# Datasets:multi_x_science_sum

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math9912167
1631980677
Author(s): Kuperberg, Greg; Thurston, Dylan P. | Abstract: We give a purely topological definition of the perturbative quantum invariants of links and 3-manifolds associated with Chern-Simons field theory. Our definition is as close as possible to one given by Kontsevich. We will also establish some basic properties of these invariants, in particular that they are universally finite type with respect to algebraically split surgery and with respect to Torelli surgery. Torelli surgery is a mutual generalization of blink surgery of Garoufalidis and Levine and clasper surgery of Habiro.
Two other generalizations that can be considered are invariants of graphs in 3-manifolds, and invariants associated to other flat connections @cite_16 . We will analyze these in future work. Among other things, there should be a general relation between flat bundles and links in 3-manifolds on the one hand and finite covers and branched covers on the other hand @cite_26 .
{ "cite_N": [ "@cite_16", "@cite_26" ], "mid": [ "1481005306", "1641082372" ], "abstract": [ "This note is a sequel to our earlier paper of the same title [4] and describes invariants of rational homology 3-spheres associated to acyclic orthogonal local systems. Our work is in the spirit of the Axelrod–Singer papers [1], generalizes some of their results, and furnishes a new setting for the purely topological implications of their work.", "Recently, Mullins calculated the Casson-Walker invariant of the 2-fold cyclic branched cover of an oriented link in S^3 in terms of its Jones polynomial and its signature, under the assumption that the 2-fold branched cover is a rational homology 3-sphere. Using elementary principles, we provide a similar calculation for the general case. In addition, we calculate the LMO invariant of the p-fold branched cover of twisted knots in S^3 in terms of the Kontsevich integral of the knot." ] }
cs9910011
2168463568
A statistical model for segmentation and word discovery in child directed speech is presented. An incremental unsupervised learning algorithm to infer word boundaries based on this model is described and results of empirical tests showing that the algorithm is competitive with other models that have been used for similar tasks are also presented.
Model Based Dynamic Programming, hereafter referred to as MBDP-1 @cite_0 , is probably the most recent work that addresses the exact same issue as that considered in this paper. Both the approach presented in this paper and Brent's MBDP-1 are based on explicit probability models. Approaches not based on explicit probability models include those based on information theoretic criteria such as MDL , transitional probability or simple recurrent networks . The maximum likelihood approach due to Olivier:SGL68 is probabilistic in the sense that it is geared towards explicitly calculating the most probable segmentation of each block of input utterances. However, it is not based on a formal statistical model. To avoid needless repetition, we only describe Brent's MBDP-1 below and direct the interested reader at Brent:EPS99 which provides an excellent review of many of the algorithms mentioned above.
{ "cite_N": [ "@cite_0" ], "mid": [ "2074546930" ], "abstract": [ "This paper presents a model-based, unsupervised algorithm for recovering word boundaries in a natural-language text from which they have been deleted. The algorithm is derived from a probability model of the source that generated the text. The fundamental structure of the model is specified abstractly so that the detailed component models of phonology, word-order, and word frequency can be replaced in a modular fashion. The model yields a language-independent, prior probability distribution on all possible sequences of all possible words over a given alphabet, based on the assumption that the input was generated by concatenating words from a fixed but unknown lexicon. The model is unusual in that it treats the generation of a complete corpus, regardless of length, as a single event in the probability space. Accordingly, the algorithm does not estimate a probability distribution on wordss instead, it attempts to calculate the prior probabilities of various word sequences that could underlie the observed text. Experiments on phonemic transcripts of spontaneous speech by parents to young children suggest that our algorithm is more effective than other proposed algorithms, at least when utterance boundaries are given and the text includes a substantial number of short utterances." ] }
cs9911003
2950670108
We solve the subgraph isomorphism problem in planar graphs in linear time, for any pattern of constant size. Our results are based on a technique of partitioning the planar graph into pieces of small tree-width, and applying dynamic programming within each piece. The same methods can be used to solve other planar graph problems including connectivity, diameter, girth, induced subgraph isomorphism, and shortest paths.
Recently we were able to characterize the graphs that can occur at most @math times as a subgraph isomorph in an @math -vertex planar graph: they are exactly the 3-connected planar graphs @cite_41 . However our proof does not lead to an efficient algorithm for 3-connected planar subgraph isomorphism. In this paper we use different techniques which do not depend on high-order connectivity.
{ "cite_N": [ "@cite_41" ], "mid": [ "2074992286" ], "abstract": [ "It is well known that any planar graph contains at most O(n) complete subgraphs. We extend this to an exact characterization: G occurs O(n) times as a subgraph of any planar graph, if and only if G is three-connected. We generalize these results to similarly characterize certain other minor-closed families of graphs; in particular, G occurs O(n) times as a subgraph of the Kb,c-free graphs, b ≥ c and c ≤ 4, iff G is c-connected. Our results use a simple Ramsey-theoretic lemma that may be of independent interest. © 1993 John Wiley & Sons, Inc." ] }
hep-th9908200
2160091034
Daviau showed the equivalence of matrix Dirac theory, formulated within a spinor bundle (S_x C _x^4 ), to a Clifford algebraic formulation within space Clifford algebra (C ( R ^3 , ) M _ 2 ( C ) P ) Pauli algebra (matrices) ≃ ℍ ⨁ ℍ ≃ biquaternions. We will show, that Daviau's map θ: ( : C ^4 M _ 2 ( C ) ) is an isomorphism. It is shown that Hestenes' and Parra's formulations are equivalent to Daviau's Clifford algebra formulation, which uses outer automorphisms. The connection between different formulations is quite remarkable, since it connects the left and right action on the Pauli algebra itself viewed as a bi-module with the left (resp. right) action of the enveloping algebra (P^ P P^T on P ). The isomorphism established in this article and given by Daviau's map does clearly show that right and left actions are of similar type. This should be compared with attempts of Hestenes, Daviau, and others to interprete the right action as the iso-spin freedom.
A further genuine and important approach to the spinor-tensor transition was developed starting probably with Crawford by P. Lounesto, @cite_6 and references there. He investigated the question, how a spinor field can be reconstructed from known tensor densities. The major characterization is derived, using Fierz-Kofink identities, from elements called Boomerangs --because they are able to come back to the spinorial picture. Lounesto's result is a characterization of spinors based on multi-vector relations which unveils a new unknown type of spinor.
{ "cite_N": [ "@cite_6" ], "mid": [ "2082565556" ], "abstract": [ "A historical review of spinors is given together with a construction of spinor spaces as minimal left ideals of Clifford algebras. Spinor spaces of euclidean spaces over reals have a natural linear structure over reals, complex numbers or quaternions. Clifford algebras have involutions which induce bilinear forms or scalar products on spinor spaces. The automorphism groups of these scalar products of spinors are determined and also classified." ] }
cs9903014
1612660921
We present an open architecture for just-in-time code generation and dynamic code optimization that is flexible, customizable, and extensible. While previous research has primarily investigated functional aspects of such a system, architectural aspects have so far remained unexplored. In this paper, we argue that these properties are important to generate optimal code for a variety of hardware architectures and different processor generations within processor families. These properties are also important to make system-level code generation useful in practice.
Pioneering research in dynamic runtime optimization was done by Hansen @cite_8 who first described a fully automated system for runtime code optimization. His system was similar in structure to our system---it was composed of a loader, a profiler, and an optimizer---but used profiling data only to decide when to optimize and what to optimize, not how to optimize. Also, his system interpreted code prior to optimization, since load time code generation was too memory and time consuming at the time.
{ "cite_N": [ "@cite_8" ], "mid": [ "2101776604" ], "abstract": [ "Abstract : This thesis investigates adaptive compiler systems that perform, during program execution, code optimizations based on the dynamic behavior of the program as opposed to current approaches that employ a fixed code generation strategy, i.e., one in which a predetermined set of code optimizations are applied at compile-time to an entire program. The main problems associated with such adaptive systems are studied in general: which optimizations to apply to what parts of the program and when. Two different optimization strategies result: an ideal scheme which is not practical to implement, and a more basic scheme that is. The design of a practical system is discussed for the FORTRAN IV language. The system was implemented and tested with programs having different behavioral characteristics." ] }
cs9903014
1612660921
We present an open architecture for just-in-time code generation and dynamic code optimization that is flexible, customizable, and extensible. While previous research has primarily investigated functional aspects of such a system, architectural aspects have so far remained unexplored. In this paper, we argue that these properties are important to generate optimal code for a variety of hardware architectures and different processor generations within processor families. These properties are also important to make system-level code generation useful in practice.
Hansen's work was followed by several other projects that have investigated the benefits of runtime optimization: the Smalltalk @cite_33 and SELF @cite_0 systems that focused on the benefits of dynamic optimization in an object-oriented environment; Morph'', a project developed at Harvard University @cite_16 ; and the system described by the authors of this paper @cite_4 @cite_30 . Other projects have experimented with optimization at link time rather than at runtime @cite_18 . At link time, many of the problems described in this paper are non-existent. Among them the decision when to optimize, what to optimize, and how to replace code. However, there is also a price to pay, namely that it cannot be performed in the presence of dynamic loading.
cs9903014
1612660921
We present an open architecture for just-in-time code generation and dynamic code optimization that is flexible, customizable, and extensible. While previous research has primarily investigated functional aspects of such a system, architectural aspects have so far remained unexplored. In this paper, we argue that these properties are important to generate optimal code for a variety of hardware architectures and different processor generations within processor families. These properties are also important to make system-level code generation useful in practice.
Common to the above-mentioned work is that the main focus has always been on functional aspects, that is how to profile and which optimizations to perform. Related to this is research on how to boost application performance by combining profiling data and code optimizations at compile time (not at runtime), including work on method dispatch optimizations for object-oriented programming languages @cite_22 @cite_35 , profile-guided intermodular optimizations @cite_3 @cite_26 , code positioning techniques @cite_13 @cite_25 , and profile-guided data cache locality optimizations @cite_29 @cite_10 @cite_12 .
cs9903018
1593496962
Scripting languages are becoming more and more important as a tool for software development, as they provide great flexibility for rapid prototyping and for configuring componentware applications. In this paper we present LuaJava, a scripting tool for Java. LuaJava adopts Lua, a dynamically typed interpreted language, as its script language. Great emphasis is given to the transparency of the integration between the two languages, so that objects from one language can be used inside the other like native objects. The final result of this integration is a tool that allows the construction of configurable Java applications, using off-the-shelf components, in a high abstraction level.
For Tcl @cite_13 two integration solutions exist: the TclBlend binding @cite_11 and the Jacl implementation @cite_14 . TclBlend is a binding between Java and Tcl, which, as LuaJava, allows Java objects to be manipulated by scripts. Some operations, such as access to fields and static method invocations, require specific functions. Calls to instance methods are handled naturally by Tcl commands.
{ "cite_N": [ "@cite_14", "@cite_13", "@cite_11" ], "mid": [ "2162914120", "2789138443", "196441419" ], "abstract": [ "This paper describes the motivations and strategies behind our group’s efforts to integrate the Tcl and Java programming languages. From the Java perspective, we wish to create a powerful scripting solution for Java applications and operating environments. From the Tcl perspective, we want to allow for cross-platform Tcl extensions and leverage the useful features and user community Java has to offer. We are specifically focusing on Java tasks like Java Bean manipulation, where a scripting solution is preferable to using straight Java code. Our goal is to create a synergy between Tcl and Java, similar to that of Visual Basic and Visual C++ on the Microsoft desktop, which makes both languages more powerful together than they are individually.", "", "A mechanical brake actuator includes a manual lever which is self-locking in the active braking position. In such position, the lever and associated cable means applies tension to a spring whose force is applied to the plunger of a hydraulic master cylinder included in the conventional turntable hydraulic brake system. In the event of minor leakage and or thermal changes in the hydraulic braking system, the spring force exerted by the mechanical actuator maintains safe braking pressure when the crane is parked. When the mechanical actuator is in a release mode, the turntable hydraulic brake is foot pedal operated from the crane operator's cab without interference from the mechanical actuator." ] }
hep-th9807171
1774239421
The thesis begins with an introduction to M-theory (at a graduate student's level), starting from perturbative string theory and proceeding to dualities, D-branes and finally Matrix theory. The following chapter treats, in a self-contained way, of general classical p-brane solutions. Black and extremal branes are reviewed, along with their semi-classical thermodynamics. We then focus on intersecting extremal branes, the intersection rules being derived both with and without the explicit use of supersymmetry. The last three chapters comprise more advanced aspects of brane physics, such as the dynamics of open branes, the little theories on the world-volume of branes and how the four dimensional Schwarzschild black hole can be mapped to an extremal configuration of branes, thus allowing for a statistical interpretation of its entropy. The original results were already reported in hep-th 9701042, hep-th 9704190, hep-th 9710027 and hep-th 9801053.
The main objective of this chapter was to study the basic @math -branes that one encounters in M-theory, and to treat them in a unified way. The need to unify the treatment is inspired by U-duality @cite_22 @cite_86 @cite_144 , which states that from the effective lower dimensional space-time point of view, all the charges carried by the different branes are on the same footing. While string theory breaks' this U-duality symmetry, choosing the NSNS string to be the fundamental object of the perturbative theory, the supergravity low-energy effective theories realize the U-duality at the classical level.
{ "cite_N": [ "@cite_86", "@cite_22", "@cite_144" ], "mid": [ "1987603965", "2141847212", "" ], "abstract": [ "Abstract The strong coupling dynamics of string theories in dimension d ⩾ 4 are studied. It is argued, among other things, that eleven-dimensional supergravity arises as a low energy limit of the ten-dimensional Type IIA superstring, and that a recently conjectured duality between the heterotic string and Type IIA superstrings controls the strong coupling dynamics of the heterotic string in five, six, and seven dimensions and implies S -duality for both heterotic and Type II strings.", "Abstract The effective action for type II string theory compactified on a six-torus is N = 8 supergravity, which is known to have an E7 duality symmetry. We show that this is broken by quantum effects to a discrete subgroup, E 7 ( Z ) , which contains both the T-duality group O(6, 6; Z ) and the S-duality group SL(2; Z ). We present evidence for the conjecture that E 7 ( Z ) is an exact ‘U-duality’ symmetry of type II string theory. This conjecture requires certain extreme black hole states to be identified with massive modes of the fundamental string. The gauge bosons from the Ramond-Ramond sector couple not to string excitations but to solitons. We discuss similar issues in the context of toroidal string compactifications to other dimensions, compactifications of the type II string on K3 × T2 and compactifications of 11-dimensional supermembrane theory.", "" ] }
hep-th9807171
1774239421
The thesis begins with an introduction to M-theory (at a graduate student's level), starting from perturbative string theory and proceeding to dualities, D-branes and finally Matrix theory. The following chapter treats, in a self-contained way, of general classical p-brane solutions. Black and extremal branes are reviewed, along with their semi-classical thermodynamics. We then focus on intersecting extremal branes, the intersection rules being derived both with and without the explicit use of supersymmetry. The last three chapters comprise more advanced aspects of brane physics, such as the dynamics of open branes, the little theories on the world-volume of branes and how the four dimensional Schwarzschild black hole can be mapped to an extremal configuration of branes, thus allowing for a statistical interpretation of its entropy. The original results were already reported in hep-th 9701042, hep-th 9704190, hep-th 9710027 and hep-th 9801053.
It should also not be underestimated that the derivation of the intersecting solutions presented in this chapter is a thorough consistency check of all the dualities acting on, and between, the supergravity theories. It is straightforward to check that, starting from one definite configuration, all its dual configurations are also found between the solutions presented here (with the exception of the solutions involving waves and KK monopoles). In this line of thoughts, we presented a recipe for building five and four dimensional extreme supersymmetric black holes. Some of these black holes were used in the literature to perform a microscopic counting of their entropy, as in @cite_191 @cite_61 for the 5-dimensional ones. Actually, the only (5 dimensional) black holes in the U-duality orbit' that were counted were the ones containing only D-branes and KK momentum. It is still an open problem to directly count the microscopic states of the same black hole but in a different M-theoretic formulation.
{ "cite_N": [ "@cite_191", "@cite_61" ], "mid": [ "2130491267", "2045285156" ], "abstract": [ "Abstract The Bekenstein-Hawking area-entropy relation S BH = A 4 is derived for a class of five-dimensional extremal black holes in string theory by counting the degeneracy of BPS solition bound states.", "Abstract Strominger and Vafa have used D-brane technology to identify and precisely count the degenerate quantum states responsible for the entropy of certain extremal, BPS-saturated black holes. Here we give a Type-II D-brane description of a class of extremal and non-extremal five-dimensional Reissner-Nordstrom solutions and identify a corresponding set of degenerate D-brane configurations. We use this information to do a string theory calculation of the entropy, radiation rate and “Hawking” temperature. The results agree perfectly with standard Hawking results for the corresponding nearly extremal Reissner-Nordstrom black holes. Although these calculations suffer from open-string strong coupling problems, we give some reasons to believe that they are nonetheless qualitatively reliable. In this optimistic scenario there would be no “information loss” in black hole quantum evolution." ] }
hep-th9807171
1774239421
The thesis begins with an introduction to M-theory (at a graduate student's level), starting from perturbative string theory and proceeding to dualities, D-branes and finally Matrix theory. The following chapter treats, in a self-contained way, of general classical p-brane solutions. Black and extremal branes are reviewed, along with their semi-classical thermodynamics. We then focus on intersecting extremal branes, the intersection rules being derived both with and without the explicit use of supersymmetry. The last three chapters comprise more advanced aspects of brane physics, such as the dynamics of open branes, the little theories on the world-volume of branes and how the four dimensional Schwarzschild black hole can be mapped to an extremal configuration of branes, thus allowing for a statistical interpretation of its entropy. The original results were already reported in hep-th 9701042, hep-th 9704190, hep-th 9710027 and hep-th 9801053.
Some of the intersection rules intersectionrules point towards an M-theory interpretation in terms of open branes ending on other branes. This idea will be elaborated and made firmer in the next chapter. It suffices to say here that this interpretation is consistent with dualities if we postulate that the open character' of a fundamental string ending on a D-brane is invariant under dualities. S-duality directly implies, for instance, that D-strings can end on NS5-branes @cite_176 . Then T-dualities imply that all the D-branes can end on the NS5-brane. In particular, the fact that the D2-brane can end on the NS5-brane should imply that the M5-brane is a D-brane for the M2-branes @cite_176 @cite_143 @cite_38 (this could also be extrapolated from the fact that a F1-string ends on a D4-brane). In the next chapter we will see how these ideas are further supported by the presence of the Chern-Simons terms in the supergravities, and by the structure of the world-volume effective actions of the branes.
{ "cite_N": [ "@cite_143", "@cite_38", "@cite_176" ], "mid": [ "1981788818", "2032622195", "" ], "abstract": [ "Abstract Various aspects of branes in the recently proposed matrix model for M-theory are discussed. A careful analysis of the supersymmetry algebra of the matrix model uncovers some central changes which can be activated only in the large N limit. We identify the states with non-zero charges as branes of different dimensions.", "Abstract We formulate boundary conditions for an open membrane that ends on the fivebrane of M -theory. We show that the dynamics of the eleven-dimensional fivebrane can be obtained from the quantization of a “small membrane” that is confined to a single fivebrane and which moves with the speed of light. This shows that the eleven-dimensional fivebrane has an interpretation as a D -brane of an open supermembrane as has recently been proposed by Strominger and Townsend. We briefly discuss the boundary dynamics of an infinitely extended planar membrane that is stretched between two parallel fivebranes.", "" ] }
hep-th9807171
1774239421
The thesis begins with an introduction to M-theory (at a graduate student's level), starting from perturbative string theory and proceeding to dualities, D-branes and finally Matrix theory. The following chapter treats, in a self-contained way, of general classical p-brane solutions. Black and extremal branes are reviewed, along with their semi-classical thermodynamics. We then focus on intersecting extremal branes, the intersection rules being derived both with and without the explicit use of supersymmetry. The last three chapters comprise more advanced aspects of brane physics, such as the dynamics of open branes, the little theories on the world-volume of branes and how the four dimensional Schwarzschild black hole can be mapped to an extremal configuration of branes, thus allowing for a statistical interpretation of its entropy. The original results were already reported in hep-th 9701042, hep-th 9704190, hep-th 9710027 and hep-th 9801053.
In this chapter we presented only extremal configurations of intersecting branes. The natural further step to take would be to consider also non-extremal configurations of intersecting branes. There is however a subtlety: there could be a difference between intersections of non-extremal branes, and non-extremal intersections of otherwise extremal branes. If we focus on bound states (and thus not on configurations of well separated branes), it appears that a non-extremal configuration would be characterized for instance by @math charges and by its mass. There is only one additional parameter with respect to the extremal configurations. Physically, we could have hardly expected to have, say, as many non-extremality parameters as the number of branes in the bound state. Indeed, non-extremality can be roughly associated to the branes being in an excited state, and it would have thus been very unlikely that the excitations did not mix between the various branes in the bound state. Non-extremal intersecting brane solutions were found first in @cite_48 , and were derived from the equations of motion following a similar approach as here in @cite_81 @cite_16 .
{ "cite_N": [ "@cite_48", "@cite_81", "@cite_16" ], "mid": [ "2054280159", "", "2086840642" ], "abstract": [ "Abstract We present non-extreme generalisations of intersecting p -brane solutions of eleven-dimensional supergravity which upon toroidal compactification reduce to non-extreme static black holes in dimensions D = 4, D = 5 and 6 ⩽ D ⩽ 9, parameterised by four, three and two charges, respectively. The D = 4 black holes are obtained either from a non-extreme configuration of three intersecting five-branes with a boost along the common string or from a non-extreme intersecting system of two two-branes and two five-branes. The D = 5 black holes arise from three intersecting two-branes or from a system of an intersecting two-brane and five-brane with a boost along the common string. The five-brane and two-brane with a boost along one direction reduce to black holes in D = 6 and D = 9, respectively, while a D = 7 black hole can be interpreted in terms of a non-extreme configuration of two intersecting two-branes. We discuss the expressions for the corresponding masses and entropies.", "", "Abstract We present a general rule determining how extremal branes can intersect in a configuration with zero binding energy. The rule is derived in a model independent way and in arbitrary spacetime dimensions D by solving the equations of motion of gravity coupled to a dilaton and several different n -form field strengths. The intersection rules are all compatible with supersymmetry, although derived without using it. We then specialize to the branes occurring in type II string theories and in M-theory. We show that the intersection rules are consistent with the picture that open branes can have boundaries on some other branes. In particular, all the D-branes of dimension q , with 1 ≤ q ≤ 6, can have boundaries on the solitonic 5-brane." ] }
hep-th9807171
1774239421
The thesis begins with an introduction to M-theory (at a graduate student's level), starting from perturbative string theory and proceeding to dualities, D-branes and finally Matrix theory. The following chapter treats, in a self-contained way, of general classical p-brane solutions. Black and extremal branes are reviewed, along with their semi-classical thermodynamics. We then focus on intersecting extremal branes, the intersection rules being derived both with and without the explicit use of supersymmetry. The last three chapters comprise more advanced aspects of brane physics, such as the dynamics of open branes, the little theories on the world-volume of branes and how the four dimensional Schwarzschild black hole can be mapped to an extremal configuration of branes, thus allowing for a statistical interpretation of its entropy. The original results were already reported in hep-th 9701042, hep-th 9704190, hep-th 9710027 and hep-th 9801053.
Supergravity solutions corresponding to D-branes at angles were found in @cite_31 @cite_59 @cite_178 . The resulting solutions contain as expected off-diagonal elements in the internal metric, and the derivation from the equations of motion as in @cite_59 is accordingly rather intricated.
{ "cite_N": [ "@cite_31", "@cite_178", "@cite_59" ], "mid": [ "2125497699", "2048444779", "" ], "abstract": [ "A low-energy background field solution is presented which describes several D-membranes oriented at angles with respect to one another. The mass and charge densities for this configuration are computed and found to saturate the Bogomol close_quote nyi-Prasad-Sommerfeld bound, implying the preservation of one-quarter of the supersymmetries. T duality is exploited to construct new solutions with nontrivial angles from the basic one. copyright ital 1997 ital The American Physical Society", "We construct the most general supersymmetric configuration of @math -branes and @math -branes on a 6-torus. It contains arbitrary numbers of branes at relative @math angles. The corresponding supergravity solutions are constructed and expressed in a remarkably simple form, using the complex geometry of the compact space. The spacetime supersymmetry of the configuration is verified explicitly, by solution of the Killing spinor equations, and the equations of motion are verified too. Our configurations can be interpreted as a 16-parameter family of regular extremal black holes in four dimensions. Their entropy is interpreted microscopically by counting the degeneracy of bound states of @math -branes. Our result agrees in detail with the prediction for the degeneracy of BPS states in terms of the quartic invariant of the E(7,7) duality group.", "" ] }
hep-th9807171
1774239421
The thesis begins with an introduction to M-theory (at a graduate student's level), starting from perturbative string theory and proceeding to dualities, D-branes and finally Matrix theory. The following chapter treats, in a self-contained way, of general classical p-brane solutions. Black and extremal branes are reviewed, along with their semi-classical thermodynamics. We then focus on intersecting extremal branes, the intersection rules being derived both with and without the explicit use of supersymmetry. The last three chapters comprise more advanced aspects of brane physics, such as the dynamics of open branes, the little theories on the world-volume of branes and how the four dimensional Schwarzschild black hole can be mapped to an extremal configuration of branes, thus allowing for a statistical interpretation of its entropy. The original results were already reported in hep-th 9701042, hep-th 9704190, hep-th 9710027 and hep-th 9801053.
Other half-supersymmetric bound states of this class are the @math multiplets of 1- and 5-branes in type IIB theory @cite_95 @cite_0 , or more precisely the configurations F1 @math D1 and NS5 @math D5, also called @math 1- and 5-branes, where @math is the NSNS charge and @math the RR charge of the compound. The classical solutions corresponding to this latter case were actually found more simply performing an @math transformation on the F1 or NS5 solutions.
{ "cite_N": [ "@cite_0", "@cite_95" ], "mid": [ "2022574854", "2127535930" ], "abstract": [ "The recent discovery of an explicit conformal field theory description of Type II p-branes makes it possible to investigate the existence of bound states of such objects. In particular, it is possible with reasonable precision to verify the prediction that the Type IIB superstring in ten dimensions has a family of soliton and bound state strings permuted by SL(2,Z). The space-time coordinates enter tantalizingly in the formalism as non-commuting matrices.", "An SL(2, Z) family of string solutions of type IIB supergravity in ten dimensions is constructed. The solutions are labeled by a pair of relatively prime integers, which characterize charges of the three-form field strengths. The string tensions depend on these charges in an SL(2, Z) covariant way. Compactifying on a circle and identifying with eleven-dimensional supergravity compactified on a torus implies that the modulus of the IIB theory should be equated to the modular parameter of the torus." ] }
hep-th9807171
1774239421
The thesis begins with an introduction to M-theory (at a graduate student's level), starting from perturbative string theory and proceeding to dualities, D-branes and finally Matrix theory. The following chapter treats, in a self-contained way, of general classical p-brane solutions. Black and extremal branes are reviewed, along with their semi-classical thermodynamics. We then focus on intersecting extremal branes, the intersection rules being derived both with and without the explicit use of supersymmetry. The last three chapters comprise more advanced aspects of brane physics, such as the dynamics of open branes, the little theories on the world-volume of branes and how the four dimensional Schwarzschild black hole can be mapped to an extremal configuration of branes, thus allowing for a statistical interpretation of its entropy. The original results were already reported in hep-th 9701042, hep-th 9704190, hep-th 9710027 and hep-th 9801053.
In @cite_106 (inspired by @cite_54 ) a solution is presented which corresponds to a M5 @math M5=1 configuration, which follows the harmonic superposition rule, provided however that the harmonic functions depend on the respective relative transverse space (i.e. they are functions of two different spaces). The problem now is that the harmonic functions do not depend on the overall transverse space (which is 1-dimensional in the case above), the configuration thus not being localized there. A method actually inspired by the one presented here to derive the intersecting brane solutions, has been applied in @cite_89 to the intersections of this second kind. Imposing that the functions depend on the relative transverse space(s) (with factorized dependence) and not on the overall one, the authors of @cite_89 arrive at a formula for the intersections very similar to intersectionrules , with @math on the l.h.s. This rule correctly reproduces the M5 @math M5=1 configuration, and moreover also all the configurations of two D-branes with 8 Neumann-Dirichlet directions, which preserve @math supersymmetries but were excluded from the intersecting solutions derived in this chapter (only the configurations with 4 ND directions were found as solutions). One such configuration is e.g. D0 @math D8.
{ "cite_N": [ "@cite_54", "@cite_106", "@cite_89" ], "mid": [ "2065552713", "1992572456", "" ], "abstract": [ "We derive an exact stringlike soliton solution of [ital D]=10 heterotic string theory. The solution possesses SU(2)[times]SU(2) instanton structure in the eight-dimensional space transverse to the world sheet of the soliton.", "We construct new supersymmetric solutions of D = 11 supergravity describing n orthogonally “overlapping” membranes and fivebranes for n = 2,…,8. Overlapping branes arise after separating intersecting branes in a direction transverse to all of the branes. The solutions, which generalize known intersecting brane solutions, preserve at least 2−n of the supersymmetry. Each pairwise overlap involves a membrane overlapping a membrane in a 0-brane, a fivebrane overlapping a fivebrane in a 3-brane or a membrane overlapping a fivebrane in a string. After reducing n overlapping membranes to obtain n overlapping D-2-branes in D = 10, T-duality generates new overlapping D-brane solutions in type IIA and type IIB string theory. Uplifting certain type IIA solutions leads to the D = 11 solutions. Some of the new solutions reduce to dilaton black holes in D = 4. Additionally, we present a D = 10 solution that describes two D-5-branes overlapping in a string. T-duality then generates further D = 10 solutions and uplifting one of the type IIA solutions gives a new D = 11 solution describing two fivebranes overlapping in a string.", "" ] }
1903.05435
2921710190
In this work, we are interested in the applications of big data in the telecommunication domain, analysing two weeks of datasets provided by Telecom Italia for Milan and Trento. Our objective is to identify hotspots which are places with very high communication traffic relative to others and measure the interaction between them. We model the hotspots as nodes in a graph and then apply node centrality metrics that quantify the importance of each node. We review five node centrality metrics and show that they can be divided into two families: the first family is composed of closeness and betweenness centrality whereas the second family consists of degree, PageRank and eigenvector centrality. We then proceed with a statistical analysis in order to evaluate the consistency of the results over the two weeks. We find out that the ranking of the hotspots under the various centrality metrics remains practically the same with the time for both Milan and Trento. We further identify that the relative difference of the values of the metrics is smaller for PageRank centrality than for closeness centrality and this holds for both Milan and Trento. Finally, our analysis reveals that the variance of the results is significantly smaller for Trento than for Milan.
Nowadays, telecom companies use widely big data in order to mine the behaviour of their customers, improve the quality of service that they provide and reduce the customers' churn. Towards this direction, demographic statistics, network deployments and call detail records (CDRs) are key factors that need to be carefully integrated in order to make accurate predictions. Though there are various open source data for the first two factors, researchers rarely have access to traffic demand data, since it is a sensitive information for the operators. Therefore, researchers need to rely on synthetic models, which do not always capture accurately large-scale mobile networks @cite_5 .
{ "cite_N": [ "@cite_5" ], "mid": [ "2741581007" ], "abstract": [ "In a world of open data and large-scale measurements, it is often feasible to obtain a real-world trace to fit to one's research problem. Feasible, however, does not imply simple. Taking next-generation cellular network planning as a case study, in this paper we describe a large-scale dataset, combining topology, traffic demand from call detail records, and demographic information throughout a whole country. We investigate how these aspects interact, revealing effects that are normally not captured by smaller-scale or synthetic datasets. In addition to making the resulting dataset available for download, we discuss how our experience can be generalized to other scenarios and case studies, i.e., how everyone can construct a similar dataset from publicly available information." ] }
1903.05435
2921710190
In this work, we are interested in the applications of big data in the telecommunication domain, analysing two weeks of datasets provided by Telecom Italia for Milan and Trento. Our objective is to identify hotspots which are places with very high communication traffic relative to others and measure the interaction between them. We model the hotspots as nodes in a graph and then apply node centrality metrics that quantify the importance of each node. We review five node centrality metrics and show that they can be divided into two families: the first family is composed of closeness and betweenness centrality whereas the second family consists of degree, PageRank and eigenvector centrality. We then proceed with a statistical analysis in order to evaluate the consistency of the results over the two weeks. We find out that the ranking of the hotspots under the various centrality metrics remains practically the same with the time for both Milan and Trento. We further identify that the relative difference of the values of the metrics is smaller for PageRank centrality than for closeness centrality and this holds for both Milan and Trento. Finally, our analysis reveals that the variance of the results is significantly smaller for Trento than for Milan.
For example, the authors in @cite_4 analyse an heterogeneous cellular network which consists of different types of nodes, such as macrocells and microcells. Nowadays a popular model is the one from Wyner @cite_0 , but it fails to fully capture a real heterogeneous cellular network because it is simplistic. Another approach is to use the spatial Poisson point process model (SPPP) @cite_9 , which can be derived from the premise that all base stations are uniformly distributed. However, a city can be classified in different areas, which have different population densities. These different areas can be characterised as dense urban, urban and suburban. To be able to classify the heterogeneous networks into these areas, the authors introduce SPPP for homogeneous and inhomogeneous sets. They show that the SPPP-model captures accurately both urban and suburban areas, whereas this is not the case for dense urban areas, because of a considerable population concentrated in small areas.
{ "cite_N": [ "@cite_0", "@cite_9", "@cite_4" ], "mid": [ "2131070905", "", "2005411736" ], "abstract": [ "The Wyner model has been widely used to model and analyze cellular networks due to its simplicity and analytical tractability. Its key aspects include fixed user locations and the deterministic and homogeneous interference intensity. While clearly a significant simplification of a real cellular system, which has random user locations and interference levels that vary by several orders of magnitude over a cell, a common presumption by theorists is that the Wyner model nevertheless captures the essential aspects of cellular interactions. But is this true? To answer this question, we compare the Wyner model to a model that includes random user locations and fading. We consider both uplink and downlink transmissions and both outage-based and average-based metrics. For the uplink, for both metrics, we conclude that the Wyner model is in fact quite accurate for systems with a sufficient number of simultaneous users, e.g., a CDMA system. Conversely, it is broadly inaccurate otherwise. Turning to the downlink, the Wyner model becomes inaccurate even for systems with a large number of simultaneous users. In addition, we derive an approximation for the main parameter in the Wyner model - the interference intensity term, which depends on the path loss exponent.", "", "In heterogeneous cellular networks spatial characteristics of base stations (BSs) influence the system performance intensively. Existing models like two-dimensional hexagonal grid model or homogeneous spatial poisson point process (SPPP) are based on the assumption that BSs are ideal or uniformly distributed, but the aggregation behavior of users in hot spots has an important effect on the location of low power nodes (LPNs), so these models fail to characterize the distribution of BSs in the current mobile cellular networks. In this paper, firstly existing spatial models are analyzed. Then, based on real data from a mobile operator in one large city of China, a set of spatial models is proposed in three typical regions: dense urban, urban and suburban. For dense urban area, “Two Tiers Poisson Cluster Superimposed Process” is proposed to model the spatial characteristics of real-world BSs. Specifically, for urban and suburban area, conventional SPPP model still can be used. Finally, the fundamental relationship between user behavior and BS distribution is illustrated and summarized. Numerous results show that SPPP is only appropriate in the urban and suburban regions where users are not gathered together obviously. Principal parameters of these models are provided as reference for the theoretical analysis and computer simulation, which describe the complex spatial configuration more reasonably and reflect the current mobile cellular network performance more precisely." ] }
1903.05355
2968491849
Learning the dynamics of robots from data can help achieve more accurate tracking controllers, or aid their navigation algorithms. However, when the actual dynamics of the robots change due to external conditions, on-line adaptation of their models is required to maintain high fidelity performance. In this work, a framework for on-line learning of robot dynamics is developed to adapt to such changes. The proposed framework employs an incremental support vector regression method to learn the model sequentially from data streams. In combination with the incremental learning, strategies for including and forgetting data are developed to obtain better generalization over the whole state space. The framework is tested in simulation and real experimental scenarios demonstrating its adaptation capabilities to changes in the robot’s dynamics.
In the field of marine robotics, @cite_10 used locally weighted projection regression to compensate the mismatch between the physics based model and the sensors reading of the AUV Nessie. Auto-regressive networks augmented with a genetic algorithm as a gating network were used to identify the model of a simulated AUV with variable mass. In a previous work @cite_16 , an on-line adaptation method was proposed to model the change in the damping forces resulting from a structural change of an AUVs mechanical structure. The algorithm showed good adaptation capability but was only limited to modelling the damping effect of an AUV model. In this work we build upon our the results of @cite_14 @cite_11 to provide a general framework for on-line learning of AUV fully coupled nonlinear dynamics, and validating the proposed approach on simulated data as well as real robot data.
{ "cite_N": [ "@cite_14", "@cite_16", "@cite_10", "@cite_11" ], "mid": [ "2766583623", "2774694743", "2022798939", "" ], "abstract": [ "This work addresses a data driven approach which employs a machine learning technique known as Support Vector Regression (SVR), to identify the coupled dynamical model of an autonomous underwater vehicle. To train the regressor, we use a dataset collected from the robot's on-board navigation sensors and actuators. To achieve a better fit to the experimental data, a variant of a radial-basis-function kernel is used in combination with the SVR which accounts for the different complexities of each of the contributing input features of the model. We compare our method to other explicit hydrodynamic damping models that were identified using the total least squares method and with less complex SVR methods. To analyze the transferability, we clearly separate training and testing data obtained in real-world experiments. Our presented method shows much better results especially compared to classical approaches.", "This paper presents an online technique which employs incremental support vector regression to learn the damping term of an underwater vehicle motion model, subject to dynamical changes in the vehicle's body. To learn the damping term, we use data collected from the robot's on-board navigation sensors and actuator encoders. We introduce a new sample-efficient methodology which accounts for adding new training samples, removing old samples, and outlier rejection. The proposed method is tested in a real-world experimental scenario to account for the model's dynamical changes due to a change in the vehicle's geometrical shape.", "Navigation is instrumental in the successful deployment of Autonomous Underwater Vehicles (AUVs). Sensor hardware is installed on AUVs to support navigational accuracy. Sensors, however, may fail during deployment, thereby jeopardizing the mission. This work proposes a solution, based on an adaptive dynamic model, to accurately predict the navigation of the AUV. A hydrodynamic model, derived from simple laws of physics, is integrated with a powerful non-parametric regression method. The incremental regression method, namely the Locally Weighted Projection Regression (LWPR), is used to compensate for un-modeled dynamics, as well as for possible changes in the operating conditions of the vehicle. The augmented hydrodynamic model is used within an Extended Kalman Filter, to provide optimal estimations of the AUV’s position and orientation. Experimental results demonstrate an overall improvement in the prediction of the vehicle’s acceleration and velocity.", "" ] }
1903.05454
2950587559
In this work, we present a camera geopositioning system based on matching a query image against a database with panoramic images. For matching, our system uses memory vectors aggregated from global image descriptors based on convolutional features to facilitate fast searching in the database. To speed up searching, a clustering algorithm is used to balance geographical positioning and computation time. We refine the obtained position from the query image using a new outlier removal algorithm. The matching of the query image is obtained with a recall@5 larger than 90 for panorama-to-panorama matching. We cluster available panoramas from geographically adjacent locations into a single compact representation and observe computational gains of approximately 50 at the cost of only a small (approximately 3 ) recall loss. Finally, we present a coordinate estimation algorithm that reduces the median geopositioning error by up to 20 .
In recent years, research regarding image matching has been influenced by the developments in other areas of computer vision. Deep learning architectures have been developed both for image matching @cite_10 @cite_1 @cite_17 and geopositioning @cite_13 @cite_5 @cite_18 with attractive results.
{ "cite_N": [ "@cite_13", "@cite_18", "@cite_1", "@cite_5", "@cite_10", "@cite_17" ], "mid": [ "1946093182", "2199890863", "2609950940", "1969891195", "2204975001", "2614218061" ], "abstract": [ "The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial or “bird's eye” imagery. In this work, we localize a ground-level query image by matching it to a reference database of aerial imagery. We use publicly available data to build a dataset of 78K aligned crossview image pairs. The primary challenge for this task is that traditional computer vision approaches cannot handle the wide baseline and appearance variation of these cross-view pairs. We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart. Our proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over traditional hand-crafted features and existing deep features learned from other large-scale databases. We show the effectiveness of Where-CNN in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations.", "We propose to use deep convolutional neural networks to address the problem of cross-view image geolocalization, in which the geolocation of a ground-level query image is estimated by matching to georeferenced aerial images. We use state-of-the-art feature representations for ground-level images and introduce a cross-view training approach for learning a joint semantic feature representation for aerial images. We also propose a network architecture that fuses features extracted from aerial images at multiple spatial scales. To support training these networks, we introduce a massive database that contains pairs of aerial and ground-level images from across the United States. Our methods significantly out-perform the state of the art on two benchmark datasets. We also show, qualitatively, that the proposed feature representations are discriminative at both local and continental spatial scales.", "Location recognition is commonly treated as visual instance retrieval on \"street view\" imagery. The dataset items and queries are panoramic views, i.e. groups of images taken at a single location. This work introduces a novel panorama-to-panorama matching process, either by aggregating features of individual images in a group or by explicitly constructing a larger panorama. In either case, multiple views are used as queries. We reach near perfect location recognition on a standard benchmark with only four query views.", "We address the problem of geo-registering ground-based multi-view stereo models by ground-to-aerial image matching. The main contribution is a fully automated geo-registration pipeline with a novel viewpoint-dependent matching method that handles ground to aerial viewpoint variation. We conduct large-scale experiments which consist of many popular outdoor landmarks in Rome. The proposed approach demonstrates a high success rate for the task, and dramatically outperforms state-of-the-art techniques, yielding geo-registration at pixel-level accuracy.", "Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It also has been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregating methods developed for local features (e.g. Fisher vectors), thus providing new powerful global descriptor. In this paper we investigate possible ways to aggregate local deep features to produce compact descriptors for image retrieval. First, we show that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated. Such re-evaluation reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides the best performance for deep convolutional features. This method is efficient, has few parameters, and bears little risk of overfitting when e.g. learning the PCA matrix. In addition, we suggest a simple yet efficient query expansion scheme suitable for the proposed aggregation method. Overall, the new compact global descriptor improves the state-of-the-art on four common benchmarks considerably.", "We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature). The new feature is based on convolutional neural networks, which are trained only with image-level annotations on a landmark image dataset. To identify semantically useful local features for image retrieval, we also propose an attention mechanism for keypoint selection, which shares most network layers with the descriptor. This framework can be used for image retrieval as a drop-in replacement for other keypoint detectors and descriptors, enabling more accurate feature matching and geometric verification. Our system produces reliable confidence scores to reject false positives---in particular, it is robust against queries that have no correct match in the database. To evaluate the proposed descriptor, we introduce a new large-scale dataset, referred to as Google-Landmarks dataset, which involves challenges in both database and query such as background clutter, partial occlusion, multiple landmarks, objects in variable scales, etc. We show that DELF outperforms the state-of-the-art global and local descriptors in the large-scale setting by significant margins. Code and dataset can be found at the project webpage: this https URL ." ] }
1903.05454
2950587559
In this work, we present a camera geopositioning system based on matching a query image against a database with panoramic images. For matching, our system uses memory vectors aggregated from global image descriptors based on convolutional features to facilitate fast searching in the database. To speed up searching, a clustering algorithm is used to balance geographical positioning and computation time. We refine the obtained position from the query image using a new outlier removal algorithm. The matching of the query image is obtained with a recall@5 larger than 90 for panorama-to-panorama matching. We cluster available panoramas from geographically adjacent locations into a single compact representation and observe computational gains of approximately 50 at the cost of only a small (approximately 3 ) recall loss. Finally, we present a coordinate estimation algorithm that reduces the median geopositioning error by up to 20 .
Convolutional features extracted from the deep layers of CNNs have shown great utility when addressing image matching and retrieval problems. Babenko @cite_10 employ pre-trained networks to generate descriptors based on high-level convolutional features used for retrieving images of various landmarks. Sunderhauf @cite_2 solve the problem of urban scene recognition, employing salient regions and convolutional features of local objects. This method is extended in @cite_8 , where additional spatial information is used to increase the algorithm performance.
{ "cite_N": [ "@cite_8", "@cite_10", "@cite_2" ], "mid": [ "2518534307", "2204975001", "1162411702" ], "abstract": [ "Recent work by [1] demonstrated improved visual place recognition using proposal regions coupled with features from convolutional neural networks (CNN) to match landmarks between views. In this work we extend the approach by introducing descriptors built from landmark features which also encode the spatial distribution of the landmarks within a view. Matching descriptors then enforces consistency of the relative positions of landmarks between views. This has a significant impact on performance. For example, in experiments on 10 image-pair datasets, each consisting of 200 urban locations with significant differences in viewing positions and conditions, we recorded average precision of around 70 (at 100 recall), compared with 58 obtained using whole image CNN features and 50 for the method in [1].", "Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It also has been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregating methods developed for local features (e.g. Fisher vectors), thus providing new powerful global descriptor. In this paper we investigate possible ways to aggregate local deep features to produce compact descriptors for image retrieval. First, we show that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated. Such re-evaluation reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides the best performance for deep convolutional features. This method is efficient, has few parameters, and bears little risk of overfitting when e.g. learning the PCA matrix. In addition, we suggest a simple yet efficient query expansion scheme suitable for the proposed aggregation method. Overall, the new compact global descriptor improves the state-of-the-art on four common benchmarks considerably.", "Place recognition has long been an incompletely solved problem in that all approaches involve significant compromises. Current methods address many but never all of the critical challenges of place recognition – viewpoint-invariance, condition-invariance and minimizing training requirements. Here we present an approach that adapts state-of-the-art object proposal techniques to identify potential landmarks within an image for place recognition. We use the astonishing power of convolutional neural network features to identify matching landmark proposals between images to perform place recognition over extreme appearance and viewpoint variations. Our system does not require any form of training, all components are generic enough to be used off-the-shelf. We present a range of challenging experiments in varied viewpoint and environmental conditions. We demonstrate superior performance to current state-of-the- art techniques. Furthermore, by building on existing and widely used recognition frameworks, this approach provides a highly compatible place recognition system with the potential for easy integration of other techniques such as object detection and semantic scene interpretation." ] }
1903.05454
2950587559
In this work, we present a camera geopositioning system based on matching a query image against a database with panoramic images. For matching, our system uses memory vectors aggregated from global image descriptors based on convolutional features to facilitate fast searching in the database. To speed up searching, a clustering algorithm is used to balance geographical positioning and computation time. We refine the obtained position from the query image using a new outlier removal algorithm. The matching of the query image is obtained with a recall@5 larger than 90 for panorama-to-panorama matching. We cluster available panoramas from geographically adjacent locations into a single compact representation and observe computational gains of approximately 50 at the cost of only a small (approximately 3 ) recall loss. Finally, we present a coordinate estimation algorithm that reduces the median geopositioning error by up to 20 .
The problem of geopositioning can be seen as a dedicated branch of image retrieval. In this case, the objective is to compute extrinsic parameters (or coordinates) of a camera capturing the query image, based on the matched georeferenced images from a database. There exist many different algorithms and neural network architectures that attempt to identify the geographical location of a street-level query image. Lin @cite_13 learn deep representations for matching aerial and ground images. Workman @cite_18 use spatial features at multiple scales which are fused with street-level features, to solve the problem of geolocalization. @cite_5 , a fully automated processing pipeline matches multi-view stereo (MVS) models to aerial images. This matching algorithm handles the viewpoint variance across aerial and street-level images.
{ "cite_N": [ "@cite_5", "@cite_18", "@cite_13" ], "mid": [ "1969891195", "2199890863", "1946093182" ], "abstract": [ "We address the problem of geo-registering ground-based multi-view stereo models by ground-to-aerial image matching. The main contribution is a fully automated geo-registration pipeline with a novel viewpoint-dependent matching method that handles ground to aerial viewpoint variation. We conduct large-scale experiments which consist of many popular outdoor landmarks in Rome. The proposed approach demonstrates a high success rate for the task, and dramatically outperforms state-of-the-art techniques, yielding geo-registration at pixel-level accuracy.", "We propose to use deep convolutional neural networks to address the problem of cross-view image geolocalization, in which the geolocation of a ground-level query image is estimated by matching to georeferenced aerial images. We use state-of-the-art feature representations for ground-level images and introduce a cross-view training approach for learning a joint semantic feature representation for aerial images. We also propose a network architecture that fuses features extracted from aerial images at multiple spatial scales. To support training these networks, we introduce a massive database that contains pairs of aerial and ground-level images from across the United States. Our methods significantly out-perform the state of the art on two benchmark datasets. We also show, qualitatively, that the proposed feature representations are discriminative at both local and continental spatial scales.", "The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial or “bird's eye” imagery. In this work, we localize a ground-level query image by matching it to a reference database of aerial imagery. We use publicly available data to build a dataset of 78K aligned crossview image pairs. The primary challenge for this task is that traditional computer vision approaches cannot handle the wide baseline and appearance variation of these cross-view pairs. We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart. Our proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over traditional hand-crafted features and existing deep features learned from other large-scale databases. We show the effectiveness of Where-CNN in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations." ] }
1903.05454
2950587559
In this work, we present a camera geopositioning system based on matching a query image against a database with panoramic images. For matching, our system uses memory vectors aggregated from global image descriptors based on convolutional features to facilitate fast searching in the database. To speed up searching, a clustering algorithm is used to balance geographical positioning and computation time. We refine the obtained position from the query image using a new outlier removal algorithm. The matching of the query image is obtained with a recall@5 larger than 90 for panorama-to-panorama matching. We cluster available panoramas from geographically adjacent locations into a single compact representation and observe computational gains of approximately 50 at the cost of only a small (approximately 3 ) recall loss. Finally, we present a coordinate estimation algorithm that reduces the median geopositioning error by up to 20 .
A common factor of the above work is that it either requires the combination of aerial and street-level images for geopositioning, or extensive training on specific datasets. Both cases and their solutions cannot be easily generalized. In our approach, we utilize georeferenced, street-level panoramic images only and a pre-trained CNN combined with image matching techniques for coordinate estimation. This avoids lengthy training and labeling procedures and assumes street-level data to be available without requiring aerial images. Furthermore, and unlike @cite_1 , we do not assume that our query and database images originate from the same imaging devices.
{ "cite_N": [ "@cite_1" ], "mid": [ "2609950940" ], "abstract": [ "Location recognition is commonly treated as visual instance retrieval on \"street view\" imagery. The dataset items and queries are panoramic views, i.e. groups of images taken at a single location. This work introduces a novel panorama-to-panorama matching process, either by aggregating features of individual images in a group or by explicitly constructing a larger panorama. In either case, multiple views are used as queries. We reach near perfect location recognition on a standard benchmark with only four query views." ] }
1903.05524
2972959293
In this paper, we study the relationship between two crucial properties in linear dynamical networks of diffusively coupled agents, that is controllability and robustness to noise and structural changes in the network. In particular, for any given network size and diameter, we identify networks that are maximally robust and then analyze their strong structural controllability. We do so by determining the minimum number of leaders to make such networks completely controllable with arbitrary coupling weights between agents. Similarly, we design networks with the same given parameters that are completely controllable independent of coupling weights through a minimum number of leaders, and then also analyze their robustness. We utilize the notion of Kirchhoff index to measure network robustness to noise and structural changes. Our controllability analysis is based on novel graph-theoretic methods that offer insights on the important connection between network robustness and strong structural controllability in such networks.
Kirchhoff index or equivalently effective graph resistance based measures have been instrumental in quantifying the effect of noise on the expected steady state dispersion in linear dynamical networks, particularly in the ones with the consensus dynamics, for instance see @cite_23 @cite_8 @cite_22 . Furthermore, limits on robustness measures that quantify expected steady-state dispersion due to external stochastic disturbances in linear dynamical networks are also studied in @cite_9 @cite_10 . To maximize robustness in networks by minimizing their Kirchhoff indices, various optimization approaches (e.g., @cite_26 @cite_1 ) including graph-theoretic ones @cite_0 have been proposed. The main objective there is to determine crucial edges that need to be added or maintained to maximize robustness under given constraints @cite_11 .
1903.05524
2972959293
In this paper, we study the relationship between two crucial properties in linear dynamical networks of diffusively coupled agents, that is controllability and robustness to noise and structural changes in the network. In particular, for any given network size and diameter, we identify networks that are maximally robust and then analyze their strong structural controllability. We do so by determining the minimum number of leaders to make such networks completely controllable with arbitrary coupling weights between agents. Similarly, we design networks with the same given parameters that are completely controllable independent of coupling weights through a minimum number of leaders, and then also analyze their robustness. We utilize the notion of Kirchhoff index to measure network robustness to noise and structural changes. Our controllability analysis is based on novel graph-theoretic methods that offer insights on the important connection between network robustness and strong structural controllability in such networks.
To quantify controllability, several approaches have been adapted, including determining the minimum number of inputs (leader nodes) needed to (structurally or strong structurally) control a network, determining the worst-case control energy, metrics based on controllability Gramians, and so on (e.g., see @cite_7 @cite_5 ). Strong structural controllability, due to its independence on coupling weights between nodes, is a generalized notion of controllability with practical implications. There have been recent studies providing graph-theoretic characterizations of this concept @cite_20 @cite_13 @cite_17 . There are numerous other studies regarding leader selection to optimize network performance measures under various constraints, such as to minimize the deviation from consensus in a noisy environment @cite_4 @cite_2 , and to maximize various controllability measures, for instance @cite_15 @cite_18 @cite_25 @cite_14 . Recently, optimization methods are also presented to select leader nodes that exploit submodularity properties of performance measures for network robustness and structural controllability @cite_5 @cite_3 .
{ "cite_N": [ "@cite_18", "@cite_14", "@cite_4", "@cite_7", "@cite_3", "@cite_2", "@cite_5", "@cite_15", "@cite_13", "@cite_25", "@cite_20", "@cite_17" ], "mid": [ "", "", "", "2111725629", "", "", "1938602245", "", "2315383458", "", "2049708951", "2763583074" ], "abstract": [ "", "", "", "This paper studies the problem of controlling complex networks, i.e., the joint problem of selecting a set of control nodes and of designing a control input to steer a network to a target state. For this problem, 1) we propose a metric to quantify the difficulty of the control problem as a function of the required control energy, 2) we derive bounds based on the system dynamics (network topology and weights) to characterize the tradeoff between the control energy and the number of control nodes, and 3) we propose an open-loop control strategy with performance guarantees. In our strategy, we select control nodes by relying on network partitioning, and we design the control input by leveraging optimal and distributed control techniques. Our findings show several control limitations and properties. For instance, for Schur stable and symmetric networks: 1) if the number of control nodes is constant, then the control energy increases exponentially with the number of network nodes; 2) if the number of control nodes is a fixed fraction of the network nodes, then certain networks can be controlled with constant energy independently of the network dimension; and 3) clustered networks may be easier to control because, for sufficiently many control nodes, the control energy depends only on the controllability properties of the clusters and on their coupling strength. We validate our results with examples from power networks, social networks and epidemics spreading.", "", "", "Controllability and observability have long been recognized as fundamental structural properties of dynamical systems, but have recently seen renewed interest in the context of large, complex networks of dynamical systems. A basic problem is sensor and actuator placement: choose a subset from a finite set of possible placements to optimize some real-valued controllability and observability metrics of the network. Surprisingly little is known about the structure of such combinatorial optimization problems. In this paper, we show that several important classes of metrics based on the controllability and observability Gramians have a strong structural property that allows for either efficient global optimization or an approximation guarantee by using a simple greedy heuristic for their maximization. In particular, the mapping from possible placements to several scalar functions of the associated Gramian is either a modular or submodular set function. The results are illustrated on randomly generated systems and on a problem of power-electronic actuator placement in a model of the European power grid.", "", "In this technical note, we study the controllability of diffusively coupled networks from a graph theoretic perspective. We consider leader-follower networks, where the external control inputs are injected to only some of the agents, namely the leaders. Our main result relates the controllability of such systems to the graph distances between the agents. More specifically, we present a graph topological lower bound on the rank of the controllability matrix. This lower bound is tight, and it is applicable to systems with arbitrary network topologies, coupling weights, and number of leaders. An algorithm for computing the lower bound is also provided. Furthermore, as a prominent application, we present how the proposed bound can be utilized to select a minimal set of leaders for achieving controllability, even when the coupling weights are unknown.", "", "This paper examines strong structural controllability of linear-time-invariant networked systems. We provide necessary and sufficient conditions for strong structural controllability involving constrained matchings over the bipartite graph representation of the network. An O(n2) algorithm to validate if a set of inputs leads to a strongly structurally controllable network and to find such an input set is proposed. The problem of finding such a set with minimal cardinality is shown to be NP-complete. Minimal cardinality results for strong and weak structural controllability are compared.", "Characterization of network controllability through its topology has recently gained a lot of attention in the systems and control community. Using the notion of balancing sets, in this note, such a network-centric approach for the controllability of certain families of undirected networks is investigated. Moreover, by introducing the notion of a generalized zero forcing set, the structural controllability of undirected networks is discussed; in this direction, lower bounds on the dimension of the controllable subspace are derived. In addition, a method is proposed that facilitates synthesis of structural and strong structural controllable networks as well as examining preservation of network controllability under structural perturbations." ] }
1903.05524
2972959293
In this paper, we study the relationship between two crucial properties in linear dynamical networks of diffusively coupled agents, that is controllability and robustness to noise and structural changes in the network. In particular, for any given network size and diameter, we identify networks that are maximally robust and then analyze their strong structural controllability. We do so by determining the minimum number of leaders to make such networks completely controllable with arbitrary coupling weights between agents. Similarly, we design networks with the same given parameters that are completely controllable independent of coupling weights through a minimum number of leaders, and then also analyze their robustness. We utilize the notion of Kirchhoff index to measure network robustness to noise and structural changes. Our controllability analysis is based on novel graph-theoretic methods that offer insights on the important connection between network robustness and strong structural controllability in such networks.
Very recently in @cite_21 , trade-off between controllability and fragility in complex networks is investigated. Fragility measures the smallest perturbation in edge weights to make the network unstable. Authors in @cite_21 show that networks that require small control energy, as measured by the eigen values of the controllability Gramian, to drive from one state to another are more fragile and vice versa. In our work, for control performance, we consider minimum leaders for strong structural controllability, which is independent of coupling weights; and for robustness, we utilize the Kirchhoff index which measures robustness to noise as well as to structural changes in the underlying network graph. Moreover, in this work we focus on designing and comparing extremal networks for these properties. The rest of the paper is organized as follows: Section describes preliminaries and network dynamics. Section explains the measures for robustness and controllability, and also outlines the main problems. Section presents maximally robust networks for a given @math and @math , and also analyzes their controllability. Section provides a design of maximally controllable networks and also evaluates their robustness. Finally, Section concludes the paper.
{ "cite_N": [ "@cite_21" ], "mid": [ "2887109490" ], "abstract": [ "Mathematical theories and empirical evidence suggest that several complex natural and man-made systems are fragile: as their size increases, arbitrarily small and localized alterations of the system parameters may trigger system-wide failures. Examples are abundant, from perturbation of the population densities leading to extinction of species in ecological networks [1], to structural changes in metabolic networks preventing reactions [2], cascading failures in power networks [3], and the onset of epileptic seizures following alterations of structural connectivity among populations of neurons [4]. While fragility of these systems has long been recognized [5], convincing theories of why natural evolution or technological advance has failed, or avoided, to enhance robustness in complex systems are still lacking. In this paper we propose a mechanistic explanation of this phenomenon. We show that a fundamental tradeoff exists between fragility of a complex network and its controllability degree, that is, the control energy needed to drive the network state to a desirable state. We provide analytical and numerical evidence that easily controllable networks are fragile, suggesting that natural and man-made systems can either be resilient to parameters perturbation or efficient to adapt their state in response to external excitations and controls." ] }
cmp-lg9408015
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Effective problem solving among multiple agents requires a better understanding of the role of communication in collaboration. In this paper we show that there are communicative strategies that greatly improve the performance of resource-bounded agents, but that these strategies are highly sensitive to the task requirements, situation parameters and agents' resource limitations. We base our argument on two sources of evidence: (1) an analysis of a corpus of 55 problem solving dialogues, and (2) experimental simulations of collaborative problem solving dialogues in an experimental world, Design-World, where we parameterize task requirements, agents' resources and communicative strategies.
Design-World is also based on the method used in Carletta's JAM simulation for the Edinburgh Map-Task @cite_10 . JAM is based on the Map-Task Dialogue corpus, where the goal of the task is for the planning agent, the instructor, to instruct the reactive agent, the instructee, how to get from one place to another on the map. JAM focuses on efficient strategies for recovery from error and parametrizes agents according to their communicative and error recovery strategies. Given good error recovery strategies, Carletta argues that high risk' strategies are more efficient, where efficiency is a measure of the number of utterances in the dialogue. While the focus here is different, we have shown that that the number of utterances is just one parameter for evaluating performance, and that the task definition determines when strategies are effective.
{ "cite_N": [ "@cite_10" ], "mid": [ "2159574206" ], "abstract": [ "The Principle of Parsimony states that people usually try to complete tasks with the least effort that will produce a satisfactory solution. In task-oriented dialogue, this produces a tension between conveying information carefully to the partner and leaving it to be inferred, risking a misunderstanding and the need for recovery. Using natural dialogue examples, primarily from the HCRC Map Task, we apply the Principle of Parsimony to a range of information types and identify a set of applicable recovery strategies. We argue that risk-taking and recovery are crucial for efficient dialogue because they pinpoint which information must be transferred and allow control of the interaction to switch to the participant who can best guide the course of the dialogue." ] }
cs9907027
2949190809
The aim of the Alma project is the design of a strongly typed constraint programming language that combines the advantages of logic and imperative programming. The first stage of the project was the design and implementation of Alma-0, a small programming language that provides a support for declarative programming within the imperative programming framework. It is obtained by extending a subset of Modula-2 by a small number of features inspired by the logic programming paradigm. In this paper we discuss the rationale for the design of Alma-0, the benefits of the resulting hybrid programming framework, and the current work on adding constraint processing capabilities to the language. In particular, we discuss the role of the logical and customary variables, the interaction between the constraint store and the program, and the need for lists.
We concentrate here on the related work involving addition of constraints to imperative languages. For an overview of related work pertaining to the language we refer the reader to @cite_0 .
{ "cite_N": [ "@cite_0" ], "mid": [ "1968265180" ], "abstract": [ "We describe here an implemented small programming language, called Alma-O, that augments the expressive power of imperative programming by a limited number of features inspired by the logic programming paradigm. These additions encourage declarative programming and make it a more attractive vehicle for problems that involve search. We illustrate the use of Alma-O by presenting solutions to a number of classical problems, including α-β search, STRIPS planning, knapsack, and Eight Queens. These solutions are substantially simpler than their counterparts written in the imperative or in the logic programming style and can be used for different purposes without any modification. We also discuss here the implementation of Alma-O and an operational, executable, semantics of a large subset of the language." ] }
cmp-lg9709007
1578881253
Automatic Text Categorization (TC) is a complex and useful task for many natural language applications, and is usually performed through the use of a set of manually classified documents, a training collection. We suggest the utilization of additional resources like lexical databases to increase the amount of information that TC systems make use of, and thus, to improve their performance. Our approach integrates WordNet information with two training approaches through the Vector Space Model. The training approaches we test are the Rocchio (relevance feedback) and the Widrow-Hoff (machine learning) algorithms. Results obtained from evaluation show that the integration of WordNet clearly outperforms training approaches, and that an integrated technique can effectively address the classification of low frequency categories.
To our knowledge, lexical databases have been used only once before in TC, apart from our previous work. Hearst @cite_10 adapted a disambiguation algorithm by Yarowsky using WordNet to recognize category occurrences. Categories are made of WordNet terms, which is not the general case of standard or user-defined categories. It is a hard task to adapt WordNet subsets to pre-existing categories, especially when they are domain dependent. Hearst's approach has shown promising results confirmed by our previous work @cite_23 and present results.
{ "cite_N": [ "@cite_10", "@cite_23" ], "mid": [ "1493108551", "1575569168" ], "abstract": [ "This dissertation investigates the role of contextual information in the automated retrieval and display of full-text documents, using robust natural language processing algorithms to automatically detect structure in and assign topic labels to texts. Many long texts are comprised of complex topic and subtopic structure, a fact ignored by existing information access methods. I present two algorithms which detect such structure, and two visual display paradigms which use the results of these algorithms to show the interactions of multiple main topics, multiple subtopics, and the relations between main topics and subtopics. The first algorithm, called TextTiling , recognizes the subtopic structure of texts as dictated by their content. It uses domain-independent lexical frequency and distribution information to partition texts into multi-paragraph passages. The results are found to correspond well to reader judgments of major subtopic boundaries. The second algorithm assigns multiple main topic labels to each text, where the labels are chosen from pre-defined, intuitive category sets; the algorithm is trained on unlabeled text. A new iconic representation, called TileBars uses TextTiles to simultaneously and compactly display query term frequency, query term distribution and relative document length. This representation provides an informative alternative to ranking long texts according to their overall similarity to a query. For example, a user can choose to view those documents that have an extended discussion of one set of terms and a brief but overlapping discussion of a second set of terms. This representation also allows for relevance feedback on patterns of term distribution. TileBars display documents only in terms of words supplied in the user query. For a given retrieved text, if the query words do not correspond to its main topics, the user cannot discern in what context the query terms were used. For example, a query on contaminants may retrieve documents whose main topics relate to nuclear power, food, or oil spills. To address this issue, I describe a graphical interface, called Cougar , that displays retrieved documents in terms of interactions among their automatically-assigned main topics, thus allowing users to familiarize themselves with the topics and terminology of a text collection.", "Automatic text categorization is a complex and useful task for many natural language processing applications. Recent approaches to text categorization focus more on algorithms than on resources involved in this operation. In contrast to this trend, we present an approach based on the integration of widely available resources as lexical databases and training collections to overcome current limitations of the task. Our approach makes use of WordNet synonymy information to increase evidence for bad trained categories. When testing a direct categorization, a WordNet based one, a training algorithm, and our integrated approach, the latter exhibits a better perfomance than any of the others. Incidentally, WordNet based approach perfomance is comparable with the training approach one." ] }
cmp-lg9709007
1578881253
Automatic Text Categorization (TC) is a complex and useful task for many natural language applications, and is usually performed through the use of a set of manually classified documents, a training collection. We suggest the utilization of additional resources like lexical databases to increase the amount of information that TC systems make use of, and thus, to improve their performance. Our approach integrates WordNet information with two training approaches through the Vector Space Model. The training approaches we test are the Rocchio (relevance feedback) and the Widrow-Hoff (machine learning) algorithms. Results obtained from evaluation show that the integration of WordNet clearly outperforms training approaches, and that an integrated technique can effectively address the classification of low frequency categories.
Lexical databases have been employed recently in word sense disambiguation. For example, Agirre and Rigau @cite_4 make use of a semantic distance that takes into account structural factors in WordNet for achieving good results for this task. Additionally, Resnik @cite_3 combines the use of WordNet and a text collection for a definition of a distance for disambiguating noun groupings. Although the text collection is not a training collection (in the sense of a collection of manually labeled texts for a pre-defined text processing task), his approach can be regarded as the most similar to ours in the disambiguation setting. Finally, Ng and Lee @cite_11 make use of several sources of information inside a training collection (neighborhood, part of speech, morphological form, etc.) to get good results in disambiguating unrestricted text.
{ "cite_N": [ "@cite_4", "@cite_3", "@cite_11" ], "mid": [ "", "1608874027", "2157025692" ], "abstract": [ "", "Word groupings useful for language processing tasks are increasingly available, as thesauri appear on-line, and as distributional word clustering techniques improve. However, for many tasks, one is interested in relationships among word senses, not words. This paper presents a method for automatic sense disambiguation of nouns appearing within sets of related nouns — the kind of data one finds in on-line thesauri, or as the output of distributional clustering algorithms. Disambiguation is performed with respect to WordNet senses, which are fairly fine-grained; however, the method also permits the assignment of higher-level WordNet categories rather than sense labels. The method is illustrated primarily by example, though results of a more rigorous evaluation are also presented.", "In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neighboring words, morphological form, the unordered set of surrounding words, local collocations, and verb-object syntactic relation. We tested our WSD program, named LEXAS, on both a common data set used in previous work, as well as on a large sense-tagged corpus that we separately constructed. LEXAS achieves a higher accuracy on the common data set, and performs better than the most frequent heuristic on the highly ambiguous words in the large corpus tagged with the refined senses of WORDNET." ] }
cmp-lg9706006
2953123431
Learning problems in the text processing domain often map the text to a space whose dimensions are the measured features of the text, e.g., its words. Three characteristic properties of this domain are (a) very high dimensionality, (b) both the learned concepts and the instances reside very sparsely in the feature space, and (c) a high variation in the number of active features in an instance. In this work we study three mistake-driven learning algorithms for a typical task of this nature -- text categorization. We argue that these algorithms -- which categorize documents by learning a linear separator in the feature space -- have a few properties that make them ideal for this domain. We then show that a quantum leap in performance is achieved when we further modify the algorithms to better address some of the specific characteristics of the domain. In particular, we demonstrate (1) how variation in document length can be tolerated by either normalizing feature weights or by using negative weights, (2) the positive effect of applying a threshold range in training, (3) alternatives in considering feature frequency, and (4) the benefits of discarding features while training. Overall, we present an algorithm, a variation of Littlestone's Winnow, which performs significantly better than any other algorithm tested on this task using a similar feature set.
The methods that are most similar to our techniques are the on-line algorithms used in @cite_16 and @cite_10 . In the first, two algorithms, a multiplicative update and additive update algorithms suggested in @cite_5 are evaluated in the domain, and are shown to perform somewhat better than Rocchio's algorithm. While both these works make use of multiplicative update algorithms, as we do, there are two major differences between those studies and the current one. First, there are some important technical differences between the algorithms used. Second, the algorithms we study here are mistake-driven; they update the weight vector only when a mistake is made, and not after every example seen. The Experts algorithm studied in @cite_10 is very similar to a basic version of the algorithm which we study here. The way we treat the negative weights is different, though, and significantly more efficient, especially in sparse domains (see ). Cohen and Singer experiment also, using the same algorithm, with more complex features (sparse n-grams) and show that, as expected, it yields better results.
{ "cite_N": [ "@cite_5", "@cite_16", "@cite_10" ], "mid": [ "2069317438", "", "2440833291" ], "abstract": [ "We consider two algorithm for on-line prediction based on a linear model. The algorithms are the well-known Gradient Descent (GD) algorithm and a new algorithm, which we call EG(+ -). They both maintain a weight vector using simple updates. For the GD algorithm, the update is based on subtracting the gradient of the squared error made on a prediction. The EG(+ -) algorithm uses the components of the gradient in the exponents of factors that are used in updating the weight vector multiplicatively. We present worst-case loss bounds for EG(+ -) and compare them to previously known bounds for the GD algorithm. The bounds suggest that the losses of the algorithms are in general incomparable, but EG(+ -) has a much smaller loss if only a few components of the input are relevant for the predictions. We have performed experiments, which show that our worst-case upper bounds are quite tight already on simple artificial data.", "", "Two recently implemented machine-learning algorithms, RIPPER and sleeping-experts for phrases, are evaluated on a number of large text categorization problems. These algorithms both construct classifiers that allow the “context” of a word w to affect how (or even whether) the presence or absence of w will contribute to a classification. However, RIPPER and sleeping-experts differ radically in many other respects: differences include different notions as to what constitutes a context, different ways of combining contexts to construct a classifier, different methods to search for a combination of contexts, and different criteria as to what contexts should be included in such a combination. In spite of these differences, both RIPPER and sleeping-experts perform extremely well across a wide variety of categorization problems, generally outperforming previously applied learning methods. We view this result as a confirmation of the usefulness of classifiers that represent contextual information." ] }
cmp-lg9701003
2952751702
In expert-consultation dialogues, it is inevitable that an agent will at times have insufficient information to determine whether to accept or reject a proposal by the other agent. This results in the need for the agent to initiate an information-sharing subdialogue to form a set of shared beliefs within which the agents can effectively re-evaluate the proposal. This paper presents a computational strategy for initiating such information-sharing subdialogues to resolve the system's uncertainty regarding the acceptance of a user proposal. Our model determines when information-sharing should be pursued, selects a focus of information-sharing among multiple uncertain beliefs, chooses the most effective information-sharing strategy, and utilizes the newly obtained information to re-evaluate the user proposal. Furthermore, our model is capable of handling embedded information-sharing subdialogues.
Grosz, Sidner and Lochbaum @cite_7 @cite_1 developed a SharedPlan approach to modelling collaborative discourse, and Sidner formulated an artificial language for modeling such discourse. Sidner viewed a collaborative planning process as proposal acceptance and proposal rejection sequences. Her artificial language treats an utterance such as Why do X? as a proposal for the hearer to provide support for his proposal to do X. However, Sidner's work is descriptive and does not provide a mechanism for determining when and how such a proposal should be made nor how responses should be formulated in information-sharing subdialogues.
{ "cite_N": [ "@cite_1", "@cite_7" ], "mid": [ "2148389694", "332028463" ], "abstract": [ "A model of plan recognition in discourse must be based on intended recognition, distinguish each agent's beliefs and intentions from the other's, and avoid assumptions about the correctness or completeness of the agents' beliefs. In this paper, we present an algorithm for plan recognition that is based on the Shared-Plan model of collaboration (Grosz and Sidner, 1990; , 1990) and that satisfies these constraints.", "Abstract : Discourses are fundamentally instances of collaboration behavior. We propose a model of the collaborative plans of agents achieving joint goals and illustrate the role of these plans in discourses. Three types of collaborative plans, called Shared Plans, are formulated for joint goals requiring simultaneous, conjoined or sequential actions on the part of the agents who participate in the plans and the discourse; a fourth type of Shared Plan is presented for the circumstance where two agents communicate, but only one acts." ] }
cmp-lg9701003
2952751702
In expert-consultation dialogues, it is inevitable that an agent will at times have insufficient information to determine whether to accept or reject a proposal by the other agent. This results in the need for the agent to initiate an information-sharing subdialogue to form a set of shared beliefs within which the agents can effectively re-evaluate the proposal. This paper presents a computational strategy for initiating such information-sharing subdialogues to resolve the system's uncertainty regarding the acceptance of a user proposal. Our model determines when information-sharing should be pursued, selects a focus of information-sharing among multiple uncertain beliefs, chooses the most effective information-sharing strategy, and utilizes the newly obtained information to re-evaluate the user proposal. Furthermore, our model is capable of handling embedded information-sharing subdialogues.
Several researchers have studied the role of clarification dialogues in disambiguating user plans @cite_3 @cite_5 and in understanding referring expressions @cite_8 . developed an automated librarian that could revise its beliefs and intentions and could generate responses as an attempt to revise the user's beliefs and intentions. Although their system had rules for asking the user whether he holds a particular belief and for telling the system's attitude toward a belief, the emphasis of their work was on conflict resolution and plan disambiguation. Thus they did not investigate a comprehensive strategy for information-sharing during proposal evaluation. For example, they did not identify situations in which information-sharing is necessary, did not address how to select a focus of information-sharing when there are multiple uncertain beliefs, did not consider requesting the user's justifications for a belief, etc. In addition, they do not provide an overall dialogue planner that takes into account discourse structure and appropriately captures embedded subdialogues.
cmp-lg9606025
2952364737
This paper presents an analysis conducted on a corpus of software instructions in French in order to establish whether task structure elements (the procedural representation of the users' tasks) are alone sufficient to control the grammatical resources of a text generator. We show that the construct of genre provides a useful additional source of control enabling us to resolve undetermined cases.
The results from our linguistic analysis are consistent with other research on sublanguages in the instructions domain, in both French and English, e.g., @cite_5 @cite_4 . Our analysis goes beyond previous work by identifying within the discourse context the means for exercising explicit control over a text generator.
{ "cite_N": [ "@cite_5", "@cite_4" ], "mid": [ "2047374384", "2131196772" ], "abstract": [ "This paper discusses an approach to planning the content of instructional texts. The research is based on a corpus study of 15 French procedural texts ranging from step-by-step device manuals to general artistic procedures. The approach taken starts from an AI task planner building a task representation, from which semantic carriers are selected. The most appropriate RST relations to communicate these carriers are then chosen according to heuristics developed during the corpus analysis.", "Instructional texts have been the object of many studies recently, motivated by the increased need to produce manuals (especially multilingual manuals) coupled with the cost of translators and technical writers. Because these studies concentrate on aspects other than the linguistic realisation of instructions -- for example, the integration of text and graphics - they all generate a sequence of steps required to achieve a task, using imperatives. Our research so far shows, however, that manuals can in fact have different styles, i. e., not all instructions are stated using a sequence of imperatives, and that, furthermore, different parts of manuals often use different styles. In this paper, we present our preliminary results from an analysis of over 30 user guides manuals for consumer appliances and discuss some of the implications." ] }
cmp-lg9505006
1617827527
In previous work we studied a new type of DCGs, Datalog grammars, which are inspired on database theory. Their efficiency was shown to be better than that of their DCG counterparts under (terminating) OLDT-resolution. In this article we motivate a variant of Datalog grammars which allows us a meta-grammatical treatment of coordination. This treatment improves in some respects over previous work on coordination in logic grammars, although more research is needed for testing it in other respects.
A notion that is central to recent work on ellipsis, and which has been present in embryonic form, as we have seen, even in the early work on coordination, is that of parallelism as a key element in the determination of implicit meanings. Asher @cite_3 defines parallelism as
{ "cite_N": [ "@cite_3" ], "mid": [ "1495022714" ], "abstract": [ "Preface. Introduction. 1. From Events to Propositions: a Tour of Abstract Entities, Eventualities and the Nominals that Denote them. 2. A Crash Course in DRT. 3. Attitudes and Attitude Descriptions. 4. The Semantic Representation for Sentential Nominals. 5. Problems for the Semantics of Nominals. 6. Anaphora and Abstract Entities. 7. A Theory of Discourse Structure for an Analysis of Abstract Entity Anaphora. 8. Applying the Theory of Discourse Structure to the Anaphoric Phenomena. 9. Applications of the Theory of Discourse Structure to Concept Anaphora and VP Ellipsis. 10. Model Theory for Abstract Entities and its Philosophical Implications. Conclusion. Bibliography. Index." ] }
cmp-lg9505006
1617827527
In previous work we studied a new type of DCGs, Datalog grammars, which are inspired on database theory. Their efficiency was shown to be better than that of their DCG counterparts under (terminating) OLDT-resolution. In this article we motivate a variant of Datalog grammars which allows us a meta-grammatical treatment of coordination. This treatment improves in some respects over previous work on coordination in logic grammars, although more research is needed for testing it in other respects.
a) neither method formulates exactly how parallelism is to be determined- it is just postulated as a prerequisite to the resolution of ellipsis (although @cite_6 speculates on possible ways of formulating this, leaving it for future work)
{ "cite_N": [ "@cite_6" ], "mid": [ "2119997945" ], "abstract": [ "We describe an implementation in Carpenter's typed feature formalism, ALE, of a discourse grammar of the kind proposed by Scha, Polanyi, et al We examine their method for resolving parallelism-dependent anaphora and show that there is a coherent feature-structural rendition of this type of grammar which uses the operations of priority union and generalization. We describe an augmentation of the ALE system to encompass these operations and we show that an appropriate choice of definition for priority union gives the desired multiple output for examples of VP-ellipsis which exhibit a strict sloppy ambiguity." ] }
cmp-lg9505006
1617827527
In previous work we studied a new type of DCGs, Datalog grammars, which are inspired on database theory. Their efficiency was shown to be better than that of their DCG counterparts under (terminating) OLDT-resolution. In this article we motivate a variant of Datalog grammars which allows us a meta-grammatical treatment of coordination. This treatment improves in some respects over previous work on coordination in logic grammars, although more research is needed for testing it in other respects.
By examining ellipsis in the context of coordinated structures, which are parallel by definition, and by using extended DLGs, we provide a method in which parallel structures are detected and resolved through syntactic and semantic criteria, and which can be applied to either grammars using different semantic representations- feature structure, @math -calculus, or other. We exemplify using a logic based semantics along the lines of @cite_8 .
{ "cite_N": [ "@cite_8" ], "mid": [ "2006589508" ], "abstract": [ "Logic grammars are grammars expressible in predicate logic. Implemented in the programming language Prolog, logic grammar systems have proved to be a good basis for natural language processing. One of the most difficult constructions for natural language grammars to treat is coordination (construction with conjunctions like 'and'). This paper describes a logic grammar formalism, modifier structure grammars (MSGs), together with an interpreter written in Prolog, which can handle coordination (and other natural language constructions) in a reasonable and general way. The system produces both syntactic analyses and logical forms, and problems of scoping for coordination and quantifiers are dealt with. The MSG formalism seems of interest in its own right (perhaps even outside natural language processing) because the notions of syntactic structure and semantic interpretation are more constrained than in many previous systems (made more implicit in the formalism itself), so that less burden is put on the grammar writer." ] }
cmp-lg9505038
2952182676
Augmented reality is a research area that tries to embody an electronic information space within the real world, through computational devices. A crucial issue within this area, is the recognition of real world objects or situations. In natural language processing, it is much easier to determine interpretations of utterances, even if they are ill-formed, when the context or situation is fixed. We therefore introduce robust, natural language processing into a system of augmented reality with situation awareness. Based on this idea, we have developed a portable system, called the Ubiquitous Talker. This consists of an LCD display that reflects the scene at which a user is looking as if it is a transparent glass, a CCD camera for recognizing real world objects with color-bar ID codes, a microphone for recognizing a human voice and a speaker which outputs a synthesized voice. The Ubiquitous Talker provides its user with some information related to a recognized object, by using the display and voice. It also accepts requests or questions as voice inputs. The user feels as if he she is talking with the object itself through the system.
Ubiquitous computing @cite_4 proposes that very small computational devices (i.e., ubiquitous computers) be embedded and integrated into physical environments in such a way that they operate seamlessly and almost transparently. These devices are aware of their physical surroundings. In contrast to ubiquitous computers, our barcode (color-code) system is a low cost and reliable solution to making everything a computer. Suppose that every page in a book has a unique barcode. When the user opens a page, its page ID is detected by the system, so it can supply specific information regarding the page. When the user adds some information to the page, the system stores it with the page ID tagged for later retrieval. This is almost the same as having a computer in every page of the book without the cost. Our ID-aware system is better than ubiquitous computers from the viewpoint of reliability and cost-performance, since it does not require batteries and never breaks down.
{ "cite_N": [ "@cite_4" ], "mid": [ "2084069552" ], "abstract": [ "Ubiquitous computing is the method of enhancing computer use by making many computers available throughout the physical environment, but making them effectively invisible to the user. Since we started this work at Xerox PARC in 1988, a number of researchers around the world have begun to work in the ubiquitous computing framework. This paper explains what is new and different about the computer science in ubiquitous computing. It starts with a brief overview of ubiquitous computing, and then elaborates through a series of examples drawn from various subdisciplines of computer science: hardware components (e.g. chips), network protocols, interaction substrates (e.g. software for screens and pens), applications, privacy, and computational methods. Ubiquitous computing offers a framework for new and exciting research across the spectrum of computer science." ] }
math9702221
2117609843
This paper reexamines univariate reduction from a toric geometric point of view. We begin by constructing a binomial variant of the @math -resultant and then retailor the generalized characteristic polynomial to fully exploit sparsity in the monomial structure of any given polynomial system. We thus obtain a fast new algorithm for univariate reduction and a better understanding of the underlying projections. As a corollary, we show that a refinement of Hilbert's Tenth Problem is decidable within single-exponential time. We also show how certain multisymmetric functions of the roots of polynomial systems can be calculated with sparse resultants.
From an applied angle, our observations on degeneracies and handling polynomial systems with infinitely many roots nicely complement the work of Emiris and Canny @cite_43 . In particular, their sparse resultant based algorithms for polynomial system solving can now be made to work even when problem B occurs. Also, an added benefit of working torically (as opposed to the classical approach of working in projective space) is the increased efficiency of the sparse resultant: the resulting matrix calculations (for polynomial system solving) are much smaller and faster. In particular, whereas it was remarked in @cite_41 that Gr "obner basis methods are likely to be faster than the GCP for sparse polynomial systems, the toric GCP appears to be far more competitive in such a comparison.
{ "cite_N": [ "@cite_41", "@cite_43" ], "mid": [ "1976392590", "2066130115" ], "abstract": [ "Multipolynomial resultants provide the most efficient methods known (in terms as asymptoticcomplexity) for solving certain systems of polynomial equations or eliminating variables (, 1988). The resultant of f\"1, ..., f\"n in K[x\"1,...,x\"m] will be a polynomial in m-n+1 variables which is zero when the system f\"1=0 has a solution in ^m ( the algebraic closure of K). Thus the resultant defines a projection operator from ^m to ^(^m^-^n^+^1^). However, resultants are only exact conditions for homogeneous systems, and in the affine case just mentioned, the resultant may be zero even if the system has no affine solution. This is most serious when the solution set of the system of polynomials has ''excess components'' (components of dimension >m-n), which may not even be affine, since these cause the resultant to vanish identically. In this paper we describe a projection operator which is not identically zero, but which is guaranteed to vanish on all the proper (dimension=m-n) components of the system f\"i=0. Thus it fills the role of a general affine projection operator or variable elimination ''black box'' which can be used for arbitrary polynomial systems. The construction is based on a generalisation of the characteristic polynomial of a linear system to polynomial systems. As a corollary, we give a single-exponential time method for finding all the isolated solution points of a system of polynomials, even in the presence of infinitely many solutions, at infinity or elsewhere.", "Abstract We propose a new and efficient algorithm for computing the sparse resultant of a system of n + 1 polynomial equations in n unknowns. This algorithm produces a matrix whose entries are coefficients of the given polynomials and is typically smaller than the matrices obtained by previous approaches. The matrix determinant is a non-trivial multiple of the sparse resultant from which the sparse resultant itself can be recovered. The algorithm is incremental in the sense that successively larger matrices are constructed until one is found with the above properties. For multigraded systems, the new algorithm produces optimal matrices, i.e. expresses the sparse resultant as a single determinant. An implementation of the algorithm is described and experimental results are presented. In addition, we propose an efficient algorithm for computing the mixed volume of n polynomials in n variables. This computation provides an upper bound on the number of common isolated roots. A publicly available implementation of the algorithm is presented and empirical results are reported which suggest that it is the fastest mixed volume code to date." ] }
cmp-lg9503009
2951769629
This paper presents an algorithm for tagging words whose part-of-speech properties are unknown. Unlike previous work, the algorithm categorizes word tokens in context instead of word types. The algorithm is evaluated on the Brown Corpus.
The simplest part-of-speech taggers are bigram or trigram models @cite_12 @cite_1 . They require a relatively large tagged training text. Transformation-based tagging as introduced by also requires a hand-tagged text for training. No pretagged text is necessary for Hidden Markov Models @cite_7 @cite_13 @cite_2 . Still, a lexicon is needed that specifies the possible parts of speech for every word. have shown that the effort necessary to construct the part-of-speech lexicon can be considerably reduced by combining learning procedures and a partial part-of-speech categorization elicited from an informant.
{ "cite_N": [ "@cite_7", "@cite_1", "@cite_2", "@cite_13", "@cite_12" ], "mid": [ "2100796029", "2166394306", "", "2046224275", "1509596266" ], "abstract": [ "Abstract A system for part-of-speech tagging is described. It is based on a hidden Markov model which can be trained using a corpus of untagged text. Several techniques are introduced to achieve robustness while maintaining high performance. Word equivalence classes are used to reduce the overall number of parameters in the model, alleviating the problem of obtaining reliable estimates for individual words. The context for category prediction is extended selectively via predefined networks, rather than using a uniformly higher-order conditioning which requires exponentially more parameters with increasing context. The networks are embedded in a first-order model and network structure is developed by analysis of erros, and also via linguistic considerations. To compensate for incomplete dictionary coverage, the categories of unknown words are predicted using both local context and suffix information to aid in disambiguation. An evaluation was performed using the Brown corpus and different dictionary arrangements were investigated. The techniques result in a model that correctly tags approximately 96 of the text. The flexibility of the methods is illustrated by their use in a tagging program for French.", "We derive from first principles the basic equations for a few of the basic hidden-Markov-model word taggers as well as equations for other models which may be novel (the descriptions in previous papers being too spare to be sure). We give performance results for all of the models. The results from our best model (96.45 on an unused test sample from the Brown corpus with 181 distinct tags) is on the upper edge of reported results. We also hope these results clear up some confusion in the literature about the best equations to use. However, the major purpose of this paper is to show how the equations for a variety of models may be derived and thus encourage future authors to give the equations for their model and the derivations thereof.", "", "We present an implementation of a part-of-speech tagger based on a hidden Markov model. The methodology enables robust and accurate tagging with few resource requirements. Only a lexicon and some unlabeled training text are required. Accuracy exceeds 96 . We describe implementation strategies and optimizations which result in high-speed operation. Three applications for tagging are described: phrase recognition; word sense disambiguation; and grammatical function assignment.", "A consideration of problems engendered by the use of concordances to study additional word senses. The use of factor analysis as a research tool in lexicography is discussed. It is shown that this method provides information not obtainable through other approaches. This includes provision of several major senses for each word, an indication of the relationship between collocational patterns, & a more detailed analysis of the senses themselves. Sample factor analyses for the collocates of certain & right are presented & discussed. 3 Tables, 11 References. B. Annesser Murray" ] }
cmp-lg9503009
2951769629
This paper presents an algorithm for tagging words whose part-of-speech properties are unknown. Unlike previous work, the algorithm categorizes word tokens in context instead of word types. The algorithm is evaluated on the Brown Corpus.
The present paper is concerned with tagging languages and sublanguages for which no a priori knowledge about grammatical categories is available, a situation that occurs often in practice @cite_4 .
{ "cite_N": [ "@cite_4" ], "mid": [ "144990771" ], "abstract": [ "In this paper, we will discuss a method for assigning part of speech tags to words in an unannotated text corpus whose structure is completely unknown, with a little bit of help from an informant. Starting from scratch, automated and semiautomated methods are employed to build a part of speech tagger for the text. There are three steps to building the tagger: uncovering a set of part of speech tags, building a lexicon which indicates for each word its most likely tag, and learning rules to both correct mistakes in the learned lexicon and discover where contextual information can repair tagging mistakes. The long term goal of this work is to create a system which would enable somebody to take a large text in a language he does not know, and with only a few hours of help from a speaker of the language, accurately annotate the text with part of speech information." ] }
cmp-lg9503009
2951769629
This paper presents an algorithm for tagging words whose part-of-speech properties are unknown. Unlike previous work, the algorithm categorizes word tokens in context instead of word types. The algorithm is evaluated on the Brown Corpus.
In a previous paper @cite_15 , we trained a neural network to disambiguate part-of-speech using context; however, no information about the word that is to be categorized was used. This scheme fails for cases like The soldiers rarely come home.'' vs. The soldiers will come home.'' where the context is identical and information about the lexical item in question ( rarely'' vs. will'') is needed in combination with context for correct classification. In this paper, we will compare two tagging algorithms, one based on classifying word types, and one based on classifying words-plus-context.
{ "cite_N": [ "@cite_15" ], "mid": [ "2163514362" ], "abstract": [ "This paper presents a method for inducing the parts of speech of a language and part-of-speech labels for individual words from a large text corpus. Vector representations for the part-of-speech of a word are formed from entries of its near lexical neighbors. A dimensionality reduction creates a space representing the syntactic categories of unambiguous words. A neural net trained on these spatial representations classifies individual contexts of occurrence of ambiguous words. The method classifies both ambiguous and unambiguous words correctly with high accuracy." ] }
cmp-lg9607020
1497662183
In this paper, we propose a novel strategy which is designed to enhance the accuracy of the parser by simplifying complex sentences before parsing. This approach involves the separate parsing of the constituent sub-sentences within a complex sentence. To achieve that, the divide-and-conquer strategy first disambiguates the roles of the link words in the sentence and segments the sentence based on these roles. The separate parse trees of the segmented sub-sentences and the noun phrases within them are then synthesized to form the final parse. To evaluate the effects of this strategy on parsing, we compare the original performance of a dependency parser with the performance when it is enhanced with the divide-and-conquer strategy. When tested on 600 sentences of the IPSM'95 data sets, the enhanced parser saw a considerable error reduction of 21.2 in its accuracy.
Magerman discussed the poor performance of his parser SPATTER on sentences with conjunctions @cite_9 . As a result, he augmented SPATTER's probabilistic model with an additional conjunction feature. However, he reported that though SPATTER's performance on conjoined sentences improves with the conjunction feature, a significant percentage is still misanalyzed, as the simple conjunction feature model finds it difficult to capture long distance dependencies.
{ "cite_N": [ "@cite_9" ], "mid": [ "1924403233" ], "abstract": [ "Traditional natural language parsers are based on rewrite rule systems developed in an arduous, time-consuming manner by grammarians. A majority of the grammarian's efforts are devoted to the disambiguation process, first hypothesizing rules which dictate constituent categories and relationships among words in ambiguous sentences, and then seeking exceptions and corrections to these rules. In this work, I propose an automatic method for acquiring a statistical parser from a set of parsed sentences which takes advantage of some initial linguistic input, but avoids the pitfalls of the iterative and seemingly endless grammar development process. Based on distributionally-derived and linguistically-based features of language, this parser acquires a set of statistical decision trees which assign a probability distribution on the space of parse trees given the input sentence. These decision trees take advantage of significant amount of contextual information, potentially including all of the lexical information in the sentence, to produce highly accurate statistical models of the disambiguation process. By basing the disambiguation criteria selection on entropy reduction rather than human intuition, this parser development method is able to consider more sentences than a human grammarian can when making individual disambiguation rules. In experiments between a parser, acquired using this statistical framework, and a grammarian's rule-based parser, developed over a ten-year period, both using the same training material and test sentences, the decision tree parser significantly outperformed the grammar-based parser on the accuracy measure which the grammarian was trying to maximize, achieving an accuracy of 78 compared to the grammar-based parser's 69 ." ] }
cmp-lg9607020
1497662183
In this paper, we propose a novel strategy which is designed to enhance the accuracy of the parser by simplifying complex sentences before parsing. This approach involves the separate parsing of the constituent sub-sentences within a complex sentence. To achieve that, the divide-and-conquer strategy first disambiguates the roles of the link words in the sentence and segments the sentence based on these roles. The separate parse trees of the segmented sub-sentences and the noun phrases within them are then synthesized to form the final parse. To evaluate the effects of this strategy on parsing, we compare the original performance of a dependency parser with the performance when it is enhanced with the divide-and-conquer strategy. When tested on 600 sentences of the IPSM'95 data sets, the enhanced parser saw a considerable error reduction of 21.2 in its accuracy.
Jones explored another type of link words, the punctuations @cite_7 . He showed successfully that for longer sentences, a grammar which makes use of punctuation massively outperforms one which does not. Besides improving parsing accuracy, the use of punctuations also significantly reduces the number of possible parses generated. However, as theoretical forays into the syntactic roles of punctuation are limited, the grammar he designed can only cover a subset of all punctuation phenomena. Unexpected constructs thus cause the grammar to fail completely.
{ "cite_N": [ "@cite_7" ], "mid": [ "2039117335" ], "abstract": [ "Few, if any, current NLP systems make any significant use of punctuation. Intuitively, a treatment of punctuation seems necessary to the analysis and production of text. Whilst this has been suggested in the fields of discourse structure, it is still unclear whether punctuation can help in the syntactic field. This investigation attempts to answer this question by parsing some corpus-based material with two similar grammars --- one including rules for punctuation, the other ignoring it. The punctuated grammar significantly out-performs the unpunctuated one, and so the conclusion is that punctuation can play a useful role in syntactic processing." ] }
1908.11823
2970705892
In this work we investigate to which extent one can recover class probabilities within the empirical risk minimization (ERM) paradigm. The main aim of our paper is to extend existing results and emphasize the tight relations between empirical risk minimization and class probability estimation. Based on existing literature on excess risk bounds and proper scoring rules, we derive a class probability estimator based on empirical risk minimization. We then derive fairly general conditions under which this estimator will converge, in the L1-norm and in probability, to the true class probabilities. Our main contribution is to present a way to derive finite sample L1-convergence rates of this estimator for different surrogate loss functions. We also study in detail which commonly used loss functions are suitable for this estimation problem and finally discuss the setting of model-misspecification as well as a possible extension to asymmetric loss functions.
perform an analysis similar to ours as they also investigate convergence properties of a class probability estimator, their start and end point are very different though. While we start with theory from proper scoring rules, their paper directly starts with the class probability estimator as found in @cite_5 . The problem is that the estimator in @cite_5 only appears as a side remark, and it is unclear to which extent this is the best, only or even the correct choice. This paper contributes to close this gap and answers those questions. They show that the estimator converges to a unique class probability model. In relation to this one can view this paper as an investigation of this unique class probability model and we give necessary and sufficient conditions that lead to convergence to the true class probabilities. Note also that their paper uses convex methods, while our work in comparison draws from the theory of proper scoring rules.
{ "cite_N": [ "@cite_5" ], "mid": [ "2023163512" ], "abstract": [ "We study how closely the optimal Bayes error rate can be approximately reached using a classification algorithm that computes a classifier by minimizing a convex upper bound of the classification error function. The measurement of closeness is characterized by the loss function used in the estimation. We show that such a classification scheme can be generally regarded as a (nonmaximum-likelihood) conditional in-class probability estimate, and we use this analysis to compare various convex loss functions that have appeared in the literature. Furthermore, the theoretical insight allows us to design good loss functions with desirable properties. Another aspect of our analysis is to demonstrate the consistency of certain classification methods using convex risk minimization. This study sheds light on the good performance of some recently proposed linear classification methods including boosting and support vector machines. It also shows their limitations and suggests possible improvements." ] }
1908.11823
2970705892
In this work we investigate to which extent one can recover class probabilities within the empirical risk minimization (ERM) paradigm. The main aim of our paper is to extend existing results and emphasize the tight relations between empirical risk minimization and class probability estimation. Based on existing literature on excess risk bounds and proper scoring rules, we derive a class probability estimator based on empirical risk minimization. We then derive fairly general conditions under which this estimator will converge, in the L1-norm and in probability, to the true class probabilities. Our main contribution is to present a way to derive finite sample L1-convergence rates of this estimator for different surrogate loss functions. We also study in detail which commonly used loss functions are suitable for this estimation problem and finally discuss the setting of model-misspecification as well as a possible extension to asymmetric loss functions.
The probability estimator we use also appears in @cite_10 where it is used to derive excess risk bounds, referred to as surrogate risk bounds, for bipartite ranking. The methods used are very similar in the sense that these are also based on proper scoring rules. The difference is again the focus, and even more so the conditions used. They introduce the notion of strongly proper scoring rules which directly allows one to bound the @math -norm, and thus the @math -norm, of the estimator in terms of the excess risk. We show that convergence can be achieved already under milder conditions. We then use the concept of modulus of continuity, of which strongly proper scoring rules are a particular case, to analyze the rate of convergence.
{ "cite_N": [ "@cite_10" ], "mid": [ "2141789531" ], "abstract": [ "The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to reduce bipartite ranking to pairwise classification; in particular, it is well known that the bipartite ranking regret can be formulated as a pairwise classification regret, which in turn can be upper bounded using usual regret bounds for classification problems. Recently, (2011) showed regret bounds for bipartite ranking in terms of the regret associated with balanced versions of the standard (non-pairwise) logistic and exponential losses. In this paper, we show that such (nonpairwise) surrogate regret bounds for bipartite ranking can be obtained in terms of a broad class of proper (composite) losses that we term as strongly proper. Our proof technique is much simpler than that of (2011), and relies on properties of proper (composite) losses as elucidated recently by Reid and Williamson (2010, 2011) and others. Our result yields explicit surrogate bounds (with no hidden balancing terms) in terms of a variety of strongly proper losses, including for example logistic, exponential, squared and squared hinge losses as special cases. An important consequence is that standard algorithms minimizing a (non-pairwise) strongly proper loss, such as logistic regression and boosting algorithms (assuming a universal function class and appropriate regularization), are in fact consistent for bipartite ranking; moreover, our results allow us to quantify the bipartite ranking regret in terms of the corresponding surrogate regret. We also obtain tighter surrogate bounds under certain low-noise conditions via a recent result of Clemencon and Robbiano (2011)." ] }
1908.11829
2970633507
We consider the minimum cut problem in undirected, weighted graphs. We give a simple algorithm to find a minimum cut that @math -respects (cuts two edges of) a spanning tree @math of a graph @math . This procedure can be used in place of the complicated subroutine given in Karger's near-linear time minimum cut algorithm (J. ACM, 2000). We give a self-contained version of Karger's algorithm with the new procedure, which is easy to state and relatively simple to implement. It produces a minimum cut on an @math -edge, @math -vertex graph in @math time with high probability. This performance matches that achieved by Karger, thereby matching the current state of the art.
On an unweighted graph, Gabow @cite_13 showed how to compute the minimum cut in @math time, where @math is the capacity of the minimum cut. Karger @cite_29 improved Gabow's algorithm by applying random sampling, achieving runtime @math The @math notation hides @math factors. Las Vegas. The sampling technique developed by Karger @cite_29 , combined with the tree-packing technique devised by Gabow @cite_13 , form the basis of Karger's near-linear time minimum cut algorithm @cite_16 . As previously mentioned, this technique finds the minimum cut in an undirected, weighted graph in @math time with high probability.
{ "cite_N": [ "@cite_29", "@cite_16", "@cite_13" ], "mid": [ "2150516767", "1964510837", "2012287357" ], "abstract": [ "We use random sampling as a tool for solving undirected graph problems. We show that the sparse graph, or skeleton, that arises when we randomly sample a graph's edges will accurately approximate the value of all cuts in the original graph with high probability. This makes sampling effective for problems involving cuts in graphs. We present fast randomized (Monte Carlo and Las Vegas) algorithms for approximating and exactly finding minimum cuts and maximum flows in unweighted, undirected graphs. Our cut-approximation algorithms extend unchanged to weighted graphs while our weighted-graph flow algorithms are somewhat slower. Our approach gives a general paradigm with potential applications to any packing problem. It has since been used in a near-linear time algorithm for finding minimum cuts, as well as faster cut and flow algorithms. Our sampling theorems also yield faster algorithms for several other cut-based problems, including approximating the best balanced cut of a graph, finding a k-connected orientation of a 2k-connected graph, and finding integral multicommodity flows in graphs with a great deal of excess capacity. Our methods also improve the efficiency of some parallel cut and flow algorithms. Our methods also apply to the network design problem, where we wish to build a network satisfying certain connectivity requirements between vertices. We can purchase edges of various costs and wish to satisfy the requirements at minimum total cost. Since our sampling theorems apply even when the sampling probabilities are different for different edges, we can apply randomized rounding to solve network design problems. This gives approximation algorithms that guarantee much better approximations than previous algorithms whenever the minimum connectivity requirement is large. As a particular example, we improve the best approximation bound for the minimum k-connected subgraph problem from 1.85 to [math not displayed].", "We significantly improve known time bounds for solving the minimum cut problem on undirected graphs. We use a \"semiduality\" between minimum cuts and maximum spanning tree packings combined with our previously developed random sampling techniques. We give a randomized (Monte Carlo) algorithm that finds a minimum cut in an m -edge, n -vertex graph with high probability in O (m log 3 n ) time. We also give a simpler randomized algorithm that finds all minimum cuts with high probability in O( m log 3 n ) time. This variant has an optimal RNC parallelization. Both variants improve on the previous best time bound of O ( n 2 log 3 n ). Other applications of the tree-packing approach are new, nearly tight bounds on the number of near-minimum cuts a graph may have and a new data structure for representing them in a space-efficient manner.", "We present an algorithm that finds the edge connectivity ? of a graph having n vectices and m edges. The running time is O(? m log(n2 m)) for directed graphs and slightly less for undirected graphs, O(m+?2n log(n ?)). This improves the previous best time bounds, O(min mn, ?2n2 ) for directed graphs and O(?n2) for undirected graphs. We present an algorithm that finds k edge-disjoint arborescences on a directed graph in time O((kn)2). This improves the previous best time bound, O(kmn + k3n2). Unlike previous work, our approach is based on two theorems of Edmonds that link these two problems and show how they can be solved." ] }
1908.11829
2970633507
We consider the minimum cut problem in undirected, weighted graphs. We give a simple algorithm to find a minimum cut that @math -respects (cuts two edges of) a spanning tree @math of a graph @math . This procedure can be used in place of the complicated subroutine given in Karger's near-linear time minimum cut algorithm (J. ACM, 2000). We give a self-contained version of Karger's algorithm with the new procedure, which is easy to state and relatively simple to implement. It produces a minimum cut on an @math -edge, @math -vertex graph in @math time with high probability. This performance matches that achieved by Karger, thereby matching the current state of the art.
A recent development uses low-conductance cuts to find the minimum cut in an undirected unweighted graph. This technique was introduced by Kawarabayashi and Thorup @cite_2 , who achieve near-linear deterministic time (estimated to be @math ). This was improved by Henzinger, Rao, and Wang @cite_31 , who achieve deterministic runtime @math . Although the algorithm of is more efficient than Karger's algorithm @cite_16 on unweighted graphs, the procedure, as well as the one it was based on @cite_2 are quite involved, thus making them largely impractical for implementation purposes.
{ "cite_N": [ "@cite_31", "@cite_16", "@cite_2" ], "mid": [ "2569104968", "1964510837", "2963972775" ], "abstract": [ "We study the problem of computing a minimum cut in a simple, undirected graph and give a deterministic O(m log2 n log log2 n) time algorithm. This improves both on the best previously known deterministic running time of O(m log12 n) (Kawarabayashi and Thorup [12]) and the best previously known randomized running time of O(m log3 n) (Karger [11]) for this problem, though Karger's algorithm can be further applied to weighted graphs. Our approach is using the Kawarabayashi and Thorup graph compression technique, which repeatedly finds low-conductance cuts. To find these cuts they use a diffusion-based local algorithm. We use instead a flow-based local algorithm and suitably adjust their framework to work with our flow-based subroutine. Both flow and diffusion based methods have a long history of being applied to finding low conductance cuts. Diffusion algorithms have several variants that are naturally local while it is more complicated to make flow methods local. Some prior work has proven nice properties for local flow based algorithms with respect to improving or cleaning up low conductance cuts. Our flow subroutine, however, is the first that is both local and produces low conductance cuts. Thus, it may be of independent interest.", "We significantly improve known time bounds for solving the minimum cut problem on undirected graphs. We use a \"semiduality\" between minimum cuts and maximum spanning tree packings combined with our previously developed random sampling techniques. We give a randomized (Monte Carlo) algorithm that finds a minimum cut in an m -edge, n -vertex graph with high probability in O (m log 3 n ) time. We also give a simpler randomized algorithm that finds all minimum cuts with high probability in O( m log 3 n ) time. This variant has an optimal RNC parallelization. Both variants improve on the previous best time bound of O ( n 2 log 3 n ). Other applications of the tree-packing approach are new, nearly tight bounds on the number of near-minimum cuts a graph may have and a new data structure for representing them in a space-efficient manner.", "We present a deterministic algorithm that computes the edge-connectivity of a graph in near-linear time. This is for a simple undirected unweighted graph G with n vertices and m edges. This is the first o(mn) time deterministic algorithm for the problem. Our algorithm is easily extended to find a concrete minimum edge-cut. In fact, we can construct the classic cactus representation of all minimum cuts in near-linear time. The previous fastest deterministic algorithm by Gabow from STOC '91 took O(m+λ2 n), where λ is the edge connectivity, but λ can be as big as n−1. Karger presented a randomized near-linear time Monte Carlo algorithm for the minimum cut problem at STOC’96, but the returned cut is only minimum with high probability. Our main technical contribution is a near-linear time algorithm that contracts vertex sets of a simple input graph G with minimum degree Δ, producing a multigraph Ḡ with O(m Δ) edges, which preserves all minimum cuts of G with at least two vertices on each side. In our deterministic near-linear time algorithm, we will decompose the problem via low-conductance cuts found using PageRank a la Brin and Page (1998), as analyzed by Andersson, Chung, and Lang at FOCS’06. Normally, such algorithms for low-conductance cuts are randomized Monte Carlo algorithms, because they rely on guessing a good start vertex. However, in our case, we have so much structure that no guessing is needed." ] }
1908.11656
2971124296
We propose LU-Net -- for LiDAR U-Net, a new method for the semantic segmentation of a 3D LiDAR point cloud. Instead of applying some global 3D segmentation method such as PointNet, we propose an end-to-end architecture for LiDAR point cloud semantic segmentation that efficiently solves the problem as an image processing problem. We first extract high-level 3D features for each point given its 3D neighbors. Then, these features are projected into a 2D multichannel range-image by considering the topology of the sensor. Thanks to these learned features and this projection, we can finally perform the segmentation using a simple U-Net segmentation network, which performs very well while being very efficient. In this way, we can exploit both the 3D nature of the data and the specificity of the LiDAR sensor. This approach outperforms the state-of-the-art by a large margin on the KITTI dataset, as our experiments show. Moreover, this approach operates at 24fps on a single GPU. This is above the acquisition rate of common LiDAR sensors which makes it suitable for real-time applications.
Semantic segmentation of images has been the subject of many works in the past years. Recently, deep learning methods have largely outperformed previous ones. The method presented in @cite_16 was the first to propose an accurate end-to-end network for semantic segmentation. This method is based on an encoder in which each scale is used to compute the final segmentation. Only a few month later, the U-Net architecture @cite_20 was proposed for the semantic segmentation of medical images. This method is an encoder-decoder able to provide highly precise segmentation. These two methods have largely influenced recent works such as DeeplabV3+ @cite_11 that uses dilated convolutional layers and spatial pyramid pooling modules in an encoder-decoder structure to improve the quality of the prediction. Other approaches explore multi-scale architectures to produce and fuse segmentations performed at different scales @cite_15 @cite_7 . Most of these methods are able to produce very accurate results, on various types of images (medical, outdoor, indoor). The survey @cite_1 of CNNs methods for semantic segmentation provides a deep analysis of some recent techniques. This work demonstrates that a combination of various components would most likely improve segmentation results on wider classes of objects.
1908.11656
2971124296
We propose LU-Net -- for LiDAR U-Net, a new method for the semantic segmentation of a 3D LiDAR point cloud. Instead of applying some global 3D segmentation method such as PointNet, we propose an end-to-end architecture for LiDAR point cloud semantic segmentation that efficiently solves the problem as an image processing problem. We first extract high-level 3D features for each point given its 3D neighbors. Then, these features are projected into a 2D multichannel range-image by considering the topology of the sensor. Thanks to these learned features and this projection, we can finally perform the segmentation using a simple U-Net segmentation network, which performs very well while being very efficient. In this way, we can exploit both the 3D nature of the data and the specificity of the LiDAR sensor. This approach outperforms the state-of-the-art by a large margin on the KITTI dataset, as our experiments show. Moreover, this approach operates at 24fps on a single GPU. This is above the acquisition rate of common LiDAR sensors which makes it suitable for real-time applications.
Recently, SqueezeSeg, a novel approach for the semantic segmentation of a LiDAR point cloud represented as a spherical range-image @cite_14 , was proposed. This representation allows to perform the segmentation by using simple 2D convolutions, which lowers the computational cost while keeping good accuracy. The architecture is derived from the SqueezeNet image segmentation method @cite_13 . The intermediate layers are "fire layers", layers made of one squeeze module and one expansion module. Later on, the same authors improved this method in @cite_3 by adding a context aggregation module and by considering focal loss and batch normalization to improve the quality of the segmentation. A similar range-image approach was proposed in @cite_17 , where a Atrous Spatial Pyramid Pooling @cite_4 and squeeze reweighting layer @cite_8 are added. Finally, in @cite_10 , the authors offer to input a range-image directly to the U-Net architecture described in @cite_20 . This method achieves results that are comparable to the state of the art of range-image methods with a much simpler and more intuitive architecture. All these range-image methods succeed in real-time computation. However, their results often lack of accuracy which limits their usage in real scenarios.
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Several temporal logics have been proposed that make joint use of actions and propositions on states: ACTL* @cite_34 , RLTL @cite_0 , SE-LTL @cite_4 , TLR* @cite_25 . There are also transition structures with mixed ingredients: LKS @cite_4 , L2TS @cite_54 . Although all of them bring actions (or transitions) to the focus, none tries to be utterly egalitarian, as we do.
{ "cite_N": [ "@cite_4", "@cite_54", "@cite_0", "@cite_34", "@cite_25" ], "mid": [ "", "2167352300", "2024215898", "1602925513", "1599831144" ], "abstract": [ "", "Three temporal logics are introduced which induce on labeled transition systems the same identifications as branching bisimulation. The first is an extension of Hennessy-Milner logic with a kind of unit operator. The second is another extension of Hennessy-Milner logic which exploits the power of backward modalities. The third is CTL* with the next-time operator interpreted over all paths, not just over maximal ones. A relevant side effect of the last characterization is that it sets a bridge between the state- and event-based approaches to the semantics of concurrent systems. >", "We study efficient translations of Regular Linear Temporal Logic ( ) into automata on infinite words. is a temporal logic that fuses Linear Temporal Logic (LTL) with regular expressions, extending its expressive power to all @math -regular languages. The first contribution of this paper is a novel bottom up translation from into alternating parity automata of linear size that requires only colors @math , @math and @math . Moreover, the resulting automata enjoy the stratified internal structure of hesitant automata. Our translation is defined inductively for every operator, and does not require an upfront transformation of the expression into a normal form. Our construction builds at every step two automata: one equivalent to the formula and another to its complement. Inspired by this construction, our second contribution is to extend with new operators, including universal sequential composition, that enrich the logic with duality laws and negation normal forms. The third contribution is a ranking translation of the resulting alternating automata into non-deterministic automata. To provide this efficient translation we introduce the notion of stratified rankings, and show how the translation is optimal for the LTL fragment of the logic.", "A temporal logic based on actions rather than on states is presented and interpreted over labelled transition systems. It is proved that it has essentially the same power as CTL*, a temporal logic interpreted over Kripke structures. The relationship between the two logics is established by introducing two mappings from Kripke structures to labelled transition systems and viceversa and two transformation functions between the two logics which preserve truth. A branching time version of the action based logic is also introduced. This new logic for transition systems can play an important role as an intermediate between Hennessy-Milner Logic and the modal μ-calculus. It is sufficiently expressive to describe safety and liveness properties but permits model checking in linear time.", "This paper presents the temporal logic of rewriting @math . Syntactically, @math is a very simple extension of @math which just adds action atoms, in the form of spatial action patterns, to @math . Semantically and pragmatically, however, when used together with rewriting logic as a \"tandem\" of system specification and property specification logics, it has substantially more expressive power than purely state-based logics like @math , or purely action-based logics like A- @math . Furthermore, it avoids the system property mismatch problem experienced in state-based or action-based logics, which makes many useful properties inexpressible in those frameworks without unnatural changes to a system's specification. The advantages in expresiveness of @math are gained without losing the ability to use existing tools and algorithms to model check its properties: a faithful translation of models and formulas is given that allows verifying @math properties with @math model checkers." ] }
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
The best move towards egalitarianism we know of is the temporal logic of rewriting, TLR* (which was an inspiration for the present work). The explanations and examples in @cite_25 are good arguments for an egalitarian view. Consider this formula to express fairness in the execution of a rule with label @math : @math . The proposition @math is on states: it means that the current state of the system admits the rule @math to be applied to it. But @math is on transitions: it means that a transition is being executed according to rule @math . The simplicity of the formula is only possible by being egalitarian.
{ "cite_N": [ "@cite_25" ], "mid": [ "1599831144" ], "abstract": [ "This paper presents the temporal logic of rewriting @math . Syntactically, @math is a very simple extension of @math which just adds action atoms, in the form of spatial action patterns, to @math . Semantically and pragmatically, however, when used together with rewriting logic as a \"tandem\" of system specification and property specification logics, it has substantially more expressive power than purely state-based logics like @math , or purely action-based logics like A- @math . Furthermore, it avoids the system property mismatch problem experienced in state-based or action-based logics, which makes many useful properties inexpressible in those frameworks without unnatural changes to a system's specification. The advantages in expresiveness of @math are gained without losing the ability to use existing tools and algorithms to model check its properties: a faithful translation of models and formulas is given that allows verifying @math properties with @math model checkers." ] }
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Plain TLR*, as described in @cite_25 , stays a step away from our goal, because transitions are given by proof terms, that univocally determine one origin state and one destination state for each transition. TLR* uses proof-term patterns (called ), that are used literally in temporal formulas. The problem is that, in this way, a TLR* formula is tied to a particular algebraic specification (one in which the pattern makes sense). In contrast, an LTL or CTL formula is meaningful by itself and can be used on any system specification by using atomic proposition definitions as interfaces. Notably, propositions on transitions have been added to plain TLR*, in some way or another, in all the implementations of model checkers for (the linear-time subset of) TLR* that we are aware of @cite_36 @cite_51 @cite_14 @cite_9 . None of them, however, tries to allow a same proposition to be defined both in states and in transitions, which we need for flexible synchronization.
{ "cite_N": [ "@cite_14", "@cite_36", "@cite_9", "@cite_51", "@cite_25" ], "mid": [ "1986424898", "1583068981", "", "2151284417", "1599831144" ], "abstract": [ "This paper presents the linear temporal logic of rewriting (LTLR) model checker under localized fairness assumptions for the Maude system. The linear temporal logic of rewriting extends linear temporal logic (LTL) with spatial action patterns that describe patterns of rewriting events. Since LTLR generalizes and extends various state-based and event-based logics, mixed properties involving both state propositions and actions, such as fairness properties, can be naturally expressed in LTLR. However, often the needed fairness assumptions cannot even be expressed as propositional temporal logic formulas because they are parametric, that is, they correspond to universally quantified temporal logic formulas. Such universal quantification is succinctly captured by the notion of localized fairness; for example, fairness is localized to the object name parameter in object fairness conditions. We summarize the foundations, and present the language design and implementation of the Maude Fair LTLR model checker, developed at the C++ level within the Maude system by extending the existing Maude LTL model checker. Our tool provides not only an efficient LTLR model checking algorithm under parameterized fairness assumptions but also suitable specification languages as part of its user interface. The expressiveness and effectiveness of the Maude Fair LTLR model checker are illustrated by five case studies. This is the first tool we are aware of that can model check temporal logic properties under parameterized fairness assumptions. We develop the LTLR model checker under localized fairness assumptions.The linear temporal logic of rewriting (LTLR) extends LTL with action patterns.Localized fairness specifies parameterized fairness over generic system entities.We present the foundations, the language design, and the implementation of our tool.We illustrate the expressiveness and effectiveness of our tool with case studies.", "This paper presents the foundation, design, and implementation of the Linear Temporal Logic of Rewriting model checker as an extension of the Maude system. The Linear Temporal Logic of Rewriting (LTLR) extends linear temporal logic with spatial action patterns which represent rewriting events. LTLR generalizes and extends various state-based and event-based logics and aims to avoid certain types of mismatches between a system and its temporal logic properties. We have implemented the LTLR model checker at the C++ level within the Maude system by extending the existing Maude LTL model checker. Our LTLR model checker provides very expressive methods to define event-related properties as well as state-related properties, or, more generally, properties involving both events and state predicates. This greater expressiveness is gained without compromising performance, because the LTLR implementation minimizes the extra costs involved in handling the events of systems.", "", "This paper presents a model checker for LTLR, a subset of the temporal logic of rewriting TLR* extending linear temporal logic with spatial action patterns. Both LTLR and TLR* are very expressive logics generalizing well-known state-based and action-based logics. Furthermore, the semantics of TLR* is given in terms of rewrite theories, so that the concurrent systems on which the LTLR properties are model checked can be specified at a very high level with rewrite rules. This paper answers a nontrivial challenge, namely, to be able to build a model checker to model check LTLR formulas on rewrite theories with relatively little effort by reusing [email protected]?s LTL model checker for rewrite theories. For this, the reflective features of both rewriting logic and its Maude implementation have proved extremely useful.", "This paper presents the temporal logic of rewriting @math . Syntactically, @math is a very simple extension of @math which just adds action atoms, in the form of spatial action patterns, to @math . Semantically and pragmatically, however, when used together with rewriting logic as a \"tandem\" of system specification and property specification logics, it has substantially more expressive power than purely state-based logics like @math , or purely action-based logics like A- @math . Furthermore, it avoids the system property mismatch problem experienced in state-based or action-based logics, which makes many useful properties inexpressible in those frameworks without unnatural changes to a system's specification. The advantages in expresiveness of @math are gained without losing the ability to use existing tools and algorithms to model check its properties: a faithful translation of models and formulas is given that allows verifying @math properties with @math model checkers." ] }
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Our paper @cite_5 contains a first definition of the synchronous composition of rewrite systems. There, we proposed to synchronise the execution of rules from different systems based on the coincidence of (atomic) rule labels. This reflects the synchronisation of actions in process algebras and in automata, for example. We also proposed to synchronise states by agreement on the Boolean values of propositions defined on them. We implemented that concept of synchronisation on Maude. That proposal had the advantage that it used standard machinery already existing in Maude: rule labels are basic elements of Maude's syntax, and propositions are customarily defined and used to build LTL formulas to be used with Maude's model checker. Why is the present, much more involved paper needed? We refer the reader to . In short: Boolean-valued propositions are not enough to allow flexible synchronisation and value-passing; we need to give more substance to transitions; we want to be able to synchronise an action at one system with several consecutive ones at the other system. A complex realistic example like the one on the alternating bit protocol in @cite_33 would not be possible in our previous setting.
{ "cite_N": [ "@cite_5", "@cite_33" ], "mid": [ "2521108378", "2890370072" ], "abstract": [ "We present a concept of module composition for rewrite systems that we call synchronous product, and also a corresponding concept for doubly labeled transition systems (as proposed by De Nicola and Vaandrager) used as semantics for the former. In both cases, synchronization happens on states and on transitions, providing in this way more flexibility and more natural specifications. We describe our implementation in Maude, a rewriting logic-based language and system. A series of examples shows their use for modular specification and hints at other possible uses, including modular verification.", "Our overall goal is compositional specification and verification in rewriting logic. In previous work, we described a way to compose system specifications using the operation we call synchronous composition. In this paper, we propose the use of parameterized programming to encapsulate and handle specifications: theories represent interfaces; modules parameterized by such theories instruct on how to assemble the parameter systems using the synchronous composition operation; the implementation of the whole system is then obtained by instantiating the parameters with implementations for the components. We show, and illustrate with examples, how this setting facilitates compositionality." ] }
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
In a different topic, the paper @cite_33 also describes the use of parameterised programming to add encapsulation to our setting. We have already mentioned it in . We outline it roughly refering to the example from , on two controlled trains. First, a so-called theory is used to state that a train is any system that defines a Boolean-valued property called "isMoving". Requirements for reckoners and controllers are similarly stated. These are our interface specifications. The composition is specified in a parameterised module, whose formal parameters are the theories (that is, the interfaces). Thus, the composition can only be specified using the formal names and the properties in the interfaces. The particular implementations of trains and the other components are written and the needed properties are defined. Finally, the parameters of the composition module are instantiated with the component implementations, producing an implementation of the complete system.
{ "cite_N": [ "@cite_33" ], "mid": [ "2890370072" ], "abstract": [ "Our overall goal is compositional specification and verification in rewriting logic. In previous work, we described a way to compose system specifications using the operation we call synchronous composition. In this paper, we propose the use of parameterized programming to encapsulate and handle specifications: theories represent interfaces; modules parameterized by such theories instruct on how to assemble the parameter systems using the synchronous composition operation; the implementation of the whole system is then obtained by instantiating the parameters with implementations for the components. We show, and illustrate with examples, how this setting facilitates compositionality." ] }
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Process algebras were initially designed as theoretical tools. They focus on actions and synchronisation, and do not provide any means to specify internal computations, or to handle complex data types. However, later developments have taken process algebras as a basis for practical modelling and verification tools. Examples are occam @cite_19 , SCEL @cite_18 , FSP+LTSA @cite_45 , CSP @math B @cite_27 , and LOTOS and the CADP tool @cite_49 .
{ "cite_N": [ "@cite_18", "@cite_19", "@cite_27", "@cite_45", "@cite_49" ], "mid": [ "24701999", "2110425399", "1532163655", "1995830301", "2757839910" ], "abstract": [ "SCEL (Service Component Ensemble Language) is a new language specifically designed to rigorously model and program autonomic components and their interaction, while supporting formal reasoning on their behaviors. SCEL brings together various programming abstractions that allow one to directly represent aggregations, behaviors and knowledge according to specific policies. It also naturally supports programming interaction, self-awareness, context-awareness, and adaptation. The solid semantic grounds of the language is exploited for developing logics, tools and methodologies for formal reasoning on system behavior to establish qualitative and quantitative properties of both the individual components and the overall systems.", "This paper suggests that input and output are basic primitives of programming and that parallel composition of communicating sequential processes is a fundamental program structuring method. When combined with a development of Dijkstra's guarded command, these concepts are surprisingly versatile. Their use is illustrated by sample solutions of a variety of a familiar programming exercises.", "ProB is a model checking tool for the B Method. In this paper we present an extension of ProB that supports checking of specifications written in a combination of CSP and B. We explain how the notations are combined semantically and give an overview of the implementation of the combination. We illustrate the benefit that appropriate use of CSP, in conjunction with our tool, gives to B developments both for specification and for verification purposes.", "Concurrency provides a thoroughly updatedapproach to the basic concepts and techniques behind concurrent programming. Concurrent programming is complex and demands a much more formal approach than sequential programming. In order to develop a thorough understanding of the topicMagee and Kramer present concepts, techniques and problems through a variety of forms: informal descriptions, illustrative examples, abstract models and concrete Java examples. These combineto provide problem patterns and associated solution techniqueswhich enablestudents torecognise problems and arrive at solutions. New features include: New chapters covering program verification and logical properties. More student exercises. Supporting website contains an updated version of the LTSA tool for modelling concurrency, model animation, and model checking. Website also includes the full set of state models, java examples, and demonstration programs and a comprehensive set of overhead slides for course presentation.", "We revisit the early publications of Ed Brinksma devoted, on the one hand, to the definition of the formal description technique LOTOS (ISO International Standard 8807:1989) for specifying communication protocols and distributed systems, and, on the other hand, to two proposals (Extended LOTOS and Modular LOTOS) for making LOTOS a simpler and more expressive language. We examine how this scientific agenda has been dealt with during the last decades. We review the successive enhancements of LOTOS that led to the definition of three languages: E-LOTOS (ISO International Standard 15437:2001), then LOTOS NT, and finally LNT. We present the software implementations (compilers and translators) developed for these new languages and report about their use in various application domains." ] }
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Typically, there are two ways to compose Petri nets. One is given by hierarchical nets, that is, nets in which a transition can represent a complete separate net, that is described independently. The second way is to identify, or , either places or transitions from two different nets. For example, the coffee machine and the scientist can be modelled and then composed by fusing transitions like this: Some approaches propose the introduction of interfaces, that allow to see each component net as a black box. That is the case of the recent work described in @cite_29 .
{ "cite_N": [ "@cite_29" ], "mid": [ "2016355163" ], "abstract": [ "A quite fourishing research thread in the recent literature on component based system is concerned with the algebraic properties of different classes of connectors. In a recent paper, an algebra of stateless connectors was presented that consists of five kinds of basic connectors, namely symmetry, synchronization, mutual exclusion, hiding and inaction, plus their duals and it was shown how they can be freely composed in series and in parallel to model sophisticated \"glues\". In this paper we explore the expressiveness of stateful connectors obtained by adding one-place buffers or unbounded buffers to the stateless connectors. The main results are: i) we show how different classes of connectors exactly correspond to suitable classes of Petri nets equipped with compositional interfaces, called nets with boundaries; ii) we show that the difference between strong and weak semantics in stateful connectors is reflected in the semantics of nets with boundaries by moving from the classic step semantics (strong case) to a novel banking semantics (weak case), where a step can be executed by taking some \"debit\" tokens to be given back during the same step; iii) we show that the corresponding bisimilarities are congruences (w.r.t. composition of connectors in series and in parallel); iv) we show that suitable monoidality laws, like those arising when representing stateful connectors in the tile model, can nicely capture concurrency aspects; and v) as a side result, we provide a basic algebra, with a finite set of symbols, out of which we can compose all P T nets, fulfilling a long standing quest." ] }
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Tile logic was introduced in @cite_28 , and is closely related to rewriting logic. In short, tile logic is rewriting logic with side effects for composition. A tile is written as @math with @math being the condition for, and @math the effect of, rewriting @math to @math . The intuitive meaning of that tile is: the term @math is rewritten to the term @math , producing an effect @math , but the rewrite can only happen if the variables of @math (that represent as yet unspecified subcomponents) are rewritten with a cumulative effect @math .'' Effects are given by terms of any complexity.
{ "cite_N": [ "@cite_28" ], "mid": [ "1909063750" ], "abstract": [ "In this paper we introduce a model for a wide class of computational systems, whose behaviour can be described by certain rewriting rules. We gathered our inspiration both from the world of term rewriting, in particular from the rewriting logic framework Mes92 , and of concurrency theory: among the others, the structured operational semantics Plo81 , the context systems LX90 and the structured transition systems CM92 approaches. Our model recollects many properties of these sources: first, it provides a compositional way to describe both the states and the sequences of transitions performed by a given system, stressing their distributed nature. Second, a suitable notion of typed proof allows to take into account also those formalisms relying on the notions of synchronization and side-effects to determine the actual behaviour of a system. Finally, an equivalence relation over sequences of transitions is defined, equipping the system under analysis with a concurrent semantics, where each equivalence class denotes a family of computationally equivalent'''' behaviours, intended to correspond to the execution of the same set of (causally unrelated) events. As a further abstraction step, our model is conveniently represented using double-categories: its operational semantics is recovered with a free construction, by means of a suitable adjunction." ] }
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Connections between tile logic and rewriting logic have been drawn in @cite_47 and @cite_39 , mainly in the language of category theory.
{ "cite_N": [ "@cite_47", "@cite_39" ], "mid": [ "1601458080", "2026741712" ], "abstract": [ "Rewriting logic extends to concurrent systems with state changes the body of theory developed within the algebraic semantics approach. It is both a foundational tool and the kernel language of several implementation efforts (Cafe, ELAN, Maude). Tile logic extends (unconditional) rewriting logic since it takes into account state changes with side effects and synchronization. It is especially useful for defining compositional models of computation of reactive systems, coordination languages, mobile calculi, and causal and located concurrent systems. In this paper, the two logics are defined and compared using a recently developed algebraic specification methodology, membership equational logic. Given a theory T, the rewriting logic of T is the free monoidal 2-category, and the tile logic of T is the free monoidal double category, both generated by T. An extended version of monoidal 2-categories, called 2VH-categories, is also defined, able to include in an appropriate sense the structure of monoidal double categories. We show that 2VH-categories correspond to an extended version of rewriting logic, which is able to embed tile logic, and which can be implemented in the basic version of rewriting logic using suitable internal strategies. These strategies can be significantly simpler when the theory is uniform. A uniform theory is provided in the paper for CCS, and it is conjectured that uniform theories exist for most process algebras.", "We propose a modular high-level approach to the specification of transactions in rewriting logic, where the operational and the abstract views are related by suitable adjunctions between categories of tile theories and of rewrite theories." ] }
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
The goal of coordination is to make different components work together. The components may have been coded in different languages, reside in different servers, with different architectures. The paper @cite_20 is a comprehensive reference, though old.
{ "cite_N": [ "@cite_20" ], "mid": [ "1500859230" ], "abstract": [ "A new class of models, formalisms and mechanisms has recently evolved for describing concurrent and distributed computations based on the concept of coordination''''. The purpose of a coordination model and associated language is to provide a means of integrating a number of possibly heterogeneous components together, by interfacing with each component in such a way that the collective set forms a single application that can execute on and take advantage of parallel and distributed systems. In this chapter we initially define and present in sufficient detail the fundamental concepts of what constitutes a coordination model or language. We then go on to classify these models and languages as either data-driven'''' or control-driven'''' (also called process-'''' or task-oriented''''). Next, the main existing coordination models and languages are described in sufficient detail to let the reader appreciate their features and put them into perspective with respect to each other. The chapter ends with a discussion comparing the various models and some conclusions." ] }
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Coordination is a very general term, and some of the proposals we have discussed above can be seen as belonging to it. Let us name a few additional examples. Linda, with all its variants and implementations, is one of the best known coordination languages. See @cite_35 for a relatively recent take. It is based on the idea of a shared repository of data and relies on each component using coordination primitives in appropriate ways. REO is at the other extreme. It enforces separation of concerns and is based on composing basic channels to provide port to port communication between components. Basic channels can be of any imaginable sort (synchronous or not, lossy ) and the means to compose them are very flexible. REO is described in @cite_44 .
{ "cite_N": [ "@cite_44", "@cite_35" ], "mid": [ "2139842876", "28813266" ], "abstract": [ "In this paper, we present Reo, which forms a paradigm for composition of software components based on the notion of mobile channels. Reo is a channel-based exogenous coordination model in which complex coordinators, called connectors, are compositionally built out of simpler ones. The simplest connectors in Reo are a set of channels with well-defined behaviour supplied by users. Reo can be used as a language for coordination of concurrent processes, or as a ‘glue language’ for compositional construction of connectors that orchestrate component instances in a component-based system. The emphasis in Reo is just on connectors and their composition, and not on the entities that connect to, communicate and cooperate through these connectors. Each connector in Reo imposes a specific coordination pattern on the entities (for example, components) that perform I O operations through that connector, without the knowledge of those entities. Channel composition in Reo is a very powerful mechanism for construction of connectors. We demonstrate the expressive power of connector composition in Reo through a number of examples. We show that exogenous coordination patterns that can be expressed as (meta-level) regular expressions over I O operations can be composed in Reo out of a small set of only five primitive channel types.", "The original Linda model of coordination has always been attractive due primarily to its simplicity, but also due to the model’s other strong features of orthogonality, and the spatialand temporaldecoupling of concurrent processes. Recently there has been a resurgence of interest in the Linda coordination model, particularly in the Java community. We believe that the simplicity of this model still has much to offer, but that there are still challenges in overcoming the performance issues inherent in the Linda approach, and extending the range of applications to which it is suited. Our prior work has focused on mechanisms for generalising the input mechanisms in the Linda model, over a range of different implementation strategies. We believe that similar optimisations may be applicable to other aspects of the model, especially in the context of middleware support for components utilising web-services. The outcome of such improvements would be to provide a simple, but highly effective coordination language, that is applicable to a wide range of different application areas." ] }
1908.11769
2971290534
Rewriting logic is naturally concurrent: several subterms of the state term can be rewritten simultaneously. But state terms are global, which makes compositionality difficult to achieve. Compositionality here means being able to decompose a complex system into its functional components and code each as an isolated and encapsulated system. Our goal is to help bringing compositionality to system specification in rewriting logic. The base of our proposal is the operation that we call synchronous composition. We discuss the motivations and implications of our proposal, formalize it for rewriting logic and also for transition structures, to be used as semantics, and show the power of our approach with some examples. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
BIP stands for , , ---the three of a composed specification, as proposed by the authors. The behaviour of atomic components is specified by automata (of a special kind) some of whose actions are also taken as port names for communication. These automata are a specification of requirements on the component, whose real implementation can be made using any language or tool. Interaction is performed through connectors linking ports in potentially complex ways. Among the interactions that are allowed at any given time, the one with the highest priority is chosen and performed. Interaction and priority together implement control. The paper @cite_17 has a good overview. Several implementations exist that allow to use the BIP framework within programming languages like Java and C++.
{ "cite_N": [ "@cite_17" ], "mid": [ "2133038101" ], "abstract": [ "We present a methodology for modeling heterogeneous real-time components. Components are obtained as the superposition of three layers : Behavior, specified as a set of transitions; Interactions between transitions of the behavior; Priorities, used to choose amongst possible interactions. A parameterized binary composition operator is used to compose components layer by layer. We present the BIP language for the description and composition of layered components as well as associated tools for executing and analyzing components on a dedicated platform. The language provides a powerful mechanism for structuring interactions involving rendezvous and broadcast. We show that synchronous and timed systems are particular classes of components. Finally, we provide examples and compare the BIP framework to existing ones for heterogeneous component-based modeling." ] }
1908.11645
2971105318
In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive ML models on embedded and mobile systems with tight power and energy constraints at low cost, as well as for boosting throughput in data centers, is growing rapidly. This has sparked a surge of research into specialized hardware accelerators. Their performance is typically limited by I O bandwidth, power consumption is dominated by I O transfers to off-chip memory, and on-chip memories occupy a large part of the silicon area. We introduce and evaluate a novel, hardware-friendly and lossless compression scheme for the feature maps present within convolutional neural networks. Its hardware implementation fits into 2.8 kGE and 1.7 kGE of silicon area for the compressor and decompressor, respectively. We show that an average compression ratio of 5.1x for AlexNet, 4x for VGG-16, 2.4x for ResNet-34 and 2.2x for MobileNetV2 can be achieved---a gain of 45--70 over existing methods. Our approach also works effectively for various number formats, has a low frame-to-frame variance on the compression ratio, and achieves compression factors for gradient map compression during training that are even better than for inference.
There are several methods out there describing hardware accelerators which exploit feature map sparsity to reduce computation: Cnvlutin @cite_2 , SCNN @cite_47 , Cambricon-X @cite_46 , NullHop @cite_24 , Eyeriss @cite_12 , EIE @cite_43 . Their focus is on power gating or skipping some of the operations and memory accesses. This entails defining a scheme to feed the data into the system. They all use one of three methods: Zero-RLE (used in SCNN): A simple run-length encoding for the zero values, i.e. a single prefix bit followed by the number of zero-values or the non-zero value. Zero-free neuron array format (ZFNAf) (used in Cnvlutin): Similarly to the widely-used compressed sparse row (CSR) format, non-zero elements are encoded with an offset and their value. Compressed column storage (CCS) format (e.g. used in EIE, and similar to NullHop): Similar to ZFNAf, but the offsets are stored in relative form, thus requiring less bits to store them. Few bits are sufficient, and in case they are all exhausted, a zero-value can be encoded as if it was non-zero.
{ "cite_N": [ "@cite_47", "@cite_24", "@cite_43", "@cite_2", "@cite_46", "@cite_12" ], "mid": [ "2625457103", "2623629680", "2285660444", "2516141709", "2565851976", "2442974303" ], "abstract": [ "Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of CNNs, especially in mobile platforms such as autonomous vehicles, cameras, and electronic personal assistants. This paper introduces the Sparse CNN (SCNN) accelerator architecture, which improves performance and energy efficiency by exploiting the zero-valued weights that stem from network pruning during training and zero-valued activations that arise from the common ReLU operator. Specifically, SCNN employs a novel dataflow that enables maintaining the sparse weights and activations in a compressed encoding, which eliminates unnecessary data transfers and reduces storage requirements. Furthermore, the SCNN dataflow facilitates efficient delivery of those weights and activations to a multiplier array, where they are extensively reused; product accumulation is performed in a novel accumulator array. On contemporary neural networks, SCNN can improve both performance and energy by a factor of 2.7x and 2.3x, respectively, over a comparably provisioned dense CNN accelerator.", "Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving many state-of-the-art (SOA) visual processing tasks. Even though graphical processing units are most often used in training and deploying CNNs, their power efficiency is less than 10 GOp s W for single-frame runtime inference. We propose a flexible and efficient CNN accelerator architecture called NullHop that implements SOA CNNs useful for low-power and low-latency application scenarios. NullHop exploits the sparsity of neuron activations in CNNs to accelerate the computation and reduce memory requirements. The flexible architecture allows high utilization of available computing resources across kernel sizes ranging from @math to @math . NullHop can process up to 128 input and 128 output feature maps per layer in a single pass. We implemented the proposed architecture on a Xilinx Zynq field-programmable gate array (FPGA) platform and presented the results showing how our implementation reduces external memory transfers and compute time in five different CNNs ranging from small ones up to the widely known large VGG16 and VGG19 CNNs. Postsynthesis simulations using Mentor Modelsim in a 28-nm process with a clock frequency of 500 MHz show that the VGG19 network achieves over 450 GOp s. By exploiting sparsity, NullHop achieves an efficiency of 368 , maintains over 98 utilization of the multiply–accumulate units, and achieves a power efficiency of over 3 TOp s W in a core area of 6.3 mm2. As further proof of NullHop’s usability, we interfaced its FPGA implementation with a neuromorphic event camera for real-time interactive demonstrations.", "State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power. Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120× energy saving; Exploiting sparsity saves 10×; Weight sharing gives 8×; Skipping zero activations from ReLU saves another 3×. Evaluated on nine DNN benchmarks, EIE is 189× and 13× faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102 GOPS working directly on a compressed network, corresponding to 3 TOPS on an uncompressed network, and processes FC layers of AlexNet at 1.88×104 frames sec with a power dissipation of only 600mW. It is 24,000× and 3,400× more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9×, 19× and 3× better throughput, energy efficiency and area efficiency.", "This work observes that a large fraction of the computations performed by Deep Neural Networks (DNNs) are intrinsically ineffectual as they involve a multiplication where one of the inputs is zero. This observation motivates Cnvlutin (CNV), a value-based approach to hardware acceleration that eliminates most of these ineffectual operations, improving performance and energy over a state-of-the-art accelerator with no accuracy loss. CNV uses hierarchical data-parallel units, allowing groups of lanes to proceed mostly independently enabling them to skip over the ineffectual computations. A co-designed data storage format encodes the computation elimination decisions taking them off the critical path while avoiding control divergence in the data parallel units. Combined, the units and the data storage format result in a data-parallel architecture that maintains wide, aligned accesses to its memory hierarchy and that keeps its data lanes busy. By loosening the ineffectual computation identification criterion, CNV enables further performance and energy efficiency improvements, and more so if a loss in accuracy is acceptable. Experimental measurements over a set of state-of-the-art DNNs for image classification show that CNV improves performance over a state-of-the-art accelerator from 1.24× to 1.55× and by 1.37× on average without any loss in accuracy by removing zero-valued operand multiplications alone. While CNV incurs an area overhead of 4.49 , it improves overall EDP (Energy Delay Product) and ED2P (Energy Delay Squared Product) on average by 1.47× and 2.01×, respectively. The average performance improvements increase to 1.52× without any loss in accuracy with a broader ineffectual identification policy. Further improvements are demonstrated with a loss in accuracy.", "Neural networks (NNs) have been demonstrated to be useful in a broad range of applications such as image recognition, automatic translation and advertisement recommendation. State-of-the-art NNs are known to be both computationally and memory intensive, due to the ever-increasing deep structure, i.e., multiple layers with massive neurons and connections (i.e., synapses). Sparse neural networks have emerged as an effective solution to reduce the amount of computation and memory required. Though existing NN accelerators are able to efficiently process dense and regular networks, they cannot benefit from the reduction of synaptic weights. In this paper, we propose a novel accelerator, Cambricon-X, to exploit the sparsity and irregularity of NN models for increased efficiency. The proposed accelerator features a PE-based architecture consisting of multiple Processing Elements (PE). An Indexing Module (IM) efficiently selects and transfers needed neurons to connected PEs with reduced bandwidth requirement, while each PE stores irregular and compressed synapses for local computation in an asynchronous fashion. With 16 PEs, our accelerator is able to achieve at most 544 GOP s in a small form factor (6.38 mm2 and 954 mW at 65 nm). Experimental results over a number of representative sparse networks show that our accelerator achieves, on average, 7.23x speedup and 6.43x energy saving against the state-of-the-art NN accelerator.", "Deep convolutional neural networks (CNNs) are widely used in modern AI systems for their superior accuracy but at the cost of high computational complexity. The complexity comes from the need to simultaneously process hundreds of filters and channels in the high-dimensional convolutions, which involve a significant amount of data movement. Although highly-parallel compute paradigms, such as SIMD SIMT, effectively address the computation requirement to achieve high throughput, energy consumption still remains high as data movement can be more expensive than computation. Accordingly, finding a dataflow that supports parallel processing with minimal data movement cost is crucial to achieving energy-efficient CNN processing without compromising accuracy. In this paper, we present a novel dataflow, called row-stationary (RS), that minimizes data movement energy consumption on a spatial architecture. This is realized by exploiting local data reuse of filter weights and feature map pixels, i.e., activations, in the high-dimensional convolutions, and minimizing data movement of partial sum accumulations. Unlike dataflows used in existing designs, which only reduce certain types of data movement, the proposed RS dataflow can adapt to different CNN shape configurations and reduces all types of data movement through maximally utilizing the processing engine (PE) local storage, direct inter-PE communication and spatial parallelism. To evaluate the energy efficiency of the different dataflows, we propose an analysis framework that compares energy cost under the same hardware area and processing parallelism constraints. Experiments using the CNN configurations of AlexNet show that the proposed RS dataflow is more energy efficient than existing dataflows in both convolutional (1.4× to 2.5×) and fully-connected layers (at least 1.3× for batch size larger than 16). The RS dataflow has also been demonstrated on a fabricated chip, which verifies our energy analysis." ] }
1908.11645
2971105318
In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive ML models on embedded and mobile systems with tight power and energy constraints at low cost, as well as for boosting throughput in data centers, is growing rapidly. This has sparked a surge of research into specialized hardware accelerators. Their performance is typically limited by I O bandwidth, power consumption is dominated by I O transfers to off-chip memory, and on-chip memories occupy a large part of the silicon area. We introduce and evaluate a novel, hardware-friendly and lossless compression scheme for the feature maps present within convolutional neural networks. Its hardware implementation fits into 2.8 kGE and 1.7 kGE of silicon area for the compressor and decompressor, respectively. We show that an average compression ratio of 5.1x for AlexNet, 4x for VGG-16, 2.4x for ResNet-34 and 2.2x for MobileNetV2 can be achieved---a gain of 45--70 over existing methods. Our approach also works effectively for various number formats, has a low frame-to-frame variance on the compression ratio, and achieves compression factors for gradient map compression during training that are even better than for inference.
Oher compression methods are focusing on minimizing the model size and are very complex (silicon area) to implement in hardware. One such method, deep compression @cite_48 , combines pruning, trained clustering-based quantization, and Huffman coding. Most of these steps cannot be applied to the intermediate feature maps, which change for every inference as opposed to the weights which are static and can be optimized off-line. Furthermore, applying Huffman coding---while being optimal in terms of compression rate and given a specification of input symbols and their statistics---implies storing a very large dictionary: encoding a 16 ,bit word requires a table with @math k entries, but effectively multiple values would have to be encoded jointly in order to exploit their joint distribution (e.g. the smoothness), immediately increasing the dictionary size to @math G even for just two values. Similar issues arise when using Lempel-Ziv-Welch (LZW) coding @cite_38 @cite_42 as present in e.g. the ZIP compression scheme, where the dictionary is encoded in the compressed data stream. This makes it unsuitable for a lightweight and energy-efficient VLSI implementation @cite_0 @cite_10 .
{ "cite_N": [ "@cite_38", "@cite_48", "@cite_42", "@cite_0", "@cite_10" ], "mid": [ "1990653637", "2964299589", "2122962290", "2162310490", "2487148512" ], "abstract": [ "", "Abstract: Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce \"deep compression\", a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9x to 13x; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35x, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3x to 4x layerwise speedup and 3x to 7x better energy efficiency.", "Compressibility of individual sequences by the class of generalized finite-state information-lossless encoders is investigated. These encoders can operate in a variable-rate mode as well as a fixed-rate one, and they allow for any finite-state scheme of variable-length-to-variable-length coding. For every individual infinite sequence x a quantity (x) is defined, called the compressibility of x , which is shown to be the asymptotically attainable lower bound on the compression ratio that can be achieved for x by any finite-state encoder. This is demonstrated by means of a constructive coding theorem and its converse that, apart from their asymptotic significance, also provide useful performance criteria for finite and practical data-compression tasks. The proposed concept of compressibility is also shown to play a role analogous to that of entropy in classical information theory where one deals with probabilistic ensembles of sequences rather than with individual sequences. While the definition of (x) allows a different machine for each different sequence to be compressed, the constructive coding theorem leads to a universal algorithm that is asymptotically optimal for all sequences.", "In this paper, we propose a new two-stage hardware architecture that combines the features of both parallel dictionary LZW (PDLZW) and an approximated adaptive Huffman (AH) algorithms. In this architecture, an ordered list instead of the tree-based structure is used in the AH algorithm for speeding up the compression data rate. The resulting architecture shows that it not only outperforms the AH algorithm at the cost of only one-fourth the hardware resource but it is also competitive to the performance of LZW algorithm (compress). In addition, both compression and decompression rates of the proposed architecture are greater than those of the AH algorithm even in the case realized by software", "LZW algorithm is one of the most famous dictionary-based compression and decompression algorithms. The main contribution of this paper is to present a hardware LZW decompression algorithm and to implement it in an FPGA. The experimental results show that one proposed module on Virtex-7 family FPGA XC7VX485T-2 runs up to 2.16 times faster than sequential LZW decompression on a single CPU, where the frequency of FPGA is 301.02MHz. Since the proposed module is compactly designed and uses a few resources of the FPGA, we have succeeded to implement 150 identical modules which works in parallel on the FPGA, where the frequency of FPGA is 245.4MHz. In other words, our implementation runs up to 264 times faster than a sequential implementation on a single CPU." ] }
1908.11645
2971105318
In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive ML models on embedded and mobile systems with tight power and energy constraints at low cost, as well as for boosting throughput in data centers, is growing rapidly. This has sparked a surge of research into specialized hardware accelerators. Their performance is typically limited by I O bandwidth, power consumption is dominated by I O transfers to off-chip memory, and on-chip memories occupy a large part of the silicon area. We introduce and evaluate a novel, hardware-friendly and lossless compression scheme for the feature maps present within convolutional neural networks. Its hardware implementation fits into 2.8 kGE and 1.7 kGE of silicon area for the compressor and decompressor, respectively. We show that an average compression ratio of 5.1x for AlexNet, 4x for VGG-16, 2.4x for ResNet-34 and 2.2x for MobileNetV2 can be achieved---a gain of 45--70 over existing methods. Our approach also works effectively for various number formats, has a low frame-to-frame variance on the compression ratio, and achieves compression factors for gradient map compression during training that are even better than for inference.
Few more methods exist by changing the CNN's structure in order to compress the weights @cite_14 @cite_37 or the feature maps @cite_1 @cite_50 @cite_18 . However, they require altering the CNN's model and or retraining, and they introduce some accuracy loss. Furthermore, they can only be used to compress a few feature maps at specific points within the network and introduce additional compute effort, such as applying a Fourier transform to the feature maps.
{ "cite_N": [ "@cite_18", "@cite_37", "@cite_14", "@cite_1", "@cite_50" ], "mid": [ "2946521496", "2732044853", "2172166488", "2787697768", "2741232386" ], "abstract": [ "We present a theoretical and experimental investigation of the quantization problem for artificial neural networks. We provide a mathematical definition of quantized neural networks and analyze their approximation capabilities, showing in particular that any Lipschitz-continuous map defined on a hypercube can be uniformly approximated by a quantized neural network. We then focus on the regularization effect of additive noise on the arguments of multi-step functions inherent to the quantization of continuous variables. In particular, when the expectation operator is applied to a non-differentiable multi-step random function, and if the underlying probability density is differentiable (in either classical or weak sense), then a differentiable function is retrieved, with explicit bounds on its Lipschitz constant. Based on these results, we propose a novel gradient-based training algorithm for quantized neural networks that generalizes the straight-through estimator, acting on noise applied to the network's parameters. We evaluate our algorithm on the CIFAR-10 and ImageNet image classification benchmarks, showing state-of-the-art performance on AlexNet and MobileNetV2 for ternary networks.", "We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entropy, which we anneal to their discrete counterparts throughout training. We showcase this method for two challenging applications: Image compression and neural network compression. While these tasks have typically been approached with different methods, our soft-to-hard quantization approach gives results competitive with the state-of-the-art for both.", "As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models. We present a novel network architecture, HashedNets, that exploits inherent redundancy in neural networks to achieve drastic reductions in model sizes. HashedNets uses a low-cost hash function to randomly group connection weights into hash buckets, and all connections within the same hash bucket share a single parameter value. These parameters are tuned to adjust to the HashedNets weight sharing architecture with standard backprop during training. Our hashing procedure introduces no additional memory overhead, and we demonstrate on several benchmark data sets that HashedNets shrink the storage requirements of neural networks substantially while mostly preserving generalization performance.", "In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Unlike previous works, the proposed method is based on converting fixed-point activations into vectors over the smallest GF(2) finite field followed by nonlinear dimensionality reduction (NDR) layers embedded into a DNN. Such an end-to-end learned representation finds more compact feature maps by exploiting quantization redundancies within the fixed-point activations along the channel or spatial dimensions. We apply the proposed network architecture to the tasks of ImageNet classification and PASCAL VOC object detection. Compared to prior approaches, the conducted experiments show a factor of 2 decrease in memory requirements with minor degradation in accuracy while adding only bitwise computations.", "" ] }
1908.11645
2971105318
In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive ML models on embedded and mobile systems with tight power and energy constraints at low cost, as well as for boosting throughput in data centers, is growing rapidly. This has sparked a surge of research into specialized hardware accelerators. Their performance is typically limited by I O bandwidth, power consumption is dominated by I O transfers to off-chip memory, and on-chip memories occupy a large part of the silicon area. We introduce and evaluate a novel, hardware-friendly and lossless compression scheme for the feature maps present within convolutional neural networks. Its hardware implementation fits into 2.8 kGE and 1.7 kGE of silicon area for the compressor and decompressor, respectively. We show that an average compression ratio of 5.1x for AlexNet, 4x for VGG-16, 2.4x for ResNet-34 and 2.2x for MobileNetV2 can be achieved---a gain of 45--70 over existing methods. Our approach also works effectively for various number formats, has a low frame-to-frame variance on the compression ratio, and achieves compression factors for gradient map compression during training that are even better than for inference.
The most directly comparable approach, cDMA @cite_25 , describes a hardware-friendly compression scheme to reduce the data size of intermediate feature maps. Their target application differs in that their main goal is to allow faster temporary offloading of the feature maps from GPU to CPU memory through the PCIe bandwidth bottleneck during training, thereby enabling larger batch sizes and deeper and wider networks without sacrificing performance. They propose to use , which takes a block of 32 activation values, and generates a 32-bit mask where only the bits to the non-zero values are set. The non-zero values are transferred after the mask. This provides the main advantage over Zero-RLE that the resulting data volume is independent of how the values of the feature maps are serialized while also providing small compression ratio advantages. Note that this is a special case of Zero-RLE with a maximum zero burst length of 1.
{ "cite_N": [ "@cite_25" ], "mid": [ "2962821792" ], "abstract": [ "Popular deep learning frameworks require users to fine-tune their memory usage so that the training data of a deep neural network (DNN) fits within the GPU physical memory. Prior work tries to address this restriction by virtualizing the memory usage of DNNs, enabling both CPU and GPU memory to be utilized for memory allocations. Despite its merits, virtualizing memory can incur significant performance overheads when the time needed to copy data back and forth from CPU memory is higher than the latency to perform DNN computations. We introduce a high-performance virtualization strategy based on a \"compressing DMA engine\" (cDMA) that drastically reduces the size of the data structures that are targeted for CPU-side allocations. The cDMA engine offers an average 2.6x (maximum 13.8x) compression ratio by exploiting the sparsity inherent in offloaded data, improving the performance of virtualized DNNs by an average 53 (maximum 79 ) when evaluated on an NVIDIA Titan Xp." ] }
1908.11645
2971105318
In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive ML models on embedded and mobile systems with tight power and energy constraints at low cost, as well as for boosting throughput in data centers, is growing rapidly. This has sparked a surge of research into specialized hardware accelerators. Their performance is typically limited by I O bandwidth, power consumption is dominated by I O transfers to off-chip memory, and on-chip memories occupy a large part of the silicon area. We introduce and evaluate a novel, hardware-friendly and lossless compression scheme for the feature maps present within convolutional neural networks. Its hardware implementation fits into 2.8 kGE and 1.7 kGE of silicon area for the compressor and decompressor, respectively. We show that an average compression ratio of 5.1x for AlexNet, 4x for VGG-16, 2.4x for ResNet-34 and 2.2x for MobileNetV2 can be achieved---a gain of 45--70 over existing methods. Our approach also works effectively for various number formats, has a low frame-to-frame variance on the compression ratio, and achieves compression factors for gradient map compression during training that are even better than for inference.
For this work, we build on a method known in the area of texture compression for GPUs, @cite_44 , fuse it with sparsity-focused compression methods, and evaluate the resulting compression algorithm on intermediate feature maps and gradient maps to show compression ratios of 5.1 (8 ,bit AlexNet), 4 (VGG-16), 2.4 (ResNet-34), 2.8 (SqueezeNet), and 2.2 (MobileNetV2).
{ "cite_N": [ "@cite_44" ], "mid": [ "2516109628" ], "abstract": [ "As key applications become more data-intensive and the computational throughput of processors increases, the amount of data to be transferred in modern memory subsystems grows. Increasing physical bandwidth to keep up with the demand growth is challenging, however, due to strict area and energy limitations. This paper presents a novel and lightweight compression algorithm, Bit-Plane Compression (BPC), to increase the effective memory bandwidth. BPC aims at homogeneously-typed memory blocks, which are prevalent in many-core architectures, and applies a smart data transformation to both improve the inherent data compressibility and to reduce the complexity of compression hardware. We demonstrate that BPC provides superior compression ratios of 4.1:1 for integer benchmarks and reduces memory bandwidth requirements significantly." ] }
1908.11787
2970281864
We present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate representation. We encode tables as graphs using a graph neural network model based on the Transformer architecture. The answers are then selected from the encoded graph using a pointer network. This model is appropriate for processing conversations around structured data, where the attention mechanism that selects the answers to a question can also be used to resolve conversational references. We demonstrate the validity of this approach with competitive results on the Sequential Question Answering (SQA) task (, 2017).
Semantic parsing models can be trained to produce gold logical forms using an encoder-decoder approach @cite_26 or by filling templates @cite_22 @cite_18 @cite_30 . When gold logical forms are not available, they are typically treated as latent variables or hidden states and the answers or denotations are used to search for correct logical forms @cite_31 @cite_29 @cite_34 . In some cases, feedback from query execution is used as a reward signal for updating the model through reinforcement learning @cite_5 @cite_6 @cite_32 @cite_16 or for refining parts of the query @cite_7 . In our work, we do not use logical forms or RL, which can be hard to train, but simplify the training process by directly matching questions to table cells.
1908.11598
2971001703
We consider a crowdsourcing data acquisition scenario, such as federated learning, where a Center collects data points from a set of rational Agents, with the aim of training a model. For linear regression models, we show how a payment structure can be designed to incentivize the agents to provide high-quality data as early as possible, based on a characterization of the influence that data points have on the loss function of the model. Our contributions can be summarized as follows: (a) we prove theoretically that this scheme ensures truthful data reporting as a game-theoretic equilibrium and further demonstrate its robustness against mixtures of truthful and heuristic data reports, (b) we design a procedure according to which the influence computation can be efficiently approximated and processed sequentially in batches over time, (c) we develop a theory that allows correcting the difference between the influence and the overall change in loss and (d) we evaluate our approach on real datasets, confirming our theoretical findings.
The topic of learning a model when the input data points are provided by strategic sources has been the focus of a growing literature at the intersection of machine learning and game theory. A significant amount of work has been devoted to the setting in which Agents are interested in the outcome of the estimation process itself, e.g., when they are trying to sway the learned model closer to their own data points @cite_21 @cite_14 @cite_5 @cite_9 @cite_15 . Our setting is concerned with the fundamental question of eliciting accurate data when data acquisition is costly for the agents, or when they are not willing to share their data without some form of monetary compensation. Another line of work considers settings in which the Agents have to be compensated for their loss of privacy @cite_2 @cite_19 .
{ "cite_N": [ "@cite_14", "@cite_9", "@cite_21", "@cite_19", "@cite_2", "@cite_5", "@cite_15" ], "mid": [ "1983916796", "2435502378", "2110135769", "2767657742", "2962877736", "2054844093", "2963668529" ], "abstract": [ "We initiate the study of incentives in a general machine learning framework. We focus on a game-theoretic regression learning setting where private information is elicited from multiple agents with different, possibly conflicting, views on how to label the points of an input space. This conflict potentially gives rise to untruthfulness on the part of the agents. In the restricted but important case when every agent cares about a single point, and under mild assumptions, we show that agents are motivated to tell the truth. In a more general setting, we study the power and limitations of mechanisms without payments. We finally establish that, in the general setting, the VCG mechanism goes a long way in guaranteeing truthfulness and economic efficiency.", "We revisit the classic problem of estimating the population mean of an unknown single-dimensional distribution from samples, taking a game-theoretic viewpoint. In our setting, samples are supplied by strategic agents, who wish to pull the estimate as close as possible to their own value. In this setting, the sample mean gives rise to manipulation opportunities, whereas the sample median does not. Our key question is whether the sample median is the best (in terms of mean squared error) truthful estimator of the population mean. We show that when the underlying distribution is symmetric, there are truthful estimators that dominate the median. Our main result is a characterization of worst-case optimal truthful estimators, which provably outperform the median, for possibly asymmetric distributions with bounded support.", "Abstract This paper introduces a whole class of estimators (clockwise repeated median estimators or CRM) for the simple regression model that are immune to strategic manipulation by the agents generating the data. We find that some well-known robust estimators proposed in the literature like the resistant line method are included in our family. Finally, we also undertake a Monte Carlo study to compare the distribution of some estimators that are robust to data manipulation with the OLS estimators under different scenarios.", "We consider a data analyst's problem of purchasing data from strategic agents to compute an unbiased estimate of a statistic of interest. Agents incur private costs to reveal their data and the costs can be arbitrarily correlated with their data. Once revealed, data are verifiable. This paper focuses on linear unbiased estimators. We design an individually rational and incentive compatible mechanism that optimizes the worst-case mean-squared error of the estimation, where the worst-case is over the unknown correlation between costs and data, subject to a budget constraint in expectation. We characterize the form of the optimal mechanism in closed-form. We further extend our results to acquiring data for estimating a parameter in regression analysis, where private costs can correlate with the values of the dependent variable but not with the values of the independent variables.", "We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy guarantee to the participants; we use differential privacy to model individuals’ privacy losses. This immediately poses a problem, as differentially private computation of a linear model necessarily produces a biased estimation, and existing approaches to design mechanisms to elicit data from privacy-sensitive individuals do not generalize well to biased estimators. We overcome this challenge through an appropriate design of the computation and payment scheme.", "The strategyproof classification problem deals with a setting where a decision maker must classify a set of input points with binary labels, while minimizing the expected error. The labels of the input points are reported by self-interested agents, who might lie in order to obtain a classifier that more closely matches their own labels, thereby creating a bias in the data; this motivates the design of truthful mechanisms that discourage false reports. In this paper we give strategyproof mechanisms for the classification problem in two restricted settings: (i) there are only two classifiers, and (ii) all agents are interested in a shared set of input points. We show that these plausible assumptions lead to strong positive results. In particular, we demonstrate that variations of a random dictator mechanism, that are truthful, can guarantee approximately optimal outcomes with respect to any family of classifiers. Moreover, these results are tight in the sense that they match the best possible approximation ratio that can be guaranteed by any truthful mechanism. We further show how our mechanisms can be used for learning classifiers from sampled data, and provide PAC-style generalization bounds on their expected error. Interestingly, our results can be applied to problems in the context of various fields beyond classification, including facility location and judgment aggregation.", "This paper is part of an emerging line of work at the intersection of machine learning and mechanism design, which aims to avoid noise in training data by correctly aligning the incentives of data sources. Specifically, we focus on the ubiquitous problem of linear regression, where strategyproof mechanisms have previously been identified in two dimensions. In our setting, agents have single-peaked preferences and can manipulate only their response variables. Our main contribution is the discovery of a family of group strategyproof linear regression mechanisms in any number of dimensions, which we call generalized resistant hyperplane mechanisms. The game-theoretic properties of these mechanisms --- and, in fact, their very existence --- are established through a connection to a discrete version of the Ham Sandwich Theorem." ] }
1908.11598
2971001703
We consider a crowdsourcing data acquisition scenario, such as federated learning, where a Center collects data points from a set of rational Agents, with the aim of training a model. For linear regression models, we show how a payment structure can be designed to incentivize the agents to provide high-quality data as early as possible, based on a characterization of the influence that data points have on the loss function of the model. Our contributions can be summarized as follows: (a) we prove theoretically that this scheme ensures truthful data reporting as a game-theoretic equilibrium and further demonstrate its robustness against mixtures of truthful and heuristic data reports, (b) we design a procedure according to which the influence computation can be efficiently approximated and processed sequentially in batches over time, (c) we develop a theory that allows correcting the difference between the influence and the overall change in loss and (d) we evaluate our approach on real datasets, confirming our theoretical findings.
Our ideas are closely related to the literature of mechanisms @cite_8 and @cite_17 . The idea behind this literature is to extract high-quality information from individuals by comparing their reports against those of randomly chosen peers. This approach has been largely successful in eliciting information. The same principle applies to our case, where the payments are dependent on the improvement of the model and therefore agents are rewarded for providing information. Finally, jia2019towards [ jia2019towards ] recently considered a setting in which the value of the provided data information is determined via the . Their approach is inherently different from ours, but it is worth noting that they consider the influence approximation of @cite_1 for approximating the Shapley value.
1908.11515
2970190087
When collecting information, local differential privacy (LDP) alleviates privacy concerns of users, as users' private information is randomized before being sent to the central aggregator. However, LDP results in loss of utility due to the amount of noise that is added. To address this issue, recent work introduced an intermediate server and with the assumption that this intermediate server did not collude with the aggregator. Using this trust model, one can add less noise to achieve the same privacy guarantee; thus improving the utility. In this paper, we investigate this multiple-party setting of LDP. We first analyze the threat model and identify potential adversaries. We then make observations about existing approaches and propose new techniques that achieve a better privacy-utility tradeoff than existing ones. Finally, we perform experiments to compare different methods and demonstrate the benefits of using our proposed method.
Frequency Oracle One basic mechanism in LDP is to estimate frequencies of values. There have been several mechanisms @cite_34 @cite_30 @cite_2 @cite_31 @cite_5 @cite_0 proposed for this task. Among them, @cite_31 introduces , which achieves low estimation errors and low communication costs. The application of is crucial for the utility of other application such as heavy hitter identification @cite_2 and frequent itemset mining @cite_4 @cite_11 . And one major contribution of this paper is to enable to enjoy the privacy amplification effect.
1908.11515
2970190087
When collecting information, local differential privacy (LDP) alleviates privacy concerns of users, as users' private information is randomized before being sent to the central aggregator. However, LDP results in loss of utility due to the amount of noise that is added. To address this issue, recent work introduced an intermediate server and with the assumption that this intermediate server did not collude with the aggregator. Using this trust model, one can add less noise to achieve the same privacy guarantee; thus improving the utility. In this paper, we investigate this multiple-party setting of LDP. We first analyze the threat model and identify potential adversaries. We then make observations about existing approaches and propose new techniques that achieve a better privacy-utility tradeoff than existing ones. Finally, we perform experiments to compare different methods and demonstrate the benefits of using our proposed method.
There also exist relaxed models that seem incompatible with the shuffler model, i.e., @cite_15 considers the inferring probability as the adversary's power; and @cite_3 utilizes the linkage between each user's sensitive and public attributes.
{ "cite_N": [ "@cite_15", "@cite_3" ], "mid": [ "2902114605", "2948055046" ], "abstract": [ "In large-scale statistical learning, data collection and model fitting are moving increasingly toward peripheral devices---phones, watches, fitness trackers---away from centralized data collection. Concomitant with this rise in decentralized data are increasing challenges of maintaining privacy while allowing enough information to fit accurate, useful statistical models. This motivates local notions of privacy---most significantly, local differential privacy, which provides strong protections against sensitive data disclosures---where data is obfuscated before a statistician or learner can even observe it, providing strong protections to individuals' data. Yet local privacy as traditionally employed may prove too stringent for practical use, especially in modern high-dimensional statistical and machine learning problems. Consequently, we revisit the types of disclosures and adversaries against which we provide protections, considering adversaries with limited prior information and ensuring that with high probability, ensuring they cannot reconstruct an individual's data within useful tolerances. By reconceptualizing these protections, we allow more useful data release---large privacy parameters in local differential privacy---and we design new (minimax) optimal locally differentially private mechanisms for statistical learning problems for privacy levels. We thus present practicable approaches to large-scale locally private model training that were previously impossible, showing theoretically and empirically that we can fit large-scale image classification and language models with little degradation in utility.", "Multi-dimensional analytical (MDA) queries are often issued against a fact table with predicates on (categorical or ordinal) dimensions and aggregations on one or more measures. In this paper, we study the problem of answering MDA queries under local differential privacy (LDP). In the absence of a trusted agent, sensitive dimensions are encoded in a privacy-preserving (LDP) way locally before being sent to the data collector. The data collector estimates the answers to MDA queries, based on the encoded dimensions. We propose several LDP encoders and estimation algorithms, to handle a large class of MDA queries with different types of predicates and aggregation functions. Our techniques are able to answer these queries with tight error bounds and scale well in high-dimensional settings (i.e., error is polylogarithmic in dimension sizes). We conduct experiments on real and synthetic data to verify our theoretical results, and compare our solution with marginal-estimation based solutions." ] }
1908.11515
2970190087
When collecting information, local differential privacy (LDP) alleviates privacy concerns of users, as users' private information is randomized before being sent to the central aggregator. However, LDP results in loss of utility due to the amount of noise that is added. To address this issue, recent work introduced an intermediate server and with the assumption that this intermediate server did not collude with the aggregator. Using this trust model, one can add less noise to achieve the same privacy guarantee; thus improving the utility. In this paper, we investigate this multiple-party setting of LDP. We first analyze the threat model and identify potential adversaries. We then make observations about existing approaches and propose new techniques that achieve a better privacy-utility tradeoff than existing ones. Finally, we perform experiments to compare different methods and demonstrate the benefits of using our proposed method.
Distributed DP In the distributed setting of DP, each data owner (or proxy) has access to a (disjoint) subset of users. For example, each patient's information is possessed by a hospital. The DP noise is added at the level of the intermediate data owners (e.g., @cite_27 ). A special case (two-party computation) is also considered @cite_18 @cite_10 . @cite_7 studies the limitation of two-party DP. @cite_16 , a distributed noise generation protocol was proposed to prevent some party from adding malicious noise. @cite_37 lays the theoretical foundation of the relationship among several kinds of computational DP definitions.
1908.11515
2970190087
When collecting information, local differential privacy (LDP) alleviates privacy concerns of users, as users' private information is randomized before being sent to the central aggregator. However, LDP results in loss of utility due to the amount of noise that is added. To address this issue, recent work introduced an intermediate server and with the assumption that this intermediate server did not collude with the aggregator. Using this trust model, one can add less noise to achieve the same privacy guarantee; thus improving the utility. In this paper, we investigate this multiple-party setting of LDP. We first analyze the threat model and identify potential adversaries. We then make observations about existing approaches and propose new techniques that achieve a better privacy-utility tradeoff than existing ones. Finally, we perform experiments to compare different methods and demonstrate the benefits of using our proposed method.
DP by Trusted Hardware In this approach, a trusted hardware (e.g., SGX) is utilized to collect data, tally the data, and add the noise within the protected hardware. The result is then sent to the analyst. Google propose Prochlo @cite_29 that uses SGX. Note that the trusted hardware can be run by the server. Thus @cite_38 and @cite_1 designed oblivious DP algorithms to overcome the threat of side information (memory access pattern may be related to the underlying data). These proposals assume the trusted hardware is safe to use. However, using trusted hardware has potential risks (e.g., @cite_23 ). This paper considers the setting without trusted hardware.
1908.11515
2970190087
When collecting information, local differential privacy (LDP) alleviates privacy concerns of users, as users' private information is randomized before being sent to the central aggregator. However, LDP results in loss of utility due to the amount of noise that is added. To address this issue, recent work introduced an intermediate server and with the assumption that this intermediate server did not collude with the aggregator. Using this trust model, one can add less noise to achieve the same privacy guarantee; thus improving the utility. In this paper, we investigate this multiple-party setting of LDP. We first analyze the threat model and identify potential adversaries. We then make observations about existing approaches and propose new techniques that achieve a better privacy-utility tradeoff than existing ones. Finally, we perform experiments to compare different methods and demonstrate the benefits of using our proposed method.
Aggregation using Secure Computation Our work shares similar threat models from work on secure aggregation such as the electronic voting @cite_12 and statistic aggregation @cite_25 . In particular, multiple users compute some information collaboratively without leaking information about their own data. Note that the primary goal of secure aggregation is different: the result must be deterministic and correct, while in , a significant amount of noise is necessary.
{ "cite_N": [ "@cite_25", "@cite_12" ], "mid": [ "2599930814", "2128227627" ], "abstract": [ "This paper presents Prio, a privacy-preserving system for the collection of aggregate statistics. Each Prio client holds a private data value (e.g., its current location), and a small set of servers compute statistical functions over the values of all clients (e.g., the most popular location). As long as at least one server is honest, the Prio servers learn nearly nothing about the clients' private data, except what they can infer from the aggregate statistics that the system computes. To protect functionality in the face of faulty or malicious clients, Prio uses secret-shared non-interactive proofs (SNIPs), a new cryptographic technique that yields a hundred-fold performance improvement over conventional zero-knowledge approaches. Prio extends classic private aggregation techniques to enable the collection of a large class of useful statistics. For example, Prio can perform a least-squares regression on high-dimensional client-provided data without ever seeing the data in the clear.", "We present new cryptographic protocols for multiauthority secret ballot elections that guarantee privacy, robustness, and universal verifiability. Application of some novel techniques, in particular the construction of witness hiding indistinguishable protocols from Cramer, Damgard and Schoenmakers, and the verifiable secret sharing scheme of Pedersen, reduce the work required by the voter or an authority to a linear number of cryptographic operations in the population size (compared to quadratic in previous schemes). Thus we get significantly closer to a practical election scheme." ] }
1908.11421
2970918254
Incorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. Traditionally, IRT models are learned using human response pattern (RP) data, presenting a significant bottleneck for large data sets like those required for training deep neural networks (DNNs). In this work we propose learning IRT models using RPs generated from artificial crowds of DNN models. We demonstrate the effectiveness of learning IRT models using DNN-generated data through quantitative and qualitative analyses for two NLP tasks. Parameters learned from human and machine RPs for natural language inference and sentiment analysis exhibit medium to large positive correlations. We demonstrate a use-case for latent difficulty item parameters, namely training set filtering, and show that using difficulty to sample training data outperforms baseline methods. Finally, we highlight cases where human expectation about item difficulty does not match difficulty as estimated from the machine RPs.
There have been a number of studies on modeling latent traits of data to identify a correct label, [e.g.][] bruce1999recognizing . There has also been work in modeling individuals to identify poor annotators @cite_35 , but neither jointly model the ability of individuals and data points, nor apply the resulting metrics to interpret DNN models. Other work has modeled the probability a label is correct along with the probability of an annotator to label an item correctly according to the @cite_15 model, but do not consider difficulty or discriminatory ability of the data points @cite_2 . In the above models an annotator's response depends on an item only through its correct label. IRT assumes a more sophisticated response mechanism involving both annotator qualities and item characteristics. The DARE model @cite_30 jointly estimates ability, difficulty and response using probabilistic inference. It was evaluated on an intelligence test of 60 multiple choice questions administered to 120 individuals.
1908.11421
2970918254
Incorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. Traditionally, IRT models are learned using human response pattern (RP) data, presenting a significant bottleneck for large data sets like those required for training deep neural networks (DNNs). In this work we propose learning IRT models using RPs generated from artificial crowds of DNN models. We demonstrate the effectiveness of learning IRT models using DNN-generated data through quantitative and qualitative analyses for two NLP tasks. Parameters learned from human and machine RPs for natural language inference and sentiment analysis exhibit medium to large positive correlations. We demonstrate a use-case for latent difficulty item parameters, namely training set filtering, and show that using difficulty to sample training data outperforms baseline methods. Finally, we highlight cases where human expectation about item difficulty does not match difficulty as estimated from the machine RPs.
There are several other areas of study regarding how best to use training data that are related to this work. Re-weighting or re-ordering training examples is a well-studied and related area of supervised learning. Often examples are re-weighted according to some notion of difficulty, or model uncertainty @cite_24 . In particular, the internal uncertainty of the model is used as the basis for selecting how training examples are weighted. However, model uncertainty depends upon the original training data the model was trained on, while here we use an external measure of uncertainty.
{ "cite_N": [ "@cite_24" ], "mid": [ "2963476860" ], "abstract": [ "Self-paced learning and hard example mining re-weight training instances to improve learning accuracy. This paper presents two improved alternatives based on lightweight estimates of sample uncertainty in stochastic gradient descent (SGD): the variance in predicted probability of the correct class across iterations of mini-batch SGD, and the proximity of the correct class probability to the decision threshold. Extensive experimental results on six datasets show that our methods reliably improve accuracy in various network architectures, including additional gains on top of other popular training techniques, such as residual learning, momentum, ADAM, batch normalization, dropout, and distillation." ] }
1908.11402
2970866601
A team of mobile agents, starting from different nodes of an unknown network, possibly at different times, have to meet at the same node and declare that they have all met. Agents have different labels and move in synchronous rounds along links of the network. The above task is known as gathering and was traditionally considered under the assumption that when some agents are at the same node then they can talk. In this paper we ask the question of whether this ability of talking is needed for gathering. The answer turns out to be no. Our main contribution are two deterministic algorithms that always accomplish gathering in a much weaker model. We only assume that at any time an agent knows how many agents are at the node that it currently occupies but agents do not see the labels of other co-located agents and cannot exchange any information with them. They also do not see other nodes than the current one. Our first algorithm works under the assumption that agents know a priori some upper bound N on the network size, and it works in time polynomial in N and in the length l of the smallest label. Our second algorithm does not assume any a priori knowledge about the network but its complexity is exponential in the network size and in the labels of agents. Its purpose is to show feasibility of gathering under this harsher scenario. As a by-product of our techniques we obtain, in the same weak model, the solution of the fundamental problem of leader election among agents. As an application of our result we also solve, in the same model, the well-known gossiping problem: if each agent has a message at the beginning, we show how to make all messages known to all agents, even without any a priori knowledge about the network. If agents know an upper bound N on the network size then our gossiping algorithm works in time polynomial in N, in l and in the length of the largest message.
For the deterministic setting a lot of effort has been dedicated to the study of the feasibility of rendezvous, and to the time required to achieve this task, when feasible. For instance, deterministic rendezvous with agents equipped with tokens used to mark nodes was considered, e.g., in @cite_28 . Deterministic rendezvous of two agents that cannot mark nodes but have unique labels was discussed in @cite_17 @cite_9 . These papers are concerned with the time of rendezvous in arbitrary graphs. In @cite_17 the authors show a rendezvous algorithm polynomial in the size of the graph, in the length of the shorter label and in the delay between the starting time of the agents. In @cite_9 rendezvous time is polynomial in the first two of these parameters and independent of the delay.
{ "cite_N": [ "@cite_28", "@cite_9", "@cite_17" ], "mid": [ "2131887891", "2623545498", "" ], "abstract": [ "In the rendezvous search problem, two mobile agents must move along the n nodes of a network so as to minimize the time required to meet or rendezvous. When the mobile agents are identical and the network is anonymous, however, the resulting symmetry can make the problem impossible to solve. Symmetry is typically broken by having the mobile agents run either a randomized algorithm or different deterministic algorithms. We investigate the use of identical tokens to break symmetry so that the two mobile agents can run the same deterministic algorithm. After deriving the explicit conditions under which identical tokens can be used to break symmetry on the n node ring, we derive the lower and upper bounds for the time and memory complexity of the rendezvous search problem with various parameter sets. While these results suggest a possible tradeoff between the mobile agents' memory and the time complexity of the rendezvous search problem, we prove that this tradeoff is limited.", "We obtain several improved solutions for the deterministic rendezvous problem in general undirected graphs. Our solutions answer several problems left open in a recent paper by We also introduce an interesting variant of the rendezvous problem which we call the deterministic treasure hunt problem. Both the rendezvous and the treasure hunt problems motivate the study of universal traversal sequences and universal exploration sequences with some strengthened properties. We call such sequences strongly universal traversal (exploration) sequences. We give an explicit construction of strongly universal exploration sequences. The existence of strongly universal traversal sequences, as well as the solution of the most difficult variant of the deterministic treasure hunt problem, are left as intriguing open problems.", "" ] }
1908.11402
2970866601
A team of mobile agents, starting from different nodes of an unknown network, possibly at different times, have to meet at the same node and declare that they have all met. Agents have different labels and move in synchronous rounds along links of the network. The above task is known as gathering and was traditionally considered under the assumption that when some agents are at the same node then they can talk. In this paper we ask the question of whether this ability of talking is needed for gathering. The answer turns out to be no. Our main contribution are two deterministic algorithms that always accomplish gathering in a much weaker model. We only assume that at any time an agent knows how many agents are at the node that it currently occupies but agents do not see the labels of other co-located agents and cannot exchange any information with them. They also do not see other nodes than the current one. Our first algorithm works under the assumption that agents know a priori some upper bound N on the network size, and it works in time polynomial in N and in the length l of the smallest label. Our second algorithm does not assume any a priori knowledge about the network but its complexity is exponential in the network size and in the labels of agents. Its purpose is to show feasibility of gathering under this harsher scenario. As a by-product of our techniques we obtain, in the same weak model, the solution of the fundamental problem of leader election among agents. As an application of our result we also solve, in the same model, the well-known gossiping problem: if each agent has a message at the beginning, we show how to make all messages known to all agents, even without any a priori knowledge about the network. If agents know an upper bound N on the network size then our gossiping algorithm works in time polynomial in N, in l and in the length of the largest message.
Memory required by two anonymous agents to achieve deterministic rendezvous has been studied in @cite_22 for trees and in @cite_0 for general graphs. Memory needed for randomized rendezvous in the ring is discussed, e.g., in @cite_25 .
{ "cite_N": [ "@cite_0", "@cite_25", "@cite_22" ], "mid": [ "1972775782", "1535538796", "2081055073" ], "abstract": [ "Two identical (anonymous) mobile agents start from arbitrary nodes in an a priori unknown graph and move synchronously from node to node with the goal of meeting. This rendezvous problem has been thoroughly studied, both for anonymous and for labeled agents, along with another basic task, that of exploring graphs by mobile agents. The rendezvous problem is known to be not easier than graph exploration. A well-known recent result on exploration, due to Reingold, states that deterministic exploration of arbitrary graphs can be performed in log-space, i.e., using an agent equipped with O(log n) bits of memory, where n is the size of the graph. In this paper we study the size of memory of mobile agents that permits us to solve the rendezvous problem deterministically. Our main result establishes the minimum size of the memory of anonymous agents that guarantees deterministic rendezvous when it is feasible. We show that this minimum size is Θ(log n), where n is the size of the graph, regardless of the delay between the starting times of the agents. More precisely, we construct identical agents equipped with Θ(log n) memory bits that solve the rendezvous problem in all graphs with at most n nodes, if they start with any delay τ, and we prove a matching lower bound Ω(log n) on the number of memory bits needed to accomplish rendezvous, even for simultaneous start. In fact, this lower bound is achieved already on the class of rings. This shows a significant contrast between rendezvous and exploration: e.g., while exploration of rings (without stopping) can be done using constant memory, rendezvous, even with simultaneous start, requires logarithmic memory. As a by-product of our techniques introduced to obtain log-space rendezvous we get the first algorithm to find a quotient graph of a given unlabeled graph in polynomial time, by means of a mobile agent moving around the graph.", "We present a tradeoff between the expected time for two identical agents to rendez-vous on a synchronous, anonymous, oriented ring and the memory requirements of the agents. In particular, we show that there exists a 2t state agent, which can achieve rendez-vous on an n node ring in expected time O(n2 2t + 2t) and that any t 2 state agent requires expected time Ω(n2 2t). As a corollary we observe that Θ(log log n) bits of memory are necessary and sufficient to achieve rendezvous in linear time.", "The aim of rendezvous in a graph is meeting of two mobile agents at some node of an unknown anonymous connected graph. In this article, we focus on rendezvous in trees, and, analogously to the efforts that have been made for solving the exploration problem with compact automata, we study the size of memory of mobile agents that permits to solve the rendezvous problem deterministically. We assume that the agents are identical, and move in synchronous rounds. We first show that if the delay between the starting times of the agents is arbitrary, then the lower bound on memory required for rendezvous is Ω(log n) bits, even for the line of length n. This lower bound meets a previously known upper bound of O(log n) bits for rendezvous in arbitrary graphs of size at most n. Our main result is a proof that the amount of memory needed for rendezvous with simultaneous start depends essentially on the number e of leaves of the tree, and is exponentially less impacted by the number n of nodes. Indeed, we present two identical agents with O(log e + log log n) bits of memory that solve the rendezvous problem in all trees with at most n nodes and at most e leaves. Hence, for the class of trees with polylogarithmically many leaves, there is an exponential gap in minimum memory size needed for rendezvous between the scenario with arbitrary delay and the scenario with delay zero. Moreover, we show that our upper bound is optimal by proving that Ω(log e + log log n) bits of memory are required for rendezvous, even in the class of trees with degrees bounded by 3." ] }
1908.11402
2970866601
A team of mobile agents, starting from different nodes of an unknown network, possibly at different times, have to meet at the same node and declare that they have all met. Agents have different labels and move in synchronous rounds along links of the network. The above task is known as gathering and was traditionally considered under the assumption that when some agents are at the same node then they can talk. In this paper we ask the question of whether this ability of talking is needed for gathering. The answer turns out to be no. Our main contribution are two deterministic algorithms that always accomplish gathering in a much weaker model. We only assume that at any time an agent knows how many agents are at the node that it currently occupies but agents do not see the labels of other co-located agents and cannot exchange any information with them. They also do not see other nodes than the current one. Our first algorithm works under the assumption that agents know a priori some upper bound N on the network size, and it works in time polynomial in N and in the length l of the smallest label. Our second algorithm does not assume any a priori knowledge about the network but its complexity is exponential in the network size and in the labels of agents. Its purpose is to show feasibility of gathering under this harsher scenario. As a by-product of our techniques we obtain, in the same weak model, the solution of the fundamental problem of leader election among agents. As an application of our result we also solve, in the same model, the well-known gossiping problem: if each agent has a message at the beginning, we show how to make all messages known to all agents, even without any a priori knowledge about the network. If agents know an upper bound N on the network size then our gossiping algorithm works in time polynomial in N, in l and in the length of the largest message.
Apart from the synchronous model used in this paper, several authors have investigated asynchronous gathering in the plane @cite_34 @cite_35 and in network environments @cite_19 @cite_1 @cite_30 @cite_13 . In the latter scenario the agent chooses the edge which it decides to traverse but the adversary controls the speed of the agent. Under this assumption rendezvous in a node cannot be guaranteed even in very simple graphs and hence the rendezvous requirement is relaxed to permit the agents to meet inside an edge.
{ "cite_N": [ "@cite_30", "@cite_35", "@cite_1", "@cite_19", "@cite_34", "@cite_13" ], "mid": [ "2050063944", "1635699204", "2100580556", "1493636406", "2087073465", "1582826078" ], "abstract": [ "Two mobile agents (robots) having distinct labels and located in nodes of an unknown anonymous connected graph have to meet. We consider the asynchronous version of this well-studied rendezvous problem and we seek fast deterministic algorithms for it. Since in the asynchronous setting, meeting at a node, which is normally required in rendezvous, is in general impossible, we relax the demand by allowing meeting of the agents inside an edge as well. The measure of performance of a rendezvous algorithm is its cost: for a given initial location of agents in a graph, this is the number of edge traversals of both agents until rendezvous is achieved. If agents are initially situated at a distance D in an infinite line, we show a rendezvous algorithm with cost O(D|Lmin|2) when D is known and O((D + |Lmax|)3) if D is unknown, where |Lmin| and |Lmax| are the lengths of the shorter and longer label of the agents, respectively. These results still hold for the case of the ring of unknown size, but then we also give an optimal algorithm of cost O(n|Lmin|), if the size n of the ring is known, and of cost O(n|Lmax|), if it is unknown. For arbitrary graphs, we show that rendezvous is feasible if an upper bound on the size of the graph is known and we give an optimal algorithm of cost O(D|Lmin|) if the topology of the graph and the initial positions are known to agents.", "We consider a collection of robots which are identical (anonymous), have limited visibility of the environment, and no memory of the past (oblivious); furthermore, they are totally asynchronous in their actions, computations, and movements. We show that, even in such a totally asynchronous setting, it is possible for the robots to gather in the same location in finite time, provided they have a compass.", "Two mobile agents (robots) with distinct labels have to meet in an arbitrary, possibly infinite, unknown connected graph or in an unknown connected terrain in the plane. Agents are modeled as points, and the route of each of them only depends on its label and on the unknown environment. The actual walk of each agent also depends on an asynchronous adversary that may arbitrarily vary the speed of the agent, stop it, or even move it back and forth, as long as the walk of the agent in each segment of its route is continuous, does not leave it and covers all of it. Meeting in a graph means that both agents must be at the same time in some node or in some point inside an edge of the graph, while meeting in a terrain means that both agents must be at the same time in some point of the terrain. Does there exist a deterministic algorithm that allows any two agents to meet in any unknown environment in spite of this very powerful adversary? We give deterministic rendezvous algorithms for agents starting at arbitrary nodes of any anonymous connected graph (finite or infinite) and for agents starting at any interior points with rational coordinates in any closed region of the plane with path-connected interior. While our algorithms work in a very general setting - agents can, indeed, meet almost everywhere - we show that none of the above few limitations imposed on the environment can be removed. On the other hand, our algorithm also guarantees the following approximate rendezvous for agents starting at arbitrary interior points of a terrain as above: agents will eventually get at an arbitrarily small positive distance from each other.", "Two anonymous mobile agents (robots) moving in an asynchronous manner have to meet in an infinite grid of dimension δ > 0, starting from two arbitrary positions at distance at most d. Since the problem is clearly infeasible in such general setting, we assume that the grid is embedded in a δ-dimensional Euclidean space and that each agent knows the Cartesian coordinates of its own initial position (but not the one of the other agent). We design an algorithm permitting the agents to meet after traversing a trajectory of length O(dδ polylog d). This bound for the case of 2D-grids subsumes the main result of [12]. The algorithm is almost optimal, since the Ω(dδ) lower bound is straightforward. Further, we apply our rendezvous method to the following network design problem. The ports of the δ-dimensional grid have to be set such that two anonymous agents starting at distance at most d from each other will always meet, moving in an asynchronous manner, after traversing a O(dδ polylog d) length trajectory. We can also apply our method to a version of the geometric rendezvous problem. Two anonymous agents move asynchronously in the δ-dimensional Euclidean space. The agents have the radii of visibility of r1 and r2, respectively. Each agent knows only its own initial position and its own radius of visibility. The agents meet when one agent is visible to the other one. We propose an algorithm designing the trajectory of each agent, so that they always meet after traveling a total distance of O((d r)δ polylog(d r)), where r = min(r1, r2) and for r ≥ 1.", "Consider a set of @math identical mobile computational entities in the plane, called robots, operating in Look-Compute-Move cycles, without any means of direct communication. The Gathering Problem is the primitive task of all entities gathering in finite time at a point not fixed in advance, without any external control. The problem has been extensively studied in the literature under a variety of strong assumptions (e.g., synchronicity of the cycles, instantaneous movements, complete memory of the past, common coordinate system, etc.). In this paper we consider the setting without those assumptions, that is, when the entities are oblivious (i.e., they do not remember results and observations from previous cycles), disoriented (i.e., have no common coordinate system), and fully asynchronous (i.e., no assumptions exist on timing of cycles and activities within a cycle). The existing algorithmic contributions for such robots are limited to solutions for @math or for restricted sets of initial configura...", "Two mobile agents starting at different nodes of an unknown network have to meet. This task is known in the literature as rendezvous. Each agent has a different label which is a positive integer known to it but unknown to the other agent. Agents move in an asynchronous way: the speed of agents may vary and is controlled by an adversary. The cost of a rendezvous algorithm is the total number of edge traversals by both agents until their meeting. The only previous deterministic algorithm solving this problem has cost exponential in the size of the graph and in the larger label. In this paper we present a deterministic rendezvous algorithm with cost polynomial in the size of the graph and in the length of the smaller label. Hence, we decrease the cost exponentially in the size of the graph and doubly exponentially in the labels of agents. As an application of our rendezvous algorithm we solve several fundamental problems involving teams of unknown size larger than 1 of labeled agents moving asynchronously in..." ] }
1908.11402
2970866601
A team of mobile agents, starting from different nodes of an unknown network, possibly at different times, have to meet at the same node and declare that they have all met. Agents have different labels and move in synchronous rounds along links of the network. The above task is known as gathering and was traditionally considered under the assumption that when some agents are at the same node then they can talk. In this paper we ask the question of whether this ability of talking is needed for gathering. The answer turns out to be no. Our main contribution are two deterministic algorithms that always accomplish gathering in a much weaker model. We only assume that at any time an agent knows how many agents are at the node that it currently occupies but agents do not see the labels of other co-located agents and cannot exchange any information with them. They also do not see other nodes than the current one. Our first algorithm works under the assumption that agents know a priori some upper bound N on the network size, and it works in time polynomial in N and in the length l of the smallest label. Our second algorithm does not assume any a priori knowledge about the network but its complexity is exponential in the network size and in the labels of agents. Its purpose is to show feasibility of gathering under this harsher scenario. As a by-product of our techniques we obtain, in the same weak model, the solution of the fundamental problem of leader election among agents. As an application of our result we also solve, in the same model, the well-known gossiping problem: if each agent has a message at the beginning, we show how to make all messages known to all agents, even without any a priori knowledge about the network. If agents know an upper bound N on the network size then our gossiping algorithm works in time polynomial in N, in l and in the length of the largest message.
A different asynchronous model for gathering in ring networks was considered in @cite_36 @cite_20 . In this model, agents were memoryless but they could perform look operations which gave them a snapshot of the entire network with the positions of all agents in it.
{ "cite_N": [ "@cite_36", "@cite_20" ], "mid": [ "2400422553", "2144182788" ], "abstract": [ "Consider a set of mobile robots placed on distinct nodes of a discrete, anonymous, and bidirectional ring. Asynchronously, each robot takes a snapshot of the ring, determining the size of the ring and which nodes are either occupied by robots or empty. Based on the observed configuration, it decides whether to move to one of its adjacent nodes or not. In the first case, it performs the computed move, eventually. This model of computation is known as Look-Compute-Move. The computation depends on the required task. In this paper, we solve both the well-known Gathering and Exclusive Searching tasks. In the former problem, all robots must simultaneously occupy the same node, eventually. In the latter problem, the aim is to clear all edges of the graph. An edge is cleared if it is traversed by a robot or if both its endpoints are occupied. We consider the exclusive searching where it must be ensured that two robots never occupy the same node. Moreover, since the robots are oblivious, the clearing is perpetual, i.e., the ring is cleared infinitely often. In the literature, most contributions are restricted to a subset of initial configurations. Here, we design two different algorithms and provide a characterization of the initial configurations that permit the resolution of the problems under very weak assumptions. More precisely, we provide a full characterization (except for few pathological cases) of the initial configurations for which gathering can be solved. The algorithm relies on the necessary assumption of the local-weak multiplicity detection. This means that during the Look phase a robot detects also whether the node it occupies is occupied by other robots, without acquiring the exact number. For the exclusive searching, we characterize all (except for few pathological cases) aperiodic configurations from which the problem is feasible. We also provide some impossibility results for the case of periodic configurations.", "We consider the problem of gathering identical, memoryless, mobile robots in one node of an anonymous unoriented ring. Robots start from different nodes of the ring. They operate in Look-Compute-Move cycles and have to end up in the same node. In one cycle, a robot takes a snapshot of the current configuration (Look), makes a decision to stay idle or to move to one of its adjacent nodes (Compute), and in the latter case makes an instantaneous move to this neighbor (Move). Cycles are performed asynchronously for each robot. For an odd number of robots we prove that gathering is feasible if and only if the initial configuration is not periodic, and we provide a gathering algorithm for any such configuration. For an even number of robots we decide the feasibility of gathering except for one type of symmetric initial configurations, and provide gathering algorithms for initial configurations proved to be gatherable." ] }
1908.11402
2970866601
A team of mobile agents, starting from different nodes of an unknown network, possibly at different times, have to meet at the same node and declare that they have all met. Agents have different labels and move in synchronous rounds along links of the network. The above task is known as gathering and was traditionally considered under the assumption that when some agents are at the same node then they can talk. In this paper we ask the question of whether this ability of talking is needed for gathering. The answer turns out to be no. Our main contribution are two deterministic algorithms that always accomplish gathering in a much weaker model. We only assume that at any time an agent knows how many agents are at the node that it currently occupies but agents do not see the labels of other co-located agents and cannot exchange any information with them. They also do not see other nodes than the current one. Our first algorithm works under the assumption that agents know a priori some upper bound N on the network size, and it works in time polynomial in N and in the length l of the smallest label. Our second algorithm does not assume any a priori knowledge about the network but its complexity is exponential in the network size and in the labels of agents. Its purpose is to show feasibility of gathering under this harsher scenario. As a by-product of our techniques we obtain, in the same weak model, the solution of the fundamental problem of leader election among agents. As an application of our result we also solve, in the same model, the well-known gossiping problem: if each agent has a message at the beginning, we show how to make all messages known to all agents, even without any a priori knowledge about the network. If agents know an upper bound N on the network size then our gossiping algorithm works in time polynomial in N, in l and in the length of the largest message.
In @cite_32 , the authors considered the problem of network exploration by many agents that could not communicate between them. However, the information available to an agent in each round was much different than in the present paper. Indeed, in @cite_32 , agents were getting local traffic reports consisting of answers to three questions: Am I alone in the node?'', Did any agent enter this node in this round?'', Did any agent leave this node in this round?''. To see that this feedback cannot be derived from our present assumption of knowing the number of agents co-located with an agent in a given round, consider the situation when an agent @math stays at a node, and in a given round one other agent leaves the node and another agent enters it. In our present model, agent @math does not notice any change, while in the model from @cite_32 it gets reports about somebody leaving the node and somebody entering it.
{ "cite_N": [ "@cite_32" ], "mid": [ "2089187143" ], "abstract": [ "A team consisting of an unknown number of mobile agents starting from different nodes of an unknown network, possibly at different times, have to explore the network: Every node must be visited by at least one agent, and all agents must eventually stop. Agents are anonymous (identical), execute the same deterministic algorithm, and move in synchronous rounds along links of the network. They are silent: They cannot send any messages to other agents or mark visited nodes in any way. In the absence of any additional information, exploration with termination of an arbitrary network in this model, devoid of any means of communication between agents, is impossible. Our aim is to solve the exploration problem by giving to agents very restricted local traffic reports. Specifically, an agent that is at a node v in a given round is provided with three bits of information answering the following questions: Am I alone at vq Did any agent enter v in this roundq Did any agent exit v in this roundq We show that this small amount of information permits us to solve the exploration problem in arbitrary networks. More precisely, we give a deterministic terminating exploration algorithm working in arbitrary networks for all initial configurations that are not perfectly symmetric; that is, in which there are agents with different views of the network. The algorithm works in polynomial time in the (unknown) size of the network. A deterministic terminating exploration algorithm working for all initial configurations in arbitrary networks does not exist." ] }
1908.11402
2970866601
A team of mobile agents, starting from different nodes of an unknown network, possibly at different times, have to meet at the same node and declare that they have all met. Agents have different labels and move in synchronous rounds along links of the network. The above task is known as gathering and was traditionally considered under the assumption that when some agents are at the same node then they can talk. In this paper we ask the question of whether this ability of talking is needed for gathering. The answer turns out to be no. Our main contribution are two deterministic algorithms that always accomplish gathering in a much weaker model. We only assume that at any time an agent knows how many agents are at the node that it currently occupies but agents do not see the labels of other co-located agents and cannot exchange any information with them. They also do not see other nodes than the current one. Our first algorithm works under the assumption that agents know a priori some upper bound N on the network size, and it works in time polynomial in N and in the length l of the smallest label. Our second algorithm does not assume any a priori knowledge about the network but its complexity is exponential in the network size and in the labels of agents. Its purpose is to show feasibility of gathering under this harsher scenario. As a by-product of our techniques we obtain, in the same weak model, the solution of the fundamental problem of leader election among agents. As an application of our result we also solve, in the same model, the well-known gossiping problem: if each agent has a message at the beginning, we show how to make all messages known to all agents, even without any a priori knowledge about the network. If agents know an upper bound N on the network size then our gossiping algorithm works in time polynomial in N, in l and in the length of the largest message.
In @cite_5 , the problem of conveying bits of information using movements of robots was considered in a context much different from ours. Mobile robots were moving in the plane and they could periodically get snapshots of the entire configuration of robots.
{ "cite_N": [ "@cite_5" ], "mid": [ "2944251479" ], "abstract": [ "We investigate avenues for the exchange of information (explicit communication) among deaf and dumb mobile robots scattered in the plane. We introduce the use of movement-signals (analogously to flight signals and bees waggle) as a mean to transfer messages, enabling the use of distributed algorithms among robots. We propose one-to-one deterministic movement protocols that implement explicit communication among asynchronous robots. We first show how the movements of robots can provide implicit acknowledgment in asynchronous systems. We use this result to design one-to-one communication among a pair of robots. Then, we propose two one-to-one communication protocols for any system of n *** 2 robots. The former works for robots equipped with observable IDs that agree on a common direction (sense of direction). The latter enables one-to-one communication assuming robots devoid of any observable IDs or sense of direction. All three protocols (for either two or any number of robots) assume that no robot remains inactive forever. However, they cannot avoid that the robots move either away or closer of each others, by the way requiring robots with an infinite visibility. In this paper, we also present how to overcome these two disadvantages. These protocols enable the use of distributing algorithms based on message exchanges among swarms of Stigmergic robots. They also allow robots to be equipped with the means of communication to tolerate faults in their communication devices." ] }
1908.11526
2970961333
The success of existing deep-learning based multi-view stereo (MVS) approaches greatly depends on the availability of large-scale supervision in the form of dense depth maps. Such supervision, while not always possible, tends to hinder the generalization ability of the learned models in never-seen-before scenarios. In this paper, we propose the first unsupervised learning based MVS network, which learns the multi-view depth maps from the input multi-view images and does not need ground-truth 3D training data. Our network is symmetric in predicting depth maps for all views simultaneously, where we enforce cross-view consistency of multi-view depth maps during both training and testing stages. Thus, the learned multi-view depth maps naturally comply with the underlying 3D scene geometry. Besides, our network also learns the multi-view occlusion maps, which further improves the robustness of our network in handling real-world occlusions. Experimental results on multiple benchmarking datasets demonstrate the effectiveness of our network and the excellent generalization ability.
Traditional MVS methods focus on designing neighbor selection and photometric error measures for efficient and accurate reconstruction @cite_26 @cite_34 @cite_9 . Furukawa al @cite_22 adopted geometric structures to reconstruct textured regions and applied Markov random fields to recover per-view depth maps. Langguth al @cite_15 used the shading-aware mechanism to improve the robustness of view selection. Wu al @cite_29 utilized the lighting and shadows information to enhance the performance of the ill-posed region. Michael al @cite_18 chose images to match (both at a per-view and per-pixel level) for addressing the dramatic changes in lighting, scale, clutter, and other effects. Schonberger al @cite_11 proposed the COLMAP framework, which applied photometric and geometric priors to optimize the view selection and used geometric consistency to refine the depth map.
1908.11526
2970961333
The success of existing deep-learning based multi-view stereo (MVS) approaches greatly depends on the availability of large-scale supervision in the form of dense depth maps. Such supervision, while not always possible, tends to hinder the generalization ability of the learned models in never-seen-before scenarios. In this paper, we propose the first unsupervised learning based MVS network, which learns the multi-view depth maps from the input multi-view images and does not need ground-truth 3D training data. Our network is symmetric in predicting depth maps for all views simultaneously, where we enforce cross-view consistency of multi-view depth maps during both training and testing stages. Thus, the learned multi-view depth maps naturally comply with the underlying 3D scene geometry. Besides, our network also learns the multi-view occlusion maps, which further improves the robustness of our network in handling real-world occlusions. Experimental results on multiple benchmarking datasets demonstrate the effectiveness of our network and the excellent generalization ability.
Different from the above geometry-based methods, learning-based approaches adopt convolution operation which has powerful feature learning capability for better pair-wise patch matching @cite_32 @cite_7 @cite_10 . Ji al @cite_24 pre-warped the multi-view images to 3D space, then used CNNs to regularize the cost volume. Huang al @cite_37 proposed DeepMVS, which aggregates information through a set of unordered images. Abhishek al @cite_31 directly leveraged camera parameters as the projection operation to form the cost volume, and achieved an end-to-end network. Yao al @cite_17 adopted a variance-based cost metric to aggregate the cost volume, then applied 3D convolutions to regularize and regress the depth map. Im al @cite_35 applied a plane sweeping approach to build a cost volume from deep features, then regularized the cost volume via a context-aware aggregation to improve depth regression. Very recently, Yao al @cite_4 introduced a scalable MVS framework based on the recurrent neural network to reduce the memory-consuming.
1908.11526
2970961333
The success of existing deep-learning based multi-view stereo (MVS) approaches greatly depends on the availability of large-scale supervision in the form of dense depth maps. Such supervision, while not always possible, tends to hinder the generalization ability of the learned models in never-seen-before scenarios. In this paper, we propose the first unsupervised learning based MVS network, which learns the multi-view depth maps from the input multi-view images and does not need ground-truth 3D training data. Our network is symmetric in predicting depth maps for all views simultaneously, where we enforce cross-view consistency of multi-view depth maps during both training and testing stages. Thus, the learned multi-view depth maps naturally comply with the underlying 3D scene geometry. Besides, our network also learns the multi-view occlusion maps, which further improves the robustness of our network in handling real-world occlusions. Experimental results on multiple benchmarking datasets demonstrate the effectiveness of our network and the excellent generalization ability.
Unsupervised learning has been developed in monocular depth estimation and binocular stereo matching by exploiting the photometric consistency and regularization. Xie al @cite_25 proposed Deep3D to automatically convert 2D videos and images to stereoscopic 3D format. Zhou al @cite_30 proposed an unsupervised monocular depth prediction method by minimizing the image reconstruction error. Mahjourian al @cite_23 explicitly considered the inferred 3D geometry of the whole scene, where consistency of the estimated 3D point clouds and ego-motion across consecutive frames are enforced. Zhong al @cite_13 @cite_14 used the image warping error as the loss function to derive the learning process for estimating the disparity map.
{ "cite_N": [ "@cite_30", "@cite_14", "@cite_23", "@cite_13", "@cite_25" ], "mid": [ "2609883120", "2887123368", "2963906250", "2751625733", "2336968928" ], "abstract": [ "We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. In common with recent work [10, 14, 16], we use an end-to-end learning approach with view synthesis as the supervisory signal. In contrast to the previous work, our method is completely unsupervised, requiring only monocular video sequences for training. Our method uses single-view depth and multiview pose networks, with a loss based on warping nearby views to the target using the computed depth and pose. The networks are thus coupled by the loss during training, but can be applied independently at test time. Empirical evaluation on the KITTI dataset demonstrates the effectiveness of our approach: 1) monocular depth performs comparably with supervised methods that use either ground-truth pose or depth for training, and 2) pose estimation performs favorably compared to established SLAM systems under comparable input settings.", "Deep Learning based stereo matching methods have shown great successes and achieved top scores across different benchmarks. However, like most data-driven methods, existing deep stereo matching networks suffer from some well-known drawbacks such as requiring large amount of labeled training data, and that their performances are fundamentally limited by the generalization ability. In this paper, we propose a novel Recurrent Neural Network (RNN) that takes a continuous (possibly previously unseen) stereo video as input, and directly predicts a depth-map at each frame without a pre-training process, and without the need of ground-truth depth-maps as supervision. Thanks to the recurrent nature (provided by two convolutional-LSTM blocks), our network is able to memorize and learn from its past experiences, and modify its inner parameters (network weights) to adapt to previously unseen or unfamiliar environments. This suggests a remarkable generalization ability of the net, making it applicable in an open world setting. Our method works robustly with changes in scene content, image statistics, and lighting and season conditions etc. By extensive experiments, we demonstrate that the proposed method seamlessly adapts between different scenarios. Equally important, in terms of the stereo matching accuracy, it outperforms state-of-the-art deep stereo approaches on standard benchmark datasets such as KITTI and Middlebury stereo.", "We present a novel approach for unsupervised learning of depth and ego-motion from monocular video. Unsupervised learning removes the need for separate supervisory signals (depth or ego-motion ground truth, or multi-view video). Prior work in unsupervised depth learning uses pixel-wise or gradient-based losses, which only consider pixels in small local neighborhoods. Our main contribution is to explicitly consider the inferred 3D geometry of the whole scene, and enforce consistency of the estimated 3D point clouds and ego-motion across consecutive frames. This is a challenging task and is solved by a novel (approximate) backpropagation algorithm for aligning 3D structures. We combine this novel 3D-based loss with 2D losses based on photometric quality of frame reconstructions using estimated depth and ego-motion from adjacent frames. We also incorporate validity masks to avoid penalizing areas in which no useful information exists. We test our algorithm on the KITTI dataset and on a video dataset captured on an uncalibrated mobile phone camera. Our proposed approach consistently improves depth estimates on both datasets, and outperforms the state-of-the-art for both depth and ego-motion. Because we only require a simple video, learning depth and ego-motion on large and varied datasets becomes possible. We demonstrate this by training on the low quality uncalibrated video dataset and evaluating on KITTI, ranking among top performing prior methods which are trained on KITTI itself.1", "Exiting deep-learning based dense stereo matching methods often rely on ground-truth disparity maps as the training signals, which are however not always available in many situations. In this paper, we design a simple convolutional neural network architecture that is able to learn to compute dense disparity maps directly from the stereo inputs. Training is performed in an end-to-end fashion without the need of ground-truth disparity maps. The idea is to use image warping error (instead of disparity-map residuals) as the loss function to drive the learning process, aiming to find a depth-map that minimizes the warping error. While this is a simple concept well-known in stereo matching, to make it work in a deep-learning framework, many non-trivial challenges must be overcome, and in this work we provide effective solutions. Our network is self-adaptive to different unseen imageries as well as to different camera settings. Experiments on KITTI and Middlebury stereo benchmark datasets show that our method outperforms many state-of-the-art stereo matching methods with a margin, and at the same time significantly faster.", "As 3D movie viewing becomes mainstream and the Virtual Reality (VR) market emerges, the demand for 3D contents is growing rapidly. Producing 3D videos, however, remains challenging. In this paper we propose to use deep neural networks to automatically convert 2D videos and images to a stereoscopic 3D format. In contrast to previous automatic 2D-to-3D conversion algorithms, which have separate stages and need ground truth depth map as supervision, our approach is trained end-to-end directly on stereo pairs extracted from existing 3D movies. This novel training scheme makes it possible to exploit orders of magnitude more data and significantly increases performance. Indeed, Deep3D outperforms baselines in both quantitative and human subject evaluations." ] }
1908.11399
2971293878
Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery. Using the data from a single high-throughput imaging assay, a classification model for predicting the biological activity of candidate compounds was introduced. The image recognition model which is based on deep convolutional neural network (CNN) architecture with residual connections achieved accuracy of 99.6 @math on a binary classification task of distinguishing untreated and treated rodent primary neuronal cells with Amyloid- @math .
Recent years have seen an explosion of applications of the deep learning methods to medical imaging, including computer-aided diagnosis (CAD) in radiology and medical image analysis @cite_37 @cite_23 @cite_27 . The efficiency of deep learning models for cytometry @cite_2 has been widely recognised and applied to cell imaging @cite_30 , virtual staining with generative adversarial networks (GAN) @cite_19 @cite_26 , fluorescence microscopy @cite_9 and reconstructing cell cycles and disease progression @cite_42 . However, despite the wide popularity and maturity of the deep learning approach, very little has been done to estimate the effect of biological activity of neuronal cells induced by compounds and searching for drugs that may protect against neurodegeneration and Alzheimer's disease. Simm in @cite_22 suggested to re-purpose high-throughput images assay to predict biological activity in drug discovery, however this approach depends on the features extracted from CellProfiler @cite_4 and lacks the flexibility of the CNN models @cite_7 which learn features directly from raw pixels of images.
1908.11518
2971189344
Non-convex optimization problems arise from various areas in science and engineering. Although many numerical methods and theories have been developed for unconstrained non-convex problems, the parallel development for constrained non-convex problems remains limited. That restricts the practices of mathematical modeling and quantitative decision making in many disciplines. In this paper, an inexact proximal-point penalty method is proposed for constrained optimization problems where both the objective function and the constraint can be non-convex. The proposed method approximately solves a sequence of subproblems, each of which is formed by adding to the original objective function a proximal term and quadratic penalty terms associated to the constraint functions. Under a weak-convexity assumption, each subproblem is made strongly convex and can be solved effectively to a required accuracy by an optimal gradient-type method. The theoretical property of the proposed method is analyzed in two different cases. In the first case, the objective function is non-convex but the constraint functions are assumed to be convex, while in the second case, both the objective function and the constraint are non-convex. For both cases, we give the complexity results in terms of the number of function value and gradient evaluations to produce near-stationary points. Due to the different structures, different definitions of near-stationary points are given for the two cases. The complexity for producing a nearly @math -stationary point is @math for the first case while it becomes @math for the second case.
There has been growing interest in first-order algorithms for non-convex minimization problems with no constraints or simple constraints in both stochastic and deterministic settings. Initially, the research in this direction mainly focuses on problems with smooth objective functions @cite_31 @cite_55 @cite_0 @cite_8 @cite_52 @cite_57 @cite_9 @cite_66 @cite_42 @cite_67 . Recently, algorithms and theories have been developed for non-convex problems with non-smooth (but weakly convex) objective functions @cite_89 @cite_88 @cite_102 @cite_50 @cite_26 @cite_79 . These works tackle the non-smoothness by introducing the Moreau envelope of the objective function. However, for with sophisticated functional constraints, these methods are not applicable.
1908.11518
2971189344
Non-convex optimization problems arise from various areas in science and engineering. Although many numerical methods and theories have been developed for unconstrained non-convex problems, the parallel development for constrained non-convex problems remains limited. That restricts the practices of mathematical modeling and quantitative decision making in many disciplines. In this paper, an inexact proximal-point penalty method is proposed for constrained optimization problems where both the objective function and the constraint can be non-convex. The proposed method approximately solves a sequence of subproblems, each of which is formed by adding to the original objective function a proximal term and quadratic penalty terms associated to the constraint functions. Under a weak-convexity assumption, each subproblem is made strongly convex and can be solved effectively to a required accuracy by an optimal gradient-type method. The theoretical property of the proposed method is analyzed in two different cases. In the first case, the objective function is non-convex but the constraint functions are assumed to be convex, while in the second case, both the objective function and the constraint are non-convex. For both cases, we give the complexity results in terms of the number of function value and gradient evaluations to produce near-stationary points. Due to the different structures, different definitions of near-stationary points are given for the two cases. The complexity for producing a nearly @math -stationary point is @math for the first case while it becomes @math for the second case.
When all constraint functions in are affine, a primal-dual Frank-Wolfe method is proposed in @cite_72 , and it finds an @math -stationary point with a complexity of @math in general and @math when there exists a strictly feasible solution. Compared to @cite_72 , this paper uses a different notion of @math -stationary point and our constraint functions can be nonlinear and non-convex.
{ "cite_N": [ "@cite_72" ], "mid": [ "2805494515" ], "abstract": [ "We study constrained stochastic programs where the decision vector at each time slot cannot be chosen freely but is tied to the realization of an underlying random state vector. The goal is to minimize a general objective function subject to linear constraints. A typical scenario where such programs appear is opportunistic scheduling over a network of time-varying channels, where the random state vector is the channel state observed, and the control vector is the transmission decision which depends on the current channel state. We consider a primal-dual type Frank-Wolfe algorithm that has a low complexity update during each slot and that learns to make efficient decisions without prior knowledge of the probability distribution of the random state vector. We establish convergence time guarantees for the case of both convex and non-convex objective functions. We also emphasize application of the algorithm to non-convex opportunistic scheduling and distributed non-convex stochastic optimization over a connected graph." ] }
1908.11518
2971189344
Non-convex optimization problems arise from various areas in science and engineering. Although many numerical methods and theories have been developed for unconstrained non-convex problems, the parallel development for constrained non-convex problems remains limited. That restricts the practices of mathematical modeling and quantitative decision making in many disciplines. In this paper, an inexact proximal-point penalty method is proposed for constrained optimization problems where both the objective function and the constraint can be non-convex. The proposed method approximately solves a sequence of subproblems, each of which is formed by adding to the original objective function a proximal term and quadratic penalty terms associated to the constraint functions. Under a weak-convexity assumption, each subproblem is made strongly convex and can be solved effectively to a required accuracy by an optimal gradient-type method. The theoretical property of the proposed method is analyzed in two different cases. In the first case, the objective function is non-convex but the constraint functions are assumed to be convex, while in the second case, both the objective function and the constraint are non-convex. For both cases, we give the complexity results in terms of the number of function value and gradient evaluations to produce near-stationary points. Due to the different structures, different definitions of near-stationary points are given for the two cases. The complexity for producing a nearly @math -stationary point is @math for the first case while it becomes @math for the second case.
As a classical approach for solving constrained optimization , a penalty method finds an approximate solution by solving a sequence of unconstrained subproblems, where the violation of constraints is penalized by the positively weighted penalty terms in the objective function of the subproblems. Unconstrained optimization techniques are then applied to the subproblems along with an updating scheme for the weighting parameters. The computational complexity of penalty methods for convex problems has been well established @cite_17 @cite_53 @cite_7 . For non-convex problems, most existing studies of penalty methods focus on the asymptotic convergence to a stationary point @cite_70 @cite_97 @cite_27 @cite_99 @cite_100 @cite_90 @cite_10 @cite_91 @cite_69 @cite_33 @cite_34 @cite_78 @cite_23 @cite_60 . On the contrary, we analyze the finite complexity of penalty methods for finding a near-stationary point.
1908.11518
2971189344
Non-convex optimization problems arise from various areas in science and engineering. Although many numerical methods and theories have been developed for unconstrained non-convex problems, the parallel development for constrained non-convex problems remains limited. That restricts the practices of mathematical modeling and quantitative decision making in many disciplines. In this paper, an inexact proximal-point penalty method is proposed for constrained optimization problems where both the objective function and the constraint can be non-convex. The proposed method approximately solves a sequence of subproblems, each of which is formed by adding to the original objective function a proximal term and quadratic penalty terms associated to the constraint functions. Under a weak-convexity assumption, each subproblem is made strongly convex and can be solved effectively to a required accuracy by an optimal gradient-type method. The theoretical property of the proposed method is analyzed in two different cases. In the first case, the objective function is non-convex but the constraint functions are assumed to be convex, while in the second case, both the objective function and the constraint are non-convex. For both cases, we give the complexity results in terms of the number of function value and gradient evaluations to produce near-stationary points. Due to the different structures, different definitions of near-stationary points are given for the two cases. The complexity for producing a nearly @math -stationary point is @math for the first case while it becomes @math for the second case.
On solving a problem with a non-convex objective and linear constraint, @cite_25 has developed a quadratic-penalty accelerated inexact proximal point method. That method can generate an @math -stationary point in the sense of with a complexity of @math . Our method is similar to that in @cite_25 by utilizing the techniques from both the proximal point method and the quadratic penalty method. Although we make a little stronger assumption than @cite_25 by requiring the boundedness of @math , our method and analysis apply to the problems with non-convex objectives and convex non-convex nonlinear constraint functions. When the constraints are convex (but possibly nonlinear), our method can find a nearly @math -stationary point with a complexity of @math that is a nearly @math improvement over the complexity in @cite_25 .
{ "cite_N": [ "@cite_25" ], "mid": [ "2787445655" ], "abstract": [ "This paper analyzes the iteration-complexity of a quadratic penalty accelerated inexact proximal point method for solving linearly constrained nonconvex composite programs. More specifically, the objective function is of the form @math where @math is a differentiable function whose gradient is Lipschitz continuous and @math is a closed convex function with bounded domain. The method, basically, consists of applying an accelerated inexact proximal point method for solving approximately a sequence of quadratic penalized subproblems associated to the linearly constrained problem. Each subproblem of the proximal point method is in turn approximately solved by an accelerated composite gradient method. It is shown that the proposed scheme generates a @math -approximate stationary point in at most @math . Finally, numerical results showing the efficiency of the proposed method are also given." ] }
1908.11518
2971189344
Non-convex optimization problems arise from various areas in science and engineering. Although many numerical methods and theories have been developed for unconstrained non-convex problems, the parallel development for constrained non-convex problems remains limited. That restricts the practices of mathematical modeling and quantitative decision making in many disciplines. In this paper, an inexact proximal-point penalty method is proposed for constrained optimization problems where both the objective function and the constraint can be non-convex. The proposed method approximately solves a sequence of subproblems, each of which is formed by adding to the original objective function a proximal term and quadratic penalty terms associated to the constraint functions. Under a weak-convexity assumption, each subproblem is made strongly convex and can be solved effectively to a required accuracy by an optimal gradient-type method. The theoretical property of the proposed method is analyzed in two different cases. In the first case, the objective function is non-convex but the constraint functions are assumed to be convex, while in the second case, both the objective function and the constraint are non-convex. For both cases, we give the complexity results in terms of the number of function value and gradient evaluations to produce near-stationary points. Due to the different structures, different definitions of near-stationary points are given for the two cases. The complexity for producing a nearly @math -stationary point is @math for the first case while it becomes @math for the second case.
Barrier methods @cite_54 @cite_83 @cite_56 @cite_74 @cite_16 @cite_11 @cite_3 @cite_36 @cite_13 are another traditional class of algorithms for constrained optimization. Similar to the penalty methods, they also solve a sequence of unconstrained subproblems with barrier functions added to objective function. The barrier functions will increase to infinity as the iterates approach the boundary of the feasible set, and thus enforce the iterates to stay in the interior of the feasible set. However, the convergence rate of barrier methods is only shown when the problem is convex @cite_3 @cite_36 @cite_13 @cite_5 , and only asymptotic convergence analysis is available for non-convex problems.
1908.11518
2971189344
Non-convex optimization problems arise from various areas in science and engineering. Although many numerical methods and theories have been developed for unconstrained non-convex problems, the parallel development for constrained non-convex problems remains limited. That restricts the practices of mathematical modeling and quantitative decision making in many disciplines. In this paper, an inexact proximal-point penalty method is proposed for constrained optimization problems where both the objective function and the constraint can be non-convex. The proposed method approximately solves a sequence of subproblems, each of which is formed by adding to the original objective function a proximal term and quadratic penalty terms associated to the constraint functions. Under a weak-convexity assumption, each subproblem is made strongly convex and can be solved effectively to a required accuracy by an optimal gradient-type method. The theoretical property of the proposed method is analyzed in two different cases. In the first case, the objective function is non-convex but the constraint functions are assumed to be convex, while in the second case, both the objective function and the constraint are non-convex. For both cases, we give the complexity results in terms of the number of function value and gradient evaluations to produce near-stationary points. Due to the different structures, different definitions of near-stationary points are given for the two cases. The complexity for producing a nearly @math -stationary point is @math for the first case while it becomes @math for the second case.
The augmented Lagrange method (ALM) @cite_32 @cite_20 @cite_18 @cite_35 is another common choice for constrained problems. Different from the exact or quadratic penalty method, ALM estimates the primal solution together with the dual solution. At each iteration, it updates the primal variable by minimizing the augmented Lagrange function and then performs a dual gradient ascent step to update the dual variable. The iteration complexity of ALM has been established for convex problems @cite_17 @cite_49 @cite_38 @cite_2 @cite_53 . For non-convex problems, most of the existing studies on ALM only show its asymptotic convergence or local convergence rate @cite_41 @cite_61 @cite_19 @cite_30 @cite_64 @cite_82 . The computational complexities of ALM for finding an @math -stationary point (under various notions of stationarity) are obtained only for linearly constrained problems @cite_39 @cite_12 @cite_63 @cite_4 . One exception is @cite_29 where they essentially assume that the smallest singular value of the Jacobian matrix of the constraint functions is uniformly bounded away from zero at all feasible points. In this paper, we do not require that assumption but, instead, need an initial nearly feasible solution when the constraints are non-convex while @cite_29 does not need.
1908.11518
2971189344
Non-convex optimization problems arise from various areas in science and engineering. Although many numerical methods and theories have been developed for unconstrained non-convex problems, the parallel development for constrained non-convex problems remains limited. That restricts the practices of mathematical modeling and quantitative decision making in many disciplines. In this paper, an inexact proximal-point penalty method is proposed for constrained optimization problems where both the objective function and the constraint can be non-convex. The proposed method approximately solves a sequence of subproblems, each of which is formed by adding to the original objective function a proximal term and quadratic penalty terms associated to the constraint functions. Under a weak-convexity assumption, each subproblem is made strongly convex and can be solved effectively to a required accuracy by an optimal gradient-type method. The theoretical property of the proposed method is analyzed in two different cases. In the first case, the objective function is non-convex but the constraint functions are assumed to be convex, while in the second case, both the objective function and the constraint are non-convex. For both cases, we give the complexity results in terms of the number of function value and gradient evaluations to produce near-stationary points. Due to the different structures, different definitions of near-stationary points are given for the two cases. The complexity for producing a nearly @math -stationary point is @math for the first case while it becomes @math for the second case.
While preparing this paper, we notice two recently posted papers @cite_86 @cite_76 on the problems with non-convex constraints. The algorithms in both works are based on the proximal point method. Different from our approach, they solve subproblems with strongly convex objective and also strongly convex constraints by adding proximal terms to the objective and constraints. Their analysis requires the uniform boundedness of the dual solutions of all subproblems and, to ensure this requirement is satisfied, @cite_48 assume that a uniform Slater's condition holds while @cite_86 assume that the Mangasarian-Fromovitz constraint qualification holds at the limiting points of the generated iterates. However, neither assumptions can be easily verified. As pointed out in @cite_86 , their assumptions can be implied by a sufficient feasibility assumption, which is an even stronger assumption. On the contrary, our analysis in the non-convex constrained case does not depend on the boundness of the dual variables, and thus does not need the aforementioned assumptions by @cite_86 @cite_76 .
{ "cite_N": [ "@cite_86", "@cite_76", "@cite_48" ], "mid": [ "2964862314", "", "2964772384" ], "abstract": [ "Nonconvex optimization is becoming more and more important in machine learning and operations research. In spite of recent progresses, the development of provably efficient algorithm for optimization with nonconvex functional constraints remains open. Such problems have potential applications in risk-averse machine learning, semisupervised learning and robust optimization among others. In this paper, we introduce a new proximal point type method for solving this important class of nonconvex problems by transforming them into a sequence of convex constrained subproblems. We establish the convergence and rate of convergence of this algorithm to the KKT point under different types of constraint qualifications. In particular, we prove that our algorithm will converge to an @math -KKT point in @math iterations under a properly defined condition. For practical use, we present inexact variants of this approach, in which approximate solutions of the subproblems are computed by either primal or primal-dual type algorithms, and establish their associated rate of convergence. To the best of our knowledge, this is the first time that proximal point type method is developed for nonlinear programing with nonconvex functional constraints, and most of the convergence and complexity results seem to be new in the literature.", "", "Optimization models with non-convex constraints arise in many tasks in machine learning, e.g., learning with fairness constraints or Neyman-Pearson classification with non-convex loss. Although many efficient methods have been developed with theoretical convergence guarantees for non-convex unconstrained problems, it remains a challenge to design provably efficient algorithms for problems with non-convex functional constraints. This paper proposes a class of subgradient methods for constrained optimization where the objective function and the constraint functions are are weakly convex. Our methods solve a sequence of strongly convex subproblems, where a proximal term is added to both the objective function and each constraint function. Each subproblem can be solved by various algorithms for strongly convex optimization. Under a uniform Slater's condition, we establish the computation complexities of our methods for finding a nearly stationary point." ] }
1908.11518
2971189344
Non-convex optimization problems arise from various areas in science and engineering. Although many numerical methods and theories have been developed for unconstrained non-convex problems, the parallel development for constrained non-convex problems remains limited. That restricts the practices of mathematical modeling and quantitative decision making in many disciplines. In this paper, an inexact proximal-point penalty method is proposed for constrained optimization problems where both the objective function and the constraint can be non-convex. The proposed method approximately solves a sequence of subproblems, each of which is formed by adding to the original objective function a proximal term and quadratic penalty terms associated to the constraint functions. Under a weak-convexity assumption, each subproblem is made strongly convex and can be solved effectively to a required accuracy by an optimal gradient-type method. The theoretical property of the proposed method is analyzed in two different cases. In the first case, the objective function is non-convex but the constraint functions are assumed to be convex, while in the second case, both the objective function and the constraint are non-convex. For both cases, we give the complexity results in terms of the number of function value and gradient evaluations to produce near-stationary points. Due to the different structures, different definitions of near-stationary points are given for the two cases. The complexity for producing a nearly @math -stationary point is @math for the first case while it becomes @math for the second case.
In addition to the methods above, algorithms that utilize Hessian information have been developed to find the second-order @math -stationary point of linearly constrained smooth non-convex optimization @cite_87 @cite_71 @cite_15 . Different from these works, we focus on finding an approximate first-order stationary point for nonlinear constrained non-convex optimization using only gradient information.
{ "cite_N": [ "@cite_15", "@cite_71", "@cite_87" ], "mid": [ "2959708829", "2895571900", "2788706426" ], "abstract": [ "This paper proposes low-complexity algorithms for finding approximate second-order stationary points (SOSPs) of problems with smooth non-convex objective and linear constraints. While finding (approximate) SOSPs is computationally intractable, we first show that generic instances of the problem can be solved efficiently. More specifically, for a generic problem instance, certain strict complementarity (SC) condition holds for all Karush-Kuhn-Tucker (KKT) solutions (with probability one). The SC condition is then used to establish an equivalence relationship between two different notions of SOSPs, one of which is computationally easy to verify. Based on this particular notion of SOSP, we design an algorithm named the Successive Negative-curvature grAdient Projection (SNAP), which successively performs either conventional gradient projection or some negative curvature based projection steps to find SOSPs. SNAP and its first-order extension SNAP @math , require @math iterations to compute an @math -SOSP, and their per-iteration computational complexities are polynomial in the number of constraints and problem dimension. To our knowledge, this is the first time that first-order algorithms with polynomial per-iteration complexity and global sublinear rate have been designed to find SOSPs of the important class of non-convex problems with linear constraints.", "We consider the problem of finding an approximate second-order stationary point of a constrained non-convex optimization problem. We first show that, unlike the unconstrained scenario, the vanilla projected gradient descent algorithm may converge to a strict saddle point even when there is only a single linear constraint. We then provide a hardness result by showing that checking ( , )-second order stationarity is NP-hard even in the presence of linear constraints. Despite our hardness result, we identify instances of the problem for which checking second order stationarity can be done efficiently. For such instances, we propose a dynamic second order Frank--Wolfe algorithm which converges to ( , )-second order stationary points in O ( ^ -2 , ^ -3 ) iterations. The proposed algorithm can be used in general constrained non-convex optimization as long as the constrained quadratic sub-problem can be solved efficiently.", "In this work, we study two first-order primal-dual based algorithms, the Gradient Primal-Dual Algorithm (GPDA) and the Gradient Alternating Direction Method of Multipliers (GADMM), for solving a class of linearly constrained non-convex optimization problems. We show that with random initialization of the primal and dual variables, both algorithms are able to compute second-order stationary solutions (ss2) with probability one. This is the first result showing that primal-dual algorithm is capable of finding ss2 when only using first-order information, it also extends the existing results for first-order, but primal-only algorithms. An important implication of our result is that it also gives rise to the first global convergence result to the ss2, for two classes of unconstrained distributed non-convex learning problems over multi-agent networks." ] }
1908.11053
2970409889
Formal query generation aims to generate correct executable queries for question answering over knowledge bases (KBs), given entity and relation linking results. Current approaches build universal paraphrasing or ranking models for the whole questions, which are likely to fail in generating queries for complex, long-tail questions. In this paper, we propose SubQG, a new query generation approach based on frequent query substructures, which helps rank the existing (but nonsignificant) query structures or build new query structures. Our experiments on two benchmark datasets show that our approach significantly outperforms the existing ones, especially for complex questions. Also, it achieves promising performance with limited training data and noisy entity relation linking results.
Semantic parsing-based approaches translate questions into formal queries using bottom up parsing @cite_5 or staged query graph generation @cite_12 . gAnswer @cite_15 @cite_1 builds up semantic query graph for question analysis and utilize subgraph matching for disambiguation. Recent studies combine parsing based approaches with neural networks, to enhance the ability for structure disambiguation. ConstraintQG ( ConstraintQG ), CQAEMNLP ( CQAEMNLP ) and SQG ( SQG ) build query graphs by staged query generation, and follow an encode-and-compare framework to rank candidate queries with neural networks. These approaches try to learn entire representations for questions with different query structures by using a single network. Thus, they may suffer from the lack of training data, especially for questions with rarely appeared structures. By contrast, our approach utilizes multiple networks to learn predictors for different query substructures, which can gain a stable performance with limited training data. Also, our approach does not require manually-written rules, and performs stably with noisy linking results.
1908.11056
2971248932
In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely. Among activities that impact the environment, oil and gas production, wastewater transport, and urbanization are included. In addition to the occurrence of anthropogenic contamination, the presence of some contaminants (e.g., methane, salt, and sulfate) of natural origin is not uncommon. Therefore, scientists sometimes find it difficult to identify the sources of contaminants in the coupled natural and human systems. In this paper, we propose a technique to simultaneously conduct source detection and prediction, which outperforms other approaches in the interdisciplinary case study of the identification of potential groundwater contamination within a region of high-density shale gas development.
Dictionary learning has been widely used in computer vision to obtain basic components and sparse representations of images @cite_7 . Recently, in order to optimize the learned dictionary for a specific task, people proposed supervised dictionary learning @cite_1 . Some methods learn discriminative dictionaries for different classes @cite_8 @cite_14 , or use label information to prune the learned dictionary by unsupervised dictionary learning @cite_5 . They actually separate the dictionary learning from the supervised learning part and may lead to inferior results. Another group of methods combine dictionary learning and supervised learning @cite_1 @cite_11 , but fail to consider the spatial temporal property for specific problems. Hence, we propose to do dictionary learning and supervised learning iteratively, and spatial and temporal regularization are added to improve the interpretation of results.
1908.11044
2971299764
We present a general paradigm for dynamic 3D reconstruction from multiple independent and uncontrolled image sources having arbitrary temporal sampling density and distribution. Our graph-theoretic formulation models the Spatio-temporal relationships among our observations in terms of the joint estimation of their 3D geometry and its discrete Laplace operator. Towards this end, we define a tri-convex optimization framework that leverages the geometric properties and dependencies found among a Euclideanshape-space and the discrete Laplace operator describing its local and global topology. We present a reconstructability analysis, experiments on motion capture data and multi-view image datasets, as well as explore applications to geometry-based event segmentation and data association.
Temporal alignment is a necessary pre-processing step for most dynamic 3D reconstruction methods. Current video synchronization or image sequencing @cite_42 @cite_44 @cite_27 @cite_18 @cite_29 @cite_19 rely on the image 2D features, foregoing the recovery of the 3D structure. Feature-based sequencing methods like @cite_42 @cite_27 @cite_16 make different assumptions on the underlying imaging geometry. For example, while @cite_42 favors an approximately static imaging geometry, @cite_27 prefers viewing configurations with large baselines. @cite_44 overcomes the limitation of static cameras and improves accuracy by leveraging the temporal info of frames in individual cameras. @cite_18 determines spatio-temporal alignment among a partially order set of observation by framing the problem as mapping of @math observations into a single line in @math , which explicitly imposes a total ordering. Unlike previous methods, @cite_20 propose a synchronization algorithm without tracking corresponding feature between video sequences. Instead, they synchronize two videos by the relative motion between two rigid objects. @cite_25 determined sequencing based on the approximate 3D intersections of viewing rays under an affine reference frame. @cite_30 jointly synchronize a pair of video sequences and reconstruct their commonly observed dense 3D structure by maximizing the spatio-temporal consistency of two-view pixel correspondences across video sequences.
{ "cite_N": [ "@cite_30", "@cite_18", "@cite_29", "@cite_42", "@cite_44", "@cite_19", "@cite_27", "@cite_16", "@cite_25", "@cite_20" ], "mid": [ "", "2167747244", "1886695513", "", "2152136819", "", "1901796002", "2126784706", "2097446893", "1850003624" ], "abstract": [ "", "In this paper, we consider the problem of estimating the spatiotemporal alignment between N unsynchronized video sequences of the same dynamic 3D scene, captured from distinct viewpoints. Unlike most existing methods, which work for N = 2 and rely on a computationally intensive search in the space of temporal alignments, we present a novel approach that reduces the problem for general N to the robust estimation of a single line in RN. This line captures all temporal relations between the sequences and can be computed without any prior knowledge of these relations. Considering that the spatial alignment is captured by the parameters of fundamental matrices, an iterative algorithm is used to refine simultaneously the parameters representing the temporal and spatial relations between the sequences. Experimental results with real-world and synthetic sequences show that our method can accurately align the videos even when they have large misalignments (e.g., hundreds of frames), when the problem is seemingly ambiguous (e.g., scenes with roughly periodic motion), and when accurate manual alignment is difficult (e.g., due to slow-moving objects).", "We present a novel algorithm for temporally synchronizing multiple videos capturing the same dynamic scene. Our algorithm relies on general image features and it does not require explicitly tracking any specific object, making it applicable to general scenes with complex motion. This is facilitated by our new trajectory filtering and matching schemes that correctly identifies matching pairs of trajectories (inliers) from a large set of potential candidate matches, of which many are outliers. We find globally optimal synchronization parameters by using a stable RANSAC-based optimization approach. For multi-video synchronization, the algorithm identifies an informative subset of video pairs which prevents the RANSAC algorithm from being biased by outliers. Experiments on two-camera and multi-camera synchronization demonstrate the performance of our algorithm.", "", "Photo-sequencing is the problem of recovering the temporal order of a set of still images of a dynamic event, taken asynchronously by a set of uncalibrated cameras. Solving this problem is a first, crucial step for analyzing (or visualizing) the dynamic content of the scene captured by a large number of freely moving spectators. We propose a geometric based solution, followed by rank aggregation to the photo-sequencing problem. Our algorithm trades spatial certainty for temporal certainty. Whereas the previous solution proposed by [4] relies on two images taken from the same static camera to eliminate uncertainty in space, we drop the static-camera assumption and replace it with temporal information available from images taken from the same (moving) camera. Our method thus overcomes the limitation of the static-camera assumption, and scales much better with the duration of the event and the spread of cameras in space. We present successful results on challenging real data sets and large scale synthetic data (250 images).", "", "We present an algorithm that synchronizes two short video sequences where an object undergoes ballistic motion against stationary scene points. The object’s motion and epipolar geometry are exploited to guide the algorithm to the correct synchronization in an iterative manner. Our algorithm accurately synchronizes videos recorded at different frame rates, and takes few iterations to converge to sub-frame accuracy. We use synthetic data to analyze our algorithm’s accuracy under the influence of noise. We demonstrate that it accurately synchronizes real video sequences, and evaluate its performance against manual synchronization.", "This paper presents a method of synchronizing video sequences that exploits the non-rigidity of sets of 3D point features (e.g., anatomical joint locations) within the scene. The theory is developed for homography, perspective and affine projection models within a unified rank constraint framework that is computationally cheap. An efficient method is then presented that recovers potential frame correspondences, estimates possible synchronization parameters via the Hough transform and refines these parameters using non-linear optimization methods in order to recover synchronization to sub-frame accuracy, even for sequences of unknown and different frame rates. The method is evaluated quantitatively using synthetic data and demonstrated qualitatively on several real sequences.", "We present a novel method for automatically synchronizing two video sequences of the same event. Unlike previously proposed methods, we do not put any restrictive constraints on the scene nor on the camera motions: our method can deal with independently moving cameras, wide baseline conditions, and general 3D scenes. It starts from five point correspondences throughout the video sequences, that are provided using wide baseline matching and tracking techniques. It is efficient, in that it can be implemented in a non-combinatorial way. The feasibility of the method is demonstrated by preliminary experimental results.", "In this work, a method that synchronizes two video sequences is proposed. Unlike previous methods, which require the existence of correspondences between features tracked in the two sequences, and or that the cameras are static or jointly moving, the proposed approach does not impose any of these constraints. It works when the cameras move independently, even if different features are tracked in the two sequences. The assumptions underlying the proposed strategy are that the intrinsic parameters of the cameras are known and that two rigid objects, with independent motions on the scene, are visible in both sequences. The relative motion between these objects is used as clue for the synchronization. The extrinsic parameters of the cameras are assumed to be unknown. A new synchronization algorithm for static or jointly moving cameras that see (possibly) different parts of a common rigidly moving object is also proposed. Proof-of-concept experiments that illustrate the performance of these methods are presented, as well as a comparison with a state-of-the-art approach." ] }
1908.11315
2970031230
Membership inference. @cite_33 present a membership inference attack in which they infer the presence of an individual's genotype within a complex genomic DNA mixture. @cite_2 improve on the attack using correlation statistics of just a few hundreds SNPs, while @cite_16 rely on regression coefficients. Shringarpure and Bustamante @cite_30 perform membership inference against the Beacon network. Beacons are web servers that answer questions e.g. does your dataset include a genome that has a specific nucleotide at a specific genomic coordinate?'' to which the Beacon responds yes or no, without referring to a specific individual; see: https: github.com ga4gh-beacon specification . They use a likelihood-ratio test to predict whether an individual is present in the Beacon, detecting membership within a Beacon with 1,000 individuals using 5,000 queries. Also, Von @cite_9 reduce the number of queries to less than 0.5 best performing attack uses a high-order Markov chain to model the SNP correlations, as described in @cite_11 . Note that, as part of the attacks described in this paper, we use inference methods from @cite_11 as our baseline inference methods.
{ "cite_N": [ "@cite_30", "@cite_33", "@cite_9", "@cite_2", "@cite_16", "@cite_11" ], "mid": [ "1838635991", "2040228409", "2952306472", "2141481372", "2134493890", "1526729971" ], "abstract": [ "The human genetics community needs robust protocols that enable secure sharing of genomic data from participants in genetic research. Beacons are web servers that answer allele-presence queries—such as “Do you have a genome that has a specific nucleotide (e.g., A) at a specific genomic position (e.g., position 11,272 on chromosome 1)?”—with either “yes” or “no.” Here, we show that individuals in a beacon are susceptible to re-identification even if the only data shared include presence or absence information about alleles in a beacon. Specifically, we propose a likelihood-ratio test of whether a given individual is present in a given genetic beacon. Our test is not dependent on allele frequencies and is the most powerful test for a specified false-positive rate. Through simulations, we showed that in a beacon with 1,000 individuals, re-identification is possible with just 5,000 queries. Relatives can also be identified in the beacon. Re-identification is possible even in the presence of sequencing errors and variant-calling differences. In a beacon constructed with 65 European individuals from the 1000 Genomes Project, we demonstrated that it is possible to detect membership in the beacon with just 250 SNPs. With just 1,000 SNP queries, we were able to detect the presence of an individual genome from the Personal Genome Project in an existing beacon. Our results show that beacons can disclose membership and implied phenotypic information about participants and do not protect privacy a priori. We discuss risk mitigation through policies and standards such as not allowing anonymous pings of genetic beacons and requiring minimum beacon sizes.", "We use high-density single nucleotide polymorphism (SNP) genotyping microarrays to demonstrate the ability to accurately and robustly determine whether individuals are in a complex genomic DNA mixture. We first develop a theoretical framework for detecting an individual's presence within a mixture, then show, through simulations, the limits associated with our method, and finally demonstrate experimentally the identification of the presence of genomic DNA of specific individuals within a series of highly complex genomic mixtures, including mixtures where an individual contributes less than 0.1 of the total genomic DNA. These findings shift the perceived utility of SNPs for identifying individual trace contributors within a forensics mixture, and suggest future research efforts into assessing the viability of previously sub-optimal DNA sources due to sample contamination. These findings also suggest that composite statistics across cohorts, such as allele frequency or genotype counts, do not mask identity within genome-wide association studies. The implications of these findings are discussed.", "Genomic datasets are often associated with sensitive phenotypes. Therefore, the leak of membership information is a major privacy risk. Genomic beacons aim to provide a secure, easy to implement, and standardized interface for data sharing by only allowing yes no queries on the presence of specific alleles in the dataset. Previously deemed secure against re-identification attacks, beacons were shown to be vulnerable despite their stringent policy. Recent studies have demonstrated that it is possible to determine whether the victim is in the dataset, by repeatedly querying the beacon for his her single nucleotide polymorphisms (SNPs). In this work, we propose a novel re-identification attack and show that the privacy risk is more serious than previously thought. Using the proposed attack, even if the victim systematically hides informative SNPs (i.e., SNPs with very low minor allele frequency -MAF-), it is possible to infer the alleles at positions of interest as well as the beacon query results with very high confidence. Our method is based on the fact that alleles at different loci are not necessarily independent. We use the linkage disequilibrium and a high-order Markov chain-based algorithm for the inference. We show that in a simulated beacon with 65 individuals from the CEU population, we can infer membership of individuals with 95 confidence with only 5 queries, even when SNPs with MAF less than 0.05 are hidden. This means, we need less than 0.5 of the number of queries that existing works require, to determine beacon membership under the same conditions. We further show that countermeasures such as hiding certain parts of the genome or setting a query budget for the user would fail to protect the privacy of the participants under our adversary model.", "Genome-wide association studies (GWAS) aim at discovering the association between genetic variations, particularly single-nucleotide polymorphism (SNP), and common diseases, which is well recognized to be one of the most important and active areas in biomedical research. Also renowned is the privacy implication of such studies, which has been brought into the limelight by the recent attack proposed by Homer's attack demonstrates that it is possible to identify a GWAS participant from the allele frequencies of a large number of SNPs. Such a threat, unfortunately, was found in our research to be significantly understated. In this paper, we show that individuals can actually be identified from even a relatively small set of statistics, as those routinely published in GWAS papers. We present two attacks. The first one extends Homer's attack with a much more powerful test statistic, based on the correlations among different SNPs described by coefficient of determination (r2). This attack can determine the presence of an individual from the statistics related to a couple of hundred SNPs. The second attack can lead to complete disclosure of hundreds of participants' SNPs, through analyzing the information derived from published statistics. We also found that those attacks can succeed even when the precisions of the statistics are low and part of data is missing. We evaluated our attacks on the real human genomes and concluded that such threats are completely realistic.", "Recent advances in genome-scale, system-level measurements of quantitative phenotypes (transcriptome, metabolome, and proteome) promise to yield unprecedented biological insights. In this environment, broad dissemination of results from genome-wide association studies (GWASs) or deep-sequencing efforts is highly desirable. However, summary results from case-control studies (allele frequencies) have been withdrawn from public access because it has been shown that they can be used for inferring participation in a study if the individual's genotype is available. A natural question that follows is how much private information is contained in summary results from quantitative trait GWAS such as regression coefficients or p values. We show that regression coefficients for many SNPs can reveal the person's participation and for participants his or her phenotype with high accuracy. Our power calculations show that regression coefficients contain as much information on individuals as allele frequencies do, if the person's phenotype is rather extreme or if multiple phenotypes are available as has been increasingly facilitated by the use of multiple-omics data sets. These findings emphasize the need to devise a mechanism that allows data sharing that will facilitate scientific progress without sacrificing privacy protection.", "As genomic data becomes widely used, the problem of genomic data privacy becomes a hot interdisciplinary research topic among geneticists, bioinformaticians and security and privacy experts. Practical attacks have been identified on genomic data, and thus break the privacy expectations of individuals who contribute their genomic data to medical research, or simply share their data online. Frustrating as it is, the problem could become even worse. Existing genomic privacy breaches rely on low-order SNV (Single Nucleotide Variant) correlations. Our work shows that far more powerful attacks can be designed if high-order correlations are utilized. We corroborate this concern by making use of different SNV correlations based on various genomic data models and applying them to an inference attack on individuals' genotype data with hidden SNVs. We also show that low-order models behave very differently from real genomic data and therefore should not be relied upon for privacy-preserving solutions." ] }
1908.11315
2970031230
Data sharing. Progress in genomics research is dependent on collaboration and data sharing among different institutions. Given the sensitive nature of the data, as well as regulatory and ethics constraints, this often proves to be a challenging task. @cite_4 propose the use of secret sharing to distribute data among several entities and, using secure multi-party computations, support privacy-friendly computations across multiple entities. @cite_3 present GENSETS, a genome-wide, privacy-preserving similar patients querying system using genomic edit distance approximation and private set difference protocols. Then, @cite_21 use Software Guard Extensions (SGX) to build a privacy-preserving international collaboration tool; this enables secure and distributed computations over encrypted data, thus supporting the analysis of rare disease genetic data across different continents. Finally, Oprisanu and De Cristofaro @cite_14 present a framework ( AnoniMME'') geared supporting anonymous queries within the Matchmaker Exchange platform, which allows researchers to perform queries for rare genetic disease discovery over multiple federated databases.
{ "cite_N": [ "@cite_14", "@cite_21", "@cite_4", "@cite_3" ], "mid": [ "2951951981", "2568218703", "2166106997", "" ], "abstract": [ "Motivation: Advances in genome sequencing and genomics research are bringing us closer to a new era of personalized medicine, where healthcare can be tailored to the individual’s genetic makeup, and to more effective diagnosis and treatment of rare genetic diseases. Much of this progress depends on collaborations and access to genomes, and thus a number of initiatives have been introduced to support seamless data sharing. Among these, the Global Alliance for Genomics and Health runs a popular platform, called Matchmaker Exchange, which allows researchers to perform queries for rare genetic disease discovery over multiple federated databases. Queries include gene variations which are linked to rare diseases, and the ability to find other researchers that have seen or have interest in those variations is extremely valuable. Nonetheless, in some cases, researchers may be reluctant to use the platform since the queries they make (thus, what they are working on) are revealed to other researchers, and this creates concerns with privacy and competitive advantage. Contributions: We present AnoniMME, a novel framework geared to enable anonymous queries within the Matchmaker Exchange platform. The framework, building on a cryptographic primitive called Reverse Private Information Retrieval (PIR), let researchers anonymously query the federated platform, in a multi-server setting. Specifically, they write their query, along with a public encryption key, anonymously in a public database. Responses are also supported, so that other researchers can respond to queries by providing their encrypted contact details. Availability and Implementation: https: github.com bristena-op AnoniMME.", "We introduce PRINCESS, a privacy-preserving international collaboration framework for analyzing rare disease genetic data that are distributed across different continents. PRINCESS leverages Software Guard Extensions (SGX) and hardware for trustworthy computation. Unlike a traditional international collaboration model, where individual-level patient DNA are physically centralized at a single site, PRINCESS performs a secure and distributed computation over encrypted data, fulfilling institutional policies and regulations for protected health information. To demonstrate PRINCESS' performance and feasibility, we conducted a family-based allelic association study for Kawasaki Disease, with data hosted in three different continents. The experimental results show that PRINCESS provides secure and accurate analyses much faster than alternative solutions, such as homomorphic encryption and garbled circuits (over 40 000× faster). https: github.com achenfengb PRINCESS_opensource. shw070@ucsd.edu. Supplementary data are available at Bioinformatics online.", "Motivation: Increased availability of various genotyping techniques has initiated a race for finding genetic markers that can be used in diagnostics and personalized medicine. Although many genetic risk factors are known, key causes of common diseases with complex heritage patterns are still unknown. Identification of such complex traits requires a targeted study over a large collection of data. Ideally, such studies bring together data from many biobanks. However, data aggregation on such a large scale raises many privacy issues. Results: We show how to conduct such studies without violating privacy of individual donors and without leaking the data to third parties. The presented solution has provable security guarantees. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.", "" ] }
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2970031230
Privacy-friendly testing. Another line of work focuses on protecting privacy in the context of personal genomic testing, i.e., computational tests run on sequenced genomes to assess, e.g., genetic susceptibility to diseases, determining the best course of treatment, etc. @cite_31 assume that each individual keeps a copy of their data and consents to tests done in such a way that only the outcome is disclosed. They present a few cryptographic protocols allowing researchers to privately search mutations in specific genes. @cite_34 rely on a semi-trusted party to store an encrypted copy of the individual's genomic data: using additively homomorphic encryption and proxy re-encryption, they allow a Medical Center to privately perform disease susceptibility tests on patients' SNPs. @cite_6 introduce a new cryptographic primitive called Controlled Functional Encryption (CFE), which allows users to learn only certain functions of the (encrypted) data, using keys obtained from an authority; however, the client is required to send a fresh key request to the authority every time they want to evaluate a function on a ciphertext. Overall, for an overview of privacy-enhancing technologies applied to genetic testing, we refer the reader to @cite_13 .
{ "cite_N": [ "@cite_31", "@cite_34", "@cite_13", "@cite_6" ], "mid": [ "2087135382", "2133711597", "2885741165", "2056556714" ], "abstract": [ "Recent advances in DNA sequencing technologies have put ubiquitous availability of fully sequenced human genomes within reach. It is no longer hard to imagine the day when everyone will have the means to obtain and store one's own DNA sequence. Widespread and affordable availability of fully sequenced genomes immediately opens up important opportunities in a number of health-related fields. In particular, common genomic applications and tests performed in vitro today will soon be conducted computationally, using digitized genomes. New applications will be developed as genome-enabled medicine becomes increasingly preventive and personalized. However, this progress also prompts significant privacy challenges associated with potential loss, theft, or misuse of genomic data. In this paper, we begin to address genomic privacy by focusing on three important applications: Paternity Tests, Personalized Medicine, and Genetic Compatibility Tests. After carefully analyzing these applications and their privacy requirements, we propose a set of efficient techniques based on private set operations. This allows us to implement in in silico some operations that are currently performed via in vitro methods, in a secure fashion. Experimental results demonstrate that proposed techniques are both feasible and practical today.", "In this paper, we propose privacy-enhancing technologies for medical tests and personalized medicine methods that use patients' genomic data. Focusing on genetic disease-susceptibility tests, we develop a new architecture (between the patient and the medical unit) and propose a \"privacy-preserving disease susceptibility test\" (PDS) by using homomorphic encryption and proxy re-encryption. Assuming the whole genome sequencing to be done by a certified institution, we propose to store patients' genomic data encrypted by their public keys at a \"storage and processing unit\" (SPU). Our proposed solution lets the medical unit retrieve the encrypted genomic data from the SPU and process it for medical tests and personalized medicine methods, while preserving the privacy of patients' genomic data. We also quantify the genomic privacy of a patient (from the medical unit's point of view) and show how a patient's genomic privacy decreases with the genetic tests he undergoes due to (i) the nature of the genetic test, and (ii) the characteristics of the genomic data. Furthermore, we show how basic policies and obfuscation methods help to keep the genomic privacy of a patient at a high level. We also implement and show, via a complexity analysis, the practicality of PDS.", "", "Motivated by privacy and usability requirements in various scenarios where existing cryptographic tools (like secure multi-party computation and functional encryption) are not adequate, we introduce a new cryptographic tool called Controlled Functional Encryption (C-FE). As in functional encryption, C-FE allows a user (client) to learn only certain functions of encrypted data, using keys obtained from an authority. However, we allow (and require) the client to send a fresh key request to the authority every time it wants to evaluate a function on a ciphertext. We obtain efficient solutions by carefully combining CCA2 secure public-key encryption (or rerandomizable RCCA secure public-key encryption, depending on the nature of security desired) with Yao's garbled circuit. Our main contributions in this work include developing and for- mally defining the notion of C-FE; designing theoretical and practical constructions of C-FE schemes achieving these definitions for specific and general classes of functions; and evaluating the performance of our constructions on various application scenarios." ] }
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# Dataset Card for Multi-XScience

### Dataset Summary

Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references.

### Languages

The text in the dataset is in English

## Dataset Structure

### Data Instances

{'abstract': 'Author(s): Kuperberg, Greg; Thurston, Dylan P. | Abstract: We give a purely topological definition of the perturbative quantum invariants of links and 3-manifolds associated with Chern-Simons field theory. Our definition is as close as possible to one given by Kontsevich. We will also establish some basic properties of these invariants, in particular that they are universally finite type with respect to algebraically split surgery and with respect to Torelli surgery. Torelli surgery is a mutual generalization of blink surgery of Garoufalidis and Levine and clasper surgery of Habiro.', 'aid': 'math9912167', 'mid': '1631980677', 'ref_abstract': {'abstract': ['This note is a sequel to our earlier paper of the same title [4] and describes invariants of rational homology 3-spheres associated to acyclic orthogonal local systems. Our work is in the spirit of the Axelrod–Singer papers [1], generalizes some of their results, and furnishes a new setting for the purely topological implications of their work.', 'Recently, Mullins calculated the Casson-Walker invariant of the 2-fold cyclic branched cover of an oriented link in S^3 in terms of its Jones polynomial and its signature, under the assumption that the 2-fold branched cover is a rational homology 3-sphere. Using elementary principles, we provide a similar calculation for the general case. In addition, we calculate the LMO invariant of the p-fold branched cover of twisted knots in S^3 in terms of the Kontsevich integral of the knot.'], 'cite_N': ['@cite_16', '@cite_26'], 'mid': ['1481005306', '1641082372']}, 'related_work': 'Two other generalizations that can be considered are invariants of graphs in 3-manifolds, and invariants associated to other flat connections @cite_16 . We will analyze these in future work. Among other things, there should be a general relation between flat bundles and links in 3-manifolds on the one hand and finite covers and branched covers on the other hand @cite_26 .'}

### Data Fields

{abstract: text of paper abstract
aid: arxiv id
mid: microsoft academic graph id
ref_abstract:
{
abstract: text of reference paper (cite_N) abstract
cite_N: special cite symbol,
mid: reference paper's (cite_N) microsoft academic graph id
},
related_work: text of paper related work
}

### Data Splits

The data is split into a training, validation and test.

train validation test
30369 5066 5093

## Considerations for Using the Data

### Citation Information

@article{lu2020multi,
title={Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
author={Lu, Yao and Dong, Yue and Charlin, Laurent},
journal={arXiv preprint arXiv:2010.14235},
year={2020}
}


### Contributions

Thanks to @moussaKam for adding this dataset.