cssi/SciSciGPT-SciSciNet huggingface dataset:
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

Motivated by product detection in supermarkets, this paper studies the problem of object proposal generation in supermarket images and other natural images. We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range. Therefore, we propose to estimate object scales of images before generating object proposals. The proposed method for predicting object scales is called ScaleNet. To validate the effectiveness of ScaleNet, we build three supermarket datasets, two of which are real-world datasets used for testing and the other one is a synthetic dataset used for training. In short, we extend the previous state-of-the-art object proposal methods by adding a scale prediction phase. The resulted method outperforms the previous state-of-the-art on the supermarket datasets by a large margin. We also show that the approach works for object proposal on other natural images and it outperforms the previous state-of-the-art object proposal methods on the MS COCO dataset. The supermarket datasets, the virtual supermarkets, and the tools for creating more synthetic datasets will be made public.
The m/n Pieri rule

The Pieri rule is an important theorem which explains how the operators e_k of multiplication by elementary symmetric functions act in the basis of Schur functions s_lambda. In this paper, for any rational number m/n, we study the relationship between the rational version e_k^{m/n} of the operators (given by the elliptic Hall algebra) and the "rational" version s_lambda^{m/n} of the basis (given by the Maulik-Okounkov stable basis construction)
Asymptotic Slopes of the Aubin–Yau Functional and Calculation of the Donaldson–Futaki Invariant

We derive an explicit formula for the asymptotic slope of the Aubin–Yau functional along a Bergman geodesic on a surface of complex dimension 2, extending the work of Phong–Sturm (On asymptotics for the Mabuchi energy functional, World Science Publishing, River Edge, 2004) on Riemann surfaces. This is equivalent to an explicit calculation of the Donaldson–Futaki invariant of a test configuration. The slope is given as a rational linear combination of period integrals of rational functions that sum to a rational number. The result gives a way to check directly whether a two-dimensional projective variety is K-stable.
The conformal four-point integrals, magic identities and representations of U(2,2)

Abstract In [3] , [5] we found mathematical interpretations of the one-loop conformal four-point Feynman integral as well as the vacuum polarization Feynman integral in the context of representations of a Lie group U ( 2 , 2 ) and quaternionic analysis. Then we raised a natural question of finding mathematical interpretation of other Feynman diagrams in the same setting. In this article we describe this interpretation for all conformal four-point integrals. Using this interpretation, we give a representation-theoretic proof of an operator version of the “magic identities” for the conformal four-point integrals described by the box diagrams. The original “magic identities” are due to J.M. Drummond, J. Henn, V.A. Smirnov and E. Sokatchev; they assert that all n-loop box integrals for four scalar massless particles are equal to each other [2] . The authors give a proof of the magic identities for the Euclidean metric case only and claim that the result is also true for the Minkowski metric case. However, the Minkowski case is much more subtle. In this article we prove an operator version of the magic identities in the Minkowski metric case and, in particular, specify the relative positions of cycles of integration that make these identities correct. No prior knowledge of physics or Feynman diagrams is assumed from the reader. We provide a summary of all relevant results from quaternionic analysis to make the article self-contained.
Covering shadows with a smaller volume

For n⩾2 a construction is given for convex bodies K and L in Rn such that the orthogonal projection Lu onto the subspace u⊥ contains a translate of Ku for every direction u, while the volumes of K and L satisfy Vn(K)>Vn(L). A more general construction is then given for n-dimensional convex bodies K and L such that each orthogonal projection Lξ onto a k-dimensional subspace ξ contains a translate of Kξ, while the mth intrinsic volumes of K and L satisfy Vm(K)>Vm(L) for all m>k. For each k=1,…,n, we then define the collection Cn,k to be the closure (under the Hausdorff topology) of all Blaschke combinations of suitably defined cylinder sets (prisms). It is subsequently shown that, if L∈Cn,k, and if the orthogonal projection Lξ contains a translate of Kξ for every k-dimensional subspace ξ of Rn, then Vn(K)⩽Vn(L). The families Cn,k, called k-cylinder bodies of Rn, form a strictly increasing chain Cn,1⊂Cn,2⊂⋯⊂Cn,n−1⊂Cn,n, where Cn,1 is precisely the collection of centrally symmetric compact convex sets in Rn, while Cn,n is the collection of all compact convex sets in Rn. Members of each family Cn,k are seen to play a fundamental role in relating covering conditions for projections to the theory of mixed volumes, and members of Cn,k are shown to satisfy certain geometric inequalities. Related open questions are also posed.
A Combinatorial Approach for Small and Strong Formulations of Disjunctive Constraints

A framework is presented for constructing strong mixed-integer programming formulations for logical disjunctive constraints. This approach is a generalization of the logarithmically sized formulati...
Mitigating Unwanted Biases with Adversarial Learning

Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases. We present a framework for mitigating such biases by including a variable for the group of interest and simultaneously learning a predictor and an adversary. The input to the network X, here text or census data, produces a prediction Y, such as an analogy completion or income bracket, while the adversary tries to model a protected variable Z, here gender or zip code. The objective is to maximize the predictor's ability to predict Y while minimizing the adversary's ability to predict Z. Applied to analogy completion, this method results in accurate predictions that exhibit less evidence of stereotyping Z. When applied to a classification task using the UCI Adult (Census) Dataset, it results in a predictive model that does not lose much accuracy while achieving very close to equality of odds (Hardt, et al., 2016). The method is flexible and applicable to multiple definitions of fairness as well as a wide range of gradient-based learning models, including both regression and classification tasks.
Presentations of NET maps

A branched covering $f: S^2 \to S^2$ is a nearly Euclidean Thurston (NET) map if each critical point is simple and its postcritical set has exactly four points. We show that up to equivalence, each NET map admits a normal form in terms of simple affine data. This data can then be used as input for algorithms developed for the computation of fundamental invariants, now systematically tabulated in a large census.
Frictional effects in biomimetic scales engagement

Scales engagement can contribute significantly to nonlinear bending behavior of elastic substrates with rigid biomimetic scales. In this letter, we investigate the role of friction in modulating the nonlinearity that arises due to self-contact of scales through an analytical investigation. We model the friction as dry Coulomb's type friction between rigid links and the substrate is taken to be linear elastic. Our results reveal that frictional effects give rise to two possible locking mechanisms, namely static friction lock and kinetic friction lock. These locks arise due to a combination of interfacial behavior and geometry. In addition to these extremes, the frictional behavior is found to increase the stiffness of the structure. This dual nature of friction which influences both the system operation and its terminal limit results in the maximum relative frictional work to lie at intermediate friction coefficients and not at the extremes of the frictional limits.
Multiscale modeling of plastic deformation of molybdenum and tungsten. III. Effects of temperature and plastic strain rate

Abstract In this paper, we develop a link between the atomic-level modeling of the glide of 1/2〈1 1 1〉 screw dislocations at 0 K and the thermally activated motion of these dislocations via nucleation of pairs of kinks. For this purpose, we introduce the concept of a hypothetical Peierls barrier, which reproduces all the aspects of the dislocation glide at 0 K resulting from the complex response to non-glide stresses expressed in a compact form by the yield criteria advanced in Part II. To achieve this, the barrier is dependent not only on the crystal symmetry and interatomic bonding but also on the applied stress tensor. Standard models are then employed to evaluate the activation enthalpy of kink-pair formation, which is now also a function of the full applied stress tensor. The transition state theory then links this mechanism with the temperature and strain rate dependence of the yield stress.
Composite social network for predicting mobile apps installation

We have carefully instrumented a large portion of the population living in a university graduate dormitory by giving participants Android smart phones running our sensing software. In this paper, we propose the novel problem of predicting mobile application (known as "apps") installation using social networks and explain its challenge. Modern smart phones, like the ones used in our study, are able to collect different social networks using built-in sensors. (e.g. Bluetooth proximity network, call log network, etc) While this information is accessible to app market makers such as the iPhone AppStore, it has not yet been studied how app market makers can use these information for marketing research and strategy development. We develop a simple computational model to better predict app installation by using a composite network computed from the different networks sensed by phones. Our model also captures individual variance and exogenous factors in app adoption. We show the importance of considering all these factors in predicting app installations, and we observe the surprising result that app installation is indeed predictable. We also show that our model achieves the best results compared with generic approaches.
Lagrangian Floer potential of orbifold spheres

Abstract For each sphere with three orbifold points, we construct an algorithm to compute the open Gromov–Witten potential, which serves as the quantum-corrected Landau–Ginzburg mirror and is an infinite series in general. This gives the first class of general-type geometries whose full potentials can be computed. As a consequence we obtain an enumerative meaning of mirror maps for elliptic curve quotients. Furthermore, we prove that the open Gromov–Witten potential is convergent, even in the general-type cases, and has an isolated singularity at the origin, which is an important ingredient of proving homological mirror symmetry.
Auxetic deformations and elliptic curves.

Abstract In materials science and engineering, auxetic behavior refers to deformations of flexible structures where stretching in some direction involves lateral widening, rather than lateral shrinking. We address the problem of detecting auxetic behavior for flexible periodic bar-and-joint frameworks. Currently, the only known algorithmic solution is based on the rather heavy machinery of fixed-dimension semi-definite programming. In this paper we present a new, simpler algorithmic approach which is applicable to a natural family of three-dimensional periodic bar-and-joint frameworks with three degrees of freedom. This class includes most zeolite structures, which are important for applications in computational materials science. We show that the existence of auxetic deformations is related to properties of an associated elliptic curve. A fast algorithm for recognizing auxetic capabilities is obtained via the classical Aronhold invariants of the cubic form defining the curve. Related algorithmic alternatives are also considered.
The bowl championship series: A mathematical review

We discuss individual components of the college football Bowl Championship Series, compare with a simple algorithm defined by random walks on a biased graph, attempt to predict whether the proposed changes will truly lead to increased BCS bowl access for non-BCS schools, and conclude by arguing that the true problem with the BCS Standings lies not in the computer algorithms, but rather in misguided addition.
Knot Theory: From Fox 3-Colorings of Links to Yang–Baxter Homology and Khovanov Homology

This paper is an extended account of my “Introductory Plenary talk at Knots in Hellas 2016” conference. We start from the short introduction to Knot Theory from the historical perspective, starting from Heraclas text (the first century AD), mentioning R. Llull (1232–1315), A. Kircher (1602–1680), Leibniz idea of Geometria Situs (1679), and J.B. Listing (student of Gauss) work of 1847. We spend some space on Ralph H. Fox (1913–1973) elementary introduction to diagram colorings (1956). In the second section we describe how Fox work was generalized to distributive colorings (racks and quandles) and eventually in the work of Jones and Turaev to link invariants via Yang–Baxter operators; here the importance of statistical mechanics to topology will be mentioned. Finally we describe recent developments which started with Mikhail Khovanov work on categorification of the Jones polynomial. By analogy to Khovanov homology we build homology of distributive structures (including homology of Fox colorings) and generalize it to homology of Yang–Baxter operators. We speculate, with supporting evidence, on co-cycle invariants of knots coming from Yang–Baxter homology. Here the work of Fenn–Rourke–Sanderson (geometric realization of pre-cubic sets of link diagrams) and Carter–Kamada–Saito (co-cycle invariants of links) will be discussed and expanded. No deep knowledge of Knot Theory, homological algebra, or statistical mechanics is assumed as we work from basic principles. Because of this, some topics will be only briefly described.
Bayesian Active Edge Evaluation on Expensive Graphs.

Robots operate in environments with varying implicit structure. For instance, a helicopter flying over terrain encounters a very different arrangement of obstacles than a robotic arm manipulating objects on a cluttered table top. State-of-the-art motion planning systems do not exploit this structure, thereby expending valuable planning effort searching for implausible solutions. We are interested in planning algorithms that actively infer the underlying structure of the valid configuration space during planning in order to find solutions with minimal effort. Consider the problem of evaluating edges on a graph to quickly discover collision-free paths. Evaluating edges is expensive, both for robots with complex geometries like robot arms, and for robots with limited onboard computation like UAVs. Until now, this challenge has been addressed via laziness i.e. deferring edge evaluation until absolutely necessary, with the hope that edges turn out to be valid. However, all edges are not alike in value - some have a lot of potentially good paths flowing through them, and some others encode the likelihood of neighbouring edges being valid. This leads to our key insight - instead of passive laziness, we can actively choose edges that reduce the uncertainty about the validity of paths. We show that this is equivalent to the Bayesian active learning paradigm of decision region determination (DRD). However, the DRD problem is not only combinatorially hard, but also requires explicit enumeration of all possible worlds. We propose a novel framework that combines two DRD algorithms, DIRECT and BISECT, to overcome both issues. We show that our approach outperforms several state-of-the-art algorithms on a spectrum of planning problems for mobile robots, manipulators and autonomous helicopters.
Photometric Stabilization for Fast-forward Videos

Videos captured by consumer cameras often exhibit temporal variations in color and tone that are caused by camera auto-adjustments like white-balance and exposure. When such videos are sub-sampled to play fast-forward, as in the increasingly popular forms of timelapse and hyperlapse videos, these temporal variations are exacerbated and appear as visually disturbing high frequency flickering. Previous techniques to photometrically stabilize videos typically rely on computing dense correspondences between video frames, and use these correspondences to remove all color changes in the video sequences. However, this approach is limited in fast-forward videos that often have large content changes and also might exhibit changes in scene illumination that should be preserved. In this work, we propose a novel photometric stabilization algorithm for fast-forward videos that is robust to large content-variation across frames. We compute pairwise color and tone transformations between neighboring frames and smooth these pair-wise transformations while taking in account the possibility of scene/content variations. This allows us to eliminate high-frequency fluctuations, while still adapting to real variations in scene characteristics. We evaluate our technique on a new dataset consisting of controlled synthetic and real videos, and demonstrate that our techniques outperforms the state-of-the-art.
Developing Far-Field Speaker System Via Teacher-Student Learning

In this study, we develop the keyword spotting (KWS) and acoustic model (AM) components in a far-field speaker system. Specifically, we use teacher-student (T/S) learning to adapt a close-talk well-trained production AM to far-field by using parallel close-talk and simulated far-field data. We also use T/S learning to compress a large-size KWS model into a small-size one to fit the device computational cost. Without the need of transcription, T/S learning well utilizes untranscribed data to boost the model performance in both the AM adaptation and KWS model compression. We further optimize the models with sequence discriminative training and live data to reach the best performance of systems. The adapted AM improved from the baseline by 72.60% and 57.16% relative word error rate reduction on play-back and live test data, respectively. The final KWS model size was reduced by 27 times from a large-size KWS model without losing accuracy.
DisCoF + : Asynchronous DisCoF with flexible decoupling for cooperative pathfinding in distributed systems

In our prior work, we outlined an approach, named DisCoF, for cooperative pathfinding in distributed systems with limited sensing and communication range. Contrasting to prior works on cooperative pathfinding with completeness guarantees which assume access to global communication and coordination, DisCoF does not make this assumption. The implication is that at any given time in DisCoF, the robots may not all be aware of each other which is often the case in distributed systems. As a result, DisCoF represents an inherently online approach since coordination can only be realized in an opportunistic manner between robots that are within each other's sensing and communication range. However, there are a few assumptions made in DisCoF to facilitate a formal analysis which must be removed to work with distributed multi-robot platforms. In this paper, we present DisCoF+ which extends DisCoF by enabling an asynchronous solution, as well as providing flexible decoupling between robots for performance improvement. Furthermore, we evaluate our implementation of DisCoF+ by implementing our distributed multi-robot algorithm in the Webots simulator. Finally, we compare DisCoF+ with DisCoF in terms of plan quality and planning performance.
Stability in the Busemann–Petty and Shephard problems

Abstract A comparison problem for volumes of convex bodies asks whether inequalities f K ( ξ ) ⩽ f L ( ξ ) for all ξ ∈ S n − 1 imply that Vol n ( K ) ⩽ Vol n ( L ) , where K, L are convex bodies in R n , and f K is a certain geometric characteristic of K. By linear stability in comparison problems we mean that there exists a constant c such that for every e > 0 , the inequalities f K ( ξ ) ⩽ f L ( ξ ) + e for all ξ ∈ S n − 1 imply that ( Vol n ( K ) ) n − 1 n ⩽ ( Vol n ( L ) ) n − 1 n + c e . We prove such results in the settings of the Busemann–Petty and Shephard problems and their generalizations. We consider the section function f K ( ξ ) = S K ( ξ ) = Vol n − 1 ( K ∩ ξ ⊥ ) and the projection function f K ( ξ ) = P K ( ξ ) = Vol n − 1 ( K | ξ ⊥ ) , where ξ ⊥ is the central hyperplane perpendicular to ξ, and K | ξ ⊥ is the orthogonal projection of K to ξ ⊥ . In these two cases we prove linear stability under additional conditions that K is an intersection body or L is a projection body, respectively. Then we consider other functions f K , which allow to remove the additional conditions on the bodies in higher dimensions.
Black holes and class groups

The theory of quadratic forms and class numbers has connections to many classical problems in number theory. Recently, class numbers have appeared in the study of black holes in string theory. We describe this connection and raise questions in the hope of inspiring new collaborations between number theorists and physicists.
The Attenuated Geodesic Ray Transform on Tensors: Generic Injectivity and Stability

We consider the attenuated geodesic ray transform defined on pairs of symmetric 2-tensors and 1-forms on a simple Riemannian manifold. We prove injectivity and stability results for a class of generic simple metrics and attenuations containing real analytic ones. In fact, methods used in this paper can be modified to generalize our results for a class of non-simple manifolds similar to Stefanov and Uhlmann Am J Math 130(1):239–268 (2008).
Electron dynamics in laser and quasi-static transverse electric and longitudinal magnetic fields

By deriving the 3/2 dimensional Hamiltonian equations for electrons in the intense laser radiation and quasi-static transverse electric and longitudinal magnetic fields, the electron heating mechanisms are examined both for low harmonic resonance of electron frequency in the static fields with the laser frequency and for high harmonic resonances where the overlapping of broadened resonances causes the stochastic heating. For both cases, the maximum electron kinetic energies, well beyond the ponderomotive scaling, depend only on a small parameter combining the laser amplitude, electrostatic field strength and the conserved dephasing rate.
The teaching dimension of linear learners

Teaching dimension is a learning theoretic quantity that species the minimum training set size to teach a target model to a learner. Previous studies on teaching dimension focused on version-space learners which maintain all hypotheses consistent with the training data, and cannot be applied to modern machine learners which select a specific hypothesis via optimization. This paper presents the first known teaching dimension for ridge regression, support vector machines, and logistic regression. We also exhibit optimal training sets that match these teaching dimensions. Our approach generalizes to other linear learners.
A quantum lower bound for distinguishing random functions from random permutations

The problem of distinguishing between a random function and a random permutation on a domain of size N is important in theoretical cryptography, where the security of many primitives depend on the problem's hardness. We study the quantum query complexity of this problem, and show that any quantum algorithm that solves this problem with bounded error must make Ω(N1/5/polylog N) queries to the input function. Our lower bound proof uses a combination of the Collision Problem lower bound and Ambainis's adversary theorem.
Completion Time in Two-User Channels: An Information-Theoretic Perspective

Consider a multi-user channel, where each user has a large but non-replenishable bit pool to transmit. Completion time refers to the number of channel uses spent by each user to complete its transmission. In this paper, an information-theoretic formulation of completion time is based on the concept of constrained rates, which are defined over possibly different number of channel uses. Analogous to the capacity region, the completion time region characterizes all possible trade-offs among users’ completion times. For a two-user multi-access channel, it is shown that the completion time region is achieved by operating the channel in two independent phases: a multi-access phase when both users are transmitting, and a point-to-point phase when one user has finished and the other is still transmitting. Using a similar two-phase approach, the completion time regions (or inner and outer bounds) are established for a two-user Gaussian broadcast channel and a two-user Gaussian interference channel. It is observed that although consisting of two convex subregions, the completion time region may not be convex in general. Finally, optimization problems of minimizing the weighted sum completion time for a Gaussian multi-access channel and a Gaussian broadcast channel are solved, demonstrating the utility of the completion time approach.
Cache aided wireless networks: Tradeoffs between storage and latency

We investigate the fundamental information theoretic limits of cache-aided wireless networks, where edge nodes (or transmitters) are endowed with caches that can store popular content such as multimedia files. This architecture aims to localize popular multimedia content by proactively pushing it closer to the edge of the wireless network, thereby alleviating backhaul load. An information theoretic model of such networks is presented, that includes the introduction of a new metric, namely normalized delivery time (NDT), which captures the worst case time to deliver any requested content to the users. We present new results on the trade-off between latency, measured via the NDT, and the cache storage capacity of the edge nodes. In particular, a novel information theoretic lower bound on NDT is presented for cache aided networks. The optimality of this bound is shown for several system parameters.
Synchronization strings: explicit constructions, local decoding, and applications

This paper gives new results for synchronization strings, a powerful combinatorial object introduced by [Haeupler, Shahrasbi; STOC’17] that allows to efficiently deal with insertions and deletions in various communication problems: - We give a deterministic, linear time synchronization string construction, improving over an O(n5) time randomized construction. Independently of this work, a deterministic O(n log2 logn) time construction was proposed by Cheng, Li, and Wu. - We give a deterministic construction of an infinite synchronization string which outputs the first n symbols in O(n) time. Previously it was not known whether such a string was computable. - Both synchronization string constructions are highly explicit, i.e., the ith symbol can be deterministically computed in O(logi) time. - This paper also introduces a generalized notion we call long-distance synchronization strings. Such strings allow for local and very fast decoding. In particular only O(log3 n) time and access to logarithmically many symbols is required to decode any index. The paper also provides several applications for these improved synchronization strings: - For any δ 0 we provide an insdel error correcting block code with rate 1 − δ − є which can correct any δ/3 fraction of insertion and deletion errors in O(n log3 n) time. This near linear computational efficiency is surprising given that we do not even know how to compute the (edit) distance between the decoding input and output in sub-quadratic time. - We show that local decodability implies that error correcting codes constructed with long-distance synchronization strings can not only efficiently recover from δ fraction of insdel errors but, similar to [Schulman, Zuckerman; TransInf’99], also from any O(δ / logn) fraction of block transpositions and block replications. These block corruptions allow arbitrarily long substrings to be swapped or replicated anywhere. - We show that highly explicitness and local decoding allow for infinite channel simulations with exponentially smaller memory and decoding time requirements. These simulations can then be used to give the first near linear time interactive coding scheme for insdel errors, similar to the result of [Brakerski, Naor; SODA’13] for Hamming errors.
Refined dimensions of cusp forms, and equidistribution and bias of signs

Abstract We refine known dimension formulas for spaces of cusp forms of squarefree level, determining the dimension of subspaces generated by newforms both with prescribed global root numbers and with prescribed local signs of Atkin–Lehner operators. This yields precise results on the distribution of signs of global functional equations and sign patterns of Atkin–Lehner eigenvalues, refining and generalizing earlier results of Iwaniec, Luo and Sarnak. In particular, we exhibit a strict bias towards root number +1 and a phenomenon that sign patterns are biased in the weight but perfectly equidistributed in the level. Another consequence is lower bounds on the number of Galois orbits.
The Kanerva Machine: A Generative Distributed Memory

We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva's sparse distributed memory, it has a robust distributed reading and writing mechanism. The memory is analytically tractable, which enables optimal on-line compression via a Bayesian update-rule. We formulate it as a hierarchical conditional generative model, where memory provides a rich data-dependent prior distribution. Consequently, the top-down memory and bottom-up perception are combined to produce the code representing an observation. Empirically, we demonstrate that the adaptive memory significantly improves generative models trained on both the Omniglot and CIFAR datasets. Compared with the Differentiable Neural Computer (DNC) and its variants, our memory model has greater capacity and is significantly easier to train.
A perturbation approach for Paneitz energy on standard three sphere

We present another proof of the sharp inequality for Paneitz operator on the standard three sphere, in the spirit of subcritical approximation for the classical Yamabe problem. To solve the perturbed problem, we use a symmetrization process which only works for extremal functions. This gives a new example of symmetrization for higher order variational problems.
The stable homology of congruence subgroups

We relate the completed cohomology groups of SLN .OF / , where OF is the ring of integers of a number field, to K–theory and Galois cohomology. Various consequences include showing that Borel’s stable classes become infinitely p–divisible up the p–congruence tower if and only if a certain p–adic zeta value is nonzero. We use our results to compute H2.N .p/;Fp/ (for sufficiently large N ), where N .p/ is the full level-p congruence subgroup of SLN .Z/ .
Algorithm and hardware design of discrete-time spiking neural networks based on back propagation with binary activations

We present a new back propagation based training algorithm for discrete-time spiking neural networks (SNN). Inspired by recent deep learning algorithms on binarized neural networks, binary activation with a straight-through gradient estimator is used to model the leaky integrate-fire spiking neuron, overcoming the difficulty in training SNNs using back propagation. Two SNN training algorithms are proposed: (1) SNN with discontinuous integration, which is suitable for rate-coded input spikes, and (2) SNN with continuous integration, which is more general and can handle input spikes with temporal information. Neuromorphic hardware designed in 28nm CMOS exploits the spike sparsity and demonstrates high classification accuracy (>98% on MNIST) and low energy (51.4–773 nJ/image).
Classification of hyperspectral imagery on embedded Grassmannians

We propose an approach for capturing the signal variability in hyperspectral imagery using the framework of the Grassmann manifold. Labeled points from each class are sampled and used to form abstract points on the Grassmannian. The resulting points on the Grassmannian have representations as orthonormal matrices and as such do not reside in Euclidean space in the usual sense. There are a variety of metrics which allow us to determine a distance matrices that can be used to realize the Grassmannian as an embedding in Euclidean space. We illustrate that we can achieve an approximately isometric embedding of the Grassmann manifold using the chordal metric while this is not the case with geodesic distances. However, non-isometric embeddings generated by using a pseudometric on the Grassmannian lead to the best classification results. We observe that as the dimension of the Grassmannian grows, the accuracy of the classification grows to 100% on two illustrative examples. We also observe a decrease in classification rates if the dimension of the points on the Grassmannian is too large for the dimension of the Euclidean space. We use sparse support vector machines to perform additional model reduction. The resulting classifier selects a subset of dimensions of the embedding without loss in classification performance.
A convex polynomial force-motion model for planar sliding: Identification and application

We propose a polynomial force-motion model for planar sliding. The set of generalized friction loads is the 1-sublevel set of a polynomial whose gradient directions correspond to generalized velocities. Additionally, the polynomial is confined to be convex even-degree homogeneous in order to obey the maximum work inequality, symmetry, shape invariance in scale, and fast invertibility. We present a simple and statistically-efficient model identification procedure using a sum-of-squares convex relaxation. Simulation and robotic experiments validate the accuracy and efficiency of our approach. We also show practical applications of our model including stable pushing of objects and free sliding dynamic simulations.
Brane tunneling and virtual brane-antibrane pairs

We survey barrier penetration by quantum tunneling for four cases: nonrelativistic point particles, scalar fields, relativistic point particles, and DBI branes. We examine two novel features that arise for DBI brane tunneling: the rate can sometimes increase as the barrier gets higher; and the instanton “wrinkles”. We show that these features can be understood as the effect of the quantum sea of virtual brane-antibrane pairs. This sea exponentially augments the decay rate, with possible cosmological consequences.
Demonstration of low emittance in the Cornell energy recovery linac injector prototype

We present a detailed study of the six-dimensional phase space of the electron beam produced by the Cornell Energy Recovery Linac Photoinjector, a high-brightness, high repetition rate (1.3 GHz) DC photoemission source designed to drive a hard x-ray energy recovery linac (ERL). A complete simulation model of the injector has been constructed, verified by measurement, and optimized. Both the horizontal and vertical 2D transverse phase spaces, as well as the time-resolved (sliced) horizontal phase space, were simulated and directly measured at the end of the injector for 19 and 77 pC bunches at roughly 8 MeV. These bunch charges were chosen because they correspond to 25 and 100 mA average current if operating at the full 1.3 GHz repetition rate. The resulting 90% normalized transverse emittances for $19\text{ }(77)\text{ }\text{ }\mathrm{pC}/\mathrm{bunch}$ were $0.23\ifmmode\pm\else\textpm\fi{}0.02$ $(0.51\ifmmode\pm\else\textpm\fi{}0.04)\text{ }\text{ }\ensuremath{\mu}\mathrm{m}$ in the horizontal plane, and $0.14\ifmmode\pm\else\textpm\fi{}0.01$ $(0.29\ifmmode\pm\else\textpm\fi{}0.02)\text{ }\text{ }\ensuremath{\mu}\mathrm{m}$ in the vertical plane, respectively. These emittances were measured with a corresponding bunch length of $2.1\ifmmode\pm\else\textpm\fi{}0.1$ $(3.0\ifmmode\pm\else\textpm\fi{}0.2)\text{ }\text{ }\mathrm{ps}$, respectively. In each case the rms momentum spread was determined to be on the order of ${10}^{\ensuremath{-}3}$. Excellent overall agreement between measurement and simulation has been demonstrated. Using the emittances and bunch length measured at $19\text{ }\text{ }\mathrm{pC}/\mathrm{bunch}$, we estimate the electron beam quality in a 1.3 GHz, 5 GeV hard x-ray ERL to be at least a factor of 20 times better than that of existing storage rings when the rms energy spread of each device is considered. These results represent a milestone for the field of high-brightness, high-current photoinjectors.
Query-Guided Regression Network with Context Policy for Phrase Grounding

Given a textual description of an image, phrase grounding localizes objects in the image referred by query phrases in the description. State-of-the-art methods address the problem by ranking a set of proposals based on the relevance to each query, which are limited by the performance of independent proposal generation systems and ignore useful cues from context in the description. In this paper, we adopt a spatial regression method to break the performance limit, and introduce reinforcement learning techniques to further leverage semantic context information. We propose a novel Query-guided Regression network with Context policy (QRC Net) which jointly learns a Proposal Generation Network (PGN), a Query-guided Regression Network (QRN) and a Context Policy Network (CPN). Experiments show QRC Net provides a significant improvement in accuracy on two popular datasets: Flickr30K Entities and Referit Game, with 14.25% and 17.14% increase over the state-of-the-arts respectively.
On-device bug reporting for Android applications

Bugs that surface in mobile applications can be difficult to reproduce and fix due to several confounding factors including the highly GUI-driven nature of mobile apps, varying contextual states, differing platform versions and device fragmentation. It is clear that developers need support in the form of automated tools that allow for more precise reporting of application defects in order to facilitate more efficient and effective bug fixes. In this paper, we present a tool aimed at supporting application testers and developers in the process of On-Device Bug Reporting. Our tool, called ODBR, leverages the uiautomator framework and low-level event stream capture to offer support for recording and replaying a series of input gesture and sensor events that describe a bug in an Android application.
Elliptic boundary value problems for Bessel operators, with applications to anti-de Sitter spacetimes

Abstract This paper considers boundary value problems for a class of singular elliptic operators that appear naturally in the study of asymptotically anti-de Sitter (aAdS) spacetimes. These problems involve a singular Bessel operator acting in the normal direction. After formulating a Lopatinskii condition, elliptic estimates are established for functions supported near the boundary. The Fredholm property follows from additional hypotheses in the interior. This paper provides a rigorous framework for mode analysis on aAdS spacetimes for a wide range of boundary conditions considered in the physics literature. Completeness of eigenfunctions for some Bessel operator pencils is shown.
Clustering using Max-norm Constrained Optimization

We suggest using the max-norm as a convex surrogate constraint for clustering. We show how this yields a better exact cluster recovery guarantee than previously suggested nuclear-norm relaxation, and study the effectiveness of our method, and other related convex relaxations, compared to other approaches.
Fast Label Embeddings for Extremely Large Output Spaces

Many modern multiclass and multilabel problems are characterized by increasingly large output spaces. For these problems, label embeddings have been shown to be a useful primitive that can improve computational and statistical efficiency. In this work we utilize a correspondence between rank constrained estimation and low dimensional label embeddings that uncovers a fast label embedding algorithm which works in both the multiclass and multilabel settings. The result is a randomized algorithm whose running time is exponentially faster than naive algorithms. We demonstrate our techniques on two large-scale public datasets, from the Large Scale Hierarchical Text Challenge and the Open Directory Project, where we obtain state of the art results. 1 Contributions We provide a statistical motivation for label embedding by demonstrating that the optimal rank-constrained least squares estimator can be constructed from an optimal unconstrained estimator of an embedding of the labels. Thus, embedding can provide beneficial sample complexity reduction even if computational constraints are not binding. We identify a natural object to define label similarity: the expected outer product of the conditional label probabilities. In particular, in conjunction with a low-rank constraint, this indicates two label embeddings are similar when their conditional probabilities are linearly dependent across the dataset. This unifies prior work utilizing the confusion matrix for multiclass [1] and the empirical label covariance for multilabel [5]. We apply techniques from randomized linear algebra [3] to develop an efficient and scalable algorithm for constructing the embeddings, essentially via a novel randomized algorithm. Intuitively, this technique implicitly decomposes the prediction matrix of a model which would be prohibitively expensive to form explicitly. 2 Proposed Algorithm Our proposal is Rembrandt, described in Algorithm 1. We use the top right singular space of ΠX,LY as a label embedding, or equivalently, the top principal components of Y ΠX,LY . Using randomized techniques, we can Algorithm 1 Rembrandt: Response EMBedding via RANDomized Techniques 1: function REMBRANDT(k,X ∈ Rn×d, Y ∈ Rn×c) 2: (p, q)← (20, 1) . These hyperparameters rarely need adjustment. 3: Q← randn(c, k + p) 4: for i ∈ {1, . . . , q} do . Randomized range finder for Y ΠX,LY 5: Z ← arg min ‖Y Q−XZ‖F 6: Q← orthogonalize(Y >XZ) 7: end for . NB: total of (q + 1) data passes, including next line 8: F ← (Y >XQ)>(Y >XQ) . F ∈ R(k+p)×(k+p) is “small” 9: (V,Σ)← eig(F, k) 10: V ← QV . V ∈ Rc×k is the embedding 11: return (V,Σ) 12: end function
Mirror duality in a Joyce manifold

Abstract Previously the two of the authors defined a notion of dual Calabi–Yau manifolds in a G 2 manifold, and described a process to obtain them. Here we apply this process to a compact G 2 manifold, constructed by Joyce, and as a result we obtain a pair of Borcea–Voisin Calabi–Yau manifolds, which are known to be mirror duals of each other.
Continuity of halo functions associated to homothecy invariant density bases

Let B be a collection of open sets in R such that, for any x ∈ R, there exists a set U ∈ B of arbitrarily small diameter containing x. B is said to be a density basis provided that, given a measurable set A ⊂ R, for a.e. x ∈ R we have lim k→∞ 1 |Rk| ∫
Product offerings in malicious hacker markets

Marketplaces specializing in malicious hacking products - including malware and exploits - have recently become more prominent on the darkweb and deepweb. We scrape 17 such sites and collect information about such products in a unified database schema. Using a combination of manual labeling and unsupervised clustering, we examine a corpus of products in order to understand their various categories and how they become specialized with respect to vendor and marketplace. This initial study presents how we effectively employed unsupervised techniques to this data as well as the types of insights we gained on various categories of malicious hacking products.
A Search for Ringed Exoplanets using Kepler Photometry

Author(s): Heising, MZ; Marcy, GW; Schlichting, HE | Abstract: Models are developed to simulate light curves of stars dimmed by transiting exoplanets with and without rings. These models are then applied to Kepler photometry to search for planetary rings in a sample of 21 exoplanets, mostly hot Jupiters, chosen to offer the best observational opportunity for discovering potential rings. We also examine what kinds of rings might be expected for these planets, in terms of both size and orientation, based on arguments involving the host planet's equilibrium temperature, its likely obliquities, and the formation and stability of possible ring systems. Finding no evidence for rings, for each of the 21 studied planets it is determined on an observational basis which potential rings can be rejected out of a representative set of fiducial rings, varying in both size and orientation. For 12 of the 21 planets, we determined that Saturn-like rings could be ruled out for at least certain orientations. Additionally, the detectability of rings is studied, and it is found that ringed planets with small obliquities (roughly 5°-10°) can yield large signals, which is encouraging for future work, since such small obliquities are expected for hot Jupiters.
The Heegner Point Kolyvagin system

In Bull. Soc. Math. France 115 (1987), 399–456, Perrin-Riou formulates a form of the Iwasawa main conjecture which relates Heegner points to the Selmer group of an elliptic curve defined over -extension of a quadratic imaginary field K. Building on the earlier work of Bertolini on this conjecture, and making use of the recent work of Mazur and Rubin on Kolyvagin's theory of Euler systems, we prove one divisibility of Perrin-Riou's conjectured equality. As a consequence, one obtains an upper bound on the rank of the Mordell–Weil group E(K) in terms of Heegner points.
Online semi-supervised learning on quantized graphs

In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose a fast approximate online SSL algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local "representative points" that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We apply our algorithm to face recognition and optical character recognition applications to show that we can take advantage of the manifold structure to outperform the previous methods. Unlike previous heuristic approaches, we show that our method yields provable performance bounds.
Multi-Scale Spatially Weighted Local Histograms in O(1)

Histograms are commonly used to characterize and analyze the region of interest within an image. Weighting the contributions of the pixels to the histogram is a key feature to handle noise and occlusion and increase object localization accuracy of many histogram-based search problems including object detection, tracking and recognition. The integral histogram method provides an optimum and complete solution to compute the plain histogram of any rectangular region in constant time. However, the matter of how accurately extract the weighted histogram of any arbitrary region within an image using integral histogram has not been addressed. This paper presents a novel fast algorithm to evaluate spatially weighted local histograms at different scale accurately and in constant time using an extension of integral histogram. Utilizing the integral histogram makes it to be fast, multi-scale and flexible to different weighting functions. The pixel-level weighting problem is addressed by decomposing the Manhattan spatial filter and fragmenting the region of interest. We evaluated and compared the computational complexity and accuracy of our proposed approach with brute-force implementation and approximation scheme. The proposed method can be integrated into any detection and tracking framework to provide an efficient exhaustive search, improve target localization accuracy and meet the demand of real-time processing.