cssi/SciSciGPT-SciSciNet huggingface dataset:
A Budget-Constrained Inverse Classification Framework for Smooth Classifiers

Inverse classification is the process of manipulating an instance such that it is more likely to conform to a specific class. Past methods that address such a problem have shortcomings. Greedy methods make changes that are overly radical, often relying on data that is strictly discrete. Other methods rely on certain data points, the presence of which cannot be guaranteed. In this paper we propose a general framework and method that overcomes these and other limitations. The formulation of our method can use any differentiable classification function. We demonstrate the method by using logistic regression and Gaussian kernel SVMs. We constrain the inverse classification to occur on features that can actually be changed, each of which incurs an individual cost. We further subject such changes to fall within a certain level of cumulative change (budget). Our framework can also accommodate the estimation of (indirectly changeable) features whose values change as a consequence of actions taken. Furthermore, we propose two methods for specifying feature-value ranges that result in different algorithmic behavior. We apply our method, and a proposed sensitivity analysis-based benchmark method, to two freely available datasets: Student Performance from the UCI Machine Learning Repository and a real-world cardiovascular disease dataset. The results obtained demonstrate the validity and benefits of our framework and method.
Minimax Rates and Efficient Algorithms for Noisy Sorting

There has been a recent surge of interest in studying permutation-based models for ranking from pairwise comparison data. Despite being structurally richer and more robust than parametric ranking models, permutation-based models are less well understood statistically and generally lack efficient learning algorithms. In this work, we study a prototype of permutation-based ranking models, namely, the noisy sorting model. We establish the optimal rates of learning the model under two sampling procedures. Furthermore, we provide a fast algorithm to achieve near-optimal rates if the observations are sampled independently. Along the way, we discover properties of the symmetric group which are of theoretical interest.
A Frobenius–Nirenberg theorem with parameter

The Newlander-Nirenberg theorem says that a formally integrable complex structure is locally equivalent to the complex structure in the complex Euclidean space. We will show two results about the Newlander-Nirenberg theorem with parameter. The first extends the Newlander-Nirenberg theorem to a parametric version, and its proof yields a sharp regularity result as Webster's proof for the Newlander-Nirenberg theorem. The second concerns a version of Nirenberg's complex Frobenius theorem and its proof yields a result with a mild loss of regularity.
On Enhancing Speech Emotion Recognition Using Generative Adversarial Networks.

Generative Adversarial Networks (GANs) have gained a lot of attention from machine learning community due to their ability to learn and mimic an input data distribution. GANs consist of a discriminator and a generator working in tandem playing a min-max game to learn a target underlying data distribution; when fed with data-points sampled from a simpler distribution (like uniform or Gaussian distribution). Once trained, they allow synthetic generation of examples sampled from the target distribution. We investigate the application of GANs to generate synthetic feature vectors used for speech emotion recognition. Specifically, we investigate two set ups: (i) a vanilla GAN that learns the distribution of a lower dimensional representation of the actual higher dimensional feature vector and, (ii) a conditional GAN that learns the distribution of the higher dimensional feature vectors conditioned on the labels or the emotional class to which it belongs. As a potential practical application of these synthetically generated samples, we measure any improvement in a classifier's performance when the synthetic data is used along with real data for training. We perform cross-validation analyses followed by a cross-corpus study.
On the absence of a normal nonabelian Sylow subgroup

Let G be a finite solvable group. We show that G does not have a normal nonabelian Sylow p-subgroup when its prime character degree graph Δ(G) satisfies a technical hypothesis.
A formula for the geometric Jacquet functor and its character sheaf analogue

Let (G,K) be a symmetric pair over the complex numbers, and let X=K∖GX=K∖G be the corresponding symmetric space. In this paper we study a nearby cycles functor associated to a degeneration of X to MN∖GMN∖G , which we call the “wonderful degeneration”. We show that on the category of character sheaves on X, this functor is isomorphic to a composition of two averaging functors (a parallel result, on the level of functions in the p-adic setting, was obtained in [BK,SV]). As an application, we obtain a formula for the geometric Jacquet functor of [ENV] and use this formula to give a geometric proof of the celebrated Casselman’s submodule theorem and establish a second adjointness theorem for Harish-Chandra modules.
Tensor triangular geometry for classical Lie superalgebras

Abstract Tensor triangular geometry as introduced by Balmer [3] is a powerful idea which can be used to extract the ambient geometry from a given tensor triangulated category. In this paper we provide a general setting for a compactly generated tensor triangulated category which enables one to classify thick tensor ideals and the Balmer spectrum. For the general linear Lie superalgebra g = g 0 ¯ ⊕ g 1 ¯ we construct a Zariski space from a detecting subalgebra of g and demonstrate that this topological space governs the tensor triangular geometry for the category of finite dimensional g -modules which are semisimple over g 0 ¯ .
Physical properties of SrSn4 single crystals

We present detailed thermodynamic and transport measurements on single crystals of the recently discovered binary intermetallic superconductor, SrSn4. We find this material to be a slightly anisotropic three-dimensional, strongly coupled, possibly multiband, superconductor. Hydrostatic pressure causes a decrease in the superconducting transition temperature at the rate of ≈ − 0.068 K kbar−1. Band structure calculations are consistent with experimental data on the Sommerfeld coefficient and upper superconducting critical field anisotropy, and suggest a complex, multi-sheet Fermi surface formed by four bands.
Tree decompositions and social graphs

AbstractRecent work has established that large informatics graphs such as social and information networks have non-trivial tree-like structure when viewed at moderate size scales. Here, we present results from the first detailed empirical evaluation of the use of tree decomposition (TD) heuristics for structure identification and extraction in social graphs. Although TDs have historically been used in structural graph theory and scientific computing, we show that—even with existing TD heuristics developed for those very different areas—TD methods can identify interesting structure in a wide range of realistic informatics graphs. Our main contributions are the following: we show that TD methods can identify structures that correlate strongly with the core-periphery structure of realistic networks, even when using simple greedy heuristics; we show that the peripheral bags of these TDs correlate well with low-conductance communities (when they exist) found using local spectral computations; and we show that ...
Deep hidden physics models: deep learning of nonlinear partial differential equations

We put forth a deep learning approach for discovering nonlinear partial differential equations from scattered and potentially noisy observations in space and time. Specifically, we approximate the unknown solution as well as the nonlinear dynamics by two deep neural networks. The first network acts as a prior on the unknown solution and essentially enables us to avoid numerical differentiations which are inherently ill-conditioned and unstable. The second network represents the nonlinear dynamics and helps us distill the mechanisms that govern the evolution of a given spatiotemporal data-set. We test the effectiveness of our approach for several benchmark problems spanning a number of scientific domains and demonstrate how the proposed framework can help us accurately learn the underlying dynamics and forecast future states of the system. In particular, we study the Burgers', Korteweg-de Vries (KdV), Kuramoto-Sivashinsky, nonlinear Schrodinger, and Navier-Stokes equations.
Periodicity of Betti numbers of monomial curves

Abstract Let K be an arbitrary field. Let a = ( a 1 ⋯ a n ) be a sequence of positive integers. Let C ( a ) be the affine monomial curve in A n parametrized by t → ( t a 1 , . . . , t a n ) . Let I ( a ) be the defining ideal of C ( a ) in K [ x 1 , . . . , x n ] . For each positive integer j, let a + j be the sequence ( a 1 + j , . . . , a n + j ) . In this paper, we prove the conjecture of Herzog and Srinivasan saying that the Betti numbers of I ( a + j ) are eventually periodic in j with period a n − a 1 . When j is large enough, we describe the Betti table for the closure of C ( a + j ) in P n .
Stability of syzygy bundles on an algebraic surface

Given a very ample line bundle L on a projective variety X, the syzygy bundle M_L associated to L is the kernel of the evaluation map on sections of L. Our main result is that if X is a smooth projective surface defined over an algebraically closed field, then M_L is slope-stable for any sufficiently positive L.
A generalized Goulden-Jackson cluster method and lattice path enumeration

Abstract The Goulden–Jackson cluster method is a powerful tool for obtaining generating functions counting words in a free monoid by occurrences of a set of subwords. We introduce a generalization of the cluster method for monoid networks, which generalize the combinatorial framework of free monoids. As a sample application of the generalized cluster method, we compute bivariate and multivariate generating functions counting Motzkin paths – both with height bounded and unbounded – by statistics corresponding to the number of occurrences of various subwords, yielding both closed-form and continued fraction formulas.
Properties of reciprocity formulas for the Rogers–Ramanujan continued fractions

Ramanujan recorded four reciprocity formulas for the Rogers–Ramanujan continued fractions. Two reciprocity formulas each are also associated with the Ramanujan–Gollnitz–Gordon continued fractions and a level-13 analog of the Rogers–Ramanujan continued fractions. We show that all eight reciprocity formulas are related to a pair of quadratic equations. The solution to the first equation generalizes the golden ratio and is used to set the value of a coefficient in the second equation; and the solution to the second equation gives a pair of values for a continued fraction. We relate the coefficients of the quadratic equations to important formulas obtained by Ramanujan, examine a pattern in the relation between a continued fraction and its parameters, and use the reciprocity formulas to obtain close approximations for all values of the continued fractions. We highlight patterns in the expressions for certain explicit values of the Rogers–Ramanujan continued fractions by expressing them in terms of the golden ratio. We extend the analysis to reciprocity formulas for Ramanujan’s cubic continued fraction and the Ramanujan–Selberg continued fraction.
Block tensors and symmetric embeddings

Abstract Well known connections exist between the singular value decomposition of a matrix A and the Schur decomposition of its symmetric embedding sym ( A ) = ( [ 0 A ; A T 0 ] ) . In particular, if σ is a singular value of A then + σ and - σ are eigenvalues of the symmetric embedding. The top and bottom halves of sym ( A ) ’s eigenvectors are singular vectors for A . Power methods applied to A can be related to power methods applied to sym ( A ) . The rank of sym ( A ) is twice the rank of A . In this paper we develop similar connections for tensors by building on L.-H. Lim’s variational approach to tensor singular values and vectors. We show how to embed a general order- d tensor A into an order- d symmetric tensor sym ( A ) . Through the embedding we relate power methods for A ’s singular values to power methods for sym ( A ) ’s eigenvalues. Finally, we connect the multilinear and outer product rank of A to the multilinear and outer product rank of sym ( A ) .
Analogues of the general theta transformation formula

A new class of integrals involving the confluent hypergeometric function 1F1(a;c;z) and the Riemann Ξ-function is considered. It generalizes a class containing some integrals of Ramanujan, Hardy and Ferrar and gives, as by-products, transformation formulae of the form F(z, α) = F(iz, β), where αβ = 1. As particular examples, we derive an extended version of the general theta transformation formula and generalizations of certain formulae of Ferrar and Hardy. A one-variable generalization of a well-known identity of Ramanujan is also given. We conclude with a generalization of a conjecture due to Ramanujan, Hardy and Littlewood involving infinite series of the Mobius function.
Coulomb and Landau Gauge Fixing in GPUs using CUDA and MILC

In this work, we present the GPU implementation of the overrelaxation and steepest descent method with Fourier acceleration methods for Laudau and Coulomb gauge fixing using CUDA for SU(N) with N>2. A multi-GPU implementation of the overrelaxation method is also presented using MPI and CUDA. The GPU performance was measured on BlueWaters and compared against the gauge fixing of the CPU MILC code.
Optimal power control and scheduling under hard deadline constraints for continuous fading channels

We consider a joint scheduling-and-power-allocation problem of a downlink cellular system. The system consists of two groups of users: real-time (RT) and non-real-time (NRT) users. Given an average power constraint on the base station, the problem is to find an algorithm that satisfies the RT hard deadline constraint and NRT queue stability constraint. We propose a sum-rate-maximizing algorithm that satisfies these constraints. We also show, through simulations, that the proposed algorithm has an average complexity that is close-to-linear in the number of RT users. The power allocation policy in the proposed algorithm has a closed-form expression for the two groups of users. However, interestingly, the power policy of the RT users differ in structure from that of the NRT users. We also show the superiority of the proposed algorithms over existing approaches using extensive simulations.
Enumerative Combinatorics of Simplicial and Cell Complexes: Kirchhoff and Trent Type Theorems

This paper considers three separate matrices associated to graphs and (each dimension of) cell complexes. It relates all the coefficients of their respective characteristic polynomials to the geometric and combinatorial enumeration of three kinds of sub-objects. The matrices are: the mesh matrix for integral d-cycles, the mesh matrix for integral d-boundaries, and the Kirchhoff matrix, i.e., the combinatorial Laplacian, for integral $$(d-1)$$ -chains. Trent’s theorem states that the determinant of the mesh matrix on 1-cycles of a connected graph is equal to the number of spanning trees (Trent in Proc Nat Acad Sci USA 40:1004–1007, 1954; J Acoust Soc Am 27(3):500–527, 1955). Here this theorem is extended to the mesh matrix on d-cycles in an arbitrary cell complex and, new even for graphs, to enumerative combinatorial interpretation of all of the coefficients of its characteristic polynomial. This last is well defined once a basis for the integral d-cycles is chosen. Additionally, a parallel result for the mesh matrix for integral d-boundaries is proved. Kirchhoff’s classical theorem on graphs (Kirchhoff in Ann Phys Chem 72:497–508, 1847) states that the product of the non-zero eigenvalues of the Kirchhoff matrix, i.e., combinatorial Laplacian, for connected graphs equals n times the number of spanning trees, with n the number of vertices. Kalai (Isr J Math 45(4): 337–351, 1983) investigated counting spanning trees in some higher dimensional settings including simplices here weighting by order of torsion homology groups entered. Lyons has generalized Kirchhoff’s result on the product of the non-zero eigenvalues of the Kirchhoff or combinatorial Laplacian on $$(d-1)$$ -chains to cell complexes for $$d >1$$ (Lyons in J Topol Anal 1(2), 153–175, 2009; Lyons and Peres in Probability on trees and networks. Cambridge series in statistical and probabilistic mathematics, vol 42, Cambridge University Press, New York, 2016). The present analysis extends this to all coefficients of the complete characteristic polynomial. An evaluation of the Reidemeister–Franz torsion of the cell complex with respect to its integral basis gives relations between these combinatorial invariants.
Uncovering Proximity of Chromosome Territories using Classical Algebraic Statistics

Exchange type chromosome aberrations (ETCAs) are rearrangements of the genome that occur when chromosomes break and the resulting fragments rejoin with fragments from other chromosomes or from other regions within the same chromosome. ETCAs are commonly observed in cancer cells and in cells exposed to radiation. The frequency of these chromosome rearrangements is correlated with their spatial proximity, therefore it can be used to infer the three dimensional organization of the genome. Extracting statistical significance of spatial proximity from cancer and radiation data has remained somewhat elusive because of the sparsity of the data. We here propose a new approach to study the three dimensional organization of the genome using algebraic statistics. We test our method on a published data set of irradiated human blood lymphocyte cells. We provide a rigorous method for testing the overall organization of the genome, and in agreement with previous results we find a random relative positioning of chromosomes with the exception of the chromosome pairs { 1,22 } and { 13,14 } that have a significantly larger number of ETCAs than the rest of the chromosome pairs suggesting their spatial proximity. We conclude that algebraic methods can successfully be used to analyze genetic data and have potential applications to larger and more complex data sets.
Decentralized and recursive identification for cooperative manipulation of unknown rigid body with local measurements

This paper proposes a fully decentralized and recursive approach to online identification of unknown kinematic and dynamic parameters for cooperative manipulation of a rigid body based on commonly used local measurements. To the best of our knowledge, this is the first paper addressing the identification problem for 3D rigid body cooperative manipulation, though the approach proposed here applies to the 2D case as well. In this work, we derive truly linear observation models for kinematic and dynamic unknowns whose state-dependent uncertainties can be exactly evaluated. Dynamic consensus in different coordinates and a filter for dual quaternion are developed with which the identification problem can be solved in a distributed way. It can be seen that in our approach all unknowns to be identified are time-invariant constants. Finally, we provide numerical simulation results to illustrate the efficacy of our approach indicating that it can be used for online identification and adaptive control of rigid body cooperative manipulation.
A $p$-adic version of AdS/CFT

In this summary of my talk at Strings 2016, I explain how classical dynamics on an infinite tree graph can be dual to a conformal field theory defined over the $p$-adic numbers. An informal introduction to $p$-adic numbers is followed by a presentation of results on holographic three- and four-point functions. The simplicity of $p$-adic field theories and their similarity to ordinary field theories are illustrated through comparisons of holographic correlators and computations of simple loop diagrams on the field theory side. I close with a discussion of challenges and directions for future work.
WHAT IS THE SHELL AROUND R CORONAE BOREALIS

The hydrogen-deficient, carbon-rich R Coronae Borealis (RCB) stars are known for being prolific producers of dust which causes their large iconic declines in brightness. Several RCB stars, including R Coronae Borealis (R CrB), itself, have large extended dust shells seen in the far-infrared. The origin of these shells is uncertain but they may give us clues to the evolution of the RCB stars. The shells could form in three possible ways. (1) They are fossil Planetary Nebula (PN) shells, which would exist if RCB stars are the result of a final, helium-shell flash, (2) they are material left over from a white-dwarf (WD) merger event which formed the RCB stars, or (3) they are material lost from the star during the RCB phase. Arecibo 21 cm observations establish an upper limit on the column density of H I in the R CrB shell implying a maximum shell mass of ≲0.3 M{sub ☉}. A low-mass fossil PN shell is still a possible source of the shell although it may not contain enough dust. The mass of gas lost during a WD merger event will not condense enough dust to produce the observed shell, assuming a reasonable gas-to-dust ratio. The third scenariomore » where the shell around R CrB has been produced during the star’s RCB phase seems most likely to produce the observed mass of dust and the observed size of the shell. But this means that R CrB has been in its RCB phase for ∼10{sup 4} years.« less
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

A Long Short-Term Memory (LSTM) network is a type of recurrent neural network architecture which has recently obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies. TreeLSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank).
The Power of Shared Randomness in Uncertain Communication.

In a recent work (Ghazi et al., SODA 2016), the authors with Komargodski and Kothari initiated the study of communication with contextual uncertainty, a setup aiming to understand how efficient communication is possible when the communicating parties imperfectly share a huge context. In this setting, Alice is given a function $f$ and an input string $x$, and Bob is given a function $g$ and an input string $y$. The pair $(x,y)$ comes from a known distribution $\mu$ and $f$ and $g$ are guaranteed to be close under this distribution. Alice and Bob wish to compute $g(x,y)$ with high probability. The previous work showed that any problem with one-way communication complexity $k$ in the standard model has public-coin communication $O(k(1+I))$ bits in the uncertain case, where $I$ is the mutual information between $x$ and $y$. A lower bound of $\Omega(\sqrt{I})$ bits on the public-coin uncertain communication was also shown. An important question that was left open is the power that public randomness brings to uncertain communication. Can Alice and Bob achieve efficient communication amid uncertainty without using public randomness? How powerful are public-coin protocols in overcoming uncertainty? Motivated by these two questions: - We prove the first separation between private-coin uncertain communication and public-coin uncertain communication. We exhibit a function class for which the communication in the standard model and the public-coin uncertain communication are $O(1)$ while the private-coin uncertain communication is a growing function of the length $n$ of the inputs. - We improve the lower-bound of the previous work on public-coin uncertain communication. We exhibit a function class and a distribution (with $I \approx n$) for which the one-way certain communication is $k$ bits but the one-way public-coin uncertain communication is at least $\Omega(\sqrt{k} \cdot \sqrt{I})$ bits.
Learning in the Machine: Random Backpropagation and the Deep Learning Channel.

Abstract Random backpropagation (RBP) is a variant of the backpropagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates. It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system. To better understand random backpropagation, we first connect it to the notions of local learning and learning channels. Through this connection, we derive several alternatives to RBP, including skipped RBP (SRBP), adaptive RBP (ARBP), sparse RBP, and their combinations (e.g. ASRBP) and analyze their computational complexity. We then study their behavior through simulations using the MNIST and CIFAR-10 benchmark datasets. These simulations show that most of these variants work robustly, almost as well as backpropagation, and that multiplication by the derivatives of the activation functions is important. As a follow-up, we study also the low-end of the number of bits required to communicate error information over the learning channel. We then provide partial intuitive explanations for some of the remarkable properties of RBP and its variations. Finally, we prove several mathematical results for RBP and its variants including: (1) the convergence to optimal fixed points for linear chains of arbitrary length; (2) convergence to fixed points for linear autoencoders with decorrelated data; (3) long-term existence of solutions for linear systems with a single hidden layer, and their convergence in special cases; and (4) convergence to fixed points of non-linear chains, when the derivative of the activation functions is included.
Galois group action and Jordan decomposition of characters of finite reductive groups with connected center

Abstract Let G be a connected reductive group with connected center defined over F q , with Frobenius morphism F. Given an irreducible complex character χ of G F with its Jordan decomposition, and a Galois automorphism σ ∈ Gal ( Q ‾ / Q ) , we give the Jordan decomposition of the image χ σ of χ under the action of σ on its character values.
The Energy Balance Relation for Weak solutions of the Density-Dependent Navier-Stokes Equations

Abstract We consider the incompressible inhomogeneous Navier–Stokes equations with constant viscosity coefficient and density which is bounded and bounded away from zero. We show that the energy balance relation for this system holds for weak solutions if the velocity, density, and pressure belong to a range of Besov spaces of smoothness 1/3. A density-dependent version of the classical Karman–Howarth–Monin relation is derived.
BPS spectrum, wall crossing and quantum dilogarithm identity

BPS spectrum with finite number of states are found for higher rank four dimensional N=2 theory engineered from six dimensional A_{N-1} (2,0) theory on a Riemann surface with various kinds of defects. The wall crossing formula is interpreted as the quantum dilogarithm identity. Various methods including quiver representation theory, maximal green mutation, and cluster algebra are used extensively. The spectral generator and its refined version for the higher rank theory are written down using the explicit spectrum information. The finite chamber has an interesting N^3 behavior in the large N limit.
On the Entity Hardening Problem in multi-layered interdependent networks

The power grid and the communication network are highly interdependent on each other for their well being. In recent times the research community has shown significant interest in modeling such interdependent networks and studying the impact of failures on these networks. Although a number of models have been proposed, many of them are simplistic in nature and fail to capture the complex interdependencies that exist between the entities of these networks. To overcome the limitations, recently an Implicative Interdependency Model that utilizes Boolean Logic, was proposed and a number of problems were studied. In this paper we study the “entity hardening” problem, where by “entity hardening” we imply the ability of the network operator to ensure that an adversary (be it Nature or human) cannot take a network entity from operative to inoperative state. Given that the network operator with a limited budget can only harden k entities, the goal of the entity hardening problem is to identify the set of k entities whose hardening will ensure maximum benefit for the operator, i.e. maximally reduce the ability of the adversary to degrade the network. We classify the problem into four cases and show that the problem is solvable in polynomial time for the first case, whereas for others it is NP-complete. We provide an inapproximability result for the second case, an approximation algorithm for the third case, and a heuristic for the fourth (general) case. We evaluate the efficacy of our heuristic using power and communication network data of Maricopa County, Arizona. The experiments show that our heuristic almost always produces near optimal results.
Differentiability of Lipschitz Maps from Metric Measure Spaces to Banach Spaces with the Radon–Nikodym Property

We prove the differentiability of Lipschitz maps X → V, where X denotes a PI space, i.e. a complete metric measure space satisfying a doubling condition and a Poincare inequality, and V denotes a Banach space with the Radon–Nikodym Property (RNP). As a consequence, we obtain a bi-Lipschitz nonembedding theorem for RNP targets. The differentiation theorem depends on a new specification of the differentiable structure for PI spaces involving directional derivatives in the direction of velocity vectors to rectifiable curves. We give two different proofs of this, the second of which relies on a new characterization of the minimal upper gradient. There are strong implications for the infinitesimal structure of PI spaces which will be discussed elsewhere.
Subsystems and independence in relativistic microscopic physics

Abstract The analyzability of the universe into subsystems requires a concept of the “independence” of the subsystems, of which the relativistic quantum world supports many distinct notions which either coincide or are trivial in the classical setting. The multitude of such notions and the complex relations between them will only be adumbrated here. The emphasis of the discussion is placed upon the warrant for and the consequences of a particular notion of subsystem independence, which, it is proposed, should be viewed as primary and, it is argued, provides a reasonable framework within which to sensibly speak of relativistic quantum subsystems.
Service Discovery for Hyperledger Fabric

Hyperledger Fabric (HLF) is a modular and extensible permissioned blockchain platform released to open-source and hosted by the Linux Foundation. The platform's design exhibits principles required by enterprise grade business applications like supply-chains, financial transactions, asset management, food safety, and many more. For that end HLF introduces several innovations, two of which are smart contracts in general purpose languages (chaincode in HLF), and flexible endorsement policies, which govern whether a transaction is considered valid.Typical blockchain applications are comprised of two tiers: the first tier focuses on the modelling of the data schema and embedding of business rules into the blockchain by means of smart contracts (chaincode) and endorsment policies; and the second tier uses the SDK (Software Development Kit) provided by HLF to implement client side application logic.However there is a gap between the two tiers that hinders the rapid adoption of changes in the chaincode and endorsement policies within the client SDK. Currently, the chaincode location and endorsement policies are statically configured into the client SDK. This limits the reliability and availability of the client in the event of changes in the platform, and makes the platform more difficult to use. In this work we address and bridge the gap by describing the design and implementation of Service Discovery.Service Discovery provides APIs which allow dynamic discovery of the configuration required for the client SDK to interact with the platform, alleviating the client from the burden of maintaining it. This enables the client to rapidly adapt to changes in the platform, thus significantly improving the reliability of the application layer. It also makes the HLF platform more consumable, simplifying the job of creating blockchain applications.
Sweep maps: A continuous family of sorting algorithms

Abstract We define a family of maps on lattice paths, called sweep maps , that assign levels to each step in the path and sort steps according to their level. Surprisingly, although sweep maps act by sorting, they appear to be bijective in general. The sweep maps give concise combinatorial formulas for the q , t -Catalan numbers, the higher q , t -Catalan numbers, the q , t -square numbers, and many more general polynomials connected to the nabla operator and rational Catalan combinatorics. We prove that many algorithms that have appeared in the literature (including maps studied by Andrews, Egge, Gorsky, Haglund, Hanusa, Jones, Killpatrick, Krattenthaler, Kremer, Orsina, Mazin, Papi, Vaille, and the present authors) are all special cases of the sweep maps or their inverses. The sweep maps provide a very simple unifying framework for understanding all of these algorithms. We explain how inversion of the sweep map (which is an open problem in general) can be solved in known special cases by finding a “bounce path” for the lattice paths under consideration. We also define a generalized sweep map acting on words over arbitrary alphabets with arbitrary weights, which is also conjectured to be bijective.
Attitude Control of a 2U Cubesat by Magnetic and Air Drag Torques

This paper describes the development of a magnetic attitude control subsystem for a 2U cubesat. Due to the presence of gravity-gradient torques, the satellite dynamics are open-loop unstable near the desired pointing configuration. Nevertheless, the linearized time-varying system is completely controllable, under easily verifiable conditions, and the system’s disturbance rejection capabilities can be enhanced by adding air drag panels exemplifying a beneficial interplay between hardware design and control. In this paper, conditions for the complete controllability for the case of a magnetically controlled satellite with passive air drag panels are developed, and simulation case studies with the linear quadratic regulator and model predictive control designs applied in combination with a nonlinear time-varying input transformation are presented to demonstrate the ability of the closed-loop system to satisfy mission objectives despite disturbance torques.
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions

Various 3D semantic attributes such as segmentation masks, geometric features, keypoints, and materials can be encoded as per-point probe functions on 3D geometries. Given a collection of related 3D shapes, we consider how to jointly analyze such probe functions over different shapes, and how to discover common latent structures using a neural network — even in the absence of any correspondence information. Our network is trained on point cloud representations of shape geometry and associated semantic functions on that point cloud. These functions express a shared semantic understanding of the shapes but are not coordinated in any way. For example, in a segmentation task, the functions can be indicator functions of arbitrary sets of shape parts, with the particular combination involved not known to the network. Our network is able to produce a small dictionary of basis functions for each shape, a dictionary whose span includes the semantic functions provided for that shape. Even though our shapes have independent discretizations and no functional correspondences are provided, the network is able to generate latent bases, in a consistent order, that reflect the shared semantic structure among the shapes. We demonstrate the effectiveness of our technique in various segmentation and keypoint selection applications.
Chip-scale cavity optomechanics in lithium niobate

We develop a chip-scale cavity optomechanical system in single-crystal lithium niobate that exhibits high optical quality factors and a large frequency-quality product as high as 3.6 × 1012 Hz at room temperature and atmosphere. The excellent optical and mechanical properties together with the strong optomechanical coupling allow us to efficiently excite the coherent regenerative optomechanical oscillation operating at 375 MHz with a threshold power of 174 μW in the air. The demonstrated lithium niobate optomechanical device enables great potential for achieving electro-optic-mechanical hybrid systems for broad applications in sensing, metrology, and quantum physics.
Differentially Private Empirical Risk Minimization Revisited: Faster and More General

In this paper we study differentially private Empirical Risk Minimization(ERM) in different settings. For smooth (strongly) convex loss function with or without (non)-smooth regularization, we give algorithms which achieve either optimal or near optimal utility bound with less gradient complexity compared with previous work. For ERM with smooth convex loss function in high-dimension($p\gg n$) setting, we give an algorithm which achieves the upper bound with less gradient complexity than previous ones. At last, we generalize the expected excess empirical risk from convex to Polyak-Lojasiewicz condition and give a tighter upper bound of the utility comparing with the result in \cite{DBLP:journals/corr/ZhangZMW17}.
Complex perspective for the projective heat map acting on pentagons

We place Schwartz's work on the real dynamics of the projective heat map $H$ into the complex perspective by computing its first dynamical degree and gleaning some corollaries about the dynamics of $H$.
Signaling Design of Two-Way MIMO Full-Duplex Channel: Optimality Under Imperfect Transmit Front-End Chain

We derive the optimal signaling for a multiple input multiple output (MIMO) full-duplex (FD) two-way channel under imperfect transmit front-end chains. We characterize two-way rates of the channel by using a game-theoretical approach, where we focus on the Pareto boundary of the achievable rate region and the Nash equilibrium (NE). For a MIMO FD channel, the Pareto boundary achieves the global optimality. However, deriving the Pareto boundary amounts to solving a family of centralized non-convex problems. By introducing auxiliary variables, we decouple and convert the Pareto boundary into a family of convex problems, which enables us to obtain the Pareto boundary with low computational complexity. In a MISO FD two-way channel, we further present a closed-form expression for the Pareto-optimal signaling. In our numerical examples, we quantify gains in the achievable rates of the Pareto-optimal signaling over the zero-forcing beamforming and NE. For a distributed MIMO FD channel, we establish the existence of NE and present a condition for the uniqueness of NE. We then propose an iterative water-filling algorithm, which is capable of reaching the NE. Through simulations, the threshold of the self-interference level is found below which the FD NE outperforms the half-duplex TDMA.
Multi-Information Source Optimization

We consider Bayesian methods for multi-information source optimization (MISO), in which we seek to optimize an expensive-to-evaluate black-box objective function while also accessing cheaper but biased and noisy approximations ("information sources"). We present a novel algorithm that outperforms the state of the art for this problem by using a Gaussian process covariance kernel better suited to MISO than those used by previous approaches, and an acquisition function based on a one-step optimality analysis supported by efficient parallelization. We also provide a novel technique to guarantee the asymptotic quality of the solution provided by this algorithm. Experimental evaluations demonstrate that this algorithm consistently finds designs of higher value at less cost than previous approaches.
Tight contact structures via admissible transverse surgery

We investigate the line between tight and overtwisted for surgeries on fibered transverse knots in contact 3-manifolds. When the contact structure ξK is supported by the fibered knot K ⊂ M, we obta...
Linear regression with an unknown permutation: Statistical and computational limits

Consider a noisy linear observation model with an unknown permutation, based on observing y = Π*Ax* + w, where x* ∈ ℝd is an unknown vector, Π* is an unknown n × n permutation matrix, and w ∈ ℝn is additive Gaussian noise. We analyze the problem of permutation recovery in a random design setting in which the entries of the matrix A are drawn i.i.d. from a standard Gaussian distribution, and establish sharp conditions on the SNR, sample size n, and dimension d under which Π* is exactly and approximately recoverable. On the computational front, we show that the maximum likelihood estimate of Π* is NP-hard to compute, while also providing a polynomial time algorithm when d = 1.
Learning to rank for synthesizing planning heuristics

We investigate learning heuristics for domain-specific planning. Prior work framed learning a heuristic as an ordinary regression problem. However, in a greedy best-first search, the ordering of states induced by a heuristic is more indicative of the resulting planner's performance than mean squared error. Thus, we instead frame learning a heuristic as a learning to rank problem which we solve using a RankSVM formulation. Additionally, we introduce new methods for computing features that capture temporal interactions in an approximate plan. Our experiments on recent International Planning Competition problems show that the RankSVM learned heuristics outperform both the original heuristics and heuristics learned through ordinary regression.
Autoequivalences of derived categories via geometric invariant theory

Abstract We study autoequivalences of the derived category of coherent sheaves of a variety arising from a variation of GIT quotient. We show that these autoequivalences are spherical twists, and describe how they result from mutations of semiorthogonal decompositions. Beyond the GIT setting, we show that all spherical twist autoequivalences of a dg-category can be obtained from mutation in this manner. Motivated by a prediction from mirror symmetry, we refine the recent notion of “grade restriction rules” in equivariant derived categories. We produce additional derived autoequivalences of a GIT quotient and propose an interpretation in terms of monodromy of the quantum connection. We generalize this observation by proving a criterion under which a spherical twist autoequivalence factors into a composition of other spherical twists.
Process monitoring on sequences of system call count vectors

We introduce a methodology for efficient monitoring of processes running on hosts in a corporate network. The methodology is based on collecting streams of system calls produced by all or selected processes on the hosts, and sending them over the network to a monitoring server, where machine learning algorithms are used to identify changes in process behavior due to malicious activity, hardware failures, or software errors. The methodology uses a sequence of system call count vectors as the data format which can handle large and varying volumes of data. Unlike previous approaches, the methodology introduced in this paper is suitable for distributed collection and processing of data in large corporate networks. We evaluate the methodology both in a laboratory setting on a real-life setup and provide statistics characterizing performance and accuracy of the methodology.
Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty

An important task in structural design is to quantify the structural performance of an object under the external forces it may experience during its use. The problem proves to be computationally very challenging as the external forces' contact locations and magnitudes may exhibit significant variations. We present an efficient analysis approach to determine the most critical force contact location in such problems with force location uncertainty. Given an input 3D model and regions on its boundary where arbitrary normal forces may make contact, our algorithm predicts the worst-case force configuration responsible for creating the highest stress within the object. Our approach uses a computationally tractable experimental design method to select number of sample force locations based on geometry only, without inspecting the stress response that requires computationally expensive finite-element analysis. Then, we construct a simple regression model on these samples and corresponding maximum stresses. Combined with a simple ranking based post-processing step, our method provides a practical solution to worst-case structural analysis problem. The results indicate that our approach achieves significant improvements over the existing work and brute force approaches. We demonstrate that further speed- up can be obtained when small amount of an error tolerance in maximum stress is allowed.
A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics

We study the Stochastic Gradient Langevin Dynamics (SGLD) algorithm for non-convex optimization. The algorithm performs stochastic gradient descent, where in each step it injects appropriately scaled Gaussian noise to the update. We analyze the algorithm's hitting time to an arbitrary subset of the parameter space. Two results follow from our general theory: First, we prove that for empirical risk minimization, if the empirical risk is point-wise close to the (smooth) population risk, then the algorithm achieves an approximate local minimum of the population risk in polynomial time, escaping suboptimal local minima that only exist in the empirical risk. Second, we show that SGLD improves on one of the best known learnability results for learning linear classifiers under the zero-one loss.
Integrated mmWave Access and Backhaul in 5G: Bandwidth Partitioning and Downlink Analysis

With the increasing network densification, it has become exceedingly difficult to provide traditional fiber backhaul access to each cell site, which is especially true for small cell base stations (SBSs). The increasing maturity of millimeter wave (mmWave) communication has opened up the possibility of providing high-speed wireless backhaul to such cell sites. Since mmWave is also suitable for access links, the third generation partnership project (3GPP) is envisioning an integrated access and backhaul (IAB) architecture for the fifth generation (5G) cellular networks in which the same infrastructure and spectral resources will be used for both access and backhaul. In this paper, we develop an analytical framework for IAB-enabled cellular network using which we provide an accurate characterization of its downlink rate coverage probability. Using this, we study the performance of two backhaul bandwidth (BW) partition strategies, (i) equal partition: when all SBSs obtain equal share of the backhaul BW, and (ii) load-based partition: when the backhaul BW share of an SBS is proportional to its load. Our analysis shows that depending on the choice of the partition strategy, there exists an optimal split of access and backhaul BW for which the rate coverage is maximized. Further, there exists a critical volume of cell-load (total number of users) beyond which the gains provided by the IAB-enabled network disappear and its performance converges to that of the traditional macro-only network with no SBSs.