cssi/SciSciGPT-SciSciNet huggingface dataset:
LUT-Lock: A Novel LUT-Based Logic Obfuscation for FPGA-Bitstream and ASIC-Hardware Protection

In this work, we propose LUT-Lock, a novel Look-Up-Table-based netlist obfuscation algorithm, for protecting the intellectual property that is mapped to an FPGA bitstream or an ASIC netlist. We, first, illustrate the effectiveness of several key features that make the LUT-based obfuscation more resilient against SAT attacks and then we embed the proposed key features into our proposed LUT-Lock algorithm. We illustrate that LUT-Lock maximizes the resiliency of the LUT-based obfuscation against SAT attacks by forcing a near exponential increase in the execution time of a SAT solver with respect to the number of obfuscated gates. Hence, by adopting LUT-Lock algorithm, SAT attack execution time could be made unreasonably long by increasing the number of utilized LUTs.
On the Combinatorial Multi-Armed Bandit Problem with Markovian Rewards

We consider a combinatorial generalization of the classical multi-armed bandit problem that is defined as follows. There is a given bipartite graph of M users and NiYM resources. For each user-resource pair (i,j), there is an associated state that evolves as an aperiodic irreducible finite-state Markov chain with unknown parameters, with transitions occurring each time the particular user i is allocated resource j. The user i receives a reward that depends on the corresponding state each time it is allocated the resource j. The system objective is to learn the best matching of users to resources so that the long-term sum of the rewards received by all users is maximized. This corresponds to minimizing regret, defined here as the gap between the expected total reward that can be obtained by the best-possible static matching and the expected total reward that can be achieved by a given algorithm. We present a polynomial-storage and polynomial-complexity-per-step matching-learning algorithm for this problem. We show that this algorithm can achieve a regret that is uniformly arbitrarily close to logarithmic in time and polynomial in the number of users and resources. This formulation is broadly applicable to scheduling and switching problems in communication networks including cognitive radio networks and significantly extends prior results in the area.
Inferring Which Medical Treatments Work from Reports of Clinical Trials

How do we know if a particular medical treatment actually works? Ideally one would consult all available evidence from relevant clinical trials. Unfortunately, such results are primarily disseminated in natural language scientific articles, imposing substantial burden on those trying to make sense of them. In this paper, we present a new task and corpus for making this unstructured published scientific evidence actionable. The task entails inferring reported findings from a full-text article describing randomized controlled trials (RCT) with respect to a given intervention, comparator, and outcome of interest, e.g., inferring if a given article provides evidence supporting the use of aspirin to reduce risk of stroke, as compared to placebo. We present a new corpus for this task comprising 10,000+ prompts coupled with full-text articles describing RCTs. Results using a suite of baseline models — ranging from heuristic (rule-based) approaches to attentive neural architectures — demonstrate the difficulty of the task, which we believe largely owes to the lengthy, technical input texts. To facilitate further work on this important, challenging problem we make the corpus, documentation, a website and leaderboard, and all source code for baselines and evaluation publicly available.
Cauchy–Binet for pseudo-determinants

Abstract The pseudo-determinant Det ( A ) of a square matrix A is defined as the product of the nonzero eigenvalues of A . It is a basis-independent number which is up to a sign the first nonzero entry of the characteristic polynomial of A . We prove Det ( F T G ) = ∑ P det ( F P ) det ( G P ) for any two n × m matrices F , G . The sum to the right runs over all k × k minors of A , where k is determined by F and G . If F = G is the incidence matrix of a graph this directly implies the Kirchhoff tree theorem as L = F T G is then the Laplacian and det 2 ( F P ) ∈ { 0 , 1 } is equal to 1 if P is a rooted spanning tree. A consequence is the following Pythagorean theorem: for any self-adjoint matrix A of rank k , one has Det 2 ( A ) = ∑ P det 2 ( A P ) , where det ( A P ) runs over k × k minors of A . More generally, we prove the polynomial identity det ( 1 + x F T G ) = ∑ P x | P | det ( F P ) det ( G P ) for classical determinants det, which holds for any two n × m matrices F , G and where the sum on the right is taken over all minors P , understanding the sum to be 1 if | P | = 0 . It implies the Pythagorean identity det ( 1 + F T F ) = ∑ P det 2 ( F P ) which holds for any n × m matrix F and sums again over all minors F P . If applied to the incidence matrix F of a finite simple graph, it produces the Chebotarev–Shamis forest theorem telling that det ( 1 + L ) is the number of rooted spanning forests in the graph with Laplacian L .
Higher order Yang-Mills flow

We define a family of functionals generalizing the Yang–Mills functional. We study the corresponding gradient flows and prove long-time existence and convergence results for subcritical dimensions as well as a bubbling criterion for critical dimensions. Consequently, we generalize the results of the convergence of Yang–Mills flow in dimensions 2 and 3 given by Rade (J Reine Angew Math 431:123–163, 1992) and the bubbling criterion in dimension 4 of Struwe (Calc Var 2:123–150, 1994) in the case where the initial flow data is smooth.
What Are You Hiding? Algorithmic Transparency and User Perceptions.

Extensive recent media focus has been directed towards the dark side of intelligent systems, how algorithms can influence society negatively. Often, transparency is proposed as a solution or step in the right direction. Unfortunately, research is mixed on the impact of transparency on the user experience. We examine transparency in the context an interactive system that predicts positive/negative emotion from a users' written text. We unify seemingly this contradictory research under a single model. We show that transparency can negatively affect accuracy perceptions for users whose expectations were not violated by the system's prediction; however, transparency also limits the damage done when users' expectations are violated by system predictions.
Human APOBEC3G prevents emergence of infectious endogenous retrovirus in mice

Endogenous retroviruses (ERV) are found throughout vertebrate genomes, and failure to silence their activation can have deleterious consequences on the host. Mutation and subsequent disruption of ERV loci is therefore an indispensable component of the cell-intrinsic defenses that maintain the integrity of the host genome. Abundant in vitro and in silico evidence have revealed that APOBEC3 cytidine-deaminases, including human APOBEC3G (hA3G), can potently restrict retrotransposition; yet, in vivo data demonstrating such activity is lacking, since no replication-competent human ERV have been identified. In mice deficient for Toll-like receptor 7 (TLR7), transcribed ERV loci can recombine and generate infectious ERV. In this study, we show that ectopic expression of hA3G can prevent the emergence of replication-competent, infectious ERV in Tlr7 -/- mice. Mice encode one copy of Apobec3 in their genome. ERV reactivation in Tlr7 -/- mice was comparable in the presence or absence of Apobec3 In contrast, expression of a human APOBEC3G transgene abrogated emergence of infectious ERV in the Tlr7 -/- background. No ERV RNA was detected in the plasma of hA3G+ Apobec3 -/- Tlr7 -/- mice, and infectious ERV virions could not be amplified through coculture with permissive cells. These data reveal that hA3G can potently restrict active ERV in vivo and suggest that expansion of the APOBEC3 locus in primates may have helped to provide for the continued restraint of ERV in the human genome.IMPORTANCE Although APOBEC3 proteins are known to be important antiviral restriction factors in both mice and humans, their roles in the restriction of endogenous retroviruses (ERV) have been limited to in vitro studies. Here, we report that human APOBEC3G expressed as a transgene in mice prevents the emergence of infectious ERV from endogenous loci. This study reveals that APOBEC3G can powerfully restrict active retrotransposons in vivo and demonstrates how transgenic mice can be used to investigate host mechanisms that inhibit retrotransposons and reinforce genomic integrity.
Characterization of magnetic Ni clusters on graphene scaffold after high vacuum annealing

Abstract Magnetic Ni nanoclusters were synthesized by electron beam deposition utilizing CVD graphene as a scaffold. The subsequent clusters were subjected to high vacuum (5−8 x10 −7 torr) annealing between 300 and 600 °C. The chemical stability, optical and morphological changes were characterized by X-ray photoemission microscopy, Raman spectroscopy, atomic force microscopy and magnetic measurement. Under ambient exposure, nickel nanoparticles were observed to be oxidized quickly, forming antiferromagnetic nickel oxide. Here, we report that the majority of the oxidized nickel is in non-stoichiometric form and can be reduced under high vacuum at temperature as low as 300 °C. Importantly, the resulting annealed clusters were relatively stable and no further oxidation was detectable after three weeks of air exposure at room temperature.
Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog

Effective models of social dialog must understand a broad range of rhetorical and figurative devices. Rhetorical questions (RQs) are a type of figurative language whose aim is to achieve a pragmatic goal, such as structuring an argument, being persuasive, emphasizing a point, or being ironic. While there are computational models for other forms of figurative language, rhetorical questions have received little attention to date. We expand a small dataset from previous work, presenting a corpus of 10,270 RQs from debate forums and Twitter that represent different discourse functions. We show that we can clearly distinguish between RQs and sincere questions (0.76 F1). We then show that RQs can be used both sarcastically and non-sarcastically, observing that non-sarcastic (other) uses of RQs are frequently argumentative in forums, and persuasive in tweets. We present experiments to distinguish between these uses of RQs using SVM and LSTM models that represent linguistic features and post-level context, achieving results as high as 0.76 F1 for “sarcastic” and 0.77 F1 for “other” in forums, and 0.83 F1 for both “sarcastic” and “other” in tweets. We supplement our quantitative experiments with an in-depth characterization of the linguistic variation in RQs.
Development of a Translator from LLVM to ACL2

In our current work a library of formally verified software components is to be created, and assembled, using the Low-Level Virtual Machine (LLVM) intermediate form, into subsystems whose top-level assurance relies on the assurance of the individual components. We have thus undertaken a project to build a translator from LLVM to the applicative subset of Common Lisp accepted by the ACL2 theorem prover. Our translator produces executable ACL2 formal models, allowing us to both prove theorems about the translated models as well as validate those models by testing. The resulting models can be translated and certified without user intervention, even for code with loops, thanks to the use of the def::ung macro which allows us to defer the question of termination. Initial measurements of concrete execution for translated LLVM functions indicate that performance is nearly 2.4 million LLVM instructions per second on a typical laptop computer. In this paper we overview the translation process and illustrate the translator’s capabilities by way of a concrete example, including both a functional correctness theorem as well as a validation test for that example.
Integer points in backward orbits

Abstract A theorem of J. Silverman states that a forward orbit of a rational map φ ( z ) on P 1 ( K ) contains finitely many S-integers in the number field K when ( φ ∘ φ ) ( z ) is not a polynomial. We state an analogous conjecture for the backward orbits using a general S-integrality notion based on the Galois conjugates of points. This conjecture is proven for the map φ ( z ) = z d , and consequently Chebyshev polynomials, by uniformly bounding the number of Galois orbits for z n − β when β ≠ 0 is a non-root of unity. In general, our conjecture is true provided that the number of Galois orbits for φ n ( z ) − β is bounded independently of n.
Two applications of the Schwarz lemma

The Schwarz lemmas are well-known characterizations for holomorphic maps and we exhibit two examples of their applications. For a sequence family of biholomorphisms $f_j$, it is useful to determine the location of $f_j(q)$ for a fixed point $q$ in source manifolds (see Proposition \ref{2.5}). With it, we extend the Fornaess-Stout's theorem of \citep{FS77} in monotone unions of balls to ellipsoids in Section \ref{sec2}. In Section \ref{sec3}, we discuss the curvature bounds of complete Kahler metric on $\rtimes$ domains defined in \citep{Liu004} with an idea of \citep{Ya76}.
Perfect Snake-in-the-Box Codes for Rank Modulation

For odd $n$ , the alternating group on $n$ elements is generated by the permutations that jump an element from an odd position to position 1. We prove Hamiltonicity of the associated directed Cayley graph for all odd $n\neq 5$ (a result of Rankin implies that the graph is not Hamiltonian for $n=5$ ). This solves a problem arising in rank modulation schemes for flash memory. Our result disproves a conjecture of Horovitz and Etzion, and proves another conjecture of Yehezkeally and Schwartz.
Adversarial Constraint Learning for Structured Prediction.

Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws. We propose a novel framework for simultaneously learning these constraints and using them for supervision, bypassing the difficulty of using domain expertise to manually specify constraints. Learning requires a black-box simulator of structured outputs, which generates valid labels, but need not model their corresponding inputs or the input-label relationship. At training time, we constrain the model to produce outputs that cannot be distinguished from simulated labels by adversarial training. Providing our framework with a small number of labeled inputs gives rise to a new semi-supervised structured prediction model; we evaluate this model on multiple tasks --- tracking, pose estimation and time series prediction --- and find that it achieves high accuracy with only a small number of labeled inputs. In some cases, no labels are required at all.
An accelerated testing approach for automated vehicles with background traffic described by joint distributions

This paper proposes a new framework based on joint statistical models for evaluating risks of automated vehicles in a naturalistic driving environment. The previous studies on the Accelerated Evaluation for automated vehicles are extended from multi-independent-variate models to joint statistics. The proposed toolkit includes exploration of the rare event (e.g. crash) sets and construction of accelerated distributions for Gaussian Mixture models using Importance Sampling techniques. Furthermore, the monotonic property is used to avoid the curse of dimensionality introduced by the joint distributions. Simulation results show that the procedure is effective and has a great potential to reduce the test cost for automated vehicles.
Nonparametric density estimation for spatial data with wavelets

Abstract Nonparametric density estimators are studied for d -dimensional, strongly spatial mixing data which are defined on a general N -dimensional lattice structure. We consider linear and nonlinear hard thresholded wavelet estimators derived from a d -dimensional multi-resolution analysis. We give sufficient criteria for the consistency of these estimators and derive rates of convergence in L p ′ for p ′ ∈ [ 1 , ∞ ) . For this reason, we study density functions which are elements of a d -dimensional Besov space B p , q s ( R d ) . We also verify the analytic correctness of our results in numerical simulations.
Influence Maximization Over Markovian Graphs: A Stochastic Optimization Approach

Depending on the initial adopters of an innovation, it can either lead to a large number of people adopting that innovation or, it might die away quickly without spreading. Therefore, an idea central to many application domains, such as viral marketing, message spreading, etc., is influence maximization: selecting a set of initial adopters from a social network that can cause a massive spread of an innovation (or, more generally an idea, a product or a message). To this end, we consider the problem of randomized influence maximization over a Markovian graph process: given a fixed set of individuals whose connectivity graph is evolving as a Markov chain, estimate the probability distribution (over this fixed set of nodes) that samples an individual that can initiate the largest information cascade (in expectation). Further, it is assumed that the sampling process affects the evolution of the graph, i.e., the sampling distribution and the transition probability matrix are functionally dependent. In this setup, recursive stochastic optimization algorithms are presented to estimate the optimal sampling distribution for two cases: 1) transition probabilities of the graph are unknown but, the graph can be observed perfectly; 2) transition probabilities of the graph are known, but the graph is observed in noise. These algorithms consist of a neighborhood size estimation algorithm combined with a variance reduction method, a Bayesian filter, and a stochastic gradient algorithm. Convergence of the algorithms is established theoretically, and numerical results are provided to illustrate how the algorithms work.
Tau Functions and Virasoro Actions for soliton Hierarchies

There is a general method for constructing a soliton hierarchy from a splitting \({{L_{\pm}}}\) of a loop group as positive and negative sub-groups together with a commuting linearly independent sequence in the positive Lie algebra \({\mathcal{L}_{+}}\). Many known soliton hierarchies can be constructed this way. The formal inverse scattering associates to each f in the negative subgroup \({L_-}\) a solution \({u_{f}}\) of the hierarchy. When there is a 2 co-cycle of the Lie algebra that vanishes on both sub-algebras, Wilson constructed a tau function \({\tau_{f}}\) for each element \({f \in L_-}\). In this paper, we give integral formulas for variations of \({\ln\tau_{f}}\) and second partials of \({\ln\tau_{f}}\), discuss whether we can recover solutions \({u_{f}}\) from \({\tau_{f}}\), and give a general construction of actions of the positive half of the Virasoro algebra on tau functions. We write down formulas relating tau functions and formal inverse scattering solutions and the Virasoro vector fields for the \({GL(n,\mathbb{C})}\)-hierarchy.
Identities for Third Order Jacobsthal Quaternions

In this paper we introduce the third order Jacobsthal quaternions and the third order Jacobsthal–Lucas quaternions and give some of their properties. We derive the relations between third order Jacobsthal numbers and third order Jacobsthal quaternions and we give the matrix representation of these quaternions.
Periodicity of d-cluster-tilted algebras

Abstract It is well-known that any maximal Cohen–Macaulay module over a hypersurface has a periodic free resolution of period 2. Auslander, Reiten (1996) [4] and Buchweitz (1998) [8] have used this periodicity to explain the existence of periodic projective resolutions over certain finite-dimensional algebras which arise as stable endomorphism rings of Cohen–Macaulay modules. These algebras are in fact periodic, meaning that they have periodic projective resolutions as bimodules and thus periodic Hochschild cohomology as well. The goal of this article is to generalize this construction of periodic algebras to the context of Iyamaʼs higher AR-theory. Let C be a maximal ( d − 1 ) -orthogonal subcategory of an exact Frobenius category B , and start by studying the projective resolutions of finitely presented functors on the stable category C , over both C and C . Under the assumption that C is fixed by Ω d , we show that Ω d induces the ( 2 + d ) th syzygy on mod- C . If C has finite type, i.e., if C = add ( T ) for a d-cluster tilting object T, then we show that the stable endomorphism ring of T has a quasi-periodic resolution over its enveloping algebra. Moreover, this resolution will be periodic if some power of Ω d is isomorphic to the identity on C . It follows, in particular, that 2-C.Y.-tilted algebras arising as stable endomorphism rings of Cohen–Macaulay modules over curve singularities, as in the work of Burban, Iyama, Keller and Reiten (2008) [9] , have periodic bimodule resolutions of period 4.
Color confinement from fluctuating topology

QCD possesses a compact gauge group, and this implies a non-trivial topological structure of the vacuum. In this contribution to the Gribov-85 Memorial volume, we first discuss the origin of Gribov copies and their interpretation in terms of fluctuating topology in the QCD vacuum. We then describe the recent work with E. Levin that links the confinement of gluons and color screening to the fluctuating topology, and discuss implications for spin physics, high energy scattering, and the physics of quark-gluon plasma.
Integer-valued polynomials on commutative rings and modules

ABSTRACTThe ring of integer-valued polynomials on an arbitrary integral domain is well studied. In this paper, we initiate and provide motivation for the study of integer-valued polynomials on commutative rings and modules. Several examples are computed, including the integer-valued polynomials over the ring R[T1,…,Tn]∕(T1(T1−r1),…,Tn(Tn−rn)) for any commutative ring R and any elements r1,…,rn of R, as well as the integer-valued polynomials over the Nagata idealization R(+)M of M over R, where M is an R-module. These examples are motivated by and provide motivation for the study of integer-valued derivatives.
The Lusternik–Schnirelmann category of metric spaces

Abstract We extend the theory of the Lusternik–Schnirelmann category to general metric spaces by means of covers by arbitrary subsets. We also generalize the definition of the strict category weight. We show that if the Bockstein homomorphism on a metric space X is non-zero, then cat X ⩾ 2 , and use this to compute the category of Pontryagin surfaces. Additionally, we prove that a Polish space with LS-category n can be presented as the inverse limit of ANR spaces of category at most n .
Exact computation of minimum sample size for estimation of binomial parameters

Abstract It is indicated by some researchers in the literature that it might be difficult to exactly determine the minimum sample size for the estimation of a binomial parameter with prescribed margin of error and confidence level. In this paper, we investigate such a very old but also extremely important problem and demonstrate that the difficulty for obtaining the exact solution is not insurmountable. Unlike the classical approximate sample size method based on the central limit theorem, we develop a new approach for computing the minimum sample size that does not require any approximation. Moreover, our approach overcomes the conservatism of existing rigorous sample size methods derived from Bernoulli's theorem or Chernoff–Hoeffding bound. Our computational machinery consists of two essential ingredients. First, we prove that the minimum of coverage probability with respect to a binomial parameter bounded in an interval is attained at a discrete set of finite many values of the binomial parameter. This allows for reducing infinite many evaluations of coverage probability to finite many evaluations. Second, a recursive bounding technique is developed to further improve the efficiency of computation.
Noncirculatory Fluid Forces on Panels with Porosity Gradients

This Note determines the unsteady non-circulatory forces on an arbitrarily deforming panel with a Holder-continuous porosity distribution. An analytical expression for the non-circulatory pressure distribution is presented and evaluated for the special cases of uniform and variable-porosity panels undergoing harmonic deformations, where special attention is paid to the influence of the panel end conditions. These results constitute the first major step towards a complete linearized, unsteady aerodynamic theory for lifting porous bodies, which may have potential application to the performance estimation of biologically-inspired swimmers and fliers and to the future assessment of aeroelastic stability and flow noise production of porous airfoils.
Projectivized Rank Two Toric Vector Bundles are Mori Dream Spaces

We prove that the Cox ring of the projectivization P(ℰ) of a rank two toric vector bundle ℰ, over a toric variety X, is a finitely generated k-algebra. As a consequence, P(ℰ) is a Mori dream space if the toric variety X is projective and simplicial.
Effective divisors on Bott-Samelson varieties

We compute the cone of effective divisors on a Bott–Samelson variety corresponding to an arbitrary sequence of simple roots. The main tool is a general result concerning effective cones of certain B-equivariant ℙ1 bundles. As an application, we compute the cone of effective codimension-two cycles on Bott–Samelson varieties corresponding to reduced words. We also obtain an auxiliary result giving criteria for a Bott–Samelson variety to contain a dense B-orbit, and we construct desingularizations of intersections of Schubert varieties. An appendix exhibits an explicit divisor showing that any Bott–Samelson variety is log Fano.
Reasoning about Hypothetical Agent Behaviours and their Parameters

Agents can achieve effective interaction with previously unknown other agents by maintaining beliefs over a set of hypothetical behaviours, or types, that these agents may have. A current limitation in this method is that it does not recognise parameters within type specifications, because types are viewed as blackbox mappings from interaction histories to probability distributions over actions. In this work, we propose a general method which allows an agent to reason about both the relative likelihood of types and the values of any bounded continuous parameters within types. The method maintains individual parameter estimates for each type and selectively updates the estimates for some types after each observation. We propose different methods for the selection of types and the estimation of parameter values. The proposed methods are evaluated in detailed experiments, showing that updating the parameter estimates of a single type after each observation can be sufficient to achieve good performance.
Online Learning: Sufficient Statistics and the Burkholder Method

We uncover a fairly general principle in online learning: If regret can be (approximately) expressed as a function of certain "sufficient statistics" for the data sequence, then there exists a special Burkholder function that 1) can be used algorithmically to achieve the regret bound and 2) only depends on these sufficient statistics, not the entire data sequence, so that the online strategy is only required to keep the sufficient statistics in memory. This characterization is achieved by bringing the full power of the Burkholder Method --- originally developed for certifying probabilistic martingale inequalities --- to bear on the online learning setting. To demonstrate the scope and effectiveness of the Burkholder method, we develop a novel online strategy for matrix prediction that attains a regret bound corresponding to the variance term in matrix concentration inequalities. We also present a linear-time/space prediction strategy for parameter free supervised learning with linear classes and general smooth norms.
Hierarchical Question-Image Co-Attention for Visual Question Answering

A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling "where to look" or visual attention, it is equally important to model "what words to listen to" or question attention. We present a novel co-attention model for VQA that jointly reasons about image and question attention. In addition, our model reasons about the question (and consequently the image via the co-attention mechanism) in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN). Our model improves the state-of-the-art on the VQA dataset from 60.3% to 60.5%, and from 61.6% to 63.3% on the COCO-QA dataset. By using ResNet, the performance is further improved to 62.1% for VQA and 65.4% for COCO-QA.
Minimum Cost Matching in a Random Graph with Random Costs

Let $G_{n,p}$ be the standard Erdos--Renyi--Gilbert random graph and let $G_{n,n,p}$ be the random bipartite graph on $n+n$ vertices, where each $e\in [n]^2$ appears as an edge independently with probability $p$. For a graph $G=(V,E)$, suppose that each edge $e\in E$ is given an independent exponential rate 1 cost $X_e$. Let $C(G)$ denote the random variable equal to the length of the minimum cost perfect matching if $G$ contains at least one perfect matching, and let $C(G)=0$ otherwise. Let $\mu(G)=\textbf{E}{C(G)}$. We show that if $np\gg{\log}^{2}n$ and $G=G_{n,n,p}$, then with high probability (w.h.p.) $\mu(G)\approx\frac{{\pi}^2}{6p}$. This generalizes the well-known result for the case $G=K_{n,n}$, where $p=1$. We also show that if $G=G_{n,p}$, then $\mu(G_{n,p}) \approx\frac{{\pi}^2}{12p}$ w.h.p. along with concentration results for both types of random graph.
Quantum and affine Schubert calculus and Macdonald polynomials

Abstract We definitively establish that the theory of symmetric Macdonald polynomials aligns with quantum and affine Schubert calculus using a discovery that distinguished weak chains can be identified by chains in the strong (Bruhat) order poset on the type- A affine Weyl group. We construct two one-parameter families of functions that respectively transition positively with Hall–Littlewood and Macdonald's P -functions, and specialize to the representatives for Schubert classes of homology and cohomology of the affine Grassmannian. Our approach leads us to conjecture that all elements in a defining set of 3-point genus 0 Gromov–Witten invariants for flag manifolds can be formulated as strong covers.
Zeros of partial sums of the Dedekind zeta function of a cyclotomic field

Abstract In this article, we study the zeros of the partial sums of the Dedekind zeta function of a cyclotomic field K defined by the truncated Dirichlet series ζ K , X ( s ) = ∑ ‖ a ‖ ⩽ X 1 ‖ a ‖ s , where the sum is to be taken over nonzero integral ideals a of K and ‖ a ‖ denotes the absolute norm of a . Specifically, we establish the zero-free regions for ζ K , X ( s ) and estimate the number of zeros of ζ K , X ( s ) up to height T.
Active discriminative text representation learning

We propose a new active learning (AL) method for text classification with convolutional neural networks (CNNs). In AL, one selects the instances to be manually labeled with the aim of maximizing model performance with minimal effort. Neural models capitalize on word embeddings as representations (features), tuning these to the task at hand. We argue that AL strategies for multi-layered neural models should focus on selecting instances that most affect the embedding space (i.e., induce discriminative word representations). This is in contrast to traditional AL approaches (e.g., entropy-based uncertainty sampling), which specify higher level objectives. We propose a simple approach for sentence classification that selects instances containing words whose embeddings are likely to be updated with the greatest magnitude, thereby rapidly learning discriminative, task-specific embeddings. We extend this approach to document classification by jointly considering: (1) the expected changes to the constituent word representations; and (2) the model's current overall uncertainty regarding the instance. The relative emphasis placed on these criteria is governed by a stochastic process that favors selecting instances likely to improve representations at the outset of learning, and then shifts toward general uncertainty sampling as AL progresses. Empirical results show that our method outperforms baseline AL approaches on both sentence and document classification tasks. We also show that, as expected, the method quickly learns discriminative word embeddings. To the best of our knowledge, this is the first work on AL addressing neural models for text classification.
A bilinear oscillatory integral estimate and bilinear refinements to Strichartz estimates on closed manifolds

We prove a bilinear $L^2(\R^d) \times L^2(\R^d) \to L^2(\R^{d+1})$ estimate for a pair of oscillatory integral operators with different asymptotic parameters and phase functions satisfying a transversality condition. This is then used to prove a bilinear refinement to Strichartz estimates on closed manifolds, similar to that on $\R^d$, but at a relevant semi-classical scale. These estimates will be employed elsewhere to prove global well-posedness below $H^1$ for the cubic nonlinear Schr\"odinger equation on closed surfaces.
Crime Incidents Embedding Using Restricted Boltzmann Machines

We present a new approach for detecting related crime series, by unsupervised learning of the latent feature embeddings from narratives of crime record via the Gaussian-Bernoulli Restricted Boltzmann Machine (GBRBM). This is a drastically different approach from prior work on crime analysis, which typically considers only time and location and at most category information. After the embedding, related cases are closer to each other in the Euclidean feature space, and the unrelated cases are far apart, which is a good property can enable subsequent analysis such as detection and clustering of related cases. Experiments over several series of related crime incidents hand labeled by the Atlanta Police Department reveal the promise of our embedding methods.
Transseries for Beginners

From the simplest point of view, transseries concern manipulations on formal series, or a new kind of expansion for real-valued functions. But transseries constitute much more than that---they have a very rich (algebraic, combinatorial, analytic) structure. The set of transseries---also known as the transline---is a large ordered field, extending the real number field, and endowed with additional operations such as exponential, logarithm, derivative, integral, composition. Over the course of the last 20 years or so, transseries have emerged in several areas of mathematics: dynamical systems, model theory, computer algebra, surreal numbers. This paper is an exposition for the non-specialist mathematician. All a mathematician needs to know in order to apply transseries.
Divide-and-conquer strategy for large-scale Eulerian solvent excluded surface.

Motivation Surface generation and visualization are some of the most important tasks in biomolecular modeling and computation. Eulerian solvent excluded surface (ESES) software provides analytical solvent excluded surface (SES) in the Cartesian grid, which is necessary for simulating many biomolecular electrostatic and ion channel models. However, large biomolecules and/or fine grid resolutions give rise to excessively large memory requirements in ESES construction. We introduce an out-of-core and parallel algorithm to improve the ESES software. Results The present approach drastically improves the spatial and temporal efficiency of ESES. The memory footprint and time complexity are analyzed and empirically verified through extensive tests with a large collection of biomolecule examples. Our results show that our algorithm can successfully reduce memory footprint through a straightforward divide-and-conquer strategy to perform the calculation of arbitrarily large proteins on a typical commodity personal computer. On multi-core computers or clusters, our algorithm can reduce the execution time by parallelizing most of the calculation as disjoint subproblems. Various comparisons with the state-of-the-art Cartesian grid based SES calculation were done to validate the present method and show the improved efficiency. This approach makes ESES a robust software for the construction of analytical solvent excluded surfaces. Availability and implementation http://weilab.math.msu.edu/ESES.
RADON: Repairable Atomic Data Object in Networks

Erasure codes offer an efficient way to decrease storage and communication costs while implementing atomic memory service in asynchronous distributed storage systems. In this paper, we provide erasure-code-based algorithms having the additional ability to perform background repair of crashed nodes. A repair operation of a node in the crashed state is triggered externally, and is carried out by the concerned node via message exchanges with other active nodes in the system. Upon completion of repair, the node re-enters active state, and resumes participation in ongoing and future read, write, and repair operations. To guarantee liveness and atomicity simultaneously, existing works assume either the presence of nodes with stable storage, or presence of nodes that never crash during the execution. We demand neither of these; instead we consider a natural, yet practical network stability condition N1 that only restricts the number of nodes in the crashed/repair state during broadcast of any message. We present an erasure-code based algorithm RADON_{C} that is always live, and guarantees atomicity as long as condition N1 holds. In situations when the number of concurrent writes is limited, RADON_{C} has significantly improved storage and communication cost over a replication-based algorithm RADON_{R}, which also works under N1. We further show how a slightly stronger network stability condition N2 can be used to construct algorithms that never violate atomicity. The guarantee of atomicity comes at the expense of having an additional phase during the read and write operations.
A filtering technique for Markov chains with applications to spectral embedding

Abstract Spectral methods have proven to be a highly effective tool in understanding the intrinsic geometry of a high-dimensional data set { x i } i = 1 n ⊂ R d . The key ingredient is the construction of a Markov chain on the set, where transition probabilities depend on the distance between elements, for example where the probability of going from x j to x i is proportional to p i j ∼ exp ⁡ ( − 1 e ‖ x i − x j ‖ l 2 ( R d ) 2 ) where e > 0 is a free parameter . We propose a method which increases the self-consistency of such Markov chains before spectral methods are applied. Instead of directly using a Markov transition matrix P, we set p i i = 0 and rescale, thereby obtaining a transition matrix P ⁎ modeling a non-lazy random walk. We then create a new transition matrix Q = ( q i j ) i , j = 1 n by demanding that for fixed j the quantity q i j be proportional to q i j ∼ min ⁡ ( ( P ⁎ ) i j , ( ( P ⁎ ) 2 ) i j , … , ( ( P ⁎ ) k ) i j ) where we usually set k = 2 . We show that the method can increase the efficiency of spectral methods and prove that it finds randomly introduced errors with high probability.
SoK: Sanitizing for Security

The C and C++ programming languages are notoriously insecure yet remain indispensable. Developers therefore resort to a multi-pronged approach to find security issues before adversaries. These include manual, static, and dynamic program analysis. Dynamic bug finding tools—henceforth "sanitizers"—can find bugs that elude other types of analysis because they observe the actual execution of a program, and can therefore directly observe incorrect program behavior as it happens. A vast number of sanitizers have been prototyped by academics and refined by practitioners. We provide a systematic overview of sanitizers with an emphasis on their role in finding security issues. Specifically, we taxonomize the available tools and the security vulnerabilities they cover, describe their performance and compatibility properties, and highlight various trade-offs.
On the Pitfalls of End-to-End Encrypted Communications: A Study of Remote Key-Fingerprint Verification

Many widely used Internet messaging and calling apps, such as WhatsApp, Viber, Telegram, and Signal, have deployed an end-to-end encryption functionality. To defeat potential man-in-the-middle attackers against the key exchange protocol, the approach crucially relies upon users to perform a code verification task whereby each user must compare the code (a fingerprint of the cryptographic keys) computed by her app with the one computed by the other user's app and reject the session if the two codes do not match. In this paper, we study the security and usability of this human-centered code verification task for a prominent setting where the end users are remotely located, and compare it as a baseline to a potentially less frequent scenario where the end users are in close proximity. We consider several variations of the code presentation and code verification methods, incorporated into representative real-world apps, including codes encoded as numbers or images, displayed on the screen, and verbally spoken by the users. We perform a carefully-designed human factors study in a lab setting to quantify the security and usability of these different methods. Our study results expose key weaknesses in the security and usability of the code verification methods employed in the remote end-to-end encryption apps. First, we show that generally most code verification methods offer poor security (high false accepts) and low usability (high false rejects and low user experience ratings) in the remote setting. Second, we demonstrate that, security and usability under the remote code verification setting is significantly lower than that in the proximity code verification setting. We attribute this result to the increased cognitive overhead associated with comparing the codes across two apps on the same device (remote setting) rather than across two devices (proximity setting). Overall, our work serves to highlight a serious fundamental vulnerability of Internet-based communication apps in the remote setting stemming from human errors.
Alternating Back-Propagation for Generator Network

This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model, the mapping from the continuous latent factors to the observed signal is parametrized by a convolutional neural network. The alternating back-propagation algorithm iterates the following two steps: (1) Inferential back-propagation, which infers the latent factors by Langevin dynamics or gradient descent. (2) Learning back-propagation, which updates the parameters given the inferred latent factors by gradient descent. The gradient computations in both steps are powered by back-propagation, and they share most of their code in common. We show that the alternating back-propagation algorithm can learn realistic generator models of natural images, video sequences, and sounds. Moreover, it can also be used to learn from incomplete or indirect training data.
Semiclassical limits of quantum affine spaces

Semiclassical limits of generic multi-parameter quantized coordinate rings A = q ( k n ) of affine spaces are constructed and related to A , for k an algebraically closed field of characteristic zero and q a multiplicatively antisymmetric matrix whose entries generate a torsion-free subgroup of k × . A semiclassical limit of A is a Poisson algebra structure on the corresponding classical coordinate ring R = ( k n ), and results of Oh, Park, Shin and the authors are used to construct homeomorphisms from the Poisson-prime and Poisson-primitive spectra of R onto the prime and primitive spectra of~ A . The Poisson-primitive spectrum of R is then identified with the space of symplectic cores in k n in the sense of Brown and Gordon, and an example is presented (over ℂ) for which the Poisson-primitive spectrum of R is not homeomorphic to the space of symplectic leaves in k n . Finally, these results are extended from quantum affine spaces to quantum affine toric varieties.
Hodge-Frobenius equations and the Hodge-Backlund transformation

Linear and nonlinear Hodge-like systems for 1-forms are studied, with an assumption equivalent to complete integrability substituted for the requirement of closure under exterior differentiation. The systems are placed in a variational context and properties of critical points investi
Line graphs and the transplantation method

Abstract We study isospectrality for mixed Dirichlet–Neumann boundary conditions and extend the previously derived graph-theoretic formulation of the transplantation method. Led by the theory of Brownian motion, we introduce vertex-colored and edge-colored line graphs that give rise to block diagonal transplantation matrices. In particular, we rephrase the transplantation method in terms of representations of free semigroups and provide a method for generating adjacency cospectral weighted directed graphs.
Embedded contact homology and its applications

Embedded contact homology (ECH) is a kind of Floer homology for contact three-manifolds. Taubes has shown that ECH is isomorphic to a version of Seiberg-Witten Floer homology (and both are conjecturally isomorphic to a version of Heegaard Floer homology). This isomorphism allows information to be transferred between topology and contact geometry in three dimensions. In this article we first give an overview of the definition of embedded contact homology. We then outline its applications to generalizations of the Weinstein conjecture, the Arnold chord conjecture, and obstructions to symplectic embeddings in four dimensions. Mathematics Subject Classification (2010). Primary 57R58; Secondary 57R17.
Application of microgrids in addressing distribution network net-load ramping

In spite of all advantages of solar energy, its deployment will significantly change the typical electric load profile, thus necessitating a change in traditional distribution grid management practices. In particular, the net load ramping, created as a result of simultaneous solar generation drop and load increase at early evening hours, is one of the major operational issues that needs to be carefully addressed. In this paper, microgrids are utilized to offer a viable and localized solution to this challenge while removing the need for costly investments by the electric utility. In this regard, first the microgrid ramping capability is determined via a min-max optimization, and second, the microgrid optimal scheduling model is developed to coordinate the microgrid net load with the distribution grid net load for addressing the ramping issue. Numerical simulations on a test distribution feeder with one microgrid exhibit the effectiveness of the proposed model.
Optimal dynamic program for r-domination problems over tree decompositions

There has been recent progress in showing that the exponential dependence on treewidth in dynamic programming algorithms for solving NP-hard problems is optimal under the Strong Exponential Time Hypothesis (SETH). We extend this work to r-domination problems. In r-dominating set, one wishes to find a minimum subset S of vertices such that every vertex of G is within r hops of some vertex in S. In connected r-dominating set, one additionally requires that the set induces a connected subgraph of G. We give a O((2r+1)^tw n) time algorithm for r-dominating set and a randomized O((2r+2)^tw n^{O(1)}) time algorithm for connected r-dominating set in n-vertex graphs of treewidth tw. We show that the running time dependence on r and tw is the best possible under SETH. This adds to earlier observations that a "+1" in the denominator is required for connectivity constraints.