Datasets:

debajyotidasgupta

/

dblp_2022_2023

like 0

Title: On bucket increasing trees, clustered increasing trees and increasing diamonds. Abstract: In this work we analyze bucket increasing tree families. We introduce two simple stochastic growth processes, generating random bucket increasing trees of size $n$, complementing the earlier result of Mahmoud and Smythe for bucket recursive trees. On the combinatorial side, we define multilabelled generalizations of the tree families $d$-ary increasing trees and generalized plane-oriented recursive trees. Additionally, we introduce a clustering process for ordinary increasing trees and relate it to bucket increasing trees. We discuss in detail the bucket size two and present a bijection between such bucket increasing tree families and certain families of graphs called increasing diamonds, providing an explanation for phenomena observed by Bodini et al.

Title: MEAN-FIELD LIMIT FOR A CLASS OF STOCHASTIC ERGODIC CONTROL PROBLEMS Abstract: We establish existence and uniqueness of an optimal control for a family of McKeanVlasov (mean-field) type ergodic optimal control problems with linear control and quadratic dependence on control of the cost function. We propose an N-particle Markovian optimal control problem approximating the McKean-Vlasov one and prove the convergence in relative entropy and total variation of the law of the former to the law of the latter when N goes to infinity.

Title: New upper bounds for the crossing numbers of crossing-critical graphs Abstract: A graph Gis k-crossing-critical if cr (G) <= k, but cr( G \ e) < k for each edge e is an element of E(G), where cr (G) is the crossing number of G. It is known that the latest upper bound of cr ( G) for a k-crossing-critical graph G is 2k + 8 root k+ 47when delta(G) >= 3, and 2k + 35when delta(G) >= 4, where delta(G) is the minimum degree of G. In this paper, we mainly show that for any k-crossing-critical graph G with nvertices, cr (G) <= 2k + 8when delta(G) >= 4, and cr (G) <= 2k - root k/2n + 35/6when delta(G) = 5.

Title: Flexible circuits in the d-dimensional rigidity matroid Abstract: A bar-joint framework ( G , p ) in R d is rigid if the only edge-length preserving continuous motions of the vertices arise from isometries of R d. It is known that, when ( G , p ) is generic, its rigidity depends only on the underlying graph G, and is determined by the rank of the edge set of G in the generic d-dimensional rigidity matroid Script capital R d. Complete combinatorial descriptions of the rank function of this matroid are known when d = 1 , 2, and imply that all circuits in Script capital R d are generically rigid in R d when d = 1 , 2. Determining the rank function of Script capital R d is a long standing open problem when d >= 3, and the existence of nonrigid circuits in Script capital R d for d >= 3 is a major contributing factor to why this problem is so difficult. We begin a study of nonrigid circuits by characterising the nonrigid circuits in Script capital R d which have at most d + 6 vertices.

Title: Memory and Entropy Abstract: I study the physical nature of traces. Surprisingly, (i) systems separation with (ii) temperature differences and (iii) long thermalization times are sufficient conditions to produce macroscopic traces. Traces of the past are ubiquitous because these conditions are largely satisfied in our universe. I quantify these thermodynamical conditions for memory and derive an expression for the maximum amount of information stored in such memories as a function of the relevant thermodynamical parameters. This mechanism transforms low entropy into available information. I suggest that all macroscopic information has this origin in past low entropy.

Title: A Kogbetliantz-type algorithm for the hyperbolic SVD Abstract: In this paper, a two-sided, parallel Kogbetliantz-type algorithm for the hyperbolic singular value decomposition (HSVD) of real and complex square matrices is developed, with a single assumption that the input matrix, of order n, admits such a decomposition into the product of a unitary, a non-negative diagonal, and a J-unitary matrix, where J is a given diagonal matrix of positive and negative signs. When J = ±I, the proposed algorithm computes the ordinary SVD. The paper’s most important contribution—a derivation of formulas for the HSVD of 2 × 2 matrices—is presented first, followed by the details of their implementation in floating-point arithmetic. Next, the effects of the hyperbolic transformations on the columns of the iteration matrix are discussed. These effects then guide a redesign of the dynamic pivot ordering, being already a well-established pivot strategy for the ordinary Kogbetliantz algorithm, for the general, n × n HSVD. A heuristic but sound convergence criterion is then proposed, which contributes to high accuracy demonstrated in the numerical testing results. Such a J-Kogbetliantz algorithm as presented here is intrinsically slow, but is nevertheless usable for matrices of small orders.

Title: The reconstruction conjecture for finite simple graphs and associated directed graphs Abstract: In this paper, we study the Reconstruction Conjecture for finite simple graphs. Let & nbsp;gamma and gamma'& nbsp;be finite simple graphs with at least three vertices such that there exists a bijective map f : V (gamma) -> V (gamma') and for any v & ISIN; V (gamma), there exists an isomorphism phi v: gamma & nbsp;-v & nbsp;->& nbsp;gamma'& nbsp;- f (v). Then we define the associated directed graph (gamma)over tilde= (gamma') over tilde (gamma, gamma', f, {phi v}(v is an element of V (gamma))) with two kinds of arrows from the graphs gamma & nbsp;and gamma', the bijective map f and the isomorphisms gamma', we study when are the two {phi(v)}(v is an element of V (gamma)). By investigating the associated directed graph graphs (gamma)ocer tilde & nbsp;and gamma'& nbsp;isomorphic. (C)& nbsp;2022 The Author(s). Published by Elsevier B.V.& nbsp;

Title: Extended Feature Pyramid Network for Small Object Detection Abstract: Small object detection remains an unsolved challenge because it is hard to extract the information of small objects with only a few pixels. While scale-level corresponding detection in feature pyramid network alleviates this problem, we find feature coupling of various scales still impairs the performance of small objects. In this paper, we propose an extended feature pyramid network (EFPN) with an extra high-resolution pyramid level specialized for small object detection. Specifically, we design a novel module, named feature texture transfer (FTT), which is used to super-resolve features and extract credible regional details simultaneously. Moreover, we introduce a cross resolution distillation mechanism to transfer the ability of perceiving details across the scales of the network, where a foreground-background-balanced loss function is designed to alleviate area imbalance of foreground and background. In our experiments, the proposed EFPN is efficient on both computation and memory, and yields state-of-the-art results on small traffic-sign dataset Tsinghua-Tencent 100 K and small category of general object detection dataset MS COCO.

Title: Universal singular exponents in catalytic variable equations Abstract: Catalytic equations appear in several combinatorial applications, most notably in the enumeration of lattice paths and in the enumeration of planar maps. The main purpose of this paper is to show that the asymptotic estimate for the coefficients of the solutions of (so-called) positive catalytic equations has a universal asymptotic behavior. In particular, this provides a rationale why the number of maps of size n in various planar map classes grows asymptotically like c⋅n−5/2γn, for suitable positive constants c and γ. Essentially we have to distinguish between linear catalytic equations (where the subexponential growth is n−3/2) and non-linear catalytic equations (where we have n−5/2 as in planar maps). Furthermore we provide a quite general central limit theorem for parameters that can be encoded by catalytic functional equations, even when they are not positive.

Title: Social Media and Misleading Information in a Democracy: A Mechanism Design Approach Abstract: In this article, we present a resource allocation mechanism to incentivize misinformation filtering among strategic social media platforms and, thus, to indirectly prevent the spread of fake news. We consider the presence of a strategic government and private knowledge of how misinformation affects the users of the social media platforms. Our proposed mechanism strongly implements all generalized ...

Title: A Rotation-Invariant Framework for Deep Point Cloud Analysis Abstract: Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to arbitrary orientations. In this article, we introduce a new low-level purely rotation-invariant representation to replace common 3D Cartesian coordinates as the network inputs. Also, we present a network architecture to embed these representations into features, encoding local relations between points and their neighbors, and the global shape structure. To alleviate inevitable global information loss caused by the rotation-invariant representations, we further introduce a region relation convolution to encode local and non-local information. We evaluate our method on multiple point cloud analysis tasks, including (i) shape classification, (ii) part segmentation, and (iii) shape retrieval. Extensive experimental results show that our method achieves consistent, and also the best performance, on inputs at arbitrary orientations, compared with all the state-of-the-art methods.

Title: An Automatic Attribute-Based Access Control Policy Extraction From Access Logs Abstract: With the rapid advances in computing and information technologies, traditional access control models have become inadequate in terms of capturing fine-grained, and expressive security requirements of newly emerging applications. An attribute-based access control (ABAC) model provides a more flexible approach to addressing the authorization needs of complex and dynamic systems. While organizations are interested in employing newer authorization models, migrating to such models pose as a significant challenge. Many large-scale businesses need to grant authorizations to their user populations that are potentially distributed across disparate and heterogeneous computing environments. Each of these computing environments may have its own access control model. The manual development of a single policy framework for an entire organization is tedious, costly, and error-prone. In this article, we present a methodology for automatically learning ABAC policy rules from access logs of a system to simplify the policy development process. The proposed approach employs an unsupervised learning-based algorithm for detecting patterns in access logs and extracting ABAC authorization rules from these patterns. In addition, we present two policy improvement algorithms, including rule pruning and policy refinement algorithms to generate a higher quality mined policy. Finally, we implement a prototype of the proposed approach to demonstrate its feasibility.

Title: Restricted Boltzmann machine representation for the groundstate and excited states of Kitaev Honeycomb model Abstract: In this work, the capability of restricted Boltzmann machines (RBMs) to find solutions for the Kitaev honeycomb model with periodic boundary conditions is investigated. The measured groundstate energy of the system is compared and, for small lattice sizes (e.g. 3 x 3 with 18 spinors), shown to agree with the analytically derived value of the energy up to a deviation of 0.09%. Moreover, the wave-functions we find have 99.89% overlap with the exact ground state wave-functions. Furthermore, the possibility of realizing anyons in the RBM is discussed and an algorithm is given to build these anyonic excitations and braid them for possible future applications in quantum computation. Using the correspondence between topological field theories in (2 + 1)d and 2d conformal field theories, we propose an identification between our RBM states with the Moore-Read state and conformal blocks of the 2d Ising model.

Title: Subdivisions of shellable complexes Abstract: In geometric, algebraic, and topological combinatorics, the unimodality of combinatorial generating polynomials is frequently studied. Unimodality follows when the polynomial is (real) stable, a property often deduced via the theory of interlacing polynomials. Many of the open questions on stability and unimodality of polynomials pertain to the enumeration of faces of cell complexes. In this paper, we relate the theory of interlacing polynomials to the shellability of cell complexes. We first derive a sufficient condition for stability of the h-polynomial of a subdivision of a shellable complex. To apply it, we generalize the notion of reciprocal domains for convex embeddings of polytopes to abstract polytopes and use this generalization to define the family of stable shellings of a polytopal complex. We characterize the stable shellings of cubical and simplicial complexes, and apply this theory to answer a question of Brenti and Welker on barycentric subdivisions for the well-known cubical polytopes. We also give a positive solution to a problem of Mohammadi and Welker on edgewise subdivisions of cell complexes. We end by relating the family of stable line shellings to the combinatorics of hyperplane arrangements. We pose related questions, answers to which would resolve some long-standing problems while strengthening ties between the theory of interlacing polynomials and the combinatorics of hyperplane arrangements.

Title: Infrasonic, Acoustic and Seismic Waves Produced by the Axion Quark Nuggets Abstract: We advocate the idea that Axion Quark Nuggets (AQN) hitting the Earth can be detected by analysing the infrasound, acoustic, and seismic waves which always accompany their passage in the atmosphere and underground. Our estimates for the infrasonic frequency nu similar or equal to 5 Hz and overpressure delta p similar to 0.3 Pa for relatively large size dark matter (DM) nuggets suggest that sensitivity of presently available instruments is already sufficient to detect very intense (but very rare) events today with existing technology. A study of much more frequent but less intense events requires a new type of instrument. We propose a detection strategy for a systematic study to search for such relatively weak and frequent events by using distributed acoustic sensing and briefly mention other possible detection methods.

Title: A Unifying Complexity Certification Framework for Active-Set Methods for Convex Quadratic Programming Abstract: In model-predictive control (MPC), an optimization problem has to be solved at each time step, which in real-time applications makes it important to solve these efficiently and to have good upper bounds on worst-case solution time. Often for linear MPC problems, the optimization problem in question is a quadratic program (QP) that depends on parameters such as system states and reference signals. A popular class of methods for solving such QPs is active-set methods, where a sequence of linear systems of equations is solved. We propose an algorithm for computing which sequence of subproblems an active-set algorithm will solve, for every parameter of interest. These sequences can be used to set worst-case bounds on how many iterations, floating-point operations, and, ultimately, the maximum solution time the active-set algorithm requires to converge. The usefulness of the proposed method is illustrated on a set of QPs originating from MPC problems, by computing the exact worst-case number of iterations primal and dual active-set algorithms require to reach optimality.

Title: Perturbed Iterate SGD for Lipschitz Continuous Loss Functions. Abstract: This paper presents an extension of stochastic gradient descent for the minimization of Lipschitz continuous loss functions. Our motivation is for use in non-smooth non-convex stochastic optimization problems, which are frequently encountered in applications such as machine learning. Using the Clarke $\epsilon$-subdifferential, we prove the non-asymptotic convergence to an approximate stationary point in expectation for the proposed method. From this result, a method with non-asymptotic convergence with high probability, as well as a method with asymptotic convergence to a Clarke stationary point almost surely are developed. Our results hold under the assumption that the stochastic loss function is a Carath\'eodory function which is almost everywhere Lipschitz continuous in the decision variables. To the best of our knowledge this is the first non-asymptotic convergence analysis under these minimal assumptions.

Title: Braitenberg Vehicles as Developmental Neurosimulation Abstract: Connecting brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. However, this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We introduce three alternate approaches to demonstrate how developmental embodied agents can be implemented. The resulting developmental Braitenberg vehicles (dBVs) will generate behaviors ranging from stimulus responses to group behavior that resembles collective motion. We will situate this work in the domain of artificial brain networks along with broader themes such as embodied cognition, feedback, and emergence. Our perspective is exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, a multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we consider future applications of the developmental neurosimulation approach.

Title: Polynomial superlevel set representation of the multistationarity region of chemical reaction networks Abstract: In this paper we introduce a new representation for the multistationarity region of a reaction network, using polynomial superlevel sets. The advantages of using this polynomial superlevel set representation over the already existing representations (cylindrical algebraic decompositions, numeric sampling, rectangular divisions) is discussed, and algorithms to compute this new representation are provided. The results are given for the general mathematical formalism of a parametric system of equations and so may be applied to other application domains.

Title: Complexity of linear relaxations in integer programming Abstract: For a set X of integer points in a polyhedron, the smallest number of facets of any polyhedron whose set of integer points coincides with X is called the relaxation complexity  $${{\,\mathrm{rc}\,}}(X)$$ . This parameter, introduced by Kaibel & Weltge (2015), captures the complexity of linear descriptions of X without using auxiliary variables. Using tools from combinatorics, geometry of numbers, and quantifier elimination, we make progress on several open questions regarding $${{\,\mathrm{rc}\,}}(X)$$ and its variant $${{\,\mathrm{rc}\,}}_\mathbb {Q}(X)$$ , restricting the descriptions of X to rational polyhedra. As our main results we show that $${{\,\mathrm{rc}\,}}(X) = {{\,\mathrm{rc}\,}}_\mathbb {Q}(X)$$ when: (a) X is at most four-dimensional, (b) X represents every residue class in $$(\mathbb {Z}/2\mathbb {Z})^d$$ , (c) the convex hull of X contains an interior integer point, or (d) the lattice-width of X is above a certain threshold. Additionally, $${{\,\mathrm{rc}\,}}(X)$$ can be algorithmically computed when X is at most three-dimensional, or X satisfies one of the conditions (b), (c), or (d) above. Moreover, we obtain an improved lower bound on $${{\,\mathrm{rc}\,}}(X)$$ in terms of the dimension of X.

Title: LOGARITHMIC HEAVY TRAFFIC ERROR BOUNDS IN GENERALIZED SWITCH AND LOAD BALANCING SYSTEMS Abstract: Motivated by applications to wireless networks, cloud computing, data centers, etc., stochastic processing networks have been studied in the literature under various asymptotic regimes. In the heavy traffic regime, the steady-state mean queue length is proved to be Theta(1/epsilon), where epsilon is the heavy traffic parameter (which goes to zero in the limit). The focus of this paper is on obtaining queue length bounds on pre-limit systems, thus establishing the rate of convergence to heavy traffic. For the generalized switch, operating under the MaxWeight algorithm, we show that the mean queue length is within O(log(1/epsilon)) of its heavy traffic limit. This result holds regardless of the complete resource pooling (CRP) condition being satisfied. Furthermore, when the CRP condition is satisfied, we show that the mean queue length under the MaxWeight algorithm is within O(log(1/epsilon)) of the optimal scheduling policy. Finally, we obtain similar results for the rate of convergence to heavy traffic of the total queue length in load balancing systems operating under the 'join the shortest queue' routeing algorithm.

Title: Gaussian Process-Aided Function Comparison Using Noisy Scattered Data Abstract: This work proposes a nonparametric method to compare the underlying mean functions given two noisy datasets. The motivation for the work stems from an application of comparing wind turbine power curves. Comparing wind turbine data presents new problems, namely the need to identify the regions of difference in the input space and to quantify the extent of difference that is statistically significant. Our proposed method, referred to as funGP, estimates the underlying functions for different data samples using Gaussian process models. We build a confidence band using the probability law of the estimated function differences under the null hypothesis. Then, the confidence band is used for the hypothesis test as well as for identifying the regions of difference. This identification of difference regions is a distinct feature, as existing methods tend to conduct an overall hypothesis test stating whether two functions are different. Understanding the difference regions can lead to further practical insights and help devise better control and maintenance strategies for wind turbines. The merit of funGP is demonstrated by using three simulation studies and four real wind turbine datasets.

Title: Dynamic Information Design: A Simple Problem on Optimal Sequential Information Disclosure Abstract: We study a dynamic information design problem in a finite-horizon setting consisting of two strategic and long-term optimizing agents, namely a principal (he) and a detector (she). The principal observes the evolution of a Markov chain that has two states, one “good” and one “bad” absorbing state, and has to decide how to sequentially disclose information to the detector. The detector’s only information consists of the messages she receives from the principal. The detector’s objective is to detect as accurately as possible the time of the jump from the good to the bad state. The principal’s objective is to delay the detector as much as possible from detecting the jump to the bad state. For this setting, we determine the optimal strategies of the principal and the detector. The detector’s optimal strategy is described by time-varying thresholds on her posterior belief of the good state. We prove that it is optimal for the principal to give no information to the detector before a time threshold, run a mixed strategy to confuse the detector at the threshold time, and reveal the true state afterward. We present an algorithm that determines both the optimal time threshold and the optimal mixed strategy that could be employed by the principal. We show, through numerical experiments, that this optimal sequential mechanism outperforms any other information disclosure strategy presented in the literature. We also show that our results can be extended to the infinite-horizon problem, to the problem where the matrix of transition probabilities of the Markov chain is time-varying, and to the case where the Markov chain has more than two states and one of the states is absorbing.

Title: Thick Weakly Distance-Regular Digraphs Abstract: A weakly distance-regular digraph is thick if its attached scheme is regular. In this paper, we show that each commutative thick weakly distance-regular digraph has a thick weakly distance-regular subdigraph such that the corresponding quotient digraph falls into six families of thick weakly distance-regular digraphs up to isomorphism.

Title: Law of large numbers for Betti numbers of homogeneous and spatially independent random simplicial complexes Abstract: The Linial-Meshulam complex model is a natural higher dimensional analog of the Erdos-Renyi graph model. In recent years, Linial and Peled established a limit theorem for Betti numbers of Linial-Meshulam complexes with an appropriate scaling of the underlying parameter. The present article aims to extend that result to more general random simplicial complex models. We introduce a class of homogeneous and spatially independent random simplicial complexes, including the Linial-Meshulam complex model and the random clique complex model as special cases, and we study the asymptotic behavior of their Betti numbers. Moreover, we obtain the convergence of the empirical spectral distributions of their Laplacians. A key element in the argument is the local weak convergence of simplicial complexes. Inspired by the work of Linial and Peled, we establish the local weak limit theorem for homogeneous and spatially independent random simplicial complexes.

Title: Multi-Task Learning With Coarse Priors for Robust Part-Aware Person Re-Identification Abstract: Part-level representations are important for robust person re-identification (ReID), but in practice feature quality suffers due to the body part misalignment problem. In this paper, we present a robust, compact, and easy-to-use method called the Multi-task Part-aware Network (MPN), which is designed to extract semantically aligned part-level features from pedestrian images. MPN solves the body part misalignment problem via multi-task learning (MTL) in the training stage. More specifically, it builds one main task (MT) and one auxiliary task (AT) for each body part on the top of the same backbone model. The ATs are equipped with a coarse prior of the body part locations for training images. ATs then transfer the concept of the body parts to the MTs via optimizing the MT parameters to identify part-relevant channels from the backbone model. Concept transfer is accomplished by means of two novel alignment strategies: namely, parameter space alignment via hard parameter sharing and feature space alignment in a class-wise manner. With the aid of the learned high-quality parameters, MTs can independently extract semantically aligned part-level features from relevant channels in the testing stage. MPN has three key advantages: 1) it does not need to conduct body part detection in the inference stage; 2) its model is very compact and efficient for both training and testing; 3) in the training stage, it requires only coarse priors of body part locations, which are easy to obtain. Systematic experiments on four large-scale ReID databases demonstrate that MPN consistently outperforms state-of-the-art approaches by significant margins.

Title: Some multivariate master polynomials for permutations, set partitions, and perfect matchings, and their continued fractions Abstract: We find Stieltjes-type and Jacobi-type continued fractions for some "master polynomials" that enumerate permutations, set partitions or perfect matchings with a large (sometimes infinite) number of simultaneous statistics. Our results contain many previously obtained identities as special cases, providing a common refinement of all of them. (C) 2022 Elsevier Inc. All rights reserved.

Title: Largest Family Without a Pair of Posets on Consecutive Levels of the Boolean Lattice Abstract: Suppose k ≥ 2 is an integer. Let Yk be the poset with elements x1,x2,y1,y2,…,yk− 1 such that y1 < y2 < ⋯ < yk− 1 < x1,x2 and let $Y_{k}^{\prime }$ be the same poset but all relations reversed. We say that a family of subsets of [n] contains a copy of Yk on consecutive levels if it contains k + 1 subsets F1,F2,G1,G2,…,Gk− 1 such that G1 ⊂ G2 ⊂⋯ ⊂ Gk− 1 ⊂ F1,F2 and |F1| = |F2| = |Gk− 1| + 1 = |Gk− 2| + 2 = ⋯ = |G1| + k − 1. If both Yk and $Y^{\prime }_{k}$ on consecutive levels are forbidden, the size of the largest such family is denoted by $\text {La}_{\mathrm {c}}\left (n, Y_{k}, Y^{\prime }_{k}\right )$ . In this paper, we will determine the exact value of $\text {La}_{\mathrm {c}}\left (n, Y_{k}, Y^{\prime }_{k}\right )$ .

Title: Cooperative conditions for the existence of rainbow matchings Abstract: Let k > 1, and let F be a family of 2n + k - 3 non-empty sets of edges in a bipartite graph. If the union of every k members of F contains a matching of size n, then there exists an F-rainbow matching of size n. Replacing 2n + k - 3 by 2n + k - 2, the result is true also for k = 1, and it can be proved (for all k) both topologically and by a relatively simple combinatorial argument. The main effort is in gaining the last 1, which makes the result sharp.

Title: Local Multiple Traces Formulation for electromagnetics: Stability and preconditioning for smooth geometries Abstract: We consider the time-harmonic electromagnetic transmission problem for the unit sphere. Appealing to a vector spherical harmonics analysis, we prove the first stability result of the local multiple traces formulation (MTF) for electromagnetics, originally introduced by Hiptmair and Jerez-Hanckes (2012) for the acoustic case, paving the way towards an extension to general piecewise homogeneous scatterers. Moreover, we investigate preconditioning techniques for the local MTF scheme and study the accumulation points of induced operators. In particular, we propose a novel second-order inverse approximation of the operator. Numerical experiments validate our claims and confirm the relevance of the preconditioning strategies proposed.

Title: Viewport-Aware Deep Reinforcement Learning Approach for 360 $^\circ$ Video Caching Abstract: 360$^{\circ }$ video is an essential component of VR/AR/MR systems that provides immersive experience to the users. However, 360$^{\circ }$ video is associated with high bandwidth requirements. The required bandwidth can be reduced by exploiting the fact tha...

Title: Knowledge-Based Prediction of Network Controllability Robustness Abstract: Network controllability robustness (CR) reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the CR is determined by attack simulations, which is computationally time-consuming or even infeasible. In this article, an improved method for predicting the network CR is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor; 2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity.

Title: Adaptive batching for Gaussian process surrogates with application in noisy level set estimation Abstract: We develop adaptive replicated designs for Gaussian process metamodels of stochastic experiments. Adaptive batching is a natural extension of sequential design heuristics with the benefit of replication growing as response features are learned, inputs concentrate, and the metamodeling overhead rises. Motivated by the problem of learning the level set of the mean simulator response, we develop five novel schemes: Multi-Level Batching (MLB), Ratchet Batching (RB), Adaptive Batched Stepwise Uncertainty Reduction (ABSUR), Adaptive Design with Stepwise Allocation (ADSA), and Deterministic Design with Stepwise Allocation (DDSA). Our algorithms simultaneously (MLB, RB, and ABSUR) or sequentially (ADSA and DDSA) determine the sequential design inputs and the respective number of replicates. Illustrations using synthetic examples and an application in quantitative finance (Bermudan option pricing via Regression Monte Carlo) show that adaptive batching brings significant computational speed-ups with minimal loss of modeling fidelity.

Title: Structural-Constrained Methods for the Identification of False Data Injection Attacks in Power Systems. Abstract: Power system functionality is determined on the basis of the power system state estimation (PSSE). Thus, corruption of the PSSE may lead to severe consequences, such as financial losses, maintenance damage, and disruptions in electricity distribution. Classical bad data detection (BDD) methods, developed to ensure PSSE reliability, are unable to detect well-designed attacks, named unobservable false data injection (FDI) attacks. In this paper, we develop novel structural-constrained methods for the detection of unobservable FDI attacks, the identification of the attacked buses' locations, and PSSE under the presence of such attacks. The proposed methods are based on formulating structural, sparse constraints on both the attack and the system loads. First, we exploit these constraints in order to compose an appropriate model selection problem. Then, we develop the associated generalized information criterion (GIC) for this problem. However, for large networks, the GIC method's computational complexity grows exponentially with the network size. Thus, based on the proposed structural and sparse constraints, we develop two novel low-complexity methods for unobservable FDI attack identification: 1) a modification of the state-of-the-art orthogonal matching pursuit (OMP); and 2) a method that utilizes the graph Markovian property in power systems, i.e. the second-neighbor relationship between the power data at the system's buses. The methods' performance is evaluated on a IEEE-30 bus test case system.

Title: Self-Guided Adaptation: Progressive Representation Alignment for Domain Adaptive Object Detection Abstract: Unsupervised domain adaptation (UDA) has achieved unprecedented success in improving the cross-domain robustness of object detection models. However, existing UDA methods largely ignore the instantaneous data distribution and the sampling strategy during model learning, which could deteriorate the feature representation given large domain shift. In this work, we propose a Self-Guided Adaptation (SGA) model, targeting at aligning feature representation and transferring object detection models across domains while considering the instantaneous alignment difficulty. The core of SGA is to calculate "hardness" factors for sample pairs indicating domain distance in a kernel space. With the hardness factor, the proposed SGA adaptively indicates the importance of samples and assigns them different constrains. Indicated by these hardness factors, Self-Guided Progressive Sampling (SPS) is implemented in an "easy-to-hard" way during model adaptation. Using multi-stage convolutional features, SGA is further aggregated to fully align hierarchical representations of detection models. Extensive experiments on commonly-used benchmarks show that SGA improves the state-of-the-art methods with significant margins especially on large domain shift cases.

Title: Approximately Supermodular Scheduling Subject to Matroid Constraints Abstract: Control scheduling refers to the problem of assigning agents or actuators to act upon a dynamical system at specific times so as to minimize a quadratic control cost, such as the objectives of the linear-quadratic-Gaussian (LQG) or linear quadratic regulator problems. When budget or operational constraints are imposed on the schedule, this problem is in general NP-hard and its solution can therefo...

Title: Adaptive total variation stable local timestepping for conservation laws Abstract: This paper proposes a first-order total variation diminishing (TVD) treatment for coarsening and refining of local timestep size in response to dynamic local variations in wave speeds for nonlinear conservation laws. The algorithm is accompanied with a proof of formal correctness showing that given a sufficiently small minimum timestep the algorithm will produce a TVD solution for nonlinear scalar conservation laws. A key feature of the algorithm is its formulation as a discrete event simulation, which allows for easy and efficient parallelization using existing software. Numerical results demonstrate the stability and adaptivity of the method for the shallow water equations. We also introduce a performance model to load balance and explain the observed performance gains. Performance results are presented for a single node on Stampede2's Skylake partition using an optimistic parallel discrete event simulator. Results show the proposed algorithm recovering 59%-77% of the theoretically achievable speed-up with the remainder being attributed to the cost of computing the CFL condition and load imbalance.

Title: TF-Coder: Program Synthesis for Tensor Manipulations Abstract: The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch, which make it easier to develop deep learning models. However, these libraries also come with steep learning curves, since programming in these frameworks is quite different from traditional imperative programming with explicit loops and conditionals. In this work, we present a tool called TF-Coder for programming by example in TensorFlow. TF-Coder uses a bottom-up weighted enumerative search, with value-based pruning of equivalent expressions and flexible type- and value-based filtering to ensure that expressions adhere to various requirements imposed by the TensorFlow library. We train models to predict TensorFlow operations from features of the input and output tensors and natural language descriptions of tasks to prioritize relevant operations during search. TF-Coder solves 63 of 70 real-world tasks within 5 minutes, sometimes finding simpler solutions in less time compared to experienced human programmers.

Title: An Attribute-Aware Attentive GCN Model for Attribute Missing in Recommendation Abstract: As important side information, attributes have been widely exploited in the existing recommender system for better performance. However, in the real-world scenarios, it is common that some attributes of items/users are missing (e.g., some movies miss the genre data). Prior studies usually use a default value (i.e., “other”) to represent the missing attribute, resulting in sub-optimal performance. To address this problem, in this paper, we present an attribute-aware attentive graph convolution network (A <inline-formula><tex-math notation="LaTeX">${^2}$</tex-math></inline-formula> -GCN). In particular, we first construct a graph, where users, items, and attributes are three types of nodes and their associations are edges. Thereafter, we leverage the graph convolution network to characterize the complicated interactions among <inline-formula><tex-math notation="LaTeX">$&lt;$</tex-math></inline-formula> users, items, attributes <inline-formula><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> . Furthermore, to learn the node representation, we adopt the message-passing strategy to aggregate the messages passed from the other directly linked types of nodes (e.g., a user or an attribute). Towards this end, we are capable of incorporating associate attributes to strengthen the user and item representation learning, and thus naturally solve the attribute missing problem. Given that for different users, the attributes of an item have different influence on their preference to this item, we design a novel attention mechanism to filter the message passed from an item to a target user by considering the attribute information. Extensive experiments have been conducted on several publicly accessible datasets to justify our model, demonstrating that our model outperforms several state-of-the-art methods and demonstrate the effectiveness of our attention method.

Title: TNT-KID: Transformer-based neural tagger for keyword identification Abstract: With growing amounts of available textual data, development of algorithms capable of automatic analysis, categorization, and summarization of these data has become a necessity. In this research, we present a novel algorithm for keyword identification, that is, an extraction of one or multiword phrases representing key aspects of a given document, called Transformer-Based Neural Tagger for Keyword IDentification (TNT-KID). By adapting the transformer architecture for a specific task at hand and leveraging language model pretraining on a domain-specific corpus, the model is capable of overcoming deficiencies of both supervised and unsupervised state-of-the-art approaches to keyword extraction by offering competitive and robust performance on a variety of different datasets while requiring only a fraction of manually labeled data required by the best-performing systems. This study also offers thorough error analysis with valuable insights into the inner workings of the model and an ablation study measuring the influence of specific components of the keyword identification workflow on the overall performance.

Title: Rates of superlinear convergence for classical quasi-Newton methods Abstract: We study the local convergence of classical quasi-Newton methods for nonlinear optimization. Although it was well established a long time ago that asymptotically these methods converge superlinearly, the corresponding rates of convergence still remain unknown. In this paper, we address this problem. We obtain first explicit non-asymptotic rates of superlinear convergence for the standard quasi-Newton methods, which are based on the updating formulas from the convex Broyden class. In particular, for the well-known DFP and BFGS methods, we obtain the rates of the form $$(\frac{n L^2}{\mu ^2 k})^{k/2}$$ and $$(\frac{n L}{\mu k})^{k/2}$$ respectively, where k is the iteration counter, n is the dimension of the problem, $$\mu $$ is the strong convexity parameter, and L is the Lipschitz constant of the gradient.

Title: Control Reconfiguration of Dynamical Systems for Improved Performance via Reverse- and Forward-Engineering Abstract: This article presents a control reconfiguration approach to improve the performance of two classes of dynamical systems. Motivated by recent research on re-engineering cyber-physical systems, we propose a three-step control retrofit procedure. First, we reverse-engineer a dynamical system to dig out an optimization problem it actually solves. Second, we forward-engineer the system by applying a corresponding faster algorithm to solve this optimization problem. Finally, by comparing the original and accelerated dynamics, we obtain the implementation of the redesigned part (the extra dynamics). As a result, the convergence rate/speed or transient behavior of the given system can be improved, while the system control structure is maintained. Internet congestion control and distributed proportional–integral control, as applications in the two different classes of target systems, show the effectiveness of the proposed approach.

Title: Detection of Information Hiding at Anti-Copying 2D Barcodes Abstract: This paper addresses the issue of detecting the use of information hiding at anti-copying 2D barcodes. Prior hidden information detection schemes have their roots in either heuristic-based or Machine Learning (ML). However, prior heuristics-based schemes lack a rigorous theoretical analysis. Prior ML-based information schemes lack robustness because a printed 2D barcode is very much environmentall...

Title: Fast generalized Nash equilibrium seeking under partial-decision information Abstract: We address the generalized Nash equilibrium seeking problem in a partial-decision information scenario, where each agent can only exchange information with some neighbors, although its cost function possibly depends on the strategies of all agents. The few existing methods build on projected pseudo-gradient dynamics, and require either double-layer iterations or conservative conditions on the step sizes. To overcome both these flaws and improve efficiency, we design the first fully-distributed single-layer algorithms based on proximal best-response. Our schemes are fixed-step and allow for inexact updates, which is crucial for reducing the computational complexity. Under standard assumptions on the game primitives, we establish convergence to a variational equilibrium (with linear rate for games without coupling constraints) by recasting our algorithms as proximal-point methods, opportunely preconditioned to distribute the computation among the agents. Since our analysis hinges on a restricted monotonicity property, we also provide new general results that significantly extend the domain of applicability of proximal-point methods. Besides, our operator-theoretic approach favors the implementation of provably correct acceleration schemes that can further improve the convergence speed. Finally, the potential of our algorithms is demonstrated numerically, revealing much faster convergence with respect to projected pseudo-gradient methods and validating our theoretical findings.

Title: LOGICS OF FORMAL INCONSISTENCY ENRICHED WITH REPLACEMENT: AN ALGEBRAIC AND MODAL ACCOUNT Abstract: It is customary to expect from a logical system that it can be algebraizable, in the sense that an algebraic companion of the deductive machinery can always be found. Since the inception of da Costa's paraconsistent calculi, algebraic equivalents for such systems have been sought. It is known, however, that these systems are not self-extensional (i.e., they do not satisfy the replacement property). More than this, they are not algebraizable in the sense of Blok-Pigozzi. The same negative results hold for several systems of the hierarchy of paraconsistent logics known as Logics of Formal Inconsistency (LFIs). Because of this, several systems belonging to this class of logics are only characterizable by semantics of a non-deterministic nature. This paper offers a solution for two open problems in the domain of paraconsistency, in particular connected to algebraization of LFIs, by extending with rules several LFIs weaker than C-1, thus obtaining the replacement property (that is, such LFIs turn out to be self-extensional). Moreover, these logics become algebraizable in the standard Lindenbaum-Tarski's sense by a suitable variety of Boolean algebras extended with additional operations. The weakest LFI satisfying replacement presented here is called RmbC, which is obtained from the basic LFI called mbC. Some axiomatic extensions of RmbC are also studied. In addition, a neighborhood semantics is defined for such systems. It is shown that RmbC can be defined within the minimal bimodal non-normal logic E circle plus E defined by the fusion of the non-normal modal logic E with itself. Finally, the framework is extended to first-order languages. RQmbC, the quantified extension of RmbC, is shown to be sound and complete w.r.t. the proposed algebraic semantics.

Title: Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning Abstract: Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization. Deep neural networks’ rapid development facilitates the learning techniques for modeling complex problems and emerges into federated deep learning under the federated setting. However, the tremendous amount of model parameters burdens the communication network with a high load of transportation. This article introduces two approaches for improving communication efficiency by dynamic sampling and top-$k$k selective masking. The former controls the fraction of selected client models dynamically, while the latter selects parameters with top-$k$k largest values of difference for federated updating. Experiments on convolutional image classification and recurrent language modeling are conducted on three public datasets to show our proposed methods’ effectiveness.

Title: NeuCrowd: neural sampling network for representation learning with crowdsourced labels Abstract: Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, smart city, and education. In practice, people refer to crowdsourcing to get annotated labels. However, due to issues like data privacy, budget limitation, shortage of domain-specific annotators, the number of crowdsourced labels is still very limited. Moreover, because of annotators’ diverse expertise, crowdsourced labels are often inconsistent. Thus, directly applying existing supervised representation learning (SRL) algorithms may easily get the overfitting problem and yield suboptimal solutions. In this paper, we propose NeuCrowd, a unified framework for SRL from crowdsourced labels. The proposed framework (1) creates a sufficient number of high-quality n-tuplet training samples by utilizing safety-aware sampling and robust anchor generation; and (2) automatically learns a neural sampling network that adaptively learns to select effective samples for SRL networks. The proposed framework is evaluated on both one synthetic and three real-world data sets. The results show that our approach outperforms a wide range of state-of-the-art baselines in terms of prediction accuracy and AUC. To encourage reproducible results, we make our code publicly available at https://github.com/tal-ai/NeuCrowd_KAIS2021 .

Title: On Information Plane Analyses of Neural Network Classifiers—A Review Abstract: We review the current literature concerned with information plane (IP) analyses of neural network (NN) classifiers. While the underlying information bottleneck theory and the claim that information-theoretic compression is causally linked to generalization are plausible, empirical evidence was found to be both supporting and conflicting. We review this evidence together with a detailed analysis of how the respective information quantities were estimated. Our survey suggests that compression visualized in IPs is not necessarily information-theoretic but is rather often compatible with geometric compression of the latent representations. This insight gives the IP a renewed justification. Aside from this, we shed light on the problem of estimating mutual information in deterministic NNs and its consequences. Specifically, we argue that, even in feedforward NNs, the data processing inequality needs not to hold for estimates of mutual information. Similarly, while a fitting phase, in which the mutual information is between the latent representation and the target increases, is necessary (but not sufficient) for good classification performance, depending on the specifics of mutual information estimation, such a fitting phase needs to not be visible in the IP.

Title: POD-Galerkin Model Order Reduction for Parametrized Nonlinear Time Dependent Optimal Flow Control: an Application to Shallow Water Equations Abstract: In this work we propose reduced order methods as a reliable strategy to efficiently solve parametrized optimal control problems governed by Shallow Waters Equations in a solution tracking setting. The physical parametrized model we deal with is nonlinear and time dependent: this leads to very time consuming simulations which can be unbearable e.g. in a marine environmental monitoring plan application. Our aim is to show how reduced order modelling could help in studying different configurations and phenomena in a fast way. After building the optimality system, we rely on a POD-Galerkin reduction in order to solve the optimal control problem in a low dimensional reduced space. The presented theoretical framework is actually suited to general nonlinear time dependent optimal control problems. The proposed methodology is finally tested with a numerical experiment: the reduced optimal control problem governed by Shallow Waters Equations reproduces the desired velocity and height profiles faster than the standard model, still remaining accurate.

Title: K-Core Based Temporal Graph Convolutional Network for Dynamic Graphs Abstract: Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information. However, many existing methods focus on static graphs while ignoring evolving graph patterns. Inspired by the success of graph convolutional networks(GCNs) in static graph embedding, we propose a novel k-core based temporal graph convolutional network, the CTGCN, to learn node representations for dynamic graphs. In contrast to previous dynamic graph embedding methods, CTGCN can preserve both local connective proximity and global structural similarity while simultaneously capturing graph dynamics. In the proposed framework, the traditional graph convolution is generalized into two phases, feature transformation and feature aggregation, which gives the CTGCN more flexibility and enables the CTGCN to learn connective and structural information under the same framework. Experimental results on 7 real-world graphs demonstrate that the CTGCN outperforms existing state-of-the-art graph embedding methods in several tasks, including link prediction and structural role classification. The source code of this work can be obtained from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jhljx/CTGCN</uri> .

Title: E2EET: from pipeline to end-to-end entity typing via transformer-based embeddings Abstract: Entity typing (ET) is the process of identifying the semantic types of every entity within a corpus. ET involves labelling each entity mention with one or more class labels. As a multi-class, multi-label task, it is considerably more challenging than named entity recognition. This means existing entity typing models require pre-identified mentions and cannot operate directly on plain text. Pipeline-based approaches are therefore used to join a mention extraction model and an entity typing model to process raw text. Another key limiting factor is that these mention-level ET models are trained on fixed context windows, which makes the entity typing results sensitive to window size selection. In light of these drawbacks, we propose an end-to-end entity typing model (E2EET) using a Bi-GRU to remove the dependency on window size. To demonstrate the effectiveness of our E2EET model, we created a stronger baseline mention-level model by incorporating the latest contextualised transformer-based embeddings (BERT). Extensive ablative studies demonstrate the competitiveness and simplicity of our end-to-end model for entity typing.

Title: Market Efficient Portfolios in a Systemic Economy Abstract: We study the ex ante minimization of market inefficiency, defined in terms of minimum deviation of market prices from fundamental values, from a centralized planner's perspective. Prices are pressured from exogenous trading actions of leveragetargeting banks, which rebalance their portfolios in response to asset shocks. We characterize market inefficiency in terms of two key drivers, the banks' systemic significance and the statistical moments of asset shocks, and develop an explicit expression for the matrix of asset holdings that minimizes such inefficiency. Our analysis shows that to reduce inefficiencies, portfolio holdings should deviate more from a full diversification strategy if there is little heterogeneity in banks' systemic significance.

Title: Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM Abstract: The Expectation Maximisation (EM) algorithm is widely used to optimise non-convex likelihood functions with latent variables. Many authors modified its simple design to fit more specific situations. For instance, the Expectation (E) step has been replaced by Monte Carlo (MC), Markov Chain Monte Carlo or tempered approximations, etc. Most of the well-studied approximations belong to the stochastic class. By comparison, the literature is lacking when it comes to deterministic approximations. In this paper, we introduce a theoretical framework, with state-of-the-art convergence guarantees, for any deterministic approximation of the E step. We analyse theoretically and empirically several approximations that fit into this framework. First, for intractable E-steps, we introduce a deterministic version of MC-EM using Riemann sums. A straightforward method, not requiring any hyper-parameter fine-tuning, useful when the low dimensionality does not warrant a MC-EM. Then, we consider the tempered approximation, borrowed from the Simulated Annealing literature and used to escape local extrema. We prove that the tempered EM verifies the convergence guarantees for a wider range of temperature profiles than previously considered. We showcase empirically how new non-trivial profiles can more successfully escape adversarial initialisations. Finally, we combine the Riemann and tempered approximations into a method that accomplishes both their purposes.

Title: Modelling High-Order Social Relations for Item Recommendation Abstract: The prevalence of online social network makes it compulsory to study how social relations affect user choice. However, most existing methods leverage only first-order social relations, that is, the direct neighbors that are connected to the target user. The high-order social relations, e.g., the friends of friends, which are very informative to reveal user preference, have been largely ignored. In this work, we focus on modeling the indirect influence from the high-order neighbors in social networks to improve the performance of item recommendation. Distinct from mainstream social recommenders that regularize the model learning with social relations, we instead propose to directly factor social relations in the predictive model, aiming at learning better user embeddings to improve recommendation. To address the challenge that high-order neighbors increase dramatically with the order size, we propose to recursively “propagate” embeddings along the social network, effectively injecting the influence of high-order neighbors into user representation. We conduct experiments on two real datasets of Yelp and Douban to verify our <i>High-Order Social Recommender</i> (HOSR) model. Empirical results show that our HOSR significantly outperforms recent graph regularization-based recommenders NSCR and IF-BPR <inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> , and graph convolutional network-based social influence prediction model DeepInf, achieving new state-of-the-arts of the task.

Title: Constrained Controller and Observer Design by Inverse Optimality Abstract: Model predictive control (MPC) is often tuned by trial and error. When a baseline linear controller exists that is already well tuned in the absence of constraints and MPC is introduced to enforce them, one would like to avoid altering the original linear feedback law whenever they are not active. We formulate this problem as a controller matching similar to the works of Di Cairano and Bemporad (2009), Di Cairano and Bemporad (2010), and Tran et al. (2015), which we extend to a more general framework. We prove that a positive-definite stage-cost matrix yielding this matching property can be computed for all stabilizing linear controllers. In addition, we prove that the constrained estimation problem can also be solved similarly, by matching a linear observer with a moving horizon estimator. Finally, we discuss various aspects of the practical implementation of the proposed technique in some examples.

Title: Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motion Abstract: We provide a comparative analysis of qualitative features of different numerical methods for the inhomogeneous geometric Brownian motion (IGBM). The limit distribution of the IGBM exists, its conditional and asymptotic mean and variance are known and the process can be characterised according to Feller’s boundary classification. We compare the frequently used Euler–Maruyama and Milstein methods, two Lie–Trotter and two Strang splitting schemes and two methods based on the ordinary differential equation (ODE) approach, namely the classical Wong–Zakai approximation and the recently proposed log-ODE scheme. First, we prove that, in contrast to the Euler–Maruyama and Milstein schemes, the splitting and ODE schemes preserve the boundary properties of the process, independently of the choice of the time discretisation step. Second, we prove that the limit distribution of the splitting and ODE methods exists for all stepsize values and parameters. Third, we derive closed-form expressions for the conditional and asymptotic means and variances of all considered schemes and analyse the resulting biases. While the Euler–Maruyama and Milstein schemes are the only methods which may have an asymptotically unbiased mean, the splitting and ODE schemes perform better in terms of variance preservation. The Strang schemes outperform the Lie–Trotter splittings, and the log-ODE scheme the classical ODE method. The mean and variance biases of the log-ODE scheme are very small for many relevant parameter settings. However, in some situations the two derived Strang splittings may be a better alternative, one of them requiring considerably less computational effort than the log-ODE method. The proposed analysis may be carried out in a similar fashion on other numerical methods and stochastic differential equations with comparable features.

Title: Error Bounds of Regularized Gap Functions for Polynomial Variational Inequalities Abstract: This paper is devoted to presenting new error bounds of regularized gap functions for polynomial variational inequalities with exponents explicitly determined by the dimension of the underlying space and the number/degree of the involved polynomials. The main techniques are based on semialgebraic geometry and variational analysis, which allow us to establish a nonsmooth extension of the seminal Łojasiewicz gradient inequality to regularized gap functions with explicitly calculated exponents.

Title: ELLIPTIC EQUATIONS WITH DEGENERATE WEIGHTS Abstract: We obtain new local Calder6n--Zygmund estimates for elliptic equations with matrixvalued weights for linear as well as nonlinear equations. We introduce a novel log-BMO condition on the weight M. In particular, we assume smallness of the logarithm of the matrix-valued weight in BMO. This allows us to include degenerate, discontinuous weights. The assumption on the smallness parameter is sharp and linear in terms of the integrability exponent of the gradient. This is a novelty even in the linear setting with nondegenerate weights compared to previously known results, where the dependency was exponential. We provide examples that show the sharpness of the estimates in terms of the log-BMO norm.

Title: Risk-Aware Submodular Optimization for Multirobot Coordination Abstract: We study the problem of incorporating risk while making combinatorial decisions under uncertainty. We formulate a discrete submodular maximization problem for selecting a set using conditional value at risk (CVaR), a risk metric commonly used in financial analysis. While the CVaR has recently been used in the optimization of linear cost functions in robotics, we take the first step toward extending this to discrete submodular optimization and provide several positive results. Specifically, we propose the sequential greedy algorithm that provides an approximation guarantee on finding the maxima of the CVaR cost function under a matroid constraint. The approximation guarantee shows that the solution produced by our algorithm is within a constant factor of the optimal and an additive term that depends on the optimal. Our analysis uses the curvature of the submodular set function and proves that the algorithm runs in polynomial time. This formulates a number of combinatorial optimization problems that appear in robotics. We use two such problems, i.e., vehicle assignment under uncertainty for mobility on demand and sensor selection with failures for environmental monitoring, as case studies to demonstrate the efficacy of our formulation. We also study the problem of adaptive risk-aware submodular maximization. We design a heuristic solution that triggers the replanning only when certain conditions are satisfied, to eliminate unnecessary planning. In particular, for the online mobility-on-demand study, we propose an adaptive triggering assignment algorithm that triggers a new assignment only when it can potentially reduce the waiting time at demand locations. We verify the performance of the proposed algorithms through simulations.

Title: Simple and robust contact-discontinuity capturing central algorithms for high speed compressible flows Abstract: The nonlinear convection terms in the governing equations of compressible fluid flows are hyperbolic in nature and are nontrivial for modelling and numerical simulation. Many numerical methods have been developed in the last few decades for this purpose and are typically based on Riemann solvers, which are strongly dependent on the underlying eigenstructure of the governing equations. Objective of the present work is to develop simple algorithms which are not dependent on the eigen-structure and yet can tackle easily the hyperbolic parts. Central schemes with smart diffusion mechanisms are apt for this purpose. For fixing the numerical diffusion, the basic ideas of satisfying the Rankine-Hugoniot (RH) conditions along with generalized Riemann invariants are proposed. Two such interesting algorithms are presented, which capture grid-aligned steady contact discontinuities exactly and yet have sufficient numerical diffusion to avoid numerical shock instabilities. Both the algorithms presented are robust in avoiding shock instabilities, apart from being accurate in capturing contact discontinuities, do not need wave speed corrections and are independent of eigen-struture of the underlying hyperbolic parts of the systems. (C) 2021 Elsevier Inc. All rights reserved.

Title: A model-free sampling method for basins of attraction using hybrid active learning (HAL) Abstract: Understanding basins of attraction (BoA) is often a paramount consideration for nonlinear systems. Most existing approaches for determining high-resolution BoA require prior knowledge of the system's underlying math model (e.g., differential equation or point mapping for continuous systems, cell mapping for discrete systems, etc.). These approaches, however, become impractical when a system's dynamics cannot be derived from first principles (e.g., modeling biological systems), or are approximate. This paper introduces a model-free sampling method to obtain BoA. The proposed method is based upon hybrid active learning (HAL) and is designed to find and label the "informative"samples, which efficiently determine the boundary for the BoA. The approach consists of three primary parts: (1) additional sampling on trajectories (AST) to maximize the number of samples obtained from each simulation or experiment; (2) an active learning (AL) algorithm to exploit the local BoA boundary; and (3) a density-based sampling (DBS) method to explore the global BoA boundary. An example of estimating the BoA for a bistable nonlinear system is presented to demonstrate the efficacy of the HAL sampling method.

Title: Points and lines configurations for perpendicular bisectors of convex cyclic polygons Abstract: We characterize the topological configurations of points and lines that may arise when placing n points on a circle and drawing the n perpendicular bisectors of the sides of the corresponding convex cyclic n-gon. We also provide exact and asymptotic formulas describing a random realizable configuration, obtained either by sampling the points uniformly at random on the circle or by sampling a realizable configuration uniformly at random.

Title: Driver Modeling Through Deep Reinforcement Learning and Behavioral Game Theory Abstract: In this work, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The modeling framework presented in this work can be used in a high-fidelity traffic simulator consisting of multiple human decision-makers. This simulator can reduce the time and effort spent for testing autonomous vehicles by allowing safe and quick assessment of self-driving control algorithms. To demonstrate the fidelity of the proposed modeling framework, game-theoretical driver models are compared with real human driver behavior patterns extracted from two different sets of traffic data.

Title: Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks With Imperfect Channels Abstract: This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels. In the heterogeneous networks, multiple wireless networks adopt different medium access control (MAC) protocols to share a common wireless spectrum and each network is unaware of the MACs of others. This paper aims to design a distributed deep reinforcement learning (DRL) based MAC protocol for a particular network, and the objective of this network is to achieve a global <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> -fairness objective. In the conventional DRL framework, feedback/reward given to the agent is always correctly received, so that the agent can optimize its strategy based on the received reward. In our wireless application where the channels are noisy, the feedback/reward (i.e., the ACK packet) may be lost due to channel noise and interference. Without correct feedback, the agent (i.e., the network user) may fail to find a good solution. Moreover, in the distributed protocol, each agent makes decisions on its own. It is a challenge to guarantee that the multiple agents will make coherent decisions and work together to achieve the same objective, particularly in the face of imperfect feedback channels. To tackle the challenge, we put forth (i) a feedback recovery mechanism to recover missing feedback information, and (ii) a two-stage action selection mechanism to aid coherent decision making to reduce transmission collisions among the agents. Extensive simulation results demonstrate the effectiveness of these two mechanisms. Last but not least, we believe that the feedback recovery mechanism and the two-stage action selection mechanism can also be used in general distributed multi-agent reinforcement learning problems in which feedback information on rewards can be corrupted.

Title: FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates Abstract: Many time series data mining problems can be solved with repeated use of distance measure. Examples of such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to use for most tasks, in most domains. Because the classic DTW algorithm has quadratic time complexity, many ideas have been introduced to reduce its amortized time, or to quickly approximate it. One of the most cited approximate approaches is FastDTW. The FastDTW algorithm has well over a thousand citations and has been explicitly used in several hundred research efforts. In this work, we make a surprising claim. In any realistic data mining application, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">approximate</i> FastDTW is much slower than the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exact</i> DTW. This fact clearly has implications for the community that uses this algorithm: allowing it to address much larger datasets, get exact results, and do so in less time.

Title: Leray numbers of complexes of graphs with bounded matching number Abstract: Given a graph G on the vertex set V, the non-matching complex of G, denoted by NMk(G), is the family of subgraphs G′⊂G whose matching number ν(G′) is strictly less than k. As an attempt to extend the result by Linusson, Shareshian and Welker on the homotopy types of NMk(Kn) and NMk(Kr,s) to arbitrary graphs G, we show that (i) NMk(G) is (3k−3)-Leray, and (ii) if G is bipartite, then NMk(G) is (2k−2)-Leray. This result is obtained by analyzing the homology of the links of non-empty faces of the complex NMk(G), which vanishes in all dimensions d≥3k−4, and all dimensions d≥2k−3 when G is bipartite. As a corollary, we have the following rainbow matching theorem which generalizes a result by Aharoni, Berger, Chudnovsky, Howard and Seymour: Let E1,…,E3k−2 be non-empty edge subsets of a graph and suppose that ν(Ei∪Ej)≥k for every i≠j. Then E=⋃Ei has a rainbow matching of size k. Furthermore, the number of edge sets Ei can be reduced to 2k−1 when E is the edge set of a bipartite graph.

Title: A Formalization of SQL with Nulls. Abstract: SQL is the world’s most popular declarative language, forming the basis of the multi-billion-dollar database industry. Although SQL has been standardized, the full standard is based on ambiguous natural language rather than formal specification. Commercial SQL implementations interpret the standard in different ways, so that, given the same input data, the same query can yield different results depending on the SQL system it is run on. Even for a particular system, mechanically checked formalization of all widely-used features of SQL remains an open problem. The lack of a well-understood formal semantics makes it very difficult to validate the soundness of database implementations. Although formal semantics for fragments of SQL were designed in the past, they usually did not support set and bag operations, lateral joins, nested subqueries, and, crucially, null values. Null values complicate SQL’s semantics in profound ways analogous to null pointers or side-effects in other programming languages. Since certain SQL queries are equivalent in the absence of null values, but produce different results when applied to tables containing incomplete data, semantics which ignore null values are able to prove query equivalences that are unsound in realistic databases. A formal semantics of SQL supporting all the aforementioned features was only proposed recently. In this paper, we report about our mechanization of SQL semantics covering set/bag operations, lateral joins, nested subqueries, and nulls, written in the Coq proof assistant, and describe the validation of key metatheoretic properties. Additionally, we are able to use the same framework to formalize the semantics of a flat relational calculus (with null values), and show a certified translation of its normal forms into SQL.

Title: Inter-Slice Mobility Management in 5G: Motivations, Standard Principles, Challenges, and Research Directions Abstract: Mobility management in a sliced 5G network introduces new and complex challenges. In a network-sliced environment, user mobility has to be managed among not only different base stations or access technologies but also different slices. Managing user mobility among slices, or inter-slice mobility, motivates the need for new solutions. This article, presented as a tutorial, focuses on the problem of...

Title: Exact discrete Lagrangian mechanics for nonholonomic mechanics Abstract: We construct the exponential map associated to a nonholonomic system that allows us to define an exact discrete nonholonomic constraint submanifold. We reproduce the continuous nonholonomic flow as a discrete flow on this discrete constraint submanifold deriving an exact discrete version of the nonholonomic equations. Finally, we derive a general family of nonholonomic integrators that includes as a particular case the exact discrete nonholonomic trajectory.

Title: Hybrid attention-based transformer block model for distant supervision relation extraction Abstract: With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information. As one basic task for natural language processing (NLP), relation extraction (RE) aims to extract semantic relations between entity pairs based on the given text. To avoid manual labeling of datasets, distant supervision relation extraction (DSRE) has been widely used, aiming to utilize knowledge base to automatically annotate datasets. Unfortunately, this method heavily suffers from wrong labelling due to its underlying strong assumptions. To address this issue, we propose a new framework using hybrid attention-based Transformer block with multi-instance learning for DSRE. More specifically, the Transformer block is, for the first time, used as a sentence encoder, which mainly utilizes multi-head self-attention to capture syntactic information at the word level. Then, a novel sentence-level attention mechanism is proposed to calculate the bag representation, aiming to exploit all useful information in each sentence. Experimental results on the public dataset New York Times (NYT) demonstrate that the proposed approach can outperform the state-of-the-art algorithms on the adopted dataset, which verifies the effectiveness of our model on the DSRE task.

Title: Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis Abstract: With the remarkable recent progress on learning deep generative models, it becomes increasingly interesting to develop models for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">controllable</i> image synthesis from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reconfigurable</i> structured inputs. This paper focuses on a recently emerged task, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">layout-to-image</i> , whose goal is to learn generative models for synthesizing photo-realistic images from a spatial layout (i.e., object bounding boxes configured in an image lattice) and its style codes (i.e., structural and appearance variations encoded by latent vectors). This paper first proposes an intuitive paradigm for the task, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">layout-to-mask-to-image</i> , which learns to unfold object masks in a weakly-supervised way based on an input layout and object style codes. The layout-to-mask component deeply interacts with layers in the generator network to bridge the gap between an input layout and synthesized images. Then, this paper presents a method built on Generative Adversarial Networks (GANs) for the proposed layout-to-mask-to-image synthesis with layout and style control at both image and object levels. The controllability is realized by a proposed novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Instance-Sensitive and Layout-Aware Normalization</i> (ISLA-Norm) scheme. A layout semi-supervised version of the proposed method is further developed without sacrificing performance. In experiments, the proposed method is tested in the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained.

Title: The Wavelet Compressibility of Compound Poisson Processes Abstract: In this paper, we precisely quantify the wavelet compressibility of compound Poisson processes. To that end, we expand the given random process over the Haar wavelet basis and we analyse its asymptotic approximation properties. By only considering the nonzero wavelet coefficients up to a given scale, what we call the greedy approximation, we exploit the extreme sparsity of the wavelet expansion th...

Title: Nonconvex Sparse Regularization for Deep Neural Networks and Its Optimality Abstract: Recent theoretical studies proved that deep neural network (DNN) estimators obtained by minimizing empirical risk with a certain sparsity constraint can attain optimal convergence rates for regression and classification problems. However, the sparsity constraint requires knowing certain properties of the true model, which are not available in practice. Moreover, computation is difficult due to the discrete nature of the sparsity constraint. In this letter, we propose a novel penalized estimation method for sparse DNNs that resolves the problems existing in the sparsity constraint. We establish an oracle inequality for the excess risk of the proposed sparse-penalized DNN estimator and derive convergence rates for several learning tasks. In particular, we prove that the sparse-penalized estimator can adaptively attain minimax convergence rates for various nonparametric regression problems. For computation, we develop an efficient gradient-based optimization algorithm that guarantees the monotonic reduction of the objective function.

Title: A Misreport- and Collusion-Proof Crowdsourcing Mechanism Without Quality Verification Abstract: Quality control plays a critical role in crowdsourcing. The state-of-the-art work is not suitable for crowdsourcing applications that require extensive validation of the tasks quality, since it is a long haul for the requestor to verify task quality or select professional workers in a one-by-one mode. In this paper, we propose a misreport- and collusion-proof crowdsourcing mechanism, guiding workers to truthfully report the quality of submitted tasks without collusion by designing a mechanism, so that workers have to act the way the requestor would like. In detail, the mechanism proposed by the requester makes no room for the workers to obtain profit through quality misreport and collusion, and thus, the quality can be controlled without any verification. Extensive simulation results verify the effectiveness of the proposed mechanism. Finally, the importance and originality of our work lie in that it reveals some interesting and even counterintuitive findings: 1) a high-quality worker may pretend to be a low-quality one; 2) the rise of task quality from high-quality workers may not result in the increased utility of the requestor; 3) the utility of the requestor may not get improved with the increasing number of workers. These findings can boost forward looking and strategic planning solutions for crowdsourcing.

Title: MEAN FIELD LIMITS OF PARTICLE-BASED STOCHASTIC REACTION-DIFFUSION MODELS Abstract: Particle-based stochastic reaction-diffusion (PBSRD) models are a popular approach for studying biological systems involving both noise in the reaction process and diffusive transport. In this work we derive coarse-grained deterministic partial integro-differential equation (PIDE) models that provide a mean field approximation to the volume reactivity PBSRD model, a model commonly used for studying cellular processes. We formulate a weak measure-valued stochastic process (MVSP) representation for the volume reactivity PBSRD model, demonstrating for a simplified but representative system that it is consistent with the commonly used Doi Fock space representation of the corresponding forward equation. We then prove the convergence of the general volume reactivity model MVSP to the mean field PIDEs in the large-population (i.e., thermodynamic) limit.

Title: Enabling Pulse-Level Programming, Compilation, and Execution in XACC Abstract: Noisy gate-model quantum processing units (QPUs) are currently available from vendors over the cloud, and digital quantum programming approaches exist to run low-depth circuits on physical hardware. These digital representations are ultimately lowered to pulse-level instructions by vendor quantum control systems to affect unitary evolution representative of the submitted digital circuit. Vendors are beginning to open this pulse-level control system to the public via specified interfaces. Robust programming methodologies, software frameworks, and backend simulation technologies for this analog model of quantum computation will prove critical to advancing pulse-level control research and development. Prototypical use cases for this include error mitigation, optimal pulse control, and physics-inspired pulse construction. Here we present an extension to the XACC quantum-classical software framework that enables pulse-level programming for superconducting, gate-model quantum computers, and a novel, general, and extensible pulse-level simulation backend for XACC that scales on classical compute clusters via MPI. Our work enables custom backend Hamiltonian definitions and gate-level compilation to available pulses with a focus on performance and scalability. We end with a demonstration of this capability, and show how to use XACC for pertinent pulse-level programming tasks.

Title: Secure Groupcast With Shared Keys Abstract: We consider a transmitter and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> receivers, each of which shares a key variable with the transmitter. Through a noiseless broadcast channel, the transmitter wishes to send a common message <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula> securely to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> out of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> receivers while the remaining <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K-N$ </tex-math></inline-formula> receivers learn no information about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula> . We are interested in the maximum message rate, i.e., the maximum number of bits of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula> that can be securely groupcast to the legitimate receivers per key block and the minimum broadcast bandwidth, i.e., the minimum number of bits of the broadcast information required to securely groupcast the message bits. We focus on the setting of combinatorial keys, where every subset of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> receivers share an independent key of arbitrary size. Under this combinatorial key setting, the maximum message rate is characterized for the following scenarios - 1) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=1$ </tex-math></inline-formula> or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=K-1$ </tex-math></inline-formula> , i.e., secure unicast to 1 receiver with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K-1$ </tex-math></inline-formula> eavesdroppers or secure groupcast to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K-1$ </tex-math></inline-formula> receivers with 1 eavesdropper, 2) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=2, K=4$ </tex-math></inline-formula> , i.e., secure groupcast to 2 out of 4 receivers, and 3) the symmetric setting where the key size for any subset of the same cardinality is equal for any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N,K$ </tex-math></inline-formula> . Further, for the latter two cases, the minimum broadcast bandwidth for the maximum message rate is characterized.

Title: The asymptotic distribution of cluster sizes for supercritical percolation on random split trees Abstract: We consider the model of random trees introduced by Devroye, the so-called random split trees. The model encompasses many important randomized algorithms and data structures. We then perform supercritical Bernoulli bond-percolation on those trees and obtain a precise weak limit theorem for the sizes of the largest clusters. We also show that the approach developed in this work may be useful for studying percolation on other classes of trees with logarithmic height, for instance, we also study the case of d-regular trees.

Title: Milking CowMask for Semi-supervised Image Classification Abstract: Consistency regularization is a technique for semi-supervised learning that underlies a number of strong results for classification with few labeled data. It works by encouraging a learned model to be robust to perturbations on unlabeled data. Here, we present a novel mask-based augmentation method called CowMask. Using it to provide perturbations for semi-supervised consistency regularization, we achieve a competitive result on ImageNet with 10% labeled data, with a top-5 error of 8.76% and top-1 error of 26.06%. Moreover, we do so with a method that is much simpler than many alternatives. We further investigate the behavior of CowMask for semi-supervised learning by running many smaller scale experiments on the SVHN, CIFAR-10 and CIFAR-100 data sets, where we achieve results competitive with the state of the art, indicating that CowMask is widely applicable. We open source our code at hfips://github.coin/google-research/google-research/tree/master/milking_cowmask.

Title: HERS: Homomorphically Encrypted Representation Search Abstract: We present a method to search for a probe (or query) image representation against a large gallery in the encrypted domain. We require that the probe and gallery images be represented in terms of a fixed-length representation, which is typical for representations obtained from learned networks. Our encryption scheme is agnostic to how the fixed-length representation is obtained and can therefore be applied to any fixed-length representation in any application domain. Our method, dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HERS</i> (Homomorphically Encrypted Representation Search), operates by (i) compressing the representation towards its estimated intrinsic dimensionality with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimal loss</i> of accuracy (ii) encrypting the compressed representation using the proposed fully homomorphic encryption scheme, and (iii) efficiently searching against a gallery of encrypted representations <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directly in the encrypted domain, without decrypting them</i> . Numerical results on large galleries of face, fingerprint, and object datasets such as ImageNet show that, for the first time, accurate and fast image search within the encrypted domain is feasible at scale (500 seconds; <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$275\times $ </tex-math></inline-formula> speed up over state-of-the-art for encrypted search against a gallery of 100 million). Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/human-analysis/hers-encrypted-image-search</uri> .

Title: Rolling Horizon Evolutionary Algorithms for General Video Game Playing Abstract: Game-playing evolutionary algorithms, specifically rolling horizon evolutionary algorithms (RHEA), have recently managed to beat the state of the art in win rate across many video games. However, the best results in a game are highly dependent on the specific configuration of modifications introduced over several papers, each adding additional parameters to the core algorithm. Furthermore, the best previously published parameters have been found from only a few human-picked combinations, as the possibility space has grown beyond exhaustive search. This article presents the state of the art in RHEA, combining all modifications described in the literature, as well as new ones. We then use a parameter optimizer, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -tuple bandit evolutionary algorithm, to find the best combination of parameters in 20 games from the general video game Artificial Intelligence (AI) framework. Furthermore, we analyze the algorithm’s parameters and some interesting combinations revealed through the optimization process. Finally, we find new state of the art solutions on several games by automatically exploring the large parameter space of RHEA.

Title: Bump functions with monotone Fourier transforms satisfying decay bounds Abstract: The existence of a smooth, nonnegative, compactly supported function with monotone (on the half-line) Fourier transform satisfying two-sided decay bounds is demonstrated.

Title: A Truthful Auction for Graph Job Allocation in Vehicular Cloud-Assisted Networks Abstract: Vehicular cloud computing has been emerged as a promising solution to fulfill users’ demands on processing computation-intensive applications in modern driving environments. Such applications are commonly represented by graphs consisting of components and edges. However, encouraging vehicles to share resources poses significant challenges owing to users’ selfishness. In this paper, an auction-based graph job allocation problem is studied in vehicular cloud-assisted networks considering resource reutilization. Our goal is to map each buyer (component) to a feasible seller (virtual machine) while maximizing the buyers’ utility-of-service, which concerns the execution time and commission cost. First, we formulate the auction-based graph job allocation as a 0-1 integer programming (0-1 IP) problem. Then, a Vickrey-Clarke-Groves based payment rule is proposed which satisfies the desired economical properties, truthfulness and individual rationality. We face two challenges: 1) the abovementioned 0-1 IP problem is NP-hard; 2) one constraint associated with the IP problem poses addressing the subgraph isomorphism problem. Thus, obtaining the optimal solution is practically infeasible in large-scale networks. Motivated by which, we develop a structure-preserved matching algorithm by maximizing the utility-of-service-gain, and the corresponding payment rule which offers economical properties and low computation complexity. Extensive simulations demonstrate that the proposed algorithm outperforms the contrast methods considering various problem sizes.

Title: Using the Split Bregman Algorithm to Solve the Self-repelling Snakes Model Abstract: Preserving contour topology during image segmentation is useful in many practical scenarios. By keeping the contours isomorphic, it is possible to prevent over-segmentation and under-segmentation, as well as to adhere to given topologies. The Self-repelling Snakes model (SR) is a variational model that preserves contour topology by combining a non-local repulsion term with the geodesic active contour model. The SR is traditionally solved using the additive operator splitting (AOS) scheme. In our paper, we propose an alternative solution to the SR using the Split Bregman method. Our algorithm breaks the problem down into simpler sub-problems to use lower-order evolution equations and a simple projection scheme rather than re-initialization. The sub-problems can be solved via fast Fourier transform or an approximate soft thresholding formula which maintains stability, shortening the convergence time, and reduces the memory requirement. The Split Bregman and AOS algorithms are compared theoretically and experimentally.

Title: Topology and Content Co-Alignment Graph Convolutional Learning Abstract: In traditional graph neural networks (GNNs), graph convolutional learning is carried out through topology-driven recursive node content aggregation for network representation learning. In reality, network topology and node content each provide unique and important information, and they are not always consistent because of noise, irrelevance, or missing links between nodes. A pure topology-driven feature aggregation approach between unaligned neighborhoods may deteriorate learning from nodes with poor structure-content consistency, due to the propagation of incorrect messages over the whole network. Alternatively, in this brief, we advocate a co-alignment graph convolutional learning (CoGL) paradigm, by aligning topology and content networks to maximize consistency. Our theme is to enforce the learning from the topology network to be consistent with the content network while simultaneously optimizing the content network to comply with the topology for optimized representation learning. Given a network, CoGL first reconstructs a content network from node features then co-aligns the content network and the original network through a unified optimization goal with: 1) minimized content loss; 2) minimized classification loss; and 3) minimized adversarial loss. Experiments on six benchmarks demonstrate that CoGL achieves comparable and even better performance compared with existing state-of-the-art GNN models.

Title: Combination Networks With End-User-Caches: Novel Achievable and Converse Bounds Under Uncoded Cache Placement Abstract: Caching is an efficient way to reduce network traffic congestion during peak hours by storing some content at the users’ local caches. For the shared-link network with end-user-caches, Maddah-Ali and Niesen proposed a two-phase coded caching strategy. In practice, users may communicate with the server through intermediate relays. This paper studies the tradeoff between the memory size <inline-form...

Title: Efficient particle smoothing for Bayesian inference in dynamic survival models Abstract: This article proposes an efficient Bayesian inference for piecewise exponential hazard (PEH) models, which allow the effect of a covariate on the survival time to vary over time. The proposed inference methodology is based on a particle smoothing algorithm that depends on three particle filters. Efficient proposal (importance) distributions for the particle filters tailored to the nature of survival data and PEH models are developed using the Laplace approximation of the posterior distribution and linear Bayes theory. The algorithm is applied to both simulated and real data, and the results show that it is faster and more efficient than a state-of-the-art MCMC sampler, and scales well in high-dimensional and relatively large data.

Title: The operating system of the neuromorphic BrainScaleS-1 system Abstract: BrainScaleS-1 is a wafer-scale mixed-signal accelerated neuromorphic system targeted for research in the fields of computational neuroscience and beyond-von-Neumann computing. Here we present the BrainScaleS Operating System (BrainScaleS OS): the software stack gives users the possibility to emulate networks described in the high-level network description language PyNN with minimal knowledge of the system, as well as expert usage facilitated by allowing access to the system at any depth of the stack. BrainScaleS OS has been used extensively in the commissioning and calibration of BrainScaleS-1 as well as in various neuromorphic experiments, e.g., rate-based deep learning, accelerated physical emulation of Bayesian inference, solving of SAT problems, and others. The tolerance to faults of individual components of the neuromorphic system is reflected in the mapping process based on information stored in an availability database. We evaluate the robustness and compensation mechanisms of the system and software stack. The software stack is designed with performance in mind, with its core implemented in C++ and most user-facing API wrapped automatically to Python. The implemented multi-FPGA orchestration allows for parallel configuration and synchronized experiments facilitating wafer-scale experiments. The initial configuration of a wafer-scale experiment with hundreds of neuromorphic ASICs is performed in a fraction of a minute. Subsequent experiments, that potentially change only a subset of parameters, can be executed with rates of typically 10Hz. The bandwidth from the host machine to the neuromorphic system is fully utilized starting from a quarter of the system’s FPGA count. Operation and development methodologies implemented for the BrainScaleS-1 neuromorphic architecture are presented and the individual components of BrainScaleS OS constituting the software stack for BrainScaleS-1 platform operation are detailed.

Title: Subgraph densities in a surface. Abstract: Given a fixed graph $H$ that embeds in a surface $\Sigma$, what is the maximum number of copies of $H$ in an $n$-vertex graph $G$ that embeds in $\Sigma$? We show that the answer is $\Theta(n^{f(H)})$, where $f(H)$ is a graph invariant called the `flap-number' of $H$, which is independent of $\Sigma$. This simultaneously answers two open problems posed by Eppstein (1993). When $H$ is a complete graph we give more precise answers.

Title: On Arithmetic Progressions in Model Sets Abstract: We establish the existence of arbitrary-length arithmetic progressions in model sets and Meyer sets in Euclidean d-space. We prove a van der Waerden-type theorem for Meyer sets. We show that subsets of Meyer sets with positive density and pure point diffraction contain arithmetic progressions of arbitrary length.

Title: Autonomous Cave Surveying With an Aerial Robot Abstract: This article presents a method for cave surveying in total darkness using an autonomous aerial vehicle equipped with a depth camera for mapping, downward-facing camera for state estimation, and forward and downward lights. Traditional methods of cave surveying are labor-intensive and dangerous due to the risk of injury when operating in darkness, and the potential structural instability of the sub...

Title: A formula on the weight distribution of linear codes with applications to AMDS codes Abstract: The determination of the weight distribution of linear codes has been a fascinating problem since the very beginning of coding theory. There has been a lot of research on weight enumerators of special cases, such as self-dual codes and codes with small Singleton's defect. We propose a new set of linear relations that must be satisfied by the coefficients of the weight distribution. From these relations we are able to derive known identities (in an easier way) for interesting cases, such as extremal codes, Hermitian codes, MDS and NMDS codes. Moreover, we are able to present for the first time the weight distribution of AMDS codes. We also discuss the link between our results and the Pless equations.

Title: Chevalley formula for anti-dominant minuscule fundamental weights in the equivariant quantum K-group of partial flag manifolds Abstract: In this paper, we give an explicit formula of Chevalley type, in terms of the Bruhat graph, for the quantum multiplication with the class of the line bundle associated to an anti-dominant minuscule fundamental weight −ϖk in the torus-equivariant quantum K-group of the partial flag manifold G/PJ (where J=I∖{k}) corresponding to the maximal (standard) parabolic subgroup PJ of minuscule type in type A, D, E, or B. This result is obtained by proving a similar formula in a torus-equivariant K-group of the semi-infinite partial flag manifold QJ of minuscule type, and then by making use of the isomorphism between the torus-equivariant quantum K-group of G/PJ and the torus-equivariant K-group of QJ, recently established by Kato.

Title: Stream/block ciphers, difference equations and algebraic attacks Abstract: In this paper we model a class of stream and block ciphers as systems of (ordinary) explicit difference equations over a finite field. We call this class “difference ciphers” and we show that ciphers of application interest, as for example systems of LFSRs with a combiner, Trivium and KeeLoq, belong to the class. By using Difference Algebra, that is, the formal theory of difference equations, we can properly define and study important properties of these ciphers, such as their invertibility and periodicity. We describe then general cryptanalytic methods for difference ciphers that follow from these properties and are useful to assess the security. We illustrate such algebraic attacks in practice by means of the ciphers Bivium and KeeLoq.

Title: Unsupervised Feature Learning Architecture With Multi-Clustering Integration RBM Abstract: In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM). In the multi-clustering integration module, we apply three clusterers (K-means, affinity propagation and spectral clustering algorithms) to obtain three different clustering partitions (CPs) witho...

Title: A <italic>k</italic>-Hop Collaborate Game Model: Extended to Community Budgets and Adaptive Nonsubmodularity Abstract: Revenue maximization (RM) is one of the most important problems in social networks, which attempts to find a small subset of users that make the expected revenue maximized. It has been studied in depth before. However, most of the existing literature was based on nonadaptive seeding strategies and simple information diffusion models. It considered the number of influenced users as a measurement unit to quantify the revenue. Until the emergence of the collaborate game model, it considered the activity as a basic object to compute the revenue. An activity initiated by a user can only influence those users whose distances are within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -hop from the initiator. Based on that, we adopt an adaptive seed strategy and formulate an RM under the size budget (RMSB) problem. If taking into account the product’s promotion, we extend it to an RM under the community budget problem, where the influence can be distributed over the whole network uniformly. We can prove that our objective function is adaptive monotone and not adaptive submodular, but it is adaptive submodular in some special cases. We study these two problems under both the special submodular cases and general nonsubmodular cases, and propose RMSBSolver and RMCBSolver to solve them with strong theoretical guarantees, respectively. In particular, we give a data-dependent approximation ratio by adaptive primal curvature for the RMSB in general nonsubmodular cases. Finally, we evaluate our proposed algorithms by conducting experiments on real datasets, and show the effectiveness and accuracy of our solutions.

Title: Riemannian Adaptive Optimization Algorithm and its Application to Natural Language Processing Abstract: This article proposes a Riemannian adaptive optimization algorithm to optimize the parameters of deep neural networks. The algorithm is an extension of both AMSGrad in Euclidean space and RAMSGrad on a Riemannian manifold. The algorithm helps to resolve two issues affecting RAMSGrad. The first is that it can solve the Riemannian stochastic optimization problem directly, in contrast to RAMSGrad which only achieves a low regret. The other is that it can use constant learning rates, which makes it implementable in practice. Additionally, we apply the proposed algorithm to Poincaré embeddings that embed the transitive closure of the WordNet nouns into the Poincaré ball model of hyperbolic space. Numerical experiments show that regardless of the initial value of the learning rate, our algorithm stably converges to the optimal solution and converges faster than the existing algorithms.

Title: Maximizing the expected number of components in an online search of a graph Abstract: The following optimal stopping problem is considered. The vertices of a graph G are revealed one by one, in a random order, to a selector. He aims to stop this process at a time t that maximizes the expected number of connected components in the graph (G) over tilde (t), induced by the currently revealed vertices. The selector knows G in advance, but different versions of the game are considered depending on the information that he gets about (G) over tilde (t). We show that when G has N vertices and maximum degree of order o(root N), then the number of components of (G) over tilde (t) is concentrated around its mean, which implies that playing the optimal strategy the selector does not benefit much by receiving more information about (G) over tilde (t). Results of similar nature were previously obtained by M. Lason for the case where G is a k-tree (for constant k). We also consider the particular cases where G is a square, triangular or hexagonal lattice, showing that an optimal selector gains cN components and we compute c with an error less than 0.005 in each case. (c) 2021 Elsevier B.V. All rights reserved. G tilde t, induced

Title: New complexity and approximability results for minimizing the total weighted completion time on a single machine subject to non-renewable resource constraints Abstract: We consider single machine scheduling problems with additional non-renewable resource constraints. Examples for non-renewable resources include raw materials, energy, or money. Usually they have an initial stock and replenishments arrive over time at a-priori known time points and quantities. The jobs have some requirements from the resources and a job can only be started if the available quantity from each of the required resources exceeds the requirements of the job. Upon starting a job, it consumes its requirements which decreases the available quantities of the respective non-renewable resources. There is a broad background for this class of problems. Most of the literature concentrate on the makespan, and the maximum lateness objectives. This paper focuses on the total weighted completion time objective for which the list of the approximation algorithms is very short. We extend that list by considering new special cases and obtain new complexity results and approximation algorithms. (c) 2022 Published by Elsevier B.V. <comment>Superscript/Subscript Available</comment

Title: Provable Sample Complexity Guarantees For Learning Of Continuous-Action Graphical Games With Nonparametric Utilities Abstract: In this paper, we study the problem of learning the exact structure of continuous-action games with non-parametric utility functions. We propose an $\ell_1$ regularized method which encourages sparsity of the coefficients of the Fourier transform of the recovered utilities. Our method works by accessing very few Nash equilibria and their noisy utilities. Under certain technical conditions, our method also recovers the exact structure of these utility functions, and thus, the exact structure of the game. Furthermore, our method only needs a logarithmic number of samples in terms of the number of players and runs in polynomial time. We follow the primal-dual witness framework to provide provable theoretical guarantees.