cssi/SciSciGPT-SciSciNet huggingface dataset:
Biometrics-as-a-service: A framework to promote innovative biometric recognition in the cloud

Biometrie recognition, or simply biometrics, is the use of biological attributes such as face, fingerprints or iris in order to recognize an individual in an automated manner. A key application of biometrics is authentication; i.e., using said biological attributes to provide access by verifying the claimed identity of an individual. This paper presents a framework for Biometrics-as-a-Service (BaaS) that performs biometric matching operations in the cloud, while relying on simple and ubiquitous consumer devices such as smartphones. Further, the framework promotes innovation by providing interfaces for a plurality of software developers to upload their matching algorithms to the cloud. When a biometric authentication request is submitted, the system uses a criteria to automatically select an appropriate matching algorithm. Every time a particular algorithm is selected, the corresponding developer is rendered a micropayment. This creates an innovative and competitive ecosystem that benefits both software developers and the consumers. As a case study, we have implemented the following: (a) an ocular recognition system using a mobile web interface providing user access to a biometric authentication service, and (b) a Linux-based virtual machine environment used by software developers for algorithm development and submission.
Reconstruction of directed networks from consensus dynamics

This paper addresses the problem of identifying the topology of an unknown, weighted, directed network running a consensus dynamics. We propose a methodology to reconstruct the network topology from the dynamic response when the system is stimulated by a wide-sense stationary noise of unknown power spectral density. The method is based on a node-knockout, or grounding, procedure wherein the grounded node broadcasts zero without being eliminated from the network. In this direction, we measure the empirical cross-power spectral densities of the outputs between every pair of nodes for both grounded and ungrounded consensus to reconstruct the unknown topology of the network. We also establish that in the special cases of undirected and purely unidirectional networks, the reconstruction does not need grounding. Finally, we extend our results to the case of a directed network assuming a general dynamics, and prove that the developed method can detect edges and their direction.
On the zeros of random harmonic polynomials: the Weyl model

Li and Wei (Proc Am Math Soc 137:195–204, 2009) studied the density of zeros of Gaussian harmonic polynomials with independent Gaussian coefficients. They derived a formula for the expected number of zeros of random harmonic polynomials as well as asymptotics for the case that the polynomials are drawn from the Kostlan ensemble. In this paper we extend their work to cover the case that the polynomials are drawn from the Weyl ensemble by deriving asymptotics for this class of harmonic polynomials.
A subanalytic triangulation theorem for real analytic orbifolds

Abstract Let X be a real analytic orbifold. Then each stratum of X is a subanalytic subset of X. We show that X has a unique subanalytic triangulation compatible with the strata of X. We also show that every C r -orbifold, 1 ⩽ r ⩽ ∞ , has a real analytic structure. This allows us to triangulate differentiable orbifolds. The results generalize the subanalytic triangulation theorems previously known for quotient orbifolds.
Nonlinear Instability Theory of Lane‐Emden Stars

We establish a nonlinear instability of the Euler-Poisson system for polytropic gases whose adiabatic exponents take value in around the Lane-Emden equilibrium star configurations. © 2014 Wiley Periodicals, Inc.
Robust max-product belief propagation

We study the problem of optimizing a graph-structured objective function under adversarial uncertainty. This problem can be modeled as a two-persons zero-sum game between an Engineer and Nature. The Engineer controls a subset of the variables (nodes in the graph), and tries to assign their values to maximize an objective function. Nature controls the complementary subset of variables and tries to minimize the same objective. This setting encompasses estimation and optimization problems under model uncertainty, and strategic problems with a graph structure. Von Neumann's minimax theorem guarantees the existence of a (minimax) pair of randomized strategies that provide optimal robustness for each player against its adversary. We prove several structural properties of this strategy pair in the case of graph-structured payoff function. In particular, the randomized minimax strategies (distributions over variable assignments) can be chosen in such a way to satisfy the Markov property with respect to the graph. This significantly reduces the problem dimensionality. Finally we introduce a message passing algorithm to solve this minimax problem. The algorithm generalizes max-product belief propagation to this new domain.
Johnson-Mehl Cell-Based Analysis of UL Cellular Network with Coupled User and BS Locations

In this work, we analyze the performance of the uplink (UL) of a cellular network where the base station (BS) locations follow a homogeneous Poisson point process (PPP), and the locations of users and BSs are spatially coupled. In order to capture this coupling, we consider that users attached to a BS are located uniformly at random independently of each other in the Johnson-Mehl (JM) cell of that BS. For this system model, we derive analytical expressions for the UL signal to interference ratio (SIR) coverage probability and average spectral efficiency (SE) of a typical user in the network. One of the key intermediate steps in our analysis is the approximate, but accurate, characterization of the area distribution of a typical JM cell. Another key intermediate step is the accurate statistical characterization of the point process formed by the interfering users that subsequently enables the coverage probability analysis. We present coverage probability and SE results for a typical user and study the interplay between different system parameters.
Traces for homogeneous Sobolev spaces in infinite strip-like domains

Abstract In this paper we construct a trace operator for homogeneous Sobolev spaces defined on infinite strip-like domains. We identify an intrinsic seminorm on the resulting trace space that makes the trace operator bounded and allows us to construct a bounded right inverse. The intrinsic seminorm involves two features not encountered in the trace theory of bounded Lipschitz domains or half-spaces. First, due to the strip-like structure of the domain, the boundary splits into two infinite disconnected components. The traces onto each component are not completely independent, and the intrinsic seminorm contains a term that measures the difference between the two traces. Second, as in the usual trace theory, there is a term in the seminorm measuring the fractional Sobolev regularity of the trace functions with a difference quotient integral. However, the finite width of the strip-like domain gives rise to a screening effect that bounds the range of the difference quotient perturbation. The screened homogeneous fractional Sobolev spaces defined by this screened seminorm on arbitrary open sets are of independent interest, and we study their basic properties. We conclude the paper with applications of the trace theory to partial differential equations.
On Variants of k-means Clustering

Clustering problems often arise in fields like data mining and machine learning. Clustering usually refers to the task of partitioning a collection of objects into groups with similar elements, with respect to a similarity (or dissimilarity) measure. Among the clustering problems, k-means clustering in particular has received much attention from researchers. Despite the fact that k-means is a well studied problem, its status in the plane is still open. In particular, it is unknown whether it admits a PTAS in the plane. The best known approximation bound achievable in polynomial time is 9+epsilon. In this paper, we consider the following variant of k-means. Given a set C of points in R^d and a real f > 0, find a finite set F of points in R^d that minimizes the quantity f*|F|+sum_{p in C} min_{q in F} {||p-q||}^2. For any fixed dimension d, we design a PTAS for this problem that is based on local search. We also give a "bi-criterion" local search algorithm for k-means which uses (1+epsilon)k centers and yields a solution whose cost is at most (1+epsilon) times the cost of an optimal k-means solution. The algorithm runs in polynomial time for any fixed dimension. The contribution of this paper is two-fold. On the one hand, we are able to handle the square of distances in an elegant manner, obtaining a near-optimal approximation bound. This leads us towards a better understanding of the k-means problem. On the other hand, our analysis of local search might also be useful for other geometric problems. This is important considering that little is known about the local search method for geometric approximation.
The classification of generalized Riemann derivatives

We characterize all pairs $(\mathcal{A}$,$\mathcal{B})$ of generalized Riemann differences for which $\mathcal{A}$-differentiability implies $\mathcal{B}$-differentiability. Two generalized Riemann derivatives $\mathcal{A}$ and $\mathcal{B}$ are equivalent if a function has a derivative in the sense of $\mathcal{A}$ at a real number $x$ if and only if it has a derivative in the sense of $\mathcal{B}$ at $x$. We determine the equivalence classes for this equivalence relation.
An empirical evaluation of social influence metrics

Predicting when an individual will adopt a new behavior is an important problem in application domains such as marketing and public health. This paper examines the performance of a wide variety of social network based measurements proposed in the literature - which have not been previously compared directly. We study the probability of an individual becoming influenced based on measurements derived from neighborhood (i.e. number of influencers, personal network exposure), structural diversity, locality, temporal measures, cascade measures, and metadata. We also examine the ability to predict influence based on choice of classifier and how the ratio of positive to negative samples in both training and testing affect prediction results - further enabling practical use of these concepts for social influence applications.
Fine-Grained Lesion Annotation in CT Images With Knowledge Mined From Radiology Reports

In radiologists’ routine work, one major task is to read a medical image, e.g., a CT scan, find significant lesions, and write sentences in the radiology report to describe them. In this paper, we study the lesion description or annotation problem as an important step of computer-aided diagnosis (CAD). Given a lesion image, our aim is to predict multiple relevant labels, such as the lesion’s body part, type, and attributes. To address this problem, we define a set of 145 labels based on RadLex to describe a large variety of lesions in the DeepLesion dataset. We directly mine training labels from the lesion’s corresponding sentence in the radiology report, which requires minimal manual effort and is easily generalizable to large data and label sets. A multi-label convolutional neural network is then proposed for images with multi-scale structure and a noise-robust loss. Quantitative and qualitative experiments demonstrate the effectiveness of the framework. The average area under ROC curve on 1,872 test lesions is 0.9083.
Weak Convergence Results for Multiple Generations of a Branching Process

We establish limit theorems involving weak convergence of multiple generations of critical and supercritical branching processes. These results arise naturally when dealing with the joint asymptotic behavior of functionals defined in terms of several generations of such processes. Applications of our main result include a functional central limit theorem (CLT), a Darling–Erdos result, and an extremal process result. The limiting process for our functional CLT is an infinite dimensional Brownian motion with sample paths in the infinite product space (C 0[0,1])∞, with the product topology, or in Banach subspaces of (C 0[0,1])∞ determined by norms related to the distribution of the population size of the branching process. As an application of this CLT we obtain a central limit theorem for ratios of weighted sums of generations of a branching processes, and also to various maximums of these generations. The Darling–Erdos result and the application to extremal distributions also include infinite-dimensional limit laws. Some branching process examples where the CLT fails are also included.
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation

Maximum a posteriori (MAP) inference over discrete Markov random fields is a fundamental task spanning a wide spectrum of real-world applications, which is known to be NP-hard for general graphs. In this paper, we propose a novel semidefinite relaxation formulation (referred to as SDR) to estimate the MAP assignment. Algorithmically, we develop an accelerated variant of the alternating direction method of multipliers (referred to as SDPAD-LR) that can effectively exploit the special structure of the new relaxation. Encouragingly, the proposed procedure allows solving SDR for large-scale problems, e.g., problems on a grid graph comprising hundreds of thousands of variables with multiple states per node. Compared with prior SDP solvers, SDPAD-LR is capable of attaining comparable accuracy while exhibiting remarkably improved scalability, in contrast to the commonly held belief that semidefinite relaxation can only been applied on small-scale MRF problems. We have evaluated the performance of SDR on various benchmark datasets including OPENGM2 and PIC in terms of boththe quality of the solutions and computation time. Experimental results demonstrate that for a broad class of problems, SDPAD-LR outperforms state-of-the-art algorithms in producing better MAP assignments in an efficient manner.
CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis

We propose and evaluate a new technique for learning hybrid automata automatically by observing the runtime behavior of a dynamical system. Working from a sequence of continuous state values and predicates about the environment, CHARDA recovers the distinct dynamic modes, learns a model for each mode from a given set of templates, and postulates causal guard conditions which trigger transitions between modes. Our main contribution is the use of information-theoretic measures (1)~as a cost function for data segmentation and model selection to penalize over-fitting and (2)~to determine the likely causes of each transition. CHARDA is easily extended with different classes of model templates, fitting methods, or predicates. In our experiments on a complex videogame character, CHARDA successfully discovers a reasonable over-approximation of the character's true behaviors. Our results also compare favorably against recent work in automatically learning probabilistic timed automata in an aircraft domain: CHARDA exactly learns the modes of these simpler automata.
Automatic Construction of Evaluation Sets and Evaluation of Document Similarity Models in Large Scholarly Retrieval Systems.

Retrieval systems for scholarly literature offer the ability for the scientific community to search, explore and download scholarly articles across various scientific disciplines. Mostly used by the experts in the particular field, these systems contain user community logs including information on user specific downloaded articles. In this paper we present a novel approach for automatically evaluating document similarity models in large collections of scholarly publications. Unlike typical evaluation settings that use test collections consisting of query documents and human annotated relevance judgments, we use download logs to automatically generate pseudo-relevant set of similar document pairs. More specifically we show that consecutively downloaded document pairs, extracted from a scholarly information retrieval (IR) system, could be utilized as a test collection for evaluating document similarity models. Another novel aspect of our approach lies in the method that we employ for evaluating the performance of the model by comparing the distribution of consecutively downloaded document pairs and random document pairs in log space. Across two families of similarity models, that represent documents in the term vector and topic spaces, we show that our evaluation approach achieves very high correlation with traditional performance metrics such as Mean Average Precision (MAP), while being more efficient to compute.
Modeling Supervisor Safe Sets for Improving Collaboration in Human-Robot Teams

When a human supervisor collaborates with a team of robots, the human's attention is divided, and cognitive resources are at a premium. We aim to optimize the distribution of these resources and the flow of attention. To this end, we propose the model of an idealized supervisor to describe human behavior. Such a supervisor employs a potentially inaccurate internal model of the the robots' dynamics to judge safety. We represent these safety judgements by constructing a safe set from this internal model using reachability theory. When a robot leaves this safe set, the idealized supervisor will intervene to assist, regardless of whether or not the robot remains objectively safe. False positives, where a human supervisor incorrectly judges a robot to be in danger, needlessly consume supervisor attention. In this work, we propose a method that decreases false positives by learning the supervisor's safe set and using that information to govern robot behavior. We prove that robots behaving according to our approach will reduce the occurrence of false positives for our idealized supervisor model. Furthermore, we empirically validate our approach with a user study that demonstrates a significant (p == 0.0328) reduction in false positives for our method compared to a baseline safety controller.
Inferring Human Traits from Facebook Statuses

This paper explores the use of language models to predict 20 human traits from users’ Facebook status updates. The data was collected by the myPersonality project, and includes user statuses along with their personality, gender, political identification, religion, race, satisfaction with life, IQ, self-disclosure, fair-mindedness, and belief in astrology. A single interpretable model meets state of the art results for well-studied tasks such as predicting gender and personality; and sets the standard on other traits such as IQ, sensational interests, political identity, and satisfaction with life. Additionally, highly weighted words are published for each trait. These lists are valuable for creating hypotheses about human behavior, as well as for understanding what information a model is extracting. Using performance and extracted features we analyze models built on social media. The real world problems we explore include gendered classification bias and Cambridge Analytica’s use of psychographic models.
Constructing large k-systems on surfaces

Abstract Let S g denote the genus g closed orientable surface. For k ∈ N , a k-system is a collection of pairwise non-homotopic simple closed curves such that no two intersect more than k times. Juvan–Malnic–Mohar [3] showed that there exists a k-system on S g whose size is on the order of g k / 4 . For each k ≥ 2 , we construct a k-system on S g with on the order of g ⌊ ( k + 1 ) / 2 ⌋ + 1 elements. The k-systems we construct behave well with respect to subsurface inclusion, analogously to how a pants decomposition contains pants decompositions of lower complexity subsurfaces.
Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks

Scalability properties of deep neural networks raise key research questions, particularly as the problems considered become larger and more challenging. This paper expands on the idea of conditional computation introduced in [2], where the nodes of a deep network are augmented by a set of gating units that determine when a node should be calculated. By factorizing the weight matrix into a low-rank approximation, an estimation of the sign of the pre-nonlinearity activation can be efficiently obtained. For networks using rectified-linear hidden units, this implies that the computation of a hidden unit with an estimated negative pre-nonlinearity can be omitted altogether, as its value will become zero when nonlinearity is applied. For sparse neural networks, this can result in considerable speed gains. Experimental results using the MNIST and SVHN data sets with a fully-connected deep neural network demonstrate the performance robustness of the proposed scheme with respect to the error introduced by the conditional computation process.
Exponential expressivity in deep neural networks through transient chaos

We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in deep neural networks with random weights. Our results reveal a phase transition in the expressivity of random deep networks, with networks in the chaotic phase computing nonlinear functions whose global curvature grows exponentially with depth, but not with width. We prove that this generic class of random functions cannot be efficiently computed by any shallow network, going beyond prior work that restricts their analysis to single functions. Moreover, we formally quantify and demonstrate the long conjectured idea that deep networks can disentangle exponentially curved manifolds in input space into flat manifolds in hidden space. Our theoretical framework for analyzing the expressive power of deep networks is broadly applicable and provides a basis for quantifying previously abstract notions about the geometry of deep functions.
Robust Learning of Fixed-Structure Bayesian Networks

We investigate the problem of learning Bayesian networks in a robust model where an $\epsilon$-fraction of the samples are adversarially corrupted. In this work, we study the fully observable discrete case where the structure of the network is given. Even in this basic setting, previous learning algorithms either run in exponential time or lose dimension-dependent factors in their error guarantees. We provide the first computationally efficient robust learning algorithm for this problem with dimension-independent error guarantees. Our algorithm has near-optimal sample complexity, runs in polynomial time, and achieves error that scales nearly-linearly with the fraction of adversarially corrupted samples. Finally, we show on both synthetic and semi-synthetic data that our algorithm performs well in practice.
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction.

Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones. However, these methods are computationally wasteful in attempt to predict 3D shapes, where information is rich only on the surfaces. In this paper, we propose a novel 3D generative modeling framework to efficiently generate object shapes in the form of dense point clouds. We use 2D convolutional operations to predict the 3D structure from multiple viewpoints and jointly apply geometric reasoning with 2D projection optimization. We introduce the pseudo-renderer, a differentiable module to approximate the true rendering operation, to synthesize novel depth maps for optimization. Experimental results for single-image 3D object reconstruction tasks show that we outperforms state-of-the-art methods in terms of shape similarity and prediction density.
CATERPILLAR: Coarse Grain Reconfigurable Architecture for accelerating the training of Deep Neural Networks

Accelerating the inference of a trained DNN is a well studied subject. In this paper we switch the focus to the training of DNNs. The training phase is compute intensive, demands complicated data communication, and contains multiple levels of data dependencies and parallelism. This paper presents an algorithm/architecture space exploration of efficient accelerators to achieve better network convergence rates and higher energy efficiency for training DNNs. We further demonstrate that an architecture with hierarchical support for collective communication semantics provides flexibility in training various networks performing both stochastic and batched gradient descent based techniques. Our results suggest that smaller networks favor non-batched techniques while performance for larger networks is higher using batched operations. At 45nm technology, CATERPILLAR achieves performance efficiencies of 177 GFLOPS/W at over 80% utilization for SGD training on small networks and 211 GFLOPS/W at over 90% utilization for pipelined SGD/CP training on larger networks using a total area of 103.2 mm2 and 178.9 mm2 respectively.
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood

Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct result, while not being misled by spurious programs: incorrect programs that coincidentally output the correct result. We connect two common learning paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML), and then present a new learning algorithm that combines the strengths of both. The new algorithm guards against spurious programs by combining the systematic search traditionally employed in MML with the randomized exploration of RL, and by updating parameters such that probability is spread more evenly across consistent programs. We apply our learning algorithm to a new neural semantic parser and show significant gains over existing state-of-the-art results on a recent context-dependent semantic parsing task.
Regularity of Solutions to a Parabolic Free Boundary Problem with Variable Coefficients

Abstract In this work the optimal Lipschitz regularity of viscosity solutions for a type of parabolic free boundary problem with variable coefficients is proved under the main assumption that the free boundary is Lipschitz.
Detecting Friendship Within Dynamic Online Interaction Networks

In many complex social systems, the timing and frequency of interactions between individuals are observable but friendship ties are hidden. Here, we investigate the accuracy of multiple statistical features, based either purely on temporal interaction patterns or on the cooperative nature of the interactions, for automatically extracting latent social ties. Using self-reported friendship and non-friendship labels derived from an anonymous online survey, we learn highly accurate predictors for recovering hidden friendships within a massive online data set encompassing 18 billion interactions among 17 million individuals of the popular online game Halo: Reach. We find that periodicities in interaction time series are sufficient to correctly classify 95% of ties, even for casual users. These results clarify the nature of friendship in online social environments and suggest new opportunities and new privacy concerns for friendship-aware applications that do not require the disclosure of private friendship information.
Mixed volumes and the Bochner method

At the heart of convex geometry lies the observation that the volume of convex bodies behaves as a polynomial. Many geometric inequalities may be expressed in terms of the coefficients of this polynomial, called mixed volumes. Among the deepest results of this theory is the Alexandrov-Fenchel inequality, which subsumes many known inequalities as special cases. The aim of this note is to give new proofs of the Alexandrov-Fenchel inequality and of its matrix counterpart, Alexandrov's inequality for mixed discriminants, that appear conceptually and technically simpler than earlier proofs and clarify the underlying structure. Our main observation is that these inequalities can be reduced by the spectral theorem to certain trivial `Bochner formulas'.
Affine Cellularity of Khovanov–Lauda–Rouquier Algebras of Finite Types

We prove that the Khovanov-Lauda-Rouquier algebras $R_\alpha$ of finite type are (graded) affine cellular in the sense of Koenig and Xi. In fact, we establish a stronger property, namely that the affine cell ideals in $R_\alpha$ are generated by idempotents. This in particular implies the (known) result that the global dimension of $R_\alpha$ is finite.
Spheroidal groups, virtual cohomology and lower dimensional G-spaces

A space is defined to be “n-spheroidal” if it has the homotopy type of an n-dimensional CW-complex X with \(H_{n}(X; \mathbb {Z})\) not zero and finitely generated. A group G is called “n-spheroidal” if its classifying space K(G, 1) is n-spheroidal. Examples include fundamental groups of compact manifold K(G, 1)s. Moreover, the class of groups G, which are n-spheroidal for some n, is closed under products, free products, and group extensions. If Y is a space with \(\pi _{1}(Y)\)n-spheroidal, and if \(H_{k}(Y;\mathbb {F}_{p})\) is non-zero and finitely generated, and if \(H_{i}(Y;\mathbb {F}_{p}) = 0\) for \(i>k\), then \(H_{n+k}(\overline{Y};\mathbb {F}_{p}) \ne 0\) for \(\overline{Y}\) a finite sheeted covering space of Y. Hence, dim\((Y) \ge n+k\). Thus, it follows that if dim\((Y) k>0\), then \(H_{k}(Y;\mathbb {F}_{p})\) is not finitely generated. Similar results follow for \(Y\subset K(G,1)\).
A delay-aware caching algorithm for wireless D2D caching networks

Recently, wireless caching techniques have been studied to satisfy lower delay requirements and offload traffic from peak periods. By storing parts of the popular files at the mobile users, users can locate some of their requested files in their own caches or the caches at their neighbors. In the latter case, when a user receives files from its neighbors, device-to-device (D2D) communication is enabled. D2D communication underlaid with cellular networks is also a new paradigm for the upcoming 5G wireless systems. By allowing a pair of adjacent D2D users to communicate directly, D2D communication can achieve higher throughput, better energy efficiency and lower traffic delay. In this work, we propose a very efficient caching algorithm for D2D-enabled cellular networks to minimize the average transmission delay. Instead of searching over all possible solutions, our algorithm finds out the best pairs, which provide the best delay improvement in each loop to form a caching policy with very low transmission delay and high throughput. This algorithm is also extended to address a more general scenario, in which the distributions of fading coefficients and values of system parameters potentially change over time. Via numerical results, the superiority of the proposed algorithm is verified by comparing it with a naive algorithm, in which all users simply cache their favorite files.
Adaptive Power System Emergency Control Using Deep Reinforcement Learning

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived “worst” case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL) by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named Reinforcement Learning for Grid Control (RLGC) has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Robustness of the developed DRL method to different simulation scenarios, model parameter uncertainty and noise in the observations is investigated. Extensive case studies performed in both the two-area, four-machine system and the IEEE 39-bus system have demonstrated excellent performance and robustness of the proposed schemes.
Flipping Cubical Meshes

We define and examine flip operations for quadrilateral and hexahedral meshes, similar to the flipping transformations previously used in triangular and tetrahedral mesh generation.
Zhang Neural Networks for fast and accurate computations of the field of values

In this paper, a new and different neural network, called Zhang Neural Network (ZNN) is appropriated from discrete time-varying matrix problems and applied to the angle parameter-varying matrix fie...
Germ Granules Coordinate RNA-Based Epigenetic Inheritance Pathways.

Summary Germ granules are biomolecular condensates that promote germ cell totipotency in animals. In C. elegans, MEG-3 and MEG-4 function redundantly to assemble germ granules in germline blastomeres. Here, we show that meg-3/4 mutant animals exhibit defects in RNA interference (RNAi) that are transgenerationally disconnected from the meg-3/4 genotype. Similar non-Mendelian inheritance is associated with other mutations disrupting germ granule formation, indicating that loss of germ granules is the likely cause of the observed disconnects between genotype and phenotype. meg-3/4 animals produce aberrant siRNAs that are propagated for ≅10 generations in wild-type descendants of meg-3/4 ancestors. Aberrant siRNAs inappropriately and heritably silence germline-expressed genes including the RNAi gene sid-1, suggesting that transgenerational silencing of sid-1 underlies inherited defects in RNAi. We conclude that one function of germ granules is to organize RNA-based epigenetic inheritance pathways and that germ granule loss has consequences that persist for many generations.
Exponentially convergent stochastic k-PCA without variance reduction

We present Matrix Krasulina, an algorithm for online k-PCA, by gen- eralizing the classic Krasulina’s method (Krasulina, 1969) from vector to matrix case. We show, both theoretically and empirically, that the algorithm naturally adapts to data low-rankness and converges exponentially fast to the ground-truth principal subspace. Notably, our result suggests that despite various recent efforts to accelerate the convergence of stochastic-gradient based methods by adding a O(n)-time variance reduction step, for the k- PCA problem, a truly online SGD variant suffices to achieve exponential convergence on intrinsically low-rank data.
Arx: an encrypted database using semantically secure encryption

In recent years, encrypted databases have emerged as a promising direction that provides data confidentiality without sacrificing functionality: queries are executed on encrypted data. However, many practical proposals rely on a set of weak encryption schemes that have been shown to leak sensitive data. In this paper, we propose Arx, a practical and functionally rich database system that encrypts the data only with semantically secure encryption schemes. We show that Arx supports real applications such as ShareLaTeX with a modest performance overhead.
Rigidity of singularities of 2D gravity water waves

Abstract We consider the Cauchy problem for the 2D gravity water wave equation. Recently Wu [27] , [28] proved the local well-posedness of the equation in a regime which allows interfaces with angled crests as initial data. In this work we study properties of these singular solutions and prove that the singularities of these solutions are “rigid”. More precisely we prove that an initial interface with angled crests remains angled crested, the Euler equation holds point-wise even on the boundary, the particle at the tip stays at the tip, the acceleration at the tip is the one due to gravity and the angle of the crest does not change nor does it tilt. We also show that the local well-posedness result of Wu [27] , [28] and our rigidity results are applicable not only to interfaces with angled crests, but also to certain types of cusps.
Price stabilization, short selling, and IPO secondary market liquidity

Abstract Prior research shows that failures to deliver, which commonly occur around initial public offerings (IPOs), typically result from underwriter price stabilization. Additionally, investors often establish short positions in IPO stocks that are unrelated to underwriter price stabilization. We study the relation between failures to deliver, short sales, and IPO trading costs. We find that failures to deliver are associated with greater liquidity, especially for IPOs likely to receive underwriter price support. However, contrary to prior research that finds that short selling is associated with greater liquidity, we find that short selling is negatively correlated with liquidity for IPO stocks with strong investor demand.
Thermofield Theory for Finite-Temperature Coupled Cluster.

We present a coupled cluster and linear response theory to compute properties of many-electron systems at nonzero temperatures. For this purpose, we make use of the thermofield dynamics, which allows for a compact wave function representation of the thermal density matrix, and extend our recently developed framework ( J. Chem. Phys. 2019, 150, 154109, DOI: 10.1063/1.5089560) to parametrize the so-called thermal state using an exponential ansatz with cluster operators that create thermal quasiparticle excitations on a mean-field reference. As benchmark examples, we apply this method to both model (one-dimensional Hubbard and Pairing) and ab initio (atomic Beryllium and molecular Hydrogen) systems, while comparing with exact results.
Imaging of morphological and biochemical hallmarks of apoptosis with optimized optogenetic tools.

Creation of optogenetic switches for specific activation of cell death pathways can provide insights into apoptosis and could also form a basis for noninvasive, next-generation therapeutic strategies. Previous work has demonstrated that cryptochrome 2 (Cry2)/cryptochrome-interacting β helix–loop–helix (CIB), a blue light–activated protein–protein dimerization module from the plant Arabidopsis thaliana, together with BCL2-associated X apoptosis regulator (BAX), an outer mitochondrial membrane–targeting pro-apoptotic protein, can be used for light-mediated initiation of mitochondrial outer membrane permeabilization (MOMP) and downstream apoptosis. In this work, we further developed the original light-activated Cry2-BAX system (hereafter referred to as OptoBAX) by improving the photophysical properties and light-independent interactions of this optogenetic switch. The resulting optogenetic constructs significantly reduced the frequency of light exposure required for membrane permeabilization activation and also decreased dark-state cytotoxicity. We used OptoBAX in a series of experiments in Neuro-2a and HEK293T cells to measure the timing of the dramatic morphological and biochemical changes occurring in cells after light-induced MOMP. In these experiments, we used OptoBAX in tandem with fluorescent reporters to image key events in early apoptosis, including membrane inversion, caspase cleavage, and actin redistribution. We then used these data to construct a timeline of biochemical and morphological events in early apoptosis, demonstrating a direct link between MOMP-induced redistribution of actin and apoptosis progression. In summary, we created a next-generation Cry2/CIB–BAX system requiring less frequent light stimulation and established a timeline of critical apoptotic events, providing detailed insights into key steps in early apoptosis.
S and VV Corona Australis: Spectroscopic Variability in Two Young Binary Star Systems

We used high-resolution near-infrared spectroscopy from the NIRSPEC instrument on the Keck II telescope, taken over multiple epochs spanning five years, to examine two young binary T Tauri star systems, S Corona Australis and VV Corona Australis. The stars in these 1-2" separation systems have optically thick circumstellar disks and high extinctions at optical and near-infrared wavelengths. Using a combination of new and archival data, we have determined the spectral types of all the stars in these two systems for the first time, examined the variable NIR veiling, measured the emission line equivalent widths, and created spectral energy distributions. They have similar spectral types (K7-M1) and are at approximately the same evolutionary stage, allowing comparison of the four stars in the two systems. We conclude that S CrA and VV CrA are young binary systems of stars bridging the Class I and Class II evolutionary stages, characterized by high accretion luminosities and variable emission lines.
PIMBALL: Binary Neural Networks in Spintronic Memory

Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for performance, energy efficiency, or hardware/software complexity. Here, we introduce a spintronic, re-configurable in-memory BNN accelerator, PIMBALL: Processing In Memory BNN AcceL(L)erator, which allows for massively parallel and energy efficient computation. PIMBALL is capable of being used as a standard spintronic memory (STT-MRAM) array and a computational substrate simultaneously. We evaluate PIMBALL using multiple image classifiers and a genomics kernel. Our simulation results show that PIMBALL is more energy efficient than alternative CPU-, GPU-, and FPGA-based implementations while delivering higher throughput.
Slow Fibonacci walks

Abstract We study Fibonacci walks, which are sequences of positive integers satisfying the recurrence w k + 2 = w k + 1 + w k . Richard Stanley suggested studying n-slow Fibonacci walks, which are Fibonacci walks with w s = n and s as large as possible. Stanley conjectured that for most n, there is a slow Fibonacci walk reaching n = w s with the property that w s + 1 is the integer closest to ϕn where ϕ = ( 1 + 5 ) / 2 . We prove that this is true for only a positive fraction of n. We give explicit formulas for the choice of the starting pairs and the determination of s by giving a characterization theorem. We also derive a number of density results concerning the distribution of down and up cases (that is, those n with w s + 1 = ⌊ ϕ n ⌋ or ⌈ ϕ n ⌉ , respectively), as well as for more general “paradoxical” cases.
ENTREPRENEURSHIP EDUCATION ENROLLMENT INTENTIONS: THE EFFECT OF ATTITUDES, NORMS AND PERSONALITY

Entrepreneurship education (EE) has grown exponentially over the past three decades, providing myriad benefits to society. Interestingly though, despite such benefits, scholarly research has rarely...
Rapid dissemination and monopolization of viral populations in mice revealed using a panel of barcoded viruses

The gastrointestinal tract presents a formidable barrier for pathogens to initiate infection. Despite this barrier, enteroviruses, including coxsackievirus B3 (CVB3), successfully penetrate the intestine to initiate infection and spread systemically prior to shedding in stool. However, the effect of the gastrointestinal barrier on CVB3 population dynamics is relatively unexplored, and the selective pressures acting on CVB3 in the intestine are not well characterized. To examine viral population dynamics in orally infected mice, we produced over 100 CVB3 clones harboring nine unique nucleotide "barcodes." Using this collection of barcoded viruses, we found diverse viral populations throughout each mouse within the first day postinfection, but by 48 h the viral populations were dominated by fewer than three barcoded viruses in intestinal and extraintestinal tissues. Using light-sensitive viruses to track replication status, we found that diverse viruses had replicated prior to loss of diversity. Sequencing whole viral genomes from samples later in infection did not reveal detectable viral adaptations. Surprisingly, orally inoculated CVB3 was detectable in pancreas and liver as soon as 20 min postinoculation, indicating rapid systemic dissemination. These results suggest rapid dissemination of diverse viral populations, followed by a major restriction in population diversity and monopolization in all examined tissues. These results underscore a complex dynamic between dissemination and clearance for an enteric virus.IMPORTANCE Enteric viruses initiate infection in the gastrointestinal tract but can disseminate to systemic sites. However, the dynamics of viral dissemination are unclear. In this study, we created a library of 135 barcoded coxsackieviruses to examine viral population diversity across time and space following oral inoculation of mice. Overall, we found that the broad population of viruses disseminates early, followed by monopolization of mouse tissues with three or fewer pool members at later time points. Interestingly, we detected virus in systemic tissues such as pancreas and liver just 20 min after oral inoculation. These results suggest rapid dissemination of diverse viral populations, followed by a major restriction in population diversity and monopolization in all examined tissues.
Inherent Weight Normalization in Stochastic Neural Networks

Multiplicative stochasticity such as Dropout improves the robustness and gener- alizability deep neural networks. Here, we further demonstrate that always-on multiplicative stochasticity combined with simple threshold neurons provide a suf- ficient substrate for deep learning machines. We call such models Neural Sampling Machines (NSM). We find that the probability of activation of the NSM exhibits a self-normalizing property that mirrors Weight Normalization, a previously studied mechanism that fulfills many of the features of Batch Normalization in an online fashion. The normalization of activities during training speeds up convergence by preventing internal covariate shift caused by changes in the distribution of inputs. The always-on stochasticity of the NSM confers the following advantages: the network is identical in the inference and learning phases, making the NSM a suitable substrate for continual learning, it can exploit stochasticity inherent to a physical substrate such as analog non-volatile memories for in memory computing, and it is suitable for Monte Carlo sampling, while requiring almost exclusively addition and comparison operations. We demonstrate NSMs on standard classification benchmarks (MNIST and CIFAR) and event-based classification benchmarks (N-MNIST and DVS Gestures). Our results show that NSMs perform comparably or better than conventional artificial neural networks with the same architecture.
Geometry of the embedding of supergravity scalar manifolds in D=11 and D=10

Abstract Several recent papers have made considerable progress in proving the existence of remarkable consistent Kaluza–Klein sphere reductions of D=10 and D=11 supergravities, to give gauged supergravities in lower dimensions. A proof of the consistency of the full gauged SO(8) reduction on S 7 from D=11 was given many years ago, but from a practical viewpoint a reduction to a smaller subset of the fields can be more manageable and explicit, for the purposes of lifting lower-dimensional solutions back to the higher dimension. The major complexity of the spherical reduction Ansatze comes from the spin-0 fields, and of these, it is the pseudoscalars that are the most difficult to handle. In this paper we address this problem in two cases. One arises in a truncation of SO(8) gauged supergravity in four dimensions to U(1) 4 , where there are three pairs of dilatons and axions in the scalar sector. The other example involves the truncation of SO(6) gauged supergravity in D=5 to a subsector containing a scalar and a pseudoscalar field, with a potential that admits a second supersymmetric vacuum aside from the maximally-supersymmetric one. We briefly discuss the use of these embedding ansatze for the lifting of solutions back to the higher dimension.