cssi/SciSciGPT-SciSciNet huggingface dataset:
Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures

Slot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities_of_residence) for a given entity from a large collection of source documents. In this paper we propose an effective DNN architecture for SF with the following new strategies: (1). Take a regularized dependency graph instead of a raw sentence as input to DNN, to compress the wide contexts between query and candidate filler; (2). Incorporate two attention mechanisms: local attention learned from query and candidate filler, and global attention learned from external knowledge bases, to guide the model to better select indicative contexts to determine slot type. Experiments show that this framework outperforms state-of-the-art on both relation extraction (16% absolute F-score gain) and slot filling validation for each individual system (up to 8.5% absolute F-score gain).
A Statistical Model for Motifs Detection

We consider a statistical model for the problem of finding subgraphs with specified topology in an otherwise random graph. This task plays an important role in the analysis of social and biological networks. In these types of networks, small subgraphs with a specific structure have important functional roles, and they are referred to as motifs. Within this model, one or multiple copies of a subgraph is added (planted) in an Erdős–Renyi random graph with $n$ vertices and edge probability $q_{0}$ . We ask whether the resulting graph can be distinguished reliably from a pure Erdős–Renyi random graph, and we present two types of result. First we investigate the question from a purely statistical perspective, and ask whether there is any test that can distinguish between the two graph models. We provide necessary and sufficient conditions that are essentially tight for small enough subgraphs. Next we study two polynomial-time algorithms for solving the same problem: a spectral algorithm and a semidefinite programming (SDP) relaxation. For the spectral algorithm, we establish sufficient conditions under which it distinguishes the two graph models with high probability. Under the same conditions the spectral algorithm indeed identifies the hidden subgraph. The spectral algorithm is substantially sub-optimal with respect to the optimal test. We show that a similar gap is present for the more sophisticated SDP approach.
Generative Modeling of Multimodal Multi-Human Behavior

This work presents a methodology for modeling and predicting human behavior in settings with $N$ humans interacting in highly multimodal scenarios (i.e. where there are many possible highly-distinct futures). A motivating example includes robots interacting with humans in crowded environments, such as self-driving cars operating alongside human-driven vehicles or human-robot collaborative bin packing in a warehouse. Our approach to model human behavior in such uncertain environments is to model humans in the scene as nodes in a graphical model, with edges encoding relationships between them. For each human, we learn a multimodal probability distribution over future actions from a dataset of multi-human interactions. Learning such distributions is made possible by recent advances in the theory of conditional variational autoencoders and deep learning approximations of probabilistic graphical models. Specifically, we learn action distributions conditioned on interaction history, neighboring human behavior, and candidate future agent behavior in order to take into account response dynamics. We demonstrate the performance of such a modeling approach in modeling basketball player trajectories, a highly multimodal, multi-human scenario which serves as a proxy for many robotic applications.
A sequence of relaxations constraining hidden variable models

Many widely studied graphical models with latent variables lead to nontrivial constraints on the distribution of the observed variables. Inspired by the Bell inequalities in quantum mechanics, we refer to any linear inequality whose violation rules out some latent variable model as a "hidden variable test" for that model. Our main contribution is to introduce a sequence of relaxations which provides progressively tighter hidden variable tests. We demonstrate applicability to mixtures of sequences of i.i.d. variables, Bell inequalities, and homophily models in social networks. For the last, we demonstrate that our method provides a test that is able to rule out latent homophily as the sole explanation for correlations on a real social network that are known to be due to influence.
QUASIPOSITIVE ANNULI (CONSTRUCTIONS OF QUASIPOSITIVE KNOTS AND LINKS, IV)

The modulus of quasipositivityq(K) of a knot K was introduced as a tool in the knot theory of complex plane curves, and can be applied to Legendrian knot theory in sym-plectic topology. It has also, however, a straightforward characterization in ordinary knot theory: q(K) is the supremum of the integers f such that the framed knot (K, f) embeds non-trivially on a fiber surface of a positive torus link. Geometric constructions show that −∞ < q(K), calculations with link polynomials that q(K) < ∞. The present paper aims to provide sharper lower bounds (by optimizing the geometry with positive plats) and more readily calculated upper bounds (by modifying known link polynomials), and so to compute q(K) for various classes of knots, such as positive closed braids (for which q(K) = μ(K) − 1) and most positive pretzels. As an aside, it is noted that a recent result of Kronheimer & Mrowka implies that q(K) < 0 if K is slice.
Joint Unsupervised Learning of Deep Representations and Image Clusters

In this paper, we propose a recurrent framework for joint unsupervised learning of deep representations and image clusters. In our framework, successive operations in a clustering algorithm are expressed as steps in a recurrent process, stacked on top of representations output by a Convolutional Neural Network (CNN). During training, image clusters and representations are updated jointly: image clustering is conducted in the forward pass, while representation learning in the backward pass. Our key idea behind this framework is that good representations are beneficial to image clustering and clustering results provide supervisory signals to representation learning. By integrating two processes into a single model with a unified weighted triplet loss function and optimizing it end-to-end, we can obtain not only more powerful representations, but also more precise image clusters. Extensive experiments show that our method outperforms the state of-the-art on image clustering across a variety of image datasets. Moreover, the learned representations generalize well when transferred to other tasks. The source code can be downloaded from https://github.com/ jwyang/joint-unsupervised-learning.
Intrinsic Hyperpolarizabilities as a Figure of Merit for Electro-optic Molecules†

We propose the scale-invariant intrinsic hyperpolarizability as a measure of the figure of merit for electro-optic molecules. By applying our analysis to the best second-order nonlinear-optical molecules that are made using the present paradigms, we conclude that it should be possible to make dye-doped polymers with electro-optic coefficients of several thousand picometers per volt.
Periodic Analog Channel Estimation Aided Beamforming for Massive MIMO Systems

Analog beamforming is an attractive and cost-effective solution to exploit the benefits of massive multiple-input-multiple-output systems, requiring only one up/down-conversion chain. However, the presence of only one chain imposes a significant overhead in estimating the channel state information required for beamforming, when conventional digital channel estimation (CE) approaches are used. As an alternative, this paper proposes a novel CE technique, called periodic analog CE (PACE), which can be performed by analog hardware. By avoiding digital processing, the estimation overhead is significantly lowered and does not scale with the number of antennas. PACE involves the periodic transmission of a sinusoidal reference signal by the transmitter, the estimation of its amplitude and phase at each receive antenna via analog hardware, and using these estimates for beamforming. To enable such non-trivial operation, two reference tone recovery techniques and a novel receiver architecture for PACE are proposed and analyzed, both theoretically and via simulations. The results suggest that in sparse, wide-band channels and above a certain signal-to-noise ratio, PACE-aided beamforming suffers only a small loss in beamforming gain and enjoys a much lower CE overhead, in comparison to conventional approaches. The benefits of using PACE-aided beamforming during the initial access phase are also discussed.
ON THE DISTRIBUTION OF SOME EULER-MAHONIAN STATISTICS

We give a direct combinatorial proof of the equidistributi on of two pairs of permutation statistics, (des,aid) and (lec,inv), which have been previously shown to have the same joint dis- tribution as (exc,maj), the major index and the number of excedances of a permutation. Moreover, the triple (pix,lec,inv) was shown to have the same distribution as (fix,exc,maj), where fix is the number of fixed points of a permutation. We define a new stat istic aix so that our bijection maps (pix,lec,inv) to (aix,des,aid). We also find an Eulerian partner das for a Mahonian statistic mix defined using mesh patterns, so that (das,mix) is equidistributed with (des,inv).
Data-Efficient Minimax Quickest Change Detection in a Decentralized System

Abstract A sensor network is considered where a sequence of random variables is observed at each sensor. At each time step, a processed version of the observations is transmitted from the sensors to a common node called the fusion center. At some unknown point in time the distribution of the observations at all of the sensor nodes changes. The objective is to detect this change in distribution as quickly as possible, subject to constraints on the false alarm rate and the cost of observations taken at each sensor. Minimax problem formulations are proposed for the above problem. A data-efficient algorithm is proposed in which an adaptive sampling strategy is used at each sensor to control the cost of observations used before change. To conserve the cost of communication an occasional binary digit is transmitted from each sensor to the fusion center. It is shown that the proposed algorithm is globally asymptotically optimal for the proposed formulations, as the false alarm rate goes to zero.
Damage spreading in spatial and small-world random Boolean networks.

The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the Hamming distance at very low connectivities ($\overline{K}\ensuremath{\ll}1$) and that the critical connectivity of stability ${K}_{s}$ changes compared to random networks. At higher $\overline{K}$, this scaling remains unchanged. We also show that the Hamming distance of spatially local networks scales with a power law as the system size $N$ increases, but with a different exponent for local and small-world networks. The scaling arguments for small-world networks are obtained with respect to the system sizes and strength of spatially local connections. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.
Robust Kullback-Leibler Divergence and Universal Hypothesis Testing for Continuous Distributions

Universal hypothesis testing (UHT) refers to the problem of deciding whether samples come from a nominal distribution or an unknown distribution that is different from the nominal distribution. Hoeffding’s test, whose test statistic is equivalent to the empirical Kullback–Leibler divergence (KL divergence), is known to be asymptotically optimal for distributions defined on finite alphabets. With continuous observations, however, the discontinuity of the KL divergence in the distribution functions results in significant complications for UHT. This paper introduces a robust version of the classical KL divergence, defined as the KL divergence from a distribution to the Levy ball of a known distribution. This robust KL divergence is shown to be continuous in the underlying distribution function with respect to the weak convergence. The continuity property enables the development of an asymptotically optimal test for the university hypothesis testing problem with continuous observations. The optimality is in the same sense as that of the Hoeffding’s test and stronger than that of Zeitouni and Gutman. Perhaps more importantly, the developed test statistic can be computed through convex programs, making it much more meaningful in practice. Numerical experiments are also conducted to evaluate its performance as compared with some kernel based goodness of fit test that has been proposed recently.
How Can I Do That with ACL2? Recent Enhancements to ACL2

The last several years have seen major enhancements to ACL2 functionality, largely driven by requests from its user community, including utilities now in common use such as make-event, mbe, and trust tags. In this paper we provide user-level summaries of some ACL2 enhancements introduced after the release of Version 3.5 (in May, 2009, at about the time of the 2009 ACL2 workshop) up through the release of Version 4.3 roughly a couple of years later. Many of these features are not particularly well known yet, but most ACL2 users could take advantage of at least some of them. Some of the changes could affect existing proof efforts, such as a change that treats pairs of functions such as member and member-equal as the same function.
Learning to Track On-The-Fly Using a Particle Filter with Annealed-Weighted QPSO Modeled after a Singular Dirac Delta Potential

This paper proposes an evolutionary Particle Filter with a memory guided proposal step size update and an improved, fully-connected Quantum-behaved Particle Swarm Optimization (QPSO) resampling scheme for visual tracking applications. The proposal update step uses importance weights proportional to velocities encountered in recent memory to limit the swarm movement within probable regions of interest. The QPSO resampling scheme uses a fitness weighted mean best update to bias the swarm towards the fittest section of particles while also employing a simulated annealing operator to avoid subpar fine tune during latter course of iterations. By moving particles closer to high likelihood landscapes of the posterior distribution using such constructs, the sample impoverishment problem that plagues the Particle Filter is mitigated to a great extent. Experimental results using benchmark sequences imply that the proposed method outperforms competitive candidate trackers such as the Particle Filter and the traditional Particle Swarm Optimization based Particle Filter on a suite of tracker performance indices.
Improvements to Context Based Self-Supervised Learning

We develop a set of methods to improve on the results of self-supervised learning using context. We start with a baseline of patch based arrangement context learning and go from there. Our methods address some overt problems such as chromatic aberration as well as other potential problems such as spatial skew and mid-level feature neglect. We prevent problems with testing generalization on common self-supervised benchmark tests by using different datasets during our development. The results of our methods combined yield top scores on all standard self-supervised benchmarks, including classification and detection on PASCAL VOC 2007, segmentation on PASCAL VOC 2012, and "linear tests" on the ImageNet and CSAIL Places datasets. We obtain an improvement over our baseline method of between 4.0 to 7.1 percentage points on transfer learning classification tests. We also show results on different standard network architectures to demonstrate generalization as well as portability. All data, models and programs are available at: https://gdo-datasci.llnl.gov/selfsupervised/.
Spin-orbit enhanced carrier lifetimes in noncentrosymmetric semiconductors

Abstract We show that the carrier recombination rate of noncentrosymmetric materials can be strongly modified by spin-orbit coupling. Our proposed mechanism involves the separation of conduction and valence bands into their respective spin components, which changes the transition dipole moments between them. The change in the carrier recombination can be either positive or negative in sign, or vary depending on the location of carriers in the Brillouin zone. We have performed a large scale DFT screening study to identify candidate materials that display this effect. We have selected three materials, Pb4SeBr6, ReTe3Br5, and CsCu(BiS2)2, which span the range of behaviors, and discuss their electronic band structure in greater detail. We find transition dipole moment enhancement factors of up to three orders of magnitude, reflecting the physical impact of spin-orbit coupling on the carrier lifetime. Therefore, further explorations of the spin-orbit coupling and lattice symmetry could prove to be useful for manipulating the photophysics of materials.
A decentralized quasi-Newton method for dual formulations of consensus optimization

This paper considers consensus optimization problems where each node of a network has access to a different summand of an aggregate cost function. Nodes try to maximize the aggregate cost function, while they exchange information only with their neighbors. We modify the dual decomposition method to incorporate a curvature correction inspired by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method. The resulting dual D-BFGS method is a fully decentralized algorithm in which nodes approximate curvature information of themselves and neighbors through the satisfaction of a secant condition. Dual D-BFGS is of interest in consensus problems that are not well conditioned, making first order decentralized methods ineffective, and in which second order information is not readily available, making decentralized second order methods infeasible. Asynchronous implementation is discussed and convergence of D-BFGS is established formally for synchronous and asynchronous implementations. Performance advantages relative to alternative decentralized algorithms are shown numerically.
Obstacle problems and free boundaries: an overview

Free boundary problems are those described by PDEs that exhibit a priori unknown (free) interfaces or boundaries. These problems appear in physics, probability, biology, finance, or industry, and the study of solutions and free boundaries uses methods from PDEs, calculus of variations, geometric measure theory, and harmonic analysis. The most important mathematical challenge in this context is to understand the structure and regularity of free boundaries. In this paper we provide an invitation to this area of research by presenting, in a completely non-technical manner, some classical results as well as some recent results of the author.
Non-equilibrium dynamics in the dual-wavelength operation of vertical external-cavity surface-emitting lasers

Microscopic many-body theory coupled with Maxwell's equation is used to study dual-wavelength operation in vertical external-cavity surface-emitting lasers. The intrinsically dynamic nature of coexisting emission wavelengths in semiconductor lasers is associated with characteristic non-equilibrium carrier dynamics, which causes significant deformations of the quasi-equilibrium gain and carrier inversion. Extended numerical simulations are employed to efficiently investigate the parameter space to identify the regime for dual-wavelength operation. Using a frequency selective intracavity etalon, two families of modes are stabilized with dynamical interchange of the strongest emission peaks. For this operation mode, anti-correlated intensity noise is observed in agreement with the experiment. A method using effective frequency selective filtering is suggested for stabilization of genuine dual-wavelength output.
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution

This work identifies and addresses two important technical challenges in single-image super-resolution: (1) how to upsample an image without magnifying noise and (2) how to preserve large scale structure when upsampling. We summarize the techniques we developed for our second place entry in Track 1 (Bicubic Downsampling), seventh place entry in Track 2 (Realistic Adverse Conditions), and seventh place entry in Track 3 (Realistic difficult) in the 2018 NTIRE Super-Resolution Challenge. Furthermore, we present new neural network architectures that specifically address the two challenges listed above: denoising and preservation of large-scale structure.
Spontaneous Excitability in the Morris--Lecar Model with Ion Channel Noise

Noise induced excitability is studied in types I and II Morris--Lecar neurons subject to constant subthreshold input, where fluctuations arise from sodium and potassium ion channels. Ion channels open and close randomly, creating current fluctuations that can induce spontaneous firing of action potentials. Both noise sources are assumed to be weak so that spontaneous action potentials occur on a longer timescale than ion channel fluctuations. Asymptotic approximations of the stationary density function and most probable paths are developed to understand the role of channel noise in spontaneous excitability. Even though the deterministic dynamical behavior of types I and II action potentials differ, results show that a single mechanism explains how ion channel noise generates spontaneous action potentials.
Graphical Mahonian Statistics on Words

Foata and Zeilberger defined the graphical major index, $\mathrm{maj}_U$, and the graphical inversion index, $\mathrm{inv}_U$, for words over the alphabet $\{1, 2, \dots, n\}$. These statistics are a generalization of the classical permutation statistics $\mathrm{maj}$ and $\mathrm{inv}$ indexed by directed graphs $U$. They showed that $\mathrm{maj}_U$ and $\mathrm{inv}_U$ are equidistributed over all rearrangement classes if and only if $U$ is bipartitional . In this paper we strengthen their result by showing that if $\mathrm{maj}_U$ and $\mathrm{inv}_U$ are equidistributed on a single rearrangement class then $U$ is essentially bipartitional . Moreover, we define a graphical sorting index, $\mathrm{sor}_U$, which generalizes the sorting index of a permutation. We then characterize the graphs $U$ for which $\mathrm{sor}_U$ is equidistributed with $\mathrm{inv}_U$ and $\mathrm{maj}_U$ on a single rearrangement class.
Inapproximability of the Standard Pebble Game and Hard to Pebble Graphs

Pebble games are single-player games on DAGs involving placing and moving pebbles on nodes of the graph according to a certain set of rules. The goal is to pebble a set of target nodes using a minimum number of pebbles. In this paper, we present a possibly simpler proof of the result in [4] and strengthen the result to show that it is PSPACE-hard to determine the minimum number of pebbles to an additive \(n^{1/3-\epsilon }\) term for all \(\epsilon > 0\), which improves upon the currently known additive constant hardness of approximation [4] in the standard pebble game. We also introduce a family of explicit, constant indegree graphs with n nodes where there exists a graph in the family such that using \(0< k < \sqrt{n}\) pebbles requires \(\varOmega ((n/k)^k)\) moves to pebble in both the standard and black-white pebble games. This independently answers an open question summarized in [14] of whether a family of DAGs exists that meets the upper bound of \(O(n^k)\) moves using constant k pebbles with a different construction than that presented in [1].
Dealing with Unknown Unknowns: Identification and Selection of Minimal Sensing for Fractional Dynamics with Unknown Inputs

This paper focuses on analysis and design of time-varying complex networks having fractional order dynamics. These systems are key in modeling the complex dynamical processes arising in several natural and man made systems. Notably, examples include neurophysiological signals such as electroencephalogram (EEG) that captures the variation in potential fields, and blood oxygenation level dependent (BOLD) signal, which serves as a proxy for neuronal activity. Notwithstanding, the complex networks originated by locally measuring EEG and BOLD are often treated as isolated networks and do not capture the dependency from external stimuli, e.g., originated in subcortical structures such as the thalamus and the brain stem. Therefore, we propose a paradigm-shift towards the analysis of such complex networks under unknown unknowns (i.e., excitations). Consequently, the main contributions of the present paper are threefold: (i) we present an alternating scheme that enables to determine the best estimate of the model parameters and unknown stimuli; (ii) we provide necessary and sufficient conditions to ensure that it is possible to retrieve the state and unknown stimuli; and (iii) upon these conditions we determine a small subset of variables that need to be measured to ensure that both state and input can be recovered, while establishing sub-optimality guarantees with respect to the smallest possible subset. Finally, we present several pedagogical examples of the main results using real data collected from an EEG wearable device.
Exploring Local Context for Multi-target Tracking in Wide Area Aerial Surveillance

Tracking many vehicles in wide coverage aerial imagery is crucial for understanding events in a large field of view. Most approaches aim to associate detections from frame differencing into tracks. However, slow or stopped vehicles result in long-term missing detections and further cause tracking discontinuities. Relying merely on appearance clue to recover missing detections is difficult as targets are extremely small and in grayscale. In this paper, we address the limitations of detection association trackers by coupling it with a local context tracker (LCT), which does not rely on motion detections. On one hand, our LCT learns neighboring spatial relation and tracks each target in consecutive frames using graph optimization. It takes the advantage of context constraints to avoid drifting to nearby targets. We generate hypotheses from sparse and dense flow efficiently to keep solutions tractable. On the other hand, we use detection association strategy to extract short tracks in batch processing. We explicitly handle merged detections by generating additional hypotheses from them. Our evaluation on wide area aerial imagery sequences shows significant improvement over state-of-the-art methods.
Optimal placement of a small order in a diffusive limit order book

We study the optimal placement problem of a stock trader who wishes to clear his/her inventory by a predetermined time horizon t, by using a limit order or a market order. For a diffusive market, we characterize the optimal limit order placement policy and analyze its behavior under different market conditions. In particular, we show that, in the presence of a negative drift, there exists a critical time t0>0 such that, for any time horizon t>t0, there exists an optimal placement, which, contrary to earlier work, is different from one that is placed "infinitesimally" close to the best ask, such as the best bid and second best bid. We also propose a simple method to approximate the critical time t0 and the optimal order placement.
Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation

The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence representation learning method that can integrate with any existing encoder-decoder dialog models for interpretable response generation. Building upon variational autoencoders (VAEs), we present two novel models, DI-VAE and DI-VST that improve VAEs and can discover interpretable semantics via either auto encoding or context predicting. Our methods have been validated on real-world dialog datasets to discover semantic representations and enhance encoder-decoder models with interpretable generation.
Scalable Bayes under Informative Sampling

Bayesian hierarchical formulations are utilized by the U.S. Bureau of Labor Statistics (BLS) with respondent‐level data for missing item imputation because these formulations are readily parameterized to capture correlation structures. BLS collects survey data under informative sampling designs that assign probabilities of inclusion to be correlated with the response on which sampling‐weighted pseudo posterior distributions are estimated for asymptotically unbiased inference about population model parameters. Computation is expensive and does not support BLS production schedules. We propose a new method to scale the computation that divides the data into smaller subsets, estimates a sampling‐weighted pseudo posterior distribution, in parallel, for every subset and combines the pseudo posterior parameter samples from all the subsets through their mean in the Wasserstein space of order 2. We construct conditions on a class of sampling designs where posterior consistency of the proposed method is achieved. We demonstrate on both synthetic data and in application to the Current Employment Statistics survey that our method produces results of similar accuracy as the usual approach while offering substantially faster computation.
Alternating minimization for dictionary learning with random initialization

We present theoretical guarantees for an alternating minimization algorithm for the dictionary learning/sparse coding problem. The dictionary learning problem is to factorize vector samples $y^{1},y^{2},\ldots, y^{n}$ into an appropriate basis (dictionary) $A^*$ and sparse vectors $x^{1*},\ldots,x^{n*}$. Our algorithm is a simple alternating minimization procedure that switches between $\ell_1$ minimization and gradient descent in alternate steps. Dictionary learning and specifically alternating minimization algorithms for dictionary learning are well studied both theoretically and empirically. However, in contrast to previous theoretical analyses for this problem, we replace a condition on the operator norm (that is, the largest magnitude singular value) of the true underlying dictionary $A^*$ with a condition on the matrix infinity norm (that is, the largest magnitude term). This not only allows us to get convergence rates for the error of the estimated dictionary measured in the matrix infinity norm, but also ensures that a random initialization will provably converge to the global optimum. Our guarantees are under a reasonable generative model that allows for dictionaries with growing operator norms, and can handle an arbitrary level of overcompleteness, while having sparsity that is information theoretically optimal. We also establish upper bounds on the sample complexity of our algorithm.
Density Estimation on Manifolds with Boundary

Density estimation is a crucial component of many machine learning methods, and manifold learning in particular, where geometry is to be constructed from data alone. A significant practical limitation of the current density estimation literature is that methods have not been developed for manifolds with boundary, except in simple cases of linear manifolds where the location of the boundary is assumed to be known. This limitation is overcome by developing a density estimation method for manifolds with boundary that does not require any prior knowledge of the location of the boundary. To construct an appropriate estimator, statistics are introduced that provably approximate the distance and direction of the boundary, which allows us to apply a cut-and-normalize boundary correction. Then, multiple cut-and-normalize estimators are used to build a consistent kernel density estimator that has uniform bias, at interior and boundary points, on manifolds with boundary.
The Effect of Infecting Curves on Knot Concordance

Various obstructions to knot concordance have been found using Casson-Gordon invariants, higher-order Alexander polynomials, as well as von-Neumann rho-invariants. Examples have been produced using (iterated) doubling operations K=R(c,J), and considering these as parametrized by invariants of the base knot J and doubling operator R. In this paper, we introduce a new mew method to obstruct concordance. We show that infinitely many distinct concordance classes may be constructed by varying the infecting curve c in S^3-R. Distinct concordance classes are found even while fixing the base knot, the doubling operator, and the order of c in the Alexander module.
A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses

We tackle the problem of learning a rotation invariant latent factor model when the training data is comprised of lower-dimensional projections of the original feature space. The main goal is the discovery of a set of 3-D bases poses that can characterize the manifold of primitive human motions, or movemes, from a training set of 2-D projected poses obtained from still images taken at various camera angles. The proposed technique for basis discovery is data-driven rather than hand-designed. The learned representation is rotation invariant, and can reconstruct any training instance from multiple viewing angles. We apply our method to modeling human poses in sports (via the Leeds Sports Dataset), and demonstrate the effectiveness of the learned bases in a range of applications such as activity classification, inference of dynamics from a single frame, and synthetic representation of movements.
Reaction–diffusion front speed enhancement by flows

Abstract We study flow-induced enhancement of the speed of pulsating traveling fronts for reaction–diffusion equations, and quenching of reaction by fluid flows. We prove, for periodic flows in two dimensions and any combustion-type reaction, that the front speed is proportional to the square root of the (homogenized) effective diffusivity of the flow. We show that this result does not hold in three and more dimensions. We also prove conjectures from Audoly, Berestycki and Pomeau (2000) [1] , Berestycki (2003) [3] , Fannjiang, Kiselev and Ryzhik (2006) [11] for cellular flows, concerning the rate of speed-up of fronts and the minimal flow amplitude necessary to quench solutions with initial data of a fixed (large) size.
Diffeomorphisms Hölder conjugate to Anosov diffeomorphisms

We show by means of a counterexample that a C 1+Lip diffeomorphism Holder conjugate to an Anosov diffeomorphism is not necessarily Anosov. Also we include a result from the 2006 PhD thesis of Fisher: a C 1+Lip diffeomorphism Holder conjugate to an Anosov diffeomorphism is Anosov itself provided that Holder exponents of the conjugacy and its inverse are sufficiently large.
Decoupling inequalities and some mean-value theorems

The purpose of this paper is to present some further applications of the general decoupling theory from [B-D] and [B-D2] to certain diophantine issues. In particular, we consider mean value estimates relevant to the Bombieri- Iwaniec approach to exponential sums and arising in the work of Robert and Sargos [R-S]. Our main input is a new mean-value theorem.
Some Results on Metric Trees

Using isometric embedding of metric trees into Banach spaces, this paper will investigate barycenters, type and cotype, and various measures of compactness of metric trees. A metric tree (T , d) is a metric space such that between any two of its points there is an unique arc that is isometric to an interval in R. We begin our investigation by examining isometric embeddings of metric trees into Banach spaces. We then investigate the possible images x0 = π((x1 + . . .+xn)/n), where π is a contractive retraction from the ambient Banach space X onto T (such a π always exists), in order to understand the “metric” barycenter of a family of points x1, . . . , xn in a tree T . Further, we consider the metric properties of trees (such as their type and cotype). We identify various measures of compactness of metric trees (their covering numbers, 2-entropy and Kolmogorov widths) and the connections between them. Additionally, we prove that the limit of the sequence of the Kolmogorov widths of a metric tree is equal to its ball measure of non-compactness.
Fine-Grained Activity Recognition in Baseball Videos

In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection. The dataset contains two settings: segmented video classification as well as activity detection in continuous videos. We experimentally compare various recognition approaches capturing temporal structure in activity videos, by classifying segmented videos and extending those approaches to continuous videos. We also compare models on the extremely difficult task of predicting pitch speed and pitch type from broadcast baseball videos. We find that learning temporal structure is valuable for fine-grained activity recognition.
Spider evaluation and representations of web groups

The topology of SU(3)-representation varieties of the fundamental groups of planar webs so that the meridians are sent to matrices with trace equal to − 1 are explored, and compared to data coming ...
Quantum Noise in Large-Scale Coherent Nonlinear Photonic Circuits

Ongoing advances in semiconductor fabrication are expected to enable nonlinear optical circuits to operate at extremely low switching energies, where quantum effects become important. This work describes semiclassical simulations to study the effects of quantum noise in large digital logic circuits containing hundreds of optical components. The authors find that the amplitudes of quantum fluctuations do not increase as signals propagate through the circuit, which is promising for scaling up devices.
Using the “Freshman's Dream” to Prove Combinatorial Congruences

AbstractRecently, William Y.C. Chen, Qing-Hu Hou, and Doron Zeilberger developed an algorithm for finding and proving congruence identities (modulo primes) of indefinite sums of many combinatorial sequences, namely those (like the Catalan and Motzkin sequences) that are expressible in terms of constant terms of powers of Laurent polynomials. We first give a leisurely exposition of their approach and then extend it in two directions. The Laurent polynomials may be of several variables, and instead of single sums we have multiple sums. In fact, we even combine these two generalizations. We conclude with some super-challenges.
Building DNN acoustic models for large vocabulary speech recognition

Empirical investigation of neural network acoustic models for speech recognition.Evaluation of DNNs up to ten times larger than those used in previous works.Comparison of densely connected, convolutional, and locally-connected untied neural networks.Results on Switchboard and a combined 2100 hour corpus.Explanation of combined corpus baseline system training recipe which is now a part of the Kaldi speech toolkit. Understanding architectural choices for deep neural networks (DNNs) is crucial to improving state-of-the-art speech recognition systems. We investigate which aspects of DNN acoustic model design are most important for speech recognition system performance, focusing on feed-forward networks. We study the effects of parameters like model size (number of layers, total parameters), architecture (convolutional networks), and training details (loss function, regularization methods) on DNN classifier performance and speech recognizer word error rates. On the Switchboard benchmark corpus we compare standard DNNs to convolutional networks, and present the first experiments using locally-connected, untied neural networks for acoustic modeling. Using a much larger 2100-hour training corpus (combining Switchboard and Fisher) we examine the performance of very large DNN models - with up to ten times more parameters than those typically used in speech recognition systems. The results suggest that a relatively simple DNN architecture and optimization technique give strong performance, and we offer intuitions about architectural choices like network depth over breadth. Our findings extend previous works to help establish a set of best practices for building DNN hybrid speech recognition systems and constitute an important first step toward analyzing more complex recurrent, sequence-discriminative, and HMM-free architectures.
Bayesian Structure Learning by Recursive Bootstrap

We address the problem of Bayesian structure learning for domains with hundreds of variables by employing non-parametric bootstrap, recursively. We propose a method that covers both model averaging and model selection in the same framework. The proposed method deals with the main weakness of constraint-based learning---sensitivity to errors in the independence tests---by a novel way of combining bootstrap with constraint-based learning. Essentially, we provide an algorithm for learning a tree, in which each node represents a scored CPDAG for a subset of variables and the level of the node corresponds to the maximal order of conditional independencies that are encoded in the graph. As higher order independencies are tested in deeper recursive calls, they benefit from more bootstrap samples, and therefore are more resistant to the curse-of-dimensionality. Moreover, the re-use of stable low order independencies allows greater computational efficiency. We also provide an algorithm for sampling CPDAGs efficiently from their posterior given the learned tree. That is, not from the full posterior, but from a reduced space of CPDAGs encoded in the learned tree. We empirically demonstrate that the proposed algorithm scales well to hundreds of variables, and learns better MAP models and more reliable causal relationships between variables, than other state-of-the-art-methods.
Greedy Sensor Placement With Cost Constraints

The problem of optimally placing sensors under a cost constraint arises naturally in the design of industrial and commercial products, as well as in scientific experiments. We consider a relaxation of the full optimization formulation of this problem and then extend a well-established greedy algorithm for the optimal sensor placement problem without cost constraints. We demonstrate the effectiveness of this algorithm on the datasets related to facial recognition, climate science, and fluid mechanics. This algorithm is scalable and often identifies sparse sensors with near-optimal reconstruction performance, while dramatically reducing the overall cost of the sensors. We find that the cost-error landscape varies by application, with intuitive connections to the underlying physics. In addition, we include experiments for various pre-processing techniques and find that a popular technique based on the singular value decomposition is often suboptimal.
Computing Quasidegrees of A-graded Modules

We describe the main functions of the Macaulay2 package Quasidegrees. The purpose of this package is to compute the quasidegree set of a finitely generated A-graded module presented as the cokernel of a monomial matrix. We provide examples with motivation coming from A-hypergeometric systems.
Modeling Algorithms in SystemC and ACL2

We describe the formal language MASC, based on a subset of SystemC and intended for modeling algorithms to be implemented in hardware. By means of a special-purpose parser, an algorithm coded in SystemC is converted to a MASC model for the purpose of documentation, which in turn is translated to ACL2 for formal verification. The parser also generates a SystemC variant that is suitable as input to a high-level synthesis tool. As an illustration of this methodology, we describe a proof of correctness of a simple 32-bit radix-4 multiplier.
Vanishing of Ext and Tor over fiber products

Consider a non-trivial fiber product $R=S\times_kT$ of local rings $S$, $T$ with common residue field $k$. Given two finitely generate $R$-modules $M$ and $N$, we show that if $\operatorname{Tor}^R_i(M,N)=0=\operatorname{Tor}^R_{i+1}(M,N)$ for some $i\geq 5$, then $\operatorname{pd}_R(M)\leq 1$ or $\operatorname{pd}_R(N)\leq 1$. From this, we deduce several consequence, for instance, that $R$ satisfies the Auslander-Reiten Conjecture.
Online Learning for Non-Stationary A/B Tests

The rollout of new versions of a feature in modern applications is a manual multi-stage process, as the feature is released to ever larger groups of users, while its performance is carefully monitored. This kind of A/B testing is ubiquitous, but suboptimal, as the monitoring requires heavy human intervention, is not guaranteed to capture consistent, but short-term fluctuations in performance, and is inefficient, as better versions take a long time to reach the full population. In this work we formulate this question as that of expert learning, and give a new algorithm Follow-The-Best-Interval, FTBI, that works in dynamic, non-stationary environments. Our approach is practical, simple, and efficient, and has rigorous guarantees on its performance. Finally, we perform a thorough evaluation on synthetic and real world datasets and show that our approach outperforms current state-of-the-art methods.
The Incremental Multiresolution Matrix Factorization Algorithm

Multiresolution analysis and matrix factorization are foundational tools in computer vision. In this work, we study the interface between these two distinct topics and obtain techniques to uncover hierarchical block structure in symmetric matrices &#x2013; an important aspect in the success of many vision problems. Our new algorithm, the incremental multiresolution matrix factorization, uncovers such structure one feature at a time, and hence scales well to large matrices. We describe how this multiscale analysis goes much farther than what a direct global factorization of the data can identify. We evaluate the efficacy of the resulting factorizations for relative leveraging within regression tasks using medical imaging data. We also use the factorization on representations learned by popular deep networks, providing evidence of their ability to infer semantic relationships even when they are not explicitly trained to do so. We show that this algorithm can be used as an exploratory tool to improve the network architecture, and within numerous other settings in vision.