cssi/SciSciGPT-SciSciNet huggingface dataset:
Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask

Using a layered representation for motion estimation has the advantage of being able to cope with discontinuities and occlusions. In this paper, we learn to estimate optical flow by combining a layered motion representation with deep learning. Instead of pre-segmenting the image to layers, the proposed approach automatically generates a layered representation of optical flow using the proposed soft-mask module. The essential components of the soft-mask module are maxout and fuse operations, which enable a disjoint layered representation of optical flow and more accurate flow estimation. We show that by using masks the motion estimate results in a quadratic function of input features in the output layer. The proposed soft-mask module can be added to any existing optical flow estimation networks by replacing their flow output layer. In this work, we use FlowNet as the base network to which we add the soft-mask module. The resulting network is tested on three well-known benchmarks with both supervised and unsupervised flow estimation tasks. Evaluation results show that the proposed network achieve better results compared with the original FlowNet.
Testing Endogeneity with High Dimensional Covariates

Abstract Modern, high dimensional data has renewed investigation on instrumental variables (IV) analysis, primarily focusing on estimation of effects of endogenous variables and putting little attention towards specification tests. This paper studies in high dimensions the Durbin–Wu–Hausman (DWH) test, a popular specification test for endogeneity in IV regression. We show, surprisingly, that the DWH test maintains its size in high dimensions, but at an expense of power. We propose a new test that remedies this issue and has better power than the DWH test. Simulation studies reveal that our test achieves near-oracle performance to detect endogeneity.
Analytical device-physics framework for non-planar solar cells

Abstract Non-planar solar cell devices have been promoted as a means to enhance current collection in absorber materials with charge-transport limitations. This work presents an analytical framework for assessing the ultimate performance of non-planar solar cells based on materials and geometry. Herein, the physics of the p-n junction is analyzed for low-injection conditions, when the junction can be considered spatially separable into quasi-neutral and space-charge regions. For the conventional planar solar cell architecture, previously established one-dimensional expressions governing charge carrier transport are recovered from the framework established herein. Space-charge region recombination statistics are compared for planar and non-planar geometries, showing variations in recombination current produced from the space-charge region. In addition, planar and non-planar solar cell performances are simulated, based on a semi-empirical expression for short-circuit current, detailing variations in charge carrier transport and efficiency as a function of geometry, thereby yielding insights into design criteria for solar cell architectures. For the conditions considered here, the expressions for generation rate and total current are shown to universally govern any solar cell geometry, while recombination within the space-charge region is shown to be directly dependent on the geometrical orientation of the p-n junction.
On the Sub-Optimality of Single-Letter Coding Over Networks

In this paper, we establish a new bound tying together the effective length and the maximum correlation between the outputs of an arbitrary pair of Boolean functions which operate on two sequences of correlated random variables. We derive a new upper bound on the correlation between the outputs of these functions. The upper bound may find applications in problems in many areas which deal with common information. We build upon Witsenhausen’s result [1] on maximum correlation. The present upper bound takes into account the effective length of the Boolean functions in characterizing the correlation. We use the new bound to characterize the communication-cooperation tradeoff in multi-terminal communications. We investigate binary block-codes (BBC). A BBC is defined as a vector of Boolean functions. We consider an ensemble of BBCs which is randomly generated using single-letter distributions. We characterize the vector of dependency spectrums of these BBCs. We use this vector to bound the correlation between the outputs of two distributed BBCs. Finally, the upper bound is used to show that the large blocklength single-letter coding schemes studied in the literature are sub-optimal in various multi-terminal communication settings.
Efficient inference on generalized fault diagrams

The generalized fault diagram, a data structure for failure analysis based on the influence diagram, is defined. Unlike the fault tree, this structure allows for dependence among the basic events and replicated logical elements. A heuristic procedure is developed for efficient processing of these structures.
The Fourier transform of order statistics with applications to Lorentz spaces

We present a formula for the Fourier transforms of order statistics in ℝ n showing that all these Fourier transforms are equal up to a constant multiple outside the coordinate planes in ℝ n . Fora 1≥...≥a n≥0 andq>0, denote by l then-dimensional Lorentz space with the norm ‖(x 1,...,x n)‖=(a 1(x 1 * ) q +...+a n(x * ) q )1/q , where (x 1 * ,...,x * ) is the non-increasing permutation of the numbers |x 1|,...,|x n|. We use the above mentioned formula and the Fourier transform criterion of isometric embeddability of Banach spaces intoL q [10] to prove that, forn≥3 andq≤1, the space l is isometric to a subspace ofL q if and only if the numbersa 1,...,a n form an arithmetic progression. Forq>1, all the numbersa i must be equal so that l = l . Consequently, the Lorentz function spaceL w,q(0, 1) is isometric to a subspace ofL q if and only ifeither 0<q<∞ and the weightw is a constant function (so thatL w,q=Lq),or q≤1 andw(t) is a decreasing linear function. Finally, we relate our results to the theory of positive definite functions.
Sequential Hypothesis Test With Online Usage-Constrained Sensor Selection

This paper investigates the sequential hypothesis testing problem with online sensor selection and sensor usage constraints. That is, in a sensor network, the fusion center sequentially acquires samples by selecting one “most informative” sensor at each time until a reliable decision can be made. In particular, the sensor selection is carried out in the online fashion since it depends on all the previous samples at each time. Our goal is to develop the sequential test (i.e., stopping rule and decision function) and sensor selection strategy that minimize the expected sample size subject to the constraints on the error probabilities and sensor usages. To this end, we first recast the usage-constrained formulation into a Bayesian optimal stopping problem with different sampling costs for the usage-contrained sensors. The Bayesian problem is then studied under both finite- and infinite-horizon setups, based on which, the optimal solution to the original usage-constrained problem can be readily established. Moreover, by capitalizing on the structures of the optimal solution, a lower bound is obtained for the optimal expected sample size. In addition, we also propose algorithms to approximately evaluate the parameters in the optimal sequential test so that the sensor usage and error probability constraints are satisfied. Finally, numerical experiments are provided to illustrate the theoretical findings, and compare with the existing methods.
Deconvolution of mixing time series on a graph

In many applications we are interested in making inference on latent time series from indirect measurements, which are often low-dimensional projections resulting from mixing oraggregation. Positron emission tomography, super-resolution, and network traffic monitoring are some examples. Inference in such settings requires solving a sequence of ill-posed inverse problems, yt = Axt, where the projection mechanism provides information on A. We consider problems in which A specifies mixing on a graph of times series that are bursty and sparse. We develop a multilevel state-space model for mixing times series and an efficient approach to inference. A simple model is used to calibrate regularization parameters that lead to efficient inference in the multilevel state-space model. We apply this method to the problem of estimating point-to-point traffic flows on a network from aggregate measurements. Our solution outperforms existing methods for this problem, and our two-stage approach suggests an efficient inference strategy for multilevel models of multivariate time series.
Existence of traveling wave solutions of parabolic–parabolic chemotaxis systems

The current paper is devoted to the study of traveling wave solutions of the following parabolic-parabolic chemotaxis systems, $$ \begin{cases} u_{t}= \Delta u-\chi \nabla \cdot (u \nabla v) + u(a-bu),\quad x\in\mathbb{R}^N \tau v_t=\Delta v-v+u, \quad x\in\mathbb{R}^N, \end{cases} $$ where $u(x,t)$ represents the population density of a mobile species and $v(x,t)$ represents the population density of a chemoattractant, and $\chi$ represents the chemotaxis sensitivity. We prove that for every $\tau >0$, there is $0<\chi_{\tau}^*<\frac{b}{2}$ such that for every $0<\chi<\chi_{\tau}^*$, there exist two positive numbers $2\sqrt a \le c^{*}(\chi,\tau)<c^{**}(\chi,\tau)$ satisfying that for every $ c\in [ c^{*}(\chi,\tau)\,\ c^{**}(\chi,\tau))$ and $\xi\in S^{N-1}$, the system has a traveling wave solution $(u(x,t),v(x,t))=(U(x\cdot\xi-ct;\tau),V(x\cdot\xi-ct;\tau))$ with speed $c$ connecting the constant solutions $(\frac{a}{b},\frac{a}{b})$ and $(0,0)$, and it does not have such traveling wave solutions of speed less than $2\sqrt a$. Moreover, $$ \lim_{\chi\to 0^+}c^{**}(\chi,\tau)=\infty,$$ $$ \lim_{\chi\to 0^+}c^{*}(\chi,\tau)=\begin{cases} 2\sqrt{a}\qquad \qquad \qquad\ \text{if} \quad 0<a\leq \frac{1+\tau a}{(1-\tau)_+}\cr \frac{1+\tau a}{(1-\tau)_{+}}+\frac{a(1-\tau)_{+}}{1+\tau a}\quad \text{if} \quad a\geq \frac{1+\tau a}{(1-\tau)_+}, \end{cases} $$ and $$ \lim_{x\to -\infty}\frac{U(x;\tau)}{e^{-\mu x}}=1, $$ where $\mu$ is the only solution of the equation $\mu+\frac{a}{\mu}=c$ in the interval $(0, \min\{\sqrt a, \sqrt{\frac{1+\tau a}{(1-\tau)_+}}\})$. Furthermore, it hods that $\lim_{\tau\to 0^+}\chi_{\tau}^*=\frac{b}{2}$.
The Cycle Structure of Unicritical Polynomials

A polynomial with integer coefficients yields a family of dynamical systems indexed by primes as follows: for any prime $p$, reduce its coefficients mod $p$ and consider its action on the field $\mathbb{F}_p$. The questions of whether and in what sense these families are random have been studied extensively, spurred in part by Pollard's famous "rho" algorithm for integer factorization (the heuristic justification of which is the randomness of one such family). However, the cycle structure of these families cannot be random, since in any such family, the number of cycles of a fixed length in any dynamical system in the family is bounded. In this paper, we show that the cycle statistics of many of these families are as random as possible. As a corollary, we show that most members of these families have many cycles, addressing a conjecture of Mans et. al.
The leading term of the Yang-Mills free energy

Abstract This article gives an explicit formula for the leading term of the free energy of three-dimensional U ( N ) lattice gauge theory for any N, as the lattice spacing tends to zero. The proof is based on a novel technique that avoids phase cell renormalization. The technique also yields a similar formula for the four-dimensional theory, but only in the weak coupling limit.
Lefschetz classes on projective varieties

The Lefschetz algebra $L^*(X)$ of a smooth complex projective variety $X$ is the subalgebra of the cohomology algebra of $X$ generated by divisor classes. We construct smooth complex projective varieties whose Lefschetz algebras do not satisfy analogues of the hard Lefschetz theorem and Poincar\'e duality.
A dynamical construction of small totally p-adic algebraic numbers

Abstract We give a dynamical construction of an infinite sequence of distinct totally p-adic algebraic numbers whose Weil heights tend to the limit log ⁡ p p − 1 , thus giving a new proof of a result of Bombieri-Zannier. The proof is essentially equivalent to the explicit calculation of the Arakelov-Zhang pairing of the maps σ ( x ) = x 2 and ϕ p ( x ) = 1 p ( x p − x ) .
A Novel Downside Risk Measure and Expected Returns

Several studies have found that the cross-section of stock returns reflects a risk premium for bearing downside risk; however, existing measures of downside risk have poor power for predicting returns. Therefore, this paper proposes a novel measure of downside risk, the ES-implied beta, to improve the prediction of the cross-section of asset returns. The ES-implied beta explains stock returns over the same period as well as the widely used downside beta, but also has strong predictive power over future returns. In the empirical analysis, although the widely used downside beta shows a weak relation with future expected returns, the ES-implied beta implies a statistically and economically significant risk premium of 0.5 percent per month. The predictive power of the ES-implied beta is not explained by the cross-sectional effects from the CAPM beta, size, book-to-market ratio, momentum, coskewness, cokurtosis or liquidity beta, nor does it depend on the design of the empirical analysis.
Planar Multilayer Circuit Quantum Electrodynamics

Experimental quantum information processing with superconducting circuits is rapidly advancing, driven by innovation in two classes of devices, one involving planar microfabricated (2D) resonators, and the other involving machined three-dimensional (3D) cavities. We demonstrate that circuit quantum electrodynamics can be implemented in a multilayer superconducting structure that combines 2D and 3D advantages. We employ standard microfabrication techniques to pattern each layer, and rely on a vacuum gap between the layers to store the electromagnetic energy. Planar qubits are lithographically defined as an aperture in a conducting boundary of the resonators. We demonstrate the aperture concept by implementing an integrated, two-cavity-mode, one-transmon-qubit system.
Tempest: Temporal Dynamics in Anonymity Systems

Many recent proposals for anonymous communication omit from their security analyses a consideration of the effects of time on important system components. In practice, many components of anonymity systems, such as the client location and network structure, exhibit changes and patterns over time. In this paper, we focus on the effect of such temporal dynamics on the security of anonymity networks. We present Tempest, a suite of novel attacks based on (1) client mobility, (2) usage patterns, and (3) changes in the underlying network routing. Using experimental analysis on real-world datasets, we demonstrate that these temporal attacks degrade user privacy across a wide range of anonymity networks, including deployed systems such as Tor; path-selection protocols for Tor such as DeNASA, TAPS, and Counter-RAPTOR; and network-layer anonymity protocols for Internet routing such as Dovetail and HORNET. The degradation is in some cases surprisingly severe. For example, a single host failure or network route change could quickly and with high certainty identify the client's ISP to a malicious host or ISP. The adversary behind each attack is relatively weak - generally passive and in control of one network location or a small number of hosts. Our findings suggest that designers of anonymity systems should rigorously consider the impact of temporal dynamics when analyzing anonymity.
Lossy compression of decimated Gaussian random walks

We consider the problem of estimating a Gaussian random walk from a lossy compression of its decimated version. Hence, the encoder operates on the decimated random walk, and the decoder estimates the original random walk from its encoded version under a mean squared error (MSE) criterion. It is well-known that the minimal distortion in this problem is attained by an estimate-and-compress (EC) source coding strategy, in which the encoder first estimates the original random walk and then compresses this estimate subject to the bit constraint. In this work, we derive a closed-form expression for this minimal distortion as a function of the bitrate and the decimation factor. Next, we consider a compress-and-estimate (CE) source coding scheme, in which the encoder first compresses the decimated sequence subject to an MSE criterion (with respect to the decimated sequence), and the original random walk is estimated only at the decoder. We evaluate the distortion under CE in a closed form and show that there exists a non-zero gap between the distortion under the two schemes. This difference in performance illustrates the importance of having the decimation factor at the encoder.
On the power of joint wavelet-DCT features for multispectral palmprint recognition

Biometric-based identification has drawn a lot of attention in the recent years. Among all biometrics, palmprint is known to possess a rich set of features. In this paper we have proposed to use DCT-based features in parallel with wavelet-based ones for palmprint identification. PCA is applied to the features to reduce their dimensionality and the majority voting algorithm is used to perform classification. The features introduced here result in a near-perfectly accurate identification. This method is tested on a well-known multispectral palmprint database and an accuracy rate of 99.97–100% is achieved, outperforming all previous methods in similar conditions.
Consensus state gram matrix estimation for stochastic switching networks from spectral distribution moments

Reaching distributed average consensus quickly and accurately over a network through iterative dynamics represents an important task in numerous distributed applications. Suitably designed filters applied to the state values can significantly improve the convergence rate. For constant networks, these filters can be viewed in terms of graph signal processing as polynomials in a single matrix, the consensus iteration matrix, with filter response evaluated at its eigenvalues. For random, time-varying networks, filter design becomes more complicated, involving eigendecompositions of sums and products of random, time-varying iteration matrices. This paper focuses on deriving an estimate for the Gram matrix of error in the state vectors over a filtering window for large-scale, stationary, switching random networks. The result depends on the moments of the empirical spectral distribution, which can be estimated through Monte-Carlo simulation. This work then defines a quadratic objective function to minimize the expected consensus estimate error norm. Simulation results provide support for the approximation.
Improved lower bound for difference bases

Abstract A difference basis with respect to n is a subset A ⊆ Z such that A − A ⊇ { 1 , … , n } . Redei and Renyi showed that the minimum size of a difference basis with respect to n is ( c + o ( 1 ) ) n for some positive constant c. The best previously known lower bound on c is c ⩾ 1.5602 … , which was obtained by Leech using a version of an earlier argument due to Redei and Renyi. In this note we use Fourier-analytic tools to show that the Leech–Redei–Renyi lower bound is not sharp.
Spacetime bounds for the energy-critical nonlinear wave equation in three spatial dimensions

Results of Struwe, Grillakis, Struwe-Shatah, Kapitanski, Bahouri-Shatah, Bahouri-G\'erard and Nakanishi have established global wellposedness, regularity, and scattering in the energy class for the energy-critical nonlinear wave equation $\Box u = u^5$ in $\R^{1+3}$, together with a spacetime bound $$ \| u \|_{L^4_t L^{12}_x(\R^{1+3})} \leq M(E(u))$$ for some finite quantity M(E(u)) depending only on the energy E(u) of u. We reprove this result, and show that this quantity obeys a bound of at most exponential type in the energy, and specifically $M(E) \leq C (1+E)^{C E^{105/2}}$ for some absolute constant C > 0. The argument combines the quantitative local potential energy decay estimates of these previous papers with arguments used by Bourgain and the author for the analogous nonlinear Schr\"odinger equation.
The Kontsevich integral and re-normalized link invariants arising from Lie superalgebras

We show that the coefficients of the re-normalized link invariants of the paper "Multivariable link invariants arising from Lie superalgebras of type I" are Vassiliev invariants which give rise to a canonical family of weight systems.
Hölder regularity for nondivergence nonlocal parabolic equations

This paper proves Holder continuity of viscosity solutions to certain nonlocal parabolic equations that involve a generalized fractional time derivative of Marchaud or Caputo type. As a necessary and preliminary result, this paper first proves Holder continuity for viscosity solutions to certain nonlinear ordinary differential equations involving the generalized fractional time derivative.
Lattice rescoring strategies for long short term memory language models in speech recognition

Recurrent neural network (RNN) language models (LMs) and Long Short Term Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform traditional N-gram LMs on speech recognition tasks. However, these models are computationally more expensive than N-gram LMs for decoding, and thus, challenging to integrate into speech recognizers. Recent research has proposed the use of lattice-rescoring algorithms using RNNLMs and LSTMLMs as an efficient strategy to integrate these models into a speech recognition system. In this paper, we evaluate existing lattice rescoring algorithms along with new variants on a YouTube speech recognition task. Lattice rescoring using LSTMLMs reduces the word error rate (WER) for this task by 8% relative to the WER obtained using an N-gram LM.
A Generative Model for Punctuation in Dependency Trees

Treebanks traditionally treat punctuation marks as ordinary words, but linguists have suggested that a tree’s “true” punctuation marks are not observed (Nunberg, 1990). These latent “underlying” ma...
Building Language Models for Text with Named Entities

Text in many domains involves a significant amount of named entities. Predicting the entity names is often challenging for a language model as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a language model which can learn the entity names by leveraging their entity type information. We also introduce two benchmark datasets based on recipes and Java programming codes, on which we evaluate the proposed model. Experimental results show that our model achieves 52.2% better perplexity in recipe generation and 22.06% on code generation than state-of-the-art language models.
Asynchronous and Distributed Tracking of Time-Varying Fixed Points

This paper develops an algorithmic framework for tracking fixed points of time-varying contraction mappings. Analytical results for the tracking error are established for the cases where: (i) the underlying contraction self-map changes at each step of the algorithm; (ii) only an imperfect information of the map is available; and, (iii) the algorithm is implemented in a distributed fashion, with communication delays and packet drops leading to asynchronous algorithmic updates. The analytical results are applicable to several classes of problems, including time-varying contraction mappings emerging from online and asynchronous implementations of gradient-based methods for time-varying convex programs. In this domain, the proposed framework can also capture the operating principles of feedback-based online algorithms, where the online gradient steps are suitably modified to accommodate actionable feedback from an underlying physical or logical network. Examples of applications and illustrative numerical results are provided.
Coarse hyperbolicity and closed orbits for quasigeodesic flows

We prove Calegari's conjecture that every quasigeodesic flow on a closed hyperbolic 3-manifold has closed orbits.
Sparse Approximation and Recovery by Greedy Algorithms in Banach Spaces

We study sparse approximation by greedy algorithms. We prove the Lebesgue-type inequalities for the weak Chebyshev greedy algorithm (WCGA), a generalization of the weak orthogonal matching pursuit to the case of a Banach space. The main novelty of these results is a Banach space setting instead of a Hilbert space setting. The results are proved for redundant dictionaries satisfying certain conditions. Then we apply these general results to the case of bases. In particular, we prove that the WCGA provides almost optimal sparse approximation for the trigonometric system in , .
Link prediction based on graph neural networks

Link prediction is a key problem for network-structured data. Link prediction heuristics use some score functions, such as common neighbors and Katz index, to measure the likelihood of links. They have obtained wide practical uses due to their simplicity, interpretability, and for some of them, scalability. However, every heuristic has a strong assumption on when two nodes are likely to link, which limits their effectiveness on networks where these assumptions fail. In this regard, a more reasonable way should be learning a suitable heuristic from a given network instead of using predefined ones. By extracting a local subgraph around each target link, we aim to learn a function mapping the subgraph patterns to link existence, thus automatically learning a "heuristic" that suits the current network. In this paper, we study this heuristic learning paradigm for link prediction. First, we develop a novel γ-decaying heuristic theory. The theory unifies a wide range of heuristics in a single framework, and proves that all these heuristics can be well approximated from local subgraphs. Our results show that local subgraphs reserve rich information related to link existence. Second, based on the γ-decaying theory, we propose a new method to learn heuristics from local subgraphs using a graph neural network (GNN). Its experimental results show unprecedented performance, working consistently well on a wide range of problems.
Retarded correlators in kinetic theory: branch cuts, poles and hydrodynamic onset transitions

In this work the collective modes of an effective kinetic theory description based on the Boltzmann equation in a relaxation-time approximation applicable to gauge theories at weak but finite coupling and low frequencies are studied. Real time retarded two-point correlators of the energy-momentum tensor and the R-charge current are calculated at finite temperature in flat space-times for large N gauge theories. It is found that the real-time correlators possess logarithmic branch cuts which in the limit of large coupling disappear and give rise to non-hydrodynamic poles that are reminiscent of quasi-normal modes in black holes. In addition to branch cuts, correlators can have simple hydrodynamic poles, generalizing the concept of hydrodynamic modes to intermediate wavelength. Surprisingly, the hydrodynamic poles cease to exist for some critical value of the wavelength and coupling reminiscent of the properties of onset transitions.
Ergodic coverage in constrained environments using stochastic trajectory optimization

In search and surveillance applications in robotics, it is intuitive to spatially distribute robot trajectories with respect to the probability of locating targets in the domain. Ergodic coverage is one such approach to trajectory planning in which a robot is directed such that the percentage of time spent in a region is in proportion to the probability of locating targets in that region. In this work, we extend the ergodic coverage algorithm to robots operating in constrained environments and present a formulation that can capture sensor footprint and avoid obstacles and restricted areas in the domain. We demonstrate that our formulation easily extends to coordination of multiple robots equipped with different sensing capabilities to perform ergodic coverage of a domain.
Symmetric group representations and Z

Abstract We discuss implications of the following statement about representation theory of symmetric groups: every integer appears infinitely often as an irreducible character evaluation and every nonnegative integer appears infinitely often as a Littlewood–Richardson coefficient and as a Kronecker coefficient.
Computing active subspaces efficiently with gradient sketching

Active subspaces are an emerging set of tools for identifying and exploiting the most important directions in the space of a computer simulation's input parameters; these directions depend on the simulation's quantity of interest, which we treat as a function from inputs to outputs. To identify a function's active subspace, one must compute the eigenpairs of a matrix derived from the function's gradient, which presents challenges when the gradient is not available as a subroutine. We numerically study two methods for estimating the necessary eigenpairs using only linear measurements of the function's gradient. In practice, these measurements can be estimated by finite differences using only two function evaluations, regardless of the dimension of the function's input space.
Constrained Convolutional-Recurrent Networks to Improve Speech Quality with Low Impact on Recognition Accuracy

For a speech-enhancement algorithm, it is highly desirable to simultaneously improve perceptual quality and recognition rate. Thanks to computational costs and model complexities, it is challenging to train a model that effectively optimizes both metrics at the same time. In this paper, we propose a method for speech enhancement that combines local and global contextual structures information through convolutional-recurrent neural networks that improves perceptual quality. At the same time, we introduce a new constraint on the objective function using a language model/decoder that limits the impact on recognition rate. Based on experiments conducted with real user data, we demonstrate that our new context-augmented machine-learning approach for speech enhancement improves PESQ and WER by an additional 24.5% and 51.3%, respectively, when compared to the best-performing methods in the literature.
An FPGA Implementation of a Time Delay Reservoir Using Stochastic Logic

This article presents and demonstrates a stochastic logic time delay reservoir design in FPGA hardware. The reservoir network approach is analyzed using a number of metrics, such as kernel quality, generalization rank, and performance on simple benchmarks and is also compared to a deterministic design. A novel re-seeding method is introduced to reduce the adverse effects of stochastic noise, which may also be implemented in other stochastic logic reservoir computing designs, such as echo state networks. Benchmark results indicate that the proposed design performs well on noise-tolerant classification problems, but more work needs to be done to improve the stochastic logic time delay reservoir's robustness for regression problems. In addition, we show that the stochastic design can significantly reduce area cost if the conversion between binary and stochastic representations is implemented efficiently.
A warped product version of the Cheeger-Gromoll splitting theorem

We prove a new generalization of the Cheeger-Gromoll splitting theorem where we obtain a warped product splitting under the existence of a line. The curvature condition in our splitting is a curvature dimension inequality of the form $CD(0,1)$. Even though we have to allow warping in our splitting, we are able to recover topological applications. In particular, for a smooth compact Riemannian manifold admitting a density which is $CD(0,1)$, we show that the fundamental group of $M$ is the fundamental group of a compact manifold with nonnegative sectional curvature. If the space is also locally homogeneous, we obtain that the space also admits a metric of non-negative sectional curvature. Both of these obstructions give many examples of Riemannian metrics which do not admit any smooth density which is $CD(0,1)$.
Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems

We consider the problem of allocating radio resources over wireless communication links to control a series of independent wireless control systems. Low-latency transmissions are necessary in enabling time-sensitive control systems with high sampling rates to operate over wireless links. Enabling low-latency through fast data rates comes at the cost of reliability in the form of higher packet error rates due to channel noise. However, the impact of such communication link errors on the control system performance depends dynamically on the control system state. We propose a novel control-aware communication design to the low-latency resource allocation problem. In our proposed method, we incorporate both control and channel state information in scheduling transmissions across time slots, frequency bands, and data rates using the next-generation Wi-Fi scheduling architecture. Control systems that are closer to instability or further from a desired range in a given control cycle are given higher packet delivery rate targets to meet. Rather than a simple priority ranking, we derive precise adaptive packet error rate targets for each system needed to satisfy control-specific performance requirements. We use these adaptive rate targets to make scheduling decisions that reduce total transmission time. The resulting control-aware low-latency scheduling (CALLS) method is tested in numerous simulation experiments that demonstrate its effectiveness in meeting control-based goals under tight latency constraints relative to control-agnostic scheduling.
Cavity optomechanics with Si3N4 membranes at cryogenic temperatures

We describe a cryogenic cavity-optomechanical system that combines Si3N4 membranes with a mechanically rigid Fabry?Perot cavity. The extremely high products of quality factor and frequency of the membranes allow us to cool a MHz mechanical mode to a phonon occupation of , starting at a bath temperature of 5?K. We show that even at cold temperatures thermally occupied mechanical modes of the cavity elements can be a limitation, and we discuss methods to reduce these effects sufficiently for achieving ground state cooling. This promising new platform should have versatile uses for hybrid devices and searches for radiation pressure shot noise.
On Optimizing Distributed Tucker Decomposition for Sparse Tensors

The Tucker decomposition generalizes the notion of Singular Value Decomposition (SVD) to tensors, the higher dimensional analogues of matrices. We study the problem of constructing the Tucker decomposition of sparse tensors on distributed memory systems via the HOOI procedure, a popular iterative method. The scheme used for distributing the input tensor among the processors (MPI ranks) critically influences the HOOI execution time. Prior work has proposed different distribution schemes: an offline scheme based on sophisticated hypergraph partitioning method and simple, lightweight alternatives that can be used real-time. While the hypergraph based scheme typically results in faster HOOI execution time, being complex, the time taken for determining the distribution is an order of magnitude higher than the execution time of a single HOOI iteration. Our main contribution is a lightweight distribution scheme, which achieves the best of both worlds. We show that the scheme is near-optimal on certain fundamental metrics associated with the HOOI procedure and as a result, near-optimal on the computational load (FLOPs). Though the scheme may incur higher communication volume, the computation time is the dominant factor and as the result, the scheme achieves better performance on the overall HOOI execution time. Our experimental evaluation on large real-life tensors (having up to 4 billion elements) shows that the scheme outperforms the prior schemes on the HOOI execution time by a factor of up to 3x. On the other hand, its distribution time is comparable to the prior lightweight schemes and is typically lesser than the execution time of a single HOOI iteration.
Ordinary primes for Abelian surfaces

Abstract We compute the density of the set of ordinary primes of an Abelian surface over a number field in terms of the l -adic monodromy group.
Invariant contact structures on 7-dimensional nilmanifolds

In this paper we give the list of all 7-dimensional nilpotent real Lie algebras that admit a contact structure. Based on this list, we describe all 7-dimensional nilmanifolds that admit an invariant contact structure.
On the Lagrangian minimal surface equation and related problems

We give a survey of various existence results for minimal Lagrangian graphs. We also discuss the mean curvature flow for Lagrangian graphs.
Visualizing and Understanding Neural Models in NLP

While neural networks have been successfully applied to many NLP tasks the resulting vectorbased models are very difficult to interpret. For example it’s not clear how they achieve compositionality, building sentence meaning from the meanings of words and phrases. In this paper we describe strategies for visualizing compositionality in neural models for NLP, inspired by similar work in computer vision. We first plot unit values to visualize compositionality of negation, intensification, and concessive clauses, allowing us to see wellknown markedness asymmetries in negation. We then introduce methods for visualizing a unit’s salience, the amount that it contributes to the final composed meaning from first-order derivatives. Our general-purpose methods may have wide applications for understanding compositionality and other semantic properties of deep networks.
Unleashing the power of Schrijver's permanental inequality with the help of the Bethe Approximation.

Let A ∈ Ωn be doubly-stochastic n × n matrix. Alexander Schrijver proved in 1998 the following remarkable inequality per(A) ≥ ∏ 1≤i,j≤n (1−A(i, j)); A(i, j) =: A(i, j)(1−A(i, j)), 1 ≤ i, j ≤ n (1) We prove in this paper the following generalization (or just clever reformulation) of (1): For all pairs of n × n matrices (P,Q), where P is nonnegative and Q is doublystochastic log(per(P )) ≥ ∑ 1≤i,j≤n log(1−Q(i, j))(1−Q(i, j))− ∑ 1≤i,j≤n Q(i, j) log ( Q(i, j) P (i, j) ) (2) The main co rollary of (2) is the following inequality for doubly-stochastic matrices: per(A) F (A) ≥ 1;F (A) =: ∏ 1≤i,j≤n (1−A(i, j))1−A(i,j) . We use this inequality to prove Friedland’s conjecture on monomerdimer entropy, so called Asymptotic Lower Matching Conjecture We present explicit doubly-stochastic n×n matrices A with the ratio per(A) F (A) = √ 2 n and conjecture that max A∈Ωn per(A) F (A) ≈ (√ 2 )n . If true, it would imply a deterministic poly-time algorithm to approximate the permanent of n× n nonnegative matrices within the relative factor (√ 2 )n . ∗gurvits@lanl.gov. Los Alamos National Laboratory, Los Alamos, NM.
Separability of reproducing kernel spaces

We demonstrate that a reproducing kernel Hilbert or Banach space of functions on a separable absolute Borel space or an analytic subset of a Polish space is separable if it possesses a Borel measurable feature map.
Encoding Neural and Synaptic Functionalities in Electron Spin: A Pathway to Efficient Neuromorphic Computing

Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform every day. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this paper attempts to provide a review of the recent developments in the field of spintronic device based neuromorphic computing. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing device structures mimicking neural and synaptic functionalities is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations.
Functors from association schemes

Abstract We construct a wide subcategory of the category of finite association schemes with a collection of desirable properties. Our subcategory has a first isomorphism theorem analogous to that of groups. Also, standard constructions taking schemes to groups (thin radicals and quotients by thin residues) or algebras (adjacency algebras) become functorial when restricted to our category. We use our category to give a more conceptual account for a result of Hanaki concerning products of characters of association schemes; i.e. we show that the virtual representations of an association scheme form a module over the representation ring of the thin quotient of the association scheme.
Blow up for the 2D Euler equation on some bounded domains

Abstract We find a smooth solution of the 2D Euler equation on a bounded domain which exists and is unique in a natural class locally in time, but blows up in finite time in the sense of its vorticity losing continuity. The domain's boundary is smooth except at two points, which are interior cusps.
Pretty good quantum state transfer in asymmetric graphs via potential

Abstract We construct infinite families of graphs in which pretty good state transfer can be induced by adding a potential to the nodes of the graph (i.e. adding a number to a diagonal entry of the adjacency matrix). Indeed, we show that given any graph with a pair of cospectral nodes, a simple modification of the graph, along with a suitable potential, yields pretty good state transfer between the nodes. This generalizes previous work, concerning graphs with an involution, to asymmetric graphs.
Full Speed Ahead: 3D Spatial Database Acceleration with GPUs

Many industries rely on visual insights to support decision- making processes in their businesses. In mining, the analysis of drills and geological shapes, represented as 3D geometries, is an important tool to assist geologists on the search for new ore deposits. Aeronautics manipulate high-resolution geometries when designing a new aircraft aided by the numerical simulation of aerodynamics. In common, these industries require scalable databases that compute spatial relationships and measurements so that decision making can be conducted without lags. However, as we show in this study, most database systems either lack support for handling 3D geometries or show poor performance when given a sheer volume of data to work with. This paper presents a pluggable acceleration engine for spatial database systems that can improve the performance of spatial operations by more than 3000x through GPU offloading. We focus on the design and evaluation of our plug-in for the PostgreSQL database system.
Adjustable Real-time Style Transfer

Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another. Although recent feed-forward neural networks can generate stylized images in real-time, these models produce a single stylization given a pair of style/content images, and the user doesn't have control over the synthesized output. Moreover, the style transfer depends on the hyper-parameters of the model with varying ``optimum" for different input images. Therefore, if the stylized output is not appealing to the user, she/he has to try multiple models or retrain one with different hyper-parameters to get a favorite stylization. In this paper, we address these issues by proposing a novel method which allows adjustment of crucial hyper-parameters, after the training and in real-time, through a set of manually adjustable parameters. These parameters enable the user to modify the synthesized outputs from the same pair of style/content images, in search of a favorite stylized image. Our quantitative and qualitative experiments indicate how adjusting these parameters is comparable to retraining the model with different hyper-parameters. We also demonstrate how these parameters can be randomized to generate results which are diverse but still very similar in style and content.
A review of reinforcement learning for autonomous building energy management

Abstract The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. Energy savings of greater than 20% are reported in the literature for more complex building energy management problems when implementing reinforcement learning. The main direction for future research and challenges in reinforcement learning are also outlined.
The Computational Complexity of Integer Programming with Alternations

We prove that integer programming with three alternating quantifiers is NP-complete, even for a fixed number of variables. This complements earlier results by Lenstra [16] [Lenstra H (1983) Integer...
Performance of gene expression analyses using de novo assembled transcripts in polyploid species

Motivation Quality of gene expression analyses using de novo assembled transcripts in species that experienced recent polyploidization remains unexplored. Results Differential gene expression (DGE) analyses using putative genes inferred by Trinity, Corset and Grouper performed slightly differently across five plant species that experienced various polyploidy histories. In species that lack recent polyploidy events that occurred in the past several millions of years, DGE analyses using de novo assembled transcriptomes identified 54-82% of the differentially expressed genes recovered by mapping reads to the reference genes. However, in species that experienced more recent polyploidy events, the percentage decreased to 21-65%. Gene co-expression network analyses using de novo assemblies versus mapping to the reference genes recovered the same module that significantly correlated with treatment in one species that lacks recent polyploidization. Availability and implementation Commands and scripts used in this study are available at https://bitbucket.org/lychen83/chen_et_al_2018_benchmark_dge/; Analysis files are available at Dryad doi: 10.5061/dryad.4p6n481. Supplementary information Supplementary data are available at Bioinformatics online.
Resilient Leader-Follower Consensus to Arbitrary Reference Values in Time-Varying Graphs

Several algorithms in prior literature have been proposed, which guarantee the consensus of normally behaving agents in a network that may contain adversarially behaving agents. These algorithms guarantee that the consensus value lies within the convex hull of initial normal agents’ states, with the exact consensus value possibly being unknown. In leader-follower consensus problems, however, the objective is for normally behaving agents to track a reference state that may take on values outside of this convex hull. In this paper, we present methods for agents in time-varying graphs with discrete-time dynamics to resiliently track a reference state propagated by a set of leaders, despite a bounded subset of the leaders and followers behaving adversarially. Our results are demonstrated through simulations.