cssi/SciSciGPT-SciSciNet huggingface dataset:
Towards Efficient Neural Networks On-A-Chip: Joint Hardware-Algorithm Approaches

Machine learning algorithms have made significant advances in many applications. However, their hardware implementation on the state-of-the-art platforms still faces several challenges and are limited by various factors, such as memory volume, memory bandwidth and interconnection overhead. The adoption of the crossbar architecture with emerging memory technology partially solves the problem but induces process variation and other concerns. In this paper, we will present novel solutions to two fundamental issues in crossbar implementation of Artificial Intelligence (AI) algorithms: device variation and insufficient interconnections. These solutions are inspired by the statistical properties of algorithms themselves, especially the redundancy in neural network nodes and connections. By Random Sparse Adaptation and pruning the connections following the Small-World model, we demonstrate robust and efficient performance on representative datasets such as MNIST and CIFAR-10. Moreover, we present Continuous Growth and Pruning algorithm for future learning and adaptation on hardware.
Women at Work : How Can Investment Incentives be Used to Enhance Economic Opportunities for Women?

Around the world, women face persistent barriers in seeking to enter the labor force, attain leadership positions, and launch businesses. Gender inequality not only prevents women from achieving their full potential and gaining economic independence, but also inhibits the growth of national economies. In a context of growing global competition for private investment, policymakers face the timely challenge of ensuring that women are not left behind in the development agenda. This working paper identifies and analyzes investment incentives that governments can provide to businesses with the aim of promoting gender equality. Barriers to gender equality in the workplace include supply-side barriers that make it difficult for women to find jobs or investment financing, and demand-side barriers that make it more costly for firms or investors to hire or fund women. The paper discusses three main types of investment incentives that governments may use to address these barriers: (i) subsidies and grants, (ii) tax incentives, and (iii) public procurement incentives. Because incentives run the risk of creating market inefficiencies and leading to redundant outcomes, their use should be carefully conceived and implemented. The effectiveness of these instruments will depend on the underlying constraints to gender equality, the targeted policy goals, implementation features, and the broader enabling environment. Relevant studies and experiences from several countries are used to explore when and how governments might use investment incentives to promote gender equality.
Hyperstructures of affine algebraic group schemes

Abstract We impose a rather unknown algebraic structure called a ‘hyperstructure’ to the underlying space of an affine algebraic group scheme. This algebraic structure generalizes the classical group structure and is canonically defined by the structure of a Hopf algebra of global sections. This paper partially generalizes the result of A. Connes and C. Consani in [1] .
Random Partitions and Cohen–Lenstra Heuristics

We investigate combinatorial properties of a family of probability distributions on finite abelian p-groups. This family includes several well-known distributions as specializations. These specializations have been studied in the context of Cohen–Lenstra heuristics and cokernels of families of random p-adic matrices.
Vectors of matroids over tracts

Abstract We enrich Baker and Bowler's theory of matroids over tracts with notions of vectors and covectors. In the case of oriented matroids, these F-vectors and F-covectors coincide with the usual signed vectors and signed covectors, and in the case of ordinary matroids, they are essentially the unions of circuits resp. unions of cocircuits. In the case of matroids over a field F, the F-covector set resp. F-vector set of an F-matroid is a linear subspace of F E resp. its orthogonal complement.
An A Posteriori KAM Theorem for Whiskered Tori in Hamiltonian Partial Differential Equations with Applications to some Ill-Posed Equations

The goal of this paper is to develop a KAM theory for tori with hyperbolic directions, which applies to Hamiltonian partial differential equations, even to some ill-posed ones. The main result has an a-posteriori format, that is, we show that if there is an approximate solution of an invariance equation which also satisfies some non-degeneracy conditions, then there is a true solution nearby. This allows, besides dealing with the quasi-integrable case, for the validation of numerical computations or formal perturbative expansions as well as for obtaining quasi-periodic solutions in degenerate situations. The a-posteriori format also has other automatic consequences (smooth dependence on parameters, bootstrap of regularity, etc.). We emphasize that the non-degeneracy conditions required are just quantities evaluated on the approximate solution (no global assumptions on the system such as twist). Hence, they are readily verifiable in perturbation expansions. We will pay attention to the quantitative relations between the sizes of the approximation and the non-degeneracy conditions. This will allow us to prove what experts call small twist theorems (the non-degeneracy conditions vanishes as the perturbation goes to zero but much slower than the error of the approximation). The method of proof is based on an iterative method for solving a functional equation for the parameterization of the torus expressing that the range of the parameterization admits an evolution and is invariant. We also solve functional equations for bundles which imply that are invariant under the linearization. The iterative method does not use transformation theory nor action-angle variables. The main result does not assume that the system is close to integrable. More surprisingly, we do not need that the equations we study define an evolution for all initial conditions and are well posed. Even if the systems we study do not admit solutions for all initial conditions, we show that there is a systematic way to choose initial conditions on which one can define an evolution which is quasi-periodic. We first develop an abstract theorem. Then, we show how this abstract result applies to some concrete examples. The examples considered in this paper are the scalar Boussinesq equation and the Boussinesq system (both are PDE models that aim to describe water waves in the long wave limit). For these equations we construct small amplitude time quasi-periodic solutions which are even in the spatial variable. The strategy for the abstract theorem is inspired by that in Fontich et al. (Electron Res Announc Math Sci 16:9–22, 2009; J Differ Equ 246(8):3136–3213, 2009). The main part of the paper is to study infinite dimensional analogues of dichotomies which applies even to ill-posed equations and which is stable under addition of unbounded perturbations. This requires that we assume smoothing properties. We also present very detailed bounds on the change of the splittings under perturbations.
Laser-Induced Spallation of Microsphere Monolayers

The detachment of a semiordered monolayer of polystyrene microspheres adhered to an aluminum-coated glass substrate is studied using a laser-induced spallation technique. The microsphere–substrate adhesion force is estimated from substrate surface displacement measurements obtained using optical interferometry, and a rigid-body model that accounts for the inertia of the microspheres. The estimated adhesion force is compared with estimates obtained using an adhesive contact model together with interferometric measurements of the out-of-plane microsphere contact resonance, and with estimated work of adhesion values for the polystyrene–aluminum interface. Scanning electron microscope images of detached monolayer regions reveal a unique morphology, namely, partially detached monolayer flakes composed of single hexagonal close packed crystalline domains. This work contributes to the fields of microsphere adhesion and contact dynamics, and demonstrates a unique monolayer delamination morphology.
Accounting for errors in quantum algorithms via individual error reduction

We discuss a surprisingly simple scheme for accounting (and removal) of error in observables determined from quantum algorithms. A correction to the value of the observable is calculated by first measuring the observable with all error sources active and subsequently measuring the observable with each error source reduced separately. We apply this scheme to the variational quantum eigensolver, simulating the calculation of the ground state energy of equilibrium H2 and LiH in the presence of several noise sources, including amplitude damping, dephasing, thermal noise, and correlated noise. We show that this scheme provides a decrease in the needed quality of the qubits by up to two orders of magnitude. In near-term quantum computing, where full fault-tolerant error correction is too expensive, this scheme provides a route to significantly more accurate calculations. The errors in quantum computing can be strongly reduced exploiting partial removal and subsequent estimation of the residual error. Matthew Otten and Stephen Gray from Argonne National Laboratory have devised a simple scheme that should be able to greatly reduce the practical requirements for quantum devices in terms of cleanliness of operation, both for computation and for some quantum sensing protocol. The approach requires being able to actively reduce, of a certain amount, each error source separately, and measure the quantity to be calculated separately each time. Then, the authors show that it is possible to combine these noisy measurements together and get a reliable estimation of the noiseless result. Numerical simulations prove that the scheme would work for many different types of noise.
MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings

E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell billions of products. Machine learning (ML) algorithms involving products are often used to improve the customer experience and increase revenue, e.g., product similarity, recommendation, and price estimation. The products are required to be represented as features before training an ML algorithm. In this paper, we propose an approach called MRNet-Product2Vec for creating generic embeddings of products within an e-commerce ecosystem. We learn a dense and low-dimensional embedding where a diverse set of signals related to a product are explicitly injected into its representation. We train a Discriminative Multi-task Bidirectional Recurrent Neural Network (RNN), where the input is a product title fed through a Bidirectional RNN and at the output, product labels corresponding to fifteen different tasks are predicted. The task set includes several intrinsic characteristics about a product such as price, weight, size, color, popularity, and material. We evaluate the proposed embedding quantitatively and qualitatively. We demonstrate that they are almost as good as sparse and extremely high-dimensional TF-IDF representation in spite of having less than 3% of the TF-IDF dimension. We also use a multimodal autoencoder for comparing products from different language-regions and show preliminary yet promising qualitative results.
Empirical likelihood for linear structural equation models with dependent errors

We consider linear structural equation models that are associated with mixed graphs. The structural equations in these models only involve observed variables, but their idiosyncratic error terms are allowed to be correlated and non-Gaussian. We propose empirical likelihood procedures for inference and suggest several modifications, including a profile likelihood, in order to improve tractability and performance of the resulting methods. Through simulations, we show that when the error distributions are non-Gaussian, the use of empirical likelihood and the proposed modifications may increase statistical efficiency and improve assessment of significance. Copyright © 2017 John Wiley & Sons, Ltd.
Size-dependent phase morphologies in LiFePO4 battery particles

Abstract Lithium iron phosphate (LiFePO4) is the prototypical two-phase battery material whose complex patterns of lithium ion intercalation provide a testing ground for theories of electrochemical thermodynamics. Using a depth-averaged (a-b plane) phase-field model of coherent phase separation driven by Faradaic reactions, we reconcile conflicting experimental observations of diamond-like phase patterns in micron-sized platelets with observations of surface-controlled patterns in nanoparticles. Elastic analysis predicts this morphological transition for particles whose a-axis dimension exceeds twice the bulk elastic stripe period. We also simulate a rich variety of non-equilibrium patterns, influenced by size-dependent spinodal points and electro-autocatalytic control of thermodynamic stability.
Structured Optimal Transport

Optimal Transport has recently gained interest in machine learning for applications ranging from domain adaptation, sentence similarities to deep learning. Yet, its ability to capture frequently occurring structure beyond the "ground metric" is limited. In this work, we develop a nonlinear generalization of (discrete) optimal transport that is able to reflect much additional structure. We demonstrate how to leverage the geometry of this new model for fast algorithms, and explore connections and properties. Illustrative experiments highlight the benefit of the induced structured couplings for tasks in domain adaptation and natural language processing.
Asynchronous Performance of Circularly Pulse-Shaped Waveforms for 5G

The fifth generation of wireless networks (5G) necessitates the use of waveforms with loose constraints on synchronization in multiuser scenarios. Also, carrier aggregation, as a way to better utilize the spectrum in 5G, needs a waveform with low out-of-band (OOB) emissions. Generalized frequency division multiplexing (GFDM) and circular filter bank multicarrier (C-FBMC) are two candidate waveforms that fulfill these requirements. Both GFDM and C-FBMC operate based on circular convolution, and use cyclic prefix to combat channel response. Because of obvious reasons, we refer to these waveform as circularly pulse-shaped. In this paper, we develop an analytical technique for examining the OOB emissions and multiuser interference (MUI) in circularly pulse-shaped waveforms. To stay focused, the study in this paper is limited to C-FBMC modulation. However, the approach we take is trivially extendable to other waveforms (including GFDM) as well. We derive equations that quantify OOB emissions and MUI. Our analysis allows us to identify the source of OOB emissions and MUI. This leads us to quantify the methods proposed by other researchers to decrease OOB emissions and MUI. Moreover, we quantify the impact of signal windowing at the transmitter and receiver in reducing OOB emissions and MUI, respectively.
THE ARCHES CLUSTER: EXTENDED STRUCTURE and TIDAL RADIUS

Author(s): Hosek, MW; Lu, JR; Anderson, J; Ghez, AM; Morris, MR; Clarkson, WI | Abstract: At a projected distance of ∼26 pc from Sgr A∗, the Arches cluster provides insight into star formation in the extreme Galactic center (GC) environment. Despite its importance, many key properties, such as the cluster's internal structure and orbital history, are not well known. We present an astrometric and photometric study of the outer region of the Arches cluster (R g 6.″25) using Hubble Space Telescope WFC3IR. Using proper motions, we calculate membership probabilities for stars down to F153M = 20 mag (∼2.5 Mo) over a 120″ × 120″ field of view, an area 144 times larger than previous astrometric studies of the cluster. We construct the radial profile of the Arches to a radius of 75″ (∼3 pc at 8 kpc), which can be well described by a single power law. From this profile we place a 3σ lower limit of 2.8 pc on the observed tidal radius, which is larger than the predicted tidal radius (1-2.5 pc). Evidence of mass segregation is observed throughout the cluster, and no tidal tail structures are apparent along the orbital path. The absence of breaks in the profile suggests that the Arches has not likely experienced its closest approach to the GC between ∼0.2 and 1 Myr ago. If accurate, this constraint indicates that the cluster is on a prograde orbit and is located in front of the sky plane that intersects Sgr A∗. However, further simulations of clusters in the GC potential are required to interpret the observed profile with more confidence.
Schur complement domain decomposition methods for the solution of multiple scattering problems

We present a Schur complement Domain Decomposition (DD) algorithm for the solution of frequency domain multiple scattering problems. Just as in the classical DD methods we (1) enclose the ensemble of scatterers in a domain bounded by an artificial boundary, (2) we subdivide this domain into a collection of nonoverlapping subdomains so that the boundaries of the subdomains do not intersect any of the scatterers, and (3) we connect the solutions of the subproblems via Robin boundary conditions matching on the common interfaces between subdomains. We use subdomain Robin-to-Robin maps to recast the DD problem as a sparse linear system whose unknown consists of Robin data on the interfaces between subdomains---two unknowns per interface. The Robin-to-Robin maps are computed in terms of well-conditioned boundary integral operators. Unlike classical DD, we do not reformulate the Domain Decomposition problem in the form a fixed point iteration, but rather we solve the ensuing linear system by Gaussian elimination of the unknowns corresponding to inner interfaces between subdomains via Schur complements. Once all the unknowns corresponding to inner subdomains interfaces have been eliminated, we solve a much smaller linear system involving unknowns on the inner and outer artificial boundary. We present numerical evidence that our Schur complement DD algorithm can produce accurate solutions of very large multiple scattering problems that are out of reach for other existing approaches.
Deep Abstract Q-Networks

We examine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards. Recent approaches have shown great successes in many Atari 2600 domains. However, domains with long horizons and sparse rewards, such as Montezuma's Revenge and Venture, remain challenging for existing methods. Methods using abstraction (Dietterich 2000; Sutton, Precup, and Singh 1999) have shown to be useful in tackling long-horizon problems. We combine recent techniques of deep reinforcement learning with existing model-based approaches using an expert-provided state abstraction. We construct toy domains that elucidate the problem of long horizons, sparse rewards and high-dimensional inputs, and show that our algorithm significantly outperforms previous methods on these domains. Our abstraction-based approach outperforms Deep Q-Networks (Mnih et al. 2015) on Montezuma's Revenge and Venture, and exhibits backtracking behavior that is absent from previous methods.
Directional Wavelet Bases Constructions with Dyadic Quincunx Subsampling

We construct directional wavelet systems that will enable building efficient signal representation schemes with good direction selectivity. In particular, we focus on wavelet bases with dyadic quincunx subsampling. In our previous work (Yin, in: Proceedings of the 2015 international conference on sampling theory and applications (SampTA), 2015), we show that the supports of orthonormal wavelets in our framework are discontinuous in the frequency domain, yet this irregularity constraint can be avoided in frames, even with redundancy factor <2. In this paper, we focus on the extension of orthonormal wavelets to biorthogonal wavelets and show that the same obstruction of regularity as in orthonormal schemes exists in biorthogonal schemes. In addition, we provide a numerical algorithm for biorthogonal wavelets construction where the dual wavelets can be optimized, though at the cost of deteriorating the primal wavelets due to the intrinsic irregularity of biorthogonal schemes.
On the AJ conjecture for cable knots

We study the AJ conjecture for $(r,2)$-cables of a knot, where $r$ is an odd integer. Using skein theory, we show that the AJ conjecture holds true for most $(r,2)$-cables of some classes of two-bridge knots and pretzel knots.
Statistical analysis of metric graph reconstruction

A metric graph is a 1-dimensional stratified metric space consisting of vertices and edges or loops glued together. Metric graphs can be naturally used to represent and model data that take the form of noisy filamentary structures, such as street maps, neurons, networks of rivers and galaxies. We consider the statistical problem of reconstructing the topology of a metric graph embedded in RD from a random sample. We derive lower and upper bounds on the minimax risk for the noiseless case and tubular noise case. The upper bound is based on the reconstruction algorithm given in Aanjaneya et al. (2012).
Parsimonious Bayesian deep networks

Combining Bayesian nonparametrics and a forward model selection strategy, we construct parsimonious Bayesian deep networks (PBDNs) that infer capacity-regularized network architectures from the data and require neither cross-validation nor fine-tuning when training the model. One of the two essential components of a PBDN is the development of a special infinite-wide single-hidden-layer neural network, whose number of active hidden units can be inferred from the data. The other one is the construction of a greedy layer-wise learning algorithm that uses a forward model selection criterion to determine when to stop adding another hidden layer. We develop both Gibbs sampling and stochastic gradient descent based maximum a posteriori inference for PBDNs, providing state-of-the-art classification accuracy and interpretable data subtypes near the decision boundaries, while maintaining low computational complexity for out-of-sample prediction.
Exact Semidefinite Formulations for a Class of (Random and Non-Random) Nonconvex Quadratic Programs

We study a class of quadratically constrained quadratic programs (QCQPs), called diagonal QCQPs, which contain no off-diagonal terms $$x_j x_k$$ for $$j \ne k$$ , and we provide a sufficient condition on the problem data guaranteeing that the basic Shor semidefinite relaxation is exact. Our condition complements and refines those already present in the literature and can be checked in polynomial time. We then extend our analysis from diagonal QCQPs to general QCQPs, i.e., ones with no particular structure. By reformulating a general QCQP into diagonal form, we establish new, polynomial-time-checkable sufficient conditions for the semidefinite relaxations of general QCQPs to be exact. Finally, these ideas are extended to show that a class of random general QCQPs has exact semidefinite relaxations with high probability as long as the number of constraints grows no faster than a fixed polynomial in the number of variables. To the best of our knowledge, this is the first result establishing the exactness of the semidefinite relaxation for random general QCQPs.
FRAPpuccino: fault-detection through runtime analysis of provenance

We present FRAPpuccino (or FRAP), a provenance-based fault detection mechanism for Platform as a Service (PaaS) users, who run many instances of an application on a large cluster of machines. FRAP models, records, and analyzes the behavior of an application and its impact on the system as a directed acyclic provenance graph. It assumes that most instances behave normally and uses their behavior to construct a model of legitimate behavior. Given a model of legitimate behavior, FRAP uses a dynamic sliding window algorithm to compare a new instance's execution to that of the model. Any instance that does not conform to the model is identified as an anomaly. We present the FRAP prototype and experimental results showing that it can accurately detect application anomalies.
Residual diffusivity in elephant random walk models with stops

We study the enhanced diffusivity in the so called elephant random walk model with stops (ERWS) by including symmetric random walk steps at small probability $\epsilon$. At any $\epsilon > 0$, the large time behavior transitions from sub-diffusive at $\epsilon = 0$ to diffusive in a wedge shaped parameter regime where the diffusivity is strictly above that in the un-perturbed ERWS model in the $\epsilon \downarrow 0$ limit. The perturbed ERWS model is shown to be solvable with the first two moments and their asymptotics calculated exactly in both one and two space dimensions. The model provides a discrete analytical setting of the residual diffusion phenomenon known for the passive scalar transport in chaotic flows (e.g. generated by time periodic cellular flows and statistically sub-diffusive) as molecular diffusivity tends to zero.
Hiding in plain sight: New virus genomes discovered via a systematic analysis of fungal public transcriptomes

The distribution and diversity of RNA viruses in fungi is incompletely understood due to the often cryptic nature of mycoviral infections and the focused study of primarily pathogenic and/or economically important fungi. As most viruses that are known to infect fungi possess either single-stranded or double-stranded RNA genomes, transcriptomic data provides the opportunity to query for viruses in diverse fungal samples without any a priori knowledge of virus infection. Here we describe a systematic survey of all transcriptomic datasets from fungi belonging to the subphylum Pezizomycotina. Using a simple but effective computational pipeline that uses reads discarded during normal RNA-seq analyses, followed by identification of a viral RNA-dependent RNA polymerase (RdRP) motif in de novo assembled contigs, 59 viruses from 44 different fungi were identified. Among the viruses identified, 88% were determined to be new species and 68% are, to our knowledge, the first virus described from the fungal species. Comprehensive analyses of both nucleotide and inferred protein sequences characterize the phylogenetic relationships between these viruses and the known set of mycoviral sequences and support the classification of up to four new families and two new genera. Thus the results provide a deeper understanding of the scope of mycoviral diversity while also increasing the distribution of fungal hosts. Further, this study demonstrates the suitability of analyzing RNA-seq data to facilitate rapid discovery of new viruses.
Self-Attentive Sequential Recommendation

Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the 'context' of users' activities on the basis of actions they have performed recently. To capture such patterns, two approaches have proliferated: Markov Chains (MCs) and Recurrent Neural Networks (RNNs). Markov Chains assume that a user's next action can be predicted on the basis of just their last (or last few) actions, while RNNs in principle allow for longer-term semantics to be uncovered. Generally speaking, MC-based methods perform best in extremely sparse datasets, where model parsimony is critical, while RNNs perform better in denser datasets where higher model complexity is affordable. The goal of our work is to balance these two goals, by proposing a self-attention based sequential model (SASRec) that allows us to capture long-term semantics (like an RNN), but, using an attention mechanism, makes its predictions based on relatively few actions (like an MC). At each time step, SASRec seeks to identify which items are 'relevant' from a user's action history, and use them to predict the next item. Extensive empirical studies show that our method outperforms various state-of-the-art sequential models (including MC/CNN/RNN-based approaches) on both sparse and dense datasets. Moreover, the model is an order of magnitude more efficient than comparable CNN/RNN-based models. Visualizations on attention weights also show how our model adaptively handles datasets with various density, and uncovers meaningful patterns in activity sequences.
The role of visual saliency in the automation of seismic interpretation

In this paper, we propose a workflow based on SalSi for the detection and delineation of geological structures such as salt domes. SalSi is a seismic attribute designed based on the modeling of human visual system that detects the salient features and captures the spatial correlation within seismic volumes for delineating seismic structures. Using SalSi, we can not only highlight the neighboring regions of salt domes to assist a seismic interpreter but also delineate such structures using a region growing method and post-processing. The proposed delineation workflow detects the salt-dome boundary with very good precision and accuracy. Experimental results show the effectiveness of the proposed workflow on a real seismic dataset acquired from the North Sea, F3 block. For the subjective evaluation of the results of different salt-dome delineation algorithms, we have used a reference salt-dome boundary interpreted by a geophysicist. For the objective evaluation of results, we have used five different metrics based on pixels, shape, and curvedness to establish the effectiveness of the proposed workflow. The proposed workflow is not only fast but also yields better results as compared to other salt-dome delineation algorithms and shows a promising potential in seismic interpretation. This article is protected by copyright. All rights reserved
Decomposition of Nonlinear Dynamical Networks Via Comparison Systems

In analysis and control of large-scale nonlinear dynamical systems, a distributed approach is often an attractive option due to its computational tractability and usually low communication requirements. Success of the distributed control design relies on the separability of the network into weakly interacting subsystems such that minimal information exchange between subsystems is sufficient to achieve satisfactory control performance. While distributed analysis and control design for dynamical network have been well studied, decomposition of nonlinear networks into weakly interacting subsystems has not received as much attention. In this article we propose a vector Lyapunov functions based approach to quantify the energy-flow in a dynamical network via a model of a comparison system. Introducing a notion of power and energy flow in a dynamical network, we use sum-of-squares programming tools to partition polynomial networks into weakly interacting subsystems. Examples are provided to illustrate the proposed method of decomposition.
Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech

In this paper, we propose a novel deep neural network architecture, Speech2Vec, for learning fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the underlying spoken words, and are close to other vectors in the embedding space if their corresponding underlying spoken words are semantically similar. The proposed model can be viewed as a speech version of Word2Vec. Its design is based on a RNN Encoder-Decoder framework, and borrows the methodology of skipgrams or continuous bag-of-words for training. Learning word embeddings directly from speech enables Speech2Vec to make use of the semantic information carried by speech that does not exist in plain text. The learned word embeddings are evaluated and analyzed on 13 widely used word similarity benchmarks, and outperform word embeddings learned by Word2Vec from the transcriptions.
The Geometry of Model Spaces for Probability-Preserving Actions of Sofic Groups

Bowen's notion of sofic entropy is a powerful invariant for classifying probability-preserving actions of sofic groups. It can be defined in terms of the covering numbers of certain metric spaces associated to such an action, the `model spaces'. The metric geometry of these model spaces can exhibit various interesting features, some of which provide other invariants of the action. This paper explores an approximate connectedness property of the model spaces, and uses it give a new proof that certain groups admit factors of Bernoulli shifts which are not Bernoulli. This was originally proved by Popa. Our proof covers fewer examples than his, but provides additional information about this phenomenon.
Exact relations for Green's functions in linear PDE and boundary field equalities: a generalization of conservation laws

Many physical equations have the form $$\mathbf{J }(\mathbf{x })=\mathbf{L }(\mathbf{x })\mathbf{E }(\mathbf{x })-\mathbf{h }(\mathbf{x })$$ with source $$\mathbf{h }(\mathbf{x })$$ and fields $$\mathbf{E }$$ and $$\mathbf{J }$$ satisfying differential constraints, symbolized by $$\mathbf{E }\in \mathcal E$$ , $$\mathbf{J }\in \mathcal J$$ where $$\mathcal E$$ , $$\mathcal J$$ are orthogonal spaces. We show that if $$\mathbf{L }(\mathbf{x })$$ takes values in certain nonlinear manifolds $$\mathcal M$$ , and coercivity and boundedness conditions hold, then the infinite body Green’s function (fundamental solution) satisfies exact identities. The theory also links Green’s functions of different problems. The analysis is based on the theory of exact relations for composites, but without assumptions about the length scales of variations in $$\mathbf{L }(\mathbf{x })$$ , and more general equations, such as for waves in lossy media, are allowed. For bodies $$\Omega $$ , inside which $$\mathbf{L }(\mathbf{x })\in \mathcal{M}$$ , the “Dirichlet-to-Neumann map” giving the response also satisfies exact relations. These boundary field equalities generalize the notion of conservation laws: the field inside $$\Omega $$ satisfies certain constraints that leave a wide choice in these fields, but which give identities satisfied by the boundary fields, and moreover provide constraints on the fields inside the body. A consequence is the following: if a matrix-valued field $$\mathbf{Q }(\mathbf{x })$$ with divergence-free columns takes values within $$\Omega $$ in a set $$\mathcal B$$ (independent of $$\mathbf{x }$$ ) that lies on a nonlinear manifold, we find conditions on the manifold, and on $$\mathcal B$$ , that with appropriate conditions on the boundary fluxes $$\mathbf{q }(\mathbf{x })=\mathbf{n }(\mathbf{x })\cdot \mathbf{Q }(\mathbf{x })$$ (where $$\mathbf{n }(\mathbf{x })$$ is the outward normal to $$\partial \Omega $$ ) force $$\mathbf{Q }(\mathbf{x })$$ within $$\Omega $$ to take values in a subspace $$\mathcal D$$ . This forces $$\mathbf{q }(\mathbf{x })$$ to take values in $$\mathbf{n }(\mathbf{x })\cdot \mathcal D$$ . We find there are additional divergence-free fields inside $$\Omega $$ that in turn generate additional boundary field equalities. Consequently, there exist partial null Lagrangians, functionals $$F(\mathbf{w },\nabla \mathbf{w })$$ of a vector potential $$\mathbf{w }$$ and its gradient that act as null Lagrangians when $$\nabla \mathbf{w }$$ is constrained for $$\mathbf{x }\in \Omega $$ to take values in certain sets $$\mathcal A$$ , of appropriate nonlinear manifolds, and when $$\mathbf{w }$$ satisfies appropriate boundary conditions. The extension to certain nonlinear minimization problems is also sketched.
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function.

Gender bias exists in natural language datasets, which neural language models tend to learn, resulting in biased text generation. In this research, we propose a debiasing approach based on the loss function modification. We introduce a new term to the loss function which attempts to equalize the probabilities of male and female words in the output. Using an array of bias evaluation metrics, we provide empirical evidence that our approach successfully mitigates gender bias in language models without increasing perplexity. In comparison to existing debiasing strategies, data augmentation, and word embedding debiasing, our method performs better in several aspects, especially in reducing gender bias in occupation words. Finally, we introduce a combination of data augmentation and our approach and show that it outperforms existing strategies in all bias evaluation metrics.
Nanowire Gold Chains: Formation Mechanisms and Conductance

Structural transformations, electronic spectra, and ballistic transport in pulled gold nanowires are investigated with ab initio simulations, and correlated with recent measurements. Strain-induced yield of an initial double-strand wire results first in formation of a bent chain which transforms upon further elongation to a linear atomic chain exhibiting dimerized atomic configurations. These structures are stabilized by directional local bonding with spd hybridization. The conductance of the initial double-stranded contact is close to 2(2e2/h) ≡ 2g0, and it drops sharply to 1g0 during the transformation to a single chain, exhibiting subsequently a ∼1g0 plateau extending over an elongation well above typical Au−Au distances.
Involutive filters of pseudo-hoops

In this paper, we introduce the notion of involutive filters of pseudo-hoops, and we emphasize their role in the probability theory on these structures. Characterizations of involutive pseudo-hoops are given, and their properties are investigated. We give a characterization of involutive filters of a good pseudo-hoop, and we prove that in the case of good pseudo-hoops, the sets of fantastic and involutive filters are equal. One of the main results consists of proving that a normal filter F of a bounded pseudo-hoop A is involutive if and only if A / F is an involutive pseudo-hoop. It is also proved that any implicative filter of a bounded pseudo-hoop is involutive and that any Boolean filter of a bounded Wajsberg pseudo-hoop is involutive. The notions of state operators and state-morphism operators on pseudo-hoops are introduced, and the relationship between these operators is investigated. For a bounded Wajsberg pseudo-hoop, we prove that the kernel of any state operator is an involutive filter.
Characterization of the X-ray coherence properties of an undulator beamline at the Advanced Photon Source

In anticipation of the increased use of coherent X-ray methods and the need to upgrade beamlines to match improved source quality, here the coherence properties of the X-rays delivered by beamline 12ID-D at the Advanced Photon Source have been characterized. The measured X-ray divergence, beam size, brightness and coherent flux at energies up to 26 keV are compared with the calculated values from the undulator source, and the effects of beamline optics such as a mirror, monochromator and compound refractive lenses are evaluated. Diffraction patterns from slits as a function of slit width are analyzed using wave propagation theory to obtain the beam divergence and thus coherence length. Imaging of the source using a compound refractive lens was found to be the most accurate method for determining the vertical divergence. While the brightness and coherent flux obtained without a monochromator (`pink beam') agree well with those calculated for the source, those measured with the monochromator were a factor of three to six lower than the source, primarily because of vertical divergence introduced by the monochromator. The methods described herein should be widely applicable for measuring the X-ray coherence properties of synchrotron beamlines.
Distributed Equilibrium-Learning for Power Network Voltage Control With a Locally Connected Communication Network

We address the problem of voltage control in power distribution networks by coordinating the distributed energy resources (DERs) at different buses. This problem has been investigated actively via either distributed optimization-based or local feedback control-based approaches. The former one requires a strongly-connected communication network among all DERs for implementing the optimization algorithms, which is not yet realistic in existing distribution systems with under-deployed communication infrastructure. The latter one, on the other hand, has been proven to suffer from loss of network-wide operational optimality. In this paper, we propose a game-theoretic characterization for semi-local voltage control with only a locally connected communication network. We analyze the existence and uniqueness of the generalized Nash equilibrium (GNE) for this characterization, and develop a fully distributed equilibrium-learning algorithm that hinges on only neighbor-to-neighbor information exchange of DERs. Provable convergence results are provided along with numerical tests, to illustrate the robust convergence property of our algorithm.
Building Mini-Categories in Product Networks

We constructed a product network based on the sales data collected and provided by a major nationwide retailer. The structure of the network is dominated by small isolated components, dense clique-based communities, and sparse stars and linear chains and pendants. We used the identified structural elements (tiles) to organize products into mini-categories—compact collections of potentially complementary and substitute items. The mini-categories extend the traditional hierarchy of retail products (group–class–subcategory) and may serve as building blocks towards exploration of consumer projects and long-term customer behavior.
The pentagram map and Y-patterns

The pentagram map, introduced by R. Schwartz, is defined by the following construction: given a polygon as input, draw all of its ``shortest'' diagonals, and output the smaller polygon which they cut out. We employ the machinery of cluster algebras to obtain explicit formulas for the iterates of the pentagram map.
Velocity Field Generation for Density Control of Swarms using Heat Equation and Smoothing Kernels

Abstract This paper presents a method to control the probability density distribution of a swarm of vehicles via velocity fields. The proposed approach synthesizes smooth velocity fields, which specify a desired velocity as a function of time and position in a decentralized manner i.e., each agent calculates the desired velocity locally by utilizing the number of agents within a prescribed communication distance. Swarm converges to the desired/commanded density distribution by following the velocity field. The local information consists, only, of agents’ positions and it is utilized to estimate the density around each agent. Local density estimation is performed by using kernel density estimation. Then the local density estimates and the desired density are utilized to generate the velocity field, which propagates the swarm probability density distribution via the well-known heat equation. The key advantage of using smooth velocity fields to control swarm density with respect to our earlier Markov chain based probabilistic control methods is that the agents move more smoothly in an organized manner and their relative velocities go to zero as they get closer to each other, i.e., they facilitate conflict/collision avoidance. The desired density distribution, which is commanded to the local controllers, can be computed by using a Markov chain that propagates the desired density distribution within prescribed requirements. Along with convergence and stability analysis, the effectiveness of the approach is illustrated via numerical examples.
Enhancing Distribution Resilience with Mobile Energy Storage: A Progressive Hedging Approach

Electrochemical energy storage (ES) units (e.g. batteries) have been field-validated as an efficient back-up resource that enhance resilience of the distribution system in case of natural disasters. However, using these units for resilience is not sufficient to economically justify their installation and, therefore, these units are often installed in locations where they incur the greatest economic value during normal operations. Motivated by the recent progress in transportable ES technologies, i.e. ES units can be moved using public transportation routes, this paper proposes to use this spatial flexibility to bridge the gap between the economically optimal locations during normal operations and disaster-specific locations where extra back-up capacity is necessary. We propose a two-stage optimization model that optimizes investments in mobile ES units in the first stage and can re-route the installed mobile ES units in the second stage to avoid the expected load shedding caused by disaster forecasts. Since the proposed model cannot be solved efficiently with off-the-shelf solvers, even for relatively small instances, we apply a progressive hedging algorithm. The proposed model and progressive hedging algorithm are tested through two illustrative examples on a 15-bus radial distribution test system.
ON COPRODUCTS IN VARIETIES, QUASIVARIETIES AND PREVARIETIES

If the free algebra F on one generator in a variety V of algebras (in the sense of universal algebra) has a subalgebra free on two generators, must it also have a subalgebra free on three generators? In general, no; but yes if F generates the variety V: Generalizing the argument, it is shown that if we are given an algebra and subalgebras, A0 An; in a prevariety (SP -closed class of algebras) P such that An generates P; and also subalgebras Bi Ai 1 (0 0 the subalgebra of Ai 1 generated by Ai and Bi is their coproduct in P; then the subalgebra of A generated by B1;:::;Bn is the coproduct in P of these algebras. Some further results on coproducts are noted: If P satises the amalgamation property, then one has the stronger \transitivity" statement, that if A has a nite family of subalgebras ( Bi)i2I such that the subalgebra of A generated by the Bi is their coproduct, and each Bi has a nite family of subalgebras ( Cij)j2Ji with the same property, then the subalgebra of A generated by all the Cij is their coproduct. For P a residually small prevariety or an arbitrary quasivariety, relationships are proved between the least number of algebras needed to generate P as a prevariety or quasivariety, and behavior of the coproduct operation in P: It is shown by example that for B a subgroup of the group S = Sym() of all permutations of an innite set ; the group S need not have a subgroup isomorphic over B to the coproduct with amalgamation S ' B S: But under various additional hypotheses on B; the question remains open.
The Power Allocation Game on A Network: Computation Issue

Abstract In this paper two algorithms with the goal of generating the equilibrium set of the power allocation game first developed in Li and Morse [2017c] are proposed. Based on the first algorithm, the geometric property of the pure strategy Nash equilibrium set will be proven to be a collection of convex polytopes. The second, simulation-based, algorithm is developed to overcome the shortcoming of the first algorithm in terms of generating the equilibrium set efficiently and then making policy-relevant predictions based on the set. The second algorithm will be usefully applied to a real-world case study, which draws on the current crisis between North Korea and certain key players including the US and China.