cssi/SciSciGPT-SciSciNet huggingface dataset:
The Shape of Data and Probability Measures

Abstract We introduce the notion of multiscale covariance tensor fields (CTF) associated with Euclidean random variables as a gateway to the shape of their distributions. Multiscale CTFs quantify variation of the data about every point in the data landscape at all spatial scales, unlike the usual covariance tensor that only quantifies global variation about the mean. Empirical forms of localized covariance previously have been used in data analysis and visualization, for example, in local principal component analysis, but we develop a framework for the systematic treatment of theoretical questions and mathematical analysis of computational models. We prove strong stability theorems with respect to the Wasserstein distance between probability measures, obtain consistency results for estimators, as well as bounds on the rate of convergence of empirical CTFs. These results show that CTFs are robust to sampling, noise and outliers. We provide numerous illustrations of how CTFs let us extract shape from data and also apply CTFs to manifold clustering, the problem of categorizing data points according to their noisy membership in a collection of possibly intersecting smooth submanifolds of Euclidean space. We prove that the proposed manifold clustering method is stable and carry out several experiments to illustrate the method.
Compact Generation of the Category of D-Modules on the Stack of G-Bundles on a Curve

Let G be a reductive group. Let BunG denote the stack of G-bundles on a smooth complete curve over a field of characteristic 0, and let D-mod(BunG) denote the DG category of D-modules on BunG. The main goal of the paper is to show that D-mod(BunG) is compactly generated (this is not automatic because BunG is not quasi-compact). The proof is based on the following observation: BunG can be written as a union of quasi-compact open substacks j : U ↪→ BunG, which are ”co-truncative”, i.e., the functor j! is defined on the entire category D-mod(U).
Hypergraphs and hypermatrices with symmetric spectrum

Abstract It is well known that a graph is bipartite if and only if the spectrum of its adjacency matrix is symmetric. In the present paper, this assertion is dissected into three separate matrix results of wider scope, which are extended to hypermatrices. To this end, the concept of bipartiteness is generalized by a new monotone property of cubical hypermatrices, called odd-colorable matrices. It is shown that a nonnegative symmetric r-matrix A has a symmetric spectrum if and only if r is even and A is odd-colorable. This result also solves a problem of Pearson and Zhang about hypergraphs with symmetric spectrum and disproves a conjecture of Zhou, Sun, Wang, and Bu. Separately, similar results are obtained for the H-spectrum of hypermatrices.
Machine Learning and Manycore Systems Design: A Serendipitous Symbiosis

Tight collaboration between manycore system designers and machine-learning experts is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge. Such a framework will be necessary to address the rising complexity of designing large-scale manycore systems and machine-learning techniques.
A new algorithm for maximum likelihood estimation in gaussian graphical models for marginal independence

Graphical models with bi-directed edges (↔) represent marginal independence: the absence of an edge between two vertices indicates that the corresponding variables are marginally independent. In this paper, we consider maximum likelihood estimation in the case of continuous variables with a Gaussian joint distribution, sometimes termed a covariance graph model. We present a new fitting algorithm which exploits standard regression techniques and establish its convergence properties. Moreover, we contrast our procedure to existing estimation algorithms.
Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency for Sequence Modeling

Recurrent neural networks have shown remarkable success in modeling sequences. However low resource situations still adversely affect the generalizability of these models. We introduce a new family of models, called Lattice Recurrent Units (LRU), to address the challenge of learning deep multi-layer recurrent models with limited resources. LRU models achieve this goal by creating distinct (but coupled) flow of information inside the units: a first flow along time dimension and a second flow along depth dimension. It also offers a symmetry in how information can flow horizontally and vertically. We analyze the effects of decoupling three different components of our LRU model: Reset Gate, Update Gate and Projected State. We evaluate this family on new LRU models on computational convergence rates and statistical efficiency. Our experiments are performed on four publicly-available datasets, comparing with Grid-LSTM and Recurrent Highway networks. Our results show that LRU has better empirical computational convergence rates and statistical efficiency values, along with learning more accurate language models.
Generic polynomials for cyclic function field extensions over certain finite fields

We find all the generic polynomials for geometric $$\ell $$ -cyclic function field extensions over the finite fields $$\mathbb {F}_q$$ where $$q= p^n$$ , p prime integer such that .
An algorithm for real-time restructuring of a ranging-based localization network

We present a method to improve the localization accuracy of robots operating in a range-based localization network. The method is favorable especially when the robots operate in harsh environments where the access to a robust and reliable localization system is limited. A state estimator is used for a six degree of freedom object using inertial sensors as well as an Ultra-wideband (UWB) range measurement sensor. The estimator is incorporated into an adaptive algorithm, improving the localization quality of an agent by using a mobile UWB ranging sensor, where the mobile anchor moves to improve localization quality. The algorithm reconstructs localization network in real-time to minimize the determinant of the covariance matrix in the sense of least square error. Finally, the proposed algorithm is experimentally validated in a network consisting of one mobile and four fixed anchors.
Bayesian structure learning for Markov Random Fields with a spike and slab prior

In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1-regularized optimization which has a number of disadvantages such as the inability to assess model uncertainty and expensive cross-validation to find the optimal regularization parameter. Moreover, the model's predictive performance may degrade dramatically with a sub-optimal value of the regularization parameter (which is sometimes desirable to induce sparseness). We propose a fully Bayesian approach based on a "spike and slab" prior (similar to L0 regularization) that does not suffer from these shortcomings. We develop an approximate MCMC method combining Langevin dynamics and reversible jump MCMC to conduct inference in this model. Experiments show that the proposed model learns a good combination of the structure and parameter values without the need for separate hyper-parameter tuning. Moreover, the model's predictive performance is much more robust than L1-based methods with hyper-parameter settings that induce highly sparse model structures.
Robust probabilistic modeling with Bayesian data reweighting

Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model's assumptions and reality. We propose a way to systematically detect and mitigate mismatch of a large class of probabilistic models. The idea is to raise the likelihood of each observation to a weight and then to infer both the latent variables and the weights from data. Inferring the weights allows a model to identify observations that match its assumptions and down-weight others. This enables robust inference and improves predictive accuracy. We study four different forms of mismatch with reality, ranging from missing latent groups to structure misspecification. A Poisson factorization analysis of the Movielens 1M dataset shows the benefits of this approach in a practical scenario.
Distributed cooperative decision-making in multiarmed bandits: Frequentist and Bayesian algorithms

We study distributed cooperative decision-making under the explore-exploit tradeoff in the multiarmed bandit (MAB) problem. We extend state-of-the-art frequentist and Bayesian algorithms for single-agent MAB problems to cooperative distributed algorithms for multi-agent MAB problems in which agents communicate according to a fixed network graph. We rely on a running consensus algorithm for each agent's estimation of mean rewards from its own rewards and the estimated rewards of its neighbors. We prove the performance of these algorithms and show that they asymptotically recover the performance of a centralized agent. Further, we rigorously characterize the influence of the communication graph structure on the decision-making performance of the group.
Crafting adversarial input sequences for recurrent neural networks

Machine learning models are frequently used to solve complex security problems, as well as to make decisions in sensitive situations like guiding autonomous vehicles or predicting financial market behaviors. Previous efforts have shown that numerous machine learning models are vulnerable to adversarial manipulations of their inputs taking the form of adversarial samples. Such inputs are crafted by adding carefully selected perturbations to legitimate inputs so as to force the machine learning model to misbehave, for instance by outputting a wrong class if the machine learning task of interest is classification. In fact, to the best of our knowledge, all previous work on adversarial samples crafting for neural networks considered models used to solve classification tasks, most frequently in computer vision applications. In this paper, we investigate adversarial input sequences for recurrent neural networks processing sequential data. We show that the classes of algorithms introduced previously to craft adversarial samples misclassified by feed-forward neural networks can be adapted to recurrent neural networks. In a experiment, we show that adversaries can craft adversarial sequences misleading both categorical and sequential recurrent neural networks.
Linear dynamical neural population models through nonlinear embeddings

A body of recent work in modeling neural activity focuses on recovering low- dimensional latent features that capture the statistical structure of large-scale neural populations. Most such approaches have focused on linear generative models, where inference is computationally tractable. Here, we propose fLDS, a general class of nonlinear generative models that permits the firing rate of each neuron to vary as an arbitrary smooth function of a latent, linear dynamical state. This extra flexibility allows the model to capture a richer set of neural variability than a purely linear model, but retains an easily visualizable low-dimensional latent space. To fit this class of non-conjugate models we propose a variational inference scheme, along with a novel approximate posterior capable of capturing rich temporal correlations across time. We show that our techniques permit inference in a wide class of generative models.We also show in application to two neural datasets that, compared to state-of-the-art neural population models, fLDS captures a much larger proportion of neural variability with a small number of latent dimensions, providing superior predictive performance and interpretability.
A Note on Perelman’s No Shrinking Breather Theorem

As an application of his entropy formula, Perelman (The entropy formula for the Ricci flow and its geometric applications, 2002) proved that every compact shrinking breather solution to the Ricci flow is a shrinking gradient Ricci soliton. Zhang (Asian J Math 18(4):727–756, 2014) and Lu and Zheng (J Geom Anal, 1–7, 2017) proved no shrinking breather theorems in the noncompact case under additional conditions. It is a natural question to ask whether one can generalize Perelman’s no shrinking breather theorem to the noncompact case assuming only bounded curvature. This is the result we prove in this paper. Our proof uses Perelman’s $${\mathcal {L}}$$ -geometry and an idea of Lu and Zheng (J Geom Anal, 1–7, 2017). The novelty of this paper is that we can remove the technical assumptions in Zhang (Asian J Math 18(4):727–756, 2014) and Lu and Zheng (J Geom Anal, 1–7, 2017).
Large jumps of q-Ornstein–Uhlenbeck processes

Abstract We continue the investigation of sample paths of q -Ornstein–Uhlenbeck processes. We show that for each q ∈ ( − 1 , 1 ) , the process has big jumps crossing from near one end point of the domain to the other with positive probability. Moreover, the number of such jumps in an appropriately enlarged window converges weakly to a Poisson random variable.
Exact pairs of homogeneous zero divisors

Abstract Let S be a standard graded Artinian algebra over a field k . We identify constraints on the Hilbert function of S which are imposed by the hypothesis that S contains an exact pair of homogeneous zero divisors. As a consequence, we prove that if S is a compressed level algebra, then S does not contain any homogeneous zero divisors.
On the similarity of AB and BA for normal and other matrices

Abstract It is known that AB and BA are similar when A and B are Hermitian matrices. In this note we answer a question of F. Zhang by demonstrating that similarity can fail if A is Hermitian and B is normal. Perhaps surprisingly, similarity does hold when A is positive semidefinite and B is normal.
Building bridges: viewing active learning from the multi-armed bandit lens

In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification. By carefully constructing an analogy between active learning and multi-armed bandits, we utilize ideas such as lower confidence bounds, and self-concordant regularization from the multi-armed bandit literature to design our proposed algorithm. Our algorithm is a sequential algorithm, which in each round assigns a sampling distribution on the pool, samples one point from this distribution, and queries the oracle for the label of this sampled point. The design of this sampling distribution is also inspired by the analogy between active learning and multi-armed bandits. We show how to derive lower confidence bounds required by our algorithm. Experimental comparisons to previously proposed active learning algorithms show superior performance on some standard UCI data-sets.
Entropy Bumps and another sufficient condition for the two-weight boundedness of sparse operators

In this short note, we give a very efficient proof of a recent result of Treil–Volberg and Lacey–Spencer giving sufficient conditions for the two-weight boundedness of a sparse operator. We also give a new sufficient condition for the two-weight boundedness of a sparse operator. We make critical use of a formula of Hytonen in [6].
Trimmed serendipity finite element differential forms

We introduce the family of trimmed serendipity finite element differential form spaces, defined on cubical meshes in any number of dimensions, for any polynomial degree, and for any form order. The relation between the trimmed serendipity family and the (non-trimmed) serendipity family developed by Arnold and Awanou [Math. Comp. 83(288) 2014] is analogous to the relation between the trimmed and (non-trimmed) polynomial finite element differential form families on simplicial meshes from finite element exterior calculus. We provide degrees of freedom in the general setting and prove that they are unisolvent for the trimmed serendipity spaces. The sequence of trimmed serendipity spaces with a fixed polynomial order r provides an explicit example of a system described by Christiansen and Gillette [ESAIM:M2AN 50(3) 2016], namely, a minimal compatible finite element system on squares or cubes containing order r-1 polynomial differential forms.
When is a crowd wise

Numerous studies and anecdotes demonstrate the "wisdom of the crowd," the surprising accuracy of a group's aggregated judgments. Less is known, however, about the generality of crowd wisdom. For example, are crowds wise even if their members have systematic judgmental biases, or can influence each other before members render their judgments? If so, are there situations in which we can expect a crowd to be less accurate than skilled individuals? We provide a precise but general definition of crowd wisdom: A crowd is wise if a linear aggregate, for example a mean, of its members' judgments is closer to the target value than a randomly, but not necessarily uniformly, sampled member of the crowd. Building on this definition, we develop a theoretical framework for examining, a priori, when and to what degree a crowd will be wise. We systematically investigate the boundary conditions for crowd wisdom within this framework and determine conditions under which the accuracy advantage for crowds is maximized. Our results demonstrate that crowd wisdom is highly robust: Even if judgments are biased and correlated, one would need to nearly deterministically select only a highly skilled judge before an individual's judgment could be expected to be more accurate than a simple averaging of the crowd. Our results also provide an accuracy rationale behind the need for diversity of judgments among group members. Contrary to folk explanations of crowd wisdom which hold that judgments should ideally be independent so that errors cancel out, we find that crowd wisdom is maximized when judgments systematically differ as much as possible. We re-analyze data from two published studies that confirm our theoretical results.
A comparison of large scale dimension of a metric space to the dimension of its boundary

Abstract Buyalo and Lebedeva have shown that the asymptotic dimension of a hyperbolic group is equal to the dimension of the group boundary plus one. Among the work presented here is a partial extension of that result to all groups admitting Z -structures; in particular, we show that asdim G ≥ dim Z + 1 where Z is the Z -boundary.
Consistency by Agreement in Zero-Shot Neural Machine Translation

Generalization and reliability of multilingual translation often highly depend on the amount of available parallel data for each language pair of interest. In this paper, we focus on zero-shot generalization—a challenging setup that tests models on translation directions they have not been optimized for at training time. To solve the problem, we (i) reformulate multilingual translation as probabilistic inference, (ii) define the notion of zero-shot consistency and show why standard training often results in models unsuitable for zero-shot tasks, and (iii) introduce a consistent agreement-based training method that encourages the model to produce equivalent translations of parallel sentences in auxiliary languages. We test our multilingual NMT models on multiple public zero-shot translation benchmarks (IWSLT17, UN corpus, Europarl) and show that agreement-based learning often results in 2-3 BLEU zero-shot improvement over strong baselines without any loss in performance on supervised translation directions.
Physical realization of topological quantum walks on IBM-Q and beyond

We discuss an efficient physical realization of topological quantum walks on a one-dimensional finite lattice with periodic boundary conditions (circle). The N-point lattice is realized with qubits, and the quantum circuit utilizes a number of quantum gates that are polynomial in the number of qubits. In a certain scaling limit, we show that a large number of steps are implemented with a number of quantum gates which are independent of the number of steps. We ran the quantum algorithm on the IBM-Q five-qubit quantum computer, thus experimentally demonstrating topological features, such as boundary bound states, on a one-dimensional lattice with N = 4 points.
Iterative Algorithms for Ptychographic Phase Retrieval

Ptychography promises diffraction limited resolution without the need for high resolution lenses. To achieve high resolution one has to solve the phase problem for many partially overlapping frames. Here we review some of the existing methods for solving ptychographic phase retrieval problem from a numerical analysis point of view, and propose alternative methods based on numerical optimization.
Multitask training with unlabeled data for end-to-end sign language fingerspelling recognition

We address the problem of automatic American Sign Language fingerspelling recognition from video. Prior work has largely relied on frame-level labels, hand-crafted features, or other constraints, and has been hampered by the scarcity of data for this task. We introduce a model for fingerspelling recognition that addresses these issues. The model consists of an auto-encoder-based feature extractor and an attention-based neural encoder-decoder, which are trained jointly. The model receives a sequence of image frames and outputs the fingerspelled word, without relying on any frame-level training labels or hand-crafted features. In addition, the auto-encoder subcomponent makes it possible to leverage unlabeled data to improve the feature learning. The model achieves 11.6% and 4.4% absolute letter accuracy improvement respectively in signer-independent and signer-adapted fingerspelling recognition over previous approaches that required frame-level training labels.
Sparse Semidefinite Programs with Near-Linear Time Complexity

Some of the strongest polynomial-time relaxations to NP-hard combinatorial optimization problems are semidefinite programs (SDPs), but their solution complexity of up to O(n6.5 L) time and O(n4) memory for $L$ accurate digits limits their use in all but the smallest problems. Given that combinatorial SDP relaxations are often sparse, a technique known as chordal conversion can sometimes reduce complexity substantially. In this paper, we describe a modification of chordal conversion that allows any general-purpose interior-point method to solve a certain class of sparse SDPs with a guaranteed complexity of O(nl.5L) time and O(n) memory. To illustrate the use of this technique, we solve the MAX k- CUT relaxation and the Lovasz Theta problem on power system models with up to n = 13659 nodes in 5 minutes, using SeDuMi v1.32 on a 1.7 GHz CPU with 16 GB of RAM. The empirical time complexity for attaining $L$ decimal digits of accuracy is ≈ 0.001nl.l $L$ seconds.
Selective Greedy Equivalence Search: finding optimal Bayesian networks using a polynomial number of score evaluations

We introduce Selective Greedy Equivalence Search (SGES), a restricted version of Greedy Equivalence Search (GES). SGES retains the asymptotic correctness of GES but, unlike GES, has polynomial performance guarantees. In particular, we show that when data are sampled independently from a distribution that is perfect with respect to a DAG G defined over the observable variables then, in the limit of large data, SGES will identify the equivalence class of G after a number of score evaluations that is (1) polynomial in the number of nodes and (2) exponential in various complexity measures including maximum-number-of-parents, maximum-clique-size, and a new measure called v-width that is at least as small as—and potentially much smaller than—the other two. More generally, we show that for any hereditary and equivalence-invariant property n known to hold in G, we retain the large-sample optimality guarantees of GES even if we ignore any GES deletion operator during the backward phase that results in a state for which II does not hold in the common-descendants subgraph.
On the Schrödinger equation outside strictly convex obstacles

We prove sharp Strichartz estimates for the semiclassical Schrodinger equation on a compact Riemannian manifold with a smooth, strictly geodesically concave boundary. We deduce classical Strichartz estimates for the Schrodinger equation outside a strictly convex obstacle, local existence for the H1-critical (quintic) Schrodinger equation, and scattering for the subcritical Schrodinger equation in three dimensions.
Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science.

Recent advancements in neutron and X-ray sources, instrumentation and data collection modes have significantly increased the experimental data size (which could easily contain 108–1010 data points), so that conventional volumetric visualization approaches become inefficient for both still imaging and interactive OpenGL rendition in a 3D setting. We introduce a new approach based on the unsupervised machine learning algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to efficiently analyze and visualize large volumetric datasets. Here we present two examples of analyzing and visualizing datasets from the diffuse scattering experiment of a single crystal sample and the tomographic reconstruction of a neutron scanning of a turbine blade. We found that by using the intensity as the weighting factor in the clustering process, DBSCAN becomes very effective in de-noising and feature/boundary detection, and thus enables better visualization of the hierarchical internal structures of the neutron scattering data.
Optimal Universal Lossless Compression with Side Information

This paper presents conditional versions of the Lempel-Ziv (LZ) algorithm for settings where compressor and decompressor have access to the same side information. We propose a fixed-length-parsing LZ algorithm with side information, motivated by the Willems algorithm, and prove its optimality for any stationary processes. In addition, we suggest strategies to improve the algorithm which lower the non-asymptotic data compression rate. A modification of a variable-length-parsing LZ algorithm with side information is proposed and proved to be asymptotically optimal for stationary and ergodic processes.
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients

Distributed model training is vulnerable to byzantine system failures and adversarial compute nodes, i.e., nodes that use malicious updates to corrupt the global model stored at a parameter server (PS). To guarantee some form of robustness, recent work suggests using variants of the geometric median as an aggregation rule, in place of gradient averaging. Unfortunately, median-based rules can incur a prohibitive computational overhead in large-scale settings, and their convergence guarantees often require strong assumptions. In this work, we present DRACO, a scalable framework for robust distributed training that uses ideas from coding theory. In DRACO, each compute node evaluates redundant gradients that are used by the parameter server to eliminate the effects of adversarial updates. DRACO comes with problem-independent robustness guarantees, and the model that it trains is identical to the one trained in the adversary-free setup. We provide extensive experiments on real datasets and distributed setups across a variety of large-scale models, where we show that DRACO is several times, to orders of magnitude faster than median-based approaches.
The Current Dilemma and Future of Software Patenting

The unprecedented division over patent eligibility based on the “abstract idea” construct among the courts and judges in the Alice v. CLS case, the post-Alice contradictory decisions and the trend of rejecting or invalidating software patents, as well as the uncertainty about the concept of “computer program as such” (or “per se”) in Europe and China, present a chaotic reality and serious dilemma as to the future direction of the software patenting regime around the world. The recent revisions to patent examination guidelines by the patent offices in the US, Europe and China, particularly the “grouping” of “abstract ideas” by the USPTO, providing examples of a “further technical effect” by the EPO, and distinguishing a “computer program per se” and “computer-implemented inventions” by the CNIPA, although helpful in patent examinations, cannot solve the root problem and fix the dilemma. A more general definition, or a “safe harbour”, for the “abstract idea” or computer program “as such” is needed. In addition, to prevent patent trolls and promote true software innovation, the scope and length of software patent protection should be limited, e.g. allowing only the means of implementation but not the function to be patented; and granting 10 years of utility-model-type or sui generis protection. All the above proposed reforms should be undertaken at the international level, e.g. by adopting a Software Treaty under the auspices of the WIPO, because the software patent dilemma is a global one that deserves an international solution, especially in the age of the internet where most of the software patents cover borderless internet technologies.
Critical metrics on connected sums of Einstein four-manifolds

Abstract We develop a gluing procedure designed to obtain canonical metrics on connected sums of Einstein four-manifolds. The main application is an existence result, using two well-known Einstein manifolds as building blocks: the Fubini–Study metric on CP 2 and the product metric on S 2 × S 2 . Using these metrics in various gluing configurations, toric-invariant critical metrics are found on connected sums for a specific Riemannian functional, which depends on the global geometry of the factors. Furthermore, using certain quotients of S 2 × S 2 as one of the gluing factors, critical metrics on several non-simply-connected manifolds are also obtained.
Higher order smallest parts functions and rank-crank moment inequalities from Bailey pairs

We generalize a result of Garvan on inequalities and interpretations of the moments of the partition rank and crank functions. In particular for nearly 30 Bailey pairs, we introduce a rank-like function, establish inequalities with the moments of the rank-like function and an associated crank-like function, and give an associated so called higher order smallest parts function. In some cases we are able to deduce inequalities among the rank-like functions. We also conjecture additional inequalities and a large number of congruences for the higher order smallest parts functions.
A New Combinatorial Design of Coded Distributed Computing

Coded distributed computing introduced by Li et al. in 2015 is an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce. In particular, Li et al. show that increasing the computation load in the Map phase by a factor of $r$ can create coded multicasting opportunities to reduce the communication load in the Reduce phase by the same factor. However, there are two major limitations in practice. First, it requires an exponentially large number of input files (data batches) when the number of computing nodes gets large. Second, it forces every $s$ computing nodes to compute one Map function, which leads to a large number of Map functions required to achieve the promised gain. In this paper, we make an attempt to overcome these two limitations by proposing a novel coded distributed computing approach based on a combinatorial design. We demonstrate that when the number of computing nodes becomes large, 1) the proposed approach requires an exponentially less number of input files; 2) the required number of Map functions is also reduced exponentially. Meanwhile, the resulting computation-communication trade-off maintains the multiplicative gain compared to conventional uncoded unicast and achieves the information theoretic lower bound asymmetrically for some system parameters.
A Unified Framework for Structured Low-rank Matrix Learning

We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices. A salient feature of the proposed factorization scheme is it decouples the low-rank and the structural constraints onto separate factors. We formulate the optimization problem on the Riemannian spectrahedron manifold, where the Riemannian framework allows to develop computationally efficient conjugate gradient and trust-region algorithms. Experiments on problems such as standard/robust/non-negative matrix completion, Hankel matrix learning and multi-task learning demonstrate the efficacy of our approach. A shorter version of this work has been published in ICML'18.
Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols

Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics. Most grammar-based fuzzers for network protocols rely on human experts to manually specify these rules. In this work we study automated learning of protocol rules from textual specifications (i.e. RFCs). We evaluate the automatically extracted protocol rules by applying them to a state-of-the-art fuzzer for transport protocols and show that it leads to a smaller number of test cases while finding the same attacks as the system that uses manually specified rules.
Connectivity of tropicalizations

We show that the tropicalization of an irreducible variety over a complete or algebraically closed valued field is connected through codimension 1, giving an affirmative answer in all characteristics to a question posed by Einsiedler, Lind, and Thomas in 2003.
On the preservers of maximally entangled states

Abstract We characterize the linear maps that preserve maximally entangled states in L ( X ⊗ Y ) in the case where dim ⁡ ( X ) divides dim ⁡ ( Y ) .
Performance of normative and approximate evidence accumulation on the dynamic clicks task

The aim of a number of psychophysics tasks is to uncover how mammals make decisions in a world that is in flux. Here we examine the characteristics of ideal and near--ideal observers in a task of this type. We ask when and how performance depends on task parameters and design, and, in turn, what observer performance tells us about their decision-making process. In the dynamic clicks task subjects hear two streams (left and right) of Poisson clicks with different rates. Subjects are rewarded when they correctly identify the side with the higher rate, as this side switches unpredictably. We show that a reduced set of task parameters defines regions in parameter space in which optimal, but not near-optimal observers, maintain constant response accuracy. We also show that for a range of task parameters an approximate normative model must be finely tuned to reach near-optimal performance, illustrating a potential way to distinguish between normative models and their approximations. In addition, we show that using the negative log-likelihood and the 0/1-loss functions to fit these types of models is not equivalent: the 0/1-loss leads to a bias in parameter recovery that increases with sensory noise. These findings suggest ways to tease apart models that are hard to distinguish when tuned exactly, and point to general pitfalls in experimental design, model fitting, and interpretation of the resulting data.
The Effects of Job Characteristics on Retirement

This paper presents results based on a survey fielded in the RAND American Life Panel that queried older workers about their current, desired, and expected job characteristics, and about how certain job characteristics would affect their retirement. Having access to flexible work hours was found to be the most consistent predictor of retirement expectations. For example, we estimated that the fraction of individuals working after age 70 would be 32.2% if all workers had flexible hours, while the fraction working would be 17.2% if none had the option of flexible hours. We further found that job stress, physical and cognitive job demands, the option to telecommute, and commuting times were also strong predictors of retirement expectations. By comparing workers’ current job characteristics with those that individuals desire, we show that people would like preretirement jobs to be less cognitively and physically demanding and more sociable compared to their current jobs. We also find that most workers worry about their health and the demands of their jobs when they think about their future work trajectory, but relatively few were worried that their employers would retain them. Having access to part-time jobs, and expected longevity were less important predictors of retirement.
Normal crossings degenerations of symplectic manifolds

We use local Hamiltonian torus actions to degenerate a symplectic manifold to a normal crossings symplectic variety in a smooth one-parameter family. This construction, motivated in part by the Gross–Siebert and B. Parker’s programs, contains a multifold version of the usual (two-fold) symplectic cut construction and in particular splits a symplectic manifold into several symplectic manifolds containing normal crossings symplectic divisors with shared irreducible components in one step.
Unit Commitment With Gas Network Awareness

Recent changes in the fuel mix for electricity generation and, in particular, the increase in Gas-Fueled Power Plants (GFPP), have created significant interdependencies between the electrical power and natural gas transmission systems. However, despite their physical and economic couplings, these networks are still operated independently, with asynchronous market mechanisms. This mode of operation may lead to significant economic and reliability risks in congested environments as revealed by the 2014 polar vortex event experienced by the northeastern United States. To mitigate these risks, while preserving the current structure of the markets, this paper explores the idea of introducing gas network awareness into the standard unit commitment model. Under the assumption that the power system operator has some (or full) knowledge of gas demand forecast and the gas network, the paper proposes a tri-level mathematical program where natural gas zonal prices are given by the dual solutions of natural-gas flux conservation constraints and commitment decisions are subject to bid-validity constraints that ensure the economic viability of the committed GFPPs. This tri-level program can be reformulated as a single-level Mixed-Integer Second-Order Cone program which can then be solved using a dedicated Benders decomposition. The approach is validated on a case study for the Northeastern United States [1] that can reproduce the gas and electricity price spikes experienced during the early winter of 2014. The results on the case study demonstrate that gas awareness in unit commitment is instrumental in avoiding the peaks in electricity prices while keeping the gas prices to reasonable levels.
Drosophila menthol sensitivity and the Precambrian origins of transient receptor potential-dependent chemosensation.

Transient receptor potential (TRP) cation channels are highly conserved, polymodal sensors which respond to a wide variety of stimuli. Perhaps most notably, TRP channels serve critical functions in...
TEN-YEAR SURVIVORSHIP OF PRIMARY TOTAL HIP ARTHROPLASTY IN PATIENTS 30 YEARS OF AGE OR UNDER: SUCCESSFUL, BUT HIGHER RISK OF REVISION

Total hip arthroplasty (THA) in the young patient has been associated with higher rates of revision and perioperative complications. For clinicians and patients alike, there remains a desire to better characterize the durability of THA in young patients. We reviewed secondary data from our institutional database for all patients who underwent primary THA from January 2000 to May 2015. Patients were identified using ICD-9 procedure code 81.51. Our primary interest was the survival of implants in patients 30 years of age or younger (n=167) while using a contemporaneous cohort of patients age 60 and older as a control (n=1359). Failure was defined as revision THA for any reason. Cox regression with robust standard errors was used to calculate hazard ratios. A population-averaged Poisson regression analysis was used to compare complication rates. The rate of all-cause revision THA was 2.4 times greater (95% CI 1.10 – 5.37, p=0.028) in young patients (7%, 12/167) undergoing primary or conversion to THA compared to their elderly counterparts (3.7%, 50/1,359). Survival at 10 years was 89% (95% CI 82 – 94) in the young cohort and 96% (94 – 97) in the elderly cohort. The primary reasons for revision in young that patients compared to their elderly counterparts included metal-on-metal implants (IRR 8.12, 95% CI 2.04 – 32.38, p=0.003). These data demonstrate that patients 30 years of age and younger substantially benefit from THA but should be warned of higher risk of subsequent revision.
Coding of Gauss-Markov sources with side information at the decoder

The problem of coding i.i.d. sources with side information available only at the decoder is solved by the Slepian-Wolf and Wyner-Ziv theorems. In this work, we study the problem of coding Gauss-Markov sources with side information. We find a rate lower bound for lossless coding and derive the rate-distortion function for lossy coding. We show that the rate-distortion function is asymptotically achievable by combining the conditional Karhunen-Loeve transform (KLT) and Wyner-Ziv coding.
Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking.

High spatiotemporal resolution dynamic magnetic resonance imaging (MRI) is a powerful clinical tool for imaging moving structures as well as to reveal and quantify other physical and physiological dynamics. The low speed of MRI necessitates acceleration methods such as deep learning reconstruction from under-sampled data. However, the massive size of many dynamic MRI problems prevents deep learning networks from directly exploiting global temporal relationships. In this work, we show that by applying deep neural networks inside a priori calculated temporal feature spaces, we enable deep learning reconstruction with global temporal modeling even for image sequences with >40,000 frames. One proposed variation of our approach using dilated multi-level Densely Connected Network (mDCN) speeds up feature space coordinate calculation by 3000x compared to conventional iterative methods, from 20 min to 0.39 s. Thus, the combination of low-rank tensor and deep learning models not only makes large-scale dynamic MRI feasible but also practical for routine clinical application (This work was supported by NIH 1R01EB028146).
Joint Shape Representation and Classification for Detecting PDAC

We aim to detect pancreatic ductal adenocarcinoma (PDAC) in abdominal CT scans, which sheds light on early diagnosis of pancreatic cancer. This is a 3D volume classification task with little training data. We propose a two-stage framework, which first segments the pancreas into a binary mask, then compresses the mask into a shape vector and performs abnormality classification. Shape representation and classification are performed in a joint manner, both to exploit the knowledge that PDAC often changes the shape of the pancreas and to prevent over-fitting. Experiments are performed on 300 normal scans and 136 PDAC cases. We achieve a specificity of \(90.2\%\) (false alarm occurs on less than 1/10 normal cases) at a sensitivity of \(80.2\%\) (less than 1/5 PDAC cases are not detected), which show promise for clinical applications.
Equitable Thresholding and Clustering: A Novel Method for Functional Magnetic Resonance Imaging Clustering in AFNI

This article describes a hybrid method to threshold functional magnetic resonance imaging (FMRI) group statistical maps derived from voxel-wise second-level statistical analyses. The proposed "Equitable Thresholding and Clustering" (ETAC) approach seeks to reduce the dependence of clustering results on arbitrary parameter values by using multiple subtests, each equivalent to a standard FMRI clustering analysis, to make decisions about which groups of voxels are potentially significant. The union of these subtest results decides which voxels are accepted. The approach adjusts the cluster-thresholding parameter of each subtest in an equitable way, so that the individual false-positive rates (FPRs) are balanced across subtests to achieve a desired final FPR (e.g., 5%). ETAC utilizes resampling methods to estimate the FPR and thus does not rely on parametric assumptions about the spatial correlation of FMRI noise. The approach was validated with pseudotask timings in resting-state brain data. In addition, a task FMRI data collection was used to compare ETACs true positive detection power versus a standard cluster detection method, demonstrating that ETAC is able to detect true results and control false positives while reducing reliance on arbitrary analysis parameters.
Radiative Heat Transfer in Solid Rocket Nozzles

The radiative heat transfer within solid propellant rocket motor nozzles due to the emission and isotropic scattering by the liquid-phase alumina particles present in the combustion products is inv...
The Effects of Enrolling in Oversubscribed Prekindergarten Programs Through Third Grade

This study leverages naturally occurring lotteries for oversubscribed Boston Public Schools prekindergarten program sites between 2007 and 2011, for 3,182 children (M = 4.5 years old) to estimate the impacts of winning a first choice lottery and enrolling in Boston prekindergarten versus losing a first choice lottery and not enrolling on children?s enrollment and persistence in district schools, grade retention, special education placement, and third-grade test scores. There are large effects on enrollment and persistence, but no effects on other examined outcomes for this subsample. Importantly, children who competed for oversubscribed seats were not representative of all appliers and almost all control-group children attended center-based preschool. Findings contribute to the larger evidence base and raise important considerations for future prekindergarten lottery-based studies.
DeepSat V2: feature augmented convolutional neural nets for satellite image classification

Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data,...
Energy and Performance Analysis of STTRAM Caches for Mobile Applications

Spin-Transfer Torque RAMs (STTRAMs) have been shown to offer much promise for implementing emerging cache architectures. This paper studies the viability of STTRAM caches for mobile workloads from the perspective of energy and latency. Specifically, we explore the benefits of reduced retention STTRAM caches for mobile applications. We analyze the characteristics of mobile applications' cache blocks and how those characteristics dictate the appropriate retention time for mobile device caches. We show that due to their inherently interactive nature, mobile applications' execution characteristics—and hence, STTRAM cache design requirements—differ from other kinds of applications. We also explore various STTRAM cache designs in both single and multicore systems, and at different cache levels, that can efficiently satisfy mobile applications' execution requirements, in order to maximize energy savings without introducing substantial latency overhead.