cssi/SciSciGPT-SciSciNet huggingface dataset:
The Sarason sub-symbol and the recovery of the symbol of densely defined Toeplitz operators over the Hardy space

Abstract While the symbol map for the collection of bounded Toeplitz operators is well studied, there has been little work on a symbol map for densely defined Toeplitz operators. In this work a family of candidate symbols, the Sarason sub-symbols, is introduced as a means of reproducing the symbol of a densely defined Toeplitz operator. This leads to a partial answer to a question posed by Donald Sarason in 2008. In the bounded case the Toeplitzness of an operator can be classified in terms of its Sarason sub-symbols. This justifies the investigation into the application of the Sarason sub-symbols on densely defined operators. It is shown that analytic closed densely defined Toeplitz operators are completely determined by their Sarason sub-symbols, and it is shown for a broader class of operators that they extend closed densely defined Toeplitz operators (of multiplication type).
Morphology and Stellar Populations in the Gas-Rich, Giant LSBGs

An unexpected characteristic of low surface brightness galaxies (LSBGs) is that a significant number are massive and possess substantial amounts of atomic gas. We present preliminary results of an ongoing program to obtain BVRIJHK imaging, along with some nuclear spectroscopy, of a well-defined sample of LSBGs which are gas-rich and of similar size to giant, high surface brightness spiral galaxies (HSBGs). These LSBGs span the entire range of Hubble disk morphologies. While their disks are bluer, on average, than comparable HSBGs, the optical morphology of massive LSBGs indicates that many of these systems have undergone previous star formation episodes. They typically have long disk scale lengths, and range from MB = −16 to −22 (H0 = 75 km s−1 Mpc−1). About half of the LSBGs with bulges show evidence of nuclear activity, and ~30% appear to be barred. These massive, gas-rich LSBGs apparently have varied, and often complex, evolutionary histories.
Remark on a Result of Constantine

In this short note we construct codes of length 4n with 8n+8 codewords and minimum distance 2n−2 whenever 4n+4 is the order of a Hadamard matrix. This generalises work of Constantine who obtained a similar result in the special case that n is a prime power.
Consistency of Hill Estimators in a Linear Preferential Attachment Model

Preferential attachment is widely used to model power-law behavior of degree distributions in both directed and undirected networks. Statistical estimates of the tail exponent of the power-law degree distribution often use the Hill estimator as one of the key summary statistics. The consistency of the Hill estimator for network data has not been explored and the major goal in this paper is to prove consistency in certain models. To do this, we first derive the asymptotic behavior of the degree sequence via embedding the degree growth of a fixed node into a birth immigration process and then show the convergence of the tail empirical measure. From these steps, the consistency of the Hill estimator is obtained. Simulations are provided as an illustration for the asymptotic distribution of the Hill estimator.
Efficient Algorithms for k-Regret Minimizing Sets

A regret minimizing set Q is a small size representation of a much larger database P so that user queries executed on Q return answers whose scores are not much worse than those on the full dataset. In particular, a k-regret minimizing set has the property that the regret ratio between the score of the top-1 item in Q and the score of the top-k item in P is minimized, where the score of an item is the inner product of the item's attributes with a user's weight (preference) vector. The problem is challenging because we want to find a single representative set Q whose regret ratio is small with respect to all possible user weight vectors. We show that k-regret minimization is NP-Complete for all dimensions d>=3, settling an open problem from Chester et al. [VLDB 2014]. Our main algorithmic contributions are two approximation algorithms, both with provable guarantees, one based on coresets and another based on hitting sets. We perform extensive experimental evaluation of our algorithms, using both real-world and synthetic data, and compare their performance against the solution proposed in [VLDB 14]. The results show that our algorithms are significantly faster and scalable to much larger sets than the greedy algorithm of Chester et al. for comparable quality answers.
Rational Points of Universal Curves in Positive Characteristics

Rational Points of Universal Curves in Positive Characteristics by Tatsunari Watanabe Department of Mathematics Duke University
Neostability in countable homogeneous metric spaces

Abstract Given a countable, totally ordered commutative monoid R = ( R , ⊕ , ≤ , 0 ) , with least element 0, there is a countable, universal and ultrahomogeneous metric space U R with distances in R . We refer to this space as the R -Urysohn space, and consider the theory of U R in a binary relational language of distance inequalities. This setting encompasses many classical structures of varying model theoretic complexity, including the rational Urysohn space, the free nth roots of the complete graph (e.g. the random graph when n = 2 ), and theories of refining equivalence relations (viewed as ultrametric spaces). We characterize model theoretic properties of Th ( U R ) by algebraic properties of R , many of which are first-order in the language of ordered monoids. This includes stability, simplicity, and Shelah's SOP n -hierarchy. Using the submonoid of idempotents in R , we also characterize superstability, supersimplicity, and weak elimination of imaginaries. Finally, we give necessary conditions for elimination of hyperimaginaries, which further develops previous work of Casanovas and Wagner.
Nonvanishing of the differential of holomorphic mappings at boundary points

In this paper we prove a general result of the ``Hopf lemma'' type for CR mappings, with nonidentically vanishing Jacobians, between real hypersurfaces in C^n with smooth or real analytic boundaries. Applications of this result to finiteness and holomorphic extendibility of such mappings are also given. The novelty here is that we make no assumption on the nonflatness of the mapping or its Jacobian, nor do we assume that the hypersurfaces are pseudoconvex or minimally convex.
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated

Given a matrix of observed data, Principal Components Analysis (PCA) computes a small number of orthogonal directions that contain most of its variability. Provably accurate solutions for PCA have been in use for a long time. However, to the best of our knowledge, all existing theoretical guarantees for it assume that the data and the corrupting noise are mutually independent, or at least uncorrelated. This is valid in practice often, but not always. In this paper, we study the PCA problem in the setting where the data and noise can be correlated. Such noise is often also referred to as ``data-dependent noise". We obtain a correctness result for the standard eigenvalue decomposition (EVD) based solution to PCA under simple assumptions on the data-noise correlation. We also develop and analyze a generalization of EVD, cluster-EVD, that improves upon EVD in certain regimes.
Transformer-XL: Attentive Language Models beyond a Fixed-Length Context.

Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch.
Update on the 2+1+1 flavor QCD equation of state with HISQ

We present recent results on the QCD equation of state with 2+1+1 flavors of highly improved staggered quarks (HISQ). We focus on three sets of ensembles with temporal extent 6, 8 and 10, that reach up to temperatures of 967, 725 and 580 MeV, respectively. The strange and charm quark masses are tuned to the physical values and the light quarks mass is set to one fifth of the strange. This corresponds to a Goldstone pion of about 300 MeV.
Induced forests in bipartite planar graphs

Akiyama and Watanabe conjectured that every simple planar bipartite graph on $n$ vertices contains an induced forest on at least $5n/8$ vertices. We apply the discharging method to show that every simple bipartite planar graph on $n$ vertices contains an induced forest on at least $\lceil (4n+3)/7 \rceil$ vertices.
On score-functions and goodness-of-fit tests for stochastic processes

The problems of the construction of asymptotically distribution free goodness-of-fit tests for two diffusion processes are considered. The null hypothesis is composite parametric. All tests are based on the score-function processes, where the unknown parameter is replaced by the maximum likelihood estimators. We show that a special change of time transforms the limit score-function processes into the Brownian bridge. This property allows us to construct asymptotically distribution-free tests for dynamical systems with small noise and ergodic diffusion processes. The proposed tests are in some sense universal. We discuss the possibilities of the construction of asymptotically distribution free tests for inhomogeneous Poisson processes and nonlinear AR time series.
Artin Group Presentations Arising from Cluster Algebras

In 2003, Fomin and Zelevinsky proved that finite type cluster algebras can be classified by Dynkin diagrams. Then in 2013, Barot and Marsh defined the presentation of a reflection group associated to a Dynkin diagram in terms of an edge-weighted, oriented graph, and proved that this group is invariant (up to isomorphism) under diagram mutations. In this paper, we extend Barot and Marsh’s results to Artin group presentations, defining new generator relations and showing mutation-invariance for these presentations.
Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state–space–time network representations

Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). This paper first proposes a new time-discretized multi-commodity network flow model for the VRPPDTW based on the integration of vehicles’ carrying states within space–time transportation networks, so as to allow a joint optimization of passenger-to-vehicle assignment and turn-by-turn routing in congested transportation networks. Our three-dimensional state–space–time network construct is able to comprehensively enumerate possible transportation states at any given time along vehicle space–time paths, and further allows a forward dynamic programming solution algorithm to solve the single vehicle VRPPDTW problem. By utilizing a Lagrangian relaxation approach, the primal multi-vehicle routing problem is decomposed to a sequence of single vehicle routing sub-problems, with Lagrangian multipliers for individual passengers’ requests being updated by sub-gradient-based algorithms. We further discuss a number of search space reduction strategies and test our algorithms, implemented through a specialized program in C++, on medium-scale and large-scale transportation networks, namely the Chicago sketch and Phoenix regional networks.
Unsupervised motion artifact detection in wrist-measured electrodermal activity data

One of the main benefits of a wrist-worn computer is its ability to collect a variety of physiological data in a minimally intrusive manner. Among these data, electrodermal activity (EDA) is readily collected and provides a window into a person's emotional and sympathetic responses. EDA data collected using a wearable wristband are easily influenced by motion artifacts (MAs) that may significantly distort the data and degrade the quality of analyses performed on the data if not identified and removed. Prior work has demonstrated that MAs can be successfully detected using supervised machine learning algorithms on a small data set collected in a lab setting. In this paper, we demonstrate that unsupervised learning algorithms perform competitively with supervised algorithms for detecting MAs on EDA data collected in both a lab-based setting and a real-world setting comprising about 23 hours of data. We also find, somewhat surprisingly, that incorporating accelerometer data as well as EDA improves detection accuracy only slightly for supervised algorithms and significantly degrades the accuracy of unsupervised algorithms.
On the Degrees-of-Freedom of Two-Unicast Wireless Networks With Delayed CSIT

We characterize the degrees-of-freedom (DoF) region of a class of two-unicast wireless networks under the assumption of delayed channel state information at the transmitters. We consider a layered topology with arbitrary connectivity, and we introduce new outer-bounds on the DoF region of such networks through the graph-theoretic notion of bottleneck nodes. Such nodes act as informational bottlenecks only under the assumption of delayed channel state information. We also present new transmission schemes that achieve the outer-bounds. We show that unlike the instantaneous channel state information model, the sum DoF of two-unicast networks with delayed channel knowledge can take an infinite set of values. In this paper, we compare our results to the best previously known outer-bounds, and we show that the gap can be arbitrary large in favor.
Arnoldi-based Sampling for High-dimensional Optimization using Imperfect Data

We present a sampling strategy suitable for optimization problems characterized by high-dimensional design spaces and noisy outputs. Such outputs can arise, for example, in time-averaged objectives that depend on chaotic states. The proposed sampling method is based on a generalization of Arnoldi’s method used in Krylov iterative methods. We show that Arnoldi-based sampling can effectively estimate the dominant eigenvalues of the underlying Hessian, even in the presence of inaccurate gradients. This spectral information can be used to build a low-rank approximation of the Hessian in a quadratic model of the objective. We also investigate two variants of the linear term in the quadratic model: one based on step averaging and one based on directional derivatives. The resulting quadratic models are used in a trust-region optimization framework called the Stochastic Arnoldi’s Method (SAM). Numerical experiments highlight the potential of SAM relative to conventional derivative-based and derivative-free methods when the design space is high-dimensional and noisy.
Actions of categories by Lipschitz morphisms on limits for the Gromov–Hausdorff propinquity

Abstract We prove a compactness result for classes of actions of many small categories on quantum compact metric spaces by Lipschitz linear maps, for the topology of the covariant Gromov–Hausdorff propinquity. In particular, our result applies to actions of proper groups by Lipschitz isomorphisms on quantum compact spaces. Our result provides a first example of a structure which passes to the limit of quantum metric spaces for the propinquity, as well as a new method to construct group actions, including from non-locally compact groups seen as inductive limits of compact groups, on unital C*-algebras. We apply our techniques to obtain some properties of closure of certain classes of quasi-Leibniz quantum compact metric spaces for the propinquity.
Constructing invariant subspaces as kernels of commuting matrices

Abstract Given an n × n matrix A over C and an invariant subspace N , a straightforward formula constructs an n × n matrix N that commutes with A and has N = ker ⁡ N . For Q a matrix putting A into Jordan canonical form, J = Q − 1 A Q , we get N = Q − 1 M where M = ker(M) is an invariant subspace for J with M commuting with J. In the formula M = P Z T − 1 P t , the matrices Z and T are m × m and P is an n × m row selection matrix. If N is a marked subspace, m = n and Z is an n × n block diagonal matrix, and if N is not a marked subspace, then m > n and Z is an m × m near-diagonal block matrix. Strikingly, each block of Z is a monomial of a finite-dimensional backward shift. Each possible form of Z is easily arranged in a lattice structure isomorphic to and thereby displaying the complete invariant subspace lattice L ( A ) for A.
Transposon-insertion sequencing screens unveil requirements for EHEC growth and intestinal colonization

Enterohemorrhagic Escherichia coli O157:H7 (EHEC) is an important food-borne pathogen that colonizes the colon. Transposon-insertion sequencing (TIS) was used to identify genes required for EHEC and E. coli K-12 growth in vitro and for EHEC growth in vivo in the infant rabbit colon. Surprisingly, many conserved loci contribute to EHEC’s but not to K-12’s growth in vitro. There was a restrictive bottleneck for EHEC colonization of the rabbit colon, which complicated identification of EHEC genes facilitating growth in vivo. Both a refined version of an existing analytic framework as well as PCA-based analysis were used to compensate for the effects of the infection bottleneck. These analyses confirmed that the EHEC LEE-encoded type III secretion apparatus is required for growth in vivo and revealed that only a few effectors are critical for in vivo fitness. Over 200 mutants not previously associated with EHEC survival/growth in vivo also appeared attenuated in vivo, and a subset of these putative in vivo fitness factors were validated. Some were found to contribute to efficient type-three secretion while others, including tatABC, oxyR, envC, acrAB, and cvpA, promote EHEC resistance to host-derived stresses. cvpA is also required for intestinal growth of several other enteric pathogens, and proved to be required for EHEC, Vibrio cholerae and Vibrio parahaemolyticus resistance to the bile salt deoxycholate, highlighting the important role of this previously uncharacterized protein in pathogen survival. Collectively, our findings provide a comprehensive framework for understanding EHEC growth in the intestine.
Understanding the Engines and Progenitors of Gamma-Ray Bursts

Our understanding of the engines and progenitors of gamma-ray bursts has expanded through the ages as a broader set of diagnostics has allowed us to test our understanding of these objects. Here we review the history of the growth in our understanding, focusing on 3 leading engines and 9 potential progenitors. The first gravitational wave detection of a short burst is the latest in a series of breakthrough observations shaping this understanding and we study the importance of multi-diagnostic, multi-messenger observations on these engines and their progenitors. Our understanding based on a detailed study of nearby bursts can be applied to make predictions for the trends expected as we begin to observe high redshift bursts and we discuss these trends.
Optimizing organic electrosynthesis through controlled voltage dosing and artificial intelligence

Organic electrosynthesis can transform the chemical industry by introducing electricity-driven processes that are more energy efficient and that can be easily integrated with renewable energy sources. However, their deployment is severely hindered by the difficulties of controlling selectivity and achieving a large energy conversion efficiency at high current density due to the low solubility of organic reactants in practical electrolytes. This control can be improved by carefully balancing the mass transport processes and electrocatalytic reaction rates at the electrode diffusion layer through pulsed electrochemical methods. In this study, we explore these methods in the context of the electrosynthesis of adiponitrile (ADN), the largest organic electrochemical process in industry. Systematically exploring voltage pulses in the timescale between 5 and 150 ms led to a 20% increase in production of ADN and a 250% increase in relative selectivity with respect to the state-of-the-art constant voltage process. Moreover, combining this systematic experimental investigation with artificial intelligence (AI) tools allowed us to rapidly discover drastically improved electrosynthetic conditions, reaching improvements of 30 and 325% in ADN production rates and selectivity, respectively. This powerful AI-enhanced experimental approach represents a paradigm shift in the design of electrified chemical transformations, which can accelerate the deployment of more sustainable electrochemical manufacturing processes.
Adapting Neural Networks for the Estimation of Treatment Effects

This paper addresses the use of neural networks for the estimation of treatment effects from observational data. Generally, estimation proceeds in two stages. First, we ﬁt models for the expected outcome and the probability of treatment (propensity score). Second, we plug these ﬁtted models into a downstream estimator. Neural networks are a natural choice for the models in the ﬁrst step. Our question is: how can we adapt the design and training of the neural networks used in this ﬁrst step in order to improve the quality of the ﬁnal estimate of the treatment effect? We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects. The ﬁrst is a new architecture, the Dragonnet, that exploits the sufﬁciency of the propensity score for estimation adjustment. The second is a regularization procedure, targeted regularization, that induces a bias towards models that have non-parametrically optimal asymptotic properties ‘out-of-the-box’. Studies on benchmark datasets for causal inference show these adaptations outperform existing methods.
MP3: A More Efficient Private Presence Protocol

This paper presents a novel private presence protocol, referred to as MP3, where the service provider does not have any knowledge of the social graph of its users. In prior work, a private presence protocol, referred to as DP5, was presented. However, the size of the presence database in this protocol grows rapidly as the number of users increases; this limits its scalability and increases its cost. In the proposed protocol, the size of the presence database is reduced significantly, enabling significantly cheaper registration and lookup compared to that of DP5. MP3 requires about two-thirds the bandwidth of DP5 for \(N={200\,000}\) users and about one-half the bandwidth of DP5 for \(N={1\,000\,000}\) users. Furthermore, these savings grow asymptotically with the number of users. Additionally, the client-facing latency in MP3 is an order of magnitude less than that of DP5. We provide an evaluation of the performance and scalability of both protocols.
Valuation of Large Variable Annuity Portfolios with Rank Order Kriging

Metamodels, which simplify the simulation models used in the valuation of large variable annuity portfolios, have recently increased in popularity. The ordinary kriging and the GB2 (generalized bet...
Automated Computation of Materials Properties

Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis techniques, underlying property trends can be identified, facilitating the formulation of new design rules. Such methods require large sets of consistently generated, programmatically accessible materials data. Computational materials design frameworks using standardized parameter sets are the ideal tools for producing such data. This work reviews the state-of-the-art in computational materials design, with a focus on these automated $\textit{ab-initio}$ frameworks. Features such as structural prototyping and automated error correction that enable rapid generation of large datasets are discussed, and the way in which integrated workflows can simplify the calculation of complex properties, such as thermal conductivity and mechanical stability, is demonstrated. The organization of large datasets composed of $\textit{ab-initio}$ calculations, and the tools that render them programmatically accessible for use in statistical learning applications, are also described. Finally, recent advances in leveraging existing data to predict novel functional materials, such as entropy stabilized ceramics, bulk metallic glasses, thermoelectrics, superalloys, and magnets, are surveyed.
Stable Matching with Proportionality Constraints

School choice programs seek to give students the option to choose their school but also close an opportunity gap. To be fair in the assignment of students, it is usually argued that the assignment ...
A 15 – 55 GHz Low-Loss Ultra-Compact Folded Inductor-Based Multi-Section Wilkinson Power Divider for Multi-Band 5G Applications

This paper presents a wideband mm-Wave ultra-compact folded inductor-based Wilkinson power divider for multi-band 5G applications. The detailed circuit analysis and design equations are presented. A proof-of-concept design is implemented in GlobalFoundries 22 nm FD-SOI CMOS process with a core chip area of 190 μm × 190 μm. Over 15 – 55 GHz, the measurement results demonstrate that the proposed circuit achieves 10 dB, and return loss > 10 dB, outperforming the state-of-the-art designs.
Junta Correlation is Testable

The problem of tolerant junta testing is a natural and challenging problem which asks if the property of a function having some specified correlation with a k-Junta is testable. In this paper we give an affirmative answer to this question: There is an algorithm which given distance parameters c, d, and oracle access to a Boolean function f on the hypercube, has query complexity exp(k).poly(1/(c-d)) and distinguishes between the following cases: 1. The distance of f from any k-junta is at least c; 2. There is a k-junta g which has distance at most d from f. This is the first non-trivial tester (i.e., query complexity is independent of the ambient dimension n) which works for all c and d (bounded by 0.5). The best previously known results by Blais et~al., required c to be at least 16d. In fact, with the same query complexity, we accomplish the stronger goal of identifying the most correlated k-junta, up to permutations of the coordinates. We can further improve the query complexity to poly(k/(c-d)) for the (weaker) task of distinguishing between the following cases: 1. The distance of f from any k'-junta is at least c. 2. There is a k-junta g which is at a distance at most d from f. Here k'=poly(k/(c-d)). Our main tools are Fourier analysis based algorithms that simulate oracle access to influential coordinates of functions.
Kernel Machines Beat Deep Neural Networks on Mask-Based Single-Channel Speech Enhancement.

We apply a fast kernel method for mask-based single-channel speech enhancement. Specifically, our method solves a kernel regression problem associated to a non-smooth kernel function (exponential power kernel) with a highly efficient iterative method (EigenPro). Due to the simplicity of this method, its hyper-parameters such as kernel bandwidth can be automatically and efficiently selected using line search with subsamples of training data. We observe an empirical correlation between the regression loss (mean square error) and regular metrics for speech enhancement. This observation justifies our training target and motivates us to achieve lower regression loss by training separate kernel model per frequency subband. We compare our method with the state-of-the-art deep neural networks on mask-based HINT and TIMIT. Experimental results show that our kernel method consistently outperforms deep neural networks while requiring less training time.
A review of community flood risk management studies in the United States

Abstract Given the substantial and diverse body of research on community flood risk management in the United States, there is a need to establish the current state of knowledge, synthesize the methodological dimensions of community flood risk management studies, and identify directions for future research on community flood risk management. The present study addresses these needs by conducting a comprehensive and systematic review of community flood risk management empirical studies in the United States. We searched three academic databases and identified 60 studies that met our selection criteria (e.g., study must be focused on flood risk management at the community level and conducted in the United States). Findings indicate that the number of studies on community flood risk management is increasing, most studies employ flood mitigation and flood impact as their dependent variables, the preferred analytical method is regression, and this literature is dominated by social scientists, among other findings. We discuss six themes that emerge, present four recommendations based on the gaps identified, and outline a robust research agenda for enhancing communities' resilience to future flood disasters.
A Canonical Form for First-Order Distributed Optimization Algorithms

We consider the distributed optimization problem in which a network of agents aims to minimize the average of local functions. To solve this problem, several algorithms have recently been proposed where agents perform various combinations of communication with neighbors, local gradient computations, and updates to local state variables. In this paper, we present a canonical form that characterizes any first-order distributed algorithm that can be implemented using a single round of communication and gradient computation per iteration, and where each agent stores up to two state variables. The canonical form features a minimal set of parameters that are both unique and expressive enough to capture any distributed algorithm in this class. The generic nature of our canonical form enables the systematic analysis and design of distributed optimization algorithms.
Designing Reactive Power Control Rules for Smart Inverters Using Support Vector Machines

Smart inverters have been advocated as a fast-responding mechanism for voltage regulation in distribution grids. Nevertheless, optimal inverter coordination can be computationally demanding, and preset local control rules are known to be subpar. Leveraging tools from machine learning, the design of customized inverter control rules is posed here as a multi-task learning problem. Each inverter control rule is modeled as a possibly nonlinear function of local and/or remote control inputs. Given the electric coupling, the function outputs interact to yield the feeder voltage profile. Using an approximate grid model, inverter rules are designed jointly to minimize a voltage deviation objective based on anticipated load and solar generation scenarios. Each control rule is described by a set of coefficients, one for each training scenario. To reduce the communication overhead between the grid operator and the inverters, we devise a voltage regulation objective that is shown to promote parsimonious descriptions for inverter control rules. Numerical tests using real-world data on a benchmark feeder demonstrate the advantages of the novel nonlinear rules and explore the trade-off between voltage regulation and sparsity in rule descriptions.
An Exploration of Supports for Increasing Classroom Physical Activity within Elementary Schools

Classroom-based physical activity (CBPA) can significantly benefit students’ health and educational outcomes, but many teachers do not utilize CBPA. This study examined teachers’ perceptions about the value and impact of several approaches to support CBPA implementation, and teachers’ weekly self-reported CBPA use. Interviews were conducted with 35 classroom teachers (including those using and not using CBPA) at two public elementary schools, and CBPA tracking logs were collected on a weekly basis. Interview transcripts were interpreted through key domains within implementation science. On average, teachers reported using one activity every other day. Interview data revealed that utilizing professional collaboration time for peer-to-peer feedback and getting informal support from the school’s physical education teacher both have some promise for increasing implementation of CBPA. However, teachers largely felt these strategies were unnecessary. Explicit administrator support was reported by teachers as the most promising mechanism for increasing their CBPA implementation.
Catalytic Asymmetric Radical-Polar Crossover Hydroalkoxylation.

Asymmetric intramolecular hydrofunctionalization of tertiary allylic alcohols is described. This metal hydride-mediated catalytic radical–polar crossover reaction delivers corresponding epoxides in...
Frequency-weighting filter selection for H/sub 2/ control of microgravity isolation systems: A consideration of the "implicit frequency weighting" problem

Many space-science experiments need an active isolation system to provide them with the requisite microgravity environment. The isolation systems planned for use with the International Space Station (ISS) have been appropriately modeled using relative position, relative velocity, and acceleration states. In theory, frequency-weighting design filters can be applied to these state-space models, in order to develop optimal H/sub 2/ or mixed-norm controllers with desired stability and performance characteristics. In practice, however, since there is a kinematic relationship among the various states, any frequency weighting applied to one state will implicitly weight other states. These implicit frequency-weighting effects must be considered, for intelligent frequency-weighting filter assignment. This paper suggests a rational approach to the assignment of frequency-weighting design filters, in the presence of the kinematic coupling among states that exists in the microgravity vibration isolation problem.
Efficient Noninteractive Certification of RSA Moduli and Beyond

In many applications, it is important to verify that an RSA public key (N, e) specifies a permutation over the entire space \(\mathbb {Z}_N\), in order to prevent attacks due to adversarially-generated public keys. We design and implement a simple and efficient noninteractive zero-knowledge protocol (in the random oracle model) for this task. Applications concerned about adversarial key generation can just append our proof to the RSA public key without any other modifications to existing code or cryptographic libraries. Users need only perform a one-time verification of the proof to ensure that raising to the power e is a permutation of the integers modulo N. For typical parameter settings, the proof consists of nine integers modulo N; generating the proof and verifying it both require about nine modular exponentiations.
Environmental Chemical Diethylhexyl Phthalate Alters Intestinal Microbiota Community Structure and Metabolite Profile in Mice

Exposure to environmental chemicals during windows of development is a potentially contributing factor in gut microbiota dysbiosis and linked to chronic diseases and developmental disorders. We used a community-level model of microbiota metabolism to investigate the effects of diethylhexyl phthalate (DEHP), a ubiquitous plasticizer implicated in neurodevelopmental disorders, on the composition and metabolite outputs of gut microbiota in young mice. Administration of DEHP by oral gavage increased the abundance of Lachnoclostridium, while decreasing Clostridium sensu stricto Addition of DEHP to in vitro-cultured cecal microbiota increased the abundance of Paenibacillus and Lachnoclostridium Untargeted metabolomics showed that DEHP broadly altered the metabolite profile in the culture. Notably, DEHP enhanced the production of p-cresol while inhibiting butyrate synthesis. Metabolic model-guided correlation analysis indicated that the likely sources of p-cresol are Clostridium species. Monoculture of Lachnoclostridium bolteae confirmed that it is capable of producing p-hydroxyphenylacetic acid, the immediate precursor of p-cresol, and that the species' growth is enhanced upon DEHP exposure. Taken together, these findings suggest a model where DEHP increases production of p-cresol, a bacterial metabolite linked with neurodevelopmental disorders, by expanding the abundance of species that synthesize the metabolite's precursor.IMPORTANCE Several previous studies have pointed to environmental chemical exposure during windows of development as a contributing factor in neurodevelopmental disorders and correlated these disorders with microbiota dysbiosis; however, little is known about how the chemicals specifically alter the microbiota to interfere with development. The findings reported in this paper unambiguously establish that a pollutant linked with neurodevelopmental disorders can directly modify the microbiota to promote the production of a potentially toxic metabolite (p-cresol) that has also been correlated with neurodevelopmental disorders. Furthermore, we used a novel modeling strategy to identify the responsible enzymes and bacterial sources of this metabolite. To the best of our knowledge, the present study is the first to characterize the functional consequence of phthalate exposure on a developed microbiota. Our results suggest that specific bacterial pathways could be developed as diagnostic and therapeutic targets against health risks posed by ingestion of environmental chemicals.
Very badly ordered cycles of interval maps

We prove that a periodic orbit P with coprime over-rotation pair is an over-twist periodic orbit iff the P-linear map has the over-rotation interval with left endpoint equal to the over-rotation nu...
Multilingual Alignment of Contextual Word Representations

We propose procedures for evaluating and strengthening contextual embedding alignment and show that they are useful in understanding and improving multilingual BERT. In particular, after our proposed alignment procedure, BERT exhibits significantly improved zero-shot performance on XNLI compared to the base model, remarkably matching fully-supervised models for Bulgarian and Greek. Further, using non-contextual and contextual versions of word retrieval, we show that BERT outperforms fastText while being able to distinguish between multiple uses of a word, suggesting that pre-training subsumes word vectors for learning cross-lingual signals. Finally, we use the contextual word retrieval task to gain a better understanding of the strengths and weaknesses of multilingual pre-training.
Presidential Selection of Supreme Court Nominees: The Characteristics Approach

Despite the importance of every nomination to the Supreme Court, a unified theory that illuminates presidential selection of nominees across the modern political era remains elusive. We propose a new theory --- the "characteristics approach" --- that envisions nominees as bundles of characteristics, such as ideology, policy reliability, and attributes of diversity. We formalize the theory, which emphasizes the political returns to presidents from a nominee's characteristics and the "costs" of finding and confirming such individuals, and derive explicit presidential demand functions for these characteristics. Using newly collected data on both nominees and short-list candidates, we estimate these demand functions. They reveal some striking and under-appreciated regularities in appointment politics. In particular, the substantial increase in presidential interest in the Supreme Court's policy output and the increased availability of candidates with desired characteristics has led to significant changes in appointment politics and the composition of the Court.
Financial Dereliction of Duty Are Charities That Aid Servicemen and Veterans Systematically Mismanaged

Charity watchdogs and the media level serious allegations of mismanagement of funds at charities serving former and current members of the U.S. armed services, affecting service recipients, familie...
Deep Tensor Factorization for Spatially-Aware Scene Decomposition

We propose a completely unsupervised method to understand audio scenes observed with random microphone arrangements by decomposing the scene into its constituent sources and their relative presence in each microphone. To this end, we formulate a neural network architecture that can be interpreted as a nonnegative tensor factorization of a multi-channel audio recording. By clustering on the learned network parameters corresponding to channel content, we can learn sources’ individual spectral dictionaries and their activation patterns over time. Our method allows us to leverage deep learning advances like end-to-end training, while also allowing stochastic minibatch training so that we can feasibly decompose realistic audio scenes that are intractable to decompose using standard methods. This neural network architecture is easily extensible to other kinds of tensor factorizations.
Chasing convex bodies optimally

In the chasing convex bodies problem, an online player receives a request sequence of N convex sets K1,..., KN contained in a normed space Rd. The player starts at [MATH HERE], and after observing each Kn picks a new point [MATH HERE]. At each step the player pays a movement cost of [MATH HERE]. The player aims to maintain a constant competitive ratio against the minimum cost possible in hindsight, i.e. knowing all requests in advance. The existence of a finite competitive ratio for convex body chasing was first conjectured in 1991 by Friedman and Linial in [FL93]. This conjecture was recently resolved in [BLLS19] which proved an exponential 2O(d) upper bound on the competitive ratio. In this paper, we drastically improve the exponential upper bound. We give an algorithm achieving competitive ratio d for arbitrary normed spaces, which is exactly tight for l∞. In Euclidean space, our algorithm achieves nearly optimal competitive ratio [MATH HERE], compared to a lower bound of [MATH HERE]. Our approach extends the recent work [BKL+20] which chases nested convex bodies using the classical Steiner point of a convex body. We define the functional Steiner point of a convex function and apply it to the work function to obtain our algorithm.
Investigating the Role of Open Costing in the Buyer-Supplier Relationship: Implications for Global Apparel Supply Chain Management:

Research in supply chain management focused on the buyer-supplier relationship (BSR) indicates that costing practices can impact relationship quality and performance. Yet, few studies have examined...
Ultrasound-Detected Venous Changes Associated with Peripheral Intravenous Placement in Children

Highlights Ultrasound shows several venous changes in pediatric PIV-containing veins. Changes were visualized by ultrasound in the absence of physical exam findings. Venous luminal narrowing, wall ...
Globalization and Willingness to Support the Poor in Developing Countries: An Experiment in India:

Does an individual’s exposure to aspects of globalization impact their willingness to redistribute to the poor? We hypothesize that the “glitter” of foreign direct investment (FDI) in developing co...
A parallel sparse tensor benchmark suite on CPUs and GPUs

Tensor computations present significant performance challenges that impact a wide spectrum of applications. Efforts on improving the performance of tensor computations include exploring data layout, execution scheduling, and parallelism in common tensor kernels. This work presents a benchmark suite for arbitrary-order sparse tensor kernels using state-of-the-art tensor formats: coordinate (COO) and hierarchical coordinate (HiCOO). It demonstrates a set of reference tensor kernel implementations and some observations on Intel CPUs and NVIDIA GPUs. The full paper can be referred to at http://arxiv.org/abs/2001.00660.
A closer look at turbulence spreading: How bistability admits intermittent, propagating turbulence fronts

In magnetic fusion plasmas, mounting evidence suggests the possibility of sustained turbulence below the linear stability threshold or more generally global turbulence bistability. The usual reduce...
Resting-state “physiological networks”

Slow changes in systemic brain physiology can elicit large fluctuations in fMRI time series, which manifest as structured spatial patterns of temporal correlations between distant brain regions. Here, we investigated whether such "physiological networks"-sets of segregated brain regions that exhibit similar responses following slow changes in systemic physiology-resemble patterns associated with large-scale networks typically attributed to remotely synchronized neuronal activity. By analyzing a large group of subjects from the 3T Human Connectome Project (HCP) database, we demonstrate brain-wide and noticeably heterogenous dynamics tightly coupled to either respiratory variation or heart rate changes. We show, using synthesized data generated from physiological recordings across subjects, that these physiologically-coupled fluctuations alone can produce networks that strongly resemble previously reported resting-state networks, suggesting that, in some cases, the "physiological networks" seem to mimic the neuronal networks. Further, we show that such physiologically-relevant connectivity estimates appear to dominate the overall connectivity observations in multiple HCP subjects, and that this apparent "physiological connectivity" cannot be removed by the use of a single nuisance regressor for the entire brain (such as global signal regression) due to the clear regional heterogeneity of the physiologically-coupled responses. Our results challenge previous notions that physiological confounds are either localized to large veins or globally coherent across the cortex, therefore emphasizing the necessity to consider potential physiological contributions in fMRI-based functional connectivity studies. The rich spatiotemporal patterns carried by such "physiological" dynamics also suggest great potential for clinical biomarkers that are complementary to large-scale neuronal networks.