cssi/SciSciGPT-SciSciNet huggingface dataset:
Higher-dimensional obstructions for star reductions

A $*$-reduction between two equivalence relations is a Baire measurable reduction which preserves generic notions, i.e., preimages of meager sets are meager. We show that a $*$-reduction between orbit equivalence relations induces generically an embedding between the associated Becker graphs. We introduce a notion of dimension for Polish $G$-spaces which is generically preserved under $*$-reductions. For every natural number $n$ we define a free action of $S_{\infty}$ whose dimension is $n$ on every invariant Baire measurable non-meager set. We also show that the $S_{\infty}$-space which induces the equivalence relation $=^{+}$ of countable sets of reals is $\infty$-dimensional on every invariant Baire measurable non-meager set. We conclude that the orbit equivalence relations associated to all these actions are pairwise incomparable with respect to $*$-reductions.
Ownership, Learning, and Beliefs

We examine how owning a good affects learning and beliefs about its quality. We show that people have more extreme reactions to information about a good they own compared with the same information about a nonowned good: ownership causes more optimistic beliefs after receiving a positive signal and more pessimistic beliefs after receiving a negative signal. Comparing learning to normative benchmarks reveals that people overextrapolate from signals about goods they own, which leads to an overreaction to information; in contrast, learning is close to Bayesian for nonowned goods. We provide direct evidence that this effect is driven by ownership channeling greater attention toward associated information, which leads people to overweight recent signals when forming beliefs. The relationship between ownership and beliefs has testable implications for trade and market expectations. In line with these predictions, we show that the endowment effect doubles in response to positive information and disappears with negative information, and demonstrate a significant relationship between ownership and overextrapolation in survey data about stock market expectations.
Analyzing Migration Restriction Regimes

This paper develops a framework for analyzing migration restriction regimes, and illustrates it with the case of U.S. immigration law and policy. Nation-states regulate the entry of foreign-born persons, and this regulation comprises three elements: the type of restriction, the apparatus of restriction, and the consequences of restriction. Restriction may be based on personal characteristics, numerical ceilings, or both. Personal restriction notices the characteristics of persons, using them as criteria for granting or denying admission. Numerical restriction places numerical ceilings on admissions. The apparatus of restriction may stipulate specific ceilings, whether some groups are exempt from the ceiling and, if so, by what criteria, and whether admission under the ceiling is first-come/first-served or by lottery or instead preferential and, if so, by what criteria. Two unintended consequences follow immediately: unauthorized migration (under both personal and numerical restriction); and visa-number backlogs (under numerical restriction). These in turn generate a range of policy devices: border enforcement, procedures for legalization and deportation, and procedures for clearing backlogs. Indeed, the history of a country's immigration law may be understood as a sequence of measures for first setting up the apparatus of restriction and then altering it in order not only to re-examine provisions of the initial setup but also to address unauthorized migration and visa-number backlogs. Viewing migration through this lens enables assessment of particular legislation and, more broadly, dynamics of a migration restriction regime, subject to world circumstances, including its possible inherent instability. The migration restriction lens also generates new metrics for a country's attractiveness and its innovativeness and creativity. To illustrate, the paper examines the migration restriction regime in the United States since the country's founding. Finally, the paper provides a checklist for a migration restriction setup that doubles as the basis for table shells for summarizing a country's migration restriction regime and its history.
Infinity-operads and Day convolution in Goodwillie calculus

We prove two theorems about Goodwillie calculus and use those theorems to describe new models for Goodwillie derivatives of functors between pointed compactly-generated infinity-categories. The first theorem say that the construction of higher derivatives for spectrum-valued functors is a Day convolution of copies of the first derivative construction. The second theorem says that the derivatives of any functor can be realized as natural transformation objects for derivatives of spectrum-valued functors. Together these results allow us to construct an infinity-operad that models the derivatives of the identity functor on any pointed compactly-generated infinity-category. Our main example is the infinity-category of algebras over a stable infinity-operad, in which case we show that the derivatives of the identity essentially recover the same infinity-operad, making precise a well-known slogan in Goodwillie calculus. We also describe a bimodule structure on the derivatives of an arbitrary functor, over the infinity-operads given by the derivatives of the identity on the source and target, and we conjecture a chain rule that generalizes previous work of Arone and the author in the case of functors of pointed spaces and spectra.
Genome-wide characterization of human minisatellite VNTRs: population-specific alleles and gene expression differences

Variable Number Tandem Repeats (VNTRs) are tandem repeat (TR) loci that vary in copy number across a population. Using our program, VNTRseek, we analyzed human whole genome sequencing datasets from 2770 individuals in order to detect minisatellite VNTRs, i.e., those with pattern sizes ≥7 bp. We detected 35 638 VNTR loci and classified 5676 as commonly polymorphic (i.e. with non-reference alleles occurring in >5% of the population). Commonly polymorphic VNTR loci were found to be enriched in genomic regions with regulatory function, i.e. transcription start sites and enhancers. Investigation of the commonly polymorphic VNTRs in the context of population ancestry revealed that 1096 loci contained population-specific alleles and that those could be used to classify individuals into super-populations with near-perfect accuracy. Search for quantitative trait loci (eQTLs), among the VNTRs proximal to genes, indicated that in 187 genes expression differences correlated with VNTR genotype. We validated our predictions in several ways, including experimentally, through the identification of predicted alleles in long reads, and by comparisons showing consistency between sequencing platforms. This study is the most comprehensive analysis of minisatellite VNTRs in the human population to date.
Atomic Origins of Friction Reduction in Metal Alloys

We present the results of large scale molecular dynamics simulations aimed at understanding the origins of high friction coefficients in pure metals, and their concomitant reduction in alloys and composites. We utilize a series of targeted simulations to demonstrate that different slip mechanisms are active in the two systems, leading to differing frictional behavior. Specifically, we show that in pure metals, sliding occurs along the crystallographic slip planes, whereas in alloys shear is accommodated by grain boundaries. In pure metals, there is significant grain growth induced by the applied shear stress and the slip planes are commensurate contacts with high friction. However, the presence of dissimilar atoms in alloys suppresses grain growth and stabilizes grain boundaries, leading to low friction via grain boundary sliding.
Tschirnhaus transformations after Hilbert

Let RD(n) denote the minimum d for which there exists a formula for the roots of the general degree n polynomial using only algebraic functions of d or fewer variables. In 1927, Hilbert sketched how the 27 lines on a cubic surface could be used to construct a 4-variable formula for the general degree 9 polynomial (implying $RD(9)\le 4$). In this paper, we turn Hilbert's sketch into a general method. We show this method produces best-to-date upper bounds on RD(n) for all n, improving earlier results of Hamilton, Sylvester, Segre and Brauer.
Age-Stratified COVID-19 Spread Analysis and Vaccination: A Multitype Random Network Approach

The risk of severe illness and mortality from COVID-19 significantly increases with age. As a result, age-stratified modeling for COVID-19 dynamics is the key to study how to reduce hospitalizations and mortality from COVID-19. By taking advantage of network theory, we develop an age-stratified epidemic model for COVID-19 in complex contact networks. Specifically, we present an extension of standard SEIR (susceptible-exposed-infectious-removed) compartmental model, called age-stratified SEAHIR (susceptible-exposed-asymptomatic-hospitalized-infectious-removed) model, to capture the spread of COVID-19 over multitype random networks with general degree distributions. We derive several key epidemiological metrics and then propose an age-stratified vaccination strategy to decrease the mortality and hospitalizations. Through extensive study, we discover that the outcome of vaccination prioritization depends on the reproduction number $R_0$ . Specifically, the elderly should be prioritized only when $R_0$ is relatively high. If ongoing intervention policies, such as universal masking, could suppress $R_0$ at a relatively low level, prioritizing the high-transmission age group (i.e., adults aged 20-39) is most effective to reduce both mortality and hospitalizations. These conclusions provide useful recommendations for age-based vaccination prioritization for COVID-19.
"Is depression related to cannabis?": A Knowledge-infused Model for Entity and Relation Extraction with Limited Supervision

With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators. However, preliminary scientific research does not conclusively associate cannabis with improved mental health. In this study, we explore the relationship between depression and consumption of cannabis in a targeted social media corpus involving personal use of cannabis with the intent to derive its potential mental health benefit. We use tweets that contain an association among three categories annotated by domain experts - Reason, Effect, and Addiction. The state-of-the-art Natural Langauge Processing techniques fall short in extracting these relationships between cannabis phrases and the depression indicators. We seek to address the limitation by using domain knowledge; specifically, the Drug Abuse Ontology for addiction augmented with Diagnostic and Statistical Manual of Mental Disorders lexicons for mental health. Because of the lack of annotations due to the limited availability of the domain experts' time, we use supervised contrastive learning in conjunction with GPT-3 trained on a vast corpus to achieve improved performance even with limited supervision. Experimental results show that our method can significantly extract cannabis-depression relationships better than the state-of-the-art relation extractor. High-quality annotations can be provided using a nearest neighbor approach using the learned representations that can be used by the scientific community to understand the association between cannabis and depression better.
Dietary Supplements and Nutraceuticals under Investigation for COVID-19 Prevention and Treatment

Coronavirus disease 2019 (COVID-19) has caused global disruption and a significant loss of life. Existing treatments that can be repurposed as prophylactic and therapeutic agents may reduce the pandemic's devastation. Emerging evidence of potential applications in other therapeutic contexts has led to the investigation of dietary supplements and nutraceuticals for COVID-19. Such products include vitamin C, vitamin D, omega 3 polyunsaturated fatty acids, probiotics, and zinc, all of which are currently under clinical investigation. In this review, we critically appraise the evidence surrounding dietary supplements and nutraceuticals for the prophylaxis and treatment of COVID-19. Overall, further study is required before evidence-based recommendations can be formulated, but nutritional status plays a significant role in patient outcomes, and these products may help alleviate deficiencies. For example, evidence indicates that vitamin D deficiency may be associated with a greater incidence of infection and severity of COVID-19, suggesting that vitamin D supplementation may hold prophylactic or therapeutic value. A growing number of scientific organizations are now considering recommending vitamin D supplementation to those at high risk of COVID-19. Because research in vitamin D and other nutraceuticals and supplements is preliminary, here we evaluate the extent to which these nutraceutical and dietary supplements hold potential in the COVID-19 crisis.IMPORTANCE Sales of dietary supplements and nutraceuticals have increased during the pandemic due to their perceived "immune-boosting" effects. However, little is known about the efficacy of these dietary supplements and nutraceuticals against the novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) or the disease that it causes, CoV disease 2019 (COVID-19). This review provides a critical overview of the potential prophylactic and therapeutic value of various dietary supplements and nutraceuticals from the evidence available to date. These include vitamin C, vitamin D, and zinc, which are often perceived by the public as treating respiratory infections or supporting immune health. Consumers need to be aware of misinformation and false promises surrounding some supplements, which may be subject to limited regulation by authorities. However, considerably more research is required to determine whether dietary supplements and nutraceuticals exhibit prophylactic and therapeutic value against SARS-CoV-2 infection and COVID-19. This review provides perspective on which nutraceuticals and supplements are involved in biological processes that are relevant to recovery from or prevention of COVID-19.
Reliable detection of episodes in event sequences

Suppose one wants to detect bad or suspicious subsequences in event sequences. Whether an observed pattern of activity (in the form of a particular subsequence) is significant and should be a cause for alarm depends on how likely it is to occur fortuitously. A long-enough sequence of observed events will almost certainly contain any subsequence, and setting thresholds for alarm is an important issue in a monitoring system that seeks to avoid false alarms. Suppose a long sequence, T, of observed events contains a suspicious subsequence pattern, S, within it, where the suspicious subsequence S consists of m events and spans a window of size w within T. We address the fundamental problem: Is a certain number of occurrences of a particular subsequence unlikely to be generated by randomness itself (i.e. indicative of suspicious activity)? If the probability of an occurrence generated by randomness is high and an automated monitoring system flags it as suspicious anyway, then such a system will suffer from generating too many false alarms. This paper quantifies the probability of such an S occurring in T within a window of size w, the number of distinct windows containing S as a subsequence, the expected number of such occurrences, its variance, and establishes its limiting distribution that allows setting up an alarm threshold so that the probability of false alarms is very small. We report on experiments confirming the theory and showing that we can detect bad subsequences with low false alarm rate.
Security and Privacy in the Emerging Cyber-Physical World: A Survey

With the emergence of low-cost smart and connected IoT devices, the area of cyber-physical security is becoming increasingly important. Past research has demonstrated new threat vectors targeting the transition process between the cyber and physical domains, where the attacker exploits the sensing system as an attack surface for signal injection or extraction of private information. Recently, there have been attempts to characterize an abstracted model for signal injection, but they primarily focus on the path of signal processing. This paper aims to systematize the existing research on security and privacy problems arising from the interaction of cyber world and physical world, with the context of broad CPS applications. The primary goals of the systematization are to (1) reveal the attack patterns and extract a general attack model of existing work; (2) understand possible new attacks; and (3) motivate development of defenses against the emerging cyber-physical threats.
The Effect of Varying Viscosity in Turbulent Channel Flow

In this article we examine channel flow subject to spatially varying viscosity in the streamwise direction. The Reynolds number is imposed locally with three different ramps. The setup is reminiscent of transient channel flow, but with a space dependent viscosity rather than a time dependent viscosity. It is also relevant to various applications in nuclear engineering and in particular in test reactors, where the viscosity changes significantly in the streamwise direction, and there is a severe lack of Direct Numerical Simulation (DNS) data to benchmark turbulence models in these conditions. As part of this work we set up a novel benchmark case: the channel is extended in the stream-wise direction up to 20p. The viscosity is kept constant in the first 4p region. This inlet region is used as a cyclic region to obtain a fully developed flow profile at the beginning of the ramping region. In the ramping region the Reynolds number is linearly increased along the channel. The flow is homogenous in the spanwise direction, while it is nonhomogenous in the stream-wise and wall-normal direction. We perform here Direct Numerical Simulation (DNS) with Nek5000, a spectral-element computational fluid dynamics (CFD) code developed at Argonne National Laboratory. In this study, specific focus is given to the investigation of turbulence properties and structures in the near-wall region along the flow direction. Turbulent statistics are collected and investigated. Similarly to transient channel flow, the results show that a variation in the Reynolds across a channel does not cause an immediate change in the size of turbulent structures in the ramp region and a delay is in fact observed in both wall shear and friction Reynolds number. The results from the present study are compared with a correlation available in the literature for the friction velocity and as a function of the Reynolds number.
A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization

In this paper, we analyze several methods for approximating gradients of noisy functions using only function values. These methods include finite differences, linear interpolation, Gaussian smoothing, and smoothing on a sphere. The methods differ in the number of functions sampled, the choice of the sample points, and the way in which the gradient approximations are derived. For each method, we derive bounds on the number of samples and the sampling radius which guarantee favorable convergence properties for a line search or fixed step size descent method. To this end, we use the results in Berahas et al. (Global convergence rate analysis of a generic line search algorithm with noise, arXiv:1910.04055 , 2019) and show how each method can satisfy the sufficient conditions, possibly only with some sufficiently large probability at each iteration, as happens to be the case with Gaussian smoothing and smoothing on a sphere. Finally, we present numerical results evaluating the quality of the gradient approximations as well as their performance in conjunction with a line search derivative-free optimization algorithm.
A multiscale 3D chemotaxis assay reveals bacterial navigation mechanisms

How motile bacteria navigate environmental chemical gradients has implications ranging from health to climate science, but the underlying behavioral mechanisms are unknown for most species. The well-studied navigation strategy of Escherichia coli forms a powerful paradigm that is widely assumed to translate to other bacterial species. This assumption is rarely tested because of a lack of techniques capable of bridging scales from individual navigation behavior to the resulting population-level chemotactic performance. Here, we present such a multiscale 3D chemotaxis assay by combining high-throughput 3D bacterial tracking with microfluidically created chemical gradients. Large datasets of 3D trajectories yield the statistical power required to assess chemotactic performance at the population level, while simultaneously resolving the underlying 3D navigation behavior for every individual. We demonstrate that surface effects confound typical 2D chemotaxis assays, and reveal that, contrary to previous reports, Caulobacter crescentus breaks with the E. coli paradigm.
The biaxial mechanics of thermally denaturing skin - Part I: Experiments

The mechanics of collageneous soft tissues, such as skin, are sensitive to heat. Thus, quantifying and modeling thermo-mechanical coupling of skin is critical to our understanding of skin9s physiology, pathophysiology, as well as its treatment. However, key gaps persist in our knowledge about skin9s coupled thermo-mechanics. Among them, we haven9t quantified the role of skin9s microstructural organization in its response to superphysiological loading. To fill this gap, we conducted a comprehensive set of experiments in which we combined biaxial mechanical testing with histology and two-photon imaging under liquid heat treatment. Among other observations, we found that unconstrained skin, when exposed to high temperatures, shrinks anisotropically with the principle direction of shrinkage being aligned with collagen9s principle orientation. Additionally, we found that when skin is isometrically constrained, it produces significant forces during denaturing that are also anisotropic. Finally, we found that denaturation significantly alters the mechanical behavior of skin. For short exposure times, this alteration is reflected in a reduction of stiffness at high strains. At long exposure times, the tissue softened to a point where it became untestable. We supplemented our findings with confirmation of collagen denaturation in skin via loss of birefringence and second harmonic generation. Finally, we captured all time-, temperature-, and direction-dependent experimental findings in a hypothetical model. Thus, this work fills a fundamental gap in our current understanding of skin thermo-mechanics and will support future developments in thermal injury prevention, thermal injury management, and thermal therapeutics of skin.
Domes over Curves

A closed PL-curve is called integral if it is comprised of unit intervals. Kenyon's problem asks whether for every integral curve $\gamma$ in $\mathbb{R}^3$, there is a dome over $\gamma$, i.e. whether $\gamma$ is a boundary of a polyhedral surface whose faces are equilateral triangles with unit edge lengths. First, we give an algebraic necessary condition when $\gamma$ is a quadrilateral, thus giving a negative solution to Kenyon's problem in full generality. We then prove that domes exist over a dense set of integral curves. Finally, we give an explicit construction of domes over all regular $n$-gons.
Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment

We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective. We demonstrate BDMA by training a model for a pair of languages rather than individual, directed source and target combinations, reducing the number of models by 50%. We show that models trained with BDMA in the “forward” (source to target) direction can successfully map words in the “reverse” (target to source) direction, yielding equivalent (or better) performance to standard unidirectional translation models where the source and target language is flipped. We also show how BDMA reduces the overall size of the model.
A nearly-linear time algorithm for linear programs with small treewidth: a multiscale representation of robust central path

Arising from structural graph theory, treewidth has become a focus of study in fixed-parameter tractable algorithms. Many NP-hard problems are known to be solvable in O(n · 2O(τ)) time, where τ is the treewidth of the input graph. Analogously, many problems in P should be solvable in O(n · τO(1)) time; however, due to the lack of appropriate tools, only a few such results are currently known. In our paper, we show this holds for linear programs: Given a linear program of the form minAx=b,l ≤ x≤ u c⊤ x whose dual graph GA has treewidth τ, and a corresponding width-τ tree decomposition, we show how to solve it in time O(n · τ2 log(1/e)), where n is the number of variables and e is the relative accuracy. When a tree decomposition is not given, we use existing techniques in vertex separators to obtain algorithms with O(n · τ4 log(1/e)) and O(n · τ2 log(1/e) + n1.5) run-times. Besides being the first of its kind, our algorithm has run-time nearly matching the fastest run-time for solving the sub-problem Ax=b (under the assumption that no fast matrix multiplication is used). We obtain these results by combining recent techniques in interior-point methods (IPMs), sketching, and a novel representation of the solution under a multiscale basis similar to the wavelet basis. This representation further yields the first IPM with o(rank(A)) time per iteration when the treewidth is small.
Unmanned Aircraft Systems Traffic Management (UTM) Safely Enabling UAS Operations in Low-Altitude Airspace

Conduct research, development and testing to identify airspace operations requirements to enable large-scale visual and beyond visual line of sight UAS operations in the low-altitude airspace. Use build-a-little-test-a-little strategy remote areas to urban areas Low density: No traffic management required but understanding of airspace constraints. Cooperative traffic management: Understanding of airspace constraints and other operations. Manned and unmanned traffic management: Scalable and heterogeneous operations. UTM construct consistent with FAAs risk-based strategy. UTM research platform is used for simulations and tests. UTM offers path towards scalability
A New Combinatorial Coded Design for Heterogeneous Distributed Computing

Coded Distributed Computing (CDC) introduced by Li et al. in 2015 offers an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce and Spark. In particular, 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 Shuffle phase by the same factor. However, the CDC scheme is designed for the homogeneous settings, where each node maps the same number of files and is assigned the same number of reduce functions. It requires an exponentially large number of input files (data batches), reduce functions and multicasting groups relative to the number of nodes to achieve the promised gain. We address the CDC limitations by proposing a novel CDC approach based on a combinatorial design, which accommodates heterogeneous networks and maintains a multiplicative computation-communication trade-off. In addition, the proposed approach requires an exponentially less number of input files compared to the original CDC scheme proposed by Li et al. Finally, we derive a new information theoretic converse for general heterogeneous CDC and show that the communication load of the proposed design is optimal within a constant factor.
On the Equivalence Between Weak BMO and the Space of Derivatives of the Zygmund Class

In this paper, we will discuss the space of functions of weak bounded mean oscillation. In particular, we will show that this space is the dual space of the special atom space, whose dual space was already known to be the space of derivative of functions (in the sense of distribution) belonging to the Zygmund class of functions. We show in particular that this proves that the Hardy space $H^1$ strictly contains the space special atom space.
Sequential Estimation of Network Cascades

We consider the problem of locating the source of a network cascade, given a noisy time-series of network data. Initially, the cascade starts with one unknown, affected vertex and spreads deterministically at each time step. The goal is to find an adaptive procedure that outputs an estimate for the source as fast as possible, subject to a bound on the estimation error. For a general class of graphs, we describe a family of matrix sequential probability ratio tests (MSPRTs) that are first-order asymptotically optimal up to a constant factor as the estimation error tends to zero. We apply our results to lattices and regular trees, and show that MSPRTs are asymptotically optimal for regular trees. We support our theoretical results with simulations.
Distance between arithmetic progressions and perfect squares

In this paper, we study how close the terms of a finite arithmetic progression can get to a perfect square. The answer depends on the initial term, the common difference and the number of terms in the arithmetic progression.
Statistical enumeration of groups by double cosets

Abstract Let H and K be subgroups of a finite group G. Pick g ∈ G uniformly at random. We study the distribution induced on double cosets. Three examples are treated in detail: 1) H = K = the Borel subgroup in G L n ( F q ) . This leads to new theorems for Mallows measure on permutations and new insights into the LU matrix factorization. 2) The double cosets of the hyperoctahedral group inside S 2 n , which leads to new applications of the Ewens's sampling formula of mathematical genetics. 3) Finally, if H and K are parabolic subgroups of S n , the double cosets are ‘contingency tables’, studied by statisticians for the past 100 years.
Classification of the streaming approximability of Boolean CSPs

A Boolean constraint satisfaction problem (CSP), Max-CSP$(f)$, is a maximization problem specified by a constraint $f:\{-1,1\}^k\to\{0,1\}$. An instance of the problem consists of $m$ constraint applications on $n$ Boolean variables, where each constraint application applies the constraint to $k$ literals chosen from the $n$ variables and their negations. The goal is to compute the maximum number of constraints that can be satisfied by a Boolean assignment to the $n$~variables. In the $(\gamma,\beta)$-approximation version of the problem for parameters $\gamma \geq \beta \in [0,1]$, the goal is to distinguish instances where at least $\gamma$ fraction of the constraints can be satisfied from instances where at most $\beta$ fraction of the constraints can be satisfied. In this work we completely characterize the approximability of all Boolean CSPs in the streaming model. Specifically, given $f$, $\gamma$ and $\beta$ we show that either (1) the $(\gamma,\beta)$-approximation version of Max-CSP$(f)$ has a probabilistic streaming algorithm using $O(\log n)$ space, or (2) for every $\varepsilon > 0$ the $(\gamma-\varepsilon,\beta+\varepsilon)$-approximation version of Max-CSP$(f)$ requires $\Omega(\sqrt{n})$ space for probabilistic streaming algorithms. Previously such a separation was known only for $k=2$. We stress that for $k=2$, there are only finitely many distinct problems to consider. Our positive results show wider applicability of bias-based algorithms used previously by [Guruswami-Velingker-Velusamy APPROX'17], [Chou-Golovnev-Velusamy FOCS'20] by giving a systematic way to explore biases. Our negative results combine the Fourier analytic methods of [Kapralov-Khanna-Sudan SODA'15], which we extend to a wider class of CSPs, with a rich collection of reductions among communication complexity problems that lie at the heart of the negative results.
Creditor Control of Corporate Acquisitions

We examine the impact of creditor control rights on corporate acquisitions. Nearly 75% of loan agreements include restrictions that limit borrower acquisition decisions throughout the life of the contract. Following a financial covenant violation, creditors use their bargaining power to tighten these restrictions and limit acquisition activity, particularly deals expected to earn negative announcement returns. Firms that do announce an acquisition after violating a financial covenant earn 1.8% higher stock returns, on average, and do not pursue less risky deals. We conclude that creditors use contractual rights and the renegotiation process to limit value-destroying acquisitions driven by managerial agency problems.
Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation.

Despite the recent success of deep reinforcement learning (RL), domain adaptation remains an open problem. Although the generalization ability of RL agents is critical for the real-world applicability of Deep RL, zero-shot policy transfer is still a challenging problem since even minor visual changes could make the trained agent completely fail in the new task. To address this issue, we propose a two-stage RL agent that first learns a latent unified state representation (LUSR) which is consistent across multiple domains in the first stage, and then do RL training in one source domain based on LUSR in the second stage. The cross-domain consistency of LUSR allows the policy acquired from the source domain to generalize to other target domains without extra training. We first demonstrate our approach in variants of CarRacing games with customized manipulations, and then verify it in CARLA, an autonomous driving simulator with more complex and realistic visual observations. Our results show that this approach can achieve state-of-the-art domain adaptation performance in related RL tasks and outperforms prior approaches based on latent-representation based RL and image-to-image translation.
Jamming criticality of near-crystals

Amorphous and crystalline solids have long been considered as two distinct kinds of rigidity at the opposite ends of the order-disorder continuum. Crystals are usually treated in equilibrium with defects arising as perturbations or excitations (Ashcroft N. W. and Mermin N. D., Solid State Physics (Thomson Brooks/Cole) 1976). Amorphous solids are frustrated and out of equilibrium where preparation protocol can be important. Nevertheless the onset of rigidity of athermal amorphous matter (Liu A. J. and Nagel S. R., Nature , 396 (1998) 21) has been established as a critical point with extended universality. The universal scaling behavior that characterizes jamming at high amorphisation has been demonstrated by a wealth of simulations and models, including an infinite-dimensional mean field theory (Charbonneau P. et al., Annu. Rev. Condens. Matter Phys. , 8 (2017) 265). At the other end, the crystal of minimal disorder has not been shown to display such universal behavior. Here we provide numerical evidence that slightly polydisperse crystals can become critically jammed at packing fractions extremely close to the maximum close-packed density. At the near-crystal jamming point some of the characteristic scalings are universal and agree with maximally amorphous jamming, others are novel. The set of scaling results we provide establishes jamming criticality of maximum-packing crystals in 2 and 3 dimensions.
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models

Attention maps, a popular heatmap-based explanation method for Visual Question Answering (VQA), are supposed to help users understand the model by highlighting portions of the image/question used by the model to infer answers. However, we see that users are often misled by current attention map visualizations that point to relevant regions despite the model producing an incorrect answer. Hence, we propose Error Maps that clarify the error by highlighting image regions where the model is prone to err. Error maps can indicate when a correctly attended region may be processed incorrectly leading to an incorrect answer, and hence, improve users' understanding of those cases. To evaluate our new explanations, we further introduce a metric that simulates users' interpretation of explanations to evaluate their potential helpfulness to understand model correctness. We finally conduct user studies to see that our new explanations help users understand model correctness better than baselines by an expected 30\% and that our proxy helpfulness metrics correlate strongly ($\rho>0.97$) with how well users can predict model correctness.
Contracts under Moral Hazard and Adverse Selection

In the classical principal-agent problem, a principal must design a contract to incentivize an agent to perform an action on behalf of the principal. We study the classical principal-agent problem in a setting where the agent can be of one of several types (affecting the outcome of actions they might take). This combines the contract theory phenomena of "moral hazard" (incomplete information about actions) with that of "adverse selection" (incomplete information about types). We examine this problem through the computational lens. We show that in this setting it is APX-hard to compute either the profit-maximizing single contract or the profit-maximizing menu of contracts (as opposed to in the absence of types, where one can efficiently compute the optimal contract). We then show that the performance of the best linear contract scales especially well in the number of types: if agent has n available actions and T possible types, the best linear contract achieves an O(n log T) approximation of the best possible profit. Finally, we apply our framework to prove tight worst-case approximation bounds between a variety of benchmarks of mechanisms for the principal.
Axion searches with two superconducting radio-frequency cavities

We propose an experimental setup to search for Axion-like particles (ALPs) using two superconducting radio-frequency cavities. In this light-shining-through-wall setup the axion is sourced by two modes with large fields and nonzero $$ \overrightarrow{E}\cdot \overrightarrow{B} $$ in an emitter cavity. In a nearby identical cavity only one of these modes, the spectator, is populated while the other is a quiet signal mode. Axions can up-convert off the spectator mode into signal photons. We discuss the physics reach of this setup finding potential to explore new ALP parameter space. Enhanced sensitivity can be achieved if high-level modes can be used, thanks to improved phase matching between the excited modes and the generated axion field. We also discuss the potential leakage noise effects and their mitigation, which is aided by $$ \mathcal{O} $$ (GHz) separation between the spectator and signal frequencies.
Discrete-Roughness Transition for Hypersonic Flight Vehicles

The importance of discrete roughness and the correlations developed to predict the onset of boundary layer transition on hypersonic flight vehicles are discussed. The paper is organized by hypersonic vehicle applications characterized in a general sense by the boundary layer: slender with hypersonic conditions at the edge of the boundary layer, moderately blunt with supersonic, and blunt with subsonic. This paper is intended to be a review of recent discrete roughness transition work completed at NASA Langley Research Center in support of agency flight test programs. First, a review is provided of discrete roughness wind tunnel data and the resulting correlations that were developed. Then, results obtained from flight vehicles, in particular the recently flown Hyper-X and Shuttle missions, are discussed and compared to the ground-based correlations.
Inhibitory gating of coincidence-dependent sensory binding in secondary auditory cortex.

Integration of multi-frequency sounds into a unified perceptual object is critical for recognizing syllables in speech. This “feature binding” relies on the precise synchrony of each component’s onset timing, but little is known regarding its neural correlates. We find that multi-frequency sounds prevalent in vocalizations, specifically harmonics, preferentially activate the mouse secondary auditory cortex (A2), whose response deteriorates with shifts in component onset timings. The temporal window for harmonics integration in A2 was broadened by inactivation of somatostatin-expressing interneurons (SOM cells), but not parvalbumin-expressing interneurons (PV cells). Importantly, A2 has functionally connected subnetworks of neurons preferentially encoding harmonic over inharmonic sounds. These subnetworks are stable across days and exist prior to experimental harmonics exposure, suggesting their formation during development. Furthermore, A2 inactivation impairs performance in a discrimination task for coincident harmonics. Together, we propose A2 as a locus for multi-frequency integration, which may form the circuit basis for vocal processing. Sound processing requires binding of frequency components into a unified perceptual object. Here the authors investigate the neural correlates in the mouse secondary auditory cortex underlying multifrequency binding in response to harmonic sounds.
Compositionality of Linearly Solvable Optimal Control in Networked Multi-Agent Systems

In this paper, we discuss the methodology of generalizing the optimal control law from learned component tasks to unlearned composite tasks on Multi-Agent Systems (MASs), by using the linearity composition principle of linearly solvable optimal control (LSOC) problems. The proposed approach achieves both the compositionality and optimality of control actions simultaneously within the cooperative MAS framework in both discrete and continuous-time in a sample-efficient manner, which reduces the burden of re-computation of the optimal control solutions for the new task on the MASs. We investigate the application of the proposed approach on the MAS with coordination between agents. The experiments show feasible results in investigated scenarios, including both discrete and continuous dynamical systems for task generalization without resampling.
Feedback-Based Dynamic Feature Selection for Constrained Continuous Data Acquisition

Relevant and high-quality data are critical to successful development of machine learning applications. For machine learning applications on dynamic systems equipped with a large number of sensors, such as connected vehicles and robots, how to find relevant and high-quality data features in an efficient way is a challenging problem. In this work, we address the problem of feature selection in constrained continuous data acquisition. We propose a feedback-based dynamic feature selection algorithm that efficiently decides on the feature set for data collection from a dynamic system in a step-wise manner. We formulate the sequential feature selection procedure as a Markov Decision Process. The machine learning model performance feedback with an exploration component is used as the reward function in an ∊-greedy action selection. Our evaluation shows that the proposed feedback-based feature selection algorithm has superior performance over constrained baseline methods and matching performance with unconstrained baseline methods.
Surveillance of COVID-19 pandemic using social media: a reddit study in North Carolina

Coronavirus disease (COVID-19) pandemic has changed various aspects of people's lives and behaviors. At this stage, there are no other ways to control the natural progression of the disease than adopting mitigation strategies such as wearing masks, watching distance, and washing hands. Moreover, at this time of social distancing, social media plays a key role in connecting people and providing a platform for expressing their feelings. In this study, we tap into social media to surveil the uptake of mitigation and detection strategies, and capture issues and concerns about the pandemic. In particular, we explore the research question, "how much can be learned regarding the public uptake of mitigation strategies and concerns about COVID-19 pandemic by using natural language processing on Reddit posts?" After extracting COVID-related posts from the four largest subreddit communities of North Carolina over six months, we performed NLP-based preprocessing to clean the noisy data. We employed a custom Named-entity Recognition (NER) system and a Latent Dirichlet Allocation (LDA) method for topic modeling on a Reddit corpus. We observed that mask, flu, and testing are the most prevalent named-entities for "Personal Protective Equipment", "symptoms", and "testing" categories, respectively. We also observed that the most discussed topics are related to testing, masks, and employment. The mitigation measures are the most prevalent theme of discussion across all subreddits.
THEORETICAL MODELS OF FLUX PINNING AND FLUX MOTION IN HIGH-Tc SUPERCONDUCTING OXIDES

Various issues involved in the development of phenomenological models of flux pinning and motion in high-Tc oxides are discussed. A simplified model is presented for the critical current density and is used to examine the question of whether flux flow results from an instability due to plasticity of the flux-line array or from pin breaking.
Analyzing recrystallization behavior of heterogeneous structures single-phase Al alloys

Abstract In this research, mixed structures are created so that they have regions where dislocations accumulate and a region that is, to some extent, free from dislocations. These heterogeneous structures that bring together both soft and hard domains in a single sample are presumed to produce a fine recrystallized grain and recovered large grain structures if they are subsequently heated, leading to limited recrystallization. In the present work, the 3D Potts model has been modified to tailor and model complex problems such as heterogeneous structure recrystallization. This is done by introducing a limited fraction of nucleating grains to the structure where the highest geometrically necessary dislocation density distributions develop in the rolled layers at early simulation time as well as by controlling structure evolution in the undeformed structure layer using grain boundary energy criteria. Additionally, the designed microstructures were investigated using electron backscatter diffraction measurements and an in-situ heating stage performed on material with various degrees of deformation.
Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection

A sequential decision process in which an adaptive radar system repeatedly interacts with a finite state target channel is studied. The radar is capable of passively sensing the spectrum at regular intervals, which provides side information for the waveform selection process. The radar transmitter uses the sequence of spectrum observations as well as feedback from a collocated receiver to select waveforms which accurately estimate target parameters. It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient. Stochastic and adversarial linear contextual bandit models are introduced, allowing the radar to achieve effective performance in broad classes of physical environments. Simulations in a radar-communication coexistence scenario, as well as in an adversarial radar-jammer scenario, demonstrate that the proposed formulation provides a substantial improvement in target detection performance when Thompson Sampling and EXP3 algorithms are used to drive the waveform selection process. Further, it is shown that the harmful impacts of pulse-agile behavior on coherently processed radar data can be mitigated by adopting a time-varying constraint on the radars waveform catalog.