cssi/SciSciGPT-SciSciNet huggingface dataset:
Approximate Sparse Linear Regression

In the Sparse Linear Regression (SLR) problem, given a d x n matrix M and a d-dimensional query q, the goal is to compute a k-sparse n-dimensional vector tau such that the error ||M tau - q|| is minimized. This problem is equivalent to the following geometric problem: given a set P of n points and a query point q in d dimensions, find the closest k-dimensional subspace to q, that is spanned by a subset of k points in P. In this paper, we present data-structures/algorithms and conditional lower bounds for several variants of this problem (such as finding the closest induced k dimensional flat/simplex instead of a subspace). In particular, we present approximation algorithms for the online variants of the above problems with query time O~(n^{k-1}), which are of interest in the "low sparsity regime" where k is small, e.g., 2 or 3. For k=d, this matches, up to polylogarithmic factors, the lower bound that relies on the affinely degenerate conjecture (i.e., deciding if n points in R^d contains d+1 points contained in a hyperplane takes Omega(n^d) time). Moreover, our algorithms involve formulating and solving several geometric subproblems, which we believe to be of independent interest.
Communication algorithms via deep learning

Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning. We study a family of sequential codes parameterized by recurrent neural network (RNN) architectures. We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms). We show strong generalizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.
Splitting numbers of links and the four-genus

The splitting number of a link is the minimum number of crossing changes between distinct components that is required to convert the link into a split link. We provide a bound on the splitting number in terms of the four-genus of related knots.
Random groups contain surface subgroups

1. IntroductionGromov famously asked the following:Surface Subgroup Question. Let G be a one-ended hyperbolic group. Does Gcontain a subgroup isomorphic to the fundamental group of a closed surface withχ < 0?Beyond its intrinsic appeal, and its obvious connections to the Virtual HakenConjecture in 3-manifold topology (now a theorem of Agol [1]), one reason Gromovwas interested in this question was the hope that such surface subgroups could beused as essential structural components of hyperbolic groups [9]. Our interest inthis question is stimulated by a belief that surface groups (not necessarily closed)can act as a sort of “bridge” between hyperbolic geometry and symplectic geometry(through their connection to causal structures, quasimorphisms, stable commutatorlength, etc.).Despite receiving considerable attention the Surface Subgroup Question is wideopen in general, although in the speciﬁc case of hyperbolic 3-manifold groups it waspositively resolved by Kahn–Markovic [10]. The main results of our paper may besummarized by saying that we show that Gromov’s question has a positive answerfor most (hyperbolic) groups. In fact, the “executive summary” says that(1) most groups contain (many) surface subgroups;(2) these surface subgroups are quasiconvex — i.e. their intrinsic and extrinsicgeometry is uniformly comparable on large scales; and(3) these surface subgroups can be constructed, and their properties certiﬁedquickly and easily.Here “most groups” is a proxy for random groups in Gromov’s few relators ordensity models, to be deﬁned presently.In [8], § 9 (also see [14]), Gromov introduced the notion of a random group. Infact, he introduced two such models: the few relators model and the density model.In either model one ﬁrst ﬁxes a free group F
Human rDNA copy number is unstable in metastatic breast cancers.

ABSTRACTChromatin-mediated silencing, including the formation of heterochromatin, silent chromosome territories, and repressed gene promoters, acts to stabilize patterns of gene regulation and the ...
Trade credit, cash holdings, and product market competition

Abstract We examine how trade credit complements cash holdings in product market competition and find empirical support for this view. First, in line with the redistribution view of trade credit, we show that trade credit is sensitive to cash flows. Second, using an augmented Faulkender and Wang (2006) model, we report that stock market values both cash holdings and trade credit. Third, we identify that three common factors, namely, firms’ financial strength, market power, and relationship specific investments, moderate the valuations of cash and trade credit. For robustness, we use the U.S. import tariff reductions as exogenous shocks to product market competition and help establish causality between trade credit practice and competition. Overall, our findings strongly support the notion that corporate cash holding and trade credit decisions are intertwined. Managers be mindful of the trade-off between holding cash vis-a-vis distributing liquidity via trade credit.
A pilot RCT of a school nurse delivered intervention to reduce student anxiety

The goal of this study was to evaluate the feasibility and impact of brief school-nurse-administered interventions for reducing anxiety. Thirty school nurses in Connecticut and Maryland were random...
EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection

Epistasis (gene-gene interaction) is crucial to predicting genetic disease. Our work tackles the computational challenges faced by previous works in epistasis detection by modeling it as a one-step Markov Decision Process where the state is genome data, the actions are the interacted genes, and the reward is an interaction measurement for the selected actions. A reinforcement learning agent using policy gradient method then learns to discover a set of highly interacted genes. Our preliminary study shows a positive result.
Visual-Inertial Navigation: A Concise Review

As inertial and visual sensors are becoming ubiquitous, visual-inertial navigation systems (VINS) have prevailed in a wide range of applications from mobile augmented reality to aerial navigation to autonomous driving, in part because of the complementary sensing capabilities and the decreasing costs and size of the sensors. In this paper, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work – which is unfortunately missing in the literature while being greatly demanded by researchers and engineers – in the hope to accelerate the VINS research and beyond in our society as a whole.
Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification

In this paper, we propose a contact-implicit trajectory optimization (CITO) method based on a variable smooth contact model (VSCM) and successive convexification (SCvx). The VSCM facilitates the convergence of gradient-based optimization without compromising physical fidelity. On the other hand, the proposed SCvx-based approach combines the advantages of direct and shooting methods for CITO. For evaluations, we consider non-prehensile manipulation tasks. The proposed method is compared to a version based on iterative linear quadratic regulator (iLQR) on a planar example. The results demonstrate that both methods can find physically-consistent motions that complete the tasks without a meaningful initial guess owing to the VSCM. The proposed SCvx-based method outperforms the iLQR-based method in terms of convergence, computation time, and the quality of motions found. Finally, the proposed SCvx-based method is tested on a standard robot platform and shown to perform efficiently for a real-world application.
Seeing parochially and acting locally: Social exposure, problem identification and social entrepreneurship

Abstract Why some communities have greater rates of social entrepreneurship in similar domains is a question of importance to scholars and practitioners alike. Much of the literature in social entrepreneurship begins with a social problem that has been identified, and then analyzes the antecedents of the entrepreneurial process that lead to organizational solutions emerging for those problems. However, why some problems gain traction as being worthy of time and effort in solving, has garnered little attention. We argue that particular problems are more or less salient triggers of action by prospective social entrepreneurs based on the distribution of such problems in the local social environment, rather than aggregate levels of need. Yet, even problems widely experienced as shared, salient, and generally worthy of action might lack the emergence of solutions in fragmented communities, such as those with high levels of residential segregation by race and income. We study this in the context of founding of advocacy and support organizations in the domain of healthcare, and find support for our predictions. We also conducted additional tests to better characterize the findings and test robustness to alternative sources of influence, such as the local pool of potential social entrepreneurs, the role of local ecology, and geographic spillovers from neighboring areas.
Suppression of p16 Induces mTORC1-Mediated Nucleotide Metabolic Reprogramming.

Summary Reprogrammed metabolism and cell cycle dysregulation are two cancer hallmarks. p16 is a cell cycle inhibitor and tumor suppressor that is upregulated during oncogene-induced senescence (OIS). Loss of p16 allows for uninhibited cell cycle progression, bypass of OIS, and tumorigenesis. Whether p16 loss affects pro-tumorigenic metabolism is unclear. We report that suppression of p16 plays a central role in reprogramming metabolism by increasing nucleotide synthesis. This occurs by activation of mTORC1 signaling, which directly mediates increased translation of the mRNA encoding ribose-5-phosphate isomerase A (RPIA), a pentose phosphate pathway enzyme. p16 loss correlates with activation of the mTORC1-RPIA axis in multiple cancer types. Suppression of RPIA inhibits proliferation only in p16-low cells by inducing senescence both in vitro and in vivo. These data reveal the molecular basis whereby p16 loss modulates pro-tumorigenic metabolism through mTORC1-mediated upregulation of nucleotide synthesis and reveals a metabolic vulnerability of p16-null cancer cells.
High-speed low-crosstalk detection of a 171 Yb + qubit using superconducting nanowire single photon detectors

Qubits used in quantum computing suffer from errors, either from the qubit interacting with the environment, or from imperfect quantum logic gates. Effective quantum error correcting codes require a high fidelity readout of ancilla qubits from which the error syndrome can be determined without affecting data qubits. Here, we present a detection scheme for 171Yb+ qubits, where we use superconducting nanowire single photon detectors and utilize photon time-of-arrival statistics to improve the fidelity and speed. Qubit shuttling allows for creating a separate detection region where an ancilla qubit can be measured without disrupting a data qubit. We achieve an average qubit state detection time of 11 μs with a fidelity of 99.931(6). The detection crosstalk error, defined as the probability that the data qubit coherence is lost due to the process of detecting an ancilla qubit, is reduced to ~2 × 10−5 by creating a separation of 370 μm between them. Trapped ions have gained momentum as a platform for quantum computing thanks to the ability of storing qubits in stable electron states of each ions and transfer of information among the ion qubits in the trap. The authors present an experimental scheme to detect trapped 171-ytterbium ion qubits using photon statistics and superconducting nanowire single photon detectors and report a qubit detection fidelity of 99.93% within 11 microseconds.
Hiring a deaf computer professional

The computer industry has a critical need for skilled professionals. Employers are already hard-pressed to find qualified workers and look with interest to the growing population of technically trained deaf individuals as a new source of competent employees. Interest in hiring deaf computer professionals has also been accelerated by employers’ growing sense of social responsibility and their need to comply with government regulations concerning affirmative action in hiring and promoting handicapped individuals. This willingness to employ deaf professionals must be accompanied, however, by specific guidelines for the actual hiring and accommodation of such workers. This paper addresses the practical concerns of managers who wish to hire deaf professionals into their organization.
Multi-Level Texture Encoding and Representation (Multer) Based on Deep Neural Networks

In this paper, we propose a multi-level texture encoding and representation network (MuLTER) for texture-related applications. Based on a multi-level pooling architecture, the MuLTER network simultaneously leverages low-and high-level features to maintain both texture details and spatial information. Such a pooling architecture involves few extra parameters and keeps feature dimensions fixed despite of the changes of image sizes. In comparison with state-of-the-art texture descriptors, the MuLTER network yields higher recognition accuracy on typical texture datasets such as MINC-2500 and GTOS-mobile with a discriminative and compact representation. In addition, we analyze the impact of combining features from different levels, which supports our claim that the fusion of multi-level features efficiently enhances recognition performance. Our source code will be published on GitHub (https://github.com/olivesgatech).
Finite-Sample Analysis for SARSA with Linear Function Approximation

SARSA is an on-policy algorithm to learn a Markov decision process policy in reinforcement learning. We investigate the SARSA algorithm with linear function approximation under the non-i.i.d.\ setting, where a single sample trajectory is available. With a Lipschitz continuous policy improvement operator that is smooth enough, SARSA has been shown to converge asymptotically. However, its non-asymptotic analysis is challenging and remains unsolved due to the non-i.i.d. samples, and the fact that the behavior policy changes dynamically with time. In this paper, we develop a novel technique to explicitly characterize the stochastic bias of a type of stochastic approximation procedures with time-varying Markov transition kernels. Our approach enables non-asymptotic convergence analyses of this type of stochastic approximation algorithms, which may be of independent interest. Using our bias characterization technique and a gradient descent type of analysis, we further provide the finite-sample analysis on the mean square error of the SARSA algorithm. In the end, we present a fitted SARSA algorithm, which includes the original SARSA algorithm and its variant as special cases. This fitted SARSA algorithm provides a framework for \textit{iterative} on-policy fitted policy iteration, which is more memory and computationally efficient. For this fitted SARSA algorithm, we also present its finite-sample analysis.
Entrainment in Resolved, Dry Thermals

AbstractEntrainment in cumulus convection remains ill understood and difficult to quantify. For instance, entrainment is widely believed to be a fundamentally turbulent process, even though Turner ...
Fast Breadth-First Search in Still Less Space

It is shown that a breadth-first search in a directed or undirected graph with n vertices and m edges can be carried out in \(O(n+m)\) time with \(n\log _2 3+O((\log n)^2)\) bits of working memory.
Simultaneous Input and State Set-Valued $\mathcal{H}_{\infty}$ -Observers For Linear Parameter-Varying Systems

A fixed-order set-valued observer is presented for linear parameter-varying systems with bounded-norm noise and under completely unknown attack signals, which simultaneously finds bounded sets of states and unknown inputs that include the true state and inputs. The proposed observer can be designed using semidefinite programming with LMI constraints and is optimal in the minimum $\mathcal{H}_{\infty}$ -norm sense. We show that the strong detectability of each constituent linear time-invariant system is a necessary condition for the existence of such an observer, as well as the boundedness of set-valued estimates. Furthermore, sufficient conditions are provided for the upper bounds of the estimation errors to converge to steady state values and finally, the results of such a set-valued observer are exhibited through an illustrative example.
Distributed Q-Learning for Dynamically Decoupled Systems

Control of large-scale networked systems often necessitates the availability of complex models for the interactions amongst the agents. However, in many applications building accurate models of these interactions might be prohibitive due to the curse of dimensionality or their inherent complexity. In the meantime, data-guided control methods can circumvent model complexity by directly synthesizing the controller from the observed data. In this paper, we propose a distributed Q-learning algorithm to design a feedback mechanism given an underlying graph structure parameterizing the agents' communication. We assume that the distributed nature of the system arises from a common cost and show that for the particular case of identical dynamically decoupled systems, the learned controller converges to the optimal Linear Quadratic Regulator controller for each subsystem. We provide a convergence analysis and verify the result with an example.
Non-enzymatic roles of human RAD51 at stalled replication forks

The central recombination enzyme RAD51 has been implicated in replication fork processing and restart in response to replication stress. Here, we use a separation-of-function allele of RAD51 that retains DNA binding, but not D-loop activity, to reveal mechanistic aspects of RAD51's roles in the response to replication stress. Here, we find that cells lacking RAD51's enzymatic activity protect replication forks from MRE11-dependent degradation, as expected from previous studies. Unexpectedly, we find that RAD51's strand exchange activity is not required to convert stalled forks to a form that can be degraded by DNA2. Such conversion was shown previously to require replication fork regression, supporting a model in which fork regression depends on a non-enzymatic function of RAD51. We also show RAD51 promotes replication restart by both strand exchange-dependent and strand exchange-independent mechanisms.
An Information-Theoretic Explanation for the Adversarial Fragility of AI Classifiers

We present a simple hypothesis about a compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations. We also propose a new method for detecting when small input perturbations cause classifier errors, and show theoretical guarantees for the performance of this detection method. We present experimental results with a voice recognition system to demonstrate this method. The ideas in this paper are motivated by a simple analogy between AI classifiers and the standard Shannon model of a communication system.1
Epigenetic memory independent of symmetric histone inheritance.

A crucial process in life is the ability of cells to pass on useful information to their descendants. Some of this information is encoded within molecules of DNA, including genes that contain specific coded instructions. Another layer of information helps to specify whether individual genes are switched on or off, which means cells with the same genes can perform different tasks. However, it remains unclear exactly how cells pass on this additional layer of “epigenetic” information. Inside human, yeast and other eukaryotic cells, DNA is wrapped around scaffold proteins known as histones. Cells modify histones by adding chemical tags to them, and histones within the same gene often have specific patterns of chemical tags. One popular hypothesis is that these marked histones constitute epigenetic information that may be passed on when DNA replicates before a cell divides to make two daughter cells. This model predicts that the marked histones need to be divided equally between the two sets of DNA to allow the epigenetic information to be faithfully passed on to both daughter cells. To test this prediction, Saxton and Rine studied a gene called HMR that is involved in mating in yeast. This gene is constantly silenced (in other words, not actively providing instructions to the cell) and contains histones with very specific patterns of chemical tags. For the experiments, Saxton and Rine made a series of mutations in the yeast that increased how often these marked histones were divided unequally when the yeast cells replicated their DNA. Unexpectedly, these mutations had little impact on the ability of the cells to pass on the silenced state of HMR to their offspring. These findings argue against the classic model that marked histones carry epigenetic information.
The Effect of Signaling Latencies and Node Refractory States on the Dynamics of Networks.

We describe the construction and theoretical analysis of a framework derived from canonical neurophysiological principles that model the competing dynamics of incident signals into nodes along dire...
Motivic characteristic classes in cohomological Hall algebras

Abstract The equivariant Chern-Schwartz-MacPherson (CSM) class and the equivariant Motivic Chern (MC) class are important characteristic classes of singular varieties in cohomology and K theory—and their theory overlaps with the theory of Okounkov's stable envelopes. We study CSM and MC classes for the orbits of Dynkin quiver representations. We show that the problem of computing the CSM and MC classes of all these orbits can be reduced to some basic classes c β o , C β o parameterized by positive roots β. We prove an identity in a deformed version of Kontsevich-Soibelman's Cohomological (and K-theoretic) Hall Algebra (CoHA, KHA), namely, that a product of exponentials of c β o (or C β o ) classes formally depending on a stability function Z, does not depend on Z. This identity—which encodes infinitely many identities among rational functions in growing number of variables—has the structure of Donaldson-Thomas type quantum dilogarithm identities. Using a wall-crossing argument we present the c β o , C β o classes as certain commutators in the CoHA, KHA.
Whole brain polarity regime dynamics are significantly disrupted in schizophrenia and correlate strongly with network connectivity measures

From a large clinical blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) study, we report several interrelated findings involving transient supra-network brainwide states characterized by a saturation phenomenon we are referring to as “polarization.” These are whole-brain states in which the voxelwise-normalized BOLD (vn-BOLD) activation of a large proportion of voxels is simultaneously either very high or very low. The presence of such states during a resting-state fMRI (rs-fMRI) scan is significantly anti-correlated with diagnosed schizophrenia, significantly anti-correlated with connectivity between subcortical networks and auditory, visual and sensorimotor networks and also significantly anti-correlated with contemporaneous occupancy of transient functional network connectivity states featuring broad disconnectivity or strong inhibitory connections between the default mode and other networks. Conversely, the presence of highly polarized vn-BOLD states is significantly correlated with connectivity strength between auditory, visual and sensorimotor networks and with contemporaneous occupancy of transient whole-brain patterns of strongly modularized network connectivity and diffuse hyperconnectivity. Despite their consistency with well-documented effects of schizophrenia on static and time-varying functional network connectivity, the observed relationships between polarization and network connectivity are with very few exceptions unmediated by schizophrenia diagnosis. Many differences observed between patients and controls are echoed within the patient population itself in the effect patterns of positive symptomology (e.g. hallucinations, delusions, grandiosity). Our findings highlight a particular whole-brain spatiotemporal BOLD activation phenomenon that differs markedly between healthy subjects and schizophrenia patients, one that also strongly informs time-resolved network connectivity patterns that are associated with this serious clinical disorder.
Braid index bounds ropelength from below

For an unoriented link &#x1d4a6;, let L(&#x1d4a6;) be the ropelength of &#x1d4a6;. It is known that in general L(&#x1d4a6;) is at least of the order O((Cr(&#x1d4a6;))3/4), and at most of the order O(Cr(&#x1d4a6;)ln5(Cr(&#x1d4a6;)) where Cr(&#x1d4a6;) is the min...
Canicide by Cop: A geographical analysis of canine killings by police in Los Angeles

Abstract Even in an era of extreme scrutiny of police use of force, the fact that a significant proportion of police shootings are directed at dogs—pet pit bulls in particular—remains largely unknown and all but completely absent from the academic literature on state violence. However, based on our analyses of officer-involved shootings from 2010 through 2017 by the County of Los Angeles Sheriff’s Department and City of Los Angeles Police Department, we reveal that not only do dog shootings and killings—or “canicide”—account for a significant share of deadly force, but they are also clustered to a statistically significant degree in predominantly low-income communities of color. In this article, we provide a much-needed socio-spatial analysis of this hitherto unexplored though prevalent form of legal deadly force. We argue that revealing this particular geography of violence committed against dogs provides yet another example of the distinctive spatiality of carcerality and state violence enacted in and on disenfranchised communities.
An efficient scheme to reduce handoff dropping in LEO satellite systems

The problem of handoffs in cellular networks is compounded in a low earth orbit (LEO) satellite-based cellular network due to the relative motion of the satellites with respect to a stationary observer on earth. Typically, the velocity of motion of mobiles can be ignored when compared to the very high velocity of the footprints of satellites. We exploit this property of LEO satellite systems and propose a handoff scheme based on a channel sharing approach that results in a substantial decrease in handoff dropping. For the same handoff dropping performance, our scheme has significantly lower new call blocking probability than the conventional reservation scheme. We also present an analytical approximation that is in very good accord with simulation results.
Literacy skills, language use, and online health information seeking among Hispanic adults in the United States.

Abstract Objective Online health information is underutilized among Hispanics with low English proficiency in the U.S. This study examines the association between a unique measure of general English literacy, language use, and online health information seeking among Hispanic adults. Methods Data for Hispanics ages 25–65 (N = 700) come from the 2012/2014 Program for International Assessment of Adult Competencies (PIAAC). Binary logistic regression models were used to predict online health information seeking as a function of literacy skill scores (0–500 points) and primary language use (Spanish vs. other). Results Literacy (Odds-Ratio = 1.012, p Conclusion Literacy skills and language use appear to be separate contributors of online health information seeking among Hispanic adults. Practice implications Online health information providers should be aware of literacy skills and Spanish language use as barriers to online health information seeking among Hispanics, particularly those who have both limited literacy skills and predominantly Spanish language use.
Cohen forcing and inner models

Given an inner model $W \subset V$ and a regular cardinal $\kappa$, we consider two alternatives for adding a subset to $\kappa$ by forcing: the Cohen poset $Add(\kappa,1)$, and the Cohen poset of the inner model $Add(\kappa,1)^W$. The forcing from $W$ will be at least as strong as the forcing from $V$ (in the sense that forcing with the former adds a generic for the latter) if and only if the two posets have the same cardinality. On the other hand, a sufficient condition is established for the poset from $V$ to fail to be as strong as that from $W$. The results are generalized to $Add(\kappa,\lambda)$, and to iterations of Cohen forcing where the poset at each stage comes from an arbitrary intermediate inner model.
Micro-Scale, Meso-Scale, Macro-Scale, and Temporal Scale: Comparing the Relative Importance for Robbery Risk in New York City

We compare the relative importance of four dimensions for explaining the micro location of robberies: 1) the micro spatial scale of street segments; 2) the meso spatial scale surrounding the street...
Acute toxicity of the plant volatile indole depends on herbivore specialization

Herbivore-induced plant volatiles (HIPVs) provide direct benefits to plants as antimicrobials and herbivore repellents, but their potential as direct toxins to herbivores is unclear. Here, we assayed the larvicidal activity of six common HIPVs from three different biochemical pathways and tested the hypothesis that the larvicidal activity of HIPVs is related to the host specialization of the insect pest. We first assessed β-caryophyllene, linalool, z-3-hexenyl acetate, z-3-hexenol, e-2-hexenal, and indole against the beet armyworm (Spodoptera exigua) and found that indole was sevenfold more toxic compared to the other volatiles when incorporated into the diet. Then, we tested the larvicidal activity of indole against six common, destructive pest caterpillars with varying host ranges. Consistent with our hypothesis, indole toxicity varied with caterpillar host range: indole toxicity was sevenfold higher in more specialized insect species relative to generalist insect species. That said, the LC50 of indole was comparable to other reported anti-herbivore agents even against the generalist caterpillars. Yet, indole in headspace had neither larvicidal nor ovicidal activity on any caterpillar species tested. These results support a key ecological precept of a trade-off between host specialization and chemical detoxification and also indicate that indole in particular is directly toxic to herbivores and therefore potentially useful in integrated pest management strategies.
Rewilding Nod2 and Atg16l1 Mutant Mice Uncovers Genetic and Environmental Contributions to Microbial Responses and Immune Cell Composition.

Summary The relative contributions of genetic and environmental factors to variation in immune responses are poorly understood. Here, we performed a phenotypic analysis of immunological parameters in laboratory mice carrying susceptibility genes implicated in inflammatory bowel disease (IBD) (Nod2 and Atg16l1) upon exposure to environmental microbes. Mice were released into an outdoor enclosure (rewilded) and then profiled for immune responses in the blood and lymph nodes. Variations of immune cell populations were largely driven by the environment, whereas cytokine production elicited by microbial antigens was more affected by the genetic mutations. We identified transcriptional signatures in the lymph nodes associated with differences in T cell populations. Subnetworks associated with responses against Clostridium perfringens, Candida albicans, and Bacteroides vulgatus were also coupled with rewilding. Therefore, exposing laboratory mice with genetic mutations to a natural environment uncovers different contributions to variations in microbial responses and immune cell composition.
28 GHz mmWave Channel Measurements and Modeling in a Library Environment

To fully exploit the millimeter-wave bands for the fifth generation cellular systems, an accurate understanding of the channel propagation characteristics is required, and hence extensive measurement campaigns in different environments are needed. In this paper, we use a rotated directional antenna-based channel sounder for measurements at 28 GHz in large indoor environments at a library setting. We present models for power angular-delay profile and large-scale path loss based on the measurements over distances ranging from 10 m to 50 m. In total, nineteen different line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios are considered, including the cases where the transmitter and the receiver are placed on different floors. Results show that the close-in free space reference distance and the floating intercept path loss models both perform well in fitting the empirical data. The path loss exponent obtained for the LOS scenarios is found to be very close to that of the free space path loss model.
Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership

Network analysis of social media provides an important new lens on politics, communication, and their interactions. This lens is particularly prominent in fast-moving events, such as conversations and action in political rallies and the use of social media by extremist groups to spread their message. We study the Twitter conversation following the August 2017 ‘Unite the Right’ rally in Charlottesville, Virginia, USA using tools from network analysis and data science. We use media followership on Twitter and principal component analysis (PCA) to compute a ‘Left’/‘Right’ media score on a one-dimensional axis to characterize Twitter accounts. We then use these scores, in concert with retweet relationships, to examine the structure of a retweet network of approximately 300,000 accounts that communicated with the #Charlottesville hashtag. The retweet network is sharply polarized, with an assortativity coefficient of 0.8 with respect to the sign of the media PCA score. Community detection using two approaches, a Louvain method and InfoMap, yields communities that tend to be homogeneous in terms of Left/Right node composition. We also examine centrality measures and find that hyperlink-induced topic search (HITS) identifies many more hubs on the Left than on the Right. When comparing tweet content, we find that tweets about ‘Trump’ were widespread in both the Left and Right, although the accompanying language (i.e., critical on the Left, but supportive on the Right) was unsurprisingly different. Nodes with large degrees in communities on the Left include accounts that are associated with disparate areas, including activism, business, arts and entertainment, media, and politics. By contrast, support of Donald Trump was a common thread among the Right communities, connecting communities with accounts that reference white-supremacist hate symbols, communities with influential personalities in the alt-right, and the largest Right community (which includes the Twitter account FoxNews).
Multi-omics Visualization Platform: An extensible Galaxy plug-in for multi-omics data visualization and exploration.

BACKGROUND Proteogenomics integrates genomics, transcriptomics, and mass spectrometry (MS)-based proteomics data to identify novel protein sequences arising from gene and transcript sequence variants. Proteogenomic data analysis requires integration of disparate 'omic software tools, as well as customized tools to view and interpret results. The flexible Galaxy platform has proven valuable for proteogenomic data analysis. Here, we describe a novel Multi-omics Visualization Platform (MVP) for organizing, visualizing, and exploring proteogenomic results, adding a critically needed tool for data exploration and interpretation. FINDINGS MVP is built as an HTML Galaxy plug-in, primarily based on JavaScript. Via the Galaxy API, MVP uses SQLite databases as input-a custom data type (mzSQLite) containing MS-based peptide identification information, a variant annotation table, and a coding sequence table. Users can interactively filter identified peptides based on sequence and data quality metrics, view annotated peptide MS data, and visualize protein-level information, along with genomic coordinates. Peptides that pass the user-defined thresholds can be sent back to Galaxy via the API for further analysis; processed data and visualizations can also be saved and shared. MVP leverages the Integrated Genomics Viewer JavaScript framework, enabling interactive visualization of peptides and corresponding transcript and genomic coding information within the MVP interface. CONCLUSIONS MVP provides a powerful, extensible platform for automated, interactive visualization of proteogenomic results within the Galaxy environment, adding a unique and critically needed tool for empowering exploration and interpretation of results. The platform is extensible, providing a basis for further development of new functionalities for proteogenomic data visualization.
Iterative computation of noncooperative equilibria in nonzero-sum differential games with weakly coupled players

We study the Nash equilibria of a class of two-person nonlinear, deterministic differential games where the players are weakly coupled through the state equation and their objective functionals. The weak coupling is characterized in terms of a small perturbation parameter e. With e=0, the problem decomposes into two independent standard optimal control problems, while for e≠0, even though it is possible to derive the necessary and sufficient conditions to be satisfied by a Nash equilibrium solution, it is not always possible to construct such a solution. In this paper, we develop an iterative scheme to obtain an approximate Nash solution when e lies in a small interval around zero. Further, after requiring strong time consistency and/or robustness of the Nash equilibrium solution when at least one of the players uses dynamic information, we address the issues of existence and uniqueness of these solutions for the cases when both players use the same information, either closed loop or open loop, and when one player uses open-loop information and the other player uses closed-loop information. We also show that, even though the original problem is nonlinear, the higher (than zero) order terms in the Nash equilibria can be obtained as solutions to LQ optimal control problems or static quadratic optimization problems.
Self-Limiting Electrospray Deposition for the Surface Modification of Additively Manufactured Parts

Electrospray deposition (ESD) is a spray coating process that utilizes a high voltage to atomize a flowing solution into charged microdroplets. These self-repulsive droplets evaporate as they trave...
The Effect of Labor Market Conditions at Entry on Workers’ Long-Term Skills

This paper studies the impact of labor market conditions during the education-to-work transition on workers' long-term skill development. Using representative survey data on measures of work-relevant cognitive skills for adults from 19 countries, I document four main findings: i) cohorts of workers who faced higher unemployment rates at ages 1825 have lower skills at ages 3659; ii) unemployment rates faced at later ages (2635) do not have such an effect; iii) the former findings hold even though, on average, people get more formal education as a response to higher unemployment in their late teens and early twenties; iv) skill inequality is affected: workers whose parents were less educated bear most of the negative effects. These findings can be rationalized by on-the-job learning during the early twenties being an important factor of skill-development, and such learning being negatively impacted by bad macroeconomic conditions. Using German panel data on skills, I show that young workers at large firms experience higher skill growth than those at small firms. This finding suggests firm heterogeneity in human capital provision to young workers as a potential mechanism since, in bad economic times, young workers disproportionately match with small firms.
Real-Time Binaural Speech Separation with Preserved Spatial Cues

Deep learning speech separation algorithms have achieved great success in improving the quality and intelligibility of separated speech from mixed audio. Most previous methods focused on generating a single-channel output for each of the target speakers, hence discarding the spatial cues needed for the localization of sound sources in space. However, preserving the spatial information is important in many applications that aim to accurately render the acoustic scene such as in hearing aids and augmented reality (AR). Here, we propose a speech separation algorithm that preserves the interaural cues of separated sound sources and can be implemented with low latency and high fidelity, therefore enabling a real-time modification of the acoustic scene. Based on the time-domain audio separation network (TasNet), a single-channel time-domain speech separation system that can be implemented in real-time, we propose a multi-input-multi-output (MIMO) end-to-end extension of TasNet that takes binaural mixed audio as input and simultaneously separates target speakers in both channels. Experimental results show that the proposed end-to-end MIMO system is able to significantly improve the separation performance and keep the perceived location of the modified sources intact in various acoustic scenes.
Long Time Behavior of Random and Nonautonomous Fisher–KPP Equations: Part I—Stability of Equilibria and Spreading Speeds

In the current series of two papers, we study the long time behavior of nonnegative solutions to the following random Fisher–KPP equation, 1 $$\begin{aligned} u_t =u_{xx}+a(\theta _t\omega )u(1-u),\quad x\in {{\mathbb {R}}}, \end{aligned}$$ where $$\omega \in \Omega $$ , $$(\Omega , {\mathcal {F}},{\mathbb {P}})$$ is a given probability space, $$\theta _t$$ is an ergodic metric dynamical system on $$\Omega $$ , and $$a(\omega )>0$$ for every $$\omega \in \Omega $$ . We also study the long time behavior of nonnegative solutions to the following nonautonomous Fisher–KPP equation, 2 $$\begin{aligned} u_t=u_{xx}+a_0(t)u(1-u),\quad x\in {{\mathbb {R}}}, \end{aligned}$$ where $$a_0(t)$$ is a positive locally Holder continuous function. In this first part of the series, we investigate the stability of positive equilibria and the spreading speeds. Under some proper assumption on $$a(\omega )$$ , we show that the constant solution $$u=1$$ of (1) is asymptotically stable with respect to strictly positive perturbations and show that (1) has a deterministic spreading speed interval $$[2\sqrt{{\underline{a}}}, 2\sqrt{{\bar{a}}}]$$ , where $${\underline{a}}$$ and $${\bar{a}}$$ are the least and the greatest means of $$a(\cdot )$$ , respectively, and hence the spreading speed interval is linearly determinate. It is shown that the solution of (1) with a nonnegative initial function which is bounded away from 0 for $$x\ll -1$$ and is 0 for $$x\gg 1$$ propagates at the speed $$2\sqrt{{\hat{a}}}$$ , where $${\hat{a}}$$ is the mean of $$a(\cdot )$$ . Under some assumption on $$a_0(\cdot )$$ , we also show that the constant solution $$u=1$$ of (2) is asymptotically stably and (2) admits a bounded spreading speed interval. It is not assumed that $$a(\omega )$$ and $$a_0(t)$$ are bounded above and below by some positive constants. The results obtained in this part are new and extend the existing results in literature on spreading speeds of Fisher–KPP equations. In the second part of the series, we will study the existence and stability of transition fronts of (1) and (2).
Dynamical Decomposition of Bilinear Control Systems Subject to Symmetries

We describe a method to analyze and decompose the dynamics of a bilinear control system subject to symmetries. The method is based on the concept of generalized Young symmetrizers of representation theory. It naturally applies to the situation where the system evolves on a tensor product space and there exists a finite group of symmetries for the dynamics which interchanges the various factors. This is the case for quantum mechanical multipartite systems, such as spin networks, where each factor of the tensor product represents the state of one of the component systems. We present several examples of application.
The force on a boundary in active matter

We present a general theory for determining the force (and torque) exerted on a boundary (or body) in active matter. The theory extends the description of passive Brownian colloids to self-propelled active particles and applies for all ratios of the thermal energy to the swimmer’s activity , where is the Stokes drag coefficient, is the swim speed and is the reorientation time of the active particles. The theory, which is valid on all length and time scales, has a natural microscopic length scale over which concentration and orientation distributions are confined near boundaries, but the microscopic length does not appear in the force. The swim pressure emerges naturally and dominates the behaviour when the boundary size is large compared to the swimmer’s run length . The theory is used to predict the motion of bodies of all sizes immersed in active matter.
Hierarchical growth is necessary and (sometimes) sufficient to self-assemble discrete self-similar fractals

In this paper, we prove that in the abstract Tile Assembly Model (aTAM), an accretion-based model which only allows for a single tile to attach to a growing assembly at each step, there are no tile assembly systems capable of self-assembling the discrete self-similar fractals known as the “H” and “U” fractals. We then show that in a related model which allows for hierarchical self-assembly, the 2-Handed Assembly Model (2HAM), there does exist a tile assembly system which self-assembles the “U” fractal and conjecture that the same holds for the “H” fractal. This is the first example of discrete self similar fractals which self-assemble in the 2HAM but not in the aTAM, providing a direct comparison of the models and greater understanding of the power of hierarchical assembly.
Structure of the Full-Length TRPV2 Channel by Cryo-EM

Transient receptor potential (TRP) proteins form a superfamily Ca(2+)-permeable cation channels regulated by a range of chemical and physical stimuli. Structural analysis of a 'minimal' TRP vanilloid subtype 1 (TRPV1) elucidated a mechanism of channel activation by agonists through changes in its outer pore region. Though homologous to TRPV1, other TRPV channels (TRPV2-6) are insensitive to TRPV1 activators including heat and vanilloids. To further understand the structural basis of TRPV channel function, we determined the structure of full-length TRPV2 at ∼5 A resolution by cryo-electron microscopy. Like TRPV1, TRPV2 contains two constrictions, one each in the pore-forming upper and lower gates. The agonist-free full-length TRPV2 has wider upper and lower gates compared with closed and agonist-activated TRPV1. We propose these newly revealed TRPV2 structural features contribute to diversity of TRPV channels.
Strategic Implications of Uncertainty over One's Own Private Value in Auctions

Suppose a bidder must decide whether and when to incur the cost of estimating his own private value in an auction. This can explain why a bidder might increase his bid ceiling in the course of an auction, and why a bidder would like to know the private values of other bidders. It also can explain sniping— flurries of bids at the end of auctions with deadlines— as the result of other bidders trying to avoid stimulating the uninformed bidder to examine his value.