cssi/SciSciGPT-SciSciNet huggingface dataset:
Convergence of Stochastic Interacting Particle Systems in Probability under a Sobolev Norm

Abstract In this paper, we consider particle systems with interaction and Brownian motion. We prove that when the initial data is from the sampling of Chorin’s method, i.e., the initial vertices are on lattice points hi ∈ Rd with mass ρ0(hi)h, where ρ0 is some initial density function, then the regularized empirical measure of the interacting particle system converges in probability to the corresponding mean-field partial differential equation with initial density ρ0, under the Sobolev norm of L ∞(L2) ∩ L2(H1). Our result is true for all those systems when the interacting function is bounded, Lipschitz continuous and satisfies certain regular condition. And if we further regularize the interacting particle system, it also holds for some of the most important systems of which the interacting functions are not. For systems with repulsive Coulomb interaction, this convergence holds globally on any interval [0, t]. And for systems with attractive Newton force as interacting function, we have convergence within the largest existence time of the regular solution of the corresponding Keller-Segel equation.
Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies

Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have real-world significance or not. In this paper, we are interested in the problem of specifically detecting anomalous instances that are known to have high real-world utility, while ignoring the low-utility statistically anomalous instances. To this end, we propose a novel method called Latent Laplacian Maximum Entropy Discrimination (LatLapMED) as a potential solution. This method uses the Entropy Minimization (EM) algorithm to simultaneously incorporate the Geometric EM principle for identifying statistical anomalies, and the MED principle to incorporate utility labels, in order to detect high-utility anomalies. We apply our method in both simulated and real datasets to demonstrate that it has superior performance over existing alternatives that independently pre-process with unsupervised anomaly detection algorithms before classifying.
Bayesian Optimization with Gradients

Bayesian optimization has shown success in global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimization typically does not use derivative information. In this paper we show how Bayesian optimization can exploit derivative information to find good solutions with fewer objective function evaluations. In particular, we develop a novel Bayesian optimization algorithm, the derivative-enabled knowledge-gradient (dKG), which is one-step Bayes-optimal, asymptotically consistent, and provides greater one-step value of information than in the derivative-free setting. dKG accommodates noisy and incomplete derivative information, comes in both sequential and batch forms, and can optionally reduce the computational cost of inference through automatically selected retention of a single directional derivative. We also compute the dKG acquisition function and its gradient using a novel fast discretization-free technique. We show dKG provides state-of-the-art performance compared to a wide range of optimization procedures with and without gradients, on benchmarks including logistic regression, deep learning, kernel learning, and k-nearest neighbors.
Age Group Classification with Speech and Metadata Multimodality Fusion

Children comprise a significant proportion of TV viewers and it is worthwhile to customize the experience for them. However, identifying who is a child in the audience can be a challenging task. We present initial studies of a novel method which combines utterances with user metadata. In particular, we develop an ensemble of different machine learning techniques on different subsets of data to improve child detection. Our initial results show an 9.2% absolute improvement over the baseline, leading to a state-of-the-art performance.
SMOOTH PARAMETRIZED TORSION A MANIFOLD APPROACH

Abstract We present a construction of a torsion invariant of bundles of smooth manifolds which is based on the work of Dwyer, Weiss and Williams on smooth structures on fibrations.
Model-Ensemble Trust-Region Policy Optimization

Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample complexity, which hinders their use in real-world domains. Alternatively, model-based reinforcement learning promises to reduce sample complexity, but tends to require careful tuning and to date have succeeded mainly in restrictive domains where simple models are sufficient for learning. In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training. To overcome this issue, we propose to use an ensemble of models to maintain the model uncertainty and regularize the learning process. We further show that the use of likelihood ratio derivatives yields much more stable learning than backpropagation through time. Altogether, our approach Model-Ensemble Trust-Region Policy Optimization (ME-TRPO) significantly reduces the sample complexity compared to model-free deep RL methods on challenging continuous control benchmark tasks.
Affine pavings and the enhanced nilpotent cone

We construct affine pavings of Springer-type fibers over the enhanced nilpotent cone. This resolves a question of Achar-Henderson and implies the existence of perverse parity sheaves on the enhanced nilpotent cone.
Parameter estimation by Density Functional Theory for a lattice-gas model of Br and Cl chemisorption on Ag (100)

Abstract We study Bromine and Chlorine chemisorption on a Ag (1 0 0) surface, using a lattice-gas model and the quantum-mechanical Density Functional Theory (DFT) method. In this model the Br and Cl ions adsorb at the fourfold hollow sites of the Ag (1 0 0) surface, which can be represented by a square lattice of adsorption sites. Five different coverages were used for each kind of adsorbate. For each adsorbate and coverage, we obtained the minimum-energy configuration, its energy, and its charge distribution. From these data we calculated dipole moments, lateral interaction energies, and binding energies. Our results show that for Br the lateral interactions obtained by fitting to the adsorption energies obtained from the DFT calculation are consistent with long-range dipole–dipole lateral interactions obtained using the dipole moments calculated from the DFT charge distribution. For Cl we found that, while the long-range dipole–dipole lateral interactions are important, short-range attractive interactions are also present. Our results are overall consistent with parameter estimates previously obtained by fitting room-temperature Monte Carlo simulations to electrochemical adsorption isotherms [I. Abou Hamad, et al., J. Electroanal. Chem. 554 (2003) 211; Electrochim. Acta 50 (2005) 5518].
The Lee spectral sequence, unknotting number, and the Knight Move Conjecture

Abstract We show that the page at which the Lee spectral sequence collapses gives a bound on the unknotting number, u ( K ) . In particular, for knots with u ( K ) ≤ 2 , we show that the Lee spectral sequence must collapse at the E 2 page. An immediate corollary is that the Knight Move Conjecture is true when u ( K ) ≤ 2 .
Total positivity in loop groups, I: Whirls and curls

Abstract This is the first of a series of papers where we develop a theory of total positivity for loop groups. In this paper, we completely describe the totally nonnegative part of the polynomial loop group G L n ( R [ t , t − 1 ] ) , and for the formal loop group G L n ( R ( ( t ) ) ) we describe the totally nonnegative points which are not totally positive. Furthermore, we make the connection with networks on the cylinder. Our approach involves the introduction of distinguished generators, called whirls and curls, and we describe the commutation relations amongst them. These matrices play the same role as the poles and zeros of the Edrei–Thoma theorem classifying totally positive functions (corresponding to our case n = 1 ). We give a solution to the “factorization problem” using limits of ratios of minors. This is in a similar spirit to the Berenstein–Fomin–Zelevinsky Chamber Ansatz where ratios of minors are used. A birational symmetric group action arising in the commutation relation of curls appeared previously in Noumi–Yamada’s study of discrete Painleve dynamical systems and Berenstein–Kazhdan’s study of geometric crystals.
Analog forecasting with dynamics-adapted kernels

Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning and state-space reconstruction for dynamical systems. A key ingredient of our approach is to replace single-analog forecasting with weighted ensembles of analogs constructed using local similarity kernels. The kernels used here employ a number of dynamics-dependent features designed to improve forecast skill, including Takens' delay-coordinate maps (to recover information in the initial data lost through partial observations) and a directional dependence on the dynamical vector field generating the data. Mathematically, our approach is closely related to kernel methods for out-of-sample extension of functions, and we discuss alternative strategies based on the Nystrom method and the multiscale Laplacian pyramids technique. We illustrate these techniques in applications to forecasting in a low-order deterministic model for atmospheric dynamics with chaotic metastability, and interannual-scale forecasting in the North Pacific sector of a comprehensive climate model. We find that forecasts based on kernel-weighted ensembles have significantly higher skill than the conventional approach following a single analog.
Robsut Wrod Reocginiton via Semi-Character Recurrent Neural Network

Language processing mechanism by humans is generally more robust than computers. The Cmabrigde Uinervtisy (Cambridge University) effect from the psycholinguistics literature has demonstrated such a robust word processing mechanism, where jumbled words (e.g. Cmabrigde / Cambridge) are recognized with little cost. On the other hand, computational models for word recognition (e.g. spelling checkers) perform poorly on data with such noise. Inspired by the findings from the Cmabrigde Uinervtisy effect, we propose a word recognition model based on a semi-character level recurrent neural network (scRNN). In our experiments, we demonstrate that scRNN has significantly more robust performance in word spelling correction (i.e. word recognition) compared to existing spelling checkers and character-based convolutional neural network. Furthermore, we demonstrate that the model is cognitively plausible by replicating a psycholinguistics experiment about human reading difficulty using our model.
Exact and asymptotic results on coarse Ricci curvature of graphs

Abstract Ricci curvature was proposed by Ollivier in a general framework of metric measure spaces, and it has been studied extensively in the context of graphs in recent years. In this paper we obtain the exact formulas for Ollivier’s Ricci-curvature for bipartite graphs and for the graphs with girth at least 5. These are the first formulas for Ricci-curvature that hold for a wide class of graphs, and extend earlier results where the Ricci-curvature for graphs with girth 6 was obtained. We also prove a general lower bound on the Ricci-curvature in terms of the size of the maximum matching in an appropriate subgraph. As a consequence, we characterize the Ricci-flat graphs of girth 5. Moreover, using our general lower bound and the Birkhoff–von Neumann theorem, we give the first necessary and sufficient condition for the structure of Ricci-flat regular graphs of girth 4. Finally, we obtain the asymptotic Ricci-curvature of random bipartite graphs G ( n , n , p ) and random graphs G ( n , p ) , in various regimes of p .
Splittings of link concordance groups

We establish several results about two short exact sequences involving lower terms of the n-solvable filtration, {ℱnm} of the string link concordance group Cm. We utilize the Thom–Pontryagin construction to show that the Sato–Levine invariants μ(iijj) must vanish for 0.5-solvable links. Using this result, we show that the short exact sequence 0 →ℱ0m/ℱ 0.5m →ℱ −0.5m/ℱ 0.5m →ℱ −0.5m/ℱ 0m → 0 does not split for links of two or more components, in contrast to the fact that it splits for knots. Considering lower terms of the filtration {ℱnm} in the short exact sequence 0 →ℱ−0.5m/ℱ 0m →&#x1d49e;m/ℱ 0m →&#x1d49e;m/ℱ −0.5m→0, we show that while the sequence does not split for m ≥ 3, it does indeed split for m = 2. This allows us to determine that the quotient &#x1d49e;2/ℱ 02≅ℤ 2 ⊕ ℤ2 ⊕ ℤ2 ⊕ ℤ.
Chevalley restriction theorem for vector-valued functions on quantum groups

We generalize Chevalley's theorem about restriction of \mathfrak{g}-invariant polynomial functions \mathfrak{g}->C to W-invariant functions on the Cartan \mathfrak{h}->C. We consider the case when \mathfrak{g} is replaced by a quantum group and the target space of the polynomial maps is replaced by a finite dimensional representation V of this quantum group. We prove that the restriction map Res:(O_q(G)\otimes V)^{U_q(\mathfrak{g})}-> O(H)\otimes V is injective and describe the image.
Probing Biomedical Embeddings from Language Models

Contextualized word embeddings derived from pre-trained language models (LMs) show significant improvements on downstream NLP tasks. Pre-training on domain-specific corpora, such as biomedical articles, further improves their performance. In this paper, we conduct probing experiments to determine what additional information is carried intrinsically by the in-domain trained contextualized embeddings. For this we use the pre-trained LMs as fixed feature extractors and restrict the downstream task models to not have additional sequence modeling layers. We compare BERT (Devlin et al. 2018), ELMo (Peters et al., 2018), BioBERT (Lee et al., 2019) and BioELMo, a biomedical version of ELMo trained on 10M PubMed abstracts. Surprisingly, while fine-tuned BioBERT is better than BioELMo in biomedical NER and NLI tasks, as a fixed feature extractor BioELMo outperforms BioBERT in our probing tasks. We use visualization and nearest neighbor analysis to show that better encoding of entity-type and relational information leads to this superiority.
Plumbagin-induced oxidative stress leads to inhibition of Na+/K+-ATPase (NKA) in canine cancer cells.

The Na+/K+-ATPase (NKA) complex is the master regulator of membrane potential and a target for anti-cancer therapies. Here, we investigate the effect of drug-induced oxidative stress on NKA activity. The natural product, plumbagin increases oxygen radicals through inhibition of oxidative phosphorylation. As a result, plumbagin treatment results in decreased production of ATP and a rapid increase in intracellular oxygen radicals. We show that plumbagin induces apoptosis in canine cancer cells via oxidative stress. We use this model to test the effect of oxidative stress on NKA activity. Using whole-cell patch-clamp electrophysiology we demonstrate that short-term exposure (4 min) to plumbagin results in 48% decrease in outward current at +50 mV. Even when exogenous ATP was supplied to the cells, plumbagin treatment resulted in 46% inhibition of outward current through NKA at +50 mV. In contrast, when the canine cancer cells were pre-treated with the oxygen radical scavenger, N-acetylcysteine, the NKA inhibitory activity of plumbagin was abrogated. These experiments demonstrate that the oxidative stress-causing agents such as plumbagin and its analogues, are a novel avenue to regulate NKA activity in tumors.
Automated Parameterized Verification of CRDTs.

Maintaining multiple replicas of data is crucial to achieving scalability, availability and low latency in distributed applications. Conflict-free Replicated Data Types (CRDTs) are important building blocks in this domain because they are designed to operate correctly under the myriad behaviors possible in a weakly-consistent distributed setting. Because of the possibility of concurrent updates to the same object at different replicas, and the absence of any ordering guarantees on these updates, convergence is an important correctness criterion for CRDTs. This property asserts that two replicas which receive the same set of updates (in any order) must nonetheless converge to the same state. One way to prove that operations on a CRDT converge is to show that they commute since commutative actions by definition behave the same regardless of the order in which they execute. In this paper, we present a framework for automatically verifying convergence of CRDTs under different weak-consistency policies. Surprisingly, depending upon the consistency policy supported by the underlying system, we show that not all operations of a CRDT need to commute to achieve convergence. We develop a proof rule parameterized by a consistency specification based on the concepts of commutativity modulo consistency policy and non-interference to commutativity. We describe the design and implementation of a verification engine equipped with this rule and show how it can be used to provide the first automated convergence proofs for a number of challenging CRDTs, including sets, lists, and graphs.
Toroidal Schubert Varieties

Levi subgroup actions on Schubert varieties are studied. In the case of partial flag varieties, the horospherical actions are determined. This leads to a characterization of the toroidal and horospherical partial flag varieties with Picard number 1. In the more general case, we provide a set of necessary conditions for the action of a Levi subgroup on a Schubert variety to be toroidal. The singular locus of a (co)minuscule Schubert variety is shown to contain all the Lmax-stable Schubert subvarieties, where Lmax is the standard Levi subgroup of the maximal parabolic which acts on the Schubert variety by left multiplication. In type A, the effect of the Billey-Postnikov decomposition on toroidal Schubert varieties is obtained.
Asymptotics for Sketching in Least Squares Regression

We consider a least squares regression problem where the data has been generated from a linear model, and we are interested to learn the unknown regression parameters. We consider "sketch-and-solve" methods that randomly project the data first, and do regression after. Previous works have analyzed the statistical and computational performance of such methods. However, the existing analysis is not fine-grained enough to show the fundamental differences between various methods, such as the Subsampled Randomized Hadamard Transform (SRHT) and Gaussian projections. In this paper, we make progress on this problem, working in an asymptotic framework where the number of datapoints and dimension of features goes to infinity. We find the limits of the accuracy loss (for estimation and test error) incurred by popular sketching methods. We show separation between different methods, so that SRHT is better than Gaussian projections. Our theoretical results are verified on both real and synthetic data. The analysis of SRHT relies on novel methods from random matrix theory that may be of independent interest.
Causal Regularization

We argue that regularizing terms in standard regression methods not only help against overfitting finite data, but sometimes also help in getting better causal models. We first consider a multi-dimensional variable linearly influencing a target variable with some multi-dimensional unobserved common cause, where the confounding effect can be decreased by keeping the penalizing term in Ridge and Lasso regression even in the population limit. The reason is a close analogy between overfitting and confounding observed for our toy model. In the case of overfitting, we can choose regularization constants via cross validation, but here we choose the regularization constant by first estimating the strength of confounding, which yielded reasonable results for simulated and real data. Further, we show a ‘causal generalization bound’ which states (subject to our particular model of confounding) that the error made by interpreting any non-linear regression as causal model can be bounded from above whenever functions are taken from a not too rich class.
Vicinal metal surfaces as potential catalysts for phosphorene epitaxial growth

Phosphorene, a single layer of black phosphorous (BLK-P), has significant potential for flexible and tunable electronics, but attempts to grow it epitaxially have been unsuccessful to date. Close-packed (111) surfaces of metals favor blue phosphorous (BL-P) over BLK-P due to the hexagonal symmetry of the former. Here, we investigate computationally the alternative offered by stepped substrates. Using the model of a Cu(311) surface, we find that surface steps can favor energetically BLK-P over BL-P. This can be rationalized in terms of surface density of states and orbital hybridization, which leads to a stronger surface bonding of BLK-P. This suggests that vicinal metal surfaces of metals can offer a viable path toward phosphorene synthesis.
Information Theoretic Model Predictive Control on Jump Diffusion Processes

In this paper we present an information theoretic approach to stochastic optimal control problems for systems with compound Poisson noise. We generalize previous work on information theoretic path integral control to discontinuous dynamics with compound Poisson noise. We also derive a control update law of the same form using a stochastic optimization approach. We develop a sampling-based iterative model predictive control (MPC) algorithm. The proposed algorithm is parallelizable and when implemented on a Graphical Processing Unit (GPU) can run in real time. We test the performance of the proposed algorithm in simulation for two control tasks using a cartpole and a quadrotor system. Our simulations demonstrate improved performance of the new scheme and indicate the importance of incorporating the statistical characteristics of stochastic disturbances in the computation of the stochastic optimal control policies.
Identifying Mood Episodes Using Dialogue Features from Clinical Interviews.

Bipolar disorder, a severe chronic mental illness characterized by pathological mood swings from depression to mania, requires ongoing symptom severity tracking to both guide and measure treatments that are critical for maintaining long-term health. Mental health professionals assess symptom severity through semi-structured clinical interviews. During these interviews, they observe their patients' spoken behaviors, including both what the patients say and how they say it. In this work, we move beyond acoustic and lexical information, investigating how higher-level interactive patterns also change during mood episodes. We then perform a secondary analysis, asking if these interactive patterns, measured through dialogue features, can be used in conjunction with acoustic features to automatically recognize mood episodes. Our results show that it is beneficial to consider dialogue features when analyzing and building automated systems for predicting and monitoring mood.
Quandle cocycle quivers

Abstract We incorporate quandle cocycle information into the quandle coloring quivers we defined in [2] to define weighted directed graph-valued invariants of oriented links we call quandle cocycle quivers. This construction turns the quandle cocycle invariant into a small category, yielding a categorification of the quandle cocycle invariant. From these graphs we define several new link invariants including a 2-variable polynomial which specializes to the usual quandle cocycle invariant. Examples and computations are provided.
Merger waves: are buyers following the herd or responding to structural queues?

While there has been a significant amount of research covering the causes of merger waves, few papers have rank ordered merger waves based on the causes nor sought to determine which rationale leads to higher bidder payouts. This paper seeks to fill this gap by examining a cross section of large, global mergers across most industries occurring over a 17 year period. I find that merger waves over this period are caused foremost by changing economic and regulatory conditions. It is the behavioral rationale of mispricing, however, that more often leads to higher bidder payouts or merger premiums among acquirers in merger waves.
Bayesian inference of network loss characteristics with applications to TCP performance prediction

Network tomography, inferring internal network behavior based on the "external" end-to-end network measurements, is of particular interest when the network itself can not cooperate in characterizing its own behavior. In particular, it is impractical to directly measure packet losses or delays at every router. On the other hand, measuring end-to-end (from sources to receivers) losses is relatively easy. In this paper, the problems of characterizing links behavior in a network is formulated as Bayesian inference problems and develop several Markov chain Monte Carlo (MCMC) algorithms to solve them. The proposed link loss algorithms are then applied to data generated by the network simulator (NS2) software, and obtain good agreements between the theoretical results and the actual measurements.
Clustering of spatiotemporal signals: application to the analysis of FMRI data

In this paper, a new paradigm for the analysis of event-related functional magnetic resonance images (fMRI) is explored. These datasets are large collections of spatiotemporal time series that are indexed by their spatial locations. The analysis of such datasets requires to partition the spatial domain into regions within which the time series are similar. Basis functions is constructed on which the projection of the fMRI time series reveals the organization of the fMRI dataset into "activated", and "non-activated" clusters. These basis functions maximally separate the time series into meaningful groups according to their time-scale behavior.
Effective aspects of Bernoulli randomness

In this paper, we study Bernoulli random sequences, i.e., sequences that are Martin-Lof random with respect to a Bernoulli measure $\mu_p$ for some $p\in[0,1]$, where we allow for the possibility that $p$ is noncomputable. We focus in particular on the case in which the underlying Bernoulli parameter $p$ is proper (that is, Martin-Lof random with respect to some computable measure). We show for every Bernoulli parameter $p$, if there is a sequence that is both proper and Martin-Lof random with respect to $\mu_p$, then $p$ itself must be proper, and explore further consequences of this result. We also study the Turing degrees of Bernoulli random sequences, showing, for instance, that the Turing degrees containing a Bernoulli random sequence do not coincide with the Turing degrees containing a Martin-Lof random sequence. Lastly, we consider several possible approaches to characterizing blind Bernoulli randomness, where the corresponding Martin-Lof tests do not have access to the Bernoulli parameter $p$, and show that these fail to characterize blind Bernoulli randomness.
The Nuts and Bolts of Cooperative Learning in Engineering

A great number of engineering students work alone most of the time. This is in sharp contrast with industry where most of the work is performed in teams. The ability to work in a team effectively is not acquired automatically. It takes interpersonal and social skills which need to be developed and practiced. In addition, research shows that the student-student interaction, often neglected in traditional ways of teaching, is a most effective way of learning. Thus, it is imperative that we encourage our students to work with each other in their efforts to achieve their educational goals. In this paper I discuss my experience with Cooperative Learning (CL) in a variety of engineering courses during the last four years. The discussion includes benefits and problems along with possible solutions. Lastly, I have made an effort to evaluate the impact of CL on student performance and attitude.
Microstructure development in impact welding of a model system

Abstract Characteristic features of impact welding were investigated in a simple model system of colliding copper single crystals. Large grained sheets were used to approximate single crystal to single crystal impact. Optical microscopy and electron backscatter diffraction (EBSD) analyses showed multiple features of the weld interface, which include deformation twinning, adiabatic shear banding, localized melting, and dynamic recrystallization. To support the microstructural findings, numerical simulation based on smoothed particle hydrodynamics method was conducted to predict the local temperature, pressure, and strain distributions. The insights gathered here will aid in the design of materials combinations and systems for impact welding.
Chapter 9 of the Bankruptcy Code: A Solution in Search of a Problem

As a result of the recent credit crisis, municipal insolvency has become a relevant and important issue. All over America cities are grappling with budget problems, and some of them are even considering bankruptcy. This Article analyzes the municipal bankruptcy process, and inquires whether it provides a sensible solution for urban fiscal crises. In order to examine this question, the Article delves into the prevailing rationales of bankruptcy law-the contractual theory and the fresh start theory. The Article makes the claim that these theories do not adequately explain the municipal bankruptcy process, and that filing for bankruptcy can damage the distressed locality as well as its state and other localities within the state. As an alternative to bankruptcy the Article suggests a proactive state oversight model. This model aims to address the economic problems that lie at the heart of the fiscal crisis, and it allows cities to undergo a genuine rehabilitation process.
Gaussian bounds for noise correlation of resilient functions

Gaussian bounds on noise correlation of functions play an important role in hardness of approximation, in quantitative social choice theory and in testing. The author (2008) obtained sharp Gaussian bounds for the expected correlation of l low influence functions f(1), …, f(l) : Ωn → [0, 1], where the inputs to the functions are correlated via the n-fold tensor of distribution $${\cal P}$$ on Ωl in the following way: For each 1 ≤ i ≤ n, the vector consisting of the i’-th inputs to the l functions is sampled according to $${\cal P}$$. It is natural to ask if the condition of low influences can be relaxed to the condition that the function has vanishing Fourier coefficients. Here and g we further show that if f, g have a noisy inner product that exceeds the Gaussian bound, then the Fourier supports of their large coefficients intersect.
Gotta Serve Somebody? Religious Liberty, Freedom of Conscience, and Religion as Comprehensive Doctrine:

This article critically assesses an account of religious liberty often associated with several legal and political philosophers: Ronald Dworkin, John Rawls, and Christopher Eisgruber and Lawrence S...
Predictive Inference for Locally Stationary Time Series With an Application to Climate Data

The model-free prediction principle of Politis has been successfully applied to general regression problems, as well as problems involving stationary time series. However, with long time series, fo...
Examining untempered social media: analyzing cascades of polarized conversations

Online social media, periodically serves as a platform for cascading polarizing topics of conversation. The inherent community structure present in online social networks (ho-mophily) and the advent of fringe outlets like Gab have created online “echo chambers” that amplify the effects of polarization, which fuels detrimental behavior. Recently, in October 2018, Gab made headlines when it was revealed that Robert Bowers, the individual behind the Pittsburgh Synagogue massacre, was an active member of this social media site and used it to express his anti-Semitic views and discuss conspiracy theories. Thus to address the need of automated data-driven analyses of such fringe outlets, this research proposes novel methods to discover topics that are prevalent in Gab and how they cascade within the network. Specifically, using approximately 34 million posts, and 3.7 million cascading conversation threads with close to 300k users; we demonstrate that there are essentially five cascading patterns that manifest in Gab and the most “viral” ones begin with an echo-chamber pattern and grow out to the entire network. Also, we empirically show, through two models viz. Susceptible-Infected and Bass, how the cascades structurally evolve from one of the five patterns to the other based on the topic of the conversation with upto 84% accuracy.
The Role of A Priori-Identified Addiction and Smoking Gene Sets in Smoking Behaviors.

Introduction Smoking is a leading cause of death, and genetic variation contributes to smoking behaviors. Identifying genes and sets of genes that contribute to risk for addiction is necessary to prioritize targets for functional characterization and for personalized medicine. Methods We performed a gene set-based association and heritable enrichment study of two addiction-related gene sets, those on the Smokescreen Genotyping Array and the nicotinic acetylcholine receptors, using the largest available GWAS summary statistics. We assessed smoking initiation, cigarettes per day, smoking cessation, and age of smoking initiation. Results Individual genes within each gene set were significantly associated with smoking behaviors. Both sets of genes were significantly associated with cigarettes per day, smoking initiation, and smoking cessation. Age of initiation was only associated with the Smokescreen gene set. Although both sets of genes were enriched for trait heritability, each accounts for only a small proportion of the single nucleotide polymorphism-based heritability (2%-12%). Conclusions These two gene sets are associated with smoking behaviors, but collectively account for a limited amount of the genetic and phenotypic variation of these complex traits, consistent with high polygenicity. Implications We evaluated evidence for the association and heritable contribution of expert-curated and bioinformatically identified sets of genes related to smoking. Although they impact smoking behaviors, these specifically targeted genes do not account for much of the heritability in smoking and will be of limited use for predictive purposes. Advanced genome-wide approaches and integration of other 'omics data will be needed to fully account for the genetic variation in smoking phenotypes.
A Cayley graph description of the symmetry breaking associated with deformation and structural phase transitions in metallic materials

Abstract Deformation and structural phase transitions are typical physical processes, which can produce variety types of defects (e.g., twinning and dislocation) in metals and alloys. Those crystalline defects are critical in determining the mechanical and functional properties of materials. Theoretically, crystalline defects belong to the so-called topological defects, which are generated by symmetry breaking processes. In spite of the well-established group theory description of crystal symmetry (for an individual crystal), a theoretical framework to characterize multiple symmetry breaking processes (e.g., multiple transformation pathways between two crystals, multiple deformation twinning modes) is still lacking. In the literature, a graph method, phase transition graph, has been introduced to describe the pathway connectivity in specific martensitic transformations. However, a general framework capturing the coupling of deformation and phase transitions is unavailable, partially due to the lack of rigorous connection between group theory (description of crystal symmetry) and graph theory (description of deformation pathway connectivity). In this paper, a theoretical connection between crystal symmetry and pathway connectivity is rigorously established for the first time. By examining the broken and preserved symmetries during crystal deformation and structural phase transitions through group theory, we employ a Cayley graph method to analyze symmetry groups, which leads to a new theoretical description of symmetry breaking based on group theory and graph theory. Our graph approach establishes a mathematical foundation to investigate the symmetry breaking associated with deformation and structural phase transitions in crystals, which also provides a new insight into the physical origin of crystalline defects.
Electrophysiological indicators of gesture perception.

Electroencephalography (EEG) activity in the mu frequency band (8–13 Hz) is suppressed during both gesture performance and observation. However, it is not clear if or how particular characteristics within the kinematic execution of gestures map onto dynamic changes in mu activity. Mapping the time course of gesture kinematics onto that of mu activity could help understand which aspects of gestures capture attention and aid in the classification of communicative intent. In this work, we test whether the timing of inflection points within gesture kinematics predicts the occurrence of oscillatory mu activity during passive gesture observation. The timing for salient features of performed gestures in video stimuli was determined by isolating inflection points in the hands’ motion trajectories. Participants passively viewed the gesture videos while continuous EEG data was collected. We used wavelet analysis to extract mu oscillations at 11 Hz and at central electrodes and occipital electrodes. We used linear regression to test for associations between the timing of inflection points in motion trajectories and mu oscillations that generalized across gesture stimuli. Separately, we also tested whether inflection point occurrences evoked mu/alpha responses that generalized across participants. Across all gestures and inflection points, and pooled across participants, peaks in 11 Hz EEG waveforms were detected 465 and 535 ms after inflection points at occipital and central electrodes, respectively. A regression model showed that inflection points in the motion trajectories strongly predicted subsequent mu oscillations ($${R}^{2}=0.921, p$$<0.01); effects were weaker and non-significant for low (17 Hz) and high (21 Hz) beta activity. When segmented by inflection point occurrence rather than stimulus onset and testing participants as a random effect, inflection points evoked mu and beta activity from 308 to 364 ms at central electrodes, and broad activity from 226 to 800 ms at occipital electrodes. The results suggest that inflection points in gesture trajectories elicit coordinated activity in the visual and motor cortices, with prominent activity in the mu/alpha frequency band and extending into the beta frequency band. The time course of activity indicates that visual processing drives subsequent activity in the motor cortex during gesture processing, with a lag of approximately 80 ms.
The complete set of minimal simple graphs that support unsatisfiable 2-CNFs

Abstract A propositional logic sentence in conjunctive normal form that has clauses of length at most two (a 2-CNF) can be associated with a multigraph in which the vertices correspond to the variables and edges to clauses. We show that every 2-CNF that has been reduced under the application of certain tautologies, is equisatisfiable to a 2-CNF whose associated multigraph is, in fact, a simple graph. Our main result is a complete characterization of graphs that can support unsatisfiable 2-CNF sentences. We show that a simple graph can support an unsatisfiable reduced 2-CNF sentence if and only if it contains any one of four specific small graphs as a topological minor. Equivalently, all reduced 2-CNF sentences supported on a given simple graph are satisfiable if and only if all subdivisions of those four graphs are forbidden as subgraphs of the original graph.
Neural-Learning Trajectory Tracking Control of Flexible-Joint Robot Manipulators with Unknown Dynamics

Fast and precise motion control is important for industrial robots in manufacturing applications. However, some collaborative robots sacrifice precision for safety, particular for high motion speed. The performance degradation is caused by the inability of the joint servo controller to address the uncertain nonlinear dynamics of the robot arm, e.g., due to joint flexibility. We consider two approaches to improve the trajectory tracking performance through feedforward compensation. The first approach uses iterative learning control, with the gradient-based iterative update generated from the robot forward dynamics model. The second approach uses dynamic inversion to directly compensate for the robot forward dynamics. If the forward dynamics is strictly proper or is non-minimum-phase (e.g., due to time delays), its stable inverse would be non-causal. Both approaches require robot dynamical models. This paper presents results of using recurrent neural networks (RNNs) to approximate these dynamical models – forward dynamics in the first case, inverse dynamics (possibly non-causal) in the second case. We use the bi-directional RNN to capture the noncausality. The RNNs are trained based on a collection of commanded trajectories and the actual robot responses. We use a Baxter robot to evaluate the two approaches. The Baxter robot exhibits significant joint flexibility due to the series-elastic joint actuators. Both approaches achieve sizable improvement over the uncompensated robot motion, for both random joint trajectories and Cartesian motion. The inverse dynamics method is particularly attractive as it may be used to more accurately track a user input as in teleoperation.
Forward Kinematics Kernel for Improved Proxy Collision Checking

Kernel functions may be used in robotics for comparing different poses of a robot, such as in collision checking, inverse kinematics, and motion planning. These comparisons provide distance metrics often based on joint space measurements and are performed hundreds or thousands of times a second, continuously for changing environments. We introduce a new kernel function based on forward kinematics (FK) to compare robot manipulator configurations. We integrate our new FK kernel into our proxy collision checker, Fastron, that previously showed significant speed improvements to collision checking and motion planning. With the new FK kernel, we realize a two-fold speedup in proxy collision check speed, 8 times less memory, and a boost in classification accuracy from 74% to over 95% for a 7 degrees-of-freedom robot arm compared to the previously-used radial basis function kernel. Compared to state-of-the-art geometric collision checkers, with the FK kernel, collision checks are now 9 times faster. To show the broadness of the approach, we apply Fastron FK in OMPL across a wide variety of motion planners, showing unanimously faster robot planning.
Allele-Specific QTL Fine Mapping with PLASMA

Although quantitative trait locus (QTL) associations have been identified for many molecular traits such as gene expression, it remains challenging to distinguish the causal nucleotide from nearby variants. In addition to traditional QTLs by association, allele-specific (AS) QTLs are a powerful measure of cis-regulation that are concordant with traditional QTLs but typically less susceptible to technical/environmental noise. However, existing methods for estimating causal variant probabilities (i.e., fine mapping) cannot produce valid estimates from asQTL signals due to complexities in linkage disequilibrium (LD). We introduce PLASMA (Population Allele-Specific Mapping), a fine-mapping method that integrates QTL and asQTL information to improve accuracy. In simulations, PLASMA accurately prioritizes causal variants over a wide range of genetic architectures. Applied to RNA-seq data from 524 kidney tumor samples, PLASMA achieves a greater power at 50 samples than conventional QTL-based fine mapping at 500 samples, with more than 17% of loci fine mapped to within five causal variants, compared to 2% by QTL-based fine mapping, and a 6.9-fold overall reduction in median credible set size compared to QTL-based fine mapping when applied to H3K27AC ChIP-seq from just 28 prostate tumor/normal samples. Variants in the PLASMA credible sets for RNA-seq and ChIP-seq were enriched for open chromatin and chromatin looping, respectively, at a comparable or greater degree than credible variants from existing methods while containing far fewer markers. Our results demonstrate how integrating AS activity can substantially improve the detection of causal variants from existing molecular data.
Iatrogenic Peroneal Nerve Palsy Rates Secondary to Open Reduction Internal Fixation for Tibial Plateau Fractures Using an Intraoperative Distractor.

OBJECTIVE To report the rate of peroneal nerve palsy after routine use of intraoperative distraction during open reduction internal fixation (ORIF) for lateral unicondylar and bicondylar tibial plateau fracture (TPF) repairs. DESIGN Retrospective chart review. SETTING Level I trauma center. PATIENTS Patients with traumatic TPF treated with ORIF between 2007 and 2017. INTERVENTION ORIF for lateral unicondylar and bicondylar TPF. MAIN OUTCOME MEASUREMENT Presence and resolution of neurovascular injury. RESULTS There were a total of 21 lateral unicondylar and 40 bicondylar TPFs repaired through ORIF in 60 patients identified during the study period with 1-year follow-up and complete records for review. Thirty-six patients had staged external fixation before ORIF while 24 were treated with ORIF initially. Of the staged patients, 9 of 36 (25%) developed nerve palsy while those undergoing initial ORIF (not staged) developed palsy in only one case (1 of 24, or 4%). Of the patients who developed nerve palsy, 9 of 10 (90%) were staged with an initial external fixator before ORIF. The incidence of iatrogenic peroneal nerve palsy secondary to intraoperative distraction was 16.4% (10 of 61). Only 60% (6 of 10) of peroneal nerve palsies recovered clinically with a mean recovery time of approximately 14 weeks. Comparison of demographics in patients with peroneal nerve palsy versus those without yielded no significant difference by sex (P = 0.08), age (P = 0.27), fracture type (P = 0.29), tobacco use (P = 0.44), or alcohol use (P = 0.78). CONCLUSIONS Peroneal nerve palsy is a common sequela of ORIF for TPFs involving the lateral compartment using an intraoperative distractor. Staged external fixation followed by definitive ORIF using intraoperative distraction was associated with significant risk for developing nerve palsy (9/10). Many patients (40%) who develop peroneal nerve palsies do not recover, leading to permanent loss of motor and/or sensory function for 7% of patients studied. None of the epidemiologic variables evaluated yielded predictive value for development of peroneal nerve palsy or subsequent resolution. Caution should be exercised in avoiding overdistraction when using intraoperative distraction, especially in those cases that had staged fixation, most notably bicondylar injuries. LEVEL OF EVIDENCE Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.