cssi/SciSciGPT-SciSciNet huggingface dataset:
Random Organization in Periodically Driven Gliding Dislocations

Abstract We numerically examine dynamical irreversible to reversible transitions and random organization for periodically driven gliding dislocation assemblies using the stroboscopic protocol developed to identify random organization in periodically driven dilute colloidal suspensions. We find that the gliding dislocations exhibit features associated with random organization and evolve into a dynamically reversible state after a transient time extending over a number of cycles. At a critical shearing amplitude, the transient time diverges. When the dislocations enter the reversible state they organize into patterns with fragmented domain wall type features.
Characteristic free measure rigidity for the action of solvable groups on homogeneous spaces

We classify measures on a homogeneous space which are invariant under a certain solvable subgroup and ergodic under its unipotent radical. Our treatment is independent of characteristic. As a result we get the first measure classification for the action of semisimple subgroups without any characteristic restriction.
Lifting divisors with imposed ramifications on a generic chain of loops

Proves that if a curve has totally split reduction and the corresponding skeleton (as a metric graph) is a chain of loops with generic edge lengths, then every divisor on the graph with imposed ramification at the rightmost vertex $p$ of the skeleton lifts to a divisor class on the curve with the same ramification at some point $P$ that retracts to $p$, extending the work of Cartwright-Jensen-Payne. Gives a positive answer to lifting proper tropical intersections within a polytopal domain in an analytic torus.
Multivariate Regular Variation of Discrete Mass Functions with Applications to Preferential Attachment Networks

Regular variation of a multivariate measure with a Lebesgue density implies the regular variation of its density provided the density satisfies some regularity conditions. Unlike the univariate case, the converse also requires regularity conditions. We extend these arguments to discrete mass functions and their associated measures using the concept that the mass function can be embedded in a joint density function with continuous arguments. We give two different conditions, monotonicity and convergence on the unit sphere, both of which can make the discrete function embeddable. Our results are then applied to the preferential attachment network model, and we conclude that the joint mass function of in- and out-degree is embeddable and thus regularly varying.
RNA folding kinetics using Monte Carlo and Gillespie algorithms

RNA secondary structure folding kinetics is known to be important for the biological function of certain processes, such as the hok/sok system in E. coli. Although linear algebra provides an exact computational solution of secondary structure folding kinetics with respect to the Turner energy model for tiny ( $$\approx $$ 20 nt) RNA sequences, the folding kinetics for larger sequences can only be approximated by binning structures into macrostates in a coarse-grained model, or by repeatedly simulating secondary structure folding with either the Monte Carlo algorithm or the Gillespie algorithm. Here we investigate the relation between the Monte Carlo algorithm and the Gillespie algorithm. We prove that asymptotically, the expected time for a K-step trajectory of the Monte Carlo algorithm is equal to $$\langle N \rangle $$ times that of the Gillespie algorithm, where $$\langle N \rangle $$ denotes the Boltzmann expected network degree. If the network is regular (i.e. every node has the same degree), then the mean first passage time (MFPT) computed by the Monte Carlo algorithm is equal to MFPT computed by the Gillespie algorithm multiplied by $$\langle N \rangle $$ ; however, this is not true for non-regular networks. In particular, RNA secondary structure folding kinetics, as computed by the Monte Carlo algorithm, is not equal to the folding kinetics, as computed by the Gillespie algorithm, although the mean first passage times are roughly correlated. Simulation software for RNA secondary structure folding according to the Monte Carlo and Gillespie algorithms is publicly available, as is our software to compute the expected degree of the network of secondary structures of a given RNA sequence—see http://bioinformatics.bc.edu/clote/RNAexpNumNbors .
Inverse Risk-Sensitive Reinforcement Learning

This work addresses the problem of inverse reinforcement learning in Markov decision processes where the decision-making agent is risk-sensitive . In particular, a risk-sensitive reinforcement learning algorithm with convergence guarantees that makes use of coherent risk metrics and models of human decision-making which have their origins in behavioral psychology and economics is presented. The risk-sensitive reinforcement learning algorithm provides the theoretical underpinning for a gradient-based inverse reinforcement learning algorithm that seeks to minimize a loss function defined on the observed behavior. It is shown that the gradient of the loss function with respect to the model parameters is well defined and computable via a contraction map argument. Evaluation of the proposed technique is performed on a Grid World example, a canonical benchmark problem.
Robust EM algorithm for model-based curve clustering

Model-based clustering approaches concern the paradigm of exploratory data analysis relying on the finite mixture model to automatically find a latent structure governing observed data. They are one of the most popular and successful approaches in cluster analysis. The mixture density estimation is generally performed by maximizing the observed-data log-likelihood by using the expectation-maximization (EM) algorithm. However, it is well-known that the EM algorithm initialization is crucial. In addition, the standard EM algorithm requires the number of clusters to be known a priori. Some solutions have been provided in [31], [12] for model-based clustering with Gaussian mixture models for multivariate data. In this paper we focus on model-based curve clustering approaches, when the data are curves rather than vectorial data, based on regression mixtures. We propose a new robust EM algorithm for clustering curves. We extend the model-based clustering approach presented in [31] for Gaussian mixture models, to the case of curve clustering by regression mixtures, including polynomial regression mixtures as well as spline or B-spline regressions mixtures. Our approach both handles the problem of initialization and the one of choosing the optimal number of clusters as the EM learning proceeds, rather than in a twofold scheme. This is achieved by optimizing a penalized log-likelihood criterion. A simulation study confirms the potential benefit of the proposed algorithm in terms of robustness regarding initialization and funding the actual number of clusters.
Cosmological parameters from statistics of strongly lensed radio sources

We calculate the expected number of strongly lensed radio sources in a sample of 6500 sources observed with the Very Large Array as part of the Cosmic Lens All Sky Survey (CLASS) during the first two sessions of its observations. A comparison between the predicted and the observed number of lensed radio sources allows a determination of the current value of m , where m is the cosmological mass density of the universe and is the normalized cosmological constant. If there are six strongly lensed sources in this sample, our 95% confidence lower limit on m is -0.58. For a flat universe with m + =1 , then, (1:2-0:5w 2 ) 0:05 (95% C.L.). We discuss the region allowed by combined gravitational lensing statistics, high redshift Type Ia supernovae distances, and globular cluster ages. Instead of a cosmological model, we can constrain the redshift distribution of faint radio sources based on the ob- served gravitational lensing rate and an assumed cosmologi- cal model. If there are six strongly lensed sources, the 68% confidence upper limit on the average redshift hzi of radio sources with flux densities less than 150 mJy at 8.4 GHz is hzi < 1:4+(m )0:1. In order to obtain a much tighter estimate on the cosmological parameters, it is essential that the redshift distribution for radio sources at the faint flux density levels be observationally determined. We strongly recommend that statistically complete optical spectroscopic programs be carried out to obtain redshifts for a representative subsample of faint background radio sources. Until such redshifts are ob- tained, it is unlikely that a major improvement could be made with respect to lensed radio source constraints on cosmological parameters.
Self-concordant inclusions: a unified framework for path-following generalized Newton-type algorithms

We study a class of monotone inclusions called “self-concordant inclusion” which covers three fundamental convex optimization formulations as special cases. We develop a new generalized Newton-type framework to solve this inclusion. Our framework subsumes three schemes: full-step, damped-step, and path-following methods as specific instances, while allows one to use inexact computation to form generalized Newton directions. We prove the local quadratic convergence of both full-step and damped-step algorithms. Then, we propose a new two-phase inexact path-following scheme for solving this monotone inclusion which possesses an $${\mathcal {O}}(\sqrt{\nu }\log (1/\varepsilon ))$$-worst-case iteration-complexity to achieve an $$\varepsilon $$-solution, where $$\nu $$ is the barrier parameter and $$\varepsilon $$ is a desired accuracy. As byproducts, we customize our scheme to solve three convex problems: the convex–concave saddle-point problem, the nonsmooth constrained convex program, and the nonsmooth convex program with linear constraints. We also provide three numerical examples to illustrate our theory and compare with existing methods.
Conformal QED$_d$, $F$-Theorem and the $\epsilon$ Expansion

We calculate the free energies F for U(1) gauge theories on the d dimensional sphere of radius R. For the theory with free Maxwell action we find the exact result as a function of d, it contains the term $\frac{d-4}{2}\mathrm{log}R$ consistent with the lack of conformal invariance in dimensions other than 4. When the U(1) gauge theory is coupled to a sufficient number N ( )f( ) of massless four-component fermions, it acquires an interacting conformal phase, which in $d\lt 4$ describes the long distance behavior of the model. The conformal phase can be studied using large N ( )f( ) methods. Generalizing the d = 3 calculation in arXiv:1112.5342, we compute its sphere free energy as a function of d, ignoring the terms of order $1/{N}_{f}$ and higher. For finite N ( )f( ), following arXiv:1409.1937 and arXiv:1507.01960, we develop the $4-\epsilon $ expansion for the sphere free energy of conformal QED( )d( ). Its extrapolation to d = 3 shows very good agreement with the large N ( )f( ) approximation for ${N}_{f}\gt 3$. For N ( )f( ) at or below some critical value ${N}_{{\rm{crit}}}$, the ${SU}(2{N}_{f})$ symmetric conformal phase of QED(3) is expected to disappear or become unstable. By using the F-theorem and comparing the sphere free energies in the conformal and broken symmetry phases, we show that ${N}_{{\rm{crit}}}\leqslant 4$. As another application of our results, we calculate the one loop beta function in conformal QED(6), where the gauge field has a four-derivative kinetic term. We show that this theory coupled to N ( )f( ) massless fermions is asymptotically free.
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization

Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and functional significance of these spatial representations remain largely mysterious. As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells. All these different functional types of neurons have been observed experimentally. The order of the emergence of grid-like and border cells is also consistent with observations from developmental studies. Together, our results suggest that grid cells, border cells and others as observed in EC may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits.
Millimeter-wave distance-dependent large-scale propagation measurements and path loss models for outdoor and indoor 5G systems

This paper presents millimeter-wave propagation measurements for urban micro-cellular and indoor office scenarios at 28 GHz and 73 GHz, and investigates the corresponding path loss using five types of path loss models, the single-frequency floating-intercept (FI) model, single-frequency close-in (CI) free space reference distance model, multi-frequency alpha-beta-gamma (ABG) model, multi-frequency CI model, and multi-frequency CI model with a frequency-weighted path loss exponent (CIF), in both line-of-sight and non-line-of-sight environments. Results show that the CI and CIF models provide good estimation and exhibit stable behavior over frequencies and distances, with a solid physical basis and less computational complexity when compared with the FI and ABG models. Furthermore, path loss in outdoor scenarios shows little dependence on frequency beyond the first meter of free space propagation, whereas path loss tends to increase with frequency in addition to the increased free space path loss in indoor environments. Therefore, the CI model is suitable for outdoor environments over multiple frequencies, while the CIF model is more appropriate for indoor modeling. This work shows that both the CI and CIF models use fewer parameters and offer more convenient closed-form expressions suitable for analysis, without compromising model accuracy when compared to current 3GPP and WINNER path loss models.
On the largest Kronecker and Littlewood--Richardson coefficients

Abstract We give new bounds and asymptotic estimates for Kronecker and Littlewood–Richardson coefficients. Notably, we resolve Stanley's questions on the shape of partitions attaining the largest Kronecker and Littlewood–Richardson coefficients. We apply the results to asymptotics of the number of standard Young tableaux of skew shapes.
Probabilistic models for daily peak loads at distribution feeders

Load forecasting at distribution networks is more challenging than load forecasting at transmission networks because its load pattern is more stochastic and unpredictable. To plan sufficient resources and estimate DER hosting capacity, it is invaluable for a distribution network planner to get the probabilistic distribution of daily peak-load under a feeder over long term. In this paper, we model the probabilistic distribution functions of daily peak-load under a feeder using power law distributions, which is tested by improved Kolmogorov-Smirnov test enhanced by the Monte Carlo simulation approach. In addition, the uncertainty of the modeling is quantified using the bootstrap method. The methodology of parameter estimation of the probabilistic model and the hypothesis test is elaborated. In the case studies, it is shown using measurement data sets that the daily peak-loads under several feeders follow the power law distribution by applying the proposed testing methods.
Optimization and Analysis of a Heat Exchanger with Encapsulated Phase Change Material

In this study, the performance of a heat exchanger (HX) with encapsulated phase change material (EPCM) undergoing alternating melting and freezing is studied with the intent of analyzing the therma...
Human-Centered Emotion Recognition in Animated GIFs

As an intuitive way of expression emotion, the animated Graphical Interchange Format (GIF) images have been widely used on social media. Most previous studies on automated GIF emotion recognition fail to effectively utilize GIF's unique properties, and this potentially limits the recognition performance. In this study, we demonstrate the importance of human related information in GIFs and conduct human-centered GIF emotion recognition with a proposed Keypoint Attended Visual Attention Network (KAVAN). The framework consists of a facial attention module and a hierarchical segment temporal module. The facial attention module exploits the strong relationship between GIF contents and human characters, and extracts frame-level visual feature with a focus on human faces. The Hierarchical Segment LSTM (HS-LSTM) module is then proposed to better learn global GIF representations. Our proposed framework outperforms the state-of-the-art on the MIT GIFGIF dataset. Furthermore, the facial attention module provides reliable facial region mask predictions, which improves the model's interpretability.
Sample-Optimal Low-Rank Approximation of Distance Matrices

A distance matrix $A \in \mathbb R^{n \times m}$ represents all pairwise distances, $A_{ij}=\mathrm{d}(x_i,y_j)$, between two point sets $x_1,...,x_n$ and $y_1,...,y_m$ in an arbitrary metric space $(\mathcal Z, \mathrm{d})$. Such matrices arise in various computational contexts such as learning image manifolds, handwriting recognition, and multi-dimensional unfolding. In this work we study algorithms for low-rank approximation of distance matrices. Recent work by Bakshi and Woodruff (NeurIPS 2018) showed it is possible to compute a rank-$k$ approximation of a distance matrix in time $O((n+m)^{1+\gamma}) \cdot \mathrm{poly}(k,1/\epsilon)$, where $\epsilon>0$ is an error parameter and $\gamma>0$ is an arbitrarily small constant. Notably, their bound is sublinear in the matrix size, which is unachievable for general matrices. We present an algorithm that is both simpler and more efficient. It reads only $O((n+m) k/\epsilon)$ entries of the input matrix, and has a running time of $O(n+m) \cdot \mathrm{poly}(k,1/\epsilon)$. We complement the sample complexity of our algorithm with a matching lower bound on the number of entries that must be read by any algorithm. We provide experimental results to validate the approximation quality and running time of our algorithm.
Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach

This paper concerns with the production of synthetic phasor measurement unit (PMU) data for research and education purposes. Due to the confidentiality of real PMU data and no public access to the real power systems infrastructure information, the lack of credible realistic data becomes a growing concern. Instead of constructing synthetic power grids and then producing synthetic PMU measurement data by time simulations, we propose a model-free approach to directly generate synthetic PMU data. we train the generative adversarial network (GAN) with real PMU data, which can be used to generate synthetic PMU data capturing the system dynamic behaviors. To validate the sequential generation by GAN to mimic PMU data, we theoretically analyze GAN's capacity of learning system dynamics. Further by evaluating the synthetic PMU data by a proposed quantitative method, we verify GAN's potential to synthesize realistic samples and meanwhile realize that GAN model in this paper still has room to improve. Moreover it is the first time that such generative model is applied to synthesize PMU data.
CDEK: Clinical Drug Experience Knowledgebase.

The Clinical Drug Experience Knowledgebase (CDEK) is a database and web platform of active pharmaceutical ingredients with evidence of clinical testing as well as the organizations involved in their research and development. CDEK was curated by disambiguating intervention and organization names from ClinicalTrials.gov and cross-referencing these entries with other prominent drug databases. Approximately 43% of active pharmaceutical ingredients in the CDEK database were sourced from ClinicalTrials.gov and cannot be found in any other prominent compound-oriented database. The contents of CDEK are structured around three pillars: active pharmaceutical ingredients (n = 22 292), clinical trials (n = 127 223) and organizations (n = 24 728). The envisioned use of the CDEK is to support the investigation of many aspects of drug development, including discovery, repurposing opportunities, chemo- and bio-informatics, clinical and translational research and regulatory sciences.
Development of a high-throughput γ-H2AX assay based on imaging flow cytometry

Measurement of γ-H2AX foci levels in cells provides a sensitive and reliable method for quantitation of the radiation-induced DNA damage response. The objective of the present study was to develop a rapid, high-throughput γ-H2AX assay based on imaging flow cytometry (IFC) using the ImageStream®X Mk II (ISX) platform to evaluate DNA double strand break (DSB) repair kinetics in human peripheral blood cells after exposure to ionizing irradiation. The γ-H2AX protocol was developed and optimized for small volumes (100 μL) of human blood in Matrix™ 96-tube format. Blood cell lymphocytes were identified and captured by ISX INSPIRE™ software and analyzed by Data Exploration and Analysis Software. Dose- and time-dependent γ-H2AX levels corresponding to radiation exposure were measured at various time points over 24 h using the IFC system. γ-H2AX fluorescence intensity at 1 h after exposure, increased linearly with increasing radiation dose (R2 = 0.98) for the four human donors tested, whereas the dose response for the mean number of γ-H2AX foci/cell was not as robust (R2 = 0.81). Radiation-induced γ-H2AX levels rapidly increased within 30 min and reached a maximum by ~ 1 h, after which time there was fast decline by 6 h, followed by a much slower rate of disappearance up to 24 h. A mathematical approach for quantifying DNA repair kinetics using the rate of γ-H2AX decay (decay constant, Kdec), and yield of residual unrepaired breaks (Fres) demonstrated differences in individual repair capacity between the healthy donors. The results indicate that the IFC-based γ-H2AX protocol may provide a practical and high-throughput platform for measurements of individual global DNA DSB repair capacity which can facilitate precision medicine by predicting individual radiosensitivity and risk of developing adverse effects related to radiotherapy treatment.
Sequential Experimental Design for Transductive Linear Bandits

In this paper we introduce the pure exploration transductive linear bandit problem: given a set of measurement vectors $\mathcal{X}\subset \mathbb{R}^d$, a set of items $\mathcal{Z}\subset \mathbb{R}^d$, a fixed confidence $\delta$, and an unknown vector $\theta^{\ast}\in \mathbb{R}^d$, the goal is to infer $\arg\max_{z\in \mathcal{Z}} z^\top\theta^\ast$ with probability $1-\delta$ by making as few sequentially chosen noisy measurements of the form $x^\top\theta^{\ast}$ as possible. When $\mathcal{X}=\mathcal{Z}$, this setting generalizes linear bandits, and when $\mathcal{X}$ is the standard basis vectors and $\mathcal{Z}\subset \{0,1\}^d$, combinatorial bandits. The transductive setting naturally arises when the set of measurement vectors is limited due to factors such as availability or cost. As an example, in drug discovery the compounds and dosages $\mathcal{X}$ a practitioner may be willing to evaluate in the lab in vitro due to cost or safety reasons may differ vastly from those compounds and dosages $\mathcal{Z}$ that can be safely administered to patients in vivo. Alternatively, in recommender systems for books, the set of books $\mathcal{X}$ a user is queried about may be restricted to known best-sellers even though the goal might be to recommend more esoteric titles $\mathcal{Z}$. In this paper, we provide instance-dependent lower bounds for the transductive setting, an algorithm that matches these up to logarithmic factors, and an evaluation. In particular, we present the first non-asymptotic algorithm for linear bandits that nearly achieves the information-theoretic lower bound.
Innovation activities and business cycles: are trademarks a leading indicator?

ABSTRACTDespite the widespread use of economic information to anticipate changes in business conditions, innovation metrics are not considered to be leading indicators. We argue that aggregate trad...
Amplitude Retrieval for Channel Estimation of MIMO Systems With One-Bit ADCs

This letter revisits the channel estimation problem for MIMO systems with one-bit analog-to-digital converters (ADCs) through a novel algorithm– Amplitude Retrieval (AR) . Unlike the state-of-the-art methods such as those based on one-bit compressive sensing, AR takes a different approach. It accounts for the lost amplitudes of the one-bit quantized measurements, and performs channel estimation and amplitude completion jointly. This way, the direction information of the propagation paths can be estimated via accurate direction finding algorithms in array processing, e.g., maximum likelihood. The upsot is that AR is able to handle off-grid angles and provide more accurate channel estimates. Simulation results are included to showcase the advantages of AR.
Specialized care improves outcomes for patients with cirrhosis who require general surgical operations

BACKGROUND General surgical operations on patients with cirrhosis have historically been associated with high morbidity and mortality rates. This study examines a contemporary series of patients with cirrhosis undergoing general surgical procedures. METHODS A retrospective evaluation of 358 cirrhotic patients undergoing general surgical operations at a single institution between 2004-2015 was performed. Thirty- and 90-day mortality along with complications and subsequent transplantation rates were examined. RESULTS 358 cirrhotic patients were identified. The majority were Child-Turcotte-Pugh class (CTP) A (55.9%) followed by class B (32.4%) and class C (11.7%). Mean MELD score differed significantly between the groups (8.7 vs. 12.1 vs. 20.1; p<0.001). The most common operations were herniorrhaphy (29.9%), cholecystectomy (19.3%), and liver resection (14.5%). The majority of cases were performed semi-electively (68.4%), however, within the CTP C patients most cases were performed emergently (73.8%). Thirty and 90-day mortality for all patients were 5% and 6%, respectively. Mortality rates increased from CTP A to CTP C (30 day: 3.0% vs. 5.2% vs. 14.3%; p = 0.01; 90 day: 4.5% vs. 6.9% vs. 16.7%; p = 0.016). Additionally, 30-day mortality (12.8% vs. 2.3%; p<0.001), 90 day mortality (16.0% vs. 3.4%; p<0.001) were higher for emergent compared to elective cases. A total of 13 (3.6%) patients underwent transplantation ≤ 90 days from surgery. No elective cases resulted in an urgent transplantation. CONCLUSION Performing general surgical operations on cirrhotic patients carries a significant morbidity and mortality. This contemporary series from a specialized liver center demonstrates improved outcomes compared to historical series. These data strongly support early referral of cirrhotic patients needing general surgical operation to centers with liver expertise to minimize morbidity and mortality.
Neural Correlates of Causal Confounding

As scientists, we are keenly aware that if putative causes perfectly covary, the independent influence of neither can be discerned—a “no confounding” constraint on inference, fundamental to philoso...
Adaptive Sparse Estimation With Side Information

ABSTRACTThe article considers the problem of estimating a high-dimensional sparse parameter in the presence of side information that encodes the sparsity structure. We develop a general framework t...
Characterization of resistance to a potent d-peptide HIV entry inhibitor

PIE12-trimer is a highly potent d-peptide HIV-1 entry inhibitor that broadly targets group M isolates. It specifically binds the three identical conserved hydrophobic pockets at the base of the gp41 N-trimer with sub-femtomolar affinity. This extremely high affinity for the transiently exposed gp41 trimer provides a reserve of binding energy (resistance capacitor) to prevent the viral resistance pathway of stepwise accumulation of modest affinity-disrupting mutations. Such modest mutations would not affect PIE12-trimer potency and therefore not confer a selective advantage. Viral passaging in the presence of escalating PIE12-trimer concentrations ultimately selected for PIE12-trimer resistant populations, but required an extremely extended timeframe (> 1 year) in comparison to other entry inhibitors. Eventually, HIV developed resistance to PIE12-trimer by mutating Q577 in the gp41 pocket. Using deep sequence analysis, we identified three mutations at Q577 (R, N and K) in our two PIE12-trimer resistant pools. Each point mutant is capable of conferring the majority of PIE12-trimer resistance seen in the polyclonal pools. Surface plasmon resonance studies demonstrated substantial affinity loss between PIE12-trimer and the Q577R-mutated gp41 pocket. A high-resolution X-ray crystal structure of PIE12 bound to the Q577R pocket revealed the loss of two hydrogen bonds, the repositioning of neighboring residues, and a small decrease in buried surface area. The Q577 mutations in an NL4-3 backbone decreased viral growth rates. Fitness was ultimately rescued in resistant viral pools by a suite of compensatory mutations in gp120 and gp41, of which we identified seven candidates from our sequencing data. These data show that PIE12-trimer exhibits a high barrier to resistance, as extended passaging was required to develop resistant virus with normal growth rates. The primary resistance mutation, Q577R/N/K, found in the conserved gp41 pocket, substantially decreases inhibitor affinity but also damages viral fitness, and candidate compensatory mutations in gp160 have been identified.
Rapid loss of an ecosystem engineer: Sphagnum decline in an experimentally warmed bog.

Sphagnum mosses are keystone components of peatland ecosystems. They facilitate the accumulation of carbon in peat deposits, but climate change is predicted to expose peatland ecosystem to sustained and unprecedented warming leading to a significant release of carbon to the atmosphere. Sphagnum responses to climate change, and their interaction with other components of the ecosystem, will determine the future trajectory of carbon fluxes in peatlands. We measured the growth and productivity of Sphagnum in an ombrotrophic bog in northern Minnesota, where ten 12.8-m-diameter plots were exposed to a range of whole-ecosystem (air and soil) warming treatments (+0 to +9°C) in ambient or elevated (+500 ppm) CO2. The experiment is unique in its spatial and temporal scale, a focus on response surface analysis encompassing the range of elevated temperature predicted to occur this century, and consideration of an effect of co-occurring CO2 altering the temperature response surface. In the second year of warming, dry matter increment of Sphagnum increased with modest warming to a maximum at 5°C above ambient and decreased with additional warming. Sphagnum cover declined from close to 100% of the ground area to <50% in the warmest enclosures. After three years of warming, annual Sphagnum productivity declined linearly with increasing temperature (13-29 g C/m2 per °C warming) due to widespread desiccation and loss of Sphagnum. Productivity was less in elevated CO2 enclosures, which we attribute to increased shading by shrubs. Sphagnum desiccation and growth responses were associated with the effects of warming on hydrology. The rapid decline of the Sphagnum community with sustained warming, which appears to be irreversible, can be expected to have many follow-on consequences to the structure and function of this and similar ecosystems, with significant feedbacks to the global carbon cycle and climate change.
Limits to surface-enhanced Raman scattering near arbitrary-shape scatterers.

In this erratum, we correct two minor algebraic errors from our previous published manuscript [Opt. Express 27, 35189 (2019)], which do not affect the main results or conclusions, and make a corresponding small change to one figure.
DNS of Hypersonic Turbulent Boundary Layers

We present a direct numerical simulation database of supersonic and hypersonic turbulent boundary layers. The systematic procedure for initializing the turbulent flow fields at controlled Mach number and Reynolds number conditions is described. It is shown that simulation transients are less than 10% of the time required for gathering statistical data of the turbulent flows. The experimental conditions of Debieve and Elena are simulated. The magnitude of velocity and temperature fluctuations, as well as the turbulent shear stresses given by the direct numerical simulations are in excellent agreement with the experimental data. Using the direct numerical simulation database we perform parametric studies varying freestream Mach number in the range of 3 to 8 and wall-temperature condition for wall-to-freestream-temperature ratio of 2 to 5.5.
Awareness of Voter Passion Greatly Improves the Distortion of Metric Social Choice

We develop new voting mechanisms for the case where voters and candidates are located in an arbitrary unknown metric space, and the goal is to choose a candidate minimizing social cost: the total distance of the voters to this candidate. Previous work has often assumed that only the ordinal preferences of the voters are known (instead of their true costs), and focused on minimizing distortion: the quality of the chosen candidate as compared to the best possible candidate. In this paper, we instead assume that a (very small) amount of information is known about the voter preference strengths, not just about their ordinal preferences. We provide mechanisms with much better distortion when this extra information is known as compared to mechanisms which use only ordinal information. We quantify tradeoffs between the amount of information known about preference strengths and the achievable distortion. We further provide advice about which type of information about preference strengths seems to be the most useful. Finally, we conclude by quantifying the ideal candidate distortion, which compares the quality of the chosen outcome with the best possible candidate that could ever exist, instead of only the best candidate that is actually in the running.
Cyberwar, International Politics, and Institutional Design

In the United States, the breadth of the president’s warmaking authority has been governed by the Constitution, the Supreme Court’s jurisprudence, and, over time, historical practice; in short, the president’s powers are constrained by a well-developed body of US foreign relations law. But the prospect of a new kind of conflict — cyberwar — potentially challenges the existing regulatory regime, which rests on assumptions that are common to traditional, conventional war. For some, the complexities of cyberwar generate new foreign relations law questions about the president’s authority to engage in offensive cyberoperations, and they thus necessitate a new regulatory framework. For others, cyberwar is not meaningfully different from traditional war for purposes of foreign relations law, and the extant regime regulating the president is sufficient. As it currently stands, the debate about the scope of the president’s cyberwar authority turns on arguments about cyberwar’s similarity or dissimilarity to conventional war. This Essay argues that any claim about regulating the president’s authority to engage in cyberwar requires consideration of the United States’ cyberstrategy and the capacity and national interests of the United States’ cybercompetitors. For the United States to achieve its foreign policy goals in cyberspace, the president must navigate both the internal constraints from domestic law and the external constraints from international politics. Building on previous work, the Essay provides two models with which to understand internal and external constraints and their consequences on any potential cyberwar regulation. It contends that a frame-work that does not consider the complex relationship between the two types of constraints might result in a regulatory regime that leaves the president overconstrained and unable to achieve US cyberpolicy goals.
Is it a Trump Bump, Spike, or Plateau? India’s Changing Interest in Canadian versus U.S. Universities

This article assesses changing interest from India in Canadian versus U.S. universities since the 2016 U.S. presidential election, as measured by search activity reports from Google Trends. The findings indicate a rise in Indian interest toward Canadian versus U.S. universities was underway before the election. After controlling for this trend effect, there remains widespread evidence of a substantial shift in interest toward Canadian universities with search activity rising 70%–85% for Canadian versus U.S. universities in the postelection period. This shift in interest toward Canadian universities shows no sign of dissipating. Canadian universities are likely to make further enrollment gains of Indian students versus U.S. counterparts in upcoming recruiting classes.
The Impact of pH on Clostridioides difficile Sporulation and Physiology.

ABSTRACT Clostridioides difficile is a pathogenic bacterium that infects the human colon to cause diarrheal disease. Growth of the bacterium is known to be dependent on certain bile acids, oxygen levels, and nutrient availability in the intestine, but how the environmental pH can influence C. difficile is mostly unknown. Previous studies indicated that C. difficile modulates the intestinal pH, and prospective cohort studies have found a strong association between a more alkaline fecal pH and C. difficile infection. Based on these data, we hypothesized that C. difficile physiology can be affected by various pH conditions. In this study, we investigated the impact of a range of pH conditions on C. difficile to assess potential effects on growth, sporulation, motility, and toxin production in the strains 630Δerm and R20291. We observed pH-dependent differences in sporulation rate, spore morphology, and viability. Sporulation frequency was lowest under acidic conditions, and differences in cell morphology were apparent at low pH. In alkaline environments, C. difficile sporulation was greater for strain 630Δerm, whereas R20291 produced relatively high levels of spores in a broad range of pH conditions. Rapid changes in pH during exponential growth impacted sporulation similarly among the strains. Furthermore, we observed an increase in C. difficile motility with increases in pH, and strain-dependent differences in toxin production under acidic conditions. The data demonstrate that pH is an important parameter that affects C. difficile physiology and may reveal relevant insights into the growth and dissemination of this pathogen. IMPORTANCEClostridioides difficile is an anaerobic bacterium that causes gastrointestinal disease. C. difficile forms dormant spores which can survive harsh environmental conditions, allowing their spread to new hosts. In this study, we determine how intestinally relevant pH conditions impact C. difficile physiology in the two divergent strains, 630Δerm and R20291. Our data demonstrate that low pH conditions reduce C. difficile growth, sporulation, and motility. However, toxin production and spore morphology were differentially impacted in the two strains at low pH. In addition, we observed that alkaline environments reduce C. difficile growth, but increase cell motility. When pH was adjusted rapidly during growth, we observed similar impacts on both strains. This study provides new insights into the phenotypic diversity of C. difficile grown under diverse pH conditions present in the intestinal tract, and demonstrates similarities and differences in the pH responses of different C. difficile isolates.
Deep neuroethology of a virtual rodent

Parallel developments in neuroscience and deep learning have led to mutually productive exchanges, pushing our understanding of real and artificial neural networks in sensory and cognitive systems. However, this interaction between fields is less developed in the study of motor control. Existing experimental research and neural network models have been focused on the production of individual behaviors, yielding little insight into how intelligent systems can produce a rich and varied set of motor behaviors. In this work we develop a virtual rodent that learns to flexibly apply a broad motor repertoire, including righting, running, leaping and rearing, to solve multiple tasks in a simulated world. We analyze the artificial neural mechanisms underlying the virtual rodent's motor capabilities using a neuroethological approach, where we characterize neural activity patterns relative to the rodent's behavior and goals. We show that the rodent solves tasks by using a shared set of force patterns that are orchestrated into task-specific behaviors over longer timescales. Through methods familiar to neuroscientists, including representational similarity analysis, dimensionality reduction techniques, and targeted perturbations, we show that the networks produce these behaviors using at least two classes of behavioral representations, one that explicitly encodes behavioral kinematics in a task-invariant manner, and a second that encodes task-specific behavioral strategies. Overall, the virtual rat promises to facilitate grounded collaborations between deep reinforcement learning and motor neuroscience.
Precise Feature Selection and Case Study of Intrusion Detection in an Industrial Control System (ICS) Environment

This paper presents analytical techniques to improve redundancy and relevance assessment for precise selection of features in practical multi-class raw datasets. We propose a matrix-rank based $k$-medoids algorithm that guarantees to output all independent medoids. The new algorithm uses matrix rank as a robust indicator, while a traditional $k$-medoids algorithm depends on specific datasets and how the distance between any of two features is defined. Another advantage is that the total number of operations in the nested loops is bounded, different from some $k$-medoids algorithms that involve random search. Sparse regression is an efficient tool for feature relevance analysis, but its outcome can depend on what labeled datasets are employed. A compensation method is introduced in this paper to handle the unequality of class-occurrence in a practical raw dataset. To assess the proposed techniques quantitatively, an existing Industrial Control System (ICS) dataset is used to perform intrusion detection. The numerical results generated from this case study validate the effectiveness and necessity of the proposed analytical framework.
Alternative Work Arrangements

Alternative work arrangements, defined both by working conditions and by workers’ relationship to their employers, are heterogeneous and common in the United States. This article reviews the litera...
Pairwise Fairness for Ranking and Regression.

We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. Our pairwise formulation supports both discrete protected groups, and continuous protected attributes. We show that the resulting training problems can be efficiently and effectively solved using existing constrained optimization and robust optimization techniques developed for fair classification. Experiments illustrate the broad applicability and trade-offs of these methods.
Gradient-based Optimization for Bayesian Preference Elicitation

Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and conceptually appealing Bayesian criterion for selecting queries is expected value of information (EVOI). Unfortunately, it is computationally prohibitive to construct queries with maximum EVOI in RSs with large item spaces. We tackle this issue by introducing a continuous formulation of EVOI as a differentiable network that can be optimized using gradient methods available in modern machine learning computational frameworks (e.g., TensorFlow, PyTorch). We exploit this to develop a novel Monte Carlo method for EVOI optimization, which is much more scalable for large item spaces than methods requiring explicit enumeration of items. While we emphasize the use of this approach for pairwise (or k-wise) comparisons of items, we also demonstrate how our method can be adapted to queries involving subsets of item attributes or “partial items,” which are often more cognitively manageable for users. Experiments show that our gradient-based EVOI technique achieves state-of-the-art performance across several domains while scaling to large item spaces.
The modulus of Whittaker functions

The paper discusses some properties of the modulus | W k , m ( z ) | of the Whittaker function W k , m ( z ) . In particular, completely monotone functions expressed in terms of | W k , m ( z ) | a...
Phase diagrams of polymer-containing liquid mixtures with a theory-embedded neural network

We develop a deep neural network (DNN) that accounts for the phase behaviors of polymer-containing liquid mixtures. The key component in the DNN consists of a theory-embedded layer that captures the characteristic features of the phase behavior via coarse-grained mean-field theory and scaling laws and substantially enhances the accuracy of the DNN. Moreover, this layer enables us to reduce the size of the DNN for the phase diagrams of the mixtures. This study also presents the predictive power of the DNN for the phase behaviors of polymer solutions and salt-free and salt-doped diblock copolymer melts.
RIPK3 upregulation confers robust proliferation and collateral cystine-dependence on breast cancer recurrence

The molecular and genetic basis of tumor recurrence is complex and poorly understood. RIPK3 is a key effector in programmed necrotic cell death and, therefore, its expression is frequently suppressed in primary tumors. In a transcriptome profiling between primary and recurrent breast tumor cells from a murine model of breast cancer recurrence, we found that RIPK3, while absent in primary tumor cells, is dramatically reexpressed in recurrent breast tumor cells by an epigenetic mechanism. Unexpectedly, we found that RIPK3 knockdown in recurrent tumor cells reduced clonogenic growth, causing cytokinesis failure, p53 stabilization, and repressed the activities of YAP/TAZ. These data uncover a surprising role of the pro-necroptotic RIPK3 kinase in enabling productive cell cycle during tumor recurrence. Remarkably, high RIPK3 expression also rendered recurrent tumor cells exquisitely dependent on extracellular cystine and undergo necroptosis upon cystine deprivation. The induction of RIPK3 in recurrent tumors unravels an unexpected mechanism that paradoxically confers on tumors both growth advantage and necrotic vulnerability, providing potential strategies to eradicate recurrent tumors.