cssi/SciSciGPT-SciSciNet huggingface dataset:
Identification of multiple noise sources improves estimation of neural responses across stimulus conditions.

Most models of neural responses are constructed to reproduce the average response to inputs but lack the flexibility to capture observed variability in responses. The origins and structure of this variability have significant implications for how information is encoded and processed in the nervous system, both by limiting information that can be conveyed and by determining processing strategies that are favorable for minimizing its negative effects. Here, we present a new modeling framework that incorporates multiple sources of noise to better capture observed features of neural response variability across stimulus conditions. We apply this model to retinal ganglion cells at two different ambient light levels and demonstrate that it captures the full distribution of responses. Further, the model reveals light level-dependent changes that could not be seen with previous models, showing both large changes in rectification of nonlinear circuit elements and systematic differences in the contributions of different noise sources under different conditions. Significance Statement Current models for neural responses typically focus on accurately estimating a neuron’s average response to a stimulus but often fail to accurately reflect response variability. Such variability is central to the accuracy with which neural responses represent inputs and with which they can guide behavior. We present a new modeling framework that accurately captures observed variability in neural responses and find that multiple stochastic model elements are necessary to capture this variability. We show that model parameters can be accurately estimated using about 8 minutes of data. We then apply the model to retinal ganglion cells, demonstrating light level-dependent changes in both deterministic and stochastic model elements changes that are either obscured or absent using more standard modeling approaches.
A Rotating-Grid Upwind Fast Sweeping Scheme for a Class of Hamilton-Jacobi Equations

We present a fast sweeping method for a class of Hamilton-Jacobi equations that arise from time-independent problems in optimal control theory. The basic method in two dimensions uses a four point stencil and is extremely simple to implement. We test our basic method against Eikonal equations in different norms, and then suggest a general method for rotating the grid and using additional approximations to the derivatives in different directions in order to more accurately capture characteristic flow. We display the utility of our method by applying it to relevant problems from engineering.
Hail Damage to Typical Aircraft Surfaces

Severe structural damage can occur when aircraft collide with hailstones. Consequently, methods of predicting hail damage to airplane surfaces are needed by the aircraft designer. This paper describes an analytical method of predicting the dent depth and final deformed shape for simple structural components impacted by hailstones. The solution was accomplished by adapting the DEPROSS computer program to the problem of normal impact of hail on flat metallic sheets and spherical metallic caps. Experimental data and analytical predictions are presented for hail damage to typical aircraft surfaces along with a description of the hail gun and hail simulation technique used in the experimental study.
Ceramic Component Reliability With the Restructured NASA/CARES Computer Program

The Ceramics Analysis and Reliability Evaluation of Structures (CARES) integrated design program on statistical fast fracture reliability of monolithic ceramic components is enhanced to include the use of a neutral data base, two-dimensional modeling and variable problem size. The data base allows for the efficient transfer of element stresses, temperatures and volumes/areas from the finite element output to the reliability analysis program. Elements are divided to insure a direct correspondence between the subelements and the Gaussian integration points. Two-dimensional modeling is accomplished by assessing the volume flaw reliability with shell elements. To demonstrate the improvements in the algorithm, example problems are selected from a round-robin conducted by WELFEP (WEakest Link failure probability prediction by Finite Element Postprocessors).Copyright © 1992 by ASME
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning

As multi-task models gain popularity in a wider range of machine learning applications, it is becoming increasingly important for practitioners to understand the fairness implications associated with those models. Most existing fairness literature focuses on learning a single task more fairly, while how ML fairness interacts with multiple tasks in the joint learning setting is largely under-explored. In this paper, we are concerned with how group fairness (e.g., equal opportunity, equalized odds) as an ML fairness concept plays out in the multi-task scenario. In multi-task learning, several tasks are learned jointly to exploit task correlations for a more efficient inductive transfer. This presents a multi-dimensional Pareto frontier on (1) the trade-off between group fairness and accuracy with respect to each task, as well as (2) the trade-offs across multiple tasks. We aim to provide a deeper understanding on how group fairness interacts with accuracy in multi-task learning, and we show that traditional approaches that mainly focus on optimizing the Pareto frontier of multi-task accuracy might not perform well on fairness goals. We propose a new set of metrics to better capture the multi-dimensional Pareto frontier of fairness-accuracy trade-offs uniquely presented in a multi-task learning setting. We further propose a Multi-Task-Aware Fairness (MTA-F) approach to improve fairness in multi-task learning. Experiments on several real-world datasets demonstrate the effectiveness of our proposed approach.
Solving Linear Equations With Separable Problem Data Over Directed Networks

This letter deals with linear algebraic equations where the global coefficient matrix and constant vector are given respectively, by the summation of the coefficient matrices and constant vectors of the individual agents. Our approach is based on reformulating the original problem as an unconstrained optimization. Based on this exact reformulation, we first provide a gradient-based, centralized algorithm which serves as a reference for the ensuing design of distributed algorithms. We propose two sets of exponentially stable continuous-time distributed algorithms that do not require the individual agent matrices to be invertible, and are based on estimating non-distributed terms in the centralized algorithm using dynamic average consensus. The first algorithm works for time-varying weight-balanced directed networks, and the second algorithm works for general directed networks for which the communication graphs might not be balanced. Numerical simulations illustrate our results.
Channel Wall Nozzle Manufacturing and Hot-Fire Testing using a Laser Wire Direct Closeout Technique for Liquid Rocket Engines

A regeneratively-cooled nozzle for liquid rocket engine applications is a significant cost of the overall engine due to the complexities of manufacturing a large thin-walled structure that must operate in extreme temperature and pressure environments. The National Aeronautics and Space Administration (NASA) has been investigating and advancing methods for fabrication of liquid rocket engine channel wall nozzles to realize further cost and schedule improvements over traditional techniques. The methods being evaluated are targeting increased scale required for current NASA and commercial space programs. Several advanced rapid fabrication methods are being investigated for forming of the inner liner, producing the coolant channels, closeout of the coolant channels, and fabrication of the manifolds. NASA's Marshall Space Flight Center (MSFC) has completed process development and subscale hot-fire testing of a series of these advanced fabrication channel wall nozzle technologies to gather performance data in a relevant environment. The primary fabrication technique being discussed in this paper is Laser Wire Direct Closeout (LWDC). This process has been developed to significantly reduce the time required for closeouts of regeneratively-cooled slotted liners. It allows for channel closeout to be formed in place in addition to the structural jacket without the need for channel fillers or complex tooling. Additional technologies were also tested as part of this program including water jet milling and arc-based additive manufacturing deposition. Each nozzle included different fabrication features, materials, and methods to demonstrate durability in a hot-fire environment. The results of design, fabrication, and hot-fire testing are discussed in this paper.
Using reweighting and free energy surface interpolation to predict solid-solid phase diagrams.

Many physical properties of small organic molecules are dependent on the current crystal packing, or polymorph, of the material, including bioavailability of pharmaceuticals, optical properties of dyes, and charge transport properties of semiconductors. Predicting the most stable crystalline form at a given temperature and pressure requires determining the crystalline form with the lowest relative Gibbs free energy. Effective computational prediction of the most stable polymorph could save significant time and effort in the design of novel molecular crystalline solids or predict their behavior under new conditions. In this study, we introduce a new approach using multistate reweighting to address the problem of determining solid-solid phase diagrams and apply this approach to the phase diagram of solid benzene. For this approach, we perform sampling at a selection of temperature and pressure states in the region of interest. We use multistate reweighting methods to determine the reduced free energy differ...
TAK1 Inhibition Elicits Mitochondrial ROS to Block Intracellular Bacterial Colonization

Mitogen-activated protein kinase kinase kinase 7 (MAP3K7), known as TAK1, is an intracellular signaling intermediate of inflammatory responses. However, a series of mouse Tak1 gene deletion analyses have revealed that ablation of TAK1 does not prevent but rather elicits inflammation, which is accompanied by elevation of reactive oxygen species (ROS). This has been considered a consequence of impaired TAK1-dependent maintenance of tissue integrity. Contrary to this view, here we propose that TAK1 inhibition-induced ROS are an active cellular process that targets intracellular bacteria. Intracellular bacterial effector proteins such as Yersinia's outer membrane protein YopJ are known to inhibit TAK1 to circumvent the inflammatory host responses. We found that such TAK1 inhibition induces mitochondrial-derived ROS, which effectively destroys intracellular bacteria. Two cell death-signaling molecules, caspase 8 and RIPK3, cooperatively participate in TAK1 inhibition-induced ROS and blockade of intracellular bacterial growth. Our results reveal a previously unrecognized host defense mechanism, which is initiated by host recognition of pathogen-induced impairment in a host protein, TAK1, but not directly of pathogens.
Lift Optimization Study of a Multi-Element Three-Segment Variable Camber Airfoil

This paper reports a detailed computational high-lift study of the Variable Camber Continuous Trailing Edge Flap (VCCTEF) system carried out to explore the best VCCTEF designs, in conjunction with a leading edge flap called the Variable Camber Krueger (VCK), for take-off and landing. For this purpose, a three-segment variable camber airfoil employed as a performance adaptive aeroelastic wing shaping control effector for a NASA Generic Transport Model (GTM) in landing and take-off configurations is considered. The objective of the study is to define optimal high-lift VCCTEF settings and VCK settings/configurations. A total of 224 combinations of VCK settings/configurations and VCCTEF settings are considered for the inboard GTM wing, where the VCCTEFs are configured as a Fowler flap that forms a slot between the VCCTEF and the main wing. For the VCK settings of deflection angles of 55deg, 60deg and 65deg, 18, 19 and 19 vck configurations, respectively, were considered for each of the 4 different VCCTEF deflection settings. Different vck configurations were defined by varying the horizontal and vertical distance of the vck from the main wing. A computational investigation using a Reynolds-Averaged Navier-Stokes (RANS) solver was carried out to complement a wind-tunnel experimental study covering three of these configurations with the goal of identifying the most optimal high-lift configurations. Four most optimal high-lift configurations, corresponding to each of the VCK deflection settings, have been identified out of all the different configurations considered in this study yielding the highest lift performance.
Random Coordinate Langevin Monte Carlo

Langevin Monte Carlo (LMC) is a popular Markov chain Monte Carlo sampling method. One drawback is that it requires the computation of the full gradient at each iteration, an expensive operation if the dimension of the problem is high. We propose a new sampling method: Random Coordinate LMC (RC-LMC). At each iteration, a single coordinate is randomly selected to be updated by a multiple of the partial derivative along this direction plus noise, and all other coordinates remain untouched. We investigate the total complexity of RC-LMC and compare it with the classical LMC for log-concave probability distributions. When the gradient of the log-density is Lipschitz, RC-LMC is less expensive than the classical LMC if the log-density is highly skewed for high dimensional problems, and when both the gradient and the Hessian of the log-density are Lipschitz, RC-LMC is always cheaper than the classical LMC, by a factor proportional to the square root of the problem dimension. In the latter case, our estimate of complexity is sharp with respect to the dimension.
Self-Attention Networks Can Process Bounded Hierarchical Languages

Despite their impressive performance in NLP, self-attention networks were recently proved to be limited for processing formal languages with hierarchical structure, such as Dyck-k, the language consisting of well-nested parentheses of k types. This suggested that natural language can be approximated well with models that are too weak for formal languages, or that the role of hierarchy and recursion in natural language might be limited. We qualify this implication by proving that self-attention networks can process Dyck-(k, D), the subset of Dyck-k with depth bounded by D, which arguably better captures the bounded hierarchical structure of natural language. Specifically, we construct a hard-attention network with D+1 layers and O(log k) memory size (per token per layer) that recognizes Dyck-(k, D), and a soft-attention network with two layers and O(log k) memory size that generates Dyck-(k, D). Experiments show that self-attention networks trained on Dyck-(k, D) generalize to longer inputs with near-perfect accuracy, and also verify the theoretical memory advantage of self-attention networks over recurrent networks.
Resolving Small Objects Using Seismic Traveltime Tomography

It is often claimed that the spatial resolution limit for traveltime tomography is the first Fresnel zone associated with the dominant frequency in the data. To test this assertion, synthetic seismic data were generated for traveltime picking and inversion for a single, small velocity anomaly embedded in a homogeneous background velocity. All picking methods produced accurate raytheoretical (infinite-frequency) picks from noise-free seismic data for objects much smaller than the dominantfrequency Fresnel zone. All methods detected the presence of objects smaller than a wavelength. The inversion of these traveltime picks always recovered the position and shape of the object. Random noise was added to the synthetic seismic data. Pick times with different noise realizations were statistically centered on the noise-free pick, not the time that would be recorded in the absence of the object. Trace stacking prior to picking or the averaging of many picks improves the signal-to-noise ratio and can extract signal that is not resolved on an individual pick. An averaging of traveltime picks also occurs during tomographic inversion. This inherent signal-to-noise improvement allows tomography to image small objects that are not resolved in individual trace picks. The resolution of tomography is limited not by the Fresnel zone associated with the dominant frequency, but by the accuracy of the traveltime picks and the density of ray coverage.
The massless Dirac equation in two dimensions: zero-energy obstructions and dispersive estimates

We investigate $L^1\to L^\infty$ dispersive estimates for the massless two dimensional Dirac equation with a potential. In particular, we show that the Dirac evolution satisfies the natural $t^{-\frac12}$ decay rate, which may be improved to $t^{-\frac12-\gamma}$ for any $0\leq \gamma<\frac{3}{2}$ at the cost of spatial weights. We classify the structure of threshold obstructions as being composed of a two dimensional space of p-wave resonances and a finite dimensional space of eigenfunctions at zero energy. We show that, in the presence of a threshold resonance, the Dirac evolution satisfies the natural decay rate except for a finite-rank piece. While in the case of a threshold eigenvalue only, the natural decay rate is preserved. In both cases we show that the decay rate may be improved at the cost of spatial weights.
Robotic additive construction of bar structures: unified sequence and motion planning

Additive robotic construction of building-scale discrete bar structures, such as trusses and space frames, is increasingly attractive due to the potential improvements in efficiency, safety, and design possibilities. However, programming complex robots, such as manipulators with seven degrees of freedom, to successfully complete construction tasks can be tedious, challenging, or impossible for a human to do manually. Namely, the structure must be constructed in a sequence that preserves structural properties, such as stiffness, at each step. At the same time, this sequence must allow for the robot to precisely manipulate elements within the in-progress structure while respecting geometric constraints that, for example, ensure the robot does not collide with what it has built. In this work, we present an automated and newly generalized planning approach for jointly finding a construction sequence and robot motion plan for additive construction that satisfies these requirements. Our approach can be applied in a variety of additive construction processes, and we demonstrate it specifically on spatial extrusion and discrete bar assembly in this paper. We demonstrate the effectiveness of our approach on several simulated and real-world extrusion and assembly tasks, including a human-scale physical prototype, for which our algorithm is deployed for the first time to plan the assembly of a complicated double tangent bar system design.
Optochemical nanosensors for intracellular chemical measurement

The development of a submicron optical fiber `supertip' has provided advantages over previously produced submicron tips, such as facilitating insertion of these sensors into cells while minimizing damage to the cell membrane. Fiber optic ion correlation-based nanosensors for sodium, potassium and chloride employing these `supertips' have been applied to the monitoring of ion concentrations in single mouse oocytes. These sensors have also been used to monitor the effect of an ion channel-blocking agent. In order to address the challenge associated with single-cell simultaneous measurement of multiple analytes, the use of submicron optical fiber multiprobes has been explored.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Racist Antitrust, Antiracist Antitrust:

If the tumultuous 2010s yielded one consistent theme, it is frustration with inequality coalescing into collective action. In response, progressive enforcers and commentators have begun to explore ...
The Necessary Interrelationship between Land Use and Preservation of Groundwater Resources

Professor Linda Malone explores the correlated relationship between groundwater resource and land use rights in this article. National, state, and local governments have made legislation concerning groundwater resources, but the regulations fail to be sufficient. For successful groundwater reform, private property rights will have to be revaluated in favor of regulation methods.
Sparse Activity Discovery in Energy Constrained Multi-Cluster IoT Networks Using Group Testing

Current IoT networks are characterized by an ultra-high density of devices with different energy budget constraints, typically having sparse and sporadic activity patterns. Access points require an efficient strategy to identify the active devices for a timely allocation of resources to enable massive machine-type communication. Recently, group testing based approaches have been studied to handle sparse activity detection in massive random access problems. In this paper, a non-adaptive group testing strategy is proposed which can take into account the energy constraints on different sensor clusters. A theoretical extension of the existing randomized group testing strategies to the case of multiple clusters is presented and the necessary constraints that the optimal sampling parameters should satisfy in order to improve the efficiency of group tests is established. The cases of fixed activity pattern where there is a fixed set of active sensors and random activity pattern where each sensor can be independently active with certain probability are examined. The theoretical results are verified and validated by Monte-Carlo simulations. In massive wireless sensor networks comprising of devices with different energy efficiencies, our proposed low-power-use mode of access can potentially extend the lifetime of battery powered sensors with finite energy budget.
Effectiveness of Policies for Addressing the US Opioid Epidemic: A Model-Based Analysis from the Stanford-Lancet Commission on the North American Opioid Crisis

Background The U.S. opioid crisis has been exacerbated by COVID-19 and the spread of synthetic opioids (e.g., fentanyl). Methods We model the effectiveness of reduced prescribing, drug rescheduling, prescription monitoring programs (PMPs), tamper-resistant drug reformulation, excess opioid disposal, naloxone availability, syringe exchange, pharmacotherapy, and psychosocial treatment. We measure life years, quality-adjusted life years (QALYs), and opioid-related deaths over five and ten years. Findings Under the status quo, our model predicts that approximately 547,000 opioid-related deaths will occur from 2020 to 2024 (range 441,000 - 613,000), rising to 1,220,000 (range 996,000 - 1,383,000) by 2029. Expanding naloxone availability by 30% had the largest effect, averting 25% of opioid deaths. Pharmacotherapy, syringe exchange, psychosocial treatment, and PMPs are uniformly beneficial, reducing opioid-related deaths while leading to gains in life years and QALYs. Reduced prescribing and increasing excess opioid disposal programs would reduce total deaths, but would lead to more heroin deaths in the short term. Drug rescheduling would increase total deaths over five years as some opioid users escalate to heroin, but decrease deaths over ten years. Combined interventions would lead to greater increases in life years, QALYs, and deaths averted, although in many cases the results are subadditive. Interpretation Expanded health services for individuals with opioid use disorder combined with PMPs and reduced opioid prescribing would moderately lessen the severity of the opioid crisis over the next decade. Tragically, even with improved public policies, significant morbidity and mortality is inevitable.
Subjectivity in Economics

Economics cannot claim to be absolutely objective. There are several significant ways in which economic analysis is subjective, and this should be recognized by the profession to a greater degree.
Electric dipole moments of three-nucleon systems in the pionless effective field theory

We calculate the electric dipole moments (EDMs) of three-nucleon systems at leading order in pionless effective field theory. The one-body contributions that arise from permanent proton and neutron EDMs and the two-body contributions that arise from charge-parity-odd nucleon-nucleon interactions are taken into account. Neglecting the Coulomb interaction, we consider the triton and $^{3}\mathrm{He}$, and also investigate them in the Wigner-SU(4) symmetric limit. We also calculate the electric dipole form factor and find numerically that the momentum dependence of the electric dipole form factor in the Wigner limit is, up to an overall constant (and numerical accuracy), the same as the momentum dependence of the charge form factor. Finally, we study the cutoff dependence of these observables and find that they are properly renormalized.
Measuring expression heterogeneity of single-cell cytoskeletal protein complexes.

Multimeric cytoskeletal protein complexes orchestrate normal cellular function. However, protein-complex distributions in stressed, heterogeneous cell populations remain unknown. Cell staining and proximity-based methods have limited selectivity and/or sensitivity for endogenous multimeric protein-complex quantification from single cells. We introduce micro-arrayed, differential detergent fractionation to simultaneously detect protein complexes in hundreds of individual cells. Fractionation occurs by 60 s size-exclusion electrophoresis with protein complex-stabilizing buffer that minimizes depolymerization. Proteins are measured with a ~5-hour immunoassay. Co-detection of cytoskeletal protein complexes in U2OS cells treated with filamentous actin (F-actin) destabilizing Latrunculin A detects a unique subpopulation (~2%) exhibiting downregulated F-actin, but upregulated microtubules. Thus, some cells may upregulate other cytoskeletal complexes to counteract the stress of Latrunculin A treatment. We also sought to understand the effect of non-chemical stress on cellular heterogeneity of F-actin. We find heat shock may dysregulate filamentous and globular actin correlation. In this work, our assay overcomes selectivity limitations to biochemically quantify single-cell protein complexes perturbed with diverse stimuli. Existing methods for multimeric protein complex quantification in single cells suffer from limited selectivity and sensitivity. Here the authors report Single-cell protein Interaction Fractionation Through Electrophoresis and immunoassay Readout (SIFTER) and use this to probe the effects of cellular stress.
Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence.

BACKGROUND: The COVID-19 pandemic fueled one of the most rapid vaccine developments in history. However, misinformation spread through online social media often leads to negative vaccine sentiment and hesitancy. METHODS: To investigate COVID-19 vaccine-related discussion in social media, we conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling on textual data collected from 13 Reddit communities focusing on the COVID-19 vaccine from Dec 1, 2020, to May 15, 2021. Data were aggregated and analyzed by month to detect changes in any sentiment and latent topics. RESULTS: Polarity analysis suggested these communities expressed more positive sentiment than negative regarding the vaccine-related discussions and has remained static over time. Topic modeling revealed community members mainly focused on side effects rather than outlandish conspiracy theories. CONCLUSION: Covid-19 vaccine-related content from 13 subreddits show that the sentiments expressed in these communities are overall more positive than negative and have not meaningfully changed since December 2020. Keywords indicating vaccine hesitancy were detected throughout the LDA topic modeling. Public sentiment and topic modeling analysis regarding vaccines could facilitate the implementation of appropriate messaging, digital interventions, and new policies to promote vaccine confidence.
Joint eQTL mapping and Inference of Gene Regulatory Network Improves Power of Detecting both cis- and trans-eQTLs.

MOTIVATION Genetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: 1) relatively small size of samples available, and 2) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identify trans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identify trans-eQTLs without increasing multiple testing burden. RESULTS In this paper, we employ the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM for eQTL mapping (SSEMQ), to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determine trans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms nine existing eQTL mapping methods. SSEMQ is further employed to analyze two real datasets of human breast and whole blood tissues, yielding a number of cis- and trans-eQTLs. AVAILABILITY R package ssemQr is available at https://github.com/Ivis4ml/ssemQr.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Two-step growth mixture model to examine heterogeneity in nonlinear trajectories

Empirical researchers are usually interested in investigating the impacts that baseline covariates have when uncovering sample heterogeneity and separating samples into more homogeneous groups. However, a considerable number of studies in the structural equation modeling (SEM) framework usually start with vague hypotheses in terms of heterogeneity and possible causes. It suggests that (1) the determination and specification of a proper model with covariates is not straightforward, and (2) the exploration process may be computationally intensive given that a model in the SEM framework is usually complicated and the pool of candidate covariates is usually huge in the psychological and educational domain where the SEM framework is widely employed. Following Bakk and Kuha (2017), this article presents a two-step growth mixture model (GMM) that examines the relationship between latent classes of nonlinear trajectories and baseline characteristics. Our simulation studies demonstrate that the proposed model is capable of clustering the nonlinear change patterns, and estimating the parameters of interest unbiasedly, precisely, as well as exhibiting appropriate confidence interval coverage. Considering the pool of candidate covariates is usually huge and highly correlated, this study also proposes implementing exploratory factor analysis (EFA) to reduce the dimension of covariate space. We illustrate how to use the hybrid method, the two-step GMM and EFA, to efficiently explore the heterogeneity of nonlinear trajectories of longitudinal mathematics achievement data.
On the Joint Effects of HPA Nonlinearities and IQ Imbalance on Mixed RF/FSO Cooperative Systems

In this work, we provide a framework analysis of dual-hop hybrid Millimeter Wave Radio Frequency (RF)/Free Space Optical (FSO) MIMO relaying system. The source is equipped with multiple antennas and employs conjugate beamforming while the destination consists of multiple apertures with selection combining. The system also consists of a relay operating at amplify-and-forward mode. The RF channels are subject to Nakagami-m fading while the optical links experience the Malaga distribution. In addition, we introduce the impairments to the relay and receiver. In fact, the relay is impaired by the High Power Amplifier (HPA) nonlinearities while the receiver suffers from the In phase and Quadrature Imbalance. Moreover, we assume two types of HPA nonlinearities impairments called Soft Envelope Limiter (SEL) and Traveling Wave Tube Amplifier (TWTA). Closed-forms of the outage probability, the bit error probability, and the ergodic capacity are derived. Capitalizing on these performances, we derive the high SNR asymptotes to unpack insightful metrics such as the diversity gain. We also address the impacts of some key factors on the system performance such as the impairments, the interferers, the number of antennas and apertures and the pointing errors, etc. Finally, the analytical expressions are confirmed by Monte Carlo simulation.
Killing in the stock market: Evidence from organ donations

Abstract Daily individual patient records for every organ transplant capable hospital in the United States from 1987 to 2018 indicate a negative relationship between stock market returns and deaths. Stress related deaths, such as heart attacks and strokes, are the most pronounced around stock market movements. Market shifts also alter the availability of organ transplants creating life altering consequences for organ wait list patients. A geographic effect exists within states as well. An interrupted time series specification mitigates some endogeneity concerns. The findings imply that wealth shocks alter current utility even at the extremes emphasizing the spillover effects of finance.
Current-conserving relativistic linear response for collisional plasmas

Abstract We investigate the response of a relativistic plasma to electromagnetic fields in the framework of the Boltzmann equation incorporating a collision term in the relaxation rate approximation selected in a form assuring current conservation. We obtain an explicit solution for the linearized perturbation of the Fermi–Dirac equilibrium distribution in terms of the average relaxation rate κ . We study the resulting covariant, gauge invariant, and current conserving form of the polarization tensor in the ultrarelativistic and non-relativistic limits. We evaluate the susceptibility in the ultrarelativistic limit and explore their dependence on κ . Finally, we study the dispersion relations for the longitudinal and transverse poles of the propagator. We show that for κ > 2 ω p , where ω p is the plasma frequency, the plasma wave modes are overdamped. In the opposite case, κ ≪ ω p , the propagating plasma modes are weakly damped.
The Whiteness of French Food: Law, Race, and Eating Culture in France

Food is fundamental to French identity. So too is the denial of structural racism and racial identity. Both tenets are central to the nation’s self-definition, making them all the more important to think about together. This article purports to identify and critique a form of French food Whiteness (blanchite alimentaire), that is, the use of food and eating practices to reify and reinforce Whiteness as the dominant racial identity. To do so, it develops four case studies of how law elevates a fiction of homogeneous French/White food as superior and normative at the expense of alternative ways of eating and their eaters—the law of geographical indications, school lunches, citizenship, and cultural heritage.
Homotopy liftings and Hochschild cohomology of some twisted tensor products

The Hochschild cohomology of a tensor product of algebras is isomorphic to a graded tensor product of Hochschild cohomology algebras, as a Gerstenhaber algebra. A similar result holds when the tens...
Corporate Social Performance and the Likelihood of Bankruptcy: Evidence from a Period of Economic Upswing

The paper aims to investigate the effects of corporate social performance (CSP) on bankruptcy likelihood in times of economic upswing. This is important because prior related literature focused on data containing times of economic crises. We measure bankruptcy likelihood with the Altman Z score and CSP with Refinitiv ESG scores. By applying static panel data regressions and instrumental variable regressions on a sample of 6696 US-firm-year observations from 2010 to 2019 our main findings are: (i) In contrast to existing research, the level of firms’ CSP seems to have no (positive) effect on the likelihood of bankruptcy during times of economic upswing. (ii) Increasing a firm’s CSP in times of economic upswing leads to a rise in bankruptcy likelihood. We conclude that the positive effects of CSP on stakeholder relationships fail to materialize in flourishing business environments. The costs of increasing CSP, thus, exceed their immediate positive effects and raise bankruptcy likelihood. However, as they reduce financial default risk in subsequent crises, CSP investments can be seen as a balancing measure. Our findings bear implications for scholars, practitioners, and policymakers.
The interplay of chromatin phase separation and lamina interactions in nuclear organization.

Abstract The genetic material of eukaryotes is segregated into transcriptionally active euchromatin and silent heterochromatin compartments. The spatial arrangement of chromatin compartments evolves over the course of cellular life in a process that remains poorly understood. The latest nuclear imaging experiments reveal a number of dynamical signatures of chromatin that are reminiscent of active multiphase liquids. This includes the observations of viscoelastic response, coherent motions, Ostwald ripening, and coalescence of chromatin compartments. There is also growing evidence that liquid-liquid phase separation of protein and nucleic acid components is the underlying mechanism for the dynamical behavior of chromatin. To dissect the organizational and dynamical implications of chromatin's liquid behavior, we have devised a phenomenological field-theoretic model of the nucleus as a multiphase condensate of liquid chromatin types. Employing the liquid chromatin model of the Drosophila nucleus, we have carried out an extensive set of simulations with an objective to shed light on the dynamics and chromatin patterning observed in the latest nuclear imaging experiments. Our simulations reveal the emergence of experimentally detected mesoscale chromatin channels and spheroidal droplets which arise from the dynamic interplay of chromatin type to type interactions and intermingling of chromosomal territories. We also quantitatively reproduce coherent motions of chromatin domains observed in displacement correlation spectroscopy measurements which are explained within the framework of our model by phase separation of chromatin types operating within constrained intrachromosomal and interchromosomal boundaries. Finally, we illuminate the role of heterochromatin-lamina interactions in the nuclear organization by showing that these interactions enhance the mobility of euchromatin and indirectly introduce correlated motions of heterochromatin droplets.
Thermal conductivity modeling of monodispersed microspheres using discrete element method

Particle beds are widely used in various systems and processes, such as particle heat exchangers, granular flow reactors, and additive manufacturing. Accurate modeling of the thermal conductivity of particle beds and understanding of their heat transfer mechanisms are important. However, previous models were based on simple cubic packing of particles, which could not accurately represent the actual heat transfer processes under certain conditions. Here, we examine the effect of the packing structure on the thermal conductivity of particle beds. We use monodispersed silica microspheres with average particle sizes ranging from 23 to 330 μm as a model material. We employ a transient hot-wire technique to measure the thermal conductivity of the particle beds with packing density of 43%–57% within a temperature range of room temperature to 500 °C and under N2 gaseous pressures of 20–760 Torr. We then use a discrete element method (DEM) to obtain the realistic packing structure of the particles, which is then fed into a finite-element model (FEM) to calculate the thermal conductivity, with the consideration of solid conduction, gas conduction, and radiation heat transfer. Our results show that the thermal conductivity model based on the more realistic random packing structure derived from the DEM shows better agreement with the experimental data compared to that based on the simple cubic-packing structure. The combined DEM and FEM methodology can serve as a useful tool to predict the effective thermal conductivity of particle beds and to quantify different heat transfer mechanisms under various conditions.
Indicator power spectra: surgical excision of non-linearities and covariance matrices for counts in cells

We here introduce indicator functions, which identify regions of a given density in order to characterize the density dependence of clustering. After a general introduction to this tool, we show that indicator-function power spectra are biased versions of the linear spectrum on large scales. We provide a calculation from first principles for this bias, we show that it reproduces simulation results, and we provide a simple functional form for the translinear portion of the indicator-function spectra. We also outline two applications: first, these spectra facilitate surgical excision of non-linearity and thus significantly increase the reach of linear theory. Second, indicator-function spectra permit calculation of theoretical covariance matrices for counts-in-cells, facilitating parameter estimation with complementary CIC methods.
A generative nonparametric Bayesian model for whole genomes

Abstract Generative probabilistic modeling of biological sequences has widespread existing and potential use across biology and biomedicine, particularly given advances in high-throughput sequencing, synthesis and editing. However, we still lack methods with nucleotide resolution that are tractable at the scale of whole genomes and that can achieve high predictive accuracy either in theory or practice. In this article we propose a new generative sequence model, the Bayesian embedded autoregressive (BEAR) model, which uses a parametric autoregressive model to specify a conjugate prior over a nonparametric Bayesian Markov model. We explore, theoretically and empirically, applications of BEAR models to a variety of statistical problems including density estimation, robust parameter estimation, goodness-of-fit tests, and two-sample tests. We prove rigorous asymptotic consistency results including nonparametric posterior concentration rates. We scale inference in BEAR models to datasets containing tens of billions of nucleotides. On genomic, transcriptomic, and metagenomic sequence data we show that BEAR models provide large increases in predictive performance as compared to parametric autoregressive models, among other results. BEAR models offer a flexible and scalable framework, with theoretical guarantees, for building and critiquing generative models at the whole genome scale.
Human-Computer Interaction Glow Up: Examining Operational Trust and Intention Towards Mars Autonomous Systems

Tactful coordination on earth between hundreds of operators from diverse disciplines and backgrounds is needed to ensure that Martian rovers have a high likelihood of achieving their science goals while enduring the harsh environment of the red planet. The operations team includes many individuals, each with independent and overlapping objectives, working to decide what to execute on the Mars surface during the next planning period. The team must work together to understand each other's objectives and constraints within a fixed time period, often requiring frequent revision. This study examines the challenges faced during Mars surface operations, from high-level science objectives to formulating a valid, safe, and optimal activity plan that is ready to be radiated to the rover. Through this examination, we aim to illuminate how planning intent can be formulated and effectively communicated to future spacecrafts that will become more and more autonomous. Our findings reveal the intricate nature of human-to-human interactions that require a large array of soft skills and core competencies to communicate concurrently with science and engineering teams during plan formulation. Additionally, our findings exposed significant challenges in eliciting planning intent from operators, which will intensify in the future, as operators on the ground asynchronously co-operate the rover with the on board autonomy. Building a marvellous robot and landing it onto the Mars surface are remarkable feats -however, ensuring that scientists can get the best out of the mission is an ongoing challenge and will not cease to be a difficult task with increased autonomy.
Asymmetric coloring of locally finite graphs and profinite permutation groups: Tucker's Conjecture confirmed

Abstract An asymmetric coloring of a graph is a coloring of its vertices that is not preserved by any non-identity automorphism of the graph. The motion of a graph is the minimal degree of its automorphism group, i.e., the minimum number of elements that are moved (not fixed) by any non-identity automorphism. We confirm Tom Tucker's “Infinite Motion Conjecture” that connected locally finite graphs with infinite motion admit an asymmetric 2-coloring. We infer this from the more general result that the inverse limit of an infinite sequence of finite permutation groups with disjoint domains, viewed as a permutation group on the union of those domains, admits an asymmetric 2-coloring. The proof is based on the study of the interaction between epimorphisms of finite permutation groups and the structure of the setwise stabilizers of subsets of their domains.
Counterbalancing Learning and Strategic Incentives in Allocation Markets

Motivated by the high discard rate of donated organs in the United States, we study an allocation problem in the presence of learning and strategic incentives. We consider a setting where a benevolent social planner decides whether and how to allocate a single indivisible object to a queue of strategic agents. The object has a common true quality, good or bad, which is ex-ante unknown to everyone. Each agent holds an informative, yet noisy, private signal about the quality. To make a correct allocation decision the planner attempts to learn the object quality by truthfully eliciting agents' signals. Under the commonly applied sequential offering mechanism, we show that learning is hampered by the presence of strategic incentives as herding may emerge. This can result in incorrect allocation and welfare loss. To overcome these issues, we propose a novel class of incentive-compatible mechanisms. Our mechanism involves a batch-by-batch, dynamic voting process using a majority rule. We prove that the proposed voting mechanisms improve the probability of correct allocation whenever agents are sufficiently well informed. Particularly, we show that such an improvement can be achieved via a simple greedy algorithm. We quantify the improvement using simulations.
Trapping interlayer excitons in van der Waals heterostructures by potential arrays

Transition metal dichalcogenide heterostructures can host interlayer excitons (IXs), which consist of electrons and holes spatially separated in different layers. IXs possess permanent dipoles and have proven to offer a wealth of novel physics. We develop a discrete, random-walk model which includes annihilation and repulsion interactions among IXs. Using this model, we simulate the trapping of IXs in traps of different depths, densities, and shapes. Our results show that dipole-dipole interactions play an important role in regulating IX trapping. The effects of dipole interactions can be mitigated with small, deep traps which are realizable with atomic defects and moir\'e potentials.