cssi/SciSciGPT-SciSciNet huggingface dataset:
Large deviations for extreme eigenvalues of deformed Wigner random matrices

We present a large deviation principle at speed N for the largest eigenvalue of some additively deformed Wigner matrices. In particular this includes Gaussian ensembles with full-rank general deformation. For the non-Gaussian ensembles, the deformation should be diagonal, and we assume that the laws of the entries have sharp sub-Gaussian Laplace transforms and satisfy certain concentration properties. For these latter ensembles we establish the large deviation principle in a restricted range (−∞,xc), where xc depends on the deformation only and can be infinite.
FoggySight: A Scheme for Facial Lookup Privacy

Advances in deep learning algorithms have enabled better-than-human performance on face recognition tasks. In parallel, private companies have been scraping social media and other public websites that tie photos to identities and have built up large databases of labeled face images. Searches in these databases are now being offered as a service to law enforcement and others and carry a multitude of privacy risks for social media users. In this work, we tackle the problem of providing privacy from such face recognition systems. We propose and evaluate FoggySight, a solution that applies lessons learned from the adversarial examples literature to modify facial photos in a privacy-preserving manner before they are uploaded to social media. FoggySight's core feature is a community protection strategy where users acting as protectors of privacy for others upload decoy photos generated by adversarial machine learning algorithms. We explore different settings for this scheme and find that it does enable protection of facial privacy -- including against a facial recognition service with unknown internals.
Fuzzy hypercubes: Linguistic learning/reasoning systems for intelligent control and identification

This paper introduces a new tool for intelligent control and identification. A robust and reliable learning and reasoning mechanism is addressed based upon fuzzy set theory and fuzzy associative memories. The mechanism storesa priori an initial knowledge base via approximate learning and utilizes this information for identification and control via fuzzy inferencing. This architecture parallels a well-known scheme in which memory implicative rules are stored disjunctively. We call this process afuzzy hypercube. Fuzzy hypercubes can be applied to a class of complex and highly nonlinear systems which suffer from vagueness uncertainty and incomplete information such as fuzziness and ignorance. Evidential aspects of a fuzzy hypercube are treated to assess the degree of certainty or reliability. The implementation issue using fuzzy hypercubes is raised, and finally, a fuzzy hypercube is applied to fuzzy linguistic control.
Chronic SSRI treatment reverses HIV-1 protein-mediated synaptodendritic damage

HIV-1 infection affects approximately 37 million individuals, and approximately 50% of seropositive individuals will develop symptoms of clinical depression and/or apathy. Dysfunctions of both serotonergic and dopaminergic neurotransmission have been implicated in the pathogenesis of motivational alterations. The present study evaluated the efficacy of a SSRI (escitalopram) in the HIV-1 transgenic (Tg) rat. Behavioral, neurochemical, and neuroanatomical outcomes with respect to HIV-1 and sex were evaluated to determine the efficacy of chronic escitalopram treatment. Escitalopram treatment restored function in each of the behavioral tasks that were sensitive to HIV-1-induced impairments. Further, escitalopram treatment restored HIV-1-mediated synaptodendritic damage in the nucleus accumbens; treatment with escitalopram significantly increased dendritic proliferation in HIV-1 Tg rats. However, restoration did not consistently occur with the neurochemical analysis in the HIV-1 rat. Taken together, these results suggest a role for SSRI therapies in repairing long-term HIV-1 protein-mediated neuronal damage and restoring function.
Ecological theory applied to environmental metabolomes reveals compositional divergence despite conserved molecular properties

Abstract Stream and river systems transport and process substantial amounts of dissolved organic matter (DOM) from terrestrial and aquatic sources to the ocean, with global biogeochemical implications. However, the underlying mechanisms affecting the spatiotemporal organization of DOM composition are under-investigated. To understand the principles governing DOM composition, we leverage the recently proposed synthesis of metacommunity ecology and metabolomics, termed ‘meta-metabolome ecology.’ Applying this novel approach to a freshwater ecosystem, we demonstrated that despite similar molecular properties across metabolomes, metabolite identity significantly diverged due to environmental filtering and variations in putative biochemical transformations. We refer to this phenomenon as ‘thermodynamic redundancy,’ which is analogous to the ecological concept of functional redundancy. We suggest that under thermodynamic redundancy, divergent metabolomes can support equivalent biogeochemical function just as divergent ecological communities can support equivalent ecosystem function. As these analyses are performed in additional ecosystems, potentially generalizable concepts, like thermodynamic redundancy, can be revealed and provide insight into DOM dynamics.
Linear and Deep Neural Network-Based Receivers for Massive MIMO Systems With One-Bit ADCs

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we present several linear receivers based on the Bussgang decomposition that show significant performance gains over conventional linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based detector, namely OBMNet, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.
GraphDF: A Discrete Flow Model for Molecular Graph Generation

We consider the problem of molecular graph generation using deep models. While graphs are discrete, most existing methods use continuous latent variables, resulting in inaccurate modeling of discrete graph structures. In this work, we propose GraphDF, a novel discrete latent variable model for molecular graph generation based on normalizing flow methods. GraphDF uses invertible modulo shift transforms to map discrete latent variables to graph nodes and edges. We show that the use of discrete latent variables reduces computational costs and eliminates the negative effect of dequantization. Comprehensive experimental results show that GraphDF outperforms prior methods on random generation, property optimization, and constrained optimization tasks.
Association Between Reproduction and Preweaning Growth Traits and ELISA Scores for Paratuberculosis in an Angus-Brahman Multibreed Herd of Cattle

Genetic evaluation of animals for reproduction and production traits in beef cattle relies on models that account for environmental and genetic effects in the records used to perform computations. Records are assumed to come from healthy animals. This assumption is likely to be appropriate for acute or subacute infectious diseases that have short subclinical stages, where sick animals can be easily identified, and their records excluded from genetic evaluations. However, for chronic infectious diseases with long subclinical stages, it may be difficult to accurately identify sick animals. Their records are likely to be included in genetic evaluations, and losses in performance due to subclinical disease effects will not be accounted for. One chronic incurable disease of cattle and other ruminants with subclinical stages that last for years is paratuberculosis. Paratuberculosis is caused by Mycobacterium avium subspecies paratuberculosis (MAP). A commonly used serological test to detect subclinical para tuberculosis is ELISA. The purpose of ELISA is to detect antibodies against MAP in the serum of infected animals. This test is primarily a herd-screening tool, and can detect approximately 50% of infected animals. In spite of its low sensitivity, Elzo et al (2006) found significant associations between ELISA scores for paratuberculosis and several cow and calf traits in a multibreed herd of beef cattle. The next step was to evaluate these associations from a genetic evaluation perspective, i.e., to quantify the association between individual traits of interest and ELISA scores. The objective of this research was to obtain regression estimates of gestation length (GL), calving interval (CI), time open (TO), weight change of cow (WC) from late November (pre-calving) to September (weaning), birth weight of calf (BW) and weaning weight of calf (WW) on ELISA scores for paratuberculosis in an Angus-Brahman multibreed herd of beef cattle.
Theoretical Foundations of Relational Incentive Contracts

This article describes the emerging game-theoretic framework for modeling long-term contractual relationships with moral hazard. The framework combines self-enforcement and external enforcement, ac...
Assessment of comprehensive analysis calculation of airloads on helicopter rotors

Blade section normal force and pitching moment were investigated for six rotors operating at transition and high speeds: H-34 in flight and wind tunnel, SA 330 (research Puma), SA 349/2, UH-60A full-scale, and BO-105 model (Higher-Harmonic Acoustics Rotor Test I). The measured data from flight and wind-tunnel tests were compared with calculations obtained using the comprehensive analysis CAMRAD II. The calculations were made using two free-wake models: rolled up and multiple trailer with consolidation models. At transition speed, there is fair to good agreement for the blade section normal force between the test data and analysis for the H-34, research Puma, and SA 349/2 with the rolled-up wake. The calculated airloads differ significantly from the measurements for the UH-60A and BO-105. Better correlation is obtained for the UH-60A and BO-105 by using the multiple trailer with consolidation wake model. In the high-speed condition, the analysis shows generally good agreement with the research Puma flight data in both magnitude and phase. However, poor agreement is obtained for the other rotors examined. The analysis shows that the aerodynamic tip design (chord length and quarter-chord location) of the research Puma has an important influence on the phase correlation.
Motif-Driven Contrastive Learning of Graph Representations

Graph motifs are significant subgraph patterns occurring frequently in graphs, and they play important roles in representing the whole graph characteristics. For example, in the chemical domain, functional groups are motifs that can determine molecule properties. Mining and utilizing motifs, however, is a non-trivial task for large graph datasets. Traditional motif discovery approaches mostly rely on exact counting or statistical estimation, which are hard to scale for a large number of graphs with continuous and high-dimension features. In light of the significance and challenges of motif mining, we propose : MICRO-Graph: a framework for \underline{M}ot\underline{I}f-driven \underline{C}ontrastive lea\underline{R}ning \underline{O}f \underline{G}raph representations to: 1) pre-train Graph Neural Networks (GNNs) in a self-supervised manner to automatically extract graph motifs from large graph datasets; 2) leverage learned motifs to guide the contrastive learning of graph representations, which further benefit various graph downstream tasks. Specifically, given a graph dataset, a motif learner cluster similar and significant subgraphs into corresponding motif slots. Based on the learned motifs, a motif-guided subgraph segmenter is trained to generate more informative subgraphs, which are used to conduct graph-to-subgraph contrastive learning of GNNs. Our discovering strategy is to simutaneously do clustering and contrastive learning on dynamically sampled subgraphs. The clustering part pull together similar subgraphs across different whole graphs, as the contrastive part push away dissimilar ones. Meanwhile, our learnable sampler will generate subgraph samples better aligned with the discoverying procedure. By pre-training on ogbn-molhiv molecule dataset with our proposed MICRO-Graph, the pre-trained GNN model can enhance various chemical property prediction downstream tasks with scarce label by 2.0%, and significantly higher than other state-of-the-art self-supervised learning baselines.
WedgeChain: A Trusted Edge-Cloud Store With Asynchronous (Lazy) Trust

We propose WedgeChain, a data store that spans both edge and cloud nodes (an edge-cloud system). WedgeChain consists of a logging layer and a data indexing layer. In this study, we encounter two challenges: (1) edge nodes are untrusted and potentially malicious, and (2) edge-cloud coordination is expensive. WedgeChain tackles these challenges by the following proposals: (1) Lazy (asynchronous) certification: where data is committed at the untrusted edge and then lazily certified at the cloud node. This lazy certification method takes advantage of the observation that an untrusted edge node is unlikely to act maliciously if it knows it will be detected (and punished) eventually. Our lazy certification method guarantees that malicious acts (i.e., lying) are eventually detected. (2) Data-free certification: our lazy certification method only needs to send digests of data to the cloud—instead of sending all data to the cloud—which enables saving network and cloud resources and reduce costs. (3) LSMerkle: we extend a trusted index (mLSM [32]) to enable indexing data at the edge while utilizing lazy and data-free certification.
Budget sharing for multi-analyst differential privacy

Large organizations that collect data about populations (like the US Census Bureau) release summary statistics that are used by multiple stakeholders for resource allocation and policy making probl...
Computational structural mechanics for engine structures

The computational structural mechanics (CSM) program at Lewis encompasses: (1) fundamental aspects for formulating and solving structural mechanics problems, and (2) development of integrated software systems to computationally simulate the performance/durability/life of engine structures. It is structured to mainly supplement, complement, and whenever possible replace, costly experimental efforts which are unavoidable during engineering research and development programs. Specific objectives include: investigate unique advantages of parallel and multiprocesses for: reformulating/solving structural mechanics and formulating/solving multidisciplinary mechanics and develop integrated structural system computational simulators for: predicting structural performances, evaluating newly developed methods, and for identifying and prioritizing improved/missing methods needed. Herein the CSM program is summarized with emphasis on the Engine Structures Computational Simulator (ESCS). Typical results obtained using ESCS are described to illustrate its versatility.
Scoring Popularity in GitHub

Popularity and engagement are the currencies of social media platforms, serving as powerful reinforcement mechanisms to keep users online. Social coding platforms such as GitHub serve a dual purpose: they are practical tools that facilitate asynchronous, distributed collaborations between software developers while also supporting passive social media style interactions. There are several mechanisms for "liking" content on GitHub: 1) forking repositories to copy their content 2) watching repositories to be notified of updates and 3) starring to express approval. This paper presents a study of popularity in GitHub and examines the relationship between these three quantitative measures of popularity. We introduce a weight-based popularity score (WTPS) that is extracted from the history line of GitHub repositories popularity indicators.
Counting tanglegrams with species

Abstract A tanglegram is a pair of binary trees with the same set of leaves. Unlabeled tanglegrams were counted recently by Billey, Konvalinka, and Matsen, who also proposed the problem of counting several variations of unlabeled tanglegrams. We use the theory of combinatorial species to solve these problems.
Propelling and perturbing appendages together facilitate strenuous ground self-righting.

Terrestrial animals must self-right when overturned on the ground, but this locomotor task is strenuous. To do so, the discoid cockroach often pushes its wings against the ground to begin a somersault which rarely succeeds. As it repeatedly attempts this, the animal probabilistically rolls to the side to self-right. During winged self-righting, the animal flails its legs vigorously. Here, we studied whether wing opening and leg flailing together facilitate strenuous ground self-righting. Adding mass to increase hind leg flailing kinetic energy increased the animal’s self-righting probability. We then developed a robot with similar strenuous self-righting behavior and used it as a physical model for systematic experiments. The robot’s self-righting probability increased with wing opening and leg flailing amplitudes. A potential energy landscape model revealed that, although wing opening did not generate sufficient kinetic energy to overcome the high pitch potential energy barrier to somersault, it reduced the barrier for rolling, facilitating the small kinetic energy from leg flailing to probabilistically overcome it to self-right. The model also revealed that the stereotyped body motion during self-righting emerged from physical interaction of the body and appendages with the ground. Our work demonstrated the usefulness of potential energy landscape for modeling self-righting transitions.
Universal Algorithms for Clustering Problems

This paper presents universal algorithms for clustering problems, including the widely studied k-median, k-means, and k-center objectives. The input is a metric space containing all potential client locations. The algorithm must select k cluster centers such that they are a good solution for any subset of clients that actually realize. Specifically, we aim for low regret, defined as the maximum over all subsets of the difference between the cost of the algorithm’s solution and that of an optimal solution. A universal algorithm’s solution sol for a clustering problem is said to be an (α, β)-approximation if for all subsets of clients C', it satisfies sol(C') ≤ α ⋅ opt(C') + β ⋅ mr, where opt(C') is the cost of the optimal solution for clients C' and mr is the minimum regret achievable by any solution. Our main results are universal algorithms for the standard clustering objectives of k-median, k-means, and k-center that achieve (O(1), O(1))-approximations. These results are obtained via a novel framework for universal algorithms using linear programming (LP) relaxations. These results generalize to other 𝓁_p-objectives and the setting where some subset of the clients are fixed. We also give hardness results showing that (α, β)-approximation is NP-hard if α or β is at most a certain constant, even for the widely studied special case of Euclidean metric spaces. This shows that in some sense, (O(1), O(1))-approximation is the strongest type of guarantee obtainable for universal clustering.
A Dual Characterization of Observability for Stochastic Systems

Abstract This paper is concerned with the definition and characterization of the observability for a continuous-time hidden Markov model where the state evolves as a continuous-time Markov process on a compact state space and the observation process is modeled as nonlinear function of the state corrupted by a Gaussian measurement noise. The main technical tool is based on the recently discovered duality relationship between minimum variance estimation and stochastic optimal control: The observability is defined as a dual of the controllability for a certain backward stochastic differential equation. Based on the dual formulation, a test for observability is presented and related to literature. The proposed duality-based framework allows one to easily relate and compare the linear and the nonlinear systems. A side-by-side summary of this relationship is given in a tabular form (Table 1).
Ranked Choice Voting for Representative Transcripts with TRaCE.

Genome sequencing projects annotate protein-coding gene models with multiple transcripts, aiming to represent all of the available transcript evidence. However, downstream analyses often operate on only one representative transcript per gene locus, sometimes known as the canonical transcript. To choose canonical transcripts, TRaCE (Transcript Ranking and Canonical Election) holds an ‘election’ in which a set of RNA-seq samples rank transcripts by annotation edit distance. These sample-specific votes are tallied along with other criteria such as protein length and InterPro domain coverage. The winner is selected as the canonical transcript, but the election proceeds through multiple rounds of voting to order all the transcripts by relevance. Based on the set of expression data provided, TRaCE can identify the most common isoforms from a broad expression atlas or prioritize alternative transcripts expressed in specific contexts.
Dynamic Networks with Multi-scale Temporal Structure

We describe a novel method for modeling non-stationary multivariate time series, with time-varying conditional dependencies represented through dynamic networks. Our proposed approach combines traditional multi-scale modeling and network based neighborhood selection, aiming at capturing temporally local structure in the data while maintaining sparsity of the potential interactions. Our multi-scale framework is based on recursive dyadic partitioning, which recursively partitions the temporal axis into finer intervals and allows us to detect local network structural changes at varying temporal resolutions. The dynamic neighborhood selection is achieved through penalized likelihood estimation, where the penalty seeks to limit the number of neighbors used to model the data. We present theoretical and numerical results describing the performance of our method, which is motivated and illustrated using task-based magnetoencephalography (MEG) data in neuroscience.
Near-field induced polarization imaging for optical data storage metrology

Near-field induced polarization imaging with a solid immersion lens (SIL) is used to provide high lateral resolution compared with conventional far-field microscopy. In addition, a new technique is used to obtain height information from the near-field induced polarization (cross polarized) image. Several optical data storage samples, including DVD-R, DVD-RW, BD-RW, are studied with this imaging technique. A calibration target indicates an accuracy of ±2nm for obtaining height information.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training.

We present the TAPAS contribution to the Shared Task on Statement Verification and Evidence Finding with Tables (SemEval 2021 Task 9, Wang et al. (2021)). SEM TAB FACT Task A is a classification task of recognizing if a statement is entailed, neutral or refuted by the content of a given table. We adopt the binary TAPAS model of Eisenschlos et al. (2020) to this task. We learn two binary classification models: A first model to predict if a statement is neutral or non-neutral and a second one to predict if it is entailed or refuted. As the shared task training set contains only entailed or refuted examples, we generate artificial neutral examples to train the first model. Both models are pre-trained using a MASKLM objective, intermediate counter-factual and synthetic data (Eisenschlos et al., 2020) and TABFACT (Chen et al., 2020), a large table entailment dataset. We find that the artificial neutral examples are somewhat effective at training the first model, achieving 68.03 test F1 versus the 60.47 of a majority baseline. For the second stage, we find that the pre-training on the intermediate data and TABFACT improves the results over MASKLM pre-training (68.03 vs 57.01).
Improved Test Planning and Analysis Through the Use of Advanced Statistical Methods

The goal of this work is, through computational simulations, to provide statistically-based evidence to convince the testing community that a distributed testing approach is superior to a clustered testing approach for most situations. For clustered testing, numerous, repeated test points are acquired at a limited number of test conditions. For distributed testing, only one or a few test points are requested at many different conditions. The statistical techniques of Analysis of Variance (ANOVA), Design of Experiments (DOE) and Response Surface Methods (RSM) are applied to enable distributed test planning, data analysis and test augmentation. The D-Optimal class of DOE is used to plan an optimally efficient single- and multi-factor test. The resulting simulated test data are analyzed via ANOVA and a parametric model is constructed using RSM. Finally, ANOVA can be used to plan a second round of testing to augment the existing data set with new data points. The use of these techniques is demonstrated through several illustrative examples. To date, many thousands of comparisons have been performed and the results strongly support the conclusion that the distributed testing approach outperforms the clustered testing approach.
Deep learning for quantile regression under right censoring: DeepQuantreg

Abstract The computational prediction algorithm of neural network, or deep learning, has drawn much attention recently in statistics as well as in image recognition and natural language processing. Particularly in statistical application for censored survival data, the loss function used for optimization has been mainly based on the partial likelihood from Cox's model and its variations to utilize existing neural network library such as Keras, which was built upon the open source library of TensorFlow. As a novel contribution to the literature, an extension of the neural network to the quantile regression is proposed for survival data with right censoring, which is adjusted by the inverse of the estimated censoring distribution in the check function. The main purpose is to show that the deep learning method could be flexible enough to predict nonlinear patterns more accurately compared to existing quantile regression methods such as traditional linear quantile regression and nonparametric quantile regression with total variation regularization, emphasizing practicality of the method for censored survival data. Simulation studies were performed to generate nonlinear censored survival data and compare the deep learning method with existing quantile regression methods in terms of prediction accuracy. The proposed method is illustrated with two publicly available breast cancer data sets with gene signatures. The method has been built into a package and is freely available at https://github.com/yicjia/DeepQuantreg .
India's Goods and Services Tax: A Unique Experiment in Cooperative Federalism and a Constitutional Crisis in Waiting

It has long been argued that federal countries, especially those with strong subnational taxing powers, might face difficulty in implementing a federal value-added tax (VAT) because of coordination issues involved, and therefore might be reluctant to adopt one. This article provides insights on how VAT structures are evolving in federal systems, where different tiers of government have separate (and sometimes overlapping) taxation powers. While the author focuses mainly on India's recently enacted goods and services tax (GST), he also offers a comparative perspective, with reference to the GST/VAT systems in Canada, Brazil, and the European Union, thus adding to the hitherto limited body of scholarly work on VAT coordination in federal jurisdictions.
Randomized Clinical Trial of Air Cleaners to Improve Indoor Air Quality and COPD Health: Results of the CLEAN AIR STUDY.

Rationale: Indoor particulate matter is associated with worse COPD outcomes. It remains unknown whether reductions of indoor pollutants improve respiratory morbidity. Methods: Eligible former smoke...
Resilience of Interdependent Urban Socio-Physical Systems using Large-Scale Mobility Data: Modeling Recovery Dynamics

Abstract Cities are complex systems comprised of socioeconomic systems relying on critical services delivered by multiple physical infrastructure networks. Due to interdependencies between social and physical systems, disruptions caused by natural hazards may cascade across systems, amplifying the impact of disasters. Despite the increasing threat posed by climate change and rapid urban growth, how to design interdependencies between social and physical systems to achieve resilient cities have been largely unexplored. Here, we study the socio-physical interdependencies in urban systems and their effects on disaster recovery and resilience, using large-scale mobility data collected from Puerto Rico during Hurricane Maria. We find that as cities grow in scale and expand their centralized infrastructure systems, the recovery efficiency of critical services improves, however, curtails the self-reliance of socio-economic systems during crises. Results show that maintaining self-reliance among social systems could be key in developing resilient urban socio-physical systems for cities facing rapid urban growth.
A Model for Crosstalk in Micropattern Gas Detectors

Crosstalk characteristics such as pulse amplitude and shape are studied with a simple PSpice model of the capacitive couplings within an MPGD. The crosstalk pulse shape can be understood as due to a CR-differentiator. Crosstalk can occur simultaneously through more than one capacitive coupling path. The crosstalk signals on these paths add differently if the signal is induced via a current source as in normal detector operation or via a voltage source as is often done in benchtop tests with an external voltage pulse generator. A few means for reducing the crosstalk are investigated with the model. It shows that a low-impedance AC path from the amplification electrode that faces the readout structure to ground is important for minimizing crosstalk. This implies a need for a sufficiently large capacitance of that amplification electrode to ground. This result has consequences for how much such an amplification electrode can be segmented without incurring crosstalk.
Aggregate Distress Risk and Equity Returns

Abstract The aggregate default probability is significantly priced in equities because of its close relation with uncertainty. Ceteris paribus, the aggregate default probability positively predicts stock market returns, and loadings on its changes correlate negatively with the cross-section of expected stock returns. These findings are consistent with multifactor models in which aggregate uncertainty is a determinant of the conditional equity premium and innovations in aggregate uncertainty are a systematic risk factor. By contrast, credit spreads have negligible explanatory power for expected stock returns. Contrary to the conventional wisdom, credit spreads are poor proxies for aggregate credit risk.
Visualization Design Sprints for Online and On-Campus Courses

We present how to integrate Design Sprints and project-based learning into introductory visualization courses. A design sprint is a unique process based on rapid prototyping and user testing to define goals and validate ideas before starting costly development. The well-defined, interactive, and time-constrained design cycle makes design sprints a promising option for teaching project-based and active-learning centered courses to increase student engagement and hands-on experience. Over the past five years, we have adjusted the design sprint methodology for teaching a range of visualization courses. We present a detailed guide on incorporating design sprints into large undergraduate and small professional development courses in both online and on-campus settings. Design sprint results, including quantitative and qualitative student feedback, show that design sprints engage students and help practice and apply visualization and design skills. We provide design sprint teaching materials, show examples of student-created work, and discuss limitations and lessons learned.
Quantifying representativeness in randomized clinical trials using machine learning fairness metrics

Objective We help identify subpopulations underrepresented in randomized clinical trials (RCTs) cohorts with respect to national, community-based or health system target populations by formulating population representativeness of RCTs as a machine learning (ML) fairness problem, deriving new representation metrics, and deploying them in easy-to-understand interactive visualization tools. Materials and Methods We represent RCT cohort enrollment as random binary classification fairness problems, and then show how ML fairness metrics based on enrollment fraction can be efficiently calculated using easily computed rates of subpopulations in RCT cohorts and target populations. We propose standardized versions of these metrics and deploy them in an interactive tool to analyze 3 RCTs with respect to type 2 diabetes and hypertension target populations in the National Health and Nutrition Examination Survey. Results We demonstrate how the proposed metrics and associated statistics enable users to rapidly examine representativeness of all subpopulations in the RCT defined by a set of categorical traits (eg, gender, race, ethnicity, smoking status, and blood pressure) with respect to target populations. Discussion The normalized metrics provide an intuitive standardized scale for evaluating representation across subgroups, which may have vastly different enrollment fractions and rates in RCT study cohorts. The metrics are beneficial complements to other approaches (eg, enrollment fractions) used to identify generalizability and health equity of RCTs. Conclusion By quantifying the gaps between RCT and target populations, the proposed methods can support generalizability evaluation of existing RCT cohorts. The interactive visualization tool can be readily applied to identified underrepresented subgroups with respect to any desired source or target populations.
Inhibitors of anti-apoptotic Bcl-2 family proteins exhibit potent and broad-spectrum anti-mammarenavirus activity via cell cycle arrest at G0/G1 phase.

Targeting host factors is a promising strategy to develop broad-spectrum antiviral drugs. Drugs targeting anti-apoptotic Bcl-2 family proteins that were originally developed as tumor suppressors have been reported to inhibit multiplication of different types of viruses. However, the mechanisms whereby Bcl-2 inhibitors exert their antiviral activity remain poorly understood. In this study, we have investigated the mechanisms by which obatoclax (OLX) and ABT-737 Bcl-2 inhibitors exhibited a potent antiviral activity against the mammarenavirus lymphocytic choriomeningitis virus (LCMV). OLX and ABT-737 potent anti-LCMV activity was not associated with their pro-apoptotic properties, but rather their ability of inducing cell arrest at G0/G1 phase. OLX and ABT-737 mediated inhibition of Bcl-2 correlated with reduced expression levels of thymidine kinase 1 (TK1), cyclin A2 (CCNA2), and cyclin B1 (CCNB1) cell cycle regulators. In addition, siRNA-mediated knock down of TK1, CCNA2, and CCNB1 resulted in reduced levels of LCMV multiplication. The antiviral activity exerted by Bcl-2 inhibitors correlated with reduced levels of viral RNA synthesis at early times of infection. Importantly, ABT-737 exhibited moderate efficacy in a mouse model of LCMV infection, and Bcl-2 inhibitors displayed broad-spectrum antiviral activities against different mammarenaviruses and SARS-CoV-2. Our results suggest that Bcl-2 inhibitors, actively being explored as anti-cancer therapeutics, might be repositioned as broad-spectrum antivirals. IMPORTANCE Anti-apoptotic Bcl-2 inhibitors have been shown to exert potent antiviral activities against various types of viruses via mechanisms that are currently poorly understood. This study has revealed that Bcl-2 inhibitors mediated cell cycle arrest at the G0/G1 phase, rather than their pro-apoptotic activity, plays a critical role in blocking mammarenavirus multiplication in cultured cells. In addition, we show that Bcl-2 inhibitor ABT-737 exhibited moderate anti-mammarenavirus activity in vivo, and that Bcl-2 inhibitors displayed broad-spectrum antiviral activities against different mammarenaviruses and SARS-CoV-2. Our results suggest that Bcl-2 inhibitors, actively being explored as anti-cancer therapeutics, might be repositioned as broad-spectrum antivirals.
Reproducibility of global electrical heterogeneity measurements on 12-lead ECG: The Multi-Ethnic Study of Atherosclerosis

Abstract Objective Vectorcardiographic (VCG) global electrical heterogeneity (GEH) metrics showed clinical usefulness. We aimed to assess the reproducibility of GEH metrics. Methods GEH was measured on two 10-s 12‑lead ECGs recorded on the same day in 4316 participants of the Multi-Ethnic Study of Atherosclerosis (age 69.4 ± 9.4 y; 2317(54%) female, 1728 (40%) white, 1138(26%) African-American, 519(12%) Asian-American, 931(22%) Hispanic-American). GEH was measured on a median beat, comprised of the normal sinus (N), atrial fibrillation/flutter (S), and ventricular-paced (VP) beats. Spatial ventricular gradient's (SVG's) scalar was measured as sum absolute QRST integral (SAIQRST) and vector magnitude QT integral (VMQTi). Results Two N ECGs with heart rate (HR) bias of −0.64 (95% limits of agreement [LOA] -5.68 to 5.21) showed spatial area QRS-T angle (aQRST) bias of −0.12 (95%LOA −14.8 to 14.5). Two S ECGs with HR bias of 0.20 (95%LOA −15.8 to 16.2) showed aQRST bias of 1.37 (95%LOA −33.2 to 35.9). Two VP ECGs with HR bias of 0.25 (95%LOA −3.0 to 3.5) showed aQRST bias of −1.03 (95%LOA −11.9 to 9.9). After excluding premature atrial or ventricular beat and two additional beats (before and after extrasystole), the number of cardiac beats included in a median beat did not affect the GEH reproducibility. Mean-centered log-transformed values of SAIQRST and VMQTi demonstrated perfect agreement (Bias 0; 95%LOA −0.092 to 0.092). Conclusion GEH measurements on N, S, and VP median beats are reproducible. SVG's scalar can be measured as either SAIQRST or VMQTi. Significance Satisfactory reproducibility of GEH metrics supports their implementation.
Rejective Sampling, Rerandomization and Regression Adjustment in Survey Experiments

Classical randomized experiments, equipped with randomization-based inference, provide assumption-free inference for treatment effects. They have been the gold standard for drawing causal inference...
A New RANS Correction to Account for Varying Viscosity Effects

It has previously been shown that by increasing the Reynolds number across a channel by spatially varying the viscosity does not cause an immediate change in the size of turbulent structures and a delay is in fact observed in both wall shear and friction Reynolds number (Coppo Leite, V. & Merzari, E., Proceedings of the ASME 2020 FEDSM, p. V003T05A019). Furthermore, it is also shown that depending on the length in which the flow condition changes, turbulence bursts are observed in the turbulence field. For the present work we propose a new version of the standard Reynolds Averaged Navier Stokes (RANS) k-tau model that includes some modifications in the production term in order to account for these effects. The new proposed model may be useful for many engineering applications as turbulent flows featuring temperature gradients and high heat transfer rates are often seen in heat exchangers, combustion chambers and nuclear reactors. In these applications, thermal and viscous properties f the working fluid are important design parameters that depende on temperature; hence it is likely to observe strong gradients on these scalars' fields. To accomplish our goal, the modifications for the k-tau model are implemented and tested for a channel flow with spatial varying viscosity in the streamwise direction. The numerical simulations are performed using Nek5000, a spectral-element code developed at Argonne National Laboratory (ANL). Finally, the results considering a turbulence channel using the proposed model are compared against data obtained using Direct Numerical Simulations from the earlier work.
Learning to drive from a world on rails

We learn an interactive vision-based driving policy from pre-recorded driving logs via a model-based approach. A forward model of the world supervises a driving policy that predicts the outcome of any potential driving trajectory. To support learning from pre-recorded logs, we assume that the world is on rails, meaning neither the agent nor its actions influence the environment. This assumption greatly simplifies the learning problem, factorizing the dynamics into a nonreactive world model and a low-dimensional and compact forward model of the ego-vehicle. Our approach computes action-values for each training trajectory using a tabular dynamic-programming evaluation of the Bellman equations; these action-values in turn supervise the final vision-based driving policy. Despite the world-on-rails assumption, the final driving policy acts well in a dynamic and reactive world. At the time of writing, our method ranks first on the CARLA leaderboard, attaining a 25% higher driving score while using 40 times less data. Our method is also an order of magnitude more sample-efficient than state-of-the-art model-free reinforcement learning techniques on navigational tasks in the ProcGen benchmark.
On the spherical slice transform

We study the spherical slice transform &#x1d516; which assigns to a function f on the unit sphere Sn in ℝn+1 the integrals of f over cross-sections of Sn by k-dimensional affine planes passing through the ...
Variability in higher order structure of noise added to weighted networks

The complex behavior of many real-world systems depends on a network of both strong and weak edges. Distinguishing between true weak edges and low-weight edges caused by noise is a common problem in data analysis, and solutions tend to either remove noise or study noise in the absence of data. In this work, we instead study how noise and data coexist, by examining the structure of noisy, weak edges that have been synthetically added to model networks. We find that the structure of low-weight, noisy edges varies according to the topology of the model network to which it is added, that at least three qualitative classes of noise structure emerge, and that these noisy edges can be used to classify the model networks. Our results demonstrate that noise does not present as a monolithic nuisance, but rather as a nuanced, topology-dependent, and even useful entity in characterizing higher-order network interactions. A common problem in reconstructing weighted networks to represent real-world systems is that low-weight edges might appear due to noise, affecting the topology of the inferred network. Here, the authors propose a method based on persistent homology that allows one to investigate the higher-order network organization that can be created by low-weight, noisy edges.
TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8

Summary Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian cis-expression quantitative trait locus (eQTL) weights through TWASs of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes, respectively, for breast and ovarian cancer, most of which are either known or near previously identified GWAS (∼95%) or TWAS (∼40%) risk genes and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWASs can provide biological insight into the transcriptional regulation of complex diseases. The TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and linkage disequilibrium (LD) information from GTEx V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.