cssi/SciSciGPT-SciSciNet huggingface dataset:
Test-retest reliability of tone- and 40 Hz train-evoked gamma oscillations in female rats and their sensitivity to low-dose NMDA channel blockade.

Schizophrenia patients consistently show deficits in sensory-evoked broadband gamma oscillations and click-evoked entrainment at 40 Hz, called the 40-Hz auditory steady-state response (ASSR). Since such evoked oscillations depend on cortical N-methyl D-aspartic acid (NMDA)-mediated network activity, they can serve as pharmacodynamic biomarkers in the preclinical and clinical development of drug candidates engaging these circuits. However, there are few test-retest reliability data in preclinical species, a prerequisite for within-subject testing paradigms. We investigated the long-term psychometric stability of these measures in a rodent model. Female rats with chronic epidural implants were used to record tone- and 40 Hz click-evoked responses at multiple time points and across six sessions, spread over 3 weeks. We assessed reliability using intraclass correlation coefficients (ICC). Separately, we used mixed-effects ANOVA to examine time and session effects. Individual subject variability was determined using the coefficient of variation (CV). Lastly, to illustrate the importance of long-term measure stability for within-subject testing design, we used low to moderate doses of an NMDA antagonist MK801 (0.025–0.15 mg/kg) to disrupt the evoked response. We found that 40-Hz ASSR showed good reliability (ICC=0.60–0.75), while the reliability of tone-evoked gamma ranged from poor to good (0.33–0.67). We noted time but no session effects. Subjects showed a lower variance for ASSR over tone-evoked gamma. Both measures were dose-dependently attenuated by NMDA antagonism. Overall, while both evoked gamma measures use NMDA transmission, 40-Hz ASSR showed superior psychometric properties of higher ICC and lower CV, relative to tone-evoked gamma.
Control Barrier Functions in Sampled-Data Systems

This letter presents conditions for ensuring forward invariance of safe sets under sampled-data system dynamics with piecewise-constant controllers and fixed time-steps. First, we introduce two different metrics to compare the conservativeness of sufficient conditions on forward invariance under piecewise-constant controllers. Then, we propose three approaches for guaranteeing forward invariance, two motivated by continuous-time barrier functions, and one motivated by discrete-time barrier functions. All proposed conditions are control affine, and thus can be incorporated into quadratic programs for control synthesis. We show that the proposed conditions are less conservative than those in earlier studies, and show via simulation how this enables the use of barrier functions that are impossible to implement with the desired time-step using existing methods.
Theory of condensin mediated loop extrusion in genomes

Condensation of hundreds of mega-base-pair-long human chromosomes in a small nuclear volume is a spectacular phenomenon. This process is driven by the formation of chromosome loops. ATP consuming motor, condensin, interacts with chromatin segments to actively extrude loops. Motivated by real-time imaging of loop extrusion (LE) and measurements using magnetic tweezer experiments, we created an analytically solvable model, predicting the LE velocity and step size distribution as a function of external load. The theory fits the experimental data quantitatively, and suggests that condensin must undergo a large conformational change, induced by ATP binding, bringing distant parts of the motor to proximity. Simulations using a simple model confirm that the motor transitions between an open to closed state in order to extrude loops by a scrunching mechanism, similar to that proposed in DNA bubble formation during bacterial transcription. Changes in the orientation of the motor domains are transmitted over ~ 50 nm, connecting the motor head and the hinge, thus providing an allosteric basis for LE.
Blackwell Online Learning for Markov Decision Processes

Ahstract-This work provides a novel interpretation of Markov Decision Processes (MDP) from the online optimization viewpoint. In such an online optimization context, the policy of the MDP is viewed as the decision variable while the corresponding value function is treated as payoff feedback from the environment. Based on this interpretation, we construct a Blackwell game induced by MDP, which bridges the gap among regret minimization, Blackwell approachability theory, and learning theory for MDP. Specifically, Based on the approachability theory, we propose 1) Blackwell value iteration for offline planning and 2) Blackwell Q-learning for online learning in MDP, both of which are shown to converge to the optimal solution. Our theoretical guarantees are corroborated by numerical experiments.
Radiation Effects in Advanced and Emerging Nonvolatile Memories

Despite hitting major roadblocks in 2-D scaling, NAND flash continues to scale in the vertical direction and dominate the commercial nonvolatile memory market. However, several emerging nonvolatile technologies are under development by major commercial foundries or are already in small volume production, motivated by storage-class memory and embedded application drivers. These include spin-transfer torque magnetic random access memory (STT-MRAM), resistive random access memory (ReRAM), phase change random access memory (PCRAM), and conductive bridge random access memory (CBRAM). Emerging memories have improved resilience to radiation effects compared to flash, which is based on storing charge, and hence may offer an expanded selection from which radiation-tolerant system designers can choose from in the future. This review discusses the material and device physics, fabrication, operational principles, and commercial status of scaled 2-D flash, 3-D flash, and emerging memory technologies. Radiation effects relevant to each of these memories are described, including the physics of and errors caused by total ionizing dose, displacement damage, and single-event effects, with an eye toward the future role of emerging technologies in radiation environments.
When Crime Moves Where Does It Go? Analyzing the Spatial Correlates of Robbery Incidents Displaced by a Place-based Policing Intervention:

Objective:Examine the place-based correlates of robbery activity displaced by a foot-patrol intervention in Newark, NJ. We use constructs from Crime Pattern and Social Disorganization theories to t...
Repurposing Nintedanib for pathological cardiac remodeling and dysfunction.

Abstract Heart Failure (HF) is the leading cause of death worldwide. Myocardial fibrosis, one of the clinical manifestations implicated in almost every form of heart disease, contributes significantly to HF development. However, there is no approved drug specifically designed to target cardiac fibrosis. Nintedanib (NTB) is an FDA approved tyrosine kinase inhibitor for idiopathic pulmonary fibrosis (IPF) and chronic fibrosing interstitial lung diseases (ILD). The favorable clinical outcome of NTB in IPF patients is well established. Furthermore, NTB is well tolerated in IPF patients irrespective of cardiovascular comorbidities. However, there is a lack of direct evidence to support the therapeutic efficacy and safety of NTB in cardiac diseases. In this study we examined the effects of NTB treatment on cardiac fibrosis and dysfunction using a murine model of HF. Specifically, 10 weeks old C57BL/6J male mice were subjected to Transverse Aortic Constriction (TAC) surgery. NTB was administered once daily by oral gavage (50 mg/kg) till 16 weeks post-TAC. Cardiac function was monitored by serial echocardiography. Histological analysis and morphometric studies were performed at 16 weeks post-TAC. In the control group, systolic dysfunction started developing from 4 weeks post-surgery and progressed till 16 weeks. However, NTB treatment prevented TAC-induced cardiac functional decline. In another experiment, NTB treatment was stopped at 8 weeks, and animals were followed till 16 weeks post-TAC. Surprisingly, NTB’s beneficial effect on cardiac function was maintained even after treatment interruption. NTB treatment remarkably reduced cardiac fibrosis as confirmed by Masson’s trichrome staining and decreased expression of collagen genes (COL1A1, COL3A1). Compared to the TAC group, NTB treated mice showed a lower HW/TL ratio and cardiomyocyte cross-sectional area. NTB treatment reduced myocardial and systemic inflammation by inhibiting pro-inflammatory subsets and promoting regulatory T cells (Tregs). Our in vitro studies demonstrated that NTB prevents myofibroblast transformation, TGFβ1-induced SMAD3 phosphorylation, and the production of fibrogenic proteins (Fibronectin-1, α-SMA). However, NTB promoted immunosuppressive phenotype in Tregs, and altered vital signaling pathways in isolated cardiac fibroblast and cardiomyocytes, suggesting that its biological effect and underlying cardiac protection mechanisms are not limited to fibroblast and fibrosis alone. Our findings provide a proof of concept for repurposing NTB to combat adverse myocardial fibrosis and encourage the need for further validation in large animal models and subsequent clinical development for HF patients.
Exploiting Implicit Beliefs to Resolve Sparse Usage Problem in Usage-Based Specification Mining

Frameworks and libraries provide application programming interfaces (APIs) that serve as building blocks in modern software development. As APIs present the opportunity of increased productivity, it also calls for correct use to avoid buggy code. The usage-based specification mining technique has shown great promise in solving this problem through a data-driven approach. These techniques leverage the use of the API in large corpora to understand the recurring usages of the APIs and infer behavioral specifications (preconditions and postconditions) from such usages. A challenge for such technique is thus inference in the presence of insufficient usages, in terms of both frequency and richness. We refer to this as a "sparse usage problem." This paper presents the first technique to solve the sparse usage problem in usage-based precondition mining. Our key insight is to leverage implicit beliefs to overcome sparse usage. An implicit belief (IB) is the knowledge implicitly derived from the fact about the code. An IB about a program is known implicitly to a programmer via the language's constructs and semantics, and thus not explicitly written or specified in the code. The technical underpinnings of our new precondition mining approach include a technique to analyze the data and control flow in the program leading to API calls to infer preconditions that are implicitly present in the code corpus, a catalog of 35 code elements in total that can be used to derive implicit beliefs from a program, and empirical evaluation of all of these ideas. We have analyzed over 350 millions lines of code and 7 libraries that suffer from the sparse usage problem. Our approach realizes 6 implicit beliefs and we have observed that addition of single-level context sensitivity can further improve the result of usage based precondition mining. The result shows that we achieve overall 60% in precision and 69% in recall and the accuracy is relatively improved by 32% in precision and 78% in recall compared to base usage-based mining approach for these libraries.
Numerical Methods to Compute the Coriolis Matrix and Christoffel Symbols for Rigid-Body Systems

The growth of model-based control strategies for robotics platforms has led to the need for additional rigid-body-dynamics algorithms to support their operation. Toward addressing this need, this article summarizes efficient numerical methods to compute the Coriolis matrix and underlying Christoffel Symbols (of the first kind) for tree-structure rigid-body systems. The resulting algorithms can be executed purely numerically, without requiring any partial derivatives that would be required in symbolic techniques that do not scale. Properties of the presented algorithms share recursive structure in common with classical methods such as the Composite-Rigid-Body Algorithm. The algorithms presented are of the lowest possible order: $O(Nd)$ for the Coriolis Matrix and $O(Nd^2)$ for the Christoffel symbols, where $N$ is the number of bodies and $d$ is the depth of the kinematic tree. A method of order $O(Nd)$ is also provided to compute the time derivative of the mass matrix. A numerical implementation of these algorithms in C/C++ is benchmarked showing computation times on the order of 10-20 $\mu$s for the computation of the Coriolis matrix and $40-120$ $\mu$s for the computation of the Christoffel symbols for systems with $20$ degrees of freedom. These results demonstrate feasibility for the adoption of these numerical methods within control loops that need to operate at $1$kHz rates or higher, as is commonly required for model-based control applications.
Adverse events associated with weekly short course isoniazid and rifapentine therapy in pediatric patients with latent tuberculosis: A chart and literature review.

BACKGROUND Effective yet safe treatment of latent tuberculosis is important for preventing the spread of tuberculosis and the progression to active disease in pediatric patients. As of 2017, the short course combination regimen of weekly isoniazid and rifapentine (3HP) administered by directly observed therapy (DOT) has replaced 9 months of isoniazid as the standard of treatment for latent tuberculosis in pediatric patients. The literature, limited in size, has established the 3HP regimen's superior safety and adherence. METHODS We completed a retrospective chart review (n = 22) of pediatric patients at our institution receiving the 3HP regimen via DOT between 2017 and 2019. Frequencies of selected outcomes were compared to previously published data collected in a literature review. RESULTS In this retrospective chart review, pediatric patients ages 2-20 years receiving 3HP with DOT for latent tuberculosis experienced frequent adverse events, more severe adverse events such as anaphylaxis, and higher treatment discontinuation than that which has been previously reported in the literature. Of note, our cohort's race/ethnicity differed from the cohorts described in the literature. CONCLUSIONS Our data suggests that the short course combination regimen for pediatric latent tuberculosis patients may have a higher adverse event rate than previously established. Although this sample size is small, this study urges further investigation of more diverse cohorts to better establish the 3HP regimen's safety and tolerability.
GRAVITAS: Graphical Reticulated Attack Vectors for Internet-of-Things Aggregate Security

Internet-of-Things (IoT) and cyber-physical systems (CPSs) may consist of thousands of devices connected in a complex network topology. The diversity and complexity of these components present an enormous attack surface, allowing adversaries to exploit security vulnerabilities of different devices to execute a potent attack. Though significant efforts have been made to improve the security of individual devices in these systems, little attention has been paid to security at the aggregate level. In this article, we describe a comprehensive risk management system, called GRAVITAS, for IoT/CPS that can identify undiscovered attack vectors and optimize the placement of defenses within the system for optimal performance and cost. While existing risk management systems consider only known attacks, our model employs a machine learning approach to extrapolate undiscovered exploits, enabling us to identify attacks overlooked by manual penetration testing (pen-testing). The model is flexible enough to analyze practically any IoT/CPS and provide the system administrator with a concrete list of suggested defenses that can reduce system vulnerability at optimal cost. GRAVITAS can be employed by governments, companies, and system administrators to design secure IoT/CPS at scale, providing a quantitative measure of security and efficiency in a world where IoT/CPS devices will soon be ubiquitous.
Measuring planetary neutron albedo fluxes by remote gamma-ray sensing

In order to measure the planetary neutron albedo fluxes, a neutron-absorbing shield which emits gamma rays of characteristic energy and serves as a neutron detector, is added to a gamma-ray spectrometer (GRS). The gamma rays representing the neutron flux are observed against interference consisting of cosmic gamma rays, planetary continuum and line emission, and gamma rays arising from the interaction of cosmic rays with the GRS and the spacecraft. The uncertainty and minimum detection limits in neutron albedo fluxes are calculated for two missions, a lunar orbiter and a comet nucleus rendezvous. A GRS on a lunar orbiter at 100 km altitude detects a thermal neutron albedo flux as low as 0.002/sq cm/s and an expected flux of about 0.6/sq cm/s is measured with an uncertainty of 0.001/sq cm/s, for a 100 h observation period. For the comet nucleus, again in a 100 h observing period, a thermal neutron albedo flux is detected at a level of 0.006/sq cm/s and an expected flux of about 0.4/sq cm/s is measured with an uncertainty of 0.004/sq cm/s. The expanded geological capabilities made possible by this technique include improvements in H sensitivity, spatial resolution, and measurement depth; and an improved model of induced gamma-ray emission.
Laboratory verification of electron-scale reconnection regions modulated by a three-dimensional instability

During magnetic reconnection in collisionless space plasma, the electron fluid decouples from the magnetic field within narrow current layers, and theoretical models for this process can be distinguished in terms of their predicted current layer widths. From theory, the off-diagonal stress in the electron pressure tensor is related to thermal non-circular orbit motion of electrons around the magnetic field lines. This stress becomes significant when the width of the reconnecting current layer approaches the small characteristic length scale of the electron motion. To aid in situ spacecraft and numerical investigations of reconnection, the structure of the electron diffusion region is here investigated using the Terrestrial Reconnection EXperiment (TREX). In agreement with the closely matched kinetic simulations, laboratory observations reveal the presence of electron-scale current layer widths. Although the layers are modulated by a current-driven instability, 3D simulations demonstrate that it is the off-diagonal stress that is responsible for breaking the frozen-in condition of the electron fluid.
Operating lease obligations and corporate cash management

Abstract Our study addresses two issues overlooked in prior research: Does a firm's future operating lease obligations influence its current cash holdings? Does this relationship contribute to the temporal increase in corporate cash holdings? We provide evidence that these future obligations significantly influence a firm's cash holdings and contribute to the temporal increase in U.S. corporate cash holdings. Consequently, our findings are consistent with the options offered by operating leases to young growing firms and the effect of these operating lease obligations on the firm's operating leverage.
Firm Unionization and Disruptions in Customer Relationships

Relationships with major customers may be advantageous to suppliers due to economies of scale and reputational benefits. In this study, we investigate whether unionization leads to disruptions in a firm’s relationships with its customers. Our goal is to provide insights regarding the impact unionization has on a firm’s sales relationships with its major customers. We predict that major customers will shift purchases away from suppliers that unionize to avoid potential disruptions. Using a difference-in-differences research design, our results show a negative association between supplier unionization and sales to major customers. Our findings are robust to addressing endogeneity concerns through a propensity score matched analysis and regression discontinuity research design. Additionally, we find that supplier firm performance declines subsequent to unionization. We also find that suppliers experience significant increases in their cost of goods sold and the number of employees after supplier unionization, suggestive of higher input prices driving the disruption with major customers. Finally, we provide evidence that higher switching costs mitigate the decline in sales to major customers. Overall, our findings suggest that employee unionization can adversely affect a firm’s relationships with their major customers.
Adaptive estimation in symmetric location model under log-concavity constraint

We revisit the problem of estimating the center of symmetry θ of an unknown symmetric density f. Although Stone (1975), Van Eeden (1970), and Sacks (1975) constructed adaptive estimators of θ in this model, their estimators depend on external tuning parameters. In an effort to reduce the burden of tuning parameters, we impose an additional restriction of log-concavity on f. We construct truncated one-step estimators which are adaptive under the log-concavity assumption. Our simulations indicate that the untruncated version of the one step estimator, which is tuning parameter free, is also asymptotically efficient. We also study the maximum likelihood estimator (MLE) of θ in the shape-restricted model.
A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines but have only recently begun to take hold in spatial applications. Spatial causal inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided.
Analysis of deep-stall characteristics of T- tailed aircraft configurations and some recovery procedures.

Deep-stall characteristics of T-tailed aircraft configurations and recovery procedures, noting parameters of pitch-up, angle of attack, etc
A jet engine control problem for evaluating minimal design software

The problem of designing feedforward and feedback compensators to achieve a specified closed loop performance matrix is shown to reduce to a minimal design problem. Existing results for the F100 turbofan engine can be interpreted to yield the closed loop specifications for the associated control problem - and also a possible solution. All possible solutions can be enumerated by computing the kernel of a polynomial matrix. It is shown how this matrix can be obtained from the formulation of the minimal design problem.
Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

This paper presents a machine learning method for event cause analysis to enhance situational awareness in distribution networks. The data streams are captured using time-synchronized high sampling rates synchro waveform measurement units (SWMU). The proposed method is formulated based on a machine learning method, the convolutional neural network (CNN). This method is capable of capturing the spatiotemporal feature of the measurements effectively and perform the event cause analysis. Several events are considered in this paper to encompass a range of possible events in real distribution networks, including capacitor bank switching, transformer energization, fault, and high impedance fault (HIF). The dataset for our study is generated using the real time digital simulator (RTDS) to simulate real-world events. The event cause analysis is performed using only one cycle of the voltage waveforms after the event is detected. The simulation results show the effectiveness of the proposed machine learning-based method compared to the state-of-the-art classifiers.
Contrastive Fine-tuning Improves Robustness for Neural Rankers

The performance of state-of-the-art neural rankers can deteriorate substantially when exposed to noisy inputs or applied to a new domain. In this paper, we present a novel method for fine-tuning neural rankers that can significantly improve their robustness to out-of-domain data and query perturbations. Specifically, a contrastive loss that compares data points in the representation space is combined with the standard ranking loss during fine-tuning. We use relevance labels to denote similar/dissimilar pairs, which allows the model to learn the underlying matching semantics across different query-document pairs and leads to improved robustness. In experiments with four passage ranking datasets, the proposed contrastive fine-tuning method obtains improvements on robustness to query reformulations, noise perturbations, and zero-shot transfer for both BERT and BART based rankers. Additionally, our experiments show that contrastive fine-tuning outperforms data augmentation for robustifying neural rankers.
Towards more equitable question answering systems: How much more data do you need?

Question answering (QA) in English has been widely explored, but multilingual datasets are relatively new, with several methods attempting to bridge the gap between high- and low-resourced languages using data augmentation through translation and cross-lingual transfer. In this project we take a step back and study which approaches allow us to take the most advantage of existing resources in order to produce QA systems in many languages. Specifically, we perform extensive analysis to measure the efficacy of few-shot approaches augmented with automatic translations and permutations of context-question-answer pairs. In addition, we make suggestions for future dataset development efforts that make better use of a fixed annotation budget, with a goal of increasing the language coverage of QA datasets and systems.
Minimum Robust Multi-Submodular Cover for Fairness

In this paper, we study a novel problem, Minimum Robust Multi-Submodular Cover for Fairness (MinRF), as follows: given a ground set V; m monotone submodular functions f_1,...,f_m; m thresholds T_1,...,T_m and a non-negative integer r; MinRF asks for the smallest set S such that f_i(S \ X) ≥ T_i for all i ∈ [m] and |X| ≤ r. We prove that MinRF is inapproximable within (1- e) ln m; and no algorithm, taking fewer than exponential number of queries in term of r, is able to output a feasible set to MinRF with high certainty. Three bicriteria approximation algorithms with performance guarantees are proposed: one for r = 0, one for r = 1, and one for general r. We further investigate our algorithms' performance in two applications of MinRF, Information Propagation for Multiple Groups and Movie Recommendation for Multiple Users. Our algorithms have shown to outperform baseline heuristics in both solution quality and the number of queries in most cases.
A Guide to Reducing Carbon Emissions through Data Center Geographical Load Shifting

Recent computing needs have lead technology companies to develop large scale, highly optimized data centers. These data centers represent large loads on electric power networks which have the unique flexibility to shift load both geographically and temporally. This paper focuses on how data centers can use their geographic load flexibility to reduce carbon emissions through clever interactions with electricity markets. Because electricity market clearing accounts for congestion and power flow physics in the electric grid, the carbon emissions associated with electricity use varies between (potentially geographically close) locations. Using our knowledge about this process, we propose a new and improved metric to guide geographic load shifting, which we refer to as the locational marginal carbon emission λCO2. We compare this and three other shifting metrics on their ability to reduce carbon emissions and generation costs throughout the course of a year. Our analysis demonstrates that λCO2 is more effective in reducing carbon emissions than more commonly proposed metrics that do not account for the specifics of the power grid.
Use of Experimental Separation Limits in the Theoretical Design of V/STOL Inlets

Experimental data from several model inlets have been used to generate two parameters which are related to the limit of operation for inlet flow separation. One parameter, called the diffusion ratio, is the ratio of the peak velocity on the inlet surface to the velocity at the diffuser exit and is related to the boundary-layer separation at low throat Mach numbers. The other parameter, the peak Mach number on the inlet surface, is related to the separation at high throat Mach numbers. These parameters are easily calculated from potential flow solutions and thus can be used as a design tool in screening proposed inlet geometries. Any of the geometric design variables can be analyzed by this technique; but, this paper is restricted to the consideration of the internal lip contraction ratio. An illustrative example of an application to an inlet design study for a tilt nacelle VTOL airplane is presented. The study will show what value of contraction ratio is required to meet the operating requirements yet allow the inlet to remain free of separation as indicated by the two separation parameters.
Individualized Group Learning

Many massive data are assembled through collections of information of a large number of individuals in a population. The analysis of such data, especially in the aspect of individualized inferences...
Nonabelian extensions and factor systems for the algebras of Loday

Factor systems are a tool for working on the extension problem of algebraic structures such as groups, Lie algebras, and associative algebras. Their applications are numerous and well-known in thes...
Alternating the population and control neural networks to solve high-dimensional stochastic mean-field games.

We present APAC-Net, an alternating population and agent control neural network for solving stochastic mean-field games (MFGs). Our algorithm is geared toward high-dimensional instances of MFGs that are not approachable with existing solution methods. We achieve this in two steps. First, we take advantage of the underlying variational primal-dual structure that MFGs exhibit and phrase it as a convex–concave saddle-point problem. Second, we parameterize the value and density functions by two neural networks, respectively. By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial network (GAN). We show the potential of our method on up to 100-dimensional MFG problems.
Who Gets What, According to Whom? An Analysis of Fairness Perceptions in Service Allocation

Algorithmic fairness research has traditionally been linked to the disciplines of philosophy, ethics, and economics, where notions of fairness are prescriptive and seek objectivity. Increasingly, however, scholars are turning to the study of what different people perceive to be fair, and how these perceptions can or should help to shape the design of machine learning, particularly in the policy realm. The present work experimentally explores five novel research questions at the intersection of the "Who," "What," and "How" of fairness perceptions. Specifically, we present the results of a multi-factor conjoint analysis study that quantifies the effects of the specific context in which a question is asked, the framing of the given question, and who is answering it. Our results broadly suggest that the "Who" and "What," at least, matter in ways that are 1) not easily explained by any one theoretical perspective, 2) have critical implications for how perceptions of fairness should be measured and/or integrated into algorithmic decision-making systems.
The Global Protein-RNA Interaction map of Epithelial Splicing Regulatory Protein 1 defines a post-transcriptional program that is essential for epithelial cell function

The epithelial splicing regulatory proteins, ESRP1 and ESRP2 are essential for mammalian development through regulation of a global program of alternative splicing of genes involved in maintenance of epithelial cell function. To further inform our understanding of the molecular functions of ESRP1 we performed enhanced crosslinking immunoprecipitation coupled with high throughput sequencing (eCLIP) in epithelial cells of mouse epidermis. The genome-wide binding sites of ESRP1 were integrated with RNA-Seq analysis of alterations in splicing and total gene expression that result from epidermal ablation of Esrp1 and Esrp2. These studies demonstrated that ESRP1 functions in splicing regulation occur primarily through direct binding in a position-dependent manner to either promote exon inclusion or skipping. In addition, we also identified widespread binding of ESRP1 in 3′ and 5′ untranslated regions (UTRs) of genes involved in epithelial cell function suggesting that its post-transcriptional functions extend beyond splicing regulation.
Temperature dependence of the optical phonon reflection band in GaP

We explore the effect of temperatures between 80 and 720 K on the energy and linewidth of zone-center transverse (TO) and longitudinal (LO) optical phonons in bulk gallium phosphide (GaP) using Fourier transform infrared ellipsometry from 0.03 to 0.60 eV. We extract the optical phonon parameters of GaP by fitting the ellipsometric angles with the Lowndes–Gervais model, which applies two different broadening parameters to the TO and LO phonons. In GaP, the two-phonon density of states is larger for the decay of TO phonons than for LO phonons. Therefore, we observed a larger TO phonon broadening (compared to the LO phonon) and an asymmetric reststrahlen line shape. This would lead to a negative imaginary part of the dielectric function just above the LO phonon energy but the addition of two-phonon absorption avoids this. We find a temperature-dependent redshift and broadening of TO and LO phonons with increasing temperature due to thermal expansion and anharmonic phonon-phonon scattering, involving three and four phonon decay processes. We also investigate the temperature dependence of the high-frequency dielectric constant. Its variation is explained by thermal expansion and the temperature dependence of the Penn gap.
Formal Methods for an Iterated Volunteer’s Dilemma

Game theory provides a paradigm through which we can study the evolving communication and phenomena that occur via rational agent interaction [10]. The Volunteer’s dilemma is a vastly studied game throughout literature that models agents as cooperative, rather than selfish, entities. In this work, we design a model framework and explore the Volunteer’s dilemma with the goals of 1) modeling it as a stochastic concurrent n-player game, 2) constructing properties to verify model correctness and reachability, 3) constructing strategy synthesis graphs to understand how the game is iteratively stepped through most optimally and, 4) analyzing a series of parameters to understand correlations with expected local and global rewards over a finite time horizon.
K-Averaging Agent-Based Model: Propagation of Chaos and Convergence to Equilibrium

The paper treats an agent-based model with averaging dynamics to which we refer as the K-averaging model. Broadly speaking, our model can be added to the growing list of dynamics exhibiting self-organization such as the well-known Vicsek-type models (Aldana et al. in: Phys Rev Lett 98(9):095702, 2007; Aldana and Huepe in: J Stat Phys 112(1–2):135–153, 2003; Pimentel in: Phys. Rev. E 77(6):061138, 2008). In the K-averaging model, each of the N particles updates their position by averaging over K randomly selected particles with additional noise. To make the K-averaging dynamics more tractable, we first establish a propagation of chaos type result in the limit of infinite particle number (i.e. $$N \rightarrow \infty $$ ) using a martingale technique. Then, we prove the convergence of the limit equation toward a suitable Gaussian distribution in the sense of Wasserstein distance as well as relative entropy. We provide additional numerical simulations to illustrate both results.
Large-scale asymmetry in galaxy spin directions: evidence from the Southern hemisphere

Recent observations using several different telescopes and sky surveys showed patterns of asymmetry in the distribution of galaxies by their spin directions as observed from Earth. These studies were done with data imaged from the Northern hemisphere, showing excellent agreement between different telescopes and different analysis methods. Here, data from the DESI Legacy Survey was used. The initial dataset contains galaxy images, reduced to galaxies annotated by their spin direction using a symmetric algorithm. That makes it not just the first analysis of its kind in which the majority of the galaxies are in the Southern hemisphere, but also by far the largest dataset used for this purpose to date. The results show strong agreement between opposite parts of the sky, such that the asymmetry in one part of the sky is similar to the inverse asymmetry in the corresponding part of the sky in the opposite hemisphere. Fitting the distribution of galaxy spin directions to cosine dependence shows a dipole axis with probability of 4.66 . Interestingly, the location of the most likely axis is within close proximity to the CMB Cold Spot. The profile of the distribution is nearly identical to the asymmetry profile of the distribution identified in Pan-STARRS, and it is within 1 difference from the distribution profile in SDSS and HST. All four telescopes show similar large-scale profile of asymmetry.
Reinforcement Learning for Optimized Beam Training in Multi-Hop Terahertz Communications

Communication at terahertz (THz) frequency bands is a promising solution for achieving extremely high data rates in next-generation wireless networks. While the THz communication is conventionally envisioned for short-range wireless applications due to the high atmospheric absorption at THz frequencies, multi-hop directional transmissions can be enabled to extend the communication range. However, to realize multi-hop THz communications, conventional beam training schemes, such as exhaustive search or hierarchical methods with a fixed number of training levels, can lead to a very large time overhead. To address this challenge, in this paper, a novel hierarchical beam training scheme with dynamic training levels is proposed to optimize the performance of multi-hop THz links. In fact, an optimization problem is formulated to maximize the overall spectral efficiency of the multi-hop THz link by dynamically and jointly selecting the number of beam training levels across all the constituent single-hop links. To solve this problem in presence of unknown channel state information, noise, and path loss, a new reinforcement learning solution based on the multi-armed bandit (MAB) is developed. Simulation results show the fast convergence of the proposed scheme in presence of random channels and noise. The results also show that the proposed scheme can yield up to 75% performance gain, in terms of spectral efficiency, compared to the conventional hierarchical beam training with a fixed number of training levels.
LEvEL: Low-Energy Neutrino Experiment at the LHC

We propose the operation of LEvEL, the Low-Energy Neutrino Experiment at the LHC, a neutrino detector near the Large Hadron Collider Beam Dump. Such a detector is capable of exploring an intense, low-energy neutrino flux and can measure neutrino cross sections that have previously never been observed. These cross sections can inform other future neutrino experiments, such as those aiming to observe neutrinos from supernovae, allowing such measurements to accomplish their fundamental physics goals. We perform detailed simulations to determine neutrino production at the LHC beam dump, as well as neutron and muon backgrounds. Measurements at a few to ten percent precision of neutrino-argon charged current and neutrino-nucleus coherent scattering cross sections are attainable with 100 ton-year and 1 ton-year exposures at LEvEL, respectively, concurrent with the operation of the High Luminosity LHC. We also estimate signal and backgrounds for an experiment exploiting the forward direction of the LHC beam dump, which could measure neutrinos above 100 GeV.
Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation

Speech-based machine learning (ML) has been heralded as a promising solution for tracking prosodic and spectrotemporal patterns in real-life that are indicative of emotional changes, providing a valuable window into one's cognitive and mental state. Yet, the scarcity of labelled data in ambulatory studies prevents the reliable training of ML models, which usually rely on "data-hungry" distribution-based learning. Leveraging the abundance of labelled speech data from acted emotions, this paper proposes a few-shot learning approach for automatically recognizing emotion in spontaneous speech from a small number of labelled samples. Few-shot learning is implemented via a metric learning approach through a siamese neural network, which models the relative distance between samples rather than relying on learning absolute patterns of the corresponding distributions of each emotion. Results indicate the feasibility of the proposed metric learning in recognizing emotions from spontaneous speech in four datasets, even with a small amount of labelled samples. They further demonstrate superior performance of the proposed metric learning compared to distribution-learning, which is implemented with feedforward neural network fine-tuning. Findings from this work provide a foundation for the ambulatory tracking of human emotion in spontaneous speech contributing to the real-life assessment of mental health degradation outcomes.
Stable surfaces and free boundary marginally outer trapped surfaces

We explore various notions of stability for surfaces embedded and immersed in spacetimes and initial data sets. The interest in such surfaces lies in their potential to go beyond the variational techniques which often underlie the study of minimal and CMC surfaces. We prove two versions of Christodoulou–Yau estimate for $${\mathbf {H}}$$ -stable surfaces, a Cohn-Vossen type inequality for non-compact stable marginally outer trapped surface (MOTS), and a global theorem on the topology of $${\mathbf {H}}$$ -stable surfaces. Moreover, we give a definition of capillary stability for MOTS with boundary. This notion of stability leads to an area upper bound inequality and a local splitting theorem for free boundary stable MOTS. Finally, we establish an index estimate and a diameter estimate for free boundary MOTS. These are straightforward generalizations of Chen–Fraser–Pang and Carlotto–Franz results for free boundary minimal surfaces, respectively.
A Bayesian Approach to Account for Misclassification in Prevalence and Trend Estimation

In this paper we present a Bayesian approach to estimate the mean of a binary variable and changes in the mean over time, when the variable is subject to misclassification error. These parameters are partially identified and we derive identified sets under various assumptions about the misclassification rates. We apply our method to estimating the prevalence and trend of prescription opioid misuse, using data from the 2002-2014 National Survey on Drug Use and Health. Using a range of priors, the posterior distribution provides evidence that the prevalence of opioid misuse increases multiple times between 2002 and 2012.
Fiber packing and morphology driven moisture diffusion mechanics in reinforced composites

Abstract Fiber reinforced polymer composite (FRPC) materials are extensively used in lightweight applications due to their high specific strength and other favorable properties including enhanced endurance and corrosion resistance. However, these materials are inevitably exposed to moisture, which is known to drastically reduce their mechanical properties caused by moisture absorption and often accompanied with plasticization, weight gain, hygrothermal swelling, and de-bonding between fiber and matrix. Hence, it is important to understand the mechanics of moisture diffusion into FRPCs. The presence of fibers, especially impermeable like Carbon fibers, introduce tortuous moisture diffusion pathways through polymer matrix. In this paper, we elucidate the impact of fiber packing and morphology on moisture diffusion in FRPC materials. Computational models are developed within a finite element framework to evaluate moisture kinetics in impermeable FRPCs. We introduce a tortuosity factor for calculating the extent of deviation in moisture diffusion pathways due to impermeable fiber reinforcements. Two-dimensional micromechanical models are analyzed with varying fiber volume fractions, spatial distributions and morphology to elucidate the influence of internal microscale fiber architectures on tortuous diffusion pathways and corresponding diffusivities. Finally, a relationship between tortuosity and diffusivity is established such that diffusivity can be calculated using tortuosity for a given micro-architecture. Tortuosity can be easily calculated for a given architecture by solving steady state diffusion governing equations, whereas time-dependent transient diffusion equations need to be solved for calculating moisture diffusivity. Hence, tortuosity, instead of diffusivity, can be used in future composites designs, multi-scale analyses, and optimization for enabling robust structures in extreme moisture environments.
What's in a frequency: new tools for graph Fourier Transform visualization

Recent progress in graph signal processing (GSP) has addressed a number of problems, including sampling and filtering. Proposed methods have focused on generic graphs and defined signals with certain characteristics, e.g., bandlimited signals, based on the graph Fourier transform (GFT). However, the effect of GFT properties (e.g., vertex localization) on the behavior of such methods is not as well understood. In this paper, we propose novel GFT visualization tools and provide some examples to illustrate certain GFT properties and their impact on sampling or wavelet transforms.