cssi/SciSciGPT-SciSciNet huggingface dataset:
Across the Apocalypse on Horseback: Imperfect Legal Responses to Biodiversity Loss

The most significant drivers of biodiversity loss can be described by HIPPO, the Greek word for horse. Habitat destruction, Invasive species, Population, Pollution, and Overkill - in that order - are causing species losses on a magnitude worthy of one of geological history's great extinctions. Unfortunately, existing legal tools for addressing biodiversity loss are aimed precisely where human agency hurts least. The Endangered Species Act, for instance, more clearly punishes overkill than either habitat destruction or the introduction of alien invasive species. This brief article describes the predicament and prescribes a modest agenda for improving the law of biodiversity conservation.
You’re Fired! New Evidence on Portfolio Manager Turnover and Performance

We study managerial turnover for both internally managed mutual funds and those managed externally by subadvisors. We argue that turnover of subadvisors provides sharper tests and helps address several unresolved issues and puzzles from the previous literature. We find dramatically stronger inverse relations between subadvisor departures and lagged returns, and new evidence on how past flow predicts turnover. We find no evidence of improvements in return performance related to departures, but flow improvements are associated with departures of poor past performers. Our findings represent new evidence on how investors, sponsors, and boards learn about and evaluate mutual fund management performance.
Pay Attention: Object Consideration as a Mechanism of Network Diffusion

Research on diffusion processes in networks relies on two broad classes of mechanisms: networks direct the flow of information, and they produce constraints for rational decision-making. This paper...
MIDMod-OSN: A Microscopic-Level Information Diffusion Model for Online Social Networks

As online social networks continue to be commonly used for the dissemination of information to the public, understanding the phenomena that govern information diffusion is crucial for many security and safety-related applications. In this study, we hypothesize that the features that contribute to information diffusion in online social networks are significantly influenced by the type of event being studied. We classify Twitter events as either informative or trending and then explore the node-to-node influence dynamics associated with information spread. We build a model based on Bayesian Logistic Regression for learning and prediction and Random Forests for feature selection. Experimental results from real-world data sets show that the proposed model outperforms state-of-the-art diffusion prediction models, achieving 93% accuracy in informative events and 86% in trending events. We observed that the models for informative and trending events differ significantly, both in the diffusion process and in the user features that govern the diffusion. Our findings show that followers play an important role in the diffusion process and it is possible to use the diffusion and OSN behavior of users for predicting the trending character of a message very early, long before being able to count the number of reactions.
Contiguous Osteomyelitis of Distal Extremities in Children

Objective. To evaluate the burden of Contiguous Osteomyelitis (COM) in pediatric patients with cellulitis/abscess of hands/feet. Methods. Children aged 0-18 years, treated from 2009 to 2019 for cel...
Equidistribution on Kuga-Sato Varieties of Torsion Points on CM Elliptic Curves

A connected Kuga-Sato variety $\mathbf{W}^r$ parameterizes tuples of $r$ points on elliptic curves (with level structure). A special point of $\mathbf{W}^r$ is a tuple of torsion points on a CM elliptic curve. A sequence of special points is strict if any CM elliptic curve appears at most finitely many times and no relation between the points in the tuple is satisfied infinitely often. The genus orbit of a special point is the $\operatorname{Gal}(\bar{\mathbb{Q}}/\mathbb{Q}^{\mathrm{ab}})$-orbit. We show that genus orbits of special points in a strict sequence equidistribute in $\mathbf{W}^r(\mathbb{C})$, assuming a congruence condition at two fixed primes. A genus orbit can be very sparse in the full Galois orbit. In particular, the number of torsion points on each elliptic curve in a genus orbit is not bounded below by the torsion order. A genus orbit corresponds to a toral packet in an extension of $\mathbf{SL}_2$ by a vector representation. These packets also arise in the study by Aka, Einsiedler and Shapira of grids orthogonal to lattice points on the $2$-sphere. As an application we establish their joint equidistribution conjecture assuming two split primes.
NON‐EULERIAN DEHN–SOMMERVILLE RELATIONS

The classical Dehn--Sommerville relations assert that the $h$-vector of an Eulerian simplicial complex is symmetric. We establish three generalizations of the Dehn--Sommerville relations: one for the $h$-vectors of pure simplicial complexes, another one for the flag $h$-vectors of balanced simplicial complexes and graded posets, and yet another one for the toric $h$-vectors of graded posets with restricted singularities. In all of these cases, we express any failure of symmetry in terms of "errors coming from the links." For simplicial complexes, this further extends Klee's semi-Eulerian relations.
Bundled references: an abstraction for highly-concurrent linearizable range queries

Bundled references are a new building block to provide linearizable range query operations for highly concurrent linked data structures. They enable range queries to traverse a path through the data structure that is consistent with the target atomic snapshot. The path consists of the minimal amount of nodes that should be accessed to preserve linearizability.
Has machine learning rendered simple rules obsolete

Epstein (Simple rules for a complex world, Harvard University Press, Cambridge, 1995) defended the superiority of simple legal rules over complex, human-designed regulations. Has Epstein’s case for simple rules become obsolete with the arrival of artificial intelligence, and in particular machine learning (ML)? Can ML deliver better algorithmic rules than traditional simple legal rules? This paper argues that the answer to these question is “no” by building an argument based on three increasingly more serious barriers that ML faces to develop legal (or quasi-legal) algorithmic rules: data availability, the Lucas’ critique, and incentive compatibility in eliciting information. Thus, the case for simple legal rules is still sound even in a world with ML.
A Probabilistic Model for Planar Sliding of Objects with Unknown Material Properties: Identification and Robust Planning

This paper introduces a new technique for learning probabilistic models of mass and friction distributions of unknown objects, and performing robust sliding actions by using the learned models. The proposed method is executed in two consecutive phases. In the exploration phase, a table-top object is poked by a robot from different angles. The observed motions of the object are compared against simulated motions with various hypothesized mass and friction models. The simulation-to-reality gap is then differentiated with respect to the unknown mass and friction parameters, and the analytically computed gradient is used to optimize those parameters. Since it is difficult to disentangle the mass from the friction coefficients in low-data and quasi-static motion regimes, our approach retains a set of locally optimal pairs of mass and friction models. A probability distribution on the models is computed based on the relative accuracy of each pair of models. In the exploitation phase, a probabilistic planner is used to select a goal configuration and waypoints that are stable with a high confidence. The proposed technique is evaluated on real objects and using a real manipulator. The results show that this technique can not only identify accurately mass and friction coefficients of non-uniform heterogeneous objects, but can also be used to successfully slide an unknown object to the edge of a table and pick it up from there, without any human assistance or feedback.
Reconstruction of Backbone Curves for Snake Robots

Snake robots composed of alternating single-axis pitch and yaw joints have many internal degrees of freedom, which make them capable of versatile three-dimensional locomotion. In motion planning process, snake robot motions are often designed kinematically by a chronological sequence of continuous backbone curves that capture desired macroscopic shapes of the robot. However, as the geometric arrangement of single-axis rotary joints creates constraints on the rotations in the robot, it is challenging for the robot to reconstruct an arbitrary 3D curve. When the robot configuration does not accurately achieve the desired shapes defined by these backbone curves, the robot can have unexpected contacts with the environment, such that the robot does not achieve the desired motion. In this work, we propose a method for snake robots to reconstruct desired backbone curves by posing an optimization problem that exploits the robot's geometric structure. We verified that our method enables fast and accurate curve-configuration conversions through its applications to commonly used 3D gaits. We also demonstrated via robot experiments that 1) our method results in smooth locomotion on the robot; 2) our method allows the robot to approach the numerically predicted locomotive performance of a sequence of continuous backbone curve.
Banks' Holdings of Government Securities and Credit to the Private Sector in Emerging Market and Developing Economies

This paper studies the relationship between banks' holdings of domestic sovereign securities and credit growth to the private sector in emerging market and developing economies. Higher banks' holdings of government debt are associated with a lower credit growth to the private sector and with a higher return on assets of the banking sector. Analysis suggests that the negative relationship between banks' claims on the government and private sector credit growth mainly reflects a portfolio rebalancing of banks towards safer, more liquid public assets in stress times and provides only limited evidence of a crowding-out effect due to financial repression.
Expected distances on manifolds of partially oriented flags

Flag manifolds are generalizations of projective spaces and other Grassmannians: they parametrize flags, which are nested sequences of subspaces in a given vector space. These are important objects in algebraic and differential geometry, but are also increasingly being used in data science, where many types of data are properly understood as subspaces rather than vectors. In this paper we discuss partially oriented flag manifolds, which parametrize flags in which some of the subspaces may be endowed with an orientation. We compute the expected distance between random points on some low-dimensional examples, which we view as a statistical baseline against which to compare the distances between particular partially oriented flags coming from geometry or data.
Aerosol generation in public restrooms

Aerosolized droplets play a central role in the transmission of various infectious diseases, including Legionnaire's disease, gastroenteritis-causing norovirus, and most recently COVID-19. Respiratory droplets are known to be the most prominent source of transmission for COVID-19; however, alternative routes may exist given the discovery of small numbers of viable viruses in urine and stool samples. Flushing biomatter can lead to the aerosolization of micro-organisms; thus, there is a likelihood that bioaerosols generated in public restrooms may pose a concern for the transmission of COVID-19, especially since these areas are relatively confined, experience heavy foot traffic, and may suffer from inadequate ventilation. To quantify the extent of aerosolization, we measure the size and number of droplets generated by flushing toilets and urinals in a public restroom. The results indicate that the particular designs tested in the study generate a large number of droplets in the size range 0.3 μ m– 3 μ m, which can reach heights of at least 1.52 m. Covering the toilet reduced aerosol levels but did not eliminate them completely, suggesting that aerosolized droplets escaped through small gaps between the cover and the seat. In addition to consistent increases in aerosol levels immediately after flushing, there was a notable rise in ambient aerosol levels due to the accumulation of droplets from multiple flushes conducted during the tests. This highlights the need for incorporating adequate ventilation in the design and operation of public spaces, which can help prevent aerosol accumulation in high occupancy areas and mitigate the risk of airborne disease transmission.
DOVE: A Data-Oblivious Virtual Environment.

Users can improve the security of remote communications by using Trusted Execution Environments (TEEs) to protect against direct introspection and tampering of sensitive data. This can even be done with applications coded in high-level languages with complex programming stacks such as R, Python, and Ruby. However, this creates a trade-off between programming convenience versus the risk of attacks using microarchitectural side channels. In this paper, we argue that it is possible to address this problem for important applications by instrumenting a complex programming environment (like R) to produce a Data-Oblivious Transcript (DOT) that is explicitly designed to support computation that excludes side channels. Such a transcript is then evaluated on a Trusted Execution Environment (TEE) containing the sensitive data using a small trusted computing base called the Data-Oblivious Virtual Environment (DOVE). To motivate the problem, we demonstrate a number of subtle side-channel vulnerabilities in the R language. We then provide an illustrative design and implementation of DOVE for R, creating the first side-channel resistant R programming stack. We demonstrate that the two-phase architecture provided by DOT generation and DOVE evaluation can provide practical support for complex programming languages with usable performance and high security assurances against side channels.
A molecular and kinetic analysis of estrogen receptor transformation.

Abstract Recent studies of the uterine estrogen receptor indicate it is a multi-subunit protein. Estradiol and temperature induce a conformational change in the 4 S estrogen-binding protein (EBP) or monomer (mol. wt. 7–8 × 104) as indicated by the high energy of activation (19–21 kcal mol−1) accompanying the formation of the 4S EBP dimer, 5S EBP (mol. wt. 13–14 × 104). The formation of the 5 S EBP suggests that new intramolecular bonding forces have been established, presumably hydrophobic interactions, which were not available in the inactive 4 S EBP monomer. Kinetic analysis of the 4 S to 5 S EBP transformation indicates a second-order reaction, the dimerization of the 4 S EBP with a second (similar or dissimilar) subunit. The 5 S EBP produced by the in vitro warming of the cytosol-[3H]-estradiol and the 5 S EBP extracted from isolated nuclei have similar molecular weights, sedimentation coefficients, molecular radii and rates of formation. These results suggest that an estradiol and temperature induced conformational change in the 4 S EBP leads to a macromolecular association process and receptor activation.
Implementation of Interstate Water Quality Plan

The water quality control plan of the interstate Delaware River Basin is part of a multifunctional master plan for water resources conservation, development, and management in the 13,000-mile, four-state region. The master plan, or Comprehensive Plan, is required by the Delaware River Basin Compact, and has the force of law. No project or development affecting the water resources of the Basin can be undertaken if it is in conflict with the Comprehensive Plan, which includes policies, standards, criteria, and general goals, as well as physical projects and facilities, deemed necessary by the Delaware River Basin Commission for the purposes of the Compact. The water quality plan is being implemented by the Basin community under the guidance and regulatory control of the Commission. Compliance is effected by:(1) Mandatory consultative planning between the Commission and sponsors of projects that may influence water quality; and (2) formal detailed review of water-related projects before construction approval is granted by the Commission.
An expanded class of histidine-accepting viral tRNA-like structures

Structured RNA elements are common in the genomes of RNA viruses, often playing critical roles during viral infection. Some viral RNA elements use forms of tRNA mimicry, but the diverse ways this mimicry can be achieved are poorly understood. Histidine-accepting tRNA-like structures (TLSHis) are examples found at the 3' termini of some positive-sense single-stranded RNA (+ssRNA) viruses where they interact with several host proteins, induce histidylation of the RNA genome, and facilitate processes important for infection, to include genome replication. As only five TLSHis examples had been reported, we explored the possible larger phylogenetic distribution and diversity of this TLS class using bioinformatic approaches. We identified many new examples of TLSHis, yielding a rigorous consensus sequence and secondary structure model that we validated by chemical probing of representative TLSHis RNAs. We confirmed new examples as authentic TLSHis by demonstrating their ability to be histidylated in vitro, then used mutational analyses to imply a tertiary interaction that is likely analogous to the D- and T-loop interaction found in canonical tRNAs. These results expand our understanding of how diverse RNA sequences achieve tRNA-like structure and function in the context of viral RNA genomes and lay the groundwork for high-resolution structural studies of tRNA mimicry by histidine-accepting TLSs.
Influence of the Indian Ocean Dipole on the Large-Scale Circulation in South America

Author(s): Sena, ACT; Magnusdottir, G | Abstract: The influence of each phase of the Indian Ocean dipole (IOD) on the large-scale circulation in South America is investigated using rainfall observations, fully coupled, large-ensemble, historical simulations (LENS), and forced experiments using the coupled model's atmospheric component. IOD events often occur when El Nino-Southern Oscillation (ENSO), the largest source of interannual variability of precipitation in South America, is active. To distinguish from effects of ENSO, only cases during neutral ENSO conditions are analyzed in LENS and observations. During the positive IOD polarity, a perturbation in the local Walker circulation leads to increased convection over equatorial South America, resulting in wet anomalies in the Amazon basin. This signal is the opposite of what is expected during El Nino events. Tropical convection anomalies in the Indian Ocean also force an extratropical Rossby wave train that reaches subtropical South America. During positive IOD, the moisture flux from the Amazon to central and southeastern Brazil weakens, resulting in a drying of the area associated with the South Atlantic convergence zone. Meanwhile, the South Atlantic subtropical high strengthens, contributing to a drying in southeastern Brazil. During negative IOD, the induced wave train from the Indian Ocean leads to increased moisture transport to the La Plata basin, leading to wet anomalies in the region.
EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction

Affect preferences vary with user demographics, and tapping into demographic information provides important cues about the users' language preferences. In this paper, we utilize the user demographics and propose EmpathBERT, a demographic-aware framework for empathy prediction based on BERT. Through several comparative experiments, we show that EmpathBERT surpasses traditional machine learning and deep learning models, and illustrate the importance of user demographics, for predicting empathy and distress in user responses to stimulative news articles. We also highlight the importance of affect information in the responses by developing affect-aware models to predict user demographic attributes.
Who Is in Control? Practical Physical Layer Attack and Defense for mmWave-Based Sensing in Autonomous Vehicles

With the wide bandwidths in millimeter wave (mmWave) frequency band that results in unprecedented accuracy, mmWave sensing has become vital for many applications, especially in autonomous vehicles (AVs). In addition, mmWave sensing has superior reliability compared to other sensing counterparts such as camera and LiDAR, which is essential for safety-critical driving. Therefore, it is critical to understand the security vulnerabilities and improve the security and reliability of mmWave sensing in AVs. To this end, we perform the end-to-end security analysis of a mmWave-based sensing system in AVs, by designing and implementing practical physical layer attack and defense strategies in a state-of-the-art mmWave testbed and an AV testbed in real-world settings. Various strategies are developed to take control of the victim AV by spoofing its mmWave sensing module, including adding fake obstacles at arbitrary locations and faking the locations of existing obstacles. Five real-world attack scenarios are constructed to spoof the victim AV and force it to make dangerous driving decisions leading to a fatal crash. Field experiments are conducted to study the impact of the various attack scenarios using a Lincoln MKZ-based AV testbed, which validate that the attacker can indeed assume control of the victim AV to compromise its security and safety. To defend the attacks, we design and implement a challenge-response authentication scheme and a RF fingerprinting scheme to reliably detect aforementioned spoofing attacks.
Sumcheck Arguments and Their Applications.

We introduce a class of interactive protocols, which we call sumcheck arguments, that establishes a novel connection between the sumcheck protocol (Lund et al. JACM 1992) and folding techniques for Pedersen commitments (Bootle et al. EUROCRYPT 2016).
Dominance Solvability in Random Games

We study the effectiveness of iterated elimination of strictly-dominated actions in random games. We show that dominance solvability of games is vanishingly small as the number of at least one player's actions grows. Furthermore, conditional on dominance solvability, the number of iterations required to converge to Nash equilibrium grows rapidly as action sets grow. Nonetheless, when games are highly imbalanced, iterated elimination simplifies the game substantially by ruling out a sizable fraction of actions. Technically, we illustrate the usefulness of recent combinatorial methods for the analysis of general games.
Safety practices for in-office laryngology procedures during clinical reintroduction amidst COVID-19.

Objective: Describe safety practices for performing in-office laryngology procedures during clinical re-introduction amidst the coronavirus disease 2019 (COVID-19) pandemic. Methods: An anonymous survey in Qualtrics was created to evaluate demographics, preprocedure testing, practice settings, anesthesia, and personal protective equipment (PPE) use for five procedure categories (non-mucosal-traversing injections, mucosal-traversing injections, endoscopy without suction, endoscopy with suction/mucosal intervention via working channel, and laser via working channel). The survey was emailed to the Fall Voice Community on Doc Matter and to members of the American Broncho-Esophagological Association (ABEA) from May to June 2020. Results: Eighty-two respondents were analyzed (response rate: 10%). Respondents represented diverse locations, including international. Most reported academic (71%) or private practices (16%), laryngology fellowship training (76%), and a significant practice devotion to laryngology and broncho-esophagology. During the early re-introduction, most continued to perform all procedure categories. The office was preferred to the OR setting for most, though 36% preferred the OR for laser procedures. There was a preference for preprocedural SARS-Cov2 testing for procedures involving a working channel (>67%), and these procedures had the highest proportion of respondents discontinuing the procedure due to COVID-19. Various types of topical anesthesia were reported, including nebulizer treatments. The most common forms of personal protective equipment utilized were gloves (>95%) and N95 masks (>67%). Powered-air purifying respirators and general surgical masks were used infrequently. Conclusions: During the early re-introduction, respondents reported generally continuing to perform office laryngology procedures, while greater mucosal manipulation affected decisions to stop procedures due to COVID-19, perform preprocedural SARS-Cov2 testing, and alter topical anesthesia. Gloves and N95 masks were the predominate PPE. Level of Evidence: N/A.
Control and Trajectory Optimization for Soft Aerial Manipulation

Manipulation and grasping with unmanned aerial vehicles (UAVs) currently require accurate positioning and are often executed at reduced speed to ensure successful grasps. This is due to the fact that typical UAV s can only accommodate rigid manipulators with few degrees of freedom, which limits their capability to compensate for disturbances caused by the vehicle positioning errors. Moreover, UAV s have to minimize external contact forces in order to maintain stability. Biological systems, on the other hand, exploit softness to overcome similar limitations, and leverage compliance to enable aggressive grasping. This paper investigates control and trajectory optimization for a soft aerial manipulator, consisting of a quadrotor and a tendon-actuated soft gripper, in which the advantages of softness can be fully exploited. To the best of our knowledge, this is the first work at the intersection between soft manipulation and UAV control. We present a decoupled approach for the quadrotor and the soft gripper, combining (i) a geometric controller and a minimum-snap trajectory optimization for the quadrotor (rigid) base, with (ii) a quasi-static finite element model and control-space interpolation for the soft gripper. We prove that the geometric controller asymptotically stabilizes the quadrotor velocity and attitude despite the addition of the soft load. Finally, we evaluate the proposed system in a realistic soft dynamics simulator, and show that: (i) the geometric controller is fairly insensitive to the soft payload, (ii) the platform can reliably grasp unknown objects despite inaccurate positioning and initial conditions, and (iii) the decoupled controller is amenable for real-time execution. Video Attachment: https://youtu.be/NNpQxP0SPFk
Scalable First-Order Methods for Robust MDPs.

Robust Markov Decision Processes (MDPs) are a powerful framework for modeling sequential decision making problems with model uncertainty. This paper proposes the first first-order framework for solving robust MDPs. Our algorithm interleaves primal-dual first-order updates with approximate Value Iteration updates. By carefully controlling the tradeoff between the accuracy and cost of Value Iteration updates, we achieve an ergodic convergence rate that is significantly better than classical Value Iteration algorithms in terms of the number of states S and the number of actions A on ellipsoidal and Kullback-Leibler s-rectangular uncertainty sets. In numerical experiments on ellipsoidal uncertainty sets we show that our algorithm is significantly more scalable than state-of-the-art approaches. Our framework is also the first one to solve robust MDPs with s-rectangular KL uncertainty sets.
Finite Element Analysis of Inviscid Subsonic Boattail Flow

A finite element code for analysis of inviscid subsonic flows over arbitrary nonlifting planar or axisymmetric bodies is described. The code solves a novel primitive variable formulation of the coupled irrotationality and compressible continuity equations. Results for flow over a cylinder, a sphere, and a NACA 0012 airfoil verify the code. Computed subcritical flows over an axisymmetric boattailed afterbody compare well with finite difference results and experimental data. Interative coupling with an integral turbulent boundary layer code shows strong viscous effects on the inviscid flow. Improvements in code efficiency and extensions to transonic flows are discussed.
Structure and Mechanism of a Bacterial t6A Biosynthesis System

The universal N(6)-threonylcarbamoyladenosine (t6A) modification at position 37 of ANN-decoding tRNAs is central to translational fidelity. In bacteria, t6A biosynthesis is catalyzed by the proteins TsaB, TsaC/TsaC2, TsaD and TsaE. Despite intense research, the molecular mechanisms underlying t6A biosynthesis are poorly understood. Here, we report biochemical and biophysical studies of the t6A biosynthesis system from Thermotoga maritima. Small angle X-ray scattering analysis reveals a symmetric 2:2 stoichiometric complex of TsaB and TsaD (TsaB2D2), as well as 2:2:2 complex (TsaB2D2E2), in which TsaB acts as a dimerization module, similar to the role of Pcc1 in the archaeal system. The TsaB2D2 complex is the minimal platform for the binding of one tRNA molecule, which can then accommodate a single TsaE subunit. Kinetic data demonstrate that TsaB2D2 alone, and a TsaB2D2E1 complex with TsaE mutants deficient in adenosine triphosphatase (ATPase) activity, can catalyze only a single cycle of t6A synthesis, while gel shift experiments provide evidence that the role of TsaE-catalyzed ATP hydrolysis occurs after the release of product tRNA. Based on these results, we propose a model for t6A biosynthesis in bacteria.
A generative model method for unsupervised multispectral image fusion in remote sensing

This paper presents a generative model method for multispectral image fusion in remote sensing which involves training without supervision. This method eases the supervision of learning and also uses a multi-objective loss function to achieve image fusion. The loss function used incorporates both spectral and spatial distortions. Two discriminators are designed to minimize the spectral and spatial distortions of the generative output. Extensive experimentations are conducted using three public domain datasets. The comparison results across four reduced-resolution and three full-resolution objective metrics show the superiority of the developed method over several recently developed methods.
Gravitational-wave Lunar Observatory for Cosmology

Several large-scale experimental facilities and space-missions are being suggested to probe the universe across the gravitational-wave (GW) spectrum. Here we propose Gravitational-wave Lunar Observatory for Cosmology (GLOC) - the first concept design in the NASA Artemis era for a GW observatory on the Moon. We find that a lunar-based observatory is ideal for probing GW frequencies in the range between deci-Hz to 5 Hz, an astrophysically rich regime that is very challenging for both Earth- and space-based detectors. GLOC can survey binaries with neutrons stars, stellar and intermediate-mass black holes to $\gtrsim 70\%$ of the observable volume of our universe without significant background contamination. The sensitivity at $\mathcal{O}(1~\mathrm{Hz})$ allows a unique window into calibrating Type Ia supernovae. Its unprecedented sensitivity would trace the Hubble expansion rate up to redshift $z \sim 3$ and test General Relativity and $\Lambda$CDM cosmology up to $z\sim 100$.
Q8BERT: Quantized 8Bit BERT

Recently, pre-trained Transformer [1] based language models such as BERT [2] and GPT [3], have shown great improvement in many Natural Language Processing (NLP) tasks. However, these models contain a large amount of parameters. The emergence of even larger and more accurate models such as GPT2 [4] and Megatron11https://github.com/NVIDIA/Megatron-LM, suggest a trend of large pre-trained Transformer models. However, using these large models in production environments is a complex task requiring a large amount of compute, memory and power resources. In this work we show how to perform quantization-aware training during the fine-tuning phase of BERT in order to compress BERT by 4x with minimal accuracy loss. Furthermore, the produced quantized model can accelerate inference speed if it is optimized for 8bit Integer supporting hardware.
Mixing enhancement by tabs in round supersonic jets

The objective of this study was to analyze jet plume mass flow entrainment rates associated with the introduction of counter-rotating streamwise vorticity by prism shaped devices (tabs) located at the lip of the nozzle. We have examined the resulting mixing process through coordinated experimental tests and numerical simulations of the supersonic flow from a model axisymmetric nozzle. In the numerical simulations, the total induced vorticity was held constant while varying the distribution of counter-rotating vorticity around the nozzle lip training edge. In the experiment, the number of tabs applied was varied while holding the total projected area constant. Evaluations were also conducted on initial vortex strength. The results of this work show that the initial growth rate of the jet shear layer is increasingly enhanced as more tabs are added, but that the lowest tab count results in the largest entrained mass flow. The numerical simulations confirm these results.
Dynamic focusing lens for volume optical memory applications

We present the design, implementation and performance of a holographic dynamic focusing lens (HDFL). This device was developed to focus a 2D array of point sources to any of the many discrete planes inside the volume memory material. The lens utilizes two components: a spatial light modulator (SLM) as an active element which allows fast switching (approximately equals 1 microsecond(s) ec), and a diffractive optical element which allows for high SBP. The unique design feature of this device is that the resolution of the lens is not dependent on the resolution of the SLM. Simulation and experimental results are presented.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Disparities in COVID-19 hospitalizations and mortality among black and Hispanic patients: cross-sectional analysis from the greater Houston metropolitan area.

Disparate racial/ethnic burdens of the Coronavirus Disease 2019 (COVID-19) pandemic may be attributable to higher susceptibility to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or to factors such as differences in hospitalization and care provision. In our cross-sectional analysis of lab-confirmed COVID-19 cases from a tertiary, eight-hospital healthcare system across greater Houston, multivariable logistic regression models were fitted to evaluate hospitalization and mortality odds for non-Hispanic Blacks (NHBs) vs. non-Hispanic Whites (NHWs) and Hispanics vs. non-Hispanics. Between March 3rd and July 18th, 2020, 70,496 individuals were tested for SARS-CoV-2; 12,084 (17.1%) tested positive, of whom 3536 (29.3%) were hospitalized. Among positive cases, NHBs and Hispanics were significantly younger than NHWs and Hispanics, respectively (mean age NHBs vs. NHWs: 46.0 vs. 51.7 years; p < 0.001 and Hispanic vs. non-Hispanic: 44.0 vs. 48.7 years; p < 0.001). Despite younger age, NHBs (vs. NHWs) had a higher prevalence of diabetes (25.2% vs. 17.6%; p < 0.001), hypertension (47.7% vs. 43.1%; p < 0.001), and chronic kidney disease (5.0% vs. 3.3%; p = 0.001). Both minority groups resided in lower median income (median income [USD]; NHBs vs. NHWs: 63,489 vs. 75,793; p < 0.001, Hispanic vs. non-Hispanic: 59,104 vs. 68,318; p < 0.001) and higher population density areas (median population density [per square mile]; NHBs vs. NHWs: 3257 vs. 2742; p < 0.001, Hispanic vs. non-Hispanic: 3381 vs. 2884; p < 0.001). In fully adjusted models, NHBs (vs. NHWs) and Hispanics (vs. non-Hispanic) had higher likelihoods of hospitalization, aOR (95% CI): 1.42 (1.24–1.63) and 1.61 (1.46–1.78), respectively. No differences were observed in intensive care unit (ICU) utilization or treatment parameters. Models adjusted for demographics, vital signs, laboratory parameters, hospital complications, and ICU admission vital signs demonstrated non-significantly lower likelihoods of in-hospital mortality among NHBs and Hispanic patients, aOR (95% CI): 0.65 (0.40–1.03) and 0.89 (0.59–1.31), respectively. Our data did not demonstrate racial and ethnic differences in care provision and hospital outcomes. Higher susceptibility of racial and ethnic minorities to SARS-CoV-2 and subsequent hospitalization may be driven primarily by social determinants.
Evaluation of two-particle properties within finite-temperature self-consistent one-particle Green’s function methods: Theory and application to GW and GF2

One-particle Green’s function methods can model molecular and solid spectra at zero or non-zero temperatures. One-particle Green’s functions directly provide electronic energies and one-particle properties, such as dipole moment. However, the evaluation of two-particle properties, such as ⟨S2⟩ and ⟨N2⟩, can be challenging because they require a solution of the computationally expensive Bethe–Salpeter equation to find two-particle Green’s functions. We demonstrate that the solution of the Bethe–Salpeter equation can be completely avoided. Applying the thermodynamic Hellmann–Feynman theorem to self-consistent one-particle Green’s function methods, we derive expressions for two-particle density matrices in a general case and provide explicit expressions for GF2 and GW methods. Such density matrices can be decomposed into an antisymmetrized product of correlated one-electron density matrices and the two-particle electronic cumulant of the density matrix. Cumulant expressions reveal a deviation from ensemble representability for GW, explaining its known deficiencies. We analyze the temperature dependence of ⟨S2⟩ and ⟨N2⟩ for a set of small closed-shell systems. Interestingly, both GF2 and GW show a non-zero spin contamination and a non-zero fluctuation of the number of particles for closed-shell systems at the zero-temperature limit.
Deformation driven Seq2Seq longitudinal tumor and organs-at-risk prediction for radiotherapy.

PURPOSE Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy. METHODS To deal with the aforementioned challenges and to comply with the clinical requirements, we present a novel 3D sequence-to-sequence model based on Convolution Long Short-Term Memory (ConvLSTM) that makes use of series of deformation vector fields (DVFs) between individual timepoints and reference pre-treatment/planning CTs to predict future anatomical deformations and changes in gross tumor volume as well as critical OARs. High-quality DVF training data are created by employing hyper-parameter optimization on the subset of the training data with DICE coefficient and mutual information metric. We validated our model on two radiotherapy datasets: a publicly available head-and-neck dataset (28 patients with manually contoured pre-, mid-, and post-treatment CTs), and an internal non-small cell lung cancer dataset (63 patients with manually contoured planning CT and 6 weekly CBCTs). RESULTS The use of DVF representation and skip connections overcomes the blurring issue of ConvLSTM prediction with the traditional image representation. The mean and standard deviation of DICE for predictions of lung GTV at weeks 4, 5, and 6 were 0.83 ± 0.09, 0.82 ± 0.08, and 0.81 ± 0.10, respectively, and for post-treatment ipsilateral and contralateral parotids, were 0.81 ± 0.06 and 0.85 ± 0.02. CONCLUSION We presented a novel DVF-based Seq2Seq model for medical images, leveraging the complete 3D imaging information of a relatively large longitudinal clinical dataset, to carry out longitudinal GTV/OAR predictions for anatomical changes in HN and lung radiotherapy patients, which has potential to improve RT outcomes.
Polarization problem on a higher-dimensional sphere for a simplex

We study the problem of maximizing the minimal value over the sphere $$S^{d-1}\subset {\mathbb {R}}^d$$ of the potential generated by a configuration of $$d+1$$ points on $$S^{d-1}$$ (the maximal discrete polarization problem). The points interact via the potential given by a function f of the Euclidean distance squared, where $$f:[0,4]\rightarrow (-\infty ,\infty ]$$ is continuous (in the extended sense), decreasing on [0, 4], and finite and convex on (0, 4], with a concave or convex derivative $$f'$$ . We prove that the configuration of the vertices of a regular d-simplex inscribed in $$S^{d-1}$$ is optimal. This result is new for $$d>3$$ (certain special cases for $$d=2$$ and $$d=3$$ are also new). As a byproduct, we find a simpler proof for the known optimal covering property of the vertices of a regular d-simplex inscribed in $$S^{d-1}$$ .
Fulfillment by Platform: Antitrust and Upstream Market Power

Fulfillment by Platform (FBP) has been widely adopted by many e-commerce sellers. Despite providing better service to customers, recently platforms have come under antitrust scrutiny, with concerns ranging from data usage to retail competition. We examine whether the mere adoption of fulfillment services offered by platforms affects competition in the upstream markets among sellers. We use data from a leading online retailing marketplace to empirically evaluate the validity of such concerns, and estimate its effect on upstream supply echelons. We employ a structural empirical model to identify consumers' demand and merchants' cost structures. We estimate cost pressure on merchants upstream when they integrate the platform's fulfillment services. We find that the adoption of fulfillment by platform, can hurt upstream market competition, measured by the Herfindahl-Hirschman Index, that is attributable to higher cost of service. In particular, smaller merchants, with lower margins, are forced to increase prices more to remain profitable with FBP, leading to a price disadvantage compared to bigger merchants.
Containerless Processing of Undercooled Melts

Containerless drop tube processing allows for significant levels of liquid undercooling through control of parameters such as sample size, surface coating and cooling rate. A laboratory scale (3m) drop tube has been developed which allows the under-cooling and solidification behavior of powder samples to be eval-uated under low gravity free fall conditions. The level of undercooling obtained in an InSb-Sb eutectic alloy has been eval-uated by comparing the eutectic spacing in drop tube samples with a spacing/undercooling relationship established using thermal analysis techniques. Undercoolings of 0.17 and 0.23 T e were produced by processing under vacuum and He gas conditions respec-tively. Alternatively, the formation of an amorphous phase in a Ni-Nb eutectic alloy indicates that undercooling levels of approximately 500°C were obtained by drop tube processing. The influence of droplet size and gas environment on undercooling behavior in the Ni-Nb eutectic was evaluated through their effect on the amorphous/crystalline phase ratio. To supplement the structural analysis, heat flow modeling has been developed to describe the undercooling history during drop tube processing and the model has been tested experimentally.
Tax Specific versus Generic Accounting-Based Textual Analysis and the Relationship with Effective Tax Rates: Building Context

A growing literature, typically using “bags of words” dictionaries, examines the information content of text in financial accounting disclosures. We generate context for our text analysis to help predict effective tax rates using two approaches. First, we create tax-specific, expert-derived, dictionaries and second, we generate the counts for those bags of words using text taken from tax-related discussions of the Form 10K, as opposed to its entirety. We find that using expertise provides more information than simply using general accounting and finance dictionaries. In addition, we find that generating general accounting text variable values from tax-related content in the Form 10K provides statistically significant improvement in model fit. Contrary to more generic accounting and finance word-based text analysis, we find that the signs on our positive and negative tax event dictionaries are different and are consistent with theoretical expectations through each of our modeled time periods.
Enhancing the Spatio-Temporal Observability of Grid-Edge Resources in Distribution Grids

Enhancing the spatio-temporal observability of distributed energy resources (DERs) is crucial for achieving secure and efficient operations in distribution grids. This paper puts forth a joint recovery framework for residential loads by leveraging the complimentary strengths of heterogeneous measurements in real time. The proposed framework integrates low-resolution smart meter data collected at every load node with fast-sampled feeder-level measurements from limited number of distribution phasor measurement units. To address the lack of data, we exploit two key characteristics for the loads and DERs, namely the sparse changes due to infrequent activities of appliances and electric vehicles (EVs) and the locational dependence of solar photovoltaic (PV) generation. Accordingly, meaningful regularization terms are introduced to cast a convex load recovery problem, which will be further simplified to reduce the computational complexity. The load recovery solutions can be utilized to identify the EV charging events at each load node and to infer the total behind-the-meter PV output. Numerical tests using real-world data have demonstrated the effectiveness of the proposed approaches in enhancing the visibility of grid-edge DERs.
Expansion in matrix-weighted graphs

Abstract A matrix-weighted graph is an undirected graph with a k × k symmetric positive semidefinite matrix assigned to each edge. Such graphs admit natural generalizations of the Laplacian and adjacency matrices, leading to a generalized notion of expansion. Extensions of some theorems about expansion hold for matrix-weighted graphs—in particular, an analogue of the expander mixing lemma and one half of a Cheeger-type inequality. These results lead to a definition of a matrix-weighted expander graph, and suggest the tantalizing possibility of families of matrix-weighted graphs with better-than-Ramanujan expansion.
On the number of independent sets in uniform, regular, linear hypergraphs

Abstract We study the problems of bounding the number weak and strong independent sets in r -uniform, d -regular, n -vertex linear hypergraphs with no cross-edges. In the case of weak independent sets, we provide an upper bound that is tight up to the first order term for all (fixed) r ≥ 3 , with d and n going to infinity. In the case of strong independent sets, for r = 3 , we provide an upper bound that is tight up to the second order term, improving on a result of Ordentlich–Roth (2004). The tightness in the strong independent set case is established by an explicit construction of a 3-uniform, d -regular, cross-edge free, linear hypergraph on n vertices which could be of interest in other contexts. We leave open the general case(s) with some conjectures. Our proofs use the occupancy method introduced by Davies, Jenssen, Perkins, and Roberts (2017).