cssi/SciSciGPT-SciSciNet huggingface dataset:
Gabriella: An Online System for Real-Time Activity Detection in Untrimmed Security Videos

Activity detection in security videos is a difficult problem due to multiple factors such as large field of view, presence of multiple activities, varying scales and viewpoints, and its untrimmed nature. The existing research in activity detection is mainly focused on datasets, such as UCF-101, JHMDB, THUMOS, and AVA, which partially address these issues. The requirement of processing security videos in real-time makes this even more challenging. In this work, we propose Gabriella, a real-time online system to perform activity detection on untrimmed security videos. The proposed method consists of three stages: tubelet extraction, activity classification, and online tubelet merging. For tubelet extraction, we propose a localization network which takes a video clip as input and spatio-temporally detects potential foreground regions at multiple scales to generate action tubelets. We propose a novel Patch-Dice loss to handle large variations in actor size. Our online processing of videos at a clip level drastically reduces the computation time in detecting activities. The detected tubelets are assigned activity class scores by the classification network and merged together using our proposed Tubelet-Merge Action-Split (TMAS) algorithm to form the final action detections. The TMAS algorithm efficiently connects the tubelets in an online fashion to generate action detections which are robust against varying length activities. We perform our experiments on the VIRAT and MEVA (Multiview Extended Video with Activities) datasets and demonstrate the effectiveness of the proposed approach in terms of speed (∼100 fps) and performance with state-of-the-art results. More details about this work are available on our project webpage11https://www.crcv.ucf.edu/research/projects/gabriella-an-online-system-for-real-time-activity-detection-in-untrimmed-security-videos.
Toward Speeding up Mutation Analysis by Memoizing Expensive Methods

Mutation analysis has many applications, such as assessing the quality of test cases, fault localization, test input generation, security analysis, etc. Such applications involve running test suite against a large number of program mutants leading to poor scalability. Much research has been aimed at speeding up this process, focusing on reducing the number of mutants, the number of executed tests, or the execution time of the mutants. This paper presents a novel approach, named MeMu, for reducing the execution time of the mutants, by memoizing the most expensive methods in the system. Memoization is an optimization technique that allows bypassing the execution of expensive methods, when repeated inputs are detected. MeMu can be used in conjunction with existing acceleration techniques. We implemented MeMu on top of PITest, a well-known JVM bytecode-level mutation analysis system, and obtained, on average, an 18.15% speed-up over PITest, in the execution time of the mutants for 12 real-world programs. These promising results and the fact that MeMu could also be used for other applications that involve repeated execution of tests (e.g., automatic program repair and regression testing), strongly support future research for improving its efficiency.
Gate array implementation of on-line algorithms for floating-point operations

We present gate array designs of on-line arithmetic units for radix-2 floating-point addition, multiplication and division operations. Performance and complexity characteristics of the implementations of on-line arithmetic units are discussed and compared with those of the compatible conventional floating-point algorithms implemented in the same technology.
Multipoint inverse design of an infinite cascade of airfoils

This paper describes a method for the design of an infinite cascade in incompressible flow. The method is based on conformal mapping and does not allow for multipoint design. The cascade blade is to determined is divided into a number of segments. Over each segment, the velocity distribution is prescribed together with an inlet or outlet flow angle at which this velocity distributions is to be achieved. In this way multipoint design requirements can be met. It is necessary to satisfy several conditions that arise to guarantee compatibility with the inlet and outlet flow as well as closure of the cascade blade. Satisfaction of these conditions does not necessarily result in a cascade with all of the desired characteristucs. For example, the cascade blades may be bulbous or crossed. Through Newtonian iteration, however, the desired characteristics may be prescribed directly. Four examples will be illustrated to demonstrate the capability of the method.
Treadmill Assisted Gait Spoofing (TAGS): An Emerging Threat to Wearable Sensor-based Gait Authentication

In this work, we examine the impact of Treadmill Assisted Gait Spoofing on Wearable Sensor-based Gait Authentication (WSGait). We consider more realistic implementation and deployment scenarios than the previous study, which focused only on the accelerometer sensor and a fixed set of features. Specifically, we consider the situations in which the implementation of WSGait could be using one or more sensors embedded into modern smartphones. In addition, it could be using different sets of features or different classification algorithms, or both. Despite the use of a variety of sensors, feature sets (ranked by mutual information), and six different classification algorithms, Treadmill Assisted Gait Spoofing was able to increase the average false accept rate from 4% to 26%. Such a considerable increase in the average false accept rate, especially under the stringent implementation and deployment scenarios considered in this study, calls for a further investigation into the design of evaluations of WSGait before its deployment for public use.
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism

We study exploration in stochastic multi-armed bandits when we have access to a divisible resource that can be allocated in varying amounts to arm pulls. We focus in particular on the allocation of distributed computing resources, where we may obtain results faster by allocating more resources per pull, but might have reduced throughput due to nonlinear scaling. For example, in simulation-based scientific studies, an expensive simulation can be sped up by running it on multiple cores. This speed-up however, is partly offset by the communication among cores, which results in lower throughput than if fewer cores were allocated per trial to run more trials in parallel. In this paper, we explore these trade-offs in two settings. First, in a fixed confidence setting, we need to find the best arm with a given target success probability as quickly as possible. We propose an algorithm which trades off between information accumulation and throughput and show that the time taken can be upper bounded by the solution of a dynamic program whose inputs are the gaps between the sub-optimal and optimal arms. We also prove a matching hardness result. Second, we present an algorithm for a fixed deadline setting, where we are given a time deadline and need to maximize the probability of finding the best arm. We corroborate our theoretical insights with simulation experiments that show that the algorithms consistently match or outperform baseline algorithms on a variety of problem instances.
UAV Swarm Position Optimization for High Capacity MIMO Backhaul

A swarm of cooperating UAVs communicating with a distant multiantenna ground station can leverage MIMO spatial multiplexing to scale the capacity. Due to the line-of-sight propagation between the swarm and the ground station, the MIMO channel is highly correlated, leading to limited multiplexing gains. In this paper, we optimize the UAV positions to attain the maximum MIMO capacity given by the single user bound. An infinite set of UAV placements that attains the capacity bound is first derived. Given an initial swarm placement, we formulate the problem of minimizing the distance traveled by the UAVs to reach a placement within the capacity maximizing set of positions. An offline centralized solution to the problem using block coordinate descent is developed assuming known initial positions of UAVs. We also propose an online distributed algorithm, where the UAVs iteratively adjust their positions to maximize the capacity. Our proposed approaches are shown to significantly increase the capacity at the expense of a bounded translation from the initial UAV placements. This capacity increase persists when using a massive MIMO ground station. Using numerical simulations, we show the robustness of our approaches in a Rician channel under UAV motion disturbances.
The COVID-19 Impact on Corporate Leverage and Financial Fragility

We study the impact of the COVID-19 recession on capital structure of publicly listed U.S. firms. Our estimates suggest leverage (Net Debt/Asset) decreased by 5.3 percentage points from the pre-shock mean of 19.6 percent, while debt maturity increased moderately. This de-leveraging effect is stronger for firms exposed to significant rollover risk, while firms whose businesses were most vulnerable to social distancing did not de-lever relative to less vulnerable companies. We rationalize our evidence through a structural model of firm value that shows lower expected growth rate and higher volatility of cash flows following COVID-19 reduced optimal levels of corporate leverage. Model-implied optimal leverage indicates firms severely affected by lockdowns became over-leveraged. We find default probability deteriorates most in large, over-leveraged firms and those that were stressed pre-COVID. Additional stress tests predict value of these firms will be less than one standard deviation away from default if cash flows decline by 20 percent.
All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text

Human evaluations are typically considered the gold standard in natural language generation, but as models’ fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts’ ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three domains (stories, news articles, and recipes). We find that, without training, evaluators distinguished between GPT3- and human-authored text at random chance level. We explore three approaches for quickly training evaluators to better identify GPT3-authored text (detailed instructions, annotated examples, and paired examples) and find that while evaluators’ accuracy improved up to 55%, it did not significantly improve across the three domains. Given the inconsistent results across text domains and the often contradictory reasons evaluators gave for their judgments, we examine the role untrained human evaluations play in NLG evaluation and provide recommendations to NLG researchers for improving human evaluations of text generated from state-of-the-art models.
Optimal Speed Sharing Characteristics of a Series-Hybrid Bearing

A series-hybrid bearing assembly consisting of a conical hydrostatic fluid-film bearing and a ball bearing is described. Computer studies are used to predict friction torque and life characteristics of a 150-millimeter ball bearing. A conical hydrostatic fluid-film bearing is designed for minimum friction and maximum speed reduction of the ball-bearing component of the series-hydrid bearing. At a thrust load of 4000 pounds and speeds corresponding to DN (bearing bore in millimeters times shaft speed in rpm) values of 3 and 4 million, ball-bearing speed may be reduced by 30 percent. This speed reduction corresponds to ball-bearing fatigue life improvement factors of 3.4 at 3 million DN and 5.9 at 4 million DN. An oil flow rate of 18.2 pounds per minute is required to maintain a fluid-film thickness of 0.001 inch in the hydrostatic bearing.
Accelerated Polynomial Evaluation and Differentiation at Power Series in Multiple Double Precision

The problem is to evaluate a polynomial in several variables and its gradient at a power series truncated to some finite degree with multiple double precision arithmetic. To compensate for the cost overhead of multiple double precision and power series arithmetic, data parallel algorithms for general purpose graphics processing units are presented. The reverse mode of algorithmic differentiation is organized into a massively parallel computation of many convolutions and additions of truncated power series. Experimental results demonstrate that teraflop performance is obtained in deca double precision with power series truncated at degree 152. The algorithms scale well for increasing precision and increasing degrees.
Viscous effects on transonic airfoil stability and response

Viscous effects on transonic airfoil stability and response are investigated using an integral boundary layer model coupled to the inviscid XTRAN2L transonic small disturbance code. Unsteady transonic airloads required for stability analyses are computed using a pulse transfer function analysis including viscous effects. The pulse analysis provides unsteady aerodynamic forces for a wide range of reduced frequency in a single flow field computation. Nonlinear time marching aeroelastic solutions are presented which show the effects of viscosity on airfoil response behavior and flutter. Effects of amplitude on time marching responses are demonstrated. A state space aeroelastic model employing Pade approximants to describe the unsteady airloads is used to study the effects of viscosity on transonic airfoil stability. State space dynamic pressure root loci are in good overall agreement with time marching damping and frequency estimates. Parallel sets of results with and without viscous effects reveal the effects of viscosity on transonic unsteady airloads and aeroelastic characteristics of airfoils.
Active Bayesian Assessment for Black-Box Classifiers

Author(s): Ji, Disi; IV, Robert L Logan; Smyth, Padhraic; Steyvers, Mark | Abstract: Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications. In many such situations there is a critical need to both reliably assess the performance of these pre-trained models and to perform this assessment in a label-efficient manner (given that labels may be scarce and costly to collect). In this paper, we introduce an active Bayesian approach for assessment of classifier performance to satisfy the desiderata of both reliability and label-efficiency. We begin by developing inference strategies to quantify uncertainty for common assessment metrics such as accuracy, misclassification cost, and calibration error. We then propose a general framework for active Bayesian assessment using inferred uncertainty to guide efficient selection of instances for labeling, enabling better performance assessment with fewer labels. We demonstrate significant gains from our proposed active Bayesian approach via a series of systematic empirical experiments assessing the performance of modern neural classifiers (e.g., ResNet and BERT) on several standard image and text classification datasets.
Optimal plane change during constant altitude hypersonic flight

In a previous paper, we addressed the problem of choosing constant values of altitude, speed, and angle of attack such that the orbital plane change during hypersonic flight is maximized for a fixed amount of propellant consumption. In the present paper, the restrictions of constant speed and angle of attack are removed. Necessary conditions for solutions to the resulting optimal control problem are derived. Numerical solutions are obtained for several specific cases under the assumption that the constant altitude trajectory that maximizes the plane change is primarily a singular arc. We find that, under the condition of constant altitude flight, it is not, in general, optimum to fly at constant angle of attack. The reduction in plane change capability resulting from a constant angle-of-attack program increases as the range over which the flight takes place increases. On the other hand, the optimum speed is nearly constant.
Extreme weather and ratings on corporate climate mitigation policies

I show that after controlling for county fixed effects, the annual number of extreme weather events (EWEs) sustained by a US county significantly improves the subsequent rating of the climate mitigation policies of a firm headquartered in that county. I also find that more recent EWEs have a more pronounced effect on climate ratings than more distant ones. For the utility industry I provide evidence that flooding events positively affect both managerial concerns for climate risk as reflected in 10-Ks/10-Qs/8-Ks and climate ratings. These results suggest that managerial experiential processing of weather information is important in determining corporate climate mitigation policies.
Wind farm yaw control set-point optimization under model parameter uncertainty

Wake steering, the intentional yaw misalignment of certain turbines in an array, has demonstrated potential as a wind farm control approach to increase collective power. Existing algorithms optimize the yaw misalignment angle set-points using steady-state wake models and either deterministic frameworks or optimizers that account for wind direction and yaw misalignment variability and uncertainty. Wake models rely on parameterizations of physical phenomena in the mean flow field, such as the wake spreading rate. The wake model parameters are uncertain and vary in time at a wind farm depending on the atmospheric conditions, including turbulence intensity, stability, shear, veer, and other atmospheric features. In this study, we develop a yaw set-point optimization approach that includes model parameter uncertainty in addition to wind condition variability and uncertainty. To enable computationally efficient online set-point optimization under model parameter uncertainty, a simplified, approximate parameter distribution estimation method is used. The optimization is tested in open-loop control numerical experiments using utility-scale wind farm operational data for which the set-point optimization framework with parametric uncertainty has a statistically significant impact on the wind farm power production for certain wind turbine layouts at low turbulence intensity, but the results are not significant for all layouts considered nor at higher turbulence intensity. The set-point optimizer is also tested for closed-loop wake steering control of a model wind farm in large eddy simulations of a convective atmospheric boundary layer (ABL). The yaw set-point optimization with model parameter uncertainty reduced the sensitivity of the closed-loop wake steering control to increases in the yaw controller update frequency. Increases in wind farm power production were not statistically significant due to the high ambient power variability in the turbulent, convective ABL.
One-dimensional system arising in stochastic gradient descent

We consider stochastic differential equations of the form are martingale differences that are almost surely bounded. This result shows that for a function F whose second derivative at degenerate saddle points is of polynomial order, it is always possible to escape saddle points via the iteration .
Learning under uncertainty with multiple priors: experimental investigation

We run an experiment to compare belief formation and learning under ambiguity and under compound risk at the individual level. We estimate a four-type mixture model assuming that, for each type of uncertainty, subjects may either learn according to Bayes’ Rule or learn according to a multiple priors model of learning. Our results indicate that majority of subjects are Bayesian, both under compound risk and under ambiguity, while the second most frequent type are subjects that are Bayesian under compound risk but who use a multiple priors model of learning under ambiguity. In addition, we find strong evidence against a common assumption that participants’ initial beliefs (and priors) are consistent with information provided about the uncertain process.
Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants (kp). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.
Reducing COVID-19 Airborne Transmission Risks on Public Transportation Buses: An Empirical Study on Aerosol Dispersion and Control

This study is one of the first COVID-19 related bus studies to fully characterize cough aerosol dispersion and control in the highly turbulent real-world environment of driving regular bus routes o...
Nonparametric market supply with variable participants

Research estimating market supply often focuses on parametric models. In this paper, we study a non-parametric approach to market supply. We characterize the necessary and sufficient conditions of profit maximization for aggregate market behavior when individual firm supply is unobservable and participants on the supply side vary. We also show how the result changes as additional properties are added such as constraints on net outputs, non-negative profits, and increasing production sets.
Weighted Average Precision: Adversarial Example Detection in the Visual Perception of Autonomous Vehicles

Recent works have shown that neural networks are vulnerable to carefully crafted adversarial examples (AE). By adding small perturbations to input images, AEs are able to make the victim model predicts incorrect outputs. Several research work in adversarial machine learning started to focus on the detection of AEs in autonomous driving. However, the existing studies either use preliminary assumption on outputs of detections or ignore the tracking system in the perception pipeline. In this paper, we firstly propose a novel distance metric for practical autonomous driving object detection outputs. Then, we bridge the gap between the current AE detection research and the real-world autonomous systems by providing a temporal detection algorithm, which takes the impact of tracking system into consideration. We perform evaluation on Berkeley Deep Drive (BDD) and CityScapes datasets to show how our approach outperforms existing single-frame-mAP based AE detections by increasing 17.76% accuracy of performance.
Occupants’ satisfaction with LEED- and non-LEED-certified apartments using social media data

Abstract Leadership in Energy and Environmental Design (LEED) certified buildings aim to offer a sustainable and healthy building environment. Previous studies have shown mixed and inconsistent results on whether occupants in LEED-certified buildings are more satisfied than in non-LEED-certified counterparts. Those studies are usually based on surveys or questionnaires for commercial buildings and were limited by sample size and pre-defined question structures. Since most people would spend more time at home after experiencing the COVID-19 pandemic due to the flexibility to work remotely, assessing the satisfaction with residential buildings benefits future environmental design and certification system development. In this work, we propose a natural language processing-based approach for such assessment. The study collected 16,761 online reviews of 232 LEED-certified and 129 non-LEED-certified apartment buildings from social media, then applied topic modeling and sentiment analysis to evaluate occupants’ satisfaction. Based on topic modeling, we categorized online comments into three topics, 1) location and transportation, 2) running cost, and 3) health and wellbeing. The subsequent sentiment analysis has shown a statistically significant but small or negligible enhancement in the satisfaction occurring in LEED-certified apartments compared to non-LEED-certified ones concerning all three topics. The “significant but small or negligible uptick” has also been found in online star rating and indoor environmental satisfaction. The only exception with a large effect size is lighting which is found to be significantly more satisfying in LEED-certified apartments. Nevertheless, the statistical significance in online star rating disappears when normalized by rent price and property house value.
Recent Results in Random Vibrations of Nonlinear Mechanical Systems

The influence of random vibration on the design of mechanical components has been considered within the framework of the linear theory of small oscillations. However, in some important cases this theory is inadequate and fails to predict some complex response characteristics that have been observed experimentally and which can only be predicted by nonlinear analyses. This paper describes some recent developments in the theory of nonlinear random vibration based on Markov methods and related problems in the design of dynamical systems. Research efforts have been focused on stability/bifurcation conditions, response statistics and reliability problems. Significant progress has been made in developing new analytical methods and conducting experimental testing. These developments have helped to resolve some controversies, and to enhance our understanding of difficult issues. Experimental and numerical simulations have revealed new phenomena that were not predicted analytically. These include on-off intermittency, snap-through phenomena, and the dependence of the response bandwidth on the excitation level. The main results of studying the responses of nonlinear single-and two-degree-of-freedom systems to random excitations obtained by the author and others are discussed in this paper.
Peer-to-peer multi-risk insurance and mutual aid

Abstract Peer-to-peer (P2P) insurance is a decentralized network in which participants pool their resources together to compensate those who suffer losses. The rise of P2P insurance in Western countries, such as Friendsurance and Lemonade, has been viewed as a disruption to the traditional insurance industry in the same way Uber is to the taxi industry. A similar business model of mutual aid, such as the model developed by Xianghubao, has become popular in the East. It is a model designed to provide financial support to those in need and to distribute the cost among all participants. Despite the fast-changing landscape in this field, there has been scarce literature on the theoretical underpinning of P2P insurance and mutual aid. This paper presents the first effort to build a unified framework to quantify and assess the exchange of risks in a network of participants from different risk classes. Under this framework, the paper aims to address several essential research questions, including the fair exchange of heterogeneous risks and the optimality of algorithmic designs under various criteria. The modeling of multi-risk exchange is done with a P2P network structure. We show that these network structures not only can be used to explain the current practice in the industry, but also provide new tools to develop better designs. The paper concludes with a comparison of traditional insurance and P2P risk sharing from the standpoint of stability and cost reduction.
Dynamics of disruption and recovery in air transportation networks

Flight delays occur in the air transportation system when disruptive events such as weather, equipment outage, or congestion create an imbalance between system capacity and demand. These cycles of disruptions and subsequent recoveries can be viewed from a dynamical systems perspective: exogenous inputs (convective weather, airspace restrictions, etc.) disrupt the system, inducing delays and inefficiencies from which the system eventually recovers. We study these disruption and recovery cycles through a state-space representation that captures the severity and spatial impact of airport delays. In particular, using US airport delay data from 2008 to 2017, we first identify representative disruption and recovery cycles. These representative cycles provide insights into the common operational patterns of disruptions and recoveries in the system. We also relate these representative cycles to specific off-nominal events such as airport outages, and elucidate the differing disruption–recovery pathways for various off-nominal events. Finally, we explore temporal trends in terms of when and how the system tends to be disrupted, and the subsequent recovery.
Transfer operators and Hankel transforms between relative trace formulas, II: Rankin-Selberg theory

Abstract The Langlands functoriality conjecture, as reformulated in the “beyond endoscopy” program, predicts comparisons between the (stable) trace formulas of different groups G 1 , G 2 for every morphism G 1 L → L G 2 between their L-groups. This conjecture can be seen as a special case of a more general conjecture, which replaces reductive groups by spherical varieties and the trace formula by its generalization, the relative trace formula. The goal of this article and its precursor [11] is to demonstrate, by example, the existence of “transfer operators” between relative trace formulas, which generalize the scalar transfer factors of endoscopy. These transfer operators have all properties that one could expect from a trace formula comparison: matching, fundamental lemma for the Hecke algebra, transfer of (relative) characters. Most importantly, and quite surprisingly, they appear to be of abelian nature (at least, in the low-rank examples considered in this paper), even though they encompass functoriality relations of non-abelian harmonic analysis. Thus, they are amenable to application of the Poisson summation formula in order to perform the global comparison. Moreover, we show that these abelian transforms have some structure — which presently escapes our understanding in its entirety — as deformations of well-understood operators when the spaces under consideration are replaced by their “asymptotic cones”. In this second paper we use Rankin–Selberg theory to prove the local transfer behind Rudnick's 1990 thesis (comparing the stable trace formula for SL 2 with the Kuznetsov formula) and Venkatesh's 2002 thesis (providing a “beyond endoscopy” proof of functorial transfer from tori to GL 2 ). As it turns out, the latter is not completely disjoint from endoscopic transfer — in fact, our proof “factors” through endoscopic transfer. We also study the functional equation of the symmetric-square L-function for GL 2 , and show that it is governed by an explicit “Hankel operator” at the level of the Kuznetsov formula, which is also of abelian nature. A similar theory for the standard L-function was previously developed (in a different language) by Jacquet.
Tributyltin protects against ovariectomy-induced trabecular bone loss in C57BL/6J mice with an attenuated effect in high fat fed mice

Abstract Risk factors for poor bone quality include estrogen loss at menopause, a high fat diet and exposures to drugs/chemicals that activate peroxisome proliferator activated receptor gamma (PPARγ). We previously reported that the PPARγ and retinoid X receptor dual ligand, tributyltin (TBT), repressed periosteal bone formation but enhanced trabecular bone formation in vivo. Here, we examined the interaction of diet, ovariectomy (OVX) and TBT exposure on bone structure. C57BL/6J mice underwent either sham surgery or OVX at 10 weeks of age. At 12 weeks of age, they were placed on a low (10% kcal) or high (45% kcal) fat, sucrose-matched diet and treated with vehicle or TBT (1 or 5 mg/kg) for 14 weeks. OVX increased body weight gain in mice on either diet. TBT enhanced body weight gain in intact mice fed a high fat diet, but decreased weight gain in OVX mice. Elemental tin concentrations increased dose-dependently in bone. TBT had marginal effects on cortical and trabecular bone in intact mice fed either diet. OVX caused a reduction in cortical and trabecular bone, regardless of diet. In high fat fed OVX mice, TBT further reduced cortical thickness, bone area and total area. Interestingly, TBT protected against OVX-induced trabecular bone loss in low fat fed mice. The protective effect of TBT was nullified by the high fat. These results show that TBT protects against trabecular bone loss, even in the presence of a strongly resorptive environment, at an even lower level of exposure than we showed repressed homeostatic resorption.
Underscreening and hidden ion structures in large scale simulations of concentrated electrolytes

The electrostatic screening length predicted by Debye–Huckel theory decreases with increasing ionic strength, but recent experiments have found that the screening length can instead increase in concentrated electrolytes. This phenomenon, referred to as underscreening, is believed to result from ion–ion correlations and short-range forces such as excluded volume interactions among ions. We use Brownian Dynamics to simulate a version of the Restrictive Primitive Model for electrolytes over a wide range of ion concentrations, ionic strengths, and ion excluded volume radii for binary electrolytes. We measure the decay of the charge–charge correlation among ions in the bulk and compare it against scaling trends found experimentally and determined in certain weak coupling theories of ion–ion correlation. Moreover, we find that additional large scale ion structures emerge at high concentrations. In this regime, the frequency of oscillations computed from the charge–charge correlation function is not dominated by electrostatic interactions but rather by excluded volume interactions and with oscillation periods on the order of the ion diameter. We also find the nearest neighbor correlation of ions sharing the same charge transitions from negative at small concentrations to positive at high concentrations, representing the formation of small, like-charge ion clusters. We conclude that the increase in local charge density due to the formation of these clusters and the topological constraints of macroscopic charged surfaces can help explain the degree of underscreening observed experimentally.
Covariance group for null geodesic expansion calculations, and its application to the apparent horizon

We show that the recipe for computing the expansions &#x1d703;l and &#x1d703;n of outgoing and ingoing null geodesics normal to a surface admits a covariance group with nonconstant scalar κ(x), corresponding to th...
Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles

We present Pylot, a platform for autonomous vehicle (AV) research and development, built with the goal to allow researchers to study the effects of the latency and accuracy of their models and algorithms on the end-to-end driving behavior of an AV. This is achieved through a modular structure enabled by our high-performance dataflow system that represents AV software pipeline components (object detectors, motion planners, etc.) as a dataflow graph of operators which communicate on data streams using timestamped messages. Pylot readily interfaces with popular AV simulators like CARLA, and is easily deployable to real-world vehicles with minimal code changes.To reduce the burden of developing an entire pipeline for evaluating a single component, Pylot provides several state-of-the-art reference implementations for the various components of an AV pipeline. Using these reference implementations, a Pylot-based AV pipeline is able to drive a real vehicle, and attains a high score on the CARLA Autonomous Driving Challenge. We also present several case studies enabled by Pylot, including evidence of a need for context-dependent components, and per-component time allocation. Pylot is open source, with the code available at https://github.com/erdos-project/pylot.
Simultaneous profiling of multiple chromatin proteins in the same cells

Methods derived from CUT&RUN and CUT&Tag enable genome-wide mapping of the localization of proteins on chromatin from as few as one cell. These and other mapping approaches focus on one protein at a time, preventing direct measurements of co-localization of different chromatin proteins in the same cells and requiring prioritization of targets where samples are limiting. Here, we describe multi-CUT&Tag, an adaptation of CUT&Tag that overcomes these hurdles by using antibody-specific barcodes to simultaneously map multiple proteins in the same cells. Highly specific multi-CUT&Tag maps of histone marks and RNA Polymerase II uncovered sites of co-localization in the same cells, active and repressed genes, and candidate cis-regulatory elements. Single-cell multi-CUT&Tag profiling facilitated identification of distinct cell types from a mixed population and characterization of cell-type-specific chromatin architecture. In sum, multi-CUT&Tag increases the information content per cell of epigenomic maps, facilitating direct analysis of the interplay of different chromatin proteins.
Local Composite Quantile Regression for Regression Discontinuity

We introduce the local composite quantile regression (LCQR) to causal inference in regression discontinuity (RD) designs. Kai et al. (2010) study the efficiency property of LCQR, while we show that its nice boundary performance translates to accurate estimation of treatment effects in RD under a variety of data generating processes. Moreover, we propose a bias-corrected and standard error-adjusted t-test for inference, which leads to confidence intervals with good coverage probabilities. A bandwidth selector is also discussed. For illustration, we conduct a simulation study and revisit a classic example from Lee (2008). A companion R package rdcqr is developed.
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing

Predicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained sequence-to-sequence Transformer models has recently led to large improvements on AMR parsing benchmarks. These parsers are simple and avoid explicit modeling of structure but lack desirable properties such as graph well-formedness guarantees or built-in graph-sentence alignments. In this work we explore the integration of general pre-trained sequence-to-sequence language models and a structure-aware transition-based approach. We depart from a pointer-based transition system and propose a simplified transition set, designed to better exploit pre-trained language models for structured fine-tuning. We also explore modeling the parser state within the pre-trained encoder-decoder architecture and different vocabulary strategies for the same purpose. We provide a detailed comparison with recent progress in AMR parsing and show that the proposed parser retains the desirable properties of previous transition-based approaches, while being simpler and reaching the new parsing state of the art for AMR 2.0, without the need for graph re-categorization.
The First Vision For Vitals (V4V) Challenge for Non-Contact Video-Based Physiological Estimation

Telehealth has the potential to offset the high demand for help during public health emergencies, such as the COVID-19 pandemic. Remote Photoplethysmography (rPPG) - the problem of non-invasively estimating blood volume variations in the microvascular tissue from video - would be well suited for these situations. Over the past few years a number of research groups have made rapid advances in remote PPG methods for estimating heart rate from digital video and obtained impressive results. How these various methods compare in naturalistic conditions, where spontaneous behavior, facial expressions, and illumination changes are present, is relatively unknown. To enable comparisons among alternative methods, the 1st Vision for Vitals Challenge (V4V) presented a novel dataset containing high-resolution videos time-locked with varied physiological signals from a diverse population. In this paper, we outline the evaluation protocol, the data used, and the results. V4V is to be held in conjunction with the 2021 International Conference on Computer Vision.
Simple self-calibrating polarimeter for measuring the Stokes parameters of light

A simple, self-calibrating, rotating-waveplate polarimeter is largely insensitive to light intensity fluctuations and is shown to be useful for determining the Stokes parameters of light. This study shows how to minimize the in situ self-calibration time, the measurement time, and the measurement uncertainty. The suggested methods are applied to measurements of spatial variations in the linear and circular polarizations of laser light passing through glass plates with a laser intensity dependent birefringence. These are crucial measurements for the ACME electron electric dipole measurements, requiring accuracies in circular and linear polarization fraction of about 0.1% and 0.4%, with laser intensities up to 100 mW/mm2 incident into the polarimeter.
Scqubits: a Python package for superconducting qubits

$\textbf{scqubits}$ is an open-source Python package for simulating and analyzing superconducting circuits. It provides convenient routines to obtain energy spectra of common superconducting qubits, such as the transmon, fluxonium, flux, cos(2$\phi$) and the 0-$\pi$ qubit. $\textbf{scqubits}$ also features a number of options for visualizing the computed spectral data, including plots of energy levels as a function of external parameters, display of matrix elements of various operators as well as means to easily plot qubit wavefunctions. Many of these tools are not limited to single qubits, but extend to composite Hilbert spaces consisting of coupled superconducting qubits and harmonic (or weakly anharmonic) modes. The library provides an extensive suite of methods for estimating qubit coherence times due to a variety of commonly considered noise channels. While all functionality of $\textbf{scqubits}$ can be accessed programatically, the package also implements GUI-like widgets that, with a few clicks can help users both create relevant Python objects, as well as explore their properties through various plots. When applicable, the library harnesses the computing power of multiple cores via multiprocessing. $\textbf{scqubits}$ further exposes a direct interface to the Quantum Toolbox in Python (QuTiP) package, allowing the user to efficiently leverage QuTiP's proven capabilities for simulating time evolution.
Delegation in Veto Bargaining

A proposer requires a veto player's approval to change a status quo. Proposer is uncertain about Vetoer's preferences. We show that Vetoer is typically given a non-singleton menu, or delegation set, of options to pick from. The optimal set balances the extent of compromise with the risk of a veto. We identify conditions for certain delegation sets to emerge, including "full delegation": Vetoer can choose any action between the status quo and Proposer's ideal action. By contrast to expertise-based delegation, Proposer gives less discretion to Vetoer when their preferences are more (likely to be) aligned.
Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition

We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their sum. In this work, we propose a fast non-convex algorithm, coined Robust Tensor CUR (RTCUR), for large-scale TRPCA problems. RTCUR considers a framework of alternating projections and utilizes the recently developed tensor Fiber CUR decomposition to dramatically lower the computational complexity. The performance advantage of RTCUR is empirically verified against the state-of-the-arts on the synthetic datasets and is further demonstrated on the real-world application such as color video background subtraction.
Portfolio rebalancing based on time series momentum and downside risk

To examine the familiar tradeoff between risk and return in financial investments, we use a rolling two-stage stochastic program to compare mean-risk optimization models with time series momentum strategies. In a backtest of allocating investment between a market index and a risk-free asset, we generate scenarios of future return according to a momentum-based stochastic process model. A new hybrid approach, time series momentum strategy controlling downside risk (TSMDR), frequently dominates traditional approaches by generating trading signals according to a modified momentum measure while setting the risky asset position to control the CVaR of return. For insight into the outperformance of TSMDR, we decompose each strategy into two aspects, the trading signal and the asset allocation model that determines the risky asset position. We find that 1) weighted moving average can better capture the trend of the stock market than time series momentum computed as past 12-month excess return, 2) mean-risk strategies generally provide better returns whereas risk parity strategies have less investment risk, and 3) controlling CVaR limits the investment risk better than controlling variance does.
The biology and psychology of moral agency

Part I. Moral Agency and Scientific Naturalism: 1. Understanding moral agency: what is a scientific naturalist view of moral agency? Part II. The Biological Bases of Moral Agency: 2. Evolution and moral agency: can evolution endow us with moral capacities? 3. Evolution and moral agency: Does Evolution endow us with moral capacities? 4. Developmental biology and psychology and moral agency: how do our biologically-based moral capacities develop? Part III. The Psychological Bases of moral Agency: 5. Behavioral psychology and moral agency: how do we learn to behave morally? 6. Social cognitive psychology and moral agency: how do we learn to act morally? 7. The neurophysiological bases of moral capacities: do the neurosciences have room for moral agents? Part IV. A Scientific Naturalist Account of Moral Agency: 8. The adequacy of moral beliefs, motivations and actions: how can biological and psychological explanations serve as justifications? 9. Moral ontology: what is the ontological status of moral values? Part V. Integrating a Personalistic and Naturalistic View of Agency: 10. The manifest and scientific images of morality: how can we integrate our ordinary and scientifically based views of moral agency?
Determining the solar-flare photospheric scale height from SMM gamma-ray measurements

A connected series of Monte Carlo programs was developed to make systematic calculations of the energy, temporal and angular dependences of the gamma-ray line and neutron emission resulting from such accelerated ion interactions. Comparing the results of these calculations with the Solar Maximum Mission/Gamma Ray Spectrometer (SMM/GRS) measurements of gamma-ray line and neutron fluxes, the total number and energy spectrum of the flare-accelerated ions trapped on magnetic loops at the Sun were determined and the angular distribution, pitch angle scattering, and mirroring of the ions on loop fields were constrained. Comparing the calculations with measurements of the time dependence of the neutron capture line emission, a determination of the He-3/H ratio in the photosphere was also made. The diagnostic capabilities of the SMM/GRS measurements were extended by developing a new technique to directly determine the effective photospheric scale height in solar flares from the neutron capture gamma-ray line measurements, and critically test current atmospheric models in the flare region.