cssi/SciSciGPT-SciSciNet huggingface dataset:
The Represented Client in a Settlement Conference: The Lessons of G. Heileman Brewing Co. v. Joseph Oat Corp.

This Article sets out various perspectives that litigants, lawyers and judges commonly bring to settlement conferences, perspectives on lawyer-client relations, negotiation, and the role of the judicial host. In examining the opinions in the Heileman case, along with other materials, the Article attempts to uncover the underlying assumptions about the settlement conference that informed the behavior of the judges and lawyers in that case, arguing that Heileman's explanation lies in the lawyers' and judges' tendency to embrace one of two radically different visions of the settlement conference. The Article then catalogs the advantages and disadvantages of involving clients in settlement conferences and describes the many different ways in which a client can participate, and offers general suggestions about when and for what purposes a judicial host should require a litigant or a representative of an organizational litigant to accompany the litigant's lawyer to a settlement conference. It then proposes an explanation for the expression "full authority to settle the case" as it applies to an organization. Finally, the Article reviews the obligations of the client or client representative once he or she appears at the settlement conference and suggests a special benefit flowing from client participation in settlement conferences.
Ensemble Learning Based Convex Approximation of Three-Phase Power Flow

Though the convex optimization has been widely used in power systems, it still cannot guarantee to yield a tight (accurate) solution to some problems. To mitigate this issue, this paper proposes an ensemble learning based convex approximation for alternating current (AC) power flow equations that differs from the existing convex relaxations. The proposed approach is based on three-phase quadratic power flow equations in rectangular coordinates. To develop this data-driven convex approximation of power flows, the polynomial regression (PR) is first deployed as a basic learner to fit convex relationships between the independent and dependent variables. Then, ensemble learning algorithms such as gradient boosting (GB) and bagging are introduced to combine learners to boost model performance. Based on the learned convex approximation of power flow, optimal power flow (OPF) is formulated as a convex quadratic programming problem. The simulation results on IEEE standard cases of both balanced and unbalanced systems show that, in the context of solving OPF, the proposed data-driven convex approximation outperforms the conventional semi-definite programming (SDP) relaxation in both accuracy and computational efficiency, especially in the cases that the conventional SDP relaxation fails.
Short-term reversals, short-term momentum, and news-driven trading activity

Abstract We find no evidence of monthly return reversals for the top quintile of small- and large-cap stocks ranked by turnover. Indeed, stocks in the top decile of turnover display short-term momentum. We argue that these findings arise from a combination of effects. First, short-term reversals stem from short-term liquidity demands. Second, news-driven returns tend to continue rather than reverse. Third, turnover acts as a proxy for both liquidity and news-driven trading activity. The evidence suggests that reversals give way to momentum as turnover increases because high-turnover stocks are more liquid than low-turnover stocks and their returns are more reflective of news-driven trading activity. For example, the correlation between the monthly returns of stocks that announce earnings during the month and their announcement-window returns increases with monthly turnover. Furthermore, sorting stocks into turnover-based portfolios that are rebalanced monthly leads to a disproportionate number of stocks with earnings announcements in the high-turnover portfolios.
JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset

Robots navigating autonomously need to perceive and track the motion of objects and other agents in its surroundings. This information enables planning and executing robust and safe trajectories. To facilitate these processes, the motion should be perceived in 3D Cartesian space. However, most recent multi-object tracking (MOT) research has focused on tracking people and moving objects in 2D RGB video sequences. In this work we present JRMOT, a novel 3D MOT system that integrates information from RGB images and 3D point clouds to achieve real-time, state-of-the-art tracking performance. Our system is built with recent neural networks for re-identification, 2D and 3D detection and track description, combined into a joint probabilistic data-association framework within a multi-modal recursive Kalman architecture. As part of our work, we release the JRDB dataset, a novel large scale 2D+3D dataset and benchmark, annotated with over 2 million boxes and 3500 time consistent 2D+3D trajectories across 54 indoor and outdoor scenes. JRDB contains over 60 minutes of data including 360◦cylindrical RGB video and 3D pointclouds in social settings that we use to develop, train and evaluate JRMOT. The presented 3D MOT system demonstrates state-of-the-art performance against competing methods on the popular 2D tracking KITTI benchmark and serves as first 3D tracking solution for our benchmark. Real-robot tests on our social robot JackRabbot indicate that the system is capable of tracking multiple pedestrians fast and reliably. We provide the ROS code of our tracker at https://sites.google.com/view/jrmot
Nifty Web Apps: Build a Web App for Any Text-Based Programming Assignment

While many students now interact with web apps across a variety of smart devices, the vast majority of our Nifty Assignments still present traditional user interfaces such as console input/output and desktop GUI. In this tutorial session, participants will learn to build simple web apps for programming assignments that execute student-written code to dynamically respond to user interactions resulting in a more modern app experience. Our approach requires up to 75% less code than similar desktop GUI apps while requiring few (if any) modifications to existing assignments. Instructors and students alike can run and modify these web apps on their own computers or deploy their apps online for access from any smart device at no cost. The tutorial presents examples from CS1 and CS2 courses in Python and Java, but the ideas apply generally.
Hitting the diagnostic sweet spot: Point-of-care SARS-CoV-2 salivary antigen testing with an off-the-shelf glucometer.

Significant barriers to the diagnosis of latent and acute SARS-CoV-2 infection continue to hamper population-based screening efforts required to contain the COVID-19 pandemic in the absence of widely available antiviral therapeutics or vaccines. We report an aptamer-based SARS-CoV-2 salivary antigen assay employing only low-cost reagents ($3.20/test) and an off-the-shelf glucometer. The test was engineered around a glucometer as it is quantitative, easy to use, and the most prevalent piece of diagnostic equipment globally, making the test highly scalable with an infrastructure that is already in place. Furthermore, many glucometers connect to smartphones, providing an opportunity to integrate with contact tracing apps, medical providers, and electronic health records. In clinical testing, the developed assay detected SARS-CoV-2 infection in patient saliva across a range of viral loads - as benchmarked by RT-qPCR - within 1 h, with 100% sensitivity (positive percent agreement) and distinguished infected specimens from off-target antigens in uninfected controls with 100% specificity (negative percent agreement). We propose that this approach provides an inexpensive, rapid, and accurate diagnostic for distributed screening of SARS-CoV-2 infection at scale.
An analogue of the Grothendieck–Springer resolution for symmetric spaces

Motivated by questions in the study of relative trace formulae, we construct a generalization of Grothendieck's simultaneous resolution over the regular locus of certain symmetric pairs. We use this space to prove a relative version of results of Donagi and Gaitsgory about the automorphism sheaf of regular stabilizers. We also obtain partial results toward applications in Springer theory for symmetric spaces.
Black Feminist Musings on Algorithmic Oppression

This paper uses a theory of oppression to ground and extend algorithmic oppression. Algorithmic oppression is then situated through a Black feminist lens part of which entails highlighting the double bind of technology. To reconcile algorithmic oppression with respect to the fairness, accountability, and transparency community, I critique the language of the community. Lastly, I place algorithmic oppression in a broader conversation of feminist science, technology, and society studies to ground the discussion of ways forward through abolition and empowering marginalized communities.
Enforcing Strong Stability of Explicit Runge-Kutta Methods with Superviscosity

A time discretization method is called strongly stable (or monotone), if the norm of its numerical solution is nonincreasing. Although this property is desirable in various of contexts, many explicit Runge-Kutta (RK) methods may fail to preserve it. In this paper, we enforce strong stability by modifying the method with superviscosity, which is a numerical technique commonly used in spectral methods. Our main focus is on strong stability under the inner-product norm for linear problems with possibly non-normal operators. We propose two approaches for stabilization: the modified method and the filtering method. The modified method is achieved by modifying the semi-negative operator with a high order superviscosity term; the filtering method is to post-process the solution by solving a diffusive or dispersive problem with small superviscosity. For linear problems, most explicit RK methods can be stabilized with either approach without accuracy degeneration. Furthermore, we prove a sharp bound (up to an equal sign) on diffusive superviscosity for ensuring strong stability. For nonlinear problems, a filtering method is investigated. Numerical examples with linear non-normal ordinary differential equation systems and for discontinuous Galerkin approximations of conservation laws are performed to validate our analysis and to test the performance.
The wake up and report problem is time-equivalent to the firing squad synchronization problem

We consider several problems relating to strongly-connected directed networks of identical finite-state processors that work synchronously in discrete time steps. The conceptually simplest of these problems is the Wake Up and Report Problem; this is the problem of having a unique "root" processor send a signal to all other processors in the network and then enter a special "done" state only when all other processors have received the signal. The most difficult of the problems we consider is the classic Firing Squad Synchronization Problem; this is the much-studied problem of achieving macro-synchrorization in a network given micro-synchronization. We show via a complex algorithmic application of the "snake" data stucture first introduced in Even, Litman, and Winkler[6] that these two problems in particular are asymptotically time-equivalent up to a constant factor. This result leads immediately to the inclusion of several other related problems into this new asymptotic time-class.
Wideband Full-Duplex Phased Array With Joint Transmit and Receive Beamforming: Optimization and Rate Gains

Full-duplex (FD) wireless and phased arrays are both promising techniques that can significantly improve data rates in future wireless networks. However, integrating FD with transmit (Tx) and receive (Rx) phased arrays is extremely challenging, due to the large number of self-interference (SI) channels. Previous work relies on either RF canceller hardware or on analog/digital Tx beamforming (TxBF) to achieve SI cancellation (SIC). However, Rx beamforming (RxBF) and the data rate gain introduced by FD nodes employing beamforming have not been considered yet. We study FD phased arrays with joint TxBF and RxBF with the objective of achieving improved FD data rates. The key idea is to carefully select the TxBF and RxBF weights to achieve wideband RF SIC in the spatial domain with minimal TxBF and RxBF gain losses. Essentially, TxBF and RxBF are repurposed , thereby not requiring specialized RF canceller circuitry. We formulate the corresponding optimization problem and develop an iterative algorithm to obtain an approximate solution with provable performance guarantees. Using SI channel measurements and datasets, we extensively evaluate the performance of the proposed approach in different use cases under various network settings. The results show that an FD phased array with 9/36/72 elements can cancel the total SI power to below the noise floor with sum TxBF and RxBF gain losses of 10.6/7.2/6.9dB, even at Tx power level of 30dBm. Moreover, the corresponding FD rate gains are at least 1.33/1.66/1.68 $\times $ .
The strong Suslin reciprocity law

We prove the strong Suslin reciprocity law conjectured by A. Goncharov. The Suslin reciprocity law is a generalization of the Weil reciprocity law to higher Milnor we construct a hyperbolic polytope (defined up to scissors congruence). The hyperbolic volume and the Dehn invariant of this polytope can be computed directly from the triple of rational functions on the curve.
Spectrum and Structure of Excited Baryons with CLAS

In this contribution I discuss recent results in light quark baryon spectroscopy involving CLAS data and higher level analysis results from the partial wave analysis by the Bonn-Gatchina group. New baryon states were discovered largely based on the open strangeness production channels γp → K + Λ and γp → K + Σ0 . The data illustrate the great potential of the kaon-hyperon channel in the discovery of higher mass baryon resonances in s-channel production. Other channels with discovery potential, such as γp → pω and γp → ϕp are also discussed. In the second part I will demonstrate on data the sensitivity of meson electroproduction to expose the active degrees of freedom underlying resonance transitions as a function of the probed distance scale. For several of the prominent excited states in the lower mass range the short distance behavior is described by a core of three dressed-quarks with running quark mass, and meson-baryon contributions make up significant parts of the excitation strength at large distances. Finally, I give an outlook of baryon resonance physics at the 12 GeV CEBAF electron accelerator.
QCD prediction of jet structure in 2D trigger-associated momentum correlations and implications for multiple parton interactions

The expression “multiple parton interactions” (MPI) denotes a conjectured QCD mechanism representing contributions from secondary (semi)hard parton scattering to the transverse azimuth region (TR) of jet-triggered p-p collisions. MPI is an object of underlying-event (UE) studies that consider variation of TR n ch or p t yields relative to a trigger condition (leading hadron or jet p t ). An alternative approach is 2D trigger-associated (TA) correlations on hadron transverse momentum p t or rapidity y t in which all hadrons from all p-p events are included. Based on a two-component (soft+hard) model (TCM) of TA correlations a jet-related TA hard component is isolated. Contributions to the hard component from the triggered dijet and from secondary dijets (MPI) can be distinguished, including their azimuth dependence relative to the trigger direction. Measured e + - e − and p-p fragmentation functions and a minimum-bias jet spectrum from 200 GeV p-p collisions are convoluted to predict the 2D hard component of TA correlations as a function of p-p collision multiplicity. The agreement between QCD predictions and TA correlation data is quantitative, confirming a dijet interpretation for the TCM hard component. The TA azimuth dependence is inconsistent with conventional UE assumptions.
Procedural environment of public engagement: an induced recall experiment of local government employees

This research (1) examines how employees’ recollections of public engagement are associated with their willingness for future engagement (WFE), (2) assesses whether employees’ discrete recollection...
Operation of Natural Gas Pipeline Networks With Storage Under Transient Flow Conditions

We formulate a nonlinear optimal control problem for intraday operation of a natural gas pipeline network that includes storage reservoirs. The dynamics of compressible gas flow through pipes, compressors, reservoirs, and wells are considered. In particular, a reservoir is modeled as a rigid, hollow container that stores gas under isothermal conditions and uniform density, and a well is modeled as a vertical pipe. For each pipe, flow dynamics are described by a coupled partial differential equation (PDE) system in density and mass flux variables, with momentum dissipation modeled using the Darcy-Wiesbach friction approximation. Compressors are modeled as scaling up the pressure of gas between the inlet and outlet. The governing equations for all network components are spatially discretized and assembled into a nonlinear differential-algebraic equation (DAE) system, which synthesizes above-ground pipeline and subsurface reservoir dynamics into a single reduced-order model. We seek to maximize an objective function that quantifies economic profit and network efficiency subject to the flow equations and inequalities that represent operating limitations. The problem is solved using a primal-dual interior point solver, and the solutions are validated in computational experiments and simulations on several pipeline test networks to demonstrate the effectiveness of the proposed methodology.
Changes in the Health of Adolescent Athletes: A Comparison of Health Measures Collected Before and During the CoVID-19 Pandemic.

CONTEXT: In the spring of 2020, schools closed to in-person teaching and sports were cancelled to control the transmission of CoVID-19. The changes that took place to the physical and mental health among young athletes during this time remain unknown, however. OBJECTIVE: Identify changes in the health (mental health, physical activity and quality of life) of athletes that occurred during the CoVID-19 pandemic. DESIGN: Cross-sectional study. SETTING: Sample recruited via social media. PATIENTS OR OTHER PARTICIPANTS: 3243 Wisconsin adolescent athletes (age=16.2±1.2 yrs., female=58% female) completed an online survey in May 2020 (DuringCoVID-19). Health measures for this cohort were compared with previously reported data for Wisconsin adolescent athletes (n=5231, age=15.7±1.2, 47% female) collected in 2016-2018 (PreCoVID-19). MAIN OUTCOME MEASURE(S): Demographic information included: sex, grade and sports played. Health assessments included the Patient Health Questionnaire-9 Item (PHQ-9) to identify depression symptoms, the Pediatric Functional Activity Brief Scale (PFABS) for physical activity, and the Pediatric Quality of Life Inventory 4.0 (PedsQL) for health related quality of life (HRQoL). Univariable comparisons of these variables between groups were made via t-tests or chi-square tests. Means and 95% confidence intervals (CI) for each group were estimated by survey weighted ANOVA models. RESULTS: Compared to PreCoVID-19, a larger proportion of the During-CoVID-19 participants reported rates of moderate to severe levels of depression (9.7% vs 32.9%, p<0.001). During-CoVID-19 participants reported 50% lower (worse) PFABS scores (mean:12.2 [95%CI: 11.9, 12.5] vs 24.7 [24.5, 24.9] p<0.001) and lower (worse) PedsQL total scores compared to the PreCoVID-19 participants (78.4 [78.0, 78.8] vs. 90.9 [90.5, 91.3] p<0.001). CONCLUSIONS: During the CoVID-19 pandemic, adolescent athletes reported increased symptoms of depression, decreased physical activity and decreased quality of life compared to adolescent athletes in previous years.
Circulating, eccentric periodic orbits at the Moon

Loosely captured orbits with circulating and pulsating eccentricity vectors have a variety of attractive mission design properties, including low insertion costs, near-circular and highly eccentric phases, mid- to high inclinations, long-term stability, and spatially distributed close approaches. Such orbits are the known result of averaging third-body perturbations over the spacecraft and system motions. Unfortunately, the doubly averaged model does not match long-term unaveraged motion well at high altitudes. Singly averaged dynamics are accurate for orbits with much higher semimajor axis values, providing a mechanism to predict and characterize high-altitude motion in the unaveraged model. In this work, singly averaged circulating, eccentric orbits are surveyed in the specific context of the dimensioned Earth–Moon system, where high-altitude orbits are expected to have the most useful applications. A global search is performed over non-impacting system resonances, and families of periodic orbits are used as a framework to map the feasible space. The resulting database of periodic orbits is provided as an online supplement. The singly averaged families are shown to provide a reliable bridge between the analytic results of the doubly averaged system and initial guesses that converge to periodic orbits in the unaveraged model. Non-impacting circulating, eccentric orbits are demonstrated to maintain their structure for multiple years in a high-fidelity force model. These circulating, eccentric, lunar orbits may be useful for human-crewed space stations, dedicated science orbits, extended missions, loitering orbits, or transfers between the Lagrange points and low-lunar orbits.
Algorithms for Fairness in Sequential Decision Making

It has recently been shown that if feedback effects of decisions are ignored, then imposing fairness constraints such as demographic parity or equality of opportunity can actually exacerbate unfairness. We propose to address this challenge by modeling feedback effects as Markov decision processes (MDPs). First, we propose analogs of fairness properties for the MDP setting. Second, we propose algorithms for learning fair decision-making policies for MDPs. Finally, we demonstrate the need to account for dynamical effects using simulations on a loan applicant MDP.
Regression-based negative control of homophily in dyadic peer effect analysis.

A prominent threat to causal inference about peer effects in social science studies is the presence of homophily bias, that is, social influence between friends and families is entangled with common characteristics or underlying similarities that form close connections. Analysis of social study data has suggested that certain health conditions such as obesity and psychological states including happiness and loneliness can spread between friends and relatives. However, such analyses of peer effects or contagion effects have come under criticism because homophily bias may compromise the causal statement. We develop a regression-based approach which leverages a negative control exposure for identification and estimation of contagion effects on additive or multiplicative scales, in the presence of homophily bias. We apply our methods to evaluate the peer effect of obesity in Framingham Offspring Study. This article is protected by copyright. All rights reserved.
Quotients of buildings by groups acting freely on chambers

Abstract We introduce certain directed multigraphs with extra structure, called Weyl graphs, which model quotients of Tits buildings by type-preserving chamber-free group actions. Their advantage over complexes of groups, which are often used for the CAT(0) Davis realization of buildings, is that Weyl graphs exploit the ultimate combinatorial W-metric structure of buildings. Weyl graphs generalize Tits's chamber systems of type M by allowing rank two residues to be quotients of generalized polygons by flag-free group actions, and Weyl graphs are easily constructed by amalgamating such quotients. We develop covering theory of Weyl graphs, which can be used to construct buildings as universal covers. We describe a method for obtaining a group presentation of the fundamental group of a Weyl graph, which acts chamber-freely on the covering building. The theory developed here is part of a fully general theory which deals with not necessarily chamber-free actions and the stacky version of buildings.
Millimeter Wave and Sub-Terahertz Spatial Statistical Channel Model for an Indoor Office Building

Millimeter-wave (mmWave) and sub-Terahertz (THz) frequencies are expected to play a vital role in 6G wireless systems and beyond due to the vast available bandwidth of many tens of GHz. This paper presents an indoor 3-D spatial statistical channel model for mmWave and sub-THz frequencies based on extensive radio propagation measurements at 28 and 140 GHz conducted in an indoor office environment from 2014 to 2020. Omnidirectional and directional path loss models and channel statistics such as the number of time clusters, cluster delays, and cluster powers were derived from over 15,000 measured power delay profiles. The resulting channel statistics show that the number of time clusters follows a Poisson distribution and the number of subpaths within each cluster follows a composite exponential distribution for both LOS and NLOS environments at 28 and 140 GHz. This paper proposes a unified indoor statistical channel model for mmWave and sub-Terahertz frequencies following the mathematical framework of the previous outdoor NYUSIM channel models. A corresponding indoor channel simulator is developed, which can recreate 3-D omnidirectional, directional, and multiple input multiple output (MIMO) channels for arbitrary mmWave and sub-THz carrier frequency up to 150 GHz, signal bandwidth, and antenna beamwidth. The presented statistical channel model and simulator will guide future air-interface, beamforming, and transceiver designs for 6G and beyond.
Optimal control of rigid body angular velocity with quadratic cost

In this paper, we consider the problem of obtaining optimal controllers which minimize a quadratic cost function for the rotational motion of a rigid body. We are not concerned with the attitude of the body and consider only the evolution of the angular velocity as described by the Euler equations. We obtain conditions which guarantee the existence of linear stabilizing optimal and suboptimal controllers. These controllers have a very simple structure.
Contrastive Self-Supervised Learning for Wireless Power Control

We propose a new approach for power control in wireless networks using self-supervised learning. We partition a multi-layer perceptron that takes as input the channel matrix and outputs the power control decisions into a backbone and a head, and we show how we can use contrastive learning to pre-train the backbone so that it produces similar embeddings at its output for similar channel matrices and vice versa, where similarity is defined in an information-theoretic sense by identifying the interference links that can be optimally treated as noise. The backbone and the head are then fine-tuned using a limited number of labeled samples. Simulation results show the effectiveness of the proposed approach, demonstrating significant gains over pure supervised learning methods in both sum-throughput and sample efficiency.1
Transformer Reasoning Network for Image- Text Matching and Retrieval

Image-text matching is an interesting and fascinating task in modern AI research. Despite the evolution of deep-learning-based image and text processing systems, multimodal matching remains a challenging problem. In this work, we consider the problem of accurate image-text matching for the task of multi-modal large-scale information retrieval. State-of-the-art results in image-text matching are achieved by inter-playing image and text features from the two different processing pipelines, usually using mutual attention mechanisms. However, this invalidates any chance to extract separate visual and textual features needed for later indexing steps in large-scale retrieval systems. In this regard, we introduce the Transformer Encoder Reasoning Network (TERN), an architecture built upon one of the modern relationship-aware self-attentive architectures, the Transformer Encoder (TE). This architecture is able to separately reason on the two different modalities and to enforce a final common abstract concept space by sharing the weights of the deeper transformer layers. Thanks to this design, the implemented network is able to produce compact and very rich visual and textual features available for the successive indexing step. Experiments are conducted on the MS-COCO dataset, and we evaluate the results using a discounted cumulative gain metric with relevance computed exploiting caption similarities, in order to assess possibly non-exact but relevant search results. We demonstrate that on this metric we are able to achieve state-of-the-art results in the image retrieval task. Our code is freely available at https://github.com/mesnico/TERN.
Say It All: Feedback for Improving Non-Visual Presentation Accessibility

Presenters commonly use slides as visual aids for informative talks. When presenters fail to verbally describe the content on their slides, blind and visually impaired audience members lose access to necessary content, making the presentation difficult to follow. Our analysis of 90 presentation videos revealed that 72% of 610 visual elements (e.g., images, text) were insufficiently described. To help presenters create accessible presentations, we introduce Presentation A11y, a system that provides real-time and post-presentation accessibility feedback. Our system analyzes visual elements on the slide and the transcript of the verbal presentation to provide element-level feedback on what visual content needs to be further described or even removed. Presenters using our system with their own slide-based presentations described more of the content on their slides, and identified 3.26 times more accessibility problems to fix after the talk than when using a traditional slide-based presentation interface. Integrating accessibility feedback into content creation tools will improve the accessibility of informational content for all.
Targeting allostery in the Dynein motor domain with small molecule inhibitors.

Summary Cytoplasmic dyneins are AAA (ATPase associated with diverse cellular activities) motor proteins responsible for microtubule minus-end-directed intracellular transport. Dynein's unusually large size, four distinct nucleotide-binding sites, and conformational dynamics pose challenges for the design of potent and selective chemical inhibitors. Here we use structural approaches to develop a model for the inhibition of a well-characterized S. cerevisiae dynein construct by pyrazolo-pyrimidinone-based compounds. These data, along with functional assays of dynein motility and mutagenesis studies, suggest that the compounds inhibit dynein by engaging the regulatory ATPase sites in the AAA3 and AAA4 domains, and not by interacting with dynein's main catalytic site in the AAA1 domain. A double Walker B mutation of the AAA3 and AAA4 sites substantially reduces enzyme activity, suggesting that targeting these regulatory domains is sufficient to inhibit dynein. Our findings reveal how chemical inhibitors can be designed to disrupt allosteric communication across dynein's AAA domains.
Transforming Programs and Tests in Tandem for Fault Localization

Localizing failure-inducing code is essential for software debugging. Manual fault localization can be quite tedious, error-prone, and time-consuming. Therefore, a huge body of research efforts have been dedicated to automated fault localization. Spectrum-based fault localization, the most intensively studied fault localization approach based on test execution information, may have limited effectiveness, since a code element executed by a failed tests may not necessarily have impact on the test outcome and cause the test failure. To bridge the gap, mutation-based fault localization has been proposed to transform the programs under test to check the impact of each code element for better fault localization. However, there are limited studies on the effectiveness of mutation-based fault localization on sufficient number of real bugs. In this paper, we perform an extensive study to compare mutation-based fault localization techniques with various state-of-the-art spectrum-based fault localization techniques on 357 real bugs from the Defects4J benchmark suite. The study results demonstrate the effectiveness of mutation-based fault localization, as well as firstly revealing a number of guidelines for further improving the mutation-based fault localization. Based on the learnt guidelines, we further transform test outputs/messages and test code to obtain various mutation information. Then, we propose TraPT, an automated Learning-to-Rank technique to fully explore the obtained mutation information for effective fault localization. The experimental results show that TraPT localizes at least 62.79% more bugs within Top-1 than state-of-the-art spectrum or mutation based techniques when using the default setting of LIBSVM.
Insider trading and the public enforcement of private prohibitions: some complications in enforcing simple rules for a complex world

Accepting the argument made by Manne, Epstein and others that firms wishing to allow their employees to insider trade should be permitted to do so, this article shows that there is still a crucial role for government in regulating insider trading. In particular, allowing employees to profit by insider trading is a form of employee compensation that, in contradistinction from conventional forms of equity compensation, results in unknowable and effectively unlimited costs to the company. Since providing employee compensation in this form causes the company to lose control of its compensation expense, even if insider trading were legal, virtually every company would rely on conventional forms of employee compensation and prohibit its employees from insider trading. But, pace Manne, Epstein and others, companies lack the means to detect insider trading by their employees, and even when they do catch employees insider trading, companies can impose only mild contractual sanctions, generally not exceeding disgorgement of profits and dismissal. As a result, although an efficient agreement between a company and its employee would prohibit the employee from insider trading, this prohibition cannot be effectively enforced by the company. Government, with its usual law enforcement powers, is better able to detect insider trading and can impose more severe sanctions on violators, including criminal penalties. Government should thus enforce a ban on insider trading in those instances, which will be virtually all instances, in which a company prohibits its employees from insider trading. The efficient solution is thus a hybrid system of private prohibition and public enforcement. Such a system is not unusual but the norm. Employers prohibit employees from embezzling their money and stealing their property, and employees are subject to contractual sanctions and dismissal for violating these prohibitions, but we still need statutes against theft to generate an optimal level of deterrence. This is all the more true when the employee misappropriates information, which is much harder to detect than a theft of money or property.
Time series relations between parking garage occupancy and traffic speed in macroscopic downtown areas – a data driven study

This paper investigates time-series correlations between macroscopic travel speed and parking garage occupancy in downtown area, using the real-time parking occupancy data via SFPark.org and travel...
Does the Strength of Incentives Matter for Elected Officials? A Look at Tax Collectors

In Pennsylvania local property taxes are collected by elected officials, known as tax collectors, whose compensation varies widely in both structure and level across municipalities. This paper analyses the existence of a pay-performance relationship for these officials. Using data on the percentage of real estate taxes that are actually collected at the municipal level, the paper finds that as the compensation tax collectors receive goes up, they collect more in taxes. This relationship is however true only for collectors who are compensated on a commission basis and not for collectors compensated on the basis of a flat salary. The paper also finds no relationship between the share of votes received by the tax collector and the percentage of property taxes collected during the previous term. This observation may account for the lack of a positive relationship between pay and performance for collectors compensated on the basis of a salary.
Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation

We propose multi-microphone complex spectral mapping, a simple way of applying deep learning for time-varying non-linear beamforming, for speaker separation in reverberant conditions. We aim at both speaker separation and dereverberation. Our study first investigates offline utterance-wise speaker separation and then extends to block-online continuous speech separation (CSS). Assuming a fixed array geometry between training and testing, we train deep neural networks (DNN) to predict the real and imaginary (RI) components of target speech at a reference microphone from the RI components of multiple microphones. We then integrate multi-microphone complex spectral mapping with minimum variance distortionless response (MVDR) beamforming and post-filtering to further improve separation, and combine it with frame-level speaker counting for block-online CSS. Although our system is trained on simulated room impulse responses (RIR) based on a fixed number of microphones arranged in a given geometry, it generalizes well to a real array with the same geometry. State-of-the-art separation performance is obtained on the simulated two-talker SMS-WSJ corpus and the real-recorded LibriCSS dataset.
A Sliding-Window Approach to Automatic Creation of Meeting Minutes

Meeting minutes record any subject matter discussed, decisions reached and actions taken at the meeting. The importance of automatic minuting cannot be overstated. In this paper, we present a sliding window approach to automatic generation of meeting minutes. It aims at addressing issues pertaining to the nature of spoken text, including the lengthy transcript and lack of document structure, which make it difficult to identify salient content to be included in meeting minutes. Our approach combines a sliding-window approach and a neural abstractive summarizer to navigate through the raw transcript to find salient content. The approach is evaluated on transcripts of natural meeting conversations, where we compare results obtained for human transcripts and two versions of automatic transcripts and discuss how and to what extent the summarizer succeeds at capturing salient content.
Robust Bandit Learning with Imperfect Context.

A standard assumption in contextual multi-arm bandit is that the true context is perfectly known before arm selection. Nonetheless, in many practical applications (e.g., cloud resource management), prior to arm selection, the context information can only be acquired by prediction subject to errors or adversarial modification. In this paper, we study a contextual bandit setting in which only imperfect context is available for arm selection while the true context is revealed at the end of each round. We propose two robust arm selection algorithms: MaxMinUCB (Maximize Minimum UCB) which maximizes the worst-case reward, and MinWD (Minimize Worst-case Degradation) which minimizes the worst-case regret. Importantly, we analyze the robustness of MaxMinUCB and MinWD by deriving both regret and reward bounds compared to an oracle that knows the true context. Our results show that as time goes on, MaxMinUCB and MinWD both perform as asymptotically well as their optimal counterparts that know the reward function. Finally, we apply MaxMinUCB and MinWD to online edge datacenter selection, and run synthetic simulations to validate our theoretical analysis.
Selected Scientific and Technical Contributions of Edward C. Polhamus

Edward C. Polhamus joined the NACA Langley Research Center staff in 1944 and was active in a broad range of aerodynamic research related to high-speed aircraft technology, aerodynamic prediction methods, and cryogenic wind-tunnel development. This lecture will focus on his 'leading-edge suction analogy' for the prediction of vortex-lift effects on slender wings. Briefer treatment of his contributions to variable-sweep aircraft and cryogenic wind tunnels is also included.
Dependence of the electronic structure of the EuS/InAs interface on the bonding configuration

Recently, the EuS/InAs interface has attracted attention for the possibility of inducing magnetic exchange correlations in a strong spin-orbit semiconductor, which could be useful for topological quantum devices. We use density functional theory with a machine-learned Hubbard $U$ correction [Yu et al., npj Comput. Mater. 6, 180 (2020)] to elucidate the effect of the bonding configuration at the interface on the electronic structure. For all interface configurations considered here, we find that the EuS valence band maximum (VBM) lies below the InAs VBM. In addition, dispersed states emerge at the top of the InAs VBM at the interface, which do not exist in either material separately. These states are contributed mainly by the InAs layer adjacent to the interface. They are localized at the interface and may be attributed to charge transfer from the EuS to the InAs. The interface configuration affects the position of the EuS VBM with respect to the InAs VBM, as well as the dispersion of the interface states. For all interface configurations studied here, the induced magnetic moment in the InAs is small. Our results suggest that this interface, in its coherent form studied here, may not be promising for inducing equilibrium magnetic properties in InAs.
RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge

Question: I have five fingers but I am not alive. What am I? Answer: a glove. Answering such a riddle-style question is a challenging cognitive process, in that it requires complex commonsense reasoning abilities, an understanding of figurative language, and counterfactual reasoning skills, which are all important abilities for advanced natural language understanding (NLU). However, there are currently no dedicated datasets aiming to test these abilities. Herein, we present RiddleSense, a new multiple-choice question answering task, which comes with the first large dataset (5.7k examples) for answering riddle-style commonsense questions. We systematically evaluate a wide range of models over the challenge, and point out that there is a large gap between the best-supervised model and human performance -- suggesting intriguing future research in the direction of higher-order commonsense reasoning and linguistic creativity towards building advanced NLU systems.
Prevalence of function-limiting late effects in Hodgkin lymphoma survivors.

Introduction Hodgkin's Lymphoma (HL) is highly curable, but survivors often develop function-limiting impairments. Screening guidelines for neuromuscular and musculoskeletal late effects are not as well recognized across medical disciplines. Early identification and management of functional late effects is instrumental in improving the longitudinal care of HL survivors. Objective Define the prevalence of neuromuscular, musculoskeletal, visceral, oncologic, and other late effects impacting function and quality of life (QOL) in HL survivors. Design A retrospective cohort analysis. Setting Outpatient cancer rehabilitation clinic. Participants One hundred HL survivors. Interventions Not applicable. Main outcome measures Prevalence of neuromuscular, musculoskeletal, visceral, oncologic, and other late effects contributing to functional impairment and disability in HL survivors. Results Among the 100 HL survivors, 43% received chemotherapy, 94% radiation therapy, and 38% a combination of chemo-radiation for their initial cancer treatment. Nearly all HL survivors were diagnosed with myelopathy (83%), radiculo-plexopathy (93%), mononeuropathy (95%) and localized myopathy (93%). Musculoskeletal sequelae included dropped head syndrome (83%), cervicalgia (79%), shoulder girdle dysfunction (73%), and dysphagia (42%). Visceral disorders included cardiovascular (70%), pulmonary (44%), endocrine (63%), gastrointestinal (29%), and genitourinary (11%) dysfunction. Lymphedema affected 21% of survivors and 30% had a history of a secondary malignancy. Pain (71%), fatigue (45%), and dyspnea (43%) were major function-limiting impairments. Nearly all (95%) of survivors were referred to at least one therapy discipline including physical therapy, occupational therapy, speech and language pathology and/or lymphedema therapy. Conclusion Neuromuscular, musculoskeletal, visceral, oncologic, and other late effects are extremely common in HL survivors seeking physiatric evaluation. Multiple function-limiting disorders can co-exist in HL survivors with the potential to severely compromise function and QOL. Safe and effective rehabilitation may depend on the physiatrist's ability to identify, evaluate, and manage the multitude of complex and often interrelated functional late effects seen in HL survivors. This article is protected by copyright. All rights reserved.
Non-stationary Reinforcement Learning without Prior Knowledge: an Optimal Black-box Approach

We propose a black-box reduction that turns a certain reinforcement learning algorithm with optimal regret in a (near-)stationary environment into another algorithm with optimal dynamic regret in a non-stationary environment, importantly without any prior knowledge on the degree of non-stationarity. By plugging different algorithms into our black-box, we provide a list of examples showing that our approach not only recovers recent results for (contextual) multi-armed bandits achieved by very specialized algorithms, but also significantly improves the state of the art for (generalzed) linear bandits, episodic MDPs, and infinite-horizon MDPs in various ways. Specifically, in most cases our algorithm achieves the optimal dynamic regret $\widetilde{\mathcal{O}}(\min\{\sqrt{LT}, \Delta^{\frac{1}{3}}T^{\frac{2}{3}}\})$ where $T$ is the number of rounds and $L$ and $\Delta$ are the number and amount of changes of the world respectively, while previous works only obtain suboptimal bounds and/or require the knowledge of $L$ and $\Delta$.
The many faces of unification and pluralism in economics: The case of Paul Samuelson's Foundations of Economic Analysis.

Abstract The history of modern economics abounds with pleas for more pluralism as well as pleas for more unification. These seem to be contradictory goals, suggesting that pluralism and unification are mutually exclusive, or at least that they involve trade-offs with more of one necessarily being traded off against less of the other. This paper will use the example of Paul Samuelson's Foundations of Economic Analysis (1947) to argue that the relationship between pluralism and unification is often more complex than this simple dichotomy suggests. In particular, Samuelson's Foundations is invariably presented as a key text in the unification of modern economics during the middle of the twentieth century; and in many ways that is entirely correct. But Samuelson's unification was not at the theoretical (causal and explanatory) level, but rather at the purely mathematical derivational level. Although this fact is recognized in the literature on Samuelson, what seems to be less recognized is that for Samuelson, much of the motivation for this unification was pluralist in spirit: not to narrow scientific economics into one single theory, but rather to allow for more than one theory to co-exist under a single unified derivational technique. This hidden pluralism will be discussed in detail. The paper concludes with a discussion of the implications for more recent developments in economics.