cssi/SciSciGPT-SciSciNet huggingface dataset:
Coercive vs. cooperative enforcement: Effect of enforcement approach on environmental management

Abstract A spirited debate explores the comparative merits of two different approaches to the enforcement of regulatory law: the coercive approach, which emphasizes the deterrence of noncompliance through inflexibly imposed sanctions, and the cooperative approach, which emphasizes the inducement of compliance through flexibility and assistance. Both scholarly and policymaking communities are interested in this topic of enforcement approach within the realms of finance, tax compliance, occupational safety, food and drug safety, consumer product safety, and environmental protection. To inform this debate, our study explores enforcement of environmental protection laws where the debate has been especially spirited yet lacking in much empirical evidence. Specifically our study empirically analyzes the effects of these two approaches on environmental management practices linked to compliance with wastewater discharge limits imposed on chemical manufacturing facilities. For this analysis, we view the enforcement approach as representing a relationship between a regulator and a regulated entity that is measured in multiple dimensions so that we are able to explore the extent of cooperation or coercion. The empirical results reveal that a more cooperative relationship induces better environmental management.
Why are university endowments large and risky

We build a model of universities combining their real production decisions with their choice of endowment size and asset allocation. Variation in opportunity cost, that is, the productivity of internal projects, has a first-order effect on these choices. Adding the UPMIFA-mandated 7% payout constraint, the endowment size and asset allocations match those empirically observed. This constraint has little effect on universities that do not value the output of their internal projects but harms those that do: it prevents the endowment's use as an effective buffer stock, thereby increasing the volatility of production, and it slows the growth of the most productive universities
Tests of equal forecast accuracy and encompassing for nested models

We examine the asymptotic and finite-sample properties of tests for equal forecast accuracy and encompassing applied to 1-step ahead forecasts from nested linear models. We first derive the asymptotic distributions of two standard tests and one new test of encompassing and provide tables of asymptotically valid critical values. Monte Carlo methods are then used to evaluate the size and power of tests of equal forecast accuracy and encompassing. The simulations indicate that post-sample tests can be reasonably well sized. Of the post-sample tests considered, the encompassing test proposed in this paper is the most powerful. We conclude with an empirical application regarding the predictive content of unemployment for inflation.
The Impact of Founders' Professional-Education Background on the Adoption of Open Science by For-Profit Biotechnology Firms

This paper investigates the effect of founders' professional-education background on the adoption of an open-science technology strategy, using a sample of 512 young biotechnology firms. After controlling for founders' prior work experience and other organizational and environmental factors, I find that firms with proportionally more Ph.D.-holding entrepreneurs on the founding team have a higher probability of adopting open science. In addition, founders' educational background can mitigate the constraint of organizational environments on strategy. A crowded technological niche provides a more challenging environment for firms to implement open science, due to higher scooping risks. The deterrent effect, however, of such a high-risk environment is smaller among firms founded by proportionally more Ph.D.-holding entrepreneurs. There is also some evidence of a stronger effect of founders' educational background on open science in an institutional environment in which open science has yet to become the industry norm. This finding is consistent with and complements the growing body of research that emphasizes the importance of entrepreneurial background in developing knowledge about new-venture strategy and structure. This paper was accepted by Olav Sorenson, organizations.
Medellin v. Dretke: Another Chapter in the Vienna Convention Narrative

The Vienna Convention on Consular Relations (Vienna Convention) enshrines consular notification rights - the rights of detained foreign nationals who have become wards of a foreign country's criminal justice system to communicate with their home country and request, among other things, legal assistance. In practice, detaining officials in the United States are typically members of state and local law enforcement agencies, and these officials often do not inform foreign national detainees of their Vienna Convention rights. By the time a foreign national raises the Vienna Convention transgression, the criminal prosecution has often progressed to a point where procedural bar rules impede a court's ability to entertain Vienna Convention-related claims and adjudicate the question of prejudice to the foreign national. Thus, during the last decade, the Vienna Convention story has evolved into a volley of litigation, bouncing between the International Court of Justice (ICJ), the Supreme Court, and lower federal and/or state courts. Medellin v. Dretke emerges as a relatively minor chapter in this Vienna Convention narrative. Although the Court granted a writ of certiorari in Medellin to answer fundamental international law questions involving the effect of an ICJ decision on lower federal and state court proceedings, the Court, following a Presidential memorandum that ordered lower courts to comply with the ICJ decision, ultimately dismissed the case for improvidently granting certiorari. Thus, the Court, for the time being, dodged the underlying international law questions, leaving other transnational actors with to grapple with the complexities that the Vienna Convention presents. The Medellin decision nonetheless highlights much about the nature of international law. In particular, Medellin (1) reminds that state courts, and thus local practitioners, remain important players in the evolution and resolution of transnational legal issues; (2) implicitly reaffirms the breadth of executive power in foreign affairs; and (3) for the Court watchers and vote counters, provides insight into how the Court may resolve related international legal issues in the future.
Racial Differences in Labor Market Values of a Statistical Life

This article constructs measures of job fatality rates for black and white workers using information on job-related fatalities from 1992–1997. The fatality rates for black employees are somewhat greater than those for whites. Each of these groups receives significant compensating wage differentials for fatality risks, controlling for nonfatal risks and expected workers' compensation benefits. The implicit value of a statistical life is lower for black workers than for whites. These results in conjunction with evidence that blacks receive less annual compensation for fatality risks than do whites imply that black and white workers face different market offer curves that are flatter for blacks than for whites.
Reflexivity, business cycles, and the new economy

Markets are not efficient as that term is currently used in academic finance. Rather, markets are reflexive in that market behavior and the fundamentals reflect each other via a two-way, interactive feedback loop. Free markets remain reflexive unless market participants close the feedback loop, which they can do, and have done, to justify and perpetuate a boom. Practical finance theory was clear on the market behavior boom-bust cycles generate, but it was silent regarding the cause of such cycles. Austrian business cycle theory, on the other hand, provides a clear theoretical explanation of the cause and effects of business cycles. By utilizing both theories in a unified manner it is possible to track each stage of a business cycle, which was demonstrated in an analysis of the recent new economy business cycle. Such an approach could be enormously beneficial to both academicians and practitioners during the next business cycle.
Rank, separability, and conditional demands

Rank R demand systems that are either conditioned on some good ~such as dura- bles! or are separable from that good, are shown to be identical to unconditional demand systems having rank R or R 1 1. More generally, the rank of a conditional demand system or a separable subsystem is a lower bound on the rank of the corresponding demand system over all goods. This relationship is applied to reinterpret some existing empirical results regarding rank, conditioning goods, and separability. Rang, separabilite et demandes conditionnelles. On montre que des systemes de demande de rang R qui sont conditionnes par rapport a un bien ~des biens durables par exemple! ou sont separables de ce bien sont identiques aux systemes de demande inconditionnels de rang R ou R 1 1. Plus generalement, le rang d'un systeme de demande conditionnel ou d'un sous- systeme separable est une limite inferieure au rang du systeme de demande correspondant pour tous les biens. Cette relation est utilisee pour re-interpreter quelques resultats empiriques pour ce qui est du rang, des biens conditionnants et la separabilite. Extending Gorman ~1981!, Lewbel ~1991! defined the general concept of the rank of a demand system, and showed that rank has many important implications for specification, estimation, aggregation, and welfare calculations. The point of this note is to clarify some relationships between demand system rank, separability, and conditioning goods. These results help to explain how differences in assump- tions regarding either separability or the presence of conditioning goods may result in different estimates of demand system rank. In particular, it is shown here that, for any positive integer R, any conditional or separable rank R demand system equals
Rationing mental health care: parity, disparity, and justice.

Recent policy debates in the US over access to mental health care have raised several philosophically complex ethical and conceptual issues. The defeat of mental health parity legislation in the US Congress has brought new urgency and relevance to theoretical and empirical investigations into the nature of mental illness and its relation to other forms of sickness and disability. Manifold, nebulous, and often competing conceptions of mental illness make the creation of coherent public policy exceedingly difficult. Referencing a variety of approaches to ethical reflection on health care, and drawing from the empirical literature on therapeutic efficacy and economic efficiency, we argue that differential rationing, ‘disparity,’ is unjustifiable.
Microstructure in the Machine Age

We demonstrate how a machine learning algorithm can be applied to predict and explain modern market microstructure phenomena. We investigate the efficacy of various microstructure measures and show that they continue to provide insights into price dynamics in current complex markets. Some microstructure features with apparent high explanatory power exhibit low predictive power, and vice versa. We also find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about the efficiency of markets. Our results are derived using 87 of the most liquid futures contracts across all asset classes.
When to haggle, when to hold firm? Lessons from the used-car retail market

Though haggling has been the conventional way for auto retailers to sell cars, the last two decades have witnessed the systematic adoption of no‐haggle prices by many large dealerships, including the largest new‐ and used‐car dealership chains. This paper develops a structural empirical model to estimate sellers' profits under posted price and haggling, and investigates how market conditions affect sellers' optimal pricing formats. The model incorporates a simple class of bargaining mechanisms into a standard random‐coefficient discrete‐choice model. With the extension, the product‐level demand system is estimated using data with only list prices, and the unobserved price discounts are also recovered in the estimation. The counterfactual experiments yield a few interesting findings. First, dealers' adopted pricing formats seem superior to the alternative ones. Second, dealers enjoying larger market power through vertical differentiation and carrying a large number of models are more likely to have posted price as their optimal pricing format.
Exponentially Growing Finite Energy Solutions for the Klein–Gordon Equation on Sub-Extremal Kerr Spacetimes

For any sub-extremal Kerr spacetime with non-zero angular momentum, we find an open family of non-zero masses for which there exist smooth, finite energy, and exponentially growing solutions to the corresponding Klein–Gordon equation. If desired, for any non-zero integer m, an exponentially growing solution can be found with mass arbitrarily close to $${\frac{\left|am\right|}{2Mr_+}}$$ . In addition to its direct relevance for the stability of Kerr as a solution to the Einstein–Klein–Gordon system, our result provides the first rigorous construction of a superradiant instability. Finally, we note that this linear instability for the Klein–Gordon equation contrasts strongly with recent work establishing linear stability for the wave equation.
Contextual gaps: privacy issues on Facebook

Social networking sites like Facebook are rapidly gaining in popularity. At the same time, they seem to present significant privacy issues for their users. We analyze two of Facebooks's more recent features, Applications and News Feed, from the perspective enabled by Helen Nissenbaum's treatment of privacy as "contextual integrity." Offline, privacy is mediated by highly granular social contexts. Online contexts, including social networking sites, lack much of this granularity. These contextual gaps are at the root of many of the sites' privacy issues. Applications, which nearly invisibly shares not just a users', but a user's friends' information with third parties, clearly violates standard norms of information flow. News Feed is a more complex case, because it involves not just questions of privacy, but also of program interface and of the meaning of "friendship" online. In both cases, many of the privacy issues on Facebook are primarily design issues, which could be ameliorated by an interface that made the flows of information more transparent to users.
Active deep learning reduces annotation burden in automatic cell segmentation

The relationship between cellular geometry and cellular state and function is apparent, but not yet completely understood. Precise characterization of cellular state is important in many fields, from pathology to synthetic biology. High-content high-throughput microscopy is accessible to researchers now more than ever. This allows for collection of large amounts of cellular images. Naturally, the analysis of this data cannot be left to manual investigation and needs the use of efficient computing algorithms for cellular detection, segmentation, and tracking. Annotation is required for building high quality algorithms. Medical professionals and researchers spend a lot of effort and time in annotating cells. This task has proved to be very repetitive and time consuming. The experts’ time is valuable and should be used effectively. Our hypothesis is that active deep learning will help to share some of the burden that researchers face in their everyday work. In this paper, we focus specifically on the problem of cellular segmentation. We approach the segmentation task using a classification framework. Each pixel in the image is classified based on whether the patch around it resides on the interior, boundary or exterior of the cell. Deep convolutional neural networks (CNN) are used to perform the classification task. Active learning is the method used to reduce the annotation burden. Uncertainty sampling, a popular active learning framework is used in conjunction with the CNN to segment the cells in the image. Three datasets of mammalian nuclei and cytoplasm are used for this work. We show that active deep learning significantly reduces the number of training samples required and also improves the quality of segmentation.
p-adic estimates of exponential sums on curves

The purpose of this article is to prove a ``Newton over Hodge'' result for exponential sums on curves. Let $X$ be a smooth proper curve over a finite field $\mathbb{F}_q$ of characteristic $p\geq 3$ and let $V \subset X$ be an affine curve. For a regular function $\overline{f}$ on $V$, we may form the $L$-function $L(\overline{f},V,s)$ associated to the exponential sums of $\overline{f}$. In this article, we prove a lower estimate on the Newton polygon of $L(\overline{f},V,s)$. The estimate depends on the local monodromy of $f$ around each point $x \in X-V$. This confirms a hope of Deligne that the irregular Hodge filtration forces bounds on $p$-adic valuations of Frobenius eigenvalues. As a corollary, we obtain a lower estimate on the Newton polygon of a curve with an action of $\mathbb{Z}/p\mathbb{Z}$ in terms of local monodromy invariants.
More 5d KK theories

In this note, we discuss circle compactifications of 6d SCFTs for which a geometric M-theory construction is not known in previous literature.
Total Quality Management and Residential Real Estate Agency Issues

This study explores the implications of applying Total Quality Management (TQM) principles to residential real estate brokerage, specifically the need for customer focus. Deming's (1986) TQM system...
Evaluation of the Effects of the COVID-19 Pandemic on Electronic Consultation Use in Primary Care.

Background: Little is known about electronic consultation (e-consult) utilization during the COVID-19 pandemic when health systems rapidly implemented and scaled telehealth alternatives to in-person care. It is also unknown if e-consult utilization during the pandemic replaced or merely deferred the need for a specialty appointment. We evaluated if primary care providers' (PCPs) e-consult utilization and specialists' recommendations for specialty appointments changed after the transition to telemedicine during the COVID-19 pandemic. Methods: This cohort study used an interrupted time series analysis of e-consult utilization in a large, urban academic health care system between December 1, 2019, and June 27, 2020; the post-telemedicine time period began March 15, 2020. The primary outcome measure was the odds of an e-consult ordered during a PCP appointment; the secondary outcome measure was the odds of a specialist recommending a specialty appointment in an e-consult. Results: During 193,263 PCP appointments, 1,318 e-consults were placed to internal medicine subspecialties. Compared to the pre-telemedicine time period, the odds of a PCP ordering an e-consult increased (OR 1.04, 95% CI [1.02-1.07]) and the odds of specialists recommending specialty appointments increased (OR 1.11, 95% CI [1.06-1.15]). Conclusions: E-consult use increased following the transition to telemedicine in the context of the COVID-19 pandemic, suggesting that PCPs consider the e-consult a valuable tool for patient care when there is limited availability of specialty appointments. However, recommendations for specialty appointments following an e-consult also increased, suggesting that the e-consult may not replace the need for a specialty appointment.
In-group interest cues do not change issue attitudes

In this paper, I investigate whether people change their attitudes about societal issues when they learn that those issues affect others like them. In three pre-registered survey experiments, I fin...
Noise effect on the temporal patterns of neural synchrony.

Abstract Neural synchrony in the brain is often present in an intermittent fashion, i.e., there are intervals of synchronized activity interspersed with intervals of desynchronized activity. A series of experimental studies showed that this kind of temporal patterning of neural synchronization may be very specific and may be correlated with behaviour (even if the average synchrony strength is not changed). Prior studies showed that a network with many short desynchronized intervals may be functionally different from a network with few long desynchronized intervals as it may be more sensitive to synchronizing input signals. In this study, we investigated the effect of channel noise on the temporal patterns of neural synchronization. We employed a small network of conductance-based model neurons that were mutually connected via excitatory synapses. The resulting dynamics of the network was studied using the same time-series analysis methods as used in prior experimental and computational studies. While it is well known that synchrony strength generally degrades with noise, we found that noise also affects the temporal patterning of synchrony. Noise, at a sufficient intensity (yet too weak to substantially affect synchrony strength), promotes dynamics with predominantly short (although potentially very numerous) desynchronizations. Thus, channel noise may be one of the mechanisms contributing to the short desynchronization dynamics observed in multiple experimental studies.
Homebound by COVID19: the benefits and consequences of non-pharmaceutical intervention strategies.

Recent research has been conducted by various countries and regions on the impact of non-pharmaceutical interventions (NPIs) on reducing the spread of COVID19. This study evaluates the tradeoffs between potential benefits (e.g., reduction in infection spread and deaths) of NPIs for COVID19 and being homebound (i.e., refraining from interactions outside of the household). An agent-based simulation model, which captures the natural history of the disease at the individual level, and the infection spread via a contact network assuming heterogeneous population mixing in households, peer groups (workplaces, schools), and communities, is adapted to project the disease spread and estimate the number of homebound people and person-days under multiple scenarios, including combinations of shelter-in-place, voluntary quarantine, and school closure in Georgia from March 1 to September 1, 2020. Compared to no intervention, under voluntary quarantine, voluntary quarantine with school closure, and shelter-in-place with school closure scenarios 4.5, 23.1, and 200+ homebound adult-days were required to prevent one infection, with the maximum number of adults homebound on a given day in the range of 119 K–248 K, 465 K–499 K, 5388 K-5389 K, respectively. Compared to no intervention, school closure only reduced the percentage of the population infected by less than 16% while more than doubling the peak number of adults homebound. Voluntary quarantine combined with school closure significantly reduced the number of infections and deaths with a considerably smaller number of homebound person-days compared to shelter-in-place.
Meta-analysis suggests the microbiome responds to Evolve and Resequence experiments in Drosophila melanogaster.

Experimental evolution has a long history of uncovering fundamental insights into evolutionary processes, but has largely neglected one underappreciated component--the microbiome. As eukaryotic hosts evolve, the microbiome may also respond to selection. However, the microbial contribution to host evolution remains poorly understood. Here, we re-analyzed genomic data to characterize the metagenomes from ten Evolve and Resequence (ER thus, the microbiome may contribute to the response of Drosophila in E&R experiments. We outline important considerations for incorporating the microbiome into E&R experiments. The E&R approach may provide critical insights into host-microbiome interactions and fundamental insight into the genomic basis of adaptation.
TableLab: An Interactive Table Extraction System with Adaptive Deep Learning

Table extraction from PDF and image documents is a ubiquitous task in the real-world. Perfect extraction quality is difficult to achieve with one single out-of-box model due to (1) the wide variety of table styles, (2) the lack of training data representing this variety and (3) the inherent ambiguity and subjectivity of table definitions between end-users. Meanwhile, building customized models from scratch can be difficult due to the expensive nature of annotating table data. We attempt to solve these challenges with TableLab by providing a system where users and models seamlessly work together to quickly customize high-quality extraction models with a few labelled examples for the user’s document collection, which contains pages with tables. Given an input document collection, TableLab first detects tables with similar structures (templates) by clustering embeddings from the extraction model. Document collections often contain tables created with a limited set of templates or similar structures. It then selects a few representative table examples already extracted with a pre-trained base deep learning model. Via an easy-to-use user interface, users provide feedback to these selections without necessarily having to identify every single error. TableLab then applies such feedback to finetune the pre-trained model and returns the results of the finetuned model back to the user. The user can choose to repeat this process iteratively until obtaining a customized model with satisfactory performance.
Bank capital, government bond holdings, and sovereign debt capacity

Abstract I develop a model where the sovereign debt capacity depends on the capitalization of domestic banks. Low-capital banks optimally tilt their government bond portfolio toward domestic securities, linking their destiny to that of the sovereign. If the sovereign risk is sufficiently high, low-capital banks lend less to the productive sector to further increase their holdings of domestic government bonds, lowering sovereign yields. In this case, a government that regulates bank capital faces a trade-off. On the one hand, high-capital banks lend more to the productive sector. On the other hand, low-capital banks support the home sovereign debt capacity.
L2C: Describing Visual Differences Needs Semantic Understanding of Individuals

Recent advances in language and vision push forward the research of captioning a single image to describing visual differences between image pairs. Suppose there are two images, I_1 and I_2, and the task is to generate a description W_1,2 comparing them, existing methods directly model I_1, I_2 -> W_1,2 mapping without the semantic understanding of individuals. In this paper, we introduce a Learning-to-Compare (L2C) model, which learns to understand the semantic structures of these two images and compare them while learning to describe each one. We demonstrate that L2C benefits from a comparison between explicit semantic representations and single-image captions, and generalizes better on the new testing image pairs. It outperforms the baseline on both automatic evaluation and human evaluation for the Birds-to-Words dataset.
Recursive spectra of flat strongly minimal theories

We show that for a model complete strongly minimal theory whose pregeometry is flat, the recursive spectrum (SRM($T$)) is either of the form $[0,\alpha)$ for $\alpha\in \omega+2$ or $[0,n]\cup\{\omega\}$ for $n\in \omega$, or $\{\omega\}$, or contained in $\{0,1,2\}$. Combined with previous results, this leaves precisely 4 sets for which it is not yet determined whether each is the spectrum of a model complete strongly minimal theory with a flat pregeometry.
Board committees and director departures

We examine whether directors utilize private information obtained through their committee memberships to depart from firms prior to the revelation of their poor performance. Such departures raise the concern that directors leave the firm when they are most needed. Utilizing private information to make decisions in their personal interest may also violate the directors’ fiduciary duties. We focus on departures of audit committee members since information regarding earnings quality should be available to them prior to public release. The departure of audit committee members who serve on multiple boards is coincident with a deterioration in earnings quality. Other directors do not appear to time their departure based on declines in earnings quality. Results from examining the reasons behind this finding are consistent with the director’s preference to lead a “quiet life” and a desire to lower their exposure to litigation risk rather than to protect their reputation in the director market.
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances.

Several works have shown that perturbation stable instances of the MAP inference problem in Potts models can be solved exactly using a natural linear programming (LP) relaxation. However, most of these works give few (or no) guarantees for the LP solutions on instances that do not satisfy the relatively strict perturbation stability definitions. In this work, we go beyond these stability results by showing that the LP approximately recovers the MAP solution of a stable instance even after the instance is corrupted by noise. This "noisy stable" model realistically fits with practical MAP inference problems: we design an algorithm for finding "close" stable instances, and show that several real-world instances from computer vision have nearby instances that are perturbation stable. These results suggest a new theoretical explanation for the excellent performance of this LP relaxation in practice.
Theoretical Analysis of Multi Integrating RX Front-Ends for Lossy Broad-Band Channels

Recent advances in wire-like communication channels without a physical wire has seen the emergence of wireline-like broadband channels but with atypical high loss. Communication links using such channels (e.g., Proximity Communication, Human Body Communication) are typically broadband, like wireline systems but noise-limited like wireless systems. Hence, employing broad-band communication through such emerging channels calls for new research on analyzing, designing and finding the theoretical limits for receiver topologies suitable for lossy broad-band channels . In this paper, we present a theoretical analysis framework for various broadband receivers in combination with low-noise amplifiers and proposed multi-integrator cascade, which provides significant gain with relatively lower power consumption than the standard gain elements. Through 1) derivation of new noise analysis of integrating-sampling receivers, 2) proposal of a new circuit topology (multi-integrating receiver, MIR) and 3) development of a thorough design space exploration framework, including use of a combination of wireless-inspired Low-noise amplifiers (LNA) with wireline-inspired strong-ARM latches, and the newly proposed MIRs, this paper demonstrates the optimum design choices for some common scenarios. Maximum achievable data rate and optimum energy-efficiency for various channel losses have been obtained theoretically for different topologies revealing their advantages and limitations, intended to serve as a guide for future receiver designs for lossy broad-band channels . All the circuits have been designed in 65 nm CMOS process with a 1 V supply voltage.
Industry herding by hedge funds

This paper investigates hedge fund herding at the industry level and its impact on industry returns. Although the level of industry herding on average is substantially weaker for hedge funds compar...
A Volatile-by-Default JVM for Server Applications

A memory consistency model (or simply memory model) defines the possible values that a shared-memory read may return in a multithreaded programming language. Choosing a memory model involves an inherent performance-programmability tradeoff. The Java language has adopted a relaxed (or weak) memory model that is designed to admit most traditional compiler optimizations and obviate the need for hardware fences on most shared-memory accesses. The downside, however, is that programmers are exposed to a complex and unintuitive semantics and must carefully declare certain variables as volatile in order to enforce program orderings that are necessary for proper behavior. This paper proposes a simpler and stronger memory model for Java through a conceptually small change: every variable has volatile semantics by default, but the language allows a programmer to tag certain variables, methods, or classes as relaxed and provides the current Java semantics for these portions of code. This volatile-by-default semantics provides sequential consistency (SC) for all programs by default. At the same time, expert programmers retain the freedom to build performance-critical libraries that violate the SC semantics. At the outset, it is unclear if the volatile-by-default semantics is practical for Java, given the cost of memory fences on today's hardware platforms. The core contribution of this paper is to demonstrate, through comprehensive empirical evaluation, that the volatile-by-default semantics is arguably acceptable for a predominant use case for Java today – server-side applications running on Intel x86 architectures. We present VBD-HotSpot, a modification to Oracle's widely used HotSpot JVM that implements the volatile-by-default semantics for x86. To our knowledge VBD-HotSpot is the first implementation of SC for Java in the context of a modern JVM. VBD-HotSpot incurs an average overhead versus the baseline HotSpot JVM of 28% for the Da Capo benchmarks, which is significant though perhaps less than commonly assumed. Further, VBD-HotSpot incurs average overheads of 12% and 19% respectively on standard benchmark suites for big-data analytics and machine learning in the widely used Spark framework.
Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms

Across the United States, a growing number of school districts are turning to matching algorithms to assign students to public schools. The designers of these algorithms aimed to promote values such as transparency, equity, and community in the process. However, school districts have encountered practical challenges in their deployment. In fact, San Francisco Unified School District voted to stop using and completely redesign their student assignment algorithm because it was frustrating for families and it was not promoting educational equity in practice. We analyze this system using a Value Sensitive Design approach and find that one reason values are not met in practice is that the system relies on modeling assumptions about families’ priorities, constraints, and goals that clash with the real world. These assumptions overlook the complex barriers to ideal participation that many families face, particularly because of socioeconomic inequalities. We argue that direct, ongoing engagement with stakeholders is central to aligning algorithmic values with real world conditions. In doing so we must broaden how we evaluate algorithms while recognizing the limitations of purely algorithmic solutions in addressing complex socio-political problems.
Stationary distributions of persistent ecological systems

We analyze ecological systems that are influenced by random environmental fluctuations. We first provide general conditions which ensure that the species coexist and the system converges to a unique invariant probability measure (stationary distribution). Since it is usually impossible to characterize this invariant probability measure analytically, we develop a powerful method for numerically approximating invariant probability measures. This allows us to shed light upon how the various parameters of the ecosystem impact the stationary distribution. We analyze different types of environmental fluctuations. At first we study ecosystems modeled by stochastic differential equations. In the second setting we look at piecewise deterministic Markov processes. These are processes where one follows a system of differential equations for a random time, after which the environmental state changes, and one follows a different set of differential equations—this procedure then gets repeated indefinitely. Finally, we look at stochastic differential equations with switching, which take into account both the white noise fluctuations and the random environmental switches. As applications of our theoretical and numerical analysis, we look at competitive Lotka–Volterra, Beddington–DeAngelis predator–prey, and rock–paper–scissors dynamics. We highlight new biological insights by analyzing the stationary distributions of the ecosystems and by seeing how various types of environmental fluctuations influence the long term fate of populations.
Opioid overprescription in adolescents and young adults undergoing hip arthroscopy.

Few studies have examined factors related to the increased consumption of opioids after hip arthroscopy in adolescents and young adults. This study sought to determine prescription patterns following hip arthroscopy in this population, and to determine clinical or surgical factors associated with increased post-operative opioid use. Daily post-operative opioid intake was obtained from pain-control logbooks of adolescents and young adults who underwent hip arthroscopy between January 2017 and 2020. Study outcomes were defined as the median total number of opioid tablets consumed, total days opioids were consumed, mean daily opioid consumption and the ratio of opioids prescribed post-operatively to consumed. Clinical and surgical factors were analyzed to determine any association with opioid consumption. Fifty-eight (20%) patients returned completed logbooks. Most patients (73%) were prescribed 30 oxycodone tablets. The median number of tablets consumed was 7 (range 0-41) over a median duration of 7 days (range 1-22). The median ratio of tablets consumed to prescribed was 20%. Increasing patient age at surgery was associated with increased total number of tablets consumed (r = 0.28, P = 0.04) and to the ratio of tablets consumed to prescribed (r = 0.30, P = 0.03). Patients who were prescribed more than 30 tablets consumed on average 7.8 more tablets than patients prescribed fewer (P = 0.003). Patients who underwent regional anesthesia consumed tablets for longer compared with those who did not (median, 10 versus 4 days; P = 0.03). After undergoing hip arthroscopy, adolescents and young adult patients are commonly overprescribed opioids, consuming on average only one-fifth of the tablets prescribed.
Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM

Network estimation and variable selection have been extensively studied in the statistical literature, but only recently have those two challenges been addressed simultaneously. In this article, we...
Optical conductivity of alpha-Mn

The optical constants were measured at room temperature in the photon-energy range 0.6-6.5 eV on evaporated thin films. Evaporation conditions were chosen that gave the alpha-Mn crystal structure with relatively large grains. The optical conductivity was separated into intraband and interband contributions by fitting to the Drude formula at low energies. The results are anomalous in comparison to other 3d transition metals: the free-electron lifetime is exceptionally short (in agreement with the large dc resistivity of Mn), and the interband transitions seem unusually weak at the lower energies. Possible explanations related to the complicated crystal structure of alpha-Mn with its loss of point symmetry at the atom sites are discussed.
Fundamental Tradeoffs in Distributionally Adversarial Training

Adversarial training is among the most effective techniques to improve the robustness of models against adversarial perturbations. However, the full effect of this approach on models is not well understood. For example, while adversarial training can reduce the adversarial risk (prediction error against an adversary), it sometimes increase standard risk (generalization error when there is no adversary). Even more, such behavior is impacted by various elements of the learning problem, including the size and quality of training data, specific forms of adversarial perturbations in the input, model overparameterization, and adversary's power, among others. In this paper, we focus on \emph{distribution perturbing} adversary framework wherein the adversary can change the test distribution within a neighborhood of the training data distribution. The neighborhood is defined via Wasserstein distance between distributions and the radius of the neighborhood is a measure of adversary's manipulative power. We study the tradeoff between standard risk and adversarial risk and derive the Pareto-optimal tradeoff, achievable over specific classes of models, in the infinite data limit with features dimension kept fixed. We consider three learning settings: 1) Regression with the class of linear models; 2) Binary classification under the Gaussian mixtures data model, with the class of linear classifiers; 3) Regression with the class of random features model (which can be equivalently represented as two-layer neural network with random first-layer weights). We show that a tradeoff between standard and adversarial risk is manifested in all three settings. We further characterize the Pareto-optimal tradeoff curves and discuss how a variety of factors, such as features correlation, adversary's power or the width of two-layer neural network would affect this tradeoff.
Analysis of High Velocity Impact on Hybrid Composite Fan Blades

Recent developments in the analysis of high velocity impact of composite blades are described, using a computerized capability which consists of coupling a composites mechanics code with the direct-time integration features of NASTRAN. The application of the capability to determine the linear dynamic response of an interply hybrid composite aircraft engine fan blade is described in detail. The results also show that the impact stresses reach sufficiently high magnitudes to cause failures in the impact region at early times of the impact event.
Accelerated discovery of a large family of quaternary chalcogenides with very low lattice thermal conductivity

The development of efficient thermal energy management devices such as thermoelectrics and barrier coatings often relies on compounds having low lattice thermal conductivity (κl). Here, we present the computational discovery of a large family of 628 thermodynamically stable quaternary chalcogenides, AMM′Q3 (A = alkali/alkaline earth/post-transition metals; M/M′ = transition metals, lanthanides; Q = chalcogens) using high-throughput density functional theory (DFT) calculations. We validate the presence of low κl in these materials by calculating κl of several predicted stable compounds using the Peierls–Boltzmann transport equation. Our analysis reveals that the low κl originates from the presence of either a strong lattice anharmonicity that enhances the phonon-scatterings or rattler cations that lead to multiple scattering channels in their crystal structures. Our thermoelectric calculations indicate that some of the predicted semiconductors may possess high energy conversion efficiency with their figure-of-merits exceeding 1 near 600 K. Our predictions suggest experimental research opportunities in the synthesis and characterization of these stable, low κl compounds.
Packing (1,1,2,4)-coloring of subcubic outerplanar graphs

Abstract For 1 ≤ s 1 ≤ s 2 ≤ … ≤ s k and a graph G , a packing ( s 1 , s 2 , … , s k ) -coloring of G is a partition of V ( G ) into sets V 1 , V 2 , … , V k such that, for each 1 ≤ i ≤ k the distance between any two distinct x , y ∈ V i is at least s i + 1 . The packing chromatic number, χ p ( G ) , of a graph G is the smallest k such that G has a packing ( 1 , 2 , … , k ) -coloring. It is known that there are trees of maximum degree 4 and subcubic graphs G with arbitrarily large χ p ( G ) . Recently, there was a series of papers on packing ( s 1 , s 2 , … , s k ) -colorings of subcubic graphs in various classes. We show that every 2-connected subcubic outerplanar graph has a packing ( 1 , 1 , 2 ) -coloring and every subcubic outerplanar graph is packing ( 1 , 1 , 2 , 4 ) -colorable. Our results are sharp in the sense that there are 2-connected subcubic outerplanar graphs that are not packing ( 1 , 1 , 3 ) -colorable and there are subcubic outerplanar graphs that are not packing ( 1 , 1 , 2 , 5 ) -colorable. We also show subcubic outerplanar graphs that are not packing ( 1 , 2 , 2 , 4 ) -colorable and not packing ( 1 , 1 , 3 , 4 ) -colorable.
No Shadow Left Behind: Removing Objects and their Shadows using Approximate Lighting and Geometry

Removing objects from images is a challenging technical problem that is important for many applications, including mixed reality. For believable results, the shadows that the object casts should also be removed. Current inpainting-based methods only remove the object itself, leaving shadows behind, or at best require specifying shadow regions to inpaint. We introduce a deep learning pipeline for removing a shadow along with its caster. We leverage rough scene models in order to remove a wide variety of shadows (hard or soft, dark or subtle, large or thin) from surfaces with a wide variety of textures. We train our pipeline on synthetically rendered data, and show qualitative and quantitative results on both synthetic and real scenes.
The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single Cell Resolution

Cutaneous melanoma is a highly immunogenic disease, surgically curable at early stages, but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially-resolved micro-region transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis from precursor states to melanoma in situ to invasive tumor. Hallmarks of immunosuppression are detectable by the precursor stage, and when tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor-stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1 and by PDL1-expressing macrophages and dendritic cells engaging activated T cells. However, a few mm away, T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can co-exist within a few millimeters of each other in a single specimen.
Trajectory Prediction with Latent Belief Energy-Based Model

Human trajectory prediction is critical for autonomous platforms like self-driving cars or social robots. We present a latent belief energy-based model (LB-EBM) for diverse human trajectory forecast. LB-EBM is a probabilistic model with cost function defined in the latent space to account for the movement history and social context. The low-dimensionality of the latent space and the high expressivity of the EBM make it easy for the model to capture the multi-modality of pedestrian trajectory distributions. LB-EBM is learned from expert demonstrations (i.e., human trajectories) projected into the latent space. Sampling from or optimizing the learned LB-EBM yields a belief vector which is used to make a path plan, which then in turn helps to predict a long-range trajectory. The effectiveness of LB-EBM and the two-step approach are supported by strong empirical results. Our model is able to make accurate, multi-modal, and social compliant trajectory predictions and improves over prior state-of-the-arts performance on the Stanford Drone trajectory prediction benchmark by 10.9% and on the ETH-UCY benchmark by 27.6%.