cssi/SciSciGPT-SciSciNet huggingface dataset:
Multi-Lagged Specification of the Ohlson Model:

This paper extends the study by Bar-Yosef, Callen, and Livnat (BCL) (1996), in which they test the single lagged formulation of the Ohlson (1995) linear valuation model by assuming that book values, earnings, and dividends are the underlying primitives in a multi-lagged Garman-Ohlson (1980) framework. Unlike BCL who focus solely on the information dynamic, this paper estimates both the information dynamic and the valuation equation. Also, since Akaike's (1969a, 1969b) final prediction error (FPE) and Akaike's (1973, 1974) information criterion (AIC) are known to overfit in small samples, the lag structure specification in this study is estimated by the AICe criterion suggested by Hurvich and Tsai (1989, 1991, 1994) rather than by the FPE criterion of BCL. Furthermore, to bias the case against a multi-lagged structure, the maximum lag structure is limited to three lags per variable rather than the five lags of BCL.Utilizing the result that the lag structure of the information dynamic determines the lag str...
Entrepreneurial Impact: The Role of Mit

The ultimate value of this study is to help us understand the economic impact of the entrepreneurial ventures of university graduates. We know that some universities play an important role in many economies through their core education, research and development, and other spillovers. However, in order to support economic growth through entrepreneurship, universities must create a culture and programs that make entrepreneurship widely accessible to students. While MIT's leadership in developing successful entrepreneurs has been evident anecdotally, this study – one of the largest surveys of entrepreneur alumni ever conducted – quantifies the significant impact of MIT's entrepreneurial ecosystem that supports firm start-ups. Furthermore, while MIT is more unique and unusual in the programs it offers and in its historical culture of entrepreneurship, MIT provides a benchmark by which other institutions can gauge the economic impact of their alumni entrepreneurs. The report also provides numerous examples of programs and practices that might be adopted, intact or modified as needed, by other universities that seek enhanced entrepreneurial development. The Appendix identifies several universities that have carried out surveys of alumni entrepreneurs.
Does the Failure of the Expectations Hypothesis Matter for Long-Term Investors?

We solve the portfolio problem of a long-run investor when the term structure is Gaussian and when the investor has access to nominal bonds and stock. We apply our method to a three-factor model that captures the failure of the expectations hypothesis. We extend this model to account for time-varying expected inflation, and estimate the model with both inflation and term structure data. The estimates imply that the bond portfolio of a long-run investor looks very different from the portfolio of a meanvariance optimizer. In particular, time-varying term premia generate large hedging demands for long-term bonds.
Mediation of Probate Matters: Leaving a Valuable Legacy

Mediation has many advantages for resolving probate matters. It has the power to bring parties to a different level of understanding about their underlying situations and about each other, to re-establish family harmony and to resolve both monetary and relationship issues that probate matters generally involve. To realize these advantages, this paper makes two suggestions. First, attorneys should urge testators to consider dispute resolution provisions in their will. Second, attorneys and mediators operating in the probate arena should tap mediation’s full powers to address all the negotiable issues the parties may have (not just the legal causes of action), so that mediation’s full potential to resolve the entire universe of issues presented by a conflict is not constrained by the adjudicative process.
The strategic choice of operator skills in CNC installations

The authors test a strategic choice model to further define the factors managers respond to when making skill level decisions for plants using CNC equipment. The results indicate that managers respond to the level of uncertainty in both the internal and external environments as well as the level of perceived discretion management has in regards to the workforce.
The New Tate Letter: Foreign Official Immunity and the Case for a Statutory Fix

Plaintiffs sometimes bring civil lawsuits in U.S. federal courts against officials or ex-officials of foreign governments accused of committing atrocities abroad. In these types of cases, the foreign individuals will almost certainly invoke the affirmative defense of foreign official immunity. In the 2010 decision, Samantar v. Yousuf, the Supreme Court unanimously held that the Foreign Sovereign Immunities Act (FSIA) — a 1976 statute governing the immunity of foreign states — did not control judicial determination of a foreign individual’s request for immunity. Instead, the Court said that foreign officials may be entitled to immunity as a matter of federal common law. Because of the sensitive foreign policy implications of these types of cases, the executive branch — specifically the State Department — has aggressively asserted control over all foreign official immunity requests. In 2012, in the so-called “Rosenberg Statement” and “Koh Letter,” the Justice Department and Legal Adviser to the State Department, Harold Hongju Koh, declared that (1) federal courts must refrain from deciding any foreign official immunity request that was not first presented to the State Department and (2) it was for the Executive, not the courts, to evaluate whether a foreign individual acted in an official capacity.While the Executive is certainly the branch of government with principal responsibility for foreign affairs, the Koh Letter and Rosenberg Statement represent executive branch overreach into judicial supervision of a federal lawsuit. Judicial deference to the Executive on foreign official immunity calls may be proper; blind obedience is not. In fact, such unilateral control ultimately hurts the State Department itself, which must balance complex and countervailing foreign policy interests. So how should the Executive’s power play be answered? Despite the Supreme Court’s reference to judge- made federal common law in Samantar, this Note argues that the tug-of-war between the executive and judicial branches requires congressional action because the courts are in disarray, with some already having acquiesced to executive control. The current disorder is a striking replay of what happened in the doctrine of foreign sovereign immunity in the late 1950s and 1960s in the aftermath of the 1952 Tate Letter — an earlier dispatch from a different State Department Legal Advisor that sparked such confusion in the federal common law of foreign sovereign immunity that Congress was compelled to intervene with the FSIA. This Note concludes with a model statute that Congress should consider enacting.
THE IMPACT OF YOUNG WORKERS ON THE AGGREGATE LABOR MARKET.

An increase in the share of youth in the working age population of one state or region relative to the rest of the United States causes a sharp reduction in that state's relative unemployment rate and a modest increase in its labor force participation rate. This is inconsistent with many theories of the labor market, but can be easily explained by a model of frictional unemployment with on-the-job search. The theory makes strong predictions regarding the behavior of wages which are shown to be consistent with the data. The paper also reconciles its findings with an existing body of apparently contradictory empirical evidence.
Machine Learning Approaches Identify Genes Containing Spatial Information From Single-Cell Transcriptomics Data

The development of single-cell sequencing technologies has allowed researchers to gain important new knowledge about the expression profile of genes in thousands of individual cells of a model organism or tissue. A common disadvantage of this technology is the loss of the three-dimensional (3-D) structure of the cells. Consequently, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized the Single-Cell Transcriptomics Challenge, in which we participated, with the aim to address the following two problems: (a) to identify the top 60, 40, and 20 genes of the Drosophila melanogaster embryo that contain the most spatial information and (b) to reconstruct the 3-D arrangement of the embryo using information from those genes. We developed two independent techniques, leveraging machine learning models from least absolute shrinkage and selection operator (Lasso) and deep neural networks (NNs), which are applied to high-dimensional single-cell sequencing data in order to accurately identify genes that contain spatial information. Our first technique, Lasso.TopX, utilizes the Lasso and ranking statistics and allows a user to define a specific number of features they are interested in. The NN approach utilizes weak supervision for linear regression to accommodate for uncertain or probabilistic training labels. We show, individually for both techniques, that we are able to identify important, stable, and a user-defined number of genes containing the most spatial information. The results from both techniques achieve high performance when reconstructing spatial information in D. melanogaster and also generalize to zebrafish (Danio rerio). Furthermore, we identified novel D. melanogaster genes that carry important positional information and were not previously suspected. We also show how the indirect use of the full datasets' information can lead to data leakage and generate bias in overestimating the model's performance. Lastly, we discuss the applicability of our approaches to other feature selection problems outside the realm of single-cell sequencing and the importance of being able to handle probabilistic training labels. Our source code and detailed documentation are available at https://github.com/TJU-CMC-Org/SingleCell-DREAM/.
Identifying Risk Factors That Distinguish Symptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infection From Common Upper Respiratory Infections in Children

Background Demographic and clinical risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children presenting with respiratory viral symptoms are not well defined An understanding of risk factors for SARS-CoV-2 infection can help prioritize testing Methodology We evaluated potential demographic and clinical factors in children who had respiratory viral symptoms and were tested by polymerase chain reaction (PCR) for SARS-CoV-2 and other respiratory viral infections Results Among the 263 symptomatic children tested for routine seasonal respiratory viruses by PCR, 18 (6 8%) tested positive for SARS-CoV-2 Overall, 22 2% of SARS-CoV-2-infected children and 37 1% of SARS-CoV-2-uninfected children had infection with one or more non-SARS-CoV-2 pathogens (p = 0 31) Higher proportions of children with compared to without SARS-CoV-2 infection were male (77 8 vs 51 8%, p = 0 05), Hispanic (44 4% vs 9 8%, p < 0 001), or had the symptoms of fatigue (22 2% vs 2 5%, p = 0 003) or anosmia/ageusia (11 1% vs 0%, p = 0 004) History of hypoxic-ischemic encephalopathy (HIE) and obesity were more common in children with versus without SARS-CoV-2 infection (11 1% vs 1 2%, p = 0 04, and 11 1% vs 0%, p = 0 (0)4, respectively) In a multivariate analysis, Hispanic ethnicity, symptoms of fatigue or anosmia/ageusia, and presence of obesity (as noted on physical examination) or HIE were independently associated with SARS-CoV-2 infection Numbers in each category were small, and these preliminary associations require confirmation in future studies Conclusions In this area of the United States, infection with other viruses did not rule out infection with SARS-CoV-2 Additionally, children with respiratory viral symptoms who were of Hispanic ethnicity, had symptoms of weakness/fatigue, or had obesity or HIE were at an increased risk for SARS-CoV-2 infection Future studies should assess if these factors are associated with risk in populations in other areas of the United States
Temporal LiDAR Frame Prediction for Autonomous Driving

Anticipating the future in a dynamic scene is critical for many fields such as autonomous driving and robotics. In this paper we propose a class of novel neural network architectures to predict future LiDAR frames given previous ones. Since the ground truth in this application is simply the next frame in the sequence, we can train our models in an self-supervised fashion. Our proposed architectures are based on FlowNet3D and Dynamic Graph CNN. We use Chamfer Distance (CD) and Earth Mover’s Distance (EMD) as loss functions and evaluation metrics. We train and evaluate our models using the newly released nuScenes dataset, and characterize their performance and complexity with several baselines. Compared to directly using FlowNet3D, our proposed architectures achieve CD and EMD nearly an order of magnitude lower. In addition, we show that our predictions generate reasonable scene flow approximations without using any labelled supervision.
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system. The estimated motion from inertial measurement unit (IMU) pre-integration de-skews point clouds and produces an initial guess for lidar odometry optimization. The obtained lidar odometry solution is used to estimate the bias of the IMU. To ensure high performance in real-time, we marginalize old lidar scans for pose optimization, rather than matching lidar scans to a global map. Scan-matching at a local scale instead of a global scale significantly improves the real-time performance of the system, as does the selective introduction of keyframes, and an efficient sliding window approach that registers a new keyframe to a fixed-size set of prior "sub-keyframes." The proposed method is extensively evaluated on datasets gathered from three platforms over various scales and environments.
Representation of networks and systems with delay: DDEs, DDFs, ODE–PDEs and PIEs

Abstract Delay-Differential Equations (DDEs) are the most common representation for systems with delay. However, the DDE representation is limited. In network models with delay, the delayed channels are low-dimensional and accounting for this heterogeneity is not possible in the DDE framework. In addition, DDEs cannot be used to model difference equations. Furthermore, estimation and control of systems in DDE format has proven challenging, despite decades of study. In this paper, we examine alternative representations for systems with delay and provide formulae for conversion between representations. First, we examine the Differential-Difference (DDF) formulation which allows us to represent the low-dimensional nature of delayed information. Next, we examine the coupled ODE–PDE formulation, for which backstepping methods have recently become available. Finally, we consider the algebraic Partial Integral Equation (PIE) representation, which allows the optimal estimation and control problems to be solved efficiently through the use of recent software packages such as PIETOOLS. In each case, we consider a very general class of delay systems, specifically accounting for all four possible sources of delay — state delay, input delay, output delay, and process delay. We then apply these representations to 3 archetypical network models.
Weak Harnack inequalities for eigenvalues and constant rank theorems

We consider convex solutions of nonlinear elliptic equations which satisfy the structure condition of Bian and Guan. We prove a weak Harnack inequality for the eigenvalues of the Hessian of these s...
Parity Nonconservation and a Two-Component Theory of the Neutrino

A two-component theory of the neutrino is discussed. The theory is possible only if parity is not conserved in interactions involving the neutrino. Various experimental implications are analyzed. Some general remarks concerning nonconservation are made.
Can Tax Policy Stop Human Trafficking

The total number of victims who are held in captivity to perform forced labor at any one time is estimated to be as high as twenty-seven million. That would be equivalent to every man, woman, and child in the states of New Hampshire, Vermont, Massachusetts, and New York being held in captivity and forced twelve to fourteen hours each day to labor in sweatshops, or toil as agricultural workers, or service sexually many customers every day with no hope that it will ever end except by death. They would live in crowded, dirty hovels, receive little food and no medical care, and live under the constant threat of beatings, rape, and other violence. Every year, new victims will be added to their numbers. This is human trafficking.This article recommends an economic incentive that would recruit as allies in this war the wealthy residents of countries where the abuse is most rampant, and where the governments themselves, or government officials are complicit in trafficking. The economic incentive that would be used is taxation.
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content

Recent years have witnessed an explosion of user-generated content (UGC) videos shared and streamed over the Internet, thanks to the evolution of affordable and reliable consumer capture devices, and the tremendous popularity of social media platforms. Accordingly, there is a great need for accurate video quality assessment (VQA) models for UGC/consumer videos to monitor, control, and optimize this vast content. Blind quality prediction of in-the-wild videos is quite challenging, since the quality degradations of UGC videos are unpredictable, complicated, and often commingled. Here we contribute to advancing the UGC-VQA problem by conducting a comprehensive evaluation of leading no-reference/blind VQA (BVQA) features and models on a fixed evaluation architecture, yielding new empirical insights on both subjective video quality studies and objective VQA model design. By employing a feature selection strategy on top of efficient BVQA models, we are able to extract 60 out of 763 statistical features used in existing methods to create a new fusion-based model, which we dub the VIDeo quality EVALuator (VIDEVAL), that effectively balances the trade-off between VQA performance and efficiency. Our experimental results show that VIDEVAL achieves state-of-the-art performance at considerably lower computational cost than other leading models. Our study protocol also defines a reliable benchmark for the UGC-VQA problem, which we believe will facilitate further research on deep learning-based VQA modeling, as well as perceptually-optimized efficient UGC video processing, transcoding, and streaming. To promote reproducible research and public evaluation, an implementation of VIDEVAL has been made available online: https://github.com/vztu/VIDEVAL .
Shenandoah Watershed Study-Virginia Trout Stream Sensitivity Study (SWAS-VTSSS): Stream water quality and hydrologic monitoring data for mid-Appalachian headwater streams

The Shenandoah Watershed Study (established in 1979) and the Virginia Trout Stream Sensitivity Study (established in 1987) serve to increase understanding of hydrological and biogeochemical changes in western Virginia mountain streams that occur in response to acidic deposition and other ecosystem stressors. The SWAS-VTSSS program has evolved over its 40+ year history to consist of a temporally robust and spatially stratified monitoring framework. Currently stream water is sampled bi-hourly during high-flow events at 3 sites and weekly at 4 sites within Shenandoah National Park (SHEN), and quarterly at 72 sites and on an approximately decadal frequency at ~ 450 sites within the wider western Virginia Appalachian region. Stream water is evaluated for pH, acid neutralizing capacity (ANC), base cations (calcium, magnesium, sodium and potassium ion), acid anions (sulfate, nitrate and chloride), silica and ammonium, with a subset of samples evaluated for monomeric aluminum and dissolved organic carbon. Hourly stream discharge (4 sites) and in-situ measurements of conductivity, water and air temperature (3 sites) are also measured within SHEN. Here we provide an overview and timeline of the SWAS-VTSSS stream water monitoring program, summarize the field and laboratory methods, describe the water chemistry and hydrologic data sets, and document major watershed disturbances that have occurred during the program history. Website links and instructions are provided to access the stream chemistry and time-series monitoring data in open-access federal databases. The purpose of this publication is to promote awareness of these unique, long-term data sets for wider use in catchment studies. The water chemistry and hydrologic data can be used to investigate a wide range of biogeochemical research questions and provide key inputs for models of these headwater stream ecosystems. SWAS-VTSSS is an ongoing program and quality assured data sets are uploaded to the databases annually.
Arabidopsis myosin XIK interacts with the exocyst complex to facilitate vesicle tethering during exocytosis.

Myosin motors are essential players in secretory vesicle trafficking and exocytosis in yeast and mammalian cells; however, similar roles in plants remain a matter for debate, at least for diffusely-growing cells. Here, we demonstrate that Arabidopsis (Arabidopsis thaliana) myosin XIK, via its globular tail domain (GTD), participates in the vesicle tethering step of exocytosis through direct interactions with the exocyst complex. Specifically, myosin XIK GTD bound directly to several exocyst subunits in vitro and functional fluorescently-tagged XIK colocalized with multiple exocyst subunits at plasma membrane (PM)-associated stationary foci. Moreover, genetic and pharmacological inhibition of myosin XI activity reduced the rate of appearance and lifetime of stationary exocyst complexes at the PM. By tracking single exocytosis events of cellulose synthase (CESA) complexes (CSCs) with high spatiotemporal resolution imaging and pair-wise colocalization of myosin XIK, exocyst subunits and CESA6, we demonstrated that XIK associates with secretory vesicles earlier than exocyst and is required for the efficient localization and normal dynamic behavior of exocyst complex at the PM tethering site. This study reveals an important functional role for myosin XI in secretion and provides insights about the dynamic regulation of exocytosis in plants.
The Family of Alpha,[a,b] Stochastic Orders: Risk vs. Expected Value

Abstract In this paper we provide a novel family of stochastic orders that generalizes second order stochastic dominance, which we call the α , [ a , b ] -concave stochastic orders. These stochastic orders are generated by a novel set of “very” concave functions where α parameterizes the degree of concavity. The α , [ a , b ] -concave stochastic orders allow us to derive novel comparative statics results for important applications in economics that cannot be derived using previous stochastic orders. In particular, our comparative statics results are useful when an increase in a lottery’s riskiness changes the agent’s optimal action in the opposite direction to an increase in the lottery’s expected value. For this kind of situation, we provide a tool to determine which of these two forces dominates — riskiness or expected value. We apply our results in consumption–savings problems, self-protection problems, and in a Bayesian game.
Corralling Stochastic Bandit Algorithms

We study the problem of corralling stochastic bandit algorithms, that is combining multiple bandit algorithms designed for a stochastic environment, with the goal of devising a corralling algorithm that performs almost as well as the best base algorithm. We give two general algorithms for this setting, which we show benefit from favorable regret guarantees. We show that the regret of the corralling algorithms is no worse than that of the best algorithm containing the arm with the highest reward, and depends on the gap between the highest reward and other rewards.
Co-design and co-simulation for engineering systems: insights from the Sustainable Infrastructure Planning Game

This paper draws on perspectives from co-design as an integrative and collaborative design activity and co-simulation as a supporting information system to advance engineering design methods for problems of societal significance. Design and implementation of the Sustainable Infrastructure Planning Game provides a prototypical co-design artifact that leverages the High Level Architecture co-simulation standard. Three role players create a strategic infrastructure plan for agricultural, water and energy sectors to meet sustainability objectives for a growing and urbaninzing population in a fictional desert nation. An observational study conducts 15 co-design sessions to understand underlying dynamics between actors and how co-simulation capabilities influence design outcomes. Results characterize the dependencies and conflicts between player roles based on technical exchange of resource flows, identifying tension between agriculture and water roles based on water demands for irrigation. Analysis shows a correlation between data exchange, facilitated by synchronous co-simulation, and highly ranked achievement of joint sustainability outcomes. Conclusions reflect on the opportunities and challenges presented by co-simulation in co-design settings to address engineering systems problems.
Focus Within or On Others: The Impact of Reviewers’ Attentional Focus on Review Helpfulness

When reviewers write online reviews, they differ in the focus of their attention: some focus on their own experiences, whereas some direct their attention to others—prospective consumers who may re...
Elogio a los títulos: una explicación sociológica de su importancia en el siglo XXI

espanolLa proliferacion de aparatos electronicos equipados con herramientas para enviar mensajes de texto ha aumentado el ritmo de degradacion en terminos de etiqueta social en los Estados Unidos de America. La popularidad del envio de textos ha sobrepasado la de los correos electronicos y de esta manera ha establecido un nuevo estandar en terminos de etiqueta escrita. Se lidia con el anonimato de la esfera publica con el uso de la falsa intimidad en el sector de servicio. Aunque los Estados Unidos son conocidos por su baja distancia de poder y por el comportamiento relajado de sus ciudadanos, cambios recientes en la omision completa de titulos dificulta la interaccion social y tiende a aumentar el numero de conflictos y por lo tanto crea inestabilidad social. EnglishThe proliferation of devices enabled with texting and messaging capabilities has exponentially increased the rate of decay in terms of social etiquette in the United States. Texting has overtaken e-mails in terms of popularity and thus set a new standard in terms of written etiquette. Anonymity in the public sphere is increasingly dealt with through the use of fake intimacy in the service sector. While the United States is known for its low power distance as well as by the easy going nature of its citizens, recent developments in the complete omission of titles makes social interaction difficult and at times may actually increase conflict and thus social instability
Colding Minicozzi entropy in hyperbolic space

Abstract This note introduces a notion of entropy for submanifolds of hyperbolic space analogous to the one introduced by Colding and Minicozzi for submanifolds of Euclidean space. Several properties are proved for this quantity including monotonicity along mean curvature flow in low dimensions and a connection with the conformal volume.
Dynamical Approach to the TAP Equations for the Sherrington-Kirkpatrick Model

We present a new dynamical proof of the Thouless–Anderson–Palmer (TAP) equations for the classical Sherrington–Kirkpatrick spin glass at sufficiently high temperature. In our derivation, the TAP equations are a simple consequence of the decay of the two point correlation functions. The methods can also be used to establish the decay of higher order correlation functions. We illustrate this by proving a suitable decay bound on the three point functions from which we derive an analogue of the TAP equations for the two point functions.
Rubio de Francia extrapolation theorem and related topics in the theory of elliptic and parabolic equations. A survey

We give a brief overview of the history of the Sobolev mixed norm theory of linear elliptic and parabolic equations and the recent development in this theory based on the Rubio de Francia extrapolation theorem. A self contained proof of this theorem along with other relevant tools of Real Analysis are also presented as well as an application to mixed norm estimates for fully nonlinear equations.
Symplectic fillings and cobordisms of lens spaces

We complete the classification of symplectic fillings of tight contact structures on lens spaces. In particular, we show that any symplectic filling $X$ of a virtually overtwisted contact structure on $L(p,q)$ has another symplectic structure that fills the universally tight contact structure on $L(p,q)$. Moreover, we show that the Stein filling of $L(p,q)$ with maximal second homology is given by the plumbing of disk bundles. We also consider the question of constructing symplectic cobordisms between lens spaces and report some partial results.
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations

Spreading processes play an increasingly important role in modeling for diffusion networks, information propagation, marketing and opinion setting. We address the problem of learning of a spreading model such that the predictions generated from this model are accurate and could be subsequently used for the optimization, and control of diffusion dynamics. We focus on a challenging setting where full observations of the dynamics are not available, and standard approaches such as maximum likelihood quickly become intractable for large network instances. We introduce a computationally efficient algorithm, based on a scalable dynamic message-passing approach, which is able to learn parameters of the effective spreading model given only limited information on the activation times of nodes in the network. The popular Independent Cascade model is used to illustrate our approach. We show that tractable inference from the learned model generates a better prediction of marginal probabilities compared to the original model. We develop a systematic procedure for learning a mixture of models which further improves the prediction quality.
The Generalized Oaxaca-Blinder Estimator

After performing a randomized experiment, researchers often use ordinary least-square (OLS) regression to adjust for baseline covariates when estimating the average treatment effect. It is widely k...
Declines and pronounced regional disparities in meperidine use in the United States.

There have been increasing concerns about adverse effects and drug interactions with meperidine. The goal of this study was to characterize meperidine use in the United States. Meperidine distribution data were obtained from the Drug Enforcement Administration's Automated of Reports and Consolidated Orders System. The Medicare Part D Prescriber Public Use File was utilized to capture overall trends in national prescriptions in this observational report. Nationally, meperidine distribution decreased by 94.6% from 2001 to 2019. In 2019, Arkansas, Alabama, Oklahoma, and Mississippi saw significantly greater distribution when compared with the US state average of 9.27 mg per 10 persons (SD = 6.82). Meperidine distribution showed an 18-fold difference between the highest state (Arkansas = 36.8 mg) and lowest state (Minnesota = 2.1 mg). Five of the six states with the lowest distribution were in the Northeast. Meperidine distribution per state was correlated with the prevalence of adult obesity (r(48) = +0.48, p < .001). Family medicine and internal medicine physicians accounted for 28.9% and 20.5%, respectively, of meperidine total daily supply (TDS) in 2017. Interventional pain management (5.66) and pain management (3.48) physicians accounted for the longest TDS per provider. The use of meperidine declined over the last two decades. Meperidine varied by geographic region with south-central states, and those with more obesity, showing greater distribution. Primary care doctors continue to account for the majority of meperidine daily supply. Increasing knowledge of meperidine's undesirable adverse effects like seizures and serious drug-drug interactions is likely responsible for these pronounced reductions.
Unified Pre-training for Program Understanding and Generation

Code summarization and generation empower conversion between programming language (PL) and natural language (NL), while code translation avails the migration of legacy code from one PL to another. This paper introduces PLBART, a sequence-to-sequence model capable of performing a broad spectrum of program and language understanding and generation tasks. PLBART is pre-trained on an extensive collection of Java and Python functions and associated NL text via denoising autoencoding. Experiments on code summarization in the English language, code generation, and code translation in seven programming languages show that PLBART outperforms or rivals state-of-the-art models. Moreover, experiments on discriminative tasks, e.g., program repair, clone detection, and vulnerable code detection, demonstrate PLBART’s effectiveness in program understanding. Furthermore, analysis reveals that PLBART learns program syntax, style (e.g., identifier naming convention), logical flow (e.g., “if“ block inside an “else“ block is equivalent to “else if“ block) that are crucial to program semantics and thus excels even with limited annotations.
FastCFI: Real-time Control-Flow Integrity Using FPGA without Code Instrumentation

Control-Flow Integrity (CFI) is an effective defense technique against a variety of memory-based cyber attacks. CFI is usually enforced through software methods, which entail considerable performan...
QCD physics at tevatron and LHC

Presented are the Tevatron Run I QCD results that have been known for the degree of controversy associated with them. Also, the prospects for the QCD-motivated studies at Tevatron Run II and LHC are briefly discussed.
Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning

Accurate and precise terrain estimation is a difficult problem for robot locomotion in real-world environments. Thus, it is useful to have systems that do not depend on accurate estimation to the point of fragility. In this paper, we explore the limits of such an approach by investigating the problem of traversing stair-like terrain without any external perception or terrain models on a bipedal robot. For such blind bipedal platforms, the problem appears difficult (even for humans) due to the surprise elevation changes. Our main contribution is to show that sim-to-real reinforcement learning (RL) can achieve robust locomotion over stair-like terrain on the bipedal robot Cassie using only proprioceptive feedback. Importantly, this only requires modifying an existing flat-terrain training RL framework to include stair-like terrain randomization, without any changes in reward function. To our knowledge, this is the first controller for a bipedal, human-scale robot capable of reliably traversing a variety of real-world stairs and other stair-like disturbances using only proprioception.
Dynamics of Ion Locking in Doubly-Polymerized Ionic Liquids

In this work, we investigate the dynamics of ion motion in “doubly-polymerized” ionic liquids (DPILs) in which both charged species of an ionic liquid are covalently linked to the same polymer chains. Broadband dielectric spectroscopy is used to characterize these materials over a broad frequency and temperature range, and their behavior is compared to that of conventional “singly-polymerized” ionic liquids (SPILs) in which only one of the charged species is attached to the polymer chains. Polymerization of the DPIL decreases the bulk ionic conductivity by four orders of magnitude relative to both SPILs. The timescales for local ionic rearrangement are similarly found to be approximately four orders of magnitude slower in the DPILs than in the SPILs, and the DPILs also have a lower static dielectric constant. These results suggest that copolymerization of the ionic monomers affects ion motion on both the bulk and the local scales, with ion pairs serving to form strong physical crosslinks between the polymer chains. This study provides quantitative insight into the energetics and timescales of ion motion that drive the phenomenon of “ion locking” currently under investigation for new classes of organic electronics.
Weak solutions for a poro-elastic plate system

We consider a recent plate model obtained as a scaled limit of the three-dimensional Biot system of poro-elasticity. The result is a ‘2.5’-dimensional linear system that couples traditional Euler–B...
Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change

This paper develops a new control design for guaranteeing a vehicle's safety during lane change maneuvers in a complex traffic environment. The proposed method uses a finite state machine (FSM), where a quadratic program based optimization problem using control Lyapunov functions and control barrier functions (CLF-CBF-QP) is used to calculate the system's optimal inputs via rule-based control strategies. The FSM can make switches between different states automatically according to the command of driver and traffic environment, which makes the ego vehicle find a safe opportunity to do a collision-free lane change maneuver. By using a convex quadratic program, the controller can guarantee the system's safety at a high update frequency. A set of pre-designed typical lane change scenarios as well as randomly generated driving scenarios are simulated to show the performance of our controller.
Discovery of ciliary G protein-coupled receptors regulating pancreatic islet insulin and glucagon secretion

Multiple G protein-coupled receptors (GPCRs) are expressed in pancreatic islet cells, but the majority have unknown functions. We observed specific GPCRs localized to primary cilia, a prominent signaling organelle, in pancreatic α and β cells. Loss of cilia disrupts β-cell endocrine function, but the molecular drivers are unknown. Using functional expression, we identified multiple GPCRs localized to cilia in mouse and human islet α and β cells, including FFAR4, PTGER4, ADRB2, KISS1R, and P2RY14. Free fatty acid receptor 4 (FFAR4) and prostaglandin E receptor 4 (PTGER4) agonists stimulate ciliary cAMP signaling and promote glucagon and insulin secretion by α- and β-cell lines and by mouse and human islets. Transport of GPCRs to primary cilia requires TULP3, whose knockdown in primary human and mouse islets relocalized ciliary FFAR4 and PTGER4 and impaired regulated glucagon or insulin secretion, without affecting ciliary structure. Our findings provide index evidence that regulated hormone secretion by islet α and β cells is controlled by ciliary GPCRs providing new targets for diabetes.
A Survey on Low-Resource Neural Machine Translation.

Neural approaches have achieved state-of-the-art accuracy on machine translation but suffer from the high cost of collecting large scale parallel data. Thus, a lot of research has been conducted for neural machine translation (NMT) with very limited parallel data, i.e., the low-resource setting. In this paper, we provide a survey for low-resource NMT and classify related works into three categories according to the auxiliary data they used: (1) exploiting monolingual data of source and/or target languages, (2) exploiting data from auxiliary languages, and (3) exploiting multi-modal data. We hope that our survey can help researchers to better understand this field and inspire them to design better algorithms, and help industry practitioners to choose appropriate algorithms for their applications.
Rapid evaluation of the spectral signal detection threshold and Stieltjes transform

Accurate detection of signal components is a frequently-encountered challenge in statistical applications with a low signal-to-noise ratio. This problem is particularly challenging in settings with heteroscedastic noise. In certain signal-plus-noise models of data, such as the classical spiked covariance model and its variants, there are closed formulas for the spectral signal detection threshold (the largest sample eigenvalue attributable solely to noise) for isotropic noise in the limit of infinitely large data matrices. However, more general noise models currently lack provably fast and accurate methods for numerically evaluating the threshold. In this work, we introduce a rapid algorithm for evaluating the spectral signal detection threshold in the limit of infinitely large data matrices. We consider noise matrices with a separable variance profile (whose variance matrix is rank 1), as these arise often in applications. The solution is based on nested applications of Newton’s method. We also devise a new algorithm for evaluating the Stieltjes transform of the spectral distribution at real values exceeding the threshold. The Stieltjes transform on this domain is known to be a key quantity in parameter estimation for spectral denoising methods. The correctness of both algorithms is proven from a detailed analysis of the master equations characterizing the Stieltjes transform, and their performance is demonstrated in numerical experiments.
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks

Risk assessment instrument (RAI) datasets, particularly ProPublica's COMPAS dataset, are commonly used in algorithmic fairness papers due to benchmarking practices of comparing algorithms on datasets used in prior work. In many cases, this data is used as a benchmark to demonstrate good performance without accounting for the complexities of criminal justice (CJ) processes. We show that pretrial RAI datasets contain numerous measurement biases and errors inherent to CJ pretrial evidence and due to disparities in discretion and deployment, are limited in making claims about real-world outcomes, making the datasets a poor fit for benchmarking under assumptions of ground truth and real-world impact. Conventional practices of simply replicating previous data experiments may implicitly inherit or edify normative positions without explicitly interrogating assumptions. With context of how interdisciplinary fields have engaged in CJ research, algorithmic fairness practices are misaligned for meaningful contribution in the context of CJ, and would benefit from transparent engagement with normative considerations and values related to fairness, justice, and equality. These factors prompt questions about whether benchmarks for intrinsically socio-technical systems like the CJ system can exist in a beneficial and ethical way.
High-resolution structure of the membrane-embedded skeletal muscle ryanodine receptor.

Summary The type 1 ryanodine receptor (RyR)/calcium release channel on the sarcoplasmic reticulum (SR) is required for skeletal muscle excitation-contraction coupling and is the largest known ion channel, composed of four 565-kDa protomers. Cryogenic electron microscopy (cryo-EM) studies of the RyR have primarily used detergent to solubilize the channel; in the present study, we have used cryo-EM to solve high-resolution structures of the channel in liposomes using a gel-filtration approach with on-column detergent removal to form liposomes and incorporate the channel simultaneously. This allowed us to resolve the structure of the channel in the primed and open states at 3.4 and 4.0 A, respectively, with a single dataset. This method offers validation for detergent-based structures of the RyR and offers a starting point for utilizing a chemical gradient mimicking the SR, where Ca2+ concentrations are millimolar in the lumen and nanomolar in the cytosol.
Evaluating Multiple Guesses by an Adversary via a Tunable Loss Function

We consider a problem of guessing, wherein an adversary is interested in knowing the value of the realization of a discrete random variable $X$ on observing another correlated random variable Y. The adversary can make multiple (say, k) guesses. The adversary's guessing strategy is assumed to minimize a-loss, a class of tunable loss functions parameterized by a. It has been shown before that this loss function captures well known loss functions including the exponential loss (a = 1/2), the log-loss (a = 1) and the 0–1 loss (a = ∞). We completely characterize the optimal adversarial strategy and the resulting expected α-loss, thereby recovering known results for a = ∞. We define an information leakage measure from the k-guesses setup and derive a condition under which the leakage is unchanged from a single guess.
Personalized PercepNet: Real-time, low-complexity target voice separation and enhancement

The presence of multiple talkers in the surrounding environment poses a difficult challenge for real-time speech communication systems considering the constraints on network size and complexity. In this paper, we present Personalized PercepNet, a real-time speech enhancement model that separates a target speaker from a noisy multi-talker mixture without compromising on complexity of the recently proposed PercepNet. To enable speaker-dependent speech enhancement, we first show how we can train a perceptually motivated speaker embedder network to produce a representative embedding vector for the given speaker. Personalized PercepNet uses the target speaker embedding as additional information to pick out and enhance only the target speaker while suppressing all other competing sounds. Our experiments show that the proposed model significantly outperforms PercepNet and other baselines, both in terms of objective speech enhancement metrics and human opinion scores.
Conformal and uniformizing maps in Borel analysis

Perturbative expansions in physical applications are generically divergent, and their physical content can be studied using Borel analysis. Given just a finite number of terms of such an expansion, these input data can be analyzed in different ways, leading to vastly different precision for the extrapolation of the expansion parameter away from its original asymptotic regime. Here, we describe how conformal maps and uniformizing maps can be used, in conjunction with Pade approximants, to increase the precision of the information that can be extracted from a finite amount of perturbative input data. We also summarize results from the physical interpretation of Pade approximations in terms of electrostatic potential theory.