cssi/SciSciGPT-SciSciNet huggingface dataset:
National and Office-Specific Measures of Auditor Industry Expertise and Effects on Audit Quality*

Our paper examines whether audit quality is higher for industry audit specialists at the national and city-office levels using the framework developed in Ferguson et al. [2003] and Francis et al. [2005]. We find that auditors who are both national and city-specific industry specialists have clients with the lowest abnormal accruals, suggesting that joint national and city-specific industry specialists have the highest audit quality. In addition, we find some evidence that abnormal accruals of firms audited by city-industry specialists alone (without also being national specific industry specialists) are lower than those audited by nonindustry specialists. Using alternative measures of audit quality, we find that when the auditor is both a national and a city-specific industry specialist, its clients are less likely to meet or beat analysts' earnings forecasts by one penny per share and more likely to be issued a going-concern audit opinion. Together these results provide consistent evidence that audit quality is higher when the auditor is both a national and city-specific industry specialist, suggesting that auditors' national positive network synergies and the individual auditors' deep industry knowledge at the office level are jointly important factors in delivering higher audit quality.
Too Close for Comfort? Immigrant Exclusion in Africa

Why do some immigrant minorities in the developing world integrate into their host societies whereas others face exclusion and hostility? This article offers new insights on the determinants of political identity and group relations in ethnically diverse societies through the lens of South-to-South migration. Using original data from surveys and interviews collected during 12 months of field research in West Africa and a unique empirical strategy that allows for single-group cross-country and single-country cross-group comparisons, this article tests the relationship between cultural proximity and immigrant exclusion. The analysis indicates that cultural similarities between immigrants and their hosts may limit immigrant integration because they motivate com- munity leaders to highlight group boundaries. The results shed light on immi- grant exclusion in Africa and contribute to the debate on the determinants of political identity in ethnically diverse societies.
REFLECTIONS ON TWENTY YEARS OF THE LAW REVIEW

The Author reflects on twenty years of working with the Western New England Law Review. Up until his arrival as dean of the law school, the College was unwilling,or at least reluctant, to provide the necessary funds for publication of a law review.
Reports of Section 337’s Death Have Been Greatly Exaggerated: The ITC’s Importance in an Evolving Patent Enforcement Environment

The United States International Trade Commission (ITC) is a federal agency with a broad trade and tariff setting mission that includes investigating patent infringement by imported goods. Throughout its existence, the ITC has been challenged by a constantly-evolving patent landscape shaped by increasing technological complexity, corporate organizational complexity, and trade globalization. Historically, the ITC has maintained its strength as a patent infringement adjudication venue by implementing statutory and administrative reforms in response to domestic and international pressures. This Note reviews critiques of Section 337’s position in the patent enforcement landscape and suggests that the ITC’s recent, narrowly tailored procedural changes have continued to maintain the relevance, uniqueness, and attractiveness of the Section 337’s adjudicative power.
Ex ante carrots instead of ex post sticks: two examples

This paper argues that the limited ability to help developing countries in a crisis should shift the focus to policies helping in reducing the ex ante probability of crises. Indirectly, such policies would also alleviate the depths of realized crises. Two specific ideas are explored: I. International reserves escrow accounts: Managing international reserves provides an effective mechanism for self insurance. The hazard of this mechanism is that international reserves are easy prey for opportunistic policy makers in polarized countries characterized by political instability. This hazard may be alleviated by escrow accounts run by the International Financial Institutions (IFIs), where part of the international reserves of a country are saved and would be used if pre-set conditions, like large TOT deteriorations, are met. The IFIs may offer a subsidized return on these escrow accounts in order to encourage countries to reduce external borrowing and to increase fiscal savings. Such subsidies may be welfare improving due to the over borrowing bias induced by sovereign risk. II. IFIs as lenders of last resort to finance fiscal reforms: I illustrate this possibility in a modified version of Cukierman, Edwards and Tabellinis (AER 1992) model. I identify conditions where IFIs function as the lenders of last resort, financing fiscal reforms. IFIs financing may shift the equilibrium from an inefficient outcome with a low tax base and high inflation to a superior outcome, associated with a more sound tax system.
Differential games with mixed leadership: The open-loop solution

This paper introduces the notion of mixed leadership in nonzero-sum differential games, where there is no fixed hierarchy in decision making with respect to the players. Whether a particular player is leader or follower depends on the instrument variable s/he is controlling, and it is possible for a player to be both leader and follower, depending on the control variable. The paper studies two-player open-loop differential games in this framework, and obtains a complete set of equations (differential and algebraic) which yield the controls in the mixed-leadership Stackelberg solution. The underlying differential equations are coupled and have mixed-boundary conditions. The paper also discusses the special case of linear-quadratic differential games, in which case solutions to the coupled differential equations can be expressed in terms of solutions to coupled Riccati differential equations which are independent of the state trajectory.
Using Salience and Influence to Narrow the Tax Gap

Most self-employed and small business taxpayers cheat on their taxes. In fact, in the aggregate, they fail to pay about half the tax they owe to the government, and this unpaid tax amounts to about $150 billion annually. This amount is about half the "tax gap," or the amount of tax due that the federal government does not collect.This paper argues that more salient government communications and greater attention to principles of influence would improve existing and proposed policies to encourage self-employed and small business taxpayers to pay their taxes. Reversing the widespread tax evasion among self-employed and small business taxpayers requires changing the existing social norm of noncompliance, which in turn demands a better connection between the government's message and the experience of taxpayers. Policymakers should recraft their anti-tax-gap messages so that they grab the attention of the target audience and take advantage of established influence tools to leverage predictable taxpayer heuristics such as availability bias and following the compliance behavior of similar peers.
CEO Incentives, Cash Flow, and Investment

We estimate the impact of chief executive officer (CEO) incentives on the sensitivity of investment to cash flow during a period of strong economic growth. Our measure of the alignment of managers’ and shareholders’ interests, pay-performance sensitivity (PPS), incorporates both stock and stock option holdings. Contrary to prior studies, we find that the dominant effect of increasing alignment is to reduce the overinvestment of free cash flow. We find no evidence that incentives exacerbate the severity of financial constraints. We find some evidence that PPS helps reduce the underinvestment of cash flow due to managerial shirking.
Microfoundations of the Rule of Law

Many social scientists rely on the rule of law in their accounts of political or economic development. Many, however, simply equate law with a stable government capable of enforcing the rules generated by a political authority. As two decades of largely failed efforts to build the rule of law in poor and transition countries and continuing struggles to build international legal order demonstrate, we still do not understand how legal order is produced, especially in places where it does not already exist. We here canvas literature in the social sciences to identify the themes and gaps in the existing accounts. We conclude that this literature has failed to produce a microfoundational account of the phenomenon of legal order. We then discuss our recent effort to develop the missing microfoundations of legal order to provide a better framework for future work on the rule of law.
Incentives for Tax Planning and Avoidance: Evidence from the Field

ABSTRACT: We analyze survey responses from nearly 600 corporate tax executives to investigate firms' incentives and disincentives for tax planning. While many researchers hypothesize that reputatio...
Sidelined Investors, Trading-Generated News, and Security Returns

This article studies information blockages and the asymmetric release of information in a security market with fixed setup costs of trading. In this setting, "sidelined" investors may delay trading until price movements validate their private signals. Trading thereby internally generates the arrival of further news to the market. This leads to (1) negative skewness following price run-ups and positive skewness following price rundowns (even though the model is ex ante symmetric), (2) a lack of correspondence between large price changes and the arrival of external information, and (3) increases in volatility following large price changes. Copyright 2002, Oxford University Press.
Networks in the Understanding of Economic Behaviors

Abstract As economists endeavor to build better models of human behavior, they cannot ignore that humans are fundamentally a social species with interaction patterns that shape their behaviors. People's opinions, which products they buy, whether they invest in education, become criminals, and so forth, are all influenced by friends and acquaintances. Ultimately, the full network of relationships—how dense it is, whether some groups are segregated, who sits in central positions—affects how information spreads and how people behave. Increased availability of data coupled with increased computing power allows us to analyze networks in economic settings in ways not previously possible. In this paper, I describe some of the ways in which networks are helping economists to model and understand behavior. I begin with an example that demonstrates the sorts of things that researchers can miss if they do not account for network patterns of interaction. Next I discuss a taxonomy of network properties and how they im...
In Search of Economic Justice: Considering Competition and Consumer Protection Law

In November 2003, I was invited to address the first summit conference of European competition and consumer protection officials held by the Directorate-General for Competition of the European Commission at their headquarters in Brussels. At that conference, officials gathered from the twenty-five nations that already were member states of the European Union (“EU”) or that would become member states as of May 1, 2004. These officials spent the better part of a day discussing the interactions and synergies between competition and consumer protection law. The goal was to create and deepen enforcement networks among the member states and between the member states and the EU officials in Brussels and to explore the possibilities for greater convergence between two bodies of law with very different histories and enforcement traditions.1 I was to be the lunch speaker for this august group—an American professor who cared deeply about both bodies of law—discussing how the two bodies of law related to each other. This task proved to be particularly complicated, both theoretically and in terms of how the laws are actually enforced, and it forced me to reflect long and hard about some items of faith that I had simply taken for granted. My thesis, then and now, is a simple one. Competition and consumer protection laws are intimately related, two sides of the same coin of consumer sovereignty and hence economic justice. Perhaps surprisingly, this relationship is only beginning to be recognized by academics and policy makers, and the way it all actually works in the
EXPLORING THE ENTREPRENEURIAL MINDSET: FEEDBACK AND ADAPTIVE DECISION-MAKING

The ability to sense and adapt to uncertainty may characterize a critical entrepreneurial and organizational resource. We investigate the roles that metacognition and feedback-type play in facilitating cognitive adaptability: the ability to inform and adapt a previously learned decision heuristic given a dynamic task environment. Across four, inter-related conjoint experiments we capture and decompose more than 10,000 ‘entrepreneurial decisions’ nested within 217 individuals, and test hypotheses positioned to assess the concomitant roles of metacognition and feedback-type in the decision-making process. Our findings suggest that metacognitive awareness and cognitive-based feedback are positively related to effective adaptation given a dynamic task environment, and also that metacognitively aware individuals use cognitive-type feedback more effectively than individuals less metacognitively aware.
The Subjective and Objective Evaluation of Incentive Stock Options

Owners of incentive options invariably hold undiversified portfolios. This paper derives a model for the subjective and objective values of such options. The subjective value—the value to the holder—is less than the market value because the option is held in an undiversified portfolio and because it is exercised suboptimally from the market perspective. The objective value is the cost to the firm of issuing the option and lies between the market and subjective values. This value recognizes the suboptimal exercise but not the undiversified discount. The model, which is the Black‐Scholes model with modified parameters, is simple to use.
Do nonpartisan ballots racialize candidate evaluations? Evidence from “Who Said What?” experiments:

At last count, U.S. voters were responsible for directly electing more than 510,000 public officials. Few of these contests feature lively campaigns or attract substantial media attention, often le...
Semi-Automating Knowledge Base Construction for Cancer Genetics

The vast and rapidly expanding volume of biomedical literature makes it difficult for domain experts to keep up with the evidence. In this work, we specifically consider the exponentially growing subarea of genetics in cancer. The need to synthesize and centralize this evidence for dissemination has motivated a team of physicians (with whom this work is a collaboration) to manually construct and maintain a knowledge base that distills key results reported in the literature. This is a laborious process that entails reading through full-text articles to understand the study design, assess study quality, and extract the reported cancer risk estimates associated with particular hereditary cancer genes (i.e., \emph{penetrance}). In this work, we propose models to automatically surface key elements from full-text cancer genetics articles, with the ultimate aim of expediting the manual workflow currently in place. We propose two challenging tasks that are critical for characterizing the findings reported cancer genetics studies: (i) Extracting snippets of text that describe \emph{ascertainment mechanisms}, which in turn inform whether the population studied may introduce bias owing to deviations from the target population; (ii) Extracting reported risk estimates (e.g., odds or hazard ratios) associated with specific germline mutations. The latter task may be viewed as a joint entity tagging and relation extraction problem. To train models for these tasks, we induce distant supervision over tokens and snippets in full-text articles using the manually constructed knowledge base. We propose and evaluate several model variants, including a transformer-based joint entity and relation extraction model to extract \texttt{ } pairs. We observe strong empirical performance, highlighting the practical potential for such models to aid KB construction in this space. We ablate components of our model, observing, e.g., that a joint model for \texttt{ } fares substantially better than a pipelined approach.
Identifying regions of inhomogeneities in spatial processes via an M‐RA and mixture priors

Soils have been heralded as a hidden resource that can be leveraged to mitigate and address some of the major global environmental challenges. Specifically, the organic carbon stored in soils, called soil organic carbon (SOC), can, through proper soil management, help offset fuel emissions, increase food productivity, and improve water quality. As collecting data on SOC are costly and time-consuming, not much data on SOC are available, although understanding the spatial variability in SOC is of fundamental importance for effective soil management. In this manuscript, we propose a modeling framework that can be used to gain a better understanding of the dependence structure of a spatial process by identifying regions within a spatial domain where the process displays the same spatial correlation range. To achieve this goal, we propose a generalization of the multiresolution approximation (M-RA) modeling framework of Katzfuss originally introduced as a strategy to reduce the computational burden encountered when analyzing massive spatial datasets. To allow for the possibility that the correlation of a spatial process might be characterized by a different range in different subregions of a spatial domain, we provide the M-RA basis functions weights with a two-component mixture prior with one of the mixture components a shrinking prior. We call our approach the mixture M-RA. Application of the mixture M-RA model to both stationary and nonstationary data show that the mixture M-RA model can handle both types of data, can correctly establish the type of spatial dependence structure in the data (e.g., stationary versus not), and can identify regions of local stationarity.
Synthesizing optical spectra using computer-generated holography techniques

Experimental control and detection of atoms and molecules often rely on optical transitions between different electronic states. In many cases, substructure such as hyperfine or spin-rotation structure leads to the need for multiple optical frequencies spaced by MHz to GHz. The task of creating multiple optical frequencies -- optical spectral engineering -- becomes challenging when the number of frequencies becomes large, a situation that one could encounter in complex molecules and atoms in large magnetic fields. In this work, we present a novel method to synthesize arbitrary optical spectra by modulating a monochromatic light field with a time-dependent phase generated through computer-generated holography techniques. Our method is compatible with non-linear optical processes such as sum frequency generation and second harmonic generation. Additional requirements that arise from the finite lifetimes of excited states can also be satisfied in our approach. As a proof-of-principle demonstration, we generate spectra suitable for cycling photons on the X-B transition in CaF, and verify via Optical Bloch Equation simulations that one can achieve high photon scattering rates, which are important for fluorescent detection and laser cooling. Our method could offer significant simplifications in future experiments that would otherwise be prohibitively complex.
Quantitative stratification of stationary connections

Let $A$ be a connection of a principal bundle $P$ over a Riemannian manifold $M$, such that its curvature $F_A\in L_{\text{loc}}^2(M)$ satisfies the stationarity equation. It is a consequence of the stationarity that $\theta_A(x,r)=e^{cr^2}r^{4-n}\int_{B_r(x)}|F_A|^2$ is monotonically increasing in $r$, for some $c$ depending only on the local geometry of $M$. We are interested in the singular set defined by $S(A)=\{x: \lim_{r\to 0}\theta_A(x,r)\neq 0\}$, and its stratification $S^k(A)=\{x: \text{no tangent measure at $x$ is $(k+1)$-symmetric}\}$. We then introduce and study the quantitative stratification $S^k_{\epsilon}(A)$. Roughly speaking, $S^k_{\epsilon}(A)$ consists of points at which no tangent measure of $A$ is $\epsilon$-close to being $(k+1)$-symmetric. In the main Theorem, we show that $S^k_{\epsilon}$ is $k$-rectifiable and satisfies the Minkowski volume estimate $\text{Vol}(B_r(S^k_{\epsilon})\cap B_1)\le Cr^{n-k}$. Lastly, we apply the main theorems to the stationary Yang-Mills connections to obtain a rectifiability theorem that extends some previously known results by G. Tian.
Singular loci of reflection arrangements and the containment problem

This paper provides insights into the role of symmetry in studying polynomial functions vanishing to high order on an algebraic variety. The varieties we study are singular loci of hyperplane arrangements in projective space, with emphasis on arrangements arising from complex reflection groups. We provide minimal sets of equations for the radical ideals defining these singular loci and study containments between the ordinary and symbolic powers of these ideals. Our work ties together and generalizes results in Bauer et al. (Int Math Res Not IMRN 24:7459–7514, 2019), Dumnicki et al. (J Algebra 393:24–29, 2013), Harbourne and Seceleanu (J Pure Appl Algebra 219(4):1062–1072, 2015) and Malara and Szpond (J Pure Appl Algebra 222(8):2323–2329, 2018) under a unified approach.
Electron generation and multiplication at the initial stage of nanosecond breakdown in water

Electrical breakdown of liquid dielectrics under nanosecond pulsed high voltage has been investigated extensively in the last decade. Prior studies have focused on either experimental characterization of the breakdown process and discharge plasma or formulation/verification of the electrostrictive cavitation mechanism of the breakdown initiation. There remain knowledge gaps toward a clear physical picture of how the first plasma is generated in a region saturated by nanoscale cavities created by electrostrictive forces in inhomogeneous fields at the nanosecond timescale. Initial plasma results from the multiplication of primary electrons that gain energy collisionlessly in the cavities to cause collisional ionization of water molecules on the cavity walls. This paper quantitatively discusses the possible sources of primary electrons that seed the plasma discharge. Electron detachment from hydroxide is shown to be the most probable and sustainable electron source. Using numerical modeling, this study demonstrates the plausibility of an electron multiplication mechanism involving two neighboring cavities. The drift of hydrated electrons from one cavity to the next is the rate-limiting step and sets the minimum electric field requirement. This work will inform subsequent experimental studies and have implications in various applications such as plasma sources in biomedical applications, cavitation study, and insulation of pulsed power equipment.
Time to Bury the Shocks the Conscience Test

The Supreme Court has acknowledged that “the Due Process Clause, like its forebear in the Magna Carta, was ‘intended to secure the individual from the arbitrary exercise of the powers of government’ . . . to prevent governmental power from being ‘used for purposes of oppression.’” 1 Historically, Magna Carta was aimed at limiting the power of the king. Today, substantive due process is invoked to challenge arbitrary deprivations of life, liberty, and property by officials, such as police officers, jail guards, public-school educators, public employers, and members of zoning boards. However, the Supreme Court has emasculated its efficacy as a limitation on executive power. In 1998, in County of Sacramento v. Lewis, it held that the “criteria to identify what is fatally arbitrary differ depending on whether it is legislation or a specific act of a governmental officer that is at issue.” 2 Whereas legislative enactments are subject to varying levels of scrutiny depending on the nature of the rights at stake, the Court asserted that only the most egregious executive misconduct, that which “shocks the conscience,” will be actionable.3 Since 1998, Lewis has created significant confusion and division in the appellate courts, severely restricting the ability of detainees, students, government employees, and landowners, to bring substantive due process challenges to the arbitrary exercise of power. Some circuits have required that litigants prove that executive misconduct both infringe on a fundamental right and shock the conscience. Because neither employment nor property are regarded as fundamental rights, most allegations of arbitrary treatment brought by government employees and landowners are dismissed. Other appellate courts allow substantive due process challenges to the deprivation of non-fundamental property or liberty interests only where the litigant demonstrates the inadequacy of state law remedies, thereby permitting the vagaries of state tort law to determine the fate of constitutional claims.
Machine learning discovery of high-temperature polymers.

Summary To formulate a machine learning (ML) model to establish the polymer's structure-property correlation for glass transition temperature T g , we collect a diverse set of nearly 13,000 real homopolymers from the largest polymer database, PoLyInfo. We train the deep neural network (DNN) model with 6,923 experimental T g values using Morgan fingerprint representations of chemical structures for these polymers. Interestingly, the trained DNN model can reasonably predict the unknown T g values of polymers with distinct molecular structures, in comparison with molecular dynamics simulations and experimental results. With the validated transferability and generalization ability, the ML model is utilized for high-throughput screening of nearly one million hypothetical polymers. We identify more than 65,000 promising candidates with T g > 200°C, which is 30 times more than existing known high-temperature polymers (∼2,000 from PoLyInfo). The discovery of this large number of promising candidates will be of significant interest in the development and design of high-temperature polymers.
A Visual Formalism for Interacting Systems

Interacting systems are increasingly commonmany examples pervade our everyday lives: automobiles, aircraft, defense systems, telephone switching systems, financial systems, national governments, and so on. Closer to computer science, embedded systems and Systems of Systems are further examples of interacting systems. Common to all of these is that some ”whole” is made up of constituent parts, and these parts interact with each other. By design, these interactions are intentional, but it is the unintended interactions that are problematic. The Systems of Systems literature uses the terms ”constituent systems” and ”constituents” to refer to systems that interact with each other. That practice is followed here. This paper presents a visual formalism, Swim Lane Event-Driven Petri Nets, that is proposed as a basis for Model-Based Testing (MBT) of interacting systems. In the absence of available tools, this model can only support the offline form of Model-Based Testing.
Randomized Scheduling of Real-Time Traffic in Wireless Networks Over Fading Channels

Despite the rich literature on scheduling algorithms for wireless networks, algorithms that can provide deadline guarantees on packet delivery for general traffic and interference models are very limited. In this paper, we study the problem of scheduling real-time traffic under a conflict-graph interference model with unreliable links due to channel fading. Packets that are not successfully delivered within their deadlines are of no value. We consider traffic (packet arrival and deadline) and fading (link reliability) processes that evolve as an unknown finite-state Markov chain. The performance metric is efficiency ratio which is the fraction of packets of each link which are delivered within their deadlines compared to that under the optimal (unknown) policy. We first show a conversion result that shows classical non-real-time scheduling algorithms can be ported to the real-time setting and yield a constant efficiency ratio, in particular, Max-Weight Scheduling (MWS) yields an efficiency ratio of 1/2. We then propose randomized algorithms that achieve efficiency ratios strictly higher than 1/2, by carefully randomizing over the maximal schedules. We further propose low-complexity and myopic distributed randomized algorithms, and characterize their efficiency ratio. Simulation results are presented that verify that randomized algorithms outperform classical algorithms such as MWS and GMS.
The Chern-Ricci flow on primary Hopf surfaces

The Hopf surfaces provide a family of minimal non-Kahler surfaces of class VII on which little is known about the Chern–Ricci flow. We use a construction of Gauduchon–Ornea for locally conformally Kahler metrics on primary Hopf surfaces of class 1 to study solutions of the Chern–Ricci flow. These solutions reach a volume collapsing singularity in finite time, and we show that the metric tensor satisfies a uniform upper bound, supporting the conjecture that the Gromov-Hausdorff limit is isometric to a round $$S^1$$ . Uniform $$C^{1+\beta }$$ estimates are also established for the potential. Previous results had only been known for the simplest examples of Hopf surfaces.
Efficient Conformal Parameterization of Multiply-Connected Surfaces Using Quasi-Conformal Theory

Conformal mapping, a classical topic in complex analysis and differential geometry, has become a subject of great interest in the area of surface parameterization in recent decades with various applications in science and engineering. However, most of the existing conformal parameterization algorithms only focus on simply-connected surfaces and cannot be directly applied to surfaces with holes. In this work, we propose two novel algorithms for computing the conformal parameterization of multiply-connected surfaces. We first develop an efficient method for conformally parameterizing an open surface with one hole to an annulus on the plane. Based on this method, we then develop an efficient method for conformally parameterizing an open surface with k holes onto a unit disk with k circular holes. The conformality and bijectivity of the mappings are ensured by quasi-conformal theory. Numerical experiments and applications are presented to demonstrate the effectiveness of the proposed methods.
On the number of discrete chains

We study a generalization of Erd\H os's unit distances problem to chains of $k$ distances. Given $\mathcal P,$ a set of $n$ points, and a sequence of distances $(\delta_1,\ldots,\delta_k)$, we study the maximum possible number of tuples of distinct points $(p_1,\ldots,p_{k+1})\in \mathcal P^{k+1}$ satisfying $|p_j p_{j+1}|=\delta_j$ for every $1\leq j \leq k$. We study the problem in $\mathbb R^2$ and in $\mathbb R^3$, and derive upper and lower bounds for this family of problems.
Traceability Transformed: Generating more Accurate Links with Pre-Trained BERT Models

Software traceability establishes and leverages associations between diverse development artifacts. Researchers have proposed the use of deep learning trace models to link natural language artifacts, such as requirements and issue descriptions, to source code; however, their effectiveness has been restricted by availability of labeled data and efficiency at runtime. In this study, we propose a novel framework called Trace BERT (T-BERT) to generate trace links between source code and natural language artifacts. To address data sparsity, we leverage a three-step training strategy to enable trace models to transfer knowledge from a closely related Software Engineering challenge, which has a rich dataset, to produce trace links with much higher accuracy than has previously been achieved. We then apply the T-BERT framework to recover links between issues and commits in Open Source Projects. We comparatively evaluated accuracy and efficiency of three BERT architectures. Results show that a Single-BERT architecture generated the most accurate links, while a Siamese-BERT architecture produced comparable results with significantly less execution time. Furthermore, by learning and transferring knowledge, all three models in the framework outperform classical IR trace models. On the three evaluated real-word OSS projects, the best T-BERT stably outperformed the VSM model with average improvements of 60.31% measured using Mean Average Precision (MAP). RNN severely underper-formed on these projects due to insufficient training data, while T-BERT overcame this problem by using pretrained language models and transfer learning.
Genetic basis of variation in cocaine and methamphetamine consumption in outbred populations of Drosophila melanogaster

We used Drosophila melanogaster to map the genetic basis of naturally occurring variation in voluntary consumption of cocaine and methamphetamine. We derived an outbred advanced intercross population (AIP) from 37 sequenced inbred wild-derived lines of the Drosophila melanogaster Genetic Reference Panel (DGRP), which are maximally genetically divergent, have minimal residual heterozygosity, are not segregating for common inversions, and are not infected with Wolbachia pipientis We assessed consumption of sucrose, methamphetamine-supplemented sucrose, and cocaine-supplemented sucrose and found considerable phenotypic variation for consumption of both drugs, in both sexes. We performed whole-genome sequencing and extreme quantitative trait locus (QTL) mapping on the top 10% of consumers for each replicate, sex, and condition and an equal number of randomly selected flies. We evaluated changes in allele frequencies among high consumers and control flies and identified 3,033 variants significantly (P < 1.9 × 10-8) associated with increased consumption, located in or near 1,962 genes. Many of these genes are associated with nervous system development and function, and 77 belong to a known gene-gene interaction subnetwork. We assessed the effects of RNA interference (RNAi) on drug consumption for 22 candidate genes; 17 had a significant effect in at least one sex. We constructed allele-specific AIPs that were homozygous for alternative candidate alleles for 10 single-nucleotide polymorphisms (SNPs) and measured average consumption for each population; 9 SNPs had significant effects in at least one sex. The genetic basis of voluntary drug consumption in Drosophila is polygenic and implicates genes with human orthologs and associated variants with sex- and drug-specific effects.
NKX2-1 controls lung cancer progression by inducing DUSP6 to dampen ERK activity

The RAS→RAF→MEK→ERK pathway is hyperactivated in the majority of human lung adenocarcinoma (LUAD). However, the initial activating mutations induce homeostatic feedback mechanisms that limit ERK activity. How ERK activation reaches the tumor-promoting levels that overcome the feedback and drive malignant progression is unclear. We show here that the lung lineage transcription factor NKX2-1 suppresses ERK activity. In human tissue samples and cell lines, xenografts, and genetic mouse models, NKX2-1 induces the ERK phosphatase DUSP6, which inactivates ERK. In tumor cells from late-stage LUAD with silenced NKX2-1, re-introduction of NKX2-1 induces DUSP6 and inhibits tumor growth and metastasis. We show that DUSP6 is necessary for NKX2-1-mediated inhibition of tumor progression in vivo and that DUSP6 expression is sufficient to inhibit RAS-driven LUAD. Our results indicate that NKX2-1 silencing, and thereby DUSP6 downregulation, is a mechanism by which early LUAD can unleash ERK hyperactivation for tumor progression.
Neurogrid simulates cortical cell-types, active dendrites, and top-down attention

A central challenge for systems neuroscience and artificial intelligence is to understand how cognitive behaviors arise from large, highly interconnected networks of neurons. Digital simulation is linking cognitive behavior to neural activity to bridge this gap in our understanding at great expense in time and electricity. A hybrid analog-digital approach, whereby slow analog circuits, operating in parallel, emulate graded integration of synaptic currents by dendrites while a fast digital bus, operating serially, emulates all-or-none transmission of action potentials by axons, may improve simulation efficacy. Due to the latter9s serial operation, this approach has not scaled beyond millions of synaptic connections (per bus). This limit was broken by following design principles the neocortex uses to minimize its wiring. The resulting hybrid analog-digital platform, Neurogrid, scales to billions of synaptic connections, between up to a million neurons, and simulates cortical models in real-time using a few watts of electricity. Here, we demonstrate that Neurogrid simulates cortical models spanning five levels of experimental investigation: biophysical, dendritic, neuronal, columnar, and area. Bridging these five levels with Neurogrid revealed a novel way active dendrites could mediate top-down attention.
AMR Parsing with Action-Pointer Transformer.

Abstract Meaning Representation parsing is a sentence-to-graph prediction task where target nodes are not explicitly aligned to sentence tokens. However, since graph nodes are semantically based on one or more sentence tokens, implicit alignments can be derived. Transition-based parsers operate over the sentence from left to right, capturing this inductive bias via alignments at the cost of limited expressiveness. In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments. We model the transitions as well as the pointer mechanism through straightforward modifications within a single Transformer architecture. Parser state and graph structure information are efficiently encoded using attention heads. We show that our action-pointer approach leads to increased expressiveness and attains large gains (+1.6 points) against the best transition-based AMR parser in very similar conditions. While using no graph re-categorization, our single model yields the second best Smatch score on AMR 2.0 (81.8), which is further improved to 83.4 with silver data and ensemble decoding.
Asymmetric Leaky Private Information Retrieval

Information-theoretic formulations of the private information retrieval (PIR) problem have been investigated under a variety of scenarios. Symmetric private information retrieval (SPIR) is a variant where a user is able to privately retrieve one out of $K$ messages from $N$ non-colluding replicated databases without learning anything about the remaining $K-1$ messages. However, the goal of perfect privacy can be too taxing for certain applications. In this paper, we investigate if the information-theoretic capacity of SPIR (equivalently, the inverse of the minimum download cost) can be increased by relaxing both user and DB privacy definitions. Such relaxation is relevant in applications where privacy can be traded for communication efficiency. We introduce and investigate the Asymmetric Leaky PIR (AL-PIR) model with different privacy leakage budgets in each direction. For user privacy leakage, we bound the probability ratios between all possible realizations of DB queries by a function of a non-negative constant $\epsilon $ . For DB privacy, we bound the mutual information between the undesired messages, the queries, and the answers, by a function of a non-negative constant $\delta $ . We propose a general AL-PIR scheme that achieves an upper bound on the optimal download cost for arbitrary $\epsilon $ and $\delta $ . We show that the optimal download cost of AL-PIR is upper-bounded as $D^{*}(\epsilon,\delta)\leq 1+\frac {1}{N-1}-\frac {\delta e^{\epsilon }}{N^{K-1}-1}$ . Second, we obtain an information-theoretic lower bound on the download cost as $D^{*}(\epsilon,\delta)\geq 1+\frac {1}{Ne^{\epsilon }-1}-\frac {\delta }{(Ne^{\epsilon })^{K-1}-1}$ . The gap analysis between the two bounds shows that our AL-PIR scheme is optimal when $\epsilon =0$ , i.e., under perfect user privacy and it is optimal within a maximum multiplicative gap of $\frac {N-e^{-\epsilon }}{N-1}$ for any $\epsilon >0$ and $\delta >0$ .
Minimizing post-shock forecasting error through aggregation of outside information

Abstract We develop a forecasting methodology for providing credible forecasts for time series that have recently undergone a shock. We achieve this by borrowing knowledge from other time series that have undergone similar shocks for which post-shock outcomes are observed. Three shock effect estimators are motivated with the aim of minimizing average forecast risk. We propose risk-reduction propositions that provide conditions that establish when our methodology works. Bootstrap and leave-one-out cross-validation procedures are provided to prospectively assess the performance of our methodology. Several simulated data examples and two real data examples of forecasting Conoco Phillips and Apple stock price are provided for verification and illustration.
Illustrating potential effects of alternate control populations on real-world evidence-based statistical analyses.

Objective Case-control study designs are commonly used in retrospective analyses of real-world evidence (RWE). Due to the increasingly wide availability of RWE, it can be difficult to determine whether findings are robust or the result of testing multiple hypotheses. Materials and Methods We investigate the potential effects of modifying cohort definitions in a case-control association study between depression and type 2 diabetes mellitus. We used a large (>75 million individuals) de-identified administrative claims database to observe the effects of minor changes to the requirements of glucose and hemoglobin A1c tests in the control group. Results We found that small permutations to the criteria used to define the control population result in significant shifts in both the demographic structure of the identified cohort as well as the odds ratio of association. These differences remain present when testing against age- and sex-matched controls. Discussion Analyses of RWE need to be carefully designed to avoid issues of multiple testing. Minor changes to control cohorts can lead to significantly different results and have the potential to alter even prospective studies through selection bias. Conclusion We believe this work offers strong support for the need for robust guidelines, best practices, and regulations around the use of observational RWE for clinical or regulatory decision-making.
Analysis of stochastic Lanczos quadrature for spectrum approximation

The cumulative empirical spectral measure (CESM) $\Phi[\mathbf{A}] : \mathbb{R} \to [0,1]$ of a $n\times n$ symmetric matrix $\mathbf{A}$ is defined as the fraction of eigenvalues of $\mathbf{A}$ less than a given threshold, i.e., $\Phi[\mathbf{A}](x) := \sum_{i=1}^{n} \frac{1}{n} {\large\unicode{x1D7D9}}[ \lambda_i[\mathbf{A}]\leq x]$. Spectral sums $\operatorname{tr}(f[\mathbf{A}])$ can be computed as the Riemann--Stieltjes integral of $f$ against $\Phi[\mathbf{A}]$, so the task of estimating CESM arises frequently in a number of applications, including machine learning. We present an error analysis for stochastic Lanczos quadrature (SLQ). We show that SLQ obtains an approximation to the CESM within a Wasserstein distance of $t \: | \lambda_{\text{max}}[\mathbf{A}] - \lambda_{\text{min}}[\mathbf{A}] |$ with probability at least $1-\eta$, by applying the Lanczos algorithm for $\lceil 12 t^{-1} + \frac{1}{2} \rceil$ iterations to $\lceil 4 ( n+2 )^{-1}t^{-2} \ln(2n\eta^{-1}) \rceil$ vectors sampled independently and uniformly from the unit sphere. We additionally provide (matrix-dependent) a posteriori error bounds for the Wasserstein and Kolmogorov--Smirnov distances between the output of this algorithm and the true CESM. The quality of our bounds is demonstrated using numerical experiments.
Two-dimensional vortex motion and 'negative temperatures.'

Explanation of the novel phenomenon, tentatively identified as the 'ergodic boundary' in a space of initial conditions for turbulent flow, suggested by the recent numerical integration of the two-dimensional Navier-Stokes equations at high Reynolds numbers reported by Deem and Zabusky (1971). The proposed explanation is presented in terms of negative temperatures for a point vortex model.
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction

Integrating extracted knowledge from the Web to knowledge graphs (KGs) can facilitate tasks like question answering. We study relation integration that aims to align free-text relations in subject-relation-object extractions to relations in a target KG. To address the challenge that free-text relations are ambiguous, previous methods exploit neighbor entities and relations for additional context. However, the predictions are made independently, which can be mutually inconsistent. We propose a two-stage Collective Relation Integration (CoRI) model, where the first stage independently makes candidate predictions, and the second stage employs a collective model that accesses all candidate predictions to make globally coherent predictions. We further improve the collective model with augmented data from the portion of the target KG that is otherwise unused. Experiment results on two datasets show that CoRI can significantly outperform the baselines, improving AUC from .677 to .748 and from .716 to .780, respectively.
1D Self-Healing Beams in Integrated Silicon Photonics

Since the first experimental observation of optical Airy beams, various applications ranging from particle and cell micromanipulation to laser micromachining have exploited their non-diffracting and accelerating properties. The later discovery that Airy beams can self-heal after being blocked by an obstacle further proved their robustness to propagate under scattering and disordered environment. Here, we report the generation of Airy beams on an integrated silicon photonic chip and demonstrate that the on-chip 1D Airy beams preserve the same properties as the 2D beams. The 1D meta-optics used to create the Airy beam has the size of only 3 by 16 microns, at least three orders of magnitude smaller than the conventional optic. The on-chip self-healing beams demonstrated here could potentially enable diffraction-free light routing for on-chip optical networks and high-precision micromanipulation of bio-molecules on an integrated photonic chip.
Macroscopic Balance Model for Wave Rotors

A mathematical model for multi-port wave rotors is described. The wave processes that effect energy exchange within the rotor passage are modeled using one-dimensional gas dynamics. Macroscopic mass and energy balances relate volume-averaged thermodynamic properties in the rotor passage control volume to the mass, momentum, and energy fluxes at the ports. Loss models account for entropy production in boundary layers and in separating flows caused by blade-blockage, incidence, and gradual opening and closing of rotor passages. The mathematical model provides a basis for predicting design-point wave rotor performance, port timing, and machine size. Model predictions are evaluated through comparisons with CFD calculations and three-port wave rotor experimental data. A four-port wave rotor design example is provided to demonstrate model applicability. The modeling approach is amenable to wave rotor optimization studies and rapid assessment of the trade-offs associated with integrating wave rotors into gas turbine engine systems.