cssi/SciSciGPT-SciSciNet huggingface dataset:
To Gain the Whole World and Lose His Own Soul: Nineteenth-Century American Dueling as Public Law and Private Code

The practice of dueling demolishes any happy notion that people are fundamentally the same regardless of the particular chronological moment in which they inhabit the world. Yet the duel flourished in the United States at least until the latter decades of the nineteenth century, when it died out in a regionally specific manner. This Article provides an antidote to static views of dueling by analyzing the duel as both a subject of legal regulation and a precursor to legal remedies for injuries to reputation. The duel carried profound public meaning; to duel was to perform the specific role of gentleman by asserting the right to vindicate personal honor in the realm of public notice. A subtle alchemy was therefore necessary for dueling to be viable as an institution: a translation from private dispute into public performance. This translation occurred frequently throughout the United States for much of the first half of the nineteenth century. By mid-century, however, growing dissonance between North and South led the two regions to diverge in their practice of dueling. Elite southerners embraced the duel as a symbol of southern exceptionalism and gentility, in contrast to what they viewed as the declining civility of northern industrial society. In clinging to the duel, southerners sought to maintain their traditional view of public and private as a single, coherent arena for human activity with a single code of behavior. Northerners, meanwhile, began to object to the duel because they viewed its merger of public and private realms as no longer appropriate for their increasingly urban, commercial, and industrial society. Instead, northern opinion gravitated toward an aspirationally modern and legal worldview in which only damage touching a male individual's public role as a merchant or a professional - and not the more diffuse persona of "gentleman" - qualified for public redress.
My Life for A Voice: The Influence of Voice on Health-Care Decisions

Are people willing to give up affordable healthcare and future years of their lives in exchange for having a voice in healthcare decision-making? Drawing upon research on the psychology of justice, we claim that the fairness of healthcare decision-making procedures, expressed by the availability of voice, can be more important than critical health-related outcomes. We examined this proposition using a forced-choice paradigm that required participants to choose between voice and better healthcare outcomes (affordable healthcare and greater life expectancy). Findings from three studies revealed that people maintain a strong preference for voice even at the expense of tangible healthcare outcomes. In study 1, participants preferred a healthcare plan that offered them a voice when it was $3,000–$12,000 more costly than a plan that did not offer such voice privileges. In study 2, participants preferred a voice plan to a no-voice plan when the no-voice plan was 5–20 years greater in its average life expectancy compared with the voice plan. In study 3, which used a more demographically diverse, non-student sample, the preference for the voice plan persisted across all conditions, even when the no-voice plan was 25 years greater in its life expectancy, and even when participants’ expected to personally live longer under the no-voice plan. These results are explained by participants’ expectation to enjoy better personal healthcare outcomes and greater autonomy when afforded voice. These findings demonstrate the importance of voice in hypothetical decision-making relevant to policy-making.
A Sea Change in Creditor Priorities

This Article argues that the operation of maritime law undermines a primary justification for creditor priorities under U.S. law. Under current law, when a debtor becomes insolvent, its secured creditors will be paid the full amount of their debt to the extent of their security interest, even if it means there is nothing left to pay unsecured creditors. This is controversial with respect to involuntary unsecured creditors, particularly those with tort claims against the debtor. Defenders of this scheme of priorities have argued that allowing greater priority to involuntary creditors would hinder the availability or increase the cost of credit. However, involuntary creditors have long enjoyed priority over secured creditors under maritime law, and it does not appear that firms subject to maritime law have experienced these predicted negative effects. Experience with the priority scheme under maritime law may thus provide support for efforts to reform current U.S. law to give greater priority to involuntary creditors more generally.
On the Conceptual and the Empirical: A Critique of John Mikhail's Cognitivism

Empirical claims are factual claims validated by the methods of science. Conceptual claims involve matters of sense. Empirical inquiry that proceeds from conceptual confusion can never yield fruitful results (i.e., knowledge). John Mikhail's speculations about UMG are an example of conceptual confusions that lead not to knowledge but to claims and assertions that lack sense.
The Unique Governance Challenges of Graduate Contract-Cohort Programs.

A growing trend in American higher education is for employers to contract with universities for a cohort of employees to complete a graduate management degree program. The experience at two univers...
Adjudicating Corporate Auctions

In light of recent developments in auction theory, this Article re-examines Delaware corporate law governing directors’ actions when structuring the sale of a corporation. A foundational doctrine of Delaware law is that when the board of directors resolves to sell a corporation, it must obtain the highest price reasonably available. Auction theory posits that, in certain circumstances germane to corporate takeovers, revenues can be maximized through the use of ex ante precommitments to the rules of the auction. Delaware law, however, does not fully endorse directors’ ability to make such precommitments, primarily out of the concern that the board will lock up a transaction for self-interested reasons.The Article’s core claim is that current Delaware law is unduly averse to precommitment devices that set the rules of the game in corporate auctions. Such devices can help maximize shareholder value and do not create the positional conflict that animates much of corporate takeover jurisprudence. Courts should draw a distinction between ex ante precommitments, characterized by ambivalence concerning the identity of the winning bidders, versus midstream or ex post lock-ups, in which the board favors a known buyer.
Inflation stabilization and economic transformation in Poland: The first year

Abstract This paper reviews Poland's experience in 1990, the first year of its program of stabilization and reform. The background is described, including previous reform efforts and the emerging crisis of the late 1980s. Then the various elements of the program are discussed, including fiscal adjustment, wage controls, the possibility of an initial liquidity overhang, the use of the exchange rate as a nominal anchor, and structural reform initiatives. The initial results of the program are assessed, and alternative explanations of the decline in output are considered.
Credibility, capital controls, and the EMS

Abstract A common rationale for membership in an exchange-rate arrangement such as the European Monetary System (EMS) is that may enhance the credibility of a central bank's commitment to stable monetary growth. In this paper we consider this idea in the lightof two other features of the EMS, namely the existence of exchange rate bands on the ongoing liberalization of capital controls. We find that capital controls themselves affect credibility in a way that depends on the exchange-rate bands. We also show that it is difficult to reconcile the non-zero width of the bands with the credibility-based interpretation of the EMS.
The Effect of Age at School Entry on Educational Attainment: An Application of Instrumental Variables with Moments from Two Samples

Abstract We present a model in which compulsory school attendance laws, which typically require school attendance until a specified birthday, induce a relationship between years of schooling and age at school entry. Variation in school starting age created by children's dates of birth provides a natural experiment for estimating the effect of age at school entry. Because no large data set contains information on both age at school entry and educational attainment, we use an instrumental variables (IV) estimator with data derived from the 1960 and 1980 Censuses to estimate and test the age-at-entry/compulsory schooling model. In most IV applications, the two covariance matrices that form the estimator are constructed from the same sample. We use a method-of-moments framework to discuss IV estimators that combine moments from different data sets. In our application, quarter of birth dummies are the instrumental variables used to link the 1960 Census, from which age at school entry can be derived for one coh...
Atom-interferometry constraints on dark energy

If dark energy, which drives the accelerated expansion of the universe, consists of a light scalar field, it might be detectable as a “fifth force” between normal-matter objects, in potential conflict with precision tests of gravity. Chameleon fields and other theories with screening mechanisms, however, can evade these tests by suppressing the forces in regions of high density, such as the laboratory. Using a cesium matter-wave interferometer near a spherical mass in an ultrahigh-vacuum chamber, we reduced the screening mechanism by probing the field with individual atoms rather than with bulk matter. We thereby constrained a wide class of dark energy theories, including a range of chameleon and other theories that reproduce the observed cosmic acceleration.
Robust Mechanism Design

The mechanism design literature assumes too much common knowledge of the environment among the players and planner. We relax this assumption by studying implementation on richer type spaces, with more higher order uncertainty. We study the "ex post equivalence" question: when is interim implementation on all possible type spaces equivalent to requiring ex post implementation on the
Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV With Thrust Vectoring Rotors

In this paper, we present a novel developmental reinforcement learning-based controller for a quadcopter with thrust vectoring capabilities. This multirotor UAV design has tilt-enabled rotors. It utilizes the rotor force magnitude and direction to achieve the desired state during flight. The control policy of this robot is learned using the policy transfer from the learned controller of the quadcopter (comparatively simple UAV design without thrust vectoring). This approach allows learning a control policy for systems with multiple inputs and multiple outputs. The performance of the learned policy is evaluated by physics-based simulations for the tasks of hovering and way-point navigation. The flight simulations utilize a flight controller based on reinforcement learning without any additional PID components. The results show faster learning with the presented approach as opposed to learning the control policy from scratch for this new UAV design created by modifications in a conventional quadcopter, i.e., the addition of more degrees of freedom (4-actuators in conventional quadcopter to 8-actuators in tilt-rotor quadcopter). We demonstrate the robustness of our learned policy by showing the recovery of the tilt-rotor platform in the simulation from various non-static initial conditions in order to reach a desired state. The developmental policy for the tilt-rotor UAV also showed superior fault tolerance when compared with the policy learned from the scratch. The results show the ability of the presented approach to bootstrap the learned behavior from a simpler system (lower-dimensional action-space) to a more complex robot (comparatively higher-dimensional action-space) and reach better performance faster.
The Impact of the Interest Rate on Insurance/Financials Industries: The Analysis of the Stock Market's Reactions to Federal Funds Rate Changes

Taking an event-study approach, this article examines the impact of the interest rate on the insurance industry and its subindustries in contrast with other financials industries through the analysis of the stock market's reactions to federal funds rate changes. In addition to calculating the stock market's reaction on event days, this article analyzes market over- or underreaction by examining the stock market's responses on the following days. Furthermore, the stock market's reaction on days preceding event days is explored to test pre-announcement effects. This article also investigates asymmetries in responses of insurance and financials stock markets to certain characteristics of federal rate changes. This research provides important insights to investors, regulators and policymakers, and the management of insurance and financials companies in understanding the link between monetary policy and asset prices and the policy transmission mechanism particularly related to insurance and financials industries.
Distribution of Demand for School Quality: Evidence from Quantile Regression

Our results show that high-income families place significantly higher value on academic achievement than low-income families. High-income families are also more likely to penalize house price for non-desirable non-academic school quality. This paper uses quantile regression to examine the distribution of demand for school quality. For academic achievement, the average effects as estimated by OLS are biased toward zero due to “aggregation” of families’ willingness to pay. We take advantage of a court-ordered redistricting as a quasi-random assignment of school quality. Subdivision and school fixed-effects are used to control for unobserved characteristics.
Negligent Misrepresentation as Contract

NEGLIGENT MISREPRESENTATION AS CONTRACT Mark P. Gergen * This article argues negligent misrepresentation is best understood as a contractual claim akin to promissory estoppel with its gist being invited reliance. This taxonomic claim may seem hopeless for the problem is treated as one of tort in the United States and everywhere else in the common law world. I argue this is an unfortunate byproduct of classical theories of contract and the idealization of contract as private legislation. It is unfortunate because the rise of the modern tort of negligence, which has at its heart a principle of liability for harm carelessly caused, creates a risk that negligent misrepresentation will be subsumed into a general tort of negligence. This will change the nature of the liability in significant ways that ought not be allowed to happen as a byproduct of classification of the claim as a species of negligence. This article also is a history of legal theory and arguments about the best theories of contract, tort, and negligence. A long and broad view on these arguments highlights a phenomenon that theorists who focus on a specific field generally overlook. The best theory to account for the core of a field of law generally does a poor job of explaining the periphery. Modern variations on classical theories of contract, such as a promise-based theory, brilliantly account for the core of contract law. The theory of negligence as liability for harm carelessly caused brilliantly accounts for the core of negligence law. But both theories fail in explaining the periphery of their respective fields of law. The negligence principle is too general and open- ended and it unhelpfully effaces the morality of the common law. Promise- based theories of contract are too parsimonious and constrictive. They tend to reduce contract to perfect circle of private legislation. Treating negligent misrepresentation as a problem of contract pushes back against both of these tendencies. Contents I. Introduction and overview 2 II. Negligent misrepresentation: an obligation of invited reliance 6 III. The history of negligent misrepresentation 12 A. Glanzer v. Shepard: a contract in all but name 12 B. Classical theories of contract constrict the field 14 1. Llewellyn and Beale 14 Professor, Berkeley Law School. Earlier versions of this paper were presented at workshops at Berkeley Law School and UCLA Law School.
Technology Adoption, Human Capital, and Growth Theory

The paper explores a model in which growth is determined by a combination of human capital and technology adoption. At the heart of the model is the notion of “contiguous knowledge”—the idea that knowledge spreads out a certain distance. Because of this property of knowledge, a country can adopt existing technology only when it is sufficiently close to the technological frontier. Unlike the neoclassical growth model, the proposed model predictions are pessimistic for countries that are far away from the frontier. The model is thus able to account both for rapid growth episodes and economic stagnation.
Start codon disruption with CRISPR/Cas9 prevents murine Fuchs` endothelial corneal dystrophy

A missense mutation of collagen type VIII alpha 2 chain (COL8A2) gene leads to early-onset Fuchs' endothelial corneal dystrophy (FECD), which progressively impairs vision through the loss of corneal endothelial cells. We demonstrate that CRISPR/Cas9-based postnatal gene editing achieves structural and functional rescue in a mouse model of FECD. A single intraocular injection of an adenovirus encoding both the Cas9 gene and guide RNA (Ad-Cas9-Col8a2gRNA) efficiently knocked down mutant COL8A2 expression in corneal endothelial cells, prevented endothelial cell loss, and rescued corneal endothelium pumping function in adult Col8a2 mutant mice. There were no adverse sequelae on histology or electroretinography. Col8a2 start codon disruption represents a non-surgical strategy to prevent vision loss in early-onset FECD. As this demonstrates the ability of Ad-Cas9-gRNA to restore the phenotype in adult post-mitotic cells, this method may be widely applicable to adult-onset diseases, even in tissues affected with disorders of non-reproducing cells.
Complexity of Proximal Augmented Lagrangian for nonconvex optimization with nonlinear equality constraints

We analyze worst-case complexity of a Proximal augmented Lagrangian (Proximal AL) framework for nonconvex optimization with nonlinear equality constraints. When an approximate first-order (second-order) optimal point is obtained in the subproblem, an $$\epsilon $$ first-order (second-order) optimal point for the original problem can be guaranteed within $${\mathcal {O}}(1/ \epsilon ^{2 - \eta })$$ outer iterations (where $$\eta $$ is a user-defined parameter with $$\eta \in [0,2]$$ for the first-order result and $$\eta \in [1,2]$$ for the second-order result) when the proximal term coefficient $$\beta $$ and penalty parameter $$\rho $$ satisfy $$\beta = {\mathcal {O}}(\epsilon ^\eta )$$ and $$\rho = \varOmega (1/\epsilon ^\eta )$$ , respectively. We also investigate the total iteration complexity and operation complexity when a Newton-conjugate-gradient algorithm is used to solve the subproblems. Finally, we discuss an adaptive scheme for determining a value of the parameter $$\rho $$ that satisfies the requirements of the analysis.
Toward Strong Field Mode-Selective Chemistry

We demonstrate a new method for coherent control of bond excitation in the strong field regime. We can selectively excite one or more Raman-active modes in a molecular liquid without the need for externally generated coherent sources separated by the Stokes frequency. The method employs excitation with an intense, ultrafast, shaped pulse in a learning control loop.
FACTORS AFFECTING ADOPTION OF DONOR FUNDED ICT PROJECTS IN THE PUBLIC SECTOR IN KENYA

Purpose : The purpose of this study was to establish the factors affecting adoption of donor funded ICT projects in the public sector. Methodology : The research was carried out through a descriptive survey research design which was used to assess the factors affecting adoption of donor funded ICT projects in the public sector. The sampling frame of this survey was a list of state owned enterprises in Nairobi County. The study targeted a population of all the 205 state owned enterprises in Kenya out of which 67 are located in Nairobi County. Results: The relationship between technological factor and adoption of donor funded ICT projects was positive and significant. The findings imply that technological factor has significant effect on adoption of donor funded ICT projects. The relationship between Infrastructural factor and adoption of donor funded ICT projects was positive and significant. Unique contribution to theory, practice and policy : It is recommended that culture has influence on the adoption of donor funded ICT project in public sectors. Therefore the organization cultures should focus on the short term, to expect quick results which influence ICT adoption.
Hubble parameter and related formulas for a Weyl scaling invariant dark energy action

We extend our previous analysis of a model for “dark energy” based on a Weyl scaling invariant dark energy action. We reexpress all prior results in terms of proper time, using the fluctuation ampl...
ConnectIt: A Framework for Static and Incremental Parallel Graph Connectivity Algorithms

Connected components is a fundamental kernel in graph applications. The fastest existing parallel multicore algorithms for connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.
Understanding migration aversion using elicited counterfactual choice probabilities

Abstract This paper investigates how migration and location choice decisions depend on a large set of location characteristics, with particular focus on measuring the importance and nature of non-monetary costs of moving. We employ a stated-preference approach to elicit respondents’ choice probabilities for a set of hypothetical choice scenarios, using two waves from the NY Fed’s Survey of Consumer Expectations. Our stated probabilistic choice approach allows us to recover the distribution of individual-level preferences for location and mobility attributes without concerns about omitted variables and selection biases that hamper analyses based on observed mobility choices alone. We estimate substantial heterogeneity in the willingness-to-pay (WTP) for location characteristics and in moving costs, both across and within demographic groups. We find moving costs to be strongly associated with attachment to the current neighborhood and dwelling and to social networks. Our results indicate evidence of sorting into current locations based on preferences for location attributes as well as a strong negative association between respondents’ non-monetary moving costs and their moving expectations and actual mobility decisions.
Tesla's fluidic diode and the electronic-hydraulic analogy

Reasoning by analogy is powerful in physics for students and researchers alike, a case in point being electronics and hydraulics as analogous studies of electric currents and fluid flows. Around 100 years ago, Nikola Tesla proposed a flow control device intended to operate similarly to an electronic diode, allowing fluid to pass easily in one direction but providing high resistance in reverse. Here, we use experimental tests of Tesla's diode to illustrate principles of the electronic-hydraulic analogy. We design and construct a differential pressure chamber (akin to a battery) that is used to measure flow rate (current) and thus resistance of a given pipe or channel (circuit element). Our results prove the validity of Tesla's device, whose anisotropic resistance derives from its asymmetric internal geometry interacting with high-inertia flows, as quantified by the Reynolds number (here, Re ∼ 10 3). Through the design and testing of new fluidic diodes, we explore the limitations of the analogy and the challenges of shape optimization in fluid mechanics. We also provide materials that may be incorporated into lesson plans for fluid dynamics courses, laboratory modules, and further research projects.
Corporate financial policy: What really matters?

Abstract Reliable access to funding, as in Myers and Majluf (1984) , is what really matters, but there are nontrivial indeterminacies in how such access is arranged and in the debt, cash-balance, and payout components of financial policy. These inferences are from a corporate “twins” comparison study of the financial policies of Henry Ford at Ford Motor Co. and Alfred P. Sloan, Jr. at General Motors Corp. The documented testimony of Ford and Sloan indicates that both focused on funding their business, with debt as a funding tool, not an element of an optimized leverage ratio. Their financial policies differ in five important respects, including (i) the use of debt versus large cash balances to meet funding needs and (ii) a commitment to paying large dividends versus a strong aversion to payouts. The data also point to the importance of the inability of managers to identify optimal policies with reliable precision.
Emerging Markets and the International Investment Law and Policy Regime

Multinational enterprises, including those headquartered in emerging markets, operate within the confines of the international investment law and policy regime. On the one hand, this regime prescribes the extent to which these firms can invest abroad, and it provides various protections for their investments. On the other hand, the regime prescribes increasingly that the operations of these firms need to be conducted in a responsible manner. The relevant standards are formulated by governments. This chapter discusses the rise of the international investment regime, its substantive and procedural content and how and why the regime has changed over time, paying special attention to issues relating to emerging markets. Accordingly, the focus of this chapter is on the actions of governments, illustrating in the process of discussions how emerging market multinational enterprises can both benefit from the regime and how they are constrained by it.
Prediction of amphipathic helix-Membrane interactions with Rosetta.

Amphipathic helices have hydrophobic and hydrophilic/charged residues situated on opposite faces of the helix. They can anchor peripheral membrane proteins to the membrane, be attached to integral membrane proteins, or exist as independent peptides. Despite the widespread presence of membrane-interacting amphipathic helices, there is no computational tool within Rosetta to model their interactions with membranes. In order to address this need, we developed the AmphiScan protocol with PyRosetta, which runs a grid search to find the most favorable position of an amphipathic helix with respect to the membrane. The performance of the algorithm was tested in benchmarks with the RosettaMembrane, ref2015_memb, and franklin2019 score functions on six engineered and 44 naturally-occurring amphipathic helices using membrane coordinates from the OPM and PDBTM databases, OREMPRO server, and MD simulations for comparison. The AmphiScan protocol predicted the coordinates of amphipathic helices within less than 3A of the reference structures and identified membrane-embedded residues with a Matthews Correlation Constant (MCC) of up to 0.57. Overall, AmphiScan stands as fast, accurate, and highly-customizable protocol that can be pipelined with other Rosetta and Python applications.
A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning

Abstract This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control in the era of mixed autonomy when AVs drive alongside human-driven vehicles (HV). It is the first-of-its-kind survey paper to comprehensively review literature in both transportation engineering and AI for mixed traffic modeling. We will discuss state-of-the-art applications of AI-guided methods, identify opportunities and obstacles, and raise open questions. We divide the stage of AV deployment into four phases: the pure HVs, the HV-dominated, the AV-dominated, and the pure AVs. This paper is primarily focused on the latter three phases. Models used for each phase are summarized, encompassing game theory, deep (reinforcement) learning, and imitation learning. While reviewing the methodologies, we primarily focus on the following research questions: (1) What scalable driving policies are to control a large number of AVs in mixed traffic comprised of human drivers and uncontrollable AVs? (2) How do we estimate human driver behaviors? (3) How should the driving behavior of uncontrollable AVs be modeled in the environment? (4) How are the interactions between human drivers and autonomous vehicles characterized? We also provide a list of public datasets and simulation software related to AVs. Hopefully this paper will not only inspire our transportation community to rethink the conventional models that are developed in the data-shortage era, but also start conversations with other disciplines, in particular robotics and machine learning, to join forces towards creating a safe and efficient mixed traffic ecosystem.
From Medals to Morality: Sportive Nationalism and the Problem of Doping in Sports

The conventional wisdom is that in the fight against doping, the government is in the best position to clean up sport and protect the integrity of competition. The premise underlying this assumption is that in the United States, sport is private, so that the government typically has no role in its regulation. It is now, advocates suggest, with the integrity of sport on the line, the government should move off the sidelines and take action. This essay challenges that premise by arguing that with respect to doping in sports the government has not merely been a sidelines observer, but was actively reaping the benefits that performance-enhanced victories produced. Accordingly, this essay aims to examine the issue of doping in sports, particularly in the American Olympic Movement, in a new light. Our current focus has been on individual athletes making misguided choices in an attempt to short-cut the usual path to athletic success. This essay seeks to expand that view, by arguing that the individual stories of cheating through doping have taken place in a win-at-all-costs sports paradigm that was fully supported by the government, which for decades did little to address the issue. Now, with the United States’ international image on the line, the government is tackling the issue seemingly with full force, using its power and influence to clean up sport. Examining more fully the government relationship to sports doping, and now its response, is important, because it can illuminate ways that the government’s anti-doping initiatives can threaten athletes’ rights and ultimately undermine the movement to eradicate doping in sport.
Single-query Path Planning Using Sample-Efficient Probability Informed Trees

In this work, we present a novel sampling-based path planning method, called SPRINT . The method finds solutions for high dimensional path planning problems quickly and robustly. Its efficiency comes from minimizing the number of collision check samples. This reduction in sampling relies on heuristics that predict the likelihood that samples will be useful in the search process. Specifically, heuristics (1) prioritize more promising search regions; (2) cull samples from local minima regions; and (3) steer the search away from previously observed collision states. Empirical evaluations show that our method finds shorter or comparable-length solution paths in significantly less time than commonly used methods. We demonstrate that these performance gains can be largely attributed to our approach to achieve sample efficiency.
Liquidity Constraints and Precautionary Saving

Economists working with numerical solutions to the optimal consumption/saving problem under uncertainty have long known that there are quantitatively important interactions between liquidity constraints and precautionary saving behavior. This paper provides the analytical basis for those interactions. First, we explain why the introduction of a liquidity constraint increases the precautionary saving motive around levels of wealth where the constraint becomes binding. Second, we provide a rigorous basis for the oft-noted similarity between the effects of introducing uncertainty and introducing constraints, by showing that in both cases the effects spring from the concavity in the consumption function which either uncertainty or constraints can induce. We further show that consumption function concavity, once created, propagates back to consumption functions in prior periods. Finally, our most surprising result is that the
BSM constraints from EW measurements

We investigate the impact of heavy new physics onWbosons productions at hadron colliders using an effective field theory. After listing all the relevant dimension-six operators, their effects are computed taking into account LEP constraints. Additionally, the effective approach is compared to anomalous couplings regarding several issues like unitarity.
Rapid generation of pigment free, immobile zebrafish embryos and larvae in any genetic background using CRISPR-Cas9 dgRNPs

The ability to carry out high-resolution, high-magnification optical imaging of living animals is one of the most attractive features of the zebrafish as a model organism. However, increasing amounts of pigmentation as development proceeds and difficulties in maintaining sustained immobilization of healthy, living animals remain challenges for live imaging. Chemical treatments can be used to suppress pigment formation and movement, but these treatments can lead to developmental defects. Genetic mutants can also be used to eliminate pigment formation and immobilize animals, but maintaining these mutants in lines carrying other combinations of transgenes and mutants is difficult and laborious. In this study, we show that CRISPR duplex guide ribonucleoproteins (dgRNPs) targeting the slc45a2 (albino) and chrna1 (nic1) genes can be used to efficiently suppress pigment formation in and immobilize F0 injected animals. CRISPR dgRNPs can be used to generate pigment-free, immobile zebrafish embryos and larvae in any transgenic and/or mutant-carrying background, greatly facilitating high-resolution imaging and analysis of the many transgenic and mutant lines available in the zebrafish.
State sponsors of terrorism disclosure and SEC financial reporting oversight

Abstract We examine whether SEC effort to review state sponsors of terrorism (SST) disclosure negatively influences financial reporting oversight. Using comment letter inquiries about SST to measure effort, we find the likelihood that the SEC fails to identify a financial reporting error increases when comment letters reference SST. Consistent with SST disclosure review crowding out financial reporting oversight, comment letters referencing SST are less likely to mention accounting, non-GAAP, and MD&A issues. These effects are unique to SST as we find comment letter references to non-SST issues complement financial reporting oversight. Data obtained through a Freedom of Information Act request reveals a temporal shift in the occupational mix of SEC reviewers towards (away from) lawyers (accountants) that coincides with an increased focus on SST. Path analysis reveals that accountants (lawyers) are more (less) likely to detect errors and comment on financial reporting topics, with an indirect path through SST exacerbating these effects.
Transcriptional control of DNA repair networks by CDK7 regulates sensitivity to radiation in MYC-driven medulloblastoma

MYC-driven medulloblastoma is a major therapeutic challenge due to frequent metastasis and a poor 5-year survival rate. MYC gene amplification results in transcriptional dysregulation, proliferation, and survival of malignant cells. To identify therapeutic targets in MYC-amplified medulloblastoma, we employ a CRISPR-Cas9 essentiality screen targeting 1,140 genes. We identify CDK7 as a mediator of medulloblastoma tumorigenesis. Using chemical inhibitors and genetic depletion, we observe cessation of tumor growth in xenograft mouse models and increases in apoptosis. The results are attributed to repression of a core set of MYC-driven transcriptional programs mediating DNA repair. CDK7 inhibition alters RNA polymerase II (RNA Pol II) and MYC association at DNA repair genes. Blocking CDK7 activity sensitizes cells to ionizing radiation leading to accrual of DNA damage, extending survival and tumor latency in xenograft mouse models. Our studies establish the selective inhibition of MYC-driven medulloblastoma by CDK7 inhibition combined with radiation as a viable therapeutic strategy.
Minimax Model Learning.

We present a novel off-policy loss function for learning a transition model in model-based reinforcement learning. Notably, our loss is derived from the off-policy policy evaluation objective with an emphasis on correcting distribution shift. Compared to previous model-based techniques, our approach allows for greater robustness under model misspecification or distribution shift induced by learning/evaluating policies that are distinct from the data-generating policy. We provide a theoretical analysis and show empirical improvements over existing model-based off-policy evaluation methods. We provide further analysis showing our loss can be used for off-policy optimization (OPO) and demonstrate its integration with more recent improvements in OPO.
Bandit-Based Monte Carlo Optimization for Nearest Neighbors

The celebrated Monte Carlo method estimates an expensive-to-compute quantity by random sampling. Bandit-based Monte Carlo optimization is a general technique for computing the minimum of many such expensive-to-compute quantities by adaptive random sampling. The technique converts an optimization problem into a statistical estimation problem which is then solved via multi-armed bandits. We apply this technique to solve the problem of high-dimensional $k$ -nearest neighbors, developing an algorithm which we prove is able to identify exact nearest neighbors with high probability. We show that under regularity assumptions on a dataset of $n$ points in $d$ -dimensional space, the complexity of our algorithm scales logarithmically with the dimension of the data as $O\left({(n+d)\log ^{2} \frac {nd}{\delta }}\right)$ for error probability $\delta $ , rather than linearly as in exact computation requiring $O(nd)$ . We corroborate our theoretical results with numerical simulations, showing that our algorithm outperforms both exact computation and state-of-the-art algorithms such as kGraph, NGT, and LSH on real datasets.
Efficient fuzz testing for apache spark using framework abstraction

The emerging data-intensive applications are increasingly dependent on data-intensive scalable computing (DISC) systems, such as Apache Spark, to process large data. Despite their popularity, DISC applications are hard to test. In recent years, fuzz testing has been remarkably successful; however, it is nontrivial to apply such traditional fuzzing to big data analytics directly because: (1) the long latency of DISC systems prohibits the applicability of fuzzing, and (2) conventional branch coverage is unlikely to identify application logic from the DISC framework implementation. We devise a novel fuzz testing tool called BigFuzz that automatically generates concrete data for an input Apache Spark program. The key essence of our approach is that we abstract the dataflow behavior of the DISC framework with executable specifications and we design schema-aware mutations based on common error types in DISC applications. Our experiments show that compared to random fuzzing, BigFuzz is able to speed up the fuzzing time by 1477X, improves application code coverage by 271%, and achieves 157% improvement in detecting application errors. The demonstration video of BigFuzz is available at https://www.youtube.com/watch?v=YvYQISILQHs&feature=youtu.be.
A Different Type of Property: White Women and the Human Property They Kept

This Essay advances three claims in its contribution to the relatively nascent legal study of white women and their legal and social involvement with slavery as purposeful participants. First, it stresses that the common erasure of white women as slaveholders renders their culpability and complicity in human chattel slavery imperceptible. Simply put, they become blameless and guiltless in an enterprise in which their involvement was far more than proximate and, in many instances, dominant. Second, scrubbing white women from the archives of antebellum history, serves not only to deny their agency, but also capacities. In other words, removing white women as profiteers and commanders in slavery serves to erase the fact that some were successful, shrewd businesswomen, albeit in a horrid enterprise. Even those who “managed households” rather than large plantations wielded authority. In troubling ways, this erasure served to recast white women as not only disinterested in work and business, which defined Supreme Court jurisprudence on sex for many years, but also it contributes to the stereotype of white women as fragile, disempowered, weak, and vulnerable to lingering effect. In recent decades, diligent efforts by historians correct inaccuracies in this record. Third, and perhaps most complicatedly, the Essay argues that reading white women as passive or submissive participants in the business of antebellum slavery, served to undermine their later employment attempts and business opportunities after slavery’s abolition and through the 1900s. Unlike the Supreme Court’s patriarchal reading of women’s capacities and destinies in Hoyt v. Florida, Bradwell v. Illinois, or In re Goodell clearly, white women were not wedded or destined to domestic duties, but had experience in financial management and business.
LEA: A Learned Encoding Advisor for Column Stores

Data warehouses organize data in a columnar format to enable faster scans and better compression. Modern systems offer a variety of column encodings that can reduce storage footprint and improve query performance. Selecting a good encoding scheme for a particular column is an optimization problem that depends on the data, the query workload, and the underlying hardware. We introduce Learned Encoding Advisor (LEA), a learned approach to column encoding selection. LEA is trained on synthetic datasets with various distributions on the target system. Once trained, LEA uses sample data and statistics (such as cardinality) from the user’s database to predict the optimal column encodings. LEA can optimize for encoded size, query performance, or a combination of the two. Compared to the heuristic-based encoding advisor of a commercial column store on TPC-H, LEA achieves 19% lower query latency while using 26% less space.
Selection favors adaptive plasticity in a long-term reciprocal transplant experiment.

Spatial and temporal environmental variation can favor the evolution of adaptive phenotypic plasticity, such that genotypes alter their phenotypes in response to local conditions to maintain fitness across heterogeneous landscapes. When individuals show greater fitness in one habitat than another, asymmetric migration can restrict adaptation to the lower quality environment. In these cases, selection is predicted to favor traits that enhance fitness in the higher-quality (source) habitat at the expense of fitness in the marginal (sink) habitat. Here, we test whether plasticity is adaptive in a system regulated by demographic source-sink dynamics. Vaccinium elliottii (Ericaceae) occurs in dry upland and flood-prone bottomland forests throughout the southeastern United States, but has larger populations and higher average individual fitness in upland sites. We conducted a multi-year field experiment to evaluate whether plasticity in foliar morphology increases survival and lifespan. Both across and within habitats, selection favored plasticity in specific leaf area, stomatal density, and leaf size. Stabilizing selection acted on plasticity in stomatal density within habitats, suggesting that extreme levels of plasticity are disadvantageous. Thus, even in systems driven by source-sink dynamics, temporal and spatial variation in conditions across the landscape and within habitat types can favor the evolution of plasticity.
Reconstruction and Segmentation of Parallel MR Data Using Image Domain Deep-SLR

The main focus of this work is a novel framework for the joint reconstruction and segmentation of parallel MRI (PMRI) brain data. We introduce an image domain deep network for calibrationless recovery of undersampled PMRI data. The proposed approach is the deep-learning (DL) based generalization of local low-rank based approaches for uncalibrated PMRI recovery including CLEAR [1]. Since the image domain approach exploits additional annihilation relations compared to k-space based approaches, we expect it to offer improved performance. To minimize segmentation errors resulting from undersampling artifacts, we combined the proposed scheme with a segmentation network and trained it in an end-to-end fashion. In addition to reducing segmentation errors, this approach also offers improved reconstruction performance by reducing overfitting; the reconstructed images exhibit reduced blurring and sharper edges than independently trained reconstruction network.
An Exponentially Stable Adaptive Control Law For Robot Manipulators

This paper presents a new exponentially stable direct adaptive control law for motion control of robot manipulators. This control law utilizes the desired trajectory information instead of the actual manipulator joint signals for feed-forward compensations and paramater adaptation, and, computationally, it is extremely efficient. The exponential stability of the adaptive control algorithm is proven without requiring the persistent excitation condition and by fully considering the non-linear dynamics of the manipulator in the analysis.