cssi/SciSciGPT-SciSciNet huggingface dataset:
Interstate circuit and conspiracy theories

An antitrust conspiracy is an unlawful horizontal agreement in restraint of trade. Proof of antitrust conspiracy requires evidence that tends to exclude the possibility of independent conduct. In most conspiracy cases, however, direct evidence is not available. Instead, circumstantial evidence is used to prove the existence of the alleged conspiracy. Interstate Circuit v. United States (1939), one of the most known Supreme Court antitrust opinions, laid the foundation of conspiracy inference in antitrust law. Hundreds of judicial opinions, books, monographs, and articles summarize and interpret the facts of the case. With some minor variations, the summaries of Interstate Circuit are similar in their details, yet materially incomplete and erroneous. As summarized in judicial opinions and the literature, Interstate Circuit concerned a powerful retailer who sent a letter to eight suppliers requiring them to amend their distribution policies to raise his rivals’ costs. The letter named all recipients, informing each supplier that its competitors received the same letter. The suppliers’ compliance was partial but uniform. This account raises the question of whether parallel compliance with an invitation to collude permits the inference of conspiracy. It became the paradigmatic illustration of hub-and-spoke conspiracies, the agreement requirement, conscious parallelism, tacit agreement, and the raising rivals’ costs strategy. Interstate Circuit, however, presents a very different set of factual findings: a powerful retailer, which was a partially-owned subsidiary of one of its suppliers, negotiated a deal with its parent company and its rivals. This Article explores the Interstate Circuit myth. It offers a detailed study of cartel formation in an industry with intricate relationships among the colluding parties. The study finds that the extensive use of an incorrect account of Interstate Circuit by courts and commentators explains, in part, some of the flaws of antitrust’s conspiracy doctrines. It, thus, refines four antitrust concepts whose origins are in Interstate Circuit: the agreement requirement, conscious parallelism, plus factors, and tacit agreement. The Interstate Circuit myth, this Article argues, demonstrates that erroneous myths may last long and influence policies.
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution

Collaborative bandit learning, i.e., bandit algorithms that utilize collaborative filtering techniques to improve sample efficiency in online interactive recommendation, has attracted much research attention as it enjoys the best of both worlds. However, all existing collaborative bandit learning solutions impose a stationary assumption about the environment, i.e., both user preferences and the dependency among users are assumed static over time. Unfortunately, this assumption hardly holds in practice due to users' ever-changing interests and dependency relations, which inevitably costs a recommender system sub-optimal performance in practice. In this work, we develop a collaborative dynamic bandit solution to handle a changing environment for recommendation. We explicitly model the underlying changes in both user preferences and their dependency relation as a stochastic process. Individual user's preference is modeled by a mixture of globally shared contextual bandit models with a Dirichlet process prior. Collaboration among users is thus achieved via Bayesian inference over the global bandit models. To balance exploitation and exploration during the interactions, Thompson sampling is used for both model selection and arm selection. Our solution is proved to maintain a standard ~O(√T) Bayesian regret in this challenging environment. Extensive empirical evaluations on both synthetic and real-world datasets further confirmed the necessity of modeling a changing environment and our algorithm's practical advantages against several state-of-the-art online learning solutions.
Improved bounds for the eigenvalues of prolate spheroidal wave functions and discrete prolate spheroidal sequences

Abstract The discrete prolate spheroidal sequences (DPSSs) are a set of orthonormal sequences in l 2 ( Z ) which are strictly bandlimited to a frequency band [ − W , W ] and maximally concentrated in a time interval { 0 , … , N − 1 } . The timelimited DPSSs (sometimes referred to as the Slepian basis) are an orthonormal set of vectors in C N whose discrete time Fourier transform (DTFT) is maximally concentrated in a frequency band [ − W , W ] . Due to these properties, DPSSs have a wide variety of signal processing applications. The DPSSs are the eigensequences of a timelimit-then-bandlimit operator and the Slepian basis vectors are the eigenvectors of the so-called prolate matrix. The eigenvalues in both cases are the same, and they exhibit a particular clustering behavior – slightly fewer than 2 N W eigenvalues are very close to 1, slightly fewer than N − 2 N W eigenvalues are very close to 0, and very few eigenvalues are not near 1 or 0. This eigenvalue behavior is critical in many of the applications in which DPSSs are used. There are many asymptotic characterizations of the number of eigenvalues not near 0 or 1. In contrast, there are very few non-asymptotic results, and these don't fully characterize the clustering behavior of the DPSS eigenvalues. In this work, we establish two novel non-asymptotic bounds on the number of DPSS eigenvalues between ϵ and 1 − ϵ . Also, we obtain bounds detailing how close the first ≈ 2 N W eigenvalues are to 1 and how close the last ≈ N − 2 N W eigenvalues are to 0. Furthermore, we extend these results to the eigenvalues of the prolate spheroidal wave functions (PSWFs), which are the continuous-time version of the DPSSs. Finally, we present numerical experiments demonstrating the quality of our non-asymptotic bounds on the number of DPSS eigenvalues between ϵ and 1 − ϵ .
Out of Hospital Sudden Death in a Rural Population: Low Rates of ICD Underutilization Among Decedents.

BACKGROUND Implantable cardioverter defibrillators (ICDs) reduce mortality in patients at risk for life-threatening arrhythmias. Implantation of ICDs in rural or economically disadvantaged populations is suspected to be low. This study examined Out of Hospital Premature Natural Death (OHPND) and electronic medical record (EMR) data to identify rates of non-implantation of ICDs among decedents in eastern North Carolina. METHODS OHPND cases in 2016 were identified using mortality data and matched with EMRs. Those meeting criteria for ICD implantation based on chart review were adjudicated by two electrophysiologists to determine whether they qualified for implantation. Comorbidity burden was established using Charlson's Comorbidity Index (CCI). RESULTS Out of 1316 OHPND cases, 967 (73.4%) had EMR records. Chart review identified 70 (7.2%) potential ICD candidates with a LVEF ≤35 of which 5 (7.1%) did not meet criteria because LVEF subsequently improved. Of the remaining 65 patients, 32 (49.2%) already received an ICD, and 33 patients (50.7%) met criteria but had not received one. Reasons for non-implantation included: limited life expectancy secondary to comorbidities, principally chronic kidney disease (CKD) (N = 11, 17%), physician non-adherence to guidelines (N = 9, 14%), loss to follow-up (N = 7, 11%), patient refusal (N = 5, 8%), and death before commencing medical therapy (N = 1, 2%). Among our cohort of 967 individuals who died unexpectedly, nine (0.9%) patients may have avoided death with an ICD. CONCLUSION This study using decedent data shows low rates of ICD-underutilization in a rural population and emphasizes the role of advanced comorbidities such as CKD in ICD-underutilization.
Causal Inference with Selectively Deconfounded Data

Given only data generated by a standard confounding graph with unobserved confounder, the Average Treatment Effect (ATE) is not identifiable. To estimate the ATE, a practitioner must then either (a) collect deconfounded data;(b) run a clinical trial; or (c) elucidate further properties of the causal graph that might render the ATE identifiable. In this paper, we consider the benefit of incorporating a large confounded observational dataset (confounder unobserved) alongside a small deconfounded observational dataset (confounder revealed) when estimating the ATE. Our theoretical results suggest that the inclusion of confounded data can significantly reduce the quantity of deconfounded data required to estimate the ATE to within a desired accuracy level. Moreover, in some cases -- say, genetics -- we could imagine retrospectively selecting samples to deconfound. We demonstrate that by actively selecting these samples based upon the (already observed) treatment and outcome, we can reduce sample complexity further. Our theoretical and empirical results establish that the worst-case relative performance of our approach (vs. a natural benchmark) is bounded while our best-case gains are unbounded. Finally, we demonstrate the benefits of selective deconfounding using a large real-world dataset related to genetic mutation in cancer.
The (In)Effectiveness of Campus Smart Locks for Reducing Crime

Door locks are a ubiquitous form of security to control access to a building with the goal of reducing crime there. However, research on door locks is often limited by methodological issues and pri...
On Robustness of Principal Component Regression

Principal component regression (PCR) is a simple, but powerful and ubiquitously utilized method. Its effectiveness is well established when the covariates exhibit low-rank structure. However, its a...
AffectiveSpotlight: Facilitating the Communication of Affective Responses from Audience Members during Online Presentations

The ability to monitor audience reactions is critical when delivering presentations. However, current videoconferencing platforms offer limited solutions to support this. This work leverages recent advances in affect sensing to capture and facilitate communication of relevant audience signals. Using an exploratory survey (N=175), we assessed the most relevant audience responses such as confusion, engagement, and head-nods. We then implemented AffectiveSpotlight, a Microsoft Teams bot that analyzes facial responses and head gestures of audience members and dynamically spotlights the most expressive ones. In a within-subjects study with 14 groups (N=117), we observed that the system made presenters significantly more aware of their audience, speak for a longer period of time, and self-assess the quality of their talk more similarly to the audience members, compared to two control conditions (randomly-selected spotlight and default platform UI). We provide design recommendations for future affective interfaces for online presentations based on feedback from the study.
Hybrid-PIC modeling of a high-voltage, high-specific-impulse Hall thruster

The primary life-limiting mechanism of Hall thrusters is the sputter erosion of the discharge channel walls by high-energy propellant ions. Because of the difficulty involved in characterizing this erosion experimentally, many past efforts have focused on numerical modeling to predict erosion rates and thruster lifespan, but those analyses were limited to Hall thrusters operating in the 200-400V discharge voltage range. Thrusters operating at higher discharge voltages (V(sub d) >= 500 V) present an erosion environment that may differ greatly from that of the lower-voltage thrusters modeled in the past. In this work, HPHall, a well-established hybrid-PIC code, is used to simulate NASA's High-Voltage Hall Accelerator (HiVHAc) at discharge voltages of 300, 400, and 500V as a first step towards modeling the discharge channel erosion. It is found that the model accurately predicts the thruster performance at all operating conditions to within 6%. The model predicts a normalized plasma potential profile that is consistent between all three operating points, with the acceleration zone appearing in the same approximate location. The expected trend of increasing electron temperature with increasing discharge voltage is observed. An analysis of the discharge current oscillations shows that the model predicts oscillations that are much greater in amplitude than those measured experimentally at all operating points, suggesting that the differences in oscillation amplitude are not strongly associated with discharge voltage.
Extended and compact X-ray emission in powerful radio galaxies.

We report ROSAT X-ray observations of two powerful radio galaxies. 3C 280 provides evidence for a mixture of unresolved and extended emission, with the latter produced by hot plasma of insufficient pressure to confine the radio lobes and insufficient density for a cooling flow to have begun. 3C 220.3 gives only an X-ray upper limit, but one consistent with our interpretation of the X-ray emission from powerful radio-loud active galactic nuclei (AGNs) in terms of obscured and unobscured components.
Extreme Multi-label Learning for Semantic Matching in Product Search

We consider the problem of semantic matching in product search: given a customer query, retrieve all semantically related products from a huge catalog of size 100 million, or more. Because of large catalog spaces and real-time latency constraints, semantic matching algorithms not only desire high recall but also need to have low latency. Conventional lexical matching approaches (e.g., Okapi-BM25) exploit inverted indices to achieve fast inference time, but fail to capture behavioral signals between queries and products. In contrast, embedding-based models learn semantic representations from customer behavior data, but the performance is often limited by shallow neural encoders due to latency constraints. Semantic product search can be viewed as an eXtreme Multi-label Classification (XMC) problem, where customer queries are input instances and products are output labels. In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products. We consider hierarchical linear models with n-gram features for fast real-time inference. Quantitatively, our method maintains a low latency of 1.25 milliseconds per query and achieves a 65% improvement of Recall@100 (60.9% v.s. 36.8%) over a competing embedding-based DSSM model. Our model is robust to weight pruning with varying thresholds, which can flexibly meet different system requirements for online deployments. Qualitatively, our method can retrieve products that are complementary to existing product search system and add diversity to the match set.
Performance Analysis and Optimization of Uplink Cellular Networks with Flexible Frame Structure

Future wireless cellular networks must support both enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) to manage heterogeneous data traffic for emerging wireless services. To achieve this goal, a promising technique is to enable flexible frame structure by dynamically changing the data frame’s numerology according to the channel information as well as traffic quality-of-service requirements. However, due to non-orthogonal subcarriers, this technique can result in an interference, known as inter numerology interference (INI), thus, degrading the network performance. In this work, a novel framework is proposed to analyze the INI in the uplink cellular communications. In particular, a closed-form expression is derived for the INI power in the uplink with a flexible frame structure and a new resource allocation problem is formulated to maximize the network spectral efficiency (SE) by jointly optimizing the power allocation and numerology selection in a multi-user uplink scenario. The simulation results validate the derived theoretical INI analyses and provide guidelines for the power allocation and numerology selection.
Moment Multicalibration for Uncertainty Estimation

We show how to achieve the notion of "multicalibration" from Hebert-Johnson et al. (2018) not just for means, but also for variances and other higher moments. Informally, this means that we can find regression functions which, given a data point, can make point predictions not just for the expectation of its label, but for higher moments of its label distribution as well—and those predictions match the true distribution quantities when averaged not just over the population as a whole, but also when averaged over an enormous number of finely defined subgroups. It yields a principled way to estimate the uncertainty of predictions on many different subgroups—and to diagnose potential sources of unfairness in the predictive power of features across subgroups. As an application, we show that our moment estimates can be used to derive marginal prediction intervals that are simultaneously valid as averaged over all of the (sufficiently large) subgroups for which moment multicalibration has been obtained.
Probabilistic evaluation of fuselage-type composite structures

A methodology is developed to simulate computationally the uncertain behavior of composite structures. The uncertain behavior includes buckling loads, natural frequencies, displacements, stress/strain, etc., which are the consequences of the random variation (scatter) of the primitive (independent random) variables in the constituent, ply, laminate and structural levels. This methodology is implemented in a computer code IPACS (integrated probabilistic assessment of composite structures). A fuselage-type composite structure is analyzed to demonstrate the code's capability. The probability distribution functions of the buckling loads, natural frequency, displacement, strain and stress are computed. The sensitivity of each primitive (independent random) variable to a given structural response is also identified from the analyses.
Effects of truncation of a predominantly compression load spectrum on the life of a notched graphite/epoxy laminate

The fatigue behavior of a notched, graphite/epoxy (T300/5208) laminate subjected to predominantly compressive loading was explored in a series of constant amplitude and transport wing spectrum tests. Results of these tests indicated that (1) the amount of local (near the notch) buckling allowed in the tests significantly affected fatigue life; (2) spectrum truncation of either the high- or low-load end of the spectrum produced lives greater than those obtained in the baseline, complete-spectrum test, but life was much more sensitive to truncations at the high-load end; and (3) the Palmgren-Miner linear cumulative damage theory always predicted lives much longer than the actual spectrum loading test lives.
Weak-Instrument robust tests in two-sample summary-data mendelian randomization

Mendelian randomization (MR) has been a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome using genetic variants as instrumental variables (IV), with two-sample summary-data MR being the most popular. Unfortunately, instruments in MR studies are often weakly associated with the exposure, which can bias effect estimates and inflate Type I errors. In this work, we propose test statistics that are robust under weak instrument asymptotics by extending the Anderson-Rubin, Kleibergen, and the conditional likelihood ratio test in econometrics to two-sample summary-data MR. We also use the proposed Anderson-Rubin test to develop a point estimator and to detect invalid instruments. We conclude with a simulation and an empirical study and show that the proposed tests control size and have better power than existing methods with weak instruments. This article is protected by copyright. All rights reserved.
Maximum likelihood estimation of potential energy in interacting particle systems from single-trajectory data

This paper concerns the parameter estimation problem for the quadratic potential energy in interacting particle systems from continuous-time and single-trajectory data. Even though such dynamical systems are high-dimensional, we show that the vanilla maximum likelihood estimator (without regularization) is able to estimate the interaction potential parameter with optimal rate of convergence simultaneously in mean-field limit and in long-time dynamics. This to some extend avoids the curse-of-dimensionality for estimating large dynamical systems under symmetry of the particle interaction.
Causal Inference Gates Corticostriatal Learning.

Attributing outcomes to your own actions or to external causes is essential for appropriately learning which actions lead to reward and which actions do not. Our previous work showed that this type of credit assignment is best explained by a Bayesian reinforcement learning model which posits that beliefs about the causal structure of the environment modulate reward prediction errors (RPEs) during action value updating. In this study, we investigated the brain networks underlying reinforcement learning that are influenced by causal beliefs using functional magnetic resonance imaging (fMRI) while human participants (n = 31; 13 males, 18 females) completed a behavioral task that manipulated beliefs about causal structure. We found evidence that RPEs modulated by causal beliefs are represented in dorsal striatum, while standard (unmodulated) RPEs are represented in ventral striatum. Further analyses revealed that beliefs about causal structure are represented in anterior insula and inferior frontal gyrus. Finally, structural equation modeling revealed effective connectivity from anterior insula to dorsal striatum. Together, these results are consistent with a possible neural architecture in which causal beliefs in anterior insula are integrated with prediction error signals in dorsal striatum to update action values. SIGNIFICANCE STATEMENT: Learning which actions lead to reward – a process known as reinforcement learning – is essential for survival. Inferring the causes of observed outcomes – a process known as causal inference – is crucial for appropriately assigning credit to one’s own actions and restricting learning to effective action-outcome contingencies. Previous studies have linked reinforcement learning to the striatum and causal inference to prefrontal regions, yet how these neural processes interact to guide adaptive behavior remains poorly understood. Here, we found evidence that causal beliefs represented in the prefrontal cortex modulate action value updating in posterior striatum, separately from the unmodulated action value update in ventral striatum posited by standard reinforcement learning models.
Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization

Motivated by online decision-making in time-varying combinatorial environments, we study the problem of transforming offline algorithms to their online counterparts. We focus on offline combinatorial problems that are amenable to a constant factor approximation using a greedy algorithm that is robust to local errors. For such problems, we provide a general framework that efficiently transforms offline robust greedy algorithms to online ones using Blackwell approachability. We show that the resulting online algorithms have O(T1/2) (approximate) regret under the full information setting. We further introduce a bandit extension of Blackwell approachability that we call Bandit Blackwell approachability. We leverage this notion to transform greedy robust offline algorithms into a O(T2/3) (approximate) regret in the bandit setting. Demonstrating the flexibility of our framework, we apply our offline-to-online transformation to several problems at the intersection of revenue management, market design, and online optimization, including product ranking optimization in online platforms, reserve price optimization in auctions, and submodular maximization. We show that our transformation, when applied to these applications, leads to new regret bounds or improves the current known bounds.
Investigating the electronic origins of the repulsion between substitutional and interstitial solutes in hcp Ti

The high solubility of oxygen in Ti, Zr, and Hf makes it difficult to stabilize protective oxide scales on their surfaces as the subsurface regions can serve as boundless sinks that continuously dissolve oxygen. Alloying elements are crucial to reduce the oxygen solubility and diffusivity within early transition metals. Past studies have shown that most substitutional alloying additions to titanium repel interstitial oxygen. Here we use first-principles calculations to show that this repulsion is short ranged and explore several factors that are likely responsible for the repulsive interaction. Calculations of Bader charges suggest the existence of short-range Coulomb interactions due to the accumulation of charge on the substitutional solute and interstitial oxygen that is drawn from the Ti host. Misfit strains due to differences in the atomic radii of the solutes and Ti are also found to play a role. We identify a unique hybridization phenomenon between dissolved substitutional elements and interstitial oxygen within hcp Ti that leads to a repulsive interaction at short distances, similar to that between closed-shell atoms, which is especially pronounced for Al and Si solutes.
A discrete dislocation dynamics study of precipitate bypass mechanisms in nickel-based superalloys

Abstract Order strengthening in nickel-based superalloys is associated with the extra stress required for dislocations to bypass the γ ′ precipitates distributed in the γ matrix. Depending on the operating conditions and microstructure, a rich variety of bypass mechanism has been identified, with various shearing and Orowan looping processes gradually giving way to climb bypass as the operating conditions change from the low/intermediate temperatures and high stress regime, to the high temperature and low stress regime. When anti phase boundary (APB) shearing and Orowan looping mechanisms operate, the classical picture is that, at for a given volume fraction, the bypass mechanism changes from shearing to looping with increased particle size and within a broad coexistence size window. Another possibility, which is supported by indirect experimental evidence, is that a third “hybrid” transition mechanism may operate. In this paper we use discrete dislocation dynamics (DDD) simulations to study dislocation bypass mechanisms in Ni-based superalloys. We develop a new method to compute generalized stacking fault forces in DDD simulations, based on a concept borrowed from complex analysis and known as the winding number of a closed curve about a point. We use this method to study the mechanisms of bypass of a square lattice of spherical γ ′ precipitates by a / 2 〈 110 〉 { 111 } edge dislocations, as a function of the precipitates volume fraction and size. We show that not only the hybrid mechanism is possible, but also that it operates as the transition mechanism between the shearing and looping regimes over a wide range of precipitates volume fraction and radii. Based on our simulation results, we propose a simple model for the strength of this mechanism. We also consider the effects of a γ / γ ′ lattice misfit on the bypass mechanisms, which we approximate by an additional precipitate stress computed according to Eshelby’s inclusion theory. We show that in the shearing and hybrid looping-shearing regimes, a lattice misfit generally results in an increased bypass stress. For sufficiently high lattice misfit, the critical bypass configuration in attractive dislocation-precipitates interactions changes dramatically, and the bypass stress is controlled by the pinning of the trailing dislocation on the exit side of the precipitates, similar to what has been reported in the high-temperature creep literature.
Word meaning in minds and machines.

Machines have achieved a broad and growing set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP). Psychologists have shown increasing interest in such models, comparing their output to psychological judgments such as similarity, association, priming, and comprehension, raising the question of whether the models could serve as psychological theories. In this article, we compare how humans and machines represent the meaning of words. We argue that contemporary NLP systems are fairly successful models of human word similarity, but they fall short in many other respects. Current models are too strongly linked to the text-based patterns in large corpora, and too weakly linked to the desires, goals, and beliefs that people express through words. Word meanings must also be grounded in perception and action and be capable of flexible combinations in ways that current systems are not. We discuss promising approaches to grounding NLP systems and argue that they will be more successful, with a more human-like, conceptual basis for word meaning. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Mixture density network estimation of continuous variable maximum likelihood using discrete training samples

Mixture density networks (MDNs) can be used to generate posterior density functions of model parameters $$\varvec{\theta }$$ given a set of observables $${\mathbf {x}}$$ . In some applications, training data are available only for discrete values of a continuous parameter $$\varvec{\theta }$$ . In such situations, a number of performance-limiting issues arise which can result in biased estimates. We demonstrate the usage of MDNs for parameter estimation, discuss the origins of the biases, and propose a corrective method for each issue.
The Effect of Small Temperature Gradients on Flow in a Continuous Flow Electrophoresis Chamber

Continuous flow electrophoresis employs an electric field to separate biological cells suspended in a flowing liquid buffer solution. Good separations based on differences in electrophoretic mobility are obtained only when a unidirectional flow is maintained. The desired flow has a parabolic structure in the narrow dimension of the chamber and is uniform across the width, except near the edges where the no-slip condition prevails. However, because of buoyancy, very small lateral or axial temperature gradients deform the flow significantly. The results of experiments conducted with a specially instrumented chamber show the origin and structure of the buoyancy-driven perturbations. It is found that very small temperature gradients can disturb the flow significantly, as was predicted by earlier theoretical work.
An International Comparison of the Academic Accounting Professoriate

We examine the academic accounting professoriate in the US, Canada, and other countries. As of 2016, US accounting programs averaged 7.9 faculty per program, versus 11.9 faculty for Canadian schools and 10.3 faculty for schools outside of the US and Canada. 78.2% of US faculty are tenure-track (or tenured), versus 84.0% of faculty in Canadian schools and 92.6% of faculty outside of the US and Canada. As of 2016, only 73.7% of individuals who obtain their degrees in Canada stay in Canada as university faculty vs. 90.9% of individuals obtaining US degrees staying in the US and 88.0% for faculty outside of either country. We examine how these proportions have shifted over time, from 1980 to 2016, and we break out our data into tenure-track vs. non-tenure track faculty. Finally, we examine the time needed to move from assistant rank to associate rank (i.e., the time to obtain tenure) and the time needed to move from associate to full professor, broken out by country of education and country of employment.
Fabrication of 3D Micro-Blades for the Cutting of Biological Structures in a Microfluidic Guillotine

Micro-blade design is an important factor in the cutting of single cells and other biological structures. This paper describes the fabrication process of three-dimensional (3D) micro-blades for the cutting of single cells in a microfluidic “guillotine” intended for fundamental wound repair and regeneration studies. Our microfluidic guillotine consists of a fixed 3D micro-blade centered in a microchannel to bisect cells flowing through. We show that the Nanoscribe two-photon polymerization direct laser writing system is capable of fabricating complex 3D micro-blade geometries. However, structures made of the Nanoscribe IP-S resin have low adhesion to silicon, and they tend to peel off from the substrate after at most two times of replica molding in poly(dimethylsiloxane) (PDMS). Our work demonstrates that the use of a secondary mold replicates Nanoscribe-printed features faithfully for at least 10 iterations. Finally, we show that complex micro-blade features can generate different degrees of cell wounding and cell survival rates compared with simple blades possessing a vertical cutting edge fabricated with conventional 2.5D photolithography. Our work lays the foundation for future applications in single cell analyses, wound repair and regeneration studies, as well as investigations of the physics of cutting and the interaction between the micro-blade and biological structures.
The coarea inequality

The aim of this paper is to provide a self-contained proof of a general case of the coarea inequality, also known as the Eilenberg inequality. The result is known, but we are not aware of any place that a proof would be written with all details. The known proof is based on a difficult result of Davies. Our proof is elementary and does not use Davies' theorem. Instead we use an elegant argument that we learned from Nazarov through MathOverflow. We also obtain some generalizations of the coarea inequality.
Influence of the large-Z effect during contact between butterfly sister species.

Recently diverged butterfly populations in North America have been found to exhibit high levels of divergence on the Z chromosome relative to autosomes, as measured by fixation index, Fst . The pattern of divergence appears to result from accumulation of incompatible alleles, obstructing introgression on the Z chromosome in hybrids (i.e., the large-Z effect); however, it is unknown whether this mechanism is sufficient to explain the data. Here, we simulate the effects of hybrid incompatibility on interbreeding butterfly populations using a model in which populations accumulate cross-incompatible alleles in allopatry prior to contact. We compute statistics for introgression and population divergence during contact between model populations and compare our results to those for 15 pairs of butterfly species interbreeding along a suture zone in central Texas. Time scales for allopatry and contact in the model are scaled to glacial and interglacial periods during which real populations evolved in isolation and contact. We find that the data for butterflies are explained well by an otherwise neutral model under slow fusion conditions. In particular, levels of divergence on the Z chromosome increase when interacting clusters of genes are closely linked, consistent with clusters of functionally related genes in butterfly genomes.
Deep Trade Agreements and Domestic Institutions in the Americas

Preferential trade agreements (PTAs) have spread rapidly around the world since the 1990s. In the Americas, the proliferation of trade agreements with countries from within and beyond the region have resulted in a “spaghetti bowl” of overlapping rules and regulations, some of which address behind-the-border issues such as investment, competition, labor, and environmental standards. Earlier research has linked trade agreements to increased foreign investment inflows. This article argues instead that the effects of PTAs on FDI depend on the domestic institutional capacities of member countries. Domestic institutions condition the benefits and effectiveness of PTAs by influencing governments’ external credibility as well as their ability to implement the agreements they sign. The empirical findings show that weak state capacity exacerbates the spaghetti-bowl effects of multiple, overlapping agreements. Moreover, it is not the quantity but the quality, and more specifically, the depth of trade agreements that matters for attracting FDI. Resumen Los acuerdos de libre comercio se han propagado rapidamente por el mundo a partir de los anos noventa. En las Americas, la proliferacion de acuerdos comerciales con paises dentro y fuera de la region ha resultado en un ‘spaghetti bowl’ de reglas y regulaciones paralelas y que se superponen. En este trabajo, argumentamos que el efecto de los acuerdos preferenciales de comercio (APC) sobre la IED depende de la calidad de las instituciones nacionales en los paises miembros. Las instituciones politicas de cada pais miembro afectan la credibilidad externa, asi tambien como su capacidad para implementar los acuerdos que firman, de esa forma condicionando los beneficios y la efectividad de los APC. De acuerdo con nuestro analisis empirico, la baja capacidad estatal exacerba efectos negativos del ‘spaghetti bowl’ comercial. Ademas, la calidad (o profundidad) de los APC importa mas que la cantidad. Los acuerdos comerciales profundos, que abarcan temas y regulaciones domesticos, tienden a atraer mayores flujos de IED que los acuerdos superficiales.
Identifying undercompensated groups defined by multiple attributes in risk adjustment.

Objective To identify undercompensated groups in plan payment risk adjustment that are defined by multiple attributes with a systematic new approach, improving on the arbitrary and inconsistent nature of existing evaluations. Methods Extending the concept of variable importance for single attributes, we construct a measure of ‘group importance’ in the random forests algorithm to identify groups with multiple attributes that are undercompensated by current risk adjustment formulas. Using 2016–2018 IBM MarketScan and 2015–2018 Medicare claims and enrolment data, we evaluate two risk adjustment scenarios: the risk adjustment formula used in the individual health insurance Marketplaces and the risk adjustment formula used in Medicare. Results A number of previously unidentified groups with multiple chronic conditions are undercompensated in the Marketplaces risk adjustment formula, while groups without chronic conditions tend to be overcompensated in the Marketplaces. The magnitude of undercompensation when defining groups with multiple attributes is many times larger than with single attributes. No complex groups were found to be consistently undercompensated or overcompensated in the Medicare risk adjustment formula. Conclusions Our method is effective at identifying complex undercompensated groups in health plan payment risk adjustment where undercompensation creates incentives for insurers to discriminate against these groups. This work provides policy-makers with new information on potential targets of discrimination in the healthcare system and a path towards more equitable health coverage.
Ambiguity and the Tradeoff Theory of Capital Structure

We examine the impact of ambiguity, or Knightian uncertainty, on the capital structure decision, using a static tradeoff theory model in which agents are both ambiguity and risk averse. The model c...
Radio sources and scintillation

A review is given of the interplay between studies of compact radio sources and the scattering and scintillations that occur as the signals travel through the irregular refractive index of the interstellar and interplanetary plasmas.
A fast multi-fidelity method with uncertainty quantification for complex data correlations: Application to vortex-induced vibrations of marine risers

Abstract We develop a fast multi-fidelity modeling method for very complex correlations between high- and low-fidelity data by working in modal space to extract the proper correlation function. We apply this method to infer the amplitude of motion of a flexible marine riser in cross-flow, subject to vortex-induced vibrations (VIV). VIV are driven by an absolute instability in the flow, which imposes a frequency (Strouhal) law that requires a matching with the impedance of the structure; this matching is easily achieved because of the rapid parametric variation of the added mass force. As a result, the wavenumber of the riser spatial response is within narrow bands of uncertainty. Hence, an error in wavenumber prediction can cause significant phase-related errors in the shape of the amplitude of response along the riser, rendering correlation between low- and high-fidelity data very complex. Working in modal space as outlined herein, dense data from low-fidelity data, provided by the semi-empirical computer code VIVA, can correlate in modal space with few high-fidelity data, obtained from experiments or fully-resolved CFD simulations , to correct both phase and amplitude and provide predictions that agree very well overall with the correct shape of the amplitude response. We also quantify the uncertainty in the prediction using Bayesian modeling and exploit this uncertainty to formulate an active learning strategy for the best possible location of the sensors providing the high fidelity measurements .
Basil: Breaking up BFT with ACID (transactions)

This paper presents Basil, the first transactional, leaderless Byzantine Fault Tolerant key-value store. Basil leverages ACID transactions to scalably implement the abstraction of a trusted shared log in the presence of Byzantine actors. Unlike traditional BFT approaches, Basil executes non-conflicting operations in parallel and commits transactions in a single round-trip during fault-free executions. Basil improves throughput over traditional BFT systems by four to five times, and is only four times slower than TAPIR, a non-Byzantine replicated system. Basil's novel recovery mechanism further minimizes the impact of failures: with 30% Byzantine clients, throughput drops by less than 25% in the worst-case.
Female Representation in the Academic Finance Profession

We present new data on female representation in the academic finance profession. In our sample of finance faculty at top-100 U.S. business schools during 2009–2017, only 16.0% are women. The gender imbalance manifests itself in several ways. First, after controlling for research productivity, women hold positions at lower-ranked institutions and are less likely to be full professors. There is also evidence that they are paid less. Second, women publish fewer papers. This gender gap exists in research quantity, not quality. Third, women have more female coauthors, suggesting smaller publication networks. Time-series data suggest shrinking gender gaps in recent years.
Imaging antiferromagnetic domain fluctuations and the effect of atomic scale disorder in a doped spin-orbit Mott insulator.

Correlated oxides can exhibit complex magnetic patterns. Understanding how magnetic domains form in the presence of disorder and their robustness to temperature variations has been of particular in...
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations

Training-time safety violations have been a major concern when we deploy reinforcement learning algorithms in the real world. This paper explores the possibility of safe RL algorithms with zero training-time safety violations in the challenging setting where we are only given a safe but trivial-reward initial policy without any prior knowledge of the dynamics model and additional offline data. We propose an algorithm, Co-trained Barrier Certificate for Safe RL (CRABS), which iteratively learns barrier certificates, dynamics models, and policies. The barrier certificates, learned via adversarial training, ensure the policy's safety assuming calibrated learned dynamics model. We also add a regularization term to encourage larger certified regions to enable better exploration. Empirical simulations show that zero safety violations are already challenging for a suite of simple environments with only 2-4 dimensional state space, especially if high-reward policies have to visit regions near the safety boundary. Prior methods require hundreds of violations to achieve decent rewards on these tasks, whereas our proposed algorithms incur zero violations.
How regulations undervalue occupational fatalities

The U.S. Occupational Safety and Health Administration (OSHA) establishes incentives for safety by setting and enforcing regulatory standards. Using four and a half decades of inspection data, this article provides a comprehensive analysis of the factors underpinning penalties following fatalities. The “fatality premium” for regulatory violations following a worker death is quite modest and is several orders of magnitude below the value-of-a-statistical-life figure needed to establish efficient levels of deterrence in the absence of all other financial incentives. Although there are low statutory caps on penalties, only 8% of the penalties for violations involving fatalities are constrained by the cap, suggesting that current statutes establish norms for reasonable penalty amounts. In situations involving a fatality at firms with union representation during the inspection, the enforcement sanctions are more stringent. Fatalities involving migrant laborers are less heavily penalized.
A language-based approach to measuring creative exploration

ABSTRACT We propose a new measure of the exploratory activities of companies based on the idea that experimentation with new courses of action and the need to describe them entails the adoption of new words in firm regulatory disclosures. Unlike traditional indicators, such as R&D spending, the proposed exploration indicator is available for all publicly traded firms across all industries. The exploration indicator predicts firm knowledge accumulation, as measured by future patenting and trademarking activities. It further shows that firm exploration declines after periods of high R&D spending and over time. The exploration indicator correlates positively with firm risk and exhibits a distinct positive impact on firm value unexplained by traditional innovation indicators. Our language-based approach can be applied to measure creative contributions in other domains, such as government grant applications and academic publications.
Floquet graphene antidot lattices

We establish the theoretical foundation of the Floquet graphene antidot lattice, whereby massless Dirac fermions are driven periodically by a circularly polarized electromagnetic field while having their motion excluded from an array of nanoholes. The properties of interest are encoded in the quasienergy spectra, which are computed nonperturbatively within the Floquet formalism. We find that a rich Floquet phase diagram emerges as the amplitude of the drive field is varied. Notably, the Dirac dispersion can be restored in real time relative to the gapped equilibrium state, which may enable the creation of an optoelectronic switch or a dynamically tunable electronic waveguide. As the amplitude is increased, the ability to shift the quasienergy gap between high-symmetry points can change which crystal momenta dominate in the scattering processes relevant to electronic transport and optical emission. Furthermore, the bands can be flattened near the $\mathrm{\ensuremath{\Gamma}}$ point, which is indicative of selective dynamical localization. Lastly, quadratic and linear dispersions emerge in orthogonal directions at the $M$ point, signaling a Floquet semi-Dirac material. Importantly, all our predictions are valid for experimentally accessible near-IR radiation, which corresponds to the above bandwidth limit for the graphene antidot lattice. Cycling between engineered Floquet electronic phases may play a key role in the development of next-generation on-chip devices for optoelectronic applications.
Effects of Head-locked Augmented Reality on User’s Performance and Perceived Workload

An augmented reality (AR) environment includes a set of digital elements with which the users interact while performing certain tasks. Recent AR head-mounted displays allow users to select how thes...
On automorphisms of flexible varieties

Abstract Let X be a flexible variety and φ : C 1 → C 2 be an isomorphism of closed one-dimensional subschemes of X. We develop several criteria which guarantee that φ extends to an automorphism of X.