cssi/SciSciGPT-SciSciNet huggingface dataset:
Bridging Erie: Customary International Law in the U.S. Legal System after Sosa v. Alvarez-Machain

This paper discusses the Supreme Court's decision in Sosa v. Alvarez-Machain, which held that federal courts may apply customary international law under the Alien Tort Statute as federal common law. It notes that the Sosa Court adopted a particularized approach, which looks at the incorporation of customary international law into the U.S. legal system issue-by-issue. It also examines Sosa's two-part analysis, which weighs both the original understanding of a provision and modern developments. The paper then applies this two-part analysis to two questions that Sosa left unanswered: the status of customary international law under Article III of the Constitution and its status under the Supremacy Clause of Article VI.
Debt and Democracy: Towards a Constitutional Theory of Bankruptcy

This Article explores certain important constitutional challenges presented by bankruptcy. Article I, Section 8, Clause 4 of the Constitution provides that Congress shall have the power to make uniform Laws on the subject of Bankruptcies. While there are many good social, political, and economic theories of bankruptcy, there has been comparatively little effort to address broadly what it means to have constitutionalized financial distress. This Article is a first step in that direction. Constitutional problems with bankruptcy are not new, but present three underappreciated puzzles: First, why did the Framers put a bankruptcy power in the Constitution, and how broadly should we construe its peculiar language today? Second, how should this power interact with structural features of our constitutional system, whether vertical (vis-a-vis states) or horizontal (vis-a-vis other branches)? Third, how should we resolve competitions between this power and substantive protections involving, for example, property, due process, and religious liberties? Recent Supreme Court decisions broadly interpreting the Bankruptcy Clause, the 2005 amendments to the Bankruptcy Code, and the continuing spate of Catholic diocese bankruptcies, among other things, give these puzzles some urgency. This Article identifies an important, and thus far undeveloped, theme in the constitutional implications of bankruptcy: bankruptcy exceptionalism. Bankruptcy exceptionalism is an operating principle that helps to explain why we have a Bankruptcy Clause and how it has sometimes permitted or compelled exceptions to constitutional rules, standards, norms, and values in order to accommodate the exigencies of financial distress. The Article argues that the bankruptcy power gives Congress broad discretion to legislate in response to financial distress, subject to certain important democratic and countermajoritarian protections
Wrong, Out of Step, and Pernicious: Erie as the Worst Decision of All Time

This essay was written for “Supreme Mistakes: Exploring the Most Maligned Decisions in Supreme Court History.” A symposium on the worst Supreme Court decision of all time risks becoming an exercise best described by Claude Rains’s memorable line in Casablanca: “Round up the usual suspects.” Two things saved this symposium from that fate. First, each of the usual suspects was appointed defense counsel, which made things more interesting. Second, a new face found its way into the line-up: Erie Railroad v. Tompkins. My goal in this essay is to explain why Erie is in fact guiltier than all of the usual suspects. I begin, in Part I, by setting out the three criteria that I believe must be satisfied for a decision to qualify as the worst of all time. I also explain briefly why each of the usual suspects fails to meet one or more of those criteria. The heart of the essay is Part II, examining in detail how Erie satisfies each of the three criteria. I close with some concluding thoughts on the surprising relationship between Erie’s flaws and those of the other suspects.
The Value of Employer‐Sponsored Child Care to Employees

This article uses the contingent valuation method for calculating the value of employer-sponsored child care to employees. Like many environmental amenities, there may be a nonuse or existence value of working for a company that offers employer-sponsored child care (ESCC), as well as a use value to parents who have children in the center. We test this hypothesis using data from three firms, two of which have on-site child care. Our findings indicate that price is a determinant of willingness to pay for the continued existence or establishment of an on-site center. We find evidence of the existence value, even for employees without young children, and a greater valuation among recent hires than among longer-term employees.
Classless Not Clueless: A Comparison of Case Management Mechanisms for Non-Class-Based Complex Litigation in California and Federal Courts

In this comparison of non-class-based complex litigation in California and federal courts, Scott Paetty examines the case management strategies employed in California's complex court system and in the federal multidistrict litigation process. Evaluating the strengths and weaknesses of both systems, Paetty describes what makes a case complex, evaluates procedural mechanisms for dealing with complex cases, and then discusses some of the informal case management techniques practiced by judges in both systems. Paetty ultimately finds the flexible approach advanced in the California complex court system to be more effective and argues California's system should be a model for other states seeking to manage complex cases.
Input and Output Inventory Dynamics

This paper develops an analytically tractable general equilibrium model of inventory dynamics based on a precautionary stockout-avoidance motive. The model's predictions are broadly consistent with the US business cycle and key features of inventory behavior. It is also shown that technological improvement of inventory management can increase, rather than decrease, the volatility of aggregate output. Key to this seemingly counterintuitive result is that a stockout-avoidance motive leads to a procyclical shadow value of inventories, which acts as an automatic stabilizer that discourages sales in booms and encourages demand in recessions, thereby reducing the variability of GDP. (JEL D92, E22, E23, E32, G31)
The impact of direct-to-consumer advertising of prescription drugs on physician visits and drug requests: Empirical findings and public policy implications

Abstract This study analyzes the effect of DTCA expenditures for anti-hyperlipidemia drugs on patient behaviors. The key findings are: (a) DTCA expenditures have a positive and long-term effect on the number of visits to physicians by newly diagnosed hyperlipidemia patients. (b) The effectiveness of DTCA in generating new patient visits varies substantially across patient sub-groups. (c) The effect of DTCA is larger on drug visits than on non-drug-only visits. (d) Own-brand DTCA expenditures increase the number of patient requests for Lipitor and Zocor, but have no effect on patient requests for Pravachol. Competing drugs’ DTCA expenditures have a positive effect only on patient requests for the leading brand, Lipitor. (e) A cost-effectiveness analysis suggests that the economic benefits of DTCA in terms of life years saved by preventing cardiovascular disease are considerably larger than the costs of advertising. (f) DTCA on TV has strong effects on underserved segments of the population, such as those on Medicaid. We believe this finding should be carefully considered by proponents of a complete ban or stricter regulations on DTCA.
Competing in the new economy: the effect of intellectual capital on corporate entrepreneurship in high-technology new ventures

Intellectual capital (IC) offers a potential source of sustainable competitive advantage and is believed to be the font from which technological development and economic growth may spring. This study proposes a three-dimensional framework for describing and measuring a firm's IC that includes human capital, intellectual property, and reputational capital. Drawing upon the resource-based view of the firm, it is argued that in high-technology new ventures (HTNVs) IC assets offer a unique source of advantage that facilitates entrepreneurship by reducing the risk and increasing the returns from investments in innovation and venturing. This paper reports the results of an empirical study of 237 HTNVs in the US that issued an initial public offering between 1994 and 1998. It is found that these firms' top management team human capital diversity and organizational reputation are of greatest significance for their entrepreneurial performance. Interestingly, these factors far outweigh the insignificant effect observed for intellectual property on subsequent innovation and venturing activities. The implications for theory and practice are discussed.
Learning Rotation-Invariant Representations of Point Clouds Using Aligned Edge Convolutional Neural Networks

Point cloud analysis is an area of increasing interest due to the development of 3D sensors that are able to rapidly measure the depth of scenes accurately. Unfortunately, applying deep learning techniques to perform point cloud analysis is non-trivial due to the inability of these methods to generalize to unseen rotations. To address this limitation, one usually has to augment the training data, which can lead to extra computation and require larger model complexity. This paper proposes a new neural network called the Aligned Edge Convolutional Neural Network (AECNN) that learns a feature representation of point clouds relative to Local Reference Frames (LRFs) to ensure invariance to rotation. In particular, features are learned locally and aligned with respect to the LRF of an automatically computed reference point. The proposed approach is evaluated on point cloud classification and part segmentation tasks. This paper illustrates that the proposed technique outperforms a variety of state of the art approaches (even those trained on augmented datasets) in terms of robustness to rotation without requiring any additional data augmentation.
Interspecies Metabolic Complementation in Cystic Fibrosis Pathogens via Purine Exchange

Cystic fibrosis (CF) is a genetic disease frequently associated with chronic lung infections caused by a consortium of pathogens. It is common for auxotrophy (the inability to biosynthesize certain essential metabolites) to develop in clinical isolates of the dominant CF pathogen Pseudomonas aeruginosa, indicating that the CF lung environment is replete in various nutrients. Many of these nutrients are likely to come from the host tissues, but some may come from the surrounding polymicrobial community within the lungs of CF patients as well. To assess the feasibility of nutrient exchange within the polymicrobial community of the CF lung, we selected P. aeruginosa and Staphylococcus aureus, two of the most prevalent species found in the CF lung environment. By comparing the polymicrobial culture of wild-type strains relative to their purine auxotrophic counterparts, we were able to observe metabolic complementation occurring in both P. aeruginosa and S. aureus when grown with a purine-producing cross-species pair. While our data indicate that some of this complementation is likely derived from extracellular DNA freed by lysis of S. aureus by the highly competitive P. aeruginosa, the partial complementation of S. aureus purine deficiency by P. aeruginosa demonstrates that bidirectional nutrient exchange between these classic competitors is possible.
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

Motivation Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios. Results To address this challenge, we developed a novel pre-trained bidirectional encoder represen-tation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. We compared DNABERT to the most widely used programs for genome-wide regulatory elements prediction and demonstrate its ease of use, accuracy, and efficiency. We show that the single pre-trained transformers model can simultaneously achieve state-of-the-art performance on prediction of promoters, splice sites, and transcription factor binding sites, after easy fine-tuning using small task-specific labelled data. Further, DNABERT enables direct visualization of nucleotide-level importance and semantic relationship within input sequences for better interpretability and accurate identification of conserved sequence motifs and functional genetic variant candidates. Finally, we demonstrate that pre-trained DNABERT with human genome can even be readily applied to other organisms with exceptional performance. We anticipate that the pre-trained DNABERT model can be fined tuned to many other sequence analyses tasks. Availability The source code, pretrained and finetuned model for DNABERT are available at GitHub https://github.com/jerryji1993/DNABERT. Supplementary information Supplementary data are available at Bioinformatics online.
A Multi-scale Materials Modeling Method with Seamless Zooming Capability based on Surfacelets

In multi-scale materials modeling, it is desirable that different levels of details can be specified in different regions of interest without the separation of scales so that the geometric and physical properties of materials can be designed and characterized. Existing materials modeling approaches focus on the representation of the distributions of material compositions captured from images. In this paper, a multi-scale materials modeling method is proposed to support interactive specification and visualization of material microstructures at multiple levels of details, where designer’s intent at multiple scales is captured. This method provides a feature-based modeling approach based on a recently developed surfacelet basis. It has the capability to support seamless zoom-in and zoom-out. The modeling, operation, and elucidation of materials are realized in both the surfacelet space and the image space.Copyright © 2014 by ASME
ORCID-linked labeled data for evaluating author name disambiguation at scale

How can we evaluate the performance of a disambiguation method implemented on big bibliographic data? This study suggests that the open researcher profile system, ORCID, can be used as an authority source to label name instances at scale. This study demonstrates the potential by evaluating the disambiguation performances of Author-ity2009 (which algorithmically disambiguates author names in MEDLINE) using 3 million name instances that are automatically labeled through linkage to 5 million ORCID researcher profiles. Results show that although ORCID-linked labeled data do not effectively represent the population of name instances in Author-ity2009, they do effectively capture the ‘high precision over high recall’ performances of Author-ity2009. In addition, ORCID-linked labeled data can provide nuanced details about the Author-ity2009’s performance when name instances are evaluated within and across ethnicity categories. As ORCID continues to be expanded to include more researchers, labeled data via ORCID-linkage can be improved in representing the population of a whole disambiguated data and updated on a regular basis. This can benefit author name disambiguation researchers and practitioners who need large-scale labeled data but lack resources for manual labeling or access to other authority sources for linkage-based labeling. The ORCID-linked labeled data for Author-ity2009 are publicly available for validation and reuse.
Rethinking PointNet Embedding for Faster and Compact Model

PointNet, which is the widely used point-wise embedding method and known as a universal approximator for continuous set functions, can process one million points per second. Nevertheless, real-time inference for the recent development of high-performing sensors is still challenging with existing neural network-based methods, including PointNet. In ordinary cases, the embedding function of PointNet behaves like a soft-indicator function that is activated when the input points exist in a certain local region of the input space. Leveraging this property, we reduce the computational costs of point-wise embedding by replacing the embedding function of PointNet with the soft-indicator function by Gaussian kernels. Moreover, we show that the Gaussian kernels also satisfy the universal approximation theorem that PointNet satisfies. In experiments, we verify that our model using the Gaussian kernels achieves comparable results to baseline methods, but with much fewer floating-point operations per sample up to 92% reduction from PointNet.
New examples of minimal non-strongly-perfect graphs

Abstract A graph is strongly perfect if every induced subgraph H has a stable set that meets every nonempty maximal clique of H . The characterization of strongly perfect graphs by a set of forbidden induced subgraphs is not known. Here we provide several new minimal non-strongly-perfect graphs.
Interneuron Origins in the Embryonic Porcine Medial Ganglionic Eminence.

Interneurons contribute to the complexity of neural circuits and maintenance of normal brain function. Rodent interneurons originate in embryonic ganglionic eminences, but developmental origins in other species are less understood. Here, we show that transcription factor expression patterns in porcine embryonic subpallium are similar to rodents, delineating a distinct medial ganglionic eminence (MGE) progenitor domain. On the basis of Nkx2.1, Lhx6 and Dlx2 expression, in vitro differentiation into neurons expressing GABA and robust migratory capacity in explant assays, we propose that cortical and hippocampal interneurons originate from a porcine MGE region. Following xenotransplantation into adult male and female rat hippocampus, we further demonstrate that porcine MGE progenitors, like those from rodents, migrate and differentiate into morphologically distinct interneurons expressing GABA. Our findings reveal that basic rules for interneuron development are conserved across species, and that porcine embryonic MGE progenitors could serve as a valuable source for interneuron-based xenotransplantation therapies. SIGNIFICANCE STATEMENT Here we demonstrate that porcine MGE, like rodents, exhibit a distinct transcriptional and interneuron-specific antibody profile, in vitro migratory capacity and are amenable to xenotransplantation. This is the first comprehensive examination of embryonic interneuron origins in the pig, and because a rich neurodevelopmental literature on embryonic mouse MGE exists (with some additional characterizations in other species like monkey and human) our work allows direct neurodevelopmental comparisons with this literature.
Geometric quantization and no-go theorems

Dedicated to the memory of Stanis law Zakrzewski Abstract. A geometric quantization of a Kahler manifold, viewed as a symplectic manifold, depends on the complex structure compatible with the symplectic form. The quantizations form a vector bundle over the space of such complex structures. Having a canonical quantization would amount to finding a natural (projectively) flat connection on this vector bundle. We prove that for a broad class of manifolds, including symplectic homogeneous spaces (e.g., the sphere), such connection does not exist. This is a consequence of a "no-go" theorem claiming that the entire Lie algebra of smooth functions on a compact symplectic manifold cannot be quantized, i.e., it has no essentially nontrivial finite-dimensional representations.
LaND: Learning to Navigate From Disengagements

Consistently testing autonomous mobile robots in real world scenarios is a necessary aspect of developing autonomous navigation systems. Each time the human safety monitor disengages the robot's autonomy system due to the robot performing an undesirable maneuver, the autonomy developers gain insight into how to improve the autonomy system. However, we believe that these disengagements not only show where the system fails, which is useful for troubleshooting, but also provide a direct learning signal by which the robot can learn to navigate. We present a reinforcement learning approach for learning to navigate from disengagements, or LaND. LaND learns a neural network model that predicts which actions lead to disengagements given the current sensory observation, and then at test time plans and executes actions that avoid disengagements. Our results demonstrate LaND can successfully learn to navigate in diverse, real world sidewalk environments, outperforming both imitation learning and reinforcement learning approaches. Videos, code, and other material are available on our website https://sites.google.com/view/sidewalk-learning .
The perverse filtration for the Hitchin fibration is locally constant

We prove that the perverse Leray filtration for the Hitchin morphism is locally constant in families, thus providing some evidence towards the validity of the $P=W$ conjecture due to de Cataldo, Hausel and Migliorini in non Abelian Hodge theory.
Role of β-arrestin-2 in short- and long-term opioid tolerance in the dorsal root ganglia.

Abstract G-protein-biased agonists with reduced β-arrestin-2 activation are being investigated as safer alternatives to clinically-used opioids. β-arrestin-2 has been implicated in the mechanism of opioid-induced antinociceptive tolerance. Opioid-induced analgesic tolerance is classically considered as centrally-mediated, but recent reports implicate nociceptive dorsal root ganglia neurons as critical mediators in this process. Here, we investigated the role of β-arrestin-2 in the mechanism of opioid tolerance in dorsal root ganglia nociceptive neurons using β-arrestin-2 knockout mice and the G-protein-biased μ-opioid receptor agonist, TRV130. Whole-cell current-clamp electrophysiology experiments revealed that 15-18-h overnight exposure to 10 μM morphine in vitro induced acute tolerance in β-arrestin-2 wild-type but not knockout neurons. Furthermore, in wild-type neurons circumventing β-arrestin-2 activation by overnight treatment with 200 nM TRV130 attenuated tolerance. Similarly, acute morphine tolerance in vivo in β-arrestin-2 knockout mice was prevented in the warm-water tail-withdrawal assay. Treatment with 30 mg/kg TRV130 s.c. also inhibited acute antinociceptive tolerance in vivo in wild-type mice. Alternately, in β-arrestin-2 knockout neurons tolerance induced by 7-day in vivo exposure to 50 mg morphine pellet was conserved. Likewise, β-arrestin-2 deletion did not mitigate in vivo antinociceptive tolerance induced by 7-day exposure to 25 mg or 50 mg morphine pellet in both female or male mice, respectively. Consequently, these results indicated that β-arrestin-2 mediates acute but not chronic opioid tolerance in dorsal root ganglia neurons and to antinociception in vivo. This suggests that opioid-induced antinociceptive tolerance may develop even in the absence of β-arrestin-2 activation, and thus significantly affect the clinical utility of biased agonists.
Prevalence of unexplained erythrocytosis and thrombocytosis - an NHANES analysis.

Polycythemia vera (PV) and essential thrombocytemia (ET) are myeloproliferative neoplasms (MPNs) characterized by erythrocytosis and thrombocytosis, respectively [1]. Diagnosis of PV requires exclu...
Chronic endometritis in women with suspected retained products of conception and their reproductive outcomes

Objective To determine whether women who underwent operative hysteroscopy for suspected retained products of conception (rPOC) have histopathologic evidence of chronic endometritis (CE). Design Retrospective cohort. Setting Academic center. Patient(s) One hundred and eleven women who underwent operative hysteroscopy for suspected rPOC between 2016 and 2018. Intervention(s) None. Main outcome measure(s) Evidence of CE on histopathology and subsequent reproductive outcomes. Result(s) One hundred and eleven women with retained products of conception were included in our study of which 26 (23.4%) were diagnosed with CE. Women without CE had a higher median gravidity (1 vs. 2, p = .021) and a higher median number of prior pregnancy losses (1 vs. 2 prior losses, p = .005) compared to those with CE. Subsequent pregnancy data were available for 63 women. There was no difference in the subsequent pregnancy rate (61.5 vs. 54%, p = .626) between those with and without CE. Once pregnant, miscarriage (37.5 vs. 25.9%, p = .524) and live birth rates (50 vs. 44.4%, p = .782) were similar between the groups. Women with CE received antibiotics 57.7% of the time, the most common of which was doxycycline (46.6%). Of the women with CE who received antibiotics (n = 10), 8 became pregnant, and 4 of whom went on to have a live birth. Conclusion(s) Nearly 1 in 4 women undergoing hysteroscopy for rPOC was incidentally diagnosed with CE. It is not clear whether CE is a causative agent for retained products or a response to the pregnancy loss. In this cohort, a diagnosis of CE did not negatively impact subsequent reproductive outcomes.
Loss channels affecting lithium niobate phononic crystal resonators at cryogenic temperature

We investigate the performance of microwave-frequency phononic crystal resonators fabricated on thin-film lithium niobate for integration with superconducting quantum circuits. For different design geometries at millikelvin temperatures, we achieve mechanical internal quality factors Qi above 10 5 – 10 6 at high microwave drive power, corresponding to 5 × 10 6 phonons inside the resonator. By sweeping the defect size of resonators with identical mirror cell designs, we are able to indirectly observe signatures of the complete phononic bandgap via the resonators' internal quality factors. Examination of quality factors' temperature dependence shows how superconducting and two-level system (TLS) loss channels impact device performance. Finally, we observe an anomalous low-temperature frequency shift consistent with resonant TLS decay and find that the material choice can help to mitigate these losses.
A Randomized Online Algorithm for Bandwidth Utilization

Protocols for data transmission over an IP computer network should not only lead to efficient network utilization but also be fair to different users. Current networks accomplish these goals by some form of end-to-end congestion control. Existing protocols, however, assume somewhat altruistic behavior from hosts. Karp et al. (2000) have initiated a study of whether or not a single host's optimum strategy (in a system where other hosts are well behaved) is altruistic. We carry this exploration further by developing an efficient randomized algorithm for bandwidth utilization in their model. The competitive ratio of this algorithm is optimal up to a constant factor. Karp et al. had earlier studied the deterministic case and left open the randomized case. What may be of some interest is that our algorithm is essentially the classical multiplicative increase, multiplicative decrease strategy, which is very aggressive and non-altruistic.
Visibility with a moving point of view

We investigate three-dimensional visibility problems in which the viewing position moves along a straight flightpath. Specifically we focus on two problems: determining the points along the flightpath at which the topology of the viewed scene changes, and answering ray-shooting queries for rays with origin on the flightpath. Three progressively more specialized problems are considered: general scenes, terrains, and terrains with vertical flightpaths.
Prospective study of emergency medicine provider wellness across ten academic and community hospitals during the initial surge of the COVID-19 pandemic.

While COVID-19 has had far-reaching consequences on society and health care providers, there is a paucity of research exploring frontline emergency medicine (EM) provider wellness over the course of a pandemic. The objective of this study was to assess the well-being, resilience, burnout, and wellness factors and needs of EM physicians and advanced practice providers (e.g., nurse practitioners and physician assistants; APPs) during the initial phase of the COVID-19 pandemic. A descriptive, prospective, cohort survey study of EM physicians and APPs was performed across ten emergency departments in a single state, including academic and community settings. Participants were recruited via email to complete four weekly, voluntary, anonymous questionnaires comprised of customized and validated tools for assessing wellness (Well Being Index), burnout (Physician Work Life Study item), and resilience (Brief Resilience Scale) during the initial acceleration phase of COVID-19. Univariate and multivariate analysis with Chi-squared, Fisher’s Exact, and logistic regression was performed. Of 213 eligible participants, response rates ranged from 31 to 53% over four weeks. Women comprised 54 to 60% of responses. Nonrespondent characteristics were similar to respondents. Concern for personal safety decreased from 85 to 61% (p 50% of respondents initially without significant change (p = 0.05 and p = 0.30 respectively). Women were nearly twice as likely to report feelings of isolation as men (OR 1.95; 95% CI 1.82–5.88). Working part-time carried twice the risk of burnout (OR, 2.45; 95% CI, 1.10–5.47). Baseline resilience was normal to high. Provider well-being improved over the four weeks (30 to 14%; p = 0.01), but burnout did not significantly change (30 to 22%; p = 0.39). This survey of frontline EM providers, including physicians and APPs, during the initial surge of COVID-19 found that despite being a resilient group, the majority experienced stress, anxiety, fear, and concerns about personal safety due to COVID-19, putting many at risk for burnout. The sustained impact of the pandemic on EM provider wellness deserves further investigation to guide targeted interventions.
Complexity and momentum

Previous work has explored the connections between three concepts — operator size, complexity, and the bulk radial momentum of an infalling object — in the context of JT gravity and the SYK model. In this paper we investigate the higher dimensional generalizations of these connections. We use a toy model to study the growth of an operator when perturbing the vacuum of a CFT. From circuit analysis we relate the operator growth to the rate of increase of complexity and check it by complexity-volume duality. We further give an empirical formula relating complexity and the bulk radial momentum that works from the time that the perturbation just comes in from the cutoff boundary, to after the scrambling time.
Sparking Creativity: Improving Electronic Brainstorming with Individual Cognitive Priming

Much of human behavior involves subconscious cognition that can be manipulated through "priming"—the presentation of a stimulus designed to subconsciously implant a concept in working memory that alters subsequent behavior. Priming is a well-known phenomenon for individual behavior, but we do not know whether priming can be used to influence group behavior. We developed a Web-based computer game that was designed to improve creativity through priming. Participants were exposed to a priming game and then worked as members of a group using electronic brainstorming (EBS) to generate ideas on a creativity task. Our results show that when users played the game, designed to improve performance, their groups generated significantly more ideas that were more creative than when they were exposed to neutral priming. Our findings extend the literature by providing evidence that individual priming substantially affects group idea generation performance. Avenues for future research include designing EBS software that ...
A combinatorial analysis of Severi degrees

Based on results by Brugalle and Mikhalkin, Fomin and Mikhalkin give formulas for computing classical Severi degrees Nd,δ using long-edge graphs. In 2012, Block, Colley and Kennedy considered the logarithmic versionof a special function associated to long-edge graphs which appeared in Fomin-Mikhalkin’s formula, and conjecturedit to be linear. They have since proved their conjecture. At the same time, motivated by their conjecture, we considera special multivariate function associated to long-edge graphs that generalizes their function. The main result of thispaper is that the multivariate function we define is always linear.The first application of our linearity result is that by applying it to classical Severi degrees, we recover quadraticity of Qd,δ and a bound δ for the threshold of polynomiality ofNd,δ.Next, in joint work with Osserman, we apply thelinearity result to a special family of toric surfaces and obtain universal polynomial results having connections to the Gottsche-Yau-Zaslow formula. As a result, we provide combinatorial formulas for the two unidentified power series B1(q) and B2(q) appearing in the Gottsche-Yau-Zaslow formula.The proof of our linearity result is completely combinatorial. We defineτ-graphs which generalize long-edge graphs,and a closely related family of combinatorial objects we call (τ,n)-words. By introducing height functions and aconcept of irreducibility, we describe ways to decompose certain families of (τ,n)-words into irreducible words,which leads to the desired results.
Analytical solutions to some generalized and polynomial eigenvalue problems

It is well-known that the finite difference discretization of the Laplacian eigenvalue problem $-\Delta u = \lambda u$ leads to a matrix eigenvalue problem (EVP) $A x= \lambda x$ where the matrix $A$ is Toeplitz-plus-Hankel. Analytical solutions to tridiagonal matrices with various boundary conditions are given in Strang and MacNamara \cite{strang2014functions}. We generalize the results and develop analytical solutions to the generalized matrix eigenvalue problems (GEVPs) $A x= \lambda Bx$ which arise from the finite element method (FEM) and isogeometric analysis (IGA). The FEM matrices are corner-overlapped block-diagonal while the IGA matrices are almost Toeplitz-plus-Hankel. In fact, IGA with a correction that results in Toeplitz-plus-Hankel matrices gives a better numerical method. In this paper, we focus on finding the analytical eigenpairs to the GEVPs while developing better numerical methods is our motivation. Analytical solutions are also obtained for some polynomial eigenvalue problems (PEVPs). Lastly, we generalize the eigenvector-eigenvalue identity (rediscovered and coined recently for EVPs) for GEVPs and derive some trigonometric identities.
Extended proper equilibrium

Abstract We introduce extended proper equilibrium, which refines proper equilibrium ( Myerson, 1978 ) by adding across-player restrictions on trembles. This refinement coincides with proper equilibrium in games with two players but adds new restrictions in games with three or more players. One implication of these additional restrictions is that any tremble that is costless in equilibrium is regarded by all as more likely than any costly tremble, even one by a different player. At least one extended proper equilibrium exists in every finite game. The refinement can also be characterized in terms of a symmetric, meta-version of the game in which players originate from a common pool: if these players tremble symmetrically and in the way of proper equilibrium, then the induced play in the original game is an extended proper equilibrium.
Multi-Modal Fingerprint Presentation Attack Detection: Evaluation on a New Dataset

Fingerprint presentation attack detection is becoming an increasingly challenging problem due to the continuous advancement of attack preparation techniques, which generate realistic-looking fake fingerprint presentations. In this work, rather than relying on legacy fingerprint images, which are widely used in the community, we study the usefulness of multiple recently introduced sensing modalities. Our study covers front-illumination imaging using short-wave-infrared, near-infrared, and laser illumination; and back-illumination imaging using near-infrared light. Toward studying the effectiveness of each of these unconventional sensing modalities and their fusion for liveness detection, we conducted a comprehensive analysis using a fully convolutional deep neural network framework. Our evaluation compares different combinations of the new sensing modalities to legacy data from one of our collections, showing the superiority of the new sensing modalities. It also covers the cases of known and unknown attacks and the cases of intra-dataset and inter-dataset evaluations. Our results indicate that the power of our approach stems from the nature of the captured data rather than the employed classification framework, which justifies the extra cost for hardware-based (or hybrid) solutions. The portion of the dataset that is under the control of the authors is publicly released, along with the associated code.
Collateral Value and Strategic Default: Evidence from Auto Loans

I study the impact of changes to collateral value on borrowers’ default decisions on auto loans using two natural experiments in Sri Lanka. Changes in vehicle import tax rates and loan-to-value ratio caps on auto loans generated plausibly exogenous variation in the resale value of vehicles already pledged as collateral. Using proprietary auto loan performance data, I estimate that a 10% drop in the collateral value corresponds to a 44% increase in the default rate. I also find that collateral value is more important for borrowers with higher outstanding loan balances.
EXMA: A Genomics Accelerator for Exact-Matching

Genomics is the foundation of precision medicine, global food security and virus surveillance. Exact-match is one of the most essential operations widely used in almost every step of genomics such as alignment, assembly, annotation, and compression. Modern genomics adopts Ferragina-Manzini Index (FMIndex) augmenting space-efficient Burrows-Wheeler transform (BWT) with additional data structures to permit ultra-fast exact-match operations. However, FM-Index is notorious for its poor spatial locality and random memory access pattern. Prior works create GPU-, FPGA-, ASIC- and even process-in-memory (PIM)based accelerators to boost FM-Index search throughput. Though they achieve the state-of-the-art FM-Index search throughput, the same as all prior conventional accelerators, FM-Index PIMs process only one DNA symbol after each DRAM row activation, thereby suffering from poor memory bandwidth utilization. In this paper, we propose a hardware accelerator, EXMA, to enhance FM-Index search throughput. We first create a novel EXMA table with a multi-task-learning (MTL)-based index to process multiple DNA symbols with each DRAM row activation. We then build an accelerator to search over an EXMA table. We propose 2-stage scheduling to increase the cache hit rate of our accelerator. We introduce dynamic page policy to improve the row buffer hit rate of DRAM main memory. We also present CHAIN compression to reduce the data structure size of EXMA tables. Compared to state-of-the-art FM-Index PIMs, EXMA improves search throughput by $4.9 \times$, and enhances search throughput per Watt by $4.8 \times$.
Proximity in concave integer quadratic programming

A classic result by Cook, Gerards, Schrijver, and Tardos provides an upper bound of $$n \Delta $$ on the proximity of optimal solutions of an Integer Linear Programming problem and its standard linear relaxation. In this bound, n is the number of variables and $$\Delta $$ denotes the maximum of the absolute values of the subdeterminants of the constraint matrix. Hochbaum and Shanthikumar, and Werman and Magagnosc showed that the same upper bound is valid if a more general convex function is minimized, instead of a linear function. No proximity result of this type is known when the objective function is nonconvex. In fact, if we minimize a concave quadratic, no upper bound can be given as a function of n and $$\Delta $$ . Our key observation is that, in this setting, proximity phenomena still occur, but only if we consider also approximate solutions instead of optimal solutions only. In our main result we provide upper bounds on the distance between approximate (resp., optimal) solutions to a Concave Integer Quadratic Programming problem and optimal (resp., approximate) solutions of its continuous relaxation. Our bounds are functions of $$n, \Delta $$ , and a parameter $$\epsilon $$ that controls the quality of the approximation. Furthermore, we discuss how far from optimal are our proximity bounds.
The role of HIV infection in the clinical spectrum of COVID-19: a population-based cohort analysis based on US National COVID Cohort Collaborative (N3C) Enclave data

Abstract Background Evidence of whether people living with HIV (PLWH) are at elevated risk of adverse COVID-19 outcomes is limited. We aimed to investigate this association using the population based National COVID Cohort Collaborative (N3C) data in the US. Methods The harmonized, high-granularity electronic health record data from 54 clinical sites in N3C were used for this study. Logistic and multinominal logistic regression models were employed to estimate the association between HIV infection and hospitalization, mortality and clinical severity of COVID-19, with models being initially adjusted for age and sex, then cumulatively adjusted for race and ethnicity, smoking and obesity, and a broad range of comorbidities. Interaction terms were added to assess modification effect by age, sex, and race. Findings Among a total of 1,436,622 adult COVID-19 cases between January 2020 and May 2021, 13,170 had HIV infection. A total of 26,130 deaths occurred, with 445 among PLWH. PLWH had higher risk of COVID-19 death and hospitalization than non-PLWH after adjusting for age and sex. The associations were attenuated, but remained significant, after adjusting for lifestyle factors and comorbidities (COVID-19 death: [adjusted OR(AOR): 1.38; 95%CI: 1.25-1.54]; hospitalization: [AOR: 1.23; 95%CI: 1.18-1.28]). In terms of COVID-19 disease severity, PLWH were less likely to have mild/moderate illness but more likely to have severe illness when only controlling for demographics, smoking and BMI, although the estimated risk was obviated after controlling for comorbidities. Interaction terms revealed that the elevated risk was higher among older age group, males and black/African American. Interpretation PLWH in the US had increased risk of COVID-19 hospitalization and mortality comparing with non-PLWH. The adverse COVID-19 outcomes might not only be accounted for by HIV but also by other risk factors that are highly prevalent in PLWH. Funding National Center for Advancing Translational Sciences, National Institute of Allergy And Infectious Diseases, US
Multi-view Subword Regularization

Multilingual pretrained representations generally rely on subword segmentation algorithms to create a shared multilingual vocabulary. However, standard heuristic algorithms often lead to sub-optimal segmentation, especially for languages with limited amounts of data. In this paper, we take two major steps towards alleviating this problem. First, we demonstrate empirically that applying existing subword regularization methods (Kudo, 2018; Provilkov et al., 2020) during fine-tuning of pre-trained multilingual representations improves the effectiveness of cross-lingual transfer. Second, to take full advantage of different possible input segmentations, we propose Multi-view Subword Regularization (MVR), a method that enforces the consistency of predictors between using inputs tokenized by the standard and probabilistic segmentations. Results on the XTREME multilingual benchmark (Hu et al., 2020) show that MVR brings consistent improvements of up to 2.5 points over using standard segmentation algorithms.
Dynamic Pricing and Matching for Two-Sided Markets with Strategic Servers

Motivated by applications in online marketplaces such as ridesharing, we study dynamic pricing and matching in two-sided queues with strategic servers. We consider a discrete-time process in which, heterogeneous customers and servers arrive. Each customer joins their type's queue, while servers might join a different type's queue depending on the prices posted by the system operator and an inconvenience cost. Then the system operator, constrained by a compatibility graph, decides the matching. The objective is to maximize the profit minus the expected waiting times. We develop a general framework that enables us to analyze a broad range of strategic behaviors. In particular, we encode servers' behavior in a properly defined cost function that can be tailored to various settings. Using this general cost function, we introduce a novel probabilistic fluid problem as an infinite dimensional optimization program. The probabilistic fluid model provides an upper bound on the achievable profit. We then study the system under a large market regime in which the arrival rates are scaled by η and present a probabilistic two-price policy and a max-weight matching policy which results in $O(η^1/3 )$ profit-loss. In addition, under a broad class of customer pricing policies, we show that any matching policy has profit-loss Ω(η1/3). Conditional on a given expected waiting time, we also establish scale-free lower and upper bounds for the profit. Our asymptotic analysis provides insight into near-optimal pricing and matching decisions, and our scale-free bounds provide insights into how different service levels impact the firm's profit.
Distributed Policy Synthesis of Multi-Agent Systems with Graph Temporal Logic Specifications

We study the distributed synthesis of policies for multi-agent systems to perform spatial-temporal tasks. We formalize the synthesis problem as a factored Markov decision process subject to graph temporal logic specifications. The transition function and task of each agent are functions of the agent itself and its neighboring agents. In this work, we develop another distributed synthesis method, which improves the scalability and runtime by two orders of magnitude compared to our prior work. The synthesis method decomposes the problem into a set of smaller problems, one for each agent by leveraging the structure in the model, and the specifications. We show that the running time of the method is linear in the number of agents. The size of the problem for each agent is exponential only in the number of neighboring agents, which is typically much smaller than the number of agents. We demonstrate the applicability of the method in case studies on disease control, urban security, and search and rescue. The numerical examples show that the method scales to hundreds of agents with hundreds of states per agent and can also handle significantly larger state spaces than our pri