cssi/SciSciGPT-SciSciNet huggingface dataset:
The Interrelationships Between Information Technology Spending, CEO Equity Incentives, and Firm Value

ABSTRACT: We examine the interrelationships between information technology spending, CEO equity compensation incentives, and firm value. We present two related pieces of evidence. First, we find that CEO equity incentives are associated with IT spending, suggesting that CEOs with higher incentives are more likely to invest in a risky asset such as IT. Second, we find that the association between IT spending and business value is stronger for firms that grant CEOs higher equity incentives. Our study contributes to the CEO compensation and IT governance literatures.
Saving and consumption in cities

Abstract This paper examines urban residential location and rent determination within a city populated by consumers acting with perfect foresight. In this setting, the consumption and saving decisions of city residents are determined jointly with their choice of location, thereby offering an initial step to understanding how housing services influence non-durable consumption, and vice versa. Housing, supplied by profit-maximizing land owners, is distinguished from more non-descript varieties of durable consumption by the attributes and amenities it provides occupants. These attributes are limited in supply, indivisible, and distinguished by their location in geographic space and time of development. In equilibrium, both household consumption and the rental price of housing are expressed in terms of the resident household’s permanent income. In contrast to models presuming hand-to-mouth dynamics, dwelling rents depend on interest rates, future realizations of economic fundamentals such as income and commuting costs, and accumulated wealth. Finally, residential saving is determined not only by wealth, income, and market prices, but also by city-specific factors such as population, supply constraints, and the opportunity cost of land, suggesting a novel and important role for regional and geographic differences in consumer behavior.
The value of a statistical life: some clarifications and puzzles

Many people have wondered why the US government conducts cost-benefit analysis with close reference to the value of a statistical life (VSL). It is helpful to answer that question by reference to the “Easy Cases,” in which those who benefit from regulatory protection must pay for it. In such cases, WTP is usually the right foundation for VSL, because beneficiaries are hardly helped by being forced to pay for regulatory protection that they believe not to be in their interests. In the Easy Cases, arguments from both welfare and autonomy support the use of WTP and VSL (with potentially important qualifications involving imperfect information and behavioral market failures). The analysis is less straightforward in harder cases, in which beneficiaries do not pay for all of the cost of what they receive (and may pay little of that cost). In such cases, arguments from welfare and autonomy might not lead in any clear direction. In the harder cases, regulation might be justified on welfare grounds even if the cost-benefit analysis (based on VSL) suggests that it is not. In principle, a direct inquiry into welfare (the master concept) would be preferable to use of cost-benefit analysis. In the harder cases, distributional considerations might also count in favor of proceeding (as prioritarianism suggests). But at the current time, direct inquiries into welfare consequences and into distributional effects are challenging in practice, and hence regulators should generally rely on cost-benefit analysis, making welfarist adjustments, or adjustments based on distributional considerations, only in compelling cases.
Gina's Genotypes

In August 2009, the Board of Trustees of the University of Akron added to the university's employment policy the following proviso: "any applicant may be asked to submit fingerprints or DNA sample for purpose of a federal criminal background check." Although the federal government does not do background checks with DNA, the policy is significant because it highlights a largely unexplored feature of the Genetic Information Nondiscrimination Act of 2008 (GINA). GINA generally prohibits employers from asking for "genetic information." The faculty senate and outside commentators have declared that the Akron policy is "of doubtful legality" because it "appears to violate" GINA. However, GINA's ban on the acquisition of "genetic information" also can be read so that it does not reach nonmedical DNA tests. Because employers have nondiscriminatory reasons to use forensic DNA identification technology, this narrower interpretation is more faithful to the express purpose of the law.
Tick size and quote revisions on the NYSE

Abstract In this paper, we analyze how tick size affects quote revisions on the NYSE and whether pre-decimalization tick sizes ($1/8 and $1/16) were binding constraints on specialists’ spread- and price-quote decisions. We find that the number of quote revisions that involve changes in the spread increased dramatically after the tick-size reduction in 1997. The number of spread-quote revisions is smaller for stocks with lower prices and larger volumes during both the pre and post tick-size change periods. We interpret this result as evidence that the minimum price variation is a binding constraint on absolute spreads even after the tick-size reduction, especially for low-price and/or large-volume stocks. The number of quote revisions that involve changes in the spread (depth) is largest (smallest) during the early hour of trading, suggesting that the minimum price variation is least likely to be the binding constraint on spreads during the early hour of trading. These results suggest that decimalization is likely to further reduce price rigidity and increase price competition. Consistent with this expectation, we find a significant increase in the frequency of spread- and price-quote revisions after decimalization.
Jury Sentencing in Noncapital Cases: A Case Study of El Paso County, Texas

Critics of jury sentencing in noncapital cases complain that jurors are less competent or qualified than judges to decide questions about probation or incarceration. Thus far, however, views on jury sentencing in noncapital cases have rested largely on speculation, not on empirical evidence. Professor Weninger attempts to bring some hard data to the discussion with a survey of 1,395 noncapital felony prosecutions commenced in the district courts during a 4-year period in El Paso County. The survey reveals that juries imposed longer and more variable prison terms than judges. Regression analysis predicted differences between the average lengths of sentences imposed for certain offenses. These differences, running in the direction of longer sentences by juries, increased with the seriousness of the offense.
Does Changing the Definition of Science Solve the Establishment Clause Problem for Teaching Intelligent Design as Science in Public Schools? Doing an End-Run Around the Constitution

When Charles Darwin published On the Origin of Species By Means of Natural Selection in 1859, it sparked some of the most contentious debates in American intellectual history, debates that continue to rage today. Although these debates have numerous political ramifications, the question posed in this paper is narrow: Does the Establishment Clause permit a particular assessment of current evolutionary theory—intelligent design (“ID”)—to be taught as science in American elementary and secondary public schools? This article shows that it does not. To understand current disputes over whether and how to teach the origins of life—including human life—in the science classes of public schools, it is necessary to understand both the legal and the scientific frameworks. I describe those general frameworks below in Section II. There I also show that Darwin’s theory of evolution meets the definition of science, an important step in showing that teaching evolution has a bona fide secular purpose as required by the Constitution. I also explain the
Conflict Minerals Legislation: The SEC’s New Role as Diplomatic and Humanitarian Watchdog

Buried in the voluminous Dodd-Frank Wall Street Reform and Consumer Protection Act is an oft-overlooked provision requiring corporate disclosure of the use of “conflict minerals” in products manufactured by issuing corporations. This Article scrutinizes the legislative history and lobbying efforts behind the conflict minerals provision to establish that, unlike the majority of the bill, its goals are moral and political, rather than financial. Analyzing the history of disclosure requirements, the Article suggests that the presence of conflict minerals in an issuer’s product is not inherently material information and that the Dodd-Frank provision statutorily renders nonmaterial information material. The provision, therefore, forces the SEC to expand beyond its congressional mandate of protecting investors and ensuring capital formation by requiring issuers to engage in additional nonfinancial disclosures in order to meet the provision’s humanitarian and diplomatic aims. Further, the Article posits that the conflict minerals provision is a wholly ineffective means to accomplish its stated humanitarian goals and likely will cause more harm than good in the Democratic Republic of the Congo. In conclusion, this Article proposes that a more efficient regulatory model for conflict minerals is the Clean Diamond Trade Act and the Kimberly Process Certification Scheme.
Bionormativity and the Construction of Parenthood

This piece explores the relationship between legal and biological parenthood. It examines how neither history, nor evolutionary biology nor moral philosophy dictate a legal regime in which parenthood must be based on biological connection, but that attraction to a biological (or "bionormative") regime remains strong. In explaining why, it suggests that much of what attracts people to bionormativity is not biology itself, but the way in which a biological regime constructs parenthood as a private, exclusive and binary enterprise. It is these ancillary qualities of bionormativity that people may care the most about. Today, a variety of forces put pressure on these ancillary qualities of bionormativity. The extent of child poverty, the rejection of (even the appearance of) monogamy and the use of donated gametes all, in different ways, bring into question the extent to which we may want parenthood to stay private, exclusive, binary and biological. The analysis presented here suggests that what is most destabilizing to a bionormative regime is not direct manipulation of biological material, but the fluid living patterns of contemporary adults. Because contemporary adult relationships are less likely to be permanently binary and exclusive, so is parenthood. When parenthood becomes less binary and exclusive, it becomes less private and less biological as well. The analysis presented also reveals that a construction of parenthood that is more public, more inclusive, less binary and less biological is a construction of hierarchical parenthood, or a parental regime in which some parents have substantially more parental rights and responsibilities than others. Recognizing degrees of parenthood may be an inevitable byproduct of a system that rejects bionormativity.
The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession

The 1981-1982 recession induced substantial variation across sites in air pollution reductions. This is used to estimate the impact of total suspended particulates (TSPs) on infant mortality. We find that a one-percent reduction in TSPs results in a 0.35 percent decline in the infant mortality rate at the county level, implying that 2,500 fewer infants died from 1980-1982 than would have in the absence of the TSPs reductions. Most of these effects are driven by fewer deaths occurring within one month of birth, suggesting that fetal exposure is a potential pathophysiologic mechanism. The analysis also reveals nonlinear effects of TSPs pollution and greater sensitivity of black infant mortality at the county level. Importantly, the estimates are stable across a variety of specifications.
The Financial Management Behavior Scale: Development and Validation

43 Introduction Individuals in the U.S. engage in financial behaviors nearly every day and these behaviors influence their financial well-being. For example, the more consumer credit households assume, the more likely they are to default on their loans and the less likely they are to have established an individual retirement account (IRA) (Bernstein, 2004; Sullivan, 1987). Thus, of necessity, personal finance researchers and financial planners and counselors measure financial management behaviors.
Does Corporate Diversification Reduce Firm Risk? Evidence from Diversifying Acquisitions

The main purpose of this paper is to investigate empirically whether corporate diversification reduces the risk of the diversifying firm. We investigate this issue using a sample of diversifying acquisitions and various risk measures. We find that corporate diversification tends to decrease the risk of some firms but increase the risk of many others. On average corporate diversification does not lower firm risk. These findings call into question the notion that corporate diversification strictly reduces firm risk.
Liquidity, Pledgeability, and the Nature of Lending

Abstract We develop a theory of how corporate lending and financial intermediation change based on the fundamentals of the firm and its environment. We focus on the interaction between the prospective net worth or liquidity of an industry and the firm’s internal governance or pledgeability. Variations in prospective liquidity can induce changes in the nature, covenants, and quantity of loans that are made, the identity of the lender, and the extent to which the lender is leveraged. We offer predictions on how these might vary over the financial cycle.
Representing Real-Time Multi-User Collaboration in Visualizations

Establishing common ground and maintaining shared awareness amongst participants is a key challenge in collaborative visualization. For real-time collaboration, existing work has primarily focused on synchronizing constituent visualizations — an approach that makes it difficult for users to work independently, or selectively attend to their collaborators’ activity. To address this gap, we introduce a design space for representing synchronous multi-user collaboration in visualizations defined by two orthogonal axes: situatedness, or whether collaborators’ interactions are overlaid on or shown outside of a user’s view, and specificity, or whether collaborators are depicted through abstract, generic representations or through specific means customized for the given visualization. We populate this de-sign space with a variety of examples including generic and custom synchronized cursors, and user legends that collect these cursors together or reproduce collaborators’ views as thumbnails. To build common ground, users can interact with these representations by peeking to take a quick look at a collaborator’s view, tracking to follow along with a collaborator in real-time, and forking to independently explore the visualization based on a collaborator’s work. We present a reference implementation of a wrapper library that converts interactive Vega-Lite charts into collaborative visualizations. We find that our approach affords synchronous collaboration across an expressive range of visual designs and interaction techniques.
Linear Convergent Decentralized Optimization with Compression

Communication compression has become a key strategy to speed up distributed optimization. However, existing decentralized algorithms with compression mainly focus on compressing DGD-type algorithms. They are unsatisfactory in terms of convergence rate, stability, and the capability to handle heterogeneous data. Motivated by primal-dual algorithms, this paper proposes the first \underline{L}in\underline{EA}r convergent \underline{D}ecentralized algorithm with compression, LEAD. Our theory describes the coupled dynamics of the inexact primal and dual update as well as compression error, and we provide the first consensus error bound in such settings without assuming bounded gradients. Experiments on convex problems validate our theoretical analysis, and empirical study on deep neural nets shows that LEAD is applicable to non-convex problems.
Product SCFTs in Class-S

We develop a technique for counting the number of stress tensor multiplets in a 4D $$ \mathcal{N} $$ = 2 SCFT. This provides a simple diagnostic for when an isolated (non-Lagrangian) SCFT is a product of two (or more) such theories. In class-S, the basic building blocks are the isolated SCFTs arising from the compactification of a 6D (2,0) theory on a 3-punctured sphere (“fixture”). We apply our technique to determine when a fixture is a product SCFT. The answer is that this phenomenon is surprisingly rare. In the low-rank AN−1, DN theories and the E6 theory studied by the first author and his collaborators, it occurs less than 1% of the time. Of the 2979 fixtures in the (untwisted and twisted) E6 theory, only 23 are product SCFTs. Of these, 22 were known to the original authors. The new one is a product of the (E7)8 Minahan-Nemeschansky theory and a new rank-2 SCFT.
COVID-19 Recommendations for Assisted Living: Implications for the Future.

Abstract Objectives Assisted living (AL) emerged over 2 decades ago as a preferred residential care option for older adults who require supportive care; however, as resident acuity increased, concern has been expressed whether AL sufficiently addresses health care needs. COVID-19 amplified those concerns, and an examination of recommendations to manage COVID-19 may shed light on the future of AL. This review summarizes recommendations from 6 key organizations related to preparation for and response to COVID-19 in AL in relation to resident health and quality of life; compares recommendations for AL with those for nursing homes (NHs); and assesses implications for the future of AL. Design Nonsystematic review involving search of gray literature. Setting and Participants Recommendations from key governmental bodies and professional societies regarding COVID-19 in AL, long-term care facilities (LTCFs) in general, and NHs. Measures We collected, categorized, and summarized these recommendations as they pertained to quality of life and health care. Results Many recommendations for AL and NHs were similar, but differences provided insight into ways the pandemic was recognized and challenged AL communities in particular: recommending more flexible visitation and group activities for AL, providing screening by AL staff or an outside provider, and suggesting that AL staff access resources to facilitate advance care planning discussions. Recommendations were that AL integrate health care into offered services, including working with consulting clinicians who know both the residents and the LTC community. Conclusions and Implications Long-term care providers and policy makers have recognized the need to modify current long-term care options. Because COVID-19 recommendations suggest AL communities would benefit from the services and expertise of social workers, licensed nurses, and physicians, it may accelerate the integration and closer coordination of psychosocial and medical care into AL. Future research should investigate different models of integrated, interdisciplinary health care in AL.
Spectrally specific temporal analyses of spike-train responses to complex sounds: A unifying framework

Significant scientific and translational questions remain in auditory neuroscience surrounding the neural correlates of perception. Relating perceptual and neural data collected from humans can be useful; however, human-based neural data are typically limited to evoked far-field responses, which lack anatomical and physiological specificity. Laboratory-controlled preclinical animal models offer the advantage of comparing single-unit and evoked responses from the same animals. This ability provides opportunities to develop invaluable insight into proper interpretations of evoked responses, which benefits both basic-science studies of neural mechanisms and translational applications, e.g., diagnostic development. However, these comparisons have been limited by a disconnect between the types of spectrotemporal analyses used with single-unit spike trains and evoked responses, which results because these response types are fundamentally different (point-process versus continuous-valued signals) even though the responses themselves are related. Here, we describe a unifying framework to study temporal coding of complex sounds that allows spike-train and evoked-response data to be analyzed and compared using the same advanced signal-processing techniques. The framework uses a set of peristimulus-time histograms computed from single-unit spike trains in response to polarity-alternating stimuli to allow advanced spectral analyses of both slow (envelope) and rapid (temporal fine structure) response components. Demonstrated benefits include: (1) novel spectrally specific temporal-coding measures that are less confounded by distortions due to hair-cell transduction, synaptic rectification, and neural stochasticity compared to previous metrics, e.g., the correlogram peak-height, (2) spectrally specific analyses of spike-train modulation coding (magnitude and phase), which can be directly compared to modern perceptually based models of speech intelligibility (e.g., that depend on modulation filter banks), and (3) superior spectral resolution in analyzing the neural representation of nonstationary sounds, such as speech and music. This unifying framework significantly expands the potential of preclinical animal models to advance our understanding of the physiological correlates of perceptual deficits in real-world listening following sensorineural hearing loss.
Deep analog-to-digital converter for wireless communication

With the advent of the 5G wireless networks, achieving tens of gigabits per second throughputs and low latency has become a reality. This level of performance will fuel numerous realtime applications where the computationally heavy tasks can be performed in the cloud. The increase in the bandwidth along with the use of dense constellations places a significant burden on the speed and accuracy of analog-to-digital converters (ADC). A popular approach to create wideband ADCs is utilizing multiple channels each operating at a lower speed in the time-interleaved pattern. However, an interleaved ADC comes with its own set of challenges. The parallel architecture is very sensitive to the inter-channel mismatch, timing jitter, clock skew between different ADC channels as well as the nonlinearity within individual channels. In this project, we utilize a deep learning algorithm to learn the complete and complex ADC behavior and to compensate for it.
A single inverse-designed photonic structure that performs parallel computing

In the search for improved computational capabilities, conventional microelectronic computers are facing various problems arising from the miniaturization and concentration of active electronics. Therefore, researchers have explored wave systems, such as photonic or quantum devices, for solving mathematical problems at higher speeds and larger capacities. However, previous devices have not fully exploited the linearity of the wave equation, which as we show here, allows for the simultaneous parallel solution of several independent mathematical problems within the same device. Here we demonstrate that a transmissive cavity filled with a judiciously tailored dielectric distribution and embedded in a multi-frequency feedback loop can calculate the solutions of a number of mathematical problems simultaneously. We design, build, and test a computing structure at microwave frequencies that solves two independent integral equations with any two arbitrary inputs and also provide numerical results for the calculation of the inverse of four 5 x 5 matrices.
Extremal eigenvalues of the Conformal Laplacian under Sire–Xu normalization

Abstract Let ( M n , g ) be a closed Riemannian manifold of dimension n ≥ 3 . We study the variational properties of the k th eigenvalue functional g ∈ [ g ] ↦ λ k ( L g ) under a non-volume normalization proposed by Sire–Xu. We discuss necessary conditions for the existence of extremal eigenvalues under such normalization. Also, we discuss the general existence problem when k = 1 .
PCB-Fire: Automated Classification and Fault Detection in PCB

Printed Circuit Boards (“PCB”) are the foundation for the functioning of any electronic device, and therefore are an essential component for various industries such as automobile, communication, computation, etc. However, one of the challenges faced by the PCB manufacturers in the process of manufacturing of the PCBs is the faulty placement of its components including missing components. In the present scenario the infrastructure required to ensure adequate quality of the PCB requires a lot of time and effort. The authors present a novel solution for detecting missing components and classifying them in a resourceful manner. The presented algorithm focuses on pixel theory and object detection, which has been used in combination to optimize the results from the given dataset.
Totally Asynchronous Large-Scale Quadratic Programming: Regularization, Convergence Rates, and Parameter Selection

Quadratic programs arise in robotics, communications, smart grids, and many other applications. As these problems grow in size, finding solutions becomes more computationally demanding, and new algorithms are needed to efficiently solve them at massive scales. Targeting large-scale problems, we develop a multiagent quadratic programming framework in which each agent updates only a small number of the total decision variables in a problem. Agents communicate their updated values to each other, though we do not impose any restrictions on the timing with which they do so, nor on the delays in these transmissions. Furthermore, we allow agents to independently choose their stepsizes, subject to mild restrictions. We further provide the means for agents to independently regularize the problems they solve, thereby improving convergence properties while preserving agents’ independence in selecting parameters. Larger regularizations accelerate convergence but increase the error in the solution obtained, and we quantify the tradeoff between convergence rates and quality of solutions. Simulation results are presented to illustrate these developments.
The Physics of Falling Raindrops in Diverse Planetary Atmospheres

The evolution of a single raindrop falling below a cloud is governed by fluid dynamics and thermodynamics fundamentally transferable to planetary atmospheres beyond modern Earth's. Here, we show how three properties that characterize falling raindrops -- raindrop shape, terminal velocity, and evaporation rate -- can be calculated as a function of raindrop size in any planetary atmosphere. We demonstrate that these simple, interrelated characteristics tightly bound the possible size range of raindrops in a given atmosphere, independently of poorly understood growth mechanisms. Starting from the equations governing raindrop falling and evaporation, we demonstrate that raindrop ability to vertically transport latent heat and condensible mass can be well captured by a new dimensionless number. Our results have implications for precipitation efficiency, convective storm dynamics, and rainfall rates, which are properties of interest for understanding planetary radiative balance and (in the case of terrestrial planets) rainfall-driven surface erosion.
Tight bounds on the number of minimum-mean cycle cancellations and related results

We prove a tight ź(min(nm log(nC), nm2)) bound on the number of iterations of the minimum-mean cycle-canceling algorithm of Goldberg and Tarjan [13]. We do this by giving the lower bound and by improving the strongly polynomial upper bound on the number of iterations toO(nm2). We also give an improved version of the maximum-mean cut canceling algorithm of [7], which is a dual of the minimum-mean cycle-canceling algorithm. Our version of the dual algorithm runs in O(nm2) iterations.
OFFICETECH®: a new paradigm in office services?

OFFICETECH® is a start-up company providing office assistance over the Internet. It certifies assistants' skills, matches them with potential clients, supervises the quality of the work done and provides software tools for on-line collaboration. The founder, Gayle Barr, believed that this type of assistance would soon be the predominant model due to a convergence of several trends. First, advances in the Internet technology GroupWare and their acceptance by managers and professionals coupled with growth in the amount of coordination across time zones due to globalization and expansion of the number of telecommuting workers indicated that clients would be willing and able to work in a virtual relationship with their assistants. Second, extensive corporate downsizing and a large number of home-based new business start-ups had created a large pool of clients who did not have adequate permanent assistance available. In addition, many companies were now using large numbers of part-time and contract workers on an ongoing basis for clerical and professional positions. Thus, Barr reasoned, the technology was accessible, the need for part-time and temporary administrative assistance was apparent and clients were now sufficiently comfortable with such arrangements that the OFFICETECH® concept should be feasible.
Square-root cancellation for sums of factorization functions over short intervals in function fields

We present new estimates for sums of the divisor function and other similar arithmetic functions in short intervals over function fields. (When the intervals are long, one obtains a good estimate from the Riemann hypothesis.) We obtain an estimate that approaches square-root cancellation as long as the characteristic of the finite field is relatively large. This is done by a geometric method, inspired by work of Hast and Matei, where we calculate the singular locus of a variety whose Fq-points control this sum. This has applications to highly unbalanced moments of L-functions.
Does constitutional entrenchment matter for economic freedom

A growing number of studies explore the determinants of economic freedom. Very few of them consider constitutional design. We study entrenchment, that is, the extent to which constitutions are more costly to change than ordinary policies and institutions. We utilize 1970–2017 data and study episodes where countries adopted meaningfully more entrenched constitutions. Using matching methods, we construct plausible counterfactuals against which to compare their post‐treatment changes in economic freedom. We report no significant effects on overall freedom. There is some evidence that entrenchment leads to smaller government size, more regulation, and weaker property rights. However, none of these results are robust.
Simple rules and the Political Economy of Income Taxation: the strengths of a uniform expense rule

The complexity of the U.S. income tax system emerged in part from the differentiated fiscal treatment of specific investments and various sorts of business expenditures that, as Richard Wagner says, creates “a tax code so large that no one can read it and which creates nearly a unique tax liability for each taxpayer.” While tax scholarship documents the costs of complexity, current scholarship lacks a normative legal theory that could inspire an alternative to this practice of targeted credits and deduction rules. This paper builds on Epstein’s general Simple Rules framework to develop a radical simplification of income tax: the operationalization of a uniform expense rule, i.e., the application of a single deduction scheme across all expenses and investments. With regard to the trade-off between targeted rules and this general approach, this paper investigates the nature of a uniform expense rule and reveals its strengths with respect to political economy. First, a stable and universal expense rule would narrow the scope of politicians’ ability to concentrate benefits on special-interest groups and would limit rent-seeking. The abolition of deviating rules for investments would restrict the scope of tax optimization, meaning that individual and corporate tax liabilities would be more determined by the ability to pay them. The same policy would save companies and government millions of dollars that they currently spend on tax compliance and administration of the tax system, respectively. Last, a deduction rule that applies across all businesses and industries would be less price distortive and thus would enhance the quality of the price signal.
Domain adaptation by topology regularization

Deep learning has become the leading approach to assisted target recognition. While these methods typically require large amounts of labeled training data, domain adaptation (DA) or transfer learning (TL) enables these algorithms to transfer knowledge from a labelled (source) data set to an unlabelled but related (target) data set of interest. DA enables networks to overcome the distribution mismatch between the source and target that leads to poor generalization in the target domain. DA techniques align these distributions by minimizing a divergence measurement between source and target, making the transfer of knowledge from source to target possible. While these algorithms have advanced significantly in recent years, most do not explicitly leverage global data manifold structure in aligning the source and target. We propose to leverage global data structure by applying a topological data analysis (TDA) technique called persistent homology to TL. In this paper, we examine the use of persistent homology in a domain adversarial (DAd) convolutional neural network (CNN) architecture. The experiments show that aligning persistence alone is insufficient for transfer, but must be considered along with the lifetimes of the topological singularities. In addition, we found that longer lifetimes indicate robust discriminative features and more favorable structure in data. We found that existing divergence minimization based approaches to DA improve the topological structure, as indicated over a baseline without these regularization techniques. We hope these experiments highlight how topological structure can be leveraged to boost performance in TL tasks.
Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure

The Gestalt laws of perceptual organization, which describe how visual elements in an image are grouped and interpreted, have traditionally been thought of as innate. Given past research showing that these laws have ecological validity, we investigate whether deep learning methods infer Gestalt laws from the statistics of natural scenes. We examine the law of closure, which asserts that human visual perception tends to “close the gap” by assembling elements that can jointly be interpreted as a complete figure or object. We demonstrate that a state-of-the-art convolutional neural network, trained to classify natural images, exhibits closure on synthetic displays of edge fragments, as assessed by similarity of internal representations. This finding provides further support for the hypothesis that the human perceptual system is even more elegant than the Gestaltists imagined: a single law—adaptation to the statistical structure of the environment—might suffice as fundamental.
Ionic conductivity optimization of composite polymer electrolytes through filler particle chemical modification

The addition of filler particles to polymer electrolytes is known to increment their ionic conductivity (IC). A detailed understanding of how the interactions between the constituent materials are responsible for the enhancement remains to be developed. A significant contribution is ascribed to an increment of the polymer amorphous fraction, induced by the fillers, resulting in the formation of higher ionic conductivity channels in the polymer matrix. However, the dependence of IC on the particle weight load and its composition on the polymer morphology is not fully understood. This work investigates Li-ion transport in composite polymer electrolytes (CPE) comprising Bi-doped LLZO particles embedded in PEO:LiTFSI matrixes. We find that the IC optimizes for very low particle weight loads (5–10%) and that both its magnitude and the load are required, strongly dependent on the garnet particle composition. Based on structural characterization results and electrochemical impedance spectroscopy, a mechanism is proposed to explain these findings. It is suggested that the Li-molar content in the garnet particle controls its interactions with the polymer matrix, resulting at the optimum loads reported, in the formation of high ionic conductivity channels. We propose that filler particle chemical manipulation of the polymer morphology is a promising avenue for the further development of composite polymer electrolytes.
Forecasting Hospitalizations Due to COVID-19 in South Dakota, USA.

Anticipating the number of hospital beds needed for patients with COVID-19 remains a challenge. Early efforts to predict hospital bed needs focused on deriving predictions from SIR models, largely at the level of countries, provinces, or states. In the USA, these models rely on data reported by state health agencies. However, predicting disease and hospitalization dynamics at the state level is complicated by geographic variation in disease parameters. In addition, it is difficult to make forecasts early in a pandemic due to minimal data. Bayesian approaches that allow models to be specified with informed prior information from areas that have already completed a disease curve can serve as prior estimates for areas that are beginning their curve. Here, a Bayesian non-linear regression (Weibull function) was used to forecast cumulative and active COVID-19 hospitalizations for SD, USA, based on data available up to 2020-07-22. As expected, early forecasts were dominated by prior information, which was derived from New York City. Importantly, hospitalization trends differed within South Dakota due to early peaks in an urban area, followed by later peaks in rural areas of the state. Combining these trends led to altered forecasts with relevant policy implications.
An outer-approximation guided optimization approach for constrained neural network inverse problems

This paper discusses an outer-approximation guided optimization method for constrained neural network inverse problems with rectified linear units. The constrained neural network inverse problems refer to an optimization problem to find the best set of input values of a given trained neural network in order to produce a predefined desired output in presence of constraints on input values. This paper analyzes the characteristics of optimal solutions of neural network inverse problems with rectified activation units and proposes an outer-approximation algorithm by exploiting their characteristics. The proposed outer-approximation guided optimization comprises primal and dual phases. The primal phase incorporates neighbor curvatures with neighbor outer-approximations to expedite the process. The dual phase identifies and utilizes the structure of local convex regions to improve the convergence to a local optimal solution. At last, computation experiments demonstrate the superiority of the proposed algorithm compared to a projected gradient method.
Stochastic adaptive sampling for mobile sensor networks using kernel regression

In this paper, we provide a stochastic adaptive sampling strategy for mobile sensor networks to estimate scalar fields over surveillance regions using kernel regression, which does not require a priori statistical knowledge of the field. Our approach builds on a Markov Chain Monte Carlo (MCMC) algorithm, viz., the fastest mixing Markov chain under a quantized finite state space, for generating the optimal sampling probability distribution asymptotically. The proposed adaptive sampling algorithm for multiple mobile sensors is numerically evaluated under scalar fields. The comparison simulation study with a random walk benchmark strategy demonstrates the excellent performance of the proposed scheme.
Limit Shapes for Gibbs Partitions of Sets

This study extends a prior investigation of limit shapes for grand canonical Gibbs ensembles of partitions of integers, which was based on analysis of sums of geometric random variables. Here we compute limit shapes for partitions of sets, which lead to the sums of Poisson random variables. Under mild monotonicity assumptions on the energy function, we derive all possible limit shapes arising from different asymptotic behaviors of the energy, and also compute local limit shape profiles for cases in which the limit shape is a step function.
Non-monogenic division fields of elliptic curves

Abstract For various positive integers n, we show the existence of infinite families of elliptic curves over Q with n-division fields that are not monogenic, i.e., such that the ring of integers does not admit a power integral basis. We parametrize some of these families explicitly. Moreover, we show that every E / Q without CM has infinitely many non-monogenic division fields. Our main technique combines a global description of the Frobenius obtained by Duke and Toth with an algorithm based on ideas of Dedekind. As a counterpoint, we are able to use different aspects of the arithmetic of elliptic curves to exhibit a family of monogenic 2-division fields.
The Effect of Pitch Distance on the Statistics and Morphology of Through-Silicon Via Extrusion

In this work, we investigated the effect of pitch distance on the statistical variation and morphology of extrusion in Cu through-silicon vias (TSVs) and the underlying mechanisms. Extrusion statistics were obtained from TSV samples with two different pitch distances. A notable increase in the magnitude of extrusion was observed in vias with smaller pitch, yet the extrusion spread was largely unaffected. The morphologies of the extruded vias were characterized and categorized, and a finiteelement analysis was carried out to study the effect of pitch distance on stress and deformation. The results suggested that the overlapping of stress fields from neighboring vias resulted in larger stress in the small-pitch vias, which subsequently led to higher extrusion. The morphologies observed in the extruded vias were related to the operation of different deformation mechanisms under the combined effect of stress and microstructure. The statistical spread of via extrusion, which was similar in both groups of vias, was related to the stochastic nature of the via microstructure. By using a thin cap layer of Ta to suppress the vacancy sources at the via top surface, the adverse effect of the pitch distance was minimized and a pronounced reduction of extrusion was achieved in vias of both pitch distances.
Impact of treatment duration on mortality among Veterans with opioid use disorder in the United States Veterans Health Administration

BACKGROUND AND AIMS While long-term medication-assisted treatment (MAT) using methadone or buprenorphine is associated with significantly lower all-cause mortality for individuals with opioid use disorder (OUD), periods of initiating or discontinuing treatment are associated with higher mortality risks relative to stable treatment. This study aimed to identify the OUD treatment durations necessary for the elevated mortality risks during treatment transitions to be balanced by reductions in mortality while receiving treatment. DESIGN Simulation model based on a compartmental model of OUD diagnosis, MAT receipt and all-cause mortality among Veterans with OUD in the United States Veterans Health Administration (VA) in 2017-2018. We simulated methadone and buprenorphine treatments of varying durations using parameters obtained through calibration and published meta-analyses of studies from North America, Europe and Australia. SETTING United States. PARTICIPANTS Simulated cohorts of 10 000 individuals with OUD. MEASUREMENTS All-cause mortality over 12 months. FINDINGS Receiving methadone for 4 months or longer or buprenorphine for 2 months or longer resulted in 54 [95% confidence interval (CI) = 5-90] and 65 (95% CI = 21-89) fewer deaths relative to not receiving MAT for the same duration, using VA-specific mortality rates. We estimated shorter treatment durations necessary to achieve net mortality benefits of 2 months or longer for methadone and 1 month or longer for buprenorphine, using non-VA population literature estimates. Sensitivity analyses demonstrated that necessary treatment durations increased more with smaller mortality reductions on treatment than with larger relative risks during treatment transitions. CONCLUSIONS Short periods (< 6 months) of treatment with either methadone or buprenorphine are likely to yield net mortality benefits for people with opioid use disorder relative to receiving no medications, despite periods of elevated all-cause mortality risk during transitions into and out of treatment. Retaining people with opioid use disorder in treatment longer can increase these benefits.
Anomaly Inflow and Holography

We systematically study the perturbative anomaly inflow by the bulk Chern-Simons (CS) theory in a slice of five-dimensional anti-de Sitter spacetime (AdS5). The introduction of UV and IR 3-branes makes the anomaly story remarkably rich and many interesting aspects can be obtained, including weakly gauging and spontaneous symmetry breaking of the global symmetries of the dual 4D CFT. Our main contribution is to provide a unified and comprehensive discussion of the subject, together with a detailed description of the dual CFT picture for each case. To this end, we employ a gauge-fixed effective action suitable for a holographic study, which allows us to incorporate general UV and IR boundary conditions (BCs). As part of the process, we reproduce many known results in the literature, such as ’t Hooft anomaly matching for unbroken symmetry (Neumann IR-BC) and (gauged) Wess-Zumino-Witten (WZW) action for broken symmetry (IR-BC breaks the bulk group G → H). In addition, we show that anomaly matching occurs for ABJ anomalies as well as ’t Hooft anomalies, which suggests anomalies inflowed from the bulk CS theory are necessarily free of mixed anomalies with the confining gauge force of the 4D dual CFT. In the case of broken symmetry, we prove that the “would-be” Goldstone bosons associated with the weakly gauged symmetry are completely removed by a proper field redefinition, provided the anomaly from the bulk is exactly cancelled by the boundary contribution, hence confirming the standard expectation. Moreover, we present a holographic formulation of Witten’s argument for the quantization condition for the WZW action, and argue in favor of an alternative way to obtain the same condition using a “deformed” theory (different BCs). We work out several examples, including a product group with mixed anomaly, and identify the corresponding dual CFT picture. We consider a fully general case typically arising in the context of dynamical electroweak symmetry breaking.
Protein sequence design with deep generative models

Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.