cssi/SciSciGPT-SciSciNet huggingface dataset:
Pool‐Point Distribution of Zero‐Inventory Products

We study zero-inventory production-distribution systems under pool-point delivery. The zero-inventory production and distribution paradigm is supported in a variety of industries in which a product cannot be inventoried because of its short shelf life. The advantages of pool-point (or hub-and-spoke) distribution, explored extensively in the literature, include the efficient use of transportation resources and effective day-to-day management of operations. The setting of our analysis is as follows: A production facility (plant) with a finite production rate distributes its single product, which cannot be inventoried, to several pool points. Each pool point may require multiple truckloads to satisfy its customers' demand. A third-party logistics provider then transports the product to individual customers surrounding each pool point. The production rate can be increased up to a certain limit by incurring additional cost. The delivery of the product is done by identical trucks, each having limited capacity and non-negligible traveling time between the plant and the pool points. Our objective is to coordinate the production and transportation operations so that the total cost of production and distribution is minimized, while respecting the product lifetime and the delivery capacity constraints. This study attempts to develop intuition into zero-inventory production-distribution systems under pool-point delivery by considering several variants of the above setting. These include multiple trucks, a modifiable production rate, and alternative objectives. Using a combination of theoretical analysis and computational experiments, we gain insights into optimizing the total cost of a production-delivery plan by understanding the trade-off between production and transportation.
Is operational hedging a substitute for or a complement to financial hedging

Abstract This paper investigates operational hedging by firms and how operational hedging is related to financial hedging by using a sample of 424 firm observations, which consist of 212 operationally hedged firms (firms with foreign sales) and a size- and industry-matched sample of 212 non-operationally hedged firms (firms with export sales). We find that non-operationally hedged firms use more financial hedging, relative to their levels of foreign currency exposure, as measured by the amount of export sales. On the other hand, though operationally hedged firms have more currency exposure, their usage of financial derivatives becomes much smaller than that of exporting firms. These results can explain why some global firms use very limited amount of financial derivatives for hedging purpose despite much higher levels of currency risk exposure. We also show that hedging increases firm value.
Should Public Retirement Plans be Fully Funded

Most state and local retirement plans strive for full funding, at least by actuarial standards. Funding measured at market values fluctuates and often falls short. A common argument for full funding is that pensions are a form of deferred compensation that does not justify a debt. The paper examines public finance, political economy, and financial market issues that bear on optimal funding, broadly and in a series of models. In a model where most taxpayers hold debt and face intermediation costs, returns on pension assets are less than taxpayers’ cost of borrowing. Pension funding is costly and hence zero funding is optimal. The model also implies that unfunded pension promises are properly discounted at a rate strictly greater than the government’s borrowing rate. If pension funds serve as collateral, funding can be warranted despite the cost. This is shown in a model with legal ambiguity and default risk. Except in special cases, the optimal funding ratio is less than full funding.
Using Game Theory and Contractarianism to Reform Corporate Governance: Why Shareholders Should Seek Disincentive Schemes in Executive Compensation Plans

Employing a model of game theory, this Article shows how current judge-made law in areas of the duty of loyalty does not adequately prevent corporate managers from violating their fiduciary duty. This Article presents a solution, advising shareholders to reform corporate governance through executive compensation contracts that would properly incentivize corporate managers to comport with their duty of loyalty. Part I examines the rise of contractarianism, the prominent legal academic view of a corporation that helps to guide judicial interpretation of corporate law pertaining to managers’ fiduciary duties. Part II examines agency costs, a subset of transaction costs, and the role of fiduciary duties. Part III employs lessons from game theory to show how courts have effectively created incentives for managers to violate their duty of loyalty. And Part IV examines how executive compensation contracts can be structured to properly incentivize managers to comply with their fiduciary duties.
From Violence to Voting: War and Political Participation in Uganda

What is the political legacy of violent conflict? I present evidence for a link from past violence to increased political engagement among excombatants. The evidence comes from northern Uganda, where rebel recruitment generated quasiexperimental variation in who was conscripted by abduction. Survey data suggest that abduction leads to substantial increases in voting and community leadership, largely due to elevated levels of violence witnessed. Meanwhile, abduction and violence do not appear to affect nonpolitical participation. These patterns are not easily explained by conventional theories of participation, including mobilization by elites, differential costs, and altruistic preferences. Qualitative interviews suggest that violence may lead to personal growth and political activation, a possibility supported by psychological research on the positive effects of traumatic events. Although the generalizability of these results requires more evidence to judge, the findings challenge our understanding of political behavior and point to important new avenues of research.
The influence of corporate social responsibility on investment efficiency and innovation

We examine two important channels through which corporate social responsibility (CSR) affects firm value: investment efficiency and innovation. We find that firms with higher CSR performance invest more efficiently: these firms are less prone to invest in negative net present value (NPV) projects (overinvestment) and less prone to forego positive NPV projects (underinvestment). We also find that firms with higher CSR performance generate more patents and patent citations. Mediation analysis indicates that firms with higher CSR performance are more profitable and valuable, consequences partially attributable to efficient investments and innovation. These results, robust to alternate model specifications, lend support to enlightened stakeholder theory.
Wiring the Labor Market

Workers and jobs are naturally heterogeneous and the quality of their interaction when paired is difficult to forecast. The Internet promises to open new channels for worker-firm communications. What are the consequences of this opening? I discuss three labor market features that may be altered: how worker-firm matches are made; how labor services are delivered; and how local markets shape labor demand. Theory predicts these developments will produce social benefits. But the gains are unlikely to be uniform and realizing them will generate novel problems. One result may be the formation of new institutions to address issues accompanying these opportunities.
Euclid Lives? The Uneasy Legacy of Progressivism in Zoning

This article responds to Euclid Lives: The Survival of Progressive Jurisprudence, by Charles Haar and Michael Allan Wolf, 115 Harv. L. Rev. 2158 (2002). In Euclid, Haar and Wolf praise a series of Progressive legal principles, most often associated with zoning and Euclid v. Ambler Realty Co., and then use those arguments to criticize recent developments in constitutional regulatory-takings law. This article examines whether these Progressive arguments do enough conceptual work to justify zoning. It concludes they do not. Instead, the article suggests that zoning and the Progressive legal principles associated with it are better understood as a practical application of general principles of Progressive political theory. To demonstrate, it interprets speeches and writings by the reformers most influential in instituting zoning - books advocating zoning, the first wave of zoning treatises, and the proceedings of the annual National Conference of City Planning. These materials defended zoning on more explicitly political, organic, and Progressive grounds. Read as a whole, these materials place faith in strong communities, local moral formation, and an organic conception of changing societal values. Many of the key institutional features of Euclidean zoning follow from these core beliefs - the commitment to homogeneous neighborhoods, stable home values, neighborhood aesthetics, centralized pre-use planning, and administration by urban-planning experts. This connection to Progressive political theory offers several lessons. First, it provides a more thorough and better-integrated theory of law and politics for zoning than one sees in conventional land-use scholarship like Haar and Wolf's. Second, it helps clarify the normative debate over zoning. It suggests that many of the most controversial debates about zoning relate back at least in part to zoning's Progressive conception of the public interest. That conception encourages strong local communities, but it also encourages the tendencies that local majorities sometimes use to prop up home prices, exclude outsiders on the basis of race or class, or stage not in my backyard! campaigns. In short, Progressive political theory identifies the bitter and the sweet in zoning more clearly than conventional land-use scholarship, and suggests that scholars who like the sweet had better be prepared to swallow the bitter.
A network analysis of global banking: 1978–2010

We analyze the global banking network using data on cross-border banking flows for 184 countries during 1978–2010. We find that the density of the global banking network defined by these flows is pro-cyclical, expanding and contracting with the global cycle of capital flows. We also find that country connectedness in the network tends to rise before banking and debt crises and to fall in their aftermath. Despite a historically unique build-up in aggregate flows prior to the global financial crisis, network density in 2007 was comparable to earlier peaks. This suggests that factors other than connectedness, such as the location of the initial shock to the core of the network, have contributed to the severity of the crisis. The global financial crisis stands out as an unusually large perturbation to the global banking network, with indicators of network density in 2008 reaching all-time lows.
Anchoring Effects on Consumers’ Willingness‐to‐Pay and Willingness‐to‐Accept

We examine the susceptibility of consumers' willingness-to-pay (WTP) and willingness-to-accept (WTA) judgments to influence by arbitrary anchors. The results of four studies suggest that, although circumstantial differences between buying and selling decisions can lead to systematic differences in susceptibility to anchoring effects, the principles governing the impact of anchoring on WTP and WTA judgments are essentially the same. In particular, robust anchoring effects are observed when the anchor relates to the focal source of uncertainty. Further, WTP and WTA judgments are most susceptible to anchoring (and endowment) effects under uncertainty about the desire to trade. The implications of this research for our understanding of anchoring and endowment effects are discussed.
Value-Enhancing Capabilities of CSR: A Brief Review of Contemporary Literature

This study reviews and synthesizes the contemporary business literature that focuses on the role of corporate social responsibility (CSR) to enhance firm value. The main objective of this review is to proffer a precise understanding of what has already been investigated and the findings of those investigations regarding the value-enhancing capabilities of CSR for public firms. In addition, this review identifies gaps in the existing literature, evaluates inconsistent findings, discusses possible data sources for empirical researchers, and provides direction for exploring other promising avenues in future studies. The thrust of the CSR literature largely acknowledges the value-enhancing capabilities of firms’ social and environmental activities. However, the predominance of inconsistent theoretical grounds in major CSR-benefits-related areas suggests that there is ample room for future research to contribute to the extant literature. Anecdotal evidence, the prevalence of theoretical arguments, and the availability of large cross-sectional firm-level data suggest that future research will enrich the literature by investigating the real insights behind several unanswered questions, by establishing implicit understandings regarding recognized findings, and by developing new theories in this emerging field.
Intellectual Property Rights in China: The Changing Political Economy of Chinese–American Interests

We review the evolution of modern Chinese intellectual property right (IPR) laws and enforcement and explore economic and political forces involved in international conflicts over Chinese IPR protection. Our analysis considers why the US and China moved from conflict to cooperation over intellectual property rights. Structural and institutional aspects of the political economy of IPRs within each country are considered, and data on Chinese-US trade in intellectual property-intensive goods are examined. We conclude that although enforcement of IPRs within China continues to be relatively weak, Chinese IPR institutions are converging on those in the OECD nations.
An Estimate of the Effect of Common Currencies on Trade and Income

To quantify the implications of common currencies for trade and income, we use data for over 200 countries and dependencies. In our two-stage approach, estimates at the first stage suggest that belonging to a currency union/board triples trade with other currency union members. Moreover, there is no evidence of trade-diversion. Our estimates at the second stage suggest that every one percent increase in a country’s overall trade (relative to GDP) raises income per capita by at least one third of a percent. We combine the two estimates to quantify the effect of common currencies on output. Our results support the hypothesis that important beneficial effects of currency unions come through the promotion of trade.
Shooting Down the ‘More Guns, Less Crime’ Hypothesis

John Lott and David Mustard have used regression analysis to argue forcefully that 'shall-issue' laws (which give citizens an unimpeded right to secure permits for concealed weapons) reduce violent crime. While certain facially plausible statistical models appear to generate this conclusion, more refined analyses of more recent state and county data undermine the more guns, less crime hypothesis. The most robust finding on the state data is that certain property crimes rise with passage of shall- issue laws, although the absence of any clear theory as to why this would be the case tends to undercut any strong conclusions. Estimating more statistically preferred disaggregated models on more complete county data, we show that in most states shall- issue laws have been associated with more crime and that the apparent stimulus to crime tends to be especially strong for those states that adopted in the last decade. While there are substantial concerns about model reliability and robustness, we present estimates based on disaggregated county data models that on net the passage of the law in 24 jurisdictions has increased the annual cost of crime slightly -- somewhere on the order of half a billion dollars. We also provide an illustration of how our jurisdiction-specific regression model has the capacity to generate more nuanced assessments concerning which states might profit from or be harmed by a particular legal intervention.
An Untethered Brittle Star-Inspired Soft Robot for Closed-Loop Underwater Locomotion

Soft robots are capable of inherently safer interactions with their environment than rigid robots since they can mechanically deform in response to unanticipated stimuli. However, their complex mechanics can make planning and control diﬃcult, particularly with tasks such as locomotion. In this work, we present a mobile and untethered underwater crawling soft robot, PATRICK, paired with a testbed that demonstrates closed-loop locomotion planning. PATRICK is inspired by the brittle star, with five flexible legs actuated by a total of 20 shape-memory alloy (SMA) wires, providing a rich variety of possible motions via its large input space. We propose a motion planning infrastructure based on a simple set of PATRICK’s motion primitives, and provide experiments showing that the planner can command the robot to locomote to a goal state. These experiments contribute the first examples of closed-loop, state-space goal seeking of an underwater, untethered, soft crawling robot, and make progress towards full autonomy of soft mobile robotic systems.
Physically significant phase shifts in matter-wave interferometry

Many different formalisms exist for computing the phase of a matter-wave interferometer. However, it can be challenging to develop physical intuition about what a particular interferometer is actually measuring or about whether a given classical measurement provides equivalent information. Here, we investigate the physical content of the interferometer phase through a series of thought experiments. In low-order potentials, a matter-wave interferometer with a single internal state provides the same information as a sum of position measurements of a classical test object. In high-order potentials, the interferometer phase becomes decoupled from the motion of the interferometer arms, and the phase contains information that cannot be obtained by any set of position measurements on the interferometer trajectory. This phase shift in a high-order potential fundamentally distinguishes matter-wave interferometers from classical measuring devices.
Hazard stocks and expected returns

Abstract Hazard stocks are the opposite of lottery stocks. We proxy hazard stocks with the minimum daily idiosyncratic return over the past month, “IMIN,” and examine the relation between hazard stocks and expected returns. The literature on lottery stocks implies that investors should discount hazard stocks. Anomalously, we find a negative relation between IMIN and future returns. Hedge portfolios that are long high IMIN stocks and short low IMIN stocks generate monthly alphas of -0.52% to -0.76%. The results are robust after controlling for numerous firm characteristics and corporate events. The hazard stock anomaly is primarily driven by limits to arbitrage and, to a lesser degree, by firm-level information uncertainty. Via the Reg SHO pilot program, we provide causal evidence that the apparent asymmetric preferences across lottery and hazard stocks are due to arbitrage asymmetry as described by Stambaugh et al. (2015). This demonstrates that asymmetric arbitrage may yield what appear to be asymmetric preferences.
On core quandles of groups

We review the definition of a quandle, and in particular of the core quandle Core ( G ) of a group G, which consists of the underlying set of G, with the binary operation x ⊲ y = x y − 1 x . This i...
Model free simulations of a high speed reacting mixing layer

The effects of compressibility, chemical reaction exothermicity and non-equilibrium chemical modeling in a combusting plane mixing layer were investigated by means of two-dimensional model free numerical simulations. It was shown that increased compressibility generally had a stabilizing effect, resulting in reduced mixing and chemical reaction conversion rate. The appearance of 'eddy shocklets' in the flow was observed at high convective Mach numbers. Reaction exothermicity was found to enhance mixing at the initial stages of the layer's growth, but had a stabilizing effect at later times. Calculations were performed for a constant rate chemical rate kinetics model and an Arrhenius type kinetics prototype. The Arrhenius model was found to cause a greater temperature increase due to reaction than the constant kinetics model. This had the same stabilizing effect as increasing the exothermicity of the reaction. Localized flame quenching was also observed when the Zeldovich number was relatively large.
A Flight-Calibrated Methodology for Determination of Cassini Thruster On-Times for Reaction Wheel Biases

This paper describes a methodology for accurate and flight-calibrated determination of the on-times of the Cassini spacecraft Reaction Control System (RCS) thrusters, without any form of dynamic simulation, for the reaction wheel biases. The hydrazine usage and the delta V vector in body frame are also computed from the respective thruster on-times. The Cassini spacecraft, the largest and most complex interplanetary spacecraft ever built, continues to undertake ambitious and unique scientific observations of planet Saturn, Titan, Enceladus, and other moons of Saturn. In order to maintain a stable attitude during the course of its mission, this three-axis stabilized spacecraft uses two different control systems: the RCS and the reaction wheel assembly control system. The RCS is used to execute a commanded spacecraft slew, to maintain three-axis attitude control, control spacecraft's attitude while performing science observations with coarse pointing requirements, e.g. during targeted low-altitude Titan and Enceladus flybys, bias the momentum of reaction wheels, and to perform RCS-based orbit trim maneuvers. The use of RCS often imparts undesired delta V on the spacecraft. The Cassini navigation team requires accurate predictions of the delta V in spacecraft coordinates and inertial frame resulting from slews using RCS thrusters and more importantly from reaction wheel bias events. It is crucial for the Cassini spacecraft attitude control and navigation teams to be able to, quickly but accurately, predict the hydrazine usage and delta V for various reaction wheel bias events without actually having to spend time and resources simulating the event in flight software-based dynamic simulation or hardware-in-the-loop simulation environments. The methodology described in this paper, and the ground software developed thereof, are designed to provide just that. This methodology assumes a priori knowledge of thrust magnitudes and thruster pulse rise and tail-off time constants for eight individual attitude control thrusters, the spacecraft's wet mass and its center of mass location, and a few other key parameters.
Host factor Rab11a is critical for efficient assembly of influenza A virus genomic segments

It is well documented that influenza A viruses selectively package 8 distinct viral ribonucleoprotein complexes (vRNPs) into each virion; however, the role of host factors in genome assembly is not completely understood. To evaluate the significance of cellular factors in genome assembly, we generated a reporter virus carrying a tetracysteine tag in the NP gene (NP-Tc virus) and assessed the dynamics of vRNP localization with cellular components by fluorescence microscopy. At early time points, vRNP complexes were preferentially exported to the MTOC; subsequently, vRNPs associated on vesicles positive for cellular factor Rab11a and formed distinct vRNP bundles that trafficked to the plasma membrane on microtubule networks. In Rab11a deficient cells, however, vRNP bundles were smaller in the cytoplasm with less co-localization between different vRNP segments. Furthermore, Rab11a deficiency increased the production of non-infectious particles with higher RNA copy number to PFU ratios, indicative of defects in specific genome assembly. These results indicate that Rab11a+ vesicles serve as hubs for the congregation of vRNP complexes and enable specific genome assembly through vRNP:vRNP interactions, revealing the importance of Rab11a as a critical host factor for influenza A virus genome assembly.
Gap time bias in incident and prevalent cohorts

Multiple event data are frequently encountered in incident and prevalent cohort studies when the multiple events are considered as the major outcomes. For incident cohorts, statistical analysis for the time to the first event, the first gap time, can be conducted using standard techniques in survival analysis under appropriate conditions. These techniques are, nevertheless, inappropriate for analyzing the second gap time because of the presence of induced informative censoring. For prevalent cohorts, because the sample is biased in general, standard methods do not apply to gap times of any order, but techniques for truncated data can be used for the analysis of the first gap time. It is shown that the combined incident and prevalent data form the usual survival data for analysis of the second gap time when certain stationarity conditions are satisfied. The problems are illustrated by a cohort example to study the natural history of Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS).
Intersections and Unions of Session Types

Prior work has extended the deep, logical connection between the linear sequent calculus and session-typed message-passing concurrent computation with equi-recursive types and a natural notion of subtyping. In this paper, we extend this further by intersection and union types in order to express multiple behavioral properties of processes in a single type. We prove session fidelity and absence of deadlock and illustrate the expressive power of our system with some simple examples. We observe that we can represent internal and external choice by intersection and union, respectively, which was previously suggested by Padovani for a different language of session types motivated by operational rather than logical concerns.
A non-canonical microRNA derived from the snaR-A non-coding RNA targets a metastasis inhibitor

MicroRNAs (miRNAs) are small noncoding RNAs that function as critical post-transcriptional regulators in various biological processes. While most miRNAs are generated from processing of long primary transcripts via sequential Drosha and Dicer cleavage, some miRNAs that bypass Drosha cleavage can be transcribed as part of another small non-coding RNA. Here, we develop the Target-Oriented miRNA Discovery (TOMiD) bioinformatic analysis method to identify Drosha-independent miRNAs from Argonaute crosslinking and sequencing of hybrids (Ago-CLASH) datasets. Using this technique, we discovered a novel miRNA derived from a primate specific non-coding RNA, the small NF90 associated RNA A (snaR-A). The miRNA derived from snaR-A (miR-snaR) arises independent of Drosha processing, but requires Exportin-5 and Dicer for biogenesis. We identify that miR-snaR is concurrently upregulated with the full snaR-A transcript in cancer cells. Functionally, miR-snaR associates with Ago proteins and targets NME1, a key metastasis inhibitor, contributing to snaR-A's role in promoting cancer cell migration. Our findings suggest a functional link between a novel miRNA and its precursor non-coding RNA.
Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles.

Purpose Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. Methods This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes. Results The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11–17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10–6.00), p = 0.03) increases in hazard of death relative to phenotype III. Conclusion We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.
The local innovation spillovers of listed firms

Abstract This paper provides evidence of local innovation spillovers (i.e., innovation by one firm fostering innovation by neighboring firms). First, I document that exogenous shocks to innovation by listed firms affect innovation by private firms in the same geographical area and that such local innovation spillovers decline rapidly with distance. Second, these local innovation spillovers stem from knowledge diffusing locally through two channels: learning across local firms and inventors moving from their employer to both existing firms and newly started spin-outs. Finally, I study the two-way relations between innovation spillovers and the availability of capital. I find that local innovation spillovers cause venture capital funds from outside the area to invest more in the local area, and that capital availability amplifies local innovation spillovers.
A Genome-Scale Antibiotic Screen in Serratia marcescens Identifies YdgH as a Conserved Modifier of Cephalosporin and Detergent Susceptibility.

Serratia marcescens, a member of the order Enterobacterales, is adept at colonizing healthcare environments and an important cause of invasive infections. Antibiotic resistance is a daunting problem in S. marcescens because in addition to plasmid-mediated mechanisms, most isolates have considerable intrinsic resistance to multiple antibiotic classes. To discover endogenous modifiers of antibiotic susceptibility in S. marcescens, a high-density transposon insertion library was subjected to sub-minimal inhibitory concentrations of two cephalosporins, cefoxitin and cefepime, as well as the fluoroquinolone ciprofloxacin. Comparisons of transposon insertion abundance before and after antibiotic exposure identified hundreds of potential modifiers of susceptibility to these agents. Using single gene deletions, we validated several candidate modifiers of cefoxitin susceptibility and chose ydgH, a gene of unknown function, for further characterization. In addition to cefoxitin, deletion of ydgH in S. marcescens resulted in decreased susceptibility to multiple 3rd generation cephalosporins, and in contrast, to increased susceptibility to both cationic and anionic detergents. YdgH is highly conserved throughout the Enterobacterales, and we observed similar phenotypes in Escherichia coli O157:H7 and Enterobacter cloacae mutants. YdgH is predicted to localize to the periplasm and we speculate that it may be involved there in cell envelope homeostasis. Collectively, our findings provide insight into chromosomal mediators of antibiotic resistance in S. marcescens and will serve as a resource for further investigations of this important pathogen.
Focal Loss Analysis of Nerve Fiber Layer Reflectance for Glaucoma Diagnosis

Purpose To evaluate nerve fiber layer (NFL) reflectance for glaucoma diagnosis. Methods Participants were imaged with 4.5 × 4.5 mm volumetric disc scans using spectral-domain optical coherence tomography. The normalized NFL reflectance map was processed by an azimuthal filter to reduce directional reflectance bias caused by variation of beam incidence angle. The peripapillary area of the map was divided into 160 superpixels. Average reflectance was the mean of superpixel reflectance. Low-reflectance superpixels were identified as those with NFL reflectance below the fifth percentile normative cutoff. Focal reflectance loss was measured by summing loss in low-reflectance superpixels. Results Thirty-five normal, 30 preperimetric, and 35 perimetric glaucoma participants were enrolled. Azimuthal filtering improved the repeatability of the normalized NFL reflectance, as measured by the pooled superpixel standard deviation (SD), from 0.73 to 0.57 dB (P < 0.001, paired t-test) and reduced the population SD from 2.14 to 1.78 dB (P < 0.001, t-test). Most glaucomatous reflectance maps showed characteristic patterns of contiguous wedge or diffuse defects. Focal NFL reflectance loss had significantly higher diagnostic sensitivity than the best NFL thickness parameter (from map or profile): 77% versus 55% (P < 0.001) in glaucoma eyes with the specificity fixed at 99%. Conclusions Azimuthal filtering reduces the variability of NFL reflectance measurements. Focal NFL reflectance loss has excellent glaucoma diagnostic accuracy compared to the standard NFL thickness parameters. The reflectance map may be useful for localizing NFL defects. Translational Relevance The high diagnostic accuracy of NFL reflectance may make population-based screening feasible.
Credit attribution and collaborative work

Abstract We examine incentives in research teams where the market, such as the scientific community, attributes credit for success based on its inference of individual efforts. A social planner who could commit to credit ex ante would induce more effort from higher ability agents in exchange for less credit per unit effort. Lacking such commitment, the Bayesian market assigns credit proportional to perceived effort. This inability to distort credit per unit effort leads to an incentive reversal across projects. For “easy” projects with a concave marginal cost of effort, in the unique interior equilibrium, higher ability agents work less and receive lower credit/utility, while the opposite holds for “difficult” projects with a sufficiently convex marginal cost of effort. Moreover, equilibrium may involve over-investment by some team members who expect to receive most of the credit. The incentives to team up and the implications of effort observability on credit attribution are also investigated.
Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution

Modeling, predicting, and forecasting ambient air pollution is an important way to deal with the degradation of the air quality in our cities because it would be helpful for decision-makers and urban city planners to understand the phenomena and to take solutions. In general, data-driven modeling, predicting, and forecasting outdoor pollution methods require an important amount of data, which may limit their accuracy. To deal with such a lack of data, we propose to train generative models, specifically conditional generative adversarial networks, to create synthetic nitrogen dioxide daily time series that will allow an unlimited generation of realistic data. The experimental results indicate that the proposed approach is able to generate accurate and diverse pollution daily time series, while requiring reduced computational time.
Temporal filtering of luminance and chromaticity in macaque visual cortex.

Summary Contrast sensitivity peaks near 10 Hz for luminance modulations and at lower frequencies for modulations between equiluminant lights. This difference is rooted in retinal filtering, but additional filtering occurs in the cerebral cortex. To measure the cortical contributions to luminance and chromatic temporal contrast sensitivity, signals in the lateral geniculate nucleus (LGN) were compared to the behavioral contrast sensitivity of macaque monkeys. Long wavelength-sensitive (L) and medium wavelength-sensitive (M) cones were modulated in phase to produce a luminance modulation (L + M) or in counterphase to produce a chromatic modulation (L − M). The sensitivity of LGN neurons was well matched to behavioral sensitivity at low temporal frequencies but was approximately 7 times greater at high temporal frequencies. Similar results were obtained for L + M and L − M modulations. These results show that differences in the shapes of the luminance and chromatic temporal contrast sensitivity functions are due almost entirely to pre-cortical mechanisms.
Data-driven prediction of multistable systems from sparse measurements.

We develop a data-driven method, based on semi-supervised classification, to predict the asymptotic state of multistable systems when only sparse spatial measurements of the system are feasible. Our method predicts the asymptotic behavior of an observed state by quantifying its proximity to the states in a precomputed library of data. To quantify this proximity, we introduce a sparsity-promoting metric-learning (SPML) optimization, which learns a metric directly from the precomputed data. The optimization problem is designed so that the resulting optimal metric satisfies two important properties: (i) it is compatible with the precomputed library and (ii) it is computable from sparse measurements. We prove that the proposed SPML optimization is convex, its minimizer is non-degenerate, and it is equivariant with respect to the scaling of the constraints. We demonstrate the application of this method on two multistable systems: a reaction–diffusion equation, arising in pattern formation, which has four asymptotically stable steady states, and a FitzHugh–Nagumo model with two asymptotically stable steady states. Classifications of the multistable reaction–diffusion equation based on SPML predict the asymptotic behavior of initial conditions based on two-point measurements with 95 % accuracy when a moderate number of labeled data are used. For the FitzHugh–Nagumo, SPML predicts the asymptotic behavior of initial conditions from one-point measurements with 90 % accuracy. The learned optimal metric also determines where the measurements need to be made to ensure accurate predictions.
First-Order Methods for Wasserstein Distributionally Robust MDP

Markov decision processes (MDPs) are known to be sensitive to parameter specification. Distributionally robust MDPs alleviate this issue by allowing for \emph{ambiguity sets} which give a set of possible distributions over parameter sets. The goal is to find an optimal policy with respect to the worst-case parameter distribution. We propose a framework for solving Distributionally robust MDPs via first-order methods, and instantiate it for several types of Wasserstein ambiguity sets. By developing efficient proximal updates, our algorithms achieve a convergence rate of $O\left(NA^{2.5}S^{3.5}\log(S)\log(\epsilon^{-1})\epsilon^{-1.5} \right)$ for the number of kernels $N$ in the support of the nominal distribution, states $S$, and actions $A$; this rate varies slightly based on the Wasserstein setup. Our dependence on $N,A$ and $S$ is significantly better than existing methods, which have a complexity of $O\left(N^{3.5}A^{3.5}S^{4.5}\log^{2}(\epsilon^{-1}) \right)$. Numerical experiments show that our algorithm is significantly more scalable than state-of-the-art approaches across several domains.
Blunted temporal activity of microvascular perfusion heterogeneity in metabolic syndrome: a new attractor for peripheral vascular disease?

A key clinical outcome for peripheral vascular disease (PVD) in patients is a progressive decay in skeletal muscle performance and its ability to resist fatigue with elevated metabolic demand. We h...
PAC-Learning for Strategic Classification

The study of strategic or adversarial manipulation of testing data to fool a classifier has attracted much recent attention. Most previous works have focused on two extreme situations where any testing data point either is completely adversarial or always equally prefers the positive label. In this paper, we generalize both of these through a unified framework for strategic classification, and introduce the notion of strategic VC-dimension (SVC) to capture the PAC-learnability in our general strategic setup. SVC provably generalizes the recent concept of adversarial VC-dimension (AVC) introduced by Cullina et al. arXiv:1806.01471. We instantiate our framework for the fundamental strategic linear classification problem. We fully characterize: (1) the statistical learnability of linear classifiers by pinning down its SVC; (2) its computational tractability by pinning down the complexity of the empirical risk minimization problem. Interestingly, the SVC of linear classifiers is always upper bounded by its standard VC-dimension. This characterization also strictly generalizes the AVC bound for linear classifiers in arXiv:1806.01471.
Language Embeddings for Typology and Cross-lingual Transfer Learning

Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data. We explore whether language representations that capture relationships among languages can be learned and subsequently leveraged in cross-lingual tasks without the use of parallel data. We generate dense embeddings for 29 languages using a denoising autoencoder, and evaluate the embeddings using the World Atlas of Language Structures (WALS) and two extrinsic tasks in a zero-shot setting: cross-lingual dependency parsing and cross-lingual natural language inference.
DeepCOVID: An Operational Deep Learning -driven Framework for Explainable Real-time COVID-19 Forecasting

How do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DeepCOVID, an operational deep learning framework designed for real-time COVID-19 forecasting. DeepCOVID works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The deployed framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction

Inferring 3D structure of a generic object from a 2D image is a long-standing objective of computer vision. Conventional approaches either learn completely from CADgenerated synthetic data, which have difficulty in inference from real images, or generate 2.5D depth image via intrinsic decomposition, which is limited compared to the full 3D reconstruction. One fundamental challenge lies in how to leverage numerous real 2D images without any 3D ground truth. To address this issue, we take an alternative approach with semi-supervised learning. That is, for a 2D image of a generic object, we decompose it into latent representations of category, shape, albedo, lighting and camera projection matrix, decode the representations to segmented 3D shape and albedo respectively, and fuse these components to render an image well approximating the input image. Using a category-adaptive 3D joint occupancy field (JOF), we show that the complete shape and albedo modeling enables us to leverage real 2D images in both modeling and model fitting. The effectiveness of our approach is demonstrated through superior 3D reconstruction from a single image, being either synthetic or real, and shape segmentation. Code is available at http://cvlab.cse.msu.edu/project-fully3dobject.html.
Debiasing Evaluations That Are Biased by Evaluations

It is common to evaluate a set of items by soliciting people to rate them. For example, universities ask students to rate the teaching quality of their instructors, and conference organizers ask authors of submissions to evaluate the quality of the reviews. However, in these applications, students often give a higher rating to a course if they receive higher grades in a course, and authors often give a higher rating to the reviews if their papers are accepted to the conference. In this work, we call these external factors the "outcome" experienced by people, and consider the problem of mitigating these outcome-induced biases in the given ratings when some information about the outcome is available. We formulate the information about the outcome as a known partial ordering on the bias. We propose a debiasing method by solving a regularized optimization problem under this ordering constraint, and also provide a carefully designed cross-validation method that adaptively chooses the appropriate amount of regularization. We provide theoretical guarantees on the performance of our algorithm, as well as experimental evaluations.
Optimal control with learning on the fly: a toy problem

We exhibit optimal control strategies for a simple toy problem in which the underlying dynamics depend on a parameter that is initially unknown and must be learned. We consider a cost function posed over a finite time interval, in contrast to much previous work that considers asymptotics as the time horizon tends to infinity. We study several different versions of the problem, including Bayesian control, in which we assume a prior distribution on the unknown parameter; and "agnostic" control, in which we assume nothing about the unknown parameter. For the agnostic problems, we compare our performance with that of an opponent who knows the value of the parameter. This comparison gives rise to several notions of "regret," and we obtain strategies that minimize the "worst-case regret" arising from the most unfavorable choice of the unknown parameter. In every case, the optimal strategy turns out to be a Bayesian strategy or a limit of Bayesian strategies.
Wide-Baseline Multi-Camera Calibration using Person Re-Identification

We address the problem of estimating the 3D pose of a network of cameras for large-environment wide-baseline scenarios, e.g., cameras for construction sites, sports stadiums, and public spaces. This task is challenging since detecting and matching the same 3D keypoint observed from two very different camera views is difficult, making standard structure-from-motion (SfM) pipelines inapplicable. In such circumstances, treating people in the scene as "keypoints" and associating them across different camera views can be an alternative method for obtaining correspondences. Based on this intuition, we propose a method that uses ideas from person re-identification (re-ID) for wide-baseline camera calibration. Our method first employs a re-ID method to associate human bounding boxes across cameras, then converts bounding box correspondences to point correspondences, and finally solves for camera pose using multi-view geometry and bundle adjustment. Since our method does not require specialized calibration targets except for visible people, it applies to situations where frequent calibration updates are required. We perform extensive experiments on datasets captured from scenes of different sizes (80m2, 350m2, 600m2), camera settings (indoor and outdoor), and human activities (walking, playing basketball, construction). Experiment results show that our method achieves similar performance to standard SfM methods relying on manually labeled point correspondences.