cssi/SciSciGPT-SciSciNet huggingface dataset:
Cooperation under the threat of expulsion in a public goods experiment

Abstract In a public goods experiment with the opportunity to vote to expel members of a group, we found that contributions rose to nearly 100% of endowments with significantly higher efficiency compared with a no-expulsion baseline. Our findings support the intuition that the threat of expulsion or ostracism is a device that helps some groups to provide public goods.
The Work-Family Conflict: An Essay on Employers, Men and Responsibility

This paper, prepared for a symposium held at the University of St. Thomas Law School, explores an issue that has been largely neglected in the work-family debate, namely why the burden should be on employers to change their practices rather than on men to change theirs. Many of the policy proposals designed to facilitate the balancing of work and family demands require employers to alter their practices by creating part-time work, providing paid leave, or devising ways to limit the penalties women face for taking extended leave. At the same time, the reluctance of men to change their behavior, which could go a significant way to altering the dynamics of work-family issues, has been largely ignored. This essay first explores the rationales for focusing on employers, including what is now defined as the business case for work-life benefits. The paper then critiques the various excuses that are typically raised for why men do not take more responsibility for work-family balance issues, including that (1) they are penalized to a greater extent than woman, that (2) it is economically rational for the burden to fall on women and that (3) men's behavior has changed significantly, none of which is empirically supported in the literature. Finally, I suggest that it is important to have a more theoretically targeted policy focus that is premised on workplace equality rather than trying to support all choices for all women.
When Pregnancy is an Injury: Rape, Law, and Culture

This Article examines criminal statutes that grade more severely sexual assaults that result in pregnancy. These laws, which define pregnancy as a “substantial bodily injury,” run directly counter to positive constructions of pregnancy within culture. The fact that the criminal law, in this instance, reflects this negative, subversive understanding of pregnancy creates the possibility that this idea may be received within culture as a construction of pregnancy that is as legitimate as positive understandings. In this way, these laws create possibilities for the reimagining of pregnancy within law and society. Moreover, these laws recall the argumentation that proponents of abortion rights once made – argumentation that one no longer hears and sees in the debates surrounding abortion. However, recent developments in antiabortion argumentation – namely the notion accepted in Carhart II that it is abortion that injures women – counsel the retrieval of the argument that unwanted pregnancies are injuries to women. Thus, the sexual assault laws are means to legitimatize a claim that may serve as an effective counterdiscourse to prevailing antiabortion argumentation. The exploration proceeds in three Parts. Part I provides an overview of sexual assault statutes that punish more severely perpetrators who cause their victims to become pregnant and suggests that these laws are worthy of cultural analysis because they define pregnancy as an injury and, as such, are wholly at odds with positive constructions of pregnancy. Part II moves the discussion outside of the context of rape. It contends that the definition of pregnancy as an injury does not solely describe women’s experience of pregnancies that result from rape, but generally describes women’s experience of unwanted pregnancy. Indeed, it is the profound unwantedness of the pregnancy that results from rape that makes it an injury. Thus, the criminal law gives legitimacy to a subversive phenomenology of unwanted pregnancy, which may have repercussions for how pregnancy – and abortion – is understood within society. Part III looks at representations of pregnancy in other areas of the law, revealing that the law frequently embodies positive constructions of pregnancy even when negative constructions might be expected. The rare times that the law appears to represent pregnancy subversively are when laws index the social effects of pregnancies. Accordingly, while the law in these instances represents pregnancy as an injury, the injury is to the body politic. Thus, the subversive nature of the representation is mitigated, as it does not endeavor to describe a bodily experience of pregnancy. A brief conclusion follows.
Child Nutrition in India in the Nineties

Abstract India experienced several years of fast economic growth during the 1990s, and according to many observers this period also saw a considerable decline in poverty, especially in urban areas. We use data from two rounds of the National Family and Health Survey to evaluate changes in nutritional status between 1992–93 and 1998–99 among children ages 0–3. We find that measures of short‐term nutritional status based on weight given height show large improvements, especially in urban areas. Height‐for‐age, an indicator of long‐term nutritional status, also shows improvement, but one limited to urban areas. However, we also document that the changes in nutritional status are much more favorable for boys than for girls. The gender differences in the changes over time appear to be driven by states in North India, where the existence of widespread son preference has been documented by an immense body of research.
An Analysis of the Judicial Panel on Multidistrict Litigation's Selection of Transferee District and Judge

When civil cases involving one or more common questions of fact are pending in multiple district courts, 28 U.S.C. § 1407 empowers the Judicial Panel on Multidistrict Litigation (JPML) to centralize the cases in a single district court for pretrial proceedings. If the JPML chooses to centralize a multidistrict action, it possesses broad discretion to select a transferee district and judge. While many litigants believe that the selection of one transferee district or judge over another can significantly impact the outcome of the litigation, they often describe the JPML’s rationale supporting selection as opaque and the resulting selection decision as difficult to predict. In order to clarify the criteria on which the JPML relies when choosing a transferee district and judge, this Note presents an empirical study of several years of JPML transfer orders. This Note argues that, while predicting where the JPML will centralize a multidistrict action is difficult, an understanding of statistical trends in JPML selection of transferee district and judge leads to a better understanding of the factors that are most likely to influence the JPML in any given multidistrict action.
Bank Lending, Inflation, and China's Stock Market (2004–2010)

The 2009 surge in bank lending in China was accompanied by allegations of substantial funds being funneled into the nation's stock and property markets. This paper uses 2004–2010 People's Bank survey data to examine the possible linkages between banking activity and the stock market as well as the associated inflation risks. In general, stock market strength in China seems to be accompanied by rising inflationary concerns, increased bank lending activity, and reduced banker confidence that stable conditions will be maintained. This suggests that the Shanghai market could serve as a useful indicator variable for Chinese monetary policy.
Progressive taxation in a tournament economy

Abstract Not enough is known about the responsiveness of individuals, in particular those who work under different incentives, to changes in marginal tax rates. We ask whether changes in tax rates are less distortionary for workers engaged in a contest. To examine this potential rationale for a more progressive tax code, we first model the effort decisions of workers faced with progressive taxation under tournaments and piece rates. Because of the difficulty identifying any distortion that may be induced by the tax code in naturally occurring data, we then report on the results of a real-effort experiment based on this model. Consistent with a behavioral approach to public finance, we find that competitive tournament workers are less sensitive and hint, in our discussion, at the possible welfare benefits of progressive taxation in tournament economies.
Translating the Human Right to Water and Sanitation into Public Policy Reform

The development of a human right to water and sanitation under international law has created an imperative to implement human rights in water and sanitation policy. Through forty-three interviews with informants in international institutions, national governments, and non-governmental organizations, this research examines interpretations of this new human right in global governance, national policy, and local practice. Exploring obstacles to the implementation of rights-based water and sanitation policy, the authors analyze the limitations of translating international human rights into local water and sanitation practice, concluding that system operators, utilities, and management boards remain largely unaffected by the changing public policy landscape for human rights realization. To understand the relevance of human rights standards to water and sanitation practitioners, this article frames a research agenda to ensure that human rights aspirations lead to public policy reforms and public health outcomes.
How Should a Firm Go Public? A Dynamic Model of the Choice between Fixed-Price Offerings and Auctions in IPOs and Privatizations

We develop a theoretical analysis of the choice of firms between fixed-price offerings and uniform-price auctions for selling shares in IPOs and privatizations. We consider a setting in which a firm goes public by selling a fraction of its equity in an IPO market where insiders have private information about intrinsic firm value. Outsiders can, however, produce information at a cost about the firm before bidding for shares. Firm insiders care about the extent of information production by outsiders, since this information will be reflected in the secondary market price, giving a higher secondary market price for higher intrinsic-value firms. We show that auctions and fixed-price offerings have different properties in terms of inducing information production. Thus, in many situations, firms prefer to go public using fixed-price offerings rather than IPO auctions in equilibrium. We relate the equilibrium choice between fixed-price offerings and IPO auctions to various characteristics of the firm going public. Unlike the existing literature, our model is able to explain not only the widely-documented empirical finding that underpricing is lower in IPO auctions than in fixed-price offerings (e.g., Derrien andWomack (2000)), but also the fact that, despite this, auctions are losing market share around the world. Our model thus suggests a resolution to the above “IPO auction puzzle,” and indicates how current IPO auction mechanisms can be reformed to become more competitive with fixed-price offerings. Our results also provide various other hypotheses for further empirical research. JEL Classification Code: G30, G32, C72, D44, D82 How Should A Firm Go Public? A Dynamic Model of the Choice Between Fixed-Price Offerings and Auctions in IPOs and Privatizations
A "Special Track" for Former Child Soldiers: Enacting a "Child Soldier Visa" as an Alternative to Asylum Protection

Approximately 300,000 child soldiers currently serve in more than 30 conflicts around the world. The international legal community widely considers these children to be victims of severe human rights violations in spite of the atrocities that many of them commit. Although few of these children will ever make it to the United States, those who do manage to escape and arrive in the U.S. face a number of challenges in achieving immigration status. In general, advocates for child soldiers have focused on how the United States’ asylum laws could be changed to make it easier for former child soldiers to receive asylum status. In particular, the proposals have urged clarification of the definition of “particular social group” and incorporation of duress and infancy defenses into the exclusionary bars to asylum. These proposals, although they have merit, fail to acknowledge the floodgates and national security concerns that have likely stalled these needed changes to the asylum laws. This article offers a solution for child soldiers outside the context of asylum law: a Child Soldier Visa that applies only to former child soldiers and which attempts to reconcile the United States’ humanitarian and national security interests. While acknowledging the need to ultimately reform U.S. asylum laws so they conform to the Refugee Convention and state practice, this article discusses a solution that may be more realistic in the short-term than amending the asylum laws.
The academic progress of Hispanic immigrants

Abstract Past research has shown that Hispanic students make test score gains relative to whites as they age through school; however, this finding stands in contrast to the experience of blacks, who show little change in their relative position over the same time frame. Distinguishing Hispanic students by immigrant generation, I find that the children of immigrants (first- and second-generation Hispanics) drive the improvement in Hispanic test scores. Later-generation Hispanics consistently perform slightly below whites, perhaps due to negative selection into ethnic identification. Thus, previous estimates vastly understate the progress of first- and second-generation Hispanic immigrants. From a negative gap in 3rd grade, these students surpass socioeconomically similar whites in math and reading by middle school and end 8th grade as much as a quarter of a standard deviation ahead. Assimilation alone cannot explain this progress; a potential explanation is that immigrant parents create a home environment that fosters achievement.
An Efficient Frontier in Organization Design: Organizational Structure as a Determinant of Exploration and Exploitation

This paper develops a parsimonious process-level theory that connects organizational structure to exploration and exploitation. Toward this end, it develops a mathematical model of organizational decision making that combines an information processing approach in the spirit of Sah and Stiglitz [Sah RK, Stiglitz JE 1986 The architecture of economic systems: Hierarchies and polyarchies. Amer. Econom. Rev. 764:716–727] with elements from signal detection theory. The model is first used to explore a “design space” of organizations and identify trade-offs and dominance relationships among alternative organization designs. The paper then studies open questions in the organization design literature, such as the extent to which exploration and exploitation can be produced by one organization and what is the effect of organization size on exploration. More broadly, this research speaks to calls for the introduction of more process-level explanations in the organizations literature. The paper concludes with testable hypotheses and managerially relevant insights.
An exploratory look at supermarket shopping paths

Abstract We present analyses of an extraordinary new dataset that reveals the path taken by individual shoppers in an actual grocery store, as provided by RFID (radio frequency identification) tags located on their shopping carts. The analysis is performed using a multivariate clustering algorithm not yet seen in the marketing literature that is able to handle data sets with unique (and numerous) spatial constraints. This allows us to take into account physical impediments (such as the location of aisles and other inaccessible areas of the store) to ensure that we only report feasible centroid paths. We also recognize that time spent in the store plays an important role, leading to different cluster configurations for short, medium, and long trips. The resulting three sets of clusters identify a total of 14 “canonical path types” that are typical of grocery store travel, and we carefully describe (and cross-validate) each set of clusters. These results dispel certain myths about shopper travel behavior that common intuition perpetuates, including behavior related to aisles, end-cap displays, and the “racetrack.” We briefly relate these results to previous research (using much more limited datasets) covering travel behavior in retail stores and other related settings.
High-precision Estimation of Random Walks in Small Space

In this paper, we provide a deterministic $\tilde{O}(\log N)$ -space algorithm for estimating random walk probabilities on undirected graphs, and more generally Eulerian directed graphs, to within inverse polynomial additive error $(\epsilon=1/\text{poly}(N))$ where $N$ is the length of the input. Previously, this problem was known to be solvable by a randomized algorithm using space $O(\log N)$ (following Aleliunas et al., FOCS '79) and by a deterministic algorithm using space $O(\log^{3/2}N)$ (Saks and Zhou, FOCS '95 and JCSS '99), both of which held for arbitrary directed graphs but had not been improved even for undirected graphs. We also give improvements on the space complexity of both of these previous algorithms for non-Eulerian directed graphs when the error is negligible $(\epsilon=1/N^{\omega(1)})$ , generalizing what Hoza and Zuckerman (FOCS '18) recently showed for the special case of distinguishing whether a random walk probability is 0 or greater than $\epsilon$ . We achieve these results by giving new reductions between powering Eulerian random-walk matrices and inverting Eulerian Laplacian matrices, providing a new notion of spectral approximation for Eulerian graphs that is preserved under powering, and giving the first deterministic $\tilde{O}(\log N)$ -space algorithm for inverting Eulerian Laplacian matrices. The latter algorithm builds on the work of Murtagh et al. (FOCS '17) that gave a deterministic $\tilde{O}(\log N)$ -space algorithm for inverting undirected Laplacian matrices, and the work of Cohen et al. (FOCS '19) that gave a randomized $\tilde{O}(N)$ -time algorithm for inverting Eulerian Laplacian matrices. A running theme throughout these contributions is an analysis of “cycle-lifted graphs,” where we take a graph and “lift” it to a new graph whose adjacency matrix is the tensor product of the original adjacency matrix and a directed cycle (or variants of one).
Single-cell transcriptomes of developing and adult olfactory receptor neurons in Drosophila .

Recognition of environmental cues is essential for the survival of all organisms. Transcriptional changes occur to enable the generation and function of the neural circuits underlying sensory perception. To gain insight into these changes, we generated single-cell transcriptomes of Drosophila olfactory- (ORNs), thermo-, and hygro-sensory neurons at an early developmental and adult stage using single-cell and single-nucleus RNA sequencing. We discovered that ORNs maintain expression of the same olfactory receptors across development. Using receptor expression and computational approaches, we matched transcriptomic clusters corresponding to anatomically and physiologically defined neuron types across multiple developmental stages. We found that cell-type-specific transcriptomes partly reflected axon trajectory choices in development and sensory modality in adults. We uncovered stage-specific genes that could regulate the wiring and sensory responses of distinct ORN types. Collectively, our data reveal transcriptomic features of sensory neuron biology and provide a resource for future studies of their development and physiology.
Financial attributes, financial behaviors, financial‐advisor‐use beliefs, and investing characteristics associated with having used a robo‐advisor

This study investigates the financial attributes, financial behaviors, financial‐advisor‐use beliefs, and investing characteristics associated with having used an automated investment solution (i.e., a robo‐advisor). Data are used from the 2015 National Financial Capability Study (both the State‐by‐State Survey and the Investor Survey) to investigate the use of robo‐advisors among 1,393 individuals with non‐retirement investment accounts. Multivariable results indicated that being younger, having low or midrange income, and having lower objective investment‐knowledge scores was positively associated with robo‐advisor use. This study provides important insight for policymakers, financial practitioners, and proprietors of robo‐advisors and other fintech products. Full Text Available Here: https://doi.org/10.1002/cfp2.1104
Power of data in quantum machine learning

The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data. Using rigorous prediction error bounds as a foundation, we develop a methodology for assessing potential quantum advantage in learning tasks. The bounds are tight asymptotically and empirically predictive for a wide range of learning models. These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems. We then propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime. For near-term implementations, we demonstrate a significant prediction advantage over some classical models on engineered data sets designed to demonstrate a maximal quantum advantage in one of the largest numerical tests for gate-based quantum machine learning to date, up to 30 qubits. Expectations for quantum machine learning are high, but there is currently a lack of rigorous results on which scenarios would actually exhibit a quantum advantage. Here, the authors show how to tell, for a given dataset, whether a quantum model would give any prediction advantage over a classical one.
Bifidelity Data-Assisted Neural Networks in Nonintrusive Reduced-Order Modeling

In this paper, we present a new nonintrusive reduced basis method when a cheap low-fidelity model and an expensive high-fidelity model are available. The method employs proper orthogonal decomposition method to generate the high-fidelity reduced basis and a shallow multilayer perceptron to learn the high-fidelity reduced coefficients. In contrast to previously proposed methods, besides the model parameters, we also augmented the features extracted from the data generated by an efficient bi-fidelity surrogate developed in Narayan et al. (SIAM J Sci Comput 36(2):A495–A521, 2014) and Zhu et al. (SIAM/ASA J Uncertain Quantif 2(1):444–463, 2014) as the input feature of the proposed neural network. By incorporating relevant bi-fidelity features, we demonstrate that such an approach can improve the predictive capability and robustness of the neural network via several benchmark examples. Due to its nonintrusive nature, it is also applicable to general parameterized problems.
Probabilistic Disclosures for Corporate and other Law

This Article explores the costs and benefts of one subset of continuous and discontinuous rules. These expressions are shown to be distinct from the familiar dichotomy expressed as standards versus rules, but they share the diffculty of dividing the world of law in two. Still, regulatory approaches that focus on discontinuities can often be made more continuous, and vice versa. A speed limit is discontinuous in the sense that one drives above or below (or within) the announced limit. But it is often made more continuous—even with more discontinuities— as when the stated limit is different for various kinds of vehicles. This Article works around these definitional problems to show that law often discourages useful disclosures by encouraging parties with information to offer continuous information in order to avoid after-the-fact lawsuits when specific disclosures prove to have inaccuracies. For example, it is common to hear or be warned that a medical procedure poses the risk of death, when a better-informed doctor or hospital could have given the precise percentages attached to various outcomes. Similarly, a corporation is on safe ground when it follows “generally accepted accounting principles,” when investors would have learned more from information about good and bad outcomes put in probabilistic terms. The Article works toward the suggestion that law might create a safe harbor in which probabilistic disclosures are protected when they are, or are certified to be, more useful than the ready alternative of fairly general disclosures.
On the size Ramsey number of all cycles versus a path

Abstract We say G → ( C , P n ) if G − E ( F ) contains an n -vertex path P n for any spanning forest F ⊂ G . The size Ramsey number R ˆ ( C , P n ) is the smallest integer m such that there exists a graph G with m edges for which G → ( C , P n ) . Dudek, Khoeini and Pralat proved that for sufficiently large n , 2 . 0036 n ≤ R ˆ ( C , P n ) ≤ 31 n . In this note, we improve both the lower and upper bounds to 2 . 066 n ≤ R ˆ ( C , P n ) ≤ 5 . 25 n + O ( 1 ) . Our construction for the upper bound is completely different than the one considered by Dudek, Khoeini and Pralat. We also have a computer assisted proof of the upper bound R ˆ ( C , P n ) ≤ 75 19 n + O ( 1 ) 3 . 947 n .
Ligand effects on phase separation of multivalent macromolecules

Biomolecular condensates enable spatial and temporal control over cellular processes by concentrating biomolecules into nonstoichiometric assemblies. Many condensates form via reversible phase transitions of condensate-specific multivalent macromolecules known as scaffolds. Phase transitions of scaffolds can be regulated by changing the concentrations of ligands, which are defined as nonscaffold molecules that bind to specific sites on scaffolds. Here, we use theory and computation to uncover rules that underlie ligand-mediated control over scaffold phase behavior. We use the stickers-and-spacers model wherein reversible noncovalent cross-links among stickers drive phase transitions of scaffolds, and spacers modulate the driving forces for phase transitions. We find that the modulatory effects of ligands are governed by the valence of ligands, whether they bind directly to stickers versus spacers, and the relative affinities of ligand–scaffold versus scaffold–scaffold interactions. In general, all ligands have a diluting effect on the concentration of scaffolds within condensates. Whereas monovalent ligands destabilize condensates, multivalent ligands can stabilize condensates by binding directly to spacers or destabilize condensates by binding directly to stickers. Bipartite ligands that bind to stickers and spacers can alter the structural organization of scaffold molecules within condensates even when they have a null effect on condensate stability. Our work highlights the importance of measuring dilute phase concentrations of scaffolds as a function of ligand concentration in cells. This can reveal whether ligands modulate scaffold phase behavior by enabling or suppressing phase separation at endogenous levels, thereby regulating the formation and dissolution of condensates in vivo.
Understanding Socioeconomic Disparities in Travel Behavior during the COVID-19 Pandemic

We document the magnitudes of and mechanisms behind socioeconomic differences in travel behavior during the COVID-19 pandemic. We focus on King County, Washington, one of the first places in North America where COVID-19 was detected. We leverage novel and rich administrative and survey data on travel volumes, modes, and preferences for different demographic groups. Large average declines in travel and public transit use due to the pandemic and related policy responses mask substantial heterogeneity across socioeconomic groups. Travel declined considerably less among less-educated and lower-income individuals, even after accounting for mode substitution and variation across neighborhoods in the impacts of public transit service reductions. As policy became less restrictive and travel increased, the size of the socioeconomic gap in travel behavior remained stable, and remote work capabilities became increasingly important in explaining this gap. Our results imply that disparities in travel behavior across socioeconomic groups may become an enduring feature of the urban landscape.
Identifying and Analyzing Cryptocurrency Manipulations in Social Media

Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in the recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraudity—such as “pump and dump” scams—where the goal is to artificially inflate the perceived worth of a currency, luring victims into investing before the fraudsters can sell their holdings. Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump. In this work, we propose and evaluate a computational approach that can automatically identify pump and dump scams as they unfold by combining information across social media platforms. We also develop a multi-modal approach for predicting whether a particular pump attempt will succeed or not. Finally, we analyze the prevalence of bots in cryptocurrency related tweets, and observe a significant increase in bot activity during the pump attempts.
One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision

Computer vision is widely deployed, has highly visible, society-altering applications, and documented problems with bias and representation. Datasets are critical for benchmarking progress in fair computer vision, and often employ broad racial categories as population groups for measuring group fairness. Similarly, diversity is often measured in computer vision datasets by ascribing and counting categorical race labels. However, racial categories are ill-defined, unstable temporally and geographically, and have a problematic history of scientific use. Although the racial categories used across datasets are superficially similar, the complexity of human race perception suggests the racial system encoded by one dataset may be substantially inconsistent with another. Using the insight that a classifier can learn the racial system encoded by a dataset, we conduct an empirical study of computer vision datasets supplying categorical race labels for face images to determine the cross-dataset consistency and generalization of racial categories. We find that each dataset encodes a substantially unique racial system, despite nominally equivalent racial categories, and some racial categories are systemically less consistent than others across datasets. We find evidence that racial categories encode stereotypes, and exclude ethnic groups from categories on the basis of nonconformity to stereotypes. Representing a billion humans under one racial category may obscure disparities and create new ones by encoding stereotypes of racial systems. The difficulty of adequately converting the abstract concept of race into a tool for measuring fairness underscores the need for a method more flexible and culturally aware than racial categories.
Topology and many-body localization

Abstract We discuss the problem of localization in two dimensional electron systems in the quantum Hall (single Landau level) regime. After briefly summarizing the well-studied problem of Anderson localization in the non-interacting case, we concentrate on the problem of disorder induced many-body localization (MBL) in the presence of electron–electron interactions using numerical exact diagonalization and eigenvalue spacing statistics as a function of system size. We provide evidence showing that MBL is not attainable in a single Landau level with short range (white noise) disorder in the thermodynamic limit. We then study the interplay of topology and localization, by contrasting the behavior of topological and nontopological subbands arising from a single Landau level in two models — (i) a pair of extremely flat Hofstadter bands with an optimally chosen periodic potential, and (ii) a Landau level with a split-off nontopological impurity band. Both models provide convincing evidence for the strong effect of topology on the feasibility of many-body localization as well as slow dynamics starting from a nonequilibrium state with charge imbalance.
Nickel-Sulfonate Mode of Substrate Binding for Forward and Reverse Reactions of Methyl-SCoM Reductase Suggest a Radical Mechanism Involving Long-Range Electron Transfer

Methyl-coenzyme M reductase (MCR) catalyzes both the synthesis and the anaerobic oxidation of methane (AOM). Its catalytic site contains Ni at the core of cofactor F430. The Ni ion, in its low-valent Ni(I) state, lights the fuse leading to homolysis of the C-S bond of methyl-coenzyme M (methyl-SCoM) to generate a methyl radical, which abstracts a hydrogen atom from coenzyme B (HSCoB) to generate methane and the mixed disulfide CoMSSCoB. Direct reversal of this reaction activates methane to initiate anaerobic methane oxidation. On the basis of the crystal structures, which reveal a Ni-thiol interaction between Ni(II)-MCR and inhibitor CoMSH, a Ni(I)-thioether complex with substrate methyl-SCoM has been transposed to canonical MCR mechanisms. Similarly, a Ni(I)-disulfide with CoMSSCoB is proposed for the reverse reaction. However, this Ni(I)-sulfur interaction poses a conundrum for the proposed hydrogen-atom abstraction reaction because the >6 A distance between the thiol group of SCoB and the thiol of SCoM observed in the structures appears to be too long for such a reaction. The spectroscopic, kinetic, structural, and computational studies described here establish that both methyl-SCoM and CoMSSCoB bind to the active Ni(I) state of MCR through their sulfonate groups, forming a hexacoordinate Ni(I)-N/O complex, not Ni(I)-S. These studies rule out direct Ni(I)-sulfur interactions in both substrate-bound states. As a solution to the mechanistic conundrum, we propose that both the forward and the reverse MCR reactions emanate through long-range electron transfer from the Ni(I)-sulfonate complexes with methyl-SCoM and CoMSSCoB, respectively.
Community detection in the sparse hypergraph stochastic block model

We consider the community detection problem in sparse random hypergraphs. Angelini et al. (2015) conjectured the existence of a sharp threshold on model parameters for community detection in sparse hypergraphs generated by a hypergraph stochastic block model. We solve the positive part of the conjecture for the case of two blocks: above the threshold, there is a spectral algorithm which asymptotically almost surely constructs a partition of the hypergraph correlated with the true partition. Our method is a generalization to random hypergraphs of the method developed by Massoulie (2014) for sparse random graphs.
Minimum Energy Analysis for Robust Gaussian Joint Source-Channel Coding with a Square-Law Profile

A distortion-noise profile is a function indicating the maximum allowed source distortion value for each noise level in the channel. In this paper, the minimum energy required to achieve a distortion noise profile is studied for Gaussian sources transmitted over Gaussian channels. Previously known lower and upper bounds for the minimum required energy to achieve the square-law profile are improved using a family of lower bounds and our proposed coding scheme.
How To Make the Gradients Small Stochastically

(This is a theory paper.) In convex stochastic optimization, convergence rates in terms of minimizing the objective have been well-established. However, in terms of making the gradients small, the best known convergence rate was eps^{-8/3} and it was left open how to improve it. In this paper, we improve this rate to eps^{-2}, which is optimal up to log factors.
Point process modeling of drug overdoses with heterogeneous and missing data

Opioid overdose rates have increased in the United States over the past decade and reflect a major public health crisis. Modeling and prediction of drug and opioid hotspots, where a high percentage of events fall in a small percentage of space–time, could help better focus limited social and health services. In this work we present a spatial-temporal point process model for drug overdose clustering. The data input into the model comes from two heterogeneous sources: (1) high volume emergency medical calls for service (EMS) records containing location and time but no information on the type of nonfatal overdose, and (2) fatal overdose toxicology reports from the coroner containing location and high-dimensional information from the toxicology screen on the drugs present at the time of death. We first use nonnegative matrix factorization to cluster toxicology reports into drug overdose categories, and we then develop an EM algorithm for integrating the two heterogeneous data sets, where the mark corresponding to overdose category is inferred for the EMS data and the high volume EMS data is used to more accurately predict drug overdose death hotspots. We apply the algorithm to drug overdose data from Indianapolis, showing that the point process defined on the integrated data out-performs point processes that use only coroner data (AUC improvement 0.81 to 0.85). We also investigate the extent to which overdoses are contagious, as a function of the type of overdose, while controlling for exogenous fluctuations in the background rate that might also contribute to clustering. We find that drug and opioid overdose deaths exhibit significant excitation with branching ratio ranging from 0.72 to 0.98.
Distributed generation of privacy preserving data with user customization

Distributed devices such as mobile phones can produce and store large amounts of data that can enhance machine learning models; however, this data may contain private information specific to the data owner that prevents the release of the data. We wish to reduce the correlation between user-specific private information and data while maintaining the useful information. Rather than learning a large model to achieve privatization from end to end, we introduce a decoupling of the creation of a latent representation and the privatization of data that allows user-specific privatization to occur in a distributed setting with limited computation and minimal disturbance on the utility of the data. We leverage a Variational Autoencoder (VAE) to create a compact latent representation of the data; however, the VAE remains fixed for all devices and all possible private labels. We then train a small generative filter to perturb the latent representation based on individual preferences regarding the private and utility information. The small filter is trained by utilizing a GAN-type robust optimization that can take place on a distributed device. We conduct experiments on three popular datasets: MNIST, UCI-Adult, and CelebA, and give a thorough evaluation including visualizing the geometry of the latent embeddings and estimating the empirical mutual information to show the effectiveness of our approach.
In-medium hadron properties from lattice QCD

I review recent results from lattice QCD calculations on the in-medium hadron properties. I discuss the thermal dilepton rates, heavy quarkonium properties as well as the chiral and U (1)A symmetries at finite temperature.
A Shortcut from Categorical Quantum Theory to Convex Operational Theories

This paper charts a very direct path between the categorical approach to quantum mechanics, due to Abramsky and Coecke, and the older convex-operational approach based on ordered vector spaces (recently reincarnated as "generalized probabilistic theories"). In the former, the objects of a symmetric monoidal category C are understood to represent physical systems and morphisms, physical processes. Elements of the monoid C(I,I) are interpreted somewhat metaphorically as probabilities. Any monoid homomorphism from the scalars of a symmetric monoidal category C gives rise to a covariant functor V_o from C to a category of dual-pairs of ordered vector spaces. Specifying a natural transformation u from V_o to 1 (where 1 is the trivial such functor) allows us to identify normalized states, and, thus, to regard the image category V_o(C) as consisting of concrete operational models. In this case, if A and B are objects in C, then V_o(A x B) defines a non-signaling composite of V_o(A) and V_o(B). Provided either that C satisfies a "local tomography" condition, or that C is compact closed, this defines a symmetric monoidal structure on the image category, and makes V_o a (strict) monoidal functor.
FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative. We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species. FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision—ideal for bench researchers with limited experience in handling computational tools.
DeACT: Architecture-Aware Virtual Memory Support for Fabric Attached Memory Systems

1 The exponential growth of data has driven technology providers to develop new protocols, such as cache coherent interconnects and memory semantic fabrics, to help users and facilities leverage advances in memory technologies to satisfy these growing memory and storage demands. Using these new protocols, fabric-attached memories (FAM) can be directly attached to a system interconnect and be easily integrated with a variety of processing elements (PEs). Moreover, systems that support FAM can be smoothly upgraded and allow multiple PEs to share the FAM memory pools using well-defined protocols. The sharing of FAM between PEs allows efficient data sharing, improves memory utilization, reduces cost by allowing flexible integration of different PEs and memory modules from several vendors, and makes it easier to upgrade the system. One promising use-case for FAMs is in High-Performance Compute (HPC) systems, where the underutilization of memory is a major challenge. However, adopting FAMs in HPC systems brings new challenges. In addition to cost, flexibility, and efficiency, one particular problem that requires rethinking is virtual memory support for security and performance. To address these challenges, this paper presents decoupled access control and address translation (DeACT), a novel virtual memory implementation that supports HPC systems equipped with FAM. Compared to the state-of-the-art two-level translation approach, DeACT achieves speedup of up to 4.59x (1.8x on average) without compromising security.1Part of this work was done when Vamsee was working under the supervision of Amro Awad at UCF. Amro Awad is now with the ECE Department at NC State.
Performance and Behavioral Flexibility on a Complex Motor Task Depend on Available Sensory Inputs in Early Blind and Sighted Short-Tailed Opossums

The early loss of vision results in a reorganization of visual cortex, which becomes commandeered by the somatosensory and auditory systems. Recent studies in the short-tailed opossum found that the connections and response properties of neurons in somatosensory cortex of blind animals are also altered. While studies in humans and other mammals show that early vision loss leads to heightened abilities on discrimination tasks involving the spared senses, if and how this superior discrimination leads to adaptive sensorimotor behavior has yet to be determined. Moreover, little is known about the extent to which blind animals rely on the spared senses. Here, we tested opossums on a sensorimotor task involving somatosensation. We found that early blind animals had increased limb placement accuracy compared to sighted controls, while showing similarities in crossing strategy. Whisker trimming nullified this advantage, resulting in decreases in limb placement accuracy and more variable forelimb movements in both groups. However, the increased reliance on tactile inputs in early blind animals led to greater deficits in limb placement and behavioral flexibility when the whiskers were trimmed. This data demonstrates that incoming sensory input from the whiskers is processed differently in the brains of early blind compared to sighted opossums, likely due, in part, to the dramatic changes to visual and somatosensory cortex. These results also provide direct evidence for the prevailing theory that the whiskers are critical for skilled forelimb control in whisking mammals, and highlight the importance of studying both performance and strategy when assessing differences in behavior.
Estimating abundance and phenology from transect count data with GLMs

Understanding organismal phenology has been an emerging interest in ecology, in part because phenological shifts are one of the most conspicuous signs of climate change. While we are seeing increased collection of phenological data and creative use of historical data sets, existing statistical tools to measure phenology are generally either limited (e.g., first day of observation, which has problematic biases) or are challenging to implement (often requiring custom coding, or enough data to fit many parameters). We present a method to fit phenological data with Gaussian curves using linear models, and show how robust phenological metrics can be obtained using standard linear regression tools. We then apply this method to eight years of Baltimore checkerspot data using generalized linear mixed models (GLMMs). This case study illustrates the ability of years with extensive data to inform years with less data and shows that butterfly flight activity is somewhat earlier in warmer years. We hope it fills a convenient midpoint between ad hoc measures and custom-coded models.