cssi/SciSciGPT-SciSciNet huggingface dataset:
An Overview of Engineering Concepts and Current Design Algorithms for Probabilistic Structural Analysis

The article begins by examining the fundamentals of traditional deterministic design philosophy. The initial section outlines the concepts of failure criteria and limit state functions two traditional notions that are embedded in deterministic design philosophy. This is followed by a discussion regarding safety factors (a possible limit state function) and the common utilization of statistical concepts in deterministic engineering design approaches. Next the fundamental aspects of a probabilistic failure analysis are explored and it is shown that deterministic design concepts mentioned in the initial portion of the article are embedded in probabilistic design methods. For components fabricated from ceramic materials (and other similarly brittle materials) the probabilistic design approach yields the widely used Weibull analysis after suitable assumptions are incorporated. The authors point out that Weibull analysis provides the rare instance where closed form solutions are available for a probabilistic failure analysis. Since numerical methods are usually required to evaluate component reliabilities, a section on Monte Carlo methods is included to introduce the concept. The article concludes with a presentation of the technical aspects that support the numerical method known as fast probability integration (FPI). This includes a discussion of the Hasofer-Lind and Rackwitz-Fiessler approximations.
Toral posets and the binary spectrum property

We introduce a family of posets which generate Lie poset subalgebras of $$A_{n-1}=\mathfrak {sl}(n)$$ whose index can be realized topologically. In particular, if $$\mathcal {P}$$ is such a toral poset, then it has a simplicial realization which is homotopic to a wedge sum of d one-spheres, where d is the index of the corresponding type-A Lie poset algebra $$\mathfrak {g}_A(\mathcal {P})$$ . Moreover, when $$\mathfrak {g}_A(\mathcal {P})$$ is Frobenius, its spectrum is binary, that is, consists of an equal number of 0’s and 1’s. We also find that all Frobenius, type-A Lie poset algebras corresponding to a poset whose largest totally ordered subset is of cardinality at most three have a binary spectrum.
Making Economics More Useful: How Technological Eclecticism Could Help

Keynes thought it would be “splendid” if economists became more like dentists. Disciplinary economics has instead become more like physics in focusing on concise, universal propositions verified through decisive tests. This focus, the author argues, limits the practical utility of the discipline because universal propositions form only a part of new policy recipes. The author further suggests that, as in engineering and medicine, developing economic recipes requires eclectic combinations of suggestive tests and judgment. Additionally, the author provides a detailed example of how a simulation model can help evaluate new policy combinations that affect the screening of loan applications.
SQLizer: Query Synthesis from Natural Language

This paper presents a new technique for automatically synthesizing SQL queries from natural language (NL). At the core of our technique is a new NL-based program synthesis methodology that combines semantic parsing techniques from the NLP community with type-directed program synthesis and automated program repair. Starting with a program sketch obtained using standard parsing techniques, our approach involves an iterative refinement loop that alternates between probabilistic type inhabitation and automated sketch repair. We use the proposed idea to build an end-to-end system called SQLIZER that can synthesize SQL queries from natural language. Our method is fully automated, works for any database without requiring additional customization, and does not require users to know the underlying database schema. We evaluate our approach on over 450 natural language queries concerning three different databases, namely MAS, IMDB, and YELP. Our experiments show that the desired query is ranked within the top 5 candidates in close to 90% of the cases and that SQLIZER outperforms NALIR, a state-of-the-art tool that won a best paper award at VLDB'14.
The NFHS-5 Sanitation Story: Case for Moving from Access to Adoption:

This commentary analyses the recently launched National Family Health Survey (NFHS-5) (2019–2020) factsheet to investigate the claim of an open defecation free (ODF) India. The official data from S...
Simulation of the transit-time optical stochastic cooling process in the Cornell Electron Storage Ring

In preparation for a demonstration of optical stochastic cooling in Cornell Electron Storage Ring, we have developed a particle tracking simulation to study the relevant beam dynamics. Optical radiation emitted in the pickup undulator gives a momentum kick to that same particle in the kicker undulator. The optics of the electron bypass from pickup to kicker couples betatron amplitude and momentum offset to path length so that the momentum kick reduces emittance and momentum spread. Nearby electrons contribute an incoherent noise. Layout of the bypass line is presented that accommodates optics with a range of transverse and longitudinal cooling parameters. The simulation is used to determine cooling rates and their dependence on bunch and lattice parameters for bypass optics with distinct emittance and momentum acceptance.
Free to Spend? Institutional Autonomy and Expenditures on Executive Compensation, Faculty Salaries, and Research Activities

System governing boards fulfill roles as both regulators and protectors for their multiple constituent campuses. While centralized control provides many benefits to institutional members, such advantages are also be accompanied by limitations upon campuses to pursue expanded missions, fully compete, and spend according to individual rather than collective priorities. In this study, I leverage a natural experiment where one state “freed” six public universities by removing the oversight of a centralized board. Given a novel opportunity to assess how institutions respond to a new intra-state market characterized by deregulation and increased competition for students, faculty, and other scarce resources, I first focus on changes in three institutional expenditure areas closely tied to entrepreneurship, competition, and prestige-seeking: executive compensation, faculty salaries, and spending on research activities. After constructing a novel dataset with administrative records on compensation and public data on expenditures, I employ complementary difference-in-differences and synthetic control approaches which yield robust evidence suggesting that newly independent universities increased the salary of their president/chancellor by approximately 6.2% (or $19,000), increased the average full professor salary by 2.2% (nearly $2,000), and increased research expenditures by an average of 12% (or $2 million). These findings not only advance the nascent literature on how state governance structures influence institutional behaviors but also provide useful evidence for policymakers considering the intended (and potentially unintended) consequences of similar governance reorganizations.
Effects of acute normovolemic hemodilution on post-cardiopulmonary bypass coagulation tests and allogeneic blood transfusion in thoracic aortic repair surgery: An observational cohort study.

BACKGROUND AND AIM Perioperative blood transfusion is associated with increased morbidity and mortality. Acute normovolemic hemodilution (ANH) is a blood conservation strategy associated with variable success, and rarely studied in more complex cardiac procedures. The study aim was to evaluate whether ANH improves coagulopathy and reduces blood transfusions in thoracic aortic surgeries. METHODS Single-center observational cohort study comparing ANH and standard institutional practice in patients who underwent thoracic aortic repair with cardiopulmonary bypass (CPB) from 2019 to 2021. RESULTS A total of 89 patients underwent ANH and 116 standard practice. There were no significant differences between the groups in terms of demographic or major perioperative characteristics. In the ANH group coagulation tests before and after transfusion of autologous blood showed decreased INR and increased platelets, fibrinogen, all with p < 0.0005. Coagulation results in the ANH and control groups were not statistically different. The average number of transfused allogeneic products per patient was lower in the ANH versus control group: FFP 1.1 ± 1.6 versus 1.9 ± 2.3 (p = 0.003), platelets 0.6 ± 0.8 versus 1.2 ± 1.3 (p = 0.0008), and cryoprecipitate 0.3 ± 0.7 versus 0.7 ± 1.1 (p = 0.008). Reduction in red blood cell transfusion was not statistically significant. The percentage of patients who received any transfusion was 53.9% in ANH and 59.5% in the control group (p = 0.42). There was no significant difference in major adverse outcomes. CONCLUSIONS ANH is a safe blood conservation strategy for surgical repairs of the thoracic aorta. Laboratory data suggests ANH can improve some coagulation values after separation from CPB, and significantly reduce the number of transfused FFP, platelets and cryoprecipitate.
Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents.

Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the motivation being that understanding the meanings of words or semantics is a key component of how humans understand, reason, and act in these worlds. However, it remains unclear to what extent artificial agents utilize semantic understanding of the text. To this end, we perform experiments to systematically reduce the amount of semantic information available to a learning agent. Surprisingly, we find that an agent is capable of achieving high scores even in the complete absence of language semantics, indicating that the currently popular experimental setup and models may be poorly designed to understand and leverage game texts. To remedy this deficiency, we propose an inverse dynamics decoder to regularize the representation space and encourage exploration, which shows improved performance on several games including Zork I. We discuss the implications of our findings for designing future agents with stronger semantic understanding.
State Relevance for Off-Policy Evaluation

Importance sampling-based estimators for off-policy evaluation (OPE) are valued for their simplicity, unbiasedness, and reliance on relatively few assumptions. However, the variance of these estimators is often high, especially when trajectories are of different lengths. In this work, we introduce Omitting-States-Irrelevant-to-Return Importance Sampling (OSIRIS), an estimator which reduces variance by strategically omitting likelihood ratios associated with certain states. We formalize the conditions under which OSIRIS is unbiased and has lower variance than ordinary importance sampling, and we demonstrate these properties empirically.
U.S. presidential election polls and the economic prospects of China and Mexico

Motivated by Mr. Trump’s agendas against China and Mexico, we investigate how a candidate’s probability of winning the U.S. presidential election affects the international financial markets that ar...
A flare-induced cascade model of gamma-ray bursts

An analytical model is developed for the source of gamma ray bursts as a stellar flare in the magnetosphere of a neutron star. It is suggested that the loss of energy through synchrotron radiation experienced by electrons moving through a sufficiently strong magnetic field at a large pitch angle may not be regained. Instead, pulsar theory is applied to show that the acceleration of electrons in an electric field parallel to the magnetic field will rapidly be inhibited by curvature radiation as the loop experiences a reconnection. It is shown that electrons passing through a curvature with a radius of one million with an electric field strength of 10 billion e.s.u. will emit photons with energies of up to 10 to the 12.6 eV by curvature radiation. The photons, gamma rays, would annihilate in the magnetosphere, which they cannot escape. The resulting cascade of electron-positron particles would eventually produce photons of sufficiently low energy to escape. Upper and low bounds are estimated for the resulting emission spectrum, which would vary according to the magnetic field geometry. The model explains the observed 511 keV annihilation line and the optical radiation which at times accompanies gamma-ray bursts.
Does technology transfer from the US to China harm American firms, workers, and consumers? A historical and analytic investigation

Decades of spectacular economic growth have made China into an important geopolitical player. As Chinese companies improve their capabilities across several areas of advanced technology, including AI, some U.S. policymakers and pundits lament the country’s “unfair trade practices” and serial “theft of American intellectual property”—particularly, through so-called forced technology transfer. China hawks claim these practices hurt U.S. companies, workers, and consumers. Do Chinese technology practices harm economic efficiency? What are their distributional consequences? To address these questions, we explore the different modalities of international technology transfer and flesh out their economic consequences. We also investigate the recent history of technology transfer, providing examples from the industrialization experiences of European countries and the Asian Tigers. We surmise that current Chinese processes are neither novel nor alarming from the standpoints of economic efficiency or distribution: U.S. firms are collecting record royalty payments for their IP from China and generating gangbuster profits due to their access to Chinese labor, suppliers, and the country’s growing consumer market. American consumers benefit from US-China economic interdependence and so do some workers. The consequences for the U.S. economy as a whole are positive. While we are agnostic about whether these practices threaten America’s national security, we offer ideas for how to prevent China from acquiring its most sensitive military technology.
TabNet: Attentive Interpretable Tabular Learning.

We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for the most salient features. We demonstrate that TabNet outperforms other variants on a wide range of non-performance-saturated tabular datasets and yields interpretable feature attributions plus insights into its global behavior. Finally, we demonstrate self-supervised learning for tabular data, significantly improving performance when unlabeled data is abundant.
Deep Lucas-Kanade Homography for Multimodal Image Alignment

Estimating homography to align image pairs captured by different sensors or image pairs with large appearance changes is an important and general challenge for many computer vision applications. In contrast to others, we propose a generic solution to pixel-wise align multimodal image pairs by extending the traditional Lucas-Kanade algorithm with networks. The key contribution in our method is how we construct feature maps, named as deep Lucas-Kanade feature map (DLKFM). The learned DLKFM can spontaneously recognize invariant features under various appearance-changing conditions. It also has two nice properties for the Lucas-Kanade algorithm: (1) The template feature map keeps brightness consistency with the input feature map, thus the color difference is very small while they are well-aligned. (2) The Lucas-Kanade objective function built on DLKFM has a smooth landscape around ground truth homography parameters, so the iterative solution of the Lucas-Kanade can easily converge to the ground truth. With those properties, directly updating the Lucas-Kanade algorithm on our feature maps will precisely align image pairs with large appearance changes. We share the datasets, code, and demo video online 1.
Empirically Evaluating Creative Arc Negotiation for Improvisational Decision-making

Action selection from many options with few constraints is crucial for improvisation and co-creativity. Our previous work proposed creative arc negotiation to solve this problem, i.e., selecting actions to follow an author-defined ‘creative arc’ or trajectory over estimates of novelty, unexpectedness, and quality for potential actions. The CARNIVAL agent architecture demonstrated this approach for playing the Props game from improv theatre in the Robot Improv Circus installation. This article evaluates the creative arc negotiation experience with CARNIVAL through two crowdsourced observer studies and one improviser laboratory study. The studies focus on subjects’ ability to identify creative arcs in performance and their preference for creative arc negotiation compared to a random selection baseline. Our results show empirically that observers successfully identified creative arcs in performances. Both groups also preferred creative arc negotiation in agent creativity and logical coherence, while observers enjoyed it more too.
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks.

Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user’s private attribute inference in social networks, and community detection. Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were developed for semi-supervised node classification. pMRF is more efficient than graph neural networks. However, existing pMRF-based methods are less accurate than graph neural networks, due to a key limitation that they assume a heuristics-based constant edge potential for all edges. In this work, we aim to address the key limitation of existing pMRF-based methods. In particular, we propose to learn edge potentials for pMRF. Our evaluation results on various types of graph datasets show that our optimized pMRF-based method consistently outperforms existing graph neural networks in terms of both accuracy and efficiency. Our results highlight that previous work may have underestimated the power of pMRF for semi-supervised node classification.
Pulse width fluctuations in mode-locked Nd:YAG lasers

The experiment consisted of a flashlamp-pumped Nd:YAG laser rod in the Brewster-Brewster configuration, passively modelocked by a thick flowing dye in contact with the high reflectivity rear mirror. A single pulse was selected by a cylindrical-ring electrode Pockel's cell, and amplified by two Nd:YAG amplifiers. Calibrated photodiodes measured the total pulse energy at the fundamental frequency and the total pulse energy at the second harmonic frequency. The signals were monitored by a digital data acquisition system and displayed on a teletype printer.
Estimating vaccine efficacy over time after a randomized study is unblinded.

The COVID-19 pandemic due to the novel coronavirus SARS CoV-2 has inspired remarkable breakthroughs in the development of vaccines against the virus and the launch of several phase 3 vaccine trials in Summer 2020 to evaluate vaccine efficacy (VE). Trials of vaccine candidates using mRNA delivery systems developed by Pfizer-BioNTech and Moderna have shown substantial VEs of 94-95%, leading the US Food and Drug Administration to issue Emergency Use Authorizations and subsequent widespread administration of the vaccines. As the trials continue, a key issue is the possibility that VE may wane over time. Ethical considerations dictate that trial participants be unblinded and those randomized to placebo be offered study vaccine, leading to trial protocol amendments specifying unblinding strategies. Crossover of placebo subjects to vaccine complicates inference on waning of VE. We focus on the particular features of the Moderna trial and propose a statistical framework based on a potential outcomes formulation within which we develop methods for inference on potential waning of VE over time and estimation of VE at any postvaccination time. The framework clarifies assumptions made regarding individual- and population-level phenomena and acknowledges the possibility that subjects who are more or less likely to become infected may be crossed over to vaccine differentially over time. The principles of the framework can be adapted straightforwardly to other trials.
Unsupervised Enrichment of Persona-grounded Dialog with Background Stories

Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich. While persona-grounded dialog models are able to generate responses that follow a given persona, they often miss out on stating detailed experiences or events related to a persona, often leaving conversations shallow and dull. In this work, we equip dialog models with ‘background stories’ related to a persona by leveraging fictional narratives from existing story datasets (e.g. ROCStories). Since current dialog datasets do not contain such narratives as responses, we perform an unsupervised adaptation of a retrieved story for generating a dialog response using a gradient-based rewriting technique. Our proposed method encourages the generated response to be fluent (i.e., highly likely) with the dialog history, minimally different from the retrieved story to preserve event ordering and consistent with the original persona. We demonstrate that our method can generate responses that are more diverse, and are rated more engaging and human-like by human evaluators, compared to outputs from existing dialog models.
Learned Initializations for Optimizing Coordinate-Based Neural Representations

Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network from randomly initialized weights for each new signal is inefficient. We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class of signals being represented (e.g., images of faces or 3D models of chairs). Despite requiring only a minor change in implementation, using these learned initial weights enables faster convergence during optimization and can serve as a strong prior over the signal class being modeled, resulting in better generalization when only partial observations of a given signal are available. We explore these benefits across a variety of tasks, including representing 2D images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D image observations.
Migration and Jobs : Issues for the 21st Century

With an estimated 724 million extreme poor people living in developing countries, and the world's demographics bifurcating into an older North and a younger South, there are substantial economic incentives and benefits for people to migrate. There are also important market and regulatory failures that constrain mobility and reduce the net benefits of migration. This paper reviews the recent literature and proposes a conceptual framework for better integration and coordination of policies that can address the different market and regulatory failures. The paper advances five types of interventions in need of particular attention in design, implementation, and evaluation; namely, (1) active labor market programs that serve local, regional, and foreign markets; (2) remittances and investment subsidies to promote job creation and labor productivity growth; (3) social insurance programs that cover all jobs and facilitate labor mobility; (4) labor taxes to internalize the social costs of migration in receiving regions; and (5) more flexible private sector driven schemes to regulate the flow of migrants and minimize irregular migration.
Larvae and adults exhibit contrasting patterns of immune gene expression and infection resistance in wild flour beetle populations

In nature, hosts face shifting patterns of parasite exposure and life history trade-offs as they develop from birth to old age. As a result, the net fitness benefit of immunological investment can change dramatically from one life stage to the next. Previous work has revealed a puzzling diversity of relative immune investment patterns among juvenile and adult stages, and it is not clear whether lessons learned from one particular population or species can be generalized to wild populations, after accounting for local adaptation and other variance-generating processes. In this study, we quantify larval and adult immune gene expression and resistance to bacterial infection in two flour beetle species (Tribolium castaneum and T. confusum) from two lab-adapted and five wild-derived populations. Our results provide a clear signal of higher infection-induced immunological investment and resistance in adults relative to larvae, despite variation among species in immune gene regulation. Better characterization of stage-specific investment in infection resistance in natural populations can inform our understanding of life history evolution and improve predictions of disease dynamics in the wild.
The Most Parsimonious Reconciliation Problem in the Presence of Incomplete Lineage Sorting and Hybridization is NP-Hard

The maximum parsimony phylogenetic reconciliation problem seeks to explain incongruity between a gene phylogeny and a species phylogeny with respect to a set of evolutionary events. While the reconciliation problem is well-studied for species and gene trees subject to events such as duplication, transfer, loss, and deep coalescence, recent work has examined species phylogenies that incorporate hybridization and are thus represented by networks rather than trees. In this paper, we show that the problem of computing a maximum parsimony reconciliation for a gene tree and species network is NP-hard even when only considering deep coalescence. This result suggests that future work on maximum parsimony reconciliation for species networks should explore approximation algorithms and heuristics.
Consistency Analysis of Replication-Based Probabilistic Key-Value Stores

Partial quorum systems are widely used in distributed key-value stores due to their latency benefits at the expense of providing weaker consistency guarantees. The probabilistically bounded staleness framework (PBS) studied the latency-consistency trade-off of Dynamo-style partial quorum systems through Monte Carlo event-based simulations. In this paper, we study the latency-consistency trade-off for such systems analytically and derive a closed-form expression for the inconsistency probability. Our approach allows fine-tuning of latency and consistency guarantees in key-value stores, which is intractable using Monte Carlo event-based simulations.
Hippocampal neurons construct a map of an abstract value space

The hippocampus is thought to encode a "cognitive map," a structural organization of knowledge about relationships in the world. Place cells, spatially selective hippocampal neurons that have been extensively studied in rodents, are one component of this map, describing the relative position of environmental features. However, whether this map extends to abstract, cognitive information remains unknown. Using the relative reward value of cues to define continuous "paths" through an abstract value space, we show that single neurons in primate hippocampus encode this space through value place fields, much like a rodent's place neurons encode paths through physical space. Value place fields remapped when cues changed but also became increasingly correlated across contexts, allowing maps to become generalized. Our findings help explain the critical contribution of the hippocampus to value-based decision-making, providing a mechanism by which knowledge of relationships in the world can be incorporated into reward predictions for guiding decisions.
TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations

Topology matters. Despite the recent success of point cloud processing with geometric deep learning, it remains arduous to capture the complex topologies of point cloud data with a learning model. Given a point cloud dataset containing objects with various genera, or scenes with multiple objects, we propose an autoencoder, TearingNet, which tackles the challenging task of representing the point clouds using a fixed-length descriptor. Unlike existing works directly deforming predefined primitives of genus zero (e.g., a 2D square patch) to an object-level point cloud, our TearingNet is characterized by a proposed Tearing network module and a Folding network module interacting with each other iteratively. Particularly, the Tearing network module learns the point cloud topology explicitly. By breaking the edges of a primitive graph, it tears the graph into patches or with holes to emulate the topology of a target point cloud, leading to faithful reconstructions. Experimentation shows the superiority of our proposal in terms of reconstructing point clouds as well as generating more topology-friendly representations than benchmarks.
Single photoelectron identification with Incom LAPPD 38

Incom Inc. Large Area Picosecond Photodetector (LAPPD) 38 has been tested at Jefferson Lab to identify single-photoelectron signals to assess the potential of this type of device for future applications in Cherenkov light detection. Single-photoelectron signals were clearly detected if a tight masking of photons impinging on the photocathode was used compared to the pixelation of the charge collection signal board.
Allogeneic CAR Invariant Natural Killer T Cells Exert Potent Antitumor Effects through Host CD8 T-Cell Cross-Priming.

Purpose: The development of allogeneic chimeric antigen receptor (CAR) T-cell therapies for off-the-shelf use is a major goal that faces two main immunologic challenges, namely the risk of graft-versus-host disease (GvHD) induction by the transferred cells and the rejection by the host immune system limiting their persistence. In this work we assessed the direct and indirect antitumor effect of allogeneic CAR-engineered invariant natural killer T (iNKT) cells, a cell population without GvHD-induction potential that displays immunomodulatory properties. Experimental Design: After assessing murine CAR iNKT cells direct antitumor effects in vitro and in vivo, we employed an immunocompetent mouse model of B-cell lymphoma to assess the interaction between allogeneic CAR iNKT cells and endogenous immune cells. Results: We demonstrate that allogeneic CAR iNKT cells exerted potent direct and indirect antitumor activity when administered across major MHC barriers by inducing tumor-specific antitumor immunity through host CD8 T-cell cross-priming. Conclusions: In addition to their known direct cytotoxic effect, allogeneic CAR iNKT cells induce host CD8 T-cell antitumor responses, resulting in a potent antitumor effect lasting longer than the physical persistence of the allogeneic cells. The utilization of off-the-shelf allogeneic CAR iNKT cells could meet significant unmet needs in the clinic.
A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread

Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, which is based on a discrete-time spatio-temporal susceptible, exposed, infected, recovered, and deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 COVID-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.
A contingent diffusion model of local climate change policy adoption: Evidence from Southern California cities

Abstract Climate change policy is an essential driver of urban sustainability, yet minimal research has examined how they emerge and spread across cities in shared metropolitan areas. While policy diffusion or policy mobility theories could explain the aforesaid, much of the international scholarship have not utilized an amalgamation of these two complementary theoretical perspectives. As a result, we lack a comprehensive understanding of the regional dynamics and spillover effects local governments can generate for urban sustainability. This article proposes a contingent diffusion model to examine the intra-regional adoption of local climate action plans within Southern California from 2000 to 2018. We find that neighboring jurisdictions that adopt climate policy increase the likelihood of a home city's adoption. Yet, neighboring effect is contingent on the home city's existing pro-environmental policy propensity. However, in contrast to conventional wisdom, this study finds no evidence that local environmental advocacy groups play a crucial role in the diffusion of municipal climate initiatives. Altogether, the results suggest that a regional diffusion model of policy innovations is more likely successful when adaptive capacity is in place to facilitate green infrastructure. These findings provide implications for policymakers and planners who want to achieve successful spillovers of urban climate policies across local governments.
Revisiting trends in the exchange current for hydrogen evolution

Norskov and collaborators proposed a simple kinetic model to explain the volcano relation for the hydrogen evolution reaction on transition metal surfaces such that j0 = k0f(ΔGH) where j0 is the exchange current density, f(ΔGH) is a function of the hydrogen adsorption free energy ΔGH as computed from density functional theory, and k0 is a universal rate constant. Herein, focusing on the hydrogen evolution reaction in acidic medium, we revisit the original experimental data and find that the fidelity of this kinetic model can be significantly improved by invoking metal-dependence on k0 such that the logarithm of k0 linearly depends on the absolute value of ΔGH. We further confirm this relationship using additional experimental data points obtained from a critical review of the available literature. Our analyses show that the new model decreases the discrepancy between calculated and experimental exchange current density values by up to four orders of magnitude. Furthermore, we show the model can be further improved using machine learning and statistical inference methods that integrate additional material properties.
Clinical characteristics and outcomes of COVID-19 breakthrough infections among vaccinated patients with systemic autoimmune rheumatic diseases.

SARS-CoV-2 vaccines reduce the risk of COVID-19.1–3 However, some disease-modifying anti-rheumatic drugs (DMARDs), particularly glucocorticoids, methotrexate, mycophenolate mofetil and rituximab, may blunt the immunological response to COVID-19 vaccination.4 Little is known about the clinical efficacy of these vaccines at preventing COVID-19 infection in patients with systemic autoimmune rheumatic diseases (SARDs). Mass General Brigham (MGB) is a large multicentre healthcare system in the Boston, Massachusetts, USA area. Patients with SARDs with a positive SARS-CoV-2 PCR or antigen test between 30 January 2020 and 30 July 2021 at MGB were identified using diagnostic billing codes or were referred by physicians, as previously described.5 From this cohort, we identified breakthrough infections in fully vaccinated patients, defined as a positive test ≥14 days after the final vaccine dose.6 Of 786 SARD patients with COVID-19, 340 occurred after the initial emergency use authorisation for COVID-19 vaccination in the USA. Of these, 16 (4.7%) were breakthrough infections (online supplemental figure 1). Among the breakthrough infections, 12 (75%) were female, 11 (69%) were white, the median age was 50 years and 12 (75%) had ≥1 comorbidity (table 1). The most common SARDs included rheumatoid arthritis (6, 38%), inflammatory myositis (3, 19%) and systemic lupus erythematosus (3, 19%). Rituximab (5, …
A Provably Convergent Information Bottleneck Solution via ADMM

The Information bottleneck (IB) method enables optimizing over the trade-off between compression of data and prediction accuracy of learned representations, and has successfully and robustly been applied to both supervised and unsupervised representation learning problems. However, IB has several limitations. First, the IB problem is hard to optimize. The IB Lagrangian $\mathcal{L}_{IB}: =I(X;Z)-\beta I(Y;Z)$ is non-convex and existing solutions guarantee only local convergence. As a result, the obtained solutions depend on initialization. Second, the evaluation of a solution is also a challenging task. Conventionally, it resorts to characterizing the information plane, that is, plotting $I(Y;Z)$ versus $I(X;Z)$ for all solutions obtained from different initial points. Furthermore, the IB Lagrangian has phase transitions while varying the multiplier $\beta$ . At phase transitions, both $I(X;Z)$ and $I(Y;Z)$ increase abruptly and the rate of convergence becomes significantly slow for existing solutions. Recent works with IB adopt variational surrogate bounds to the IB Lagrangian. Although allowing efficient optimization, how close are these surrogates to the IB Lagrangian is not clear. In this work, we solve the IB Lagrangian using augmented Lagrangian methods. With augmented variables, we show that the IB objective can be solved with the alternating direction method of multipliers (ADMM). Different from prior works, we prove that the proposed algorithm is consistently convergent, regardless of the value of $\beta$ . Empirically, our gradient-descent-based method results in information plane points that are comparable to those obtained through the conventional Blahut-Arimoto-based solvers, and is convergent for a wider range of the penalty coefficient than previous ADMM-based solvers.
The Kuramoto model on a sphere: Explaining its low-dimensional dynamics with group theory and hyperbolic geometry

We study a system of N identical interacting particles moving on the unit sphere in d-dimensional space. The particles are self-propelled and coupled all to all, and their motion is heavily overdamped. For d=2, the system reduces to the classic Kuramoto model of coupled oscillators; for d=3, it has been proposed to describe the orientation dynamics of swarms of drones or other entities moving about in three-dimensional space. Here, we use group theory to explain the recent discovery that the model shows low-dimensional dynamics for all N≥3 and to clarify why it admits the analog of the Ott–Antonsen ansatz in the continuum limit N→∞. The underlying reason is that the system is intimately connected to the natural hyperbolic geometry on the unit ball Bd. In this geometry, the isometries form a Lie group consisting of higher-dimensional generalizations of the Mobius transformations used in complex analysis. Once these connections are realized, the reduced dynamics and the generalized Ott–Antonsen ansatz follow immediately. This framework also reveals the seamless connection between the finite and infinite- N cases. Finally, we show that special forms of coupling yield gradient dynamics with respect to the hyperbolic metric and use that fact to obtain global stability results about convergence to the synchronized state.
GhostiPy: An Efficient Signal Processing and Spectral Analysis Toolbox for Large Data.

Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large scale modalities. Here we introduce ghostipy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time-frequency transforms. ghostipy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis. Significance Statement Due to technological innovation the size of neural recordings has increased dramatically, but downstream analysis code is often not optimized to handle such large scales of data efficiently. Here we have developed GhostiPy, an open source Python package prioritizing performance and efficiency for large data in the context of typical spectral analysis and signal processing algorithms. Users can control hardware resource consumption (such as system memory) by setting the level of parallelization and enabling out-of-core processing. Thus algorithms can be run on a variety of hardware, from laptops to dedicated compute servers. Overall, GhostiPy improves experimental throughput by increasing the portability of analyses.
Valuation Risk in Mutual Fund Portfolio Disclosure

Valuation risk of a security—uncertainty about its fair value—is a subject of considerable concern in the mutual fund industry. If funds report different values for identical securities, investors cannot easily compare performance. Yet it is not unusual to see identical illiquid stocks, small-cap stocks, stocks with high analyst dispersion, stocks with less analyst coverage, and newly listed stocks valued differently across mutual funds. An equity fund that has positive price dispersion in its portfolio holdings, that performs poorly, that belongs to a fund family with an inclination for aggressive reporting, that holds more stocks subject to stale prices, that holds more pre-IPO firms, or that experiences net outflows will tend to show positive price dispersion again in the next quarter. This behavior is significant in a volatile market. Aggressive reporting helps funds gain in the mutual fund tournament.
Parameter Estimation for Undirected Graphical Models With Hard Constraints

The hardcore model on a graph $G$ with parameter $\lambda > 0$ is a probability measure on the collection of all independent sets of $G$ , that assigns to each independent set $I$ a probability proportional to $\lambda ^{|I|}$ . In this paper we consider the problem of estimating the parameter $\lambda $ given a single sample from the hardcore model on a graph $G$ . To bypass the computational intractability of the maximum likelihood method, we use the maximum pseudo-likelihood (MPL) estimator, which for the hardcore model has a surprisingly simple closed form expression. We show that for any sequence of graphs $\{G_{N}\}_{N \geq 1}$ , where $G_{N}$ is a graph on $N$ vertices, the MPL estimate of $\lambda $ is $\sqrt {N}$ -consistent (that is, it converges to the true parameter at rate $1/\sqrt {N}$ ), whenever the graph sequence has uniformly bounded average degree. We then extend our methods to obtain estimates for the vector of activity parameters in general $H$ -coloring models, in which restrictions between adjacent colors are encoded by a constraint graph $H$ . These constitute an important class of Markov random fields that includes all hard-constraint models, which arise in a broad array of fields including combinatorics, statistical physics, and communication networks. Given a single sample from an $H$ -coloring model, we derive sufficient conditions under which the MPL estimate is $\sqrt {N}$ -consistent. Moreover, we verify the sufficient conditions for $H$ -coloring models for which there is at least one ‘unconstrained’ color (that is, there exists at least one vertex in the constraint graph $H$ that is connected to all vertices), as long as the graph sequence has uniformly bounded average degree. This applies to many $H$ -coloring examples such as the Widom-Rowlinson and multi-state hard-core models. On the other hand, for the $q$ -coloring model, which falls outside this class, we show that the condition can fail and consistent estimation may be impossible even for graphs with bounded average degree. Nevertheless, we show that the MPL estimate is $\sqrt {N}$ -consistent in the $q$ -coloring model when $\{G_{N}\}_{N \geq 1}$ has bounded average double neighborhood. The presence of hard constraints, as opposed to soft constraints, leads to new challenges, and our proofs entail applications of the method of exchangeable pairs as well as combinatorial arguments that employ the probabilistic method.
Computing molecular excited states on a D-Wave quantum annealer

The possibility of using quantum computers for electronic structure calculations has opened up a promising avenue for computational chemistry. Towards this direction, numerous algorithmic advances have been made in the last five years. The potential of quantum annealers, which are the prototypes of adiabatic quantum computers, is yet to be fully explored. In this work, we demonstrate the use of a D-Wave quantum annealer for the calculation of excited electronic states of molecular systems. These simulations play an important role in a number of areas, such as photovoltaics, semiconductor technology and nanoscience. The excited states are treated using two methods, time-dependent Hartree–Fock (TDHF) and time-dependent density-functional theory (TDDFT), both within a commonly used Tamm–Dancoff approximation (TDA). The resulting TDA eigenvalue equations are solved on a D-Wave quantum annealer using the Quantum Annealer Eigensolver (QAE), developed previously. The method is shown to reproduce a typical basis set convergence on the example $$\hbox {H}_2$$ molecule and is also applied to several other molecular species. Characteristic properties such as transition dipole moments and oscillator strengths are computed as well. Three potential energy profiles for excited states are computed for $$\hbox {NH}_3$$ as a function of the molecular geometry. Similar to previous studies, the accuracy of the method is dependent on the accuracy of the intermediate meta-heuristic software called qbsolv.