cssi/SciSciGPT-SciSciNet huggingface dataset:
Presidents and Patronage

To what extent do presidents select appointees based upon campaign experience and connections? The answer to this question has important implications for our understanding of presidential management and political leadership. This article presents a theory explaining where presidents place different types of appointees and why, focusing on differences in ideology, competence, and non-policy patronage benefits among potential appointees. We develop a formal model and test its implications with new data on 1,307 persons appointed in the first six months of the Obama administration. The empirical results broadly support the theory, suggesting that President Obama was more likely to place appointees selected for non-policy patronage reasons in agencies off his agenda, in agencies that shared his policy views, and where appointees are least able to affect agency performance. We conclude that patronage continues to play an important role in American politics, with important consequences for campaigns, presidential politics, and governance.
Expanding Secularism's Scope: An Indian Case Study

Since September 11, 2001, the relationship between religion and world politics has become a most contested issue, especially in the context of developing nations. Disagreements about the role of religion in the new nation's laws constituted a major delaying factor in the drafting of the Afghani constitution. Similar disagreements remain a major obstacle to conflict resolution in the re-building efforts in Iraq. Most of the debates on law and religion refer to examples of secular structures in the United States or Europe. Perhaps India, the largest democracy with the oldest constitution in the developing world, may have more to offer to other emerging nations grappling with the issue. In this paper, I attempt to define and re-define secularism and propose that secularism can find different expressions in different contexts. Specifically, I look at India where a constituent assembly elected by and representative of the people wrote a constitution that creates legal and political space for a progressive secular ideal. In Part I, I look at various meanings of the concept of secularism. I then discuss how secularism developed in the Christian European context. I argue that secularism came about not as an anti-religion ideology, but rather from within Christian discourse, as a response to keep the growing centralized state out of the religious sphere. I argue that this idea was never allowed to evolve in the developing world context, where secularism was implemented by elites, most often removed from the realities of the masses.
Climate Change, Fragmentation, and the Challenges of Global Environmental Law: Elements of a Post-Copenhagen Assemblage

The 2009 United Nations climate conference in Copenhagen has been widely viewed as a failure—a referendum in the eyes of many on the top-down, comprehensive approach to climate governance embodied in the Kyoto Protocol and carried forward in efforts to negotiate a successor regime. Despite a modest agreement on future work toward a new agreement, the most recent climate meeting in Cancun Mexico reinforces this view, underscoring the conclusion that Copenhagen represents an important inflection point for international climate policy. Although much of the post-Copenhagen commentary has correctly identified various problems, even fatal flaws, with the process, very little has been particularly helpful in marking out a constructive way forward. This Article takes some steps in that direction, offering a partial re-conceptualization of the nature and possibilities of global climate governance in the post-Copenhagen era. It starts from the premise that any realistic approach to climate governance must begin with the facts of globalization, legal * Associate Professor of Law, University of Colorado Law School. J.D. Stanford Law School; Ph.D. University of California, Berkeley, Energy & Resources Group. Many thanks to Ann Carlson, Brigham Daniels, Dan Kahan, Sarah Krakoff, Doug Kysar, Andrew Long, Catherine O’Neill, Jed Purdy, Carol Rose, J.B. Ruhl, Jim Salzman, and Anna Spain for helpful comments. Vanessa Finch and Katie Patterson provided excellent research assistance. This Article benefitted from presentations and discussion at the Stanford Law School Environment & Energy Workshop and the Colorado-Duke Climate Change Law & Policy Works-in-Progress Symposium. Some of the underlying research for this Article draws on work that I have been conducting since 2008 as senior advisor and project lead for the Governors’ Climate and Forests Task Force (“GCF”). That work has been generously supported by the Gordon & Betty Moore Foundation, the David & Lucille Packard Foundation and the ClimateWorks Foundation. All of the views expressed in this Article are my own and do not reflect the views or opinions of the GCF, its member states and provinces, the foundations supporting the GCF, or any of the people involved in the GCF effort.
The Role of Trading Halts in Monitoring a Specialist Market

When a collection of specialists organize as an exchange, each can reap net private benefits at the expense of the exchange by quoting a privately optimal pricing schedule. Coordination makes all specialists and customers better off, but requires a system of monitoring and punishment that breaks down when information asymmetries between the exchange and a specialist are high. The specialist may then seek a temporary trading halt to alleviate unjustified punishment, or the exchange may halt trading to prevent the quoting of damaging privately optimal pricing schedules. We test this theory on a sample of NYSE halts. As predicted, we find a significant increase in estimated information asymmetry immediately preceding trading halts. Copyright 2003, Oxford University Press.
Competing at Home to Win Abroad: Evidence from Japanese Industry

The study explores the influence of domestic competition on international trade performance, using data from a broad sample of Japanese industries. Domestic rivalry is measured directly using market-share instability rather than employing structural variables such as seller concentration. We find robust evidence that domestic rivalry has a positive and significant relationship with trade performance measured by world export share, particularly when R&D intensity reveals opportunities for dynamic improvement and innovation. Conversely, trade protection reduces export performance. These findings support the view that local competition—not monopoly, collusion, or a sheltered home market— pressures dynamic improvement that leads to international competitiveness.
A Potential Role for Aminoacylation in Primordial RNA Copying Chemistry

Aminoacylated tRNAs are the substrates for ribosomal protein synthesis in all branches of life, implying an ancient origin for aminoacylation chemistry. In the 1970s, Orgel and colleagues reported potentially prebiotic routes to aminoacylated nucleotides and their RNA-templated condensation to form amino acid-bridged dinucleotides. However, it is unclear whether such reactions would have aided or impeded non-enzymatic RNA replication. Determining whether aminoacylated RNAs could have been advantageous in evolution prior to the emergence of protein synthesis remains a key challenge. We therefore tested the ability of aminoacylated RNA to participate in both templated primer extension and ligation reactions. We find that at low magnesium concentrations that favor fatty acid-based protocells, these reactions proceed orders of magnitude more rapidly than when initiated from the cis-diol of unmodified RNA. We further demonstrate that amino acid-bridged RNAs can act as templates in a subsequent round of copying. Our results suggest that aminoacylation facilitated non-enzymatic RNA replication, thus outlining a potentially primordial functional link between aminoacylation chemistry and RNA replication.
Proof-Carrying Data Without Succinct Arguments.

Proof-carrying data (PCD) is a powerful cryptographic primitive that enables mutually distrustful parties to perform distributed computations that run indefinitely. Known approaches to construct PCD are based on succinct non-interactive arguments of knowledge (SNARKs) that have a succinct verifier or a succinct accumulation scheme.
De novo assembly of a chromosome-scale reference genome for the northern flicker Colaptes auratus.

The northern flicker, Colaptes auratus, is a widely distributed North American woodpecker and a long-standing focal species for the study of ecology, behavior, phenotypic differentiation, and hybridization. We present here a highly contiguous de novo genome assembly of C. auratus, the first such assembly for the species and the first published chromosome-level assembly for woodpeckers (Picidae). The assembly was generated using a combination of short-read Chromium 10× and long-read PacBio sequencing, and further scaffolded with chromatin conformation capture (Hi-C) reads. The resulting genome assembly is 1.378 Gb in size, with a scaffold N50 of 11 and a scaffold L50 of 43.948 Mb. This assembly contains 87.4-91.7% of genes present across four sets of universal single-copy orthologs found in tetrapods and birds. We annotated the assembly both for genes and repetitive content, identifying 18,745 genes and a prevalence of ∼28.0% repetitive elements. Lastly, we used fourfold degenerate sites from neutrally evolving genes to estimate a mutation rate for C. auratus, which we estimated to be 4.007 × 10-9 substitutions/site/year, about 1.5× times faster than an earlier mutation rate estimate of the family. The highly contiguous assembly and annotations we report will serve as a resource for future studies on the genomics of C. auratus and comparative evolution of woodpeckers.
Efficient reservoir computing using field programmable gate array and electro-optic modulation

We experimentally demonstrate a hybrid reservoir computing system consisting of an electro-optic modulator and field programmable gate array (FPGA). It implements delay lines and filters digitally for flexible dynamics and high connectivity, while supporting a large number of reservoir nodes. To evaluate the system’s performance and versatility, three benchmark tests are performed. The first is the 10th order Nonlinear Auto-Regressive Moving Average test (NARMA-10), where the predictions of 1000 and 25,000 steps yield impressively low normalized root mean square errors (NRMSE’s) of 0.142 and 0.148, respectively. Such accurate predictions over into the far future speak to its capability of large sample size processing, as enabled by the present hybrid design. The second is the Santa Fe laser data prediction, where a normalized mean square error (NMSE) of 6.73 × 10−3 is demonstrated. The third is the isolate spoken digit recognition, with a word error rate close to 0.34%. Accurate, versatile, flexibly reconfigurable, and capable of long-term prediction, this reservoir computing system could find a wealth of impactful applications in real-time information processing, weather forecasting, and financial analysis.
Update on Minos

The MINOS (Main Injector Neutrino Oscillation Search) experiment will use the Main Injector neutrino beam to make high-precision oscillation measurements on all possible oscillation channels relevant to the atmospheric neutrino problem. Construction of the detector and the beamline are underway. Data acquisition with atmospheric neutrinos will begin in 2002 while the first beam neutrinos are expected in 2004.
Street Network Models and Indicators for Every Urban Area in the World

Author(s): Boeing, Geoff | Abstract: Cities worldwide exhibit a variety of street network patterns and configurations that shape human mobility, equity, health, and livelihoods. This study models and analyzes the street networks of every urban area in the world, using boundaries derived from the Global Human Settlement Layer. Street network data are acquired and modeled from OpenStreetMap with the open‐source OSMnx software. In total, this study models over 160 million OpenStreetMap street network nodes and over 320 million edges across 8,914 urban areas in 178 countries, and attaches elevation and grade data. This article presents the study’s reproducible computational workflow, introduces two new open data repositories of ready‐to‐use global street network models and calculated indicators, and discusses summary findings on street network form worldwide. It makes four contributions. First, it reports the methodological advances of this open‐source workflow. Second, it produces an open data repository containing street network models for each urban area. Third, it analyzes these models to produce an open data repository containing street network form indicators for each urban area. No such global urban street network indicator data set has previously existed. Fourth, it presents a summary analysis of urban street network form, reporting the first such worldwide results in the literature.
Cobalt- and iron-catalyzed regiodivergent alkene hydrosilylations

A complementary set of base metal catalysts has been developed for regiodivergent alkene hydrosilylations: iron complexes of amine-iminopyridine ligands are selective for Markovnikov hydrosilylations (branched/linear up to >99 : 1), while the cobalt complexes bearing the same type of ligands provide excellent levels of anti-Markovnikov selectivity (linear/branched up to >99 : 1). Both systems exhibit high efficiency and wide functional group tolerance.
Labor market returns to a prison GED

Abstract Educational and skill-building programs are commonplace in prisons and have been the focus of recent prominent policy initiatives. These educational programs are expected to increase prisoners’ post-release employability, with the hope that these lead to both private and public benefits. One of the most popular programs prepares prisoners to pass the GED exam, which is an academic certification for those without a high-school diploma. We analyze the labor market returns to a GED earned in prison using new administrative data on all released prisoners in the state of Missouri over nearly 25 years, and a matched comparison group difference-in-differences design with individual fixed effects. We find that the GED can lead to higher short-term quarterly earnings and employment, with the largest benefits experienced soon after release. These effects are strongest for those who did not have strong work histories prior to entering prison and for those who had access to post-release support. We also find that the effect of the GED is of a similar magnitude for White and Black formerly incarcerated individuals.
The role of neutral and adaptive genomic variation in population diversification and speciation in two ground squirrel species of conservation concern

Understanding the neutral (demographic) and adaptive processes leading to the differentiation of species and populations is a critical component of evolutionary and conservation biology. In this context, recently diverged taxa represent a unique opportunity to study the process of genetic differentiation. Northern and southern Idaho ground squirrels (Urocitellus brunneus-NIDGS, and U. endemicus-SIDGS, respectively) are a recently diverged pair of sister species that have undergone dramatic declines in the last 50 years and are currently found in metapopulations across restricted spatial areas with distinct environmental pressures. Here we genotyped single-nucleotide polymorphisms (SNPs) from buccal swabs with restriction site-associated DNA sequencing (RADseq). With these data we evaluated neutral genetic structure at both the inter- and intraspecific level, and identified putatively adaptive SNPs using population structure outlier detection and genotype-environment association (GEA) analyses. At the interspecific level, we detected a clear separation between NIDGS and SIDGS, and evidence for adaptive differentiation putatively linked to torpor patterns. At the intraspecific level, we found evidence of both neutral and adaptive differentiation. For NIDGS, elevation appears to be the main driver of adaptive differentiation, while neutral variation patterns match and expand information on the low connectivity between some populations identified in previous studies using microsatellite markers. For SIDGS, neutral substructure generally reflected natural geographical barriers, while adaptive variation reflected differences in land cover and temperature, as well as elevation. These results clearly highlight the roles of neutral and adaptive processes for understanding the complexity of the processes leading to species and population differentiation, which can have important conservation implications in susceptible and threatened species.
On a Kantorovich-Rubinstein inequality

Abstract An easy consequence of Kantorovich-Rubinstein duality is the following: if f : [ 0 , 1 ] d → R is Lipschitz and { x 1 , … , x N } ⊂ [ 0 , 1 ] d , then | ∫ [ 0 , 1 ] d f ( x ) d x − 1 N ∑ k = 1 N f ( x k ) | ≤ ‖ ∇ f ‖ L ∞ ⋅ W 1 ( 1 N ∑ k = 1 N δ x k , d x ) , where W 1 denotes the 1−Wasserstein (or Earth Mover's) Distance. We prove a similar inequality with a smaller norm on ∇f and a larger Wasserstein distance ( W ∞ instead of W 1 ). Our inequality is sharp when the points are very regular, i.e. W ∞ ∼ N − 1 / d . This prompts the question whether these two inequalities are specific instances of an entire underlying family of estimates capturing a duality between transport distance and function space.
Exponential increase in neutralizing and spike specific antibodies following vaccination of COVID-19 convalescent plasma donors.

BACKGROUND: With the recent approval of COVID-19 vaccines, recovered COVID-19 subjects who are vaccinated may be ideal candidates to donate COVID-19 convalescent plasma (CCP). CASE SERIES: Eleven recovered COVID-19 patients were screened to donate CCP. All had molecularly confirmed COVID-19, and all but one were antibody positive by chemiluminescence immunoassay (DiaSorin) prior to vaccination. All were tested again for antibodies 11-21 days after they were vaccinated (Pfizer/Moderna). All showed dramatic increases (~50-fold) in spike-specific antibody levels and had at least a 20-fold increase in the IC50 neutralizing antibody titer based on plaque reduction neutralization testing (PRNT). The spike-specific antibody levels following vaccination were significantly higher than those seen in any non-vaccinated COVID-19 subjects tested to date at our facility. CONCLUSION: Spike-specific and neutralizing antibodies demonstrated dramatic increases following a single vaccination after COVID-19 infection, which significantly exceeded values seen with COVID-19 infection alone. Recovered COVID-19 subjects who are vaccinated may make ideal candidates for CCP donation.
Monopoly rents, institutions, and bribery

Why do some firms pay more bribes than others? We extend the literature by examining the role of one crucial, but overlooked industry characteristic---fixed asset intensity---in shaping firms' bribe payments. High fixed asset intensity creates natural entry barriers, thereby leading to market concentration and opportunities for monopoly rent extraction. High rents, in turn, increase the value of government officials' ``control rights'' and thus their incentive to engage in predatory behavior. Firms in fixed-asset intensive industries therefore have strong incentives to pay bribes in exchange for de facto property rights. We further posit that strong legal institutions weaken this quid pro quo by providing security for property rights and increasing the risk for government officials' behaving corruptly. We find empirical support for our arguments based on data from a large firm survey in China. Our findings have important implications for governance and industrial regulations in developing countries.
TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics.

Tasks, Datasets and Evaluation Metrics are important concepts for understanding experimental scientific papers. However, previous work on information extraction for scientific literature mainly focuses on the abstracts only, and does not treat datasets as a separate type of entity (Zadeh and Schumann, 2016; Luan et al., 2018). In this paper, we present a new corpus that contains domain expert annotations for Task (T), Dataset (D), Metric (M) entities 2,000 sentences extracted from NLP papers. We report experiment results on TDM extraction using a simple data augmentation strategy and apply our tagger to around 30,000 NLP papers from the ACL Anthology. The corpus is made publicly available to the community for fostering research on scientific publication summarization (Erera et al., 2019) and knowledge discovery.
On Hallucination and Predictive Uncertainty in Conditional Language Generation

Despite improvements in performances on different natural language generation tasks, deep neural models are prone to hallucinating facts that are incorrect or nonexistent. Different hypotheses are proposed and examined separately for different tasks, but no systematic explanations are available across these tasks. In this study, we draw connections between hallucinations and predictive uncertainty in conditional language generation. We investigate their relationship in both image captioning and data-to-text generation and propose a simple extension to beam search to reduce hallucination. Our analysis shows that higher predictive uncertainty corresponds to a higher chance of hallucination. Epistemic uncertainty is more indicative of hallucination than aleatoric or total uncertainties. It helps to achieve better results of trading performance in standard metric for less hallucination with the proposed beam search variant.
Extreme C-to-A Hypermutation at a Site of Cytosine-N4 Methylation.

Methylation of cytosine in DNA at position C5 increases the rate of C→T mutations in bacteria and eukaryotes. Methylation at the N4 position, employed by some restriction-modification systems, is not known to increase the mutation rate. Here, I report that a Salmonella enterica Type III restriction-modification system that includes a cytosine-N4 methyltransferase causes an enormous increase in the rate of mutation of the methylated cytosines, which occur at the overlined C in the motif CACCGT Mutations consist mainly of C→A transversions, the rate of which is increased ∼500-fold by the restriction-modification system. The rate of C→T transitions is also increased and somewhat exceeds that at C5-methylated cytosines in Dcm sites. Two other Salmonella N4 methyltransferases investigated do not have such dramatic effects, although in one case there is a modest increase in C→A mutations along with an increase in C→T mutations. The sensitivity of the C→A rate to orientation with respect to both DNA replication and transcription is higher at hypermutable sites than at other cytosines, suggesting a fundamental mechanistic difference between hypermutation and ordinary mutation.IMPORTANCE Mutation produces the raw material for adaptive evolution but also imposes a burden because most mutations are deleterious. The rate of mutation at a particular site is affected by a variety of factors. In both prokaryotes and eukaryotes, methylation of C at the C5 position, a naturally occurring DNA modification, greatly increases the rate of C→T mutation. A distinct C modification that occurs in prokaryotes, methylation at N4, is not known to increase mutation rate. Here, I report that a bacterial restriction-modification system, found in some Salmonella bacteria, increases the rate of C→A mutation by a factor of 500 at sites that it methylates at N4. This rate increase is much greater than that caused by C5 methylation. Although fewer than 1 in 1,600 positions analyzed are methylation sites, over 10% of all mutations occur at these sites. Like other examples of extremely high mutation rate, whether naturally occurring or the result of laboratory mutation, this phenomenon may shed light on the mechanism of mutation in general.
Perturbations of circuit evolution matrices with Jordan blocks

In our prior studies, we synthesized special circuits with evolution matrices featuring degenerate eigenfrequencies and nontrivial Jordan blocks. The degeneracy of this type is sometimes referred to as exceptional point of degeneracy (EPD). Our focus here is on the simplest of our circuits featuring EPDs that are composed of only two LC-loops coupled by a gyrator. These circuits, when near an EPD state, can be used for enhanced sensitivity applications. With that in mind, we develop here a comprehensive perturbation theory for these simple circuits near an EPD. Using this theory, we propose an approach to sensing, allowing one to benefit from the proximity to an EPD on the one hand when providing for stable operation on the other hand. We also address a broader scope of problems related to perturbations of Jordan blocks and their numerical treatment that allow us to effectively detect proximity to Jordan blocks.
The QuaStar Survey: Detecting Hidden Low-velocity Gas in the Milky Way's Circumgalactic Medium

From our position embedded within the Milky Way's interstellar medium (ISM), we have limited ability to detect gas at low relative velocities in the extended Galactic halo because those spectral lines are blended with much stronger signals from dense foreground gas. As a result, the content of the Milky Way's circumgalactic medium (CGM) is poorly constrained at $|v_{\rm LSR}|$ $\lesssim$ 150 km s$^{-1}$. To overcome this complication, the QuaStar Survey applies a spectral differencing technique using paired quasar-star sightlines to measure the obscured content of the Milky Way's CGM for the first time. We present measurements of the CIV doublet ($\lambda\lambda$ 1548 A, 1550 A), a rest-frame UV metal line transition detected in HST/COS spectra of 30 halo-star/quasar pairs evenly distributed across the sky at Galactic latitudes $|b|>30^\circ$. The 30 halo stars have well-constrained distances (d$\approx$5-14 kpc), and are paired with quasars separated by $ 13.65)=$ 20% [6/30], a value significantly lower than the covering fraction for star-forming galaxies at low redshift. Our results suggest either that the bulk of Milky Way's CIV-traced CGM lies at low Galactic latitudes, or that the Milky Way's CGM is lacking in warm, ionized material compared to low-redshift ($z < 0.1$) star-forming galaxy halos.
A survey on practical adversarial examples for malware classifiers

Machine learning based solutions have been very helpful in solving problems that deal with immense amounts of data, such as malware detection and classification. However, deep neural networks have been found to be vulnerable to adversarial examples, or inputs that have been purposefully perturbed to result in an incorrect label. Researchers have shown that this vulnerability can be exploited to create evasive malware samples. However, many proposed attacks do not generate an executable and instead generate a feature vector. To fully understand the impact of adversarial examples on malware detection, we review practical attacks against malware classifiers that generate executable adversarial malware examples. We also discuss current challenges in this area of research, as well as suggestions for improvement and future research directions.
Graph Neural Networks for Decentralized Controllers

Dynamical systems comprised of autonomous agents arise in many relevant problems such as multi-agent robotics, smart grids, or smart cities. Controlling these systems is of paramount importance to guarantee a successful deployment. Optimal centralized controllers are readily available but face limitations in terms of scalability and practical implementation. Optimal decentralized controllers, on the other hand, are difficult to find. In this paper, we propose a framework using graph neural networks (GNNs) to learn decentralized controllers from data. While GNNs are naturally distributed architectures, making them perfectly suited for the task, we adapt them to handle delayed communications as well. Furthermore, they are equivariant and stable, leading to good scalability and transferability properties. The problem of flocking is explored to illustrate the potential of GNNs in learning decentralized controllers.
Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

Abstract We introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation and flow processes. The proposed framework utilizes a hypothesized coarse-graining methodology with manifold learning and surrogate-based optimization techniques. Coarse-grained high-dimensional data describing quantities of interest of the multiscale models are projected onto a nonlinear manifold whose geometric and topological structure is exploited for measuring behavioral discrepancies in the form of manifold distances. A surrogate model is constructed using Gaussian process regression to identify a mapping between stochastic parameters and distances. Derivative-free optimization is employed to adaptively identify a unique set of parameters of the upper-scale model capable of rapidly reproducing the system’s behavior while maintaining consistency with coarse-grained atomic-level simulations. The proposed method is applied to learn the parameters of the shear transformation zone (STZ) theory of plasticity that describes plastic deformation in amorphous solids as well as coarse-graining parameters needed to translate between atomistic and continuum representations. We show that the methodology is able to successfully link coarse-grained microscale simulations to macroscale observables and achieve a high-level of parity between the models across scales.
Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning

Portable parallel programming models provide the potential for high performance and productivity, however they come with a multitude of runtime parameters that can have significant impact on execution performance. Selecting the optimal set of those parameters is non-trivial, so that HPC applications perform well in different system environments and on different input data sets, without the need of time consuming parameter exploration or major algorithmic adjustments.
Provably Efficient Algorithms for Multi-Objective Competitive RL

We study multi-objective reinforcement learning (RL) where an agent's reward is represented as a vector. In settings where an agent competes against opponents, its performance is measured by the distance of its average return vector to a target set. We develop statistically and computationally efficient algorithms to approach the associated target set. Our results extend Blackwell's approachability theorem (Blackwell, 1956) to tabular RL, where strategic exploration becomes essential. The algorithms presented are adaptive; their guarantees hold even without Blackwell's approachability condition. If the opponents use fixed policies, we give an improved rate of approaching the target set while also tackling the more ambitious goal of simultaneously minimizing a scalar cost function. We discuss our analysis for this special case by relating our results to previous works on constrained RL. To our knowledge, this work provides the first provably efficient algorithms for vector-valued Markov games and our theoretical guarantees are near-optimal.
Clustering-based Inference for Biomedical Entity Linking.

Due to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data. This necessitates entity linking models which are able to link mentions of unseen entities using learned representations of entities. Previous approaches link each mention independently, ignoring the relationships within and across documents between the entity mentions. These relations can be very useful for linking mentions in biomedical text where linking decisions are often difficult due mentions having a generic or a highly specialized form. In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions. In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for entity linking by 3.0 points of accuracy, and our clustering-based inference model further improves entity linking by 2.3 points.
Towards Lefschetz Thimbles in Sigma Models, I

We study two dimensional path integral Lefschetz thimbles, i.e. the possible path integration contours. Specifically, in the examples of the O(N) and $$\mathbb{C}{{\mathbb{P}}^{{N - 1}}}$$ models, we find a large class of complex critical points of the sigma model actions which are relevant for the theory in finite volume at finite temperature, with various chemical potentials corresponding to the symmetries of the models. In this paper we discuss the case of the O(2m) and the $$\mathbb{C}{{\mathbb{P}}^{{N - 1}}}$$ models in the sector of zero instanton charge, as well as some solutions of the O(2m + 1) model. The $$\mathbb{C}{{\mathbb{P}}^{{N - 1}}}$$ -model for all instanton charges and a more general class of solutions of the O(N)-model with odd N will be discussed in the forthcoming paper.
Electron Charge Density: A Clue from Quantum Chemistry for Quantum Foundations

Within quantum chemistry, the electron clouds that surround nuclei in atoms and molecules are sometimes treated as clouds of probability and sometimes as clouds of charge. These two roles, tracing back to Schrodinger and Born, are in tension with one another but are not incompatible. Schrodinger’s idea that the nucleus of an atom is surrounded by a spread-out electron charge density is supported by a variety of evidence from quantum chemistry, including two methods that are used to determine atomic and molecular structure: the Hartree-Fock method and density functional theory. Taking this evidence as a clue to the foundations of quantum physics, Schrodinger’s electron charge density can be incorporated into many different interpretations of quantum mechanics (and extensions of such interpretations to quantum field theory).
Spectral density functions of bivariable stable polynomials

The relationship between a stable multivariable polynomial p(z) and the Fourier coefficients of its spectral density function $$1/|p(z)|^2$$ , is further investigated. In this paper we focus on the radial asymptotics of the Fourier coefficients for a specific choice of a two variable polynomial. Hypergeometric functions appear in the analysis, and new results are derived for these as well.
X-Fact: A New Benchmark Dataset for Multilingual Fact Checking

In this work, we introduce : the largest publicly available multilingual dataset for factual verification of naturally existing real-world claims. The dataset contains short statements in 25 languages and is labeled for veracity by expert fact-checkers. The dataset includes a multilingual evaluation benchmark that measures both out-of-domain generalization, and zero-shot capabilities of the multilingual models. Using state-of-the-art multilingual transformer-based models, we develop several automated fact-checking models that, along with textual claims, make use of additional metadata and evidence from news stories retrieved using a search engine. Empirically, our best model attains an F-score of around 40%, suggesting that our dataset is a challenging benchmark for the evaluation of multilingual fact-checking models.
What are Table Cartograms Good for Anyway? An Algebraic Analysis

Unfamiliar or esoteric visual forms arise in many areas of visualization. While such forms can be intriguing, it can be unclear how to make effective use of them without long periods of practice or costly user studies. In this work we analyze the table cartogram-a graphic which visualizes tabular data by bringing the areas of a grid of quadrilaterals into correspondence with the input data, like a heat map that has been "area-ed" rather than colored. Despite having existed for several years, little is known about its appropriate usage. We mend this gap by using Algebraic Visualization Design to show that they are best suited to relatively small tables with ordinal axes for some comparison and outlier identification tasks. In doing so we demonstrate a discount theory-based analysis that can be used to cheaply determine best practices for unknown visualizations.
Mathematics of Digital Hyperspace

Social media, e-commerce, streaming video, e-mail, cloud documents, web pages, traffic flows, and network packets fill vast digital lakes, rivers, and oceans that we each navigate daily. This digital hyperspace is an amorphous flow of data supported by continuous streams that stretch standard concepts of type and dimension. The unstructured data of digital hyperspace can be elegantly represented, traversed, and transformed via the mathematics of hypergraphs, hypersparse matrices, and associative array algebra. This paper explores a novel mathematical concept, the semilink, that combines pairs of semirings to provide the essential operations for graph analytics, database operations, and machine learning. The GraphBLAS standard currently supports hypergraphs, hypersparse matrices, the mathematics required for semilinks, and seamlessly performs graph, network, and matrix operations. With the addition of key based indices (such as pointers to strings) and semilinks, GraphBLAS can become a richer associative array algebra and be a plug-in replacement for spreadsheets, database tables, and data centric operating systems, enhancing the navigation of unstructured data found in digital hyperspace.
Non-destructive vacuum-assisted measurement of lung elastic modulus.

Abstract In living tissues, mechanical stiffness and biological function are intrinsically linked. Alterations in the stiffness of tissues can induce pathological interactions that affect cellular activity and tissue function. Underlying connections between tissue stiffness and disease highlights the importance of accurate quantitative characterizations of soft tissue mechanics, which can improve our understanding of disease and inform therapeutic development. In particular, accurate measurement of lung mechanical properties has been especially challenging due to the anatomical and mechanobiological complexities of the lung. Discrepancies between measured mechanical properties of dissected lung tissue samples and intact lung tissues in vivo has limited the ability to accurately characterize integral lung mechanics. Here, we report a non-destructive vacuum-assisted method to evaluate mechanical properties of soft biomaterials, including intact tissues and hydrogels. Using this approach, we measured elastic moduli of rat lung tissue that varied depending on stress-strain distribution throughout the lung. We also observed that the elastic moduli of enzymatically disrupted lung parenchyma increased by at least 64%. The reported methodology enables assessment of the nonlinear viscoelastic characteristics of intact lungs under normal and abnormal (i.e., injured, diseased) conditions and allows measurement of mechanical properties of tissue-mimetic biomaterials for use in therapeutics or in vitro models. Statement of significance Accurate quantification of tissue stiffness is critical for understanding mechanisms of disease and developing effective therapeutics. Current modalities to measure tissue stiffness are destructive and preclude accurate assessment of lung mechanical properties, as lung mechanics are determined by complex features of the intact lung. To address the need for alternative methods to assess lung mechanics, we report a non-destructive vacuum-based approach to quantify tissue stiffness. We applied this method to correlate lung tissue mechanics with tissue disruption, and to assess the stiffness of biomaterials. This method can be used to inform the development of tissue-mimetic materials for use in therapeutics and disease models, and could potentially be applied for in-situ evaluation of tissue stiffness as a diagnostic or prognostic tool.
Identification of Auger mechanisms responsible for low energy electron emission from graphene on copper using Auger-gamma coincidence spectroscopy

Abstract We have applied positron annihilation induced Auger-gamma coincidence spectroscopy to identify important mechanisms responsible for the emission of low energy electrons following the sudden creation of holes in bilayer graphene on copper substrate. The novel surface spectroscopic method measures the energy of the Doppler shifted annihilation gamma photon in coincidence with the Auger electron emitted following the relaxation of the hole created by the annihilation of a surface electron with a surface trapped positron. By extracting and theoretically modelling the annihilation gamma spectra coincident with low energy electrons, we associate majority of the intensity in the low energy (7 eV–25 eV) region of the Auger spectrum to electron emission following Auger decays of 2s holes in adsorbed oxygen and deep valence holes in graphene. We provide additional support to this conclusion by showing that most of the Auger electrons with energy less than ∼ 25 eV are coincident with gamma photons with small Doppler shift (511 keV–512 keV) indicating that the primary electron whose annihilation resulted in low energy Auger electron emission had small momentum parallel to the gamma emission direction. On the other hand, majority of the Auger electrons measured in coincidence with the photons having maximum Doppler shift (515 keV–521 keV) were emitted following the decay of core holes (C 1s and O 1s). By selecting annihilation gamma photons coincident only with the O KVV (from surface adsorbed oxygen), C KVV (graphene), and Cu MVV (copper substrate) Auger electrons, we have also experimentally derived the energy spectrum of Doppler shifted gamma photons resulting from the annihilation of positrons with 1s electrons of O, 1s electrons of C and 3p electrons of Cu respectively. Model Doppler broadened gamma spectra produced using ab-initio calculations agree well with the experimentally derived line shapes. This demonstrates the ability of Auger-gamma coincidence method to experimentally resolve the Doppler broadened annihilation gamma spectrum from surfaces into its veiled electronic level constituents which has, heretofore, relied solely on theoretical analysis. Our results also demonstrate that the line shape of the Doppler broadened annihilation gamma peak reflect the chemical composition of the topmost atomic layer and can thus be used to characterize both external and inaccessible surfaces of porous materials.
Are Capital Goods Tariffs Different

In this paper we demonstrate the importance of distinguishing capital goods tariffs from other tariffs. Using exposure to a quasi-natural experiment induced by a trade reform in Colombia, we find that firms that have been more exposed to a reduction in intermediate and consumption input or output tariffs do not significantly increase their investment rates. However, firms' investment rate increase strongly in response to a reduction in capital goods input tariffs. Firms do not substitute capital with labor, but instead also increase employment, especially for production workers. Reduction in other tariff rates do not increase investment and employment. Our results suggest that a reduction in the relative price of capital goods can significantly boost investment and employment and does not seem to lead to a decline in the labor share.
Does Transportation Mean Transplantation? Impact of New Airline Routes on Sharing of Cadaveric Kidneys

Every year, nearly 5,000 patients die while waiting for kidney transplants, and yet an estimated 3,500 procured kidneys are discarded. Such a polarized coexistence of dire scarcity and massive wast...
Real-Time Ellipse Detection for Robotics Applications

We propose a new algorithm for real-time detection and tracking of elliptic patterns suitable for real-world robotics applications. The method fits ellipses to each contour in the image frame and rejects ellipses that do not yield a good fit. The resulting detection and tracking method is lightweight enough to be used on robots’ resource-limited onboard computers, can deal with lighting variations and detect the pattern even when the view is partial. The method is tested on an example application of an autonomous UAV landing on a fast-moving vehicle to show its performance indoors, outdoors, and in simulation on a real-world robotics task. The comparison with other well-known ellipse detection methods shows that our proposed algorithm outperforms other methods with the F1 score of 0.981 on a dataset with over 1500 frames. The videos of experiments, the source codes, and the collected dataset are provided with the letter.
Safety-Critical Control using Optimal-decay Control Barrier Function with Guaranteed Point-wise Feasibility

Safety is one of the fundamental problems in robotics. Recently, a quadratic program based control barrier function (CBF) method has emerged as a way to enforce safety-critical constraints. Together with control Lyapunov function (CLF), it forms a safety-critical control strategy, named CLF-CBF-QP, which can mediate between achieving the control objective and ensuring safety, while being executable in real-time. However, once additional constraints such as input constraints are introduced, the CLF-CBF-QP may encounter infeasibility. In order to address the challenge arises due to the infeasibility, we propose an optimal-decay form for safety-critical control wherein the decay rate of the CBF is optimized point-wise in time so as to guarantee point-wise feasibility when the state lies inside the safe set. The proposed control design is numerically validated using an adaptive cruise control example.
Shigella OspC3 suppresses murine cytosolic LPS sensing.

Shigella flexneri, a cytosol-invasive gram-negative pathogen, deploys an array of type III-secreted effector proteins to evade host cell defenses. Caspase-11 and its human ortholog caspase-4 detect cytosolic lipopolysaccharide (LPS) and trigger gasdermin D-mediated pyroptosis to eliminate intra-cytoplasmic bacterial threats. However, the role of caspase-11 in combating S. flexneri is unclear. The Shigella T3SS effector OspC3 reportedly suppresses cytosolic LPS sensing by inhibiting caspase-4 but not caspase-11 activity. Surprisingly, we found that S. flexneri also uses OspC3 to inhibit murine caspase-11 activity. Mechanistically, we found that OspC3 binds only to primed caspase-11. Importantly, we demonstrate that S. flexneri employs OspC3 to prevent caspase-11-mediated pyroptosis in neutrophils, enabling bacteria to disseminate and evade clearance following intraperitoneal challenge. In contrast, S. flexneri lacking OspC3 is attenuated in a caspase-11- and gasdermin D-dependent fashion. Overall, our study reveals that OspC3 suppresses cytosolic LPS detection in a broad array of mammals.
Age Debt: A General Framework for Minimizing Age of Information

We consider the problem of minimizing age of information in general single-hop and multihop wireless networks. First, we formulate a way to convert AoI optimization problems into equivalent network stability problems. Then, we propose a heuristic low complexity approach for achieving stability that can handle general network topologies; unicast, multicast and broadcast flows; interference constraints; link reliabilities; and AoI cost functions. We provide numerical results to show that our proposed algorithms behave as well as the best known scheduling and routing schemes available in the literature for a wide variety of network settings.