cssi/SciSciGPT-SciSciNet huggingface dataset:
M-estimation in Multistage Sampling Procedures

Multi-stage (designed) procedures, obtained by splitting the sampling budget suitably across stages, and designing the sampling at a particular stage based on information about the parameter obtained from previous stages, are often advantageous from the perspective of precise inference. We develop a generic framework for M-estimation in a multistage setting and apply empirical process techniques to develop limit theorems that describe the large sample behavior of the resulting M-estimates. Applications to change-point estimation, inverse isotonic regression, classification, mode estimation and cusp estimation are provided: it is typically seen that the multistage procedure accentuates the efficiency of the M-estimates by accelerating the rate of convergence, relative to one-stage procedures. The step-by-step process induces dependence across stages and complicates the analysis in such problems, which we address through careful conditioning arguments.
The Evolving Consequences of OxyContin Reformulation on Drug Overdoses

Recent evidence suggests that the short-term transition of the opioid crisis from prescription opioids to heroin can be attributed to the reformulation of OxyContin, which substantially reduced access to abusable prescription opioids. In this paper, we find that over a longer time horizon, reformulation stimulated illicit drug markets to grow and evolve. We compare overdose trajectories in areas more exposed to reformulation, defined as states with higher rates of nonmedical OxyContin use before reformulation, to less exposed areas. More exposed areas experienced disproportionate increases in fatal overdoses involving synthetic opioids (fentanyl) and nonopioid substances like cocaine, suggesting that these new epidemics are related to the same factors driving the rise in heroin deaths. Instead of just short-term substitution from prescription opioid to heroin overdoses, the transition to illicit markets spurred by reformulation led to growth in the overall overdose rate to unprecedented levels.
An Electromagnetic Trinity from “Negative Permittivity” and “Negative Permeability”

An electromagnetic trinity comprising vacuum, anti–vacuum, and nihility is postulated — after making use of materials with “negative permittivity” and “negative permeability” — to illuminate the structure of electromagnetic theory, at least insofar as the relationship of phase velocity with Poynting vector is concerned.
Ab initio electron-defect interactions using Wannier functions

Computing electron–defect (e–d) interactions from first principles has remained impractical due to computational cost. Here we develop an interpolation scheme based on maximally localized Wannier functions (WFs) to efficiently compute e–d interaction matrix elements. The interpolated matrix elements can accurately reproduce those computed directly without interpolation and the approach can significantly speed up calculations of e–d relaxation times and defect-limited charge transport. We show example calculations of neutral vacancy defects in silicon and copper, for which we compute the e–d relaxation times on fine uniform and random Brillouin zone grids (and for copper, directly on the Fermi surface), as well as the defect-limited resistivity at low temperature. Our interpolation approach opens doors for atomistic calculations of charge carrier dynamics in the presence of defects.
Experimental demonstration of memory-enhanced quantum communication.

The ability to communicate quantum information over long distances is of central importance in quantum science and engineering1. Although some applications of quantum communication such as secure quantum key distribution2,3 are already being successfully deployed4–7, their range is currently limited by photon losses and cannot be extended using straightforward measure-and-repeat strategies without compromising unconditional security8. Alternatively, quantum repeaters9, which utilize intermediate quantum memory nodes and error correction techniques, can extend the range of quantum channels. However, their implementation remains an outstanding challenge10–16, requiring a combination of efficient and high-fidelity quantum memories, gate operations, and measurements. Here we use a single solid-state spin memory integrated in a nanophotonic diamond resonator17–19 to implement asynchronous photonic Bell-state measurements, which are a key component of quantum repeaters. In a proof-of-principle experiment, we demonstrate high-fidelity operation that effectively enables quantum communication at a rate that surpasses the ideal loss-equivalent direct-transmission method while operating at megahertz clock speeds. These results represent a crucial step towards practical quantum repeaters and large-scale quantum networks20,21. A solid-state spin memory is used to demonstrate quantum repeater functionality, which has the potential to overcome photon losses involved in long-distance transmission of quantum information.
clustifyr: an R package for automated single-cell RNA sequencing cluster classification

Assignment of cell types from single-cell RNA sequencing (scRNA-seq) data remains a time-consuming and error-prone process. Current packages for identity assignment use limited types of reference data and often have rigid data structure requirements. We developed the clustifyr R package to leverage several external data types, including gene expression profiles to assign likely cell types using data from scRNA-seq, bulk RNA-seq, microarray expression data, or signature gene lists. We benchmark various parameters of a correlation-based approach and implement gene list enrichment methods. clustifyr is a lightweight and effective cell-type assignment tool developed for compatibility with various scRNA-seq analysis workflows. clustifyr is publicly available at https://github.com/rnabioco/clustifyr.
Unexpected predicted length variation for the coding sequence of the sleep related gene, BHLHE41 in gorilla amidst strong purifying selection across mammals

There is a molecular basis for many sleep patterns and disorders involving circadian clock genes. In humans, "short-sleeper" behavior has been linked to specific amino acid substitutions in BHLHE41 (DEC2), yet little is known about variation at these sites and across this gene in mammals. We compare BHLHE41 coding sequences for 27 mammals. Approximately half of the coding sequence was invariable at the nucleotide level and close to three-quarters of the amino acid alignment was identical. No other mammals had the same "short-sleeper" amino acid substitutions previously described from humans. Phylogenetic analyses based on the nucleotides of the coding sequence alignment are consistent with established mammalian relationships confirming orthology among the sampled sequences. Significant purifying selection was detected in about two-thirds of the variable codons and no codons exhibited significant signs of positive selection. Unexpectedly, the gorilla BHLHE41 sequence has a 318 bp insertion at the 5' end of the coding sequence and a deletion of 195 bp near the 3' end of the coding sequence (including the two short sleeper variable sites). Given the strong signal of purifying selection across this gene, phylogenetic congruence with expected relationships and generally conserved function among mammals investigated thus far, we suggest the indels predicted in the gorilla BHLHE41 may represent an annotation error and warrant experimental validation.
Revised scale lengths for cometary NH

New values for the Haser model scale lengths of NH are derived from extensive narrow-band photometry of comet P/Halley. The observations require a parent scale length which is 5-10 times larger than previously published values. It is shown that the newly determined scale lengths also give better agreement with recent observations of two other comets: P/Borrelly and Liller. When combined with fluorescence efficiency calculations by others, the present results indicate that NH production in typical comets, relative to that of other molecular species, is about 20 times greater than previously believed.
PDBMine: A Reformulation of the Protein Data Bank to Facilitate Structural Data Mining

Large scale initiatives such as the Human Genome Project, Structural Genomics, and individual research teams have provided large deposits of genomic and proteomic data. The transfer of data to knowledge has become one of the existing challenges, which is a consequence of capturing data in databases that are optimally designed for archiving and not mining. In this research, we have targeted the Protein Databank (PDB) and demonstrated a transformation of its content, named PDBMine, that reduces storage space by an order of magnitude, and allows for powerful mining in relation to the topic of protein structure determination. We have demonstrated the utility of PDBMine in exploring the prevalence of dimeric and trimeric amino acid sequences and provided a mechanism of predicting protein structure.
Synaptic Plasticity in Correlated Balanced Networks

Abstract The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory– inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How does the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a general theory of plasticity in balanced networks. We show that balance can be attained and maintained under plasticity induced weight changes. We find that correlations in the input mildly, but significantly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.
A machine learning approach for increased throughput of density functional theory substitutional alloy studies

Abstract In this study, a machine learning-based technique is developed to reduce the computational cost required to explore large design spaces of substitutional alloys. The first advancement is based on a neural network (NN) approach to predict the initial position of minority and majority ions prior to DFT relaxation. The second advancement is to allow the NN to predict the total energy for every possibility minority ion position and select the most stable configuration in the absence of relaxing each trial minority configuration. A bismuth oxide materials system, ( Bi x La y Yb z )2 MoO 6 , was used as the model system to demonstrate the developed methods and quantify the resulting computational speedup. Compared to a brute force method that requires the calculation of every permutation of minority configuration and subsequent DFT relaxation, a 1.3× speedup was realized if the NN predicted the initial configuration of ions prior to relaxation. Implementation of the second advancement allowed the NN to predict the total energy for all possible trial configurations and downselect the most stable configurations prior to relaxation, resulting in a speedup of approximately 37×. Validation was done by comparing position and energy between the NN and DFT predictions. A maximum position vector mean squared error (MSE) of 1.6 × 10−2 and a maximum energy MSE of 2.3 × 10−7 was predicted for the worst case configuration. This method demonstrates a significant computational speedup, which has the potential for even greater computational savings for larger compositional design spaces.
The Really Long-Run Performance of Initial Public Offerings: The Pre-Nasdaq Evidence

Financial economists have intensely debated the performance of IPOs using data after the formation of Nasdaq. This paper sheds light on this controversy by undertaking a large, out-of-sample study: We examine the performance for five years after listing of 3,661 U.S. IPOs from 1935 to 1972. The sample displays some underperformance when event-time buy-and-hold abnormal returns are used. The underperformance disappears, however, when cumulative abnormal returns are utilized. A calendar-time analysis shows that over the entire period, IPOs return as much as the market. The intercepts in CAPM and Fama-French regressions are insignificantly different from zero, suggesting no abnormal performance. Copyright (c) 2003 by the American Finance Association.
Chapter 7 – String Manipulation

The chapter covers many built-in string manipulation functions to perform common string applications such as concatenating strings, removing white space, changing case, and comparing strings. Functions that allow one to find, replace, and separate strings are introduced. Creating custom strings with formatting is demonstrated; several examples include using custom strings in plot labels and input prompts. Converting between string and number types is shown. “Is” functions that return true or false for strings are also introduced.
Primes with Beatty and Chebotarev conditions

Abstract We study the prime numbers that lie in Beatty sequences of the form ⌊ α n + β ⌋ and have prescribed algebraic splitting conditions. We prove that the density of primes in both a fixed Beatty sequence with α of finite type and a Chebotarev class of some Galois extension is precisely the product of the densities α − 1 ⋅ | C | | G | . Moreover, we show that the primes in the intersection of these sets satisfy a Bombieri–Vinogradov type theorem. This allows us to prove the existence of bounded gaps for such primes. As a final application, we prove a common generalization of the aforementioned bounded gaps result and the Green–Tao theorem.
TACE-dependent EGFR ligand shedding: A new target in basal breast cancer

297 Epidermal Growth Factor Receptor (EGFR)-positivity has emerged as a common feature of the basal subtype of breast cancer, a clinical entity with poor prognosis and no current targeted therapies. Existing therapeutic strategies in other tissues have focused on targeting the receptor itself, resulting in the approval of drugs such as gefitinib and erlotinib which have proved useful, albeit in relatively small populations of patients. We have used 3D culture models of breast cancer cell lines representative of the basal phenotype to examine the regulation of EGFR-dependent signaling in this disease. As an alternative approach to inhibiting the receptor directly, we have determined the key regulatory steps upstream of the receptor, on which receptor activation and pro-growth/survival signaling are dependent. We have identified the metalloproteinase TACE/ADAM17 as the key regulator of EGFR ligand shedding in these cell lines, and we have shown using both small molecule inhibitors and siRNAs that inhibition of TACE blocks EGFR signaling by preventing the mobilization of ligands for this receptor. TACE inhibition leads to a downregulation of flux through the EGFR pathway, reduces proliferation and restores the ability to form growth-arrested polarized mammary acinar structures to malignant cells in 3D culture. We report that expression of TACE and the EGFR ligand, TGF-alpha, are positively correlated in human breast cancer samples, in which they are predictive of poor prognosis and that both factors are highly associated with tumors of the basal subtype. Furthermore, we have found that breast cancer cell response to existing EGFR inhibitors, such as gefitinib, is strongly dependent on the levels of free EGFR ligands in the extracellular milieu, which suggests that impeding ligand bioavailability using TACE inhibitors may prove an important means of sensitizing tumors to existing EGFR inhibitors. These data implicate TACE-dependent TGF-alpha shedding as playing a key role in the proliferation of basal breast cancer cells and provide a strong proof of principle that this important pathway in epithelial tumorigenesis may be effectively inhibited using the strategies we have developed.
A logic for reasoning about evidence

We introduce a logic for reasoning about evidence that essentially views evidence as a function from prior beliefs (before making an observation) to posterior beliefs (after making the observation). We provide a sound and complete axiomatization for the logic, and consider the complexity of the decision problem. Although the reasoning in the logic is mainly propositional, we allow variables representing numbers and quantification over them. This expressive power seems necessary to capture important properties of evidence.
The Cosmic X-Ray Background

Evidence for a diffuse, isotropic non-terrestrial component of X-ray radiation has been accumulating since the very beginnings of X-ray astronomy. This evidence now seems secure beyond reasonable doubt, especially in the light of recent experiments, e.g. one in which a very clean separation from the cosmic ray and terrestrial background has been achieved (Pounds, 1967), and another in which a counting rate proportional to the solid angle of the sky subtended to three different detectors was found (Matsuoka et al., 1967).
How Transparency into Internal and External Responsibility Initiatives Influences Consumer Choice

Amid growing calls for transparency and social and environmental responsibility, companies are employing different strategies to improve consumer perceptions of their brands. Some pursue internal i...
Large-Scale, In-House Production of Viral Transport Media To Support SARS-CoV-2 PCR Testing in a Multihospital Health Care Network during the COVID-19 Pandemic.

The COVID-19 pandemic has severely disrupted worldwide supplies of viral transport media (VTM) due to widespread demand for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcription-PCR (RT-PCR) testing. In response to this ongoing shortage, we began production of VTM in-house in support of diagnostic testing in our hospital network. As our diagnostic laboratory was not equipped for reagent production, we took advantage of space and personnel that became available due to closure of the research division of our medical center. We utilized a formulation of VTM described by the CDC that was simple to produce, did not require filtration for sterilization, and used reagents that were available from commercial suppliers. Performance of VTM was evaluated by several quality assurance measures. Based on cycle threshold (CT ) values of spiking experiments, we found that our VTM supported highly consistent amplification of the SARS-CoV-2 target (coefficient of variation = 2.95%) using the Abbott RealTime SARS-CoV-2 Emergency Use Authorization (EUA) assay on the Abbott m2000 platform. VTM was also found to be compatible with multiple swab types and, based on accelerated stability studies, able to maintain functionality for at least 4 months at room temperature. We further discuss how we met logistical challenges associated with large-scale VTM production in a crisis setting, including use of a staged assembly line for VTM transport tube production.
Gene therapy for prostate cancer.

Basic research continues to unravel the molecular complexity of normal and abnormal biologic processes. The development of means to affect the expression level of genes that promote or contribute to cellular transformation, invasion, and metastasis has spawned the concept of gene therapy. This relatively new field seeks to reverse or suspend the pathologic progression of a variety of diseases including the malignant transfor-mation of prostatic epithelial cells. Initial clinical trials for prostate cancer have thus far shown gene therapy to be relatively safe, although definitive evidence of durable therapeutic efficacy remains to be demonstrated. In this article, recent preclinical research, current therapeutic strategies, and recent results of gene therapy clinical trials for the treatment of prostate cancer are reviewed.
Electrostatic discharge (ESD) protection in silicon-on-insulator (SOI) CMOS technology with aluminum and copper interconnects in advanced microprocessor semiconductor chips

Abstract This paper discusses the electrostatic discharge (ESD) robustness in silicon-on-insulator (SOI) high-pin-count high-performance semiconductor chips. The ESD results demonstrate that sufficient ESD protection levels are achievable in SOI microprocessors using lateral ESD SOI polysilicon-bound gated diodes without the need for additional masking steps, process implants or ESD design area.
Multigrid in H(div) on axisymmetric domains

Abstract In this paper, we will construct and analyze a multigrid algorithm that can be applied to weighted H ( div ) problems on a two-dimensional domain. These problems arise after performing a dimension reduction to a three-dimensional axisymmetric H ( div ) problem. We will use recently developed Fourier finite element spaces that can be applied to axisymmetric H ( div ) problems with general data. We prove that if the axisymmetric domain is convex, then the multigrid V-cycle with modern smoothers will converge uniformly with respect to the meshsize.
A novel on-chip ESD protection circuit for GaAs HBT RF power amplifiers☆

Abstract A low loading capacitance, 0.1 pF, on-chip electrostatic discharge (ESD) protection circuit for 2000 V HBM for GaAs power amplifiers that does not degrade RF circuit performance is introduced. Its principle of operation, loading capacitance, leakage current, ESD clamping characteristics, and robustness over process variation and temperature are investigated. Finally, a case study of its application to a 5.8 GHz power amplifier used for the wireless 802.11 A local area network is discussed.
Ad-Blockers: A Blessing or a Curse?

Users who have an ad-blocker installed present a genuine predicament for a website (also known as the publisher): On the one hand, these users do not generate revenue for the website; on the other hand, denying them access can shrink the user base and adversely affect the popularity of the website, ultimately reducing traffic over the long run. This has led some websites to require that ad-block users “white-list” them for obtaining access to an “ad-light” experience. We model the decision problem for a website facing two user segments: regular users and ad-block users . The first-level decision or gating strategy is whether to allow ad-free access to ad-block users or require them to white-list the website for gaining access. When ad-block users are allowed ad-free access, the second-level decision is the level of advertising (or ad-intensity ) for regular users. When ad-block users are required to white-list, the second-level decisions are the ad-intensities for regular users and ad-block users. The net utility of a user from visiting the website depends on the intrinsic value of the website’s content, the value obtained due to network effects driven by the amount of traffic/popularity of the website, and the cost incurred due to the presence of ads. We derive an optimal gating and ad-intensity strategy for the website and also solve an identical model for a world without ad-block software. We show that the website can increase its revenue by discriminating between regular and ad-block users via the ad-intensities shown to them. More interestingly, we find that the discriminatory power bestowed on the website by ad-blockers can increase the social surplus and, in particular, increase the surplus of both user segments, namely, regular users and ad-block users, when the utility from their outside option is below a threshold. Thus, the advent of ad-blockers can lead to a win-win for both the website and its users. Finally, we propose a superior selective-gating strategy in which only a fraction of ad-block users are gated. We establish the robustness of our conclusions under several enhancements to our base setting: (a) heterogeneous profitabilities from regular users and ad-block users, (b) endogenous adoption of ad-blockers, (c) the presence of a subscription option, and (d) negative externality due to increased traffic. Our analysis ends with recommendations for three stakeholders in this problem, namely, publishers, web-browser developers, and policy makers.
Possibility, Novelty, and Creativity

I am trying to develop an account of possibility that is consistent with the changing world of our experience. Possibility is often viewed as something that has the same form as actuality, minus existence. Or it is taken that what a possibility is, is a (re)combination of the elements of actuality. Neither of these views of possibility can countenance radical novelty. Using Bergson and Whitehead, I begin to construct an account of possibility compatible with genuine novelty.
A Statistical Learning-Based Algorithm for Topology Verification in Natural Gas Networks Based on Noisy Sensor Measurements

Accurate knowledge of natural gas network topology is critical for the proper operation of natural gas networks. Failures, physical attacks, and cyber attacks can cause the actual natural gas network topology to differ from what the operator believes to be present. Incorrect topology information misleads the operator to apply inappropriate control causing damage and lack of gas supply. Several methods for verifying the topology have been suggested in the literature for electrical power distribution networks, but we are not aware of any publications for natural gas networks. In this paper, we develop a useful topology verification algorithm for natural gas networks based on modifying a general known statistics-based approach to eliminate serious limitations for this application while maintaining good performance. We prove that the new algorithm is equivalent to the original statistics-based approach for a sufficiently large number of sensor observations. We provide new closed-form expressions for the asymptotic performance that are shown to be accurate for the typical number of sensor observations required to achieve reliable performance.
Fast Differentiable Sorting and Ranking

The sorting operation is one of the most commonly used building blocks in computer programming. In machine learning, it is often used for robust statistics. However, seen as a function, it is piecewise linear and as a result includes many kinks where it is non-differentiable. More problematic is the related ranking operator, often used for order statistics and ranking metrics. It is a piecewise constant function, meaning that its derivatives are null or undefined. While numerous works have proposed differentiable proxies to sorting and ranking, they do not achieve the $O(n \log n)$ time complexity one would expect from sorting and ranking operations. In this paper, we propose the first differentiable sorting and ranking operators with $O(n \log n)$ time and $O(n)$ space complexity. Our proposal in addition enjoys exact computation and differentiation. We achieve this feat by constructing differentiable operators as projections onto the permutahedron, the convex hull of permutations, and using a reduction to isotonic optimization. Empirically, we confirm that our approach is an order of magnitude faster than existing approaches and showcase two novel applications: differentiable Spearman's rank correlation coefficient and least trimmed squares.
Predicting tumour mutational burden from histopathological images using multiscale deep learning

Tumour mutational burden (TMB) is an important biomarker for predicting the response to immunotherapy in patients with cancer. Gold-standard measurement of TMB is performed using whole exome sequencing (WES), which is not available at most hospitals because of its high cost, operational complexity and long turnover times. We have developed a machine learning algorithm, Image2TMB, which can predict TMB from readily available lung adenocarcinoma histopathological images. Image2TMB integrates the predictions of three deep learning models that operate at different resolution scales (×5, ×10 and ×20 magnification) to determine if the TMB of a cancer is high or low. On a held-out set of patients, Image2TMB achieves an area under the precision recall curve of 0.92, an average precision of 0.89, and has the predictive power of a targeted sequencing panel of ~100 genes. This study demonstrates that it is possible to infer genomic features from histopathology images, and potentially opens avenues for exploring genotype–phenotype relationships. Tumour mutational burden (TMB) shows promise as a biomarker in cancer immunotherapy, but it usually requires whole-exome sequencing, which is costly, time-consuming and unavailable at most hospitals. The authors develop a machine learning algorithm that uses standard H&E histopathological images to quickly, inexpensively and accurately predict TMB. The approach may have applications as a tool to screen and prioritize patient samples and subsequent treatments.
Webina: an open-source library and web app that runs AutoDock Vina entirely in the web browser.

Motivation Molecular docking is a computational technique for predicting how a small molecule might bind a macromolecular target. Among docking programs, AutoDock Vina is particularly popular. Like many docking programs, Vina requires users to download/install an executable file and to run that file from a command-line interface. Choosing proper configuration parameters and analyzing Vina output is also sometimes challenging. These issues are particularly problematic for students and novice researchers. Results We created Webina, a new version of Vina, to address these challenges. Webina runs Vina entirely in a web browser, so users need only visit a Webina-enabled webpage. The docking calculations take place on the user's own computer rather than a remote server. Availability and implementation A working version of the open-source Webina app can be accessed free of charge from http://durrantlab.com/webina. Supplementary information Supplementary data are available at Bioinformatics online.
Fungal communities living within leaves of native Hawaiian dicots are structured by landscape-scale variables as well as by host plants.

A phylogenetically diverse array of fungi live within healthy leaf tissue of dicotyledonous plants. Many studies have examined these endophytes within a single plant species and/or at small spatial scales, but landscape-scale variables that determine their community composition are not well understood, either across geographic space, across climatic conditions, or in the context of host plant phylogeny. Here, we evaluate the contributions of these variables to endophyte beta diversity using a survey of foliar endophytic fungi in native Hawaiian dicots sampled across the Hawaiian archipelago. We used Illumina technology to sequence fungal ITS1 amplicons to characterize foliar endophyte communities across five islands and 80 host plant genera. We found that communities of foliar endophytic fungi showed strong geographic structuring between distances of 7 and 36 km. Endophyte community structure was most strongly associated with host plant phylogeny and evapotranspiration, and was also significantly associated with NDVI, elevation and solar radiation. Additionally, our bipartite network analysis revealed that the five islands we sampled each harboured significantly specialized endophyte communities. These results demonstrate how the interaction of factors at large and small spatial and phylogenetic scales shapes fungal symbiont communities.
Minimal log discrepancies of determinantal varieties via jet schemes

Abstract We compute the minimal log discrepancies of determinantal varieties of square matrices, and more generally of pairs ( D k , ∑ α i D k i ) consisting of a determinantal variety (of square matrices) and an R -linear sum of determinantal subvarieties. Our result implies the semicontinuity conjecture for minimal log discrepancies of such pairs. For these computations, we use the description of minimal log discrepancies via codimensions of cylinders in the space of jets; this necessitates the computations of an explicit generator for the canonical differential forms and the Nash ideal of determinantal varieties, which may be of independent interest.
Porcine reproductive and respiratory syndrome virus dissemination across pig production systems in the United States.

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widespread in the North American pig population. Despite improvements in virus characterization, it is unclear whether PRRSV infections are a product of viral circulation within production systems (local) or across production systems (external). Here, we examined the local and external dissemination dynamics of PRRSV and the processes facilitating its spread in three production systems. Overall, PRRSV genetic diversity has declined since 2018, while phylodynamic results support frequent external transmission. We found that PRRSV dissemination predominantly occurred mostly through transmission between farms of different production companies for several months, especially from November until May, a timeframe already established as PRRSV season. Although local PRRSV dissemination occurred mainly through regular pig flow (from sow to nursery and then to finisher farms), an important flux of PRRSV dissemination also occurred in the opposite direction, from finisher to sow and nursery farms, highlighting the importance of downstream farms as sources of the virus. Our results also showed that farms with pig densities of 500 to 1,000 pig/km2 and farms located at a range within 0.5 km and 0.7 km from major roads were more likely to be infected by PRRSV, whereas farms at an elevation of 41 to 61 meters and surrounded by denser vegetation were less likely to be infected, indicating their role as dissemination barriers. In conclusion, our results demonstrate that external dissemination was intense, and reinforce the importance of farm proximity on PRRSV spread. Thus, consideration of farm location, geographic characteristics and animal densities across production systems may help to forecast PRRSV collateral dissemination.
Specification tests for spatial panel data models

Specification of a model is one of the most fundamental problems in econometrics. In practice, specification tests are generally carried out in a piecemeal fashion, for example, testing the presence of one-effect at a time ignoring the potential presence of other forms of misspecification. Many of the suggested tests in the literature require estimation of complex models and even then those tests cannot account for multiple forms of departures from the model under the null hypothesis. Using Bera and Yoon (Econom Theory 9(04):649–658, 1993) general test principle and a spatial panel model framework, we first propose an overall test for “all” possible misspecification. Then, we derive adjusted Rao’s score tests for random effect, serial correlation, spatial lag and spatial error, which can identify the definite cause(s) of rejection of the basic model and thus aiding in the steps for model revision. For empirical researchers, our suggested procedures provide simple strategies for model specification search employing only the ordinary least squares residuals from a standard linear panel regression. Through an extensive simulation study, we evaluate the finite sample performance of our suggested tests and some of the existing procedures. We find that our proposed tests have good finite sample properties both in terms of size and power. Finally, to illustrate the usefulness of our procedures, we provide an empirical application of our test strategy in the context of the convergence theory of incomes of different economies, which is a widely studied empirical problem in macro-economic growth theory. Our empirical illustration reveals the problems in using and interpreting unadjusted tests, and demonstrates how these problems are rectified in using our proposed adjusted tests.
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback

We study small-loss bounds for adversarial multi-armed bandits with graph feedback, that is, adaptive regret bounds that depend on the loss of the best arm or related quantities, instead of the total number of rounds. We derive the first small-loss bound for general strongly observable graphs, resolving an open problem of Lykouris et al. (2018). Specifically, we develop an algorithm with regret $\mathcal{\tilde{O}}(\sqrt{\kappa L_*})$ where $\kappa$ is the clique partition number and $L_*$ is the loss of the best arm, and for the special case of self-aware graphs where every arm has a self-loop, we improve the regret to $\mathcal{\tilde{O}}(\min\{\sqrt{\alpha T}, \sqrt{\kappa L_*}\})$ where $\alpha \leq \kappa$ is the independence number. Our results significantly improve and extend those by Lykouris et al. (2018) who only consider self-aware undirected graphs. Furthermore, we also take the first attempt at deriving small-loss bounds for weakly observable graphs. We first prove that no typical small-loss bounds are achievable in this case, and then propose algorithms with alternative small-loss bounds in terms of the loss of some specific subset of arms. A surprising side result is that $\mathcal{\tilde{O}}(\sqrt{T})$ regret is achievable even for weakly observable graphs as long as the best arm has a self-loop. Our algorithms are based on the Online Mirror Descent framework but require a suite of novel techniques that might be of independent interest. Moreover, all our algorithms can be made parameter-free without the knowledge of the environment.
Durability characterization of mechanical interfaces in solar sail membrane structures

Abstract The construction of a solar sail from commercially available metallized film presents several challenges. The solar sail membrane is made by seaming together precut lengths of ultrathin metallized polymer film into the required geometry. This assembled sail membrane is then folded into a small stowage volume prior to launch. The sail membranes must have additional features for connecting to rigid structural elements (e.g., sail booms) and must be electrically grounded to the spacecraft bus to prevent charge build up. Space durability of the material and mechanical interfaces of the sail membrane assemblies will be critical for the success of any solar sail mission. In this study, interfaces of polymer/metal joints in a representative solar sail membrane assembly were tested to ensure that the adhesive interfaces and the fastening grommets could withstand the temperature range and expected loads required for mission success. Various adhesion methods, such as surface treatment, commercial adhesives, and fastening systems, were experimentally tested in order to determine the most suitable method of construction.
Towards scalable dataframe systems

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in R and Python, dataframes face performance issues even on moderately large datasets. Moreover, there is significant ambiguity regarding dataframe semantics. In this paper we lay out a vision and roadmap for scalable dataframe systems. To demonstrate the potential in this area, we report on our experience building Modin, a scaled-up implementation of the most widely-used and complex dataframe API today, Python's pandas. With pandas as a reference, we propose a simple data model and algebra for dataframes to ground discussion in the field. Given this foundation, we lay out an agenda of open research opportunities where the distinct features of dataframes will require extending the state of the art in many dimensions of data management. We discuss the implications of signature dataframe features including flexible schemas, ordering, row/column equivalence, and data/metadata fluidity, as well as the piecemeal, trial-and-error-based approach to interacting with dataframes.
Commentary on the Liquid Metallic Hydrogen Model of the Sun III. Insight into Solar Lithium Abundances

The apparent depletion of lithium represents one of the greatest challenges to modern gaseous solar models. As a result, lithium has been hypothesized to undergo nuclear burning deep within the Sun. Conversely, extremely low lithium abundances can be easily accounted for within the liquid metallic hydrogen model, as lithium has been hypothesized to greatly stabilize the formation of metallic hydrogen (E. Zurek et al. A little bit of lithium does a lot for hydrogen. Proc. Nat. Acad. Sci. USA, 2009, v. 106, no. 42, 17640–17643). Hence, the abundances of lithium on the solar surface can be explained, not by requiring the nuclear burning of this element, but rather, by suggesting that the Sun is retaining lithium within the solar body in order to help stabilize its liquid metallic hydrogen lattice. Unlike lithium, many of the other elements synthesized within the Sun should experience powerful lattice exclusionary forces as they are driven out of the intercalate regions between the layered liquid metallic hydrogen hexagonal planes (Robitaille J.C. and Robitaille P.M. Liquid Metallic Hydrogen III. Intercalation and Lattice Exclusion Versus Gravitational Settling and Their Consequences Relative to Internal Structure, Surface Activity, and Solar Winds in the Sun. Progr. Phys., 2013, v. 2, in press). As for lithium, its stabilizing role within the solar interior helps to account for the lack of this element on the surface of the Sun.
Synthesis and properties of urea greases based on aminoamides of plant oil phosphatides

The possibility of using the wastes of the oil and fat industry – phosphatide concentrates as components of lubricating materials was demonstrated in this paper. At the first stage, amidation of phosphatide concentrate from the purification of rapeseed oil by ethylene diamine was carried out using two procedures: without a catalyst and with the use of reagent-catalyst CaO. The samples of urea greases were subsequently synthesized by interaction of amidated phosphatide concentrates with polyisocyanate in the oil media. Aminoamides of fatty acids with polyisocyanate form urea dispersion phase of thixotropic systems, and glycerolphosphatides and calcium glycerolrophosphatides perform the function of tribological additives. The method of infrared spectroscopy proved that the full interaction between polyisocyanate and amidated phosphatide concentrate takes place at the molar ratio of 1:3. Physicochemical properties of the developed urea greases were studied and comparative analysis of their quality indicators with the Maspol brand lubricant was performed. The synthesized urea greases are characterized by high mechanical (a change in penetrations after moving of 100,000 double cycles of 42–45 mm·10 -1 ), colloidal stability (5.2–5.6 % of extracted oil) and high-temperature properties (dropping point above 230 °C). In addition, these thixotropic systems are resistant to oxidation, do not cause corrosion of non-ferrous metals, and are able to operate in contact with water. Phosphorous residues improve the lubricating properties of synthesized compositions without any additional introduction of tribological modifiers (critical load is 980–1,039 N, welding load – 1,568–1,744 N). Due to the use of raw materials of plant origin in the composition of lubricating compositions, their biodegradation is enhanced by 6–7 %
Networks of materials: Construction and structural analysis

Modeling and analysis of the materials universe is an emerging area of research with many important applications in materials science. The main goal is to create a map of materials which allows not only to visualize and navigate the materials space, but also reveal complex relationships and “connections” among materials and potentially find clusters of materials with similar properties. In this paper, we consider the problem of mapping and exploring the materials universe using network science tools and concepts. The networks are based on the open-source materials data repository AFLOW.org where each material is represented as a node, and each pair of nodes is connected by a link if the respective materials exhibit a high level of similarity between their Density of States (DOS) functions. We discuss the importance of similarity measure selection, investigate basic structural properties of the resulting networks, and demonstrate advantages and limitations of the proposed approaches.
Housing Stability and Hepatitis C Infection for Young Adults Who Inject Drugs: Examining the Relationship of Consistent and Intermittent Housing Status on HCV Infection Risk.

Housing status affects drug using behaviors, but less is known about the relationship between housing patterns and hepatitis C virus (HCV) infection. HCV-negative young people who inject drugs (PWID) were enrolled into a prospective cohort (2003–2019) with quarterly study visits. We used Cox regression to estimate the independent association of recent housing status (housed vs. unhoused, housing stability, and housing trajectory) on HCV incidence. Among 712 participants, 245 incident HCV infections occurred over 963.8 person-years (py) (cumulative incidence 24.4/100 py). An inverse relationship between time housed and HCV incidence was observed (always unhoused 45.0/100 py, 95% confidence interval (CI) 37.1, 54.5; variably housed 18.0/100 py, 95% CI 15.0, 21.3; and always housed 7.0/100 py, 95% CI 3.0, 17.3). In Cox regression models controlling for confounders, those unhoused versus housed at baseline had a 1.9-fold increased infection risk (95% CI 1.4, 2.6). Those always unhoused versus always housed had a 1.5 times greater risk of HCV (95% CI 1.0, 2.3), and those spending a portion of time in stable housing a lower risk (adjusted relative hazard 0.05, 95% CI 0.3, 0.9) with a similar trend for those being housed for less time. Young adult PWID experiencing both recent and chronic states of being unhoused are at elevated risk for HCV infection. Importantly for this group of PWID, our findings indicate that some frequency of residential housing significantly reduces HCV infection risk.