cssi/SciSciGPT-SciSciNet huggingface dataset:
Evaluating Job Training in Two Chinese Cities

Recent years have seen a surge in work on the impacts of active labor market programs for numerous countries. However, little evidence has been presented on the effectiveness of such programs in China. Recent economic reforms, associated with massive lay-offs, and the accompanying public retraining programs make China fertile ground for rigorous impact evaluations. This study uses survey data from the two large industrial cities Shenyang and Wuhan, covering the period 1998 to 2000, to evaluate retraining programs for over 2000 workers two years after they had been observed as displaced and unemployed. Using a comparison group design, this study is, to our knowledge, the first evaluation of its kind in China. The evidence suggests that retraining helped workers find jobs in Wuhan, but had little effect in Shenyang. The study raises questions about the overall effectiveness of retraining expenditures, and it offers some directions for policy-makers about future interventions to help laid-off workers.
Exploring the relationship between nuclear matter and finite nuclei with chiral two- and three-nucleon forces

We address the connection between the saturating behavior of infinite nuclear matter and the description of finite nuclei based on state-of-the-art chiral two- and three-nucleon forces. We observe that chiral two- and three-nucleon interactions (at ${\mathrm{N}}^{2}\mathrm{L}\mathrm{O}$ and at ${\mathrm{N}}^{3}\mathrm{L}\mathrm{O}$), which have been found to predict realistic binding energies and radii for a wide range of finite nuclei (from $p$-shell nuclei up to nickel isotopes) are unable to saturate infinite nuclear matter. On the other hand, it has been shown that, when the fits of the ${c}_{D}$ and ${c}_{E}$ couplings of the chiral three-nucleon interactions include the constraint of nuclear matter saturation in addition to, as is typically the case, the triton binding energy, medium-mass nuclei are underbound and their radii are systematically too large. We discuss this apparent inconsistency and perform test calculations for various scenarios to shed light on the issue.
Chapter 9 – Session Activities

Publisher Summary This chapter contains a collection of interventions that were designed to address specific developmental and content-related goals. The author discusses three content areas relevant to planning individual activities for use in play therapy. First, the developmental experience of children in first three stages of life, Level I (ages 0–2), Level II (ages 2–6), and Level III (ages 6–11), was described. The focus of these descriptions include the way in which these children obtain, process, store, and retrieve information, as well as the primary social task they face during each stage. In this developmental context, the way in which children at each level may respond to traumatic events are also discussed. The second area is a discussion of the primary therapeutic issues to be considered when planning a play therapy intervention for children at each of these developmental levels. Finally, examples are given of session activities and related interpretations that could be used to facilitate the process of the therapy. The discussion concludes that therapists must consider the specific developmental task to be addressed. They should also consider the specific therapeutic content to be included, and both their own and their clients' personalities when planning effective play therapy interventions.
Simulating farm level response to crop diversification policy

One of the new political instruments of the upcoming European Common Agricultural Policy-reform is the crop diversification measure. To comply with this measure, arable farmers will have to grow a minimum number of crops on their land, in given proportions. In this paper a non-parametric simulation model is developed to predict land cover changes while tackling the self-selection problem. Farmers’ behaviour is based on their closest peer‘s behaviour. A comparison between the results on diversity, measured through the Shannon Diversity Index, and the policy impact on farms, shows a clear trade-of and a potential for targeting.
Principal Components and Extensions

In this paper, we discuss some extensions of principal components analysis. We focus in particular on extensions that deal with categorical multivariate data. We also briefly discuss various approaches that try to enable the techniques that handle nonlinearities in the data. Keywords: categorical data; eigenvalue decomposition; nonlinear multivariate analysis; optimal scoring; projection methods
Chapter 9 – Analysis of Trusses, Beams, and Frames

The derivations and applications of one-dimensional elements, including truss, beam, and frame elements are presented. The differences between truss, beam, and frame elements are identified. The stiffness matrices of a space truss element in local and global coordinates are derived. The derivations of consistent load vectors due to initial (thermal) strains and body forces are presented. The stiffness matrix of a one-dimensional beam element and consistent load vectors are derived. The computational details in finding the stresses in a beam subjected to loads are also given. The derivation of the stiffness matrix of the space frame element is considered an assembly of the stiffness matrices of the member under axial forces, torsional forces, and bending loads in two mutually perpendicular (xy and xz) planes. The total 12 × 12 stiffness matrix of the space frame element, and the 12 × 12 coordinate transformation matrix are given. The special cases of the space frame element for use in planar frames and beams are also derived. Finally the general characteristics of stiffness matrices such as symmetry, positive diagonal elements, and singularity are discussed.
Operations Research, Simulation†

Simulation models have been indispensable tools for decision makers in many different public and private environments. The methodology of developing a simulation model involves the abstraction of a real-life system into a symbolic model format. To discuss the multiple stages required in a Monte Carlo simulation study, including the (1) Problem Statement; (2) Information Gathering and Data Preparation; (3) Model Conceptualization; (4) Model Implementation; (5) Analysis and Interpretation; (6) Verification and Validation; and (7) Implementation and Documentation. Simulation models are created in order to provide valuable insight to decision makers by replicating real-life decision scenarios and providing more complete information about the risks and uncertainties in those environments. Once a model has been verified and validated as accurately representing the real-world environment, many “what if ” questions about the system can be posed. Illustrative examples are presented. Keywords: Monte Carlo; simulation; simulation-modeling; models; probability distributions
8 – Systems of Linear Equations

Publisher Summary Systems of linear equations are used in many different disciplines. Systems of linear equations can be used to solve multiple-loop circuit problems in electronics, kinship patterns in anthropology, genetics problems in biology, and profit-and-cost problems in economics. There are many other applications as well. Solving a system of linear equations by graphing is the least accurate method. If the coordinates of the point of intersection are not integers, it can be difficult to read the solution set from the graph. There is another method of solving a linear system that does not depend on the graph. It is called the addition method. A system of linear equations consists of two or more linear equations considered simultaneously. The solution set to a linear system in two variables is the set of ordered pairs that satisfy both equations. The solution set to a linear system in three variables consists of all the ordered triples that satisfy each equation in the system.
Chapter 5 – Enzyme Inhibition and Inactivation

Building on the concepts provided in Chapters 3 and 4 Chapter 3 Chapter 4, this chapter focuses on the mechanism of action of enzyme inhibitors and enzyme inactivators and their consequent usefulness as drugs. With extensive use of examples and case histories, several subcategories of reversible inhibition are covered, including competitive inhibition, stabilization of an enzyme conformation, substrate mimicry, transition state mimicry, and slow, tight-binding inhibition. Irreversible enzyme inhibition, including the subcategories of affinity labeling and mechanism-based inactivation, is discussed with the use of multiple examples.
MCT2 overexpression rescues metabolic vulnerability and protects retinal ganglion cells in two models of glaucoma.

Abstract Improving cellular access to energy substrates is one strategy to overcome observed declines in energy production and utilization in the aged and pathologic central nervous system. Monocarboxylate transporters (MCTs), the movers of lactate, pyruvate, and ketone bodies into or out of a cell, are significantly decreased in the DBA/2 J mouse model of glaucoma. In order to confirm MCT decreases are disease-associated, we decreased MCT2 in the retinas of MCT2fl/+ mice using an injection of AAV2-cre, observing significant decline in ATP production and visual evoked potential. Restoring MCT2 levels in retinal ganglion cells (RGCs) via intraocular injection of AAV2-GFP-MCT2 in two models of glaucoma, the DBA/2 J (D2), and a magnetic bead model of ocular hypertension (OHT), preserved RGCs and their function. Viral-mediated overexpression of MCT2 increased RGC density and axon number, reduced energy imbalance, and increased mitochondrial function as measured by cytochrome c oxidase and succinate dehydrogenase activity in both models of glaucoma. Ocular hypertensive mice injected with AAV2:MCT2 had significantly greater P1 amplitude as measured by pattern electroretinogram than mice with OHT alone. These findings indicate overexpression of MCT2 improves energy homeostasis in the glaucomatous visual system, suggesting that expanding energy input options for cells is a viable option to combat neurodegeneration.
Facially Neutral No-Rehire Rules and the Americans with Disabilities Act

Imagine a man who works as a technician for a large federal defense contractor, arriving at work with alcohol on his breath. He consents to a drug test that reveals cocaine in his system, and then quits in the face of almost certain termination. Several years later, after getting his act together through Alcoholics Anonymous, the former employee reapplies to the same employer. The employer refuses to rehire him because of its universal, albeit unwritten, company policy not to rehire employees who quit rather than be fired for violation of misconduct rules. The employee sues, maintaining that he is clean and sober and that the employer’s refusal to reconsider him once he was qualified again is a violation of the Americans with Disabilities Act (“ADA”). Is the employee correct? It is common for businesses to promulgate rules regarding discipline and discharge for alcohol and illegal drug use while on duty. It is also not uncommon for employers to test employees for alcohol or drugs where the employee appears to be impaired while at work. Employers often require applicants for employment to submit to such tests prior to finalizing offers of employment. Certainly employers have legitimate business concerns about safety and sobriety in the workplace. Intoxicated employees may harm themselves or others while under the influ-
“Racially-Tailored” Medicine Unraveled

In June 2005, the FDA approved BiDil, a heart failure medication that is labeled for use only by AfricanAmericans and thus is the first treatment of its kind. The drug likely portends a future of growing interest in "race-based" medicine. This phenomenon is emerging at the same time that scientists, in light of the Human Genome Project, are reaching an understanding that "race" has no biological meaning, and consequently, "racially-tailored" medicine is both puzzling and troubling. This Article explores the reasons for the new focus on "racial-profiling" in medicine. It analyzes the risks and dangers of this approach, including medical mistakes, stigmatizations, discrimination, exacerbation of health disparities, and violation of antidiscrimination mandates. The author does not argue against the pursuit of attribute-based therapies, but cautions that the attribute or attributes at issue must be carefully determined and will not be equivalent to what is conventionally thought of as "race." The article develops recommendations for safeguards that should be implemented by scientific review boards, IRBs, researchers, health care providers, and journalists involved with attribute-based research and therapeutic practices to ensure that this new approach promotes rather than diminishes public health and welfare.
Genotype concordance and polygenic risk score estimation across consumer genetic testing data.

The consumer genomics industry is steadily growing and delivering genetic information to over 10 million individuals. Yet, the implications of using data from different services remain unclear. We investigated the genotyped sites, concordance, and genetic risk estimation using data from three consumer services-two single nucleotide polymorphism (SNP)-array based and one sequencing based. In an N-of-one setting, we found the three services genotyped predominantly distinct sets of sites. While there was a high concordance between overlapping sites of the two SNP-array services (99.6%), there was a lower concordance between these services and a low-pass whole-genome service (73.0%). The discrepancy between the three sets of data resulted in different APOE genotypes and genetic risk scores of Alzheimer's disease. Our results demonstrate genotype results across consumer genomics platforms may lead to different genetic risk estimates, highlighting the need for careful quality control and interpretation.
Machine learned feature identification for predicting phase and Young's modulus of low-, medium- and high-entropy alloys

Abstract The growth in the interest and research on high-entropy alloys (HEAs) over the last decade is due to their unique material phases responsible for their remarkable structural properties. A conventional approach to discovering new HEAs requires scavenging an enormous search space consisting of over half a trillion new material compositions comprising of three to six principal elements. Machine learning has emerged as a potential tool to rapidly accelerate the search for and design of new materials, due to its rapidity, scalability, and now, reasonably accurate material property predictions. Here, we implement machine learning tools, to predict the crystallographic phase and Young's modulus of low-, medium- and high-entropy alloys composed of a family of 5 refractory elements. Our results, in conjunction with experimental validation, reveal that the mean melting point and electronegativity difference exert the strongest contributions to the phase formation in these alloys, while the melting temperature and the enthalpy of mixing are the key features impacting the Young's modulus of these materials. Additionally, and more importantly, we find that the entropy of mixing only negligibly influences the phase or the Young's modulus, reigniting the issue of its actual impact on the material phase and properties of HEAs.
Elliptic curves and lower bounds for class numbers

Abstract Ideal class pairings map the rational points of rank r ≥ 1 elliptic curves E / Q to the ideal class groups CL ( − D ) of certain imaginary quadratic fields. These pairings imply that h ( − D ) ≥ 1 2 ( c ( E ) − e ) ( log ⁡ D ) r 2 for sufficiently large discriminants −D in certain families, where c ( E ) is a natural constant. These bounds are effective, and they offer improvements to known lower bounds for many discriminants.
High-Throughput Reclassification of SCN5A Variants.

Partial or complete loss-of-function variants in SCN5A are the most common genetic cause of the arrhythmia disorder Brugada syndrome (BrS1). However, the pathogenicity of SCN5A variants is often unknown or disputed; 80% of the 1,390 SCN5A missense variants observed in at least one individual to date are variants of uncertain significance (VUSs). The designation of VUS is a barrier to the use of sequence data in clinical care. We selected 83 variants: 10 previously studied control variants, 10 suspected benign variants, and 63 suspected Brugada syndrome-associated variants, selected on the basis of their frequency in the general population and in individuals with Brugada syndrome. We used high-throughput automated patch clamping to study the function of the 83 variants, with the goal of reclassifying variants with functional data. The ten previously studied controls had functional properties concordant with published manual patch clamp data. All 10 suspected benign variants had wild-type-like function. 22 suspected BrS variants had loss of channel function (
Dynamic Pricing and Matching for Two-Sided Queues

Motivated by diverse applications in sharing economy and online marketplaces, we consider optimal pricing and matching control in a two-sided queueing system. We assume that heterogeneous customers and servers arrive to the system with price-dependent arrival rates. The compatibility between servers and customers is specified by a bipartite graph. Once a pair of customer and server are matched, they depart from the system instantaneously. The objective is to maximize the long-run average profits of the system while minimizing average waiting time. We first propose a static pricing and max-weight matching policy, which achieves O(√ n) optimality rate when all of the arrival rates are scaled by n. We further show that a dynamic pricing and modified max-weight matching policy achieves an improved O(n1/3) optimality rate. In addition, we propose a constraint generation algorithm that solves value function approximation of the MDP and demonstrate strong numerical performance of this algorithm.
Calibrated Vehicle Paint Signatures for Simulating Hyperspectral Imagery

We investigate a procedure for rapidly adding calibrated vehicle visible-near infrared (VNIR) paint signatures to an existing hyperspectral simulator - The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model - to create more diversity in simulated urban scenes. The DIRSIG model can produce synthetic hyperspectral imagery with user-specified geometry, atmospheric conditions, and ground target spectra. To render an object pixel's spectral signature, DIRSIG uses a large database of reflectance curves for the corresponding object material and a bidirectional reflectance model to introduce s due to orientation and surface structure. However, this database contains only a few spectral curves for vehicle paints and generates new paint signatures by combining these curves internally. In this paper we demonstrate a method to rapidly generate multiple paint spectra, flying a drone carrying a pushbroom hyperspectral camera to image a university parking lot. We then process the images to convert them from the digital count space to spectral reflectance without the need of calibration panels in the scene, and port the paint signatures into DIRSIG for successful integration into the newly rendered sets of synthetic VNIR hyperspectral scenes.
Inversion-vibration and inversion-rotation interactions in the ammonia molecule

Abstract An attempt has been made to extend the theory of ammonia inversion in order to account for the dependence of the inversion splitting on the full set of vibrational and rotational quantum numbers. The potential energy of ammonia is approximated by a double minimum potential V(ζ) plus the potential of a system of harmonic oscillators in the remaining five vibrational coordinates. V(ζ) has been chosen to have the form V(ζ) = −2F cos ( ζ L ) + 2G cos ( 2ζ L ) in which ζ is an inversion coordinate and L a constant, (ζ | ≦ πL). The double minimum wave functions are computed numerically. Inversion-vibration interactions are obtained by developing the parameters F and G, which are regarded as mild functions of the five vibrational coordinates, in a Taylor expansion in the vibrational coordinates. With the exception of the state 2ν 4 0 this potential accounts for the dependence of the inversion splittings on the vibrational quantum numbers of the two doubly degenerate modes ν2 and ν4 (eleven experimental data are fitted with four empirical interaction constants). However, the potential fails to describe completely the interaction between the inversion coordinate and the remaining nondegenerate vibrational coordinate associated with ν1. Since the task of diagonalizing the complete rotation-inversion Hamiltonian is complicated by the presence of several resonances, the rotation-inversion constants B− − B+ and C− − C+ are calculated only from the lowest order vibration-rotation-inversion Hamiltonian. The calculated constants for the pure inversion states n2 = 0, 1, 2, and 3 and the states n2 = 1 in combination with the remaining vibrational modes agree surprisingly well with the experimentally observed values.
MGpi: A Computational Model of Multiagent Group Perception and Interaction

Toward enabling next-generation robots capable of socially intelligent interaction with humans, we present a computational model of interactions in a social environment of multiple agents and multiple groups. The Multiagent Group Perception and Interaction (MGpi) network is a deep neural network that predicts the appropriate social action to execute in a group conversation (e.g., speak, listen, respond, leave), taking into account neighbors' observable features (e.g., location of people, gaze orientation, distraction, etc.). A central component of MGpi is the Kinesic-Proxemic-Message (KPM) gate, that performs social signal gating to extract important information from a group conversation. In particular, KPM gate filters incoming social cues from nearby agents by observing their body gestures (kinesics) and spatial behavior (proxemics). The MGpi network and its KPM gate are learned via imitation learning, using demonstrations from our designed social interaction simulator. Further, we demonstrate the efficacy of the KPM gate as a social attention mechanism, achieving state-of-the-art performance on the task of group identification without using explicit group annotations, layout assumptions, or manually chosen parameters.
Property Testing of LP-Type Problems.

Given query access to a set of constraints S, we wish to quickly check if some objective function φ subject to these constraints is at most a given value k. We approach this problem using the framework of property testing where our goal is to distinguish the case φ(S) ≤ k from the case that at least an e fraction of the constraints in S need to be removed for φ(S) ≤ k to hold. We restrict our attention to the case where (S,φ) are LP-Type problems which is a rich family of combinatorial optimization problems with an inherent geometric structure. By utilizing a simple sampling procedure which has been used previously to study these problems, we are able to create property testers for any LP-Type problem whose query complexities are independent of the number of constraints. To the best of our knowledge, this is the first work that connects the area of LP-Type problems and property testing in a systematic way. Among our results are property testers for a variety of LP-Type problems that are new and also problems that have been studied previously such as a tight upper bound on the query complexity of testing clusterability with one cluster considered by Alon, Dar, Parnas, and Ron (FOCS 2000). We also supply a corresponding tight lower bound for this problem and other LP-Type problems using geometric constructions.
Supporting I/O and IPC via fine-grained OS isolation for mixed-criticality real-time tasks

Efforts towards hosting safety-critical, real-time applications on multicore platforms have been stymied by a problem dubbed the “one-out-of-m” problem: due to excessive analysis pessimism, the overall capacity of an m-core platform can easily be reduced to roughly just one core. The predominant approach for addressing this problem introduces hardware-isolation techniques that ameliorate contention experienced by tasks when accessing shared hardware components, such as DRAM memory or caches. Unfortunately, in work on such techniques, the operating system (OS), which is a key source of potential interference, has been largely ignored. Most real-time OSs do facilitate the use of a coarse-grained partitioning strategy to separate the OS from user-level tasks. However, such a strategy by itself fails to address any data sharing between the OS and tasks, such as when OS services are required for interprocess communication (IPC) or I/O. This paper presents techniques for lessening the impacts of such sharing, specifically in the context of $${\textsf {MC}}^{\textsf {2}}$$ , a hardware-isolation framework designed for mixed-criticality systems. Additionally, it presents the results from micro-benchmark experiments and a large-scale schedulability study conducted to evaluate the efficacy of the proposed techniques and also to elucidate sharing vs. isolation tradeoffs involving the OS. This is the first paper to systematically consider such tradeoffs and consequent impacts of OS-induced sharing on the one-out-of-m problem.
Multiplexing cell-cell communication

The engineering of advanced multicellular behaviors, such as the programmed growth of biofilms or tissues, requires cells to communicate multiple aspects of physiological information. Unfortunately, few cell-cell communication systems have been developed for synthetic biology. Here, we engineer a genetically encoded channel selector device that enables a single communication system to transmit two separate intercellular conversations. Our design comprises multiplexer and demultiplexer sub-circuits constructed from a total of 12 CRISPRi-based transcriptional logic gates, an acyl homoserine lactone-based communication module, and three inducible promoters that enable small molecule control over the conversations. Experimentally parameterized mathematical models of the sub-components predict the steady state and dynamical performance of the full system. Multiplexed cell-cell communication has applications in synthetic development, metabolic engineering, and other areas requiring the coordination of multiple pathways among a community of cells.
Internal wave beam propagation in non-uniform stratifications

In addition to being observable in laboratory experiments, internal wave beams are reported in geophysical settings, which are characterized by non-uniform density stratifications. Here, we perform a combined theoretical and experimental study of the propagation of internal wave beams in non-uniform density stratifications. Transmission and reflection coefficients, which can differ greatly for different physical quantities, are determined for sharp density-gradient interfaces and finite-width transition regions, accounting for viscous dissipation. Thereafter, we consider even more complex stratifications to model geophysical scenarios. We show that wave beam ducting can occur under conditions that do not necessitate evanescent layers, obtaining close agreement between theory and quantitative laboratory experiments. The results are also used to explain recent field observations of a vanishing wave beam at the Keana Ridge, Hawaii.
Anomalous magnetoresistance in centrosymmetric skyrmion-lattice magnet Gd2PdSi3

We performed a systematic study of the temperature- and field-dependence of magnetization and resistivity of Gd2PdSi3, which is a centrosymmetric skyrmion crystal. While the magnetization behavior is consistent with the reported phase diagram based on susceptibility, we show that a phase diagram can also be constructed based on the anomalous magnetoresistance with one-to-one correspondence among all the features. In addition, the crossover boundary into the field-induced ferromagnetic state is also identified. Our results suggest that the ferromagnetic spin fluctuations above the Neel temperature play a key role in the high sensitivity of the resistivity anomalies to magnetic field, pointing to the rich interplay of different magnetic correlations at zero and finite wave vectors underlying the skyrmion lattice in this frustrated itinerant magnet.
Adverse Neurological Effects of Short-Term Sleep Deprivation in Aging Mice Are Prevented by SS31 Peptide

Sleep deprivation is a potent stress factor that disrupts regulatory pathways in the brain resulting in cognitive dysfunction and increased risk of neurodegenerative disease with increasing age. Prevention of the adverse effects of sleep deprivation could be beneficial in older individuals by restoring healthy brain function. We report here on the ability of SS31, a mitochondrial specific peptide, to attenuate the negative neurological effects of short-term sleep deprivation in aging mice. C57BL/6 female mice, 20 months old, were subcutaneously injected with SS31 (3 mg/kg) or saline daily for four days. Sleep deprivation was 4 h daily for the last two days of SS31 treatment. Mice were immediately tested for learning ability followed by collection of brain and other tissues. In sleep deprived mice treated with SS31, learning impairment was prevented, brain mitochondrial ATP levels and synaptic plasticity regulatory proteins were restored, and reactive oxygen species (ROS) and inflammatory cytokines levels were decreased in the hippocampus. This observation suggests possible therapeutic benefits of SS31 for alleviating adverse neurological effects of short-term sleep loss.
Simulation as a sustainable trading zone: Aiming at intergenerational justice:

The paper, drawing on the example of simulation codes used in nuclear physics and high-energy physics, seeks to highlight the ethical implications of discontinuing support for simulation codes and ...
Populations of Planets in Multiple Star Systems

Astronomers have discovered that both planets and binaries are abundant throughout the Galaxy. In combination, we know of over 100 planets in binary and higher-order multi-star systems, in both circumbinary and circumstellar configurations. In this chapter we review these findings and some of their implications for the formation of both stars and planets. Most of the planets found have been circumstellar, where there is seemingly a ruinous influence of the second star if sufficiently close ( 100 AU). This was thought to be a sign of Kozai-Lidov migration, however recent studies have shown this mechanism to be too inefficient to account for the majority of hot Jupiters. A couple of dozen circumbinary planets have been proposed around both main sequence and evolved binaries. Around main sequence binaries there are preliminary indications that the frequency of gas giants is as high as those around single stars. There is however a conspicuous absence of circumbinary planets around the tightest main sequence binaries with periods of just a few days, suggesting a unique, more disruptive formation history of such close stellar pairs.
Solutions to arithmetic differential equations in algebraically closed fields

Abstract Arithmetic differential equations or δ-geometry exploits analogies between derivations and p-derivations δ arising from lifts of Frobenius to study problems in arithmetic geometry. Along the way, two main classes such functions, describable as series, arose prominently namely δ-characters of abelian schemes and (isogeny covariant) δ-modular forms. However, the theory of these δ-functions is not as straightforward in ramified settings. Overconvergence was introduced in [13] to account for these issues which essentially imposes growth conditions extensions of these series to a fixed level of ramification; necessary as such extensions have non-trivial fractional coefficients. In this article, we introduce a rescaling process which identifies a class of δ-functions we call totally overconvergent, which extend all the way to the algebraic closure of ring of integers of the maximally unramified extension of Q p . Applications built on these functions allow one to remove boundedness assumptions on ramification. The bulk of the article is devoted to establishing that most δ-functions arising in practice, namely those in the applications described in [5] , [7] , [8] , are totally overconvergent, which essentially extends results in [13] to unbounded ramification.
Resolvent analysis of an airfoil laminar separation bubble at Re=500 000

The perturbation dynamics over an airfoil laminar separation bubble (LSB) is examined via resolvent analysis. The computational burden of singular value decomposition (SVD) is relieved through the randomized method, and the local energy amplification mechanisms of the LSB are extracted from the global resolvent operator with the discounting approach. With the applications of input and output windows, it is shown that the Kelvin-Helmholtz instability dominates the energy amplification over the LSB, and the optimal momentum-based forcing aligns with the tangential direction of the surface.
Robust polarimetry via convex optimization

We present mathematical methods, based on convex optimization, for correcting non-physical coherency matrices measured in polarimetry. We also develop the method for recovering the coherency matrices corresponding to the smallest and largest values of the degree of polarization given the experimental data and a specified tolerance. We use experimental non-physical results obtained with the standard polarimetry scheme and a commercial polarimeter to illustrate these methods. Our techniques are applied in post-processing, which complements other experimental methods for robust polarimetry.
THH and traces of enriched categories

We prove that topological Hochschild homology (THH) arises from a presheaf of circles on a certain combinatorial category, which gives a universal construction of THH for any enriched infinity category. Our results rely crucially on an elementary, model-independent framework for enriched higher category theory, which may be of independent interest.
Extra-hepatic comorbidity burden significantly increases 90-day mortality in patients with cirrhosis and high model for endstage liver disease

We examined how extra-hepatic comorbidity burden impacts mortality in patients with cirrhosis referred for liver transplantation (LT). Adults with cirrhosis evaluated for their first LT in 2012 were followed through their clinical course with last follow up in 2019. Extra-hepatic comorbidity burden was measured using the Charlson Comorbidity Index (CCI). The endpoints were 90-day transplant free survival (Cox-Proportional Hazard regression), and overall mortality (competing risk analysis). The study included 340 patients, mean age 56 ± 11, 63% male and MELD-Na 17.2 ± 6.6. The CCI was 0 (no comorbidities) in 44%, 1–2 in 44% and > 2 (highest decile) in 12%, with no differences based on gender but higher CCI in patients with fatty and cryptogenic liver disease. Thirty-three (10%) of 332 patients not receiving LT within 90 days died. Beyond MELD-Na, the CCI was independently associated with 90-day mortality (hazard ratio (HR), 1.32 (95% confidence interval (CI) 1.02–1.72). Ninety-day mortality was specifically increased with higher CCI category and MELD ≥18 (12% (CCI = 0), 22% (CCI = 1–2) and 33% (CCI > 2), (p = 0.002)) but not MELD-Na ≤17. At last follow-up, 69 patients were alive, 100 underwent LT and 171 died without LT. CCI was associated with increased overall mortality in the competing risk analysis (Sub-HR 1.24, 95%CI 1.1–1.4). Extra-hepatic comorbidity burden significantly impacts short-term mortality in patients with cirrhosis and high MELD-Na. This has implications in determining urgency of LT and mortality models in cirrhosis and LT waitlisting, especially with an ageing population with increasing prevalence of fatty liver disease.
Isotope abundances of solar coronal material derived from solar energetic particle measurements

Coronal isotopic abundances for the elements He, C, N, O, Ne, and Mg are derived from previously published measurements of the isotopic composition of solar energetic particles by first measuring, and then correcting for, the charge-to-mass-dependent fractionation due to solar flare acceleration and propagation processes. The resulting coronal composition generally agrees with that of other samples of solar system material, but the previously noted difference between the solar flare and solar wind Ne-22/Ne-20 ratios remains unresolved. 18 references.
Learning to Organize Knowledge with N-Gram Machines.

Deep neural networks (DNNs) had great success on NLP tasks such as language modeling, machine translation and certain question answering (QA) tasks. However, the success is limited at more knowledge intensive tasks such as QA from a big corpus. Existing end-to-end deep QA models (Miller et al., 2016; Weston et al., 2014) need to read the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size. This is prohibitive for practical tasks such as QA from Wikipedia, a novel, or the Web. We propose to solve this scalability issue by using symbolic meaning representations, which can be indexed and retrieved efficiently with complexity that is independent of the text size. More specifically, we use sequence-to-sequence models to encode knowledge symbolically and generate programs to answer questions from the encoded knowledge. We apply our approach, called the N-Gram Machine (NGM), to the bAbI tasks (Weston et al., 2015) and a special version of them ("life-long bAbI") which has stories of up to 10 million sentences. Our experiments show that NGM can successfully solve both of these tasks accurately and efficiently. Unlike fully differentiable memory models, NGM's time complexity and answering quality are not affected by the story length. The whole system of NGM is trained end-to-end with REINFORCE (Williams, 1992). To avoid high variance in gradient estimation, which is typical in discrete latent variable models, we use beam search instead of sampling. To tackle the exponentially large search space, we use a stabilized auto-encoding objective and a structure tweak procedure to iteratively reduce and refine the search space.
Mapping the “long tail” of research funding: A topic analysis of NSF grant proposals in the division of astronomical sciences

"Long tail" data are considered to be smaller, heterogeneous, researcher-held data, which present unique data management and scholarly communication challenges. These data are presumably concentrated within relatively lower-funded projects due to insufficient resources for curation. To better understand the nature and distribution of long tail data, we examine National Science Foundation (NSF) funding patterns using Latent Dirichlet Analysis (LDA) and bibliographic data. We also introduce the concept of "Topic Investment" to capture differences in topics across funding levels and to illuminate the distribution of funding across topics. This study uses the discipline of astronomy as a case study, overall exploring possible associations between topic, funding level and research output, with implications for research policy and practice. We find that while different topics demonstrate different funding levels and publication patterns, dynamics predicted by the "long tail" theoretical framework presented here can be observed within NSF-funded topics in astronomy.
Context-Aware Collaborative Intelligence With Spatio-Temporal In-Sensor-Analytics for Efficient Communication in a Large-Area IoT Testbed

Decades of continuous scaling has reduced the energy of unit computing to virtually zero, while energy-efficient communication has remained the primary bottleneck in achieving fully energy-autonomous Internet-of-Things (IoT) nodes. This article presents and analyzes the tradeoffs between the energies required for communication and computation in a wireless sensor network, deployed in a mesh architecture over a 2400-acre university campus, and is targeted toward multisensor measurement of temperature, humidity and water nitrate concentration for smart agriculture. Several scenarios involving in-sensor analytics (ISA), collaborative intelligence (CI), and context-aware switching (CAS) of the cluster head during CI has been considered. A real-time co-optimization algorithm has been developed for minimizing the energy consumption in the network, hence maximizing the overall battery lifetime. Measurement results show that the proposed ISA consumes $\approx 467\times $ lower energy as compared to traditional Bluetooth low energy (BLE) communication, and $\approx 69500\times $ lower energy as compared with long-range (LoRa) communication. When the ISA is implemented in conjunction with LoRa, the lifetime of the node increases from a mere 4.3 h to 66.6 days with a 230-mAh coin cell battery, while preserving >99% of the total information. The CI and CAS algorithms help in extending the worst case node lifetime by an additional 50%, thereby exhibiting an overall network lifetime of $\approx 104$ days, which is >90% of the theoretical limits as posed by the leakage current present in the system, while effectively transferring information sampled every second. A Web-based monitoring system was developed to continuously archive the measured data, and for reporting real-time anomalies.
The Higgs mass and new physics scales in the minimal standard model

We study the theoretical correlation between the Higgs mass of the minimal standard model and the scale at which new physics is expected to occur. In addition to the classic constraints of unitarity, triviality and vacuum stability, we reexamine the constraints imposed by the precision electroweak data. We then pay particular attention to the constraint imposed by the absence of fine-tuning in the Higgs mass parameter (the Veltman condition). We find that the fine-tuning condition places a significant constraint on the new physics scale for the Higgs mass range 100 GeV mh200 GeV mostly unconstrained by the classic constraints.
Worldsheets in holography: Computing corrections to the Veneziano amplitude

We provide a brief summary of a method to calculate improvements to the Veneziano amplitude, creating sub-leading nonlinearities in the Regge trajectory of states. We formulate it as an extension o...
Lifespan estimates via Neumann heat kernel

We study the lower bound of the lifespan $$T^{*}$$ for the heat equation $$u_t=\Delta u$$ in a bounded domain $$\Omega \subset \mathbb {R}^{n}(n\ge 2)$$ with positive initial data $$u_{0}$$ and a nonlinear radiation condition on partial boundary: the exterior normal derivative $$\partial u/\partial n=u^{q}$$ on $$\Gamma _1\subseteq \partial \Omega $$ for some $$q>1$$ , while $$\partial u/\partial n=0$$ on the other part of the boundary. Previously, under the convexity assumption of $$\Omega $$ , the asymptotic behaviours of $$T^{*}$$ on the maximum $$M_{0}$$ of $$u_{0}$$ and the surface area $$|\Gamma _{1}|$$ of $$\Gamma _{1}$$ were explored. This paper is intended to remove the convexity assumption of $$\Omega $$ since it is very restrictive in real applications. We will show that as $$M_{0}\rightarrow 0^{+}$$ , $$T^{*}$$ is at least of order $$M_{0}^{-(q-1)}$$ which is optimal, and meanwhile prove that as $$|\Gamma _{1}|\rightarrow 0^{+}$$ , $$T^{*}$$ is at least of order $$|\Gamma _{1}|^{-\frac{1}{n-1}}$$ for $$n\ge 3$$ and $$|\Gamma _{1}|^{-1}\big /\ln \left( |\Gamma _{1}|^{-1}\right) $$ for $$n=2$$ . The order of $$T^{*}$$ on $$|\Gamma _{1}|$$ when $$n=2$$ is almost optimal. Instead of the usual energy method, the proofs in this paper are carried out by carefully analysing the representation formula of u in terms of the Neumann heat kernel.
Scientific discovery with the James Webb Space Telescope

For the past 400 years, astronomers have sought to observe and interpret the Universe by building more powerful telescopes. These incredible instruments extend the capabilities of one of our most i...
A Class of Variational Functionals in Conformal Geometry

We derive a class of variational functionals which arise naturally in conformal geometry. In the special case when the Riemannian manifold is locally conformal flat, the functional coincides with the well-studied functional which is the integration over the manifold of the k-symmetric function of the Schouten tensor of the metric on the manifold.
The Hamiltonian problem and $t$-path traceable graphs

The problem of characterizing maximal non-Hamiltonian graphs may be naturally extended to characterizing graphs that are maximal with respect to nontraceability and beyond that to t-path traceability. We show how t-path traceability behaves with respect to disjoint union of graphs and the join with a complete graph. Our main result is a decomposition theorem that reduces the problem of characterizing maximal t-path traceable graphs to characterizing those that have no universal vertex. We generalize a construction of maximal nontraceable graphs by Zelinka to t-path traceable graphs.
Ultra-low-energy computing paradigm using giant spin Hall devices

The spin Hall effect converts charge current to spin current, which can exert spin-torque to switch the magnetization of a nanomagnet. Recently, it has been shown that the ratio of spin current to charge current using the spin Hall effect can be made greater than unity by using the areal geometry judiciously, unlike the case of conventional spin-transfer-torque switching of nanomagnets. This can enable an energy-efficient means to write a bit of information in nanomagnets. Here, we study the energy dissipation in such spin Hall devices. By solving the stochastic Landau?Lifshitz?Gilbert equation of magnetization dynamics in the presence of room-temperature thermal fluctuations, we show a methodology to simultaneously reduce the switching delay, its variance and energy dissipation, while lateral dimensions of the spin Hall devices are scaled down.
FUZZY FLUID MECHANICS IN THREE DIMENSIONS

We introduce a rotation invariant short distance cutoff in the theory of an ideal fluid in three space dimensions, by requiring momenta to take values in a sphere. This leads to an algebra of functions in position space that is noncommutative. Nevertheless it is possible to find appropriate analogues of the Euler equations of an ideal fluid. The system still has a Hamiltonian structure. It is hoped that this will be useful in the study of possible singularities in the evolution of Euler (or Navier–Stokes) equations in three dimensions.
Möbius Fermions: Improved Domain Wall Chiral Fermions

A new class of domain wall fermions is defined that interpolates between Shamir's and Borici's form without increasing the number of Dirac applications per CG iteration. This class represents a full (real) Mobius transformation of the Wilson kernel. Simulations on quenched Wilson lattices with β = 6.0 show that the number of lattice sites ( L s ) in the fifth dimension can be reduced by a factor of 2 or more at fixed value of chiral symmetry violations measured by the residual mass ( m res ).
Mid-infrared frequency comb generation via cascaded quadratic nonlinearities in quasi-phase-matched waveguides.

We experimentally demonstrate a simple configuration for mid-infrared (MIR) frequency comb generation in quasi-phase-matched lithium niobate waveguides using the cascaded-χ(2) nonlinearity. With nanojoule-scale pulses from an Er:fiber laser, we observe octave-spanning supercontinuum in the near-infrared with dispersive wave generation in the 2.5–3 μm region and intrapulse difference frequency generation in the 4–5 μm region. By engineering the quasi-phase-matched grating profiles, tunable, narrowband MIR and broadband MIR spectra are both observed in this geometry. Finally, we perform numerical modeling using a nonlinear envelope equation, which shows good quantitative agreement with the experiment—and can be used to inform waveguide designs to tailor the MIR frequency combs. Our results identify a path to a simple single-branch approach to mid-infrared frequency comb generation in a compact platform using commercial Er:fiber technology.