cssi/SciSciGPT-SciSciNet huggingface dataset:
Kinetic control of stationary flux ratios for a wide range of biochemical processes.

One of the most intriguing features of biological systems is their ability to regulate the steady-state fluxes of the underlying biochemical reactions; however, the regulatory mechanisms and their physicochemical properties are not fully understood. Fundamentally, flux regulation can be explained with a chemical kinetic formalism describing the transitions between discrete states, with the reaction rates defined by an underlying free energy landscape. Which features of the energy landscape affect the flux distribution? Here we prove that the ratios of the steady-state fluxes of quasi–first-order biochemical processes are invariant to energy perturbations of the discrete states and are only affected by the energy barriers. In other words, the nonequilibrium flux distribution is under kinetic and not thermodynamic control. We illustrate the generality of this result for three biological processes. For the network describing protein folding along competing pathways, the probabilities of proceeding via these pathways are shown to be invariant to the stability of the intermediates or to the presence of additional misfolded states. For the network describing protein synthesis, the error rate and the energy expenditure per peptide bond is proven to be independent of the stability of the intermediate states. For molecular motors such as myosin-V, the ratio of forward to backward steps and the number of adenosine 5′-triphosphate (ATP) molecules hydrolyzed per step is demonstrated to be invariant to energy perturbations of the intermediate states. These findings place important constraints on the ability of mutations and drug perturbations to affect the steady-state flux distribution for a wide class of biological processes.
A design algorithm for reconfigurable ATM networks

In this paper, we study the reconfiguration capability in ATM networks and its ability to reduce ATM cell losses. Digital cross-connect systems have been used in traditional reconfigurable networks to concatenate channels to avoid store- and-forward delays. We propose an implementation of the reconfiguration capability in ATM networks that restricts the transmission of cells of an O-D pair to particular channels in each physical link in a simple path from origin to destination. With this proposed implementation, digital cross-connect systems are not needed. We formulate the problem of jointly determining logical networks and the routing assignments on the logical network of a reconfigurable ATM network as a nonlinear mixed integer programming problem. We prove that the joint problem with the integrality constraints relaxed can be simplified to a convex programming problem. This provides a method to compute a lower bound on the minimum cell losses for the original problem. An algorithm is developed to solve the nonlinear mixed integer programming problem. In the computational experiments, the proposed algorithm determined a good feasible solution in a few minutes of CPU time on SUN SPARCsystem 400. In the experiments, the reconfiguration capability decreased the total cell losses by up to 80%. Problem parameters that affect the effectiveness of the reconfiguration capability in reducing ATM cell losses are identified.
Wine Descriptions Provide Information: A Text Analysis

I use a computational linguistic algorithm to measure the topics covered in textual descriptions of wine. I ask whether there is information in the text that consumers value. Wine is a prominent example of an experience good. There is substantial product differentiation in the market, and consumers only have limited information on the utility they will receive when consumed. Thus, information is expected to be valuable. Evaluating descriptions of wine produced across the United States, I use a hedonic price regression to explore whether the descriptions provide any new information not already available to the consumer. Initial results suggest that text descriptions are shown to lose their explanatory value when varietal and numerical ratings are included as controls. I then show that once the varietal, region, and numerical ratings are adequately controlled for, there is information in the descriptions that consumers value. (JEL Classifications: C81, D83, L15)
A long lost key opens an ancient lock: Drosophila Myb causes a synthetic multivulval phenotype in nematodes.

The five-protein MuvB core complex is highly conserved in animals. This nuclear complex interacts with RB family tumor suppressor proteins and E2F-DP transcription factors to form DREAM complexes that repress genes that regulate cell cycle progression and cell fate. The MuvB core complex also interacts with proteins Myb family oncoproteins to form the Myb-MuvB complexes that activate many of the same genes. We show that animal-type Myb genes are present in Bilateria, Cnidaria, and Placozoa, the latter including the simplest known animal species. However, bilaterian nematode worms lost their animal-type Myb genes hundreds of millions of years ago. Nevertheless, amino acids in the LIN9 and LIN52 proteins that directly interact with the MuvB-binding domains of human B-Myb and Drosophila Myb are conserved in C. elegans. Here we show that, despite greater than 500 million years since their last common ancestor, the Drosophila melanogaster Myb protein can bind to the nematode LIN9-LIN52 proteins in vitro and can cause a synthetic multivulval (synMuv) phenotype in vivo. This phenotype is similar to that caused by loss-of-function mutations in C. elegans synMuvB class genes including those that encode homologs of the MuvB core, RB, E2F, and DP. Furthermore, amino acid substitutions in the MuvB-binding domain of Drosophila Myb that disrupt its functions in vitro and in vivo also disrupt these activities in C. elegans. We speculate that nematodes and other animals may contain another protein that can bind to LIN9 and LIN52 in order to activate transcription of genes repressed by DREAM complexes.
Pandemics and Political Development: The Electoral Legacy of the Black Death in Germany

Do pandemics have lasting consequences for political behavior? We address this question by examining the consequences of the most deadly pandemic of the last millennium: the Black Death (1347-1351). Our claim is that pandemics can influence politics in the long run if they impose sufficient loss of life so as to augment the price of labor relative to other factors of production. When this occurs, labor repressive regimes (such as serfdom) become untenable, which ultimately leads to the development of proto-democratic institutions and associated political cultures that shape modalities of political engagement for generations. We test our theory by tracing out the local consequences of the Black Death in German-speaking Central Europe. We find that areas hit hardest by the pandemic were more likely to: (1) adopt inclusive political institutions and equitable land ownership patterns; (2)exhibit electoral behavior indicating independence from landed elite influence during the transition to mass politics.
Government contracts and trade credit

Abstract This study examines the association between government contracts and firms' use of trade credit. Firms with government contracts may demand less trade credit because of their lower operational risk, higher firm performance, stronger capacity to generate internal funds, and better access to other sources of financing. On the other hand, government contractors could receive more trade credit extensions from suppliers. We examine a sample of U.S. listed firms from 2000 to 2016 and find that firms with government contracts have a lower level of trade credit. We also find that government contractors make quicker payments for trade credit contracts than other firms. Moreover, we provide empirical evidence of government contractors' lower levels of operational risk and higher firm performance, which may enable government contractors to generate adequate internal funds for their operations or to obtain other forms of financing at a lower cost and thus lower their demand for trade credit. Incremental to prior research, our study suggests that having government contracts is one of the factors determining trade credit and firms' financing decisions.
Chapter 9 – Regression

Publisher Summary This chapter deals with the topic of regression. Both simple linear regression—including such subtopics as regression to the mean, residual analysis, and weighted least squares and multiple linear regressions are considered. A simple linear regression model supposes a linear relationship between the mean response and the value of a single independent variable. It is important to estimate the actual value of a future response rather than its mean value. The multiple regression model helps to determine the least squares estimators. The distribution of the estimators is necessary to make additional assumptions about the random errors aside from just assuming that their mean is 0. The chapter also provides various solved examples and a practice exercise.
Incentives, Optimality, and Publicly Provided Goods: the Case of Mental Health Services

In this article, the authors investigate the incentives present in intergovernment transfers for public mental health care. This represents an important issue because of the large portion of mental health care that is provided by local governments, the central role of states in financing care via intergovernment transfers, and recent innovations adopted by some states altering the traditional terms of these transfers. Using a relatively simple model, the authors show that when a state government provides both financing and a free input into local government production, there will be excessive use of that input. If local government and society differ in their preferences, setting the price of the input at marginal cost will not induce optimal behavior. The optimal prices are proportional to the sum of the elasticities of the provider's expected supply of services with respect to the subsidy (tax). These results are analogous to those for optimal commodity taxation. Examination of the transfer contracts for...
On the Construction of Polyalgorithms for Automatic Numerical Analysis

This paper presents a summary of the experiences and viewpoints in the development of polyalgorithms for NAPSS. The polyalgorithms depend essentially on their objectives which are presented and discussed in the next section. The objectives of the NAPSS polyalgorithms are quite high and the four primary difficulties (met so far) in achieving them are discussed in the third section. These four difficulties are common sense, error control, flexibility versus simplicity, and reliability versus efficiency. Some of the approaches used in NAPSS to overcome these difficulties are given.
Lasers, Free-electron

Free-electron lasers are radiation sources, based on the coherent emission of synchrotron radiation of relativistic electrons within an undulator or wiggler. The resonant radiation wavelength depends on the electron beam energy and can be tuned over the entire spectrum from micrometer to X-ray radiation. The emission level of free-electron lasers is several orders of magnitude larger than the emission level of spontaneous synchrotron radiation, because the interaction between the electron beam and the radiation field modulates the beam current with the periodicity of the resonant radiation wavelength. The high brightness and the spectral range of this kind of radiation source allows studying physical and chemical processes on a femtosecond scale with angstrom resolution. Keywords: free-electron laser; undulator; microbunching; SASE FEL; FEL oscillator; FEL amplifier; FEL parameter; gain length
Chapter 12 – The Geysers Dry-Steam Power Plants, Sonoma and Lake Counties, California, U.S.A.

Publisher Summary The Geysers, the world's largest geothermal power complex, has been the subject of numerous books, articles, technical papers, theses, conferences and workshops. Geothermal chronicler Susan Hodgson has divided the history of The Geysers into five distinct eras beginning with the time when it was in its natural state. The Geysers steam field lies some 80 miles north of San Francisco in the Mayacamas Mountains. The reservoir is hosted in fractured greywacke, characterized by a matrix porosity associated with veins, which lies above a deep felsite intrusion. The reservoir is hosted in fractured greywacke, characterized by a matrix porosity associated with veins that lies above a deep felsite intrusion. This chapter shows diagrams of the piping used at the Geysers between the wells and the power house. It provides a summary of information on all Geysers plants that have ever been in operation. To illustrate the impact of the steam purchase agreement on the design of a geothermal plant, the study considers the case of the 72 MW SMUDGEO #1 plant, now called Sonoma. Under this arrangement, SMUD should try to generate as much electricity as is feasible from each pound of steam. This drove the design to include many features that could not be economically justified under the PG&E-type of contract. One of the problems in the management of a dry steam field is the replenishment of the fluid that is withdrawn through production. The Geysers' history of power generation demonstrates that geothermal energy may be used in a manner that allows it to approach sustainability, or it can be overexploited resulting a premature demise of the resource. The study describes the life cycle of a geothermal field as passing through four distinct phases or periods: development, sustaining, declining, and renewable.
Corruption of Journal Impact Factors

Requiring authors to cite articles from the same journal in which they hope to publish is a practice that the Editors of all Trends journals disapprove of. Beyond pointing authors in the direction of papers that they might have missed and would improve the quality of their articles, we do not require authors to cite articles from our journals.
CHAPTER 12 – Miscellaneous Useful Results

Publisher Summary Herbert Federer's treatise presents many basic methods of geometry and analysis in a generality that embraces manifold applications. This chapter describes his treatment of Sard's Theorem, Green's Theorem, relative homology, and functions of bounded variation. The usual statement of Sard's Theorem says that the set of critical values of a C∞ function f: Rm → Rn has Lebesgue measure 0. Herbert Federer's refinement shows precisely how the Hausdorff measure of the image depends on the rank of Df and the smoothness class of f. An important class of functions in analysis is the space BVloc of functions of locally bounded variation.
Mechanistic insight into crossing over during mouse meiosis

Crossover recombination is critical for meiotic chromosome segregation, but how mammalian crossing over is accomplished is poorly understood. Here, we illuminate how strands exchange during meiotic recombination in male mice by analyzing patterns of heteroduplex DNA in recombinant molecules preserved by the mismatch correction deficiency of Msh2-/- mutants. Surprisingly, MSH2-dependent recombination suppression was not evident. However, a substantial fraction of crossover products retained heteroduplex DNA, and some provided evidence of MSH2-independent correction. Biased crossover resolution was observed, consistent with asymmetry between DNA ends in earlier intermediates. Many crossover products yielded no heteroduplex DNA, suggesting dismantling by D-loop migration. Unlike the complexity of crossovers in yeast, these simple modifications of the original double-strand break repair model-asymmetry in recombination intermediates and D-loop migration-may be sufficient to explain most meiotic crossing over in mice while also addressing long-standing questions related to Holliday junction resolution.
Tax policy and foreign direct investment

Abstract This paper examines the implications of the ‘residence’ approach to taxing foreign source income such as employed by the United States. It is argued that, because the repatriation of earnings to the home country investor and not the earnings themselves are typically the source of tax liability, the foreign source income tax should affect foreign investment differently depending on the required transfers of funds within the firm. One implication of viewing the tax in this way is that in order to maximize after-tax profits, a firm should finance its foreign investment out of foreign earnings to the greatest extent possible. That is, a firm's required foreign return jumps at the point at which desired foreign investment just exhausts foreign earnings. This allows us to draw a distinction between ‘mature’ foreign operations, which are at any point in time financed at the margin by investing earnings (and perhaps also pay dividends to their parent firm in the home country), and ‘immature’ foreign affiliates, which rely on funding from their parents (and should not be paying dividends). It is noted that survey evidence on multinational firm behavior is consistent with this distinction. Direct investment data indicate that mature foreign operations probably account for nearly 90 percent of U.S. foreign direct investment. The discussion then turns to investment incentives. It is shown that the home country's rate of tax on foreign source income and the presence or absence of a foreign tax credit should be irrelevant to a mature foreign operations's investment and dividend decisions. This conclusion, which conflicts sharply with the conventional wisdom, follows because the home country tax acts as an unavoidable cost. New firms' investment decisions are, on the other hand, influenced by home country taxes.
Genetic inactivation of the translin/trax microRNA-degrading enzyme phenocopies the robust adiposity induced by Translin (Tsn) deletion.

Abstract Objective Deletion of Translin (Tsn) from mice induces an unusual metabolic profile characterized by robust adiposity, normal body weight and glucose tolerance. Translin (TN) protein and its partner, trax (TX), form the TN/TX microRNA-degrading enzyme. Since the microRNA system plays a prominent role in regulating metabolism, we reasoned that the metabolic profile displayed by Tsn KO mice might reflect dysregulation of microRNA signaling. Methods To test this hypothesis, we inserted a mutation, E126A, in Tsnax, the gene encoding TX, that abolishes the microRNA-degrading enzymatic activity of the TN/TX complex. In addition, to help define the cell types that drive the adiposity phenotype, we have also generated mice with floxed alleles of Tsn or Tsnax. Results Introduction of the E126A mutation in Tsnax does not impair expression of TN or TX proteins or their co-precipitation. Furthermore, these mice display selective increases in microRNAs that match those induced by Tsn deletion, confirming that this mutation in Tsnax inactivates the microRNA-degrading activity of the TN/TX complex. Mice homozygous for the Tsnax (E126A) mutation display a metabolic profile that closely mimics that of Tsn KO mice. Selective deletion of Tsn or Tsnax from either adipocytes or hepatocytes, two candidate cell types, does not phenocopy the elevated adiposity displayed by mice with constitutive Tsn deletion or the Tsnax (E126A) mutation. Furthermore, global, conditional deletion of Tsn in adulthood does not elicit increased adiposity. Conclusion Taken together, these findings indicate that inactivation of the TN/TX microRNA-degrading enzyme during development is necessary to drive the robust adiposity displayed by Tsn KO mice.
School finance reform: Assessing general equilibrium effects

Abstract In 1994 the state of Michigan implemented one of the most comprehensive school finance reforms undertaken to date in any of the states. Understanding the effects of the reform is thus of value in informing other potential reform initiatives. In addition, the reform and associated changes in the economic environment provide an opportunity to assess whether a simple general equilibrium model can be of value in framing the study of such reform initiatives. In this paper, we present and use such a model to derive predictions about the effects of the reform on housing prices and neighborhood demographic compositions. Broadly, our analysis implies that the effects of the reform and changes in the economic environment are likely to have been reflected primarily in housing prices and only modestly on neighborhood demographics. We find that evidence for the Detroit metropolitan area from the decade encompassing the reform is largely consistent with the predictions of the model.
Oil–water transport in clay-hosted nanopores: Effects of long-range electrostatic forces

Charged clay surfaces can impact the storage and mobility of hydrocarbon and water mixtures. Here, we use equilibrium molecular dynamics (MD) and nonequilibrium MD simulations to investigate hydrocarbon-water mixtures and their transport in slit-shaped illite nanopores. We construct two illite pore models with different surface chemistries: potassium-hydroxyl (PH) and hydroxyl-hydroxyl (HH) structures. In HH nanopore, we observe water adsorption on the clay surfaces. In PH nanopores, however, we observe the formation of water bridges because of the existence of a local, long-range electric field. Our NEMD simulations demonstrate that the velocity profiles across the pore depends strongly on water concentration, pore width and the presence or absence of the water bridge. This fundamental study provides a theoretical basis for understanding nanofluidics with charged surfaces and can be applied in such as biological processes, chemical and physical fields, and the oil and gas extraction in clay-rich formations.
Towards Secure Cryptographic Software Implementation Against Side-Channel Power Analysis Attacks.

Side-channel attacks have been a real threat against many critical embedded systems that rely on cryptographic algorithms as their security engine. A commonly used algorithmic countermeasure, random masking, incurs large execution delay and resource overhead. The other countermeasure, operation shuffling or permutation, can mitigate side-channel leakage effectively with minimal overhead. In this paper, we target utilizing the independence among operations in cryptographic algorithms and randomizing their execution order. We design a tool to automatically detect such independence between statements at the source code level and devise an algorithm for automatic operation shuffling. We test our algorithm on the new SHA3 standard, Keccak. Results show that the tool has effectively implemented operation-shuffling to reduce the side-channel leakage significantly, and therefore can guide automatic secure cryptographic software implementations against differential power analysis attacks. Keywords-Side-channel attacks, shuffling, Keccak
Conceptualizing grade inflation

Abstract Evidence of grade inflation in U.S. high schools is often misinterpreted due to confusion about how grade inflation is, or should be, defined. This note reduces the confusion by introducing a typology of grade inflation and discussing the implications of each type. We then provide empirical examples of each type of grade inflation using transcript and test-score data from Algebra I classes in North Carolina over a recent ten-year period. Year-specific (static) grade inflation has been, and remains, higher in schools serving relatively disadvantaged student populations; however, differential changes over the past ten years (what we term dynamic grade inflation) have significantly narrowed the socioeconomic gap in static grade inflation.
Consumption of a high energy density diet triggers microbiota dysbiosis, hepatic lipidosis, and microglia activation in the nucleus of the solitary tract in rats.

Obesity is a multifactorial chronic inflammatory disease. Consumption of high energy density (HED) diets is associated with hyperphagia, increased body weight and body fat accumulation, and obesity. Our lab has previously shown that short-term (4 weeks) consumption of a HED diet triggers gut microbiota dysbiosis, gut inflammation, and reorganization of the gut-brain vagal communication. The aim of this study was to investigate the effect of long-term (6 months) consumption of HED diet on body composition, gut microbiome, hepatocellular lipidosis, microglia activation in the nucleus of the solitary tract, and systemic inflammation. Male Sprague–Dawley rats were fed a low energy density (LED) diet for 2 weeks and then switched to a HED diet for 26 weeks. Twenty-four-hour food intake, body weight, and body composition were measured twice a week. Blood serum and fecal samples were collected at baseline, 1, 4, 8, and 26 weeks after introduction of the HED diet. Serum samples were used to measure insulin, leptin, and inflammatory cytokines using Enzyme-linked Immunosorbent Assay. Fecal samples were assessed for 16 S rRNA genome sequencing. HED diet induced microbiota dysbiosis within a week of introducing the diet. In addition, there was significant microglia activation in the intermediate NTS and marked hepatic lipidosis after 4 weeks of HED diet. We further observed changes in the serum cytokine profile after 26 weeks of HED feeding. These data suggest that microbiota dysbiosis is the first response of the organism to HED diets, followed by increased liver fat accumulation, microglia activation in the brain, and circulating levels of inflammatory markers. To our knowledge, this is the first study to present longitudinal and cross-sectional results on effect of long-term consumption of HED diets on all these parameters in a single cohort of animals.
A Free-Energy Principle for Representation Learning

This paper employs a formal connection of machine learning with thermodynamics to characterize the quality of learnt representations for transfer learning. We discuss how information-theoretic functional such as rate, distortion and classification loss of a model lie on a convex, so-called equilibrium surface.We prescribe dynamical processes to traverse this surface under constraints, e.g., an iso-classification process that trades off rate and distortion to keep the classification loss unchanged. We demonstrate how this process can be used for transferring representations from a source dataset to a target dataset while keeping the classification loss constant. Experimental validation of the theoretical results is provided on standard image-classification datasets.
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-Layer Networks

We develop exact representations of training two-layer neural networks with rectified linear units (ReLUs) in terms of a single convex program with number of variables polynomial in the number of training samples and the number of hidden neurons. Our theory utilizes semi-infinite duality and minimum norm regularization. We show that ReLU networks trained with standard weight decay are equivalent to block $\ell_1$ penalized convex models. Moreover, we show that certain standard convolutional linear networks are equivalent semi-definite programs which can be simplified to $\ell_1$ regularized linear models in a polynomial sized discrete Fourier feature space.
Timemory: Modular Performance Analysis for HPC

HPC has undergone a significant transition toward heterogeneous architectures. This transition has introduced several issues in code migration to support multiple frameworks for targeting the various architectures. In order to cope with these challenges, projects such as Kokkos and LLVM create abstractions which map a generic front-end API to the backend that supports the targeted architecture. This paper presents a complementary framework for performance measurement and analysis. Several performance measurement and analysis tools in existence provide their capabilities through various methods but the common theme among these tools are prohibitive limitations in terms of user-level extensions. For this reason, software developers commonly have to learn multiple tools and valuable analysis methods, such as the roofline model, are frequently required to be generated manually. The timemory framework provides complete modularity for performance measurement and analysis and eliminates all restrictions on user-level extensions. The timemory framework also provides a highly-efficient and intuitive method for handling multiple tools/measurements (i.e., “components”) concurrently. The intersection of these characteristics provide ample evidence that timemory can serve as the common interface for existing performance measurement and analysis tools. Timemory components are developed in C++ but includes multi-language support for C, Fortran, and Python codes. Numerous components are provided by the library itself – including, but not limited to, timers, memory usage, hardware counters, and FLOP and instruction roofline models. Additionally, analysis of the intrinsic overhead demonstrates superior performance in comparison with popular tools.
Online Learning with Imperfect Hints

We consider a variant of the classical online linear optimization problem in which at every step, the online player receives a "hint" vector before choosing the action for that round. Rather surprisingly, it was shown that if the hint vector is guaranteed to have a positive correlation with the cost vector, then the online player can achieve a regret of $O(\log T)$, thus significantly improving over the $O(\sqrt{T})$ regret in the general setting. However, the result and analysis require the correlation property at \emph{all} time steps, thus raising the natural question: can we design online learning algorithms that are resilient to bad hints? In this paper we develop algorithms and nearly matching lower bounds for online learning with imperfect directional hints. Our algorithms are oblivious to the quality of the hints, and the regret bounds interpolate between the always-correlated hints case and the no-hints case. Our results also generalize, simplify, and improve upon previous results on optimistic regret bounds, which can be viewed as an additive version of hints.
Graded basic elements

Abstract We prove the existence theorem for basic elements in the quasi-projective case, extending results of Eisenbud-Evans and Bruns from the affine case. We give several geometric applications. For example, we show that every local complete intersection of pure codimension two in a smooth projective variety of dimension d over an infinite field is the degeneracy locus of d − 1 sections of a rank d bundle.
Data-driven discovery of 3D and 2D thermoelectric materials.

In this work, we first perform a systematic search for high-efficiency three-dimensional (3D) and two-dimensional (2D) thermoelectric materials by combining semiclassical transport techniques with density functional theory (DFT) calculations and then train machine-learning models on the thermoelectric data. Out of 36 000 three-dimensional and 900 two-dimensional materials currently in the publicly available JARVIS-DFT database, we identify 2932 3D and 148 2D promising thermoelectric materials using a multi-steps screening procedure, where specific thresholds are chosen for key quantities like bandgaps, Seebeck coefficients and power factors. We compute the Seebeck coefficients for all the materials currently in the database and validate our calculations by comparing our results, for a subset of materials, to experimental and existing computational datasets. We also investigate the effect of chemical, structural, crystallographic and dimensionality trends on thermoelectric performance. We predict several classes of efficient 3D and 2D materials such as Ba(MgX)2 (X = P, As, Bi), X2YZ6 (X = K, Rb, Y=Pd, Pt, Z = Cl, Br), K2PtX2 (X = S, Se), NbCu3X4 (X = S, Se, Te), Sr2XYO6 (X = Ta, Zn, Y=Ga, Mo), TaCu3X4 (X = S, Se, Te), and XYN (X = Ti, Zr, Y=Cl, Br). Finally, as high-throughput DFT is computationally expensive, we train machine learning models using gradient boosting decision trees and classical force-field inspired descriptors for n-and p-type Seebeck coefficients and power factors, to quickly pre-screen materials for guiding the next set of DFT calculations. The dataset and tools are made publicly available at the websites: https://www.ctcms.nist.gov/~knc6/JVASP.html, https://www.ctcms.nist.gov/jarvisml/and https://jarvis.nist.gov/.
Anomalous Diffusion Characterization by Fourier Transform-FRAP with Patterned Illumination

Fourier transform fluorescence recovery after photobleaching (FT-FRAP) with patterned illumination is theorized and demonstrated for quantitatively evaluating normal and anomalous diffusion. Diffusion characterization is routinely performed to assess mobility in cell biology, pharmacology, and food science. Conventional FRAP is noninvasive, has low sample volume requirements, and can rapidly measure diffusion over distances of a few micrometers. However, conventional point-bleach measurements are complicated by signal-to-noise limitations, the need for precise knowledge of the photobleach beam profile, potential for bias due to sample heterogeneity, and poor compatibility with multiphoton excitation because of local heating. In FT-FRAP with patterned illumination, the time-dependent fluorescence recovery signal is concentrated to puncta in the spatial Fourier domain, with substantial improvements in signal-to-noise, mathematical simplicity, representative sampling, and multiphoton compatibility. A custom nonlinear optical beam-scanning microscope enabled patterned illumination for photobleaching through two-photon excitation. Measurements in the spatial Fourier domain removed dependence on the photobleach profile, suppressing bias from imprecise knowledge of the point spread function. For normal diffusion, the fluorescence recovery produced a simple single-exponential decay in the spatial Fourier domain, in excellent agreement with theoretical predictions. Simultaneous measurement of diffusion at multiple length scales was enabled through analysis of multiple spatial harmonics of the photobleaching pattern. Anomalous diffusion was characterized by FT-FRAP through a nonlinear fit to multiple spatial harmonics of the fluorescence recovery. Constraining the fit to describe diffusion over multiple length scales resulted in higher confidence in the recovered fitting parameters. Additionally, phase analysis in FT-FRAP was shown to inform on flow/sample translation.
Configuration models of random hypergraphs

Many empirical networks are intrinsically polyadic, with interactions occurring within groups of agents of arbitrary size. There are, however, few flexible null models that can support statistical inference for such polyadic networks. We define a class of null random hypergraphs that hold constant both the node degree and edge dimension sequences, generalizing the classical dyadic configuration model. We provide a Markov Chain Monte Carlo scheme for sampling from these models, and discuss connections and distinctions between our proposed models and previous approaches. We then illustrate these models through a triplet of applications. We start with two classical network topics -- triadic clustering and degree-assortativity. In each, we emphasize the importance of randomizing over hypergraph space rather than projected graph space, showing that this choice can dramatically alter statistical inference and study findings. We then define and study the edge intersection profile of a hypergraph as a measure of higher-order correlation between edges, and derive asymptotic approximations under the stub-labeled null. Our experiments emphasize the ability of explicit, statistically-grounded polyadic modeling to significantly enhance the toolbox of network data science. We close with suggestions for multiple avenues of future work.
Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations

We propose a generative model for the spatio-temporal distribution of high dimensional categorical observations. These are commonly produced by robots equipped with an imaging sensor such as a camera, paired with an image classifier, potentially producing observations over thousands of categories. The proposed approach combines the use of Dirichlet distributions to model sparse co-occurrence relations between the observed categories using a latent variable, and Gaussian processes to model the latent variable’s spatio-temporal distribution. Experiments in this paper show that the resulting model is able to efficiently and accurately approximate the temporal distribution of high dimensional categorical measurements such as taxonomic observations of microscopic organisms in the ocean, even in unobserved (held out) locations, far from other samples. This work’s primary motivation is to enable deployment of informative path planning techniques over high dimensional categorical fields, which until now have been limited to scalar or low dimensional vector observations.
Structural Diversity and Phylogenetic Distribution of Valyl tRNA-like Structures in Viruses

Viruses commonly use specifically folded RNA elements that interact with both host and viral proteins to perform functions important for diverse viral processes. Examples are found at the 3' termini of certain positive-sense ssRNA virus genomes where they partially mimic tRNAs, including being aminoacylated by host cell enzymes. Valine-accepting tRNA-like structures (TLSVal) are an example that share some clear homology to canonical tRNAs but have several important structural differences. Although many examples of TLSVal have been identified, we lacked a full understanding of their structural diversity and phylogenetic distribution. To address this, we undertook an in-depth bioinformatic and biochemical investigation of these RNAs, guided by recent high-resolution structures of a TLSVal. We cataloged many new examples in plant-infecting viruses but also in unrelated insect-specific viruses. Using biochemical and structural approaches, we verified the secondary structure of representative TLSVal substrates and tested their ability to be valylated, finding intriguing structural heterogeneity within this class. In a few cases, large stem-loop structures are inserted within distinct variable regions located in an area of the TLS distal to known host cell factor binding sites. In addition, we identified one virus whose TLS has switched its anticodon away from valine; the implications of this remain unclear. These results refine our understanding of the structural and functional mechanistic details of tRNA mimicry and how this may be used in viral infection.
Blockchain Governance—A New Way of Organizing Collaborations?

The recent emergence of blockchains may be considered a critical turning point in organizing collaborations. We outline the historical background and the fundamental features of blockchains and pre...
Thresholds for one-parameter families of affine iterated function systems*

This paper examines thresholds for certain properties of the attractor of a general one-parameter affine family of iterated functions systems. As the parameter increases, the iterated function system becomes less contractive, and the attractor evolves. Thresholds are studied for the following properties: the existence of an attractor, the connectivity of the attractor, and the existence of non-empty interior of the attractor. Also discussed are transition phenomena between existence and non-existence of an attractor.
BTZ one-loop determinants via the Selberg zeta function for general spin

We relate the heat kernel and quasinormal mode methods of computing the 1-loop partition function of arbitrary spin fields on a rotating (Euclidean) BTZ background using the Selberg zeta function associated with ℍ3/ℤ, extending ( arXiv:1811.08433 ) [1]. Previously, Perry and Williams [2] showed for a scalar field that the zeros of the Selberg zeta function coincide with the poles of the associated scattering operator upon a relabeling of integers. We extend the integer relabeling to the case of general spin, and discuss its relationship to the removal of non-square-integrable Euclidean zero modes.
Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data.

Motivation The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now. Results We systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2. Availability and implementation Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573. Supplementary information Supplementary data are available at Bioinformatics online.
Domain adaptive relational reasoning for 3D multi-organ segmentation

In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains). Our method is inspired by the fact that the spatial relationship between internal structures in medical images is relatively fixed, e.g., a spleen is always located at the tail of a pancreas, which serves as a latent variable to transfer the knowledge shared across multiple domains. We formulate the spatial relationship by solving a jigsaw puzzle task, i.e., recovering a CT scan from its shuffled patches, and jointly train it with the organ segmentation task. To guarantee the transferability of the learned spatial relationship to multiple domains, we additionally introduce two schemes: 1) Employing a super-resolution network also jointly trained with the segmentation model to standardize medical images from different domain to a certain spatial resolution; 2) Adapting the spatial relationship for a test image by test-time jigsaw puzzle training. Experimental results show that our method improves the performance by \(29.60\%\) DSC on target datasets on average without using any data from the target domain during training.
Discrete R symmetries and anomalies

We comment on aspects of discrete anomaly conditions focussing particularly on R symmetries. We review the Green-Schwarz cancellation of discrete anomalies, providing a heuristic explanation why, in the heterotic string, only the “model-independent dilaton” transforms non-linearly under discrete symmetries; this argument suggests that, in other theories, multiple fields might play a role in anomaly cancellations, further weakening any anomaly constraints at low energies. We provide examples in open string theories of non-universal discrete anomalies at low energies. We then consider the fact that R symmetries are necessarily broken at low energies. We exhibit dynamical models, in which fields charged under the Standard Model gauge group (for example, a doublet and a triplet) gain roughly equal masses, but where the doublet and the triplet possess different discrete charges and the low-energy anomaly conditions fail.
Single-Track Melt-Pool Measurements and Microstructures in Inconel 625

We use single-track laser melting experiments and simulations on Inconel 625 to estimate the dimensions and microstructure of the resulting melt pool. Our work is based on a design-of-experiments approach which uses multiple laser power and scan speed combinations. Single-track experiments generated melt pools of certain dimensions that showed reasonable agreement with our finite-element calculations. Phase-field simulations were used to predict the size and segregation of the cellular microstructure that formed along the melt-pool boundaries for the solidification conditions that changed as a function of melt-pool dimensions.
Coherence and other properties of sheaves in the Kohn algorithm

In the smooth case, we prove quasi-flasqueness for the sheaves of all subelliptic multipliers as well as at each of the steps of the Kohn algorithm on a pseudoconvex domain in ℂn. We use techniques by Jean-Claude Tougeron to show that if the domain has a real-analytic defining function, the modified Kohn algorithm involving generating ideals and taking real radicals only in the ring of real-analytic germs yields quasi-coherent sheaves. This sharpens a result obtained by J. J. Kohn in 1979.
Pushing the Limits of Broadband and High-Frequency Metamaterial Silicon Antireflection Coatings

Broadband refractive optics realized from high-index materials provide compelling design solutions for the next generation of observatories for the cosmic microwave background and for sub-millimeter astronomy. In this paper, work is presented which extends the state of the art in silicon lenses with metamaterial antireflection coatings toward larger-bandwidth and higher-frequency operation. Examples presented include octave bandwidth coatings with less than $$0.5\%$$ reflection, a prototype 4:1 bandwidth coating, and a coating optimized for 1.4 THz. For these coatings, the detailed design, fabrication and testing processes are described as well as the inherent performance trade-offs.
Carrier statistics and quantum capacitance effects on mobility extraction in two-dimensional crystal semiconductor field-effect transistors

In this work, the consequence of the high band-edge density of states on the carrier statistics and quantum capacitance in transition metal dichalcogenide two-dimensional semiconductor devices is explored. The study questions the validity of commonly used expressions for extracting carrier densities and field-effect mobilities from the transfer characteristics of transistors with such channel materials. By comparison to experimental data, a new method for the accurate extraction of carrier densities and mobilities is outlined. The work thus highlights a fundamental difference between these materials and traditional semiconductors that must be considered in future experimental measurements.
A Generic Slice of the Moduli Space of Line Arrangements

We study the compactification of the locus parametrizing lines with a fixed intersection to a given line, inside the moduli space of line arrangements in the projective plane constructed for weight one by Hacking-Keel-Tevelev and Alexeev for general weights. We show that this space is smooth, with normal crossing boundary, and that it has a morphism to the moduli space of marked rational curves which can be understood as a natural continuation of the blow up construction of Kapranov. In addition, we prove that it is isomorphic to a closed subvariety inside a non-reductive Chow quotient. The parametrized objects are surfaces with broken lines, whose dual graphs are rooted trees with possibly repeated markings.
SUPERSYMMETRIC YANG–MILLS THEORIES WITH EXACT SUPERSYMMETRY ON THE LATTICE

Inspired by the ideas from topological field theory it is possible to rewrite the supersymmetric charges of certain classes of extended supersymmetric Yang–Mills (SYM) theories in such a way that they are compatible with the discretization on a Euclidean space–time lattice. Such theories are known as maximally twisted SYM theories. In this review we discuss the construction and some applications of such classes of theories. The one-loop perturbative renormalization of the four-dimensional lattice SYM is discussed in particular. The lattice theories constructed using twisted approach play an important role in investigating the thermal phases of strongly coupled SYM theories and also the thermodynamic properties of their dual gravitational theories.