cssi/SciSciGPT-SciSciNet huggingface dataset:
Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles

Testing and evaluation is a crucial step in the development and deployment of connected and automated vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is necessary to test t...
Modeling the evolution of retina neural network

Vital to primary visual processing, retinal circuitry shows many similar structures across a very broad array of species, both vertebrate and non-vertebrate, especially functional components such as lateral inhibition. This surprisingly conservative pattern raises a question of how evolution leads to it, and whether there is any alternative that can also prompt helpful preprocessing. Here we design a method using genetic algorithm that, with many degrees of freedom, leads to architectures whose functions are similar to biological retina, as well as effective alternatives that are different in structures and functions. We compare this model to natural evolution and discuss how our framework can inspire neuroevolution.
The quantum confinement effect on the spectrum of near-field thermal radiation by quantum dots

The quantum confinement effect on the spectrum of near-field thermal radiation by periodic and random arrays of quantum dots (QDs) is investigated. The local density of states (LDOS) thermally emitted by QD arrays made of three lead chalcogenides, namely, lead sulfide, lead selenide, and lead telluride, is computed at a near-field distance from the arrays. The dielectric function of the QDs is extracted from their absorption spectra by utilizing an optimization technique. The thermal discrete dipole approximation is used for computing the LDOS. It is shown that the peak wavenumber of near-field LDOS emitted by periodic arrays of lead chalcogenide QDs can be significantly modulated (up to 4490 cm−1) by varying the size of the dots. The LDOS is proportional to the imaginary part of the QDs' polarizability, which peaks at the bandgap energy of the QDs. The bandgap energy of the QDs (and thus the LDOS peak) is significantly affected by the quantum confinement effect, which is size dependent. While the magnitude of thermal radiation by random arrays of QDs can be different from the periodic arrays with the same filling factor by up to ±26%, the LDOS spectrum and peak location are the same for both periodic and random arrays. The peak wavenumber of near-field radiative heat transfer between the QD arrays is also strongly affected by quantum confinement in the QDs, and thus, it can be tuned by changing the size of the QDs.
Efficient valuation of variable annuity portfolios with dynamic programming

The valuation of variable annuity (VA) portfolios presents major challenges for life insurers. Recent studies propose approximation methods based on selecting a few representative guarantees. In contrast, I present a "bottom-up" valuation approach using recursive dynamic programming (RDP). An in-depth numerical analysis shows that the RDP estimation is able to value a large VA portfolio with a high degree of accuracy and within a few seconds---even under stochastic interest rates and volatility---since the heavy computational burden can be fully front-loaded (in a one-time effort at the guarantee's pricing stage). The RDP approach outperforms competing methods in both speed and accuracy and is thus ideally suited for various VA-related applications, including the computation of GAAP reserves, statutory reserves and capital requirements, as well as to determine the insurer's hedging position. Moreover, RDP can naturally incorporate optimal policyholder behavior into the insurer's valuation.
Weak-Disorder Limit at Criticality for Directed Polymers on Hierarchical Graphs

We prove a distributional limit theorem conjectured in Clark (J Stat Phys 174(6):1372–1403, 2019) for partition functions defining models of directed polymers on diamond hierarchical graphs with disorder variables placed at the graphical edges. The limiting regime involves a joint scaling in which the number of hierarchical layers, $$n\in {\mathbb {N}}$$ , of the graphs grows as the inverse temperature, $$\beta \equiv \beta (n)$$ , vanishes with a fine-tuned dependence on n. The conjecture pertains to the marginally relevant disorder case of the model wherein the branching parameter $$b \in \{2,3,\ldots \}$$ and the segmenting parameter $$s \in \{2,3,\ldots \}$$ determining the hierarchical graphs are equal, which coincides with the diamond fractal embedding the graphs having Hausdorff dimension two. Unlike the analogous weak-disorder scaling limit for random polymer models on hierarchical graphs in the disorder relevant $$b<s$$ case (or for the (1+1)-dimensional polymer on the rectangular lattice), the distributional convergence of the partition function when $$b=s$$ cannot be approached through a term-by-term convergence to a Wiener chaos expansion, which does not exist for the continuum model emerging in the limit. The analysis proceeds by controlling the distributional convergence of the partition functions in terms of the Wasserstein distance through a perturbative generalization of Stein’s method at a critical step.
Application of microgravity and containerless environments to the investigation of fusion target fabrication technology

During the past few years we have been studying several of the physical processes relevant to the production of spherical shells for inertial confinement fusion targets, both in a microgravity environment and in a containerless environment. The work has led to the development of several experimental facilities. Those which are most unique are described here, and fall into three categories as follows: 1. Ones which provide an induced low- or microgravity containerless environment, such as a vertical drag-free wind tunnel, two differing low-pressure and/or high-temperature drop towers for processing metallic or metallic-glass specimens, and a neutral buoyancy tank, 2. Ones providing containerless processing capability, such as a focusing radiator and an electrostatic levitator and 3. Ones providing extended microgravity and containerless capabilities, such as the KC-135 aircraft and the Space Processing Application Rockets. The physical processes which we have been studying include, but are not limited to, those which establish the shell sphericity, concentricity, surface topology, material properties, coatings, heating and cooling requirements and the effects of gravity on fusion pellet fabrication processes.
Constructions of generalized MSTD sets in higher dimensions

Abstract Text Let A be a set of finite integers, define A + A = { a 1 + a 2 : a 1 , a 2 ∈ A } , A − A = { a 1 − a 2 : a 1 , a 2 ∈ A } , and for non-negative integers s and d define s A − d A = A + ⋯ + A ︸ s − A − ⋯ − A ︸ d . A More Sums than Differences (MSTD) set is an A where | A + A | > | A − A | . It was initially thought that the percentage of subsets of [ 0 , n ] that are MSTD would go to zero as n approaches infinity as addition is commutative and subtraction is not. However, in a surprising 2006 result, Martin and O'Bryant proved that a positive percentage of sets are MSTD, although this percentage is extremely small, about 10 − 4 percent. This result was extended by Iyer, Lazarev, Miller, and Zhang [ILMZ] who showed that a positive percentage of sets are generalized MSTD sets, sets for { s 1 , d 1 } ≠ { s 2 , d 2 } and s 1 + d 1 = s 2 + d 2 with | s 1 A − d 1 A | > | s 2 A − d 2 A | , and that in d-dimensions, a positive percentage of sets are MSTD. For many such results, establishing explicit MSTD sets in 1-dimensions relies on the specific choice of the elements on the left and right fringes of the set to force certain differences to be missed while desired sums are attained. In higher dimensions, the geometry forces a more careful assessment of what elements have the same behavior as 1-dimensional fringe elements. We study fringes in d-dimensions and use these to create new explicit constructions. We prove the existence of generalized MSTD sets in d-dimensions and the existence of k-generational sets, which are sets where | c A + c A | > | c A − c A | for all 1 ≤ c ≤ k . We then prove that under certain conditions, there are no sets with | k A + k A | > | k A − k A | for all k ∈ N . Video For a video summary of this paper, please visit https://youtu.be/rojbhVqN90Q .
Magnetic properties of NbSi2N4, VSi2N4, and VSi2P4 monolayers

The recent demonstration of MoSi2N4 and its exceptional stability to air, water, acid, and heat has generated intense interest in this family of two-dimensional materials. Among these materials, monolayers of NbSi2N4, VSi2N4, and VSi2P4 are semiconducting, easy-plane ferromagnets with negligible in-plane magnetic anisotropy. They, thus, satisfy a necessary condition for exhibiting a dissipationless spin superfluid mode. The Curie temperatures of monolayer VSi2P4 and VSi2N4 are determined to be above room temperature based on Monte Carlo and density functional theory calculations. The magnetic moments of VSi2N4 can be switched from in-plane to out-of-plane by applying tensile biaxial strain or electron doping.
On the Computation of Recurrence Coefficients for Univariate Orthogonal Polynomials

Associated to a finite measure on the real line with finite moments are recurrence coefficients in a three-term formula for orthogonal polynomials with respect to this measure. These recurrence coefficients are frequently inputs to modern computational tools that facilitate evaluation and manipulation of polynomials with respect to the measure, and such tasks are foundational in numerical approximation and quadrature. Although the recurrence coefficients for classical measures are known explicitly, those for nonclassical measures must typically be numerically computed. We survey and review existing approaches for computing these recurrence coefficients for univariate orthogonal polynomial families and propose a novel “predictor–corrector” algorithm for a general class of continuous measures. We combine the predictor–corrector scheme with a stabilized Lanczos procedure for a new hybrid algorithm that computes recurrence coefficients for a fairly wide class of measures that can have both continuous and discrete parts. We evaluate the new algorithms against existing methods in terms of accuracy and efficiency.
Optical direct write of Dolan-Niemeyer-bridge junctions for transmon qubits

We characterize highly coherent transmon qubits fabricated with a direct-write photolithography system. Multi-layer evaporation and oxidation allow us to change the critical current density by reducing the effective tunneling area and increasing the barrier thickness. Surface treatments before resist application and again before evaporation result in high-coherence devices. With optimized surface treatments, we achieve energy relaxation T1 times in excess of 80 μs for three dimensional transmon qubits with Josephson junction lithographic areas of 2 μm2.
Statistical Analysis of Complex Computer Models in Astronomy

We introduce statistical techniques required to handle complex computer models with potential applications to astronomy. Computer experiments play a critical role in almost all fields of scientific research and engineering. These computer experiments, or simulators, are often computationally expensive, leading to the use of emulators for rapidly approximating the outcome of the experiment. Gaussian process models, also known as Kriging, are the most common choice of emulator. While emulators offer significant improvements in computation over computer simulators, they require a selection of inputs along with the corresponding outputs of the computer experiment to function well. Thus, it is important to select inputs judiciously for the full computer simulation to construct an accurate emulator. Space-filling designs are efficient when the general response surface of the outcome is unknown, and thus they are a popular choice when selecting simulator inputs for building an emulator. In this tutorial we discuss how to construct these space filling designs, perform the subsequent fitting of the Gaussian process surrogates, and briefly indicate their potential applications to astronomy research.
Mechanisms that ensure speed and fidelity in eukaryotic translation termination

Translation termination, which liberates a nascent polypeptide from the ribosome specifically at stop codons, must occur accurately and rapidly. We established single-molecule fluorescence assays to track the dynamics of ribosomes and two requisite release factors (eRF1 and eRF3) throughout termination using an in vitro-reconstituted yeast translation system. We found that the two eukaryotic release factors bound together to recognize stop codons rapidly and elicit termination through a tightly regulated, multistep process that resembles transfer RNA selection during translation elongation. Because the release factors are conserved from yeast to humans, the molecular events that underlie yeast translation termination are likely broadly fundamental to eukaryotic protein synthesis.
Grain pricing and transportation: dynamics and changes in markets

Changing underlying market fundamentals has resulted in a more robust outlook for grain shipping from the United States. This has resulted in changes in the spatial distribution of production among crops and greater volatility in shipping demands, both intra- and interseasonally. Basis relationships have become more volatile and less predictable. Greater volatility results in more risk for producers and shippers, and exacerbates planning and investment for handling and shipping infrastructure. The purpose of this study is to analyze relationships and impacts among shipping costs and basis values at origins and destinations. The results indicate that (1) basis values have become more risky; (2) all marketing costs have increased; (3) increases in rail tariffs were less than those for barges; (4) daily car values, on average, declined; (5) fuel service charges had moderate changes in absolute terms; and (6) handling margins have had fairly substantial increases, particularly at port. The econometric results indicate that the following variables were significant in explaining variability in origin basis values: shipping costs, Gulf-PNW ocean rate spreads, outstanding export sales, shipping industry concentration, measures of rail cars late, the ratio of stocks to storage capacity, futures prices, and varying measures of futures and destination spreads. Finally, implications for rail pricing, capacity and understanding basis pricing are discussed. [Econ Lit citations: Q130, L900]. © 2011 Wiley Periodicals, Inc.
Understanding Entropic Regularization in GANs

Generative Adversarial Networks (GANs) are a popular method for learning distributions from data by modeling the target distribution as a function of a known distribution. The function, often referred to as the generator, is optimized to minimize a chosen distance measure between the generated and target distributions. One commonly used measure for this purpose is the Wasserstein distance. However, Wasserstein distance is hard to compute and optimize, and in practice entropic regularization techniques are used to facilitate its computation and improve numerical convergence. The influence of regularization on the learned solution, however, remains not well-understood. In this paper, we study how several popular entropic regularizations of Wasserstein distance impact the solution learned by a Wasserstein GAN in a simple benchmark setting where the generator is linear and the target distribution is high-dimensional Gaussian. We show that entropy regularization of Wasserstein distance promotes sparsification of the solution, while replacing the Wasserstein distance with the Sinkhorn divergence recovers the unregularized solution. The significant benefit of both regularization techniques is that they remove the curse of dimensionality suffered by Wasserstein distance. We show that in both cases the optimal generator can be learned to accuracy ∊ with $O$ (1/ ∊2) samples from the target distribution without requiring to constrain the discriminator. We thus conclude that these regularization techniques can improve the quality of the generator learned from empirical data in a way that is applicable for a large class of distributions.
A rationally designed c-di-AMP FRET biosensor to monitor nucleotide dynamics

3'3'-cyclic di-adenosine monophosphate (c-di-AMP) is an important nucleotide second messenger found throughout the bacterial domain of life. C-di-AMP is essential in many bacteria and regulates a diverse array of effector proteins controlling pathogenesis, cell wall homeostasis, osmoregulation, and central metabolism. Despite the ubiquity and importance of c-di-AMP, methods to detect this signaling molecule are limited, particularly at single cell resolution. In this work, crystallization of the Listeria monocytogenes c-di-AMP effector protein Lmo0553 enabled structure guided design of a Forster resonance energy transfer (FRET) based biosensor, which we have named CDA5. CDA5 is a fully genetically encodable, specific, and reversible biosensor which allows for the detection of c-di-AMP dynamics both in vitro and within live cells in a nondestructive manner. Our initial studies identify a distribution of c-di-AMP in Bacillus subtilis populations first grown in Luria Broth and then resuspended in diluted Luria Broth compatible with florescence analysis. Furthermore, we find that B. subtilis mutants lacking either a c-di-AMP phosphodiesterase or cyclase have respectively higher and lower FRET responses. These findings provide novel insight into the c-di-AMP distribution within bacterial populations and establish CDA5 as a powerful platform for characterizing new aspects of c-di-AMP regulation. Importance C-di-AMP is an important nucleotide second messenger for which detection methods are severely limited. In this work we engineer and implement a c-di-AMP specific FRET biosensor to remedy this dearth. We present this biosensor, CDA5, as a versatile tool to investigate previously intractable facets of c-di-AMP biology.
Approximate Support Recovery using Codes for Unsourced Multiple Access

We consider the approximate support recovery (ASR) task of inferring the support of a $K$ -sparse vector $\mathrm{x}\in \mathbb{R}^{n}$ from $m$ noisy measurements. We examine the case where $n$ is large, which precludes the application of standard compressed sensing solvers, thereby necessitating solutions with lower complexity. We design a scheme for ASR by leveraging techniques developed for unsourced multiple access. We present two decoding algorithms with computational complexities $\mathcal{O}(K^{2}\log n+ K\log n\log\log n)$ and $\mathcal{O}(K^{3}+K^{2}\log n+K\log n\log \log n)$ per iteration, respectively. When $K\ll n$ , this is much lower than the complexity of approximate message passing with a minimum mean squared error denoiser, which requires $\mathcal{O}(mn)$ operations per iteration. This gain comes at a slight performance cost. Our findings suggest that notions from multiple access can play an important role in the design of measurement schemes for ASR.
Retributivism and Over-Punishment

Lately it has become a commonplace to complain about the injustice of mass incarceration. I share the sentiment that this phenomenon has been an injustice. But it also has become orthodoxy to allege that the acceptance of a retributive penal philosophy has been one of the chief factors that has brought about mass incarceration in the first place. As a self-proclaimed retributivist, I find these allegations to be troubling and unwarranted. The point of this paper is to take steps to rebut them. I begin by making four conceptual points about retributivism. If I am correct, retributivism comes in countless flavors, and the particular variety to which I am most attracted can be applied to show why some punishments should be less severe than those presently imposed. Next I argue that many persons deserve less punishment than our legal system currently inflicts. Reflection about whether perpetrators should be afforded a complete or partial defense reveals retributivism to be less punitive than conventional wisdom would suggest.
On Data-centric Forwarding in Mobile Ad-hoc Networks: Baseline Design and Simulation Analysis

IP networking deals with end-to-end communication where the network layer routing protocols maintain the reachability from one address to another. However, challenging environments, such as mobile ad-hoc networks or MANETs, lead to frequent path failures and changes between the sender and receiver, incurring higher packet loss. The obligatory route setup and maintenance of a device-to-device stable path in MANETs incur significant data retrieval delay and transmission overhead. Such overhead exaggerates the packet loss manifold. Named Data Networking (NDN) can avoid such delays and overhead and significantly improve the overall network performance. It does so with direct application-controlled named-data retrieval from any node in a network instead of reaching a specific IP address with protocol message exchange. However, existing works lack any explicit or systematic analysis to justify such claims. Our work analyzes the core NDN and IP architectures in a MANET at a baseline level. The extensive simulations show that NDN, when applied correctly, yields much lower data retrieval latency than IP and can lower the network transmission overhead in most cases. As a result, NDN’s stateful forwarder can significantly increase the retrieval rate, offering a better trade-off at the network layer. Such performance comes from its caching, built-in multicast, and request aggregation without requiring an IP-like separate routing control plane.
A Roadmap Toward the Theory of Vibrational Polariton Chemistry

The field of vibrational polariton chemistry was firmly established in 2016 when a chemical reaction rate at room temperature was modified within a resonantly tuned infrared cavity without externally driving the system. Despite intense efforts by scientists around the world to understand why the reaction rate changes, no convincing theoretical explanation exists. In this perspective, first, we briefly review this seminal experiment, as well as relevant experiments that have since followed that have hinted at the roles of reactant concentration, cavity frequency, and symmetry. Then, we analyze the relevance of leading theories, such as quantum electrodynamics-modified transition rate theories, the photonic solvent cage effect, the impact of dissipation from dark states, bond strengthening via intramolecular vibrational energy redistribution, and collectively enhanced local molecular properties. Finally, we construct a roadmap toward the theory of vibrational polariton chemistry by suggesting experiments to test theories and new paths for theorists. We believe that understanding the importance of the onset of the strong coupling regime, designing experiments to capture changes in reaction pathways, and further developing the theories of cavity-modified intramolecular vibrational energy redistribution and collectively enhanced local molecular properties are crucial next steps. We hope this perspective will be a valuable resource for guiding research in the field of vibrational polariton chemistry.
Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs.

Maternal exposure to environmental chemicals during pregnancy can alter birth and children's health outcomes. Research seeks to identify critical windows, time periods when exposures can change future health outcomes, and estimate the exposure-response relationship. Existing statistical approaches focus on estimation of the association between maternal exposure to a single environmental chemical observed at high temporal resolution (e.g. weekly throughout pregnancy) and children's health outcomes. Extending to multiple chemicals observed at high temporal resolution poses a dimensionality problem and statistical methods are lacking. We propose a regression tree-based model for mixtures of exposures observed at high temporal resolution. The proposed approach uses an additive ensemble of tree pairs that defines structured main effects and interactions between time-resolved predictors and performs variable selection to select out of the model predictors not correlated with the outcome. In simulation, we show that the tree-based approach performs better than existing methods for a single exposure and can accurately estimate critical windows in the exposure-response relation for mixtures. We apply our method to estimate the relationship between five exposures measured weekly throughout pregnancy and birth weight in a Denver, Colorado birth cohort. We identified critical windows during which fine particulate matter, sulfur dioxide, and temperature are negatively associated with birth weight and an interaction between fine particulate matter and temperature. Software is made available in the R package dlmtree. This article is protected by copyright. All rights reserved.
Secure Random Sampling in Differential Privacy.

Differential privacy is among the most prominent techniques for preserving privacy of sensitive data, oweing to its robust mathematical guarantees and general applicability to a vast array of computations on data, including statistical analysis and machine learning. Previous work demonstrated that concrete implementations of differential privacy mechanisms are vulnerable to statistical attacks. This vulnerability is caused by the approximation of real values to floating point numbers. This paper presents a practical solution to the finite-precision floating point vulnerability, where the inverse transform sampling of the Laplace distribution can itself be inverted, thus enabling an attack where the original value can be retrieved with non-negligible advantage.
The length of the shortest closed geodesic on positively curved 2-spheres

We show that the shortest closed geodesic on a 2-sphere with non-negative curvature has length bounded above by three times the diameter. We prove a new isoperimetric inequality for 2-spheres with pinched curvature; this allows us to improve our bound on the length of the shortest closed geodesic in the pinched curvature setting.
Conformal inference of counterfactuals and individual treatment effects

Evaluating treatment effect heterogeneity widely informs treatment decision making. At the moment, much emphasis is placed on the estimation of the conditional average treatment effect via flexible machine learning algorithms. While these methods enjoy some theoretical appeal in terms of consistency and convergence rates, they generally perform poorly in terms of uncertainty quantification. This is troubling since assessing risk is crucial for reliable decision-making in sensitive and uncertain environments. In this work, we propose a conformal inference-based approach that can produce reliable interval estimates for counterfactuals and individual treatment effects under the potential outcome framework. For completely randomized or stratified randomized experiments with perfect compliance, the intervals have guaranteed average coverage in finite samples regardless of the unknown data generating mechanism. For randomized experiments with ignorable compliance and general observational studies obeying the strong ignorability assumption, the intervals satisfy a doubly robust property which states the following: the average coverage is approximately controlled if either the propensity score or the conditional quantiles of potential outcomes can be estimated accurately. Numerical studies on both synthetic and real datasets empirically demonstrate that existing methods suffer from a significant coverage deficit even in simple models. In contrast, our methods achieve the desired coverage with reasonably short intervals.
VI-modules in non-describing characteristic, part II

We classify all irreducible generic $\mathrm{VI}$-modules in non-describing characteristic. Our result degenerates to yield a classification of irreducible generic $\mathrm{FI}$-modules in arbitrary characteristic. Our result can also be viewed as a classification theorem for a natural class of representations of $\mathbf{GL}_{\infty}(\mathbf{F}_q)$.
A consensus-based ensemble approach to improve transcriptome assembly.

Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble. Without using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/ .
Non-deal Roadshows, Informed Trading, and Analyst Conflicts of Interest

Non-deal roadshows (NDRs) are private meetings between management and institutional investors, typically organized by sell-side analysts. We find that around NDRs, local institutional investors trade heavily and profitably, while retail trading is significantly less informed. Analysts who sponsor NDRs issue significantly more optimistic recommendations and target prices, coupled with more “beatable” earnings forecasts, consistent with analysts issuing strategically biased forecasts in order to win NDR business. Our results suggest that NDRs result in a substantial information advantage for institutional investors and create significant conflicts of interests for the analysts that organize them.
Exploring the Hazards of Scaling Up Clinical Data Analyses: A Drug Side Effect Discovery Case Report

We assessed the scalability of pharmacological signal detection use case from a single-site CDW to a large aggregated clinical data warehouse (single-site database with 754,214 distinct patient IDs vs. multisite database with 49.8M). We aimed to explore whether a larger clinical dataset would provide clearer signals for secondary analyses such as detecting the known relationship between prednisone and weight. We found significant weight gain rate using the single-site data but not from using aggregated data (0.0104 kg/day, p<0.0001 vs. -0.050 kg/day, p<.0001). This rate was also found more consistently across 30 age and gender subgroups using the single-site data than in the aggregated data (26 vs. 18 significant weight gain findings). Contrary to our expectations, analyses of much larger aggregated clinical datasets did not yield stronger signals. Researchers must check the underlying model assumptions and account for greater heterogeneity when analyzing aggregated multisite data to ensure reliable findings.
Neural Basis of Acoustic Species Recognition in a Cryptic Species Complex.

Sexual traits that promote species recognition are important drivers of reproductive isolation, especially among closely related species. Identifying neural processes that shape species differences in recognition is crucial for understanding the causal mechanisms of reproductive isolation. Temporal patterns are salient features of sexual signals widely used in species recognition by several taxa, including anurans. Recent advances in our understanding of temporal processing by the anuran auditory system provide an opportunity to investigate the neural basis of species-specific recognition. The anuran inferior colliculus (IC) consists of neurons that are selective for temporal features of calls. Of potential relevance are auditory neurons known as interval-counting neurons (ICNs) that are often selective for the pulse rate of conspecific advertisement calls. Here, we tested the hypothesis that ICNs mediate acoustic species recognition by exploiting the known differences in temporal selectivity in two cryptic species of gray treefrog (Hyla chrysoscelis and Hyla versicolor). We examined the extent to which the threshold number of pulses required to elicit behavioral responses from females and neural responses from ICNs was similar within each species but potentially different between the two species. In support of our hypothesis, we found that a species difference in behavioral pulse number thresholds closely matched the species difference in neural pulse number thresholds. However, this relationship held only for ICNs that exhibited band-pass tuning for conspecific pulse rates. Together, these findings suggest that differences in temporal processing of a subset of ICNs provide a mechanistic explanation for reproductive isolation between two cryptic treefrog species.
Programmable and contractile materials through cell encapsulation in fibrous hydrogel assemblies.

The natural extracellular matrix (ECM) within tissues is physically contracted and remodeled by cells, allowing the collective shaping of functional tissue architectures. Synthetic materials that f...
Genomic and common garden approaches yield complementary results for quantifying environmental drivers of local adaptation in rubber rabbitbrush, a foundational Great Basin shrub

The spatial structure of genomic and phenotypic variation across populations reflects historical and demographic processes as well as evolution via natural selection. Characterizing such variation can provide an important perspective for understanding the evolutionary consequences of changing climate and for guiding ecological restoration. While evidence for local adaptation has been traditionally evaluated using phenotypic data, modern methods for generating and analyzing landscape genomic data can directly quantify local adaptation by associating allelic variation with environmental variation. Here, we analyze both genomic and phenotypic variation of rubber rabbitbrush (Ericameria nauseosa), a foundational shrub species of western North America. To quantify landscape genomic structure and provide perspective on patterns of local adaptation, we generated reduced representation sequencing data for 17 wild populations (222 individuals; 38,615 loci) spanning a range of environmental conditions. Population genetic analyses illustrated pronounced landscape genomic structure jointly shaped by geography and environment. Genetic-environment association (GEA) analyses using both redundancy analysis (RDA) and a machine-learning approach (Gradient Forest) indicated environmental variables (precipitation seasonality, slope, aspect, elevation, and annual precipitation) influenced spatial genomic structure, and were correlated with allele frequency shifts indicative of local adaptation at a consistent set of genomic regions. We compared our GEA based inference of local adaptation with phenotypic data collected by growing seeds from each population in a greenhouse common garden. Population differentiation in seed weight, emergence, and seedling traits was associated with environmental variables (e.g., precipitation seasonality) that were also implicated in GEA analyses, suggesting complementary conclusions about the drivers of local adaptation across different methods and data sources. Our results provide a baseline understanding of spatial genomic structure for E. nauseosa across the western Great Basin and illustrate the utility of GEA analyses for detecting the environmental causes and genetic signatures of local adaptation in a widely distributed plant species of restoration significance.
Inclusion of Energy Loss in Models of Laser-Irradiated Gold Films via Classical Molecular Dynamics

The structural evolution of laser-excited systems of gold has previously been measured through ultrafast MeV electron diffraction. 1 - 3 However, there has been a long-standing inability of atomistic simulations to provide a consistent picture of the melt process, concluding in large discrepancies between the predicted threshold energy density for complete melt, as well as the transition between heterogeneous and homogeneous melting. 1 We make use of two-temperature classical molecular dynamics simulations utilizing two highly successful interatomic potentials and reproduce electron diffraction data presented by Mo et al.. We recreate the experimental electron diffraction data employing both a constant and temperature-dependent electron-ion equilibration rate. In all cases we are able to match time-resolved electron diffraction data, and find consistency between atomistic simulations and experiments, only by allowing laser energy to be transported away from the interaction region for the duration of the melt process. This additional energy-loss pathway, which scales strongly with laser fluence, we attribute to hot electrons leaving the target during melt before refluxing.
Spin interactions and topological magnonics in chromium trihalide CrClBrI

The discovery of spontaneous magnetism in van der Waal (vdW) magnetic monolayers has opened up an unprecedented platform for investigating magnetism in purely two-dimensional systems. Recently, it has been shown that the magnetic properties of vdW magnets can be easily tuned by adjusting the relative composition of halides. Motivated by these experimental advances, here we derive a model for a trihalide Cr(I, Br, Cl) monolayer from symmetry principles and we find that, in contrast to its single-halide counterparts, it can display highly anisotropic nearest- and next-to-nearest neighbor Dzyaloshinskii-Moriya amd Heisenberg interactions. Depending on the parameters, the DM interactions are responsible for the formation of exotic chiral spin states, such as skyrmions and spin cycloids, as shown by our Monte Carlo simulations. Focusing on a ground state with a two-sublattice unit cell, we find spin-wave bands with nonvanishing Chern numbers. The resulting magnon edge states yield a magnon thermal Hall conductivity that changes sign as function of temperature and magnetic field, suggesting chromium trihalides as a candidate for testing topological magnon transport in two-dimensional noncollinear spin systems.
Steady state and transient behavior of a continuous fermentor

The fermentation kinetics of the homofermentative orga­ nism Lactobacillus delbrueckii in a glucose-yeast extract medium is studied in both batch and continuous culture under conditions of controlled pH. Prom a graphical analysis of the experimental data, a mathematical model of the batch process is derived which relates bacterial growth, glucose utilization and lactic acid formation. The parameters in the model represent the activity of the organism and are a function of pH having a maximum value at about pH 5.90. The batch growth curves of L. delbrueckii exhibit several exponential phases. The position of the slope changes and the overall bacterial yield is related to the initial amount of yeast extract in the medium. It is postulated that this behavior is due to several stimulatory substances in the yeast extract that accelerate growth and are consumed by the bacteria during the course of the fermentation. Using a Monod-type expression to represent the effect of the essential and stimulatory components, the following growth equation is proposed to represent the observed behavior.
Profiles of Fatigue Damage in Graphite/Epoxy Composites from Ultrasonic Transmission Power Spectra

Early fatigue damage in non-unidirectional multi-ply graphite/epoxy composites is manifested by a distribution of cracks and disbonds through the bulk of the material. Such damage is subtle and is difficult to detect with conventional ultrasonic technology. Consequently, a new ultrasonic measurement technique called phase-insensitive tone-burst spectroscopy has been developed. The new technique eliminates problems associated with phase cancellation and pulse shape artifacts inherent to conventional broadband ultrasonic spectral measurement systems and produces clean spectral information irrespective of specimen inhomogeneity or irregularities in surface geometry. Application of the new technique to measurements of graphite/epoxy composites has yielded frequency-domain profiles that show distinct differences in ultrasonic attenuation, attenuation slope, and velocity for each specimen experiencing a different level of fatigue damage.
Three-Dimensional Computational Analysis of Accumulated Body MOSFETs

Accumulated body approach for short- and narrow-channel (10-nm scale) bulk Si MOSFETs is analyzed through 3-D finite-element studies. Accumulation of the side interfaces is achieved by a side-gate structure surrounding the body of the transistor, which leads to the accumulation of the body for narrow structures. A separately controlled top gate is used for transistor action. The simulation results show the suppression of leakage currents by 106 times for no side-interface charges, and by 1010 times for an interface positive fixed charge density of 1012 cm−2. The threshold voltage (VT ) can be dynamically increased by over 1 V with the accumulation of the body ( $V_{{\rm side}} = 0$ to −3 V) for W × $L = 10$ -nm × 15-nm structures, enabling electrostatic VT control and reliable high-temperature (>600 K) operation. Improvements in subthreshold slope and drain-induced barrier lowering are significant for narrower channel devices.
An Electron Microscope Study of Configurational Equilibrium at Twin-Grain Boundary Intersections in FCC Metals.

A transmission electron microscopy study of 304 stainless steel films has been undertaken to systematically study the interrelationships of the degrees of freedom characterizing a grain boundary. From this study a configurational theory has been developed which is useful in explaining the existence of interfacial torques at twin-grain boundary intersections. The grain boundary misorientation (Θ) is defined as the relative rotation of the directions in the adjacent grains of identical (110) orientation. The two remaining degrees of freedom are represented by the tilt or inclination (θ) and the asymmetry (Φ) of the grain boundary plane. Torques arise because of a difference in grain boundary energy with a change in misorientation or tilt. 90° twin configurations (twin plane along a direction) are essentially high-torque situations, as a result of the change in misorientation (ΔΘ) between the twinned grain and its neighboring grain. 35° twins (twin plane along a direction) are low-torque configurations, but can exhibit high torque anomalies when there is a sufficient variation in tilt across the intersection, Δθ. Misorientation, Θ, appears to be the dominant torque producing parameter for high-torque configurations, and dominates the variations in grain boundary free energy. Also, a functional relationship between Δθ and ΔΦ observed for both high-torque occurrences. Spreads in the histograms for twin boundary-grain boundary energy ratios are due to torque terms or variations in grain boundary energy with changes in grain boundary parameters.
Plasma protein fractions in normal pregnancy, labor, and puerperium

Abstract A cumulative study has been made of the various protein fractions in the blood plasma of normal nonpregnant, pregnant, parturient, and puerperal women, using the Wu method of analysis. It was hoped to relate the changes in some one fraction, globulin or fibrin particularly, with the changes in watery dilution of the blood during the reproductive cycle. The curve developed for fibrin corresponds rather closely with that for plasma volume percentage, and it is suggested that perhaps the dilution of the plasma may be directly related to the increase of fibrin, although it is thought that other factors may have a role. The cause of the fibrin increase is admitted to be unknown, but not much consideration is given to the idea that it may be the result of a mild gestational injury to the liver. The accurate relationship between the rise in fibrin and the increased sedimentation rate of the blood cells, in the absence of any similar changes in the globulin fraction, leads us to agree with those who believe that the latter is the result of the former, the increased agglutination of the red cells into rouleaux being an intermediate and necessary stage for the increase in sinking velocity. There is a rise of globulin on the third day after delivery which may be directly associated with the onset of milk secretion.
Value-based policy teaching with active indirect elicitation

Many situations arise in which an interested party's utility is dependent on the actions of an agent; e.g., a teacher is interested in a student learning effectively and a firm is interested in a consumer's behavior. We consider an environment in which the interested party can provide incentives to affect the agent's actions but cannot otherwise enforce actions. In value-based policy teaching, we situate this within the framework of sequential decision tasks modeled by Markov Decision Processes, and seek to associate limited rewards with states that induce the agent to follow a policy that maximizes the total expected value of the interested party. We show value-based policy teaching is NP-hard and provide a mixed integer program formulation. Focusing in particular on environments in which the agent's reward is unknown to the interested party, we provide a method for active indirect elicitation wherein the agent's reward function is inferred from observations about its response to incentives. Experimental results suggest that we can generally find the optimal incentive provision in a small number of elicitation rounds.
Expressive completeness failure in branching time structures

Abstract A propositional logic is expressively complete if there is a finite set of connectives which define all truth tables. Kamp, Stavi, and Gabbay proved that all tense logics over linear time are expressively complete. Amir and Gabbay brought examples of expressively complete non-linear time structures. Gabbay showed that the general time structure is not expressively complete. Here we narrow the gap and prove that for branching time, if the model is an infinite tree with an unbounded branching factor then there is no expressive completeness.