cssi/SciSciGPT-SciSciNet huggingface dataset:
Farmer trust in producer- and investor-owned firms: evidence from Missouri corn and soybean producers

The authors examine whether cooperatives are characterized by greater trust than investor-owned firms. They surveyed 2,000 Missouri corn and soybean farmers and found that trust and farmer perceptions of honesty and competence are higher in cooperatives than in investor-owned firms and that trust is a significant factor explaining the choice of farmers to market to cooperatives rather than investor-owned firms. Interestingly, they found that trust is more significant in producers' decisions for marketing soybeans than for corn. [JEL classifications: Q130, Z130]. © 2006 Wiley Periodicals, Inc. Agribusiness 22: 135–153, 2006.
Optimal Control by Polynomial Approximation: The Discrete Time Case

Abstract This paper discusses optimal control of discrete time nonlinear systems. All of the functions discussed are assumed to be analytic. Each function is expanded in a Taylor series. By parameterizing the series using tensor algebraic techniques, explicit expressions for the terms in the series expansion for the feedback controller are obtained. These expressions are recursive in the sense that series terms depend only on equal or lower order terms from the expansions for the system equation and the cost. Some special relationships between the optimal performance series terms and the optimal feedback series terms are obtained. In particular, the optimal performance terms of order j are shown to be independent of the optimal feedback terms of order greater than j/2.
Medico–Legal Cases and Statistics†

Medico-legal statistics concerns the use of data from animal studies, case–control and cohort studies and clinical trials in the context of the legal and regulatory system. Statistical findings are submitted to courts and governmental agencies to demonstrate that a new drug is safe and efficacious; exposure to a chemical in the workplace is harmful or not as well as in product liability cases. The validity of clinical findings that are used in advertisements claiming a product is superior to a competitor's or in support of a patent are subject to legal challenge. Recently, the adequacy of the information provided to patients who participate in clinical trials and similar studies has become an issue. This aspect of trials is especially important when the main investigators or the University have a financial interest in the outcome either through stock ownership or options or a royalty agreement. Keywords: clinical trials; confidentiality; misleading advertisements; regulation of drugs and toxic exposure; responsibilities of researchers
Chapter 2 – Integumentary Systems

To be small and live on land requires some exceptional adaptations, and the components that comprise the exoskeleton make an overwhelming contribution to the terrestrial success that arthropods can claim. Like the skin of vertebrates, the exoskeleton completely covers the insect and in addition provides an armor-like protective suit as a supportive skeleton. Its most critical function is to serve as an interface between the insect and the environment, providing a barrier to the movement of water, ions, parasites, and environmental chemicals, including insecticides. This barrier is especially significant for small animals like insects that have a high surface-to-volume ratio and therefore present a relatively large amount of surface area to the environment. The ‘icing on the cake’ is that it can serve as a temporary food store, with its components available for withdrawal during times of nutritive stress. The nature of the exoskeleton has thus had profound implications for growth, respiration, locomotion, and from a human perspective, the design of chemicals that must penetrate the integument to be used as control agents. The terms ‘integument’ and ‘exoskeleton’ are often used interchangeably, depending on which of its functional components are being considered.
Virtual rational tangles

We use Kauffman's bracket polynomial to define a complex-valued invariant of virtual rational tangles that generalizes the well-known fraction invariant for classical rational tangles. We provide a recursive formula for computing the invariant, and use it to compute several examples.
Prognostic significance of human papillomavirus and Epstein-Bar virus in nasopharyngeal carcinoma.

Background The clinical significance of Epstein-Barr virus (EBV) and human papillomavirus (HPV) infection in nasopharyngeal carcinoma (NPC) is unclear. Methods Three hundred and forty three patients with NPC diagnosed between 1998 and 2017 and treated at our institution were included. Chi-square was used to identify characteristics associated with viral status. Kaplan-Meier methods were used to estimate overall survival (OS) and Cox proportional regression was used to identify prognostic factors. Results Patients with HPV-associated NPC were more likely to have a positive smoking history and to present at a higher T classification. At a median follow-up time of 59.9 months (range: 0.1-222.4 months), there were no differences in OS (P = .198), time to local failure (LF, P = .403), or time to distant metastasis (DM, P = .849) between the viral subgroups. Older age (hazard ratio [HR]: 2.242, 95% confidence interval [CI] 1.374-3.659, P = .001) and higher overall stage (HR: 2.047, 95% CI 1.235-3.391, P = .005) were prognostic for worse OS. Conclusion In our population, viral status was not prognostic for OS, LF, or DM.
Discovering candidate imprinted genes and imprinting control regions in the human genome

Genomic imprinting is a process thereby a subset of genes is expressed in a parent-of-origin specific manner. This evolutionary novelty is restricted to mammals and controlled by genomic DNA segments known as Imprinting Control Regions (ICRs) and germline Differentially Methylated Regions (gDMRs). Previously, I showed that in the mouse genome, the fully characterized ICRs/gDMRs often includes clusters of 2 or more of a set of composite-DNA-elements known as ZFBS-morph overlaps. Because of the importance of the ICRs to regulating parent-of-origin specific gene expression, I developed a genome-wide strategy for predicting their positions in the human genome. My strategy consists of creating plots to display the density of ZFBS-morph overlaps along the entire chromosomal DNA sequences. In initial evaluations, I found that peaks in these plots pinpointed several of the known ICRs/gDMRs along the DNA in chromosomal bands. I deduced that in density-plots, robust peaks corresponded to actual or candidate ICRs in the DNA. By locating the genes in the vicinity of candidate ICRs, I could discover potential imprinting genes. Additionally, my assessments revealed a connection between several of the potential imprinted genes and human developmental anomalies. Examples include Leber congenital amaurosis 11, Coffin-Siris syndrome, progressive myoclonic epilepsy-10, microcephalic osteodysplastic primordial dwarfism type II, and microphthalmia, cleft lip and palate, and agenesis of the corpus callosum. With plots displaying the density of ZFBS-morph overlaps, researchers could locate candidate ICRs and imprinted genes. Since the datafiles are available for download and display at the UCSC genome browser, it is possible to examine the plots in the context of Single nucleotide polymorphisms (SNPs) to design experiments to discover novel ICRs and imprinted genes in the human genome.
Controlling thermal expansion within mixed cocrystals by tuning molecular motion capability

Controlling thermal expansion (TE) behaviors of organic materials is challenging because several mechanisms can govern TE, such as noncovalent interaction strength and structural motions. Here, we report the first demonstration of tuning TE within organic solids by using a mixed cocrystal approach. The mixed cocrystals contain three unique molecules, two of which are present in variable ratios. These two molecules either lack or exhibit the ability to undergo molecular motion in the solid state. Incorporation of higher ratios of motion-capable molecules results in larger, positive TE along the motion direction. Addition of a motion-incapable molecule affords solids that undergo less TE. Fine-tuned TE behavior was attained by systematically controlling the ratio of motion-capable and -incapable molecules in each solid.
Detecting and Tracking Communal Bird Roosts in Weather Radar Data

The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years. Communally roosting birds congregate in large numbers at nighttime roosting locations, and their morning exodus from the roost is often visible as a distinctive pattern in radar images. This paper describes a machine learning system to detect and track roost signatures in weather radar data. A significant challenge is that labels were collected opportunistically from previous research studies and there are systematic differences in labeling style. We contribute a latent-variable model and EM algorithm to learn a detection model together with models of labeling styles for individual annotators. By properly accounting for these variations we learn a significantly more accurate detector. The resulting system detects previously unknown roosting locations and provides comprehensive spatio-temporal data about roosts across the US. This data will provide biologists important information about the poorly understood phenomena of broad-scale habitat use and movements of communally roosting birds during the non-breeding season.
Deep Divergence Learning

Classical linear metric learning methods have recently been extended along two distinct lines: deep metric learning methods for learning embeddings of the data using neural networks, and Bregman divergence learning approaches for extending learning Euclidean distances to more general divergence measures such as divergences over distributions. In this paper, we introduce deep Bregman divergences, which are based on learning and parameterizing functional Bregman divergences using neural networks, and which unify and extend these existing lines of work. We show in particular how deep metric learning formulations, kernel metric learning, Mahalanobis metric learning, and moment-matching functions for comparing distributions arise as special cases of these divergences in the symmetric setting. We then describe a deep learning framework for learning general functional Bregman divergences, and show in experiments that this method yields superior performance on benchmark datasets as compared to existing deep metric learning approaches. We also discuss novel applications, including a semi-supervised distributional clustering problem, and a new loss function for unsupervised data generation.
A self-supervised workflow for particle picking in cryo-EM

High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 A or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE and EMAN2.
Persistent telomere cohesion protects aged cells from premature senescence

Human telomeres are bound by the telomere repeat binding proteins TRF1 and TRF2. Telomere shortening in human cells leads to a DNA damage response that signals replicative senescence. While insufficient loading of TRF2 at shortened telomeres contributes to the DNA damage response in senescence, the contribution of TRF1 to senescence induction has not been determined. Here we show that counter to TRF2 deficiency-mediated induction of DNA damage, TRF1 deficiency serves a protective role to limit induction of DNA damage induced by subtelomere recombination. Shortened telomeres recruit insufficient TRF1 and as a consequence inadequate tankyrase 1 to resolve sister telomere cohesion. Our findings suggest that the persistent cohesion protects short telomeres from inappropriate recombination. Ultimately, in the final division, telomeres are no longer able to maintain cohesion and subtelomere copying ensues. Thus, the gradual loss of TRF1 and concomitant persistent cohesion that occurs with telomere shortening ensures a measured approach to replicative senescence.
Grid Cyber-Security Strategy in an Attacker-Defender Model

This paper uses the Probabilistic, Learning Attacker, Dynamic Defender (PLADD) model to analyze the evolution of cyber-attacks on a power grid infrastructure. PLADD was created to evaluate the effectiveness of moving target defense (MTD) techniques. We utilize the PLADD model to evaluate AND as well as OR configurations for attack scenarios in the power grid. The paper introduces a strategy that can help cyber-security managers optimize their defense strategies. Our analysis of PLADD games in AND and OR configurations provides mathematical proofs and insight into when access controls (such as passwords, internet protocol addresses, session keys, etc.) should be reset to minimize the probability of a successful attack. In particular, we provide a mathematical proof for the OR configuration of multiple PLADD games showing that it is best if all access controls for all of the PLADD games are reset at the same time. We also provide a proof to show that it is best (in terms of minimizing the attacker's average probability of success) for the AND configuration of multiple PLADD games that the resets are equally spaced apart.
Bayesian inference for high-dimensional nonstationary Gaussian processes

In spite of the diverse literature on nonstationary spatial modelling and approximate Gaussian process (GP) methods, there are no general approaches for conducting fully Bayesian inference for mode...
A Modular Microscale Granuloma Model for Immune-Microenvironment Signaling Studies in vitro

Tuberculosis (TB) is one of the most potent infectious diseases in the world, causing more deaths than any other single infectious agent. TB infection is caused by inhalation of Mycobacterium tuberculosis (Mtb) and subsequent phagocytosis and migration into the lung tissue by innate immune cells (e.g., alveolar macrophages, neutrophils, and dendritic cells), resulting in the formation of a fused mass of immune cells known as the granuloma. Considered the pathological hallmark of TB, the granuloma is a complex microenvironment that is crucial for pathogen containment as well as pathogen survival. Disruption of the delicate granuloma microenvironment via numerous stimuli, such as variations in cytokine secretions, nutrient availability, and the makeup of immune cell population, can lead to an active infection. Herein, we present a novel in vitro model to examine the soluble factor signaling between a mycobacterial infection and its surrounding environment. Adapting a newly developed suspended microfluidic platform, known as Stacks, we established a modular microscale infection model containing human immune cells and a model mycobacterial strain that can easily integrate with different microenvironmental cues through simple spatial and temporal "stacking" of each module of the platform. We validate the establishment of suspended microscale (4 μL) infection cultures that secrete increased levels of proinflammatory factors IL-6, VEGF, and TNFα upon infection and form 3D aggregates (granuloma model) encapsulating the mycobacteria. As a proof of concept to demonstrate the capability of our platform to examine soluble factor signaling, we cocultured an in vitro angiogenesis model with the granuloma model and quantified morphology changes in endothelial structures as a result of culture conditions (P < 0.05 when comparing infected vs. uninfected coculture systems). We envision our modular in vitro granuloma model can be further expanded and adapted for studies focusing on the complex interplay between granulomatous structures and their surrounding microenvironment, as well as a complementary tool to augment in vivo signaling and mechanistic studies.
Microsimulation of energy and flow effects from optimal automated driving in mixed traffic

Abstract This paper studies the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the unconnected mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%–20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%–9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.
Semiparametric partial common principal component analysis for covariance matrices

We consider the problem of jointly modeling multiple covariance matrices by partial common principal component analysis (PCPCA), which assumes a proportion of eigenvectors to be shared across covariance matrices and the rest to be individual-specific. This paper proposes consistent estimators of the shared eigenvectors in the PCPCA as the number of matrices or the number of samples to estimate each matrix goes to infinity. We prove such asymptotic results without making any assumptions on the ranks of eigenvalues that are associated with the shared eigenvectors. When the number of samples goes to infinity, our results do not require the data to be Gaussian distributed. Furthermore, this paper introduces a sequential testing procedure to identify the number of shared eigenvectors in the PCPCA. In simulation studies, our method shows higher accuracy in estimating the shared eigenvectors than competing methods. Applied to a motor-task functional magnetic resonance imaging data set, our estimator identifies meaningful brain networks that are consistent with current scientific understandings of motor networks during a motor paradigm.
Transverse momentum dependent PDFs at N 3 LO

We compute the quark and gluon transverse momentum dependent parton distribution functions at next-to-next-to-next-to-leading order (N3LO) in perturbative QCD. Our calculation is based on an expansion of the differential Drell-Yan and gluon fusion Higgs production cross sections about their collinear limit. This method allows us to employ cutting edge multiloop techniques for the computation of cross sections to extract these universal building blocks of the collinear limit of QCD. The corresponding perturbative matching kernels for all channels are expressed in terms of simple harmonic polylogarithms up to weight five. As a byproduct, we confirm a previous computation of the soft function for transverse momentum factorization at N3LO. Our results are the last missing ingredient to extend the qT subtraction methods to N3LO and to obtain resummed qT spectra at N3LL′ accuracy both for gluon as well as for quark initiated processes.
Targeted Intervention in Random Graphs

We consider a setting where individuals interact in a network, each choosing actions which optimize utility as a function of neighbors’ actions. A central authority aiming to maximize social welfare at equilibrium can intervene by paying some cost to shift individual incentives, and the optimal intervention can be computed using the spectral decomposition of the graph, yet this is infeasible in practice if the adjacency matrix is unknown. In this paper, we study the question of designing intervention strategies for graphs where the adjacency matrix is unknown and is drawn from some distribution. For several commonly studied random graph models, we show that there is a single intervention, proportional to the first eigenvector of the expected adjacency matrix, which is near-optimal for almost all generated graphs when the budget is sufficiently large. We also provide several efficient sampling-based approaches for approximately recovering the first eigenvector when we do not know the distribution. On the whole, our analysis compares three categories of interventions: those which use no data about the network, those which use some data (such as distributional knowledge or queries to the graph), and those which are fully optimal. We evaluate these intervention strategies on synthetic and real-world network data, and our results suggest that analysis of random graph models can be useful for determining when certain heuristics may perform well in practice.
A pipeline for precise and efficient genome editing by sgRNA-Cas9 RNPs in Drosophila.

Genome editing via homology-directed repair (HDR) has made possible precise and deliberate modifications to gene sequences. CRISPR/Cas9-mediated HDR is the simplest means to carry this out. However...
Joint learning for seismic inversion: An acoustic impedance estimation case study

Seismic inversion helps geophysicists build accurate reservoir models for exploration and production purposes. Deep learning-based seismic inversion works by training a neural network to learn a mapping from seismic data to rock properties using well log data as the labels. However, well logs are often very limited in number due to the high cost of drilling wells. Machine learning models can suffer overfitting and poor generalization if trained on limited data. In such cases, well log data from other surveys can provide much needed useful information for better generalization. We propose a learning scheme where we simultaneously train two network architectures, each on a different dataset. By placing a soft constraint on the weight similarity between the two networks, we make them learn from each other where useful for better generalization performance on their respective datasets. Using less than 3$\%$ of the available training data, we were able to achieve an average $r^{2}$ coefficient of 0.8399 on the acoustic impedance pseudologs of the SEAM dataset via joint learning with the Marmousi dataset.
Deficits in hippocampal neurogenesis in obesity-dependent and -independent type-2 diabetes mellitus mouse models

Hippocampal neurogenesis plays an important role in learning and memory function throughout life. Declines in this process have been observed in both aging and Alzheimer's disease (AD). Type 2 Diabetes mellitus (T2DM) is a disorder characterized by insulin resistance and impaired glucose metabolism. T2DM often results in cognitive decline in adults, and significantly increases the risk of AD development. The pathways underlying T2DM-induced cognitive deficits are not known. Some studies suggest that alterations in hippocampal neurogenesis may contribute to cognitive deterioration, however, the fate of neurogenesis in these studies is highly controversial. To address this problem, we utilized two models of T2DM: (1) obesity-independent MKR transgenic mice expressing a mutated form of the human insulin-like growth factor 1 receptor (IGF-1R) in skeletal muscle, and (2) Obesity-dependent db/db mice harboring a mutation in the leptin receptor. Our results show that both models of T2DM display compromised hippocampal neurogenesis. We show that the number of new neurons in the hippocampus of these mice is reduced. Clone formation capacity of neural progenitor cells isolated from the db/db mice is deficient. Expression of insulin receptor and epidermal growth factor receptor was reduced in hippocampal neurospheres isolated from db/db mice. Results from this study warrant further investigation into the mechanisms underlying decreased neurogenesis in T2DM and its link to the cognitive decline observed in this disorder.
Force-free electrodynamics and foliations in an arbitrary spacetime

In this paper we formulate the relationship between force-free electrodynamics and foliations. The background metric, is considered predetermined and electrically neutral, but otherwise arbitrary. As it turns out, solutions to force-free electrodynamics is intimately connected to the existence of foliations of a spacetime with prescribed properties. We also prove a local existence and uniqueness theorem and provide a recipe for constructing the unique solution/class of solutions when certain conditions are met. We clarify the theorem with examples. We are also able to also prove a singularity theorem for when non-null solutions approach the null limit. Here too, we construct an explicit example to illustrate the singularity theorem.
Towards Practical Lipschitz Bandits

Stochastic Lipschitz bandit algorithms balance exploration and exploitation, and have been used for a variety of important task domains. In this paper, we present a framework for Lipschitz bandit methods that adaptively learns partitions of context- and arm-space. Due to this flexibility, the algorithm is able to efficiently optimize rewards and minimize regret, by focusing on the portions of the space that are most relevant. In our analysis, we link tree-based methods to Gaussian processes. In light of our analysis, we design a novel hierarchical Bayesian model for Lipschitz bandit problems. Our experiments show that our algorithms can achieve state-of-the-art performance in challenging real-world tasks such as neural network hyperparameter tuning.
IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage

Massive open online courses (MOOCs) promise to make rigorous higher education accessible to everyone, but prior research has shown that registrants tend to come from backgrounds of higher socioeconomic status. We study geographically granular economic patterns in ~76,000 U.S. registrations for ~600 HarvardX and MITx courses between 2012 and 2018, identifying registrants' locations using both IP geolocation and user-reported mailing addresses. By either metric, we find higher registration rates among postal codes with greater prosperity or population density. However, we also find evidence of bias in IP geolocation: it makes greater errors, both geographically and economically, for users from more economically distressed areas; it disproportionately places users in prosperous areas; and it underestimates the regressive pattern in MOOC registration. Researchers should use IP geolocation in MOOC studies with care, and consider the possibility of similar economic biases affecting its other academic, commercial, and legal uses.
Renormalization group equation and QCD coupling constant in the presence of SU(3) chromo-electric field

We solve the renormalization group equation in QCD in the presence of a SU(3) constant chromo-electric field Ea with arbitrary color index a=1,2,…,8 and find that the QCD coupling constant αs depends on two independent Casimir/gauge invariants C1=[EaEa] and C2=[dabcEaEbEc]2 instead of one gauge invariant C1=[EaEa]. The β function is derived from the one-loop effective action. This coupling constant may be useful to study hadron formation from color flux tubes/strings at high energy colliders and to study quark–gluon plasma formation at RHIC and LHC.
Molecular beam epitaxy growth of nonmagnetic Weyl semimetal LaAlGe thin film

Here, the authors report a detailed method of growing LaAlGe, a nonmagnetic Weyl semimetal, thin film on silicon(100) substrates by molecular beam epitaxy and their structural and electrical characterizations. About 50-nm-thick LaAlGe films were deposited and annealed for 16 h in situ at a temperature of 793 K. As-grown high-quality films showed uniform surface topography and near ideal stoichiometry with a body-centered tetragonal crystal structure. Temperature-dependent longitudinal resistivity can be understood with dominant interband s-d electron-phonon scattering in the temperature range of 5-40 K. Hall measurements confirmed the semimetallic nature of the films with an electron-dominated charge carrier density of ~7.15 × 1021 cm−3 at 5 K.
Moving Objects in the Hubble Ultra Deep Field

We identify proper motion objects in the Hubble Ultra Deep Field (UDF) using the optical data from the original UDF program in 2004 and the near-infrared data from the 128 orbit UDF 2012 campaign. There are 12 sources brighter than I = 27 mag that display >3σ significant proper motions. We do not find any proper motion objects fainter than this magnitude limit. Combining optical and near-infrared photometry, we model the spectral energy distribution of each point-source using stellar templates and state-of-the-art white dwarf models. For I ≤ 27 mag, we identify 23 stars with K0-M6 spectral types and two faint blue objects that are clearly old, thick disk white dwarfs. We measure a thick disk white dwarf space density of 0.1-1.7 × 10–3 pc–3 from these two objects. There are no halo white dwarfs in the UDF down to I = 27 mag. Combining the Hubble Deep Field North, South, and the UDF data, we do not see any evidence for dark matter in the form of faint halo white dwarfs, and the observed population of white dwarfs can be explained with the standard Galactic models.
A deontic logic analysis of autonomous systems' safety

We consider the pressing question of how to model, verify, and ensure that autonomous systems meet certain obligations (like the obligation to respect traffic laws), and refrain from impermissible behavior (like recklessly changing lanes). Temporal logics are heavily used in autonomous system design; however, as we illustrate here, temporal (alethic) logics alone are inappropriate for reasoning about obligations of autonomous systems. This paper proposes the use of Dominance Act Utilitarianism (DAU), a deontic logic of agency, to encode and reason about obligations of autonomous systems. We use DAU to analyze Intel's Responsibility-Sensitive Safety (RSS) proposal as a real-world case study. We demonstrate that DAU can express well-posed RSS rules, formally derive undesirable consequences of these rules, illustrate how DAU could help design systems that have specific obligations, and how to model-check DAU obligations.
Relating eye-tracking measures with changes in knowledge on search tasks

We conducted an eye-tracking study where 30 participants performed searches on the web. We measured their topical knowledge before and after each task. Their eye-fixations were labelled as "reading" or "scanning". The series of reading fixations in a line, called "reading-sequences" were characterized by their length in pixels, fixation duration, and the number of fixations making up the sequence. We hypothesize that differences in knowledge-change of participants are reflected in their eye-tracking measures related to reading. Our results show that the participants with higher change in knowledge differ significantly in terms of their total reading-sequence-length, reading-sequence-duration, and number of reading fixations, when compared to participants with lower knowledge-change.
Generalized statistical models of voids and hierarchical structure in cosmology

Generalized statistical models of voids and hierarchical structure in cosmology are developed. The often quoted negative binomial model and the frequently used thermodynamic model are shown to be special cases of a more general distribution that contains a parameter a. This parameter is related to the Levy index α and the Fisher critical exponent τ, the latter of which describes the power-law falloff of clumps of matter around a phase transition. The parameter a, exponent τ, or index α can be obtained from properties of a void scaling function. A stochastic probability variable p is introduced into a statistical model, which represents the adhesive growth of galaxy structure. The galaxy count distribution decays exponentially quickly with size for p 1/2, adhesive growth can go on indefinitely, thereby forming an infinite supercluster. At p = 1/2, a scale-free power-law distribution for the galaxy count distribution is present. The stochastic description also leads to consequences that have some parallels with cosmic string results, percolation theory, and phase transitions.
Massive Dirac fermions in single-layer graphene

Motivated by the results of recent photoemission and tunneling studies, we discuss potential many-body sources of a finite gap in the Dirac fermion spectrum of graphene. Specifically, we focus on the putative Peierls- and Cooper-like pairing instabilities, which can be driven by sufficiently strong Coulomb and electron-phonon interactions, respectively. Our results compare favorably with the available experimental and Monte Carlo data.
FANO HYPERSURFACES WITH ARBITRARILY LARGE DEGREES OF IRRATIONALITY

We show that complex Fano hypersurfaces can have arbitrarily large degrees of irrationality. More precisely, if we fix a Fano index argument, which Kollar used to prove nonrationality of Fano hypersurfaces. Along the way, we show that in a family of varieties, the invariant ‘the minimal degree of a dominant rational map to a ruled variety’ can only drop on special fibers. As a consequence, we show that for certain low-dimensional families of varieties, the degree of irrationality also behaves well under specialization.
SIRS epidemics on complex networks: Concurrence of exact Markov chain and approximated models

We study the SIRS (Susceptible-Infected-Recovered-Susceptible) spreading processes over complex networks, by considering its exact 3n-state Markov chain model. The Markov chain model exhibits an interesting connection with its 2n-state nonlinear “mean-field” approximation and the latter's corresponding linear approximation. We show that under the specific threshold where the disease-free state is a globally stable fixed point of both the linear and nonlinear models, the exact underlying Markov chain has an O(log n) mixing time, which means the epidemic dies out quickly. In fact, the epidemic eradication condition coincides for all the three models. Furthermore, when the threshold condition is violated, which indicates that the linear model is not stable, we show that there exists a unique second fixed point for the nonlinear model, which corresponds to the endemic state. We also investigate the effect of adding immunization to the SIRS epidemics by introducing two different models, depending on the efficacy of the vaccine. Our results indicate that immunization improves the threshold of epidemic eradication. Furthermore, the common threshold for fast-mixing of the Markov chain and global stability of the disease-free fixed point improves by the same factor for the vaccination-dominant model.
Renormalization Group Analysis of Quantum Critical Points in d-WAVE Superconductors

We describe a search for renormalization group fixed points which control a second-order quantum phase transition between a dx2-y2-superconductor and some other superconducting ground state. Only a few candidate fixed points are found. In the finite temperature (T) quantum-critical region of some of these fixed points, the fermion quasiparticle lifetime is very short and the spectral function has an energy width of order kBT near the Fermi points. Under the same conditions, the thermal conductivity is infinite in the scaling limit. We thus provide simple, explicit, examples of quantum theories in two dimensions for which a purely fermionic quasiparticle description of transport is badly violated.
On dynamic mode decomposition: Theory and applications

Originally introduced in the fluid mechanics community, dynamic mode decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. However, existing DMD theory deals primarily with sequential time series for which the measurement dimension is much larger than the number of measurements taken. We present a theoretical framework in which we define DMD as the eigendecomposition of an approximating linear operator. This generalizes DMD to a larger class of datasets, including nonsequential time series. We demonstrate the utility of this approach by presenting novel sampling strategies that increase computational efficiency and mitigate the effects of noise, respectively. We also introduce the concept of linear consistency, which helps explain the potential pitfalls of applying DMD to rank-deficient datasets, illustrating with examples. Such computations are not considered in the existing literature but can be understood using our more general framework. In addition, we show that our theory strengthens the connections between DMD and Koopman operator theory. It also establishes connections between DMD and other techniques, including the eigensystem realization algorithm (ERA), a system identification method, and linear inverse modeling (LIM), a method from climate science. We show that under certain conditions, DMD is equivalent to LIM.
Remote Source Coding Under Gaussian Noise: Dueling Roles of Power and Entropy Power

The distributed remote source coding (the so-called CEO) problem is studied in the case where the underlying source, not necessarily Gaussian, has finite differential entropy and the observation noise is Gaussian. The main result is a new lower bound for the sum-rate-distortion function under arbitrary distortion measures. When specialized to the case of mean-squared error, it is shown that the bound exactly mirrors a corresponding upper bound, except that the upper bound has the source power (variance), whereas the lower bound has the source entropy power. Bounds exhibiting this pleasing duality of power and entropy power have been well known for direct and centralized source coding since Shannon’s work. While the bounds hold generally, their value is most pronounced when interpreted as a function of the number of agents in the CEO problem.
Spectral calibration and modeling of the NuSTAR CdZnTe pixel detectors

The Nuclear Spectroscopic Telescope Array (NuSTAR) will be the first space mission to focus in the hard X-ray (5-80 keV) band. The NuSTAR instrument carries two co-aligned grazing incidence hard X-ray telescopes. Each NuSTAR focal plane consists of four 2 mm CdZnTe hybrid pixel detectors, each with an active collecting area of 2 cm x 2 cm. Each hybrid consists of a 32x32 array of 605 μm pixels, read out with the Caltech custom low-noise NuCIT ASIC. In order to characterize the spectral response of each pixel to the degree required to meet the science calibration requirements, we have developed a model based on Geant4 together with the Shockley-Ramo theorem customized to the NuSTAR hybrid design. This model combines a Monte Carlo of the X-ray interactions with subsequent charge transport within the detector. The combination of this model and calibration data taken using radioactive sources of ^(57)Co, ^(155)Eu and ^(241)Am enables us to determine electron and hole mobility-lifetime products for each pixel, and to compare actual to ideal performance expected for defect-free material.
Matching in Selective and Balanced Representation Space for Treatment Effects Estimation

The dramatically growing availability of observational data is being witnessed in various domains of science and technology, which facilitates the study of causal inference. However, estimating treatment effects from observational data is faced with two major challenges, missing counterfactual outcomes and treatment selection bias. Matching methods are among the mostly widely used and fundamental approaches to estimating treatment effects, but existing matching methods have poor performance when facing data with high dimensional and complicated variables. We propose a feature selection representation matching (FSRM) method based on deep representation learning and matching, which maps the original covariate space into a selective, nonlinear, and balanced representation space, and then conducts matching in the learned representation space. FSRM adopts deep feature selection to minimize the influence of irrelevant variables for estimating treatment effects and incorporates a regularizer based on the Wasserstein distance to learn balanced representations. We evaluate the performance of our FSRM method on three datasets, and the results demonstrate superiority over the state-of-the-art methods.
Extracting Interpretable Physical Parameters from Spatiotemporal Systems Using Unsupervised Learning

A new approach to analyzing systems with complex dynamics uses machine learning to extract physical parameters and predict their behavior all without knowledge of the underlying system.
Monte Carlo simulation of quantum Zeno effect in the brain

Environmental decoherence appears to be the biggest obstacle for successful construction of quantum mind theories. Nevertheless, the quantum physicist Henry Stapp promoted the view that the mind could utilize quantum Zeno effect to influence brain dynamics and that the efficacy of such mental efforts would not be undermined by environmental decoherence of the brain. To address the physical plausibility of Stapp's claim, we modeled the brain using quantum tunneling of an electron in a multiple-well structure such as the voltage sensor in neuronal ion channels and performed Monte Carlo simulations of quantum Zeno effect exerted by the mind upon the brain in the presence or absence of environmental decoherence. The simulations unambiguously showed that the quantum Zeno effect breaks down for timescales greater than the brain decoherence time. To generalize the Monte Carlo simulation results for any n-level quantum system, we further analyzed the change of brain entropy due to the mind probing actions and proved a theorem according to which local projections cannot decrease the von Neumann entropy of the unconditional brain density matrix. The latter theorem establishes that Stapp's model is physically implausible but leaves a door open for future development of quantum mind theories provided the brain has a decoherence-free subspace.
ON THE PROPER USE OF THE REDUCED SPEED OF LIGHT APPROXIMATION

I show that the Reduced Speed of Light (RSL) approximation, when used properly (i.e. as originally designed - only for the local sources but not for the cosmic background), remains a highly accurate numerical method for modeling cosmic reionization. Simulated ionization and star formation histories from the "Cosmic Reionization On Computers" (CROC) project are insensitive to the adopted value of the reduced speed of light for as long as that value does not fall below about 10% of the true speed of light. Here, a recent claim of the failure of the RSL approximation in the Illustris reionization model appears to be due to the effective speed of light being reduced in the equation for the cosmic background too, and, hence, illustrates the importance of maintaining the correct speed of light in modeling the cosmic background.
Pinwheels and bypasses

We give a necessary and sufficient condition for the addition of a collection of disjoint bypasses to a convex surface to be universally tight - namely the nonexistence of a polygonal region which we call a virtual pinwheel. AMS Classification 57M50; 53C15
Cubic-scaling algorithm and self-consistent field for the random-phase approximation with second-order screened exchange

The random-phase approximation with second-order screened exchange (RPA+SOSEX) is a model of electron correlation energy with two caveats: its accuracy depends on an arbitrary choice of mean field, and it scales as O(n(5)) operations and O(n(3)) memory for n electrons. We derive a new algorithm that reduces its scaling to O(n(3)) operations and O(n(2)) memory using controlled approximations and a new self-consistent field that approximates Brueckner coupled-cluster doubles theory with RPA+SOSEX, referred to as Brueckner RPA theory. The algorithm comparably reduces the scaling of second-order Moller-Plesset perturbation theory with smaller cost prefactors than RPA+SOSEX. Within a semiempirical model, we study H2 dissociation to test accuracy and Hn rings to verify scaling.
Imposed magnetic field and hot electron propagation in inertial fusion hohlraums

The effects of an imposed, axial magnetic field $$B_{z0}$$ on hydrodynamics and energetic electrons in inertial confinement fusion indirect-drive hohlraums are studied. We present simulations from the radiation-hydrodynamics code HYDRA of a low-adiabat ignition design for the National Ignition Facility, with and without $$B_{z0}=70~\text{T}$$. The field’s main hydrodynamic effect is to significantly reduce electron thermal conduction perpendicular to the field. This results in hotter and less dense plasma on the equator between the capsule and hohlraum wall. The inner laser beams experience less inverse bremsstrahlung absorption before reaching the wall. The X-ray drive is thus stronger from the equator with the imposed field. We study superthermal, or ‘hot’, electron dynamics with the particle-in-cell code ZUMA, using plasma conditions from HYDRA. During the early-time laser picket, hot electrons based on two-plasmon decay in the laser entrance hole (Regan et al., Phys. Plasmas, vol. 17(2), 2010, 020703) are guided to the capsule by a 70 T field. Twelve times more energy deposits in the deuterium–tritium fuel. For plasma conditions early in peak laser power, we present mono-energetic test-case studies with ZUMA as well as sources based on inner-beam stimulated Raman scattering. Furthermore, the effect of the field on deuterium–tritium deposition depends strongly on the source location, namely whether hot electrons are generated on field lines that connect to the capsule.