cssi/SciSciGPT-SciSciNet huggingface dataset:
FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data.

Informatics tools to support the integration and subsequent interrogation of spatiotemporal data such as clinical data and environmental exposures data are lacking. Such tools are needed to support research in environmental health and any biomedical field that is challenged by the need for integrated spatiotemporal data to examine individual-level determinants of health and disease. We have developed an open-source software application—FHIR PIT (Health Level 7 Fast Healthcare Interoperability Resources Patient data Integration Tool)—to enable studies on the impact of individual-level environmental exposures on health and disease. FHIR PIT was motivated by the need to integrate patient data derived from our institution’s clinical warehouse with a variety of public data sources on environmental exposures and then openly expose the data via ICEES (Integrated Clinical and Environmental Exposures Service). FHIR PIT consists of transformation steps or building blocks that can be chained together to form a transformation and integration workflow. Several transformation steps are generic and thus can be reused. As such, new types of data can be incorporated into the modular FHIR PIT pipeline by simply reusing generic steps or adding new ones. We validated FHIR PIT in the context of a driving use case designed to investigate the impact of airborne pollutant exposures on asthma. Specifically, we replicated published findings demonstrating racial disparities in the impact of airborne pollutants on asthma exacerbations. While FHIR PIT was developed to support our driving use case on asthma, the software can be used to integrate any type and number of spatiotemporal data sources at a level of granularity that enables individual-level study. We expect FHIR PIT to facilitate research in environmental health and numerous other biomedical disciplines.
Decontamination of ambient RNA in single-cell RNA-seq with DecontX.

Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq). However, ambient RNA present in the cell suspension can be aberrantly counted along with a cell’s native mRNA and result in cross-contamination of transcripts between different cell populations. DecontX is a novel Bayesian method to estimate and remove contamination in individual cells. DecontX accurately predicts contamination levels in a mouse-human mixture dataset and removes aberrant expression of marker genes in PBMC datasets. We also compare the contamination levels between four different scRNA-seq protocols. Overall, DecontX can be incorporated into scRNA-seq workflows to improve downstream analyses.
Adversarial Hypothesis Testing and a Quantum Stein’s Lemma for Restricted Measurements

Recall the classical hypothesis testing setting with two sets of probability distributions $P$ and $Q$ . One receives either $n$ i.i.d. samples from a distribution $p \in P$ or from a distribution $q \in Q$ and wants to decide from which set the points were sampled. It is known that the optimal exponential rate at which errors decrease can be achieved by a simple maximum-likelihood ratio test which does not depend on $p$ or $q$ , but only on the sets $P$ and $Q$ . We consider an adaptive generalization of this model where the choice of $p \in P$ and $q \in Q$ can change in each sample in some way that depends arbitrarily on the previous samples. In other words, in the $k^{th}$ round, an adversary, having observed all the previous samples in rounds $1,\ldots,k-1$ , chooses $p_{k} \in P$ and $q_{k} \in Q$ , with the goal of confusing the hypothesis test. We prove that even in this case, the optimal exponential error rate can be achieved by a simple maximum-likelihood test that depends only on $P$ and $Q$ . We then show that the adversarial model has applications in hypothesis testing for quantum states using restricted measurements. For example, it can be used to study the problem of distinguishing entangled states from the set of all separable states using only measurements that can be implemented with local operations and classical communication (LOCC). The basic idea is that in our setup, the deleterious effects of entanglement can be simulated by an adaptive classical adversary. We prove a quantum Stein’s Lemma in this setting: In many circumstances, the optimal hypothesis testing rate is equal to an appropriate notion of quantum relative entropy between two states. In particular, our arguments yield an alternate proof of Li and Winter’s recent strengthening of strong subadditivity for von Neumann entropy.
Bernoulli Ballot Polling: A Manifest Improvement for Risk-Limiting Audits

We present a method and software for ballot-polling risk-limiting audits (RLAs) based on Bernoulli sampling: ballots are included in the sample with probability p, independently. Bernoulli sampling has several advantages: (1) it does not require a ballot manifest; (2) it can be conducted independently at different locations, rather than requiring a central authority to select the sample from the whole population of cast ballots or requiring stratified sampling; (3) it can start in polling places on election night, before margins are known. If the reported margins for the 2016 U.S. Presidential election are correct, a Bernoulli ballot-polling audit with a risk limit of 5% and a sampling rate of \(p_0=1\%\) would have had at least a 99% probability of confirming the outcome in 42 states. (The other states were more likely to have needed to examine additional ballots). Logistical and security advantages that auditing in the polling place affords may outweigh the cost of examining more ballots than some other methods might require.
The Kreuzer-Skarke axiverse

We study the topological properties of Calabi-Yau threefold hypersurfaces at large h1,1. We obtain two million threefolds X by triangulating polytopes from the Kreuzer-Skarke list, including all polytopes with 240 ≤ h1,1 ≤ 491. We show that the Kahler cone of X is very narrow at large h1,1, and as a consequence, control of the α′ expansion in string compactifications on X is correlated with the presence of ultralight axions. If every effective curve has volume ≥ 1 in string units, then the typical volumes of irreducible effective curves and divisors, and of X itself, scale as (h1,1)p, with 3 ≲ p ≲ 7 depending on the type of cycle in question. Instantons from branes wrapping these cycles are thus highly suppressed.
Molecular Basis for Acetyl‐CoA Production by ATP‐Citrate Lyase

ATP-citrate lyase (ACLY) synthesizes cytosolic acetyl coenzyme A (acetyl-CoA), a fundamental cellular building block. Accordingly, aberrant ACLY activity is observed in many diseases. Here we report cryo-EM structures of human ACLY, alone or bound to substrates or products. ACLY forms a homotetramer with a rigid citrate synthase homology (CSH) module, flanked by four flexible acetyl-CoA synthetase homology (ASH) domains; CoA is bound at the CSH-ASH interface in mutually exclusive productive or unproductive conformations. The structure of a catalytic mutant of ACLY in the presence of ATP, citrate and CoA substrates reveals a phospho-citryl-CoA intermediate in the ASH domain. ACLY with acetyl-CoA and oxaloacetate products shows the products bound in the ASH domain, with an additional oxaloacetate in the CSH domain, which could function in ACLY autoinhibition. These structures, which are supported by biochemical and biophysical data, challenge previous proposals of the ACLY catalytic mechanism and suggest additional therapeutic possibilities for ACLY-associated metabolic disorders.
ON AGNOSTIC LEARNING OF PARITIES, MONOMIALS, AND HALFSPACES

We study the learnability of several fundamental concept classes in the agnostic learning framework of [D. Haussler, Inform. and Comput., 100 (1992), pp. 78-150] and [M. Kearns, R. Schapire, and L. Sellie, Machine Learning, 17 (1994), pp. 115-141]. We show that under the uniform distribution, agnostically learning parities reduce to learning parities with random classification noise, commonly referred to as the noisy parity problem. Together with the parity learning algorithm of [A. Blum, A. Kalai, and H. Wasserman, J. ACM, 50 (2003), pp. 506-519], this gives the first nontrivial algorithm for agnostic learning of parities. We use similar techniques to reduce learning of two other fundamental concept classes under the uniform distribution to learning of noisy parities. Namely, we show that learning of disjunctive normal form (DNF) expressions reduces to learning noisy parities of just logarithmic number of variables, and learning of k-juntas reduces to learning noisy parities of k variables. We give essentially optimal hardness results for agnostic learning of monomials over {0, 1} n and halfspaces over ℚ n . We show that for any constant e finding a monomial (halfspace) that agrees with an unknown function on 1/2 + e fraction of the examples is NP-hard even when there exists a monomial (halfspace) that agrees with the unknown function on 1 — e fraction of the examples. This resolves an open question due to Blum and significantly improves on a number of previous hardness results for these problems. We extend these results to ∈ = 2-log 1-λ n (e = 2 -√log n in the case of halfspaces) for any constant λ > 0 under stronger complexity assumptions.
Deep Learning Techniques for Inverse Problems in Imaging

Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learning is supervised or unsupervised, i.e., whether or not the training relies on access to matched ground truth image and measurement pairs. We also discuss the tradeoffs associated with these different reconstruction approaches, caveats and common failure modes, plus open problems and avenues for future work.
Observations of room-temperature creep recovery in titanium alloys

Conventional α(hcp) and α(hcp)/β(bcc) titanium alloys exhibit significant primary creep strains at room temperature and at stresses well below their macroscopic yield strength. It has been previously reported in various materials systems that repeated unloading during primary creep testing may either accelerate or retard the accumulation of creep strains. These effects have been demonstrated to depend on both microstructure and the applied stress. This article demonstrates that significant room-temperature recovery occurs in technologically relevant titanium alloys. These recovery mechanisms are manifested as a dramatic increase in creep rates (by several orders of magnitude) upon the introduction of individual unloading events, ranging from 1 minute to 365 days, during primary creep tests. Significant increases in both creep rate and the total accumulated creep strain were observed in polycrystalline single α-phase Ti-6Al, polycrystalline α/β Ti-6Al-2Sn-4Zr-2Mo-0.1Si, and individual α/β colonies of Ti-6242. Based on transmission electron microscopy (TEM) studies of the active deformation mechanisms, it is proposed that the presence of significant stress concentrations within the α phase of these materials, in the form of dislocation pileups, is a prerequisite for significant room-temperature recovery.
Product Risk Management: Testing and Warranties†

Manufacturers employ various testing strategies to identify and rectify defects before products are released to the markets. A carefully planned test strategy can improve product quality and customer satisfaction and reduce life cycle cost. Testing can also help reveal potential hazards associated with a product, evaluate associated risks, and discover factors that can be controlled to reduce risks. This article presents some of the tests that are performed during the prototype and production phases. These include the design verification test (DVT) and the highly accelerated life test (HALT) during the prototype phase; and the reliability qualification test (RQT) and the ongoing reliability test (ORT) in the production phase. We also describe how the results of a warranty performance analysis can be used as feedback to improve the test coverage for the tests run during production. Keywords: reliability; reliability prediction; reliability growth; warranty; Crow-AMSAA; Weibull
1 – Heat Conduction

This chapter provides a concise review of the mathematical theory of heat conduction and its engineering applications. Fourier’s law and the general heat conduction equation are developed. Solutions of the fundamental equations are presented for a number of one-dimensional steady-state problems, and these solutions are used to develop the concepts of thermal resistance, the R-value of insulating materials and the conduction shape factor. It is shown how these concepts can be applied to solve a number of problems of the type frequently encountered in process engineering. Finally, a few unsteady-state solutions of the conduction equation are presented and their use in solving engineering problems is illustrated.
Does the Source of Capital Affect Capital Structure

Prior work on leverage implicitly assumes capital availability depends solely on firm characteristics. However, market frictions that make capital structure relevant may also be associated with a firm's source of capital. Examining this intuition, we find firms that have access to the public bond markets, as measured by having a debt rating, have significantly more leverage. Although firms with a rating are fundamentally different, these differences do not explain our findings. Even after controlling for firm characteristics that determine observed capital structure, and instrumenting for the possible endogeneity of having a rating, firms with access have 35% more debt. Copyright 2006, Oxford University Press.
An Interactive Console Operating as Background in a Large Computer System

In order to fill the gap between small desktop calculators and conventional computer programming, an interactive console system has been developed to permit engineers and mathematicians to solve small scale problems with a simple algebra-like language, EASY (Elementary Algebraic Solutions for You). In order to achieve effective operation at low cost, the consoles (IBM 2260) are supported as a low level background function on an IBM 360 Model 40 computer Attached Support Processor (ASP), which is simultaneously supplying data processing capability to support multiple printers, plotters, card readers and punches, auxiliary storage devices, and the monitoring and job scheduling for an attached IBM 360 Model 65 computer (Fig. 1).
“Used fuel” vectors and waste minimization strategies for VHTRS operating without refueling

Abstract Generation IV Very High Temperature Reactors (VHTRs) are well-known for their flexibility with respect to feasible fuel cycle options. In this paper, the LEU- and TRU-fueled VHTR configurations are analyzed accounting for their capabilities to attain an extended single-batch OTTO (Once-Through-Then-Out) mode of operation without intermediate refueling. The requirement of waste minimization is imposed as one of the design constraints defining possible system configurations and deployment strategies. The resulting “used fuel” vectors are examined considering anticipated disposal options as well as viability of fuel reprocessing. A Monte Carlo-deterministic analysis methodology has been implemented for coupled design studies of VHTRs with TRUs using the ORNL SCALE 5.1 code system. The developed modeling approach provides an exact-geometry 3D representation of the VHTR core details properly capturing VHTR physics. The presented analysis is focused on prismatic block core concepts for VHTRs. It is being performed within the scope of the U.S. DOE NERI project on utilization of higher actinides (TRUs and partitioned MAs) as a fuel component for extended-life VHTR configurations.
A Low-Voltage Processor for Sensing Applications With Picowatt Standby Mode

Recent progress in ultra-low-power circuit design is creating new opportunities for cubic millimeter computing. Robust low-voltage operation has reduced active mode power consumption considerably, but standby mode power consumption has received relatively little attention from low-voltage designers. In this work, we describe a low-voltage processor called the Phoenix Processor that has been designed at the device, circuit, and architecture levels to minimize standby power. A test chip has been implemented in a carefully selected 0.18 μm process in an area of only 915 x 915 μm 2 . Measurements show that Phoenix consumes 35.4 pW in standby mode and 226 nW in active mode.
Persistent appreciations and overshooting : a normative analysis

Most economies experience episodes of persistent real exchange rate appreciations, when the question arises whether there is a need for intervention to protect the export sector. This paper presents a model of irreversible destruction where exchange rate intervention may be justified if the export sector is financially constrained. However, the criterion for intervention is not whether there are bankruptcies or not, but whether these can cause a large exchange rate overshooting once the factors behind the appreciation subside. The optimal policy includes ex-ante and ex-post interventions. Ex-ante (that is, during the appreciation phase) interventions have limited effects if the financial resources in the export sector are relatively abundant. In this case the bulk of the intervention takes place ex-post, and is concentrated in the first period of the depreciation phase. In contrast, if the financial constraint in the export sector is tight, the policy is shifted toward ex-ante intervention and it is optimal to lean against the appreciation.
Reye's Syndrome

Reye's syndrome (encephalopathy with fatty infiltration of the viscera) is an acute illness of childhood that produces hepatic dysfunction and metabolic encephalopathy. The disease is fatal in as many as 40% of cases. The cause is unknown. Several environmental agents, particularly salicylates and aflatoxin, have been implicated as possible toxins in this disorder. Treatment is directed at controlling intracranial pressure, reversing metabolic abnormalities and providing intensive supportive care. Normal neurologic function returns in most survivors.
Histone Acetyltransferase 1 is Required for DNA Replication Fork Function and Stability

The replisome is a protein complex on the DNA replication fork and functions in a dynamic environment at the intersection of parental and nascent chromatin. Parental nucleosomes are disrupted in front of the replication fork. The daughter DNA duplexes are packaged with an equal amount of parental and newly synthesized histones in the wake of the replication fork through the activity of the replication-coupled chromatin assembly pathway. Histone acetyltransferase 1 (HAT1) is responsible for the cytosolic diacetylation of newly synthesized histone H4 on lysines 5 and 12, which accompanies replication-coupled chromatin assembly. Here, using proximity ligation assay-based chromatin assembly assays and DNA fiber analysis, we analyzed the role of murine HAT1 in replication-coupled chromatin assembly. We demonstrate that HAT1 physically associates with chromatin near DNA replication sites. We found that the association of HAT1 with newly replicated DNA is transient, but can be stabilized by replication fork stalling. The association of HAT1 with nascent chromatin may be functionally relevant, as HAT1 loss decreased replication fork progression and increased replication fork stalling. Moreover, in the absence of HAT1, stalled replication forks were unstable, and newly synthesized DNA became susceptible to MRE11-dependent degradation. These results suggest that HAT1 links replication fork function to the proper processing and assembly of newly synthesized histones.
Credit and Deferral as International Investment Incentives

Abstract Many governments tax the foreign income of their firms, using a system that grants credits for foreign taxes paid and permits tax deferral for unrepatriated income. This paper shows that such tax systems encourage firms to restrict their equity stakes in new foreign investments, and to finance their new investments with considerable debt. These incentives exist even when transfer price regulation effectively limits the profit rates foreign subsidiaries can earn. The behavior of the foreign subsidiaries of US multinationals in 1984 appears to be consistent with these incentives.
Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI Using Deep 3D Convolutional Neural Networks

Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. With the advances in functional Magnetic Resonance Imaging (fMRI), recent studies focus on brain functional connectivity and have provided new insight into development of the human brain before birth. Deep Convolutional Neural Networks (CNN) have achieved remarkable success on learning directly from image data, yet have not been applied on fetal fMRI for understanding fetal neurodevelopment. Here, we bridge this gap by applying a novel application of 3D CNN to fetal blood oxygen-level dependence (BOLD) resting-state fMRI data. We build supervised CNN to isolate variation in fMRI signals that relate to younger v.s. older fetal age groups. Sensitivity analysis is then performed to identify brain regions in which changes in BOLD signal are strongly associated with fetal brain age. Based on the analysis, we discovered that regions that most strongly differentiate groups are largely bilateral, share similar distribution in older and younger age groups, and are areas of heightened metabolic activity in early human development.
Assessing exposure effects on gene expression

In observational genomics data sets, there is often confounding of the effect of an exposure on gene expression. To adjust for confounding when estimating the exposure effect, a common approach involves including potential confounders as covariates with the exposure in a regression model of gene expression. However, when the exposure and confounders interact to influence gene expression, the fitted regression model does not necessarily estimate the overall effect of the exposure. Using inverse probability weighting (IPW) or the parametric g-formula in these instances is straightforward to apply and yields consistent effect estimates. IPW can readily be integrated into a genomics data analysis pipeline with upstream data processing and normalization, while the g-formula can be implemented by making simple alterations to the regression model. The regression, IPW, and g-formula approaches to exposure effect estimation are compared herein using simulations; advantages and disadvantages of each approach are explored. The methods are applied to a case study estimating the effect of current smoking on gene expression in adipose tissue.
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution

In this paper, we explore the space-time video super-resolution task, which aims to generate a high-resolution (HR) slow-motion video from a low frame rate (LFR), low-resolution (LR) video. A simple solution is to split it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR). However, temporal interpolation and spatial super-resolution are intra-related in this task. Two-stage methods cannot fully take advantage of the natural property. In addition, state-of-the-art VFI or VSR networks require a large frame-synthesis or reconstruction module for predicting high-quality video frames, which makes the two-stage methods have large model sizes and thus be time-consuming. To overcome the problems, we propose a one-stage space-time video super-resolution framework, which directly synthesizes an HR slow-motion video from an LFR, LR video. Rather than synthesizing missing LR video frames as VFI networks do, we firstly temporally interpolate LR frame features in missing LR video frames capturing local temporal contexts by the proposed feature temporal interpolation network. Then, we propose a deformable ConvLSTM to align and aggregate temporal information simultaneously for better leveraging global temporal contexts. Finally, a deep reconstruction network is adopted to predict HR slow-motion video frames. Extensive experiments on benchmark datasets demonstrate that the proposed method not only achieves better quantitative and qualitative performance but also is more than three times faster than recent two-stage state-of-the-art methods, e.g., DAIN+EDVR and DAIN+RBPN.
Income elasticity of demand versus consumption: Implications for energy policy analysis

Abstract The income elasticity of consumption depends not only on the demand function but also on the characteristics of the supply function. If the supply of the underlying good (e.g., gasoline, natural gas, or housing) is not completely elastic, the income elasticity of equilibrium consumption will be less than the income elasticity of demand, with the difference depending on the shapes of both the demand and supply functions. We derive analytical expressions for the wedge between the two elasticities and discuss the implications for the energy policy analysis.
Estimating the nationwide transmission risk of measles in US schools and impacts of vaccination and supplemental infection control strategies.

The spread of airborne infectious diseases such as measles is a critical public health concern. The U.S. was certified measles-free in 2000, but the number of measles cases has increased in recent years breaking the record of the nationwide annual number of cases since 1992. Although the characteristics of schools have made them one of the most vulnerable environments during infection outbreaks, the transmission risk of measles among students is not completely understood. We aimed to evaluate how three factors influence measles transmission in schools: personal (vaccination), social (compartmentalizing), and building systems (ventilation, purification, and filtration). We used a combination of a newly developed multi-zone transient Wells-Riley approach, a nationwide representative School Building Archetype (SBA) model, and a Monte-Carlo simulation to estimate measles risk among U.S. students. We compared our risk results with the range of reported transmission rates of measles in school outbreaks to validate the risk model. We also investigated the effectiveness of vaccination and ten supplemental infection control scenarios for reducing the risk of measles transmission among students. Our best nationwide estimate of measles transmission risk in U.S. schools were 3.5 and 32% among all (both unvaccinated and immunized) and unvaccinated students, respectively. The results showed the transmission risk of measles among unvaccinated students is > 70 times higher than properly immunized ones. We also demonstrated that the transmission risk of measles in primary schools (assuming teacher self-contained classrooms) is less than secondary schools (assuming departmentalized systems). For building-level interventions, schools with ductless-with-air-filter and ductless-without-air-filter systems have the lowest and highest transmission risks of measles, respectively. Finally, our simulation showed that infection control strategies could cut the average number of infected cases among all students in half when a combination of advanced air filtration, ventilation, and purification was adopted in the modeled schools. Our results highlight the primary importance of vaccination for reducing the risk of measles transmission among students. Yet, additional and significant risk reduction can be achieved through compartmentalizing students and enhancing building ventilation and filtration systems.
Novel Lagrangian hierarchies, generalized variational ODE's, and families of regular and embedded solitary waves

Hierarchies of Lagrangians of degree two, each only partly determined by the choice of leading terms and with some coefficients remaining free, are considered. The free coefficients they contain satisfy the most general differential geometric criterion currently known for the existence of a Lagrangian and variational formulation, and derived by solution of the full inverse problem of the calculus of variations for scalar fourth-order ODEs respectively. However, our Lagrangians have significantly greater freedom since our existence conditions are for individual coefficients in the Lagrangian. In particular, the classes of Lagrangians derived here have four arbitrary or free functions, including allowing the leading coefficient in the resulting variational ODEs to be arbitrary, and with models based on the earlier general criteria for a variational representation being special cases. For different choices of leading coefficients, the resulting variational equations could also represent traveling waves of various nonlinear evolution equations, some of which recover known physical models. Families of regular and embedded solitary waves are derived for some of these generalized variational ODEs in appropriate parameter regimes, with the embedded solitons occurring only on isolated curves in the part of parameter space where they exist. Future work will involve higher order Lagrangians, the resulting equations of motion, and their solitary wave solutions.
Simphony: An Open-Source Photonic Integrated Circuit Simulation Framework

We present Simphony, a free and open-source software toolbox for abstracting and simulating photonic integrated circuits, implemented in Python. The toolbox is both fast and easily extensible; plugins can be written to provide compatibility with existing layout tools, and device libraries can be easily created without a deep knowledge of programming. We include several examples of photonic circuit simulations with novel features and demonstrate a speedup of more than 20× over a leading commercially available software tool.
Demiclosedness Principles for Generalized Nonexpansive Mappings

Demiclosedness principles are powerful tools in the study of convergence of iterative methods. For instance, a multi-operator demiclosedness principle for firmly nonexpansive mappings is useful in obtaining simple and transparent arguments for the weak convergence of the shadow sequence generated by the Douglas–Rachford algorithm. We provide extensions of this principle, which are compatible with the framework of more general families of mappings such as cocoercive and conically averaged mappings. As an application, we derive the weak convergence of the shadow sequence generated by the adaptive Douglas–Rachford algorithm.
Robust Iris Presentation Attack Detection Fusing 2D and 3D Information

Diversity and unpredictability of artifacts potentially presented to an iris sensor calls for presentation attack detection methods that are agnostic to specificity of presentation attack instruments. This article proposes a method that combines two-dimensional and three-dimensional properties of the observed iris to address the problem of spoof detection in case when some properties of artifacts are unknown. The 2D (textural) iris features are extracted by a state-of-the-art method employing Binary Statistical Image Features (BSIF) and an ensemble of classifiers is used to deliver 2D modality-related decision. The 3D (shape) iris features are reconstructed by a photometric stereo method from only two images captured under near-infrared illumination placed at two different angles, as in many current commercial iris recognition sensors. The map of normal vectors is used to assess the convexity of the observed iris surface. The combination of these two approaches has been applied to detect whether a subject is wearing a textured contact lens to disguise their identity. Extensive experiments with NDCLD’15 dataset, and a newly collected NDIris3D dataset show that the proposed method is highly robust under various open-set testing scenarios, and that it outperforms all available open-source iris PAD methods tested in identical scenarios. The source code and the newly prepared benchmark are made available along with this article.
Identifying candidate hosts for quantum defects via data mining

Atom-like defects in solid-state hosts are promising candidates for the development of quantum information systems, but despite their importance, the host substrate/defect combinations currently under study have almost exclusively been found serendipitously. Here we systematically evaluate the suitability of host materials by applying a combined four-stage data mining and manual screening process to all entries in the Materials Project database, with literature-based experimental confirmation of band gap values. We identify a total of 541 viable hosts (16 unary and 74 binary) for quantum defect introduction and potential use in quantum information systems. This represents a significant (99.57%) reduction from the total number of known inorganic phases, and the application of additional selection criteria for specific applications will reduce their number even further. The screening principles outlined may easily be applied to previously unrealized phases and other technologically important materials systems.
On the convergence complexity of Gibbs samplers for a family of simple Bayesian random effects models

The emergence of big data has led to so-called convergence complexity analysis, which is the study of how Markov chain Monte Carlo (MCMC) algorithms behave as the sample size, n, and/or the number of parameters, p, in the underlying data set increase. This type of analysis is often quite challenging, in part because existing results for fixed n and p are simply not sharp enough to yield good asymptotic results. One of the first convergence complexity results for an MCMC algorithm on a continuous state space is due to Yang and Rosenthal (2019), who established a mixing time result for a Gibbs sampler (for a simple Bayesian random effects model) that was introduced and studied by Rosenthal (Stat Comput 6:269–275, 1996). The asymptotic behavior of the spectral gap of this Gibbs sampler is, however, still unknown. We use a recently developed simulation technique (Qin et al. Electron J Stat 13:1790–1812, 2019) to provide substantial numerical evidence that the gap is bounded away from 0 as n → ∞. We also establish a pair of rigorous convergence complexity results for two different Gibbs samplers associated with a generalization of the random effects model considered by Rosenthal (Stat Comput 6:269–275, 1996). Our results show that, under a strong growth condition, the spectral gaps of these Gibbs samplers converge to 1 as the sample size increases.
A mathematical model of asynchronous data flow in parallel computers

We present a simplified model of data flow on processors in a high performance computing framework involving computations necessitating inter-processor communications. From this ordinary differential model, we take its asymptotic limit, resulting in a model which treats the computer as a continuum of processors and data flow as an Eulerian fluid governed by a conservation law. We derive a Hamilton-Jacobi equation associated with this conservation law for which the existence and uniqueness of solutions can be proven. We then present the results of numerical experiments for both discrete and continuum models; these show a qualitative agreement between the two and the effect of variations in the computing environment's processing capabilities on the progress of the modeled computation.
UBAT: On Jointly Optimizing UAV Trajectories and Placement of Battery Swap Stations

Unmanned aerial vehicles (UAVs) have been widely used in many applications. The limited flight time of UAVs, however, still remains as a major challenge. Although numerous approaches have been developed to recharge the battery of UAVs effectively, little is known about optimal methodologies to deploy charging stations. In this paper, we address the charging station deployment problem with an aim to find the optimal number and locations of charging stations such that the system performance is maximized. We show that the problem is NP-Hard and propose UBAT, a heuristic framework based on the ant colony optimization (ACO) to solve the problem. Additionally, a suite of algorithms are designed to enhance the execution time and the quality of the solutions for UBAT. Through extensive simulations, we demonstrate that UBAT effectively performs multi-objective optimization of generation of UAV trajectories and placement of charging stations that are within 8.3% and 7.3% of the true optimal solutions, respectively.
Clinical predictors for etiology of acute diarrhea in children in resource-limited settings.

Background Diarrhea is one of the leading causes of childhood morbidity and mortality in lower- and middle-income countries. In such settings, access to laboratory diagnostics are often limited, and decisions for use of antimicrobials often empiric. Clinical predictors are a potential non-laboratory method to more accurately assess diarrheal etiology, the knowledge of which could improve management of pediatric diarrhea. Methods We used clinical and quantitative molecular etiologic data from the Global Enteric Multicenter Study (GEMS), a prospective, case-control study, to develop predictive models for the etiology of diarrhea. Using random forests, we screened the available variables and then assessed the performance of predictions from random forest regression models and logistic regression models using 5-fold cross-validation. Results We identified 1049 cases where a virus was the only etiology, and developed predictive models against 2317 cases where the etiology was known but non-viral (bacterial, protozoal, or mixed). Variables predictive of a viral etiology included lower age, a dry and cold season, increased height-for-age z-score (HAZ), lack of bloody diarrhea, and presence of vomiting. Cross-validation suggests an AUC of 0.825 can be achieved with a parsimonious model of 5 variables, achieving a specificity of 0.85, a sensitivity of 0.59, a NPV of 0.82 and a PPV of 0.64. Conclusion Predictors of the etiology of pediatric diarrhea can be used by providers in low-resource settings to inform clinical decision-making. The use of non-laboratory methods to diagnose viral causes of diarrhea could be a step towards reducing inappropriate antibiotic prescription worldwide.
The Conway–Sloane calculus for 2-adic lattices

We motivate and explain the system introduced by Conway and Sloane for working with quadratic forms over the 2-adic integers, and prove its validity. Their system is far better for actual calculations than earlier methods, and has been used for many years, but no proof has been published before now.
A Comparison of Bayesian Accelerated Failure Time Models with Spatially Varying Coefficients

The accelerated failure time (AFT) model is a commonly used tool in analyzing survival data. In public health studies, data is often collected from medical service providers in different locations. Survival rates from different locations often present geographically varying patterns. In this paper, we focus on the accelerated failure time model with spatially varying coefficients. We compare three different types of priors for spatially varying coefficients. A model selection criterion, logarithm of the pseudo-marginal likelihood (LPML), is employed to assess the fit of the AFT model with different priors. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. Finally, we apply our model to SEER data on prostate cancer in Louisiana and demonstrate the existence of spatially varying effects on survival rates from prostate cancer.
The Fluid Dynamics of Disease Transmission

For an infectious disease such as the coronavirus disease 2019 (COVID-19) to spread, contact needs to be established between an infected host and a susceptible one. In a range of populations and in...
A double Sylvester determinant

Given two ( n +1) × ( n +1) -matrices A and B over a commutative ring, and some k ∈ {0, 1, …, n } , we consider the ( n choose k ) × ( n choose k ) -matrix W whose entries are ( k +1) × ( k +1) -minors of A multiplied by corresponding ( k +1) × ( k +1) -minors of B . Here we require the minors to use the last row and the last column (which is why we obtain an ( n choose k ) × ( n choose k ) -matrix, not a ( n + 1 choose k + 1) × ( n + 1 choose k + 1) -matrix). We prove that the determinant det W is a multiple of det A if the ( n +1, n +1) -th entry of B is 0 . Furthermore, if the ( n +1, n +1) -th entries of both A and B are 0 , then det W is a multiple of (det A )(det B ) . This extends a previous result of Olver and the author.
MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC–MS metabolomics data

Despite the availability of several pre-processing software, poor peak integration remains a prevalent problem in untargeted metabolomics data generated using liquid chromatography high–resolution mass spectrometry (LC–MS). As a result, the output of these pre-processing software may retain incorrectly calculated metabolite abundances that can perpetuate in downstream analyses. To address this problem, we propose a computational methodology that combines machine learning and peak quality metrics to filter out low quality peaks. Specifically, we comprehensively and systematically compared the performance of 24 different classifiers generated by combining eight classification algorithms and three sets of peak quality metrics on the task of distinguishing reliably integrated peaks from poorly integrated ones. These classifiers were compared to using a residual standard deviation (RSD) cut-off in pooled quality-control (QC) samples, which aims to remove peaks with analytical error. The best performing classifier was found to be a combination of the AdaBoost algorithm and a set of 11 peak quality metrics previously explored in untargeted metabolomics and proteomics studies. As a complementary approach, applying our framework to peaks retained after filtering by 30% RSD across pooled QC samples was able to further distinguish poorly integrated peaks that were not removed from filtering alone. An R implementation of these classifiers and the overall computational approach is available as the MetaClean package at https://CRAN.R-project.org/package=MetaClean . Our work represents an important step forward in developing an automated tool for filtering out unreliable peak integrations in untargeted LC–MS metabolomics data.
Do PCAOB Inspections Improve the Accuracy of Accounting Estimates

Despite issuing extensive guidance related to the evaluation of accounting estimates, the PCAOB continues to identify deficiencies related to the audit of estimates through their inspections process. We examine whether PCAOB inspections lead to more accurate audited accounting estimates, defined as those that more closely match economic reality, by examining a significant estimate within the banking industry. We find that in contrast with the PCAOB's goal of more accurate and unbiased estimates, allowance for loan losses (ALL) estimates become less accurate and more conservative with higher levels of ALL‐related inspection findings for public company audits. We find no evidence of auditor response to PCAOB inspection findings for private‐company audits, which are not subject to PCAOB inspection. Overall, our findings cast doubt on the efficacy of PCAOB inspections in improving estimate accuracy and suggest that firms are managing inspection risk to the potential detriment of audit quality.
Social Capital Among Black South African Grandmothers Raising Grandchildren

While the significance of social capital to the well-being of black South African grandmothers raising grandchildren has been well documented, few studies have systemically investigated the sources, types, and patterns of use of social capital in this population. The aim of the current qualitative study is to use the social capital framework to explore how 75 grandmothers accessed and utilized available social capital (i.e., bridging, bonding, and linking) to enhance their own and their families' well-being. Results from a thematic analysis revealed an inside-out pattern of social capital; bonding and bridging social capital were the most significant sources of immediate support for grandmothers, with relatives providing emotional and instrumental support and neighbors and friends mainly providing informational support. Linking capital (i.e., government and community leaders) helped grandmothers access public resources. Implications for policy and practice are addressed.
Computation of Atmospheric Entry Flow about a Leonid Meteoroid

The flow field is computed around a 1 cm diameter Leonid meteoroid as it enters the Earth's atmosphere at an altitude of 95 km and a velocity of 72 km/s. These conditions correspond to a Knudsen number of 4 and a Mach number of 270. To accurately compute the gas flow, these extreme nonequilibrium conditions require application of a kinetic approach and the present work employs the direct simulation Monte Carlo method. A meteoroid ablation model is included in the computations and is found to play a significant role. The computational results predict that a large region of the flow field is affected by meteoroid ablation that produces an extended wake at high temperature in a sate of thermal equilibrium. These findings are in qualitative agreement with spectroscopic observations of the 1998 Leonid meteoroid shower. The computations indicate that the results are sensitive to the material properties assumed for the meteoroid.
A simplicial construction of G-equivariant Floer homology

For G a Lie group acting on a symplectic manifold $(M,\omega)$ preserving a pair of Lagrangians $L_0$, $L_1$, under certain hypotheses not including equivariant transversality we construct a G-equivariant Floer cohomology of $L_0$ and $L_1$.
Fractional Local Dimension

The original notion of dimension for posets was introduced by Dushnik and Miller in 1941 and has been studied extensively in the literature. In 1992, Brightwell and Scheinerman developed the notion of fractional dimension as the natural linear programming relaxation of the Dushnik-Miller concept. In 2016, Ueckerdt introduced the concept of local dimension, and in just three years, several research papers studying this new parameter have been published. In this paper, we introduce and study fractional local dimension. As suggested by the terminology, our parameter is a common generalization of fractional dimension and local dimension. For a pair (n, d) with 2 ≤ d < n, we consider the poset P(1, d; n) consisting of all 1-element and d-element subsets of $\{1,\dots ,n\}$ partially ordered by inclusion. This poset has fractional dimension d + 1, but for fixed d ≥ 2, its local dimension goes to infinity with n. On the other hand, we show that as n tends to infinity, the fractional local dimension of P(1,d; n) tends to a value FLD(d) which we will be able to determine exactly. For all d ≥ 2, FLD(d) is strictly less than d + 1, and for large d, $\text{FLD} (d)\sim d/(\log d-\log \log d-o(1))$ . As an immediate corollary, we show that if P is a poset, and d is the maximum degree of a vertex in the comparability graph of P, then the fractional local dimension of P, is at most 2 + FLD(d). Our arguments use both discrete and continuous methods.