cssi/SciSciGPT-SciSciNet huggingface dataset:
Combining visible and infrared spectrum imagery using machine learning for small unmanned aerial system detection

There is an increasing demand for technology and solutions to counter commercial, off-the-shelf small unmanned aerial systems (sUAS). Advances in machine learning and deep neural networks for object detection, coupled with lower cost and power requirements of cameras, led to promising vision-based solutions for sUAS detection. However, solely relying on the visible spectrum has previously led to reliability issues in low contrast scenarios such as sUAS flying below the treeline and against bright sources of light. Alternatively, due to the relatively high heat signatures emitted from sUAS during ight, a long-wave infrared (LWIR) sensor is able to produce images that clearly contrast the sUAS from its background. However, compared to widely available visible spectrum sensors, LWIR sensors have lower resolution and may produce more false positives when exposed to birds or other heat sources. This research work proposes combining the advantages of the LWIR and visible spectrum sensors using machine learning for vision-based detection of sUAS. Utilizing the heightened background contrast from the LWIR sensor combined and synchronized with the relatively increased resolution of the visible spectrum sensor, a deep learning model was trained to detect the sUAS through previously difficult environments. More specifically, the approach demonstrated effective detection of multiple sUAS flying above and below the treeline, in the presence of heat sources, and glare from the sun. Our approach achieved a detection rate of 71.2 ± 8.3%, improving by 69% when compared to LWIR and by 30.4% when visible spectrum alone, and achieved false alarm rate of 2.7 ± 2.6%, decreasing by 74.1% and by 47.1% when compared to LWIR and visible spectrum alone, respectively, on average, for single and multiple drone scenarios, controlled for the same confidence metric of the machine learning object detector of at least 50%. With a network of these small and affordable sensors, one can accurately estimate the 3D position of the sUAS, which could then be used for elimination or further localization from more narrow sensors, like a fire-control radar (FCR). Videos of the solution's performance can be seen at https://sites.google.com/view/tamudrone-spie2020/.
Kappa-Slender Modules

For an arbitrary infinite cardinal $\kappa$, we define classes of coordinatewise $\kappa$-slender and tailwise $\kappa$-slender modules as well as related classes of $h\kappa$-modules and initiate a study of these classes.
Sequentially rationalizable choice with transitive rationales

Abstract In this note, we analyze a sequentially rationalizable choice model with a transitive rationale and a standard preference. The model in this note is more restrictive than the Rational Shortlist Method (RSM) model which is proposed in Manzini and Mariotti (2007) to capture cyclic behavior. Yet, a decision maker in our model exhibits cyclic behavior in general. We prove that the cyclicity of an indirectly revealed preference is exactly what distinguishes the RSM framework (a sequential choice model with a non-standard preference) from our transitive-RSM framework (a sequential choice model with a standard preference). We also provide a partial identification result on the representation.
High-Definition Television†

A high-definition television (HDTV) system is a new television system that delivers spectacular video and compact-disc quality sound. The system also has many features that are absent in conventional systems, such as data channels and interoperability with computers and telecommunications networks. This article summarizes the characteristics of an HDTV system, the history of its standardization in the United States and the enabling technologies. Keywords: high-definition television; HDTV; digital television
Chapter 2 – Challenges in Handling Fresh Fruits and Vegetables

Publisher Summary This chapter highlights the challenges in postharvest handling that affect consumer acceptability. The challenges include stress physiology, quality management, marketing, and food safety. Almost any handling technique used to keep harvested crops fresh for an extended period of time causes some stress to that tissue. Temperature extremes, desiccation, microbial invasion, gaseous atmosphere, light, and mechanical handling can all induce stress in a harvested fruit or vegetable. Certain fruits and vegetables are susceptible to disorders, such as chilling, freezing, and CO2 injury. Quality assurance is an integral part of most manufacturing industries, including food processing. There is less motivation to develop quality management programs for fresh produce than for other food items, partly because of the generic nature of produce marketing and the difficulty of applying principles developed for processed foods to living respiring tissue. Fresh produce is a major profit center for supermarket food chains. Fierce competition among chains is changing the merchandising of fresh items. With the exception of a few commodities, most fresh fruits and vegetables are marketed at retail in bulk displays without brand identification. Brands are used in marketing schemes of shippers directed at wholesale distributors, but whether brands will have an impact at retail distribution points is still uncertain. The growing demand for fresh fruits and vegetables by health-conscious consumers also results in increased concern about food safety. Media attention to the use of agricultural chemicals to maintain “cosmetic” quality of fresh produce has heightened this concern. It is not clear how much pesticide use can be reduced without loss of visual quality of fresh fruits and vegetables, nor is it clear how lower visual quality would affect consumption.
Reduction of Micro-Cracks in Large Diameter InxGa1-xSb Bulk Crystals

The ternary alloy, InxGa1-xSb, is a compound semiconductor of tunable bandgap in the range of 0.18 - 0.72 eV, making it useful for infrared optoelectronic devices. Utilizing a unique system based upon vertical Bridgman technique, large diameter (50 millimeter) InxGa1-xSb polycrystals of composition ranging in x from 0.015 to 0.988 have been grown. Methods of mixing the melt during solidification, including the accelerated crucible rotation technique (ACRT), have been used in conjunction with optimization of the furnace temperature gradient profile to significantly reduce micro-cracking in the crystal boules while accelerating the growth rate from less than a millimeter per hour to three millimeters per hour. In this paper, the experimental system and crystal growth parameters for a set of ternary experiments will be detailed. Scanning electron microscopy (SEM) images of vertical cross-sections of In0.15Ga0.85Sb samples show a 95.8 percent reduction in micro-cracking resulting from specific melt mixing schemes. The mechanism for micro-crack removal during bulk alloy growth will be discussed briefly.
Regions, Resources, and Economic Geography: Sources of U.S. Regional Comparative Advantage, 1880-1987

Abstract This paper estimates the Rybczynski equation matrix for the twenty two-digit U.S. manufacturing industries for various years between 1880 and 1987. As predicted by the standard general equilibrium theory of interregional trade, the regression estimates show that a consistent set of factor endowments explains a significant amount of the geographic distribution of manufacturing activities over time. Although these results do not rule out the importance of increasing returns, they do suggest certain limits on how increasing returns affect U.S. economic geography.
The Challenge of Distinguishing Cell–Cell Complexes from Singlet Cells in Non-Imaging Flow Cytometry and Single-Cell Sorting

Our recent work has highlighted that care needs to be taken when interpreting single cell data originating from flow cytometry acquisition or cell sorting: We found that doublets of T cells bound to other immune cells are often present in the live singlet gate of human peripheral blood samples acquired by flow cytometry. This hidden "contamination" generates atypical gene signatures of mixed cell lineage in what is assumed to be single cells, which can lead to data misinterpretation, such as the description of novel immune cell types. Here, based on the example of T cell-monocyte complexes, we identify experimental and data analysis strategies to help distinguishing between singlets and cell-cell complexes in non-imaging flow cytometry and single-cell sorting. We found robust molecular signatures in both T cell-monocyte and T cell-B cell complexes that can distinguish them from singlets at both protein and mRNA levels. Imaging flow cytometry with appropriate gating strategy (matching the one used in cell sorting) and direct microscopy imaging after cell sorting were the two methods of choice to detect the presence of cell-cell complexes in suspicious dual-expressing cells. We finally applied this knowledge to highlight the likely presence of T cell-B cell complexes in a recently published dataset describing a novel cell population with mixed T cell and B cell lineage properties. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
Expanding the search for galaxies at z ~7-10 with new NICMOS Parallel Fields

We have carried out a search for galaxies at z ~ 7-10 in ~14.4 arcmin^2 of new NICMOS parallel imaging taken in the Great Observatories Origins Deep Survey (5.9 arcmin^2), the Cosmic Origins Survey (7.2 arcmin^2), and SSA22 (1.3 arcmin^2). These images reach 5σ sensitivities of J_(110) = 26.0-27.5 (AB), and combined they increase the amount of deep near-infrared data by more than 60% in fields where the investment in deep optical data has already been made. We find no z > 7 candidates in our survey area, consistent with the Bouwens et al. measurements at z ~ 7 and 9 (over 23 arcmin^2), which predict 0.7 galaxies at z ~ 7 and 7 galaxies are missed by this survey, due to incompleteness from foreground contamination by faint sources. For the case of luminosity evolution, assuming a Schecter parameterization with a typical Φ_* = 10^(–3) Mpc^(–3), we find M^*> – 20.0 for z ~ 7 and M^*> – 20.7 for z ~ 9 (68% confidence). This suggests that the downward luminosity evolution of Lyman break galaxies continues to z ~ 7, although our result is marginally consistent with the z ~ 6 luminosity function of Bouwens et al. In addition, we present newly acquired deep MMT/Megacam imaging of the z ~ 9 candidate JD2325+1433, first presented in Henry et al. The resulting weak but significant detection at i' indicates that this galaxy is most likely an interloper at z ~ 2.7.
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions

We introduce a class of auto-encoder neural networks tailored to data from the natural exponential family (e.g., count data). The architectures are inspired by the problem of learning the filters in a convolutional generative model with sparsity constraints, often referred to as convolutional dictionary learning (CDL). Our work is the first to combine ideas from convolutional generative models and deep learning for data that are naturally modeled with a non-Gaussian distribution (e.g., binomial and Poisson). This perspective provides us with a scalable and flexible framework that can be re-purposed for a wide range of tasks and assumptions on the generative model. Specifically, the iterative optimization procedure for solving CDL, an unsupervised task, is mapped to an unfolded and constrained neural network, with iterative adjustments to the inputs to account for the generative distribution. We also show that the framework can easily be extended for discriminative training, appropriate for a supervised task. We demonstrate 1) that fitting the generative model to learn, in an unsupervised fashion, the latent stimulus that underlies neural spiking data leads to better goodness-of-fit compared to other baselines, 2) competitive performance compared to state-of-the-art algorithms for supervised Poisson image denoising, with significantly fewer parameters, and 3) gradient dynamics of shallow binomial auto-encoder.
Slc26a9P2ACre : a new CRE driver to regulate gene expression in the otic placode lineage and other FGFR2b-dependent epithelia.

Pan-otic CRE drivers enable gene regulation throughout the otic placode lineage, comprising the inner ear epithelium and neurons. However, intersection of extra-otic gene-of-interest expression with the CRE lineage can compromise viability and impede auditory analyses. Furthermore, extant pan-otic CREs recombine in auditory and vestibular brain nuclei, making it difficult to ascribe resulting phenotypes solely to the inner ear. We have previously identified Slc26a9 as an otic placode-specific target of the FGFR2b ligands FGF3 and FGF10. We show here that Slc26a9 is otic specific through E10.5, but is not required for hearing. We targeted P2ACre to the Slc26a9 stop codon, generating Slc26a9P2ACre mice, and observed CRE activity throughout the otic epithelium and neurons, with little activity evident in the brain. Notably, recombination was detected in many FGFR2b ligand-dependent epithelia. We generated Fgf10 and Fgf8 conditional mutants, and activated an FGFR2b ligand trap from E17.5 to P3. In contrast to analogous mice generated with other pan-otic CREs, these were viable. Auditory thresholds were elevated in mutants, and correlated with cochlear epithelial cell losses. Thus, Slc26a9P2ACre provides a useful complement to existing pan-otic CRE drivers, particularly for postnatal analyses.
Breaking the Curse of Many Agents: Provable Mean Embedding $Q$-Iteration for Mean-Field Reinforcement Learning

Multi-agent reinforcement learning (MARL) achieves significant empirical successes. However, MARL suffers from the curse of many agents. In this paper, we exploit the symmetry of agents in MARL. In the most generic form, we study a mean-field MARL problem. Such a mean-field MARL is defined on mean-field states, which are distributions that are supported on continuous space. Based on the mean embedding of the distributions, we propose MF-FQI algorithm that solves the mean-field MARL and establishes a non-asymptotic analysis for MF-FQI algorithm. We highlight that MF-FQI algorithm enjoys a "blessing of many agents" property in the sense that a larger number of observed agents improves the performance of MF-FQI algorithm.
Common Ownership and Corporate Social Responsibility

This paper studies the effect of common ownership on corporate social responsibility (CSR). We find that common ownership is positively associated with a firm's CSR score. The effect is stronger for firms in more competitive industries. We propose a two-stage duopoly game in which CSR serves as a commitment device to expand output aggressively to understand the empirical results.
Wave breaking in a class of non-local conservation laws

Abstract For models describing water waves, Constantin and Escher [3] 's works have long been considered as the cornerstone method for proving wave breaking phenomena. Their rigorous analytic proof shows that if the lowest slope of flows can be controlled by its highest slope initially, then the wave-breaking occur for the Whitham-type equation. Since this breakthrough, there have been numerous refined wave-breaking results established by generalizing the kernel which describes the dispersion relation of water waves. In this work, from a rich class of non-local conservation laws, a Riccati-type system that governs the flow's gradient is extracted and investigated. The system's leading coefficient functions are allowed to change their values and signs over time as opposed to the ones in many of other previous works are fixed constants. The blow-up analysis is illustrated via the Whitham-type equation with nonlinear drift.
Taxes, tariffs, and the global corporation

In this paper we develop some simple models of optimal tax and tariff policy in the presence of global corporations that operate in an imperfectly competitive environment. The models emphasize two important differences in the practical application of tax and tariff policy - tax, but not tariff, policy can apply to offshore output, and tariff, but not tax, policy can be industryspecific. Recognizing that multinationals’ production decisions are endogenous to the tax and tariff policies they face, we investigate how a government should tax (or subsidize) domestically owned firms and how government should set trade policy.
Using satellite imagery to understand and promote sustainable development

Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and improving resolution (spatial, temporal, and spectral) of satellite imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of model performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight research directions for the field.
A picture of pseudogap phase related to charge fluxes

Recently, charge density fluctuations or charge fluxes attract strong interests in understanding the unconventional superconductivity. In this paper, a new emergent configuration in cuprates is identified by density functional theory simulations, called the charge pseudoplane, which exhibits the property of confining the dynamic charge fluxes for higher superconducting transition temperatures. It further redefines the fundamental collective excitation in cuprates as pQon with the momentum-dependent and ultrafast localization-delocalization duality. It is shown that both pseudogap and superconducting phases can be born from and intertwined through the charge flux confinement property of the charge pseudoplane region. Our experimental simulations based on the new picture provide good agreements with previous angle resolved photoemission spectroscopy and scanning tunneling microscopy results. Our work thus opens a new perspective into the origin of the pseudogap phase and other related phases in cuprates, and further provides a critical descriptor to search and design higher temperature superconductors.
Simultaneous Detection of Multiple Change Points and Community Structures in Time Series of Networks

In many complex systems, networks and graphs arise in a natural manner. Often, time evolving behavior can be easily found and modeled using time-series methodology. Amongst others, two common research problems in network analysis are community detection and change-point detection. Community detection aims at finding specific sub-structures within the networks, and change-point detection tries to find the time points at which sub-structures change. We propose a novel methodology to detect both community structures and change points simultaneously based on a model selection framework in which the Minimum Description Length Principle (MDL) is utilized as minimizing objective criterion. The promising practical performance of the proposed method is illustrated via a series of numerical experiments and real data analysis.
Genetic hitchhiking, mitonuclear coadaptation, and the origins of mt DNA barcode gaps

DNA barcoding based on mitochondrial (mt) nucleotide sequences is an enigma. Neutral models of mt evolution predict DNA barcoding cannot work for recently diverged taxa, and yet, mt DNA barcoding accurately delimits species for many bilaterian animals. Meanwhile, mt DNA barcoding often fails for plants and fungi. I propose that because mt gene products must cofunction with nuclear gene products, the evolution of mt genomes is best understood with full consideration of the two environments that impose selective pressure on mt genes: the external environment and the internal genomic environment. Moreover, it is critical to fully consider the potential for adaptive evolution of not just protein products of mt genes but also of mt transfer RNAs and mt ribosomal RNAs. The tight linkage of genes on mt genomes that do not engage in recombination could facilitate selective sweeps whenever there is positive selection on any element in the mt genome, leading to the purging of mt genetic diversity within a population and to the rapid fixation of novel mt DNA sequences. Accordingly, the most important factor determining whether or not mt DNA sequences diagnose species boundaries may be the extent to which the mt chromosomes engage in recombination.
Elevated LiDAR Placement under Energy and Throughput Capacity Constraints

Elevated LiDAR (ELiD) has the potential to hasten the deployment of Autonomous Vehicles (AV), as ELiD can reduce energy expenditures associated with AVs, and can also be utilized for other intelligent Transportation Systems applications such as urban 3D mapping. In this paper, we address the need for a planning framework in order for ITS operators to have an effective tool for determining what resources are required to achieve a desired level of coverage of urban roadways. To this end, we develop a mixed-integer nonlinear constrained optimization problem, with the aim of maximizing effective area coverage of a roadway, while satisfying energy and throughput capacity constraints. Due to the non-linearity of the problem, we utilize Particle Swarm Optimization (PSO) to solve the problem. After demonstrating its effectiveness in finding a solution for a realistic scenario, we perform a sensitivity analysis to test the model’s general ability.
Mapping Road Safety Features from Streetview Imagery: A Deep Learning Approach

Each year, an average of around 6 million car accidents occur in the United States. Road safety features (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety features is an important component of safety management systems for federal or state transportation agencies, helping traffic engineers identify locations to invest in safety infrastructure. In current practice, mapping road safety features is largely done manually (e.g., observations on the road or visual interpretation of streetview imagery), which is both expensive and time consuming. In this article, we propose a deep learning approach to automatically map road safety features from streetview imagery. Unlike existing convolutional neural networks that classify each image individually, we propose to further add a recurrent neural network (long short-term memory) to capture geographic context of images (spatial autocorrelation effect along linear road network paths). Evaluations on real-world streetview imagery show that our proposed model outperforms several baseline methods.
Dilogarithm identities for solutions to Pell’s equation in terms of continued fraction convergents

We describe a new connection between the dilogarithm function and the solutions of Pell’s equation $$x^2-ny^2 = \pm 1$$ . For each solution x, y to Pell’s equation, we obtain a dilogarithm identity whose terms are given by the continued fraction expansion of the associated unit $$x+y\sqrt{n} \in {\mathbb {Z}}[\sqrt{n}]$$ . We further show that Ramanujan’s dilogarithm value-identities correspond to an identity for the regular ideal hyperbolic hexagon.
SPARK: A Framework for Multi-Scale Agent-Based Biomedical Modeling

Multi-scale modeling of complex biological systems remains a central challenge in the systems biology community. A method of dynamic knowledge representation known as agent-based modeling enables the study of higher level behavior emerging from discrete events performed by individual components. With the advancement of computer technology, agent-based modeling has emerged as an innovative technique to model the complexities of systems biology. In this work, the authors describe SPARK Simple Platform for Agent-based Representation of Knowledge, a framework for agent-based modeling specifically designed for systems-level biomedical model development. SPARK is a stand-alone application written in Java. It provides a user-friendly interface, and a simple programming language for developing Agent-Based Models ABMs. SPARK has the following features specialized for modeling biomedical systems: 1 continuous space that can simulate real physical space; 2 flexible agent size and shape that can represent the relative proportions of various cell types; 3 multiple spaces that can concurrently simulate and visualize multiple scales in biomedical models; 4 a convenient graphical user interface. Existing ABMs of diabetic foot ulcers and acute inflammation were implemented in SPARK. Models of identical complexity were run in both NetLogo and SPARK; the SPARK-based models ran two to three times faster.
A Hybrid FeMFET-CMOS Analog Synapse Circuit for Neural Network Training and Inference

An analog synapse circuit based on ferroelectric-metal field-effect transistors is proposed, that offers 6-bit weight precision. The circuit is comprised of volatile least significant bits (LSBs) used solely during training, and non-volatile most significant bits (MSBs) used for both training and inference. The design works at a 1.8V logic-compatible voltage, provides 1010 endurance cycles, and requires only 250ps update pulses. A variant of LeNet trained with the proposed synapse achieves 98.2% accuracy on MNIST, which is only 0.4% lower than an ideal implementation of the same network with the same bit precision. Furthermore, the proposed synapse offers improvements of up to 26% in area, 44.8% in leakage power, 16.7% in LSB update pulse duration, and two orders of magnitude in endurance cycles, when compared to state-of-the-art hybrid synaptic circuits. Our proposed synapse can be extended to an 8-bit design, enabling a VGG-like network to achieve 88.8% accuracy on CIFAR-10 (only 0.8% lower than an ideal implementation of the same network).
Keratin 17 regulates nuclear morphology and chromatin organization.

Keratin 17 (KRT17; K17), a non-lamin intermediate filament protein, was recently found to occur in the nucleus. We report here on K17-dependent differences in nuclear morphology, chromatin organization, and cell proliferation. Human tumor keratinocyte cell lines lacking K17 exhibit flatter nuclei relative to normal. Re-expression of wildtype K17, but not a mutant form lacking an intact nuclear localization signal (NLS), rescues nuclear morphology in KRT17 null cells. Analyses of primary cultures of skin keratinocytes from a mouse strain expressing K17 with a mutated NLS corroborated these findings. Proteomics screens identified K17-interacting nuclear proteins with known roles in gene expression, chromatin organization, and RNA processing. Key histone modifications and LAP2β localization within the nucleus are altered in the absence of K17, correlating with decreased cell proliferation and suppression of GLI1 target genes. Nuclear K17 thus impacts nuclear morphology with an associated impact on chromatin organization, gene expression, and proliferation in epithelial cells.
Simulation-based lidar super-resolution for ground vehicles

Abstract We propose a methodology for lidar super-resolution with ground vehicles driving on roadways, which relies completely on a driving simulator to enhance, via deep learning, the apparent resolution of a physical lidar. To increase the resolution of the point cloud captured by a sparse 3D lidar, we convert this problem from 3D Euclidean space into an image super-resolution problem in 2D image space, which is solved using a deep convolutional neural network. By projecting a point cloud onto a range image, we are able to efficiently enhance the resolution of such an image using a deep neural network. Typically, the training of a deep neural network requires vast real-world data. Our approach does not require any real-world data, as we train the network purely using computer-generated data. Thus our method is applicable to the enhancement of any type of 3D lidar theoretically. By novelly applying Monte-Carlo dropout in the network and removing the predictions with high uncertainty, our method produces high accuracy point clouds comparable with the observations of a real high resolution lidar. We present experimental results applying our method to several simulated and real-world datasets. We argue for the method’s potential benefits in real-world robotics applications such as occupancy mapping and terrain modeling.
SequelTools: a suite of tools for working with PacBio Sequel raw sequence data

PacBio sequencing is an incredibly valuable third-generation DNA sequencing method due to very long read lengths, ability to detect methylated bases, and its real-time sequencing methodology. Yet, hitherto no tool was available for analyzing the quality of, subsampling, and filtering PacBio data. Here we present SequelTools, a command-line program containing three tools: Quality Control, Read Subsampling, and Read Filtering. The Quality Control tool quickly processes PacBio Sequel raw sequence data from multiple SMRTcells producing multiple statistics and publication-quality plots describing the quality of the data including N50, read length and count statistics, PSR, and ZOR. The Read Subsampling tool allows the user to subsample reads by one or more of the following criteria: longest subreads per CLR or random CLR selection. The Read Filtering tool provides options for normalizing data by filtering out certain low-quality scraps reads and/or by minimum CLR length. SequelTools is implemented in bash, R, and Python using only standard libraries and packages and is platform independent. SequelTools is a program that provides the only free, fast, and easy-to-use quality control tool, and the only program providing this kind of read subsampling and read filtering for PacBio Sequel raw sequence data, and is available at https://github.com/ISUgenomics/SequelTools .
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development.

Background: The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies. Classic machine learning and artificial intelligence techniques cannot provide a full scientific understanding of the inner workings of the underlying models. This raises credibility issues due to the lack of transparency and generalizability. Explainable artificial intelligence is an emerging approach for promoting credibility, accountability, and trust in mission-critical areas such as medicine by combining machine-learning approaches with explanatory techniques that explicitly show what the decision criteria are and why (or how) they have been established. Hence, thinking about how machine learning could benefit from knowledge graphs that combine “common sense” knowledge as well as semantic reasoning and causality models is a potential solution to this problem. Objective: In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve mental health surveillance. Methods: We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology. Results: To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children’s hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation. Conclusions: This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners’ ability to provide explanations for the decisions they make.
Safety-Critical Control of Active Interventions for COVID-19 Mitigation

The world has recently undergone the most ambitious mitigation effort in a century, consisting of wide-spread quarantines aimed at preventing the spread of COVID-19. The use of influential epidemiological models of COVID-19 helped to encourage decision makers to take drastic non-pharmaceutical interventions. Yet, inherent in these models are often assumptions that the active interventions are static, e.g., that social distancing is enforced until infections are minimized, which can lead to inaccurate predictions that are ever evolving as new data is assimilated. We present a methodology to dynamically guide the active intervention by shifting the focus from viewing epidemiological models as systems that evolve in autonomous fashion to control systems with an “input” that can be varied in time in order to change the evolution of the system. We show that a safety-critical control approach to COVID-19 mitigation gives active intervention policies that formally guarantee the safe evolution of compartmental epidemiological models. This perspective is applied to current US data on cases while taking into account reduction of mobility, and we find that it accurately describes the current trends when time delays associated with incubation and testing are incorporated. Optimal active intervention policies are synthesized to determine future mitigations necessary to bound infections, hospitalizations, and death, both at national and state levels. We therefore provide means in which to model and modulate active interventions with a view toward the phased reopenings that are currently beginning across the US and the world in a decentralized fashion. This framework can be converted into public policies, accounting for the fractured landscape of COVID-19 mitigation in a safety-critical fashion.
Combining Observational and Experimental Data Using First-stage Covariates

Randomized controlled trials generate experimental variation that can credibly identify causal effects, but often suffer from limited scale, while observational datasets are large, but often violate desired identification assumptions. To improve estimation efficiency, I propose a method that combines experimental and observational datasets when 1) units from these two datasets are sampled from the same population and 2) some characteristics of these units are observed. I show that if these characteristics can partially explain treatment assignment in the observational data, they can be used to derive moment restrictions that, in combination with the experimental data, improve estimation efficiency. I outline three estimators (weighting, shrinkage, or GMM) for implementing this strategy, and show that my methods can reduce variance by up to 50% in typical experimental designs; therefore, only half of the experimental sample is required to attain the same statistical precision. If researchers are allowed to design experiments differently, I show that they can further improve the precision by directly leveraging this correlation between characteristics and assignment. I apply my method to a search listing dataset from Expedia that studies the causal effect of search rankings, and show that the method can substantially improve the precision.
Effect of length of surgery on the incidence of venous thromboembolism after benign hysterectomy.

Background Venous thromboembolism is a leading cause of morbidity and mortality postoperatively. The current venous thromboembolism risk assessment tools have not been validated in gynecologic patients. Most patients undergoing hysterectomy for benign indications will receive mechanical or pharmacologic prophylaxis based on preoperative risk assessment. However, current guidelines do not incorporate newer data that indicate additional risk of venous thromboembolism with prolonged surgery times or mode of hysterectomy. Objective This study aimed to determine the effect of length of surgery, or operative time, on the risk of venous thromboembolism within 30 days after hysterectomy and determine whether differences in the effect of operative time exist across age, body mass index, and surgical approach. Study Design We performed a secondary analysis of prospectively collected surgical quality improvement data using the American College of Surgeons National Surgical Quality Improvement Program database, which contains demographic and perioperative information and 30-day postoperative outcomes from >500 hospitals, and targeted data files including procedure-specific risk factors and outcomes for a subset of hospitals. We analyzed patients undergoing abdominal, vaginal, or laparoscopic hysterectomy for benign conditions from 2014 to 2017, identified by the Current Procedural Terminology codes. We excluded patients with cancer, patients whose surgery was not performed by a gynecologist, patients who were not in the targeted files, and patients with missing operative time or with an operative time of Results A total of 70,606 patients were included. The 30-day venous thromboembolism incidence was 0.4% (n=259). Patients with venous thromboembolism were more likely to be obese, have inpatient procedures, and had, on average, greater uterine weight. Hysterectomy approach was vaginal in 11,641 patients, laparoscopic in 41,557 patients, and abdominal in 17,408 patients. After adjustment, for each 60-minute increase in operative time, there was a 35% increase in the odds of venous thromboembolism (adjusted odds ratio, 1.35; 95% confidence interval, 1.25–1.45). Stratified by surgical approach, the odds of venous thromboembolism per 60-minute increase in operative time was greatest among abdominal hysterectomy (adjusted odds ratio, 1.49; 95% confidence interval, 1.35–1.65) compared with laparoscopic hysterectomy (adjusted odds ratio, 1.20; 95% confidence interval, 1.05–1.38) and vaginal hysterectomy (adjusted odds ratio, 1.27; 95% confidence interval, 0.97–1.66) (P=.01). Increasing body mass index and increasing age did not modify the impact of operative time on venous thromboembolism incidence (P=.66 and P=.58, respectively). Conclusion Every 60-minute increase in operative time was independently associated with a 35% increased odds of venous thromboembolism within 30 days of hysterectomy, and this risk was cumulative. Minimally invasive hysterectomy had lower odds of venous thromboembolism than abdominal hysterectomy across all time points.
Neuroanatomical underpinning of diffusion kurtosis measurements in the cerebral cortex of healthy macaque brains.

PURPOSE To investigate the neuroanatomical underpinning of healthy macaque brain cortical microstructure measured by diffusion kurtosis imaging (DKI), which characterizes non-Gaussian water diffusion. METHODS High-resolution DKI was acquired from 6 postmortem macaque brains. Neurofilament density (ND) was quantified based on structure tensor from neurofilament histological images of a different macaque brain sample. After alignment of DKI-derived mean kurtosis (MK) maps to the histological images, MK and histology-based ND were measured at corresponding regions of interests characterized by distinguished cortical MK values in the prefrontal/precentral-postcentral and temporal cortices. Pearson correlation was performed to test significant correlation between these cortical MK and ND measurements. RESULTS Heterogeneity of cortical MK across different cortical regions was revealed, with significantly and consistently higher MK measurements in the prefrontal/precentral-postcentral cortex compared to those in the temporal cortex across all six scanned macaque brains. Corresponding higher ND measurements in the prefrontal/precentral-postcentral cortex than in the temporal cortex were also found. The heterogeneity of cortical MK is associated with heterogeneity of histology-based ND measurements, with significant correlation between cortical MK and corresponding ND measurements (P < .005). CONCLUSION These findings suggested that DKI-derived MK can potentially be an effective noninvasive biomarker quantifying underlying neuroanatomical complexity inside the cerebral cortical mantle for clinical and neuroscientific research.
Functional evidence for biased inhibition of G protein signaling by YM-254890 in human coronary artery endothelial cells.

Abstract Small molecular chemicals targeting individual subtype of G proteins including Gs, Gi/o and Gq has been lacking, except for pertussis toxin being an established selective peptide inhibitor of the Gi/o protein. Recently, a cyclic depsipeptide compound YM-254890 isolated from culture broth of Chromobacterium sp. was reported as a selective inhibitor for the Gq protein by blocking GDP exchange of GTP on the α subunit of Gq complex. However, functional selectivity of YM-254890 towards various G proteins was not fully characterized, primarily due to its restricted availability before 2017. Here, using human coronary artery endothelial cells as a model, we performed a systemic pharmacological evaluation on the functional selectivity of YM-254890 on multiple G protein-mediated receptor signaling. First, we confirmed that YM-254890, at 30 nM, abolished UTP-activated P2Y2 receptor-mediated Ca2+ signaling and ERK1/2 phosphorylation, indicating its potent inhibition on the Gq protein. However, we unexpectedly found that YM-254890 also significantly suppressed cAMP elevation and ERK1/2 phosphorylation induced by multiple Gs-coupled receptors including β2-adrenegic, adenosine A2 and PGI2 receptors. Surprisingly, although YM-254890 had no impact on CXCR4/Gi/o protein-mediated suppression of cAMP production, it abolished ERK1/2 activation. Further, no cellular toxicity was observed for YM-254890, and it neither affected A23187- or thapsigargin-induced Ca2+ signaling, nor forskolin-induced cAMP elevation and growth factor-induced MAPK signaling. We conclude that YM-254890 is not a selective inhibitor for Gq protein; instead, it acts as a broad-spectrum inhibitor for Gq and Gs proteins and exhibits a biased inhibition on Gi/o signaling, without affecting non-GPCR-mediated cellular signaling.
Local Labor Markets and Welfare Spells: Do Demand Conditions Matter?

This paper examines the impact of changes in labor market conditions on participation in the Aid to Families with Dependent Children (AFDC) program in California. Transitions off welfare and transi...
Management and outcomes of severe pelvic fractures in level I and II ACS verified trauma centers.

Abstract Background The aim of this study was to evaluate the management strategies and outcomes of isolated severe pelvic fractures in level I and II ACS verified trauma centers. Methods ACS-TQIP database study, including patients with blunt, isolated severe pelvic facture (AIS 3–5). Results 2629 level I and 1277 level II patients were included. Early blood product transfusion was significantly higher, pharmacological VTE prophylaxis significantly lower and ICU length of stay significantly longer in level II centers (p Conclusions In isolated severe pelvic fractures there was a significantly higher use of early blood products, less VTE pharmacological prophylaxis, longer ICU length of stay and higher overall complications and ARDS in level II centers. Blood product utilization and pharmacological VTE prophylaxis are potential areas of quality improvement in level II centers.
Pruned and Structurally Sparse Neural Networks

Advances in designing and training deep neural networks have led to the principle that the large and deeper a network is, the better it can perform. As a result, computational resources have become a key limiting factor in achieving better performance. One strategy to improve network capabilities while decreasing computation required is to replace dense fully-connected and convolutional layers with sparse layers. In this paper we experiment with training on sparse neural network topologies. First, we test pruning-based sparse topologies, which use a network topology obtained by initially training a dense network and then pruning low-weight connections. Second, we test RadiX-Nets, a class of sparse network structures with proven connectivity and sparsity properties. Results show that compared to dense topologies, sparse structures show promise in training potential but also can exhibit highly nonlinear convergence, which merits further study.
Scalable Algorithms for Large Competing Risks Data

This article develops two orthogonal contributions to scalable sparse regression for competing risks time-to-event data. First, we study and accelerate the broken adaptive ridge method (BAR), a sur...
Millennials' adoption of personal financial management (PFM) technology and financial behavior

This paper uses the Technology Acceptance Model to analyze the factors associated with personal financial management technology (PFM) adoption among millennials and extends the analysis to understand how PFM adoption is associated with financial behavior. Data from the 2018 National Financial Capability Study was used for this analysis. Evidence suggests that millennials engaging in digital side hustles, such as Uber or Lyft, are significantly more likely to adopt PFM technology. Individuals experiencing higher financial pressure or exhibiting higher financial confidence are more likely to adopt PFM technology. After extending the analysis to include financial behavior, heavy adopters of PFM technology are more likely to own an emergency fund, payoff their credit card in full every month, own a retirement account, own an investment account, save for retirement, and own a will. Surprisingly, heavy adopters of PFM are also more likely to spend more than they earn and experience overdrafts. Full Text Available Here: https://doi.org/10.1002/cfp2.1095
netboxr: Automated discovery of biological process modules by network analysis in R

Summary Large-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have generated high throughput sequencing and molecular profiling data sets, but it is still challenging to identify potentially causal changes in cellular processes in cancer as well as in other diseases in an automated fashion. We developed the netboxr package written in the R programming language, which makes use of the NetBox algorithm to identify candidate cancer-related functional modules. The algorithm makes use of a data-driven, network-based approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of independently curated functionally labeled gene sets. The method can combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology. Availability and implementation The netboxr package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/netboxr.html.
The Canonical Luminous Blue Variable AG Car and Its Neighbor Hen 3-519 Are Much Closer than Previously Assumed

National Science Foundation (NSF) [AST-1210599, AST-1312221]; NSF PAARE [AST-1358862]; national institutions