cssi/SciSciGPT-SciSciNet huggingface dataset:
Theories and Contemporary Development of Organizational Perspectives in Social Sciences. Feminist approach to organizational analysis. Part 3

This paper is purely theoretical in which I have illustrated the contributions of the founding theorist of Western sociology, by focusing on how they addressed (or didn’t address) organizations. Then, I have discussed (in brief) the development of organizational theory and how organizational theorists are responding to the emergence of challenges to the traditional rational approaches to understanding organizations. These analyses are situated on the historical contexts include major contributions of each theorist. This research is solely based on the secondary information. Paper contents four Sections: first the work of the three founding theorists of Western Sociology, Karl Marx; Emile Durkheim and Max Weber, secondly, I have exemplified the development of organizational theory and the emergence of challenges to the traditional rational approaches to understand the organization; where I have analyzed the work of Classical theorists- Max Weber, Henri Fayol, Frederick Taylor, Luther Halsey Gulick, Herbert A. Simon, Berton H. Kaplan, modern theorist- Michel Foucault, Jurgen Habermas, Jacques Derrida, Jurgen Habermas etc. Third Section covers the contemporary theories and perspectives. In this section I have exemplified how Philip Selznick, Peter Blau, James David Thompson and Charles Perrow incorporated the Weber notion of bureaucracy followed by DiMaggio, Paul, J. and Walter W. Powell etc. and in forth section, covers the feminist approach in theory building with focus of organizational analysis (with the focus of Arlene Daniels, Dorothy Smith, Marjorie DeVault, Gisela Bock and Susan James, Martha Calas, Linda Smircich etc. work). This paper has detailed footnotes quoted from the original sources and contents useful reference of the sociological theory and practices for concerned social scientist to build their knowledge base and research direction.
LncRNA GAS5 attenuates fibroblast activation through inhibiting Smad3 signaling.

Transforming growth factor-β (TGF-β)-induced fibroblast activation is a key pathological event during tissue fibrosis. Long noncoding RNA (lncRNA) is a class of versatile gene regulators participat...
Chapter 4 – Light

Publisher Summary Light is often defined as electromagnetic radiation that is perceivable by the human eye. However, the word light is frequently used to refer to a wider range of electromagnetic radiation, which refers to propagating waves that have both electric and magnetic components. The chapter begins with the discussion of the range of electromagnetic radiation important in biology, including the subdivisions into various wavelength intervals. The wavelength of light is shown to be inversely proportional to its energy. After noting various conventions used to describe and measure radiation, the chapter briefly considers some of the characteristics of solar radiation reaching the earth. The chapter is primarily concerned with the physical nature of light and the mechanism of light absorption by molecules. It discusses how molecular states excited by light absorption can promote endergonic (energy-requiring) reactions or be dissipated by other deexcitation processes.
Stochastic and Distributionally Robust Load Ensemble Control

Demand response (DR) programs aim to engage distributed demand-side resources in providing ancillary services for electric power systems. Previously, aggregated thermostatically controlled loads (TCLs) have been demonstrated as a technically viable and economically valuable provider of such services that can effectively compete with conventional generation resources in reducing load peaks and smoothing demand fluctuations. Yet, to provide these services at scale, a large number of TCLs must be accurately aggregated and operated in sync. This paper describes a Markov Decision Process (MDP) that aggregates and models an ensemble of TCLs. Using the MDP framework, we propose to internalize the exogenous uncertain dynamics of TCLs by means of stochastic and distributionally robust optimization. First, under mild assumptions on the underlying uncertainty, we derive analytical stochastic and distributionally robust control policies for dispatching a given TCL ensemble. Second, we further relax these mild assumptions to allow for a more delicate treatment of uncertainty, which leads to distributionally robust MDP formulations with moment- and Wasserstein-based ambiguity sets that can be efficiently solved numerically. The case study compares the analytical and numerical control policies using a simulated ensemble of 1,000 air conditioners.
The rings of Saturn

Consideration is given to the development of theories concerning the rings of Saturn. Particular attention is given to ring structure, noting its thinness, the separations between rings, and observed variations in brightness. Data gathered via infrared, radio and radar techniques are described in terms of ring particle composition and size. Hypotheses about ring origin and evolution are outlined, including the tidal disruption model, calculations of Saturn's gravitational contraction history, grazing, and meteoroid bombardment. Prospects for future observations of Saturn's rings are reviewed, such as the variation in their radar reflectivity as a function of the tilt of the ring plane.
Self-Selection and International Migration: New Evidence from Mexico

This paper examines the selection of Mexican migrants to the United States using novel data with rich premigration characteristics that include permanent migrants, return migrants, and migrating households. Results indicate that Mexican migrants are more likely to be young, male, and from rural areas compared to nonmigrants, but they are similar to nonmigrants in cognitive ability and health. Migrants are selected from the middle of the education distribution. Male Mexican migrants are negatively selected on earnings, and this result is largely explained by differential returns to labor market skill between the United States and Mexico rather than proxies for differential costs of migration.
Computer-based Testing†

Most broadly defined, computer-based tests (CBTs) include not just tests administered on computers or workstations but also exams delivered via telephones, PDAs, and other electronic devices. There have been three main reasons for test developers to move beyond conventional paper and pencil administration. The first is to change the nature of what is being measured. The second is to improve measurement precision or efficiency. The third is to make test administration more convenient for examinees, test sponsors, or both. This entry details some of the advantages of CBTs relative to conventional paper and pencil tests, and describes some of the methods used for test administration and scoring. Keywords: computer-based testing; tailored testing
A symmetrization inequality shorn of symmetry

An inequality of Brascamp-Lieb-Luttinger and of Rogers states that among subsets of Euclidean space $\mathbb{R}^d$ of specified Lebesgue measures, balls centered at the origin are maximizers of certain functionals defined by multidimensional integrals. For $d>1$, this inequality only applies to functionals invariant under a diagonal action of $\text{Sl}(d)$. We investigate functionals of this type, and their maximizers, in perhaps the simplest situation in which $\text{Sl}(d)$ invariance does not hold. Assuming a more limited symmetry involving dilations but not rotations, we show under natural hypotheses that maximizers exist, and moreover, that there exist distinguished maximizers whose structure reflects this limited symmetry. For small perturbations of the $\text{Sl}(d)$--invariant framework we show that these distinguished maximizers are strongly convex sets with infinitely differentiable boundaries. It is shown that maximizers fail to exist for certain arbitrarily small perturbations of $\text{Sl}(d)$--invariant structures.
Heterogeneous Impact Dynamics of a Rural Business Development Program in Nicaragua

Abstract We study the impacts of a rural development program designed to boost the income of the small-farm sector in Nicaragua. Exploiting the random assignment of treatment, we find statistically and economically significant impacts on gross farm income and investment in productive farm capital. Using continuous treatment estimation techniques, we examine the evolution of program impacts over time and find that the estimated income increase persists and that the impacts on productive capital stock continue to rise even after the program concluded. Additionally, panel quantile methods reveal striking heterogeneity of program impacts on both income and investment. We show that this heterogeneity is not random and that there appear to exist low-performing household types who benefit little from the program, whereas high-performing (upper quantile) households benefit more substantially. Analysis using generalized random forests, a machine learning algorithm, points toward greater program impacts for households who were disadvantaged at baseline. Even after controlling for this source of heterogeneity, we find large and persistent differences in how much different types of households benefited from the program. While the benefit-cost ratio of the program is on average positive, the impact heterogeneity suggests that business development programs aiming to engage farm households as agricultural entrepreneurs have limitations as instruments to eliminate rural poverty.
Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model

Crystal plasticity theory is often employed to predict the mesoscopic states of poly- crystalline metals, and is well-known to be costly to simulate. Using a neural network with convolutional layers encoding correlations in time and space, we were able to pre- dict the evolution of the dominant component of the stress field given only the initial microstructure and external loading. In comparison to our recent work, we were able to predict not only the spatial average of the stress response but the evolution of the field itself. We show that the stress fields and their rates are in good agreement with the two dimensional crystal plasticity data and have no visible artifacts. Furthermore the distribution of stress throughout the elastic to fully plastic transition match the truth provided by held out crystal plasticity data. Lastly we demonstrate the efficacy of the trained model in material characterization and optimization tasks.
Iterative Computation of Security Strategies of Matrix Games with Growing Action Set

This paper studies how to efficiently update the saddle-point strategy, or security strategy of one player in a matrix game when the other player develops new actions in the game. It is well known that the saddle-point strategy of one player can be computed by solving a linear program. Developing a new action will add a new constraint to the existing LP. Therefore, our problem becomes how to efficiently solve the new LP with a new constraint. Considering the potentially huge number of constraints, which corresponds to the large size of the other player’s action set, we use the shadow vertex simplex method, whose computational complexity is lower than linear with respect to the size of the constraints, as the basis of our iterative algorithm. We first rebuild the main theorems in the shadow vertex method with a relaxed non-degeneracy assumption to make sure such a method works well in our model, then analyze the probability that the old optimum remains optimal in the new LP, and finally provide the iterative shadow vertex method whose average computational complexity is shown to be strictly less than that of the shadow vertex method. The simulation results demonstrate our main results about the probability of re-computing the optimum and the computational complexity of the iterative shadow vertex method.
The great pandemic of 2020: A defining moment for Heart Rhythm Societies and their members.

The pandemic caused by SARS-CoV-2 has affected communities throughout the world. The global nature of health care disparities is exacerbated by COVID-19. Patients in Low-and Middle-Income Countries have limited health care resources and marginal support for the evaluation and treatment of cardiac rhythm disorders. Heart Rhythm Societies and their members need to advocate for increased subsidies and assistance for these patients.
Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

Abstract To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.
Adaptive evolution among cytoplasmic piRNA proteins leads to decreased genomic auto-immunity

In metazoan germlines, the piRNA pathway acts as a genomic immune system, employing small RNA-mediated silencing to defend host DNA from the harmful effects of transposable elements (TEs). Expression of genomic TEs is proposed to initiate self regulation by increasing the production of repressive piRNAs, thereby "adapting" piRNA-mediated control to the most active TE families. Surprisingly, however, piRNA pathway proteins, which execute piRNA biogenesis and enforce silencing of targeted sequences, evolve rapidly and adaptively in animals. If TE silencing is ensured through piRNA biogenesis, what necessitates changes in piRNA pathway proteins? Here we used interspecific complementation to test for functional differences between Drosophila melanogaster and D. simulans alleles of three adaptively evolving piRNA pathway proteins: Armitage, Aubergine and Spindle-E. In contrast to piRNA-mediated transcriptional regulators examined in previous studies, these three proteins have cytoplasmic functions in piRNA maturation and post-transcriptional silencing. Across all three proteins we observed interspecific divergence in the regulation of only a handful of TE families, which were more robustly silenced by the heterospecific piRNA pathway protein. This unexpected result suggests that unlike transcriptional regulators, positive selection has not acted on cytoplasmic piRNA effector proteins to enhance their function in TE repression. Rather, TEs may evolve to "escape" silencing by host proteins. We further discovered that D. simulans alleles of aub and armi exhibit enhanced off-target effects on host transcripts in a D. melanogaster background, as well as modest reductions in the efficiency of piRNA biogenesis, suggesting that promiscuous binding of D. simulans Aub and Armi proteins to host transcripts reduces their participation in piRNA production. Avoidance of genomic auto-immunity may therefore be a critical target of selection. Our observations suggest that piRNA effector proteins are subject to an evolutionary trade-off between defending the host genome from the harmful effect of TEs while also minimizing collateral damage to host genes.
Background Matting: The World Is Your Green Screen

We propose a method for creating a matte – the per-pixel foreground color and alpha – of a person by taking photos or videos in an everyday setting with a handheld camera. Most existing matting methods require a green screen background or a manually created trimap to produce a good matte. Automatic, trimap-free methods are appearing, but are not of comparable quality. In our trimap free approach, we ask the user to take an additional photo of the background without the subject at the time of capture. This step requires a small amount of foresight but is far less timeconsuming than creating a trimap. We train a deep network with an adversarial loss to predict the matte. We first train a matting network with a supervised loss on ground truth data with synthetic composites. To bridge the domain gap to real imagery with no labeling, we train another matting network guided by the first network and by a discriminator that judges the quality of composites. We demonstrate results on a wide variety of photos and videos and show significant improvement over the state of the art.
Coresets for clustering in Euclidean spaces: importance sampling is nearly optimal

Given a collection of n points in ℝ d , the goal of the (k,z)-clustering problem is to find a subset of k “centers” that minimizes the sum of the z-th powers of the Euclidean distance of each point to the closest center. Special cases of the (k,z)-clustering problem include the k-median and k-means problems. Our main result is a unified two-stage importance sampling framework that constructs an e-coreset for the (k,z)-clustering problem. Compared to the results for (k,z)-clustering in [Feldman and Langberg, STOC 2011], our framework saves a e2 d factor in the coreset size. Compared to the results for (k,z)-clustering in [Sohler and Woodruff, FOCS 2018], our framework saves a poly(k) factor in the coreset size and avoids the exp(k/e) term in the construction time. Specifically, our coreset for k-median (z=1) has size O(e−4 k) which, when compared to the result in [Sohler and Woodruff, STOC 2018], saves a k factor in the coreset size. Our algorithmic results rely on a new dimensionality reduction technique that connects two well-known shape fitting problems: subspace approximation and clustering, and may be of independent interest. We also provide a size lower bound of Ω(k· min{2 z/20,d }) for a 0.01-coreset for (k,z)-clustering, which has a linear dependence of size on k and an exponential dependence on z that matches our algorithmic results.
Environmental and Genetic Contributions to Imperfect wMel-Like Wolbachia Transmission and Frequency Variation.

Maternally transmitted Wolbachia bacteria infect about half of all insect species. They usually show imperfect maternal transmission and often produce cytoplasmic incompatibility (CI). Irrespective of CI, Wolbachia frequencies tend to increase when rare only if they benefit host fitness. Several Wolbachia, including wMel that infects Drosophila melanogaster, cause weak or no CI and persist at intermediate frequencies. On the island of Sao Tome off West Africa, the frequencies of wMel-like Wolbachia infecting Drosophila yakuba (wYak) and Drosophila santomea (wSan) fluctuate, and the contributions of imperfect maternal transmission, fitness effects, and CI to these fluctuations are unknown. We demonstrate spatial variation in wYak frequency and transmission on Sao Tome. Concurrent field estimates of imperfect maternal transmission do not predict spatial variation in wYak frequencies, which are highest at high altitudes where maternal transmission is the most imperfect. Genomic and genetic analyses provide little support for D. yakuba effects on wYak transmission. Instead, rearing at cool temperatures reduces wYak titer and increases imperfect transmission to levels observed on Sao Tome. Using mathematical models of Wolbachia frequency dynamics and equilibria, we infer that temporally variable imperfect transmission or spatially variable effects on host fitness and reproduction are required to explain wYak frequencies. In contrast, spatially stable wSan frequencies are plausibly explained by imperfect transmission, modest fitness effects, and weak CI. Our results provide insight into causes of wMel-like frequency variation in divergent hosts. Understanding this variation is crucial to explain Wolbachia spread and to improve wMel biocontrol of human disease in transinfected mosquito systems.
CREBBP and STAT6 co-mutation and 16p13 and 1p36 loss define the t(14;18)-negative diffuse variant of follicular lymphoma

The diffuse variant of follicular lymphoma (dFL) is a rare variant of FL lacking t(14;18) that was first described in 2009. In this study, we use a comprehensive approach to define unifying pathologic and genetic features through gold-standard pathologic review, FISH, SNP-microarray, and next-generation sequencing of 16 cases of dFL. We found unique morphologic features, including interstitial sclerosis, microfollicle formation, and rounded nuclear cytology, confirmed absence of t(14;18) and recurrent deletion of 1p36, and showed a novel association with deletion/CN-LOH of 16p13 (inclusive of CREBBP, CIITA, and SOCS1). Mutational profiling demonstrated near-uniform mutations in CREBBP and STAT6, with clonal dominance of CREBBP, among other mutations typical of germinal-center B-cell lymphomas. Frequent CREBBP and CIITA codeletion/mutation suggested a mechanism for immune evasion, while subclonal STAT6 activating mutations with concurrent SOCS1 loss suggested a mechanism of BCL-xL/BCL2L1 upregulation in the absence of BCL2 rearrangements. A review of the literature showed significant enrichment for 16p13 and 1p36 loss/CN-LOH, STAT6 mutation, and CREBBP and STAT6 comutation in dFL, as compared with conventional FL. With this comprehensive approach, our study demonstrates confirmatory and novel genetic associations that can aid in the diagnosis and subclassification of this rare type of lymphoma.
A simple new approach to variable selection in regression, with application to genetic fine mapping

We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model—the ‘sum of single effects’ model, called ‘SuSiE’—which comes from writing the sparse vector of regression coefficients as a sum of ‘single‐effect’ vectors, each with one non‐zero element. We also introduce a corresponding new fitting procedure—iterative Bayesian stepwise selection (IBSS)—which is a Bayesian analogue of stepwise selection methods. IBSS shares the computational simplicity and speed of traditional stepwise methods but, instead of selecting a single variable at each step, IBSS computes a distribution on variables that captures uncertainty in which variable to select. We provide a formal justification of this intuitive algorithm by showing that it optimizes a variational approximation to the posterior distribution under SuSiE. Further, this approximate posterior distribution naturally yields convenient novel summaries of uncertainty in variable selection, providing a credible set of variables for each selection. Our methods are particularly well suited to settings where variables are highly correlated and detectable effects are sparse, both of which are characteristics of genetic fine mapping applications. We demonstrate through numerical experiments that our methods outperform existing methods for this task, and we illustrate their application to fine mapping genetic variants influencing alternative splicing in human cell lines. We also discuss the potential and challenges for applying these methods to generic variable‐selection problems.
The suboptimality of perceptual decision making with multiple alternatives

It is becoming widely appreciated that human perceptual decision making is suboptimal but the nature and origins of this suboptimality remain poorly understood. Most past research has employed tasks with two stimulus categories, but such designs cannot fully capture the limitations inherent in naturalistic perceptual decisions where choices are rarely between only two alternatives. We conduct four experiments with tasks involving multiple alternatives and use computational modeling to determine the decision-level representation on which the perceptual decisions are based. The results from all four experiments point to the existence of robust suboptimality such that most of the information in the sensory representation is lost during the transformation to a decision-level representation. These results reveal severe limits in the quality of decision-level representations for multiple alternatives and have strong implications about perceptual decision making in naturalistic settings. What sensory information is available for decision making? Here, using multi-alternative decisions, the authors show that a substantial amount of information from sensory representations is lost during the transformation to a decision-level representation.
Improving the Affordability of Robustness Training for DNNs

Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational complexity associated with this approach, due to the maximization of the loss function when finding adversaries, is a longstanding problem and may be prohibitive when using larger and more complex models. In this paper we show that the initial phase of adversarial training is redundant and can be replaced with natural training which significantly improves the computational efficiency. We demonstrate that this efficiency gain can be achieved without any loss in accuracy on natural and adversarial test samples. We support our argument with insights on the nature of the adversaries and their relative strength during the training process. We show that our proposed method can reduce the training time by a factor of up to 2.5 with comparable or better model test accuracy and generalization on various strengths of adversarial attacks.
A Near-Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

U.S. National Science FoundationNational Science Foundation (NSF) [CNS-1737424, DEB-1753639, DBI-1933016, DEB-1926050, DEB-1926388]; Virginia Tech Global Change Center; Fralin Life Sciences Institute
Fear conditioning prompts sparser representations of conditioned threat in primary visual cortex.

Repeated exposure to threatening stimuli alters sensory responses. We investigated the underlying neural mechanism by re-analyzing previously published simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) data from humans viewing oriented gratings during Pavlovian fear conditioning. In acquisition, one grating (CS+) was paired with a noxious noise, the unconditioned stimulus (US). The other grating (CS-) was never paired with the US. In habituation, which preceded acquisition, and in extinction, the same two gratings were presented without US. Using fMRI multivoxel patterns in primary visual cortex during habituation as reference, we found that during acquisition, aversive learning selectively prompted systematic changes in multivoxel patterns evoked by CS+. Specifically, CS+ evoked voxel patterns in V1 became sparser as aversive learning progressed, and the sparsified pattern appeared to be preserved in extinction. Concomitant with the voxel pattern changes, occipital alpha oscillations were increasingly more desynchronized during CS+ (but not CS-) trials. Across acquisition trials, the rate of change in CS+-related alpha desynchronization was correlated with the rate of change in multivoxel pattern representations of CS+. Furthermore, alpha oscillations co-varied with blood-oxygen-level-dependent (BOLD) data in the ventral attention network, but not with BOLD in the amygdala. Thus, fear conditioning prompts persistent sparsification of voxel patterns evoked by threat, likely mediated by attention-related mechanisms.
Littoral sediment from rivers: Patterns, rates and processes of river mouth morphodynamics

Rivers provide important sediment inputs to many littoral cells, thereby replenishing sand and gravel of beaches around the world. However, there is limited information about the patterns and processes of littoral-grade sediment transfer from rivers into coastal systems. Here I address these information gaps by examining topographic and bathymetric data of river mouths and constructing sediment budgets to characterize time-dependent patterns of onshore, offshore, and alongshore transport. Two river deltas, which differ in their morphology, were used in this study: the Elwha River, Washington, which builds a mixed sediment Gilbert-style delta, and the Santa Clara River, California, which builds a cross-shore dispersed sand delta from hyperpycnal flows. During and after sediment discharge events, both systems exhibited a similar evolution composed of three phases: (i) submarine delta growth during offshore transport of river sediment, (ii) onshore-dominated transport from the submarine delta to a subaerial river mouth berm, and (iii) longshore-dominated transport away from the river mouth following subaerial berm development. Stage (ii) occurred within days to weeks for the systems studied and was associated with the greatest rates of net erosion and deposition. These morphodynamics were similar to simple equilibrium profile concepts that included an onshore-dominated cross-shore transport rule. Additionally, both study sites revealed that littoral-grade sediment was initially exported to depths beyond the active littoral cell (i.e., below the depth of closure) during the stage (i). Following several years of reworking by coastal processes, bathymetric surveys suggested that 14% and 46% of the original volume of littoral-grade sediment discharged by the Santa Clara and Elwha Rivers, respectively, continued to be below the depth of closure. This provides evidence that the full volume of littoral-grade sediment discharged by small rivers may not be integrated into littoral cells because of sand and gravel ‘losses’ to the continental shelf.
Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) is a powerful profiling technique at the single-cell resolution. Appropriate analysis of scRNA-seq data can characterize molecular heterogeneity and shed light into the underlying cellular process to better understand development and disease mechanisms. The unique analytic challenge is to appropriately model highly over-dispersed scRNA-seq count data with prevalent dropouts (zero counts), making zero-inflated dimensionality reduction techniques popular for scRNA-seq data analyses. Employing zero-inflated distributions, however, may place extra emphasis on zero counts, leading to potential bias when identifying the latent structure of the data. In this paper, we propose a fully generative hierarchical gamma-negative binomial (hGNB) model of scRNA-seq data, obviating the need for explicitly modeling zero inflation. At the same time, hGNB can naturally account for covariate effects at both the gene and cell levels to identify complex latent representations of scRNA-seq data, without the need for commonly adopted pre-processing steps such as normalization. Efficient Bayesian model inference is derived by exploiting conditional conjugacy via novel data augmentation techniques. Experimental results on both simulated data and several real-world scRNA-seq datasets suggest that hGNB is a powerful tool for cell cluster discovery as well as cell lineage inference.
Deep Water Cycling and the Multi-Stage Cooling of the Earth

Paleo-temperature data indicates that the Earth’s mantle did not cool at a constant rate over geologic time. The data are consistent with slow cooling from 3.8 to ~2.5 billion years ago with a tran...
Water purification performance and energy consumption of gradient nanocomposite membranes

Abstract Reverse Osmosis (RO) membrane-based desalination technology accounts for 2/3 water desalination installation capacity all over the world but suffers from intensive energy consumption, where the membrane filtration process accounts for ~71% of total energy consumption. In this research, gradient membranes were designed and assembled with graphene oxide and celluloses laminates, and then used for RO desalination. The water flowing path was tuned by graphene oxide sizes while salt/water separation was tailored by the graphene oxide interlayer crosslinking. The resultant gradient nanocomposite membranes demonstrated a water yield as high as 21.34 L h−1 m−2 bar−1 with a salt rejection rate of 96.08% when the graphene oxide lateral size was reduced to 300 nm, and such a yield was 5 times higher than that of current commercial RO membranes. In addition, such a gradient nanocomposite membrane also resulted in a 35.8% energy-saving in the membrane filtration process. Furthermore, the gradient nanoporous structure also exhibits decent scalability and long-time stability.
Posterior Sampling for Anytime Motion Planning on Graphs with Expensive-to-Evaluate Edges

Collision checking is a computational bottleneck in motion planning, requiring lazy algorithms that explicitly reason about when to perform this computation. Optimism in the face of collision uncertainty minimizes the number of checks before finding the shortest path. However, this may take a prohibitively long time to compute, with no other feasible paths discovered during this period. For many real-time applications, we instead demand strong anytime performance, defined as minimizing the cumulative lengths of the feasible paths yielded over time. We introduce Posterior Sampling for Motion Planning (PSMP), an anytime lazy motion planning algorithm that leverages learned posteriors on edge collisions to quickly discover an initial feasible path and progressively yield p shorter paths. PSMP obtains an expected regret bound of $\tilde O(\sqrt {\mathcal{S}\mathcal{A}T} )$ and outperforms comparative baselines on a set of 2D and 7D planning problems.
New multivariate tests for assessing covariate balance in matched observational studies

We propose new tests for assessing whether covariates in a treatment group and matched control group are balanced in observational studies. The tests exhibit high power under a wide range of multivariate alternatives, some of which existing tests have little power for. The asymptotic permutation null distributions of the proposed tests are studied and the P-values calculated through the asymptotic results work well in simulation studies, facilitating the application of the test to large data sets. The tests are illustrated in a study of the effect of smoking on blood lead levels. The proposed tests are implemented in an R package BalanceCheck.
AGATHA: Automatic Graph Mining And Transformer based Hypothesis Generation Approach

Medical research is risky and expensive. Drug discovery requires researchers to efficiently winnow thousands of potential targets to a small candidate set. However, scientists spend significant time and money long before seeing the intermediate results that ultimately determine this smaller set. Hypothesis generation systems address this challenge by mining the wealth of publicly available scientific information to predict plausible research directions. We present AGATHA, a deep-learning hypothesis generation system that learns a data-driven ranking criteria to recommend new biomedical connections. We massively validate our system with a temporal holdout wherein we predict connections first introduced after 2015 using data published beforehand. We additionally explore biomedical sub-domains, and demonstrate AGATHA's predictive capacity across the twenty most popular relationship types. Furthermore, we perform an ablation study to examine the aspects of our semantic network that most contribute to recommendation quality. Overall, AGATHA achieves best-in-class recommendation quality when compared to other hypothesis generation systems built to predict across all available biomedical literature. Reproducibility: All code, experimental data, and pre-trained models are available online: sybrandt.com/2020/agatha.
Origami Embedding of Piecewise-Linear Two-Manifolds

We show that any compact, orientable, piecewise-linear two-manifold with Euclidean metric can be realized as a flat origami, meaning a set of non-crossing polygons in Euclidean 2-space “plus layers”. This result implies a weak form of a theorem of Burago and Zalgaller: any orientable, piecewise-linear two-manifold can be embedded into Euclidean 3-space “nearly” isometrically. We also correct a mistake in our previously published construction for cutting any polygon out of a folded sheet of paper with one straight cut.
The signature of positive braids is linearly bounded by their first Betti number

We provide linear lower bounds for the signature of positive braids in terms of the three-genus of their braid closure. This yields linear bounds for the topological slice genus of knots that arise as closures of positive braids.
The Effect of Unionism on Productivity in Privately and Publicly Owned Hospitals and Nursing Homes

This paper examines the effect of unions on productivity within a sample of publicly and privately owned hospitals and nursing homes to determine whether public ownership influences union behavior. The results show that the productivity of union contractors is much greater in private than in public projects. Within the sample of private projects, the estimates of the union-nonunion productivity difference are generally positive but very imprecise.
Structure and evolution of pulsating hot subdwarfs

Hot subdwarfs are evolved low-mass stars that have survived core helium ignition and are now in (or recently finished with) the core helium burning stage. At the hot end of the Horizontal Branch (HB), many of these stars are multiperiodic pulsators. These pulsations have revealed details of their global and internal structure, and provide important constraints on the origin of hot HB stars. While many features of their structure deduced from seismic fits have confirmed what we expected from evolutionary considerations, there have been some surprises as well (© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
SLAC Microresonator Radio Frequency (SMuRF) Electronics for Read Out of Frequency-Division-Multiplexed Cryogenic Sensors

Large arrays of cryogenic sensors for various imaging applications ranging across x-ray, gamma-ray, cosmic microwave background, mm/sub-mm, as well as particle detection increasingly rely on superconducting microresonators for high multiplexing factors. These microresonators take the form of microwave SQUIDs that couple to transition-edge sensors or microwave kinetic inductance detectors. In principle, such arrays can be read out with vastly scalable software-defined radio using suitable FPGAs, ADCs and DACs. In this work, we share plans and show initial results for SLAC Microresonator Radio Frequency (SMuRF) electronics, a next-generation control and readout system for superconducting microresonators. SMuRF electronics are unique in their implementation of specialized algorithms for closed-loop tone tracking, which consists of fast feedback and feedforward to each resonator’s excitation parameters based on transmission measurements. Closed-loop tone tracking enables improved system linearity, a significant increase in sensor count per readout line, and the possibility of overcoupled resonator designs for enhanced dynamic range. Low-bandwidth prototype electronics were used to demonstrate closed-loop tone tracking on twelve 300-kHz-wide microwave SQUID resonators, spaced at $$\sim $$ 6 MHz with center frequencies $$\sim $$ 5–6 GHz. We achieve multi-kHz tracking bandwidth and demonstrate that the noise floor of the electronics is subdominant to the noise intrinsic in the multiplexer.
Quasi-dynamic load and battery sizing and scheduling for stand-alone solar system using mixed-integer linear programming

Considering the intermittency of renewable energy systems, a sizing and scheduling model is proposed for a finite number of static electric loads. The model objective is to maximize solar energy utilization with and without storage. For the application of optimal load size selection, the energy production of a solar photovoltaic is assumed to be consumed by a finite number of discrete loads in an off-grid system using mixed-integer linear programming. Additional constraints are battery charge and discharge limitations and minimum uptime and downtime for each unit. For a certain solar power profile the model outputs optimal unit size as well as the optimal scheduling for both units and battery charge and discharge (if applicable). The impact of different solar power profiles and minimum up and down time constraints on the optimal unit and battery sizes are studied. The battery size required to achieve full solar energy utilization decreases with the number of units and with increased flexibility of the units (shorter on and off-time). A novel formulation is introduced to model quasi-dynamic units that gradually start and stop and the quasi-dynamic units increase solar energy utilization. The model can also be applied to search for the optimal number of units for a given cost function.
Double-Disk Dark Matter

Abstract Based on observational tests of large scale structure and constraints on halo structure, dark matter is generally taken to be cold and essentially collisionless. On the other hand, given the large number of particles and forces in the visible world, a more complex dark sector could be a reasonable or even likely possibility. This hypothesis leads to testable consequences, perhaps portending the discovery of a rich hidden world neighboring our own. We consider a scenario that readily satisfies current bounds that we call Partially Interacting Dark Matter (PIDM). This scenario contains self-interacting dark matter, but it is not the dominant component. Even if PIDM contains only a fraction of the net dark matter density, comparable to the baryonic fraction, the subdominant component’s interactions can lead to interesting and potentially observable consequences. Our primary focus will be the special case of Double-Disk Dark Matter (DDDM), in which self-interactions allow the dark matter to lose enough energy to lead to dynamics similar to those in the baryonic sector. We explore a simple model in which DDDM can cool efficiently and form a disk within galaxies, and we evaluate some of the possible observational signatures. The most prominent signal of such a scenario could be an enhanced indirect detection signature with a distinctive spatial distribution. Even though subdominant, the enhanced density at the center of the galaxy and possibly throughout the plane of the galaxy (depending on precise alignment) can lead to large boost factors, and could even explain a signature as large as the 130 GeV Fermi line. Such scenarios also predict additional dark radiation degrees of freedom that could soon be detectable and would influence the interpretation of future data, such as that from Planck and from the Gaia satellite. We consider this to be the first step toward exploring a rich array of new possibilities for dark matter dynamics.
COMPOSITION OF LOW-REDSHIFT HALO GAS

Halo gas in low-z (z 106 K, respectively. Utilizing O VI λλ1032, 1038 absorption lines, the warm component is compared to observations, and agreement is found with respect to the galaxy-O VI line correlation, the ratio of the O VI line incidence rate in blue to red galaxies, and the amount of O VI mass in star-forming galaxies. A detailed account of the sources of warm halo gas (stellar feedback heating, gravitational shock heating, and accretion from the intergalactic medium), inflowing and outflowing warm halo gas metallicity disparities, and their dependencies on galaxy types and environment is also presented. With the warm component securely anchored, our simulations make the following additional predictions. First, cold gas is the primary component in inner regions with its mass comprising 50% of all gas within galactocentric radius r = (30, 150) kpc in (red, blue) galaxies. Second, at r > (30, 200) kpc in (red, blue) galaxies the hot component becomes the majority. Third, the warm component is a perpetual minority, with its contribution peaking at ~30% at r = 100-300 kpc in blue galaxies and never exceeding 5% in red galaxies. The significant amount of cold gas in low-z early-type galaxies, which was found in simulations and in agreement with recent observations (Thom et al.), is intriguing, as is the dominance of hot gas at large radii in blue galaxies.
Grand Challenges in Planetary Nebulae Studies: Binary Evolution and MHD

We review work on the evolution of planetary nebulae and proto-planetaries via magneto-rotational mechanisms showing that a dynamo generated magnetic field can produce the energy and momentum needed to drive pPN and PNe outflows. Angular momentum considerations lead to the conclusion that single stars can not support strong fields for long times. Thus we take the working hypothesis that most PN may form via binary stars. We propose that the grand challenge for PN studies is fully understanding the diverse physical processes at work in binary late stage evolution including the development of disks, fields and outflows.
Sequential bottomonium production at high temperatures

Bottomonium production in heavy ion collisions is modified compared with any simple extrapolation from elementary collisions. This modification is most likely caused by the presence of a deconfined system of quarks and gluons for times of several fm/c. In such a medium, bottomonium can be destroyed, but the constituent bottom quarks will likely stay spatially correlated due to small mean free paths in this system. With these facts in mind, we describe bottomonium formation with a coupled set of equations. A rate equation describes the destruction of \(\Upsilon (1S)\) particles, while a Langevin equation describes how the bottom quarks stay correlated for a sufficiently long time so that recombination into bottomonia is possible. We show that within this approach it is possible to understand the magnitude of \(\Upsilon (1S)\) suppression in heavy ion collisions and the larger suppression of the \(\Upsilon (2S)\) state, implying that the reduction in the ratio of \(\Upsilon (1S)/\Upsilon (2S)\) yield in heavy ion collision does not necessarily correspond to the sequential melting picture.
Activated Magnetospheres of Magnetars

Like the solar corona, the external magnetic field of magnetars is twisted by surface motions of the star. The twist energy is dissipated over time. We discuss the theory of this activity and its observational status. (1) Theory predicts that the magnetosphere tends to untwist in a peculiar way: a bundle of electric currents (the “j-bundle”) is formed with a sharp boundary, which shrinks toward the magnetic dipole axis. Recent observations of shrinking hot spots on magnetars are consistent with this behavior. (2) Continual discharge fills the j-bundle with e± plasma, maintaining a nonthermal corona around the neutron star. The corona outside a few stellar radii strongly interacts with the stellar radiation and forms a “radiatively locked” outflow with a high e± multiplicity. The locked plasma annihilates near the apexes of the closed magnetic field lines. (3) New radiative-transfer simulations suggest a simple mechanismthat shapes the observed X-ray spectrumfrom 0.1 keV to 1MeV: part of the thermal X-rays emitted by the neutron star are reflected from the outer corona and then upscattered by the inner relativistic outflow in the j-bundle, producing a beam of hard X-rays.