cssi/SciSciGPT-SciSciNet huggingface dataset:
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud

Earlier work demonstrates the promise of deep-learning-based approaches for point cloud segmentation; however, these approaches need to be improved to be practically useful. To this end, we introduce a new model SqueezeSegV2. With an improved model structure, SqueezeSetV2 is more robust against dropout noises in LiDAR point cloud and therefore achieves significant accuracy improvement. Training models for point cloud segmentation requires large amounts of labeled data, which is expensive to obtain. To sidestep the cost of data collection and annotation, simulators such as GTA-V can be used to create unlimited amounts of labeled, synthetic data. However, due to domain shift, models trained on synthetic data often do not generalize well to the real world. Existing domain-adaptation methods mainly focus on images and most of them cannot be directly applied to point clouds. We address this problem with a domain-adaptation training pipeline consisting of three major components: 1) learned intensity rendering, 2) geodesic correlation alignment, and 3) progressive domain calibration. When trained on real data, our new model exhibits segmentation accuracy improvements of 6.0-8.6% over the original SqueezeSeg. When training our new model on synthetic data using the proposed domain adaptation pipeline, we nearly double test accuracy on real-world data, from 29.0% to 57.4%. Our source code and synthetic dataset are open sourced11https://github.com/xuanyuzhou98/SqueezeSegV2
Delocalized Eta Invariants, Algebraicity, and K-Theory of Group C*-Algebras

In this paper, we establish a precise connection between higher rho invariants and delocalized eta invariants. Given an element in a discrete group, if its conjugacy class has polynomial growth, then there is a natural trace map on the $K_0$-group of its group $C^\ast$-algebra. For each such trace map, we construct a determinant map on secondary higher invariants. We show that, under the evaluation of this determinant map, the image of a higher rho invariant is precisely the corresponding delocalized eta invariant of Lott. As a consequence, we show that if the Baum-Connes conjecture holds for a group, then Lott's delocalized eta invariants take values in algebraic numbers. We also generalize Lott's delocalized eta invariant to the case where the corresponding conjugacy class does not have polynomial growth, provided that the strong Novikov conjecture holds for the group.
A Microwell Cell Capture Device Reveals Variable Response to Dobutamine in Isolated Cardiomyocytes.

Abstract Isolated ventricular cardiomyocytes exhibit substantial cell-to-cell variability, even when obtained from the same small volume of myocardium. In this study, we investigated the possibility that cardiomyocyte responses to β-adrenergic stimulus are also highly heterogeneous. To achieve the throughput and measurement duration desired for these experiments, we designed and validated a novel microwell system that immobilizes and uniformly orients isolated adult cardiomyocytes. In this configuration, detailed drug responses of dozens of cells can be followed for intervals exceeding 1 h. At the conclusion of an experiment, specific cells can also be harvested via a precision aspirator for single-cell gene expression profiling. Using this system, we followed changes in Ca2+ signaling and contractility of individual cells under sustained application of either dobutamine or omecamtiv mecarbil. Both compounds increased average cardiomyocyte contractility over the course of an hour, but responses of individual cells to dobutamine were significantly more variable. Surprisingly, some dobutamine-treated cardiomyocytes augmented Ca2+ release without increasing contractility. Other cells responded with increased contractility despite unchanged Ca2+ release. Single-cell gene expression analysis revealed significant co-expression of β-adrenergic pathway genes PKA regulatory subunit type I, PKA regulatory subunit type II, and Ca2+/calmodulin-dependent protein kinase II across cardiomyocytes. Other data supported a connection between the effects of dobutamine on relaxation rate and the expression of protein phosphatase 2. These findings suggest that variable drug responses among cells are not merely experimental artifacts. By enabling direct comparison of the functional behavior of an individual cell and the genes it expresses, this new system constitutes a unique tool for interrogating cardiomyocyte drug responses and discovering the genes that modulate them.
Improving Neural Story Generation by Targeted Common Sense Grounding.

Stories generated with neural language models have shown promise in grammatical and stylistic consistency. However, the generated stories are still lacking in common sense reasoning, e.g., they often contain sentences deprived of world knowledge. We propose a simple multi-task learning scheme to achieve quantitatively better common sense reasoning in language models by leveraging auxiliary training signals from datasets designed to provide common sense grounding. When combined with our two-stage fine-tuning pipeline, our method achieves improved common sense reasoning and state-of-the-art perplexity on the WritingPrompts (Fan et al., 2018) story generation dataset.
Are deep ResNets provably better than linear predictors

Recent results in the literature indicate that a residual network (ResNet) composed of a single residual block outperforms linear predictors, in the sense that all local minima in its optimization landscape are at least as good as the best linear predictor. However, these results are limited to a single residual block (i.e., shallow ResNets), instead of the deep ResNets composed of multiple residual blocks. We take a step towards extending this result to deep ResNets. We start by two motivating examples. First, we show that there exist datasets for which all local minima of a fully-connected ReLU network are no better than the best linear predictor, whereas a ResNet has strictly better local minima. Second, we show that even at the global minimum, the representation obtained from the residual block outputs of a 2-block ResNet do not necessarily improve monotonically over subsequent blocks, which highlights a fundamental difficulty in analyzing deep ResNets. Our main theorem on deep ResNets shows under simple geometric conditions that, any critical point in the optimization landscape is either (i) at least as good as the best linear predictor; or (ii) the Hessian at this critical point has a strictly negative eigenvalue. Notably, our theorem shows that a chain of multiple skip-connections can improve the optimization landscape, whereas existing results study direct skip-connections to the last hidden layer or output layer. Finally, we complement our results by showing benign properties of the "near-identity regions" of deep ResNets, showing depth-independent upper bounds for the risk attained at critical points as well as the Rademacher complexity.
Multi-particle collisions in microgravity: Coefficient of restitution and sticking threshold for systems of mm-sized particles

Context. The current model of planet formation lacks a good understanding of the growth of dust particles inside the protoplanetary disk beyond mm sizes. A similar collisional regime exists in dense planetary rings. In order to investigate the low-velocity collisions between this type of particles, the NanoRocks experiment was flown on the International Space Station (ISS) between September 2014 and March 2016. We present the results of this experiment.Aims. The objectives of our data analysis are the quantification of the damping of energy in systems of multiple particles in the 0.1–1 mm size range while they are in the bouncing regime, and the study of the formation of clusters through sticking collisions between particles.Methods. We developed statistical methods for the analysis of the large quantity of collision data collected by the experiment. We measured the average motion of particles, the moment of clustering, and the cluster size formed. In addition, we ran simple numerical simulations in order to validate our measurements.Results. We computed the average coefficient of restitution (COR) of collisions and find values ranging from 0.55 for systems including a population of fine grains to 0.94 for systems of denser particles. We also measured the sticking threshold velocities and find values around 1 cm s−1 , consistent with the current dust collision models based on independently collected experimental data.Conclusions. Our findings have the following implications that can be useful for the simulation of particles in PPDs and planetary rings: (1) The average COR of collisions between same-sized free-floating particles at low speeds ( ) is not dependent on the collision velocity; (2) The simplified approach of using a constant COR value will accurately reproduce the average behavior of a particle system during collisional cooling; (3) At speeds below 5 mm s−1 , the influence of particle rotation becomes apparent on the collision behavior; (4) Current dust collision models predicting sticking thresholds are robust.
Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals.

Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not e...
The global value chain under imperfect capital markets

This paper develops a model to study how suppliers' financial constraint interact with suppliers' position in a global value chain. I embed financial frictions into the property‐rights model of the global value chain, as in Antras and Chor (Econometrica, 2013, 81, 2127), to derive the optimal allocation of ownership rights along the global value chain. The model predicts that multinational firms are more likely to integrate downstream intermediate input suppliers in countries with weak financial institutions when the production process is sequential complements. Using US intrafirm trade data for the years 2000–10, together with a triple‐interaction term between "downstreamness" of an industry, demand elasticity of an industry and financial development of a country, I provide empirical evidence that supports the key prediction of the model.
Poster Abstract: A Dynamic Data-Driven Prediction Model for Sparse Collaborative Sensing Applications

A fundamental problem in collaborative sensing lies in providing an accurate prediction of critical events (e.g., hazardous environmental condition, urban abnormalities, economic trends). However, due to the resource constraints, collaborative sensing applications normally only collect measurements from a subset of physical locations and predict the measurements for the rest of locations. This problem is referred to as sparse collaborative sensing prediction. In this poster, we present a novel closed-loop prediction model by leveraging topic modeling and online learning techniques. We evaluate our scheme using a realworld collaborative sensing dataset. The initial results show that our proposed scheme outperforms the state-of-the-art baselines.
Competition and Opacity in the Financial System

This paper studies the effect of competition on opacity in the financial system. In my model, two financial institutions competing for investors simultaneously make a public disclosure decision whe...
The Plenary Power Meets the Police Power: Federalism at the Intersection of Health & Immigration.

On January 8, 2019, shortly after his inauguration, California's Governor Gavin Newsom announced a proposal to provide health coverage to undocumented immigrants up to age 26. That same day, New York Mayor Bill de Blasio offered a $100 million plan to cover 600,000 uninsured New Yorkers, regardless of immigration status. These proposals from public officials on opposite sides of the continent stood in stark contrast to the Trump Administration's demand for a wall at the southern border, and its proposed public charge regulations that are expected to lead millions of lawfully present immigrants and their families to forego health insurance.
Dynamic stability enhancement of power system using PSO optimized fuzzy power system stabilizer

This paper presents the PSO optimized fuzzy logic power system stabilizer (FPSS) to enhance the dynamic stability of the entire power system using PHILLIPS HEFFRON MODEL for single machine infinite bus (SMIB) system. Various input and output parameters of the fuzzy logic controller are optimized using the Particle swarm optimization (PSO) technique to achieve better results and enhanced stability. Out of the various choices for input to the fuzzy logic controller, the angular speed deviation Aio and acceleration Δώ are taken as the input to fuzzy logic controller. Moreover the behavior of the conventional controller, fuzzy logic controller and PSO optimized FPSS is compared for various parameters.
Somatic Mutations and Genome Stability Maintenance in Clonal Coral Colonies.

One challenge for multicellular organisms is maintaining genome stability in the face of mutagens across long life spans. Imperfect genome maintenance leads to mutation accumulation in somatic cells, which is associated with tumors and senescence in vertebrates. Colonial reef-building corals are often large, can live for hundreds of years, rarely develop recognizable tumors, and are thought to convert somatic cells into gamete producers, so they are a pivotal group in which to understand long-term genome maintenance. To measure rates and patterns of somatic mutations, we analyzed transcriptomes from 17 to 22 branches from each of four Acropora hyacinthus colonies, determined putative single nucleotide variants, and verified them with Sanger resequencing. Unlike for human skin carcinomas, there is no signature of mutations caused by UV damage, indicating either higher efficiency of repair than in vertebrates, or strong sunscreen protection in these shallow water tropical animals. The somatic mutation frequency per nucleotide in A. hyacinthus is on the same order of magnitude (10-7) as noncancerous human somatic cells, and accumulation of mutations with age is similar. Loss of heterozygosity variants outnumber gain of heterozygosity mutations ∼2:1. Although the mutation frequency is similar in mammals and corals, the preponderance of loss of heterozygosity changes and potential selection may reduce the frequency of deleterious mutations in colonial animals like corals. This may limit the deleterious effects of somatic mutations on the coral organism as well as potential offspring.
Lyapunov spectrum of forced homogeneous isotropic turbulent flows

A local point of view is adopted on the chaoticity of turbulent flow, using the Lyapunov analysis. Among other findings, it is found that chaotic perturbation growth is a highly localized phenomenon that occurs in regions of high-velocity gradient but not in regions of high turbulence intensity.
Halving the Bounds for the Markov, Chebyshev, and Chernoff Inequalities Using Smoothing

The Markov, Chebyshev, and Chernoff inequalities are some of the most widely used methods for bounding the tail probabilities of random variables. In all three cases, the bounds are tight in the se...
Empirically Investigating Judicial Emotion

The empirical study of judicial emotion has enormous but largely untapped potential to illuminate a previously underexplored aspect of judging, its processes, outputs, and impacts. After defining judicial emotion, this article proposes a theoretical taxonomy of approaches to its empirical exploration. It then presents and analyses extant examples of such research, with a focus on how the questions they ask fit within the taxonomy and the methods they use to answer those questions. It concludes by identifying areas for growth in the disciplined, data-based exploration of the many facets of judicial emotion. El estudio empirico de las emociones en la judicatura tiene un potencial enorme, pero poco explorado, para arrojar luz sobre un aspecto de la profesion judicial poco explorado previamente, sus procesos, resultados y efectos. Tras definir la emocion judicial, el articulo propone una taxonomia teorica de abordajes a su estudio empirico. Despues, presenta y analiza ejemplos existentes de esa investigacion, poniendo especial atencion en la forma en que las preguntas que se formulan encajan en la taxonomia y en los metodos que utilizan para responder a aquellas. Concluye identificando areas de crecimiento en el estudio disciplinado y basado en datos de las multiples facetas de la emocion judicial. Available from: https://doi.org/10.35295/osls.iisl/0000-0000-0000-1089
Origins of the Opioid Crisis and Its Enduring Impacts

Overdose deaths involving opioids have increased dramatically since the mid-1990s, leading to the worst drug overdose epidemic in U.S. history, but there is limited empirical evidence on the initial causes. In this paper, we examine the role of the 1996 introduction and marketing of OxyContin as a potential leading cause of the opioid crisis. We leverage cross-state variation in exposure to OxyContin’s introduction due to a state policy that substantially limited OxyContin’s early entry and marketing in select states. Recently-unsealed court documents involving Purdue Pharma show that state-based triplicate prescription programs posed a major obstacle to sales of OxyContin and suggest that less marketing was targeted to states with these programs. We find that OxyContin distribution was about 50% lower in “triplicate states” in the years after the launch. While triplicate states had higher rates of overdose deaths prior to 1996, this relationship flipped shortly after the launch and triplicate states saw substantially slower growth in overdose deaths, continuing even twenty years after OxyContin's introduction. Our results show that the introduction and marketing of OxyContin explain a substantial share of overdose deaths over the last two decades.
Markov Chain Monte Carlo Methods for Simulations of Biomolecules

The computer revolution has been driven by a sustained increase of computational speed of approximately one order of magnitude (a factor of ten) every 5 years since about 1950. In natural sciences, this has led to a continuous increase of the importance of computer simulations. Major enabling techniques are Markov Chain Monte Carlo (MCMC) and molecular dynamics (MD) simulations.
Progression from Feature-Specific Brain Activity to Hippocampal Binding during Episodic Encoding

The hallmark of episodic memory is recollecting multiple perceptual details tied to a specific spatial-temporal context. To remember an event, it is therefore necessary to integrate such details into a coherent representation during initial encoding. Here we tested how the brain encodes and binds multiple, distinct kinds of features in parallel, and how this process evolves over time during the event itself. We analyzed data from 27 human subjects (16 females, 11 males) who learned a series of objects uniquely associated with a color, a panoramic scene location, and an emotional sound while fMRI data were collected. By modeling how brain activity relates to memory for upcoming or just-viewed information, we were able to test how the neural signatures of individual features as well as the integrated event changed over the course of encoding. We observed a striking dissociation between early and late encoding processes: left inferior frontal and visuo-perceptual signals at the onset of an event tracked the amount of detail subsequently recalled and were dissociable based on distinct remembered features. In contrast, memory-related brain activity shifted to the left hippocampus toward the end of an event, which was particularly sensitive to binding item color and sound associations with spatial information. These results provide evidence of early, simultaneous feature-specific neural responses during episodic encoding that predict later remembering and suggest that the hippocampus integrates these features into a coherent experience at an event transition.SIGNIFICANCE STATEMENT Understanding and remembering complex experiences are crucial for many socio-cognitive abilities, including being able to navigate our environment, predict the future, and share experiences with others. Probing the neural mechanisms by which features become bound into meaningful episodes is a vital part of understanding how we view and reconstruct the rich detail of our environment. By testing memory for multimodal events, our findings show a functional dissociation between early encoding processes that engage lateral frontal and sensory regions to successfully encode event features, and later encoding processes that recruit hippocampus to bind these features together. These results highlight the importance of considering the temporal dynamics of encoding processes supporting multimodal event representations.
Effect of light deprivation on binding activity of serotonin with light and heavy synaptosomes from various brain formations.

The degree of binding of serotonin (5-HT) with light and heavy synaptosomes in various brain formations of control and visually deprived rabbits was studied by the writer's gel-filtration method. The activity of this process in heavy synaptosomes of control rabbits was equal in the visual cortex and superior colliculus and significantly higher than in the motor cortex. Light synaptosomes of all structures tested were similar in their degree of 5-HT binding. During light deprivation the intensity of this process in the heavy synaptosomes in formations of the visual system fell by 74–81%, whereas in the same synaptosomes of the motor cortex it fell by 31%. The decrease in 5-HT binding in the light synaptosomes of all formations tested was equal, mainly by about 73% of normal. The results are discussed in the light of the possible mediator or modulating role of 5-HT in formations of the visual system.
Natively oxidized amino acid residues in the spinach PS I-LHC I supercomplex.

Reactive oxygen species (ROS) production is an unavoidable byproduct of electron transport under aerobic conditions. Photosystem II (PS II), the cytochrome b6/f complex and Photosystem I (PS I) are all demonstrated sources of ROS. It has been proposed that PS I produces substantial levels of a variety of ROS including O2.−, 1O2, H2O2 and, possibly, •OH; however, the site(s) of ROS production within PS I has been the subject of significant debate. We hypothesize that amino acid residues close to the sites of ROS generation will be more susceptible to oxidative modification than distant residues. In this study, we have identified oxidized amino acid residues in spinach PS I which was isolated from field-grown spinach. The modified residues were identified by high-resolution tandem mass spectrometry. As expected, many of the modified residues lie on the surface of the complex. However, a well-defined group of oxidized residues, both buried and surface-exposed, lead from the chl a’ of P700 to the surface of PS I. These residues (PsaB: 609F, 611E, 617M, 619W, 620L, and PsaF: 139L, 142A,143D) may identify a preferred route for ROS, probably 1O2, to egress the complex from the vicinity of P700. Additionally, two buried residues located in close proximity to A1B (PsaB:712H and 714S) were modified, which appears consistent with A1B being a source of O2.−. Surprisingly, no oxidatively modified residues were identified in close proximity to the 4Fe–FS clusters FX, FA or FB. These cofactors had been identified as principal targets for ROS damage in the photosystem. Finally, a large number of residues located in the hydrophobic cores of Lhca1–Lhca4 are oxidatively modified. These appear to be the result of 1O2 production by the distal antennae for the photosystem.
Exact computation of a manifold metric, via lipschitz embeddings and shortest paths on a graph

Data-sensitive metrics adapt distances locally based the density of data points with the goal of aligning distances and some notion of similarity. In this paper, we give the first exact algorithm for computing a data-sensitive metric called the nearest neighbor metric. In fact, we prove the surprising result that a previously published 3-approximation is an exact algorithm. The nearest neighbor metric can be viewed as a special case of a density-based distance used in machine learning, or it can be seen as an example of a manifold metric. Previous computational research on such metrics despaired of computing exact distances on account of the apparent difficulty of minimizing over all continuous paths between a pair of points. We leverage the exact computation of the nearest neighbor metric to compute sparse spanners and persistent homology. We also explore the behavior of the metric built from point sets drawn from an underlying distribution and consider the more general case of inputs that are finite collections of path-connected compact sets. The main results connect several classical theories such as the conformal change of Riemannian metrics, the theory of positive definite functions of Schoenberg, and screw function theory of Schoenberg and Von Neumann. We also develop some novel proof techniques based on the combination of screw functions and Lipschitz extensions that may be of independent interest.
Competitive Contagion with Sparse Seeding

Abstract This paper studies a strategic model of marketing and product diffusion in social networks. We consider two firms offering substitutable products which can improve their market share by seeding the key individuals in the market. Consumers update their consumption level for each of the two products as the best response to the consumption of their neighbors in the previous period. This results in linear update dynamics for the product consumption. Each consumer receives externality from the consumption of each neighbor where the strength of the externality is higher for consumption of the products of the same firm. We represent the above setting as a duopoly game between the firms and introduce a novel framework that allows for sparse seeding to emerge as an equilibrium strategy. We then study the effect of the network structure on the optimal seeding strategies and the extent to which the strategies can be sparsified. In particular, we derive conditions under which near Nash equilibrium strategies can asymptotically lead to sparse seeding in large populations. The results are illustrated using a core-periphery network.
Estimating the Market Effect of a Trade War: The Case of Soybean Tariffs

Abstract In 2018, China retaliated to U.S. trade actions by levying a 25% retaliatory tariff on U.S. soybean exports. That tariff shifted market preferences so that Chinese buyers—who make up a substantial share of total world consumption—favored Brazilian soybeans. We use the relative price of a substitute (RPS) method to estimate that the resulting trade disruption effectively drove a wedge into the world soybean market, lowering U.S. prices at Gulf export locations by $0.74/bu on average for about five months, and increasing Brazilian prices by about $0.97/bu, compared to what would have been observed without the tariff in place. By the end of that period, world markets adjusted and the soybean prices in both countries returned to the ex-ante state of near parity, even if U.S. export volume did not recover until the end of the following marketing year. Our price impact estimate is substantially lower than subsequent U.S. government “trade aid” payments to American soybean producers: although actual payments to producers varied based on county-level differences, USDA’s nominal calculation of the commodity-specific payment rate for soybeans under MFP summed to $3.70 for two bushels produced over the course of two years. We project that USDA’s near-$8.5 billion in trade aid to U.S. soybean producers exceeded the tariff damage by about $5.4 billion. These difference could be attributed to USDA’s broader definition of “economic injury”, beyond the short-run price impacts we estimate.
Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation

Fast and efficient path generation is critical for robots operating in complex environments. This motion planning problem is often performed in a robot’s actuation or configuration space, where popular pathfinding methods such as A*, RRT*, get exponentially more computationally expensive to execute as the dimensionality increases or the spaces become more cluttered and complex. On the other hand, if one were to save the entire set of paths connecting all pair of locations in the configuration space a priori, one would run out of memory very quickly. In this work, we introduce a novel way of producing fast and optimal motion plans for static environments by using a stepping neural network approach, called OracleNet. OracleNet uses Recurrent Neural Networks to determine end-to-end trajectories in an iterative manner that implicitly generates optimal motion plans with minimal loss in performance in a compact form. The algorithm is straightforward in implementation while consistently generating near-optimal paths in a single, iterative, end-to-end roll-out. In practice, OracleNet generally has fixed-time execution regardless of the configuration space complexity while outperforming popular pathfinding algorithms in complex environments and higher dimensions1.
A Simple Characterization of Proportionally 2-choosable Graphs

We recently introduced proportional choosability, a new list analogue of equitable coloring. Like equitable coloring, and unlike list equitable coloring (a.k.a. equitable choosability), proportional choosability bounds sizes of color classes both from above and from below. In this note, we show that a graph is proportionally 2-choosable if and only if it is a linear forest such that its largest component has at most five vertices and all of its other components have two or fewer vertices. We also construct examples that show that characterizing equitably 2-choosable graphs is still open.
SurfaceGenie: a web-based application for prioritizing cell-type-specific marker candidates

MOTIVATION Cell-type-specific surface proteins can be exploited as valuable markers for a range of applications including immunophenotyping live cells, targeted drug delivery and in vivo imaging. Despite their utility and relevance, the unique combination of molecules present at the cell surface are not yet described for most cell types. A significant challenge in analyzing 'omic' discovery datasets is the selection of candidate markers that are most applicable for downstream applications. RESULTS Here, we developed GenieScore, a prioritization metric that integrates a consensus-based prediction of cell surface localization with user-input data to rank-order candidate cell-type-specific surface markers. In this report, we demonstrate the utility of GenieScore for analyzing human and rodent data from proteomic and transcriptomic experiments in the areas of cancer, stem cell and islet biology. We also demonstrate that permutations of GenieScore, termed IsoGenieScore and OmniGenieScore, can efficiently prioritize co-expressed and intracellular cell-type-specific markers, respectively. AVAILABILITY AND IMPLEMENTATION Calculation of GenieScores and lookup of SPC scores is made freely accessible via the SurfaceGenie web application: www.cellsurfer.net/surfacegenie. CONTACT Rebekah.gundry@unmc.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Short-pulse grating formation in photorefractive materials

The space charge field and refractive-index grating in a photorefractive material are derived in the limit that the pulse length of the illumination grating is short compared with the carrier drift and diffusion times. The short-pulse limit is of interest because observed cw response times are too long for many applications, and short-pulse response times are observed to be of the order of nanoseconds.1,2
M-SPARC: MATLAB-Simulation Package for Ab-initio Real-space Calculations

Abstract We present M-SPARC: Matlab -Simulation Package for Ab-initio Real-space Calculations. It can perform pseudopotential spin-polarized and unpolarized Kohn–Sham Density Functional Theory (DFT) simulations for isolated systems such as molecules as well as extended systems such as crystals, surfaces, and nanowires. M-SPARC provides a rapid prototyping platform for the development and testing of new algorithms and methods in real-space DFT, with the potential to significantly accelerate the rate of advancements in the field. It also provides a convenient avenue for the accurate first principles study of small to moderate sized systems.
Covariance-Based Sample Selection for Heterogeneous Data: Applications to Gene Expression and Autism Risk Gene Detection

Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, “guilt by as...
Generalized stochastic Frank–Wolfe algorithm with stochastic “substitute” gradient for structured convex optimization

The stochastic Frank–Wolfe method has recently attracted much general interest in the context of optimization for statistical and machine learning due to its ability to work with a more general feasible region. However, there has been a complexity gap in the dependence on the optimality tolerance $$\varepsilon $$ in the guaranteed convergence rate for stochastic Frank–Wolfe compared to its deterministic counterpart. In this work, we present a new generalized stochastic Frank–Wolfe method which closes this gap for the class of structured optimization problems encountered in statistical and machine learning characterized by empirical loss minimization with a certain type of “linear prediction” property (formally defined in the paper), which is typically present in loss minimization problems in practice. Our method also introduces the notion of a “substitute gradient” that is a not-necessarily-unbiased estimate of the gradient. We show that our new method is equivalent to a particular randomized coordinate mirror descent algorithm applied to the dual problem, which in turn provides a new interpretation of randomized dual coordinate descent in the primal space. Also, in the special case of a strongly convex regularizer our generalized stochastic Frank–Wolfe method (as well as the randomized dual coordinate descent method) exhibits linear convergence. Furthermore, we present computational experiments that indicate that our method outperforms other stochastic Frank–Wolfe methods for a sufficiently small optimality tolerance, consistent with the theory developed herein.
Explaining AI: fairly? well?

Explainable AI (XAI) has started experiencing explosive growth, echoing the explosive growth that has preceded it of AI becoming used for practical purposes that impact the general public. This spread of AI into the world outside of research labs brings with it pressures and requirements that many of us have perhaps not thought about deeply enough. In this keynote address, I will explain why I think we have a very long way to go. One way to characterize our current state is that we're doing "fairly well", doing some explaining of some things. In a sense, this is reasonable: the XAI field is young, and still finding its way. However, moving forward demands progress in (at least) three areas. (1) How we go about XAI research: Explainable AI cannot succeed if the only research foundations brought to bear on it are AI foundations. Likewise, it cannot succeed if the only foundations used are from psychology, education, etc. Thus, a challenge for our emerging field is how to conduct XAI research in a truly effective multi-disciplinary fashion, that is based on an integration of foundations behind what we can make AI algorithms do, with solid, well-founded principles of explaining the complex ideas behind the algorithms to real people. Fortunately, a few researchers have started to build such foundations. (2) What we can succeed at explaining: So far, we as a field are doing a certain amount of cherry picking as to what we explain. We tend to choose what to explain by what we can figure out how to explain---but we are leaving too much out. One urgent case in point is the societal and legal need to explain fairness properties of AI systems. The above challenges are important, but the field is already becoming aware of them. Thus, this keynote will focus mostly on the third challenge, namely: (3) Who we can explain to. Who are the people we've even tried to explain AI to, so far? What are the societal implications of who we explain to well and who we do not? Our field has not even begun to consider this question. In this keynote I'll discuss why we have to explain to populations to whom we've given little thought---diverse people in many dimensions, including gender diversity, cognitive diversity, and age diversity. Addressing all of these challenges is necessary before we can claim to explain AI fairly and well.
Ligand-Mediated Phase Control in Colloidal AgInSe2 Nanocrystals

Synthetic studies of colloidal nanoparticles that crystallize in metastable structures represent an emerging area of interest in the development of novel functional materials, as metastable nanomat...
Regularity Theory for Second Order Integro-PDEs

This paper is concerned with higher Holder regularity for viscosity solutions to non-translation invariant second order integro-PDEs, compared to Mou (2018). We first obtain C1,α regularity estimates for fully nonlinear integro-PDEs. We then prove the Schauder estimates for solutions if the equation is convex.
Improved lifetime of a high spin polarization superlattice photocathode

Negative Electron Affinity (NEA) activated surfaces are required to extract highly spin-polarized electron beams from GaAs-based photocathodes, but they suffer extreme sensitivity to poor vacuum conditions that results in rapid degradation of quantum efficiency. We report on a series of unconventional NEA activations on surfaces of bulk GaAs with Cs, Sb, and O 2 using different methods of oxygen exposure for optimizing photocathode performance. One order of magnitude improvement in lifetime with respect to the standard Cs – O 2 activation is achieved without significant loss of electron spin polarization and quantum efficiency by codepositing Cs, Sb, and O 2. A strained GaAs/GaAsP superlattice sample activated with the codeposition method demonstrated similar enhancement in lifetime near the photoemission threshold while maintaining 90% spin polarization.
Antiretroviral Therapy and Demand for HIV Testing: Evidence from Zambia

This paper examines the effects of antiretroviral therapy (ART) on demand for HIV testing and of ART-induced testing on demand for risky sexual behavior. I provide a model of sexual behavior decision-making under uncertainty and estimate the structural parameters of the model using nationally representative survey data from Zambia on HIV testing decisions before and after the introduction of ART. The empirical results indicate that although the introduction of ART appears to have increased HIV testing rates by upwards of 50 percent, the ART allocation process may have limited the prevention benefit of ART-induced testing. Simulation results show that eliminating this prevention inefficiency while holding the supply of ART constant would increase the prevention impact of ART-induced testing more than four-fold. More generally, the analysis indicates that existing studies which examine “universal” testing or quasi-experimental testing programs understate the efficacy of standard voluntary counseling and testing programs.
Symmetrically substituted dichlorophenes inhibit N-acyl-phosphatidylethanolamine phospholipase D.

N-Acyl-phosphatidylethanolamine phospholipase D (NAPE-PLD) (EC 3.1.4.4) catalyzes the final step in the biosynthesis of N-acyl-ethanolamides. Reduced NAPE-PLD expression and activity may contribute to obesity and inflammation, but a lack of effective NAPE-PLD inhibitors has been a major obstacle to elucidating the role of NAPE-PLD and N-acyl-ethanolamide biosynthesis in these processes. The endogenous bile acid lithocholic acid (LCA) inhibits NAPE-PLD activity (with an IC50 of 68 μm), but LCA is also a highly potent ligand for TGR5 (EC50 0.52 μm). Recently, the first selective small-molecule inhibitor of NAPE-PLD, ARN19874, has been reported (having an IC50 of 34 μm). To identify more potent inhibitors of NAPE-PLD, here we used a quenched fluorescent NAPE analog, PED-A1, as a substrate for recombinant mouse Nape-pld to screen a panel of bile acids and a library of experimental compounds (the Spectrum Collection). Muricholic acids and several other bile acids inhibited Nape-pld with potency similar to that of LCA. We identified 14 potent Nape-pld inhibitors in the Spectrum Collection, with the two most potent (IC50 = ∼2 μm) being symmetrically substituted dichlorophenes, i.e. hexachlorophene and bithionol. Structure–activity relationship assays using additional substituted dichlorophenes identified key moieties needed for Nape-pld inhibition. Both hexachlorophene and bithionol exhibited significant selectivity for Nape-pld compared with nontarget lipase activities such as Streptomyces chromofuscus PLD or serum lipase. Both also effectively inhibited NAPE-PLD activity in cultured HEK293 cells. We conclude that symmetrically substituted dichlorophenes potently inhibit NAPE-PLD in cultured cells and have significant selectivity for NAPE-PLD versus other tissue-associated lipases.
Semiconducting and metallic polymers: the fourth generation of polymeric materials

Abstract When asked to explain the importance of the discovery of conducting polymers, I offer two basic answers: First, they did not (could not?) exist; and second, that they offer a unique combination of properties not available from any other known materials. The first is an intellectual challenge; the second expresses a promise for utility in a wide variety of applications.
Re-Evaluating the Conventional Wisdom about Binding Assays

Analytical technologies based on binding assays have evolved substantially since their inception nearly 60 years ago, but our conceptual understanding of molecular recognition has not kept pace. Contemporary technologies, such as single-molecule and digital measurements, have challenged, or even rendered obsolete, core concepts behind conventional binding assay design. Here, we explore the fundamental principles underlying molecular recognition systems, which we consider in terms of signals generated through concentration-dependent shifts in equilibrium. We challenge certain orthodoxies related to binding-based detection assays, including the primary importance of a low dissociation constant (KD) and the extent to which this parameter constrains dynamic range and limit of detection. Lastly, we identify key principles for designing binding assays that are optimally suited for a given detection application.
Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification. Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation. PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.
Chapter 13 – Sights and Sounds

The chapter briefly discusses sound files, and introduces image processing. Different methods of representing pixel color, using both colormaps and RGB 3-dimensional matrices, are introduced. Creating custom colormaps and reading and manipulating image files are shown. An introduction to programming Graphical User Interfaces (GUIs) is also given, including the creation of a button group and embedding images in a GUI. The graphical user interface development environment is introduced. The App Designer, which creates object-based code, is also introduced. The concepts of event-driven programming and callback functions, are introduced and examples are given, both programmatically and using App Designer.
Engagement design in studies on pregnancy and infant health using social media: Systematic review.

Abstract Social media utilization is prevalent among reproductive-age women. The literature on how researchers engage women in studies using social media platforms is scarce. This systematic review analyzed participant engagement design in studies using social media and focused on pregnancy and infant health. Methods: A literature search of EBSCO and PubMed databases was conducted. Included studies had to be completed with quantitative data, focus on pregnancy, postpartum or infant health, and use social media in the research process. A matrix of three engagement designs (passive, interactive, independent) and three research processes (recruitment, data analysis, intervention) was used for analysis. Findings: Thirty-one articles that reported 30 studies met the inclusion criteria. Of these, four were randomized controlled trials (RCT), four were non-RCT interventions, and 22 were observational/descriptive studies. The main purpose of using social media was for recruitment (n = 16), data analysis (n = 6), intervention (n = 8), or both recruitment and intervention (n = 1). Passive engagement was a fundamental design approach in all studies to access a data source that was either the participant or the data provided by the participants in social media. Interactive engagement, mostly for recruitment and intervention, was to engage participants in completing study enrollment or in interacting with the study team or fellow participants. Independent engagement involved off-line activities and appeared sporadically in intervention studies. Conclusions: Passive and interactive engagement designs are more frequently used than independent engagement design. Researchers should select suitable designs when studying pregnancy and infant health using social media.
Surface segmentation based on the luminance and color statistics of natural scenes

The luminance and color of surfaces in natural scenes are relatively independent under certain linear transformations, with the luminance of a surface providing little information about the color of that surface, and vice versa. However, differences in luminance between two locations in a natural scene remain strongly associated with differences in color. We used the statistics of the spatiochromatic structure of natural scenes as the priors for a Bayesian model that decides whether or not two points within an image fall on the same surface. This model provides a biologically plausible algorithm for surface segmentation that models observer segmentations well.