cssi/SciSciGPT-SciSciNet huggingface dataset:
Reward impacts visual statistical learning.

Humans automatically detect and remember regularities in the visual environment—a type of learning termed visual statistical learning (VSL). Many aspects of learning from reward resemble VSL in certain respects, yet whether and how reward learning impacts VSL is largely unexamined. In two studies, we found that reward contingencies affect VSL, with high-value associated with stronger behavioral and neural signatures of such learning than low-value images. In Experiment 1, participants learned values (high or low) of images through a trial-and-error risky choice task. Unbeknownst to them, images were paired as four types—High-High, High-Low, Low-High, and Low-Low. In subsequent recognition and reward memory tests, participants chose the more familiar of two pairs (a target and a foil) and recalled the value of images. We found better recognition when the first images of pairs have high-values, with High-High pairs showing the highest recognition rate. In Experiment 2, we provided evidence that both value and statistical contingencies affected brain responses. When we compared responses between the high-value first image and the low-value first image, greater activation in regions that included inferior frontal gyrus, anterior cingulate gyrus, hippocampus, among other regions, were found. These findings were driven by the interaction between statistically structured information and reward—the same value contrast yielded no regions for second-image contrasts and for singletons. Our results suggest that when reward information is embedded in stimulus-stimulus associations, it may alter the learning process; specifically, the higher-value first image potentially enables better memory for statistically learned pairs and reward information.
Characterizing the dynamical phase diagram of the Dicke model via classical and quantum probes

The paper shows that the quench dynamics of an ensemble of spins coupled to a single bosonic oscillator features classical and quantum dynamical critical points.
Unified Principal Component Analysis for Sparse and Dense Functional Data under Spatial Dependency

We consider spatially dependent functional data collected under a geostatistics setting, where locations are sampled from a spatial point process. The functional response is the sum of a spatially ...
Democratizing the Economic Sphere: A Case for the Political Boycott

The political boycott, though recently under attack through litigation aimed at compelled disclosure regimes, is a critical tool in constructing American democracy. Defining political boycotts as those refusals by consumers to buy goods or patronize businesses in order to effect political or social change, this Article is the first paper to place the political boycott at home in all three classic theories underlying the First Amendment: the marketplace of ideas, democracy and self-governance, and self-expression and autonomy. It also places the boycott alongside current campaign finance doctrine via Citizens United v. FEC. Just as money amassed by corporations in the economic marketplace can be used to influence the political, the boycott allows those whose main economic resource is their participation in the market as consumers to aggregate that resource, with other like-minded consumers, to influence the political marketplace. The paper also explores the doctrinal implications of these arguments for ongoing lawsuits challenging compelled disclosure regimes. As-applied challenges to such laws can be granted upon a sufficient evidentiary showing of “threats, harassment, or reprisals.” This Article argues that the boycott cannot be categorized in this way. Especially in the case of initiatives and referenda, the political boycott is a critical tool of petition and should not be considered in this as-applied harassment analysis.
Modeling Neutral Densities Downstream of a Gridded Ion Thruster

The details of a model for determining the neutral density downstream of a gridded ion thruster are presented. An investigation of the possible sources of neutrals emanating from and surrounding a NEXT ion thruster determined that the most significant contributors to the downstream neutral density include discharge chamber neutrals escaping through the perforated grids, neutrals escaping from the neutralizer, and vacuum facility background neutrals. For the neutral flux through the grids, near- and far-field equations are presented for rigorously determining the neutral density downstream of a cylindrical aperture. These equations are integrated into a spherically-domed convex grid geometry with a hexagonal array of apertures for determining neutral densities downstream of the ion thruster grids. The neutrals escaping from an off-center neutralizer are also modeled assuming diffuse neutral emission from the neutralizer keeper orifice. Finally, the effect of the surrounding vacuum facility neutrals is included and assumed to be constant. The model is used to predict the neutral density downstream of a NEXT ion thruster with and without neutralizer flow and a vacuum facility background pressure. The impacts of past simplifying assumptions for predicting downstream neutral densities are also examined for a NEXT ion thruster.
Phase II Trial of Flaxseed to Prevent Acute Complications After Chemoradiation for Lung Cancer.

Background: Thoracic radiotherapy is complicated by acute radiation-induced adverse events such as radiation pneumonitis (RP) and radiation esophagitis (RE). Based on preclinical work and a randomi...
Three-dimensional nonlinear stability analysis of the sun-perturbed earth-moon equilateral points.

Nonlinear analysis of earth-moon system motion stability in three dimensions near L4 libration point when perturbed by sun
Mercer Features for Efficient Combinatorial Bayesian Optimization.

Bayesian optimization (BO) is an efficient framework for solving black-box optimization problems with expensive function evaluations. This paper addresses the BO problem setting for combinatorial spaces (e.g., sequences and graphs) that occurs naturally in science and engineering applications. A prototypical example is molecular optimization guided by expensive experiments. The key challenge is to balance the complexity of statistical models and tractability of search to select combinatorial structures for evaluation. In this paper, we propose an efficient approach referred as Mercer Features for Combinatorial Bayesian Optimization (MerCBO). The key idea behind MerCBO is to provide explicit feature maps for diffusion kernels over discrete objects by exploiting the structure of their combinatorial graph representation. These Mercer features combined with Thompson sampling as the acquisition function allows us to employ efficient solvers for finding the next structure for evaluation. Experimental evaluation on diverse real-world benchmarks demonstrates that MerCBO performs similarly or better than prior methods.
Focal laser stimulation of fly nociceptors activates distinct axonal and dendritic Ca2+ signals.

Abstract Drosophila class IV neurons are polymodal nociceptors that detect noxious mechanical, thermal, optical, and chemical stimuli. Escape behaviors in response to attacks by parasitoid wasps are dependent on class IV cells, whose highly branched dendritic arbors form a fine meshwork that is thought to enable detection of the wasp’s needle-like ovipositor barb. To understand how mechanical stimuli trigger cellular responses, we used a focused 405-nm laser to create highly localized lesions to probe the precise position needed to evoke responses. By imaging calcium signals in dendrites, axons, and soma in response to stimuli of varying positions, intensities, and spatial profiles, we discovered that there are two distinct nociceptive pathways. Direct stimulation to dendrites (the contact pathway) produces calcium responses in axons, dendrites, and the cell body, whereas stimulation adjacent to the dendrite (the noncontact pathway) produces calcium responses in the axons only. We interpret the noncontact pathway as damage to adjacent cells releasing diffusible molecules that act on the dendrites. Axonal responses have higher sensitivities and shorter latencies. In contrast, dendritic responses have lower sensitivities and longer latencies. Stimulation of finer, distal dendrites leads to smaller responses than stimulation of coarser, proximal dendrites, as expected if the contact response depends on the geometric overlap of the laser profile and the dendrite diameter. Because the axon signals to the central nervous system to trigger escape behaviors, we propose that the density of the dendritic meshwork is high not only to enable direct contact with the ovipositor but also to enable neuronal activation via diffusing signals from damaged surrounding cells. Dendritic contact evokes responses throughout the dendritic arbor, even to regions distant and distal from the stimulus. These dendrite-wide calcium signals may facilitate hyperalgesia or cellular morphological changes after dendritic damage.
On the Lack of Robust Interpretability of Neural Text Classifiers

With the ever-increasing complexity of neural language models, practitioners have turned to methods for understanding the predictions of these models. One of the most well-adopted approaches for model interpretability is feature-based interpretability, i.e., ranking the features in terms of their impact on model predictions. Several prior studies have focused on assessing the fidelity of feature-based interpretability methods, i.e., measuring the impact of dropping the top-ranked features on the model output. However, relatively little work has been conducted on quantifying the robustness of interpretations. In this work, we assess the robustness of interpretations of neural text classifiers, specifically, those based on pretrained Transformer encoders, using two randomization tests. The first compares the interpretations of two models that are identical except for their initializations. The second measures whether the interpretations differ between a model with trained parameters and a model with random parameters. Both tests show surprising deviations from expected behavior, raising questions about the extent of insights that practitioners may draw from interpretations.
A perceptual model for eccentricity-dependent spatio-temporal flicker fusion and its applications to foveated graphics

Virtual and augmented reality (VR/AR) displays strive to provide a resolution, framerate and field of view that matches the perceptual capabilities of the human visual system, all while constrained by limited compute budgets and transmission bandwidths of wearable computing systems. Foveated graphics techniques have emerged that could achieve these goals by exploiting the falloff of spatial acuity in the periphery of the visual field. However, considerably less attention has been given to temporal aspects of human vision, which also vary across the retina. This is in part due to limitations of current eccentricity-dependent models of the visual system. We introduce a new model, experimentally measuring and computationally fitting eccentricity-dependent critical flicker fusion thresholds jointly for both space and time. In this way, our model is unique in enabling the prediction of temporal information that is imperceptible for a certain spatial frequency, eccentricity, and range of luminance levels. We validate our model with an image quality user study, and use it to predict potential bandwidth savings 7X higher than those afforded by current spatial-only foveated models. As such, this work forms the enabling foundation for new temporally foveated graphics techniques.
Compactly Supported Wannier Functions and Strictly Local Projectors

Wannier functions that are maximally localized help in understanding many properties of crystalline materials. In the absence of topological obstructions, they are at least exponentially localized. In some cases such as flat-band Hamiltonians, it is possible to construct Wannier functions that are even more localized, so that they are compactly supported thus having zero support outside their corresponding locations. Under what general conditions is it possible to construct compactly supported Wannier functions? We answer this question in this paper. Specifically, we show that in 1d non-interacting tight-binding models, strict locality of the projection operator is a necessary and sufficient condition for a subspace to be spanned by a compactly supported orthogonal basis, independent of lattice translation symmetry. For any strictly local projector, we provide a procedure for obtaining such a basis. For higher dimensional systems, we discuss some additional conditions under which an occupied subspace is spanned by a compactly supported orthogonal basis, and show that the corresponding projectors are topologically trivial in many cases. We also show that a projector in arbitrary dimensions is strictly local if and only if for any chosen axis, its image is spanned by hybrid Wannier functions that are compactly supported along that axis.
Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials

Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically determining macroscopic material properties. Here, we develop a graph neural network (GNN) model for obtaining an embedding of polycrystalline microstructure which incorporates not only the physical features of individual grains but also their interactions. The embedding is then linked to the target property using a feed-forward neural network. Using the magnetostriction of polycrystalline Tb0.3Dy0.7Fe2 alloys as an example, we show that a single GNN model with fixed network architecture and hyperparameters allows for a low prediction error of ~10% over a group of remarkably different microstructures as well as quantifying the importance of each feature in each grain of a microstructure to its magnetostriction. Such a microstructure-graph-based GNN model, therefore, enables an accurate and interpretable prediction of the properties of polycrystalline materials.
A Survey of Perception-Based Visualization Studies by Task.

Knowledge of human perception has long been incorporated into visualizations to enhance their quality and effectiveness. The last decade, in particular, has shown an increase in perception-based visualization research studies. With all of this recent progress, the visualization community lacks a comprehensive guide to contextualize their results. In this report, we provide a systematic and comprehensive evaluation of research studies on perception related to visualization. This survey reviews perception-focused visualization studies since 1980 and summarizes their research developments focusing on low-level tasks, further breaking techniques down by visual encoding and visualization type. In particular, we focus on how perception is used to evaluate the effectiveness of visualizations, to help readers understand and apply the principles of perception of their visualization designs through a task-optimized approach. We concluded our report with a summary of the weaknesses and open research questions in the area.
On-Orbit Servicing Optimization Framework with High- and Low-Thrust Propulsion Tradeoff

This paper proposes an on-orbit servicing logistics optimization framework capable of performing the short-term operational scheduling and long-term strategic planning of sustainable servicing infr...
Playbacks of food-associated calls attract chimpanzees towards known food patches in a captive setting

Food-associated calls have received much research attention due to their potential to refer to discovered food in a word-like manner. Studies have found that in many species, food-associated calls attract receivers to the food patch, suggesting these calls play roles in food sharing, cooperation and competition. Additionally, in various species, these calls play a role that has received much less attention: mediating social interactions among foragers that are already nearby or within the food patch, independently of whether they attract outside foragers. In order to increase understanding of the function of the chimpanzee (Pan troglodytes) food-associated rough grunt, we conducted captive playback studies testing whether rough grunt playbacks attract, repel or have no effect on the proximity of foragers already familiarized with the presence of food. We tested how acoustic playbacks of rough grunts (or control calls) from one of two known, identical feeding sites affected receivers’ approach and feeding behaviors. More often than expected, participants first approached the feeding site from which rough grunts, but not control calls, were broadcast. However, neither condition increased the likelihood that participants fed first from a given site. Our results support the hypothesis that rough grunts elicit an approach response in receivers, while providing no evidence that they repel. In addition, our study provides evidence that receivers may approach rough grunts even if they do not intend to feed. We discuss the information rough grunts may convey to receivers beyond information about discovered food and the potential benefits signalers may gain from this calling behavior.
Breeding with Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance

Disease resistance in plants is mostly quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs that are needed to select both major and minor genes for resistance. In this study, stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) of wheat (Triticum aestivum L.) was used as a model for quantitative disease resistance. The quantitative nature of stripe rust is usually phenotyped with two disease traits, infection type (IT) and disease severity (SEV). We compared two types of training populations composed of 2,630 breeding lines (BLs) phenotyped in single-plot trials from 4 years (2016-2020) and 475 diversity panel (DP) lines from 4 years (2013-2016), both across two locations. We also compared the accuracy of models using four different major gene markers and genome-wide association study (GWAS) markers as fixed effects. The prediction models used 31,975 markers that are replicated 50 times using a 5-fold cross-validation. We then compared GS models using a marker-assisted selection (MAS) to compare the prediction accuracy of the markers alone and in combination. GS models had higher accuracies than MAS and reached an accuracy of 0.72 for disease SEV. The major gene and GWAS markers had only a small to nil increase in the prediction accuracy more than the base GS model, with the highest accuracy increase of 0.03 for the major markers and 0.06 for the GWAS markers. There was a statistical increase in the accuracy using the disease SEV trait, BLs, population type, and combining years. There was also a statistical increase in the accuracy using the major markers in the validation sets as the mean accuracy decreased. The inclusion of fixed effects in low prediction scenarios increased the accuracy up to 0.06 for GS models using significant GWAS markers. Our results indicate that GS can accurately predict quantitative disease resistance in the presence of major and minor genes.
Efficient Coding and Risky Choice

We experimentally test a theory of risky choice in which the perception of a lottery payoff is noisy due to information processing constraints in the brain. We model perception using the principle of efficient coding, which implies that perception is most accurate for those payoffs that occur most frequently. Across two pre-registered laboratory experiments, we manipulate the distribution from which payoffs in the choice set are drawn. In our first experiment, we find that risk taking is more sensitive to payoffs that are presented more frequently. In a follow-up task, we incentivize subjects to classify which of two symbolic numbers is larger. Subjects exhibit higher accuracy and faster response times for numbers they have observed more frequently. In our second experiment, we manipulate the payoff distribution so that efficient coding modulates the strength of valuation biases. As we experimentally increase the frequency of large payoffs, we find that subjects perceive the upside of a risky lottery more accurately and take greater risk. Together, our experimental results suggest that risk taking depends systematically on the payoff distribution to which the decision maker's perceptual system has recently adapted. More broadly, our findings highlight the importance of imprecise and efficient coding in economic decision-making.
Lorentzian peak sharpening and sparse blind source separation for NMR spectroscopy

In this paper, we introduce a preprocessing technique for blind source separation of nonnegative and overlapped data. For nuclear magnetic resonance spectroscopy (NMR), the classical method of Naanaa and Nuzillard (NN) requires the condition that source signals to be non-overlapping at certain locations, while they are allowed to overlap with each other elsewhere. NN’s method works well with data signals that possess stand-alone peaks (SAPs). The SAP does not hold completely for realistic NMR spectra, however. Violation of SAP often introduces errors or artifacts in the NN’s separation results. To address this issue, a preprocessing technique is developed here based on Lorentzian peak shapes and weighted peak sharpening. The idea is to superimpose the original peak signal with its weighted negative second-order derivative. The resulting sharpened (narrower and taller) peaks enable NN’s method to work with a more relaxed SAP condition, the so-called dominant peaks condition, and deliver improved results. To achieve an optimal sharpening while preserving the data nonnegativity, we prove the existence of an upper bound of the weight parameter and propose a selection criterion. Numerical experiments on NMR spectroscopy data show satisfactory performance of our proposed method.
Power Grid Cascading Failure Mitigation by Reinforcement Learning

This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL). The motivation of the Multi-Stage Cascading Failure (MSCF) problem and its connection with the challenge of climate change are introduced. The bottom-level corrective control of the MCSF problem is formulated based on DCOPF (Direct Current Optimal Power Flow). Then, to mitigate the MSCF issue by a high-level RL-based strategy, physics-informed reward, action, and state are devised. Besides, both shallow and deep neural network architectures are tested. Experiments on the IEEE 118-bus system by the proposed mitigation strategy demonstrate a promising performance in reducing system collapses.
Challenges to Global Standardization of Outcome Measures

Global standardization of outcome measures for disease states can help researchers and healthcare providers compare healthcare institutions' and populations' health outcomes. Despite the creation of standardized outcome sets, clinical institutions' adoption of these sets is not common. A literature review shows that among the challenges to standardizing outcome measures include the difficulties of achieving consensus in the working groups creating these outcome sets, the tradeoffs made when selecting outcome measurement tools, and the high costs of implementing a new or different set of outcome measures. The duplication of effort to create these standard sets can also limit standardization, which could be minimized through increased transparency of how these standard sets are developed. We propose some approaches to improve how to create and implement standard sets to broaden their usability across institutions.
Existence of standing pulse solutions to a skew-gradient system

Abstract Reaction-diffusion systems have been primary tools for studying pattern formation. A skew-gradient system is well known to encompass a class of activator-inhibitor type reaction-diffusion systems that exhibit localized patterns such as fronts and pulses. While there is a substantial literature for the case of a linear inhibitor equation, the study of nonlinear inhibitor effect is still limited. To fill this research gap, we investigate standing pulse solutions to a skew-gradient system in which both activator and inhibitor reaction terms inherit nonlinear structures. Using a variational approach that involves several nonlocal terms, we establish the existence of standing pulse solutions with a sign change. In addition, we explore some qualitative properties of the standing pulse solutions.
A Practical Coding Scheme for the BSC with Feedback

We provide a practical implementation of the rubber method of Ahlswede et al. for binary channels. The idea is to create the “skeleton” sequence therein via an arithmetic decoder designed for a particular k-th order Markov chain. For the stochastic binary symmetric channel, we show that the scheme is nearly optimal in a strong sense for certain parameters. A byproduct of the analysis is a strict enlargement of the rates for which the sphere-packing bound is known to be achievable with feedback for this channel.
Singularities in mixed characteristic via perfectoid big Cohen-Macaulay algebras

We utilize recent results of Andre and Gabber on the existence of weakly functorial, integral perfectoid big Cohen–Macaulay (BCM) algebras to study singularities of local rings in mixed characteristic. In particular, we introduce a mixed characteristic BCM-variant of rational/F-rational singularities, of log terminal/F-regular singularities, and of multiplier/test ideals of divisor pairs. We prove a number of results about these objects including a restriction theorem for perfectoid BCM multiplier/test ideals and deformation statements for perfectoid BCM-regular and BCM-rational singularities. As an application, we obtain results on the behavior of F-regular and F-rational singularities in arithmetic families.
The right to work and to live: The implications of India's NREGS program for missing women

Limited economic opportunity for women reduces their household bargaining power and the economic value of daughters, amplifying son preference. This paper studies India’s National Rural Employment Guarantee Scheme, the largest workfare program in the world, which paid men and women equal salary and mandated at least one-third of workers be women. In a setting where the gender gap in employment and wages are sizeable, NREGS represented both an income shock and a large relative improvement in the labor market for women. We use the staggered roll-out of NREGS to show that districts which implemented the program earlier experienced an improvement in child sex ratios in favor of girls. Although program implementation was nonrandom, we find impacts exist only in rural areas, not in the urban counterparts of the same district, where NREGS did not operate. Furthermore, effects are larger in middle and upper income districts and districts with the most skewed initial sex ratios, results which are inconsistent with an alternative selection story. Finally, the effects appear only for rural youth sex ratios, not for adult sex ratios, suggesting endogenous migration is not driving the results.
PAC Bayesian Performance Guarantees for Deep (Stochastic) Networks in Medical Imaging.

Application of deep neural networks to medical imaging tasks has in some sense become commonplace. Still, a “thorn in the side” of the deep learning movement is the argument that deep networks are prone to overfitting and are thus unable to generalize well when datasets are small (as is common in medical imaging tasks). One way to bolster confidence is to provide mathematical guarantees, or bounds, on network performance after training which explicitly quantify the possibility of overfitting. In this work, we explore recent advances using the PAC-Bayesian framework to provide bounds on generalization error for large (stochastic) networks. While previous efforts focus on classification in larger natural image datasets (e.g., MNIST and CIFAR-10), we apply these techniques to both classification and segmentation in a smaller medical imagining dataset: the ISIC 2018 challenge set. We observe the resultant bounds are competitive compared to a simpler baseline, while also being more explainable and alleviating the need for holdout sets.
Mobile App Adoption and Its Differential Impact on Consumer Shopping Behavior

The mobile application (app) market is one of the fastest growing markets in recent years. In this study, we investigate how app adoption changes customers' hotel booking behaviors using a dataset of around 16,000 randomly selected customers' hotel booking and stay information from one of the largest hotel chains in China. Our analyses find that customer purchase frequency and total sales increased after app adoption. Additional investigation shows that 'customers' search behaviors also evolved post-adoption. Specifically, customers tended to book rooms earlier and select options with less discount, which indicates that app users reduced their search intensity after app adoption. We further investigate the differential impact of mobile app adoption on sales, taking into account consumer heterogeneity in price sensitivity and purchase frequency. We find that both price sensitive and insensitive customers increased the number of bookings, and they tended to book earlier post-adoption. Furthermore, price sensitive customers on average booked more expensive rooms with less discount after app adoption, suggesting that these customers likely decreased their search effort. In terms of hotel stay frequency, app adoption increased low-frequency customers' purchase frequency but did not have any significant effect on high-frequency customers. These analyses provide a more nuanced view of how mobile apps change consumer search and purchase behaviors. Our findings have important managerial implications. With a better understanding of customer behaviors, firms can provide incentives to customers who are more likely to be affected by mobile app adoption.
Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas

This paper presents a novel multi-robot coverage path planning (CPP) algorithm - aka SCoPP - that provides a time-efficient solution, with workload balanced plans for each robot in a multi-robot system, based on their initial states. This algorithm accounts for discontinuities (e.g., no-fly zones) in a specified area of interest, and provides an optimized ordered list of way-points per robot using a discrete, computationally efficient, nearest neighbor path planning algorithm. This algorithm involves five main stages, which include the transformation of the user’s input as a set of vertices in geographical coordinates, discretization, load-balanced partitioning, auctioning of conflict cells in a discretized space, and a path planning procedure. To evaluate the effectiveness of the primary algorithm, a multi-unmanned aerial vehicle (UAV) post-flood assessment application is considered, and the performance of the algorithm is tested on three test maps of varying sizes. Additionally, our method is compared with a state-of-the-art method created by Guasella et al. Further analyses on scalability and computational time of SCoPP are conducted. The results show that SCoPP is superior in terms of mission completion time; its computing time is found to be under 2 mins for a large map covered by a 150-robot team, thereby demonstrating its computationally scalability.
Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis.

Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of 19F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring 10 copolymer compositions that outperformed state-of-the-art materials.
The Page curve and baby universes

Black hole thermodynamics suggests that, in order to describe the physics of distant observers, one may model a black hole as a standard quantum system with density of states set by the Bekenstein-Hawking entropy $S_\mathrm{BH}$. This idea has long been considered to be in strong tension with Hawking's prediction that radiation from black holes is nearly thermal, and with low-energy gravity more generally. But the past two years have shown that low-energy gravity does offer a self-consistent description of black hole evaporation consistent with the above idea, and which in particular reproduces the famous Page curve. We provide a brief overview of this new paradigm, focusing on Lorentz-signature asymptotically-flat spacetimes, and emphasising operationally-defined observables that probe the entropy of Hawking radiation.
Fluoroscopy-free ablation in congenital heart disease of moderate or great complexity

BACKGROUND Fluoroscopy-free (FF) ablation has been demonstrated to be safe and successful in patients with structurally normal hearts, but has not been systematically evaluated in patients with congenital heart disease (CHD) of moderate or great (M/G) complexity. This study aimed to evaluate and compare feasibility, safety, and outcomes of FF ablation in patients with or without M/G-CHD. METHODS Consecutive patients undergoing electrophysiologic study and intended catheter ablation over a 24-month period were included. Subgroups were created based on presence and complexity of CHD-M/G-CHD or simple complexity/no CHD (S/N-CHD). Cases with total radiation dose of zero qualified as FF. Demographic and peri-procedural variables and outcome data were analyzed. RESULTS A total of 89 procedures were included with 62 comprising the S/N-CHD group and 27 comprising the M/G-CHD group. Of the M/G-CHD patients, 13 had CHD of great complexity (including 6 single ventricle/Fontan and 2 atrial switch patients). Patients with M/G-CHD were older, had higher BMI, had higher incidence of ventricular dysfunction, and greater incidence of complex arrhythmias. Fluoroscopy-free ablation was achieved in 59% of M/G-CHD and 69% of S/N-CHD patients. Both groups had similar rates of acute procedural success, recurrence, and complications. Fluoroscopy was primarily used to visualize pre-existing transvenous leads and peripheral venous anomalies or to guide transbaffle/transseptal puncture. CONCLUSIONS A fluoroscopy-free ablation approach is feasible, safe, and successful even in patients with M/G-CHD with comparable outcomes to those with S/N-CHD.
Experimental Observations on the Characteristics of an Anode Spot Onset

Experimental observations of the characteristic features of anode spot onset and stabilization in response to a stepped voltage applied to an anode immersed in a low pressure (100 mTorr) helium afterglow are reported in this poster presentation. These observations include spatial and temporal evolution of metastable species measured by planar Laser induced fluorescence (PLIF), electron densities as measured laser-collision induced fluorescence (LCIF) and electric fields around and in the spot as measured by laser-induced fluorescence-dip (LIF -dip) spectroscopy. Oscillations observed during spot formation process are correlated to transient response of the host plasma induced by sudden loss of electron species by the spot. This work was supported by the Office of Fusion Energy Science at the U.S. Department of Energy under contracts DE-AC04-94SL85000 and DE-SC0001939.
Declining HIV incidence in sub-Saharan Africa: a systematic review and meta-analysis of empiric data

Introduction UNAIDS models suggest HIV incidence is declining in sub-Saharan Africa. The objective of this study was to assess whether modelled trends are supported by empirical evidence. Methods We conducted a systematic review and meta-analysis of adult HIV incidence data from sub-Saharan Africa by searching Embase, Scopus, PubMed and OVID databases and technical reports published between 1 January 2010 and 23 July 2019. We included prospective and cross-sectional studies that directly measured incidence from blood samples. Incidence data were abstracted according to population risk group, geographic location, sex, intervention arm and calendar period. Weighted regression models were used to assess incidence trends across general population studies by sex. We also identified studies reporting greater than or equal to three incidence measurements since 2010 and assessed trends within them. Results Total 291 studies, including 22 sub-Saharan African countries, met inclusion criteria. Most studies were conducted in South Africa (n = 102), Uganda (n = 46) and Kenya (n = 41); there were 26 countries with no published incidence data, most in western and central Africa. Data were most commonly derived from prospective observational studies (n = 163; 56%) and from geographically defined populations with limited demographic or risk-based enrolment criteria other than age (i.e., general population studies; n = 151; 52%). Across general population studies, average annual incidence declines since 2010 were 0.12/100 person-years (95% CI: 0.06-0.18; p = 0.001) among men and 0.10/100 person-years (95% CI: -0.02-0.22; p = 0.093) among women in eastern Africa, and 0.25/100 person-years (95% CI: 0.17-034; p Conclusions Empirical incidence data show the rate of new HIV infections is declining in eastern and southern Africa. However, recent incidence data are non-existent or very limited for many countries and key populations.
Estimating DNA methylation potential energy landscapes from nanopore sequencing data.

High-throughput third-generation nanopore sequencing devices have enormous potential for simultaneously observing epigenetic modifications in human cells over large regions of the genome. However, signals generated by these devices are subject to considerable noise that can lead to unsatisfactory detection performance and hamper downstream analysis. Here we develop a statistical method, CpelNano, for the quantification and analysis of 5mC methylation landscapes using nanopore data. CpelNano takes into account nanopore noise by means of a hidden Markov model (HMM) in which the true but unknown (“hidden”) methylation state is modeled through an Ising probability distribution that is consistent with methylation means and pairwise correlations, whereas nanopore current signals constitute the observed state. It then estimates the associated methylation potential energy function by employing the expectation-maximization (EM) algorithm and performs differential methylation analysis via permutation-based hypothesis testing. Using simulations and analysis of published data obtained from three human cell lines (GM12878, MCF-10A, and MDA-MB-231), we show that CpelNano can faithfully estimate DNA methylation potential energy landscapes, substantially improving current methods and leading to a powerful tool for the modeling and analysis of epigenetic landscapes using nanopore sequencing data.
An energy, momentum and angular momentum conserving scheme for a regularization model of incompressible flow

We introduce a new regularization model for incompressible fluid flow, which is a regularization of the EMAC formulation of the Navier-Stokes equations (NSE) that we call EMAC-Reg. The EMAC formulation has proved to be a useful formulation because it conserves energy, momentum and angular momentum even when the divergence constraint is only weakly enforced. However it is still a NSE formulation and so cannot resolve higher Reynolds number flows without very fine meshes. By carefully introducing regularization into the EMAC formulation, we create a model more suitable for coarser mesh computations but that still conserves the same quantities as EMAC, i.e., energy, momentum, and angular momentum. We show that EMAC-Reg, when equipped with a finite element spatial discretization, is well-posed, conserves all the important quantities that EMAC does, and is optimally accurate. Numerical results are provided that show EMAC-Reg is a robust coarse mesh model.
Do Vision Transformers See Like Convolutional Neural Networks

Convolutional neural networks (CNNs) have so far been the de-facto model for visual data. Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or even superior performance on image classification tasks. This raises a central question: how are Vision Transformers solving these tasks? Are they acting like convolutional networks, or learning entirely different visual representations? Analyzing the internal representation structure of ViTs and CNNs on image classification benchmarks, we find striking differences between the two architectures, such as ViT having more uniform representations across all layers. We explore how these differences arise, finding crucial roles played by self-attention, which enables early aggregation of global information, and ViT residual connections, which strongly propagate features from lower to higher layers. We study the ramifications for spatial localization, demonstrating ViTs successfully preserve input spatial information, with noticeable effects from different classification methods. Finally, we study the effect of (pretraining) dataset scale on intermediate features and transfer learning, and conclude with a discussion on connections to new architectures such as the MLP-Mixer.
Multiplication in Sobolev spaces, revisited

In this article, we re-examine some of the classical pointwise multiplication theorems in Sobolev-Slobodeckij spaces, in part motivated by a simple counter-example that illustrates how certain multiplication theorems fail in Sobolev-Slobodeckij spaces when a bounded domain is replaced by Rn. We identify the source of the failure, and examine why the same failure is not encountered in Bessel potential spaces. To analyze the situation, we begin with a survey of the classical multiplication results stated and proved in the 1977 article of Zolesio, and carefully distinguish between the case of spaces defined on the all of Rn and spaces defined on a bounded domain (with e.g. a Lipschitz boundary). However, the survey we give has a few new wrinkles; the proofs we include are based almost exclusively on interpolation theory rather than Littlewood-Paley theory and Besov spaces, and some of the results we give and their proofs, including the results for negative exponents, do not appear in the literature in this form. We also include a particularly important variation of one of the multiplication theorems that is relevant to the study of nonlinear PDE systems arising in general relativity and other areas. The conditions for multiplication to be continuous in the case of Sobolev-Slobodeckij spaces are somewhat subtle and intertwined, and as a result, the multiplication theorems of Zolesio in 1977 have been cited (more than once) in the standard literature in slightly more generality than what is actually proved by Zolesio, and in cases that allow for the construction of counter-examples such as the one included here.
Cytochrome P-450 from bovine adrenocortical mitochondria: an enzyme for the side chain cleavage of cholesterol. II. Subunit structure.

Abstract One mitochondrial cytochrome P-450 from bovine adrenal cortex is capable of catalyzing the conversion of cholesterol to pregnenolone (Shikita, M., and Hall, P. F. (1973) J. Biol. Chem. 248, 5598). This cytochrome (molecular weight, 850,000) was dissociated into subunits of molecular weight 53,000 in 6 m guanidine as demonstrated by sedimentation equilibrium analysis and by treatment with sodium dodecyl sulfate followed by electrophoresis on polyacrylamide gel. Sedimentation equilibrium in buffer of high ionic strength (µ = 0.23) revealed dissociation of the cytochrome into subunits of molecular weights of 470,000 and 200,000. It appears that this adrenocortical P-450 is isolated in a form consisting of 16 subunits and can exist in forms of 8 and 4 subunits; more drastic treatments result in the formation of monomer units. Maximal enzymatic activity is observed at an ionic strength (µ = 0.03 to 0.05) at which the enzyme is seen as protein 16 on analytical ultracentrifugation.
Search for the Higgs Boson Produced in Association with a W Boson at CDF Run II

We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions (p{bar p} {yields} W{sup {+-}}H {yields} {ell}{nu}b{bar b}) at a center of mass energy of 1.96 TeV. WH candidate events have a signature of a single lepton (E{sup {+-}}/{mu}{sup {+-}}), missing transverse energy, and two jets. The search looks for candidate events in approximately 2.7 fb{sup -1} of data recorded with the CDF II detector. The high-p{sub T} lepton (e,{mu}) in the events provides a distinct signature for triggering and most of the events in the dataset come from high-p{sub t} lepton triggers. Our analysis improves on prior searches by including events recorded on the E{sub T} + 2 Jets trigger with a lepton reconstructed as an isolated high-p{sub T} charged particle. We increase the sample purity by identifying ('tagging') long-lived b-hadrons in jets. A neural network combines distinguishing kinematic information into a function optimized for WH sensitivity. The neural network output distributions are consistent with the standard model background expectations and we set limits upper limits on the rate of Higgs production. We set 95% confidence level upper limits on the WH production cross section times branching ratio for Higgsmore » masses from 100 to 150 GeV/c{sup 2} and express our results as a ratio of the experimental limit to the theoretical Standard Model production rate. Our limits range from 3.6 (4.3 expected) to 61.1 (43.2 expected) for Higgs masses from 100 to 150 GeV/c{sup 2}, respectively.« less
Human colostral cells: II. Response to mitogens

Abstract The ability of colostral lymphocytes to respond to pokeweed mitogen, phytohemagglutinin, or Epstein-Barr virus was examined. None of these mitogens induced colostral cells to differentiate into immunoglobulin-containing cells, either in the absence or in the presence of mitomycin C-treated mononuclear cells or T-cell-enriched populations from peripheral blood. Cocultivation of mononuclear cells from the peripheral blood of normal adults with mitomycin C-treated colostral cells resulted in a marked suppression of the generation of immunoglobulin-containing cells in response to pokeweed mitogen. The inhibitory effect was seen in peripheral blood mono-nuclear cell:colostral cell ratios of 1:1, 5:1, and 10:1. However, colostral cells had little effect on the ability of peripheral blood lymphocytes to proliferate in response to phytohemagglutinin or to allogeneic stimulation.
THE PRENATAL PEDIATRIC VISIT.

Various topics of discussion which occur between expectant couples and a pediatrician have been presented. The subject matter in the interview varies in individual circumstances. The relationship which can be established prenatally with the pediatrician offers a valuable supporting force as expectant couples approach parenthood. The pediatrician finds it easier to provide professional service after the birth of the baby when the relationship between physician and parents is well established during the pregnancy.