cssi/SciSciGPT-SciSciNet huggingface dataset:
The role of mesotocin on social bonding in pinyon jays

Abstract The neuropeptide oxytocin influences mammalian social bonding by facilitating the building and maintenance of parental, sexual, and same-sex social relationships. However, we do not know whether the function of the avian homologue mesotocin is evolutionarily conserved across birds. While it does influence avian prosocial behavior, mesotocin’s role in avian social bonding remains unclear. Here, we investigated whether mesotocin regulates the formation and maintenance of same-sex social bonding in pinyon jays (Gymnorhinus cyanocephalus), a member of the crow family. We formed squads of four individually housed birds. In the first, ‘pair-formation’ phase of the experiment, we repeatedly placed pairs of birds from within the squad together in a cage for short periods of time. Prior to entering the cage, we intranasally administered one of three hormone solutions to both members of the pair: mesotocin, oxytocin antagonist, or saline. Pairs received repeated sessions with administration of the same hormone. In the second, ‘pair-maintenance’ phase of the experiment, all four members of the squad were placed together in a large cage, and no hormones were administered. For both phases, we measured the physical proximity between pairs as our proxy for social bonding. We found that, compared to saline, administering mesotocin or oxytocin antagonist did not result in different proximities in either the pair-formation or pair-maintenance phase of the experiment. Therefore, at the dosages and time frames used here, exogenously introduced mesotocin did not influence same-sex social bond formation or maintenance. Like oxytocin in mammals, mesotocin regulates avian prosocial behavior; however, unlike oxytocin, we do not have evidence that mesotocin regulates social bonds in birds.
Tackling Challenges in Seebeck Coefficient Measurement of Ultra-High Resistance Samples with an AC Technique

Author(s): Pan, Z; Zhu, Z; Wilcox, J; Urban, JJ; Yang, F; Wang, H | Abstract: Seebeck coefficient is a widely studied semiconductor property. Conventional Seebeck coefficient measurements are based on DC voltage measurement. Normally this is performed on samples with moderate resistances (e.g., below a few MΩ level). Certain semiconductors are intrinsic and highly resistive. Many examples can be found in optical and photovoltaic materials. The hybrid halide perovskites that have gained extensive attention recently are a good example. Despite great attention from the materials and physics communities, few successful studies exist of the Seebeck coefficient of these compounds, for example CH3NH3PbI3. An AC-technique-based Seebeck coefficient measurement is reported, which makes high-quality Seebeck voltage measurements on samples with resistances up to the 100 GΩ level. This is achieved through a specifically designed setup to enhance sample isolation and increase capacitive impedance. As a demonstration, Seebeck coefficient measurement of a CH3NH3PbI3 thin film is performed at dark, with sample resistance 150 GΩ, and found S = +550 µV K−1. The strategy reported could be applied to the studies of fundamental transport parameters of all intrinsic semiconductors that have not been feasible.
Seasonal Variability and Shared Molecular Signatures of Inactivated Influenza Vaccination in Young and Older Adults

The seasonal influenza vaccine is an important public health tool but is only effective in a subset of individuals. The identification of molecular signatures provides a mechanism to understand the drivers of vaccine-induced immunity. Most previously reported molecular signatures of human influenza vaccination were derived from a single age group or season, ignoring the effects of immunosenescence or vaccine composition. Thus, it remains unclear how immune signatures of vaccine response change with age across multiple seasons. In this study we profile the transcriptional landscape of young and older adults over five consecutive vaccination seasons to identify shared signatures of vaccine response as well as marked seasonal differences. Along with substantial variability in vaccine-induced signatures across seasons, we uncovered a common transcriptional signature 28 days postvaccination in both young and older adults. However, gene expression patterns associated with vaccine-induced Ab responses were distinct in young and older adults; for example, increased expression of killer cell lectin-like receptor B1 (KLRB1; CD161) 28 days postvaccination positively and negatively predicted vaccine-induced Ab responses in young and older adults, respectively. These findings contribute new insights for developing more effective influenza vaccines, particularly in older adults.
Summation and the Poisson formula

By giving the definition of the sum of a series indexed by a set on which a group operates, we prove that the sum of the series that defines the Riemann zeta function, the Epstein zeta function and a few other series indexed by $${\mathbb {Z}}^k$$ has an intrinsic meaning as a complex number, independent of the requirements of analytic continuation. The definition of the sum requires nothing more than algebra and the concept of absolute convergence. The analytical significance of the algebraically defined sum is then explained by an argument that relies on the Poisson formula for tempered distributions.
Colorectal Polyp Segmentation by U-Net with Dilation Convolution

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and a leading cause of cancer deaths in the United States. Colorectal polyps that grow on the intima of the colon or rectum is an important precursor for CRC. Currently, the most common way for colorectal polyp detection and precancerous pathology is the colonoscopy. Therefore, accurate colorectal polyp segmentation during the colonoscopy procedure has great clinical significance in CRC early detection and prevention. In this paper, we propose a novel end-to-end deep learning framework for the colorectal polyp segmentation. The model we design consists of an encoder to extract multi-scale semantic features and a decoder to expand the feature maps to a polyp segmentation map. We improve the feature representation ability of the encoder by introducing the dilated convolution to learn high-level semantic features without resolution reduction. We further design a simplified decoder which combines multi-scale semantic features with fewer parameters than the traditional architecture. Furthermore, we apply three post processing techniques on the output segmentation map to improve colorectal polyp detection performance. Our method achieves state-of-the-art results on CVC-ClinicDB and ETIS-Larib Polyp DB.
Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence

Abstract The development of rational techniques to discover new mechanically relevant proteins for use in variety of applications ranging from mechanics, agriculture to biotechnology remains an outstanding nanomechanical design problem. The key barrier is to design a sequence to fold into a predictable structure to achieve a certain material function. Focused on alpha-helical proteins (as found in skin, hair, and many other mechanically relevant protein materials), we report a Multi-scale Neighborhood-based Neural Network (MNNN) model to learn how a specific amino acid sequence folds into a protein structure. The algorithm predicts the protein structure without using a template or co-evolutional information at a maximum error of 2.1 A. We find that the prediction accuracy is higher than other models and the prediction consumes less than six orders of magnitude time than ab initio folding methods. We demonstrate that MNNN can predict the structure of an unknown protein that agrees with experiments, and our model hence shows a great advantage in the rational design of de novo proteins.
Widespread diversity in the transcriptomes of functionally divergent limb tendons

Key points Tendon is a hypocellular, matrix-rich tissue that has been excluded from comparative transcriptional atlases. These atlases have provided important knowledge about biological heterogeneity between tissues, and our study addresses this important gap. We performed measures on four of the most studied tendons, the Achilles, forepaw flexor, patellar and supraspinatus tendons of both mice and rats. These tendons are functionally distinct and are also among the most commonly injured, and therefore of important translational interest. Approximately one-third of the filtered transcriptome was differentially regulated between Achilles, forepaw flexor, patellar and supraspinatus tendons within either mice or rats. Nearly two-thirds of the transcripts that are expressed in anatomically similar tendons were different between mice and rats. The overall findings from this study identified that although tendons across the body share a common anatomical definition based on their physical location between skeletal muscle and bone, tendon is a surprisingly genetically heterogeneous tissue. Abstract Tendon is a functionally important connective tissue that transmits force between skeletal muscle and bone. Previous studies have evaluated the architectural designs and mechanical properties of different tendons throughout the body. However, less is known about the underlying transcriptional differences between tendons that may dictate their designs and properties. Therefore, our objective was to develop a comprehensive atlas of the transcriptome of limb tendons in adult mice and rats using systems biology techniques. We selected the Achilles, forepaw digit flexor, patellar, and supraspinatus tendons due to their divergent functions and high rates of injury and tendinopathies in patients. Using RNA sequencing data, we generated the Comparative Tendon Transcriptional Database (CTTDb) that identified substantial diversity in the transcriptomes of tendons both within and across species. Approximately 30% of filtered transcripts were differentially regulated between tendons of a given species, and nearly 60% of the filtered transcripts present in anatomically similar tendons were different between species. Many of the genes that differed between tendons and across species are important in tissue specification and limb morphogenesis, tendon cell biology and tenogenesis, growth factor signalling, and production and maintenance of the extracellular matrix. This study indicates that tendon is a surprisingly heterogenous tissue with substantial genetic variation based on anatomical location and species.
Unpaired Image-to-Speech Synthesis With Multimodal Information Bottleneck

Deep generative models have led to significant advances in cross-modal generation such as text-to-image synthesis. Training these models typically requires paired data with direct correspondence between modalities. We introduce the novel problem of translating instances from one modality to another without paired data by leveraging an intermediate modality shared by the two other modalities. To demonstrate this, we take the problem of translating images to speech. In this case, one could leverage disjoint datasets with one shared modality, e.g., image-text pairs and text-speech pairs, with text as the shared modality. We call this problem ``skip-modal generation'' because the shared modality is skipped during the generation process. We propose a multimodal information bottleneck approach that learns the correspondence between modalities from unpaired data (image and speech) by leveraging the shared modality (text). We address fundamental challenges of skip-modal generation: 1) learning multimodal representations using a single model, 2) bridging the domain gap between two unrelated datasets, and 3) learning the correspondence between modalities from unpaired data. We show qualitative results on image-to-speech synthesis; this is the first time such results have been reported in the literature. We also show that our approach improves performance on traditional cross-modal generation, suggesting that it improves data efficiency in solving individual tasks.
A Survey of Tuning Parameter Selection for High-Dimensional Regression

Penalized (or regularized) regression, as represented by lasso and its variants, has become a standard technique for analyzing high-dimensional data when the number of variables substantially excee...
Optimal bipartite network clustering

Author(s): Zhou, Zhixin | Advisor(s): Amini, Arash Ali | Abstract: We consider the problem of bipartite community detection in networks, or more generally the network biclustering problem. We present a fast two-stage procedure based on spectral initialization followed by the application of a pseudo-likelihood classifier twice. Under mild regularity conditions, we establish the weak consistency of the procedure (i.e., the convergence of the misclassification rate to zero) under a general bipartite stochastic block model. We show that the procedure is optimal in the sense that it achieves the optimal convergence rate that is achievable by a biclustering oracle, adaptively over the whole class, up to constants. The optimal rate we obtain sharpens some of the existing results and generalizes others to a wide regime of average degree growth. As a special case, we recover the known exact recovery threshold in the $\log n$ regime of sparsity. To obtain the general consistency result, as part of the provable version of the algorithm, we introduce a block partitioning scheme that is also computationally attractive, allowing for distributed implementation of the algorithm without sacrificing optimality. The provable version of the algorithm is derived from a general blueprint for pseudo-likelihood biclustering algorithms that employ simple EM type updates. We show the effectiveness of this general class by numerical simulations.
Pattern Equivariant Mass Transport in Aperiodic Tilings and Cohomology

Suppose that we have a repetitive and aperiodic tiling $\bf T$ of $\mathbb{R}^n$, and two mass distributions $f_1$ and $f_2$ on $\mathbb{R}^n$, each pattern equivariant with respect to $\bf T$. Under what circumstances is it possible to do a bounded transport from $f_1$ to $f_2$? When is it possible to do this transport in a strongly or weakly pattern-equivariant way? We reduce these questions to properties of the \v Cech cohomology of the hull of $\bf T$, properties that in most common examples are already well-understood.
The Synthesizability of Molecules Proposed by Generative Models

The discovery of functional molecules is an expensive and time-consuming process, exemplified by the rising costs of small molecule therapeutic discovery. One class of techniques of growing interest for early stage drug discovery is de novo molecular generation and optimization, catalyzed by the development of new deep learning approaches. These techniques can suggest novel molecular structures intended to maximize a multiobjective function, e.g., suitability as a therapeutic against a particular target, without relying on brute-force exploration of a chemical space. However, the utility of these approaches is stymied by ignorance of synthesizability. To highlight the severity of this issue, we use a data-driven computer-aided synthesis planning program to quantify how often molecules proposed by state-of-the-art generative models cannot be readily synthesized. Our analysis demonstrates that there are several tasks for which these models generate unrealistic molecular structures despite performing well on popular quantitative benchmarks. Synthetic complexity heuristics can successfully bias generation toward synthetically tractable chemical space, although doing so necessarily detracts from the primary objective. This analysis suggests that to improve the utility of these models in real discovery workflows, new algorithm development is warranted.
COVID-19: responsibility and accountability in a world of rationing

The COVID-19 pandemic is the first modern public health crisis with the potential to overwhelm the public health care system When rationing of services, drugs, and equipment must occur, health care providers have a responsibility to make rationing decisions fairly, both procedurally and substantively In addition, health care providers, like all professionals, are accountable for their decisions The legal standard of care requires providers to exercise the skill and knowledge normally possessed by providers in good standing in the same field or class of practice acting in the same or similar circumstances But making rationing decisions in crisis conditions, like those created by COVID-19, is not the same as or similar to decision making in non-crisis conditions Thus, the standard of care, properly applied, expects less of providers practicing under the stress of COVID-19’s triage conditions Because many health care providers do not perceive this is true, and for pragmatic and normative reasons, policymakers should articulate rules limiting providers’ liability for rationing decisions—as well as other acts and omissions—occurring in and attendant to crisis conditions These rules should not, however, create absolute immunities As the COVID-19 pandemic unfolds, more states are embracing this approach
Highly Variable COI Haplotype Diversity Between Three Species of Invasive Pest Fruit Fly Reflects Remarkably Incongruent Demographic Histories

Distance decay principles predict that species with larger geographic ranges would have greater intraspecific genetic diversity than more restricted species. However, invasive pest species may not follow this prediction, with confounding implications for tracking phenomena including original ranges, invasion pathways and source populations. We sequenced an 815 base-pair section of the COI gene for 441 specimens of Bactrocera correcta, 214 B. zonata and 372 Zeugodacus cucurbitae; three invasive pest fruit fly species with overlapping hostplants. For each species, we explored how many individuals would need to be included in a study to sample the majority of their haplotype diversity. We also tested for phylogeographic signal and used demographic estimators as a proxy for invasion potency. We find contrasting patterns of haplotype diversity amongst the species, where B. zonata has the highest diversity but most haplotypes were represented by singletons; B. correcta has ~7 dominant haplotypes more evenly distributed; Z. cucurbitae has a single dominant haplotype with closely related singletons in a 'star-shape' surrounding it. We discuss how these differing patterns relate to their invasion histories. None of the species showed meaningful phylogeographic patterns, possibly due to gene-flow between areas across their distributions, obscuring or eliminating substructure.
Large bipolarons and superconductivity

Superconductivity has long been speculated to result from charge carriers paired as mobile charged bosons. Although the pairing of carriers as small (single-site) bipolarons is known, small bipolarons readily localize. By contrast, large (multi-site) bipolarons, in analogy with large polarons, should be mobile. It is shown that large bipolarons can form in solids with very displaceable ions, e.g., many oxides. Large-polaronic (but not small-polaronic) carriers produce absorption spectra like the carrier-induced absorptions observed in cuprates. Redistribution of the self-trapped carriers of large bipolarons among sites of carriers' molecular orbitals in response to atomic motions lowers phonon frequencies. The dependence of the phonon zero-point energy on the spatial distribution of large bipolarons produces a phonon-mediated attraction between them. This dynamic quantum-mechanical attraction fosters the condensation of large bipolarons into a liquid. Superconductivity can result when the large-bipolarons' groundstate remains liquid rather than solidifying.
Pituitary germinomas: a multi-institutional study analyzing patient demographics and management patterns

Intracranial germinomas are exceedingly rare tumors found in the pineal and suprasellar regions. The extremely low incidence of pituitary germinoma has resulted in a significant gap in knowledge regarding its demographics, management, and treatment outcomes. We present the largest multicenter analysis of pituitary germinomas to date, focused on analyzing demographic and management patterns. This study utilizes the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program (2004–2016) to study patients with a primary intracranial germinoma of the pituitary gland. We analyzed demographic information and management strategies among adult and pediatric populations and conducted a 20-year overall survival analysis using Kaplan-Meier curve for a descriptive evaluation of survival outcomes between age groups and treatment groups. 92 patients were included in the study, consisting of 58% pediatric patients and 42% adults, with overall 60% males. 82% patients received radiation as part of the treatment, with no significant difference between pediatric and adult groups. Chemotherapy was used significantly more in pediatrics (p = 0.0002) while surgery was significantly more common in adults (p = 0.0117). The most common treatment in pediatrics was radiation + chemotherapy (47%), while the most common treatment in adults was radiation + gross total resection + chemotherapy (23%) followed by radiation + gross total resection (19%). Younger age, radiotherapy, and chemotherapy were associated with increased 20-year survival on Kaplan–Meier curves. There exist significant differences in the management of pediatric and adult populations with pituitary germinomas. The low incidence of these tumors makes them challenging to study, but also highlights the importance of national cancer registries in amassing sufficient patient data from which to draw evidence-based conclusions.
Spatiotemporal Risk Analysis

This article focuses on the issues related to risk assessment when the domain consists of space and time. Spatiotemporal risk assessment could involve separate analyses of risk in a geographical domain, risk in a temporal window, or the interaction of space and time. Often the focus will be on magnitude and frequency of an outcome but this will have a spatial and temporal expression. The link with surveillance for adverse outcomes is made, and health risk is a focus of the review, although the concepts illustrated can be applied in other areas such as financial or industrial risk analysis. Keywords: space; time; surveillance; risk; syndromes; aberrations
Reconciling Diverging Views on Mineral Depletion: A Modified Cumulative Availability Curve Applied to Copper Resources

Abstract Mineral depletion is a perennial concern in natural resources management. Despite a multiplicity of perspectives, most analyses fall largely into one of two groups. Studies in the first group take the fixed quantity of any material in the earth as the starting point for analysis, focus primarily on physical stocks and flows of mineral resources, and assess when society will run out of a resource or when production and use will peak (the fixed-stock viewpoint). Studies in the second group start by noting the heterogeneous nature of mineral resources and our incomplete knowledge of the quantity and quality of minerals in the earth, focus on the dynamic nature of mineral availability, and assess the ability of society to adjust to mineral depletion at existing mines though mineral exploration and mine development, technological innovation, and substitution (the opportunity-cost viewpoint). This paper seeks to reconcile these diverging perspectives by developing a modified cumulative availability curve (CAC) – which combines physical stocks and flows (from the fixed-stock perspective) with geologic stock uncertainty, different demand scenarios and extraction costs (from the opportunity-cost perspective). When applied to copper resources, the modified CAC suggests that copper demand is likely to be satisfied from known deposits until about 2075. Thereafter, society's ability to discover and develop previously unknown deposits and improve the efficiency of their production - as well as efforts at substitution and improving recycling and re-use of copper - will importantly influence whether copper demand is satisfied and at what copper price.
Surface Bubble Growth in Plasmonic Nanoparticle Suspension

Understanding the growth dynamics of the microbubbles produced by plasmonic heating can benefit a wide range of applications like microfluidics, catalysis, micropatterning, and photothermal energy conversion. Usually, surface plasmonic bubbles are generated on plasmonic structures predeposited on the surface subject to laser heating. In this work, we investigate the growth dynamics of surface microbubbles generated in plasmonic nanoparticle (NP) suspension. We observe much faster bubble growth rates compared to those in pure water with surface plasmonic structures. Our analyses show that the volumetric heating effect around the surface bubble due to the existence of NPs in the suspension is the key to explaining this difference. Such volumetric heating increases the temperature around the surface bubble more efficiently compared to surface heating which enhances the expelling of dissolved gas. We also find that the bubble growth rates can be tuned in a very wide range by changing the concentration of NPs, besides laser power and dissolved gas concentration.
Trends in wheelchair recommendations in a dedicated seating department

In the early 2000 s, investigators at the federal government noted growing Medicare expenditures for power mobility devices, which were related to high profit margins and fraudulent activity. Medic...
Features or Shape? Tackling the False Dichotomy of Time Series Classification.

Time series classification is an important task in its own right, and it is often a precursor to further downstream analytics. To date, virtually all works in the literature have used either shape-based classification using a distance measure or feature-based classification after finding some suitable features for the domain. It seems to be underappreciated that in many datasets it is the case that some classes are best discriminated with features, while others are best discriminated with shape. Thus, making the shape vs. feature choice will condemn us to poor results, at least for some classes. In this work, we propose a new model for classifying time series that allows the use of both shape and feature-based measures, when warranted. Our algorithm automatically decides which approach is best for which class, and at query time chooses which classifier to trust the most. We evaluate our idea on real world datasets and demonstrate that our ideas produce statistically significant improvement in classification accuracy.
STATE AND NEEDS OF AGRICULTURAL KNOWLEDGE AND INNOVATION SYSTEM IN BULGARIA

The goal of this paper is to access the state, specify trends, and identify intervention needs of Agricultural Knowledge and Innovation System (AKIS) in Bulgaria, and assist policy formation for the next programing period. Modern scientific approaches of SWOT, Strategic Orientation, Gap Analysis, Comparative Institutional Analysis, etc. are used to identify actors and relations, trends in development, assess Strengths, Weaknesses, Opportunities and Threats, formulate an adequate strategy, and specify overall and public intervention needs of AKIS in the country. Bulgarian AKIS demonstrates low resource endowment and efficiency, domination of outdated public institutions and undeveloped private sector, insufficient sharing of knowledge and innovations, slow and uneven application of modern technologies, varieties, production and management methods, digitalization, etc. in different types of farms, subsectors of agriculture and regions of the country. The list of specified AKIS needs is provided to the government for taking a political decision about appropriate measures for public intervention. This study demonstrates that preparation of country’s RDP is (has to be) based on the comprehensive scientific approach while the research community proves that it can contribute to solving an important academic and practical problem.
Learning Robust Models for e-Commerce Product Search

Showing items that do not match search query intent degrades customer experience in e-commerce. These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search logs. Mitigating the problem requires a large labeled dataset, which is expensive and time-consuming to obtain. In this paper, we develop a deep, end-to-end model that learns to effectively classify mismatches and to generate hard mismatched examples to improve the classifier. We train the model end-to-end by introducing a latent variable into the cross-entropy loss that alternates between using the real and generated samples. This not only makes the classifier more robust but also boosts the overall ranking performance. Our model achieves a relative gain compared to baselines by over 26% in F-score, and over 17% in Area Under PR curve. On live search traffic, our model gains significant improvement in multiple countries.
Exploiting Colorimetry for Fidelity in Data Visualization

Advances in multimodal characterization methods fuel a generation of increasing immense hyper-dimensional datasets. Color mapping is employed for conveying higher dimensional data in two-dimensiona...
A locally hyperbolic 3-manifold that is not homotopy equivalent to any hyperbolic 3-manifold

We construct a locally hyperbolic 3-manifold $M$ such that $\pi_ 1(M)$ has no divisible subgroups. We then show that $M$ is not homotopy equivalent to any complete hyperbolic manifold.
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models

Starting with Gilmer et al. (2018), several works have demonstrated the inevitability of adversarial examples based on different assumptions about the underlying input probability space. It remains unclear, however, whether these results apply to natural image distributions. In this work, we assume the underlying data distribution is captured by some conditional generative model, and prove intrinsic robustness bounds for a general class of classifiers, which solves an open problem in Fawzi et al. (2018). Building upon the state-of-the-art conditional generative models, we study the intrinsic robustness of two common image benchmarks under $\ell_2$ perturbations, and show the existence of a large gap between the robustness limits implied by our theory and the adversarial robustness achieved by current state-of-the-art robust models. Code for all our experiments is available at this https URL.
Elasticity of attention and optimal monetary policy

Abstract Optimal monetary policy depends on whether agents have exogenous, endogenous–inelastic, or endogenous–elastic attention. Under elastic attention, optimal monetary policy induces equilibria that are not possible under the other two settings: no attention to any shocks that generate inefficient economic fluctuations.
Vacuum acceleration of electrons in a dynamic laser pulse

A planar laser pulse propagating in vacuum can exhibit an extremely large ponderomotive force. This force, however, cannot impart net energy to an electron: As the pulse overtakes the electron, the initial impulse from its rising edge is completely undone by an equal and opposite impulse from its trailing edge. Here we show that planarlike "flying focus" pulses can break this symmetry, imparting relativistic energies to electrons. The intensity peak of a flying focus-a moving focal point resulting from a chirped laser pulse focused by a chromatic lens-can travel at any subluminal velocity, forward or backward. As a result, an electron can gain enough momentum in the rising edge of the intensity peak to outrun and avoid the trailing edge. Accelerating the intensity peak can further boost the momentum gain. Theory and simulations demonstrate that these dynamic intensity peaks can backwards accelerate electrons to the MeV energies required for radiation and electron diffraction probes of high energy density materials.
TIMELY: pushing data movements and interfaces in PIM accelerators towards local and in time domain

Resistive-random-access-memory (ReRAM) based processing-in-memory $(\mathrm{R}^{2}\mathrm{P}\mathrm{I}\mathrm{M})$ accelerators show promise in bridging the gap between Internet of Thing devices’ constrained resources and Convolutional/Deep Neural Networks’ (CNNs/DNNs’) prohibitive energy cost. Specifically, $\mathrm{R}^{2}\mathrm{P}\mathrm{I}\mathrm{M}$ accelerators enhance energy efficiency by eliminating the cost of weight movements and improving the computational density through ReRAM’s high density. However, the energy efficiency is still limited by the dominant energy cost of input and partial sum (Psum) movements and the cost of digital-to-analog (D/A) and analog-to-digital (AD) interfaces. In this work, we identify three energy-saving opportunities in $\mathrm{R}^{2}\mathrm{P}\mathrm{I}\mathrm{M}$ accelerators: analog data locality, time-domain interfacing, and input access reduction, and propose an innovative $\mathrm{R}^{2}\mathrm{P}\mathrm{I}\mathrm{M}$ accelerator called TIMELY, with three key contributions: (1) TIMELY adopts analog local buffers (ALBs) within ReRAM crossbars to greatly enhance the data locality, minimizing the energy overheads of both input and Psum movements; (2) TIMELY largely reduces the energy of each single D/A (and AD) conversion and the total number of conversions by using time-domain interfaces (TDIs) and the employed ALBs, respectively; (3) we develop an only-once input read $(\mathrm{O}^{2}\mathrm{I}\mathrm{R})$ mapping method to further decrease the energy of input accesses and the number of D/A conversions. The evaluation with more than 10 CNN/DNN models and various chip configurations shows that, TIMELY outperforms the baseline $\mathrm{R}^{2}\mathrm{P}\mathrm{I}\mathrm{M}$ accelerator, PRIME, by one order of magnitude in energy efficiency while maintaining better computational density (up to $31.2\times$) and throughput (up to $736.6\times$). Furthermore, comprehensive studies are performed to evaluate the effectiveness of the proposed ALB, TDI, and $\mathrm{O}^{2}\mathrm{I}\mathrm{R}$ in terms of energy savings and area reduction.
Bactopia: a Flexible Pipeline for Complete Analysis of Bacterial Genomes

Sequencing of bacterial genomes using Illumina technology has become such a standard procedure that often data are generated faster than can be conveniently analyzed. We created a new series of pipelines called Bactopia, built using Nextflow workflow software, to provide efficient comparative genomic analyses for bacterial species or genera. Bactopia consists of a data set setup step (Bactopia Data Sets [BaDs]), which creates a series of customizable data sets for the species of interest, the Bactopia Analysis Pipeline (BaAP), which performs quality control, genome assembly, and several other functions based on the available data sets and outputs the processed data to a structured directory format, and a series of Bactopia Tools (BaTs) that perform specific postprocessing on some or all of the processed data. BaTs include pan-genome analysis, computing average nucleotide identity between samples, extracting and profiling the 16S genes, and taxonomic classification using highly conserved genes. It is expected that the number of BaTs will increase to fill specific applications in the future. As a demonstration, we performed an analysis of 1,664 public Lactobacillus genomes, focusing on Lactobacillus crispatus, a species that is a common part of the human vaginal microbiome. Bactopia is an open source system that can scale from projects as small as one bacterial genome to ones including thousands of genomes and that allows for great flexibility in choosing comparison data sets and options for downstream analysis. Bactopia code can be accessed at https://www.github.com/bactopia/bactopiaIMPORTANCE It is now relatively easy to obtain a high-quality draft genome sequence of a bacterium, but bioinformatic analysis requires organization and optimization of multiple open source software tools. We present Bactopia, a pipeline for bacterial genome analysis, as an option for processing bacterial genome data. Bactopia also automates downloading of data from multiple public sources and species-specific customization. Because the pipeline is written in the Nextflow language, analyses can be scaled from individual genomes on a local computer to thousands of genomes using cloud resources. As a usage example, we processed 1,664 Lactobacillus genomes from public sources and used comparative analysis workflows (Bactopia Tools) to identify and analyze members of the L. crispatus species.
The Information Content of Capital Controls

Capital controls, policy measures used by governments to regulate cross-country financial flows, have become standard policy options in many emerging market economies. This paper will focus on what capital controls reveal about the state of the economy and the implications of such revelation for policy efficacy. Using a small open economy model with a collateral constraint and overborrowing relative to the social optimum, this paper incorporates a representative agent's Bayesian updating of information in response to change in policy and show that the efficacy of capital controls to contain financial crises and improve welfare could be undermined if the agent rationally learns from policy. Empirically, this paper finds that capital controls convey important information market participants use to improve their understanding of fundamentals. This paper highlights the need for policymakers to consider the unintended consequences of information revelation in the design of capital flow management policies.
Analytic Study of Double Descent in Binary Classification: The Impact of Loss

Extensive empirical evidence reveals that, for a wide range of different learning methods and data sets, the risk curve exhibits a double-descent (DD) trend as a function of the model size. In our recent coauthored paper [Deng et al., ’19], we proposed simple binary linear classification models and showed that the test error of gradient descent (GD) with logistic loss undergoes a DD. In this paper, we complement these results by extending them to GD with square loss. We show that the DD phenomenon persists, but we also identify several differences compared to logistic loss. This emphasizes that crucial features of DD curves (such as their transition threshold and global minima) depend both on the training data and on the learning algorithm. We further study the dependence of DD curves on the size of the training set. Similar to [Deng et al., ’19] our results are analytic: we plot the DD curves by first deriving sharp asymptotics for the test error under Gaussian features. Albeit simple, the models permit a principled study, the outcomes of which theoretically corroborate related empirical findings occurring in more complex learning tasks.
X-ray scattering from a plane parallel slab using radiation transfer considerations

The X-ray scattering power of a plane parallel homogeneous slab of material is derived using radiation intensity transfer equations. The scattering power scales with the ratio of the scattering coefficient of interest to the total attenuation coefficient. The results can be used to guide the choice of slab thickness, scattering geometry and photon energy to maximize the scattering power in both elastic and inelastic X-ray scattering experiments.
Kesten–McKay Law for Random Subensembles of Paley Equiangular Tight Frames

We apply the method of moments to prove a recent conjecture of Haikin, Zamir and Gavish concerning the distribution of the singular values of random subensembles of Paley equiangular tight frames. Our analysis applies more generally to real equiangular tight frames of redundancy 2, and we suspect similar ideas will eventually produce more general results for arbitrary choices of redundancy.
Cortical Presynaptic Boutons Progressively Engulf Spinules as They Mature.

Despite decades of discussion in the neuroanatomical literature, the role of the synaptic ‘spinule’ in synaptic development and function remains elusive. Canonically, spinules are finger-like projections that emerge from postsynaptic spines and can become enveloped by presynaptic boutons. When a presynaptic bouton encapsulates a spinule in this manner, the membrane apposition between the spinule and surrounding bouton can be significantly larger than the membrane interface at the synaptic active zone. Hence, spinules may represent a mechanism for extrasynaptic neuronal communication, and/or may function as structural ‘anchors’ that increase the stability of cortical synapses. Yet despite their potential to impact synaptic function, we have little information on the percentages of developing and adult cortical bouton populations that contain spinules, the percentages of these cortical spinule-bearing boutons (SBBs) that contain spinules from distinct neuronal/glial origins, or whether the onset of activity or cortical plasticity are correlated with increased prevalence of cortical SBBs. Here, we employed 2D and 3D electron microscopy to determine the prevalence of spinules in excitatory presynaptic boutons at key developmental time points in the primary visual cortex (V1) of female and male ferrets. We find that the prevalence of SBBs in V1 increases across postnatal development, such that ∼25% of excitatory boutons in late adolescent ferret V1 contain spinules. In addition, we find that a majority of spinules within SBBs at later developmental time points emerge from postsynaptic spines and adjacent boutons/axons, suggesting that synaptic spinules may enhance synaptic stability and allow for axo-axonal communication in mature sensory cortex. Significance Statement Synaptic spinules are finger-like projections from neurites that can become completely embedded within presynaptic boutons, potentially enhancing synaptic communication and stability. Yet while their existence has been discussed for decades, spinule prevalence, projection origins, and relationship to neocortical sensory activity remain unknown. In this study, we employed 2D and 3D electron microscopy techniques to characterize the development of excitatory cortical spinule-bearing boutons (SBBs) and their relationship to sensory activity and plasticity. Our results demonstrate that neocortical presynaptic SBB prevalence is not correlated with the onset of sensory activity or heightened cortical plasticity, and that by late adolescence nearly one-quarter of presynaptic boutons contain a spinule. Hence, synaptic spinules may play progressively important roles in cortical function as these synapses mature.
Enterococcus faecalis Polymicrobial Interactions Facilitate Biofilm Formation, Antibiotic Recalcitrance, and Persistent Colonization of the Catheterized Urinary Tract

Indwelling urinary catheters are common in health care settings and can lead to catheter-associated urinary tract infection (CAUTI). Long-term catheterization causes polymicrobial colonization of the catheter and urine, for which the clinical significance is poorly understood. Through prospective assessment of catheter urine colonization, we identified Enterococcus faecalis and Proteus mirabilis as the most prevalent and persistent co-colonizers. Clinical isolates of both species successfully co-colonized in a murine model of CAUTI, and they were observed to co-localize on catheter biofilms during infection. We further demonstrate that P. mirabilis preferentially adheres to E. faecalis during biofilm formation, and that contact-dependent interactions between E. faecalis and P. mirabilis facilitate establishment of a robust biofilm architecture that enhances antimicrobial resistance for both species. E. faecalis may therefore act as a pioneer species on urinary catheters, establishing an ideal surface for persistent colonization by more traditional pathogens such as P. mirabilis.
Degradation of Lon in Caulobacter crescentus

ABSTRACT Protein degradation is an essential process in all organisms. This process is irreversible and energetically costly; therefore, protein destruction must be tightly controlled. While environmental stresses often lead to upregulation of proteases at the transcriptional level, little is known about posttranslational control of these critical machines. In this study, we show that in Caulobacter crescentus levels of the Lon protease are controlled through proteolysis. Lon turnover requires active Lon and ClpAP proteases. We show that specific determinants dictate Lon stability with a key carboxy-terminal histidine residue driving recognition. Expression of stabilized Lon variants results in toxic levels of protease that deplete normal Lon substrates, such as the replication initiator DnaA, to lethally low levels. Taken together, results of this work demonstrate a feedback mechanism in which ClpAP and Lon collaborate to tune Lon proteolytic capacity for the cell. IMPORTANCE Proteases are essential, but unrestrained activity can also kill cells by degrading essential proteins. The quality-control protease Lon must degrade many misfolded and native substrates. We show that Lon is itself controlled through proteolysis and that bypassing this control results in toxic consequences for the cell.
Innovative Empirical Model for Predicting National Banks’ Financial Failure with Artificial Intelligence Subset Data Analysis in the United States

The principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks’ equity investment valuations and create an empirical model for predicting national banks’ financial failure in the United States. The focal period of the study was from 2009 to 2012, and public data sources used. It is not known to what extent national banks’ stock value investments are based on the return on equity. This causal-comparative study explores the degree to which national banks’ value investment in terms of the price to earnings ratio impacts their return on equity and the extent to which these banks’ stock value investment in terms of dividend yield impacts their return on equity. We used statistical modeling and the machine learning model to find hidden patterns in the input data. The principal finding of this research is that the median earnings per share in 2012 and the dividend yield in 2009 were significantly larger than the median return on equity in 2009 and 2012. Additionally, the dividend yield in 2012 was significantly smaller than the median return on equity in 2012. These findings can contribute to improving our understanding of how banks can predict financial failure using the new machine learning features of artificial intelligence to build an early warning system with the innovative risk measurement tool.