cssi/SciSciGPT-SciSciNet huggingface dataset:
Autonomous Control of a Line Follower Robot Using a Q-Learning Controller

In this paper, a MIMO simulated annealing (SA)-based Q-learning method is proposed to control a line follower robot. The conventional controller for these types of robots is the proportional (P) controller. Considering the unknown mechanical characteristics of the robot and uncertainties such as friction and slippery surfaces, system modeling and controller designing can be extremely challenging. The mathematical modeling for the robot is presented in this paper, and a simulator is designed based on this model. The basic Q-learning methods are based pure exploitation and the $\varepsilon$ -greedy methods, which help exploration, can harm the controller performance after learning completion by exploring nonoptimal actions. The simulated annealing–based Q-learning method tackles this drawback by decreasing the exploration rate when the learning increases. The simulation and experimental results are provided to evaluate the effectiveness of the proposed controller.
Access schedules mediate the impact of high fat diet on ethanol intake and insulin and glucose function in mice

Abstract Alcoholism and high fat diet (HFD)-induced obesity individually promote insulin resistance and glucose intolerance in clinical populations, increasing risk for metabolic diseases. HFD can also stimulate alcohol intake in short-term clinical studies. Unfortunately, there is currently a disconnect between animal models and the clinical findings, as animal studies typically show that HFD decreases ethanol intake while ethanol intake mitigates HFD-induced effects on insulin and glucose dysfunction. However, most previous animal studies utilized forced or continuous HFD and/or ethanol. In three experiments we sought to determine whether HFD (HFD = 60% calories from fat) vs. control diet (chow = 16% fat) alters voluntary two-bottle choice ethanol intake in male C57Bl/6J mice given differing access schedules for 6–7 weeks, and we assessed the resultant impact on metabolic function via insulin and glucose tolerance tests. Experiment 1: Unlimited Access Ethanol + HFD (UAE + HFD; n = 15; 10% ethanol v/v, ad libitum diet and ethanol) or UAE + Chow (n = 15). Experiment 2: Limited Access Ethanol + HFD (LAE + HFD; n = 15; ethanol = 4 h/day; 3 days/week, ad libitum diet) or LAE + Chow (n = 15) with increasing ethanol concentrations (10%, 15%, 20%). Experiment 3: Intermittent HFD with limited access to ethanol (iHFD-E; HFD = single 24-h session/week; ethanol = 4 h/day; 4 days/week) (n = 10). UAE + HFD mice consumed significantly less ethanol and were insulin-resistant and hyperglycemic compared with UAE + Chow mice. LAE + HFD mice consumed ethanol similarly to LAE + Chow mice, but exhibited hyperglycemia, insulin resistance, and glucose intolerance. iHFD-E mice displayed binge eating-like behaviors and consumed significantly more ethanol than mice given ad libitum chow or HFD. iHFD-E mice did not have significantly altered body composition, but developed insulin insensitivity and glucose intolerance. These findings suggest that access schedules influence HFD effects on ethanol consumption and resultant metabolic dysfunction, ethanol intake does not improve HFD-induced metabolic dysfunction, and binge eating-like behaviors can transfer to binge drinking behaviors.
The SDP value for random two-eigenvalue CSPs

We precisely determine the SDP value (equivalently, quantum value) of large random instances of certain kinds of constraint satisfaction problems, ``two-eigenvalue 2CSPs''. We show this SDP value coincides with the spectral relaxation value, possibly indicating a computational threshold. Our analysis extends the previously resolved cases of random regular $\mathsf{2XOR}$ and $\textsf{NAE-3SAT}$, and includes new cases such as random $\mathsf{Sort}_4$ (equivalently, $\mathsf{CHSH}$) and $\mathsf{Forrelation}$ CSPs. Our techniques include new generalizations of the nonbacktracking operator, the Ihara--Bass Formula, and the Friedman/Bordenave proof of Alon's Conjecture.
Stable single axial mode operation of injection-seeded Q-switched Nd:YAG laser by real-time resonance tracking method

A simple real-time resonance tracking method is described and experimentally demonstrated for stable single axial mode operation of an injection-seeded Q-switched Nd:YAG laser. We have achieved stable single axial mode operation with mW of seeding power in noisy environment over hours. The mode selectivity dependence on finite Q-switch opening time is demonstrated and quantitatively modeled for Q-switch opening time optimization.
The effect of sell-by dates on purchase volume and food waste

Abstract This article constructs a theoretical model of rational food waste and analyzes how an extension of a food product’s sell-by date affects households’ decisions of purchase volume and food waste. We then use the elimination of New York City’s regulation of sell-by dates for pasteurized milk products as an empirical case and examine whether the city’s new policy effectively reduces food waste and improves consumer welfare. Our results suggest that the new policy reduces purchase volume by about 10%. Both theoretically and empirically, we show that while the observed quantity declines, the actual consumption of milk increases, implying a reduction in food waste by more than 10%. Furthermore, we prove that this consumer welfare-improving pattern is generalizable to all types of perishable food with price-inelastic demand.
The artificial morality of private law: The persistence of an illusion

In the public at large, property and contract law are commonly thought to reflect moral proprietary and promissory rights. Contemporary philosophers are mostly sceptical about natural property righ...
Stochastic differential equations with critical drifts

Abstract We establish the well-posedness of SDE with the additive noise when a singular drift belongs to the critical spaces. We prove that if the drift belongs to the Orlicz-critical space L q , 1 ( [ 0 , T ] , L x p ) for p , q ∈ ( 1 , ∞ ) satisfying 2 q + d p = 1 , then the corresponding SDE admits a unique strong solution. We also derive the Sobolev regularity of a solution under the Orlicz-critical condition.
Neonicotinoids in U.S. Maize: Insecticide Substitution Effects and Environmental Risk

Abstract This study exploits a novel dataset containing more than 89,000 farm-level surveys over a 17-year period to investigate how neonicotinoid seed treatments in maize, now ubiquitous, have affected the use of other insecticides. Neonicotinoid insecticides are the most used class of insecticides in the world, but they are controversial because of their high toxicity to honeybees. In the United States, maize production accounts for the majority of neonicotinoid use, mostly as seed treatments. We find that neonicotinoids substituted for other major insecticides: plots planted with neonicotinoid-treated seeds were 52% and 47% less likely to be treated with pyrethroid and organophosphate insecticides, respectively. Although honeybees have been put at greater risk by neonicotinoids, the changed pattern of pest control instruments has reduced toxicity risk for mammals, birds, and fish. We also find that adoption of genetically engineered insect-resistant maize varieties significantly reduced the use of organophosphate and pyrethroid insecticides, thereby reducing toxicity exposure to all examined taxa. Policies aimed at restricting neonicotinoid use may need to account for undesirable unintended consequences.
Few-Shot Acoustic Event Detection Via Meta Learning

We study few-shot acoustic event detection (AED) in this paper. Few-shot learning enables detection of new events with very limited labeled data. Compared to other research areas like computer vision, few-shot learning for audio recognition has been under-studied. We formulate few-shot AED problem and explore different ways of utilizing traditional supervised methods for this setting as well as a variety of meta-learning approaches, which are conventionally used to solve few-shot classification problem. Compared to supervised baselines, meta-learning models achieve superior performance, thus showing its effectiveness on generalization to new audio events. Our analysis including impact of initialization and domain discrepancy further validate the advantage of meta-learning approaches in few-shot AED.
The Implications of Heterogeneity and Inequality for Asset Pricing

Does heterogeneity matter for asset pricing and in particular for risk premiums? Starting with an irrelevance result, I classify the literature into two groups of papers taking different routes to link investor heterogeneity and risk premiums. The first group contains models of investors who differ in terms of their preferences, beliefs, or access to markets. Despite their differences, these models have similar implications, and can be analyzed in a unified way. The second group of papers consists of models where investors experience uninsurable income shocks. The goal of this survey is to provide one unified framework to better understand this large literature, and especially to reconcile several of the seemingly inconsistent results found in some seminal papers.
Temporal Dynamics of the Adult Female Lower Urinary Tract Microbiota

ABSTRACT Temporal dynamics of certain human microbiotas have been described in longitudinal studies; variability often relates to modifiable factors or behaviors. Early studies of the urinary microbiota preferentially used samples obtained by transurethral catheterization to minimize vulvovaginal microbial contributions. Whereas voided specimens are preferred for longitudinal studies, the few studies that reported longitudinal data were limited to women with lower urinary tract (LUT) symptoms, due to ease of accessing a clinical population for sampling and the impracticality and risk of collecting repeated catheterized urine specimens in a nonclinical population. Here, we studied the microbiota of the LUT of nonsymptomatic, premenopausal women using midstream voided urine (MSU) specimens to investigate relationships between microbial dynamics and personal factors. Using 16S rRNA gene sequencing and a metaculturomics method called expanded quantitative urine culture (EQUC), we characterized the microbiotas of MSU and periurethral swab specimens collected daily for approximately 3 months from a small cohort of adult women. Participants were screened for eligibility, including the ability to self-collect paired urogenital specimens prior to enrollment. In this population, we found that measures of microbial dynamics related to specific participant-reported factors, particularly menstruation and vaginal intercourse. Further investigation of the trends revealed differences in the composition and diversity of LUT microbiotas within and across participants. These data, in combination with previous studies showing relationships between the LUT microbiota and LUT symptoms, suggest that personal factors relating to the genitourinary system may be an important consideration in the etiology, prevention, and/or treatment of LUT disorders. IMPORTANCE Following the discovery of the collective human urinary microbiota, important knowledge gaps remain, including the stability and variability of this microbial niche over time. Initial urinary studies preferentially utilized samples obtained by transurethral catheterization to minimize contributions from vulvovaginal microbes. However, catheterization has the potential to alter the urinary microbiota; therefore, voided specimens are preferred for longitudinal studies. In this report, we describe microbial findings obtained by daily assessment over 3 months in a small cohort of adult women. We found that, similarly to vaginal microbiotas, lower urinary tract (LUT) microbiotas are dynamic, with changes relating to several factors, particularly menstruation and vaginal intercourse. Our study results show that LUT microbiotas are both dynamic and resilient. They also offer novel opportunities to target LUT microbiotas by preventative or therapeutic means, through risk and/or protective factor modification.
Therapeutic Strategies and Biomarkers to Modulate PARP Activity for Targeted Cancer Therapy

Poly-(ADP-ribose) polymerase 1 (PARP1) is commonly known for its vital role in DNA damage response and repair. However, its enzymatic activity has been linked to a plethora of physiological and pathophysiological transactions ranging from cellular proliferation, survival and death. For instance, malignancies with BRCA1/2 mutations heavily rely on PARP activity for survival. Thus, the use of PARP inhibitors is a well-established intervention in these types of tumors. However, recent studies indicate that the therapeutic potential of attenuating PARP1 activity in recalcitrant tumors, especially where PARP1 is aberrantly overexpressed and hyperactivated, may extend its therapeutic utility in wider cancer types beyond BRCA-deficiency. Here, we discuss treatment strategies to expand the tumor-selective therapeutic application of PARP inhibitors and novel approaches with predictive biomarkers to perturb NAD+ levels and hyperPARylation that inactivate PARP in recalcitrant tumors. We also provide an overview of genetic alterations that transform non-BRCA mutant cancers to a state of "BRCAness" as potential biomarkers for synthetic lethality with PARP inhibitors. Finally, we discuss a paradigm shift for the use of novel PARP inhibitors outside of cancer treatment, where it has the potential to rescue normal cells from severe oxidative damage during ischemia-reperfusion injury induced by surgery and radiotherapy.
Effect of polyvinylpyrrolidone on the physical properties of titanium dioxide suspensions

Abstract The effect of polyvinylpyrrolidone (PVP) on the physical properties of aqueous and non-aqueous (methanol-based) TiO 2 suspensions was studied by measuring PVP adsorption and the rheological properties and dispersion stability of the suspensions. The amount of PVP adsorbed onto TiO 2 from the methanol solution is much larger than that from the aqueous solution and the effect on the dispersion stability of the suspension is much greater in the methanol-based suspension than in the aqueous suspension. The amount of adsorption of PVP is independent of the molecular weight, while the thickness of the adsorbed layer increases as the molecular weight increases. As the PVP concentration increases, the viscosity of the PVP solution increases, while the relative viscosity and the dynamic rheological parameters (G′, G″) of the suspension decrease, indicating that the network structure formed by dispersed TiO 2 particles in the absence of PVP diminishes with the increase in the PVP concentration. The dispersion stability of the methanol-based suspension increases with the increase in the concentration of PVP of high molecular weight, while the dispersion stability of the aqueous suspension is not significantly improved by PVP. It appears from the conformation of the adsorbed PVP molecules, determined by the measurement of the amount adsorbed and the thickness of the adsorbed layer, that the stabilization of the methanol-based suspension by PVP is due to entropic repulsion caused by the interaction of protruded tails of the adsorbed PVP molecules. It also appears from the measurement of the adsorption isotherm and the rheological properties that the increase in the dispersion stability with PVP at high PVP concentration is due to the increase in the concentration of non-adsorbing free PVP molecules.
1 – MACROECONOMIC PROBLEMS

Publisher Summary This chapter discusses economics, macroeconomics, and macroeconomic problems. Economics is the study of how society chooses to produce and distribute goods and services in a world of scarce resources. In a market economy, like the United States, most of the production and distribution choices are made by individuals and business firms buying and selling goods in free markets. Basic economic decisions are made in a different way in a command economy. Microeconomics and macroeconomics are not two opposing schools of economics. They are simply two different ways of viewing economic events. Microeconomics focuses on individual decisions. Macroeconomics, conversely, looks at the broad issues of the national economy. Macroeconomics looks not so much at individual actions as at the combined consequences of those actions. The year 1929 began the long period that has come to be called the Great Depression. The 1940s brought a different set of economic problems to the nation. The 1950s are full of interest to economists. The 1960s present another picture entirely. The 1970 recession proved that high inflation and high unemployment rates could occur simultaneously. Macroeconomics in the 1980s and beyond is the study of a complicated and interrelated world trying to achieve goals that may be mutually inconsistent. The chapter also highlight a few economic controversies in brief.
Optimal Taxation When Consumers Have Endogenous Benchmark Levels of Consumption

I examine optimal taxes in an overlapping generations economy in which each consumer’s utility depends on consumption relative to a weighted average of consumption by others (the benchmark level of consumption) as well as on the level of the consumer’s own consumption. The socially optimal balanced growth path is characterized by the ModiÞed Golden Rule and by a condition on the intergenerational allocation of consumption in each period. A competitive economy can be induced to attain the social optimum by a lump-sum pay-as-you-go social security system and a tax on capital income.
A game-theoretic approach to deciphering the dynamics of amyloid-β aggregation along competing pathways

Aggregation of amyloid-β (Aβ) peptides is a significant event that underpins Alzheimer's disease (AD). Aβ aggregates, especially the low-molecular weight oligomers, are the primary toxic agents in ...
Symmetry operators of generalized wreath products and their applications to chemical physics

Symmetry operators of generalized wreath product groups are formulated. Several applications of these operators to nonrigid molecular problems in chemical physics are outlined.
Distributing Frank–Wolfe via Map-Reduce

Large-scale optimization problems abound in data mining and machine learning applications, and the computational challenges they pose are often addressed through parallelization. We identify structural properties under which a convex optimization problem can be massively parallelized via map-reduce operations using the Frank–Wolfe (FW) algorithm. The class of problems that can be tackled this way is quite broad and includes experimental design, AdaBoost, and projection to a convex hull. Implementing FW via map-reduce eases parallelization and deployment via commercial distributed computing frameworks. We demonstrate this by implementing FW over Spark, an engine for parallel data processing, and establish that parallelization through map-reduce yields significant performance improvements: We solve problems with 20 million variables using 350 cores in 79 min; the same operation takes 48 h when executed serially.
5 – The Integral

Publisher Summary This chapter discusses integrals. Integration is an important concept in mathematics and, together with differentiation, is one of the two main operations in calculus. The theorem linking these concepts together is called the fundamental theorem of calculus. This theorem simply asserts that a definite integral can be calculated by evaluating any antiderivative at the endpoints of the interval of integration and then subtracting. There are two types of integrals: definite and indefinite integrals. The problem of finding a definite or indefinite integral is solved if an antiderivative for the function f can be found. However, this task can be often difficult. There are many other functions for which indefinite integrals can be found, but learning to recognize them takes a lot of experience.
Using Python for Text Analysis in Accounting Research

The prominence of textual data in accounting research has increased dramatically. To assist researchers in understanding and using textual data, this monograph defines and describes common measures of textual data and then demonstrates the collection and processing of textual data using the Python programming language. The monograph is replete with sample code that replicates textual analysis tasks from recent research papers. In the first part of the monograph, we provide guidance on getting started in Python. We first describe Anaconda, a distribution of Python that provides the requisite libraries for textual analysis, and its installation. We then introduce the Jupyter notebook, a programming environment that improves research workflows and promotes replicable research. Next, we teach the basics of Python programming and demonstrate the basics of working with tabular data in the Pandas package. The second part of the monograph focuses on specific textual analysis methods and techniques commonly used in accounting research. We first introduce regular expressions, a sophisticated language for finding patterns in text. We then show how to use regular expressions to extract specific parts from text. Next, we introduce the idea of transforming text data (unstructured data) into numerical measures representing variables of interest (structured data). Specifically, we introduce dictionary-based methods of (1) measuring document sentiment, (2) computing text complexity, (3) identifying forward-looking sentences and risk disclosures, (4) collecting informative numbers in text, and (5) computing the similarity of different pieces of text. For each of these tasks, we cite relevant papers and provide code snippets to implement the relevant metrics from these papers. Finally, the third part of the monograph focuses on automating the collection of textual data. We introduce web scraping and provide code for downloading filings from EDGAR.
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks

Chemical representations derived from deep learning are emerging as a powerful tool in areas such as drug discovery and materials innovation. Currently, this methodology has three major limitations - the cost of representation generation, risk of inherited bias, and the requirement for large amounts of data. We propose the use of multi-task learning in tandem with transfer learning to address these limitations directly. In order to avoid introducing unknown bias into multi-task learning through the task selection itself, we calculate task similarity through pairwise task affinity, and use this measure to programmatically select tasks. We test this methodology on several real-world data sets to demonstrate its potential for execution in complex and low-data environments. Finally, we utilise the task similarity to further probe the expressiveness of the learned representation through a comparison to a commonly used cheminformatics fingerprint, and show that the deep representation is able to capture more expressive task-based information.
Nek8445, a protein kinase required for microtubule regulation and cytokinesis in Giardia lamblia

Here we study the role of Nek8445 in regulating cell division and microtubule array organization in Giardia. Depletion of Nek8445 results in 87% of cells being stalled or blocked in cytokinesis. Ne...
Vitamin D Receptor Protects Against Dysbiosis and Tumorigenesis via the JAK/STAT Pathway in Intestine

Background & Aims Vitamin D exerts regulatory roles via vitamin D receptor (VDR) in mucosal immunity, host defense, and inflammation involving host factors and microbiome. Human Vdr gene variation shapes the microbiome and VDR deletion leads to dysbiosis. Low VDR expression and diminished vitamin D/VDR signaling are observed in colon cancer. Nevertheless, how intestinal epithelial VDR is involved in tumorigenesis through gut microbiota remains unknown. We hypothesized that intestinal VDR protects mice against dysbiosis via modulating the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway in tumorigenesis. Methods To test our hypothesis, we used an azoxymethane/dextran sulfate sodium–induced cancer model in intestinal VDR conditional knockout (VDRΔIEC) mice, cell cultures, stem cell–derived colonoids, and human colon cancer samples. Results VDRΔIEC mice have higher numbers of tumors, with the location shifted from the distal to proximal colon. Fecal microbiota analysis showed that VDR deletion leads to a bacterial profile shift from normal to susceptible carcinogenesis. We found enhanced bacterial staining in mouse and human tumors. Microbial metabolites from VDRΔIEC mice showed increased secondary bile acids, consistent with observations in human CRC. We further identified that VDR protein bound to the Jak2 promoter, suggesting that VDR transcriptionally regulated Jak2. The JAK/STAT pathway is critical in intestinal and microbial homeostasis. Fecal samples from VDRΔIEC mice activate the STAT3 signaling in human and mouse organoids. Lack of VDR led to hyperfunction of Jak2 in response to intestinal dysbiosis. A JAK/STAT inhibitor abolished the microbiome-induced activation of STAT3. Conclusions We provide insights into the mechanism of VDR dysfunction leading to dysbiosis and tumorigenesis. It indicates a new target: microbiome and VDR for the prevention of cancer.
SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings

Spatial reasoning is an important component of human intelligence. We can imagine the shapes of 3D objects and reason about their spatial relations by merely looking at their three-view line drawings in 2D, with different levels of competence. Can deep networks be trained to perform spatial reasoning tasks? How can we measure their "spatial intelligence"? To answer these questions, we present the SPARE3D dataset. Based on cognitive science and psychometrics, SPARE3D contains three types of 2D-3D reasoning tasks on view consistency, camera pose, and shape generation, with increasing difficulty. We then design a method to automatically generate a large number of challenging questions with ground truth answers for each task. They are used to provide supervision for training our baseline models using state-of-the-art architectures like ResNet. Our experiments show that although convolutional networks have achieved superhuman performance in many visual learning tasks, their spatial reasoning performance in SPARE3D is almost equal to random guesses. We hope SPARE3D can stimulate new problem formulations and network designs for spatial reasoning to empower intelligent robots to operate effectively in the 3D world via 2D sensors.
Robust Bayesian Classification Using An Optimistic Score Ratio

We build a Bayesian contextual classification model using an optimistic score ratio for robust binary classification when there is limited information on the class-conditional, or contextual, distribution. The optimistic score searches for the distribution that is most plausible to explain the observed outcomes in the testing sample among all distributions belonging to the contextual ambiguity set which is prescribed using a limited structural constraint on the mean vector and the covariance matrix of the underlying contextual distribution. We show that the Bayesian classifier using the optimistic score ratio is conceptually attractive, delivers solid statistical guarantees and is computationally tractable. We showcase the power of the proposed optimistic score ratio classifier on both synthetic and empirical data.
EUS pancreatic function testing and dynamic pancreatic duct evaluation for the diagnosis of exocrine pancreatic insufficiency and chronic pancreatitis.

Background and Aims EUS and endoscopic pancreatic function tests (ePFTs) may be used to diagnose minimal-change chronic pancreatitis (MCCP). The impact of evaluation for exocrine pancreatic insufficiency (EPI) and real-time assessment of EUS changes after intravenous secretin on the clinical diagnosis of MCCP is unknown. Methods Patients with suspected MCCP underwent baseline EUS assessment of the pancreatic parenchyma and measurement of the main pancreatic duct (B-MPD) in the head, body, and tail. Human secretin 0.2 μg/kg was given intravenously followed 4, 8, and 12 minutes later by repeat MPD (S-MPD) measurements. Duodenal samples at 15, 30, and 45 minutes were aspirated to assess bicarbonate concentration. Endoscopists rated the percentage clinical likelihood of chronic pancreatitis (1) before secretin; (2) after secretin but before aspiration; and (3) after bicarbonate results. Results A total of 145 consecutive patients (mean age, 44±13 years; 98 females) were diagnosed with EPI (n = 32; 22%) or normal exocrine pancreatic function (n = 131, 78%). S-MPD/B-MPD ratios in the tail 4 and 8 minutes after secretin were higher in the group with normal exocrine function. Ratios at other times, locations, and duodenal fluid volumes were similar between the 2 groups. A statistically significant change in the median percentage likelihood of chronic pancreatitis was noted after secretin in all groups. The sensitivity and specificity of EPI for the EUS diagnosis of chronic pancreatitis (≥5 criteria) were 23.4% (95% confidence interval, 12.3-38.0) and 78.6% (95% confidence interval, 69.1-86.2), respectively. Conclusion Real-time EUS findings and ePFTs have a significant impact on the clinical assessment of MCCP. The diagnosis of EPI shows poor correlation with the EUS diagnosis of MCCP. (Clinical trial registration number: NCT01997476 .)
A Replication-Competent Vesicular Stomatitis Virus for Studies of SARS-CoV-2 Spike-Mediated Cell Entry and Its Inhibition.

There is an urgent need for vaccines and therapeutics to prevent and treat COVID-19. Rapid SARS-CoV-2 countermeasure development is contingent on the availability of robust, scalable, and readily deployable surrogate viral assays to screen antiviral humoral responses, define correlates of immune protection, and down-select candidate antivirals. Here, we generate a highly infectious recombinant vesicular stomatitis virus (VSV) bearing the SARS-CoV-2 spike glycoprotein S as its sole entry glycoprotein and show that this recombinant virus, rVSV-SARS-CoV-2 S, closely resembles SARS-CoV-2 in its entry-related properties. The neutralizing activities of a large panel of COVID-19 convalescent sera can be assessed in a high-throughput fluorescent reporter assay with rVSV-SARS-CoV-2 S, and neutralization of rVSV-SARS-CoV-2 S and authentic SARS-CoV-2 by spike-specific antibodies in these antisera is highly correlated. Our findings underscore the utility of rVSV-SARS-CoV-2 S for the development of spike-specific therapeutics and for mechanistic studies of viral entry and its inhibition.
Judged in Hindsight: Regulatory Incentives in Approving Innovations

I study how limited information and ex-post evaluation by third parties with the benefit of hindsight affect how regulators approve innovations. In the face of ambiguity over innovation characteristics, such a regulator limits or delays product approval, even when she is not waiting for new information to arrive. When evidence is costly for firms to generate but can be selectively reported, the regulator delegates information acquisition to the firm with the objective of minimizing max-regret. This model can explain observed patterns of correlation between firm costs and benefits of approval, why regulators drag their feet on approval decisions even in the face of strong favorable evidence, and support for regulatory sandboxes even when they do not hasten learning.
First-Principles Analysis of Coverage, Ensemble, and Solvation Effects on Selectivity Trends in NO Electroreduction on Pt3Sn Alloys

Contamination of groundwater by runoff of NOx ¬species from agricultural land is a growing problem with increasingly negative consequences for public health. Among other technologies, denitrificati...
Does the phase of ongoing EEG oscillations predict auditory perception

Summary Effective processing of information from the environment requires the brain to selectively sample relevant inputs. The visual perceptual system has been shown to sample information rhythmically, oscillating rapidly between more and less input-favorable states. Evidence of parallel effects in auditory perception is inconclusive. Here, we combined a bilateral pitch-identification task with electroencephalography (EEG) to investigate whether the phase of ongoing EEG predicts auditory discrimination accuracy. We compared prestimulus phase distributions between correct and incorrect trials. Shortly before stimulus onset, each of these distributions showed significant phase concentration, but centered at different phase angles. The effects were strongest in theta and beta frequency bands. The divergence between phase distributions showed a linear relation with accuracy, accounting for at least 10% of inter-individual variance. Discrimination performance oscillated rhythmically at a rate predicted by the neural data. These findings indicate that auditory discrimination threshold oscillates over time along with the phase of ongoing EEG activity. Thus, it appears that auditory perception is discrete rather than continuous, with the phase of ongoing EEG oscillations shaping auditory perception by providing a temporal reference frame for information processing.
Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single-reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wave function theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. To reduce the cost of generating inputs to the VAT model, we leverage the VAT model's robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in an MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening.
Surface plasmon enhanced fast electron emission from metallised fibre optic nanotips

Physical mechanisms of electron emission from fibre optic nanotips, namely, tunnelling, multi-photon, and thermionic emission, either prevent fast switching or require intense laser fields. Time-resolved electron emission from nano-sized sources finds applications ranging from material characterisation to fundamental studies of quantum coherence. We present a nano-sized electron source capable of fast-switching (<1 ns) that can be driven with low-power femtosecond lasers. The physical mechanism that can explain emission at low laser power is surface plasmon enhanced above-threshold photoemission. An electron emission peak is observed and provides support for resonant plasmonic excitation. The electron source is a metal-coated optical fibre tapered into a nano-sized tip. The fibre is flexible and back illuminated facilitating ease of positioning. The source operates with a few nJ per laser pulse, making this a versatile emitter that enables nanometrology, multisource electron-lithography and scanning probe microscopy. Keywords: fibre optic nanotip, surface plasmon resonance, multi-photon emission, above-threshold emission
Transcranial focused ultrasound generates skull-conducted shear waves: Computational model and implications for neuromodulation.

Focused ultrasound (FUS) is an established technique for non-invasive surgery and has recently attracted considerable attention as a potential method for non-invasive neuromodulation. While the pressure waves in FUS procedures have been extensively studied in this context, the accompanying shear waves are often neglected due to the relatively high shear compliance of soft tissues. However, in bony structures such as the skull, acoustic pressure can also induce significant shear waves that could propagate outside the ultrasound focus. Here, we investigate wave propagation in the human cranium by means of a finite-element model that accounts for the anatomy, elasticity, and viscoelasticity of the skull and brain. We show that, when a region on the scalp is subjected to FUS, the skull acts as a waveguide for shear waves that propagate with a speed close to 1500 m/s, reaching off-target structures such as the cochlea. In particular, when a sharp onset of FUS is introduced in a zone proximal to the intersection of the parietal and temporal cranium, the bone-propagated shear waves reach the inner ear in about 40 μs, leading to cumulative displacements of about 1 μm. We further quantify the effect of ramped and sharp application of FUS on the cumulative displacements in the inner ear. Our results help explain the off-target auditory responses observed during neuromodulation experiments and inform the development of mitigation and sham control strategies.
Disaggregation and the Application

This paper examines disaggregated data center architectures from the perspective of the applications that would run on these data centers, and challenges the abstractions that have been proposed to date. In particular, we argue that operating systems for disaggregated data centers should not abstract disaggregated hardware resources, such as memory, compute, and storage away from applications, but should instead give them information about, and control over, these resources. To this end, we propose additional OS abstractions and interfaces for disaggregation and show how they can improve data transfer in data parallel frameworks and speed up failure recovery in replicated, fault-tolerant applications. This paper studies the technical challenges in providing applications with this additional functionality and advances several preliminary proposals to overcome these challenges.
Gas–liquid flows through porous media in microgravity: The International Space Station Packed Bed Reactor Experiment

Experimental results on pressure drop and flow patterns for gas-liquid flow through packed beds obtained in the International Space Station with two types of packing are presented and analyzed. It is found that the pressure drop depends on the packing wettability in the viscous-capillary (V-C) regime and this dependence is compared with previously published results developed using short duration low-gravity aircraft tests. Within the V-C regime, the capillary contribution is the dominant force contributing to the pressure drop for the wetting case (glass) versus the viscous contribution dominating for the non-wetting case (Teflon). Outside of the V-C regime, it is also found that hysteresis effects that are often strong in normal gravity gas-liquid flows are greatly diminished in microgravity and pressure drop is nearly independent of packing wettability. A flow pattern transition map from bubble to pulse flow is also compared with the earlier aircraft data.
Direct Observation of Vesicle Transport on the Synaptic Ribbon Provides Evidence That Vesicles Are Mobilized and Prepared Rapidly for Release

Synaptic ribbons are thought to provide vesicles for continuous release in some retinal nonspiking neurons, yet recent studies indicate that genetic removal of the ribbon has little effect on release kinetics. To investigate vesicle replenishment at synaptic ribbons, we used total internal reflection fluorescence microscopy to image synaptic vesicles and ribbons in retinal bipolar cells of goldfish (Carassius auratus) of both sexes. Analysis of vesicles released by trains of 30 ms depolarizations revealed that most releasable vesicles reside within 300 nm of the ribbon center. A single 30 ms step to 0 mV was sufficient to deplete the membrane-proximal vesicle pool, while triggering rapid stepwise movements of distal vesicles along the ribbon and toward the plasma membrane. Replenishment only becomes rate-limiting for recovery from paired-pulse depression for interstimulus intervals shorter than 250 ms. For longer interstimulus intervals, vesicle movement down the ribbon is fast enough to replenish released vesicles, but newly arrived vesicles are not release-ready. Notably, the rates of vesicle resupply and maturation of newcomers are among the fastest measured optically at any synapse. Lastly, our data show that the delay in vesicle departure increases and vesicle speed decreases with multiple stimuli. Our results support a role for ribbons in the supply of vesicles for release, provide direct measurements of vesicle movement down the ribbon, and suggest that multiple factors contribute to paired-pulse depression.SIGNIFICANCE STATEMENT Synaptic ribbons are macromolecular scaffolds that tether synaptic vesicles close to release sites in nonspiking neurons of the retina and cochlea. Because these neurons release neurotransmitter continuously, synaptic ribbons are assumed to act as platforms for supplying vesicles rapidly in the face of prolonged stimulation. Yet, ribbon synapses suffer from profound paired-pulse depression, which takes seconds to subside. We investigated the mechanistic origin of this phenomenon by directly imaging triggered vesicle movement and release at ribbon sites in retinal bipolar cells, and find that, although ribbon synapses deliver and prime vesicles faster than most conventional synapses, both vesicle absence and vesicle priming contribute to the long recovery from paired-pulse depression.
Malware detection using artificial bee colony algorithm

Malware detection has become a challenging task due to the increase in the number of malware families. Universal malware detection algorithms that can detect all the malware families are needed to make the whole process feasible. However, the more universal an algorithm is, the higher number of feature dimensions it needs to work with, and that inevitably causes the emerging problem of Curse of Dimensionality (CoD). Besides, it is also difficult to make this solution work due to the real-time behavior of malware analysis. In this paper, we address this problem and aim to propose a feature selection based malware detection algorithm using an evolutionary algorithm that is referred to as Artificial Bee Colony (ABC). The proposed algorithm enables researchers to decrease the feature dimension and as a result, boost the process of malware detection. The experimental results reveal that the proposed method outperforms the state-of-the-art.
Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders

It is proposed to use analogs of the forecast mean to generate an ensemble of perturbations for use in ensemble optimal interpolation (EnOI) or ensemble variational (EnVar) methods. A new method of constructing analogs using variational autoencoders (VAEs; a machine learning method) is proposed. The resulting analog methods using analogs from a catalog (AnEnOI), and using constructed analogs (cAnEnOI), are tested in the context of a multiscale Lorenz-`96 model, with standard EnOI and an ensemble square root filter for comparison. The use of analogs from a modestly-sized catalog is shown to improve the performance of EnOI, with limited marginal improvements resulting from increases in the catalog size. The method using constructed analogs (cAnEnOI) is found to perform as well as a full ensemble square root filter, and to be robust over a wide range of tuning parameters.
Coupled MHD – Hybrid Simulations of Space Plasmas

Heliospheric plasmas require multi-scale and multi-physics considerations. On one hand, MHD codes are widely used for global simulations of the solar-terrestrial environments, but do not provide the most elaborate physical description of space plasmas. Hybrid codes, on the other hand, capture important physical processes, such as electric currents and effects of finite Larmor radius, but they can be used locally only, since the limitations in available computational resources do not allow for their use throughout a global computational domain. In the present work, we present a new coupled scheme which allows to switch blocks in the block-adaptive grids from fluid MHD to hybrid simulations, without modifying the self-consistent computation of the electromagnetic fields acting on fluids (in MHD simulation) or charged ion macroparticles (in hybrid simulation). In this way, the hybrid scheme can refine the description in specified regions of interest without compromising the efficiency of the global MHD code.
Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking

A new generation of automated bin picking systems using deep learning is evolving to support increasing demand for e-commerce. To accommodate a wide variety of products, many automated systems include multiple gripper types and/or tool changers. However, for some objects, sequential grasp failures are common: when a computed grasp fails to lift and remove the object, the bin is often left unchanged; as the sensor input is consistent, the system retries the same grasp over and over, resulting in a significant reduction in mean successful picks per hour (MPPH). Based on an empirical study of sequential failures, we characterize a class of “sequential failure objects” (SFOs) – objects prone to sequential failures based on a novel taxonomy. We then propose three non-Markov picking policies that incorporate memory of past failures to modify subsequent actions. Simulation experiments on SFO models and the EGAD dataset [19] suggest that the non-Markov policies significantly outperform the Markov policy in terms of the sequential failure rate and MPPH. In physical experiments on 50 heaps of 12 SFOs the most effective Non-Markov policy increased MPPH over the Dex-Net Markov policy by 107%.
Self-assembling 3D spheroid cultures of human neonatal keratinocytes have enhanced regenerative properties.

Abstract Relative to conventional two-dimensional (2-D) culture, three-dimensional (3-D) suspension culture of epithelial cells more closely mimics the in vivo cell microenvironment regarding cell architecture, cell to matrix interaction, and osmosis exchange. However, primary normal human keratinocytes (NHKc) rapidly undergo terminal differentiation and detachment-induced cell death (anoikis) upon disconnection from the basement membrane, thus greatly constraining their use in 3-D suspension culture models. Here, we examined the 3-D anchorage-free growth potential of NHKc isolated from neonatal skin explants of 59 different individuals. We found that 40% of all isolates naturally self-assembled into multicellular spheroids within 24 h in anchorage-free culture, while 60% did not. Placing a single spheroid back into 2-D monolayer culture yielded proliferating cells that expressed elevated levels of nuclear P63 and basal cytokeratin 14. These cells also displayed prolonged keratinocyte renewal and a gene expression profile corresponding to cellular heterogeneity, quiescence, and de-differentiation. Notably, spheroid-derived (SD) NHKc were enriched for a P63/K14 double-positive population that formed holoclonal colonies and reassembled into multicellular spheroids during 3-D suspension subculture. This study reveals marked phenotypic differences in neonatal keratinocyte suspension cultures isolated from different individuals and present a model system that can be readily employed to study epithelial cell behavior, along with a variety of dermatological diseases.