cssi/SciSciGPT-SciSciNet huggingface dataset:
Intelligent Learning for Deformable Object Manipulation

The majority of manipulation systems are designed with the assumption that the objects being handled are rigid and do not deform when grasped. This paper addresses the problem of robotic grasping and manipulation of 3-D deformable objects, such as rubber balls or bags filled with sand. Specifically, we have developed a generalized learning algorithm for handling of 3-D deformable objects in which prior knowledge of object attributes is not required and thus it can be applied to a large class of object types. Our methodology relies on the implementation of two main tasks. Our first task is to calculate deformation characteristics for a non-rigid object represented by a physically-based model. Using nonlinear partial differential equations, we model the particle motion of the deformable object in order to calculate the deformation characteristics. For our second task, we must calculate the minimum force required to successfully lift the deformable object. This minimum lifting force can be learned using a technique called ‘iterative lifting’. Once the deformation characteristics and the associated lifting force term are determined, they are used to train a neural network for extracting the minimum force required for subsequent deformable object manipulation tasks. Our developed algorithm is validated with two sets of experiments. The first experimental results are derived from the implementation of the algorithm in a simulated environment. The second set involves a physical implementation of the technique whose outcome is compared with the simulation results to test the real world validity of the developed methodology.
Total variation estimates in the Breuer–Major theorem

This paper provides estimates for the convergence rate of the total variation distance in the framework of the Breuer–Major theorem, assuming some smoothness properties of the underlying function. The results are proved by applying new bounds for the total variation distance between a random variable expressed as a divergence and a standard Gaussian random variable, which are derived by a combination of techniques of Malliavin calculus and Stein’s method. The representation of a functional of a Gaussian sequence as a divergence is established by introducing a shift operator on the expansion in Hermite polynomials. Some applications to the asymptotic behavior of power variations of the fractional Brownian motions and to the estimation of the Hurst parameter using power variations are presented.
Online Covariance Matrix Estimation in Stochastic Gradient Descent

The stochastic gradient descent (SGD) algorithm is widely used for parameter estimation, especially for huge datasets and online learning. While this recursive algorithm is popular for computation ...
Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks.

Existing works on information extraction (IE) have mainly solved the four main tasks separately (entity mention recognition, relation extraction, event trigger detection, and argument extraction), thus failing to benefit from inter-dependencies between tasks. This paper presents a novel deep learning model to simultaneously solve the four tasks of IE in a single model (called FourIE). Compared to few prior work on jointly performing four IE tasks, FourIE features two novel contributions to capture inter-dependencies between tasks. First, at the representation level, we introduce an interaction graph between instances of the four tasks that is used to enrich the prediction representation for one instance with those from related instances of other tasks. Second, at the label level, we propose a dependency graph for the information types in the four IE tasks that captures the connections between the types expressed in an input sentence. A new regularization mechanism is introduced to enforce the consistency between the golden and predicted type dependency graphs to improve representation learning. We show that the proposed model achieves the state-of-the-art performance for joint IE on both monolingual and multilingual learning settings with three different languages.
Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States

Background: The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 240,000 lives worldwide, causing tremendous public health, social, and economic damages. While the ris...
Structure-Grounded Pretraining for Text-to-SQL

Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema. In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (STRUG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus. We identify a set of novel pretraining tasks: column grounding, value grounding and column-value mapping, and leverage them to pretrain a text-table encoder. Additionally, to evaluate different methods under more realistic text-table alignment settings, we create a new evaluation set Spider-Realistic based on Spider dev set with explicit mentions of column names removed, and adopt eight existing text-to-SQL datasets for cross-database evaluation. STRUG brings significant improvement over BERTLARGE in all settings. Compared with existing pretraining methods such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms all baselines on more realistic sets. All the code and data used in this work will be open-sourced to facilitate future research.
Heat kernel bounds for elliptic partial differential operators in divergence form with Robin-type boundary conditions

One of the principal topics of this paper concerns the realization of self-adjoint operators L Θ,Ω in L 2(Ω; d n x) m , m, n ∈ ℕ, associated with divergence form elliptic partial differential expressions L with (nonlocal) Robin-type boundary conditions in bounded Lipschitz domains Ω ⊂ ℝ n . In particular, we develop the theory in the vector-valued case and hence focus on matrix-valued differential expressions L which act as $$Lu = - \left( {\sum\limits_{j,k = 1}^n {\partial _j } \left( {\sum\limits_{\beta = 1}^m {a_{j,k}^{\alpha ,\beta } \partial _k u_\beta } } \right)} \right)_{1 \leqslant \alpha \leqslant m} , u = \left( {u_1 , \ldots ,u_m } \right).$$ The (nonlocal) Robin-type boundary conditions are then of the form $$v \cdot ADu + \Theta [u|_{\partial \Omega } ] = 0{\text{ on }}\partial \Omega ,$$ where Θ represents an appropriate operator acting on Sobolev spaces associated with the boundary ∂Ω of Ω, ν denotes the outward pointing normal unit vector on ∂Ω, and $$Du: = \left( {\partial _j u_\alpha } \right)_{_{1 \leqslant j \leqslant n}^{1 \leqslant \alpha \leqslant m} } .$$ Assuming Θ ≥ 0 in the scalar case m = 1, we prove Gaussian heat kernel bounds for L Θ,Ω, by employing positivity preserving arguments for the associated semigroups and reducing the problem to the corresponding Gaussian heat kernel bounds for the case of Neumann boundary conditions on ∂Ω. We also discuss additional zero-order potential coefficients V and hence operators corresponding to the form sum L Θ,Ω + V.
Adapting to misspecification in contextual bandits with offline regression oracles

Computationally efficient contextual bandits are often based on estimating a predictive model of rewards given contexts and arms using past data. However, when the reward model is not well-specified, the bandit algorithm may incur unexpected regret, so recent work has focused on algorithms that are robust to misspecification. We propose a simple family of contextual bandit algorithms that adapt to misspecification error by reverting to a good safe policy when there is evidence that misspecification is causing a regret increase. Our algorithm requires only an offline regression oracle to ensure regret guarantees that gracefully degrade in terms of a measure of the average misspecification level. Compared to prior work, we attain similar regret guarantees, but we do not rely on a master algorithm, and do not require more robust oracles like online or constrained regression oracles [e.g., Foster et al. (2020a); Krishnamurthy et al. (2020)]. This allows us to design algorithms for more general function approximation classes.
A Two Step Procedure for Testing Partial Parameter Stability in Cointegrated Regression Models

Kejriwal and Perron (2010, KP) provided a comprehensive treatment for the problem of testing multiple structural changes in cointegrated regression models. A variety of models were considered depending on whether all regression coefficients are allowed to change (pure structural change) or a subset of the coefficients is held A–xed (partial structural change). In this note, we A–rst show that the limit distributions of the test statistics in the latter case are not invariant to changes in the coeÂ¢ cients not being tested; in fact, they diverge as the sample size increases. To address this issue, we propose a simple two step procedure to test for partial parameter stability. The A–rst entails the application of a joint test of stability for all coeÂ¢ cients as in KP. Upon a rejection, the second conducts a stability test on the subset of coeÂ¢ cients of interest while allowing the other coeÂ¢ cients to change at the estimated breakpoints. Its limit distribution is standard chi-square. The relevant asymptotic theory is provided along with simulations that illustrates the usefulness of the procedure in finite samples.
Preclinical Stage Alzheimer's Disease Detection Using Magnetic Resonance Image Scans

Alzheimer's disease is one of the diseases that mostly affects older people without being a part of aging. The most common symptoms include problems with communicating and abstract thinking, as well as disorientation. It is important to detect Alzheimer's disease in early stages so that cognitive functioning would be improved by medication and training. In this paper, we propose two attention model networks for detecting Alzheimer's disease from MRI images to help early detection efforts at the preclinical stage. We also compare the performance of these two attention network models with a baseline model. Recently available OASIS-3 Longitudinal Neuroimaging, Clinical, and Cognitive Dataset is used to train, evaluate and compare our models. The novelty of this research resides in the fact that we aim to detect Alzheimer's disease when all the parameters, physical assessments, and clinical data state that the patient is healthy and showing no symptoms.
Using Numerical Simulations to Explore Top-Mounted Propulsion on a Conceptual Commercial Supersonic Transport Aircraft

Reynolds Averaged Navier-Stokes (RANS) simulations were performed on an N+2 conceptual commercial supersonic transport aircraft to explore the possibility of relocating the engines from below the wings to above the wings in order to capitalize upon potential noise shielding benefits. The simulations focused on the feasibility of the top-mounted propulsion configuration in terms of inlet performance and flow separation around the nacelles at take-off conditions for both 0deg and 8deg angle of attack. The results showed overall good inlet performance and little separation around the nacelles. The results were comparable to the engines in their original underwing configuration.
Privacy Budget Scheduling.

Machine learning (ML) models trained on personal data have been shown to leak information about users. Differential privacy (DP) enables model training with a guaranteed bound on this leakage. Each new model trained with DP increases the bound on data leakage and can be seen as consuming part of a global privacy budget that should not be exceeded. This budget is a scarce resource that must be carefully managed to maximize the number of successfully trained models. We describe PrivateKube, an extension to the popular Kubernetes datacenter orchestrator that adds privacy as a new type of resource to be managed alongside other traditional compute resources, such as CPU, GPU, and memory. The abstractions we design for the privacy resource mirror those defined by Kubernetes for traditional resources, but there are also major differences. For example, traditional compute resources are replenishable while privacy is not: a CPU can be regained after a model finishes execution while privacy budget cannot. This distinction forces a re-design of the scheduler. We present DPF (Dominant Private Block Fairness) -- a variant of the popular Dominant Resource Fairness (DRF) algorithm -- that is geared toward the non-replenishable privacy resource but enjoys similar theoretical properties as DRF. We evaluate PrivateKube and DPF on microbenchmarks and an ML workload on Amazon Reviews data. Compared to existing baselines, DPF allows training more models under the same global privacy guarantee. This is especially true for DPF over Renyi DP, a highly composable form of DP.
RealFormer: Transformer Likes Residual Attention

Transformer is the backbone of modern NLP models. In this paper, we propose RealFormer, a simple Residual Attention Layer Transformer architecture that significantly outperforms canonical Transformers on a spectrum of tasks including Masked Language Modeling, GLUE, and SQuAD. Qualitatively, RealFormer is easy to implement and requires minimal hyper-parameter tuning. It also stabilizes training and leads to models with sparser attentions. Code will be open-sourced upon paper acceptance.
Steady State Modeling for Variable Frequency AC Power Flow

Advantages of operating portions of a power system at frequencies different from the standard 50 or 60 Hz have been demonstrated in the low frequency AC (LFAC) and high voltage DC (HVDC) literature. Branches constrained by stability or thermal limits can benefit from increased capacity and flexibility. Since advances in power electronics enable the choice of an operating frequency, tools are needed to make this choice. In order to quantify the advantages as functions of frequency, this paper provides models for steady state calculations with frequency as a variable and validates the modeling assumptions. It then introduces an analytical quantification of the power flow capacity of a transmission branch as a function of frequency, demonstrating different active constraints across the range of frequency. The modeling and power flow calculations are demonstrated for a practical transmission line using manufacturer data. The models presented here provide a foundation for system level studies with variable frequency using optimal power flow.
Beneficial Product Returns in Supply Chains

Many retailers and manufacturers accept product returns from consumers (via money-back guarantees) and retailers (via full-credit return policies) respectively. While the extant literature focuses on either manufacturer or retailer return policies, this paper investigates both return policies and finds that a manufacturer's return policy can induce the retailer to adopt a return policy. Although accepting consumer returns hurts the manufacturer when the manufacturer sells through non-competing retailers, we find that the manufacturer is not destined to be hurt by accepting returns when selling to competing retailers that are differentiated in their returns rates.
CNN-Based Cardiac Motion Extraction to Generate Deformable Geometric Left Ventricle Myocardial Models from Cine MRI

Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images to reconstruct patient-specific geometric models of the LV myocardium. With the advent of deep learning, accurate segmentation of cardiac chambers from cine cardiac MR images and unsupervised learning for image registration for cardiac motion estimation on a large number of image datasets is attainable. Here, we propose a deep leaning-based framework for the development of patient-specific geometric models of LV myocardium from cine cardiac MR images, using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We use the deformation field estimated from the VoxelMorph-based convolutional neural network (CNN) to propagate the isosurface mesh and volume mesh of the end-diastole (ED) frame to the subsequent frames of the cardiac cycle. We assess the CNN-based propagated models against segmented models at each cardiac phase, as well as models propagated using another traditional nonrigid image registration technique. Additionally, we generate dynamic LV myocardial volume meshes at all phases of the cardiac cycle using the log barrier-based mesh warping (LBWARP) method and compare them with the CNN-propagated volume meshes.
Variable Selection With Second-Generation P-Values

Many statistical methods have been proposed for variable selection in the past century, but few balance inference and prediction tasks well. Here, we report on a novel variable selection approach c...
Long-range correlations of sequences modulo 1

Abstract In this paper we consider the fractional parts of a general sequence, for example the sequence α n or α n 2 . We give a general method, which allows one to show that long-range correlations (correlations where the support of the test function grows as we consider more points) are Poissonian. We show that these statements about convergence can be reduced to bounds on associated Weyl sums. In particular we apply this methodology to the aforementioned examples. In so doing, we recover a recent result of Technau-Walker (2020) for the triple correlation of α n 2 and generalize the result to higher moments. For both of the aforementioned sequences this is one of the only results which indicates the pseudo-random nature of the higher level ( m ≥ 3 ) correlations.
Chia (Salvia hispanica) Gene Expression Atlas Elucidates Dynamic Spatio-Temporal Changes Associated With Plant Growth and Development

Chia (Salvia hispanica L.), now a popular superfood and a pseudocereal, is one of the richest sources of dietary nutrients such as protein, fiber, and polyunsaturated fatty acids (PUFAs). At present, the genomic and genetic information available in the public domain for this crop are scanty, which hinders an understanding of its growth and development and genetic improvement. We report an RNA-sequencing (RNA-Seq)-based comprehensive transcriptome atlas of Chia sampled from 13 tissue types covering vegetative and reproductive growth stages. We used ~355 million high-quality reads of total ~394 million raw reads from transcriptome sequencing to generate de novo reference transcriptome assembly and the tissue-specific transcript assemblies. After the quality assessment of the merged assemblies and implementing redundancy reduction methods, 82,663 reference transcripts were identified. About 65,587 of 82,663 transcripts were translated into 99,307 peptides, and we were successful in assigning InterPro annotations to 45,209 peptides and gene ontology (GO) terms to 32,638 peptides. The assembled transcriptome is estimated to have the complete sequence information for ~86% of the genes found in the Chia genome. Furthermore, the analysis of 53,200 differentially expressed transcripts (DETs) revealed their distinct expression patterns in Chia's vegetative and reproductive tissues; tissue-specific networks and developmental stage-specific networks of transcription factors (TFs); and the regulation of the expression of enzyme-coding genes associated with important metabolic pathways. In addition, we identified 2,411 simple sequence repeats (SSRs) as potential genetic markers from the transcripts. Overall, this study provides a comprehensive transcriptome atlas, and SSRs, contributing to building essential genomic resources to support basic research, genome annotation, functional genomics, and molecular breeding of Chia.
The structure of the antenna pattern of an adaptive array

The antenna pattern of a receiving adaptive array is investigated for the case of its operation in an environment of one desirable narrow-band signal and (K-1) narrow-band jammers. Results show that the adapted (voltage) antenna pattern of the array is a linear combination of k (or less) basis-patterns, each of which is a function of one source only. It is determined that these basis-patterns have a simple physical meaning in that the k-th basis-pattern is the pattern formed by the array when the transmission of source k is considered a desired signal and all other sources are turned off.
A variational discrete element method for the computation of Cosserat elasticity

The variational discrete element method developed in Marazzato et al. (Int J Numer Methods Eng, 121(23):5295–5319, 2020) for dynamic elasto-plastic computations is adapted to compute the deformation of elastic Cosserat materials. In addition to cellwise displacement degrees of freedom (dofs), cellwise rotational dofs are added. A reconstruction is devised to obtain $$P^1$$ non-conforming polynomials in each cell and thus constant strains and stresses in each cell. The method requires only the usual macroscopic parameters of a Cosserat material and no microscopic parameter. Numerical examples show the robustness of the method for both static and dynamic computations in two and three dimensions.
Quantitative Analysis of Plasmonic Metal Oxide Nanocrystal Ensembles Reveals the Influence of Dopant Selection on Intrinsic Optoelectronic Properties

LSPR arising from free charge carriers in doped metal oxide (MO) nanocrystals (NCs) has been explored abundantly already but there is still much to learn about the effect of dopant identity on the electronic structure of the host and, surface depletion layers. Here, using In2O3 as the host lattice, we discuss the contribution of a dopant to the electronic structure and rationalize an empirical understanding of how a particular dopant can impact surface depletion, carrier concentration, and carrier damping in doped MO NCs. To do this, we leverage a slow injection synthesis to incorporate four different dopants (Sn, Zr, Ti, and Ce) in In2O3 NCs of different radius. By fitting their plasmonic response, we extracted intrinsic electronic properties of the NCs such as surface depletion layer thickness, carrier concentration, and carrier damping, and rationalized the influence of dopant selection on each parameter.
Evaluating Medical Underwriters in Life Settlements: Problem of Unreported Deaths

The valuation of policies in the life settlement market hinges upon life expectancy assessments provided by medical underwriters. Because accurate assessments are essential to the market functionin...
Mitotic Spindle Positioning (MISP) is an Actin Bundler that Selectively Stabilizes the Rootlets of Epithelial Microvilli

Microvilli are conserved actin-based surface protrusions that have been repurposed throughout evolution to fulfill diverse cell functions. In the case of transporting epithelia, microvilli are supported by a core of actin filaments bundled in parallel by villin, fimbrin, and espin. Remarkably, microvilli biogenesis persists in mice lacking all three of these factors, suggesting the existence of unknown bundlers. We identified Mitotic Spindle Positioning (MISP) as an actin binding factor that localizes specifically to the rootlet end of the microvillus. MISP promotes rootlet elongation in cells, and purified MISP exhibits potent filament bundling activity in vitro. MISP-bundled filaments also recruit fimbrin, which further elongates and stabilizes bundles. MISP confinement to the rootlet is enforced by ezrin, which prevents decoration of the membrane-wrapped distal end of the core bundle. These discoveries reveal how epithelial cells optimize apical membrane surface area and offer insight on the remarkable robustness of microvilli biogenesis.
Dirichlet series associated to sum-of-digits functions

We study the Dirichlet series Fb(s) =∑n=1∞d b(n)n−s, where db(n) is the sum of the base-b digits of the integer n, and Gb(s) =∑n=1∞S b(n)n−s, where Sb(n) =∑m=1n−1d b(m) is the summatory function of...
A two phase boundary obstacle-type problem for the bi-Laplacian

Abstract In this paper we are concerned with a two phase boundary obstacle-type problem for the bi-Laplace operator in the upper unit ball. The problem arises in connection with unilateral phenomena for flat elastic plates. It can also be seen as an extension problem to an obstacle problem for the fractional Laplacian ( − Δ ) 3 / 2 , as first observed in Yang (2013). We establish the well-posedness and the optimal regularity of the solution, and we study the structure of the free boundary. Our proofs are based on monotonicity formulas of Almgren- and Monneau-type.
Release of endogenous dynorphin opioids in the prefrontal cortex disrupts cognition

Following repeated opioid use, some dependent individuals experience persistent cognitive deficits that contribute to relapse of drug-taking behaviors, and one component of this response may be mediated by the endogenous dynorphin/kappa opioid system in neocortex. In C57BL/6 male mice, we find that acute morphine withdrawal evokes dynorphin release in the medial prefrontal cortex (PFC) and disrupts cognitive function by activation of local kappa opioid receptors (KORs). Immunohistochemical analyses using a phospho-KOR antibody confirmed that both withdrawal-induced and optically evoked dynorphin release activated KOR in PFC. Using a genetically encoded sensor based on inert KOR (kLight1.2a), we revealed the in vivo dynamics of endogenous dynorphin release in the PFC. Local activation of KOR in PFC produced multi-phasic disruptions of memory processing in an operant-delayed alternation behavioral task, which manifest as reductions in response number and accuracy during early and late phases of an operant session. Local pretreatment in PFC with the selective KOR antagonist norbinaltorphimine (norBNI) blocked the disruptive effect of systemic KOR activation during both early and late phases of the session. The early, but not late phase disruption was blocked by viral excision of PFC KORs, suggesting an anatomically dissociable contribution of pre- and postsynaptic KORs. Naloxone-precipitated withdrawal in morphine-dependent mice or optical stimulation of pdynCre neurons using Channelrhodopsin-2 disrupted delayed alternation performance, and the dynorphin-induced effect was blocked by local norBNI. Our findings describe a mechanism for control of cortical function during opioid dependence and suggest that KOR antagonism could promote abstinence.
IowaRain: A Statewide Rain Event Dataset Based on Weather Radars and Quantitative Precipitation Estimation

Effective environmental planning and management to address climate change could be achieved through extensive environmental modeling with machine learning and conventional physical models. In order to develop and improve these models, practitioners and researchers need comprehensive benchmark datasets that are prepared and processed with environmental expertise that they can rely on. This study presents an extensive dataset of rainfall events for the state of Iowa (2016-2019) acquired from the National Weather Service Next Generation Weather Radar (NEXRAD) system and processed by a quantitative precipitation estimation system. The dataset presented in this study could be used for better disaster monitoring, response and recovery by paving the way for both predictive and prescriptive modeling.
Isogenies of certain K3 surfaces of rank 18

We construct geometric isogenies between three types of two-parameter families of K3 surfaces of Picard rank 18. One is the family of Kummer surfaces associated with Jacobians of genus-two curves admitting an elliptic involution, another is the family of Kummer surfaces associated with the product of two non-isogenous elliptic curves, and the third is the twisted Legendre pencil. The isogenies imply the existence of algebraic correspondences between these K3 surfaces and prove that the associated four-dimensional Galois representations are isomorphic. We also apply our result to several subfamilies of Picard rank 19. The result generalizes work of van Geemen and Top (Bull Lond Math Soc 38(2):209–223, 2006).
Full-Velocity Radar Returns by Radar-Camera Fusion.

A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points. However, the missing tangential velocity component hampers object velocity estimation as well as temporal integration of radar sweeps in dynamic scenes. Recognizing that fusing camera with radar provides complementary information to radar, in this paper we present a closed-form solution for the point-wise, full-velocity estimate of Doppler returns using the corresponding optical flow from camera images. Additionally, we address the association problem between radar returns and camera images with a neural network that is trained to estimate radar-camera correspondences. Experimental results on the nuScenes dataset verify the validity of the method and show significant improvements over the state-of-the-art in velocity estimation and accumulation of radar points.
Bayesian optimization of nanoporous materials

Nanoporous materials (NPMs) could be used to store, capture, and sense many different gases. Given an adsorption task, we often wish to search a library of NPMs for the one with the optimal adsorption property. The high cost of NPM synthesis and gas adsorption measurements, whether these experiments are in the lab or in a simulation, often precludes exhaustive search. We explain, demonstrate, and advocate Bayesian optimization (BO) to actively search for the optimal NPM in a library of NPMs—and find it using the fewest experiments. The two ingredients of BO are a surrogate model and an acquisition function. The surrogate model is a probabilistic model reflecting our beliefs about the NPM-structure–property relationship based on observations from past experiments. The acquisition function uses the surrogate model to score each NPM according to the utility of picking it for the next experiment. It balances two competing goals: (a) exploitation of our current approximation of the structure–property relationship to pick the NPM we believe [under uncertainty] will be the highest-performing, and (b) exploration of regions of NPM space we have not visited, to pick an NPM we are uncertain about and improve our approximation of the structure–property relationship. We demonstrate BO by searching an open database of ∼70 000 hypothetical covalent organic frameworks (COFs) for the COF with the highest simulated methane deliverable capacity (pertinent for vehicular adsorbed natural gas storage). BO finds the optimal COF and acquires ∼30% of the top 100 highest-ranked COFs after evaluating only ∼140 COFs. More, BO searches more efficiently than evolutionary and one-shot supervised machine learning approaches.
Osteoclast-mediated bone loss observed in a COVID-19 mouse model.

Abstract The consequences of SARS-CoV-2 infection on the musculoskeletal system represent a dangerous knowledge gap. Aging patients are at added risk for SARS-CoV-2 infection; therefore, a greater understanding of the resulting musculoskeletal sequelae of SARS-CoV-2 infection may help guide clinical strategies. This study examined fundamental bone parameters among mice treated with escalating viral loads. Male C57BL/6J (WT, n = 17) and B6.Cg-Tg(K18-ACE2)2Prlmn/J mice (K18-hACE2 transgenic mice, n = 21) expressing human ACE2 (TG) were divided into eight groups (n = 4–6/group) and subjected to intranasal dosing of 0, 1 × 103, 1 × 104, and 1 × 105 PFU (plaque forming units) of human SARS-CoV-2. Animal health was assessed daily by veterinary staff using established and validated scoring criteria (activity, posture, body condition scores and body weight). We report here that mock and WT infected mice were healthy and completed the study, surviving until 12–14 days post infection (dpi). In contrast, the TG mice infected with 1 × 105 PFU all experienced severe health declines that necessitated early euthanasia (6–7 dpi). For TG mice infected with 1 × 104 PFU, 2 mice were also euthanized after 7 dpi, while 3 mice showed signs of moderate disease at day 6 dpi, but recovered fully by day 11 dpi. Four of the 5 TG mice that were infected with 1 × 103 PFU remained healthy throughout the study. This suggests that our study mimics what is seen during human disease, where some patients develop severe disease resulting in death, while others have moderate to severe disease but recover, and others are asymptomatic. At necropsy, femurs were extracted and analyzed by μCT. No difference was found in μCT determined bone parameters among the WT groups. There was, however, a significant 24.4% decrease in trabecular bone volume fraction (p = 0.0009), 19.0% decrease in trabecular number (p = 0.004), 6.2% decrease in trabecular thickness (p = 0.04), and a 9.8% increase in trabecular separation (p = 0.04) among surviving TG mice receiving any viral load compared to non-infected controls. No differences in cortical bone parameters were detected. TRAP staining revealed surviving infected mice had a significant 64% increase in osteoclast number, a 27% increase in osteoclast surface, and a 38% increase in osteoclasts per bone surface. While more studies are needed to investigate the long-term consequences of SARS-CoV-2 infection on skeletal health, this study demonstrates a significant reduction in several bone parameters and corresponding robust increases in osteoclast number observed within 2 weeks post-infection in surviving asymptomatic and moderately affected mice.
Temporal fine structure influences voicing confusions for consonant identification in multi-talker babble

To understand the mechanisms of speech perception in everyday listening environments, it is important to elucidate the relative contributions of different acoustic cues in transmitting phonetic content. Previous studies suggest that the envelope of speech in different frequency bands conveys most speech content, while the temporal fine structure (TFS) can aid in segregating target speech from background noise. However, the role of TFS in conveying phonetic content beyond what envelopes convey for intact speech in complex acoustic scenes is poorly understood. The present study addressed this question using online psychophysical experiments to measure the identification of consonants in multi-talker babble for intelligibility-matched intact and 64-channel envelope-vocoded stimuli. Consonant confusion patterns revealed that listeners had a greater tendency in the vocoded (versus intact) condition to be biased toward reporting that they heard an unvoiced consonant, despite envelope and place cues being largely preserved. This result was replicated when babble instances were varied across independent experiments, suggesting that TFS conveys voicing information beyond what is conveyed by envelopes for intact speech in babble. Given that multi-talker babble is a masker that is ubiquitous in everyday environments, this finding has implications for the design of assistive listening devices such as cochlear implants.
Free Convolution Powers via Roots of Polynomials

Let $\mu$ be a compactly supported probability measure on the real line. Bercovici-Voiculescu and Nica-Speicher proved the existence of a free convolution power $\mu^{\boxplus k}$ for any real $k \geq 1$. The purpose of this short note is to give an elementary description of $\mu^{\boxplus k}$ in terms of of polynomials and roots of their derivatives. This bridge allows us to switch back and forth between free probability and the asymptotic behavior of polynomials.
Inq, a Modern GPU-Accelerated Computational Framework for (Time-Dependent) Density Functional Theory.

Author(s): Andrade, Xavier; Pemmaraju, Chaitanya Das; Kartsev, Alexey; Xiao, Jun; Lindenberg, Aaron; Rajpurohit, Sangeeta; Tan, Liang Z; Ogitsu, Tadashi; Correa, Alfredo A | Abstract: We present INQ, a new implementation of density functional theory (DFT) and time-dependent DFT (TDDFT) written from scratch to work on graphical processing units (GPUs). Besides GPU support, INQ makes use of modern code design features and takes advantage of newly available hardware. By designing the code around algorithms, rather than against specific implementations and numerical libraries, we aim to provide a concise and modular code. The result is a fairly complete DFT/TDDFT implementation in roughly 12,000 lines of open-source C++ code representing a modular platform for community-driven application development on emerging high-performance computing architectures for the simulation of materials.
Visual Thalamocortical Mechanisms of Waking State Dependent Activity and Alpha Oscillations

Summary The brain exhibits distinct patterns of recurrent activity closely related to behavioral state. The neural mechanisms that underlie state-dependent activity in the awake animal are incompletely understood. Here, we demonstrate that two types of state-dependent activity, rapid arousal/movement-related signals and a 3–5 Hz alpha-like rhythm, in the primary visual cortex (V1) of mice strongly correlate with activity in the visual thalamus. Inactivation of V1 does not interrupt arousal/movement signals in most visual thalamic neurons, but it abolishes the 3–5 Hz oscillation. Silencing of the visual thalamus similarly eradicates the alpha-like rhythm and perturbs arousal/movement-related activation in V1. Intracellular recordings in thalamic neurons reveal the 3–5 Hz oscillation to be associated with rhythmic low-threshold Ca2+ spikes. Our results indicate that thalamocortical interactions through ionotropic signaling, together with cell-intrinsic properties of thalamocortical cells, play a crucial role in shaping state-dependent activity in V1 of the awake animal.
Gaussian Process-based Model Predictive Controller for Connected Vehicles with Uncertain Wireless Channel

In this paper, we present a data-driven Model Predictive Controller that leverages a Gaussian Process to generate optimal motion policies for connected autonomous vehicles in regions with uncertainty in the wireless channel. The communication channel between the vehicles of a platoon can be easily influenced by numerous factors, e.g. the surrounding environment, and the relative states of the connected vehicles, etc. In addition, the trajectories of the vehicles depend significantly on the motion policies of the preceding vehicle shared via the wireless channel and any delay can impact the safety and optimality of its performance. In the presented algorithm, Gaussian Process learns the wireless channel model and is involved in the Model Predictive Controller to generate a control sequence that not only minimizes the conventional motion costs, but also minimizes the estimated delay of the wireless channel in the future. This results in a farsighted controller that maximizes the amount of transferred information beyond the controller's time horizon, which in turn guarantees the safety and optimality of the generated trajectories in the future. To decrease computational cost, the algorithm finds the reachable set from the current state and focuses on that region to minimize the size of the kernel matrix and related calculations. In addition, we present an efficient recursive approach to decrease the time complexity of developing the data-driven model and involving it in Model Predictive Control. We demonstrate the capability of the presented algorithm in a simulated scenario.
Meet the Press: Survey Evidence on Financial Journalists as Information Intermediaries

Abstract We survey 462 financial journalists and conduct 18 interviews to obtain insights on the inputs to their reporting, the incentives they face, and the factors that influence their coverage decisions. We report many findings relevant to the accounting literature and identify multiple avenues for future research. For example, financial journalists say the likelihood they write about a specific company or CEO increases when the company is controversial or the CEO has a colorful personality, suggesting journalists gravitate toward provocative topics. We also find that financial journalists routinely use company-issued disclosures and private phone calls with company management when developing articles, and that they believe they are evaluated primarily on the accuracy, timeliness, and depth of their articles. Journalists also believe monitoring companies to hold them accountable is one of financial journalism’s most important objectives, but they often face negative consequences for writing articles that portray companies in an unfavorable light.
Inflation expectations and consumption: Evidence from 1951

We use rich microdata from the 1951 Survey of Consumer Finances to study inflation expectations and consumption in the United States around the start of the Korean War. At the time, the Treasury required the Federal Reserve to hold nominal interest rates constant, so increases in expected inflation corresponded to reductions in the ex-ante real interest rate (similar to a zero lower bound episode). The survey data includes measures of respondents' actual spending on durables, cars, and homes in 1950, and expected expenditures in these categories in 1951, at both the extensive and intensive margins. The data also includes detailed income and asset data from which we can infer respondents' total consumption in 1950. We find that durables consumption and total consumption in 1950 increase with expected inflation, while expected consumption in 1951 of durables, cars, and homes decreases (though only statistically significantly for cars). Our results are consistent with consumers shifting consumption forward intertemporally in response to lower real interest rates.
Time-of-day and day-of-week variations in Amazon Mechanical Turk survey responses

Abstract Social science research studies are frequently conducted on Amazon Mechanical Turk (MTurk). I use data from four previous surveys of inflation expectations on MTurk, with a total of 2780 observations, to study how participant characteristics and behaviors depend on the day of the week and time of day of participation. Saturday participants are older, less educated, and more likely to have low income. Even controlling for demographics, Saturday participants are more likely to answer objective knowledge questions correctly and to provide reasonable inflation forecasts, and less likely to provide “don’t know” responses. They are also less likely to provide nonsensical responses when asked for open-ended comments. Time-of-day effects are less notable. Standard data cleaning procedures typically neither reduce nor exacerbate these patterns. I add data from several other studies in political science and psychology, and find similar Saturday effects with a sample size of over 8000. I discuss implications for various research designs, especially in macroeconomics applications.
Systematic experimental comparison of particle filtration efficiency test methods for commercial respirators and face masks.

Respirators, medical masks, and barrier face coverings all filter airborne particles using similar physical principles. However, they are tested for certification using a variety of standardized test methods, creating challenges for the comparison of differently certified products. We have performed systematic experiments to quantify and understand the differences between standardized test methods for N95 respirators (NIOSH TEB-APR-STP-0059 under US 42 CFR 84), medical face masks (ASTM F2299/F2100), and COVID-19-related barrier face coverings (ASTM F3502-21). Our experiments demonstrate the role of face velocity, particle properties (mean size, size variability, electric charge, density, and shape), measurement techniques, and environmental preconditioning. The measured filtration efficiency was most sensitive to changes in face velocity and particle charge. Relative to the NIOSH method, users of the ASTM F2299/F2100 method have commonly used non-neutralized (highly charged) aerosols as well as smaller face velocities, each of which may result in approximately 10% higher measured filtration efficiencies. In the NIOSH method, environmental conditioning at elevated humidity increased filtration efficiency in some commercial samples while decreasing it in others, indicating that measurement should be performed both with and without conditioning. More generally, our results provide an experimental basis for the comparison of respirators certified under various international methods, including FFP2, KN95, P2, Korea 1st Class, and DS2.
General Low-rank Matrix Optimization: Geometric Analysis and Sharper Bounds

This paper considers the global geometry of general low-rank minimization problems via the Burer-Monterio factorization approach. For the rank-$1$ case, we prove that there is no spurious second-order critical point for both symmetric and asymmetric problems if the rank-$2$ RIP constant $\delta$ is less than $1/2$. Combining with a counterexample with $\delta=1/2$, we show that the derived bound is the sharpest possible. For the arbitrary rank-$r$ case, the same property is established when the rank-$2r$ RIP constant $\delta$ is at most $1/3$. We design a counterexample to show that the non-existence of spurious second-order critical points may not hold if $\delta$ is at least $1/2$. In addition, for any problem with $\delta$ between $1/3$ and $1/2$, we prove that all second-order critical points have a positive correlation to the ground truth. Finally, the strict saddle property, which can lead to the polynomial-time global convergence of various algorithms, is established for both the symmetric and asymmetric problems when the rank-$2r$ RIP constant $\delta$ is less than $1/3$. The results of this paper significantly extend several existing bounds in the literature.
Distinguishing Random and Black Hole Microstates

This is an expanded version of the short report [Phys. Rev. Lett. 126, 171603 (2021)], where the relative entropy was used to distinguish random states drawn from the Wishart ensemble as well as black hole microstates. In this work, we expand these ideas by computing many generalizations including the Petz Renyi relative entropy, sandwiched Renyi relative entropy, fidelities, and trace distances. These generalized quantities are able to teach us about new structures in the space of random states and black hole microstates where the von Neumann and relative entropies were insufficient. We further generalize to generic random tensor networks where new phenomena arise due to the locality in the networks. These phenomena sharpen the relationship between holographic states and random tensor networks. We discuss the implications of our results on the black hole information problem using replica wormholes, specifically the state dependence (hair) in Hawking radiation. Understanding the differences between Hawking radiation of distinct evaporating black holes is an important piece of the information problem that was not addressed by entropy calculations using the island formula. We interpret our results in the language of quantum hypothesis testing and the subsystem eigenstate thermalization hypothesis (ETH), deriving that chaotic (including holographic) systems obey subsystem ETH for all subsystems less than half the total system size.