cssi/SciSciGPT-SciSciNet huggingface dataset:
Binding of the Andes Virus Nucleocapsid Protein to RhoGDI Induces the Release and Activation of the Permeability Factor RhoA

Andes virus (ANDV) nonlytically infects pulmonary microvascular endothelial cells (PMECs), causing acute pulmonary edema termed hantavirus pulmonary syndrome (HPS). In HPS patients, virtually every PMEC is infected; however, the mechanism by which ANDV induces vascular permeability and edema remains to be resolved. The ANDV nucleocapsid (N) protein activates the GTPase RhoA in primary human PMECs, causing VE-cadherin internalization from adherens junctions and PMEC permeability. We found that ANDV N protein failed to bind RhoA but coprecipitates RhoGDI (Rho GDP dissociation inhibitor), the primary RhoA repressor that normally sequesters RhoA in an inactive state. ANDV N protein selectively binds the RhoGDI C terminus (residues 69 to 204) but fails to form ternary complexes with RhoA or inhibit RhoA binding to the RhoGDI N terminus (residues 1 to 69). However, we found that ANDV N protein uniquely inhibits RhoA binding to an S34D phosphomimetic RhoGDI mutant. Hypoxia and vascular endothelial growth factor (VEGF) increase RhoA-induced PMEC permeability by directing protein kinase Cα (PKCα) phosphorylation of S34 on RhoGDI. Collectively, ANDV N protein alone activates RhoA by sequestering and reducing RhoGDI available to suppress RhoA. In response to hypoxia and VEGF-activated PKCα, ANDV N protein additionally directs the release of RhoA from S34-phosphorylated RhoGDI, synergistically activating RhoA and PMEC permeability. These findings reveal a fundamental edemagenic mechanism that permits ANDV to amplify PMEC permeability in hypoxic HPS patients. Our results rationalize therapeutically targeting PKCα and opposing protein kinase A (PKA) pathways that control RhoGDI phosphorylation as a means of resolving ANDV-induced capillary permeability, edema, and HPS. IMPORTANCE HPS-causing hantaviruses infect pulmonary endothelial cells (ECs), causing vascular leakage, pulmonary edema, and a 35% fatal acute respiratory distress syndrome (ARDS). Hantaviruses do not lyse or disrupt the endothelium but dysregulate normal EC barrier functions and increase hypoxia-directed permeability. Our findings reveal a novel underlying mechanism of EC permeability resulting from ANDV N protein binding to RhoGDI, a regulatory protein that normally maintains edemagenic RhoA in an inactive state and inhibits EC permeability. ANDV N sequesters RhoGDI and enhances the release of RhoA from S34-phosphorylated RhoGDI. These findings indicate that ANDV N induces the release of RhoA from PKC-phosphorylated RhoGDI, synergistically enhancing hypoxia-directed RhoA activation and PMEC permeability. Our data suggest inhibiting PKC and activating PKA phosphorylation of RhoGDI as mechanisms of inhibiting ANDV-directed EC permeability and therapeutically restricting edema in HPS patients. These findings may be broadly applicable to other causes of ARDS.
Low-Visibility Visual Simulation with Real Fog

An environmental fog simulation (EFS) attachment was developed to aid in the study of natural low-visibility visual cues and subsequently used to examine the realism effect upon the aircraft simulator visual scene. A review of the basic fog equations indicated that the two major factors must be accounted for in the simulation of low visibility-one due to atmospheric attenuation and one due to veiling luminance. These factors are compared systematically by: comparing actual measurements lo those computed from the Fog equations, and comparing runway-visual-range-related visual-scene contrast values with the calculated values. These values are also compared with the simulated equivalent equations and with contrast measurements obtained from a current electronic fog synthesizer to help identify areas in which improvements are needed. These differences in technique, the measured values, the Features of both systems, a pilot opinion survey of the EFS fog, and improvements (by combining features of both systems) that are expected to significantly increase the potential as well as flexibility for producing a very high-fidelity, low-visibility visual simulation are discussed.
ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems

Autonomous Cyber Physical Systems (CPSs) are often required to handle uncertainties and self-manage the system operation in response to problems and increasing risk in the operating paradigm. This risk may arise due to distribution shifts, environmental context, or failure of software or hardware components. Traditional techniques for risk assessment focus on design-time techniques such as hazard analysis, risk reduction, and assurance cases among others. However, these static, design-time techniques do not consider the dynamic contexts and failures the systems face at runtime. We hypothesize that this requires a dynamic assurance approach that computes the likelihood of unsafe conditions or system failures considering the safety requirements, assumptions made at design time, past failures in a given operating context, and the likelihood of system component failures. We introduce the ReSonAte dynamic risk estimation framework for autonomous systems. ReSonAte reasons over Bow-Tie Diagrams (BTDs) which capture information about hazard propagation paths and control strategies. Our innovation is the extension of the BTD formalism with attributes for modeling the conditional relationships with the state of the system and environment. We also describe a technique for estimating these conditional relationships and equations for estimating risk based on the state of the system and environment. To help with this process, we provide a scenario modeling procedure that can use the prior distributions of the scenes and threat conditions to generate the data required for estimating the conditional relationships. To improve scalability and reduce the amount of data required, this process considers each control strategy in isolation and composes several single-variate distributions into one complete multi-variate distribution for the control strategy in question. Lastly, we describe the effectiveness of our approach using two separate autonomous system simulations: CARLA and an unmanned underwater vehicle.
Cerebrovascular responses to O2-CO2 exchange ratio under brief breath-hold challenge in patients with chronic mild traumatic brain injury

Breath-by-breath oxygen-carbon dioxide (O2-CO2) exchange ratio (bER) is a respiratory gas exchange (RGE) metric, which is the ratio of the changes in the partial pressure of O2 (ΔPO2) to CO2 (ΔPCO2...
Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition

We study the stochastic shortest path problem with adversarial costs and known transition, and show that the minimax regret is $O(\sqrt{DT_\star K})$ and $O(\sqrt{DT_\star SA K})$ for the full-information setting and the bandit feedback setting respectively, where $D$ is the diameter, $T_\star$ is the expected hitting time of the optimal policy, $S$ is the number of states, $A$ is the number of actions, and $K$ is the number of episodes. Our results significantly improve upon the recent work of (Rosenberg and Mansour, 2020) which only considers the full-information setting and achieves suboptimal regret. Our work is also the first to consider bandit feedback with adversarial costs. Our algorithms are built on top of the Online Mirror Descent framework with a variety of new techniques that might be of independent interest, including an improved multi-scale expert algorithm, a reduction from general stochastic shortest path to a special loop-free case, a skewed occupancy measure space, and a novel correction term added to the cost estimators. Interestingly, the last two elements reduce the variance of the learner via positive bias and the variance of the optimal policy via negative bias respectively, and having them simultaneously is critical for obtaining the optimal high-probability bound in the bandit feedback setting.
DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks

Highly directional millimeter wave (mmWave) radios need to perform beam management to establish and maintain reliable links. To achieve this objective, existing solutions mostly rely on explicit coordination between the transmitter (TX) and the receiver (RX), which significantly reduces the airtime available for communication and further complicates the network protocol design. This paper advances the state of the art by presenting DeepBeam, a framework for beam management that does not require pilot sequences from the TX, nor any beam sweeping or synchronization from the RX. This is achieved by inferring (i) the Angle of Arrival (AoA) of the beam and (ii) the actual beam being used by the transmitter through waveform-level deep learning on ongoing transmissions between the TX to other receivers. In this way, the RX can associate Signal-to-Noise-Ratio (SNR) levels to beams without explicit coordination with the TX. This is possible because different beam patterns introduce different "impairments" to the waveform, which can be subsequently learned by a convolutional neural network (CNN). To demonstrate the generality of DeepBeam, we conduct an extensive experimental data collection campaign where we collect more than 4 TB of mmWave waveforms with (i) 4 phased array antennas at 60.48 GHz, (ii) 2 codebooks containing 24 one-dimensional beams and 12 two-dimensional beams; (iii) 3 receiver gains; (iv) 3 different AoAs; (v) multiple TX and RX locations. Moreover, we collect waveform data with two custom-designed mmWave software-defined radios with fully-digital beamforming architectures at 58 GHz. We also implement our learning models in FPGA to evaluate latency performance. Results show that DeepBeam (i) achieves accuracy of up to 96%, 84% and 77% with a 5-beam, 12-beam and 24-beam codebook, respectively; (ii) reduces latency by up to 7x with respect to the 5G NR initial beam sweep in a default configuration and with a 12-beam codebook. The waveform dataset and the full DeepBeam code repository are publicly available.
EventGAN: Leveraging Large Scale Image Datasets for Event Cameras

Event cameras provide a number of benefits over traditional cameras, such as the ability to track incredibly fast motions, high dynamic range, and low power consumption. However, their application into computer vision problems, many of which are primarily dominated by deep learning solutions, has been limited by the lack of labeled training data for events. In this work, we propose a method which leverages the existing labeled data for images by simulating events from a pair of temporal image frames, using a convolutional neural network. We train this network on pairs of images and events, using an adversarial discriminator loss and a pair of cycle consistency losses. The cycle consistency losses utilize a pair of pre-trained self-supervised networks which perform optical flow estimation and image reconstruction from events, and constrain our network to generate events which result in accurate outputs from both of these networks. Trained fully end to end, our network learns a generative model for events from images without the need for accurate modeling of the motion in the scene, exhibited by modeling based methods, while also implicitly modeling event noise. Using this simulator, we train a pair of downstream networks on object detection and 2D human pose estimation from events, using simulated data from large scale image datasets, and demonstrate the networks' abilities to generalize to datasets with real events. The code and dataset in this paper are available here: https://github.com/alexzzhu/EventGAN.
ELRUNA: Elimination Rule-based Network Alignment

Networks model a variety of complex phenomena across different domains. In many applications, one of the most essential tasks is to align two or more networks to infer the similarities between cross-network vertices and to discover potential node-level correspondence. In this article, we propose ELRUNA (elimination rule-based network alignment), a novel network alignment algorithm that relies exclusively on the underlying graph structure. Under the guidance of the elimination rules that we defined, ELRUNA computes the similarity between a pair of cross-network vertices iteratively by accumulating the similarities between their selected neighbors. The resulting cross-network similarity matrix is then used to infer a permutation matrix that encodes the final alignment of cross-network vertices. In addition to the novel alignment algorithm, we improve the performance of local search, a commonly used postprocessing step for solving the network alignment problem, by introducing a novel selection method RAWSEM (random-walk-based selection method) based on the propagation of vertices’ mismatching across the networks. The key idea is to pass on the initial levels of mismatching of vertices throughout the entire network in a random-walk fashion. Through extensive numerical experiments on real networks, we demonstrate that ELRUNA significantly outperforms the state-of-the-art alignment methods in terms of alignment accuracy under lower or comparable running time. Moreover, ELRUNA is robust to network perturbations such that it can maintain a close-to-optimal objective value under a high level of noise added to the original networks. Finally, the proposed RAWSEM can further improve the alignment quality with a smaller number of iterations compared with the naive local search method. Reproducibility: The source code and data are available at https://tinyurl.com/uwn35an.
Quantum Size Effects, Multiple Dirac Cones, and Edge States in Ultrathin Bi(110) Films.

The presence of inherently strong spin-orbit coupling in bismuth, its unique layer-dependent band topology and high carrier mobility make it an interesting system for both fundamental studies and applications. Theoretically, it has been suggested that strong quantum size effects should be present in the Bi(110) films, with the possibility of Dirac Fermion states in the odd-bilayer (BL) films, originating from dangling pz orbitals and quantum-spin hall (QSH) states in the even-bilayer films. However, the experimental verification of these claims has been lacking. Here, we study the electronic structure of Bi(110) films grown on a high-Tc superconductor, Bi2Sr2CaCu2O8+δ (Bi2212) using angle-resolved photoemission spectroscopy (ARPES). We observe an oscillatory behavior of electronic structure with the film thickness and identify the Dirac-states in the odd-bilayer films, consistent with the theoretical predictions. In the even-bilayer films, we find another Dirac state that was predicted to play a crucial role in the QSH effect. In the low thickness limit, we observe several extremely one-dimensional states, probably originating from the edge-states of Bi(110) islands. Our results provide a much needed experimental insight into the electronic and structural properties of Bi(110) films.
A recursive system-free single-step temporal discretization method for finite difference methods

Abstract Single-stage or single-step high-order temporal discretizations of partial differential equations (PDEs) have shown great promise in delivering high-order accuracy in time with efficient use of computational resources. There has been much success in developing such methods for finite volume method (FVM) discretizations of PDEs. The Picard Integral formulation (PIF) has recently made such single-stage temporal methods accessible for finite difference method (FDM) discretizations. PIF methods rely on the so-called Lax-Wendroff procedures to tightly couple spatial and temporal derivatives through the governing PDE system to construct high-order Taylor series expansions in time. Going to higher than third order in time requires the calculation of Jacobian-like derivative tensor-vector contractions of an increasingly larger degree, greatly adding to the complexity of such schemes. To that end, we present in this paper a method for calculating these tensor contractions through a recursive application of a discrete Jacobian operator that readily and efficiently computes the needed contractions entirely agnostic of the system of partial differential equations (PDEs) being solved.
CORe: Capitalizing On Rewards in Bandit Exploration

We propose a bandit algorithm that explores purely by randomizing its past observations. In particular, the sufficient optimism in the mean reward estimates is achieved by exploiting the variance in the past observed rewards. We name the algorithm Capitalizing On Rewards (CORe). The algorithm is general and can be easily applied to different bandit settings. The main benefit of CORe is that its exploration is fully data-dependent. It does not rely on any external noise and adapts to different problems without parameter tuning. We derive a $\tilde O(d\sqrt{n\log K})$ gap-free bound on the n-round regret of CORe in a stochastic linear bandit, where d is the number of features and K is the number of arms. Extensive empirical evaluation on multiple synthetic and real-world problems demonstrates the effectiveness of CORe.
Pivot Rules for (Overconfident) Entrepreneurs

We develop a computational simulation of entrepreneurial learning and decision-making to investigate four emergent propositions from the lean startup movement: (1) entrepreneurs should be prepared ...
Emergence of directional bias in tau deposition from axonal transport dynamics

Defects in axonal transport may partly underpin the differences between the observed pathophysiology of Alzheimer's disease (AD) and that of other non-amyloidogenic tauopathies. Particularly, pathological tau variants may have molecular properties that dysregulate motor proteins responsible for the anterograde-directed transport of tau in a disease-specific fashion. Here we develop the first computational model of tau-modified axonal transport that produces directional biases in the spread of tau pathology. We simulated the spatiotemporal profiles of soluble and insoluble tau species in a multicompartment, two-neuron system using biologically plausible parameters and time scales. Changes in the balance of tau transport feedback parameters can elicit anterograde and retrograde biases in the distributions of soluble and insoluble tau between compartments in the system. Aggregation and fragmentation parameters can also perturb this balance, suggesting a complex interplay between these distinct molecular processes. Critically, we show that the model faithfully recreates the characteristic network spread biases in both AD-like and non-AD-like mouse tauopathy models. Tau transport feedback may therefore help link microscopic differences in tau conformational states and the resulting variety in clinical presentations.
Asymptotic limit-cycle analysis of oscillating chemical reactions

The asymptotic limit-cycle analysis of mathematical models for oscillating chemical reactions is presented. In this work, after a brief presentation of mathematical preliminaries applied to the biased Van der Pol oscillator, we consider a two-dimensional model of the Chlorine dioxide Iodine Malonic-Acid (CIMA) reactions and the three-dimensional and two-dimensional Oregonator models of the Belousov–Zhabotinsky reactions. Explicit analytical expressions are given for the relaxation-oscillation periods of these chemical reactions that are accurate within 5% of their numerical values. In the two-dimensional CIMA and Oregonator models, we also derive critical parameter values leading to canard explosions and implosions in their associated limit cycles.
A Theory of Liquidity Spillover between Bond and CDS Markets

This paper builds a search-theoretic model of bond and CDS markets that features endogenous funding liquidity and interdependent bond and CDS market liquidity. I show that, in the long run, speculative CDS trades attract liquidity into the CDS market than then, due to search frictions, spills over into the bond market and increases bond market liquidity. In the short run, however, speculative CDS buyers instead attract liquidity away from the bond market. In a separate empirical paper, I document how a series of European policies that banned speculative CDS purchases affected bond market liquidity. The implications from the longand short-run effects help rationalize the observed changes in bond market liquidity.
Point Spectrum of Periodic Operators on Universal Covering Trees

For any multi-graph $G$ with edge weights and vertex potential, and its universal covering tree $\mathcal{T}$, we completely characterize the point spectrum of operators $A_{\mathcal{T}}$ on $\mathcal{T}$ arising as pull-backs of local, self-adjoint operators $A_{G}$ on $G$. This builds on work of Aomoto, and includes an alternative proof of the necessary condition for point spectrum he derived in (Aomoto, 1991). Our result gives a finite time algorithm to compute the point spectrum of $A_{\mathcal{T}}$ from the graph $G$, and additionally allows us to show that this point spectrum is contained in the spectrum of $A_{G}$. Finally, we prove that typical pull-back operators have a spectral delocalization property: the set of edge weight and vertex potential parameters of $A_{G}$ giving rise to $A_{\mathcal{T}}$ with purely absolutely continuous spectrum is open and its complement has large codimension.
Entanglement Dynamics from Random Product States at Long Times

We study the entanglement dynamics of quantum many-body systems at long times and prove the following: (I) For any geometrically local Hamiltonian on a lattice, starting from a random product state the entanglement entropy almost never approaches the Page curve. (II) In a spin-glass model with random all-to-all interactions, starting from any product state the average entanglement entropy does not approach the Page curve. We also extend these results to any unitary evolution with charge conservation and to the Sachdev-Ye-Kitaev model. Our results highlight the difference between the entanglement generated by (chaotic) Hamiltonian dynamics and that of random states.
Kobayashi hyperbolic convex domains not biholomorphic to bounded convex domains

We construct families of convex domains that are biholomorphic to bounded domains, but not bounded convex domains. This is accomplished by finding an obstruction related to the Gromov hyperbolicity of the Kobayashi metric.
A high-order/low-order (HOLO) algorithm for preserving conservation in time-dependent low-rank transport calculations

Abstract Dynamical low-rank (DLR) approximation methods have previously been developed for time-dependent radiation transport problems. One crucial drawback of DLR is that it does not conserve important quantities of the calculation, which limits the applicability of the method. Here we address this conservation issue by solving a low-order equation with closure terms computed via a high-order solution calculated with DLR. We observe that the high-order solution well approximates the closure term, and the low-order solution can be used to correct the conservation bias in the DLR evolution. We also apply the linear discontinuous Galerkin method for the spatial discretization. We then demonstrate with the numerical results that this so-called high-order/low-order (HOLO) algorithm is conservative without sacrificing computational efficiency and accuracy.
Handling Neumann and Robin boundary conditions in a fictitious domain volume penalization framework

Abstract Sakurai et al. (2019) [14] presented a flux-based volume penalization (VP) approach for imposing inhomogeneous Neumann boundary conditions on embedded interfaces. The flux-based VP method modifies the diffusion coefficient of the original elliptic (Poisson) equation and uses a flux-forcing function as a source term in the equation to impose the Neumann boundary conditions. As such, the flux-based VP method can be easily incorporated into existing fictitious domain codes. Sakurai et al. relied on an analytical construction of flux-forcing functions, which limits the practicality of the approach. Because of the analytical approach taken in the prior work, only (spatially) constant flux values on simple interfaces were considered. In this paper, we present a numerical technique for constructing flux-forcing functions for arbitrarily complex boundaries. The imposed flux values are also allowed to vary spatially in our approach. Furthermore, the flux-based VP method is extended to include (spatially varying) Robin boundary conditions, which makes the flux-based VP method even more general. The numerical construction of the flux-forcing functions relies only on a signed distance function that describes the distance of a grid point from the interface and can be constructed for any irregular boundary. We consider several two- and three-dimensional test examples to access the spatial accuracy of the numerical solutions. The method is also used to simulate flux-driven thermal convection in a concentric annular domain. We formally derive the flux-based volume penalized Poisson equation satisfying Neumann/Robin boundary condition in strong form; such a derivation was not presented in Sakurai et al., where the equation first appeared for the Neumann problem. The derivation reveals that the flux-based VP approach relies on a surface delta function to impose inhomogeneous Neumann/Robin boundary conditions. However, explicit construction of the delta function is not necessary for the flux-based VP method, which makes it different from other diffuse domain equations presented in the literature.
A universal quantum circuit design for periodical functions

We propose a universal quantum circuit design that can estimate any arbitrary one-dimensional periodic functions based on the corresponding Fourier expansion. The quantum circuit contains N-qubits to store the information on the different N-Fourier components and $M+2$ auxiliary qubits with $M = \lceil{\log_2{N}}\rceil$ for control operations. The desired output will be measured in the last qubit $q_N$ with a time complexity of the computation of $O(N^2\lceil \log_2N\rceil^2)$. We illustrate the approach by constructing the quantum circuit for the square wave function with accurate results obtained by direct simulations using the IBM-QASM simulator. The approach is general and can be applied to any arbitrary periodic function.
Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation

A key assumption of top-down human pose estimation approaches is their expectation of having a single person/instance present in the input bounding box. This often leads to failures in crowded scenes with occlusions. We propose a novel solution to overcome the limitations of this fundamental assumption. Our Multi-Instance Pose Network (MIPNet) allows for predicting multiple 2D pose instances within a given bounding box. We introduce a Multi-Instance Modulation Block (MIMB) that can adaptively modulate channel-wise feature responses for each instance and is parameter efficient. We demonstrate the efficacy of our approach by evaluating on COCO, CrowdPose, and OCHuman datasets. Specifically, we achieve 70.0 AP on CrowdPose and 42.5 AP on OCHuman test sets, a significant improvement of 2.4 AP and 6.5 AP over the prior art, respectively. When using ground truth bounding boxes for inference, MIPNet achieves an improvement of 0.7 AP on COCO, 0.9 AP on CrowdPose, and 9.1 AP on OCHuman validation sets compared to HRNet. Interestingly, when fewer, high confidence bounding boxes are used, HRNet's performance degrades (by 5 AP) on OCHuman, whereas MIPNet maintains a relatively stable performance (drop of 1 AP) for the same inputs.
Generalized Contingency Analysis Based on Graph Theory and Line Outage Distribution Factor

Identifying the multiple critical components in power systems whose absence together has severe impact on system performance is a crucial problem for power systems known as $(N-x)$ contingency analysis. However, the inherent combinatorial feature of the $N-x$ contingency analysis problem incurs by the increase of $x$ in the $(N-x)$ term, making the problem intractable for even relatively small test systems. We present a new framework for identifying the $N-x$ contingencies that captures both topology and physics of the network. Graph theory provides many ways to measure power grid graphs, i.e., buses as nodes and lines as edges, allowing researchers to characterize system structure and optimize algorithms. This article proposes a scalable approach based on the group betweenness centrality concept that measures the impact of multiple components in the electric power grid as well as line outage distribution factors that find the lines whose loss has the highest impact on the power flow in the network. The proposed approach is a quick and efficient solution for identifying the most critical lines in power networks. The proposed approach is validated using various test cases, and results show that the proposed approach is able to quickly identify multiple contingencies that result in violations.
Cumulative Assessment for Urban 3D Modeling

Urban 3D modeling from satellite images requires accurate semantic segmentation to delineate urban features, multiple view stereo for 3D reconstruction of surface heights, and 3D model fitting to produce compact models with accurate surface slopes. In this work, we present a cumulative assessment metric that succinctly captures error contributions from each of these components. We demonstrate our approach by providing challenging public datasets and extending two open source projects to provide an end-to-end 3D modeling baseline solution to stimulate further research and evaluation with a public leaderboard.
Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning

How to select between policies and value functions produced by different training algorithms in offline reinforcement learning (RL) -- which is crucial for hyperpa-rameter tuning -- is an important open question. Existing approaches based on off-policy evaluation (OPE) often require additional function approximation and hence hyperparameters, creating a chicken-and-egg situation. In this paper, we design hyperparameter-free algorithms for policy selection based on BVFT [XJ21], a recent theoretical advance in value-function selection, and demonstrate their effectiveness in discrete-action benchmarks such as Atari. To address performance degradation due to poor critics in continuous-action domains, we further combine BVFT with OPE to get the best of both worlds, and obtain a hyperparameter-tuning method for Q-function based OPE with theoretical guarantees as a side product.
Comparing 911 and emergency hotline calls for domestic violence in seven cities: What happened when people started staying home due to COVID-19?

Abstract Research Summary: We examine changes in help-seeking for domestic violence (DV) in seven U.S. cities during the COVID-19 pandemic. Using Bayesian structural time-series modeling with daily data to construct a synthetic counterfactual, we test whether calls to police and/or emergency hotlines varied in 2020 as people stayed home due to COVID-19. Across this sample, we estimate there were approximately 1030 more calls to police and 1671 more calls to emergency hotlines than would have occurred absent the pandemic. Policy Implications: Interagency data sharing and analysis holds great promise for better understanding localized trends in DV in real time. Research-practitioner partnerships can help DV coordinated community response teams (CCRTs) develop accessible and sustainable dashboards to visualize data and advance community transparency. As calls for drastic changes in policing are realized, prioritization of finite resources will become critical. Data-driven decision-making by CCRTs provides an opportunity to work within resource constraints without compromising the safety of DV victims.
Efficient constrained sampling via the mirror-Langevin algorithm

We propose a new discretization of the mirror-Langevin diffusion and give a crisp proof of its convergence. Our analysis uses relative convexity/smoothness and self-concordance, ideas which originated in convex optimization, together with a new result in optimal transport that generalizes the displacement convexity of the entropy. Unlike prior works, our result both (1) requires much weaker assumptions on the mirror map and the target distribution, and (2) has vanishing bias as the step size tends to zero. In particular, for the task of sampling from a log-concave distribution supported on a compact set, our theoretical results are significantly better than the existing guarantees.
Sanctioning the Homeland: Diasporas’ Influence on American Economic Sanctions Policy:

Why do some immigrant groups succeed in influencing the U.S. government to impose economic sanctions on their former dictators, while others do not? This paper begins by noting that the president i...
Integration of transcriptomics and network analysis reveals co-expressed genes in Frankliniella occidentalis larval guts that respond to tomato spotted wilt virus infection.

Background The gut is the first barrier to infection by viruses that are internally borne and transmitted persistently by arthropod vectors to plant and animal hosts. Tomato spotted wilt virus (TSWV), a plant-pathogenic virus, is transmitted exclusively by thrips vectors in a circulative-propagative manner. Frankliniella occidentalis (western flower thrips), the principal thrips vector of TSWV, is transmission-competent only if the virus is acquired by young larvae. To begin to understand the larval gut response to TSWV infection and accumulation, a genome-assisted, transcriptomic analysis of F. occidentalis gut tissues of first (early L1) and second (early L2 and late L2) instar larvae was conducted using RNA-Seq to identify differentially-expressed transcripts (DETs) in response to TSWV compared to non-exposed cohorts. Results The larval gut responded in a developmental stage-dependent manner, with the majority of DETs (71%) associated with the early L1 stage at a time when virus infection is limited to the midgut epithelium. Provisional annotations of these DETs inferred roles in digestion and absorption, insect innate immunity, and detoxification. Weighted gene co-expression network analysis using all assembled transcripts of the gut transcriptome revealed eight gene modules that distinguish larval development. Intra-module interaction network analysis of the three most DET-enriched modules revealed ten central hub genes. Droplet digital PCR-expression analyses of select network hub and connecting genes revealed temporal changes in gut expression during and post exposure to TSWV. Conclusions These findings expand our understanding of the developmentally-mediated interaction between thrips vectors and orthotospoviruses, and provide opportunities for probing pathways for biomarkers of thrips vector competence.
GGNB: Graph-Based Gaussian Naive Bayes Intrusion Detection System for CAN Bus

Abstract The national highway traffic safety administration (NHTSA) identified cybersecurity of the automobile systems are more critical than the security of other information systems. Researchers already demonstrated remote attacks on critical vehicular electronic control units (ECUs) using controller area network (CAN). Besides, existing intrusion detection systems (IDSs) often propose to tackle a specific type of attack, which may leave a system vulnerable to numerous other types of attacks. A generalizable IDS that can identify a wide range of attacks within the shortest possible time has more practical value than attack-specific IDSs, which is not a trivial task to accomplish. In this paper we propose a novel graph-based Gaussian naive Bayes (GGNB) intrusion detection algorithm by leveraging graph properties and PageRank-related features. The GGNB on the real rawCAN data set [1] yields 99.61%, 99.83%, 96.79%, and 96.20% detection accuracy for denial of service (DoS), fuzzy, spoofing, replay, mixed attacks, respectively. Also, using OpelAstra data set [2] , the proposed methodology has 100%, 99.85%, 99.92%, 100%, 99.92%, 97.75% and 99.57% detection accuracy considering DoS, diagnostic, fuzzing CAN ID, fuzzing payload, replay, suspension, and mixed attacks, respectively. The GGNB-based methodology requires about 239× and 135× lower training and tests times, respectively, compared to the SVM classifier used in the same application. Using Xilinx Zybo Z7 field-programmable gate array (FPGA) board, the proposed GGNB requires 5.7×, 5.9×, 5.1×, and 3.6× fewer slices, LUTs, flip-flops, and DSP units, respectively, than conventional NN architecture.
Formula Apportionment, Tax Competition, and the Provision of Local Goods

The paper develops an analytical framework where regional governments strategically determine the structure of the corporate profit tax system and profits are regionally allocated using an apportionment formula. Two important results emerge in a symmetric Nash equilibrium: (i) investment decisions are distorted, i.e., regional governments will not allow complete deduction of capital costs from taxable corporate profits; and (ii) there is underprovision of the good provided by the regional government, consistent with the literature on property tax competition. The paper also shows that the degree of underprovision may be less severe when the formula employs sales shares to apportion corporate profits. The model allows us to presume that the recent shift by most states in the U.S. towards a formula apportionment that gives a higher weight to the sales proportion may constitute a welfare improvement for all regions, compared to the original formula that weighs all factors equally.
Further studies on the androgen therapy of gynecologic disorders

Abstract A follow-up of patients treated from seven to twelve months previously with testosterone propionate for gynecologic ailments with satisfactory results, revealed that more than 50 per cent continued to derive appreciable benefit after discontinuing the therapy. The patients with dysmenorrhea tended to relapse more often than those with excessive uterine bleeding but the smallness of our series of patients does not permit more definite conclusions. Twelve additional patients who were treated more recently, and of whom three had menorrhagia, one had menorrhagia and dysmenorrhea, three had menometrorrhagia, four had painful breasts, and one had premenstrual migraine, responded satisfactorily to androgen therapy except in one case of menometrorrhagia. The only disturbance observed occurred in a patient who received 350 mg. of testosterone propionate in one month and who developed considerable hirsutism and acne.
A Review of “Understanding the British Empire”

Distinguished Cambridge historian Ronald Hyam explains from the start that Understanding the British Empire is not “a comprehensive guide to the understanding of the British empire” (xiii). Readers...
An Experiment in Combining Estimates of Uncertainty

This research explores how subjects combine estimates of uncertainty. Four major computer programs written in Common Lisp asked the subjects questions about the likelihoods of single events and conjunctions of independent events. The results suggest that the subjects did not widely use the product rule, appeared to use a minimum rule, and varied the combining heuristic as the magnitude of the likelihoods varied. The results suggest that inaccurate risk perceptions and judgments may, in part, result from deficient reasoning about uncertainty. Future research should encompass dependent events and time and explore other methods of representing uncertainty.
Drug Insight: role of the androgen receptor in the development and progression of prostate cancer

Functional androgen receptor (AR) signaling is necessary for the development of prostate cancer. The therapeutic effect of androgen deprivation therapy for prostate cancer was described over 60 years ago and this treatment remains the mainstay of systemic therapy despite its transient response duration. It has become clear that AR expression and signaling remains intact as the disease evolves from androgen-sensitive cancer to classically (but perhaps inaccurately) termed hormone refractory prostate cancer. Through several genetic and epigenetic adaptations, prostate tumors continue to rely on AR growth signaling and they thus remain targets of 'hormonal' therapy. The development of new strategies and drugs that can abrogate AR signaling will probably result in important clinical benefits. The biology of androgen independence and the development of new approaches targeting AR signaling are reviewed herein.
Microsized particles of Aza222 polymer as a regenerable ultrahigh affinity sorbent for the removal of mercury from aqueous solutions

Abstract This paper reports the preparation and characterization of a microparticulate sorbent based on a novel p-xylylene-peraza[2.2.2]cryptand (Aza222) polymer that exhibits selectivity and high affinity for mercury cations [1] , [2] , [3] . The sorbent particles dispersed into an aqueous suspension were characterized in terms of surface morphology, size distribution, porosity, and surface charge. Based on measured adsorption isotherms, two possible mechanisms of adsorption by this novel sorbent are proposed: high affinity stoichiometric complexation of Hg2+ within the Aza222 cage, which proceeds until the stoichiometric capacity of the sorbent is saturated; then excess mercury removal by nonspecific physisorption onto the surface of the Aza222 polymer. The experimentally determined mercury loading capacity of the sorbent was in accord with stoichiometric calculations at both high (mg/L) and low (μg/L) concentrations, opening possibilities for the use of the polymer for mercury detection or other analytical applications. Desorption studies were carried out and a preliminary study demonstrated that the sorbent is regenerable. The mercury capacity and selectivity of the sorbent in the presence of calcium ions compared favorably against corresponding properties of both a commercial ion exchange resin used for mercury removal and a related but non-macrocyclic polyamine-based polymer. Density functional (B3LYP) calculations using the 6-31G* basis set for light elements (C, H, Li, N, O) and the LANL2DZ effective core potential and basis set combination were used to compute the relative stability constants of the various metal-cryptand complexes. The agreement between theoretical and experimental Ks values, where known, was generally excellent, supporting the prediction of unknowns. Of particular relevance to the experimental work herein, the calculated difference between log KS for Hg2+ vs. Ca2+ ion binding in the hexamethylated peraza[2.2.2]cryptand is 23.0, indicating an overwhelming preference for binding mercury in this all-nitrogen complexant model for the ligand covalently embedded in the Aza222 polymer sorbent.
Solidification of nb-bearing superalloys : Part I. Reaction sequences

The solidification reaction sequences of experimental superalloys containing systematic variations in Fe, Nb, Si, and C were studied using differential thermal analysis (DTA) and microstructural characterization techniques. The reaction sequences responsible for microstructural development were found to be similar to those expected in the Ni-Nb-C ternary system and commercial superalloys of comparable composition. The solute-rich interdendritic liquid generally exhibited two eutectic-type reactions at the terminal stages of solidification: L → (γ+NbC) and L → (γ+Laves). The Ni-base alloys with a high C/Nb ratio represented the only exception to this general solidification sequence. This group of alloys terminated solidification with the L → (γ + NbC) reaction and did not exhibit the γ/Laves constituent. At similar levels of solute elements (Nb, Si, and C), the Fe-base alloys always formed more of the γ/Laves eutectic-type constituent than the corresponding Ni-base alloys. Silicon additions also increased the amount of the γ/Laves constituent that formed in the assolidified microstructure, while C additions promoted formation of γ/NbC. The influence of Nb was dependent on the C content of the alloy. When the C content was low, Nb additions generally promoted formation of γ/Laves, while Nb additions to alloys with high C led to formation of the γ/NbC constituent. The results of this work are combined with quantitative analyses for developing γ-Nb-C pseudoternary solidification diagrams in a companion article.
The effects of perturbations on matrix-based signal processing

The effects of perturbations of the elements of a matrix on functions of the matrix, such as eigenvalues and eigenvectors, was a subject of great interest to applied mathematicians who were working in the early part of this century. For example, Rayleigh and others analyzed complicated linear systems by considering them to be perturbed versions of simpler, analytically tractable systems. Calculation of eigenvalue changes due to the perturbations were used to calculate corresponding changes in the natural frequencies of the system. This is of course very similar to recent work, except that in recent work the perturbations are usually assumed to be due to measurement noise. >
Eating, drinking and temperature response of chicks to brain catecholamine injections.

Abstract The effects of dopamine, norepinephrine and epinephrine on feed intake, water intake, water consumption and rectal body temperature (T R ) were investigated using broiler cockerels. Chicks were maintained in a thermoneutral environment with feed and water available ad lib. Solutions of dopamine, l-norepinephrine and l-epinephrine were prepared in concentrations of 33, 67 and 100 μg/10 μl in artificial cerebrospinal fluid and injected into the lateral ventricles. Dopamine and norepinephrine caused no effect on feed consumption while epinephrine caused a significant increase. Although norepinephrine had no effect on feed intake, it did precipitate a narcoleptic response which may have masked a stimulation of feed intake. Epinephrine and norepinephrine significantly decreased T R while dopamine had no effect. Water consumption was not affected by any of the catecholamines. These results suggest that epinephrine and possibly norepinephrine are involved in neural control of feed intake, but not water consumption in chickens.
Laminar boundary-layer separation control by Görtler-scale blowing

Abstract Recent experimental work has succeeded in retarding or removing boundary-layer separation by means of blowing supersonic microjets transversely through the wall. To provide some theoretical context for such work, the current study examines the removal of separation by transverse blowing within the framework of the standard Prandtl scalings for incompressible boundary layers. One key result, obtained using asymptotic analysis, is that such removal is not possible for two-dimensional flow. Neither is removal of separation possible by three-dimensional blowing in an initially two-dimensional separated boundary layer if the blowing distribution has a finite-scale spanwise variation. The second key result obtained is that the previous conclusion is no longer valid when there is nontrivial short-scale spanwise variation of the blowing distribution. This result is obtained by providing a numerical counter-example in which blowing, with a Gortler scale spanwise variation, creates an attached boundary layer flow where none existed before the blowing. One consequence is that there are at least some flows in which transverse Gortler-scale blowing can turn a separated flow into an attached flow, with a vanishingly small drag that is inversely proportional to the square root of the Reynolds number. The flow physics of the computed example is analyzed to obtain a better understanding of how the Gortler-scale blowing affects the flow.
Local structural anomaly near Tc observed by pulsed neutron scattering

Abstract Pulsed neutron scattering studies carried out with the atomic pair distribution method show that the actual atomic structure of superconducting oxides has significant local deviation from the crystallographic average structure, and the deviation shows an anomalous change through the superconducting transition. This anomaly is likely to provide information regarding the coupling mechanism.