cssi/SciSciGPT-SciSciNet huggingface dataset:
Superconvergence of $$C^0$$-$$Q^k$$ Finite Element Method for Elliptic Equations with Approximated Coefficients

We prove that the superconvergence of $$C^0$$-$$Q^k$$ finite element method at the Gauss–Lobatto quadrature points still holds if variable coefficients in an elliptic problem are replaced by their piecewise $$Q^k$$ Lagrange interpolants at the Gauss–Lobatto points in each rectangular cell. In particular, a fourth order finite difference type scheme can be constructed using $$C^0$$-$$Q^2$$ finite element method with $$Q^2$$ approximated coefficients.
Chronic repeated predatory stress induces resistance to quinine adulteration of ethanol in male mice.

Abstract Background Trauma related psychiatric disorders, such as posttraumatic stress disorder (PTSD), and alcohol use disorder (AUD) are highly comorbid illnesses that separately present an opposing, sex-specific pattern, with increased prevalence of PTSD in females and increased prevalence of AUD diagnoses in males. Likewise, PTSD is a risk factor in the development of AUD, with conflicting data on the impact of sex in the comorbid development of both disorders. Because the likelihood of experiencing more than one traumatic event is high, we aim to utilize chronic repeated predatory stress (CRPS) to query the extent to which sex interacts with CRPS to influence alcohol consumption, or cessation of consumption. Methods Male (n = 16) and female (n = 15) C57BL/6 J mice underwent CRPS or daily handling for two weeks during adolescence (P35-P49) and two weeks during adulthood (P65-P79). Following the conclusion of two rounds of repeated stress, behavior was assessed in the open field. Mice subsequently underwent a two-bottle choice intermittent ethanol access (IEA) assessment (P90-131) with the options of 20 % ethanol or water. After establishing drinking behavior, increasing concentrations of quinine were added to the ethanol to assess the drinking response to adulteration of the alcohol. Results CRPS increased fecal corticosterone concentrations and anxiety-like behaviors in the open field in both male and female mice as compared to control mice that had not been exposed to CRPS. Consistent with previous reports, we observed a sex difference in alcohol consumption such that females consumed more ethanol per gram of body mass than males. In addition, CRPS reduced alcohol aversion in male mice such that higher concentrations of quinine were necessary to reduce alcohol intake as compared to control mice. CRPS did not alter alcohol-related behaviors in female mice. Conclusion Collectively, we demonstrate that repeated CRPS can induce anxiety-like behavior in both sexes but selectively influences the response to ethanol adulteration in males.
Candida albicans white and opaque cells exhibit distinct spectra of organ colonization in mouse models of infection

Candida albicans, a species of fungi, can thrive in diverse niches of its mammalian hosts; it is a normal resident of the GI tract and mucosal surfaces but it can also enter the bloodstream and colonize internal organs causing serious disease. The ability of C. albicans to thrive in these different host environments has been attributed, at least in part, to its ability to assume different morphological forms. In this work, we examine one such morphological change known as white-opaque switching. White cells are the default state of C. albicans, and most animal studies have been carried out exclusively with white cells. Here, we compared the proliferation of white and opaque cells in two murine models of infection and also monitored, using specially constructed strains, switching between the two states in the host. We found that white cells outcompeted opaque cells in many niches; however, we show for the first time that in some organs (specifically, the heart and spleen), opaque cells competed favorably with white cells and, when injected on their own, could colonize these organs. In environments where the introduced white cells outcompeted the introduced opaque cells, we observed high rates of opaque-to-white switching. We did not observe white-to-opaque switching in any of the niches we examined.
An Adaptive Homotopy Method for Computing Bifurcations of Nonlinear Parametric Systems

In this paper, we present an adaptive step-size homotopy tracking method for computing bifurcation points of nonlinear systems. There are four components in this new method: (1) an adaptive tracking technique is developed near bifurcation points; (2) an inflation technique is backed up when the adaptive tracking fails; (3) Puiseux series interpolation is used to compute bifurcation points; and (4) the tangent cone structure of the bifurcation point is approximated numerically to compute solutions on different branches. Various numerical examples of nonlinear systems are given to illustrate the efficiency of this new approach. This new adaptive homotopy tracking method is also applied to a system of nonlinear PDEs and shows robustness and efficiency for large-scale nonlinear discretized systems.
Plasmonic Electricity II: The Effect of Particle Size, Solvent Permittivity, Applied Voltage, and Temperature on Fluorophore-Induced Plasmonic Current

In a recent paper, our laboratory has shown that fluorophores in close proximity to a non-continuous metal nanoparticle film can induce a detectable electrical current in the film. This current was...
Towards k-Connectivity in Heterogeneous Sensor Networks under Pairwise Key Predistribution

We study the secure and reliable connectivity of wireless sensor networks under the heterogeneous pairwise key predistribution scheme. This scheme was recently introduced as an extension of the random pairwise key predistribution scheme of Chan et al. to accommodate networks where the constituent sensors have different capabilities or requirements for security and connectivity. For simplicity, we consider a heterogeneous network where each of the n sensors is classified as type-1 (respectively, type-2) with probability μ (respectively, 1-μ) where 0<μ<1. Each type-1 (respectively, type-2) node selects 1 (respectively, Kn) other nodes uniformly at random to be paired with; according to the pairwise scheme each pair is then assigned a unique pairwise key so that they can securely communicate with each other. We establish critical conditions on n, μ, and Kn such that the resulting network has minimum node degree of at least k with high probability in the limit of large network size. Our result constitutes a zero-one law for the minimum node degree of the recently introduced inhomogeneous random K-out graph model. This constitutes a crucial step towards establishing a similar zero-one law for the k-connectivity of the graph; i.e., for the property that the network remains connected despite the failure of any k-1 nodes or links. We present numerical results that indicate the usefulness of our results in selecting the parameters of the scheme in practical settings with finite number of sensors.
The Costs and Benefits of Early College High Schools.

Early Colleges (ECs) provide high school students access to college coursework with the goal of increasing postsecondary opportunities for traditionally underrepresented students. We examine the im...
An Optimal Iterative Placement Algorithm for PIR from Heterogeneous Storage-Constrained Databases

We propose a capacity-achieving scheme for private information retrieval (PIR) from databases (DBs) with heterogeneous storage constraints. In the PIR setting, a user queries a set of DBs to privately download a message, where privacy implies that no one DB can infer which message the user desires. Our PIR scheme uses an uncoded storage placement and we derive sufficient conditions to meet capacity in this design architecture. We translate the storage placement design to a "filling problem" where messages are partitioned into sub- messages and stored at subsets of DBs. We prove a set of necessary and sufficient conditions for the existence of the filling problem solution and design an iterative algorithm to find a filling problem solution. Our proposed algorithm requires at most a number of iterations equal to the number of DBs. Furthermore, we significantly reduce the number of sub-messages compared to the state-of- the-art PIR scheme, as our proposed PIR scheme requires that each message is split into a polynomial number of sub-messages with respect to the number of DBs.
Mutation effect estimation on protein–protein interactions using deep contextualized representation learning

The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein-protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations is commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations on PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR (Mutation Effects in Protein-protein Interaction PRediction Using Contextualized Representations), to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wild-type protein pair and its mutation pair. Multi-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that, with only sequence information, MuPIPR outperforms various state-of-the-art systems on estimating the changes of binding affinity for SKEMPI v1, and offers comparable performance on SKEMPI v2. Meanwhile, MuPIPR also demonstrates state-of-the-art performance on estimating the changes of buried surface areas. The software implementation is available at https://github.com/guangyu-zhou/MuPIPR.
Comparison of permeability predictions on cemented sandstones with physics-based and machine learning approaches

Abstract Permeability prediction has been an important problem since the time of Darcy. Most approaches to solve this problem have used either idealized physical models or empirical relations. In recent years, machine learning (ML) has led to more accurate and robust, but less interpretable empirical models. Using 211 core samples collected from 12 wells in the Garn Sandstone from the North Sea, this study compared idealized physical models based on the Carman-Kozeny equation to interpretable ML models. We found that ML models trained on estimates of physical properties are more accurate than physical models. Also, the results show evidence of a threshold of about 10% volume fraction, above which pore-filling cement strongly affects permeability.
Linearly-Solvable Mean-Field Approximation for Multi-Team Road Traffic Games

We study the traffic routing game among a large number of selfish drivers over a traffic network. We consider a specific scenario where the strategic drivers can be classified into teams, where drivers in the same team have identical payoff functions. An incentive mechanism is considered to mitigate congestion, where each driver is subject to dynamic tax penalties. We explore a special case in which the tax is affine in the logarithm of the number of drivers selecting the same route from each team. It is shown via a mean-field approximation that a Nash equilibrium in the limit of a large population can be found by linearly solvable algorithms.
High Mortality for Patients Weaned off Inotrope Therapy Not Receiving Advanced Therapies

Purpose Patients with heart failure (HF) requiring advanced therapies (AT) or palliative inotropes have been well described in the literature, but less is known regarding their earlier clinical course. Patients started on inotropes may be appropriate candidates for AT, yet do not always receive timely evaluations. Given the high mortality associated with end stage HF, we investigated the clinical characteristics and outcomes of HF patients after initiation of inotrope therapy. Methods We retrospectively evaluated 3,948 patients with HF admitted to the University of Pittsburgh Medical Center's 40 hospital network between 2010-2015 from the time of their first identified inotrope therapy to present. Patients post-cardiotomy or with prior heart transplant (HT) or ventricular assist device (VAD) were excluded. Results The patients were predominantly male (61%), white (80%) with a mean age of 68 (±15). Common comorbidities included ventricular arrhythmia (9%), atrial fibrillation (21%) and CKD (59%). 13% had an ICD and 7% had biventricular pacing. Initial EF was reduced (32%±18). During hospitalization 38% were treated with milrinone, 52% with dobutamine and 10% had been on either. Overall, median survival was 6.2 months (95% CI 5.2-7.4). On index hospitalization 1,005 (26%) patients died, 83 (2%) received AT (13 HT and 70 VAD), 491 (12%) were discharged on home inotrope therapy (HI) and 2369 (60%) without HI. Excluding patients that received AT after discharge, Kaplan-Meier analysis showed significantly worse 1-year mortality in patients discharged with HI (51%) versus those without HI (41%) (p Conclusion Most HF patients admitted for inotrope therapy were discharged without HI, with survival remaining low in this group. Patients discharged without HI have longer median survival time yet few go on to follow-up with outpatient cardiology or receive AT. This may represent an opportunity for earlier AT evaluation before further clinical deterioration.
Versatile Compressive mmWave Hybrid Beamformer Codebook Design Framework

Hybrid beamforming (HB) architectures are attractive for wireless communication systems with large antenna arrays because the analog beamforming stage can significantly reduce the number of RF transceivers and hence power consumption. In HB systems, channel estimation (CE) becomes challenging due to indirect access by the baseband processing to the communication channels and due to low SNR before beam alignment. Compressed sensing (CS) based algorithms have been adopted to address these challenges by leveraging the sparse nature of millimeter wave multi-input multi-output (mmWave MIMO) channels. In many CS algorithms for narrowband CE, the hybrid beamformers are randomly configured which does not always yield the low-coherence sensing matrices desirable for those CS algorithms whose recovery guarantees rely on coherence. In this paper, we propose a versatile deterministic HB codebook design framework for CS algorithms with coherencebased recovery guarantees to enhance CE accuracy. Simulation results show that the proposed design can obtain lower channel estimation error and higher spectral efficiency compared with random codebook for phase-shifter-, switch-, and lens-based HB architectures.
On central-peripheral appendage numbers of uniform central graphs

In a uniform central graph (UCG) the set of eccentric vertices of a central vertex is the same for all central vertices. This collection of eccentric vertices is the centered periphery. For a pair of graphs ( C , P ) the central-peripheral appendage number, A ucg ( C , P ), is the minimum number vertices needed to be adjoined to the graphs C and P in order to construct a uniform central graph H with center V ( C ) and centered-periphery V ( P ). We compute A ucg ( C , P ) in terms of the radius and diameter of P and whether or not C is a complete graph. In the process we show A ucg ( C , P ) ≤ 6 if diam( P ) > 2. We also provide structure theorems for UCGs in terms of the centered periphery.
Transparent Polynomial Delegation and Its Applications to Zero Knowledge Proof

We present a new succinct zero knowledge argument scheme for layered arithmetic circuits without trusted setup. The prover time is O(C + nlogn) and the proof size is O(D logC +log2 n) for a D-depth circuit with n inputs and C gates. The verification time is also succinct, O(D logC + log2 n), if the circuit is structured. Our scheme only uses lightweight cryptographic primitives such as collision-resistant hash functions and is plausibly post-quantum secure. We implement a zero knowledge argument system, Virgo, based on our new scheme and compare its performance to existing schemes. Experiments show that it only takes 53 seconds to generate a proof for a circuit computing a Merkle tree with 256 leaves, at least an order of magnitude faster than all other succinct zero knowledge argument schemes. The verification time is 50ms, and the proof size is 253KB, both competitive to existing systems.Underlying Virgo is a new transparent zero knowledge verifiable polynomial delegation scheme with logarithmic proof size and verification time. The scheme is in the interactive oracle proof model and may be of independent interest.
A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy

An image dataset of 10 different size molecules, where each molecule has 2,000 structural variants, is generated from the 2D cross-sectional projection of Molecular Dynamics trajectories. The purpose of this dataset is to provide a benchmark dataset for the increasing need of machine learning, deep learning and image processing on the study of scattering, imaging and microscopy.
Proteomic and biological profiling of extracellular vesicles from Alzheimer's disease human brain tissues.

Introduction Extracellular vesicles (EVs) from human Alzheimer's disease (AD) biospecimens contain amyloid beta (Aβ) peptide and tau. While AD EVs are known to affect brain disease pathobiology, their biochemical and molecular characterizations remain ill defined. Methods EVs were isolated from the cortical gray matter of 20 AD and 18 control brains. Tau and Aβ levels were measured by immunoassay. Differentially expressed EV proteins were assessed by quantitative proteomics and machine learning. Results Levels of pS396 tau and Aβ1-42 were significantly elevated in AD EVs. High levels of neuron- and glia-specific factors are detected in control and AD EVs, respectively. Machine learning identified ANXA5, VGF, GPM6A, and ACTZ in AD EV compared to controls. They distinguished AD EVs from controls in the test sets with 88% accuracy. Discussion In addition to Aβ and tau, ANXA5, VGF, GPM6A, and ACTZ are new signature proteins in AD EVs.
Hyperbolic metric, punctured Riemann sphere, and modular functions

We derive a precise asymptotic expansion of the complete Kahler-Einstein metric on the punctured Riemann sphere with three or more omitting points. By using Schwarzian derivative, we prove that the coefficients of the expansion are polynomials on the two parameters which are uniquely determined by the omitting points. Futhermore, we use the modular form and Schwarzian derivative to explicitly determine the coefficients in the expansion of the complete Kahler-Einstein metric for punctured Riemann sphere with $3, 4, 6$ or $12$ omitting points.
CHAPTER 4 – Resilient Ribonucleases

Ribonucleases (RNases) are a family of enzymes present in virtually all living cells. These enzymes can degrade RNA molecules through both endonucleolytic and exonucleolytic activities. RNases are bad when an investigator is trying to isolate intact RNA for reverse transcriptase-polymerase chain reaction (RT-PCR), Northern analysis, or any other laboratory applications. RNases are good because of their great utility in vitro when looking at RNA turnover and processing. Particularly, resilient ribonucleases are RNases of the pancreatic variety. These small, remarkably stable enzymes maintain their tertiary configurations by virtue of four disulfide bridges that allow these enzymes to renature quickly, following treatment with most denaturants, even after boiling. RNases have minimal cofactor requirements and are active over a wide pH range. The method selected for controlling RNase activity must first demonstrate compatibility with the cell lysis procedure. Nuclease inhibitors are most often added to the relatively gentle lysis buffers when subcellular organelles must be purified.. The method of nuclease inhibition must support the integrity of the RNA throughout the subsequent fractionation or purification steps. In addition, the reagents used to inhibit the RNase activity must be easily removed from purified RNA preparations so, as not to interfere with subsequent manipulations.
Quantifying Artistic Success: Ranking French Painters—and Paintings—from Impressionism to Cubism

Abstract For 35 leading painters who lived in France during the first century of modern art, this article uses textbook illustrations as the basis for measuring the importance of both painters and individual paintings. The rankings pose an interesting puzzle: Why do some of the greatest artists not produce famous paintings, and why do some relatively minor artists produce some of the most famous individual paintings? The answer may lie in an important difference in approach between experimental and conceptual painters. Experimental artists work incrementally, their innovations appear gradually, and they generally do their best work late in their careers; conceptual artists innovate more suddenly, produce individual breakthrough works, and usually do their best work early in their careers. This article demonstrates that artistic success can usefully be quantified, and that doing so increases our understanding of the working methods of modern painters.
Precipitation of Ferric Phosphate in Activated Sludge: A Chemical Model and Its Verification

A chemical model of ferric phosphate precipitation was developed describing ferric hydroxy-phosphate precipitation either alone or together with ferric hydroxide. Conditions for formation of one or two precipitates are examined. The model also incorporates an additional mechanism for phosphate removal through adsorption of PO 4 3− ions on the precipitate. Experimental verification of the proposed model was carried out in lab-scale batch and continuous activated sludge units fed with municipal primary effluent and at five pH values in the range of 6.5 to 8.0. Solubility of ferric phosphate in the activated sludge system was significantly different from that reported in the literature for distilled water systems and was pH-dependent with a minimum at pH of approx. 7.0. It is proposed that the composition of precipitating ferric hydroxy-phosphate can be represented by the empirical formula Fe 2.5 PO 4 (OH) 4.5 . Corresponding solubility product was estimated at pK sp =96.7. The adsorption mechanism has an important effect on total phosphate removal, especially at low residual phosphate concentrations.
Corrosion inhibition of AA2024-T3 by smart polyelectrolyte coacervates responsive to both acidic and alkaline environments

Abstract Previously reported pH-responsive corrosion-protective coatings based on the layer-by-layer assembled polyelectrolytes responded to the pH increase caused by cathodic reactions in corrosion but were less sensitive to the pH decrease in anodic regions. In this study, polyacrylic acid was directly added into polyethylenimine to make polyelectrolyte coacervates capable of releasing SrCrO4 inhibitor at both acidic and alkaline pH. Due to a higher amount of the released SrCrO4 at both low and high pH, AA2024-T3 substrates exhibited better corrosion resistance during cyclic potentiodynamic polarization compared with that in neutral pH chloride solutions. Electrochemical impedance spectroscopy further proved that the corrosion resistance of AA2024-T3 was improved by the application of SrCrO4-embedded polyelectrolyte coacervates. The developed polyelectrolyte coacervates open a pathway for creating a release system for corrosion protection of AA2024-T3 triggered by low and high pH in a facile and fast way.
Gene-Environment Correlation†

Genetic and environmental effects are not independent sources of influence on behavior, but rather, genotype can influence an individual's exposure to certain environments, how that individual experiences the environment, and the degree of influence that certain environments exert. This phenomenon is called gene-environment correlation. Three types of gene-environment correlation are discussed: passive, evocative, and active. Examples of each type of gene-environment correlation, and support for these effects from the literature are presented. Keywords: gene-environment correlation; passive; active; evocative
A Battery-Powered Activity-Dependent Intracortical Microstimulation IC for Brain-Machine-Brain Interface

This paper describes an activity-dependent intracortical microstimulation (ICMS) system-on-chip (SoC) that converts extracellular neural spikes recorded from one brain region to electrical stimuli delivered to another brain region in real time in vivo. The 10.9-mm 2 SoC incorporates two identical 4-channel modules, each comprising an analog recording front-end with total input noise voltage of 3.12 μV rms and noise efficiency factor (NEF) of 2.68, 5.9-μW 10-bit successive approximation register analog-to-digital converters (SAR ADCs), 12.4-μW digital spike discrimination processor, and a programmable constant-current microstimulating back-end that delivers up to 94.5 μA with 6-bit resolution to stimulate the cortical tissue when triggered by neural activity. For autonomous operation, the SoC also integrates biasing and clock generation circuitry, frequency-shift-keyed (FSK) transmitter at 433 MHz, and dc-dc converter that generates a power supply of 5.05 V for the microstimulating back-end from a single 1.5-V battery. Measured results from electrical performance characterization and biological experiments with anesthetized rats are presented from a prototype chip fabricated in AMS 0.35 μm two-poly four-metal (2P/4M) CMOS. A noise analysis for the selected low-noise amplifier (LNA) topology is presented that obtains a minimum NEF of 2.33 for a practical design given the technology parameters and power supply voltage. Future considerations in the SoC design with respect to silicon area and power consumption when increasing the number of channels are also discussed.
Cornfield's Inequality †

Cornfield developed an inequality on the observed relative risk, R O, between an exposed and unexposed group, which could be explained by an unmeasured confounder, U. To be precise, R O is no greater than the ratio of the prevalence of U in the exposed to that in the unexposed population. It is also true that R O ≤ R U, where R U is the ratio of risk in those with U compared to those without U. We discuss a sharper bound on R O, which can be computed when information on R U and on the prevalences of U in the exposed and unexposed populations are available. We also consider related bounds for risk differences and sensitivity analyses for tests of hypotheses in the presence of an unmeasured confounder. Keywords: Cornfield's inequality; confounder; confounder risk ratio; sensitivity analysis; missing covariate
LUNAR EJECTA IN HELIOCENTRIC SPACE

Measurements of the flux of picogram dust particles in selenocentric, cislunar, and interplanetary space between 1.0 and 1.6 AU were made by cosmic dust experiments on Lunar Explorer 35 (LE35), Lunar Orbiters (LO), HEOS, Mariner IV (MIV) and Pioneers 8/9 between 1964 and 1974. The cumulative flux-mass distribution (CFMD) as a function of heliocentric range was determined from MIV data, and the CFMD in selenocentric space was determined by analysis of LE35 data. Evidence of lunar ejecta from impacts of interplanetary dust particles was found in the LE35 data. Results of recent laboratory studies of hypervelocity impact of micron-size simulated cosmic dust indicate that a major portion of the lunar ejecta escapes the earth-moon system and dwells in a very limited volume of heliocentric space that is symetrical with the ecliptic plane. The recent Gegenschein measurements eeported by Roosen show a maximum intensity in the ecliptic plane, but the intensity distribution is asymmetric. The possible relation of these observations to cosmic dust measurements is discussed.
Catching up and falling behind

This paper studies the interaction between technology, a public input that flows in from abroad, and human capital, a private input that is accumulated domestically, as twin engines of growth in a developing economy. The model displays two types of long run behavior, depending on policies and initial conditions. One is sustained growth, where the economy keeps pace with the technology frontier. The other is stagnation, where the economy converges to a minimal technology level that is independent of the world frontier. In a calibrated version of the model, transition paths after a policy change can display rapid growth, as in modern growth ‘miracles.’ In these economies policies that promote technology inflows are much more effective than subsidies to human capital accumulation in accelerating growth. A policy reversal produces a ‘lost decade,’ a period of slow growth that permanently reduces the level of income and consumption.
Cite Unseen: How Neutral Citation And America's Law Schools Can Cure Our Strange Devotion To Bibliographical Orthodoxy And The Constriction Of Open And Equal Access To The Law

This article looks at the phenomenon of legal citation and its unintended consequences. After considering the reasons for the American legal system's devotion to precisely accurate and detailed citations and the history of American legal citation, the article looks at the effect the bibliographical orthodoxy promoted by the two leading citation manuals - The Bluebook and the ALWD Manual - has on open access to the law. In particular, the article looks at how the required common law citation format prescribed by both of these manuals helps to consolidate the market position of West and LexisNexis, the duopoly of legal publishing in this country. After considering the inadequacy of some present-day open access legal information sites, and exploring why it is that market pressures make it unlikely that a viable commercial competitor to the West/Lexis duopoly will emerge, the article concludes that the best approach to ensuring that the law remain free and open to all is through the use of a neutral citation format to describe case law and the formation of a consortium of American law schools to publish the law on the internet.
Observing Rivers With Varying Spatial Scales.

The National Aeronautics and Space Administration/Centre national d'etudes spatiales Surface Water and Ocean Topography (SWOT) mission will estimate global river discharge using remote sensing. Synoptic remote sensing data extend in situ point measurements but, at any given point, are generally less accurate. We address two questions: (1)What are the scales at which river dynamics can be observed, given spatial sampling and measurement noise characteristics? (2) Is there an equation whose variables are the averaged hydraulic quantities obtained by remote sensing and which describes the dynamics of spatially averaged rivers? We use calibrated hydraulic models to examine the power spectra of the different terms in the momentum equation and conclude that the measurement of river slope sets the scale at which rivers can be observed. We introduce the reach-averaged Saint Venant equations that involve only observable hydraulic variations and which parametrize within-reach variability with a variability index that multiplies the friction coefficient and leads to an increased "effective" friction coefficient. An exact expression is derived for the increase in the effective friction coefficient, and we propose an approximation that requires only estimates of the hydraulic parameter variances. We validate the results using a large set of hydraulic models and find that the approximated variability index is most faithful when the river parameters obey lognormal statistics. The effective friction coefficient, which can vary from a few percent to more than 50% of the point friction coefficient, is proportional to the riverbed elevation variance and inversely proportional to the depth. This has significant implications for estimating discharge from SWOT data.
The Relationship of Special Education Placement and Student Academic Outcomes.

This study investigates the academic outcomes of a special education student cohort in the state of Indiana placed in high and low inclusion settings. Student scores in these two settings from the ...
Differential Impacts on Host Transcription by ROP and GRA Effectors from the Intracellular Parasite Toxoplasma gondii

ABSTRACT The intracellular parasite Toxoplasma gondii employs a vast array of effector proteins from the rhoptry and dense granule organelles to modulate host cell biology; these effectors are known as ROPs and GRAs, respectively. To examine the individual impacts of ROPs and GRAs on host gene expression, we developed a robust, novel protocol to enrich for ultrapure populations of a naturally occurring and reproducible population of host cells called uninfected-injected (U-I) cells, which Toxoplasma injects with ROPs but subsequently fails to invade. We then performed single-cell transcriptomic analysis at 1 to 3 h postinfection on U-I cells (as well as on uninfected and infected controls) arising from infection with either wild-type parasites or parasites lacking the MYR1 protein, which is required for soluble GRAs to cross the parasitophorous vacuole membrane (PVM) and reach the host cell cytosol. Based on comparisons of infected and U-I cells, the host’s earliest response to infection appears to be driven primarily by the injected ROPs, which appear to induce immune and cellular stress pathways. These ROP-dependent proinflammatory signatures appear to be counteracted by at least some of the MYR1-dependent GRAs and may be enhanced by the MYR-independent GRAs (which are found embedded within the PVM). Finally, signatures detected in uninfected bystander cells from the infected monolayers suggest that MYR1-dependent paracrine effects also counteract inflammatory ROP-dependent processes. IMPORTANCE This work performs transcriptomic analysis of U-I cells, captures the earliest stage of a host cell’s interaction with Toxoplasma gondii, and dissects the effects of individual classes of parasite effectors on host cell biology.
The adoption, diffusion, and evolution of organizational form: insights from the agrifood sector

The adoption and diffusion of contract farming and vertical integration in modern agriculture has varied widely across regions, commodities, or farm types. This paper lays out a framework for understanding the evolution of organizational practices in U.S. agriculture by drawing on theories of the diffusion of technology and organizational complementarities. Using recent trends as stylized facts, with case studies from various agricultural industries, we argue that research identifying complementarities within specific sectors of the agrifood system will greatly improve our understanding of the organizational structure of agricultural production, and we identify several specific lines of inquiry. Copyright © 2011 John Wiley & Sons, Ltd.
Scalable Analysis of Authentic Viral Envelopes on FRONTERA

Enveloped viruses, such as SARS-CoV-2, infect cells via fusion of their envelope with the host membrane. By employing molecular simulations to characterize viral envelopes, researchers can gain insights into key determinants of infection. Here, the Frontera supercomputer is leveraged for large-scale modeling and analysis of authentic viral envelopes, whose lipid compositions are complex and realistic. Visual Molecular Dynamics (VMD) with support for MPI is employed, overcoming previous computational limitations and enabling investigation into virus biology at an unprecedented scale. The techniques applied here to an authentic HIV-1 envelope at two levels of spatial resolution (29 million particles and 280 million atoms) are broadly applicable to the study of other viruses. The authors are actively employing these techniques to develop and characterize an authentic SARS-CoV-2 envelope. A general framework for carrying out scalable analysis of simulation trajectories on Frontera is presented, expanding the utility of the machine in humanity's ongoing fight against infectious diseases.
External Reference Pricing and Parallel Imports of Pharmaceuticals: A Policy Comparison

Governments across the world constantly use a variety of measures to contain drug costs in order to increase patient access to critical medications. External Reference Pricing (ERP), or international price comparison, is a commonly employed policy to control prices of pharmaceuticals. ERP uses the price(s) of a medicinal product in one or several countries in order to derive a benchmark or reference price for the purposes of setting the price of the product in a given country. Despite potential drawbacks, empirical studies show ERP has been successful in reducing drug prices. Another strategy that can reduce drug prices is legalizing parallel imports of drugs from low-price countries. Motivated by the differences between government policies and the absence of national price management policies in the U.S., in this paper we compare the effects of implementing ERP or authorizing parallel imports on firm profit and different social welfare measures. We find that authorizing parallel imports can result in higher firm profit and/or social welfare than using ERP. We also examine, in a decentralized supply chain, two variations of ERP that are used in practice, the ex-factory-based ERP and the pharmacy purchase price (PPP)-based ERP. We show that despite the ex-factory-based ERP being more common, PPP-based ERP can increase both firm profits and social welfare. Therefore, governments should consider using PPP-based ERP policies. Our findings help generate insights for ongoing policy discussions around measures for controlling drug prices in the U.S.
A neural network for determination of latent dimensionality in Nonnegative Matrix Factorization

Non-negative Matrix Factorization (NMF) has proven to be a powerful unsupervised learning method for uncovering hidden features in complex and noisy data sets with applications in data mining, text recognition, dimension reduction, face recognition, anomaly detection, blind source separation, and many other fields. An important input for NMF is the latent dimensionality of the data, that is, the number of hidden features, K, present in the explored data set. Unfortunately, this quantity is rarely known a priori. We utilize a supervised machine learning approach in combination with a recent method for model determination, called NMFk, to determine the number of hidden features automatically. NMFk performs a set of NMF simulations on an ensemble of matrices, obtained by bootstrapping the initial data set, and determines which K produces stable groups of latent features that reconstruct the initial data set well. We then train a Multi-Layer Perceptron (MLP) classifier network to determine the correct number of latent features utilizing the statistics and characteristics of the NMF solutions, obtained from NMFk. In order to train the MLP classifier, a training set of 58,660 matrices with predetermined latent features were factorized with NMFk. The MLP classifier in conjunction with NMFk maintains a greater than 95% success rate when applied to a held out test set. Additionally, when applied to two well-known benchmark data sets, the swimmer and MIT face data, NMFk/MLP correctly recovered the established number of hidden features. Finally, we compared the accuracy of our method to the ARD, AIC and Stability-based methods.
Improved Attention Models for Memory Augmented Neural Network Adaptive Controllers

We introduced a working memory augmented adaptive controller in our recent work. The controller uses attention to read from and write to the working memory. Attention allows the controller to read specific information that is relevant and update its working memory with information based on its relevance, similar to how humans pick relevant information from the enormous amount of information that is received through various senses. The retrieved information is used to modify the final control input computed by the controller. We showed that this modification speeds up learning.In the above work, we used a soft-attention mechanism for the adaptive controller. Controllers that use soft attention update and read information from all memory locations at all the times, the extent of which is determined by their relevance. But, for the same reason, the information stored in the memory can be lost. In contrast, hard attention updates and reads from only one location at any point of time, which allows the memory to retain information stored in other locations. The downside is that the controller can fail to shift attention when the information in the current location becomes less relevant.We propose an attention mechanism that comprises of (i) a hard attention mechanism and additionally (ii) an attention reallocation mechanism. The attention reallocation enables the controller to reallocate attention to a different location when the relevance of the location it is reading from diminishes. The reallocation also ensures that the information stored in the memory before the shift in attention is retained which can be lost in both soft and hard attention mechanisms. Through detailed simulations of various scenarios for two link robot robot arm systems we illustrate the effectiveness of the proposed attention mechanism.
Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition

As electric grids experience high penetration levels of renewable generation, fundamental changes are required to address real-time situational awareness. This letter utilizes unique traits of tensors to devise a model-free situational awareness and energy forecasting framework for distribution networks. This letter formulates the state of the network at multiple time instants as a three-way tensor; hence, recovering full state information of the network is tantamount to estimating all the values of the tensor. Given measurements received from $\mu $ phasor measurement units and/or smart meters, the recovery of unobserved quantities is carried out using the low-rank canonical polyadic decomposition of the state tensor—that is, the state estimation task is posed as a tensor imputation problem utilizing observed patterns in measured (sampled) quantities. Two structured sampling schemes are considered, namely, asynchronous slab and fiber sampling. For both schemes, we present sufficient conditions on the number of sampled slabs and fibers that guarantee identifiability of the factors of the state tensor. Numerical results demonstrate the ability of the proposed framework to achieve high estimation accuracy in multiple sampling scenarios.
Greedy Finite-Horizon Covariance Steering for Discrete-Time Stochastic Nonlinear Systems Based on the Unscented Transform.

In this work, we consider the problem of steering the first two moments of the uncertain state of a discrete time nonlinear stochastic system to prescribed goal quantities at a given final time. In principle, the latter problem can be formulated as a density tracking problem, which seeks for a feedback policy that will keep the probability density function of the state of the system close, in terms of an appropriate metric, to the goal density. The solution to the latter infinite-dimensional problem can be, however, a complex and computationally expensive task. Instead, we propose a more tractable and intuitive approach which relies on a greedy control policy. The latter control policy is comprised of the first elements of the control policies that solve a sequence of corresponding linearized covariance steering problems. Each of these covariance steering problems relies only on information available about the state mean and state covariance at the current stage and can be formulated as a tractable (finite-dimensional) convex program. At each stage, the information on the state statistics is updated by computing approximations of the predicted state mean and covariance of the resulting closed-loop nonlinear system at the next stage by utilizing the (scaled) unscented transform. Numerical simulations that illustrate the key ideas of our approach are also presented.
Critical bridge spheres for links with arbitrarily many bridges

We show that for every integer $$b\ge 3$$ and every integer k with $$1\le k\le b$$ , there exists an alternating k-component link in a b-bridge position with respect to a critical bridge sphere. In fact, for each b, we construct an infinite family of links whose bridge spheres are critical.
CoBeam: Beamforming-based Spectrum Sharing With Zero Cross-Technology Signaling for 5G Wireless Networks

This article studies an essential yet challenging problem in 5G wireless networks: Is it possible to enable spectrally-efficient spectrum sharing for heterogeneous wireless networks with different, possibly incompatible, spectrum access technologies on the same spectrum bands; without modifying the protocol stacks of existing wireless networks? To answer this question, this article explores the system challenges that need to be addressed to enable a new spectrum sharing paradigm based on beamforming, which we refer to as CoBeam. In CoBeam, a secondary wireless network is allowed to access a spectrum band based on cognitive beam-forming without mutual temporal exclusion, i.e., without interrupting the ongoing transmissions of coexisting wireless networks on the same bands; and without cross-technology communication. We first describe the main components of CoBeam, including programmable physical layer driver, cognitive sensing engine, and beamforming engine, and then we showcase the potential of the CoBeam framework by designing a practical coexistence scheme between Wi-Fi and LTE on unlicensed bands. We present a prototype of the resulting coexisting Wi-Fi/U-LTE network built on off-the-shelf software radios based on which we evaluate the performance of CoBeam through an extensive experimental campaign. Performance evaluation results indicate that CoBeam can achieve on average 169% throughput gain while requiring no signaling exchange between the coexisting wireless networks.
Wireless Power Control via Counterfactual Optimization of Graph Neural Networks

We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating with each other over a single shared wireless medium. To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture, and we then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions. We show how the counterfactual optimization technique allows us to guarantee a minimum rate constraint, which adapts to the network size, hence achieving the right balance between average and 5th percentile user rates throughout a range of network configurations.