cssi/SciSciGPT-SciSciNet huggingface dataset:
Chapter 18 – Magmamax Binary Power Plant, East Mesa, Imperial Valley, California, USA

The East Mesa geothermal field lies roughly 17 miles east of Heber in California’s Imperial Valley, and, like the Heber field, is a “hidden” geothermal resource, lacking any surface thermal manifestations. Interest in the area began in 1968, and exploration and tests were conducted in the 1970s that paved the way for the power plants that would be built in the coming years. The Magamamax binary power plant came online in 1979, and the features and technical details of the design are discussed within the chapter. The plant was shut down after about 2 years for an extensive modification. The new plant began its operating life in October 1982 and technical specifications of the redesigned plant are discussed in detail. The technology is now flourishing in the desert of East Mesa, generating power in a reliable and environmentally friendly fashion.
The plasma proteins and their fractionation

The large scale fractionation of the pro teins of blood plasma was direct1y a product of the needs of military medicine in the United States during the war. Fundamentally, however, this development rests on a long series of advances in protein chemistry which have occurred over the last forty years in many laboratories, notably in England, Scandinavia, and the United States. The methods used for the fractionation of proteins depend on the laws governing the solubility of these substances. Our understanding of these laws has been derived, not only from the work of the protein and amino acid chemisis themselves, but also from other fundamental studies in the field of pure physical chemistry. All the advances of the last quarter century have been profoundly influenced by two fundamental studies, both published in 1923: the interionic attraction theory of DEBYE and HUCKEL (41) and the convincing demonstration by BJERRUM (11) that amino acids and peptides and, therefore, proteins also exist as dipolar ions in the neutral state. The theory of DEBYE and HUCKEL, as originally given, provided a basis for calculating the thermodynamic activities and hence the solubilities of electrolytes, as a function of ionic strength, dielectric constant, and temperature. The subsequent extensions of the theory by SCATCHARD and KIRKWOOD (126) and by KIRKWOOD (87) gave the basis for the treatment of dipolar ions along the same lines. Between 1930 and 1940 a large body of experimental evidence was obtained concerning the solubilities of amino acids, polypeptides, and related compounds in relation to their structure. This great body of empirical material could then be evaluated in relation to the theoretical treatments already indicated above, with the additional aid of some empirical generalizations that emerged from the data [for a summary of the work in this field, see the monograph of COHN and EDSALL (35)].
Carbon Limitation Leads to Thermodynamic Regulation of Aerobic Metabolism

Organic matter (OM) metabolism in freshwater ecosystems is a critical source of uncertainty in global biogeochemical cycles, yet the processes regulating aerobic respiration in aquatic environments...
Medicaid at 50: No Longer Limited to the "Deserving" Poor?

For the first fifty years of its existence, Medicaid suffered from a serious defect. While it was adopted to meet the health care needs of the poor, it only met the needs of the so-called “deserving” poor — children, pregnant women, single caretakers of children, and disabled persons — people who could not fairly be held accountable for their inability to afford health care insurance.The Affordable Care Act (ACA) seemingly has abandoned Medicaid’s conception of the deserving poor with its expansion of the Medicaid program to all persons up to 138% of the federal poverty level. One no longer needs to be a child, disabled, pregnant, or a caretaker of a child to be eligible for Medicaid; it is sufficient simply to be poor.In this essay, I consider the significance of this major modification of the Medicaid program. Does the ACA signal a more generous view of the deserving poor, or even an abandonment of the distinction between the poor and the “deserving” poor? Or does the ACA tell us more about the nature of health care than about societal views of the poor? And what do the answers to these questions tell us about the durability of the Medicaid expansion? Can we expect Congress to maintain the ACA’s revision of Medicaid for the next fifty years?Most likely, the Medicaid expansion reflects concerns about the high costs of health care rather than an evolution in societal thinking about the “deserving” poor. As a result, the expansion may not provide a durable source of health care coverage for the expansion population.
Lipid Mediators Regulate Pulmonary Fibrosis: Potential Mechanisms and Signaling Pathways

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease of unknown etiology characterized by distorted distal lung architecture, inflammation, and fibrosis. The molecular mechanisms involved in the pathophysiology of IPF are incompletely defined. Several lung cell types including alveolar epithelial cells, fibroblasts, monocyte-derived macrophages, and endothelial cells have been implicated in the development and progression of fibrosis. Regardless of the cell types involved, changes in gene expression, disrupted glycolysis, and mitochondrial oxidation, dysregulated protein folding, and altered phospholipid and sphingolipid metabolism result in activation of myofibroblast, deposition of extracellular matrix proteins, remodeling of lung architecture and fibrosis. Lipid mediators derived from phospholipids, sphingolipids, and polyunsaturated fatty acids play an important role in the pathogenesis of pulmonary fibrosis and have been described to exhibit pro- and anti-fibrotic effects in IPF and in preclinical animal models of lung fibrosis. This review describes the current understanding of the role and signaling pathways of prostanoids, lysophospholipids, and sphingolipids and their metabolizing enzymes in the development of lung fibrosis. Further, several of the lipid mediators and enzymes involved in their metabolism are therapeutic targets for drug development to treat IPF.
Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements

Abstract. Cloud condensation nuclei (CCN) number concentrations are an important aspect of aerosol–cloud interactions and the subsequent climate effects; however, their measurements are very limited. We use a machine learning tool, random decision forests, to develop a Random Forest Regression Model (RFRM) to derive CCN at 0.4 % supersaturation ([CCN0.4]) from commonly available measurements. The RFRM is trained on the long-term simulations in a global size-resolved particle microphysics model. Using atmospheric state and composition variables as predictors, through associations of their variabilities, the RFRM is able to learn the underlying dependence of [CCN0.4] on these predictors, which are: 8 fractions of PM2.5 (NH4, SO4, NO3, secondary organic aerosol (SOA), black carbon (BC), primary organic carbon (POC), dust, and salt), 7 gaseous species (NOx, NH3, O3, SO2, OH, isoprene, and monoterpene), and 4 meteorological variables (temperature (T), relative humidity (RH), precipitation, and solar radiation). The RFRM is highly robust: median mean fractional bias (MFB) of 4.4 % with ~ 96.33 % of the derived [CCN0.4] within a good agreement range of −60 %
Monte Carlo simulation of entry in the Martian atmosphere

The Direct Simulation Monte Carlo method of Bird is used to investigate the characteristics of low density hypersonic flowfields for typical aerobrakes during Martian atmospheric entry. The method allows for both thermal and chemical nonequilibrium. Results are presented for a sixty-degree spherically blunt cone for various nose radii and altitudes.
Privacy-Utility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks

Payment channel networks (PCNs) are viewed as one of the most promising scalability solutions for cryptocurrencies today. Roughly, PCNs are networks where each node represents a user and each directed, weighted edge represents funds escrowed on a blockchain; these funds can be transacted only between the endpoints of the edge. Users efficiently transmit funds from node A to B by relaying them over a path connecting A to B, as long as each edge in the path contains enough balance (escrowed funds) to support the transaction. Whenever a transaction succeeds, the edge weights are updated accordingly. In deployed PCNs, channel balances (i.e., edge weights) are not revealed to users for privacy reasons; users know only the initial weights at time 0. Hence, when routing transactions, users typically first guess a path, then check if it supports the transaction. This guess-and-check process dramatically reduces the success rate of transactions. At the other extreme, knowing full channel balances can give substantial improvements in transaction success rate at the expense of privacy. In this work, we ask whether a network can reveal noisy channel balances to trade off privacy for utility. We show fundamental limits on such a tradeoff, and propose noise mechanisms that achieve the fundamental limit for a general class of graph topologies. Our results suggest that in practice, PCNs should operate either in the low-privacy or low-utility regime; it is not possible to get large gains in utility by giving up a little privacy, or large gains in privacy by sacrificing a little utility.
Optimizing Data Usage via Differentiable Rewards

To acquire a new skill, humans learn better and faster if a tutor, based on their current knowledge level, informs them of how much attention they should pay to particular content or practice problems. Similarly, a machine learning model could potentially be trained better with a scorer that "adapts" to its current learning state and estimates the importance of each training data instance. Training such an adaptive scorer efficiently is a challenging problem; in order to precisely quantify the effect of a data instance at a given time during the training, it is typically necessary to first complete the entire training process. To efficiently optimize data usage, we propose a reinforcement learning approach called Differentiable Data Selection (DDS). In DDS, we formulate a scorer network as a learnable function of the training data, which can be efficiently updated along with the main model being trained. Specifically, DDS updates the scorer with an intuitive reward signal: it should up-weigh the data that has a similar gradient with a dev set upon which we would finally like to perform well. Without significant computing overhead, DDS delivers strong and consistent improvements over several strong baselines on two very different tasks of machine translation and image classification.
Reward prediction errors create event boundaries in memory.

We remember when things change. Particularly salient are experiences where there is a change in rewards, eliciting reward prediction errors (RPEs). How do RPEs influence our memory of those experiences? One idea is that this signal directly enhances the encoding of memory. Another, not mutually exclusive, idea is that the RPE signals a deeper change in the environment, leading to the mnemonic separation of subsequent experiences from what came before, thereby creating a new latent context and a more separate memory trace. We tested this in four experiments where participants learned to predict rewards associated with a series of trial-unique images. High-magnitude RPEs indicated a change in the underlying distribution of rewards. To test whether these large RPEs created a new latent context, we first assessed recognition priming for sequential pairs that included a high-RPE event or not (Exp. 1: n = 27 & Exp. 2: n = 83). We found evidence of recognition priming for the high-RPE event, indicating that the high-RPE event is bound to its predecessor in memory. Given that high-RPE events are themselves preferentially remembered (Rouhani, Norman, & Niv, 2018), we next tested whether there was an event boundary across a high-RPE event (i.e., excluding the high-RPE event itself; Exp. 3: n = 85). Here, sequential pairs across a high RPE no longer showed recognition priming whereas pairs within the same latent reward state did, providing initial evidence for an RPE-modulated event boundary. We then investigated whether RPE event boundaries disrupt temporal memory by asking participants to order and estimate the distance between two events that had either included a high-RPE event between them or not (Exp. 4). We found (n = 49) and replicated (n = 77) worse sequence memory for events across a high RPE. In line with our recognition priming results, we did not find sequence memory to be impaired between the high-RPE event and its predecessor, but instead found worse sequence memory for pairs across a high-RPE event. Moreover, greater distance between events at encoding led to better sequence memory for events across a low-RPE event, but not a high-RPE event, suggesting separate mechanisms for the temporal ordering of events within versus across a latent reward context. Altogether, these findings demonstrate that high-RPE events are both more strongly encoded, show intact links with their predecessor, and act as event boundaries that interrupt the sequential integration of events. We captured these effects in a variant of the Context Maintenance and Retrieval model (CMR; Polyn, Norman, & Kahana, 2009), modified to incorporate RPEs into the encoding process.
Quantum logspace algorithm for powering matrices with bounded norm

We give a quantum logspace algorithm for powering contraction matrices, that is, matrices with spectral norm at most~1. The algorithm gets as an input an arbitrary $n\times n$ contraction matrix $A$, and a parameter $T \leq \mathrm{poly}(n)$ and outputs the entries of $A^T$, up to (arbitrary) polynomially small additive error. The algorithm applies only unitary operators, without intermediate measurements. We show various implications and applications of this result: First, we use this algorithm to show that the class of quantum logspace algorithms with only quantum memory and with intermediate measurements is equivalent to the class of quantum logspace algorithms with only quantum memory without intermediate measurements. This shows that the deferred-measurement principle, a fundamental principle of quantum computing, applies also for quantum logspace algorithms (without classical memory). More generally, we give a quantum algorithm with space $O(S + \log T)$ that takes as an input the description of a quantum algorithm with quantum space $S$ and time $T$, with intermediate measurements (without classical memory), and simulates it unitarily with polynomially small error, without intermediate measurements. Since unitary transformations are reversible (while measurements are irreversible) an interesting aspect of this result is that it shows that any quantum logspace algorithm (without classical memory) can be simulated by a reversible quantum logspace algorithm. This proves a quantum analogue of the result of Lange, McKenzie and Tapp that deterministic logspace is equal to reversible logspace [LMT00]. Finally, we use our results to show non-trivial classical simulations of quantum logspace learning algorithms.
Optimal Bayesian estimation for random dot product graphs

We propose a Bayesian approach, called the posterior spectral embedding, for estimating the latent positions in random dot product graphs, and prove its optimality. Unlike the classical spectral-based adjacency/Laplacian spectral embedding, the posterior spectral embedding is a fully-likelihood based graph estimation method taking advantage of the Bernoulli likelihood information of the observed adjacency matrix. We develop a minimax-lower bound for estimating the latent positions, and show that the posterior spectral embedding achieves this lower bound since it both results in a minimax-optimal posterior contraction rate, and yields a point estimator achieving the minimax risk asymptotically. The convergence results are subsequently applied to clustering in stochastic block models, the result of which strengthens an existing result concerning the number of mis-clustered vertices. We also study a spectral-based Gaussian spectral embedding as a natural Bayesian analogy of the adjacency spectral embedding, but the resulting posterior contraction rate is sub-optimal with an extra logarithmic factor. The practical performance of the proposed methodology is illustrated through extensive synthetic examples and the analysis of a Wikipedia graph data.
Modeling EEG Data Distribution With a Wasserstein Generative Adversarial Network to Predict RSVP Events

Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing. This poses challenges to train powerful deep learning model with the limited EEG data. Being able to generate EEG data computationally could address this limitation. We propose a novel Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to synthesize EEG data. This network addresses several modeling challenges of simulating time-series EEG data including frequency artifacts and training instability. We further extended this network to a class-conditioned variant that also includes a classification branch to perform event-related classification. We trained the proposed networks to generate one and 64-channel data resembling EEG signals routinely seen in a rapid serial visual presentation (RSVP) experiment and demonstrated the validity of the generated samples. We also tested intra-subject cross-session classification performance for classifying the RSVP target events and showed that class-conditioned WGAN-GP can achieve improved event-classification performance over EEGNet.
Performance and Its Limits in Rigid Body Protein-Protein Docking

The development of fast Fourier transform (FFT) algorithms enabled the sampling of billions of complex conformations and thus revolutionized protein-protein docking. FFT-based methods are now widely available and have been used in hundreds of thousands of docking calculations. Although the methods perform "soft" docking, which allows for some overlap of component proteins, the rigid body assumption clearly introduces limitations on accuracy and reliability. In addition, the method can work only with energy expressions represented by sums of correlation functions. In this paper we use a well-established protein-protein docking benchmark set to evaluate the results of these limitations by focusing on the performance of the docking server ClusPro, which implements one of the best rigid body methods. Furthermore, we explore the theoretical limits of accuracy when using established energy terms for scoring, provide comparison with flexible docking algorithms, and review the historical performance of servers in the CAPRI docking experiment.
In situ adaptive reduction of nonlinear multiscale structural dynamics models

Conventional offline training of reduced-order bases in a predetermined region of a parameter space leads to parametric reduced-order models that are vulnerable to extrapolation. This vulnerability manifests itself whenever a queried parameter point lies in an unexplored region of the parameter space. This paper addresses this issue by presenting an in-situ, adaptive framework for nonlinear model reduction where computations are performed by default online, and shifted offline as needed. The framework is based on the concept of a database of local Reduced-Order Bases (ROBs), where locality is defined in the parameter space of interest. It achieves accuracy by updating on-the-fly a pre-computed ROB, and approximating the solution of a dynamical system along its trajectory using a sequence of most-appropriate local ROBs. It achieves efficiency by managing the dimension of a local ROB, and incorporating hyperreduction in the process. While sufficiently comprehensive, the framework is described in the context of dynamic multiscale computations in solid mechanics. In this context, even in a nonparametric setting of the macroscale problem and when all offline, online, and adaptation overhead costs are accounted for, the proposed computational framework can accelerate a single three-dimensional, nonlinear, multiscale computation by an order of magnitude, without compromising accuracy.
Real-time signal queue length prediction using long short-term memory neural network

Optimal traffic control and signal planning can significantly reduce traffic congestion and potential delays at intersections. However, a major challenge to optimize traffic signal timing is to accurately predict traffic before commencing the next cycle. An optimal strategy cannot be achieved with a poor prediction of future traffic. In this study, using a deep learning approach, we develop a data-driven real-time queue length prediction technique. We consider a connected corridor where information from vehicle detectors (located at the intersection) will be shared to consecutive intersections. We assume that the queue length of an intersection in the next cycle will depend on the queue length of the target and two upstream intersections in the current cycle. We use InSync adaptive traffic control system data to train a long short-term memory neural network model capturing time-dependent patterns of a queue of a signal. To avoid overfitting and select the best combination of hyperparameters, we use sequential model-based optimization technique. Our experiment results show that the proposed model performs very well to predict the queue length. Although we run our experiments predicting the queue length for a single movement, the proposed method can be applied for other movements as well.
Patterns of drug screen results and court-ordered substance use treatment referrals and completion among justice-involved youth.

Abstract Background Substance use is prevalent among justice-involved youth and given the risk of recidivism and other poor outcomes associated with substance use, justice systems have implemented efforts to improve substance use screening and connection to treatment. Although many justice systems use drug screening to monitor substance use, research on patterns of substance use based on drug screen records is lacking. The current study examined court records of drug screens among youth to explore patterns of substance use as well as rates of court-ordered referral to substance use treatment and treatment completion. We also examined differences in these patterns of use and treatment referral and completion by race, ethnicity, and gender. Method We examined court records for N = 3440 youth with records of positive oral drug screen (ODS) between 2011 and 2016 to assess patterns of ODS results (e.g., number and of positive screens), court-ordered referrals to substance use treatment, and rates of treatment completion. Results Of 3440 youth with a positive ODS, 96% tested positive for cannabis and 9.8% for opioids at least once; 48.5% were court-ordered to substance use treatment. Of those referred, 67% had history of completing at least one treatment episode; black youth (OR = 0.54, p Conclusion Our results underscore the need to utilize objective measures as well as validated self-reports of substance use history in both research and justice system decision-making to aid in identifying youth in need of services. Additional research should identify barriers to substance use treatment completion among this population.
Fast Convergence Rates of Distributed Subgradient Methods With Adaptive Quantization

We study distributed optimization problems over a network when the communication between the nodes is constrained, and therefore, information that is exchanged between the nodes must be quantized. Recent advances using the distributed gradient algorithm with a quantization scheme at a fixed resolution have established convergence, but at rates significantly slower than when the communications are unquantized. In this article, we introduce a novel quantization method, which we refer to as adaptive quantization, that allows us to match the convergence rates under perfect communications. Our approach adjusts the quantization scheme used by each node as the algorithm progresses: as we approach the solution, we become more certain about where the state variables are localized and adapt the quantizer codebook accordingly. We bound the convergence rates of the proposed method as a function of the communication bandwidth, the underlying network topology, and structural properties of the constituent objective functions. In particular, we show that if the objective functions are convex or strongly convex, then using adaptive quantization does not affect the rate of convergence of the distributed subgradient methods when the communications are quantized, except for a constant that depends on the resolution of the quantizer. To the best of our knowledge, the rates achieved in this article are better than any existing work in the literature for distributed gradient methods under finite communication bandwidths. We also provide numerical simulations that compare convergence properties of the distributed gradient methods with and without quantization for solving distributed regression problems for both quadratic and absolute loss functions.
FPDetect: Efficient Reasoning About Stencil Programs Using Selective Direct Evaluation

We present FPDetect, a low-overhead approach for detecting logical errors and soft errors affecting stencil computations without generating false positives. We develop an offline analysis that tightly estimates the number of floating-point bits preserved across stencil applications. This estimate rigorously bounds the values expected in the data space of the computation. Violations of this bound can be attributed with certainty to errors. FPDetect helps synthesize error detectors customized for user-specified levels of accuracy and coverage. FPDetect also enables overhead reduction techniques based on deploying these detectors coarsely in space and time. Experimental evaluations demonstrate the practicality of our approach.
Self-field, radiated energy, and radiated linear momentum of an accelerated point charge: part 2

Working within the framework of the classical theory of electrodynamics, we derive an exact mathematical solution to the problem of self-force (or radiation reaction) of an accelerated point-charge traveling in free space. In addition to deriving relativistic expressions for self electromagnetic fields, we obtain exact formulas for the rates of radiated energy and linear momentum without the need to renormalize the particle’s mass – or to discard undesirable infinities. The relativistic expression of self-force known as the Abraham- Lorentz-Dirac equation is derived in two different ways. Certain properties of the self-force are examined, and an approximate formula for the self-force, first proposed by Landau and Lifshitz, is discussed in some detail.
Differences in characteristics of Medicare patients treated by ophthalmologists and optometrists.

Purpose To quantify differences in the age, gender, race, and clinical complexity of Medicare beneficiaries treated by ophthalmologists and optometrists in each of the United States. Design Cross-sectional study based on publicly accessible Medicare payment and utilization data from 2012 through 2017. Methods For each ophthalmic and optometric provider, demographic information of treated Medicare beneficiaries was obtained from the Medicare Provider Utilization and Payment Data from the Centers for Medicare and Medicaid Services (CMS) for the years 2012 through 2017. Clinical complexity was defined using CMS Hierarchical Condition Category (HCC) coding. Results From 2012 through 2017, ophthalmologists in every state treated statistically significantly older beneficiaries, with the greatest difference (4.99 years in 2014) between provider groups seen in Rhode Island. In most states there was no gender difference among patients treated by the providers but in 46 states ophthalmologists saw a more racially diverse group of beneficiaries. HCC risk score analysis demonstrated that ophthalmologists in all 50 states saw more medically complex beneficiaries and the differences were statistically significant in 47 states throughout all six years. Conclusions Although there are regional variations in the characteristics of patients treated by ophthalmologists and optometrists, ophthalmologists throughout the United States manage older, more racially diverse, and more medically complex Medicare beneficiaries.
A Decentralized Framework for Serverless Edge Computing in the Internet of Things

Serverless computing is becoming widely adopted among cloud providers, thus making increasingly popular the Function-as-a-Service (FaaS) programming model, where the developers realize services by packaging sequences of stateless function calls. The current technologies are very well suited to data centers, but cannot provide equally good performance in decentralized environments, such as edge computing systems, which are expected to be typical for Internet of Things (IoT) applications. In this article, we fill this gap by proposing a framework for efficient dispatching of stateless tasks to in-network executors so as to minimize the response times while exhibiting short- and long-term fairness, also leveraging information from a virtualized network infrastructure when available. Our solution is shown to be simple enough to be installed on devices with limited computational capabilities, such as IoT gateways, especially when using a hierarchical forwarding extension. We evaluate the proposed platform by means of extensive emulation experiments with a prototype implementation in realistic conditions. The results show that it is able to smoothly adapt to the mobility of clients and to the variations of their service request patterns, while coping promptly with network congestion.
Episodic Koopman Learning of Nonlinear Robot Dynamics with Application to Fast Multirotor Landing

This paper presents a novel episodic method to learn a robot’s nonlinear dynamics model and an increasingly optimal control sequence for a set of tasks. The method is based on the Koopman operator approach to nonlinear dynamical systems analysis, which models the flow of observables in a function space, rather than a flow in a state space. Practically, this method estimates a nonlinear diffeomorphism that lifts the dynamics to a higher dimensional space where they are linear. Efficient Model Predictive Control methods can then be applied to the lifted model. This approach allows for real time implementation in on-board hardware, with rigorous incorporation of both input and state constraints during learning. We demonstrate the method in a real-time implementation of fast multirotor landing, where the nonlinear ground effect is learned and used to improve landing speed and quality.
Nonconventional magnetic phenomena in neodymium thin film

The authors study neodymium thin films and observe a cross-over behavior in magnetoresistance, which is ascribed to the field-induced ferromagnetism at low temperature.
Remote Lasing in Humid Air from Atomic Hydrogen

Over the last few years, air lasing has emerged as an attractive topic in standoff detection research. However, existing approaches have relied on the major species of air as the gain medium. This ...
MD&A Disclosure of Critical Accounting Policies and Financial Reporting Risk: Evidence From Restatements:

In this study, I examine whether management discloses areas with higher financial reporting risk as critical accounting policies (CAPs) and how investors react to restatements of areas previously d...
Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity.

Parkinson’s disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an \(F_1\)-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments. The code is available at https://github.com/mlu355/PD-Motor-Severity-Estimation.
Sustained cellular immune dysregulation in individuals recovering from SARS-CoV-2 infection.

SARS-CoV-2 causes a wide spectrum of clinical manifestations and significant mortality. Studies investigating underlying immune characteristics are needed to understand disease pathogenesis and inform vaccine design. In this study, we examined immune cell subsets in hospitalized and non-hospitalized individuals. In hospitalized patients, many adaptive and innate immune cells were decreased in frequency compared to healthy and convalescent individuals, with the exception of B lymphocytes which increased. Our findings show increased frequencies of T-cell activation markers (CD69, Ox40, HLA-DR and CD154) in hospitalized patients, with other T-cell activation/exhaustion markers (CD25, PD-L1 and TIGIT) remaining elevated in hospitalized and non-hospitalized individuals. B cells had a similar pattern of activation/exhaustion, with increased frequency of CD69 and CD95 during hospitalization, followed by an increase in PD1 frequencies in non-hospitalized individuals. Interestingly, many of these changes were found to increase over time in non-hospitalized longitudinal samples, suggesting a prolonged period of immune dysregulation following SARS-CoV-2 infection. Changes in T-cell activation/exhaustion in non-hospitalized patients were found to positively correlate with age. Severely infected individuals had increased expression of activation and exhaustion markers. These data suggest a prolonged period of immune dysregulation following SARS-CoV-2 infection highlighting the need for additional studies investigating immune dysregulation in convalescent individuals.
ECONOMIC DEVELOPMENT AND THE MARGINS OF TRADE: ARE THE LEAST DEVELOPED COUNTRIES DIFFERENT?

We use the Exporter Dynamics Database to identify differences in the margins of export growth for three country groups: high-income, middle-income, and least developed countries. The growing literature on trade dynamics suggests that economic development comes with trade growth on two margins -- the extensive margin (number of exporters) and the intensive margin (average exporter size). By controlling for country groups, we reveal novel relationships between economic development and the margin of export growth. While previous studies find a positive link between economic development and average exporter size, we find that this link is weakest for least developed countries (LDCs) and strongest for high-income countries. For the number of exporters, we find a positive relationship between the extensive margin and economic development for LDCs, but negative or weak correlations between the extensive margin and economic development for the other country groups. The findings, which are robust to various alternative specifications, imply that the drivers of export growth and economic development for the poorest countries differ significantly from growth drivers in middle-income and high-income economies.
Characterizing Sociolinguistic Variation in the Competing Vaccination Communities.

Public health practitioners and policy makers grapple with the challenge of devising effective message-based interventions for debunking public health misinformation in cyber communities. Framing and personalization of the message is one of the key features for devising a persuasive messaging strategy. For an effective health communication, it is imperative to focus on preference-based framing where the preferences of the target sub-community are taken into consideration. To achieve that, it is important to understand and hence characterize the target sub-communities in terms of their social interactions. In the context of health-related misinformation, vaccination remains to be the most prevalent topic of discord. Hence, in this paper, we conduct a sociolinguistic analysis of the two competing vaccination communities on Twitter: pro-vaxxers or individuals who believe in the effectiveness of vaccinations, and anti-vaxxers or individuals who are opposed to vaccinations. Our data analysis show significant linguistic variation between the two communities in terms of their usage of linguistic intensifiers, pronouns, and uncertainty words. Our network-level analysis show significant differences between the two communities in terms of their network density, echo-chamberness, and the EI index. We hypothesize that these sociolinguistic differences can be used as proxies to characterize and understand these communities to devise better message interventions.
Learning to scale multilingual representations for vision-language tasks

Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added. In this paper, we-9*6 propose a Scalable Multilingual Aligned Language Representation (SMALR) that supports many languages with few model parameters without sacrificing downstream task performance. SMALR learns a fixed size language-agnostic representation for most words in a multilingual vocabulary, keeping language-specific features for just a few. We use a masked cross-language modeling loss to align features with context from other languages. Additionally, we propose a cross-lingual consistency module that ensures predictions made for a query and its machine translation are comparable. The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date. We evaluate on multilingual image-sentence retrieval and outperform prior work by 3–4% with less than 1/5th the training parameters compared to other word embedding methods.
Sentence Level Estimation of Psycholinguistic Norms Using Joint Multidimensional Annotations.

Psycholinguistic normatives represent various affective and mental constructs using numeric scores and are used in a variety of applications in natural language processing. They are commonly used at the sentence level, the scores of which are estimated by extrapolating word level scores using simple aggregation strategies, which may not always be optimal. In this work, we present a novel approach to estimate the psycholinguistic norms at sentence level. We apply a multidimensional annotation fusion model on annotations at the word level to estimate a parameter which captures relationships between different norms. We then use this parameter at sentence level to estimate the norms. We evaluate our approach by predicting sentence level scores for various normative dimensions and compare with standard word aggregation schemes.
Atomistic full-band simulations of monolayer MoS2 transistors

We study the transport properties of deeply scaled monolayer MoS2 n-channel metal-oxide-semiconductor field effect transistors (MOSFETs), using full-band ballistic quantum transport simulations, with an atomistic tight-binding Hamiltonian obtained from density functional theory. Our simulations suggest that monolayer MoS2 MOSFETs can provide near-ideal subthreshold slope, suppression of drain-induced barrier lowering, and gate-induced drain leakage. However, these full-band simulations exhibit limited transconductance. These ballistic simulations also exhibit negative differential resistance (NDR) in the output characteristics associated with the narrow width in energy of the lowest conduction band, but this NDR may be substantially reduced or eliminated by scattering in MoS2.
Star Formation Activity in CLASH Brightest Cluster Galaxies

The CLASH X-ray selected sample of 20 galaxy clusters contains ten brightest cluster galaxies (BCGs) that exhibit significant ($>$5 $\sigma$) extinction-corrected star formation rates (SFRs). Star formation activity is inferred from photometric estimates of UV and H$\alpha$+[\ion{N}{2}] emission in knots and filaments detected in CLASH HST ACS and WFC3 observations. UV-derived SFRs in these BCGs span two orders of magnitude, including two with a SFR $\gtrsim$ 100 M$_{\odot}$ yr$^{-1}$. These measurements are supplemented with [\ion{O}{2}], [\ion{O}{3}], and H$\beta$ fluxes measured from spectra obtained with the SOAR telescope. We confirm that photoionization from ongoing star formation powers the line emission nebulae in these BCGs, although in many BCGs there is also evidence of a LINER-like contribution to the line emission. Coupling these data with Chandra X-ray measurements, we infer that the star formation occurs exclusively in low-entropy cluster cores and exhibits a correlation with gas properties related to cooling. We also perform an in-depth study of the starburst history of the BCG in the cluster RXJ1532.9+3021, and create 2D maps of stellar properties on scales down to $\sim$350 pc. These maps reveal evidence for an ongoing burst occurring in elongated filaments, generally on $\sim$ 0.5-1.0 Gyr timescales, although some filaments are consistent with much younger ($\lesssim$ 100 Myr) burst timescales and may be correlated with recent activity from the AGN. The relationship between BCG SFRs and the surrounding ICM gas properties provide new support for the process of feedback-regulated cooling in galaxy clusters and is consistent with recent theoretical predictions.
Bifurcations in a quasi-two-dimensional Kolmogorov-like flow

We present a combined experimental and theoretical study of the primary and secondary instabilities in a Kolmogorov-like flow. The experiment uses electromagnetic forcing with an approximately sinusoidal spatial profile to drive a quasi-two-dimensional (Q2D) shear flow in a thin layer of electrolyte suspended on a thin lubricating layer of a dielectric fluid. Theoretical analysis is based on a two-dimensional (2D) model (Suri et al., Phys. Fluids, vol. 26 (5), 2014, 053601), derived from first principles by depth-averaging the full three-dimensional Navier–Stokes equations. As the strength of the forcing is increased, the Q2D flow in the experiment undergoes a series of bifurcations, which is compared with results from direct numerical simulations of the 2D model. The effects of confinement and the forcing profile are studied by performing simulations that assume spatial periodicity and strictly sinusoidal forcing, as well as simulations with realistic no-slip boundary conditions and an experimentally validated forcing profile. We find that only the simulation subject to physical no-slip boundary conditions and a realistic forcing profile provides close, quantitative agreement with the experiment. Our analysis offers additional validation of the 2D model as well as a demonstration of the importance of properly modelling the forcing and boundary conditions.
Distributed Deep Learning for Question Answering

This paper is an empirical study of the distributed deep learning for question answering subtasks: answer selection and question classification. Comparison studies of SGD, MSGD, ADADELTA, ADAGRAD, ADAM/ADAMAX, RMSPROP, DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results show that the distributed framework based on the message passing interface can accelerate the convergence speed at a sublinear scale. This paper demonstrates the importance of distributed training. For example, with 48 workers, a 24x speedup is achievable for the answer selection task and running time is decreased from 138.2 hours to 5.81 hours, which will increase the productivity significantly.
PhoSim-NIRCam: photon-by-photon image simulations of the James Webb Space Telescope’s near-infrared camera

Recent instrumentation projects have allocated resources to develop codes for simulating astronomical images. Physics-based models are essential for understanding telescope, instrument, and environmental systematics in observations. A deep understanding of these systematics is especially important in the context of weak gravitational lensing, galaxy morphology, and other sensitive measurements. We present an adaptation of a physics-based ab initio image simulator: the photon simulator (PhoSim). We modify PhoSim for use with the near-infrared camera (NIRCam)—the primary imaging instrument aboard the James Webb Space Telescope. This photon Monte Carlo code replicates the observational catalog, telescope and camera optics, detector physics, and readout modes/electronics. Importantly, PhoSim-NIRCam simulates both geometric aberration and diffraction across the field of view. Full field- and wavelength-dependent point spread functions are presented. Simulated images of an extragalactic field are presented. Extensive validation is planned during in-orbit commissioning.
Effect of chemical doping on the thermoelectric properties of FeGa3

Thermoelectric properties of the chemically-doped intermetallic narrow-band semiconductor FeGa3 are reported. The parent compound shows semiconductor-like behavior with a small bandgap (Eg = 0.2 eV), a carrier density of ∼1018 cm−3, and a large n-type Seebeck coefficient (S ∼ − 400 μV/K) at room temperature. Hall effect measurements indicate that chemical doping significantly increases the carrier density, resulting in a metallic state, while the Seebeck coefficient still remains fairly large (∼− 150 μV/K). The largest power factor (S2/ρ = 62 μW/m K2) was observed for Fe0.99Co0.01(Ga0.997Ge0.003)3, and its corresponding figure of merit (ZT = 1.3 × 10−2) at 390 K improved by over a factor of 5 from the pure material.
Taut foliations, positive 3-braids, and the L-space conjecture

We construct taut foliations in every closed 3-manifold obtained by $r$-framed Dehn surgery along a positive 3-braid knot $K$ in $S^3$, where $r < 2g(K)-1$ and $g(K)$ denotes the Seifert genus of $K$. This confirms a prediction of the L-space Conjecture. For instance, we produce taut foliations in every non-L-space obtained by surgery along the pretzel knot $P(-2,3,7)$, and indeed along every pretzel knot $P(-2,3,q)$, for $q$ a positive odd integer. This is the first construction of taut foliations for every non-L-space obtained by surgery along an infinite family of hyperbolic L-space knots. Additionally, we construct taut foliations in every closed 3-manifold obtained by $r$-framed Dehn surgery along a positive 1-bridge braid in $S^3$, where $r <g(K)$.