cssi/SciSciGPT-SciSciNet huggingface dataset:
The Impact of Changes in Diagnostic Testing Practices on Estimates of COVID-19 Transmission in the United States.

Estimates of the reproductive number for novel pathogens such as severe acute respiratory syndrome coronavirus 2 are essential for understanding the potential trajectory of the epidemic and the level of intervention that is needed to bring the epidemic under control. However, most methods for estimating the basic reproductive number (R0) and time-varying effective reproductive number (Rt) assume that the fraction of cases detected and reported is constant through time. We explore the impact of secular changes in diagnostic testing and reporting on estimates of R0 and Rt using simulated data. We then compare these patterns to data on reported cases of coronavirus disease and testing practices from different states in the United States from March 4 to August 30, 2020. We find that changes in testing practices and delays in reporting can result in biased estimates of R0 and Rt. Examination of changes in the daily number of tests conducted and the percent of patients testing positive may be helpful for identifying the potential direction of bias. Changes in diagnostic testing and reporting processes should be monitored and taken into consideration when interpreting estimates of the reproductive number of coronavirus disease.
High-resolution radioluminescence microscopy of FDG uptake in an engineered 3D tumor-stoma model

The increased glucose metabolism of cancer cells is the basis for 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). However, due to its coarse image resolution, PET is unable to resolve the metabolic role of cancer-associated stroma, which often influences the metabolic reprogramming of a tumor. This study investigates the use of radioluminescence microscopy for imaging FDG uptake in engineered 3D tumor models with high resolution. Multicellular tumor spheroids (A549 lung adenocarcinoma) were co-cultured with GFP-expressing human umbilical vein endothelial cells (HUVECs) within an artificial extracellular matrix to mimic a tumor and its surrounding stroma. The tumor model was constructed as a 200-μm-thin 3D layer over a transparent CdWO4 scintillator plate to allow high-resolution imaging of the cultured cells. After incubation with FDG, the radioluminescence signal was collected by a highly sensitive widefield microscope. Fluorescence microscopy was performed using the same instrument to localize endothelial and tumor cells. Simultaneous and co-localized brightfield, fluorescence, and radioluminescence imaging provided high-resolution information on the distribution of FDG in the engineered tissue. The microvascular stromal compartment as a whole took up a large fraction of the FDG, comparable to the uptake of the tumor spheroids. In vitro gamma counting confirmed that A549 and HUVEC cells were both highly glycolytic with rapid FDG uptake kinetics. Despite the relative thickness of the tissue constructs, an average spatial resolution of 64 ± 4 μm was achieved for imaging FDG. Our study demonstrates the feasibility of imaging the distribution of FDG uptake in engineered in vitro tumor models. With its high spatial resolution, the method can separately resolve tumor and stromal components. The approach could be extended to more advanced engineered cancer models but also to surgical tissue slices and tumor biopsies.
Unsupervised speech representation learning for behavior modeling using triplet enhanced contextualized networks

Abstract Speech encodes a wealth of information related to human behavior and has been used in a variety of automated behavior recognition tasks. However, extracting behavioral information from speech remains challenging including due to inadequate training data resources stemming from the often low occurrence frequencies of specific behavioral patterns. Moreover, supervised behavioral modeling typically relies on domain-specific construct definitions and corresponding manually-annotated data, rendering generalizing across domains challenging. In this paper, we exploit the stationary properties of human behavior within an interaction and present a representation learning method to capture behavioral information from speech in an unsupervised way. We hypothesize that nearby segments of speech share the same behavioral context and hence map onto similar underlying behavioral representations. We present an encoder-decoder based Deep Contextualized Network (DCN) as well as a Triplet-Enhanced DCN (TE-DCN) framework to capture the behavioral context and derive a manifold representation, where speech frames with similar behaviors are closer while frames of different behaviors maintain larger distances. The models are trained on movie audio data and validated on diverse domains including on a couples therapy corpus and other publicly collected data (e.g., stand-up comedy). With encouraging results, our proposed framework shows the feasibility of unsupervised learning within cross-domain behavioral modeling.
Gravitational Collapse of a Fluid with Torsion into a Universe in a Black Hole

We consider gravitational collapse of a spherically symmetric sphere of a fluid with spin and torsion into a black hole. We use the Tolman metric and the Einstein–Cartan field equations with a relativistic spin fluid as a source. We show that gravitational repulsion of torsion prevents a singularity and replaces it with a nonsingular bounce. Quantum particle production during contraction helps torsion to dominate over shear. Particle production during expansion can generate a finite period of inflation and produce enormous amounts of matter. The resulting closed universe on the other side of the event horizon may have several bounces. Such a universe is oscillatory, with each cycle larger in size then the previous cycle, until it reaches the cosmological size and expands indefinitely. Our universe might have therefore originated from a black hole.
Deep Fourier Kernel for Self-Attentive Point Processes.

We present a novel attention-based model for discrete event data to capture complex non-linear temporal dependence structures. We borrow the idea from the attention mechanism and incorporate it into the point processes' conditional intensity function. We further introduce a novel score function using Fourier kernel embedding, whose spectrum is represented using neural networks, which drastically differs from the traditional dot-product kernel and can capture a more complex similarity structure. We establish our approach's theoretical properties and demonstrate our approach's competitive performance compared to the state-of-the-art for synthetic and real data.
Exact sequences and estimates for the $$\overline{\partial }$$ ∂ ¯ -problem

We study Sobolev estimates for solutions of the inhomogenous Cauchy–Riemann equations on annuli in $${\mathbb {C}}^n$$ , by constructing exact sequences relating the Dolbeault cohomology of the annulus with respect to Sobolev spaces of forms with those of the envelope and the hole. We also obtain solutions with prescribed support and estimates in Sobolev spaces using our method.
Representation stability for pure braid group Milnor fibers

We prove a representation stability result for the Milnor fiber associated to the pure braid group. Our result connects previous work of Simona Settepenella to representation stability in the sense of Church--Ellenberg--Farb, answering a question of Graham Denham. We also use our result to compute the stable integral homology of the Milnor fiber.
Temporal Graph Signal Decomposition

Temporal graph signals are multivariate time series with individual components associated with nodes of a fixed graph structure. Data of this kind arises in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Traditional matrix decomposition methods applied to such data fall short of exploiting structural regularities encoded in the underlying graph and also in the temporal patterns of the signal. How can we take into account such structure to obtain a succinct and interpretable representation of temporal graph signals? We propose a general, dictionary-based framework for temporal graph signal decomposition (TGSD). The key idea is to learn a low-rank, joint encoding of the data via a combination of graph and time dictionaries. We propose a highly scalable decomposition algorithm for both complete and incomplete data, and demonstrate its advantage for matrix decomposition, imputation of missing values, temporal interpolation, clustering, period estimation, and rank estimation in synthetic and real-world data ranging from traffic patterns to social media activity. Our framework achieves 28% reduction in RMSE compared to baselines for temporal interpolation when as many as 75% of the observations are missing. It scales best among baselines taking under 20 seconds on 3.5 million data points and produces the most parsimonious models. To the best of our knowledge, TGSD is the first framework to jointly model graph signals by temporal and graph dictionaries.
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions

Tracking and collecting fast-evolving online discussions provides vast data for studying social media usage and its role in people's public lives. However, collecting social media data using a static set of keywords fails to satisfy the growing need to monitor dynamic conversations and to study fast-changing topics. We propose a dynamic keyword search method to maximize the coverage of relevant information in fast-evolving online discussions. The method uses word embedding models to represent the semantic relations between keywords and predictive models to forecast the future trajectory of keywords. We also implement a visual user interface to aid in the decision making process in each round of keyword updates. This allows for both human-assisted tracking and fully-automated data collection. In simulations using historical #MeToo data in 2017, our human-assisted tracking method outperforms the traditional static baseline method significantly, achieving 37.1% improvement in F-1 score in the task of tracking the top trending keywords. We conduct a contemporary case study to cover dynamic conversations about the recent Presidential Inauguration and to test the dynamic data collection system. Our case studies reflect the effectiveness of our process and also points to the potential challenges in future deployment.
Inverse problems for nonlinear Maxwell's equations with second harmonic generation

Abstract In the current paper we consider an inverse boundary value problem of electromagnetism with nonlinear Second Harmonic Generation (SHG) process. We show the unique determination of the electromagnetic material parameters and the SHG susceptibility parameter of the medium by making electromagnetic measurements on the boundary. We are interested in the case when a frequency is fixed.
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers

The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains. Recently, large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQL parsing, improving zero-shot generalization to unseen databases. In this work, we examine the challenges that still prevent these techniques from practical deployment. First, we present KaggleDBQA, a new cross-domain evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions. Second, we re-examine the choice of evaluation tasks for text-to-SQL parsers as applied in real-life settings. Finally, we augment our in-domain evaluation task with database documentation, a naturally occurring source of implicit domain knowledge. We show that KaggleDBQA presents a challenge to state-of-the-art zero-shot parsers but a more realistic evaluation setting and creative use of associated database documentation boosts their accuracy by over 13.2%, doubling their performance.
Adaptive Configuration of In Situ Lossy Compression for Cosmology Simulations via Fine-Grained Rate-Quality Modeling

Extreme-scale cosmological simulations have been widely used by today's researchers and scientists on leadership supercomputers. A new generation of error-bounded lossy compressors has been used in workflows to reduce storage requirements and minimize the impact of throughput limitations while saving large snapshots of high-fidelity data for post-hoc analysis. In this paper, we propose to adaptively provide compression configurations to compute partitions of cosmological simulations with newly designed post-analysis aware rate-quality modeling. The contribution is fourfold: (1) We propose a novel adaptive approach to select feasible error bounds for different partitions, showing the possibility and efficiency of adaptively configuring lossy compression for each partition individually. (2) We build models to estimate the overall loss of post-analysis result due to lossy compression and to estimate compression ratio, based on the property of each partition. (3) We develop an efficient optimization guideline to determine the best-fit configuration of error bounds combination in order to maximize the compression ratio under acceptable post-analysis quality loss. (4) Our approach introduces negligible overheads for feature extraction and error-bound optimization for each partition, enabling post-analysis-aware in situ lossy compression for cosmological simulations. Experiments show that our proposed models are highly accurate and reliable. Our fine-grained adaptive configuration approach improves the compression ratio of up to 73% on the tested datasets with the same post-analysis distortion with only 1% performance overhead.
Knowing More About Questions Can Help: Improving Calibration in Question Answering

We study calibration in question answering, estimating whether model correctly predicts answer for each question. Unlike prior work which mainly rely on the model's confidence score, our calibrator incorporates information about the input example (e.g., question and the evidence context). Together with data augmentation via back translation, our simple approach achieves 5-10% gains in calibration accuracy on reading comprehension benchmarks. Furthermore, we present the first calibration study in the open retrieval setting, comparing the calibration accuracy of retrieval-based span prediction models and answer generation models. Here again, our approach shows consistent gains over calibrators relying on the model confidence. Our simple and efficient calibrator can be easily adapted to many tasks and model architectures, showing robust gains in all settings.
Effect of viscous shearing stresses on optimal material designs for flow of fluids through porous media

Topology optimization offers optimal material layouts, enabling automation in the design of devices. Given the recent advances in computer technology and additive manufacturing, topology optimization is increasingly being used to design complex porous structures, for example, microfluidic devices. For the flow of fluids in such miniature-sized porous structures, viscous shearing stresses will be significant. However, the Darcy model—the most popular mathematical model describing the flow of a single-phase incompressible fluid in rigid porous media—neglects the internal friction arising from viscous shearing stresses. We, therefore, develop a material design framework under the topology optimization based on the Darcy–Brinkman model—a mathematical model for the flow of fluids through porous media that accounts for internal friction besides the drag considered in the Darcy model. The proposed framework uses the total rate of mechanical dissipation—a physical quantity—for the objective function. To understand the effect of viscous shearing stresses on the design, we compare the optimal material layouts provided by the Darcy and Darcy–Brinkman models under topology optimization. In particular, we show, using analytical solutions corroborated by numerical simulations, that the optimal material layouts are alike for the class of problems exhibiting axisymmetry, for which viscous shearing stresses vanish. These analytical solutions will be valuable to check the veracity of numerical simulators. For those problems with dominant viscous shearing stresses (e.g., flow in a backward-facing step domain), we show, using numerical solutions, the optimal material layouts under the Darcy and Darcy–Brinkman models are very different. Also, the associated solution fields (i.e., velocity and pressure) are qualitatively different under their respective optimal layouts for these two models.
Biased by Choice: How Financial Constraints Can Reduce Financial Mistakes

We show that constraints can improve financial decision-making by disciplining behavioral biases. In financial markets, restrictions on leverage limit traders' ability to borrow to open new positions. We demonstrate that regulation which restricts the provision of leverage to retail traders increases trading performance. By increasing the opportunity cost of postponing the realization of losses, leverage constraints improve traders' market timing and reduce their disposition effect. We replicate these findings in two distinct experimental settings, further isolating the mechanism and demonstrating generality of the results. The interaction between constraints and behavioral biases has implications for policy and choice architecture.
The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood

In this paper we consider the problem of computing the likelihood of the profile of a discrete distribution, i.e., the probability of observing the multiset of element frequencies, and computing a profile maximum likelihood (PML) distribution, i.e., a distribution with the maximum profile likelihood. For each problem we provide polynomial time algorithms that given $n$ i.i.d.\ samples from a discrete distribution, achieve an approximation factor of $\exp\left(-O(\sqrt{n} \log n) \right)$, improving upon the previous best-known bound achievable in polynomial time of $\exp(-O(n^{2/3} \log n))$ (Charikar, Shiragur and Sidford, 2019). Through the work of Acharya, Das, Orlitsky and Suresh (2016), this implies a polynomial time universal estimator for symmetric properties of discrete distributions in a broader range of error parameter. To obtain our results on PML we establish new connections between PML and the well-studied Bethe and Sinkhorn approximations to the permanent (Vontobel, 2012 and 2014). It is known that the PML objective is proportional to the permanent of a certain Vandermonde matrix (Vontobel, 2012) with $\sqrt{n}$ distinct columns, i.e. with non-negative rank at most $\sqrt{n}$. This allows us to show that the convex approximation to computing PML distributions studied in (Charikar, Shiragur and Sidford, 2019) is governed, in part, by the quality of Sinkhorn approximations to the permanent. We show that both Bethe and Sinkhorn permanents are $\exp(O(k \log(N/k)))$ approximations to the permanent of $N \times N$ matrices with non-negative rank at most $k$. This improves upon the previous known bounds of $\exp(O(N))$ and combining these insights with careful rounding of the convex relaxation yields our results.
Amelioration of Diabetic Nephropathy Using a Retinoic Acid Receptor β2 Agonist

Vitamin A (VA) and its derivatives, known as retinoids, play critical roles in renal development through retinoic acid receptor β2 (RARβ2). Disruptions in VA signaling pathways are associated with the onset of diabetic nephropathy (DN). Despite the known role of RARβ2 in renal development, the effects of selective agonists for RARβ2 in a high-fat diet (HFD) model of DN are unknown. Here we examined whether AC261066 (AC261), a highly selective agonist for RARβ2, exhibited therapeutic effects in a HFD model of DN in C57BL/6 mice. Twelve weeks of AC261 administration to HFD-fed mice was well tolerated with no observable side effects. Compared with HFD-fed mice, HFD + AC261-treated mice had improved glycemic control and reductions in proteinuria and urine albumin-to-creatinine ratio. Several cellular hallmarks of DN were mitigated in HFD + AC261-treated mice, including reductions in tubule lipid droplets, podocyte (POD) effacement, endothelial cell collapse, mesangial expansion, and glomerular basement membrane thickening. Mesangial and tubule interstitial expression of the myofibroblast markers α-smooth muscle actin (α-SMA) and type IV collagen (Col-IV) was lower in HFD + AC261-treated mice compared with HFD alone. Ultrastructural and immunohistochemistry analyses showed that, compared with HFD-fed mice, HFD + AC261-treated mice showed preservation of POD foot process and slit-diaphragm morphology, an increase in the levels of slit-diagram protein podocin, and the transcription factor Wilms tumor-suppressor gene 1 in PODs. Given the need for novel DN therapies, our results warrant further studies of the therapeutic properties of AC261 in DN.
Atomistic-to-meso multi-scale data-driven graph surrogate modeling of dislocation glide

Abstract From their birth in the manufacturing process, materials inherently contain defects that affect the mechanical behavior across multiple length and time-scales, including vacancies, dislocations, voids and cracks. Understanding, modeling, and real-time simulation of the underlying stochastic micro-structure defect evolution is therefore vital towards multi-scale coupling and propagating numerous sources of uncertainty from atomistic to eventually aging continuum mechanics. We develop a graph-based surrogate model of dislocation glide for computation of stochastic dislocation mobility. We model an edge dislocation as a random walker, jumping between neighboring nodes of a graph following a Poisson stochastic process. The network representation functions as a coarse-graining of a molecular dynamics simulation that provides dislocation trajectories for an empirical computation of jump rates. With this construction, we recover the original atomistic mobility estimates, with remarkable computational speed-up and accuracy. Furthermore, the underlying stochastic process provides the statistics of dislocation mobility associated to the original molecular dynamics simulation, allowing an efficient propagation of material parameters and uncertainties across the scales.
Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking

Cross-language entity linking grounds mentions in multiple languages to a single-language knowledge base. We propose a neural ranking architecture for this task that uses multilingual BERT representations of the mention and the context in a neural network. We find that the multilingual ability of BERT leads to robust performance in monolingual and multilingual settings. Furthermore, we explore zero-shot language transfer and find surprisingly robust performance. We investigate the zero-shot degradation and find that it can be partially mitigated by a proposed auxiliary training objective, but that the remaining error can best be attributed to domain shift rather than language transfer.
Diversity-By-Design for Dependable and Secure Cyber-Physical Systems: A Survey

Diversity-based security approaches have been studied for several decades since the 1970s. The concept of diversity-by-design emerged in the 1980s. Since then, diversity-based system design research has been explored to provide more secure and dependable services in cyber-physical systems (CPSs). In this work, we are particularly interested in providing an in-depth, comprehensive survey of existing diversity-based approaches, their insights, and associated future work directions for building secure and dependable CPSs. This will allow us to provide promising ways of providing quality network and services based on key diversity-by-design principles for those who want to conduct research on developing secure and dependable CPSs using diversity as a system design feature. This survey paper mainly provides: (i) The common concept of diversity based on its multidisciplinary nature along with the historical evolution of the concept of diversity-by-design for providing secure and dependable services; (ii) the key diversity-by-design principles; (iii) the key benefits and caveats of using the diversity-by-design; (iv) the main concerns of CPS environments utilizing the diversity-by-design; (v) an extensive survey and discussions of existing diversity-based approaches based on five different classifications; (vi) the types of attacks considered by diversity-based approaches; (vii) the overall trends of evaluation methodologies used for diversity-based approaches, in terms of metrics, datasets, and testbeds; and (viii) the insights, lessons, and gaps identified from this extensive survey and future work directions.
High-field magnetization of Dy2O3

Abstract The magnetization of powdered samples of Dy 2 O 3 has been measured at temperatures between 1.45 and 4.2 K, in applied magnetic fields ranging to 70 kilogauss. A linear dependence of magnetization on applied field is observable in the high-field region, the slope of which is independent of temperature over the range investigated. The extrapolated saturation magnetic moment is 2.77 ± 0.08 Bohr magnetons per ion.
On Simple Mechanisms for Dependent Items

We study the problem of selling n heterogeneous items to a single buyer, whose values for different items are dependent. Under arbitrary dependence, Hart and Nisan[30] show that no simple mechanism can achieve a non-negligible fraction of the optimal revenue even with only two items. We consider the setting where the buyer's type is drawn from a correlated distribution that can be captured by a Markov Random Field, one of the most prominent frameworks for modeling high-dimensional distributions with structure. If the buyer's valuation is additive or unit-demand, we extend the result to all MRFs and show that $\max\srev,brev\ $ can achieve an Omega(1/eO(Δ))-fraction of the optimal revenue, where Δ is a parameter of the MRF that is determined by how much the value of an item can be influenced by the values of the other items. We further show that the exponential dependence on Δ is unavoidable for our approach and a polynomial dependence on Δ is unavoidable for any approach. When the buyer has a XOS valuation, we show that max(SRev,BRev) achieves at least an $Omega(1/eO(Δ) + 1/√nγ)-fraction of the optimal revenue, where γ is the spectral gap of the Glauber dynamics of the MRF. Note that the values of Δ and 1/nγ increase as the dependence between items strengthens. In the special case of independently distributed items, Δ=0 and 1/nγ ≥ 1, and our results recover the known constant factor approximations for a XOS buyer[41]. We further extend our parametric approximation to several other well-studied dependency measures such as the Dobrushin coefficient [27] and the inverse temperature. In particular, we show that if the MRF is in the high temperature regime, max(SRev,BRev) is still a constant factor approximation to the optimal revenue even for a XOS buyer. Our results are based on the Duality-Framework by Cai et al.[14] and a new concentration inequality for XOS functions over dependent random variables.
Multilingual neural semantic parsing for low-resourced languages

Multilingual semantic parsing is a cost-effective method that allows a single model to understand different languages. However, researchers face a great imbalance of availability of training data, with English being resource rich, and other languages having much less data. To tackle the data limitation problem, we propose using machine translation to bootstrap multilingual training data from the more abundant English data. To compensate for the data quality of machine translated training data, we utilize transfer learning from pretrained multilingual encoders to further improve the model. To evaluate our multilingual models on human-written sentences as opposed to machine translated ones, we introduce a new multilingual semantic parsing dataset in English, Italian and Japanese based on the Facebook Task Oriented Parsing (TOP) dataset. We show that joint multilingual training with pretrained encoders substantially outperforms our baselines on the TOP dataset and outperforms the state-of-the-art model on the public NLMaps dataset. We also establish a new baseline for zero-shot learning on the TOP dataset. We find that a semantic parser trained only on English data achieves a zero-shot performance of 44.9% exact-match accuracy on Italian sentences.
On Factors Affecting the Performance of Dynamically Groomed Optical WDM Mesh Networks

In this paper, we consider the problem of traffic grooming in optical wavelength division multiplexed (WDM) mesh topology-based networks, under dynamic traffic conditions. Individual channels in a WDM network have capacities of up to 40 Gbps and are expected to increase further. However, individual connections (or sessions) typically do not require the entire wavelength capacity. Thus, allocating a lightpath with a full wavelength capacity for a single connection results in low bandwidth utilization. One way to solve this is by using electronic SONET add-drop multiplexers (SADMs) at each node to multiplex several low-rate connections on to the high-capacity wavelength channel, and provision them as a single lightpath. This is referred to as traffic grooming. However, SADMs are costly devices and it is expensive to equip each port and wavelength in every node with grooming capabilities. This leads to the concept of limited grooming that requires fewer SADMs at the nodes: only a subset of the ports and wavelengths will be equipped with SADMs. In this paper, we propose four different limited grooming node architectures and develop grooming policies for each architecture. We also conduct a systematic performance evaluation of a limited grooming optical network supporting dynamic traffic requests. We consider the effect of several important factors including: the connection granularity, traffic grooming policy, the number of SADMs at a node, the grooming port tunability and wavelength conversion. The results indicate that limited grooming at each node is sufficient to attain the performance obtained with full grooming, especially when connections occupy a small fraction of the wavelength capacity. Further, the connection granularity, the grooming policy and the number of wavelengths used per link for a connection are also seen to have a significant effect on the blocking performance. Interestingly, the SADM tunability and the port-sharing architecture are not seen to have a significant impact on the performance.
Towards Automated and Marker-less Parkinson Disease Assessment: Predicting UPDRS Scores using Sit-stand videos

This paper presents a novel deep learning enabled, video based analysis framework for assessing the Unified Parkinson’s Disease Rating Scale (UPDRS) that can be used in the clinic or at home. We report results from comparing the performance of the framework to that of trained clinicians on a population of 32 Parkinson’s disease (PD) patients. In-person clinical assessments by trained neurologists are used as the ground truth for training our framework and for comparing the performance. We find that the standard sit-to-stand activity can be used to evaluate the UPDRS sub-scores of bradykinesia (BRADY) and posture instability and gait disorders (PIGD). For BRADY we find Fl-scores of 0.75 using our framework compared to 0.50 for the video based rater clinicians, while for PIGD we find 0.78 for the framework and 0.45 for the video based rater clinicians. We believe our proposed framework has potential to provide clinically acceptable end points of PD in greater granularity without imposing burdens on patients and clinicians, which empowers a variety of use cases such as passive tracking of PD progression in spaces such as nursing homes, in-home self-assessment, and enhanced tele-medicine.
Expiring Eviction Moratoriums and COVID-19 Incidence and Mortality.

The COVID-19 pandemic and associated economic crisis have placed millions of U.S. households at risk of eviction. Evictions may accelerate COVID-19 transmission by decreasing individuals' ability to socially distance. We leveraged variation in the expiration of eviction moratoriums in U.S. states to test for associations between evictions and COVID-19 incidence and mortality. The study included 44 U.S. states that instituted eviction moratoriums, followed from March 13th to September 3rd, 2020. We modeled associations using a difference-in-difference approach with an event-study specification. Negative-binomial-regression models of cases and deaths included fixed effects for state and week and controlled for time-varying indicators of testing, stay-at-home orders, school closures, and mask mandates. COVID-19 incidence and mortality increased steadily in states after eviction moratoriums expired, and were associated with doubling of COVID-19 incidence (incidence rate ratio 2.1; 95% confidence interval (CI) 1.1,3.9) and a five-fold increase in COVID-19 mortality (mortality rate ratio 5.4; CI 3.1,9.3) 16 weeks after moratoriums lapsed. These results imply an estimated 433,700 excess cases (CI 365200, 502200) and 10,700 excess deaths (CI 8900,12500) nationally by September 3, 2020. The expiration of eviction moratoriums was associated with increased COVID-19 incidence and mortality, supporting the public-health rationale for eviction prevention to limit COVID-19 cases and deaths.
Estimated Regional White Matter Hyperintensity Burden, Resting State Functional Connectivity, and Cognitive Functions in Older Adults.

ABSTRACT Objective White matter hyperintensities (WMH) are linked to deficits in cognitive functioning, including cognitive control and memory; however, the structural, and functional mechanisms are largely unknown. We investigated the relationship between estimated regional disruptions to white matter fiber tracts from WMH, resting state functional connectivity (RSFC), and cognitive functions in older adults. Design Cross-sectional study. Setting Community. Participants Fifty-eight cognitively-healthy older adults. Measurements Tasks of cognitive control and memory, structural MRI, and resting state fMRI. We estimated the disruption to white matter fiber tracts from WMH and its impact on gray matter regions in the cortical and subcortical frontoparietal network, default mode network, and ventral attention network by overlaying each subject's WMH mask on a normative tractogram dataset. We calculated RSFC between nodes in those same networks. We evaluated the interaction of regional WMH burden and RSFC in predicting cognitive control and memory. Results The interaction of estimated regional WMH burden and RSFC in cortico-striatal regions of the default mode network and frontoparietal network was associated with delayed recall. Models predicting working memory, cognitive inhibition, and set-shifting were not significant. Conclusion Findings highlight the role of network-level structural and functional alterations in resting state networks that are related to WMH and impact memory in older adults.
Fiber product homotopy method for multiparameter eigenvalue problems

We develop a new homotopy method for solving multiparameter eigenvalue problems (MEPs) called the fiber product homotopy method. For a k-parameter eigenvalue problem with matrices of sizes $$n_1,\ldots ,n_k = O(n)$$ , fiber product homotopy method requires deformation of O(1) linear equations, while existing homotopy methods for MEPs require O(n) nonlinear equations. We show that the fiber product homotopy method theoretically finds all eigenpairs of an MEP with probability one. It is especially well-suited for a class of problems we call dimension-deficient singular problems that are generic with respect to intrinsic dimension, as the fiber product homotopy method is provably convergent with probability one for such problems as well, a fact borne out by numerical experiments. More generally, our numerical experiments indicate that the fiber product homotopy method significantly outperforms the standard Delta method in terms of accuracy, with consistent backward errors on the order of $$10^{-16}$$ without any use of extended precision. In terms of speed, it significantly outperforms previous homotopy-based methods on all problems and outperforms the Delta method on larger problems, and is also highly parallelizable. We show that the fiber product MEP that we solve in the fiber product homotopy method, although mathematically equivalent to a standard MEP, is typically a much better conditioned problem.
A computational model of dopaminergic modulation of hippocampal Schaffer collateral-CA1 long-term plasticity

Dopamine plays a critical role in modulating the long-term synaptic plasticity of the hippocampal Schaffer collateral-CA1 pyramidal neuron synapses (SC-CA1), a widely accepted cellular model of learning and memory. Limited results from hippocampal slice experiments over the last four decades have shown that the timing of the activation of dopamine D1/D5 receptors relative to a high/low-frequency stimulation (HFS/LFS) in SC-CA1 synapses regulates the modulation of HFS/LFS-induced long-term potentiation/depression (LTP/LTD) in these synapses. However, the existing literature lacks a complete picture of how various concentrations of D1/D5 agonists and the relative timing between the activation of D1/D5 receptors and LTP/LTD induction by HFS/LFS, affect the spatiotemporal modulation of SC-CA1 synaptic dynamics. In this paper, we have developed a computational model, a first of its kind, to make quantitative predictions of the temporal dose-dependent modulation of the HFS/LFS induced LTP/LTD in SC-CA1 synapses by various D1/D5 agonists. Our model combines the biochemical effects with the electrical effects at the electrophysiological level. We have estimated the model parameters from the published electrophysiological data, available from diverse hippocampal CA1 slice experiments, in a Bayesian framework. Our modeling results demonstrate the capability of our model in making quantitative predictions of the available experimental results under diverse HFS/LFS protocols. The predictions from our model show a strong nonlinear dependency of the modulated LTP/LTD by D1/D5 agonists on the relative timing between the activated D1/D5 receptors and the HFS/LFS protocol and the applied concentration of D1/D5 agonists.
Smoking is associated with worse outcomes of COVID-19 particularly among younger adults: a systematic review and meta-analysis.

BACKGROUND: Smoking impairs lung immune function and damages upper airways, increasing risks of contracting and severity of infectious diseases. This paper quantifies the association between smoking and COVID-19 disease progression. METHODS: We searched PubMed and Embase for studies published from January 1-May 25, 2020. We included studies reporting smoking behavior of COVID-19 patients and progression of disease, including death. We used random effects meta-analysis, meta-regression and locally weighted regression and smoothing to examine relationships in the data. RESULTS: We identified 46 peer-reviewed papers with a total of 22,939 COVID-19 patients, 5421 (23.6%) experienced disease progression and 2914 (12.7%) with a history of smoking (current and former smokers). Among those with a history of smoking, 33.5% experienced disease progression, compared with 21.9% of non-smokers. The meta-analysis confirmed an association between ever smoking and COVID-19 progression (OR 1.59, 95% CI 1.33-1.89, p = 0.001). Ever smoking was associated with increased risk of death from COVID-19 (OR 1.19, 95% CI 1.02-1.39, p = 0.003). We found no significant difference (p = 0.864) between the effects of ever smoking on COVID-19 disease progression between adjusted and unadjusted analyses, suggesting that smoking is an independent risk factor for COVID-19 disease progression. We also found the risk of having COVID-19 progression higher among younger adults (p = 0.001), with the effect most pronounced among younger adults under about 45 years old. CONCLUSIONS: Smoking is an independent risk for having progression of COVID-19, including mortality. The effects seem to be higher among young people. Smoking prevention and cessation should remain a priority for the public, physicians, and public health professionals during the COVID-19 pandemic.
Path integrals and the discrete Weyl representation

This paper uses Schwinger's representation of quantum systems of a finite number of degrees of freedom to formulate discrete path integral representations of the dynamics. The representation starts with an observable with a finite number of distinct eigenvalues. Using that observable Schwinger constructs complementary pairs of finite dimensional unitary operators that are finite dimensional analogs of the irreducible Weyl algebra. These can be decomposed into products of irreducible sub-algebras that reduce to q-bit gates for the case of $2^N$ degrees of freedom. In the limit of a large number of discrete degrees of freedom these representations can be used to model quantum systems with continuous degrees of freedom. This limit recovers the continuum Weyl algebra. Path integrals can be formulated in the discrete case. They have the advantage that the number of paths for a finite number of time slices is finite. In this work the path integral is interpreted as the expectation value of a potential functional with respect to a complex probability distribution on the space of "paths". An application to scattering from a short-range potential is given. Multi-resolution wavelet bases are used exactly represent a local $\phi^4(x)$ quantum field theory as a theory with an infinite number of discrete local modes. The discrete path integral is illustrated by computing time evolution when this theory is truncated to two coupled modes.
Global existence for the two-dimensional Kuramoto-Sivashinsky equation with advection

We study the Kuramoto-Sivashinsky equation (KSE) in scalar form on the two-dimensional torus with and without advection by an incompressible vector field. We prove local existence of mild solutions...
A palmitoylation code controls PI4KIIIα complex formation and PI(4,5)P2 homeostasis at the plasma membrane.

PI4KIIIα is the major enzyme responsible for generating the phosphoinositide PI(4)P at the plasma membrane. This lipid kinase forms two multicomponent complexes, both including a palmitoylated anchor, EFR3. Whereas both PI4KIIIα complexes support production of PI(4)P, the distinct functions of each complex and mechanisms underlying the interplay between them remain unknown. Here, we present roles for differential palmitoylation patterns within a tri-Cys motif in EFR3B (Cys5/Cys7/Cys8) in controlling the distribution of PI4KIIIα between these two complexes at the plasma membrane and corresponding functions in phosphoinositide homeostasis. Spacing of palmitoyl groups within three doubly palmitoylated EFR3B "lipoforms" affects both its interactions with TMEM150A, a transmembrane protein governing formation of a PI4KIIIα complex functioning in rapid PI(4,5)P2 resynthesis following PLC signaling, and its partitioning within liquid-ordered and -disordered regions of the plasma membrane. This work identifies a palmitoylation code in controlling protein-protein and protein-lipid interactions affecting a plasma membrane-resident lipid biosynthetic pathway.
Binary signed-digit integers and the Stern diatomic sequence

Stern’s diatomic sequence is a well-studied and simply defined sequence with many fascinating characteristics. The binary signed-digit representation of integers is an alternative representation of integers with much use in efficient computation, coding theory and cryptography. We link these two ideas here, showing that the number of i-bit binary signed-digit representations of an integer n with $$n<2^i$$ is the $$(2^i-n)^{\text {th}}$$ element in Stern’s diatomic sequence. This correspondence makes the vast range of results known for Stern’s diatomic sequence available for consideration in the study of binary signed-digit integers.
Self-Supervised Vessel Enhancement Using Flow-Based Consistencies

Vessel segmentation is an essential task in many clinical applications. Although supervised methods have achieved state-of-art performance, acquiring expert annotation is laborious and mostly limited for two-dimensional datasets with a small sample size. On the contrary, unsupervised methods rely on handcrafted features to detect tube-like structures such as vessels. However, those methods require complex pipelines involving several hyper-parameters and design choices rendering the procedure sensitive, dataset-specific, and not generalizable. We propose a self-supervised method with a limited number of hyper-parameters that is generalizable across modalities. Our method uses tube-like structure properties, such as connectivity, profile consistency, and bifurcation, to introduce inductive bias into a learning algorithm. To model those properties, we generate a vector field that we refer to as a flow. Our experiments on various public datasets in 2D and 3D show that our method performs better than unsupervised methods while learning useful transferable features from unlabeled data. Unlike generic self-supervised methods, the learned features learn vessel-relevant features that are transferable for supervised approaches, which is essential when the number of annotated data is limited.
A chromosome-level genome assembly for the eastern fence lizard (Sceloporus undulatus), a reptile model for physiological and evolutionary ecology

Background High-quality genomic resources facilitate investigations into behavioral ecology, morphological and physiological adaptations, and the evolution of genomic architecture. Lizards in the genus Sceloporus have a long history as important ecological, evolutionary, and physiological models, making them a valuable target for the development of genomic resources. Findings We present a high-quality chromosome-level reference genome assembly, SceUnd1.0 (using 10X Genomics Chromium, HiC, and Pacific Biosciences data), and tissue/developmental stage transcriptomes for the eastern fence lizard, Sceloporus undulatus. We performed synteny analysis with other snake and lizard assemblies to identify broad patterns of chromosome evolution including the fusion of micro- and macrochromosomes. We also used this new assembly to provide improved reference-based genome assemblies for 34 additional Sceloporus species. Finally, we used RNAseq and whole-genome resequencing data to compare 3 assemblies, each representing an increased level of cost and effort: Supernova Assembly with data from 10X Genomics Chromium, HiRise Assembly that added data from HiC, and PBJelly Assembly that added data from Pacific Biosciences sequencing. We found that the Supernova Assembly contained the full genome and was a suitable reference for RNAseq and single-nucleotide polymorphism calling, but the chromosome-level scaffolds provided by the addition of HiC data allowed synteny and whole-genome association mapping analyses. The subsequent addition of PacBio data doubled the contig N50 but provided negligible gains in scaffold length. Conclusions These new genomic resources provide valuable tools for advanced molecular analysis of an organism that has become a model in physiology and evolutionary ecology.