cssi/SciSciGPT-SciSciNet huggingface dataset:
Do users talk about the software in my product? Analyzing user reviews on IoT products.

Consumer product reviews are an invaluable source of data because they contain a wide range of information that could help requirement engineers to meet user needs. Recent studies have shown that tweets about software applications and reviews on App Stores contain useful information, which enable a more responsive software requirements elicitation. However, all of these studies' subjects are merely software applications. Information on system software, such as embedded software, operating systems, and firmware, are overlooked, unless reviews of a product using them are investigated. Challenges in investigating these reviews could come from the fact that there is a huge volume of data available, as well as the fact that reviews of such products are diverse in nature, meaning that they may contain information mostly on hardware components or broadly on the product as a whole. Motivated by these observations, we conduct an exploratory study using a dataset of 7198 review sentences from 6 Internet of Things (IoT) products. Our qualitative analysis demonstrates that a sufficient quantity of software related information exists in these reviews. In addition, we investigate the performance of two supervised machine learning techniques (Support Vector Machines and Convolutional Neural Networks) for classification of information contained in the reviews. Our results suggest that, with a certain setup, these two techniques can be used to classify the information automatically with high precision and recall.
Alternative Intersection Designs with Connected and Automated Vehicle

Alternative intersection designs (AIDs) can improve the performance of an intersection by not only reducing the number of signal phases but also change the configuration of the conflicting points by re-routing traffic. However the AID studies have rarely been extended to Connected and Automated Vehicle (CAV) which is expected to revolutionize our transportation system. In this study, we investigate the potential benefits of CAV to two AIDs: the diverging diamond interchange (DDI) and the restricted crossing U-turn intersection. The potential enhancements of AID, CAV, and the combination of both are quantified via microscopic traffic simulation. We found that CAV is able to positively contribute to the performance of an intersection. However, converting an existing conventional diamond interchange (CDI) to a diverging one is a more effective way according to the simulation results. DDI improves the throughput of a CDI by 950 vehicles per hour, a near 20% improvement; whereas with full penetration of CAV, the throughput of a CDI is increased only by 300 vehicles per hour. A similar trend is observed in the average delay per vehicle as well. Furthermore, we assess the impact for the driver's confusion, a concern for deploying AIDs, on the traffic flow. According to the ANOVA test, the negative impacts of driver's confusion are of statistical significance.
Distributed Capacity of A Multiple Access Channel

In distributed communication, each transmitter prepares an ensemble of channel codes corresponding to different values of communication parameters. To encode a message, a transmitter chooses a channel code individually without sharing the coding choice with other transmitters or with the receiver. Upon receiving the block of channel output symbols, the receiver either decodes the messages of interest if a pre-determined reliability requirement can be met, or reports collision otherwise. Revised from the existing distributed channel coding theorems, distributed capacity of a discrete-time memoryless multiple access channel is defined and derived under the assumption that codeword length can be taken to infinity. An improved achievable error performance bound is presented for the case when codeword length is finite.11This work was supported by the National Science Foundation under Grants CCF-1420608 and CNS-1618960. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Structures, functions, and mechanisms of filament forming enzymes: a renaissance of enzyme filamentation

Filament formation by non-cytoskeletal enzymes has been known for decades, yet only relatively recently has its wide-spread role in enzyme regulation and biology come to be appreciated. This comprehensive review summarizes what is known for each enzyme confirmed to form filamentous structures in vitro, and for the many that are known only to form large self-assemblies within cells. For some enzymes, studies describing both the in vitro filamentous structures and cellular self-assembly formation are also known and described. Special attention is paid to the detailed structures of each type of enzyme filament, as well as the roles the structures play in enzyme regulation and in biology. Where it is known or hypothesized, the advantages conferred by enzyme filamentation are reviewed. Finally, the similarities, differences, and comparison to the SgrAI endonuclease system are also highlighted.
Health Monitoring in Smart Homes Utilizing Internet of Things

In recent years the concept of the Internet of Things (IoT) has evolved to connect commercial gadgets together with the medical field to facilitate an unprecedented range of accessibility. The development of medical devices connected to internet of things has been praised for the potential of alleviating the strain on the modern healthcare system by giving users the opportunity to reside in the home during treatment or recovery. With the IoT becoming more prevalent and available at a commercial level, there exists room for integration into emerging, intelligent environments such as smart homes. When used in tandem with conventional healthcare, the IoT offers a vast range of custom-tailored treatment options. This paper studies recent state-of-the-art research on the field of IoT for health monitoring and smart homes, examines several potential use-cases of blending the technology, and proposes integration with an existing smart home testbed for further study. Challenges of adoption and future research on the topic are also discussed.
Unsupervised Multi-Task Feature Learning on Point Clouds

We introduce an unsupervised multi-task model to jointly learn point and shape features on point clouds. We define three unsupervised tasks including clustering, reconstruction, and self-supervised classification to train a multi-scale graph-based encoder. We evaluate our model on shape classification and segmentation benchmarks. The results suggest that it outperforms prior state-of-the-art unsupervised models: In the ModelNet40 classification task, it achieves an accuracy of 89.1% and in ShapeNet segmentation task, it achieves an mIoU of 68.2 and accuracy of 88.6%.
Probabilistic Models of Larval Zebrafish Behavior Reveal Structure on Many Scales

Summary Nervous systems have evolved to combine environmental information with internal state to select and generate adaptive behavioral sequences. To better understand these computations and their implementation in neural circuits, natural behavior must be carefully measured and quantified. Here, we collect high spatial resolution video of single zebrafish larvae swimming in a naturalistic environment and develop models of their action selection across exploration and hunting. Zebrafish larvae swim in punctuated bouts separated by longer periods of rest called interbout intervals. We take advantage of this structure by categorizing bouts into discrete types and representing their behavior as labeled sequences of bout types emitted over time. We then construct probabilistic models—specifically, marked renewal processes—to evaluate how bout types and interbout intervals are selected by the fish as a function of its internal hunger state, behavioral history, and the locations and properties of nearby prey. Finally, we evaluate the models by their predictive likelihood and their ability to generate realistic trajectories of virtual fish swimming through simulated environments. Our simulations capture multiple timescales of structure in larval zebrafish behavior and expose many ways in which hunger state influences their action selection to promote food seeking during hunger and safety during satiety.
Impact of Travel Distance on Radiation Treatment Modality for Central Nervous System Disease

Background Stereotactic body radiation therapy (SBRT) has emerged as a popular alternative to conventional radiation therapy (RT) over the past 15 years. Unfortunately, the impact of patient distance from radiation treatment centers and utilization of SBRT versus conventional RT has been sparsely investigated. This report represents the first analysis of the impact of patient distance on radiation treatment modality for central nervous system (CNS) disease. Materials and Methods Since the inception of our RADIation oncology And Neuro-Surgery (RADIANS) multidisciplinary clinic at a community hospital in 2016, 27 patients have received either SBRT or conventional RT as their sole radiation treatment modality for CNS disease. Twenty-four (88.9%) presented with metastatic disease. Fisher’s exact test evaluated the relationship between patient residence from treatment (in miles) and radiation treatment modality received. Results Mean patient distance from our RADIANS clinic was 50.6 miles (median = 15.3). Twenty-one patients (77.8%) received SBRT; the remaining six received conventional RT. Mean patient distance from SBRT was 63.6 miles, and mean patient distance for conventional RT was 5.1 miles; this finding was statistically significant (p = 0.0433; 95% confidence interval = 1.9–115.1). Conclusion Our findings indicate that patients with CNS disease who receive SBRT over conventional RT are statistically more likely to reside further from treatment centers. This is similar to findings of national studies comparing proton versus photon treatment for pediatric solid malignancies. The results from our work have implications for neuro-oncology treatment and the development of community hospital-based clinic models similar to RADIANS in the future.
Aeroelastic Indicial Response Reduced-Order Modeling for Flexible Flight Vehicles

A reduced-order modeling method capable of providing computationally efficient predictions of the nonlinear, unsteady aerodynamics encountered by flexible flight vehicles is presented. Models are d...
The increasing risk of mortality in breast cancer: A socioeconomic analysis between countries

Abstract. The risk of mortality in breast cancer among women is a critical health issue worldwide. Scholars argue that breast cancer mortality rates have decreased in many advanced countries overall. However, about 50% of world population in 2017 was in poor and developing countries (more than 3,652 million with 50.24% female) and breast cancer mortality rates differ among nations also because of socioeconomic factors. This study investigates, at global level, breast cancer mortality in association with breast cancer incidence and some factors of socioeconomic ecosystem between poor and rich countries, to explain trends that can be used to gain insights into country-level “best practices” for health improvement. Global data regarding breast cancer incidence and mortality as the age standardized rate per 100,000 population in 78low-to-middle income countries (LMICs), 50 upper-to-middle-income countries (UMICs) and 63 high income countries (HCIs) were obtained from IARC/WHO for 2012 and 2018. Data regarding GDP per capita, population and mammography (MMG) were obtained from World Bank, United Nations and WHO. Data, transformed in log scale to have normal distribution, were analyzed with descriptive statistics, partial correlation, regression analyses and paired-Samples T Test procedure to assess the statistical significance of increase or decrease of mortality and incidence in breast cancer from 2012 to 2018.Results reveal that a 1% higher level of breast cancer incidence, increases the expected mortality by 0.79% ( p-value < .001) in LMICs, by 0.50% ( p-value <.001) in UMICs and by 0.31% ( p-value < .008) in HICs. These results, confirmed by other analyses here, seem to suggest that breast cancer mortality is increasing over time worldwide in rich and in particular developing countries. The global analysis here reveals that though an improvement of wealth and wellbeing worldwide, the risk of incidence and mortality in breast cancer is increasing. This result suggests that situational factors in the ecosystem of countries support the growing increase and mortality of breast cancer that improvement in healthcare and medicine of the last 40 years are not been sufficient to slowdown. These conclusions need for much more detailed research to investigate into the interaction between factors of socioeconomic systems, health improvement, and breast cancer causes. Keywords. Breast cancer, Wealth of nations, Epidemiology. JEL. I14, I15, I18, I39, O10, O3, O55, Q50.
A ratio of alternants formula for loop Schur functions

Lam and Pylyavskyy introduced loop symmetric functions as a generalization of symmetric functions. They defined loop Schur functions as generating functions over semistandard tableaux with respect to a `colored weight,' and they proved a Jacobi--Trudi-style determinantal formula for these generating functions. We prove that loop Schur functions can be expressed as a ratio of `loop alternants,' extending the analogy with Schur functions. As an application, we give a new proof of the loop version of the Murnaghan--Nakayama rule.
Sharp Matrix Weighted Strong Type Inequalities for the Dyadic Square Function

In this paper we refine the recent sparse domination of the integrated p = 2 matrix weighted dyadic square function by T. Hytonen, S. Petermichl, and A. Volberg to prove a pointwise sparse domination of general matrix weighted dyadic square functions. We then use this to prove sharp two matrix weighted strong type inequalities for matrix weighted dyadic square functions when 1 < p ≤ 2.
The Very Massive and Hot LMC Star VFTS 682: Progenitor of a Future Dark Gamma-Ray Burst?

VFTS 682, a very massive and very hot Wolf-Rayet (WR) star recently discovered in the Large Magellanic Cloud near the famous star cluster R136, might be providing us with a glimpse of a missing link in our understanding of Long Gamma-Ray Bursts (LGRBs), including dark GRBs. It is likely its properties result from chemically homogeneous evolution (CHE), believed to be a key process for a massive star to become a GRB. It is also heavily obscured by dust extinction, which could make it a dark GRB upon explosion. Using Spitzer data we investigate the properties of interstellar dust in the vicinity of R136, and argue that its high obscuration is not unusual for its environment and that it could indeed be a slow runaway ("walkaway") from R136. Unfortunately, based on its current mass loss rate, VFTS 682 is unlikely to become a GRB, because it will lose too much angular momentum at its death. If it were to become a GRB, it probably would also not be dark, either escaping or destroying its surrounding dusty region. Nevertheless, it is a very interesting star, deserving further studies, and being one of only three presently identified WR stars (two others in the Small Magellanic Cloud) that seems to be undergoing CHE.
Skein relations for tangle Floer homology

In a previous paper, Vertesi and the first author used grid-like Heegaard diagrams to define tangle Floer homology, which associates to a tangle $T$ a differential graded bimodule $\widetilde{\mathrm{CT}} (T)$. If $L$ is obtained by gluing together $T_1, \dotsc, T_m$, then the knot Floer homology $\hat{\mathrm{HFK}}(L)$ of $L$ can be recovered from $\widetilde{\mathrm{CT}} (T_1), \dotsc, \widetilde{\mathrm{CT}} (T_m)$. In the present paper, we prove combinatorially that tangle Floer homology satisfies unoriented and oriented skein relations, generalizing the skein exact triangles for knot Floer homology.
Tumor mutational burden predicts survival in patients with low-grade gliomas expressing mutated IDH1.

Background Gliomas are the most common primary brain tumors. High-Grade Gliomas have a median survival (MS) of 18 months, while Low-Grade Gliomas (LGGs) have an MS of approximately 7.3 years. Seventy-six percent of patients with LGG express mutated isocitrate dehydrogenase (mIDH) enzyme. Survival of these patients ranges from 1 to 15 years, and tumor mutational burden ranges from 0.28 to 3.85 somatic mutations/megabase per tumor. We tested the hypothesis that the tumor mutational burden would predict the survival of patients with tumors bearing mIDH. Methods We analyzed the effect of tumor mutational burden on patients' survival using clinical and genomic data of 1199 glioma patients from The Cancer Genome Atlas and validated our results using the Glioma Longitudinal AnalySiS consortium. Results High tumor mutational burden negatively correlates with the survival of patients with LGG harboring mIDH (P = .005). This effect was significant for both Oligodendroglioma (LGG-mIDH-O; MS = 2379 vs 4459 days in high vs low, respectively; P = .005) and Astrocytoma (LGG-mIDH-A; MS = 2286 vs 4412 days in high vs low respectively; P = .005). There was no differential representation of frequently mutated genes (eg, TP53, ATRX, CIC, and FUBP) in either group. Gene set enrichment analysis revealed an enrichment in Gene Ontologies related to cell cycle, DNA-damage response in high versus low tumor mutational burden. Finally, we identified 6 gene sets that predict survival for LGG-mIDH-A and LGG-mIDH-O. Conclusions we demonstrate that tumor mutational burden is a powerful, robust, and clinically relevant prognostic factor of MS in mIDH patients.
Topological and thermoelectric properties of double antiperovskite pnictides.

Doubling the perovskite cell (double perovskite) has been found to open new possibilities for engineering functional materials, magnetic materials in particular. This route should be applicable to the antiperovskite (aPV) class. In the pnictide based double aPV (2aPV) class introduced here magnetism is very rare, and we address them as new topological materials, possibly with thermoelectric interest. We have found that the 2aPV supercell provides a systematically larger band gap that can serve to inhibit bulk conductivity, and also large spin-orbit coupling (SOC) for band inversion. We present examples from a broad study of double antiperovskites focusing on the X$_6$AA$'$B$_2$ configuration, where X is the alkaline earth element and A and B are the group 5A pnictogens. We find that an ``extended s'' state at the valence band minimum, described alternatively as a cation valence state or a modulated interstitial planewave state, plays a crucial role in both topological and thermoelectric properties. Several of these compounds may house topological phases, while transport calculations indicate they may also find themselves useful in thermoelectric applications.
CD4+ T cell expression of the IL-10 receptor is necessary for facial motoneuron survival after axotomy

After peripheral nerve transection, facial motoneuron (FMN) survival depends on an intact CD4+ T cell population and a central source of interleukin-10 (IL-10). However, it has not been determined previously whether CD4+ T cells participate in the central neuroprotective IL-10 cascade after facial nerve axotomy (FNA). Immunohistochemical labeling of CD4+ T cells, pontine vasculature, and central microglia was used to determine whether CD4+ T cells cross the blood-brain barrier and enter the facial motor nucleus (FMNuc) after FNA. The importance of IL-10 signaling in CD4+ T cells was assessed by performing adoptive transfer of IL-10 receptor beta (IL-10RB)-deficient CD4+ T cells into immunodeficient mice prior to injury. Histology and qPCR were utilized to determine the impact of IL-10RB-deficient T cells on FMN survival and central gene expression after FNA. Flow cytometry was used to determine whether IL-10 signaling in T cells was necessary for their differentiation into neuroprotective subsets. CD4+ T cells were capable of crossing the blood-brain barrier and associating with reactive microglial nodules in the axotomized FMNuc. Full induction of central IL-10R gene expression after FNA was dependent on CD4+ T cells, regardless of their own IL-10R signaling capability. Surprisingly, CD4+ T cells lacking IL-10RB were incapable of mediating neuroprotection after axotomy and promoted increased central expression of genes associated with microglial activation, antigen presentation, T cell co-stimulation, and complement deposition. There was reduced differentiation of IL-10RB-deficient CD4+ T cells into regulatory CD4+ T cells in vitro. These findings support the interdependence of IL-10- and CD4+ T cell-mediated mechanisms of neuroprotection after axotomy. CD4+ T cells may potentiate central responsiveness to IL-10, while IL-10 signaling within CD4+ T cells is necessary for their ability to rescue axotomized motoneuron survival. We propose that loss of IL-10 signaling in CD4+ T cells promotes non-neuroprotective autoimmunity after FNA.
Machine learning for multi-fidelity scale bridging and dynamical simulations of materials

Molecular dynamics (MD) is a powerful and popular tool for understanding the dynamical evolution of materials at the nano and mesoscopic scales. There are various flavors of MD ranging from the high fidelity albeit computationally expensive ab-initio MD to relatively lower fidelity but much more efficient classical MD such as atomistic and coarse-grained models. Each of these different flavors of MD have been independently used by materials scientists to bring about breakthroughs in materials discovery and design. A significant gulf exists between the various MD flavors, each having varying levels of fidelity. The accuracy of DFT or ab-initio MD is generally much higher than that of classical atomistic simulations which is higher than that of coarse-grained models. Multi-fidelity scale bridging to combine the accuracy and flexibility of ab-initio MD with efficiency classical MD has been a longstanding goal. The advent of big-data analytics has brought to the forefront powerful machine learning methods that can be deployed to achieve this goal. Here, we provide our perspective on the challenges in multi-fidelity scale bridging and trace the developments leading up to the use of machine learning algorithms and data-science towards addressing this grand challenge.
Flavor-specific enhancement of electronic cigarette liquid consumption and preference in mice

Abstract Background The use of electronic cigarettes has increased over the past decade. To determine how the abuse liability of electronic cigarette liquids (e-liquids) differs from nicotine alone, and to determine the impact of flavor, we compared nicotine-containing fruit- and tobacco-flavored e-liquids, and their nicotine-free versions, to nicotine alone in mouse models of oral consumption, reward and aversion. Methods Adult male C57BL/6 J mice voluntarily consumed oral nicotine, equivalent nicotine concentrations of fruit- and tobacco-flavored e-liquid, and equivalent dilutions of the nicotine-free versions in 2-bottle choice tests. Conditioned place preference and place aversion were assessed with peripherally administered e-liquids or nicotine. Serum nicotine and cotinine levels were measured after subcutaneous injections of e-liquid or nicotine. Results Mice showed higher consumption and preference for the fruit-flavored e-liquid compared with nicotine alone. This increase was not due to the flavor itself as consumption of the nicotine-free fruit-flavored e-liquid was not elevated until the highest concentration tested. The increased consumption and preference were not observed with the tobacco-flavored e-liquid. The conditioned place preference, place aversion and nicotine pharmacokinetics of the fruit-flavored e-liquid were not significantly different from nicotine alone. Conclusions Our data suggest that fruit, but not tobacco flavor, increased the oral consumption of e-liquid compared with nicotine alone. Moreover, this enhancement was not due to increased consumption of the flavor itself, altered rewarding or aversive properties after peripheral administration, or altered pharmacokinetics. This flavor-specific enhancement suggests that some flavors may lead to higher nicotine intake and increased use of e-liquids compared with nicotine alone.
Understanding population displacements on location-based call records using road data

Large population displacements are usually observed after nature disasters. The best approximations to real world population movement in such a short temporal scale are the users' movements patterns derived from the cell phone usage data. However, due to a lot of political, economic and privacy constraints, these sensitive data are not always available. On the other hand, population movements are usually observed on the underlying road network. The correlation between the cell phone users' movement and the road network has yet been examined. The aim of this research is to compare the topological structure and the network metrics of the road network to the cell phone users' movement network in Abidjan, Cote D'Ivoire, and to inspect the correlations of movement volume and road connectivity. A flooding scenario was assumed to inspect the responds from both the movement network and road network. Our analysis shows that the cellphone users' movement network and the road network present significant similarities in terms of network partition. Our research also indicates that the road topology could be utilized as a proxy to approximate the population movement volume on above. We present an initial step to help the data-scarce area understand the population movement pattern from more readily-available road network data. Furthermore, our results suggest that traditional evacuation planning should consider the social perspective of population connections and periodical movements
Value iteration networks

We introduce the value iteration network (VIN): a fully differentiable neural network with a 'planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.
Communication Cost Function for Parallel CFD Using Variable Time Stepping Algorithms

Publisher Summary Recent parallel computational fluid dynamics (CFD) algorithm development demonstrates that variable time-stepping approach can significantly reduce the computation and communication time requirement. The variable time-stepping algorithm means that different sizes of time-steps can be selected independently in different subdomains (blocks) for each time-step. The new communication cost function has been tested on an IBM SP computer. The test case showed that the cost function could predict the execution time of variable time step parallel CFD code. This chapter describes a new communication cost function for parallel CFD using variable time stepping algorithms. The experiments demonstrate the accuracy of the proposed communication cost function. Workstation networks are widely used for parallel CFD. A unique problem in parallel CFD is load balancing. This chapter focuses on dynamic load balancing for parallel CFD on a heterogeneous and multi-user environment for several years. The approach is to cut the problem domain into “n” blocks and distribute the blocks among “m” processors, where m < n. Computer load is balanced by distributing blocks among computers such that the maximum elapsed execution time of all blocks is minimized.
Efficiency and Large Deviations in Time-Asymmetric Stochastic Heat Engines

In a stochastic heat engine driven by a cyclic non-equilibrium protocol, fluctuations in work and heat give rise to a fluctuating efficiency. Using computer simulations and tools from large deviation theory, we have examined these fluctuations in detail for a model two-state engine. We find in general that the form of efficiency probability distributions is similar to those described by Verley et al (2014 Nat. Commun. 5 4721), in particular featuring a local minimum in the long-time limit. In contrast to the time-symmetric engine protocols studied previously, however, this minimum need not occur at the value characteristic of a reversible Carnot engine. Furthermore, while the local minimum may reside at the global minimum of a large deviation rate function, it does not generally correspond to the least likely efficiency measured over finite time. We introduce a general approximation for the finite-time efficiency distribution, , based on large deviation statistics of work and heat, that remains very accurate even when deviates significantly from its large deviation form.
A Biological Model of Unions

This paper applies principles from evolutionary biology to the study of unions. We show that unions that implement the preferred wage and organizing policies of workers will be displaced in evolutionary competition by unions that either extract less from firms, allowing them to live longer, or spend more on union organizing, or both. This implies that unions with constitutional incumbency advantages that allow leaders to depart from members' preferences may have a selective advantage, allowing them to grow at the expense of unions lacking such provisions. Evidence from the history of American unions supports these predictions. (
Fieller's Theorem

There are numerous environmental problems where the main quantity of interest is a ratio of two parameters, say ρ = θ1/θ2. Fieller's theorem is one technique for finding a confidence set for the ratio ρ.
Speculative Trade under Unawareness: The Infinite Case

We generalize the \No-trade" theorem for nite unawareness belief structures in Heifetz, Meier, and Schipper (2009) to the innite case.
The Elasticity of Taxable Income: Evidence and Implications

Abstract A central tax policy parameter that has recently received much attention, but about which there is substantial uncertainty, is the overall elasticity of taxable income. We provide new estimates of this elasticity which address identification problems with previous work, by exploiting a long panel of tax returns to study a series of tax reforms throughout the 1980s. This identification strategy also allows us to provide new evidence on both the income effects of tax changes on taxable income, and on variation in the elasticity of taxable income by income group. We find that the overall elasticity of taxable income is approximately 0.4; the elasticity of real income, not including tax preferences, is much lower. We estimate small income effects of tax changes on reported income, implying that the compensated and uncompensated elasticities of taxable income are very similar. We estimate that this overall elasticity is primarily due to a very elastic response of taxable income for taxpayers who have incomes above $100 000 per year, who have an elasticity of 0.57, while for those with incomes below $100 000 per year the elasticity is less than one-third as large. Moreover, high income taxpayers who itemize are particularly responsive to taxation. Our estimates suggest that optimal tax structures may feature tightly targeted transfers to lower income taxpayers and a flat or even declining marginal rate structure for middle and high income taxpayers.
Using experimental economics to measure the effects of a natural educational experiment on altruism

Abstract Economic research examining how educational intervention programs affect primary and secondary schooling focuses largely on test scores although the interventions can affect many other outcomes. This paper examines how an educational intervention, a voucher program, affected students' altruism. The voucher program used a lottery to allocate scholarships among low-income applicant families with children in K-8th grade. By exploiting the lottery to identify the voucher effects, and using experimental economic methods, we measure the effects of the intervention on children's altruism. We also measure the voucher program's effects on parents' altruism and several academic outcomes including test scores. We find that the educational intervention positively affects students' altruism towards charitable organizations but not towards their peers. We fail to find statistically significant effects of the vouchers on parents' altruism or test scores.
LDP-Fed: federated learning with local differential privacy

This paper presents LDP-Fed, a novel federated learning system with a formal privacy guarantee using local differential privacy (LDP). Existing LDP protocols are developed primarily to ensure data privacy in the collection of single numerical or categorical values, such as click count in Web access logs. However, in federated learning model parameter updates are collected iteratively from each participant and consist of high dimensional, continuous values with high precision (10s of digits after the decimal point), making existing LDP protocols inapplicable. To address this challenge in LDP-Fed, we design and develop two novel approaches. First, LDP-Fed's LDP Module provides a formal differential privacy guarantee for the repeated collection of model training parameters in the federated training of large-scale neural networks over multiple individual participants' private datasets. Second, LDP-Fed implements a suite of selection and filtering techniques for perturbing and sharing select parameter updates with the parameter server. We validate our system deployed with a condensed LDP protocol in training deep neural networks on public data. We compare this version of LDP-Fed, coined CLDP-Fed, with other state-of-the-art approaches with respect to model accuracy, privacy preservation, and system capabilities.
Tissue failure propagation as mediated by circulatory flow

Abstract Aging is driven by subcellular processes that are relatively well understood. However, the qualitative mechanisms and quantitative dynamics of how these micro-level failures cascade to a macro-level catastrophe in a tissue or organs remain largely unexplored. Here, we experimentally and theoretically study how cell failure propagates in an engineered tissue in the presence of advective flow. We argue that cells secrete cooperative factors, thereby forming a network of interdependence governed by diffusion and flow, which fails with a propagating front parallel to advective circulation.
Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders

Manifold-valued data naturally arises in medical imaging. In cognitive neuroscience for instance, brain connectomes base the analysis of coactivation patterns between different brain regions on the analysis of the correlations of their functional Magnetic Resonance Imaging (fMRI) time series – an object thus constrained by construction to belong to the manifold of symmetric positive definite matrices. One of the challenges that naturally arises in these studies consists in finding a lower-dimensional subspace for representing such manifold-valued and typically high-dimensional data. Traditional techniques, like principal component analysis, are ill-adapted to tackle non-Euclidean spaces and may fail to achieve a lower-dimensional representation of the data – thus potentially pointing to the absence of lower-dimensional representation of the data. However, these techniques are restricted in that: (i) they do not leverage the assumption that the connectomes belong on a pre-specified manifold, therefore discarding information; (ii) they can only fit a linear subspace to the data. In this paper, we are interested in variants to learn potentially highly curved submanifolds of manifold-valued data. Motivated by the brain connectomes example, we investigate a latent variable generative model, which has the added benefit of providing us with uncertainty estimates – a crucial quantity in the medical applications we are considering. While latent variable models have been proposed to learn linear and nonlinear spaces for Euclidean data, or geodesic subspaces for manifold data, no intrinsic latent variable model exists to learn non-geodesic subspaces for manifold data. This paper fills this gap and formulates a Riemannian variational autoencoder with an intrinsic generative model of manifold-valued data. We evaluate its performances on synthetic and real datasets, by introducing the formalism of weighted Riemannian submanifolds.
The macro response Monte Carlo method for electron transport

This thesis demonstrates the feasibility of basing dose calculations for electrons in radiotherapy on first-principles single scatterphysics, in a calculation time that is comparable to or better than current electron Monte Carlo methods. The macro response Monte Carlo (MRMC) method achieves run times that have potential to be much faster than conventional electron transport methods such as condensed history. The problem is broken down into two separate transport calculations. The first stage is a local, single scatter calculation, which generatesprobability distribution functions (PDFs) to describe the electron’s energy, position, and trajectory after leaving the local geometry, a small sphere or “kugel.” A number of local kugel calculations were run for calcium and carbon, creating a library of kugel data sets over a range of incident energies (0.25–8 MeV) and sizes (0.025 to 0.1 cm in radius). The second transport stage is a global calculation, in which steps that conform to the size of the kugels in the library are taken through the geometry of a phantom. For each step, the appropriate PDFs from the MRMC library are sampled to determine the electron’s new energy, position, and trajectory. The electron is immediately advanced to the end of the step and then another kugel is sampled. The process continues in this manner until transport is completed. The MRMC global stepping code was benchmarked as a series of subroutines inside of the Peregrine Monte Carlo code against EGS4 and MCNP for depth dose in simple phantoms having density inhomogeneities. The energy deposition algorithms for spreading dose across 5–10 transport zones per kugel were tested. Most resulting depth dose calculations were within 2%–3% of well-benchmarked Monte Carlo codes.
Simulations of spatially developing plane jets

A computational study has been performed of spatially evolving two- and three-dimensional planar jets using direct numerical simulation of the Navier Stokes equations. The basic structural differences between two-dimensional "weak" jets with a strong co-flow and "strong" jets with a small co-flow have been investigated, as well as the usefulness of these simulations in modelling the development of "real" three-dimensional jets. It was found that "weak" two-dimensional jets behave in a manner similar to spatially evolving shear layers, and are strongly influenced by the effects of inflow forcing. The slow downstream development of these simulations limits their usefullness in studying "real" jets. Twodimensional "strong" jet simulations led to a fundamentally different flow field than is observed experimentally for planar jets. This was a result of the dominance of a two-dimensional mechanism of vortex dipole ejection in the flow due to the lack of spanwise instabilities. Three-dimensional strong jet simulations showed good qualitative and quantitative agreement with experimental data. The rapid evolution of three-dimensi onality in these simulations effectively prevented the two-dimensional vortex dipole ejection, as expected from experimental data.
Plasticity, repeatability and phenotypic correlations of aerobic metabolic traits in a small estuarine fish.

Standard metabolic rate (SMR), maximum metabolic rate (MMR), absolute aerobic scope (AAS), and critical oxygen tension (Pcrit) were determined for the Gulf killifish, Fundulus grandis, an ecologically dominant estuarine fish, acclimated to lowered salinity, elevated temperature, and lowered oxygen concentration. Acclimation to low salinity resulted in a small, but significant, elevation of Pcrit (suggesting lower tolerance of hypoxia); acclimation to elevated temperature increased SMR, MMR, AAS, and Pcrit; acclimation to low oxygen led to a small increase in SMR, but substantial decreases in MMR, AAS, and Pcrit. Variation in these metabolic traits among individuals was consistent and repeatable when measured during multiple control exposures over seven months. Trait repeatability was unaffected by acclimation condition suggesting that repeatability of these traits is not context dependent. There were significant phenotypic correlations between specific metabolic traits: SMR was positively correlated with MMR and Pcrit; MMR was positively correlated with AAS; and AAS was negatively correlated with Pcrit. In general, within-individual variation contributed more than among-individual variation to these phenotypic correlations. The effects of acclimation on these traits demonstrate that aerobic metabolism is plastic and influenced by the conditions experienced by these fish in the dynamic habitats in which they occur; however, the repeatability of these traits and the correlations among them suggest that these traits change in ways that maintains the rank order of performance among individuals across a range of environmental variation.
Deformation theory of the blown-up Seiberg–Witten equation in dimension three

Associated with every quaternionic representation of a compact, connected Lie group there is a Seiberg–Witten equation in dimension three. The moduli spaces of solutions to these equations are typically non-compact. We construct Kuranishi models around boundary points of a partially compactified moduli space. The Haydys correspondence identifies such boundary points with Fueter sections—solutions of a non-linear Dirac equation—of the bundle of hyperkahler quotients associated with the quaternionic representation. We discuss when such a Fueter section can be deformed to a solution of the Seiberg–Witten equation.
Awake delta and theta-rhythmic hippocampal network modes during intermittent locomotor behaviors in the rat.

Delta-frequency network activity is commonly associated with sleep or behavioral disengagement accompanied by a dearth of cortical spiking, but delta in awake behaving animals is not well understood. We show that hippocampal (HC) synchronization in the delta frequency band (1-4 Hz) is related to animals' locomotor behavior using detailed analyses of the HC local field potential (LFP) and simultaneous head- and body-tracking data. In contrast to running-speed modulation of the theta rhythm (6-10 Hz), delta was most prominent when animals were stationary or moving slowly, that is, when theta and fast gamma (65-120 Hz) were weak, and often developed rapidly when animals paused briefly between runs. We next combined time-frequency decomposition of the LFP with hierarchical clustering algorithms to categorize momentary estimations of the power spectral density (PSD) into putative modes of HC activity. Delta and theta power were strikingly orthogonal across spectral modes, as well as across bouts of precisely defined running and stationary behavior. Delta-band and theta-band coherences between HC recording sites were monotonically related to theta-delta ratios across modes; and whereas theta coherence between HC and medial prefrontal cortex (mPFC) increased during running, delta-band coherence between mPFC and HC increased during stationary bouts. Taken together, our findings suggest that delta-dominated network modes (and corresponding mPFC-HC couplings) represent functionally distinct circuit dynamics that are temporally and behaviorally interspersed among theta-dominated modes during navigation. As such, delta modes could play a fundamental role in coordinating encoding and retrieval mechanisms or decision-making processes at a timescale that segments event sequences within behavioral episodes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
A Deep-Learning View of Chemical Space Designed to Facilitate Drug Discovery.

Drug discovery projects entail cycles of design, synthesis, and testing that yield a series of chemically related small molecules whose properties, such as binding affinity to a given target protei...
Arithmetic statistics on cubic surfaces

In this paper, we compute the distributions of various markings on smooth cubic surfaces defined over the finite field $$\mathbb {F}_q$$ , for example the distribution of pairs of points, ‘tritangents’ or ‘double sixes’. We also compute the (rational) cohomology of certain associated bundles and covers over complex numbers.
Asynchronous Byzantine Approximate Consensus in Directed Networks

This paper considers the problem of approximate consensus in directed asynchronous message-passing networks where some nodes may become Byzantine faulty. We obtain a tight necessary and sufficient condition on the underlying directed communication network for asynchronous Byzantine approximate consensus to be achievable. Interestingly, this condition coincides with the tight condition for synchronous Byzantine exact consensus. Our consensus algorithm may be viewed as a non-trivial generalization of an algorithm previously proposed for the special case of complete networks. The tight condition and techniques identified in the paper shed light on the fundamental properties for solving approximate consensus in asynchronous directed networks.
An $\widetilde\mathcal{O}(m/\varepsilon^3.5)$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints

We study semidefinite programs with diagonal constraints. This problem class appears in combinatorial optimization and has a wide range of engineering applications such as in circuit design, channel assignment in wireless networks, phase recovery, covariance matrix estimation, and low-order controller design. In this paper, we give an algorithm to solve this problem to $\varepsilon$-accuracy, with a run time of $\widetilde{\mathcal{O}}(m/\varepsilon^{3.5})$, where $m$ is the number of non-zero entries in the cost matrix. We improve upon the previous best run time of $\widetilde{\mathcal{O}}(m/\varepsilon^{4.5})$ by Arora and Kale. As a corollary of our result, given an instance of the Max-Cut problem with $n$ vertices and $m \gg n$ edges, our algorithm when applied to the standard SDP relaxation of Max-Cut returns a $(1 - \varepsilon)\alpha_{GW}$ cut in time $\widetilde{\mathcal{O}}(m/\varepsilon^{3.5})$, where $\alpha_{GW} \approx 0.878567$ is the Goemans-Williamson approximation ratio. We obtain this run time via the stochastic variance reduction framework applied to the Arora-Kale algorithm, by constructing a constant-accuracy estimator to maintain the primal iterates.
Epigastric Abdominal Pain and Weight Loss in a 17-year-old Male

Introduction: Takayasu arteritis is an idiopathic, chronic, large vessel arteritis involving the aorta and its primary branches. Although the disease has a worldwide distribution, it has a high prevalence in Japan (40 per million) and a low prevalence in the United States (0.9 per million). Our patient, an African-American teenage male with TA, is exceedingly rare. Case Report: A 17-year-old African-American male with no significant past medical history presented to the Emergency Department with one month of epigastric abdominal pain and a 20-pound weight loss. The pain was dull, without …