cssi/SciSciGPT-SciSciNet huggingface dataset:
Wave Propagation for Reaction-Diffusion Equations on Infinite Random Trees

The asymptotic wave speed for FKPP type reaction-diffusion equations on a class of infinite random metric trees are considered. We show that a travelling wavefront emerges, provided that the reaction rate is large enough. The wavefront travels at a speed that can be quantified via a variational formula involving the random branching degrees $$\vec {d}$$ and the random branch lengths $$\vec {\ell }$$ of the tree $$\mathbb {T}_{\vec {d}, \vec {\ell }}$$ . This speed is slower than that of the same equation on the real line $$\mathbb {R}$$ , and we estimate this slow down in terms of $$\vec {d}$$ and $$\vec {\ell }$$ . The key idea is to project the Brownian motion on the tree onto a one-dimensional axis along the direction of the wave propagation. The projected process is a multi-skewed Brownian motion, introduced by Ramirez [31], with skewness and interface sets that encode the metric structure $$(\vec {d}, \vec {\ell })$$ of the tree. Combined with analytic arguments based on the Feynman-Kac formula, this idea connects our analysis of the wavefront propagation to the large deviations principle (LDP) of the multi-skewed Brownian motion with random skewness and random interface set. Our LDP analysis involves delicate estimates for an infinite product of $$2\times 2$$ random matrices parametrized by $$\vec {d}$$ and $$\vec {\ell }$$ and for hitting times of a random walk in random environment.
Center-of-mass ambiguity for Bondi–Metzner–Sachs charges

Dray and Streubel proposed a definition of angular momentum in general relativity based on `Bondi-Metzner-Sachs (BMS) charges'. I show here that the natural definition of center of mass in this program has an infinite-dimensional ambiguity. (This seems not to have been noticed before because previous work has tacitly carried over a special-relativistic assumption.) A related point is that the natural definition of spin in this context is translation-, but not supertranslation-, invariant.
Brand as Promise

Brands are widely regarded as a constellation of shared associations surrounding a company and its offerings. On the traditional view of brands, these associations are regarded as perceptions and attitudes in consumers’ minds in relation to a company. We argue that this traditional framing of brands faces an explanatory problem: the inability to satisfactorily explain why certain branding activism initiatives elicit the moralized reactive attitudes that are paradigmatic responses to wrongdoing. In this paper, we argue for a reframing of brands that calls for viewing brands as a series of normatively binding expectations that are ethically akin to promises. Our promissory framing of brands avoids the explanatory problem, illuminates a number of ethical requirements on branding, and reconceptualizes the role of brand managers.
Social Contact Frequency and Pain among Older Adults with HIV: An Ecological Momentary Assessment Study

The purpose of the present study was to examine the relationship between social contact frequency and pain, and the role of negative and positive affect in this relationship, among older adults with HIV in real-time, real world settings using ecological momentary assessment (EMA). A total of 66 (Mage=59.3, SD=6.3) older adults with HIV completed EMA surveys that included social contact frequency, pain level, and negative and positive affect four times per day for two weeks. Mixed-effects regression models were used to examine concurrent and lagged associations between social contact frequency, pain, and negative and positive affect. Greater recent social contract frequency was associated with less severe current pain (B=-0.04, SE=0.02, p
Multi-cover Inequalities for Totally-Ordered Multiple Knapsack Sets

We propose a method to generate cutting-planes from multiple covers of knapsack constraints. The covers may come from different knapsack inequalities if the weights in the inequalities form a totally-ordered set. Thus, we introduce and study the structure of a totally-ordered multiple knapsack set. The valid multi-cover inequalities we derive for its convex hull have a number of interesting properties. First, they generalize the well-known (1, k)-configuration inequalities. Second, they are not aggregation cuts. Third, they cannot be generated as a rank-1 Chvatal-Gomory cut from the inequality system consisting of the knapsack constraints and all their minimal covers. Finally, we provide conditions under which the inequalities are facet-defining for the convex hull of the totally-ordered knapsack set.
Toward Better Generalization Bounds with Locally Elastic Stability

Classical approaches in learning theory are often seen to yield very loose generalization bounds for deep neural networks. Using the example of "stability and generalization" \citep{bousquet2002stability}, however, we demonstrate that generalization bounds can be significantly improved by taking into account refined characteristics of modern neural networks. Specifically, this paper proposes a new notion of algorithmic stability termed \textit{locally elastic stability} in light of a certain phenomenon in the training of neural networks \citep{he2020local}. We prove that locally elastic stability implies a tighter generalization bound than that derived based on uniform stability in many situations. When applied to deep neural networks, our new generalization bound attaches much more meaningful confidence statements to the performance on unseen data than existing algorithmic stability notions, thereby shedding light on the effectiveness of modern neural networks in real-world applications.
Vagus Nerve and Stomach Synucleinopathy in Parkinson's Disease, Incidental Lewy Body Disease, and Normal Elderly Subjects: Evidence Against the "Body-First" Hypothesis.

Background Braak and others have proposed that Lewy-type α-synucleinopathy in Parkinson's disease (PD) may arise from an exogenous pathogen that passes across the gastric mucosa and then is retrogradely transported up the vagus nerve to the medulla. Objective We tested this hypothesis by immunohistochemically staining, with a method specific for p-serine 129 α-synuclein (pSyn), stomach and vagus nerve tissue from an autopsy series of 111 normal elderly subjects, 33 with incidental Lewy body disease (ILBD) and 53 with PD. Methods Vagus nerve samples were taken adjacent to the carotid artery in the neck. Stomach samples were taken from the gastric body, midway along the greater curvature. Formalin-fixed paraffin-embedded sections were immunohistochemically stained for pSyn, shown to be highly specific and sensitive for α-synuclein pathology. Results Median disease duration for the PD group was 13 years. In the vagus nerve none of the 111 normal subjects had pSyn in the vagus, while 12/26 ILBD (46%) and 32/36 PD (89%) subjects were pSyn-positive. In the stomach none of the 102 normal subjects had pSyn while 5/30 (17%) ILBD and 42/52 (81%) of PD subjects were pSyn-positive. Conclusion As there was no pSyn in the vagus nerve or stomach of subjects without brain pSyn, these results support initiation of pSyn in the brain. The presence of pSyn in the vagus nerve and stomach of a subset of ILBD cases indicates that synucleinopathy within the peripheral nervous system may occur, within a subset of individuals, at preclinical stages of Lewy body disease.
Subset Node Representation Learning over Large Dynamic Graphs

Dynamic graph representation learning is a task to learn node embeddings over dynamic networks, and has many important applications, including knowledge graphs, citation networks to social networks. Graphs of this type are usually large-scale but only a small subset of vertices are related in downstream tasks. Current methods are too expensive to this setting as the complexity is at best linear-dependent on both the number of nodes and edges. In this paper, we propose a new method, namely Dynamic Personalized PageRank Embedding (DynamicPPE) for learning a target subset of node representations over large-scale dynamic networks. Based on recent advances in local node embedding and a novel computation of dynamic personalized PageRank vector (PPV), DynamicPPE has two key ingredients: 1) the per-PPV complexity is O (m d / e) where m, d, and e are the number of edges received, average degree, global precision error respectively. Thus, the per-edge event update of a single node is only dependent on d in average; and 2) by using these high quality PPVs and hash kernels, the learned embeddings have properties of both locality and global consistency. These two make it possible to capture the evolution of graph structure effectively. Experimental results demonstrate both the effectiveness and efficiency of the proposed method over large-scale dynamic networks. We apply DynamicPPE to capture the embedding change of Chinese cities in the Wikipedia graph during this ongoing COVID-19 pandemic. https://en.wikipedia.org/wiki/COVID-19_pandemic. Our results show that these representations successfully encode the dynamics of the Wikipedia graph.
Understanding Instance-Level Label Noise: Disparate Impacts and Treatments

This paper aims to provide understandings for the effect of an over-parameterized model, e.g. a deep neural network, memorizing instance-dependent noisy labels. We first quantify the harms caused by memorizing noisy instances, and show the disparate impacts of noisy labels for sample instances with different representation frequencies. We then analyze how several popular solutions for learning with noisy labels mitigate this harm at the instance level. Our analysis reveals that existing approaches lead to disparate treatments when handling noisy instances. While higher-frequency instances often enjoy a high probability of an improvement by applying these solutions, lower-frequency instances do not. Our analysis reveals new understandings for when these approaches work, and provides theoretical justifications for previously reported empirical observations. This observation requires us to rethink the distribution of label noise across instances and calls for different treatments for instances in different regimes.
How Do On-demand Ridesharing Services Affect Traffic Congestion? The Moderating Role of Urban Compactness

Sharing economy platform, which leverages information technology (IT) to re-distribute unused or underutilized assets to people who are willing to pay for the services, has received tremendous attention in the last few years. Its creative business models have disrupted many traditional industries (e.g., transportation, hotel) by fundamentally changing the mechanism to match demand with supply in real time. In this research, we investigate how Uber, a peer-to-peer mobile ride-sharing platform, affects traffic congestion and environment (carbon emissions) in the urban areas of the United States. Leveraging a unique data set combining data from Uber and the Urban Mobility Report, we examine whether the entry of Uber car services affects traffic congestion using a difference-in-difference framework. Our findings provide empirical evidence that ride-sharing services such as Uber significantly decrease the traffic congestion after entering an urban area. We perform further analysis including the use of instrumental variables, alternative measures, a relative time model using more granular data to assess the robustness of the results. A few plausible underlining mechanisms are discussed to help explain our findings.
Sequential Late Fusion Technique for Multi-modal Sentiment Analysis

Multi-modal sentiment analysis plays an important role for providing better interactive experiences to users. Each modality in multi-modal data can provide different viewpoints or reveal unique aspects of a user’s emotional state. In this work, we use text, audio and visual modalities from MOSI dataset and we propose a novel fusion technique using a multi-head attention LSTM network. Finally, we perform a classification task and evaluate its performance.
Cospectral constructions for several graph matrices using cousin vertices

Graphs can be associated with a matrix according to some rule and we can find the spectrum of a graph with respect to that matrix. Two graphs are cospectral if they have the same spectrum. Constructions of cospectral graphs help us establish patterns about structural information not preserved by the spectrum. We generalize a construction for cospectral graphs previously given for the distance Laplacian matrix to a larger family of graphs. In addition, we show that with appropriate assumptions this generalized construction extends to the adjacency matrix, combinatorial Laplacian matrix, signless Laplacian matrix, normalized Laplacian matrix, and distance matrix.
Simulation Studies and the Design of the First GARP Global Experiment

Simulation studies have played an increasingly important role in planning for the First GARP Global Experiment (FGGE) during the last two years by providing information on the meteorological impact of various untested or incompletely tested elements of the GARP observing system. The studies are intended to provide information to FGGE planners on the capabilities of alternature systems for meeting a specific FGGE requirement. The simulation studies also provide a valuable input to cost-effectiveness studies by indicating tradeoffs of one possible type of observing platform against another, such as constant-level balloons vs buoys. These tradeoffs can lead to major gains in research yield per dollar of expenditure on the observing system.
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding

Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g. image classification, segmentation, object detection), these datasets are hardly suitable for post disaster damage assessments. On the other hand, existing natural disaster datasets include mainly satellite imagery which has low spatial resolution and a high revisit period. Therefore, they do not have a scope to provide quick and efficient damage assessment tasks. Unmanned Aerial Vehicle (UAV) can effortlessly access difficult places during any disaster and collect high resolution imagery that is required for aforementioned tasks of computer vision. To address these issues we present a high resolution UAV imagery, FloodNet, captured after the hurricane Harvey. This dataset demonstrates the post flooded damages of the affected areas. The images are labeled pixel-wise for semantic segmentation task and questions are produced for the task of visual question answering. FloodNet poses several challenges including detection of flooded roads and buildings and distinguishing between natural water and flooded water. With the advancement of deep learning algorithms, we can analyze the impact of any disaster which can make a precise understanding of the affected areas. In this paper, we compare and contrast the performances of baseline methods for image classification, semantic segmentation, and visual question answering on our dataset. FloodNet dataset can be downloaded from here: https://github.com/BinaLab/FloodNet-Supervised_v1.0 .
Engine-Over-the-Wing Noise Research

Acoustic measurements for large model eingine-over-the-wing (EOW) research configurations having both conventional and powered lift applications were taken for flap positions typical of takeoff and approach and at locations simulating flyover and sideline. The results indicate that the noise is shielded by the wing and redirected above it, making the EOW concept a prime contender for quiet aircraft. The large-scale noise data are in agreement with earlier small-model results. Below the wing, the EOW configuration is about 10 PNdb quieter than the engine-under-the-wing externally-blown-flap for powered lift, and up to 10 db quieter than the nozzle alone at high frequencies for conventional lift applications.
Optimal design of membrane cascades for gaseous and liquid mixtures via MINLP

Abstract Given the growing concern of reducing CO2 emissions, it is desirable to identify, for a given separation carried out through a membrane cascade, the optimum design that yields the lowest power demand. Nevertheless, designing a membrane cascade is challenging since, there are often multiple feasible configurations that differ in their energy consumption and cost. In this work, we develop a Mixed Integer Non-linear Program (MINLP) that, for a given binary separation, which may be either liquid or gaseous, finds the cascade and its operating conditions that minimize power requirement. To model the separation at each membrane in the cascade, we utilize the analytical solution of a system of differential and algebraic equations derived from the crossflow model and the solution–diffusion theory. We provide numerical evidence which shows that our single-stage membrane model accurately predicts experimental data. The resulting membrane model is non-convex and, even state-of-the-art solvers struggle to prove global optimality of the cascades and the operating conditions identified. In this paper, we derive various cuts that help with relaxation quality and, consequently, accelerate convergence of branch-and-bound based solvers. More specifically, we demonstrate, on various examples, that our cuts help branch-and-bound solvers converge within 5% optimality gap in a reasonable amount of time and such a tolerance level was not achieved by a simple formulation of the membrane model. The proposed optimization model is an easy-to-use tool for practitioners and researchers to design energy efficient membrane cascades.
A Byzantine Fault-Tolerant Consensus Library for Hyperledger Fabric

Hyperledger Fabric is an enterprise grade permissioned distributed ledger platform that offers modularity for a broad set of industry use cases. One modular component is a pluggable ordering service that establishes consensus on the order of transactions and batches them into blocks. However, as of the time of this writing, there is no production grade Byzantine Fault-Tolerant (BFT) ordering service for Fabric, with the latest version (v2.3) supporting only Crash Fault-Tolerance (CFT).In this work we describe the design and implementation of a BFT ordering service for Fabric, employing a new BFT consensus library. The new library, based on the BFT-SMART protocol and written in Go, is tailored to the blockchain use-case, yet is general enough to cater to a wide variety of other uses. The BFT library’s design and integration into Fabric address crucial aspects that were left unsolved in all prior work, making them unfit for production use. We evaluate the new BFT ordering service by comparing it with the currently supported Raft-based CFT ordering service in Hyperledger Fabric.
Defined diets for freshwater planarians.

Background Planarian flatworms are popular invertebrate models for basic research on stem cell biology and regeneration. These animals are commonly maintained on a diet of homogenized calf liver or boiled egg yolk in the laboratory, introducing a source of uncontrolled experimental variability. Results Here, we report the development of defined diets, prepared entirely from standardized, commercially sourced ingredients, for the freshwater species Schmidtea mediterranea, Dugesia japonica, and Girardia dorotocephala. These food sources provide an opportunity to test the effects of specific nutritional variables on biological phenomena of interest. Defined diet consumption was not sufficient for growth and only partially induced the increase in stem cell division that normally accompanies feeding, suggesting these responses are not solely determined by caloric intake. Our defined diet formulations enable delivery of double-stranded RNA for gene knockdown in a manner that provides unique advantages in some experimental contexts. We also present a new approach for preserving tissue integrity during hydrogen peroxide bleaching of liver-fed animals. Conclusions These tools will empower research on the connections between diet, metabolism, and stem cell biology in the experimentally tractable planarian system. This article is protected by copyright. All rights reserved.
On Modeling Human Perceptions of Allocation Policies with Uncertain Outcomes

Many policies allocate harms or benefits that are uncertain in nature: they produce distributions over the population in which individuals have different probabilities of incurring harm or benefit. Comparing different policies thus involves a comparison of their corresponding probability distributions, and we observe that in many instances the policies selected in practice are hard to explain by preferences based only on the expected value of the total harm or benefit they produce. In cases where the expected value analysis is not a sufficient explanatory framework, what would be a reasonable model for societal preferences over these distributions? Here we investigate explanations based on the framework of probability weighting from the behavioral sciences, which over several decades has identified systematic biases in how people perceive probabilities. We show that probability weighting can be used to make predictions about preferences over probabilistic distributions of harm and benefit that function quite differently from expected-value analysis, and in a number of cases provide potential explanations for policy preferences that appear hard to motivate by other means. In particular, we identify optimal policies for minimizing perceived total harm and maximizing perceived total benefit that take the distorting effects of probability weighting into account, and we discuss a number of real-world policies that resemble such allocational strategies. Our analysis does not provide specific recommendations for policy choices, but is instead fundamentally interpretive in nature, seeking to describe observed phenomena in policy choices.
Maximum Likelihood Estimation of Translational Acceleration Derivatives from Flight Data

This paper shows that translational acceleration derivatives, such as pitching moment due to rate of change of angle of attack, can be estimated from flight data with the use of appropriately designed maneuvers. No new development of estimation methodology is necessary to analyze these maneuvers. Flight data from a T-37B airplane were used to verify that rate of change of angle of attack could be estimated from rolling maneuvers.
Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning with Global-Local Policy Search

In this paper, we develop a grid-interactive multi-zone building controller based on a deep reinforcement learning (RL) approach. The controller is designed to facilitate building operation during normal conditions and demand response events, while ensuring occupants comfort and energy efficiency. We leverage a continuous action space RL formulation, and devise a two-stage global-local RL training framework. In the first stage, a global fast policy search is performed using a gradient-free RL algorithm. In the second stage, a local fine-tuning is conducted using a policy gradient method. In contrast to the state-of-the-art model predictive control (MPC) approach, the proposed RL controller does not require complex computation during real-time operation and can adapt to nonlinear building models. We illustrate the controller performance numerically using a five-zone commercial building.
LWC and temperature effects on ice accretion formation on swept wings at glaze ice conditions

An experiment was conducted to study the effect of liquid water content and temperature on the critical distance in ice accretion formation on swept wings at glaze ice conditions. The critical distance is defined as the distance from the attachment line to tile beginning of the zone where roughness elements develop into glaze ice feathers. A baseline case of 150 mph, 25 F, 0.75 g/cu m. Cloud Liquid Water Content (LWC) and 20 micrometers in Water Droplet Median Volume Diameter (MVD) was chosen. Icing runs were performed on a NACA 0012 swept wing tip at 150 mph and MVD of 20 micrometers for liquid water contents of 0.5 g/cu m, 0.75 g/cu m, and 1.0 g/cu m, and for total temperatures of 20 F, 25 F and 30 F. At each tunnel condition, the sweep angle was changed from 0 deg to 45 deg in 5 deg increments. Casting data, ice shape tracings, and close-up photographic data were obtained. The results showed that decreasing the LWC to 0.5 g/cu m decreases the value of the critical distance at a given sweep angle compared to the baseline case, and starts the formation of complete scallops at 30 sweep angle. Increasing the LWC to 1.0 g/cu m increases the value of the critical distance compared to the baseline case, the critical distance remains always above 0 millimeters and complete scallops are not formed. Decreasing the total temperature to 20 F decreases the critical distance with respect to the baseline case and formation of complete scallops begins at 25 deg sweep angle. When the total temperature is increased to 30 F, bumps covered with roughness elements appear on the ice accretion at 25 deg and 30 deg sweep angles, large ice structures appear at 35 deg and 40 deg sweep angles, and complete scallops are formed at 45 deg sweep angle.
Influencers and the Giant Component: The Fundamental Hardness in Privacy Protection for Socially Contagious Attributes.

The presence of correlation is known to make privacy protection more difficult. We investigate the privacy of socially contagious attributes on a network of individuals, where each individual possessing that attribute may influence a number of others into adopting it. We show that for contagions following the Independent Cascade model there exists a giant connected component of infected nodes, containing a constant fraction of all the nodes who all receive the contagion from the same set of sources. We further show that it is extremely hard to hide the existence of this giant connected component if we want to obtain an estimate of the activated users at an acceptable level. Moreover, an adversary possessing this knowledge can predict the real status ("active" or "inactive") with decent probability for many of the individuals regardless of the privacy (perturbation) mechanism used. As a case study, we show that the Wasserstein mechanism, a state-of-the-art privacy mechanism designed specifically for correlated data, introduces a noise with magnitude of order $\Omega(n)$ in the count estimation in our setting. We provide theoretical guarantees for two classes of random networks: Erdos Renyi graphs and Chung-Lu power-law graphs under the Independent Cascade model. Experiments demonstrate that a giant connected component of infected nodes can and does appear in real-world networks and that a simple inference attack can reveal the status of a good fraction of nodes.
A sequence-based method for predicting extant fold switchers that undergo α-helix ↔ β-strand transitions.

Extant fold-switching proteins remodel their secondary structures and change their functions in response to cellular stimuli, regulating biological processes and affecting human health. Despite their biological importance, these proteins remain understudied. Predictive methods are needed to expedite the process of discovering and characterizing more of these shapeshifting proteins. Most previous approaches require a solved structure or all-atom simulations, greatly constraining their use. Here, we propose a high-throughput sequence-based method for predicting extant fold switchers that transition from α-helix in one conformation to β-strand in the other. This method leverages two previous observations: (a) α-helix ↔ β-strand prediction discrepancies from JPred4 are a robust predictor of fold switching, and (b) the fold-switching regions (FSRs) of some extant fold switchers have different secondary structure propensities when expressed by themselves (isolated FSRs) than when expressed within the context of their parent protein (contextualized FSRs). Combining these two observations, we ran JPred4 on 99-fold-switching proteins and found strong correspondence between predicted and experimentally observed α-helix ↔ β-strand discrepancies. To test the overall robustness of this finding, we randomly selected regions of proteins not expected to switch folds (single-fold proteins) and found significantly fewer predicted α-helix ↔ β-strand discrepancies. Combining these discrepancies with the overall percentage of predicted secondary structure, we developed a classifier to identify extant fold switchers (Matthews correlation coefficient of .71). Although this classifier had a high false-negative rate (7/17), its false-positive rate was very low (2/136), suggesting that it can be used to predict a subset of extant fold switchers from a multitude of available genomic sequences.
Influence of visual feedback persistence on visuo-motor skill improvement

Towards the larger goal of understanding factors relevant for improving visuo-motor control, we investigated the role of visual feedback for modulating the effectiveness of a simple hand-eye training protocol. The regimen comprised a series of curve tracing tasks undertaken over a period of one week by neurologically healthy individuals with their non-dominant hands. Our three subject groups differed in the training they experienced: those who received 'Persistent' visual-feedback by seeing their hand and trace evolve in real-time superimposed upon the reference patterns, those who received 'Non-Persistent' visual-feedback seeing their hand movement but not the emerging trace, and a 'Control' group that underwent no training. Improvements in performance were evaluated along two dimensions-accuracy and steadiness, to assess visuo-motor and motor skills, respectively. We found that persistent feedback leads to a significantly greater improvement in accuracy than non-persistent feedback. Steadiness, on the other hand, benefits from training irrespective of the persistence of feedback. Our results not only demonstrate the feasibility of rapid visuo-motor learning in adulthood, but more specifically, the influence of visual veridicality and a critical role for dynamically emergent visual information.
Timely Broadcasting in Erasure Networks: Age-Rate Tradeoffs

The interplay between timeliness and rate efficiency is investigated in packet erasure broadcast channels with feedback. A scheduling framework is proposed in which coding actions, as opposed to users, are scheduled to attain desired tradeoffs between rate and age of information (AoI). This tradeoff is formalized by an upper bound on AoI as a function of the target rate constraints and two lower bounds: one as a function of the communication rate and one as a function of the arrival rate. Simulation results show that (i) surprisingly, coding can be beneficial in reducing AoI in the regime of moderate arrival rates even without rate constraints and the benefit increases with the number of users, and (ii) AoI increases with both the target rate constraint and the arrival rate when either is kept fixed, but decreases with them when they are set to be equal.
Land use mix and leukocyte telomere length in Mexican Americans.

It has been well-known that built environment features influence the risk of chronic diseases. However, the existing data of its relationship with telomere length, a biomarker of biological aging, is still limited, with no study available for Mexican Americans. This study investigates the relationship between several factors of the built environment with leukocyte telomere length among 5508 Mexican American adults enrolled in Mano-A-Mano, the Mexican American Cohort Study (MACS). Based on the quartile levels of telomere length, the study population was categorized into four groups, from the lowest (1st quartile) to the highest telomere length group (4th quartile). For individual built environment factors, their levels did not differ significantly across four groups. However, in the multinominal logistic regression analysis, increased Rundle’s land use mixture (LUM) and Frank’s LUM were found statistically significantly associated with increased odds of having high levels of telomere length (Rundle’s LUM: 2nd quartile: Odds ratio (OR) 1.26, 95% Confidence interval (CI) 1.07, 1.48; 3rd quartile: OR 1.25, 95% CI 1.06, 1.46; 4th quartile: OR 1.19, 95% CI 1.01, 1.41; Frank’s LUM: 2nd quartile: OR 1.34, 95% CI 1.02, 2.63; 3rd quartile: OR 1.55, 95% CI 1.04, 2.91; 4th quartile: OR 1.36, 95% CI 1.05, 2.72, respectively). The associations for Rundle’s LUM remained significant after further adjusting other non-redundant built environment factors. Finally, in stratified analysis, we found the association between Rundle’s LUM and telomere length was more evident among younger individuals (< 38 years old), women, and those with obesity, born in Mexico, having low levels of physical activity, and having low levels of acculturation than their relative counterparts. In summary, our results indicate that land use mixture may impact telomere length in leukocytes in Mexican Americans.
Non-abelian bosonization in two and three spatial dimensions and applications

Abstract In this paper, we generalize Witten's non-abelian bosonization in ( 1 + 1 ) -D to two and three spatial dimensions. Our theory applies to fermions with relativistic dispersion. The bosonized theories are non-linear sigma models with level-1 Wess-Zumino-Witten terms. We apply the bosonization results to the S U ( 2 ) gauge theory of the π-flux phase, critical spin liquids in 1,2,3 spatial dimensions, and twisted bilayer graphene.
The gendered implications of energy gaps in health care: A comparative analysis of Haiti, Senegal, and the Democratic Republic of Congo

Delivering quality primary health care requires reliable energy access. In rural health facilities, electricity is often unreliable or absent. Low energy access has a gendered impact, affecting the...
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping

We propose a framework for tightly-coupled lidar-visual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high accuracy and robustness. LVI-SAM is built atop a factor graph and is composed of two sub-systems: a visual-inertial system (VIS) and a lidar-inertial system (LIS). The two sub-systems are designed in a tightly-coupled manner, in which the VIS leverages LIS estimation to facilitate initialization. The accuracy of the VIS is improved by extracting depth information for visual features using lidar measurements. In turn, the LIS utilizes VIS estimation for initial guesses to support scan-matching. Loop closures are first identified by the VIS and further refined by the LIS. LVI-SAM can also function when one of the two sub-systems fails, which increases its robustness in both texture-less and feature-less environments. LVI-SAM is extensively evaluated on datasets gathered from several platforms over a variety of scales and environments. Our implementation is available at https://git.io/lvi-sam.
The Star Formation History of a Post-starburst Galaxy Determined from Its Cluster Population

The recent star formation histories (SFHs) of post-starburst galaxies have been determined almost exclusively from detailed modeling of their composite star light. This has provided important but limited information on the number, strength, and duration of bursts of star formation. In this work, we present a direct and independent measure of the recent SFH of S12 (plate-mjd-fiber for SDSS 623-52051-207; designated EAS12 in Smercina et al. 2018) from its star cluster population. We detect clusters from high resolution, $UBR$ optical observations from HST, and compare their luminosities and colors with stellar population models to estimate the ages and masses of the clusters. No clusters younger than $\sim$70 Myr are found, indicating star formation shut off at this time. Clusters formed $\sim$120 Myr ago reach masses up to $\sim \mbox{few}\times10^7~M_{\odot}$, several times higher than similar age counterparts formed in actively merging galaxies like the Antennae and NGC 3256. We develop a new calibration based on known properties for 8 nearby galaxies to estimate the star formation rate (SFR) of a galaxy from the mass of the most massive cluster, $M_{\rm max}$. The cluster population indicates that S12 experienced an extremely intense but short-lived burst $\sim$120 Myr ago, with an estimated peak of $500^{+500}_{-250}~M_{\odot}~\mbox{yr}^{-1}$ and duration of $50\pm25$ Myr, one of the highest SFRs estimated for any galaxy in the nearby universe. Prior to the recent, intense burst, S12 was forming stars at a moderate rate of $\sim 3{-}5~M_{\odot}~\mbox{yr}^{-1}$, typical of spiral galaxies. However, the system also experienced an earlier burst approximately $1{-}3$ Gyr ago. While fairly uncertain, we estimate that the SFR during this earlier burst was $\sim20{-}30~M_{\odot}~\mbox{yr}^{-1}$, similar to the current SFR in the Antennae and NGC 3256.
Resting-state network topology characterizing callous-unemotional traits in adolescence

Abstract Background Callous-unemotional (CU) traits, a youth antisocial phenotype, are hypothesized to associate with aberrant connectivity (dis-integration) across the salience (SAL), default mode (DMN), and frontoparietal (FPN) networks. However, CU traits have a heterogeneous presentation and previous research has not modeled individual heterogeneity in resting-state connectivity amongst adolescents with CU traits. The present study models individual-specific network maps and examines topological features of individual and subgroup maps in relation to CU traits. Methods Participants aged 13-17 (n=84, male = 55%, female = 45%) completed resting-state functional connectivity and the inventory of callous-unemotional traits as part of the Nathan Klein Rockland study. A sparse network approach (GIMME) was used to derive individual-level and subgroup maps of all participants. We then examined heterogeneous network features, including positive and negative connection density, associated with CU traits. Results Higher rates of CU traits increased probability of inclusion in one subgroup, which had the highest mean level of CU traits. Analysis of network features reveals less density (positive and negative) within the FPN and greater density between DMN-FPN associated with CU traits. Discussion Findings indicate heterogeneous person-specific connections and some subgroup connections amongst adolescents associate with CU traits. Higher CU traits associate with lower density in the FPN, which has been associated with attention and inhibition, and higher density between the DMN-FPN, which have been linked with cognitive control, social working memory, and empathy. Our findings suggest less efficiency in FPN function which, when considered mechanistically, could result in difficulty suppressing DMN when task positive networks are engaged. This is an area for further exploration but could explain cognitive and socio-affective impairments in CU traits.
Convex-Concave Min-Max Stackelberg Games.

Min-max optimization problems (i.e., min-max games) have been attracting a great deal of attention because of their applicability to a wide range of machine learning problems. Although significant progress has been made recently, the literature to date has focused on games with independent strategy sets; little is known about solving games with dependent strategy sets, which can be characterized as min-max Stackelberg games. We introduce two first-order methods that solve a large class of convex-concave min-max Stackelberg games, and show that our methods converge in polynomial time. Min-max Stackelberg games were first studied by Wald, under the posthumous name of Wald's maximin model, a variant of which is the main paradigm used in robust optimization, which means that our methods can likewise solve many convex robust optimization problems. We observe that the computation of competitive equilibria in Fisher markets also comprises a min-max Stackelberg game. Further, we demonstrate the efficacy and efficiency of our algorithms in practice by computing competitive equilibria in Fisher markets with varying utility structures. Our experiments suggest potential ways to extend our theoretical results, by demonstrating how different smoothness properties can affect the convergence rate of our algorithms.
Winner Takes All? Tech Clusters, Population Centers, and the Spatial Transformation of U.S. Invention

Abstract U.S. invention has become increasingly concentrated around major tech centers since the 1970s, with implications for how much cities across the country share in concomitant local benefits. Is invention becoming a winner-takes-all race? We explore the rising spatial concentration of patents and identify an underlying stability in their distribution. Software patents have exploded to account for about half of patents today, and these patents are highly concentrated in tech centers. Tech centers also account for a growing share of non-software patents, but the reallocation, by contrast, is entirely from the five largest population centers in 1980. Non-software patenting is stable for most cities, with anchor tenants like universities playing important roles, suggesting the growing concentration of invention may be nearing its end. Immigrant inventors and new businesses aided in the spatial transformation. One Sentence Summary The growing concentration of patenting in tech centers masks an important stability in non-software patenting for most U.S. cities.
The M2 Gene Is a Determinant of Reovirus-Induced Myocarditis.

Although a broad range of viruses cause myocarditis, the mechanisms that underlie viral myocarditis are poorly understood. Here, we report that the M2 gene is a determinant of reovirus myocarditis. The M2 gene encodes outer capsid protein μ1, which mediates host membrane penetration during reovirus entry. We infected newborn C57BL/6 mice with reovirus strain type 1 Lang (T1L) or a reassortant reovirus in which the M2 gene from strain type 3 Dearing (T3D) was substituted into the T1L genetic background (T1L/T3DM2). T1L was non-lethal in wild-type mice, whereas greater than 90% of mice succumbed to T1L/T3DM2 infection. T1L/T3DM2 produced higher viral loads than T1L at the site of inoculation. In secondary organs, T1L/T3DM2 was detected with more rapid kinetics and reached higher peak titers than T1L. We found that hearts from T1L/T3DM2-infected mice were grossly abnormal, with large lesions indicative of substantial inflammatory infiltrate. Lesions in T1L/T3DM2-infected mice contained necrotic cardiomyocytes with pyknotic debris, and extensive lymphocyte and histiocyte infiltration. In contrast, T1L induced the formation of small purulent lesions in a small subset of animals, consistent with T1L being mildly myocarditic. Finally, more activated caspase-3-positive cells were observed in hearts from animals infected with T1L/T3DM2 compared to T1L. Together, our findings indicate that substitution of the T3D M2 allele into an otherwise T1L genetic background is sufficient to change a non-lethal infection into a lethal infection. Our results further indicate that T3D M2 enhances T1L replication and dissemination in vivo, which potentiates the capacity of reovirus to cause myocarditis. IMPORTANCE Reovirus is a non-enveloped virus with a segmented double-stranded RNA genome that serves as a model for studying viral myocarditis. The mechanisms by which reovirus drives myocarditis development are not fully elucidated. We found that substituting the M2 gene from strain type 3 Dearing (T3D) into an otherwise type 1 Lang (T1L) genetic background (T1L/T3DM2) was sufficient to convert the non-lethal T1L strain into a lethal infection in neonatal C57BL/6 mice. T1L/T3DM2 disseminated more efficiently and reached higher maximum titers than T1L in all organs tested, including the heart. T1L is mildly myocarditic and induced small areas of cardiac inflammation in a subset of mice. In contrast, hearts from mice infected with T1L/T3DM2 contained extensive cardiac inflammatory infiltration and more activated caspase-3-positive cells, which is indicative of apoptosis. Together, our findings identify the reovirus M2 gene as a new determinant of reovirus-induced myocarditis.
Energy and cost minimization of bidirectional frequency regulation service by EV following FERC order 755

Increasing share of renewable energy sources in the electric sector, forces the system operators to include new resources for frequency regulations with fast ramping. Electric vehicles (EVs) with fast charging and short battery reaction time are becoming promising source for frequency regulations. Therefore, it is worthwhile to evaluate EV's abilities and associated cost to participate in the frequency regulation activity. In this paper, we first present a linear model of EV operating in charging/discharging mode and impact of battery degradation during these activities. We then define a cost function for EV operation and its minimization using linear programming. After calculating the optimal operating points of EV, a novel framework was developed to calculate optimal bidding components of EV, regulation capacity and energy cost function to participate in the regulation market. Simulation results show the efficiency of the developed linear model. Results also provide pathways for possible opportunities of revenue stream for the EV owner, by offering the regulation up/down services, without limiting the EV owners level of comfort.
High Spectral Resolution Interferometric Planetary Observations in the 7–25 μ Region

The thermal emission spectra of Venus, Mars, Jupiter, and the moon were observed at the coude focus of the McDonald Observatory 107-inch telescope in the 400–1400 cm-1 spectral range with spectral resolutions of 0.3–0.7 cm-1. A preliminary interpretation of the Venus/lunar ratio spectrum allows identification of four upper state CO2 bands in the Venusian atmosphere at 791, 828, 865, and 961 cm-1 and confirms previous observations of the broad absorption-like depression around 890 cm-1. The rotational structure of the 791 and 961 cm-1 bands is well developed at this spectral resolution.
Enhancement of Diamond Nucleation by Graphite Fibers Local to Substrate Surfaces in H2 - CH4 rf Discharges.

Abstract A method has been discovered for enhancing diamond nucleation without using any mechanical treatment to the surface. We have observed that the presence of graphite fibers tangential to a substrate surface greatly enhances the nucleation of diamond crystals immediately underneath the fiber. Diamond growth has been observed along lines and in small clusters replicating the graphite fiber pattern following exposures of unscratched silicon, nickel, and fused quartz substrates to 2% CH 4 in H 2 rf-discharges.
Turnover in dirty work occupations: A focus on pre‐entry individual characteristics

The current study explored newcomer turnover in a unique population: dirty workers. Based on social identity theory (SIT) and conservation of resources theory (COR), a set of individual level predictors theoretically relevant to dirty work turnover were identified. We examined whether differences in access to job information prior to hire, career commitment, belief in the value of the job, negative affectivity (NA), and maladaptive coping style were related to turnover of animal shelter employees with euthanasia responsibilities. Results supported the potential importance of all variables for understanding turnover amongst these dirty workers. Access to job information, NA, and maladaptive coping style were found to have the highest relative importance amongst the set of predictors.