cssi/SciSciGPT-SciSciNet huggingface dataset:
Materials with the CrVO4 structure type as candidate superprotonic conductors

Proton conducting oxides have the potential to improve the efficiency of solid oxide fuel cells and electrolyzers, yet many oxide structures remain relatively unexplored for the ability to conduct protons. To accelerate the search for novel proton-conducting oxides, we have performed a computational screen of the proton migration energy in 41 different commonly-occurring oxide structure types. The results of this screen, which are supported by a comprehensive set of density functional theory calculations, indicate that known materials with the CrVO4 structure type have an average migration energy for proton diffusion of less than 0.2 eV, with several known materials having calculated migration energies below 0.1 eV. These results indicate that materials with the CrVO4 structure type, which to our knowledge have not been previously explored as candidate proton conductors, may exhibit very high proton conductivity that surpasses that of leading proton-conducting oxides. We present the results of our screen as well as diffusion dimensionality analysis and thermodynamic stability analysis for materials with the CrVO4 structure.
General Schwarz Lemmata and their applications

We prove estimates interpolating the Schwarz Lemmata of Royden–Yau and the ones recently established by the author. These more flexible estimates provide additional information on (algebraic) geome...
MGDrivE: A modular simulation framework for the spread of gene drives through spatially explicit mosquito populations

Author(s): Sanchez C., HM; Wu, SL; Bennett, JB; Marshall, JM | Abstract: Malaria, dengue, Zika and other mosquito-borne diseases continue to pose a major global health burden through much of the world, despite the widespread distribution of insecticide-based tools and antimalarial drugs. The advent of CRISPR/Cas9-based gene editing and its demonstrated ability to streamline the development of gene drive systems has reignited interest in the application of this technology to the control of mosquitoes and the diseases they transmit. The versatility of this technology has enabled a wide range of gene drive architectures to be realized, creating a need for their population-level and spatial dynamics to be explored. We present MGDrivE (Mosquito Gene Drive Explorer): a simulation framework designed to investigate the population dynamics of a variety of gene drive architectures and their spread through spatially explicit mosquito populations. A key strength of the MGDrivE framework is its modularity: (a) a genetic inheritance module accommodates the dynamics of gene drive systems displaying user-defined inheritance patterns, (b) a population dynamic module accommodates the life history of a variety of mosquito disease vectors and insect agricultural pests, and (c) a landscape module generates the metapopulation model by which insect populations are connected via migration over space. Example MGDrivE simulations are presented to demonstrate the application of the framework to CRISPR/Cas9-based homing gene drive for: (a) driving a disease-refractory gene into a population (i.e. population replacement), and (b) disrupting a gene required for female fertility (i.e. population suppression), incorporating homing-resistant alleles in both cases. Further documentation and use examples are provided at the project's Github repository. MGDrivE is an open-source r package freely available on CRAN. We intend the package to provide a flexible tool capable of modelling novel inheritance-modifying constructs as they are proposed and become available. The field of gene drive is moving very quickly, and we welcome suggestions for future development.
Anaerobic degradation of syringic acid by an adapted strain of Rhodopseudomonas palustris

ABSTRACT While lignin represents a major fraction of the carbon in plant biomass, biological strategies to convert the components of this heterogeneous polymer into products of industrial and biotechnological value are lacking. Syringic acid (3,5-dimethoxy-4-hydroxybenzoic acid) is a by-product of lignin degradation, appearing in lignocellulosic hydrolysates, deconstructed lignin streams, and other agricultural products. Rhodopseudomonas palustris CGA009 is a known degrader of phenolic compounds under photoheterotrophic conditions via the benzoyl coenzyme A (CoA) degradation (BAD) pathway. However, R. palustris CGA009 is reported to be unable to metabolize meta-methoxylated phenolics, such as syringic acid. We isolated a strain of R. palustris (strain SA008.1.07), adapted from CGA009, which can grow on syringic acid under photoheterotrophic conditions, utilizing it as a sole source of organic carbon and reducing power. An SA008.1.07 mutant with an inactive benzoyl-CoA reductase structural gene was able to grow on syringic acid, demonstrating that the metabolism of this aromatic compound is not through the BAD pathway. Comparative gene expression analyses of SA008.1.07 implicated the involvement of products of the vanARB operon (rpa3619, rpa3620, rpa3621), which has been described as catalyzing aerobic aromatic ring demethylation in other bacteria, in anaerobic syringic acid degradation. In addition, experiments with a vanARB deletion mutant demonstrated the involvement of the vanARB operon in anaerobic syringic acid degradation. These observations provide new insights into the anaerobic degradation of meta-methoxylated and other aromatics by R. palustris. IMPORTANCE Lignin is the most abundant aromatic polymer on Earth and a resource that could eventually substitute for fossil fuels as a source of aromatic compounds for industrial and biotechnological applications. Engineering microorganisms for the production of aromatic-based biochemicals requires detailed knowledge of the metabolic pathways for the degradation of aromatics that are present in lignin. Our isolation and analysis of a Rhodopseudomonas palustris strain capable of syringic acid degradation reveal a previously unknown metabolic route for aromatic degradation in R. palustris. This study highlights several key features of this pathway and sets the stage for a more complete understanding of the microbial metabolic repertoire required to metabolize aromatic compounds from lignin and other renewable sources.
On distributionally robust chance constrained programs with Wasserstein distance

This paper studies a distributionally robust chance constrained program (DRCCP) with Wasserstein ambiguity set, where the uncertain constraints should be satisfied with a probability at least a given threshold for all the probability distributions of the uncertain parameters within a chosen Wasserstein distance from an empirical distribution. In this work, we investigate equivalent reformulations and approximations of such problems. We first show that a DRCCP can be reformulated as a conditional value-at-risk constrained optimization problem, and thus admits tight inner and outer approximations. We also show that a DRCCP of bounded feasible region is mixed integer representable by introducing big-M coefficients and additional binary variables. For a DRCCP with pure binary decision variables, by exploring the submodular structure, we show that it admits a big-M free formulation, which can be solved by a branch and cut algorithm. Finally, we present a numerical study to illustrate the effectiveness of the proposed formulations.
Augmenting Survey Completion and Rates of Returns for Patients with Low Literacy: A Randomized Control Trial of Telephone Follow-up

AbstractÂ Aim.Â The aim of the study was to determine the best possible means to achieve survey-item completion and high rates of return among low-literacy patients in regard to their perspectives on their health and quality of care post-hospital discharge.Background.Â The Hospital Consumer Assessment of Healthcare Providers and Systems Survey (HCAHPS) is designed for individuals with a sixth-grade reading level. Â One hospital, however, who had mailed the HCAHPS to discharged patients had low return rates and low item completion, which hospital personnel felt were due to low literacy levels. Â Design.Â An experimental design was used to conduct this descriptive/comparative study. Methods. Â A total of 286 adult patients with low literacy volunteered to participate in the study. Â The survey was disseminated in English or Spanish to individuals with low literacy, using two different modes of dissemination (mailing of the survey or telephone with follow-up reading/clarification of the survey items, if needed) to patients with low literacy who were hospital discharged.Results.Â Participants in the telephone group were 7.4 times more likely to complete the HCAHPS as compared to those who received the HCAHPS by mail. Â These telephone participants also were more likely to complete all items compared to those who were mailed the survey. Â Discussion.Â Assessing the health literacy of patients is important to ensure that HCAHPS is understood and that the survey is returned and items are completed.Â Telephone dissemination should be considered for patients with low literacy. Â Keywords:Â health, hospital, literacy, patient perspective, patient satisfaction, quality of care, surveyÂ
Random Khatri-Rao-Product Codes for Numerically-Stable Distributed Matrix Multiplication

We propose a class of codes called random Khatri- Rao-Product (RKRP) codes for distributed matrix multiplication in the presence of stragglers. The main advantage of the proposed codes is that decoding of RKRP codes is highly numerically stable in comparison to decoding of Polynomial codes [1] and decoding of the recently proposed OrthoPoly codes [2]. We show that RKRP codes are maximum distance separable with probability 1. The communication cost and encoding complexity for RKRP codes are identical to that of OrthoPoly codes and Polynomial codes and the average decoding complexity of RKRP codes is lower than that of OrthoPoly codes. Numerical results show that the average relative L 2 -norm of the reconstruction error for RKRP codes is substantially better than that of OrthoPoly codes.
Experimentation, Innovation, and Economics

The experimental method not only helps identify causal relationships, but also provides economists with a rich sense of context, focuses research on specific practical questions, stimulates collaboration with practitioners and specialists from other fields, and allows for rapid iteration. In this lecture, I present a series of examples illustrating how together these features make the experimental approach a powerful tool for advancing scientific understanding, informing policy, and promoting innovation. I then discuss how institutions can be designed to accelerate innovation and direct it toward the world's most pressing needs.
Melting Can Hinder Impact-Induced Adhesion.

Melting has long been used to join metallic materials, from welding to selective laser melting in additive manufacturing. In the same school of thought, localized melting has been generally perceived as an advantage, if not the main mechanism, for the adhesion of metallic microparticles to substrates during a supersonic impact. Here, we conduct the first in situ supersonic impact observations of individual metallic microparticles aimed at the explicit study of melting effects. Counterintuitively, we find that under at least some conditions melting is disadvantageous and hinders impact-induced adhesion. In the parameter space explored, i.e., $\ensuremath{\sim}10\text{ }\text{ }\ensuremath{\mu}\mathrm{m}$ particle size and $\ensuremath{\sim}1\text{ }\text{ }\mathrm{km}/\mathrm{s}$ particle velocity, we argue that the solidification time is much longer than the residence time of the particle on the substrate, so that resolidification cannot be a significant factor in adhesion.
The local residential land use regulatory environment across U.S. housing markets: Evidence from a new Wharton index

Abstract We report results from a new survey of local residential land use regulatory regimes for nearly 2,500 primarily suburban communities across the United States. Key stylized facts are documented and compared to findings from a previous survey (Gyourko, Saiz, Summers, 2008). We are able to observe how the local regulatory environment has changed in over 800 communities in both samples. This represents the first consistent nationwide data documenting changes in residential land use regulation at the local jurisdictional level. Finally, we discuss how these changes can and should broaden the research questions for housing and urban economists investigating the local residential land use environment.
Exploring the Structure of Misconceptions in the Force and Motion Conceptual Evaluation with Modified Module Analysis.

The Force and Motion Conceptual Evaluation may be a better than the Force Concept Inventory for characterizing the breadth of student misconceptions about Newtonian mechanics.
Quasi-Lie Bialgebroids and Twisted Poisson Manifolds

We develop a theory of quasi-Lie bialgebroids using a homological approach. This notion is a generalization of quasi-Lie bialgebras, as well as twisted Poisson structures with a 3-form background which have recently appeared in the context of string theory, and were studied by Severa and Weinstein using a different method.
The value of early physical maturity to young adult labor market outcomes

Puberty is the most important developmental milestone closely preceding a young adult's labor market decisions. Thus, we examine the variation in the timing of physical maturity during adolescence to isolate its association with employment and hourly wages for US young adults. Using the National Longitudinal Study of Adolescent to Adult Health data, we find an early maturity premium of about 6% for females and 8% for males, but no employment advantage, in excess of gains from height and physical attractiveness. Cognitive and personality factors significantly explain this premium for both genders, but job attributes are also important for males.
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs

To accelerate inference of Convolutional Neural Networks (CNNs), various techniques have been proposed to reduce computation redundancy. Converting convolutional layers into frequency domain significantly reduces the computation complexity of the sliding window operations in space domain. On the other hand, weight pruning techniques address the redundancy in model parameters by converting dense convolutional kernels into sparse ones. To obtain high-throughput FPGA implementation, we propose spec - the first work to prune and accelerate spectral CNNs. First, we propose a systematic pruning algorithm based on Alternative Direction Method of Multipliers (ADMM). The offline pruning iteratively sets the majority of spectral weights to zero, without using any handcrafted heuristics. Then, we design an optimized pipeline architecture on FPGA that has efficient random access into the sparse kernels and exploits various dimensions of parallelism in convolutional layers. Overall, achieves high inference throughput with extremely low computation complexity and negligible accuracy degradation. We demonstrate by pruning and implementing LeNet and VGG16 on the Xilinx Virtex platform. After pruning 75% of the spectral weights, achieves 0% accuracy loss for LeNet, and <1% accuracy loss for VGG16. The resulting accelerators achieve up to 24× higher throughput, compared with the state-of-the-art FPGA implementations for VGG16.
Coded Distributed Computing: Performance Limits and Code Designs

We consider the problem of coded distributed computing where a large linear computational job, such as a matrix multiplication, is divided into k smaller tasks, encoded using an $(n,\ k)$ linear code, and performed over n distributed nodes. The goal is to reduce the average execution time of the computational job. We provide a connection between the problem of characterizing the average execution time of a coded distributed computing system and the problem of analyzing the error probability of codes of length n used over erasure channels. Accordingly, we present closed-form expressions for the execution time using binary random linear codes and the best execution time any linear-coded distributed computing system can achieve. It is also shown that there exist good binary linear codes that attain, asymptotically, the best performance any linear code, not necessarily binary, can achieve. We also investigate the performance of coded distributed computing systems using polar and Reed-Muller (RM) codes that can benefit from low-complexity decoding, and superior performance, respectively, as well as explicit constructions. The proposed framework in this paper can enable efficient designs of distributed computing systems given the rich literature in the channel coding theory.
Strangeness in stellar matter

A protoneutron star is formed immediately after the gravitational collapse of the core of a massive star. At birth, the hot and high density matter in such a star contains a large number of neutrinos trapped during collapse. Trapped neutrinos generally inhibit the presence of exotic matter — hyperons, a kaon condensate, or quarks. However, as the neutrinos diffuse out in about 10–15 s, the threshold for the appearance of strangeness is reduced; hence, the composition and the structure of the star can change significantly. The effect of exotic, negatively-charged, strangeness-bearing components is always to soften the equation of state, and the possibility exists that the star collapses to a black hole at this time. This could explain why no neutron star has yet been seen in the remnant of supernova SN1987A, even though one certainly existed when neutrinos were detected on Feb. 23, 1987. With new generation neutrino detectors it is feasible to test different theoretical scenarios observationally.
Aerial bombardment and educational attainment

This paper provides evidence that the Allied bombing of Vietnam, the longest and heaviest aerial bombardment in the history, imposed detrimental effects on educational attainment of school-age individuals. By exploiting the plausibly exogenous district-by-cohort variation in bomb destruction under a difference-in-differences framework, we find that an increase in bomb intensity leads to significantly fewer educational years completed by school-age children exposed to the bombardment. A series of robustness checks, falsification tests, and the instrumental-variable strategy further support our results. The findings underline the importance of policies targeting children after wartime.
Simultaneous Determination of Spectral Signatures and Decay Kinetics of Excited State Species in Semiconductor Nanocrystals Probed by Transient Absorption Spectroscopy

Characterization of excited-state relaxation of semiconductor nanocrystals by ultrafast spectroscopy is complicated by the fact that an excitation pulse produces a distribution of excited-state pop...
A scrapbook of inadmissible line complexes for the X-ray transform

Abstract We consider a finite field model of the X-ray transform that integrates functions along lines in dimension 3, within the context of finite fields. The admissibility problem asks for minimal sets of lines for which the restricted transform is invertible. Graph theoretic conditions are known which characterize admissible collections of lines, and these have been counted using a brute force computer program. Here we perform the count by hand and, at the same time, produce a detailed illustration of the possible structures of inadmissible complexes. The resulting scrapbook may be of interest in an artificial intelligence approach to enumerating and illustrating admissible complexes in arbitrary dimensions (arbitrarily large ambient spaces, with transforms integrating over subspaces of arbitrary dimensions.)
Analysis of a membrane enclosed catalytic reactor for butane dehydrogenation

Abstract A mathematical model consisting of component mass balances and a mechanical energy balance was formulated and simulated for butane dehydrogenation in a cocurrent, packed bed, isothermal membrane enclosed catalytic reactor at 723 K and 152 kPa (1.5 atm). Properties of a γ-alumina membrane with 4-nm diameter pores were use din the simulations. The mechanical energy balance accounted for the axial pressure change and affected the flows in the reactor, but due to the low reaction rate, it did not significantly alter the conversion calculated. Butane conversion higher than the equilibrium conversion were calculated. A single reactor system that consists of a packed catalyst bed enclosed in a tube with an impermeable section at the reactor entrance, followed by a permeable section was proposed. For maximum butane conversion, the length of the impermeable section was determined to be about 2.4 cm, and the amount of butane converted in this section was 2.2% (equilibrium conversion at these conditions was 10.3%). For reactors with butane space times less than 0.1 sec, permeation reduced the butane conversion. Gas that entered the reactor on the low pressure side displayed a reversal in the permeation direction. This phenomenon is observed for the gases that permeable through the membrane by Knudsen diffusion, and have sufficient reactor space time. This sufficient space time limit was 0.5 sec for the investigated system.
Littlewood’s Problem for Sets with Multidimensional Structure

We give $L^1$-norm estimates for exponential sums of a finite sets $A$ consisting of integers or lattice points. Under the assumption that $A$ possesses sufficient multidimensional structure, our estimates are stronger than those of McGehee-Pigno-Smith and Konyagin. These theorems improve upon past work of Petridis.
An in Situ Atlas of Mitochondrial DNA in Mammalian Tissues Reveals High Content in Stem and Proliferative Compartments.

Mitochondria regulate ATP production, metabolism, and cell death. Alterations in mitochondrial DNA (mtDNA) sequence and copy number are implicated in aging and organ dysfunction in diverse inherited and sporadic diseases. Because most measurements of mtDNA use homogenates of complex tissues, little is known about cell-type-specific mtDNA copy number heterogeneity in normal physiology, aging, and disease. Thus, the precise cell types whose loss of mitochondrial activity and altered mtDNA copy number that result in organ dysfunction in aging and disease have often not been clarified. Here, an in situ hybridization approach to generate a single-cell-resolution atlas of mtDNA content in mammalian tissues was validated. In hierarchically organized self-renewing tissues, higher levels of mtDNA were observed in stem/proliferative compartments compared with differentiated compartments. Striking zonal patterns of mtDNA levels in the liver reflected the known oxygen tension gradient. In the kidney, proximal and distal tubules had markedly higher mtDNA levels compared with cells within glomeruli and collecting duct epithelial cells. In mice, decreased mtDNA levels were visualized in renal tubules as a function of aging, which was prevented by calorie restriction. This study provides a novel approach for quantifying species- and cell-type-specific mtDNA copy number and dynamics in any normal or diseased tissue that can be used for monitoring the effects of interventions in animal and human studies.
When does turbulence spreading matter

Few, if any, of the many papers on turbulence spreading address the key question of how turbulence spreading actually affects the profile structure. Here, we are using a reduced model to answer that question. Turbulence spreading is most relevant near regions where the profiles support a strong intensity gradient ∇ I. One such case is at the edge of an L mode discharge, near a source of turbulence [i.e., either a localized source of edge turbulence or an influx of turbulence from the scrape-off layer (SOL)]. Another is in “No Man's Land” (NML), which connects the pedestal to the stiff core in H mode. In the case of L mode, without an edge intensity source, the turbulence intensity profile is nearly flat and spreading has a weak effect. An edge localized source increases the edge ∇ I, which then drives inward spreading. Invasion of turbulence from the SOL to the edge softens the edge pressure gradient. In H mode, the strong shear suppression of pedestal turbulence necessarily forces a sharp ∇ I in NML. This sharp ∇ I drives a significant flux of turbulence from the core to the pedestal, where it is ultimately dissipated by shearing. Counter-intuitively, the results indicate that spreading actually increases the pedestal height and width and hence the energy content in H mode. This suggests that models of the pedestal structure should include NML turbulence spreading effects. The relation of avalanches to spreading is studied. Spreading weakly affects the avalanche distribution, but the spatiotemporal correlation of intensity increases with spreading.
CluStrat: A Structure Informed Clustering Strategy for Population Stratification

Genome-wide association studies (GWAS) have been extensively used to estimate the signed effects of trait-associated alleles. One of the key challenges in GWAS are confounding factors, such as population stratification, which can lead to spurious genotype-trait associations. Recent independent studies [1, 8, 10] failed to replicate the strong evidence of previously reported signals of directional selection on height in Europeans in the UK Biobank cohort, and attributed the loss of signal to cryptic relatedness in populations.
Foreclosure, Vacancy and Crime

Abstract This paper examines the impact of residential foreclosures and vacancies on violent and property crime. To overcome confounding factors, a difference-in-difference research design is applied to a unique data set containing geocoded foreclosure and crime data from Pittsburgh, Pennsylvania. Results indicate that while foreclosure alone has no effect on crime, violent crime rates increase by roughly 19% once the foreclosed home becomes vacant – an effect that increases with length of vacancy. We find weak evidence suggesting a potential vacancy effect for property crime that is much lower in magnitude.
Measurement point selection and modal damping identification for bladed disks

Abstract A novel measurement point selection (MPS) technique for bladed disks and integrally bladed disks (blisks) is presented and applied to a new modal damping identification method. When gathering data to be used for applications such as damping identification in blisks, it is important to measure points which provide sufficient and accurate information for the analysis. However, to reduce the experimental time and cost, the measurement points should be chosen optimally so that the minimum number of measurements have to be collected. This paper discusses a modified form of the effective independence distribution vector (EIDV) method presented by Penny et al. and adapted by Holland et al. The key aspect of the proposed method is that it uses only single sector-level calculations instead of the full system. A residual weighting optimizes the MPS technique by ranking individual measurement locations according to their noise sensitivity. The method presented provides results equivalent to the full-system EIDV method, but it decreases the computational cost, increases the robustness of the identification, and minimizes the measurement time. Also, a novel method to identify a damping parameter for each mode in a frequency range of interest is presented. This method utilizes the proposed MPS technique to increase the accuracy of the identification. Measurement locations and modal damping results for a 30 degree of freedom system and a blisk with a more complex geometry are presented. Using the proposed methodologies it is possible to obtain an accurate modal damping identification with a decreased computational and measurement cost.
Institutions and U.S. Regional Development: A Study of Massachusetts and Virginia

The development of the American economy was accompanied by significant spatial income inequalities between the northern and southern regions. While many factors contributed to northern industrialization and southern stagnation, an important factor was differences in the region's institutions. In the North, a democratic institution fostered growth whereas in the South, oligarchic institutions favored status quo. To gain insights on the nature and causes of this divergence, this paper examines the development of political and legal institutions in Massachusetts and Virginia, the two leading states in the North and the South.
The Agricultural Wage Gap: Evidence from Brazilian Micro-data

A key feature of developing economies is that wages in the agricultural sector are significantly below those of other sectors. Using a panel data set on the universe of formal workers in Brazil, I use information on workers that switch sectors to decompose the drivers of this inter-sector gap. I find that most of the gap between sectors is explained by unobservable differences in the skill composition of workers, as opposed to differential pay of workers with similar skills. The evidence speaks against the existence of large short-term wage gains from the reallocation of workers out of agriculture and favors recently proposed Roy models of inter-sector sorting as drivers of lower average wages in agriculture. A calibrated model of worker sorting can account for the wage gap observed in 1996 Brazil and a share of both the wage gap decline and the diminishing worker participation in agriculture observed during the period between 1996 and 2013.
How good are default investment policies in defined contribution pension plans

Defined contribution (DC) pension plans have been gaining ground in the last 10–20 years as the preferred system for many countries and other agencies, both private and public. The central question for a DC plan is how to invest in order to reach the participant's retirement goals. Given the financial illiteracy of the general population, it is common to offer a default policy for members who do not actively make investment choices. Using data from the Chilean system, we discuss an investment model with fixed contribution rates and compare the results with the existing default policy under multiple objectives. Our results indicate that the Chilean default policy has good overall performance, but specific closed-loop policies have a higher probability of achieving desired retirement goals and can reduce the expected shortfall at retirement.
Insulin Signal Transduction is Impaired in the Type 2 Diabetic Retina

Rates of type 2 diabetes are reaching epidemic levels. Yet, the tissue specific alterations due to insulin resistance are only recently being investigated. The goal of the present study was to evaluate retinal insulin signal transduction in a common mouse model of type 2 diabetes, the db/db mouse. Retinal lysates from five month old male db/db and db/+ (control) mice were collected and processed for Western blotting or ELISA analyses for insulin receptor, insulin receptor substrate-1 (IRS-1), Akt, tumor necrosis factor alpha (TNFα) and caspase 3 levels. Data demonstrate increased TNFα and IRS-1 phosphorylation on serine 307. This led to decreased Akt phosphorylation on serine 473 and increased cleavage of caspase 3. Taken together, the data suggest dysfunctional insulin signaling in the retina of the db/db mouse. insulin.
Remyelination alters the pattern of myelin in the cerebral cortex

Destruction of oligodendrocytes and myelin sheaths in cortical gray matter profoundly alters neural activity and is associated with cognitive disability in multiple sclerosis (MS). Myelin can be restored by regenerating oligodendrocytes from resident progenitors; however, it is not known whether regeneration restores the complex myelination patterns in cortical circuits. Here, we performed time lapse in vivo two photon imaging in somatosensory cortex of adult mice to define the kinetics and specificity of myelin regeneration after acute oligodendrocyte ablation. These longitudinal studies revealed that the pattern of myelination in cortex changed dramatically after regeneration, as new oligodendrocytes were formed in different locations and new sheaths were often established along axon segments previously lacking myelin. Despite the dramatic increase in axonal territory available, oligodendrogenesis was persistently impaired in deeper cortical layers that experienced higher gliosis. Repeated reorganization of myelin patterns in MS may alter circuit function and contribute to cognitive decline.
Weak Solutions of Ideal MHD Which Do Not Conserve Magnetic Helicity

We construct weak solutions to the ideal magneto-hydrodynamic (MHD) equations which have finite total energy, and whose magnetic helicity is not a constant function of time. In view of Taylor’s conjecture, this proves that there exist finite energy weak solutions to ideal MHD which cannot be attained in the infinite conductivity and zero viscosity limit. Our proof is based on a Nash-type convex integration scheme with intermittent building blocks adapted to the geometry of the MHD system.
Effect of short-term prescription opioids on DNA methylation of the OPRM1 promoter.

A long-term opioid use has been associated with hypermethylation of the opioid receptor mu 1 (OPRM1) promoter. Very little is currently known about the early epigenetic response to therapeutic opioids. Here, we examine whether we can detect DNA methylation changes associated with a few days’ use of prescribed opioids. Genome-wide DNA methylation was assayed in a cohort of 33 opioid-naive participants who underwent standard dental surgery followed by opioid self-administration. Saliva samples were collected before surgery (visit 1), and at two postsurgery visits at 2.7 ± 1.5 days (visit 2), and 39 ± 10 days (visit 3) after the discontinuation of opioid analgesics. The perioperative methylome underwent significant changes over the three visits that were primarily due to postoperative inflammatory response and cell heterogeneity. To specifically examine the effect of opioids, we started with a candidate gene approach and evaluated 10 CpGs located in the OPRM1 promoter. There was a significant cross-sectional variability in opioid use, and for participants who self-administered the prescribed drugs, the total dosage ranged from 5–210 morphine milligram equivalent (MME). Participants were categorized by cumulative dosage into three groups: < 25 MME, 25–90 MME, and ≥ 90 MME. Using mixed-effects modeling, 4 CpGs had significant positive associations with opioid dose at two-tailed p value < 0.05, and overall, 9 of the 10 OPRM1 promoter CpGs showed the predicted higher methylation in the higher dose groups relative to the lowest dose group. After adjustment for age, cellular heterogeneity, and past tobacco use, the promoter mean methylation also had positive associations with cumulative MME (regression coefficient = 0.0002, one-tailed p value = 0.02) and duration of opioid use (regression coefficient = 0.003, one-tailed p value = 0.001), but this effect was significant only for visit 3. A preliminary epigenome-wide association study identified a significant CpG in the promoter of the RAS-related signaling gene, RASL10A, that may be predictive of opioid dosage. The present study provides evidence that the hypermethylation of the OPRM1 promoter is in response to opioid use and that epigenetic differences in OPRM1 and other sites are associated with a short-term use of therapeutic opioids.
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle

Although SGD with random reshuffle has been widely-used in machine learning applications, there is a limited understanding of how model characteristics affect the convergence of the algorithm. In this work, we introduce model incoherence to characterize the diversity of model characteristics and study its impact on convergence of SGD with random reshuffle under weak strong convexity. Specifically, minimizer incoherence measures the discrepancy between the global minimizers of a sample loss and those of the total loss and affects the convergence error of SGD with random reshuffle. In particular, we show that the variable sequence generated by SGD with random reshuffle converges to a certain global minimizer of the total loss under full minimizer coherence. The other curvature incoherence measures the quality of condition numbers of the sample losses and determines the convergence rate of SGD. With model incoherence, our results show that SGD has a faster convergence rate and smaller convergence error under random reshuffle than those under random sampling, and hence provide justifications to the superior practical performance of SGD with random reshuffle.
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification

Learning how to predict future events from patterns of past events is difficult when the set of possible event types is large. Training an unrestricted neural model might overfit to spurious patterns. To exploit domain-specific knowledge of how past events might affect an event’s present probability, we propose using a temporal deductive database to track structured facts over time. Rules serve to prove facts from other facts and from past events. Each fact has a time-varying state—a vector computed by a neural net whose topology is determined by the fact’s provenance, including its experience of past events. The possible event types at any time are given by special facts, whose probabilities are neurally modeled alongside their states. In both synthetic and real-world domains, we show that neural probabilistic models derived from concise Datalog programs improve prediction by encoding appropriate domain knowledge in their architecture.
Anchor-Constrained Viterbi for Set-Supervised Action Segmentation

This paper is about weakly supervised action segmentation, where the ground truth specifies only a set of actions present in a training video, but not their true temporal ordering. Prior work typically uses a classifier that independently labels video frames for generating the pseudo ground truth, and multiple instance learning for training the classifier. We extend this framework by specifying an HMM, which accounts for co-occurrences of action classes and their temporal lengths, and by explicitly training the HMM on a Viterbi-based loss. Our first contribution is the formulation of a new set-constrained Viterbi algorithm (SCV). Given a video, the SCV generates the MAP action segmentation that satisfies the ground truth. This prediction is used as a framewise pseudo ground truth in our HMM training. Our second contribution in training is a new regularization of feature affinities between training videos that share the same action classes. Evaluation on action segmentation and alignment on the Breakfast, MPII Cooking2, Hollywood Extended datasets demonstrates our significant performance improvement for the two tasks over prior work.
Generalizable semi-supervised learning method to estimate mass from sparsely annotated images

Abstract Mass flow estimation is of great importance to several industries, and it can be quite challenging to obtain accurate estimates due to limitation in expense or general infeasibility. In the context of agricultural applications, yield monitoring is a key component to precision agriculture and mass flow is the critical factor to measure. Measuring mass flow allows for field productivity analysis, cost minimization, and adjustments to machine efficiency. Methods such as volume or force-impact have been used to measure mass flow; however, these methods are limited in application and accuracy. In this work, we use deep learning to develop and test a vision system that can accurately estimate the mass of sugarcane while running in real-time on a sugarcane harvester during operation. The deep learning algorithm that is used to estimate mass flow is trained using very sparsely annotated images (semi-supervised) using only final load weights (aggregated weights over a certain period of time). The deep neural network (DNN) succeeds in capturing the mass of sugarcane accurately and surpasses older volumetric-based methods, despite highly varying lighting and material colors in the images. The deep neural network is initially trained to predict mass on laboratory data (bamboo) and then transfer learning is utilized to apply the same methods to estimate mass of sugarcane. Using a vision system with a relatively lightweight deep neural network we are able to estimate mass of bamboo with an average error of 4.5% and 5.9% for a select season of sugarcane.
Density Map Guided Object Detection in Aerial Images

Object detection in high-resolution aerial images is a challenging task because of 1) the large variation in object size, and 2) non-uniform distribution of objects. A common solution is to divide the large aerial image into small (uniform) crops and then apply object detection on each small crop. In this paper, we investigate the image cropping strategy to address these challenges. Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map. As pixel intensity varies, it is able to tell whether a region has objects or not, which in turn provides guidance for cropping images statistically. DMNet has three key components: a density map generation module, an image cropping module and an object detector. DMNet generates a density map and learns scale information based on density intensities to form cropping regions. Extensive experiments show that DMNet achieves state-of-the-art performance on two popular aerial image datasets, i.e. VisionDrone [30] and UAVDT [4].
Hypergames and Cyber-Physical Security for Control Systems

The identification of the Stuxnet worm in 2010 provided a highly publicized example of a cyber attack that physically damaged an industrial control system. This raised public awareness about the possibility of similar attacks against other industrial targets—including critical infrastructure. In this article, we use hypergames to analyze how strategic perturbations of sensor readings and calibrated parameters can be used to manipulate a system that employs optimal control. Hypergames form an extension of game theory that enables us to model strategic interactions where the players may have significantly different perceptions of the game(s) they are playing. Past work with hypergames has focused on relatively simple interactions consisting of a small set of discrete choices for each player. Here, we apply single-stage hypergames to larger systems with continuous variables. We find that manipulating constraints can be a more effective attacker strategy than manipulating objective function parameters. Moreover, the attacker need not change the underlying system to carry out a successful attack—it may be sufficient to deceive the defender controlling the system. It is possible to scale our approach up to even larger systems, but this will depend on the characteristics of the system in question, and we identify several characteristics that will make those systems amenable to hypergame analysis.
Nesterov Acceleration for Equality-Constrained Convex Optimization via Continuously Differentiable Penalty Functions

We propose a framework to use Nesterov’s accelerated method for constrained convex optimization problems. Our approach consists of first reformulating the original problem as an unconstrained optimization problem using a continuously differentiable exact penalty function. This reformulation is based on replacing the Lagrange multipliers in the augmented Lagrangian of the original problem by Lagrange multiplier functions. The expressions of these Lagrange multiplier functions, which depend upon the gradients of the objective function and the constraints, can make the unconstrained penalty function non-convex in general even if the original problem is convex. We establish sufficient conditions on the objective function and the constraints of the original problem under which the unconstrained penalty function is convex. This enables us to use Nesterov’s accelerated gradient method for unconstrained convex optimization and achieve a guaranteed rate of convergence which is better than the state-of-the-art first-order algorithms for constrained convex optimization. Simulations illustrate our results.
Inducing Equilibria in Networked Public Goods Games through Network Structure Modification

Networked public goods games model scenarios in which self-interested agents decide whether or how much to invest in an action that benefits not only themselves, but also their network neighbors. Examples include vaccination, security investment, and crime reporting. While every agent's utility is increasing in their neighbors' joint investment, the specific form can vary widely depending on the scenario. A principal, such as a policymaker, may wish to induce large investment from the agents. Besides direct incentives, an important lever here is the network structure itself: by adding and removing edges, for example, through community meetings, the principal can change the nature of the utility functions, resulting in different, and perhaps socially preferable, equilibrium outcomes. We initiate an algorithmic study of targeted network modifications with the goal of inducing equilibria of a particular form. We study this question for a variety of equilibrium forms (induce all agents to invest, at least a given set S, exactly a given set S, at least k agents), and for a variety of utility functions. While we show that the problem is NP-complete for a number of these scenarios, we exhibit a broad array of scenarios in which the problem can be solved in polynomial time by non-trivial reductions to (minimum-cost) matching problems.
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach.

In this paper we consider solving saddle point problems using two variants of Gradient Descent-Ascent algorithms, Extra-gradient (EG) and Optimistic Gradient Descent Ascent (OGDA) methods. We show that both of these algorithms admit a unified analysis as approximations of the classical proximal point method for solving saddle point problems. This viewpoint enables us to develop a new framework for analyzing EG and OGDA for bilinear and strongly convex-strongly concave settings. Moreover, we use the proximal point approximation interpretation to generalize the results for OGDA for a wide range of parameters.
The mapping class group of a minimal subshift

For a homeomorphism $T \colon X \to X$ of a Cantor set $X$, the mapping class group $\mathcal{M}(T)$ is the group of isotopy classes of orientation-preserving self-homeomorphisms of the suspension $\Sigma_{T}X$. The group $\mathcal{M}(T)$ can be interpreted as the symmetry group of the system $(X,T)$ with respect to the flow equivalence relation. We study $\mathcal{M}(T)$, focusing on the case when $(X,T)$ is a minimal subshift. We show that when $(X,T)$ is a subshift associated to a substitution, the group $\mathcal{M}(T)$ is an extension of $\mathbb{Z}$ by a finite group; for a large class of substitutions including Pisot type, this finite group is a quotient of the automorphism group of $(X,T)$. When $(X,T)$ is a minimal subshift of linear complexity satisfying a no-infinitesimals condition, we show that $\mathcal{M}(T)$ is virtually abelian. We also show that when $(X,T)$ is minimal, $\mathcal{M}(T)$ embeds into the Picard group of the crossed product algebra $C(X) \rtimes_{T} \mathbb{Z}$.