cssi/SciSciGPT-SciSciNet huggingface dataset:
Is the Supply of Charitable Donations Fixed? Evidence from Deadly Tornadoes

Do natural disasters increase charitable giving or simply reallocate a fixed supply of donations? We study this question using Internal Revenue Service data in the context of deadly tornadoes. We find that among zip codes located in the same state but more than 20 miles away from a tornado's path, donations by households increase by about $2 million per tornado fatality. We find no negative effects of tornado fatalities on donations to charities located in these zip codes. The results imply that giving in response to new needs need not come at the expense of other causes.
The Comprehensive Tax Gain from Leverage

We expand the traditional specification of the tax gain from leverage by accounting for the choice between issuing debt and utilizing internal retained earnings equity. Standard analyses focus sole...
Poisson Brackets in Kontsevich's "Lie World"

Abstract In this note we develop the theory of double brackets in the sense of van den Bergh (2008) in Kontsevich’s non-commutative “Lie World”. These double brackets can be thought of as Poisson structures defined by formal expressions only involving the structure maps of a quadratic Lie algebra. The basic example is the Kirillov–Kostant–Souriau (KKS) Poisson bracket. We introduce a notion of non-degenerate double brackets. Surprisingly, in this framework the KKS bracket turns out to be non-degenerate. The main result of the paper is the uniqueness theorem for double brackets with a given moment map. As applications, we establish a monoidal equivalence between Hamiltonian quasi-Poisson spaces and Hamiltonian spaces and give a new proof of the theorem by L. Jeffrey in Jeffrey (1994) on symplectic structure on the moduli space of flat g -connections on a surface of genus 0.
Incidence and determinants of QT interval prolongation in COVID-19 patients treated with hydroxychloroquine and azithromycin.

Combined use of hydroxychloroquine and azithromycin was globally adopted, in part due to paucity and high cost of alternative therapies. However the utility of these medications has been questioned; and thus safety becomes a major concern given clinical equipoise regarding efficacy. Both hydroxychloroquine and azithromycin continue to be administered in US clinical trials examining their potential role in prevention of infection, treatment of mild infection in ambulatory patients, and in combination with other medical regimens in treatment of patients with severe disease. These drugs also continue to be clinically utilized in hospitalized patients around the globe, often without continuous telemetry due to lack of resources. Concern regarding use of hydroxychloroquine without adequate rhythm monitoring in clinical trials has been recently expressed.1 A review of clinicaltrials.gov at the time of submission of this correspondence reveals actively recruiting trials of combined hydroxychloroquine/azithromycin with or without additional COVID-19 therapies, for both ambulatory and hospitalized patients within and outside the US. The potential for hydroxychloroquine and azithromycin to cause QT prolongation is counterbalanced by very low risk of pro-arrhythmia in the general population, and emerging evidence of relatively low risk of Torsades de Pointes (TdP) in COVID-19 patients.2,3,4,5 Thus delineation of the determinants of significant QTc prolongation and pro-arrhythmic risk for hydroxychloroquine/azithromycin is very important, especially given mounting evidence of inefficacy in COVID-19 treatment.
The Age of Information in Networks: Moments, Distributions, and Sampling

A source provides status updates to monitors through a network with state defined by a continuous-time finite Markov chain. An age of information (AoI) metric is used to characterize timeliness by the vector of ages tracked by the monitors. Based on a stochastic hybrid systems (SHS) approach, first order linear differential equations are derived for the temporal evolution of both the moments and the moment generating function (MGF) of the age vector components. It is shown that the existence of a non-negative fixed point for the first moment is sufficient to guarantee convergence of all higher order moments as well as a region of convergence for the stationary MGF vector of the age. The stationary MGF vector is then found for the age on a line network of preemptive memoryless servers. From this MGF, it is found that the age at a node is identical in distribution to the sum of independent exponential service times. This observation is then generalized to linear status sampling networks in which each node receives samples of the update process at each preceding node according to a renewal point process. For each node in the line, the age is shown to be identical in distribution to a sum of independent renewal process age random variables.
Almost Tune-Free Variance Reduction

The variance reduction class of algorithms including the representative ones, SVRG and SARAH, have well documented merits for empirical risk minimization problems. However, they require grid search to tune parameters (step size and the number of iterations per inner loop) for optimal performance. This work introduces `almost tune-free' SVRG and SARAH schemes equipped with i) Barzilai-Borwein (BB) step sizes; ii) averaging; and, iii) the inner loop length adjusted to the BB step sizes. In particular, SVRG, SARAH, and their BB variants are first reexamined through an `estimate sequence' lens to enable new averaging methods that tighten their convergence rates theoretically, and improve their performance empirically when the step size or the inner loop length is chosen large. Then a simple yet effective means to adjust the number of iterations per inner loop is developed to enhance the merits of the proposed averaging schemes and BB step sizes. Numerical tests corroborate the proposed methods.
XOR lemmas for resilient functions against polynomials

A major challenge in complexity theory is to explicitly construct functions that have small correlation with low-degree polynomials over F2. We introduce a new technique to prove such correlation bounds with F2 polynomials. Using this technique, we bound the correlation of an XOR of Majorities with constant degree polynomials. In fact, we prove a more general XOR lemma that extends to arbitrary resilient functions. We conjecture that the technique generalizes to higher degree polynomials as well. A key ingredient in our new approach is a structural result about the Fourier spectrum of low degree polynomials over F2. We show that for any n-variate polynomial p over F2 of degree at most d, there is a small set S ⊂ [n] of variables, such that almost all of the Fourier mass of p lies on Fourier coefficients that intersect with S. In fact our result is more general, and finds such a set S for any low-dimensional subspace of polynomials. This generality is crucial in deriving the new XOR lemmas.
Pesticide Contamination of Milkweeds Across the Agricultural, Urban, and Open Spaces of Low-Elevation Northern California

Monarch butterflies (Danaus plexippus) are in decline in the western United States and are encountering a range of anthropogenic stressors. Pesticides are among the factors that likely contribute to this decline, though the concentrations of these chemicals in non-crop plants is not well documented, especially in complex landscapes with a diversity of crop types and land uses. In this study, we collected 227 milkweed (Asclepias spp.) leaf samples from 19 sites representing different land use types across the Central Valley of California. We also sampled plants purchased from two stores that sell to home gardeners. We found 64 pesticides (25 insecticides, 27 fungicides, and 11 herbicides, as well as 1 adjuvant) out of a possible 262 in our screen. Pesticides were detected in every sample, even at sites with little or no pesticide use based on information from landowners. On average, approximately 9 compounds were detected per plant across all sites, with a range of 1 to 25 compounds in any one sample. For the vast majority of pesticides detected, we do not know the biological effects on monarch caterpillars that consume these plants, however we did detect a few compounds for which effects on monarchs have been experimentally investigated. Chlorantraniliprole in particular was identified in 91% of our samples and found to exceed a tested LD50 for monarchs in 58 out of 227 samples. Our primary conclusion is the ubiquity of pesticide presence in milkweeds in an early-summer window of time that monarch larvae are likely to be present in the area. Thus, these results are consistent with the hypothesis that pesticide exposure could be a contributing factor to monarch declines in the western United States. This both highlights the need for a greater understanding of the lethal and sublethal effects of these compounds (individually, additively, and synergistically) and suggests the urgent need for strategies that reduce pesticide use and movement on the landscape.
Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation

Open Domain dialog system evaluation is one of the most important challenges in dialog research. Existing automatic evaluation metrics, such as BLEU are mostly reference-based. They calculate the difference between the generated response and a limited number of available references. Likert-score based self-reported user rating is widely adopted by social conversational systems, such as Amazon Alexa Prize chatbots. However, self-reported user rating suffers from bias and variance among different users. To alleviate this problem, we formulate dialog evaluation as a comparison task. We also propose an automatic evaluation model CMADE (Comparison Model for Automatic Dialog Evaluation) that automatically cleans self-reported user ratings as it trains on them. Specifically, we first use a self-supervised method to learn better dialog feature representation, and then use KNN and Shapley to remove confusing samples. Our experiments show that CMADE achieves 89.2% accuracy in the dialog comparison task.
Faster Approximation Algorithms for Geometric Set Cover

We improve the running times of O(1)-approximation algorithms for the set cover problem in geometric settings, specifically, covering points by disks in the plane, or covering points by halfspaces in three dimensions. In the unweighted case, Agarwal and Pan [SoCG 2014] gave a randomized O(n log⁴n)-time, O(1)-approximation algorithm, by using variants of the multiplicative weight update (MWU) method combined with geometric data structures. We simplify the data structure requirement in one of their methods and obtain a deterministic O(n log³n log log n)-time algorithm. With further new ideas, we obtain a still faster randomized O(n log n(log log n)^O(1))-time algorithm. For the weighted problem, we also give a randomized O(n log⁴n log log n)-time, O(1)-approximation algorithm, by simple modifications to the MWU method and the quasi-uniform sampling technique.
A semi-automated machine learning-aided approach to quantitative analysis of centrosomes and microtubule organization

Microtubules (MTs) promote important cellular functions including migration, intracellular trafficking, and chromosome segregation. The centrosome, comprised of two centrioles surrounded by the pericentriolar material (PCM), is the cell's central MT organizing center. Centrosomes in cancer cells are commonly numerically amplified. However, the question of how amplification of centrosomes alters the MT organization capacity is not well-studied. We developed a quantitative image-processing and machine learning-aided approach for the semi-automated analysis of MT organization. We designed a convolutional neural network-based approach for detecting centrosomes and an automated pipeline for analyzing MT organization around centrosomes, encapsulated in a semi-automatic graphical tool. Using this tool, we find that breast cancer cells with supernumerary centrosomes not only have more PCM protein per centrosome, which gradually increases with increasing centriole numbers, but also exhibit expansion in PCM size. Furthermore, these cells with amplified centrosomes cells have more growing MT ends, higher MT density and altered spatial distribution of MTs around amplified centrosomes. Thus, the semi-automated approach developed here enables rapid and quantitative analyses revealing important facets of centrosomal aberrations.
Dynamical History of the Uranian System

We numerically simulate the past tidal evolution of the five large moons of Uranus (Miranda, Ariel, Umbriel, Titania, and Oberon). We find that the most recent major mean-motion resonance (MMR) between any two moons, the Ariel-Umbriel 5:3 MMR, had a large effect on the whole system. Our results suggest that this resonance is responsible for the current 4.3$^{\circ}$ inclination of Miranda (instead of previously proposed 3:1 Miranda-Umbriel MMR), and that all five moons had their inclinations excited during this resonance. Miranda experienced significant tidal heating during the Ariel-Umbriel 5:3 MMR due to its eccentricity being excited by Ariel's secular perturbations. This tidal heating draws energy from shrinking of Miranda's orbit, rather than Ariel's outward evolution, and can generate heat flows in excess of 100 mW m$^{-2}$, sufficient to produce young coronae on Miranda. We find that this MMR was followed by a sequence of secular resonances, which reshuffled the moons' eccentricities and inclinations. We also find that the precession of Oberon's spin axis is close to a resonance with the precession of Umbriel's orbital plane, and that this spin-orbit resonance was likely excited during the Ariel-Umbriel 5:3 MMR. After the exit from the MMR, subsequent Ariel-Umbriel secular resonance and Oberon-Umbriel spin-orbit resonance may be able to explain the current low inclinations of Ariel and Umbriel. The age of Miranda's surface features tentatively suggests Uranian tidal $Q=15,000-20,000$, which can be further refined in future work.
Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers

Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.
Multi-level convergence of complex traits and the evolution of bioluminescence

Evolutionary convergence provides natural opportunities to investigate how, when, and why novel traits evolve. Many convergent traits are complex, highlighting the importance of explicitly considering convergence at different levels of biological organization, or 'multi-level convergent evolution'. To investigate multi-level convergent evolution, we propose a holistic and hierarchical framework that emphasizes breaking down traits into several functional modules. We begin by identifying long-standing questions on the origins of complexity and the diverse evolutionary processes underlying phenotypic convergence to discuss how they can be addressed by examining convergent systems. We argue that bioluminescence, a complex trait that evolved dozens of times through either novel mechanisms or conserved toolkits, is particularly well suited for these studies. We present an updated estimate of at least 94 independent origins of bioluminescence across the tree of life, which we calculated by reviewing and summarizing all estimates of independent origins. Then, we use our framework to review the biology, chemistry, and evolution of bioluminescence, and for each biological level identify questions that arise from our systematic review. We focus on luminous organisms that use the shared luciferin substrates coelenterazine or vargulin to produce light because these organisms convergently evolved bioluminescent proteins that use the same luciferins to produce bioluminescence. Evolutionary convergence does not necessarily extend across biological levels, as exemplified by cases of conservation and disparity in biological functions, organs, cells, and molecules associated with bioluminescence systems. Investigating differences across bioluminescent organisms will address fundamental questions on predictability and contingency in convergent evolution. Lastly, we highlight unexplored areas of bioluminescence research and advances in sequencing and chemical techniques useful for developing bioluminescence as a model system for studying multi-level convergent evolution.
Dynamic vision training transfers positively to batting practice performance among collegiate baseball batters

Abstract A growing body of evidence demonstrates visual, perceptual, and oculomotor abilities contribute to batting performance in baseball and there is interest in whether training such abilities can transfer positively to batting performance. The current study tested this question through a pre-registered, randomized, and placebo-controlled intervention, conducted with 24 collegiate baseball players at two NCAA Division 1 universities. Athletes were randomized to receive either dynamic vision training consisting of stroboscopic, anticipatory timing, and eye quickness drills, or placebo drills stylized after control procedures in previous vision therapy studies. Generalized near-transfer was tested via a digital visual-motor task battery (n = 20), while sports-specific intermediate and far transfer of training were evaluated through instrumented batting practice metrics (n = 14) and box score performance in NCAA-sanctioned games (n = 12), respectively. The effects of training group were tested on these outcome measures while controlling for covariates such as pre-training expectations and site. Participants averaged 8.50 hours of training with no significant group differences in training adherence, expectations, or baseline assessments. ANCOVA revealed no group differences in measures of visual-motor skills or NCAA game statistics. However, batting practice demonstrated significant improvements in launch angle (p = 0.002, Cohen’s d = 0.74) and hit distance (p
Prophet Inequalities with Linear Correlations and Augmentations

In a classical online decision problem, a decision-maker who is trying to maximize her value inspects a sequence of arriving items to learn their values (drawn from known distributions), and decides when to stop the process by taking the current item. The goal is to prove a "prophet inequality": that she can do approximately as well as a prophet with foreknowledge of all the values. In this work, we investigate this problem when the values are allowed to be correlated. Since non-trivial guarantees are impossible for arbitrary correlations, we consider a natural "linear" correlation structure introduced by Bateni et al. [ESA'15] as a generalization of the common-base value model of Chawla et al. [GEB'15]. A key challenge is that threshold-based algorithms, which are commonly used for prophet inequalities, no longer guarantee good performance for linear correlations. We relate this roadblock to another "augmentations" challenge that might be of independent interest: many existing prophet inequality algorithms are not robust to slight increase in the values of the arriving items. We leverage this intuition to prove bounds (matching up to constant factors) that decay gracefully with the amount of correlation of the arriving items. We extend these results to the case of selecting multiple items by designing a new $(1+o(1))$ approximation ratio algorithm that is robust to augmentations.
Cryo-EM structure of catalytic ribonucleoprotein complex RNase MRP.

RNase MRP is an essential eukaryotic ribonucleoprotein complex involved in the maturation of rRNA and the regulation of the cell cycle. RNase MRP is related to the ribozyme-based RNase P, but it has evolved to have distinct cellular roles. We report a cryo-EM structure of the S. cerevisiae RNase MRP holoenzyme solved to 3.0 A. We describe the structure of this 450 kDa complex, interactions between its components, and the organization of its catalytic RNA. We show that some of the RNase MRP proteins shared with RNase P undergo an unexpected RNA-driven remodeling that allows them to bind to divergent RNAs. Further, we reveal how this RNA-driven protein remodeling, acting together with the introduction of new auxiliary elements, results in the functional diversification of RNase MRP and its progenitor, RNase P, and demonstrate structural underpinnings of the acquisition of new functions by catalytic RNPs. Ribozyme-based RNase MRP is an essential eukaryotic enzyme involved in the maturation of rRNA and is evolutionarily related to RNase P. Here, the authors present the 3.0 A cryo-EM structure of the S. cerevisiae RNase MRP holoenzyme, a 450 kDa ribonucleoprotein complex and compare it with RNase P.
Differential effects of two early life stress paradigms on cerebellar-dependent delay eyeblink conditioning.

Abstract Early life stress paradigms have become prominent in the animal literature to model atypical development. Currently, two models have prevailed within the literature: (1) limited bedding or nesting and (2) maternal separation or deprivation. Both models have produced aberrations spanning behavior and neural circuitry. Surprisingly, these two models have yet to be directly compared. The current study utilized delay eyeblink conditioning, an associative learning task with a well-defined cerebellar circuit, to compare the behavioral effects of standard limited bedding (postnatal day 2–9, n = 15) and maternal separation (60 min per day during postnatal day 2–14, n = 13) early life stress paradigms. Animals in all groups exhibited robust learning curves. Surprisingly, facilitated conditioning was observed in the maternal separation group. Rats that underwent limited bedding did not differ from the control or maternal separation groups on any conditioning measures. This study contributes to a clearer understanding of early life stress paradigms and the claims made about their mechanisms, which if better clarified can be properly leveraged to increase translational value.
Deciphering cellular transcriptional alterations in Alzheimer's disease brains.

Large-scale brain bulk-RNAseq studies identified molecular pathways implicated in Alzheimer’s disease (AD), however these findings can be confounded by cellular composition changes in bulk-tissue. To identify cell intrinsic gene expression alterations of individual cell types, we designed a bioinformatics pipeline and analyzed three AD and control bulk-RNAseq datasets of temporal and dorsolateral prefrontal cortex from 685 brain samples. We detected cell-proportion changes in AD brains that are robustly replicable across the three independently assessed cohorts. We applied three different algorithms including our in-house algorithm to identify cell intrinsic differentially expressed genes in individual cell types (CI-DEGs). We assessed the performance of all algorithms by comparison to single nucleus RNAseq data. We identified consensus CI-DEGs that are common to multiple brain regions. Despite significant overlap between consensus CI-DEGs and bulk-DEGs, many CI-DEGs were absent from bulk-DEGs. Consensus CI-DEGs and their enriched GO terms include genes and pathways previously implicated in AD or neurodegeneration, as well as novel ones. We demonstrated that the detection of CI-DEGs through computational deconvolution methods is promising and highlight remaining challenges. These findings provide novel insights into cell-intrinsic transcriptional changes of individual cell types in AD and may refine discovery and modeling of molecular targets that drive this complex disease.
Compositional knockoff filter for high-dimensional regression analysis of microbiome data

A critical task in microbiome data analysis is to explore the association between a scalar response of interest and a large number of microbial taxa that are summarized as compositional data at different taxonomic levels. Motivated by fine-mapping of the microbiome, we propose a two-step compositional knockoff filter to provide the effective finite-sample false discovery rate (FDR) control in high-dimensional linear log-contrast regression analysis of microbiome compositional data. In the first step, we propose a new compositional screening procedure to remove insignificant microbial taxa while retaining the essential sum-to-zero constraint. In the second step, we extend the knockoff filter to identify the significant microbial taxa in the sparse regression model for compositional data. Thereby, a subset of the microbes is selected from the high-dimensional microbial taxa as related to the response under a prespecified FDR threshold. We study the theoretical properties of the proposed two-step procedure, including both sure screening and effective false discovery control. We demonstrate these properties in numerical simulation studies to compare our methods to some existing ones and show power gain of the new method while controlling the nominal FDR. The potential usefulness of the proposed method is also illustrated with application to an inflammatory bowel disease data set to identify microbial taxa that influence host gene expressions.
Heterogeneous Effects of State Enterprise Zone Programs in the Shorter Run and Longer Run

The authors take up two questions that have not been explored in research on enterprise zones. First, does a considerably longer-run perspective on the effects of state enterprise zones lead to dif...
Online Parameter Estimation for Human Driver Behavior Prediction

Driver models are invaluable for planning in autonomous vehicles as well as validating their safety in simulation. Highly parameterized black-box driver models are very expressive, and can capture nuanced behavior. However, they usually lack interpretability and sometimes exhibit unrealistic- even dangerous-behavior. Rule-based models are interpretable, and can be designed to guarantee "safe" behavior, but are less expressive due to their low number of parameters. In this article, we show that online parameter estimation applied to the Intelligent Driver Model captures nuanced individual driving behavior while providing collision free trajectories. We solve the online parameter estimation problem using particle filtering, and benchmark performance against rule-based and black-box driver models on two real world driving data sets. We evaluate the closeness of our driver model to ground truth data demonstration and also assess the safety of the resulting emergent driving behavior.
A Simplicial Construction for Noncommutative Settings

In this paper we present a general construction that can be used to define the higher Hochschild homology for a noncommutative algebra. We also discuss other examples where this construction can be used.
Information Technology and the pandemic: a preliminary multinational analysis of the impact of mobile tracking technology on the COVID-19 contagion control

This paper explores the benefits and drawbacks of government surveillance within a public health crisis, specifically the COVID-19 outbreak of 2020. We review the current state of COVID-19 infectio...
Computer aided design of inverted strip dielectric waveguide millimeter-wave couplers

A computer aided design technique has been developed for the design of a beam splitter coupler and a distributed type coupler using inverted strip dielectric waveguides. The optical beam principle has been used for the beam splitter coupler. Numerical results for these couplers at 80 GHZ with minimum directivity of 30 dB and VSWR of 1.15 have been presented.
Intranasal exposure of African green monkeys to SARS-CoV-2 results in acute phase pneumonia with shedding and lung injury still present in the early convalescence phase

We recently reported the development of the first African green monkey (AGM) model for COVID-19 based on a combined liquid intranasal (i.n.) and intratracheal (i.t.) exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we followed up on this work by assessing an i.n. particle only route of exposure using the LMA mucosal atomization device (MAD). Six AGMs were infected with SARS-CoV-2; three animals were euthanized near the peak stage of virus replication (day 5) and three animals were euthanized during the early convalescence period (day 34). All six AGMs supported robust SARS-CoV-2 replication and developed respiratory disease. Evidence of coagulation dysfunction as noted by a transient increases in aPTT and circulating levels of fibrinogen was observed in all AGMs. The level of SARS-CoV-2 replication and lung pathology was not quite as pronounced as previously reported with AGMs exposed by the combined i.n. and i.t. routes; however, SARS-CoV-2 RNA was detected in nasal swabs of some animals as late as day 15 and rectal swabs as late as day 28 after virus challenge. Of particular importance to this study, all three AGMs that were followed until the early convalescence stage of COVID-19 showed substantial lung pathology at necropsy as evidenced by multifocal chronic interstitial pneumonia and increased collagen deposition in alveolar walls despite the absence of detectable SARS-CoV-2 in any of the lungs of these animals. These findings are consistent with human COVID-19 further demonstrating that the AGM faithfully reproduces the human condition.
Seabed classification using physics-based modeling and machine learning

In this work, model-based methods are employed, along with machine learning techniques, to classify sediments in oceanic environments based on the geoacoustic properties of a two-layer seabed. Two different scenarios are investigated. First, a simple low-frequency case is set up, in which the acoustic field is modeled with normal modes. Four different hypotheses are made for seafloor sediment possibilities, and these are explored using both various machine learning techniques and a simple matched-field approach. For most noise levels, the latter has an inferior performance to the machine learning methods. Second, the high-frequency model of the scattering from a rough, two-layer seafloor is considered. Again, four different sediment possibilities are classified with machine learning. For higher accuracy, one-dimensional convolutional neural networks are employed. In both cases, the machine learning methods, both in simple and more complex formulations, lead to effective sediment characterization. The results assess the robustness to noise and model misspecification of different classifiers.
Capacity Bounds for Communication Systems with Quantization and Spectral Constraints

Low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) have attracted considerable attention in efforts to reduce power consumption in millimeter wave (mmWave) and massive MIMO systems. This paper presents an information-theoretic analysis with capacity bounds for classes of linear transceivers with quantization. The transmitter modulates symbols via a unitary transform followed by a DAC and the receiver employs an ADC followed by the inverse unitary transform. If the unitary transform is set to an FFT matrix, the model naturally captures filtering and spectral constraints which are essential to model in any practical transceiver. In particular, this model allows studying the impact of quantization on out-of-band emission constraints. In the limit of a large random unitary transform, it is shown that the effect of quantization can be precisely described via an additive Gaussian noise model. This model in turn leads to simple and intuitive expressions for the power spectrum of the transmitted signal and a lower bound to the capacity with quantization. Comparison with non-quantized capacity and a capacity upper bound that does not make linearity assumptions suggests that while low resolution quantization has minimal impact on the achievable rate at typical parameters in 5G systems today, satisfying out-of-band emissions are potentially much more of a challenge.
Compliance with universal background check gun laws

Some states have enacted laws extending background checks on persons seeking to acquire a firearm to cover gun transfers among private persons, not just those involving licensed gun dealers. The sa...
BIG-TECH MERGERS: Innovation, Competition for the Market, and the Acquisition of Emerging Competitors

Abstract There is broad concern that merger policy toward Big Tech has been too lenient. Big Tech typically operates in markets characterized by innovation-driven “competition for the market.” I show that this fact provides a rationale for heightened scrutiny of incumbents’ acquisitions of emerging or potential competitors. I also address the widespread argument that permissive merger policy promotes innovative entry by facilitating entry for buyout. I show that permissive merger policy can also discourage entrant innovation. One way is by diminishing entrants’ incentives to invest in marginal product improvements when such improvements reduce the gains from merger. A second way is by facilitating incumbency for buyout, under which an incumbent makes investments in order to extract rents from an entrant through merger.
His Memory has Misled Him? Two Supposed Errors in Adam Smith's Theory of Moral Sentiments

Abstract D.D. Raphael and A. L. Macfie, the editors of the Glasgow Edition of The Theory of Moral Sentiments, document numerous errors made by Adam Smith. We examine two alleged errors, both regarding stories found in Cicero, to evaluate the extent to which they might be esoteric: one involving Parmenides and Plato, the other involving Ulysses. We argue there is good reason to suspect that the first error is deliberate and contains hidden meaning, but that, in the second case, Raphael and Macfie are mistaken in their claim that Smith erred. Finally, given Smith's discussion of dissimulation, we comment on his probable attitude toward defensive esotericism.
Reductions of Some Two-Dimensional Crystalline Representations via Kisin Modules

We determine rational Kisin modules associated with two-dimensional, irreducible, crystalline representations of $\mathrm{Gal}(\overline{\mathbb{Q}}_p/\mathbb{Q}_p)$ of Hodge-Tate weights $0, k-1$. If the slope is larger than $\lfloor \frac{k-1}{p} \rfloor$, we further identify an integral Kisin module, which we use to calculate the semisimple reduction of the Galois representation. In that range, we find that the reduction is constant, thereby improving on a theorem of Berger, Li, and Zhu.
CVR: A Continuously Variable Rate LDPC Decoder Using Parity Check Extension for Minimum Latency

This paper presents a novel IEEE 802.16e (WiMAX) based decoder that performs close to the 5G code but without the expensive hardware re-development cost. The design uses an extension of the existing WiMAX parity check code to reduce the initial decoding latency and power consumption while keeping the decoder throughput at maximum. It achieves similar Frame Error Rate (FER) compared to 5G (0.1 dB off), and most notably the error curves trend down like 5G instead flooring. At FER= 10−3 there is 0.1 dB gain in the FER code performance compared to WiMAX. An implementation of the design is a modified version of the existing fully-parallel WiMAX decoder that supports multi-rate codeword size and reduces the initial latency by 33%. Additionally, for SNR greater than 3 dB, decoding only the shorter code reduces the power consumption by 34%.
Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly

Is it possible to learn policies for robotic assembly that can generalize to new objects? We explore this idea in the context of the kit assembly task. Since classic methods rely heavily on object pose estimation, they often struggle to generalize to new objects without 3D CAD models or task-specific training data. In this work, we propose to formulate the kit assembly task as a shape matching problem, where the goal is to learn a shape descriptor that establishes geometric correspondences between object surfaces and their target placement locations from visual input. This formulation enables the model to acquire a broader understanding of how shapes and surfaces fit together for assembly – allowing it to generalize to new objects and kits. To obtain training data for our model, we present a self-supervised data-collection pipeline that obtains ground truth object-to-placement correspondences by disassembling complete kits. Our resulting real-world system, Form2Fit, learns effective pick and place strategies for assembling objects into a variety of kits – achieving 90% average success rates under different initial conditions (e.g. varying object and kit poses), 94% success under new configurations of multiple kits, and over 86% success with completely new objects and kits. Code, videos, and supplemental material are available at https://form2fit.github.io1
Perceived Ineffectiveness of Pharmacological Treatments for Dysmenorrhea.

Background: Dysmenorrhea affects most reproductive-aged women. Common dysmenorrhea treatments vary in their effectiveness across individuals. Little is known about factors associated with perceived treatment ineffectiveness. The objectives of this study were to describe the perceived ineffectiveness of common pharmacological treatments for dysmenorrhea and investigate factors associated with perceived treatment ineffectiveness. Materials and Methods: In this cross-sectional study, 678 women with dysmenorrhea (aged 14-42) provided data on perceived treatment ineffectiveness, dysmenorrhea symptom-based phenotypes, demographics, clinical factors, and psychobehavioral characteristics. We used Fisher's exact tests to compare treatment ineffectiveness across three symptom-based phenotypes. We used logistic regressions to explore associations of phenotype, demographic, clinical, and psychobehavioral correlates of perceived treatment ineffectiveness. Results: Percentages perceiving treatments as ineffective were 29.3%-35.6% nonsteroidal anti-inflammatory drugs, 49.9% acetaminophen, and 39.3% combined oral contraceptive pills (OCPs). Factors associated with perceived ineffectiveness varied across treatments and included symptom-based phenotypes, clinical, and psychobehavioral factors. For ibuprofen and acetaminophen, women with severe (vs. mild) pain phenotype and higher number of chronic pain conditions were more likely to perceive the treatments as ineffective. For OCPs, women with severe pain (vs. mild) phenotype, comorbid gynecological condition, less anxiety, and worse depressive symptoms were more likely to perceive the treatment as ineffective. Conclusion: A significant percentage of women reported ineffectiveness of dysmenorrhea treatments. Phenotypes, clinical, and psychobehavioral factors were associated with treatment ineffectiveness. Future research should test if symptom-based phenotypes are associated with treatment effectiveness in clinical trials and investigate other factors that affect dysmenorrhea treatment effectiveness, so treatments can be tailored to individuals.
Jump on the Bandwagon? – Characterizing Bandwagon Phenomenon in Online NBA Fan Communities

Understanding user dynamics in online communities has become an active research topic and can provide valuable insights for human behavior analysis and community management. In this work, we investigate the “bandwagon fan” phenomenon, a special case of user dynamics, to provide a large-scale characterization of online fan loyalty in the context of professional sports teams. We leverage the existing structure of NBA-related discussion forums on Reddit, investigate the general bandwagon patterns, and trace the behavior of bandwagon fans to capture latent behavioral characteristics. We observe that better teams attract more bandwagon fans, but they do not necessarily come from weak teams. Our analysis of bandwagon fan flow also shows different trends for different teams, as the playoff season progresses. Furthermore, we compare bandwagon users with non-bandwagon users in terms of their activity and language usage. We find that bandwagon users write shorter comments but receive better feedback, and use words that show less attachment to their affiliated teams. Our observations allow for more effective identification of bandwagon users and prediction of users’ future bandwagon behavior in a season, as demonstrated by the significant improvement over the baseline method in our evaluation results.
Oncogenic mutant RAS signaling activity is rescaled by the ERK/MAPK pathway.

Activating mutations in RAS are present in ~ 30% of human tumors, and the resulting aberrations in ERK/MAPK signaling play a central role in oncogenesis. However, the form of these signaling changes is uncertain, with activating RAS mutants linked to both increased and decreased ERK activation in vivo. Rationally targeting the kinase activity of this pathway requires clarification of the quantitative effects of RAS mutations. Here, we use live-cell imaging in cells expressing only one RAS isoform to quantify ERK activity with a new level of accuracy. We find that despite large differences in their biochemical activity, mutant KRAS isoforms within cells have similar ranges of ERK output. We identify roles for pathway-level effects, including variation in feedback strength and feedforward modulation of phosphatase activity, that act to rescale pathway sensitivity, ultimately resisting changes in the dynamic range of ERK activity while preserving responsiveness to growth factor stimuli. Our results reconcile seemingly inconsistent reports within the literature and imply that the signaling changes induced by RAS mutations early in oncogenesis are subtle.
Spatially resolved measurement of dynamic glucose uptake in live ex vivo tissues.

Abstract Highly proliferative cells depend heavily on glycolysis as a source of energy and biological precursor molecules, and glucose uptake is a useful readout of this aspect of metabolic activity. Glucose uptake is commonly quantified by using flow cytometry for cell cultures and positron emission tomography for organs in vivo. However, methods to detect spatiotemporally resolved glucose uptake in intact tissues are far more limited, particularly those that can quantify changes in uptake over time in specific tissue regions and cell types. Using lymph node metabolism as a case study, we developed an optimized method to detect dynamic and spatially resolved glucose uptake in living tissue by combining ex vivo tissue slice culture with a fluorescent glucose analogue. Live slices of murine lymph node were treated with the glucose analogue 2-[N-(7-nitrobenz-2-oxa-1,3-dia-xol-4-yl)amino]-2-deoxyglucose (2-NBDG). Incubation parameters were optimized to differentiate glucose uptake in activated versus naive lymphocytes. Regional glucose uptake could be imaged at both the tissue level, by widefield microscopy, and at the cellular level, by confocal microscopy. Furthermore, the glucose assay was readily multiplexed with live immunofluorescence labelling to generate maps of 2-NBDG uptake across tissue regions, revealing highest uptake in T cell-dense regions. The signal was predominantly intracellular and localized to lymphocytes rather than stromal cells. Finally, we demonstrated that the assay was repeatable in the same slices, and imaged the dynamic distribution of glucose uptake in response to ex vivo T cell stimulation for the first time. We anticipate that this method will serve as a broadly applicable, user-friendly platform to quantify dynamic metabolic activities in complex tissue microenvironments.
SOTER on ROS: A Run-Time Assurance Framework on the Robot Operating System

We present an implementation of SOTER, a run-time assurance framework for building safe distributed mobile robotic (DMR) systems, on top of the Robot Operating System (ROS). The safety of DMR systems cannot always be guaranteed at design time, especially when complex, off-the-shelf components are used that cannot be verified easily. SOTER addresses this by providing a language-based approach for run-time assurance for DMR systems. SOTER implements the reactive robotic software using the language P, a domain-specific language designed for implementing asynchronous event-driven systems, along with an integrated run-time assurance system that allows programmers to use unfortified components but still provide safety guarantees. We describe an implementation of SOTER for ROS and demonstrate its efficacy using a multi-robot surveillance case study, with multiple run-time assurance modules. Through rigorous simulation, we show that SOTER enabled systems ensure safety, even when using unknown and untrusted components.
A topological zero-one law and elementary equivalence of finitely generated groups

Abstract Let G denote the space of finitely generated marked groups. We give equivalent characterizations of closed subspaces S ⊆ G satisfying the following zero-one law: for any sentence σ in the infinitary logic L ω 1 , ω , the set of all models of σ in S is either meager or comeager. In particular, we prove that the zero-one law holds for certain natural spaces associated to hyperbolic groups and their generalizations. As an application, we show that generic torsion-free lacunary hyperbolic groups are elementarily equivalent; the same claim holds for lacunary hyperbolic groups without non-trivial finite normal subgroups. Our paper has a substantial expository component. We give streamlined proofs of some known results and survey ideas from topology, logic, and geometric group theory relevant to our work. We also discuss some open problems.
Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video with Applications for Virtual Reality

We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for appli...
The universal surface bundle over the Torelli space has no sections

For $$g>3$$ , we give two proofs of the fact that the Birman exact sequence for the Torelli group $$\begin{aligned} 1\rightarrow \pi _1(S_g)\rightarrow {\mathcal I}_{g,1}\rightarrow {\mathcal I}_g\rightarrow 1 \end{aligned}$$ does not split. This result was claimed by Mess (Unit tangent bundle subgroups of the mapping class groups, MSRI Pre-print, 1990), but his proof has a critical and unrepairable error which will be discussed in the introduction. Let $${\mathcal U\mathcal I}_{g,n}\xrightarrow {Tu'_{g,n}} {\mathcal B} {\mathcal I}_{g,n}$$ (resp. $${\mathcal U}{\mathcal P}{\mathcal I}_{g,n}\xrightarrow {Tu_{g,n}}{\mathcal B}{\mathcal P}{\mathcal I}_{g,n}$$ ) denote the universal surface bundle over the Torelli space fixing n points as a set (resp. pointwise). We also deduce that $$Tu'_{g,n}$$ has no sections when $$n>1$$ and that $$Tu_{g,n}$$ has precisely n distinct sections for $$n\ge 0$$ up to homotopy.
Classical analysis of Bianchi types I and II in Ashtekar variables

We solve the complex Einstein equations for Bianchi I and II models formulated in Ashtekar variables. We then solve the reality conditions to obtain a parametrization of the space of Lorentzian solutions in terms of real canonically conjugate variables. In the Ashtekar variables, the dynamics of the universe point particle is governed by only a curved supermetric---there is no potential term. In the usual metric formulation the particle bounces off a potential wall in flat superspace. We consider possible characterizations of this `bounce' in the potential-free Ashtekar variables.