cssi/SciSciGPT-SciSciNet huggingface dataset:
Particle-based energetic variational inference

We introduce a new variational inference (VI) framework, called energetic variational inference (EVI). It minimizes the VI objective function based on a prescribed energy-dissipation law. Using the EVI framework, we can derive many existing particle-based variational inference (ParVI) methods, including the popular Stein variational gradient descent (SVGD). More importantly, many new ParVI schemes can be created under this framework. For illustration, we propose a new particle-based EVI scheme, which performs the particle-based approximation of the density first and then uses the approximated density in the variational procedure, or “Approximation-then-Variation” for short. Thanks to this order of approximation and variation, the new scheme can maintain the variational structure at the particle level, and can significantly decrease the KL-divergence in each iteration. Numerical experiments show the proposed method outperforms some existing ParVI methods in terms of fidelity to the target distribution.
Self-joinings for 3-IETs

We show that typical interval exchange transformations on three intervals are not 2-simple answering a question of Veech. Moreover, the set of self-joinings of almost every 3-IET is a Paulsen simplex.
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming

Data cleaning can be naturally framed as probabilistic inference in a generative model, combining a prior distribution over ground-truth databases with a likelihood that models the noisy channel by which the data are filtered and corrupted to yield incomplete, dirty, and denormalized datasets. Based on this view, we present PClean, a probabilistic programming language for leveraging dataset-specific knowledge to clean and normalize dirty data. PClean is powered by three modeling and inference contributions: (1) a non-parametric model of relational database instances, customizable via probabilistic programs, (2) a sequential Monte Carlo inference algorithm that exploits the model's structure, and (3) near-optimal SMC proposals and blocked Gibbs rejuvenation moves constructed on a per-dataset basis. We show empirically that short (< 50-line) PClean programs can be faster and more accurate than generic PPL inference on multiple data-cleaning benchmarks; perform comparably in terms of accuracy and runtime to state-of-the-art data-cleaning systems (unlike generic PPL inference given the same runtime); and scale to real-world datasets with millions of records.
A Streptococcus Quorum Sensing System Enables Suppression of Innate Immunity.

ABSTRACT Some bacterial pathogens utilize cell-cell communication systems, such as quorum sensing (QS), to coordinate genetic programs during host colonization and infection. The human-restricted pathosymbiont Streptococcus pyogenes (group A streptococcus [GAS]) uses the Rgg2/Rgg3 QS system to modify the bacterial surface, enabling biofilm formation and lysozyme resistance. Here, we demonstrate that innate immune cell responses to GAS are substantially altered by the QS status of the bacteria. We found that macrophage activation, stimulated by multiple agonists and assessed by cytokine production and NF-κB activity, was substantially suppressed upon interaction with QS-active GAS but not QS-inactive bacteria. Neither macrophage viability nor bacterial adherence, internalization, or survival were altered by the QS activation status, yet tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), and interferon beta (IFN-β) levels and NF-κB reporter activity were drastically lower following infection with QS-active GAS. Suppression required contact between viable bacteria and macrophages. A QS-regulated biosynthetic gene cluster (BGC) in the GAS genome, encoding several putative enzymes, was also required for macrophage modulation. Our findings suggest a model wherein upon contact with macrophages, QS-active GAS produce a BGC-derived factor capable of suppressing inflammatory responses. The suppressive capability of QS-active GAS is abolished after treatment with a specific QS inhibitor. These observations suggest that interfering with the ability of bacteria to collaborate via QS can serve as a strategy to counteract microbial efforts to manipulate host defenses. IMPORTANCEStreptococcus pyogenes is restricted to human hosts and commonly causes superficial diseases such as pharyngitis; it can also cause severe and deadly manifestations including necrotizing skin disease or severe postinfectious sequelae like rheumatic heart disease. Understanding the complex mechanisms used by this pathogen to manipulate host defenses could aid in developing new therapeutics to treat infections. Here, we examine the impact of a bacterial cell-cell communication system, which is highly conserved across S. pyogenes, on host innate immune responses. We find that S. pyogenes uses this system to suppress macrophage proinflammatory cytokine responses in vitro. Interference with this communication system could serve as a strategy to disarm bacteria and maintain an effective immune response.
Optimal Curing Strategy for Competing Epidemics Spreading Over Complex Networks

Optimal curing strategy of suppressing competing epidemics spreading over complex networks is a critical issue. In this paper, we first establish a framework to capture the coupling between two epidemics, and then analyze the system's equilibrium states by categorizing them into three classes, and deriving their stability conditions. The designed curing strategy globally optimizes the trade-off between the curing cost and the severity of epidemics in the network. In addition, we provide structural results on the predictability of epidemic spreading by showing the existence and uniqueness of the solution. We also demonstrate the robustness of curing strategy by showing the continuity of epidemic severity with respect to the applied curing effort. A gradient descent algorithm based on a fixed-point iterative scheme is proposed to find the optimal curing strategy. Depending on the system parameters, the curing strategy can lead to switching between equilibria of the epidemic network as the control cost varies. Finally, we use case studies to corroborate and illustrate the obtained theoretical results.
Restitution in Public Concern Cases

Enron Corporation. Arthur Andersen. Guns. Tobacco. Lead. Asbestos. Water Pollution. All are in the news as allegedly having caused injury. All involve restitution. Plaintiffs are bringing suits claiming that not only have they been injured, but also that the companies involved have been unjustly enriched at the plaintiffs' expense. The plaintiffs use, either explicitly or implicitly, the broad concept of restitution found in section one of the Restatement of the Law of Restitution. That section, entitled "Unjust Enrichment," says "[a] person who has been unjustly enriched at the expense of another is required to make restitution to the other."' In this Article, I advocate retention of those broad concepts in the current revision of the Restatement of Restitution.
Estimation of Longitudinal Unsteady Aerodynamics of a Wing-Tail Combination from Wind Tunnel Data

This paper presents an initial step toward model identification from wind tunnel data for an airliner configuration. Two approaches to modeling a transport configuration are considered and applied to both steady and large-amplitude forced-oscillation wind tunnel data taken over a wide range of angles of attack. Only limited conclusions could be drawn from this initial data set. Although model estimated time histories of normal force and pitching moment agree reasonably well with the corresponding measured values, model damping parameters did not, for some cases, have values consistent with small amplitude oscillatory data. In addition, large parameter standard errors implied poor information content for model structure determination and parameter estimation. Further investigation of the modeling problem for more general aerodynamic models is recommended with close attention to experiment design for obtaining parameters with high accuracy.
NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization

Neural architecture search (NAS) typically consists of three main steps: training a super-network, training and evaluating sampled deep neural networks (DNNs), and training the discovered DNN. Most of the existing efforts speed up some steps at the cost of a significant slowdown of other steps or sacrificing the support of non-differentiable search metrics. The unbalanced reduction in the time spent per step limits the total search time reduction, and the inability to support non-differentiable search metrics limits the performance of discovered DNNs.In this paper, we present NetAdaptV2 with three innovations to better balance the time spent for each step while supporting non-differentiable search metrics. First, we propose channel-level bypass connections that merge network depth and layer width into a single search dimension to reduce the time for training and evaluating sampled DNNs. Second, ordered dropout is proposed to train multiple DNNs in a single forward-backward pass to decrease the time for training a super-network. Third, we propose the multi-layer coordinate descent optimizer that considers the interplay of multiple layers in each iteration of optimization to improve the performance of discovered DNNs while supporting non-differentiable search metrics. With these innovations, NetAdaptV2 reduces the total search time by up to 5.8× on ImageNet and 2.4× on NYU Depth V2, respectively, and discovers DNNs with better accuracy-latency/accuracy-MAC trade-offs than state-of-the-art NAS works. Moreover, the discovered DNN outperforms NAS-discovered MobileNetV3 by 1.8% higher top-1 accuracy with the same latency.1
Does banking deregulation affect accounting conservatism

Abstract This study examines the effect of banking competition on borrowing firms’ conditional accounting conservatism (i.e., asymmetric timely loss recognition). The context of the study is the staggered passage of the Interstate Banking and Branching Efficiency Act (IBBEA), the deregulation that permits banks to establish branches across state lines and increases bank competition. I find that firms report less conservatively after the passage of the IBBEA in their headquarter states. The effect on conditional conservatism is stronger for firms in states with a greater increase in competition among banks, firms that are more likely to borrow from in-state banks, firms with greater financial constraint, and firms subject to less external monitoring. Additional tests confirm that the decline in conditional conservatism is observed only after the adoption of IBBEA and lasts for two years. The findings indicate that banks tend to “lowball” borrowers when competition arises by relaxing their demand for conservative reporting. Overall, this study highlights the unintended impacts of banking competition on borrowing firms’ financial reporting.
Private DNA Sequencing: Hiding Information in Discrete Noise

When an individual’s DNA is sequenced, sensitive medical information becomes available to the sequencing laboratory. A recently proposed way to hide an individual’s genetic information is to mix in DNA samples of other individuals. We assume these samples are known to the individual but unknown to the sequencing laboratory. Thus, these DNA samples act as "noise" to the sequencing laboratory, but still allow the individual to recover their own DNA samples afterward. Motivated by this idea, we study the problem of hiding a binary random variable X (a genetic marker) with the additive noise provided by mixing DNA samples, using mutual information as a privacy metric. This is equivalent to the problem of finding a worst-case noise distribution for recovering X from the noisy observation among a set of feasible discrete distributions. We characterize upper and lower bounds to the solution of this problem, which are empirically shown to be very close. The lower bound is obtained through a convex relaxation of the original discrete optimization problem, and yields a closed-form expression. The upper bound is computed via a greedy algorithm for selecting the mixing proportions.
Blockholder exit threats and corporate cash holdings

We investigate the effects of blockholder exit threats, which increase with stock liquidity, on corporate cash holdings and the value of cash to shareholders. Exploiting decimalization as an exogenous shock to stock liquidity to identify the effects of blockholder exit threats, we find robust evidence that blockholder exit threats have a negative effect on corporate cash holdings and a positive effect on the value of cash to shareholders. Further analysis indicates that exit threat induces firms to increase stock repurchases and improve investment efficiency. Our evidence is consistent with the view that blockholder exit threats are an effective corporate governance mechanism.
Factors associated with clinical severity in emergency department patients presenting with symptomatic SARS-CoV-2 infection

Abstract Objective To measure the association of race, ethnicity, comorbidities, and insurance status with need for hospitalization of symptomatic emergency department patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods This study is a cohort study of symptomatic patients presenting to a single emergency department (ED) with laboratory-confirmed SARS-CoV-2 infection from March 7-August 9, 2020. We collected patient-level information regarding demographics, insurance status, comorbidities, level of care, and mortality using a structured chart review. We compared characteristics of patients categorized by (1) home discharge, (2) general hospital ward admission, and (3) intensive care unit (ICU) admission or death within 30 days of the index visit. Univariate and multivariable logistic regression analyses were performed to report odds ratios (OR) and 95% confidence intervals (95% CI) between hospital admission versus ED discharge home and between ICU care versus general hospital ward admission. Results In total, 994 patients who presented to the ED with symptoms were included in the analysis with 551 (55.4%) patients discharged home, 314 (31.6%) patients admitted to the general hospital ward, and 129 (13.0%) admitted to the ICU or dying. Patients requiring admission were more likely to be Black or to have public insurance (Medicaid and/or Medicare). Patients who were admitted to the ICU or dying were more likely aged ≥ 65 years or male. In multivariable logistic regression, old age, public insurance, diabetes, hypertension, obesity, heart failure, and hyperlipidemia were independent predictors of hospital admission. When comparing those who needed ICU care versus general hospital ward admission in univariate logistic regression, patients with Medicaid (OR 2.4, 95% CI 1.2?4.6), Medicare (OR 4.2, 95% CI 2.1?8.4), Medicaid and Medicare (OR 4.3, 95% CI 2.4?7.7), history of chronic obstructive pulmonary disease (OR 2.2, 95% CI 1.2?4.2), hypertension (OR 1.7, 95% CI 1.1?2.7), and heart failure (OR 2.6, 95% CI 1.4?4.7) were more likely to be admitted into the ICU or die;Black (OR 1.1, 95% CI 0.4?2.9) and Hispanic/Latino (OR 1.0, 95% CI 0.6?1.8) patients were less likely to be admitted into the ICU;however, the associations were not statistically significant. In multivariable logistic regression, old age, male sex, public insurance, and heart failure were independent predictors of ICU care/death. Conclusion Comorbidities and public insurance are predictors of more severe illness for patients with SARS-CoV-2. This study suggests that the disparities in severity seen in COVID-19 among Black patients may be attributable, in part, to low socioeconomic status and chronic health conditions.
Fee Simple Failures: Rural Landscapes and Race

Property law’s roots are rural. America pursued an early agrarian vision that understood real property rights as instrumental to achieving a country of free, engaged citizens who cared for their communities and stewarded their physical place in it. But, we have drifted far from this ideal. Today, American agriculture is industrialized, and rural communities are in decline. The fee simple ownership form has failed every agrarian objective but one: the maintenance of white land ownership. For it was also embedded in the original American experiment that land ownership would be racialized for the benefit of its white citizens, via acts of colonialism, slavery, and explicit race-based exclusion in property law. Today, rather than undoing this racialized legacy, modern property rules only further concentrate and homogenize rural land ownership. Agricultural land ownership remains almost entirely—98 percent—white. This is a critical racial justice issue that converges directly with our impending environmental crisis and the decline of rural communities more generally. This Article builds on work of rural sociologists and farm advocates who demonstrate, again and again, that despite a pervasive narrative of rural places dying for want of population and agricultural systems too far gone for reform, the reality is a crowd of emerging beginning farmers—and farmers of color in particular—clamoring for access. Existing policy efforts to support beginning farmers have focused primarily on supporting a few private land transactions within existing systems. This Article brings property theory to the table for the first time, arguing that property law itself is not only responsible for the original racialized distributions of agricultural land but also actively perpetuates both ongoing racialized disparities and the currently industrialized and depopulated rural landscape. This Article deconstructs our most fundamental land-tenure choice—the fee simple itself—and calls on our collective legal imagination to develop more adaptive, inclusive, and dynamic land-tenure designs rooted in these otherwise overlooked rural places.
Braided Picard groups and graded extensions of braided tensor categories

We classify various types of graded extensions of a finite braided tensor category $$\mathcal {B}$$ in terms of its 2-categorical Picard groups. In particular, we prove that braided extensions of $$\mathcal {B}$$ by a finite group A correspond to braided monoidal 2-functors from A to the braided 2-categorical Picard group of $$\mathcal {B}$$ (consisting of invertible central $$\mathcal {B}$$ -module categories). Such functors can be expressed in terms of the Eilnberg-Mac Lane cohomology. We describe in detail braided 2-categorical Picard groups of symmetric fusion categories and of pointed braided fusion categories.
Singular cohomology from supersymmetric field theories

Abstract We show that Sullivan's model of rational differential forms on a simplicial set X may be interpreted as a (kind of) 0 | 1 -dimensional supersymmetric quantum field theory over X, and, as a consequence, concordance classes of such theories represent the rational cohomology of X. We introduce the notion of superalgebraic cartesian sets, a concept of space which should roughly be thought of as a blend of simplicial sets and supermanifolds, but valid over an arbitrary base ring. Every simplicial set gives rise to a superalgebraic cartesian set and so we can formulate the notion of 0 | 1 -dimensional supersymmetric quantum field theory over X, entirely within the language of such spaces. We explore several variations in the kind of field theory and discuss their cohomological interpretations. Finally, utilizing a theorem of Cartan-Miller, we describe a variant of our theory which is valid over any commutative ring S and allows one to recover the S-cohomology H ⁎ ( X ; S ) additively and with multiples of the cup product structure.
Cusp forms as p-adic limits

Abstract Ahlgren and Samart relate three cusp forms with complex multiplication to certain weakly holomorphic modular forms using p-adic bounds related to their Fourier coefficients. In these three examples, their result strengthens a theorem of Guerzhoy, Kent, and Ono which pairs certain CM forms with weakly holomorphic modular forms via p-adic limits. Ahlgren and Samart use only the theory of modular forms and Hecke operators, whereas Guerzhoy, Kent, and Ono use the theory of harmonic Maass forms. Here we extend Ahlgren and Samart's work to all cases where the cusp form space is one-dimensional and has trivial Nebentypus. Along the way, we obtain a duality result relating two families of weakly holomorphic modular forms that arise naturally in each case.
The publicness of publicly funded research

In this paper, we chronicle key U.S. legislative initiatives during the post-World War II history of public policy related to the ownership of publicly funded research-based knowledge. Our motivation for recording this history is the observation that many scholars, who are appropriately concerned about taxpayer rights, have argued for the need for public policy to clarify ownership of the publicness or openness of publicly funded research results when in fact such public policies have long been in place. We conclude this historical trace with the proposition that if the past is prologue to the future, one might expect future administrations to continue to acknowledge the importance of public access to findings from publicly funded research, be that research having occurred in federal laboratories, universities, or private-sector organizations.
140 GHz Urban Microcell Propagation Measurements for Spatial Consistency Modeling

Sub-Terahertz frequencies (frequencies above 100 GHz) have the potential to satisfy the unprecedented demand on data rate on the order of hundreds of Gbps for sixth-generation (6G) wireless communications and beyond. Accurate beam tracking and rapid beam selection are increasingly important since antenna arrays with more elements generate narrower beams to compensate for additional path loss within the first meter of propagation distance at sub-THz frequencies. Realistic channel models for above 100 GHz are needed, and should include spatial consistency to model the spatial and temporal channel evolution along the user trajectory. This paper introduces recent outdoor urban microcell (UMi) propagation measurements at 142 GHz along a 39 m × 12 m rectangular route (102 m long), where each consecutive and adjacent receiver location is 3 m apart from each other. The measured power delay profiles and angular power spectrum at each receiver location are used to study spatial autocorrelation properties of various channel parameters such as shadow fading, delay spread, and angular spread along the track. Compared to the correlation distances reported in the 3GPP TR 38.901 for frequencies below 100 GHz, the measured correlation distance of shadow fading at 142 GHz (3.8 m) is much shorter than the 10-13 m as specified in 3GPP; the measured correlation distances of delay spread and angular spread at 142 GHz (both 12 m) are comparable to the 7-10 m as specified in 3GPP. This result may guide the development of a statistical spatially consistent channel model for frequencies above 100 GHz in the UMi street canyon environment.
An Efficient and Parallel Gaussian Sampler for Lattices.

At the heart of many recent lattice-based cryptographic schemes is a polynomial-time algorithm that, given a ‘high-quality’ basis, generates a lattice point according to a Gaussian-like distribution. Unlike most other operations in lattice-based cryptography, however, the known algorithm for this task (due to Gentry, Peikert, and Vaikuntanathan; STOC 2008) is rather inefficient, and is inherently sequential. We present a new Gaussian sampling algorithm for lattices that is efficient and highly parallelizable. We also show that in most cryptographic applications, the algorithm’s efficiency comes at almost no cost in asymptotic security. At a high level, our algorithm resembles the “perturbation” heuristic proposed as part of NTRUSign (Hoffstein et al., CT-RSA 2003), though the details are quite different. To our knowledge, this is the first algorithm and rigorous analysis demonstrating the security of a perturbation-like technique.
Dirac Solitons in Optical Microresonators

We demonstrate optical Dirac solitons in microresonators by polarization mode coupling. The solitons show unusual properties such as polarization twisting and asymmetrical spectra, and they provide a road-map for visible-band soliton generation in microresonators.
Diverse wMel variants of Wolbachia pipientis differentially rescue fertility and cytological defects of the bag of marbles partial loss of function mutation in Drosophila melanogaster.

In Drosophila melanogaster, the maternally inherited endosymbiont Wolbachia pipientis interacts with germline stem cell genes during oogenesis. One such gene, bag of marbles (bam) is the key switch for differentiation and also shows signals of adaptive evolution for protein diversification. These observations have led us to hypothesize that W. pipientis could be driving the adaptive evolution of bam for control of oogenesis. To test this hypothesis, we must understand the specificity of the genetic interaction between bam and W. pipientis. Previously, we documented that the W. pipientis variant, wMel, rescued the fertility of the bamBW hypomorphic mutant as a transheterozygote over a bam null. However, bamBW was generated more than 20 years ago in an uncontrolled genetic background and maintained over a balancer chromosome. Consequently, the chromosome carrying bamBW accumulated mutations that have prevented controlled experiments to further assess the interaction. Here, we used CRISPR/Cas9 to engineer the same single amino acid bam hypomorphic mutation (bamL255F) and a new bam null disruption mutation into the w1118 isogenic background. We assess the fertility of wildtype bam, bamL255F/bamnull hypomorphic, and bamL255F/bamL255F mutant females, each infected individually with 10 W. pipientis wMel variants representing three phylogenetic clades. Overall, we find that all of the W. pipientis variants tested here rescue bam hypomorphic fertility defects with wMelCS-like variants exhibiting the strongest rescue effects. In addition, these variants did not increase wildtype bam female fertility. Therefore, both bam and W. pipientis interact in genotype-specific ways to modulate female fertility, a critical fitness phenotype.
Mechanism of negative membrane curvature generation by I-BAR domains.

Summary The membrane sculpting ability of BAR domains has been attributed to the intrinsic curvature of their banana-shaped dimeric structure. However, there is often a mismatch between this intrinsic curvature and the diameter of the membrane tubules generated. I-BAR domains are especially mysterious since they are almost flat but generate high negative membrane curvature. Here, we use atomistic implicit-solvent computer modeling to show that the membrane bending of the IRSp53 I-BAR domain is dictated by its higher oligomeric structure, whose curvature is completely unrelated to the intrinsic curvature of the dimer. Two other I-BARs give similar results, whereas a flat F-BAR sheet develops a concave membrane-binding interface, consistent with its observed positive membrane curvature generation. Laterally interacting helical spirals of I-BAR dimers on tube interiors are stable and have an enhanced binding energy that is sufficient for membrane bending to experimentally observed tubule diameters at a reasonable surface density.
Improving RNN-T ASR performance with date-time and location awareness

In this paper, we explore the benefits of incorporating context into a Recurrent Neural Network (RNN-T) based Automatic Speech Recognition (ASR) model to improve the speech recognition for virtual assistants. Specifically, we use meta information extracted from the time at which the utterance is spoken and the approximate location information to make ASR context aware. We show that these contextual information, when used individually, improves overall performance by as much as 3.48% relative to the baseline and when the contexts are combined, the model learns complementary features and the recognition improves by 4.62%. On specific domains, these contextual signals show improvements as high as 11.5%, without any significant degradation on others. We ran experiments with models trained on data of sizes 30K hours and 10K hours. We show that the scale of improvement with the 10K hours dataset is much higher than the one obtained with 30K hours dataset. Our results indicate that with limited data to train the ASR model, contextual signals can improve the performance significantly.
Impulsive Fire Disturbance in a Savanna Model: Tree–Grass Coexistence States, Multiple Stable System States, and Resilience

Savanna ecosystems are shaped by the frequency and intensity of regular fires. We model savannas via an ordinary differential equation (ODE) encoding a one-sided inhibitory Lotka–Volterra interaction between trees and grass. By applying fire as a discrete disturbance, we create an impulsive dynamical system that allows us to identify the impact of variation in fire frequency and intensity. The model exhibits three different bistability regimes: between savanna and grassland; two savanna states; and savanna and woodland. The impulsive model reveals rich bifurcation structures in response to changes in fire intensity and frequency—structures that are largely invisible to analogous ODE models with continuous fire. In addition, by using the amount of grass as an example of a socially valued function of the system state, we examine the resilience of the social value to different disturbance regimes. We find that large transitions (“tipping”) in the valued quantity can be triggered by small changes in disturbance regime.
Embodied intelligence via learning and evolution.

The intertwined processes of learning and evolution in complex environmental niches have resulted in a remarkable diversity of morphological forms. Moreover, many aspects of animal intelligence are deeply embodied in these evolved morphologies. However, the principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control, remain elusive, because performing large-scale in silico experiments on evolution and learning is challenging. Here, we introduce Deep Evolutionary Reinforcement Learning (DERL): a computational framework which can evolve diverse agent morphologies to learn challenging locomotion and manipulation tasks in complex environments. Leveraging DERL we demonstrate several relations between environmental complexity, morphological intelligence and the learnability of control. First, environmental complexity fosters the evolution of morphological intelligence as quantified by the ability of a morphology to facilitate the learning of novel tasks. Second, we demonstrate a morphological Baldwin effect i.e., in our simulations evolution rapidly selects morphologies that learn faster, thereby enabling behaviors learned late in the lifetime of early ancestors to be expressed early in the descendants lifetime. Third, we suggest a mechanistic basis for the above relationships through the evolution of morphologies that are more physically stable and energy efficient, and can therefore facilitate learning and control. The authors propose a new framework, deep evolutionary reinforcement learning, evolves agents with diverse morphologies to learn hard locomotion and manipulation tasks in complex environments, and reveals insights into relations between environmental physics, embodied intelligence, and the evolution of rapid learning.
Decision-Theoretic Question Generation for Situated Reference Resolution: An Empirical Study and Computational Model

Dialogue agents that interact with humans in situated environments need to manage referential ambiguity across multiple modalities and ask for help as needed. However, it is not clear what kinds of questions such agents should ask nor how the answers to such questions can be used to resolve ambiguity. To address this, we analyzed dialogue data from an interactive study in which participants controlled a virtual robot tasked with organizing a set of tools while engaging in dialogue with a live, remote experimenter. We discovered a number of novel results, including the distribution of question types used to resolve ambiguity and the influence of dialogue-level factors on the reference resolution process. Based on these empirical findings we: (1) developed a computational model for clarification requests using a decision network with an entropy-based utility assignment method that operates across modalities, (2) evaluated the model, showing that it outperforms a slot-filling baseline in environments of varying ambiguity, and (3) interpreted the results to offer insight into the ways that agents can ask questions to facilitate situated reference resolution.
Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example.

Abstract Background Machine learning (ML) models are beginning to proliferate in psychiatry, however machine learning models in psychiatric genetics have not always accounted for ancestry. Using an empirical example of a proposed genetic test for OUD, and exploring a similar test for tobacco dependence and a simulated binary phenotype, we show that genetic prediction using ML is vulnerable to ancestral confounding. Methods We utilize five ML algorithms trained with 16 brain reward-derived “candidate” SNPs proposed for commercial use and examine their ability to predict OUD vs. ancestry in an out-of-sample test set (N = 1000, stratified into equal groups of n = 250 cases and controls each of European and African ancestry). We rerun analyses with 8 random sets of allele-frequency matched SNPs. We contrast findings with 11 genome-wide significant variants for tobacco smoking. To document generalizability, we generate and test a random phenotype. Results None of the 5 ML algorithms predict OUD better than chance when ancestry was balanced but were confounded with ancestry in an out-of-sample test. In addition, the algorithms preferentially predicted admixed subpopulations. Random sets of variants matched to the candidate SNPs by allele frequency produced similar bias. Genome-wide significant tobacco smoking variants were also confounded by ancestry. Finally, random SNPs predicting a random simulated phenotype show that the bias attributable to ancestral confounding could impact any ML-based genetic prediction. Conclusions Researchers and clinicians are encouraged to be skeptical of claims of high prediction accuracy from ML-derived genetic algorithms for polygenic traits like addiction, particularly when using candidate variants.
Simple models highlight differences in the walking biomechanics of young children and adults.

Adults conserve metabolic energy during walking by minimizing the step-to-step transition work performed by the legs during double support and by utilizing spring-like mechanisms in their legs, but little is known as to whether children utilize these same mechanisms. To gain a better understanding, we studied how children (5-6 years) and adults modulate the mechanical and metabolic demands of walking at their preferred speed, across slow (75%), preferred (100%) and fast (125%) step frequencies. We quantified (1) the positive mass-specific work done by the trailing leg during step-to-step transitions and (2) the leg's spring-like behavior during single support. On average, children walked with a 36% greater net cost of transport (COT; J kg-1 m-1) than adults (P=0.03), yet both groups increased their net COT at varying step frequencies. After scaling for speed, children generated ∼2-fold less trailing limb positive scaled mechanical work during the step-to-step transition (P=0.02). Unlike adults, children did not modulate their trailing limb positive work to meet the demands of walking at 75% and 125% of their preferred step frequency. In single support, young children operated their stance limb with much greater compliance than adults ( versus 11.35; P=0.023). Our observations suggest that the mechanics of walking in children aged 5-6 years are fundamentally distinct from the mechanics of walking in adults and may help to explain a child's higher net COT. These insights have implications for the design of assistive devices for children and suggest that children cannot be simply treated as scaled-down versions of adults.
Virtual Flight Demonstration of the Stratospheric Dual-Aircraft Platform

A baseline configuration for the dual-aircraft platform (DAP) concept is described and evaluated in a physics-based flight dynamics simulations for two month-long missions as a communications relay in the lower stratosphere above central Florida. The DAP features two unmanned aerial vehicles connected via a long adjustable cable which effectively sail back-and-forth using wind velocity gradients and solar energy. Detailed atmospheric profiles in the vicinity of 60,000-ft derived from archived data measured by the 50-Mhz Doppler Radar Wind Profiler at Cape Canaveral are used in the flight simulations. An overview of the novel guidance and flight control strategies are provided. The energy-usage of the baseline configuration during month-long stationkeeping missions (i.e., within 150-mile radius of downtown Orlando) is characterized and compared to that of a pure solar aircraft.
Multi-Constraint Predictive Control System with Auxiliary Emergency Controllers for Autonomous Vehicles

This paper introduces a multi-constraint predictive control algorithm along with a safety layer to guarantee path tracking and object avoidance in emergency situations. A controller switching mechanism is designed which switches the controller between a main and an emergency controller. As the main controller, a nonlinear multi-constraint model predictive controller (MPC) is designed. The MPC algorithm is compared with Stanley and PID methods in terms of their efficiency to validate the MPC as the main controller. However, in unexpected situations, the high computational time of the planner and MPC modules threatens the safety of the vehicle. In order to respond as quickly as possible, emergency braking and maneuver systems are added which could be triggered separately per different situations. Two different emergency scenarios have been implemented in CARLA simulator and Python environment to evaluate the proposed method.
Seismic Wave Propagation and Inversion with Neural Operators

Seismic wave propagation forms the basis for most aspects of seismological research, yet solving the wave equation is a major computational burden that inhibits the progress of research. This is exaspirated by the fact that new simulations must be performed when the velocity structure or source location is perturbed. Here, we explore a prototype framework for learning general solutions using a recently developed machine learning paradigm called Neural Operator. A trained Neural Operator can compute a solution in negligible time for any velocity structure or source location. We develop a scheme to train Neural Operators on an ensemble of simulations performed with random velocity models and source locations. As Neural Operators are grid-free, it is possible to evaluate solutions on higher resolution velocity models than trained on, providing additional computational efficiency. We illustrate the method with the 2D acoustic wave equation and demonstrate the method's applicability to seismic tomography, using reverse mode automatic differentiation to compute gradients of the wavefield with respect to the velocity structure. The developed procedure is nearly an order of magnitude faster than using conventional numerical methods for full waveform inversion.
Gibbs posterior inference on multivariate quantiles

Abstract Bayesian and other likelihood-based methods require specification of a statistical model and may not be fully satisfactory for inference on quantities, such as quantiles, that are not naturally defined as model parameters. In this paper, we construct a direct and model-free Gibbs posterior distribution for multivariate quantiles. Being model-free means that inferences drawn from the Gibbs posterior are not subject to model misspecification bias, and being direct means that no priors for or marginalization over nuisance parameters are required. We show here that the Gibbs posterior enjoys a root- n convergence rate and a Bernstein–von Mises property, i.e., for large n , the Gibbs posterior distribution can be approximated by a Gaussian. Moreover, we present numerical results showing the validity and efficiency of credible sets derived from a suitably scaled Gibbs posterior.
Choose a Transformer: Fourier or Galerkin

In this paper, we apply the self-attention from the state-of-the-art Transformer in Attention Is All You Need the first time to a data-driven operator learning problem related to partial differential equations. We put together an effort to explain the heuristics of, and improve the efficacy of the self-attention by demonstrating that the softmax normalization in the scaled dot-product attention is sufficient but not necessary, and have proved the approximation capacity of a linear variant as a Petrov-Galerkin projection. A new layer normalization scheme is proposed to allow a scaling to propagate through attention layers, which helps the model achieve remarkable accuracy in operator learning tasks with unnormalized data. Finally, we present three operator learning experiments, including the viscid Burgers' equation, an interface Darcy flow, and an inverse interface coefficient identification problem. All experiments validate the improvements of the newly proposed simple attention-based operator learner over their softmax-normalized counterparts.
Diffusion Normalizing Flow

We present a novel generative modeling method called diffusion normalizing flow based on stochastic differential equations (SDEs). The algorithm consists of two neural SDEs: a forward SDE that gradually adds noise to the data to transform the data into Gaussian random noise, and a backward SDE that gradually removes the noise to sample from the data distribution. By jointly training the two neural SDEs to minimize a common cost function that quantifies the difference between the two, the backward SDE converges to a diffusion process the starts with a Gaussian distribution and ends with the desired data distribution. Our method is closely related to normalizing flow and diffusion probabilistic models and can be viewed as a combination of the two. Compared with normalizing flow, diffusion normalizing flow is able to learn distributions with sharp boundaries. Compared with diffusion probabilistic models, diffusion normalizing flow requires fewer discretization steps and thus has better sampling efficiency. Our algorithm demonstrates competitive performance in both high-dimension data density estimation and image generation tasks.
SoK: Cryptographic Confidentiality of Data on Mobile Devices

Mobile devices have become an indispensable component of modern life. Their high storage capacity gives these devices the capability to store vast amounts of sensitive personal data, which makes them a high-value target: these devices are routinely stolen by criminals for data theft, and are increasingly viewed by law enforcement agencies as a valuable source of forensic data. Over the past several years, providers have deployed a number of advanced cryptographic features intended to protect data on mobile devices, even in the strong setting where an attacker has physical access to a device. Many of these techniques draw from the research literature, but have been adapted to this entirely new problem setting. This involves a number of novel challenges, which are incompletely addressed in the literature. In this work, we outline those challenges, and systematize the known approaches to securing user data against extraction attacks. Our work proposes a methodology that researchers can use to analyze cryptographic data confidentiality for mobile devices. We evaluate the existing literature for securing devices against data extraction adversaries with powerful capabilities including access to devices and to the cloud services they rely on. We then analyze existing mobile device confidentiality measures to identify research areas that have not received proper attention from the community and represent opportunities for future research.
The Price Effect of Drug Price Ceilings: Intended and Unintended Consequences

Policy makers in many developing countries use maximum price or markup policies to control pharmaceutical costs, which represent 20%–60% of their overall healthcare expenditure. We study the price ...
Tabular data: Deep learning is not all you need

Abstract A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.
Bioinformatic and cell-based tools for pooled CRISPR knockout screening in mosquitos

Mosquito-borne diseases present a worldwide public health burden. Current efforts to understand and counteract them have been aided by the use of cultured mosquito cells. Moreover, application in mammalian cells of forward genetic approaches such as CRISPR screens have identified essential genes and genes required for host-pathogen interactions, and in general, aided in functional annotation of genes. An equivalent approach for genetic screening of mosquito cell lines has been lacking. To develop such an approach, we design a new bioinformatic portal for sgRNA library design in several mosquito genomes, engineer mosquito cell lines to express Cas9 and accept sgRNA at scale, and identify optimal promoters for sgRNA expression in several mosquito species. We then optimize a recombination-mediated cassette exchange system to deliver CRISPR sgRNA and perform pooled CRISPR screens in an Anopheles cell line. Altogether, we provide a platform for high-throughput genome-scale screening in cell lines from disease vector species.