cssi/SciSciGPT-SciSciNet huggingface dataset:
Learning Deep Neural Networks under Agnostic Corrupted Supervision

Training deep neural models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact the generalization performance. To alleviate this problem, we present an efficient robust algorithm that achieves strong guarantees without any assumption on the type of corruption and provides a unified framework for both classification and regression problems. Unlike many existing approaches that quantify the quality of the data points (e.g., based on their individual loss values), and filter them accordingly, the proposed algorithm focuses on controlling the collective impact of data points on the average gradient. Even when a corrupted data point failed to be excluded by our algorithm, the data point will have a very limited impact on the overall loss, as compared with state-of-the-art filtering methods based on loss values. Extensive experiments on multiple benchmark datasets have demonstrated the robustness of our algorithm under different types of corruption.
Superparticle phenomenology from the natural mini-landscape

The methodology of the heterotic mini-landscape attempts to zero in on phenomenologically viable corners of the string landscape where the effective low energy theory is the Minimal Supersymmetric Standard Model with localized grand unification. The gaugino mass pattern is that of mirage-mediation. The magnitudes of various SM Yukawa couplings point to a picture where scalar soft SUSY breaking terms are related to the geography of fields in the compactified dimensions. Higgs fields and third generation scalars extend to the bulk and occur in split multiplets with TeV scale soft masses. First and second generation scalars, localized at orbifold fixed points or tori with enhanced symmetry, occur in complete GUT multiplets and have much larger masses. This picture can be matched onto the parameter space of generalized mirage mediation. Naturalness considerations, the requirement of the observed electroweak symmetry breaking pattern, and LHC bounds on m g together limit the gravitino mass to the m 3/2 ∼ 5-60 TeV range. The mirage unification scale is bounded from below with the limit depending on the ratio of squark to gravitino masses. We show that while natural SUSY in this realization may escape detection even at the high luminosity LHC, the high energy LHC with $$ \sqrt{s}=33 $$ TeV could unequivocally confirm or exclude this scenario. It should be possible to detect the expected light higgsinos at the ILC if these are kinematically accessible, and possibly also discriminate the expected compression of gaugino masses in the natural mini-landscape picture from the mass pattern expected in models with gaugino mass unification. The thermal WIMP signal should be accessible via direct detection searches at the multi-ton noble liquid detectors such as XENONnT or LZ.
Human-Robot Teaming: From Space Robotics to Self-Driving Cars

In this talk, I describe how NASA Ames has been developing and testing robots for space exploration. In our research, we have focused on studying how human-robot teams can increase the performance, reduce the cost, and increase the success of space missions. A key tenet of our work is that humans and robots should support one another in order to compensate for limitations of manual control and autonomy. This principle has broad applicability beyond space exploration. Thus, I will conclude by discussing how we have worked with Nissan to apply our methods to self-driving cars, enabling humans to support autonomous vehicles operating in unpredictable and difficult situations.
Localization in the Crowd with Topological Constraints

We address the problem of crowd localization, i.e., the prediction of dots corresponding to people in a crowded scene. Due to various challenges, a localization method is prone to spatial semantic errors, i.e., predicting multiple dots within a same person or collapsing multiple dots in a cluttered region. We propose a topological approach targeting these semantic errors. We introduce a topological constraint that teaches the model to reason about the spatial arrangement of dots. To enforce this constraint, we define a persistence loss based on the theory of persistent homology. The loss compares the topographic landscape of the likelihood map and the topology of the ground truth. Topological reasoning improves the quality of the localization algorithm especially near cluttered regions. On multiple public benchmarks, our method outperforms previous localization methods. Additionally, we demonstrate the potential of our method in improving the performance in the crowd counting task.
Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detection

Existing methods for object detection in UAV images ignored an important challenge – imbalanced class distribution in UAV images – which leads to poor performance on tail classes. We systematically investigate existing solutions to long-tail problems and unveil that re-balancing methods that are effective on natural image datasets cannot be trivially applied to UAV datasets. To this end, we rethink longtailed object detection in UAV images and propose the Dual Sampler and Head detection Network (DSHNet), which is the first work that aims to resolve long-tail distribution in UAV images. The key components in DSHNet include Class-Biased Samplers (CBS) and Bilateral Box Heads (BBH), which are developed to cope with tail classes and head classes in a dual-path manner. Without bells and whistles, DSHNet significantly boosts the performance of tail classes on different detection frameworks. Moreover, DSHNet significantly outperforms base detectors and generic approaches for long-tail problems on VisDrone and UAVDT datasets. It achieves new state-of-the-art performance when combining with image cropping methods. Code is available at https://github.com/we1pingyu/DSHNet
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform

Many platforms collect crowdsourced information primarily from volunteers. As this type of knowledge curation has become widespread, contribution formats vary substantially and are driven by diverse processes across differing platforms. Thus, models for one platform are not necessarily applicable to others. Here, we study the temporal dynamics of Genius, a platform mainly designed for user-contributed annotations of song lyrics. A unique aspect of Genius is that the annotations are extremely local --- an annotated lyric may just be a few lines of a song --- but also highly related, e.g., by song, album, artist, or genre. We analyze several dynamical processes associated with lyric annotations and their edits, which differ substantially from models for other platforms. For example, expertise on song annotations follows a "U shape" where experts are both early and late contributors with non-experts contributing intermediately; we develop a user utility model that offers one possible explanation for such behavior. We also find several traits appearing early in a user's lifespan of contributions that distinguish (eventual) experts from non-experts. Combining our findings, we develop a model for early prediction of user expertise.
Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses.

Exome association studies to date have generally been underpowered to systematically evaluate the phenotypic impact of very rare coding variants. We leveraged extensive haplotype sharing between 49,960 exome-sequenced UK Biobank participants and the remainder of the cohort (total n ≈ 500,000) to impute exome-wide variants with accuracy R2 > 0.5 down to minor allele frequency (MAF) ~0.00005. Association and fine-mapping analyses of 54 quantitative traits identified 1,189 significant associations (P < 5 × 10−8) involving 675 distinct rare protein-altering variants (MAF < 0.01) that passed stringent filters for likely causality. Across all traits, 49% of associations (578/1,189) occurred in genes with two or more hits; follow-up analyses of these genes identified allelic series containing up to 45 distinct ‘likely-causal’ variants. Our results demonstrate the utility of within-cohort imputation in population-scale genome-wide association studies, provide a catalog of likely-causal, large-effect coding variant associations and foreshadow the insights that will be revealed as genetic biobank studies continue to grow. Imputation of rare coding variants in the UK Biobank enables association and fine-mapping analyses of rare (minor allele frequency (MAF) = 0.00005) genotypes, identifying 600 new variant–trait associations, including long allelic series in individual genes.
Studies of Leading-Edge Thrust Phenomena

A study of practical limitations on achievement of theoretical leading-edge thrust has been made and an empirical method for estimation of attainable thrust has been developed. The method is based on a theoretical analysis of a set of two-dimensional airfoils to define thrust dependence on airfoil geometric characteristics and arbitrarily defined limiting pressures, an examination of two-dimensional airfoil experimental data to provide an estimate of limiting pressure dependence on local Mach number and Reynolds number, and employment of simple sweep theory to adapt the method to three-dimensional wings. Because the method takes into account the spanwise variation of airfoil section characteristics, an opportunity is afforded for design by iteration to maximize the attainable thrust and the attendant performance benefits. The applicability of the method was demonstrated by comparisons of theoretical and experimental aerodynamic characteristics for a series of wing-body configurations. Generally, good predictions of the attainable thrust and its influence on lift and drag characteristics were obtained over a range of Mach numbers from 0.24 to 2.0.
Arc1 and the microbiota together modulate growth and metabolic traits in Drosophila.

Perturbations to animal-associated microbial communities (the microbiota) have deleterious effects on various aspects of host fitness, but the molecular processes underlying these impacts are poorly understood. Here we identify a novel connection between the microbiota and the neuronal factor Arc1 that affects growth and metabolism in Drosophila. We find that Arc1 exhibits tissue-specific microbiota-dependent expression changes, and that germ-free flies bearing a null mutation of Arc1 exhibit delayed and stunted larval growth, along with a variety of molecular, cellular, and organismal traits indicative of metabolic dysregulation. Remarkably, we show that the majority of these phenotypes can be fully suppressed by mono-association with a single Acetobacter sp. isolate, through mechanisms involving both bacterial diet modification and live bacteria. Additionally, we provide evidence that Arc1 function in key neuroendocrine cells of the larval brain modulates growth and metabolic homeostasis under germ-free conditions. Our results reveal a novel role for Arc1 in modulating physiological responses to the microbial environment, and highlight how host-microbe interactions can profoundly impact the phenotypic consequences of genetic mutations in an animal host.
Nationwide Ethnic/Racial Differences in Surgical Treatment of Discoid Meniscus in Children: A PHIS Database Study.

BACKGROUND Discoid meniscus, a congenital meniscus variant, may have greater incidence in Asian populations. No US population-based studies have examined the discoid meniscus ethnic/racial distribution. In pediatric patients undergoing meniscus surgery, it is hypothesized that ethnic/racial variability exists in patients with discoid meniscus and this variability is different than in patients with medial meniscus tears. METHODS The Pediatric Health Information System was queried from 48 hospitals to examine patients 18 years of age and younger between 2015 and 2019, using International Classification of Diseases, 10th Revision, Clinical Modification codes. A cohort of patients treated surgically for discoid meniscus was compared with a cohort of patients treated surgically for medial meniscal tear. These 2 populations were compared based on age, sex, ethnicity/race, Current Procedural Terminology code, insurance, urban versus rural, and region of country. Univariate testing and multivariable logistic modeling were used to test for associations. RESULTS A discoid meniscus cohort of 399 children (median age, 13.0 y) was compared with a medial meniscus tear cohort of 3157 children (median age, 16.0 y) (P<0.001). Hispanic/Latino children accounted for 36.8% of the discoid lateral meniscus and 22.7% of the medial meniscus populations (P<0.001). Among pediatric patients that had surgery for discoid lateral meniscus or medial meniscus, Hispanic/Latino children had 2.36 times the odds of surgery for discoid meniscus compared with White patients after adjusting for age and insurance (P<0.001). Asian children also had 2.41 times the odds of surgery for discoid meniscus compared with White patients (P=0.017). CONCLUSIONS This study shows a significant association of ethnicity/race with discoid versus medial meniscus surgical treatment in children. Among pediatric patients undergoing surgery for discoid meniscus, Hispanic/Latino and Asian patients were a significantly larger percentage of the population than White patients. Hispanic/Latino children made up a greater percentage of the population having surgery for a torn discoid meniscus versus a torn medial mensicus. When evaluating pediatric patients, younger age and Asian or Hispanic/Latino ethnicity should increase attention to the possibility of a discoid meniscus. LEVEL OF EVIDENCE Level IV.
Elevated Mortality Among People Experiencing Homelessness With COVID-19

We reviewed publicly available data from major US health jurisdictions to compare severe acute respiratory syndrome coronavirus 2 case fatality rates in people experiencing homelessness with the general population. The case fatality rate among people experiencing homelessness was 1.3 times (95% CI, 1.1-1.5) that of the general population, suggesting that people experiencing homelessness should be prioritized for vaccination.
Constrained Block Nonlinear Neural Dynamical Models

Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical models by embedding local model structure and constraints. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. For handling partially observable dynamical systems, we utilize a state observer neural network to estimate the states of the system's latent dynamics. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems: a continuous stirred tank reactor, a two tank interacting system, and an aero-dynamics body. Models optimized with a few thousand system state observations accurately represent system dynamics in open loop simulation over thousands of time steps from a single set of initial conditions. Experimental results demonstrate an order of magnitude reduction in open-loop simulation mean squared error for our constrained, block-structured neural models when compared to traditional unstructured and unconstrained neural network models.
The PAIR-R24M Dataset for Multi-animal 3D Pose Estimation

Understanding the biological basis of social and collective behaviors in animals is a key goal of the life sciences, and may yield important insights for engineering intelligent multi-agent systems. A critical step in interrogating the mechanisms underlying social behaviors is a precise readout of the 3D pose of interacting animals. While approaches for multi-animal pose estimation are beginning to emerge, they remain challenging to compare due to the lack of standardized training and benchmark datasets. Here we introduce the PAIR-R24M (Paired Acquisition of Interacting oRganisms - Rat) dataset for multi-animal 3D pose estimation, which contains 24.3 million frames of RGB video and 3D ground-truth motion capture of dyadic interactions in laboratory rats. PAIR-R24M contains data from 18 distinct pairs of rats and 24 different viewpoints. We annotated the data with 11 behavioral labels and 3 interaction categories to facilitate benchmarking in rare but challenging behaviors. To establish a baseline for markerless multi-animal 3D pose estimation, we developed a multi-animal extension of DANNCE, a recently published network for 3D pose estimation in freely behaving laboratory animals. As the first large multi-animal 3D pose estimation dataset, PAIR-R24M will help advance 3D animal tracking approaches and aid in elucidating the neural basis of social behaviors.
An oral SARS-CoV-2 Mpro inhibitor clinical candidate for the treatment of COVID-19.

The worldwide outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global pandemic. Alongside vaccines, antiviral ther...
In vivo assessment of the safety of standard fractionation Temporally Feathered Radiation Therapy (TFRT) for head and neck squamous cell carcinoma: An R-IDEAL Stage 1/2a first-in-humans/feasibility demonstration of new technology implementation.

Abstract Introduction Prior in silico simulations of studies of Temporally Feathered Radiation Therapy (TFRT) have demonstrated potential reduction in normal tissue toxicity. This R-IDEAL Stage 1/2A study seeks to demonstrate the first-in-human implementation of TFRT in treating patients with head and neck squamous cell carcinoma (HNSCC). Materials and methods Patients with HNSCC treated with definitive radiation therapy were eligible (70 Gy in 35 fractions) were eligible. The primary endpoint was feasibility of TFRT planning as defined by radiation start within 15 business days of CT simulation. Secondary endpoints included estimates of acute grade 3–5 toxicity. Results The study met its accrual goal of 5 patients. TFRT plans were generated in four of the five patients within 15 business days of CT simulation, therefore meeting the primary endpoint. One patient was not treated with TFRT at the physician’s discretion, though the TFRT plan had been generated within sufficient time from the CT simulation. For patients who received TFRT, the median time from CT simulation to radiation start was 10 business days (range 8–15). The average time required for radiation planning was 6 business days. In all patients receiving TFRT, each subplan and every daily fraction was delivered in the correct sequence without error. The OARs feathered included: oral cavity, each submandibular gland, each parotid gland, supraglottis, and posterior pharyngeal wall (OAR pharynx). Prescription dose PTV coverage (>95%) was ensured in each TFRT subplan and the composite TFRT plan. One of five patients developed an acute grade 3 toxicity. Conclusions This study demonstrates the first-in-human implementation of TFRT (R-IDEAL Stage 1), proving its feasibility in the modern clinical workflow. Additionally, assessments of acute toxicities and dosimetric comparisons to a standard radiotherapy plan were described (R-IDEAL Stage 2a).
Sparse Linear Ensemble Systems and Structural Controllability

The paper introduces and solves a structural controllability problem for continuum ensembles of linear time-invariant systems. All the individual linear systems of an ensemble are sparse, governed by the same sparsity pattern. Controllability of an ensemble system is, by convention, the capability of using a common control input to simultaneously steer every individual systems in it. A sparsity pattern is structurally controllable if it admits a controllable linear ensemble system. A main contribution of the paper is to provide a graphical condition that is necessary and sufficient for a sparsity pattern to be structurally controllable. Like other structural problems, the property of being structural controllable is monotone. We provide a complete characterization of minimal sparsity patterns as well.
The Euler characteristic: A general topological descriptor for complex data

Abstract Datasets are mathematical objects (e.g., point clouds, matrices, graphs, images, fields/functions) that have shape. This shape encodes important knowledge about the system under study. Topology is an area of mathematics that provides diverse tools to characterize the shape of data objects. In this work, we study a specific tool known as the Euler characteristic (EC). The EC is a general, low-dimensional, and interpretable descriptor of topological spaces defined by data objects. We revise the mathematical foundations of the EC and highlight its connections with statistics, linear algebra, field theory, and graph theory. We discuss advantages offered by the use of the EC in the characterization of complex datasets; to do so, we illustrate its use in different applications of interest in chemical engineering such as process monitoring, flow cytometry, and microscopy. We show that the EC provides a descriptor that effectively reduces complex datasets and that this reduction facilitates tasks such as visualization, regression, classification, and clustering.
Picometer sensitivity metrology for EUV absorber phase

With growing interest in EUV attenuated phase-shift masks due to their superior image quality for applications such as dense contact and pillar arrays, it is becoming critical to model, measure, and monitor the intensity and relative phase of multilayer and absorber reflections. We present a solution based on physical modeling of reflectometry data, which can achieve single picometer phase precision and sensitivity to changes in average film thickness below one atomic monolayer. We measure absorber and multilayer reflectivity to determine thin-film parameters with a multidimensional optimization and then acquire a new measurement of either multilayer or absorber to determine perturbations in surface contamination thickness. While it is difficult to assess the accuracy of the first step, the simplicity of the second step allows us to characterize our sensitivity to changes in contamination thickness. We apply this analysis using an initial set of measurements and repeated measurements after a period of storage. For the multilayer, the total contamination growth was 1068 pm, which occurred almost exclusively during storage (1085 pm) and decreased very slightly during repeated measurements (−17 pm). For the absorber, the behavior was quite different, with a total growth of 126 pm, which occurred much less during storage (28 pm) and primarily during repeated measurements (98 pm). Ultimately, the change in relative phase (absorber minus multilayer) was −0.86 deg for the multilayer and −1.12 deg for the absorber. We estimate the precision of the surface contamination measurement to be 3σ < 6 pm for measuring thickness and 3σ < 0.2 deg for measuring phase.
Empirical Bayes Mean Estimation With Nonparametric Errors Via Order Statistic Regression on Replicated Data

We study empirical Bayes estimation of the effect sizes of N units from K noisy observations on each unit. We show that it is possible to achieve near-Bayes optimal mean squared error, without any ...
Height, but not binding epitope, affects the potency of synthetic TCR agonists.

Abstract Under physiological conditions, peptide-major histocompatibility complex (pMHC) molecules can trigger T cell receptors (TCRs) as monovalent ligands that are sparsely distributed on the plasma membrane of an antigen-presenting cell. TCRs can also be triggered by artificial clustering, such as with pMHC tetramers or antibodies; however, these strategies circumvent many of the natural ligand discrimination mechanisms of the T cell and can elicit nonphysiological signaling activity. We have recently introduced a synthetic TCR agonist composed of an anti-TCRβ Fab′ antibody fragment covalently bound to a DNA oligonucleotide, which serves as a membrane anchor. This Fab′-DNA ligand efficiently triggers TCR as a monomer when membrane associated and exhibits a potency and activation profile resembling agonist pMHC. In this report, we explore the geometric requirements for efficient TCR triggering and cellular activation by Fab′-DNA ligands. We find that T cells are insensitive to the ligand binding epitope on the TCR complex but that length of the DNA tether is important. Increasing, the intermembrane distance spanned by Fab′-DNA:TCR complexes decreases TCR triggering efficiency and T cell activation potency, consistent with the kinetic-segregation model of TCR triggering. These results establish design parameters for constructing synthetic TCR agonists that are able to activate polyclonal T cell populations, such as T cells from a human patient, in a similar manner as the native pMHC ligand.
Cost Structure, Operating Leverage, and CDS Spreads

We provide evidence that credit investors do not fully impound the implications of firms’ cost structure (or operating leverage) when pricing credit default swaps. Information about firms’ cost structure is not disclosed and needs to be estimated. Furthermore, the performance implications of firms’ cost structure depend on the expected macroeconomic conditions. We focus on the debt market because of the strong emphasis of this market on downside risk. To measure expected aggregate macroeconomic conditions, we employ the change in the anxious index (AI), which is the probability of a decline in real GDP provided by the SPF—the survey of professional forecasters. We find that the interaction between the firm’s cost structure and change in AI predicts one-quarter-ahead CDS spreads. Portfolio-level analysis confirms this result.
Nonparametric spectral methods for multivariate spatial and spatial–temporal data

Abstract We propose computationally efficient methods for estimating stationary multivariate spatial and spatial–temporal spectra from incomplete gridded data. The methods are iterative and rely on successive imputation of data and updating of model estimates. Imputations are done according to a periodic model on an expanded domain. The periodicity of the imputations is a key feature that reduces edge effects in the periodogram and is facilitated by efficient circulant embedding techniques. In addition, we describe efficient methods for decomposing the estimated cross spectral density function into a linear model of coregionalization plus a residual process. The methods are applied to two storm datasets, one of which is from Hurricane Florence, which struck the southeastern United States in September 2018. The application demonstrates how fitted models from different datasets can be compared, and how the methods are computationally feasible on datasets with more than 200,000 total observations.
Has European Corporatism Delivered? A Survey with Preliminary Evidence

The modern stakeholder movement in the U.S. is arguably an attempt to import European corporatism into U.S. corporate practice and public policy. Hence, it is useful to ask (i) do European firms really invest in stakeholder welfare; and (ii) are such investments accomplished at the cost of lower shareholder rights or shareholder value? I survey ESG (environmental, social and governance) related legislative developments in Europe and the related academic literature to hypothesize that better E&S disclosure records in Europe do not compensate for the valuation discount imposed by weaker Gs. I go on to provide preliminary evidence on this hypothesis by focusing on largest 50 public firms in four key European countries (Netherlands, France, Germany and the United Kingdom) matched by industry and market capitalization to a sample of comparable U.S. public firms. These top 50 European firms account for between 72% and 99% of the equity market capitalizations of all domestically incorporated equities in their respective countries. The valuation discount between continental European firms and U.S. firms in Tobin’s Q is correlated with presence of large block holders in European firms. The poorer disclosure record of U.S. firms on the environmental (E) and social (“S”) dimensions of labor is not correlated with this valuation discount. When U.S. firms do disclose E and S information, their track record is no worse than their European counterparts. My data tentatively suggest that poorer G in continental Europe destroys more shareholder value than potentially better E and S disclosure can add.
X-ray diffraction and equation of state of the C–S–H room-temperature superconductor

X-ray diffraction indicates that the structure of the recently discovered carbonaceous sulfur hydride (C–S–H) room-temperature superconductor is derived from previously established van der Waals compounds found in the H2S–H2 and CH4–H2 systems. Crystals of the superconducting phase were produced by a photochemical synthesis technique, leading to the superconducting critical temperature Tc of 288 K at 267 GPa. X-ray diffraction patterns measured from 124 to 178 GPa, within the pressure range of the superconducting phase, are consistent with an orthorhombic structure derived from the Al2Cu-type determined for (H2S)2H2 and (CH4)2H2 that differs from those predicted and observed for the S–H system at these pressures. The formation and stability of the C–S–H compound can be understood in terms of the close similarity in effective volumes of the H2S and CH4 components, and denser carbon-bearing S–H phases may form at higher pressures. The results are crucial for understanding the very high superconducting Tc found in the C–S–H system at megabar pressures.
ROBIN: A Robust Optical Binary Neural Network Accelerator

Domain specific neural network accelerators have garnered attention because of their improved energy efficiency and inference performance compared to CPUs and GPUs. Such accelerators are thus well suited for resource-constrained embedded systems. However, mapping sophisticated neural network models on these accelerators still entails significant energy and memory consumption, along with high inference time overhead. Binarized neural networks (BNNs), which utilize single-bit weights, represent an efficient way to implement and deploy neural network models on accelerators. In this paper, we present a novel optical-domain BNN accelerator, named ROBIN, which intelligently integrates heterogeneous microring resonator optical devices with complementary capabilities to efficiently implement the key functionalities in BNNs. We perform detailed fabrication-process variation analyses at the optical device level, explore efficient corrective tuning for these devices, and integrate circuit-level optimization to counter thermal variations. As a result, our proposed ROBIN architecture possesses the desirable traits of being robust, energy-efficient, low latency, and high throughput, when executing BNN models. Our analysis shows that ROBIN can outperform the best-known optical BNN accelerators and many electronic accelerators. Specifically, our energy-efficient ROBIN design exhibits energy-per-bit values that are ∼4 × lower than electronic BNN accelerators and ∼933 × lower than a recently proposed photonic BNN accelerator, while a performance-efficient ROBIN design shows ∼3 × and ∼25 × better performance than electronic and photonic BNN accelerators, respectively.
Physical Characterization of Metal-rich Near-Earth Asteroids 6178 (1986 DA) and 2016 ED85

Metal-rich near-Earth asteroids (NEAs) represent a small fraction of the NEA population that is mostly dominated by S- and C-type asteroids. Because of this, their identification and study provide us with a unique opportunity to learn more about the formation and evolution of this particular type of bodies, as well as their relationship with meteorites found on Earth. We present near-infrared (NIR) spectroscopic data of NEAs 6178 (1986 DA) and 2016 ED85. We found that the spectral characteristics of these objects are consistent with those of metal-rich asteroids, showing red slopes, convex shapes, and a weak pyroxene absorption band at $\sim$0.93 $\mu$m. The compositional analysis showed that they have a pyroxene chemistry of Fs$_{40.6\pm3.3}$Wo$_{8.9\pm1.1}$ and a mineral abundance of $\sim$15% pyroxene and 85% metal. We determined that these objects were likely transported to the near-Earth space via the 5:2 mean motion resonance with Jupiter. Asteroid spectra were compared with the spectra of mesosiderites and bencubbinites. Differences in the NIR spectra and pyroxene chemistry suggest that bencubbinites are not good meteorite analogs. Mesosiderites were found to have a similar pyroxene chemistry and produced a good spectral match when metal was added to the silicate component. We estimated that the amounts of Fe, Ni, Co, and the platinum group metals present in 1986 DA could exceed the reserves worldwide.
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs.

Interactive partially observable Markov decision processes (I-POMDPs) provide a principled framework for planning and acting in a partially observable, stochastic and multi-agent environment. It extends POMDPs to multi-agent settings by including models of other agents in the state space and forming a hierarchical belief structure. In order to predict other agents' actions using I-POMDPs, we propose an approach that effectively uses Bayesian inference and sequential Monte Carlo sampling to learn others' intentional models which ascribe to them beliefs, preferences and rationality in action selection. Empirical results show that our algorithm accurately learns models of the other agent and has superior performance than methods that use subintentional models. Our approach serves as a generalized Bayesian learning algorithm that learns other agents' beliefs, strategy levels, and transition, observation and reward functions. It also effectively mitigates the belief space complexity due to the nested belief hierarchy.
How adaptation, training, and customization contribute to benefits from exoskeleton assistance.

Exoskeletons can enhance human mobility, but we still know little about why they are effective. For example, we do not know the relative importance of training, how much is required, or what type is most effective; how people adapt with the device; or the relative benefits of customizing assistance. We conducted experiments in which naive users learned to walk with ankle exoskeletons under one of three training regimens characterized by different levels of variation in device behavior. Assistance was also customized for one group. After moderate-variation training, the benefits of customized assistance were large; metabolic rate was reduced by 39% compared with walking with the exoskeleton turned off. Training contributed about half of this benefit and customization about one-quarter; a generic controller reduced energy cost by 10% before training and 31% afterward. Training required much more exposure than typical of exoskeleton studies, about 109 minutes of assisted walking. Type of training also had a strong effect; the low-variation group required twice as long as the moderate-variation group to become expert, and the high-variation group never acquired this level of expertise. Curiously, all users adapted in a way that resulted in less mechanical power from the exoskeleton as they gained expertise. Customizing assistance required less time than training for all parameters except peak torque magnitude, which grew slowly over the study, suggesting a longer time scale adaptation in the person. These results underscore the importance of training to the benefits of exoskeleton assistance and suggest the topic deserves more attention.
The Effect of Network Topology on Credit Network Throughput

Abstract Credit networks rely on decentralized, pairwise trust relationships (channels) to exchange money or goods. Credit networks arise naturally in many financial systems, including the recent construct of payment channel networks in blockchain systems. An important performance metric for these networks is their transaction throughput. However, predicting the throughput of a credit network is nontrivial. Unlike traditional communication channels, credit channels can become imbalanced; they are unable to support more transactions in a given direction once the credit limit has been reached. This potential for imbalance creates a complex dependency between a network’s throughput and its topology, path choices, and the credit balances (state) on every channel. Even worse, certain combinations of these factors can lead the credit network to deadlocked states where no transactions can make progress. In this paper, we study the relationship between the throughput of a credit network and its topology and credit state. We show that the presence of deadlocks completely characterizes a network’s throughput sensitivity to different credit states. Although we show that identifying deadlocks in an arbitrary topology is NP-hard, we propose a peeling algorithm inspired by decoding algorithms for erasure codes that upper bounds the severity of the deadlock. We use the peeling algorithm as a tool to compare the performance of different topologies as well as to aid in the synthesis of topologies robust to deadlocks.
A Lagrangian Approach Towards Quantitative Analysis of Flow-mediated Infection Transmission in Indoor Spaces with Application to SARS-COV-2

The ongoing SARS-CoV-2 (Covid-19) pandemic has ushered an unforeseen level of global health and economic burden. As a respiratory infection, Covid-19 is known to have a dominant airborne transmissi...
Structures of Spurious Local Minima in k-means

The k-means clustering problem concerns finding a partition of the data points into k clusters such that the total within-cluster squared distance is minimized. This optimization objective is non-convex, and not everywhere differentiable. In general, there exist spurious local solutions other than the global optimum. Moreover, the simplest and most popular algorithm for k-means, namely Lloyd’s algorithm, generally converges to such spurious local solutions both in theory and in practice. In this paper, we investigate the structures of these spurious local solutions under a probabilistic generative model with k ground truth clusters. As soon as k = 3, spurious local minima provably exist, even for well-separated clusters. One such local minimum puts two centers at one true cluster, and the third center in the middle of the other two true clusters. We prove that this is essentially the only type of spurious local minima under a separation condition. In particular, any local minimum solution only involves a configuration that puts multiple centers at a true cluster, and one center in the middle of multiple true clusters. Our results pertain to the k-means formulation for mixtures of Gaussians or bounded distributions, and hold in the over- and under-parametrization regimes where the number of centers in k-means may not equal to the number of true clusters. Our theoretical results corroborate existing empirical observations and provide justification for popular heuristics for k-means clustering.
OpenCDA: An Open Cooperative Driving Automation Framework Integrated with Co-Simulation

Although Cooperative Driving Automation (CDA) has attracted considerable attention in recent years, there remain numerous open challenges in this field. The gap between existing simulation platforms that mainly concentrate on single-vehicle intelligence and CDA development is one of the critical gaps, as it inhibits researchers from validating and comparing different CDA algorithms conveniently. To this end, we propose OpenCDA, a generalized framework and tool for developing and testing CDA systems. Specifically, OpenCDA is composed of three major components: a co-simulation platform with simulators of different purposes and resolutions, a full-stack cooperative driving system, and a scenario manager. Through the interactions of these three components, our framework offers a straightforward way for researchers to test different CDA algorithms at both levels of traffic and individual autonomy. More importantly, OpenCDA is highly modularized and installed with benchmark algorithms and test cases. Users can conveniently replace any default module with customized algorithms and use other default modules of the CDA platform to perform evaluations of the effectiveness of new functionalities in enhancing the overall CDA performance. An example of platooning implementation is used to illustrate the framework's capability for CDA research. The codes of OpenCDA are available in the UCLA Mobility Lab GitHub page.
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets

Hierarchical Bayesian methods enable information sharing across multiple related regression problems. While standard practice is to model regression parameters (effects) as (1) exchangeable across datasets and (2) correlated to differing degrees across covariates, we show that this approach exhibits poor statistical performance when the number of covariates exceeds the number of datasets. For instance, in statistical genetics, we might regress dozens of traits (defining datasets) for thousands of individuals (responses) on up to millions of genetic variants (covariates). When an analyst has more covariates than datasets, we argue that it is often more natural to instead model effects as (1) exchangeable across covariates and (2) correlated to differing degrees across datasets. To this end, we propose a hierarchical model expressing our alternative perspective. We devise an empirical Bayes estimator for learning the degree of correlation between datasets. We develop theory that demonstrates that our method outperforms the classic approach when the number of covariates dominates the number of datasets, and corroborate this result empirically on several high-dimensional multiple regression and classification problems.
Excess Capacity and Backdoor Poisoning

A backdoor data poisoning attack is an adversarial attack wherein the attacker injects several watermarked, mislabeled training examples into a training set. The watermark does not impact the test-time performance of the model on typical data; however, the model reliably errs on watermarked examples. To gain a better foundational understanding of backdoor data poisoning attacks, we present a formal theoretical framework within which one can discuss backdoor data poisoning attacks for classification problems. We then use this to analyze important statistical and computational issues surrounding these attacks. On the statistical front, we identify a parameter we call the memorization capacity that captures the intrinsic vulnerability of a learning problem to a backdoor attack. This allows us to argue about the robustness of several natural learning problems to backdoor attacks. Our results favoring the attacker involve presenting explicit constructions of backdoor attacks, and our robustness results show that some natural problem settings cannot yield successful backdoor attacks. From a computational standpoint, we show that under certain assumptions, adversarial training can detect the presence of backdoors in a training set. We then show that under similar assumptions, two closely related problems we call backdoor filtering and robust generalization are nearly equivalent. This implies that it is both asymptotically necessary and sufficient to design algorithms that can identify watermarked examples in the training set in order to obtain a learning algorithm that both generalizes well to unseen data and is robust to backdoors.
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints

We study reinforcement learning (RL) with linear function approximation under the adaptivity constraint. We consider two popular limited adaptivity models: batch learning model and rare policy switch model, and propose two efficient online RL algorithms for linear Markov decision processes. In specific, for the batch learning model, our proposed LSVI-UCB-Batch algorithm achieves an $\tilde O(\sqrt{d^3H^3T} + dHT/B)$ regret, where $d$ is the dimension of the feature mapping, $H$ is the episode length, $T$ is the number of interactions and $B$ is the number of batches. Our result suggests that it suffices to use only $\sqrt{T/dH}$ batches to obtain $\tilde O(\sqrt{d^3H^3T})$ regret. For the rare policy switch model, our proposed LSVI-UCB-RareSwitch algorithm enjoys an $\tilde O(\sqrt{d^3H^3T[1+T/(dH)]^{dH/B}})$ regret, which implies that $dH\log T$ policy switches suffice to obtain the $\tilde O(\sqrt{d^3H^3T})$ regret. Our algorithms achieve the same regret as the LSVI-UCB algorithm (Jin et al., 2019), yet with a substantially smaller amount of adaptivity.
The role of divestitures in horizontal mergers

Abstract We study the effectiveness of divestitures as a merger remedy. We show that divestitures are more effective in mitigating the market power impact of mergers if the merging firms divest assets to buyers outside the industry rather than existing rivals. Divestitures are also more effective as merger remedies when the merging industry is concentrated and when powerful customers are absent. Notably, stock price reactions of the acquirer and rivals suggest that firms are more concerned with maintaining a competitive edge relative to each other than gaining market power relative to customers.
SARS-CoV-2 antibody changes in patients receiving COVID-19 convalescent plasma from normal and vaccinated donors

Abstract Vaccination has been shown to stimulate remarkably high antibody levels in donors who have recovered from COVID-19. Our objective was to measure patient antibody levels before and after transfusion with COVID-19 Convalescent Plasma (CCP) and compare the antibody levels following transfusion of CCP from vaccinated and nonvaccinated donors. Plasma samples before and after transfusion were obtained from 25 recipients of CCP and COVID-19 antibody levels measured. Factors that effect changes in antibody levels were examined. In the 21 patients who received CCP from nonvaccinated donors, modest increases in antibody levels were observed. Patients who received two units were more likely to seroconvert than those receiving just one unit. The strongest predictor of changes in patient antibody level was the CCP dose, calculated by the unit volume multiplied by the donor antibody level. Using patient plasma volume and donor antibody level, the post-transfusion antibody level could be predicted with reasonable accuracy(R 2 > 0.90). In contrast, the 4 patients who received CCP from vaccinated donors all had dramatic increases in antibody levels following transfusion of a single unit. In this subset of recipients, antibody levels observed after transfusion of CCP were comparable to those seen in donors who had fully recovered from COVID-19. If available, CCP from vaccinated donors with very high antibody levels should be used. One unit of CCP from vaccinated donors increases patient antibody levels much more than 1 or 2 units of CCP from unvaccinated donors.