cssi/SciSciGPT-SciSciNet huggingface dataset:
Secure Communication Channel Establishment: TLS 1.3 (over TCP Fast Open) vs. QUIC

Secure channel establishment protocols such as TLS are some of the most important cryptographic protocols, enabling the encryption of Internet traffic. Reducing the latency (the number of interactions between parties) in such protocols has become an important design goal to improve user experience. The most important protocols addressing this goal are TLS 1.3 over TCP Fast Open (TFO), Google’s QUIC over UDP, and QUIC[TLS] (a new design for QUIC that uses TLS 1.3 key exchange) over UDP. There have been a number of formal security analyses for TLS 1.3 and QUIC, but their security, when layered with their underlying transport protocols, cannot be easily compared. Our work is the first to thoroughly compare the security and availability properties of these protocols. Towards this goal, we develop novel security models that permit “layered” security analysis. In addition to the standard goals of server authentication and data privacy and integrity, we consider the goals of IP spoofing prevention, key exchange packet integrity, secure channel header integrity, and reset authentication, which capture a range of practical threats not usually taken into account by existing security models that focus mainly on the crypto cores of the protocols. Equipped with our new models we provide a detailed comparison of the above three protocols. We hope that our results will help protocol designers in their future protocol analyses and practitioners to better understand the advantages and limitations of novel secure channel establishment protocols.
libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations

Almost all applications stop scaling at some point; those that don't are seldom performant when considering time to solution on anything but aspirational/unicorn resources. Recognizing these tradeoffs as well as greater user functionality in a near-term exascale computing era, we present libEnsemble, a library aimed at particular scalability- and capability-stretching uses. libEnsemble enables running concurrent instances of an application in dynamically allocated ensembles through an extensible Python library. We highlight the structure, execution, and capabilities of the library on leading pre-exascale environments as well as advanced capabilities for exascale environments and beyond.
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability

In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users' opportunities for discovery. We propose an evaluation procedure based on stochastic reachability to quantify the maximum probability of recommending a target piece of content to an user for a set of allowable strategic modifications. This framework allows us to compute an upper bound on the likelihood of recommendation with minimal assumptions about user behavior. Stochastic reachability can be used to detect biases in the availability of content and diagnose limitations in the opportunities for discovery granted to users. We show that this metric can be computed efficiently as a convex program for a variety of practical settings, and further argue that reachability is not inherently at odds with accuracy. We demonstrate evaluations of recommendation algorithms trained on large datasets of explicit and implicit ratings. Our results illustrate how preference models, selection rules, and user interventions impact reachability and how these effects can be distributed unevenly.
The fragile X mental retardation protein promotes adjustments in cocaine self-administration that preserve reinforcement level

The fragile X mental retardation protein (FMRP), an RNA-binding protein, regulates cocaine-induced neuronal plasticity and is critical for the normal development of drug-induced locomotor sensitization, as well as reward-related learning in the conditioned place preference assay. However, it is unknown whether FMRP impacts behaviors that are used to more closely model substance use disorders. Utilizing a cocaine intravenous self-administration (IVSA) assay in Fmr1 knockout (KO) and wild-type (WT) littermate mice, we find that, despite normal acquisition and extinction learning, Fmr1 KO mice fail to make a normal upward shift in responding during dose-response testing. Later, when given access to the original acquisition dose under increasing fixed ratio (FR) schedules of reinforcement (FR1, FR3, and FR5), Fmr1 KO mice earn significantly fewer cocaine infusions than WT mice. Importantly, similar deficits are not present in operant conditioning using a palatable food reinforcer, indicating that our results do not represent broad learning or reward-related deficits in Fmr1 KO mice. Additionally, we find an FMRP target, the activity-regulated cytoskeleton-associated protein (Arc), to be significantly reduced in synaptic cellular fractions prepared from the nucleus accumbens of Fmr1 KO, compared with WT, mice following operant tasks reinforced with cocaine but not food. Overall, our findings suggest that FMRP facilitates adjustments in drug self-administration behavior that generally serve to preserve reinforcement level, and combined with our similar IVSA findings in Arc KO mice may implicate Arc, along with FMRP, in behavioral shifts that occur in drug taking when drug availability is altered.
Event Representation with Sequential, Semi-Supervised Discrete Variables.

Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential neural variational autoencoder, which uses Gumbel-Softmax reparametrization within a carefully defined encoder, to allow for successful backpropagation during training. The core idea is to allow semi-supervised external discrete knowledge to guide, but not restrict, the variational latent parameters during training. Our experiments indicate that our approach not only outperforms multiple baselines and the state-of-the-art in narrative script induction, but also converges more quickly.
Principled Simplicial Neural Networks for Trajectory Prediction

We consider the construction of neural network architectures for data on simplicial complexes. In studying maps on the chain complex of a simplicial complex, we define three desirable properties of a simplicial neural network architecture: namely, permutation equivariance, orientation equivariance, and simplicial awareness. The first two properties respectively account for the fact that the node indexing and the simplex orientations in a simplicial complex are arbitrary. The last property encodes the desirable feature that the output of the neural network depends on the entire simplicial complex and not on a subset of its dimensions. Based on these properties, we propose a simple convolutional architecture, rooted in tools from algebraic topology, for the problem of trajectory prediction, and show that it obeys all three of these properties when an odd, nonlinear activation function is used. We then demonstrate the effectiveness of this architecture in extrapolating trajectories on synthetic and real datasets, with particular emphasis on the gains in generalizability to unseen trajectories.
Larval zebrafish display dynamic learning of aversive stimuli in a constant visual surrounding

Balancing exploration and anti-predation are fundamental to the fitness and survival of all animal species from early life stages. How these basic survival instincts drive learning remains poorly understood. Here, using a light/dark preference paradigm with well-controlled luminance history and constant visual surrounding in larval zebrafish, we analyzed intra- and intertrial dynamics for two behavioral components, dark avoidance and center avoidance. We uncover that larval zebrafish display short-term learning of dark avoidance with initial sensitization followed by habituation; they also exhibit long-term learning that is sensitive to trial interval length. We further show that such stereotyped learning patterns is stimulus-specific, as they are not observed for center avoidance. Finally, we demonstrate at individual levels that long-term learning is under homeostatic control. Together, our work has established a novel paradigm to understand learning, uncovered sequential sensitization and habituation, and demonstrated stimulus specificity, individuality, as well as dynamicity in learning.
An Integer Linear Programming Framework for Mining Constraints from Data

Structured output prediction problems (e.g., sequential tagging, hierarchical multi-class classification) often involve constraints over the output label space. These constraints interact with the learned models to filter infeasible solutions and facilitate in building an accountable system. However, although constraints are useful, they are often based on hand-crafted rules. This raises a question -- \emph{can we mine constraints and rules from data based on a learning algorithm?} In this paper, we present a general framework for mining constraints from data. In particular, we consider the inference in structured output prediction as an integer linear programming (ILP) problem. Then, given the coefficients of the objective function and the corresponding solution, we mine the underlying constraints by estimating the outer and inner polytopes of the feasible set. We verify the proposed constraint mining algorithm in various synthetic and real-world applications and demonstrate that the proposed approach successfully identifies the feasible set at scale. In particular, we show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules. Our algorithm can also integrate with a neural network model to learn the hierarchical label structure of a multi-label classification task. Besides, we provide a theoretical analysis about the tightness of the polytopes and the reliability of the mined constraints.
Large spin-to-charge conversion in ultrathin gold-silicon multilayers

Investigation of the spin Hall effect in gold has triggered increasing interest over the past decade, since gold combines the properties of a large bulk spin-diffusion length and strong interfacial spin-orbit coupling. However, discrepancies between the values of the spin Hall angle of gold reported in the literature have brought into question the microscopic origin of the spin Hall effect in Au. Here, we investigate the thickness dependence of the effective spin-charge conversion efficiency in single Au films and ultrathin Au/Si multilayers by nonlocal transport and spin-torque ferromagnetic resonance measurements. We show that the inferred effective spin-charge conversion efficiency is strongly enhanced in ultrathin Au/Si multilayers, reaching exceedingly large values of 0.99 \ifmmode\pm\else\textpm\fi{} 0.34 when the thickness of the individual Au layers is scaled down to 2 nm. These findings reveal the coexistence of a strong interfacial spin-to-charge conversion effect, which becomes dominant in ultrathin Au, and bulk spin Hall effect with a relatively low bulk spin Hall angle of 0.012 \ifmmode\pm\else\textpm\fi{} 0.005. Our experimental results suggest the key role of the intrinsic spin Hall effect enhancement along with a strong interfacial spin-orbit coupling-related effect in the large spin-to-charge conversion in ultrathin Au.
Corruption-robust exploration in episodic reinforcement learning

We initiate the study of episodic reinforcement learning under adversarial corruptions in both the rewards and the transition probabilities of the underlying system extending recent results for the special case of multi-armed bandits. We provide a framework which modifies the aggressive exploration enjoyed by existing reinforcement learning approaches based on “optimism in the face of uncertainty”, by complementing them with principles from “action elimination”. Importantly, our framework circumvents the major challenges posed by naively applying action elimination in the RL setting, as formalized by a lower bound we demonstrate. Our framework yields efficient algorithms which (a) attain near-optimal regret in the absence of corruptions and (b) adapt to unknown levels corruption, enjoying regret guarantees which degrade gracefully in the total corruption encountered. To showcase the generality of our approach, we derive results for both tabular settings (where states and actions are finite) as well as linear MDP settings (where the dynamics and rewards admit a linear underlying representation). Notably, our work provides the first sublinear regret guarantee which accommodates any deviation from purely i.i.d. transitions in the bandit feedback model for episodic reinforcement learning.
Collaborative Experiments and Computations in Aircraft Icing

The formation of ice over lifting surfaces can affect aerodynamic performance. The ability to predict ice accumulation and the resulting degradation in vehicle performance is essential to determine the limitations of aircraft in icing encounters. The consequences of underestimating performance degradation can be serious and so it is important to produce accurate predictions, particularly for severe icing conditions. The simulation of ice accretion is a challenging multidisciplinary problem that requires close collaboration between the computational and ground test communities. This paper describes three recent case studies and the lessons learned through collaborative experiments and computations in aircraft icing- one for large commercial transports, one for rotorcraft, and one dealing with icing on regional jets.
Stochastic Approximation for Online Tensorial Independent Component Analysis

Independent component analysis (ICA) has been a popular dimension reduction tool in statistical machine learning and signal processing. In this paper, we present a convergence analysis for an online tensorial ICA algorithm, by viewing the problem as a nonconvex stochastic approximation problem. For estimating one component, we provide a dynamics-based analysis to prove that our online tensorial ICA algorithm with a specific choice of stepsize achieves a sharp finite-sample error bound. In particular, under a mild assumption on the data-generating distribution and a scaling condition such that $d^4/T$ is sufficiently small up to a polylogarithmic factor of data dimension $d$ and sample size $T$, a sharp finite-sample error bound of $\tilde{O}(\sqrt{d/T})$ can be obtained.
SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption Evaluation via Typicality Analysis

The open-ended nature of visual captioning makes it a challenging area for evaluation. The majority of proposed models rely on specialized training to improve human-correlation, resulting in limited adoption, generalizability, and explainabilty. We introduce “typicality”, a new formulation of evaluation rooted in information theory, which is uniquely suited for problems lacking a definite ground truth. Typicality serves as our framework to develop a novel semantic comparison, SPARCS, as well as referenceless fluency evaluation metrics. Over the course of our analysis, two separate dimensions of fluency naturally emerge: style, captured by metric SPURTS, and grammar, captured in the form of grammatical outlier penalties. Through extensive experiments and ablation studies on benchmark datasets, we show how these decomposed dimensions of semantics and fluency provide greater system-level insight into captioner differences. Our proposed metrics along with their combination, SMURF, achieve state-of-the-art correlation with human judgment when compared with other rule-based evaluation metrics.
A Recovery Algorithm and Pooling Designs for One-Stage Noisy Group Testing Under the Probabilistic Framework

Group testing saves time and resources by testing each preassigned group instead of each individual, and one-stage group testing emerged as essential for cost-effectively controlling the current COVID-19 pandemic. Yet, the practical challenge of adjusting pooling designs based on infection rate has not been systematically addressed. In particular, there are both theoretical interests and practical motivation to analyze one-stage group testing at finite, practical problem sizes, rather than asymptotic ones, under noisy, rather than perfect tests, and when the number of positives is randomly distributed, rather than fixed.
Learning How to Dynamically Route Autonomous Vehicles on Shared Roads

Abstract Road congestion induces significant costs across the world, and road network disturbances, such as traffic accidents, can cause highly congested traffic patterns. If a planner had control over the routing of all vehicles in the network, they could easily reverse this effect. In a more realistic scenario, we consider a planner that controls autonomous cars, which are a fraction of all present cars. We study a dynamic routing game, in which the route choices of autonomous cars can be controlled and the human drivers react selfishly and dynamically. As the problem is prohibitively large, we use deep reinforcement learning to learn a policy for controlling the autonomous vehicles. This policy indirectly influences human drivers to route themselves in such a way that minimizes congestion on the network. To gauge the effectiveness of our learned policies, we establish theoretical results characterizing equilibria and empirically compare the learned policy results with best possible equilibria. We prove properties of equilibria on parallel roads and provide a polynomial-time optimization for computing the most efficient equilibrium. Moreover, we show that in the absence of these policies, high demand and network perturbations would result in large congestion, whereas using the policy greatly decreases the travel times by minimizing the congestion. To the best of our knowledge, this is the first work that employs deep reinforcement learning to reduce congestion by indirectly influencing humans’ routing decisions in mixed-autonomy traffic.
Association of heart failure subtypes and atrial fibrillation: Data from the Atherosclerosis Risk in Communities (ARIC) study

Abstract Aims To determine the prevalence and incidence of AF among HF subtypes in a biracial community-based cohort. Methods We studied 6496 participants in the Atherosclerosis Risk in Community study (mean age, 75.8 ± 5.3, 59% women, 23% black) who attended the 2011–2013 visit. HF was identified from physician adjudicated diagnosis, hospital discharges, and self-report. HF subtypes were based on echocardiography. A left ventricular ejection fraction Results Among eligible participants, 393 had HF (HFpEF = 232, HFmEF = 41, HFrEF = 35 and unclassified HF = 85) and 735 had AF. Compared to those without HF, all HF subtypes were more likely to have prevalent AF [odds ratio (95% confidence interval (CI)) 7.4 (5.6–9.9) for HFpEF, 8.1 (4.3–15.3) for HFmEF, 10.0 (5.0–20.2) for HFrEF, 8.8 (5.6–14.0) for unclassified HF]. Among participants without AF at baseline (n = 5761), 610 of them developed AF. Prevalent HF was associated with increased risk of AF [hazard ratio (95%CI) 2.3 (1.6–3.2) for HFpEF, 5.0 (2.7–9.3) for HFmEF, 3.5 (1.7–7.6) for HFrEF, 1.9 (0.9–3.7) for unclassified HF]. Conclusion AF and HF frequently co-occur, with small differences by HF subtype, underscoring the importance of understanding the interplay of these two epidemics and evaluating shared preventive and therapeutic strategies.
Automatic enhancement in 6D supergravity and F-theory models

We observe that in many F-theory models, tuning a specific gauge group G and matter content M under certain circumstances leads to an automatic enhancement to a larger gauge group G′ ⊃ G and matter content M′ ⊃ M. We propose that this is true for any theory G, M whenever there exists a containing theory G′, M′ that cannot be Higgsed down to G, M. We give a number of examples including non-Higgsable gauge factors, nonabelian gauge factors, abelian gauge factors, and exotic matter. In each of these cases, tuning an F-theory model with the desired features produces either an enhancement or an inconsistency, often when the associated anomaly coefficient becomes too large. This principle applies to a variety of models in the apparent 6D supergravity swampland, including some of the simplest cases with U(1) and SU(N) gauge groups and generic matter, as well as infinite families of U(1) models with higher charges presented in the prior literature, potentially ruling out all these apparent swampland theories.
Hydrodynamic transport and violation of the viscosity-to-entropy ratio bound in nodal-line semimetals

The ratio between the shear viscosity and the entropy $\ensuremath{\eta}/s$ is considered a universal measure of the strength of interactions in quantum systems. This quantity was conjectured to have a universal lower bound $(1/4\ensuremath{\pi})\ensuremath{\hbar}/{k}_{B}$, which indicates a very strongly correlated quantum fluid. By solving the quantum kinetic theory for a nodal-line semimetal in the hydrodynamic regime, we show that $\ensuremath{\eta}/s\ensuremath{\propto}T$ violates the universal lower bound, scaling toward zero with decreasing temperature $T$ in the perturbative limit. We find that the hydrodynamic scattering time between collisions is nearly temperature independent, up to logarithmic scaling corrections, and can be extremely short for large nodal lines, near the Mott-Ragel-Ioffe limit. Our finding suggests that nodal-line semimetals can be very strongly correlated quantum systems.
A kinetic model of friction in strongly coupled strongly magnetized plasmas

Plasmas that are strongly magnetized in the sense that the gyrofrequency exceeds the plasma frequency exhibit novel transport properties that are not well understood. As a representative example, we compute the friction force acting on a massive test charge moving through a strongly coupled and strongly magnetized one-component plasma using a generalized Boltzmann kinetic theory. Recent works studying the weakly coupled regime have shown that strong magnetization leads to a transverse component of the friction force that is perpendicular to both the Lorentz force and velocity of the test charge, in addition to the stopping power component aligned antiparallel to the velocity. Recent molecular dynamics simulations have also shown that strong Coulomb coupling in addition to strong magnetization gives rise to a third component of the friction force in the direction of the Lorentz force. Here, we show that the generalized Boltzmann kinetic theory captures these effects and generally agrees well with the molecular dynamics simulations over a broad range of Coulomb coupling and magnetization strength regimes. The theory is also used to show that the “gyro” component of the friction in the direction of the Lorentz force arises due to asymmetries associated with gyromotion during short-range collisions. Computing the average motion of the test charge through the background plasma, the transverse force is found to strongly influence the trajectory by changing the gyroradius and the gyrofriction force is found to slightly change the gyrofrequency of the test charge resulting in a phase shift.
Fund manager skill in an era of globalization: Offshore concentration and fund performance

Abstract We study how mutual fund managers gain an edge in selecting stocks in an era of globalization. We use textual analysis to construct a measure that captures a mutual fund's offshore exposure concentration through holding US multinational firms. We find that funds with a higher offshore concentration index (OCI) perform significantly better, with the difference in four-factor alpha between the top and bottom deciles amounting to 2.95% per annum. Fund managers’ overweighting of firms with operations in certain countries can be partly attributed to their foreign ethnicity. High OCI fund managers have an information advantage regarding firms’ fundamentals, such as earnings.
IBRNet: Learning Multi-View Image-Based Rendering

We present a method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views. The core of our method is a network architecture that includes a multilayer perceptron and a ray transformer that estimates radiance and volume density at continuous 5D locations (3D spatial locations and 2D viewing directions), drawing appearance information on the fly from multiple source views. By drawing on source views at render time, our method hearkens back to classic work on image-based rendering (IBR), and allows us to render high-resolution imagery. Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that generalizes to novel scenes. We render images using classic volume rendering, which is fully differentiable and allows us to train using only multi-view posed images as supervision. Experiments show that our method outperforms recent novel view synthesis methods that also seek to generalize to novel scenes. Further, if fine-tuned on each scene, our method is competitive with state-of-the-art single-scene neural rendering methods.1
The structural basis of odorant recognition in insect olfactory receptors.

Olfactory systems must detect and discriminate amongst an enormous variety of odorants1. To contend with this challenge, diverse species have converged on a common strategy in which odorant identity is encoded through the combinatorial activation of large families of olfactory receptors1–3, thus allowing a finite number of receptors to detect a vast chemical world. Here we offer structural and mechanistic insight into how an individual olfactory receptor can flexibly recognize diverse odorants. We show that the olfactory receptor MhOR5 from the jumping bristletail4 Machilis hrabei assembles as a homotetrameric odorant-gated ion channel with broad chemical tuning. Using cryo-electron microscopy, we elucidated the structure of MhOR5 in multiple gating states, alone and in complex with two of its agonists—the odorant eugenol and the insect repellent DEET. Both ligands are recognized through distributed hydrophobic interactions within the same geometrically simple binding pocket located in the transmembrane region of each subunit, suggesting a structural logic for the promiscuous chemical sensitivity of this receptor. Mutation of individual residues lining the binding pocket predictably altered the sensitivity of MhOR5 to eugenol and DEET and broadly reconfigured the receptor’s tuning. Together, our data support a model in which diverse odorants share the same structural determinants for binding, shedding light on the molecular recognition mechanisms that ultimately endow the olfactory system with its immense discriminatory capacity. Structural and functional analysis of an insect olfactory receptor shed light on how receptors can be activated by diverse odorants.
User experience with on-road electric vehicles in the U.S.A. and Canada

Approximately 3000 on-road electric passenger cars and delivery vans are now in use in the U.S.A. and Canada. The owners and operators of almost one-third of these vehicles have been surveyed directly in an attempt to determine the suitability of commercially sold electric vehicles for real on-road jobs. This paper is primarily concerned with the analysis of the engineering aspects of the user experience with electric vehicles, i.e., mileage and application, failure modes and rates, energy economy, maintenance requirements, life cycle costs, and vehicle performance characteristics. It is concluded that existing electric vehicles can perform satisfactorily in applications that have limited performance requirements, particularly in terms of range.
Communication-Efficient and Byzantine-Robust Distributed Learning With Error Feedback

We develop a communication-efficient distributed learning algorithm that is robust against Byzantine worker machines. We propose and analyze a distributed gradient-descent algorithm that performs a simple thresholding based on gradient norms to mitigate Byzantine failures. We show the (statistical) error-rate of our algorithm matches that of Yin et al. (2018), which uses more complicated schemes (coordinate-wise median, trimmed mean). Furthermore, for communication efficiency, we consider a generic class of $\delta $ -approximate compressors from Karimireddi et al. (2019) that encompasses sign-based compressors and top- $k$ sparsification. Our algorithm uses compressed gradients and gradient norms for aggregation and Byzantine removal respectively. We establish the statistical error rate for non-convex smooth loss functions. We show that, in certain range of the compression factor $\delta $ , the (order-wise) rate of convergence is not affected by the compression operation. Moreover, we analyze the compressed gradient descent algorithm with error feedback (proposed in Karimireddi et al. 2019) in a distributed setting and in the presence of Byzantine worker machines. We show that exploiting error feedback improves the statistical error rate. Finally, we experimentally validate our results and show good performance in convergence for convex (least-square regression) and non-convex (neural network training) problems.
Taxonomic Uncertainty and the Anomaly Zone: Phylogenomics Disentangle a Rapid Radiation to Resolve Contentious Species (Gila robusta Complex) in the Colorado River

Species are indisputable units for biodiversity conservation, yet their delimitation is fraught with both conceptual and methodological difficulties. A classic example is the taxonomic controversy surrounding the Gila robusta complex in the lower Colorado River of southwestern North America. Nominal species designations were originally defined according to weakly diagnostic morphological differences, but these conflicted with subsequent genetic analyses. Given this ambiguity, the complex was re-defined as a single polytypic unit, with the proposed "threatened" status under the U.S. Endangered Species Act of two elements being withdrawn. Here we re-evaluated the status of the complex by utilizing dense spatial and genomic sampling (n = 387 and >22 k loci), coupled with SNP-based coalescent and polymorphism-aware phylogenetic models. In doing so, we found that all three species were indeed supported as evolutionarily independent lineages, despite widespread phylogenetic discordance. To juxtapose this discrepancy with previous studies, we first categorized those evolutionary mechanisms driving discordance, then tested (and subsequently rejected) prior hypotheses which argued phylogenetic discord in the complex was driven by the hybrid origin of Gila nigra. The inconsistent patterns of diversity we found within G. robusta were instead associated with rapid Plio-Pleistocene drainage evolution, with subsequent divergence within the "anomaly zone" of tree space producing ambiguities that served to confound prior studies. Our results not only support the resurrection of the three species as distinct entities but also offer an empirical example of how phylogenetic discordance can be categorized within other recalcitrant taxa, particularly when variation is primarily partitioned at the species level.
Evaluation of Portable Acceleration Solutions for LArTPC Simulation Using Wire-Cell Toolkit

The Liquid Argon Time Projection Chamber (LArTPC) technology plays an essential role in many current and future neutrino experiments. Accurate and fast simulation is critical to developing efficient analysis algorithms and precise physics model projections. The speed of simulation becomes more important as Deep Learning algorithms are getting more widely used in LArTPC analysis and their training requires a large simulated dataset. Heterogeneous computing is an efficient way to delegate computationally intensive tasks to specialized hardware. However, as the landscape of compute accelerators quickly evolves, it becomes increasingly difficult to manually adapt the code to the latest hardware or software environments. A solution which is portable to multiple hardware architectures without substantially compromising performance would thus be very beneficial, especially for long-term projects such as the LArTPC simulations. In search of a portable, scalable and maintainable software solution for LArTPC simulations, we have started to explore high-level portable programming frameworks that support several hardware backends. In this paper, we present our experience porting the LArTPC simulation code in the Wire-Cell Toolkit to NVIDIA GPUs, first with the CUDA programming model and then with a portable library called Kokkos. Preliminary performance results on NVIDIA V100 GPUs and multi-core CPUs are presented, followed by a discussion of the factors affiecting the performance and plans for future improvements.
Free fermion entanglement with a semitransparent interface: the effect of graybody factors on entanglement islands

We study the entanglement entropy of free fermions in 2d in the presence of a partially transmitting interface that splits Minkowski space into two half-spaces. We focus on the case of a single interval that straddles the defect, and compute its entanglement entropy in three limits: Perturbing away from the fully transmitting and fully reflecting cases, and perturbing in the amount of asymmetry of the interval about the defect. Using these results within the setup of the Poincare patch of AdS$_2$ statically coupled to a zero temperature flat space bath, we calculate the effect of a partially transmitting AdS$_2$ boundary on the location of the entanglement island region. The partially transmitting boundary is a toy model for black hole graybody factors. Our results indicate that the entanglement island region behaves in a monotonic fashion as a function of the transmission/reflection coefficient at the interface.
Delving into Macro Placement with Reinforcement Learning

In physical design, human designers typically place macros via trial and error, which is a Markov decision process. Reinforcement learning (RL) methods have demonstrated superhuman performance on the macro placement. In this paper, we propose an extension to this prior work [1]. We first describe the details of the policy and value network architecture. We replace the force-directed method with DREAMPlace for placing standard cells in the RL environment. We also compare our improved method with other academic placers on public benchmarks.
Caring for the psychosocial needs of the acutely traumatized patient.

ABSTRACT Support for a patient in the aftermath of a potentially traumatic event is enhanced when clinicians understand the normal reactions that may occur during the traumatization process. This article discusses recommendations from international and national guidelines as well as best practices from the medical and psychiatric literature to help guide clinicians providing care for acutely traumatized patients.
Collaborative and Low-Rank Graph for Discriminant Analysis of Hyperspectral Imagery

Sparse graph-based discriminant analysis has drawn much attention to represent high dimensional data into low-dimensional subspace by using $l_{1}$ -norm optimization. There exists sparse and low-rank graph-based discriminant analysis (SLGDA) for incorporating local and global data structure together by combining both sparsity and low-rankness. Deviating from the concept of sparse representation, collaborative and low-rank representation-based discriminant analysis is proposed (CLGDA) in this paper with the assumption that collaboration among atoms is more important to estimate appropriate representation coefficients by accommodating within-class variation. The experimental results obtained on several hyperspectral datasets illustrate the superior classification performance of the proposed CLGDA in comparison to SLGDA and state-of-the-art approaches.
Timely Updating with Intermittent Energy and Data for Multiple Sources over Erasure Channels

A status updating system is considered in which multiple data sources generate packets to be delivered to a destination through a shared energy harvesting sensor. Only one source's data, when available, can be transmitted by the sensor at a time, subject to energy availability. Transmissions are prune to erasures, and each successful transmission constitutes a status update for its corresponding source at the destination. The goal is to schedule source transmissions such that the collective longterm average age-of-information (AoI) is minimized. AoI is defined as the time elapsed since the latest successfully-received data has been generated at its source. To solve this problem, the case with a single source is first considered, with a focus on threshold waiting policies, in which the sensor attempts transmission only if the time until both energy and data are available grows above a certain threshold. The distribution of the AoI is fully characterized under such a policy. This is then used to analyze the performance of the multiple sources case under maximum-age-first scheduling, in which the sensor's resources are dedicated to the source with the maximum AoI at any given time. The achievable collective longterm average AoI is derived in closed-form. Multiple numerical evaluations are demonstrated to show how the optimal threshold value behaves as a function of the system parameters, and showcase the benefits of a threshold-based waiting policy with intermittent energy and data arrivals.
Designing Transparency Cues in Online News Platforms to Promote Trust: Journalists' & Consumers' Perspectives

As news organizations embrace transparency practices on their websites to distinguish themselves from those spreading misinformation, HCI designers have the opportunity to help them effectively utilize the ideals of transparency to build trust. How can we utilize transparency to promote trust in news? We examine this question through a qualitative lens by interviewing journalists and news consumers---the two stakeholders in a news system. We designed a scenario to demonstrate transparency features using two fundamental news attributes that convey the trustworthiness of a news article: source and message. In the interviews, our news consumers expressed the idea that news transparency could be best shown by providing indicators of objectivity in two areas (news selection and framing) and by providing indicators of evidence in four areas (presence of source materials, anonymous sourcing, verification, and corrections upon erroneous reporting). While our journalists agreed with news consumers' suggestions of using evidence indicators, they also suggested additional transparency indicators in areas such as the news reporting process and personal/organizational conflicts of interest. Prompted by our scenario, participants offered new design considerations for building trustworthy news platforms, such as designing for easy comprehension, presenting appropriate details in news articles (e.g., showing the number and nature of corrections made to an article), and comparing attributes across news organizations to highlight diverging practices. Comparing the responses from our two stakeholder groups reveals conflicting suggestions with trade-offs between them. Our study has implications for HCI designers in building trustworthy news systems.
Proca Q-balls and Q-shells

Non-topological solitons such as Q-balls and Q-shells have been studied for scalar fields invariant under global and gauged U(1) symmetries. We generalize this frame-work to include a Proca mass for the gauge boson, which can arise either from spontaneous symmetry breaking or via the Stuckelberg mechanism. A heavy (light) gauge boson leads to solitons reminiscent of the global (gauged) case, but for intermediate values these Proca solitons exhibit completely novel features such as disconnected regions of viable parameter space and Q-shells with unbounded radius. We provide numerical solutions and excellent analytic approximations for both Proca Q-balls and Q-shells. These allow us to not only demonstrate the novel features numerically, but also understand and predict their origin analytically.
Support for Integrable Hopf Algebras via Noncommutative Hypersurfaces

We consider finite-dimensional Hopf algebras $u$ which admit a smooth deformation $U\to u$ by a Noetherian Hopf algebra $U$ of finite global dimension. Examples of such Hopf algebras include small quantum groups over the complex numbers, restricted enveloping algebras in finite characteristic, and Drinfeld doubles of height $1$ group schemes. We provide a means of analyzing (cohomological) support for representations over such $u$, via the singularity categories of the hypersurfaces $U/(f)$ associated to functions $f$ on the corresponding parametrization space. We use this hypersurface approach to establish the tensor product property for cohomological support, for the following examples: functions on a finite group scheme, Drinfeld doubles of certain height 1 solvable finite group schemes, bosonized quantum complete intersections, and the small quantum Borel in type $A$.
LTN: Long-Term Network for Long-Term Motion Prediction

Making accurate motion prediction of surrounding agents such as pedestrians and vehicles is a critical task when robots are trying to perform autonomous navigation tasks. Recent research on multi-modal trajectory prediction, including regression and classification approaches, perform very well at short-term prediction. However, when it comes to long-term prediction, most Long Short-Term Memory (LSTM) based models tend to diverge far away from the ground truth. Therefore, in this work, we present a two-stage framework for long-term trajectory prediction, which is named as Long-Term Network (LTN). Our Long-Term Network integrates both the regression and classification approaches. We first generate a set of proposed trajectories with our proposed distribution using a Conditional Variational Autoencoder (CVAE), and then classify them with binary labels, and output the trajectories with the highest score. We demonstrate our Long-Term Network's performance with experiments on two real-world pedestrian datasets: ETH/UCY, Stanford Drone Dataset (SDD), and one challenging real-world driving forecasting dataset: nuScenes. The results show that our method outperforms multiple state-of-the-art approaches in long-term trajectory prediction in terms of accuracy by around 20 percent.
A Biased Graph Neural Network Sampler with Near-Optimal Regret

Graph neural networks (GNN) have recently emerged as a vehicle for applying deep network architectures to graph and relational data. However, given the increasing size of industrial datasets, in many practical situations, the message passing computations required for sharing information across GNN layers are no longer scalable. Although various sampling methods have been introduced to approximate full-graph training within a tractable budget, there remain unresolved complications such as high variances and limited theoretical guarantees. To address these issues, we build upon existing work and treat GNN neighbor sampling as a multi-armed bandit problem but with a newly-designed reward function that introduces some degree of bias designed to reduce variance and avoid unstable, possibly-unbounded payouts. And unlike prior bandit-GNN use cases, the resulting policy leads to near-optimal regret while accounting for the GNN training dynamics introduced by SGD. From a practical standpoint, this translates into lower variance estimates and competitive or superior test accuracy across several benchmarks.
Relation-Guided Pre-Training for Open-Domain Question Answering

Answering complex open-domain questions requires understanding the latent relations between involving entities. However, we found that the existing QA datasets are extremely imbalanced in some types of relations, which hurts the generalization performance over questions with long-tail relations. To remedy this problem, in this paper, we propose a Relation-Guided Pre-Training (RGPT-QA) framework. We first generate a relational QA dataset covering a wide range of relations from both the Wikidata triplets and Wikipedia hyperlinks. We then pre-train a QA model to infer the latent relations from the question, and then conduct extractive QA to get the target answer entity. We demonstrate that by pre-training with propoed RGPT-QA techique, the popular open-domain QA model, Dense Passage Retriever (DPR), achieves 2.2%, 2.4%, and 6.3% absolute improvement in Exact Match accuracy on Natural Questions, TriviaQA, and WebQuestions. Particularly, we show that RGPT-QA improves significantly on questions with long-tail relations.
Cardinality constrained submodular maximization for random streams

We consider the problem of maximizing submodular functions in single-pass streaming and secretaries-with-shortlists models, both with random arrival order. For cardinality constrained monotone functions, Agrawal, Shadravan, and Stein gave a single-pass $(1-1/e-\varepsilon)$-approximation algorithm using only linear memory, but their exponential dependence on $\varepsilon$ makes it impractical even for $\varepsilon=0.1$. We simplify both the algorithm and the analysis, obtaining an exponential improvement in the $\varepsilon$-dependence (in particular, $O(k/\varepsilon)$ memory). Extending these techniques, we also give a simple $(1/e-\varepsilon)$-approximation for non-monotone functions in $O(k/\varepsilon)$ memory. For the monotone case, we also give a corresponding unconditional hardness barrier of $1-1/e+\varepsilon$ for single-pass algorithms in randomly ordered streams, even assuming unlimited computation. Finally, we show that the algorithms are simple to implement and work well on real world datasets.
RoboRun: A Robot Runtime to Exploit Spatial Heterogeneity

The limited onboard energy of autonomous mobile robots poses a tremendous challenge for practical deployment. Hence, efficient computing solutions are imperative. A crucial shortcoming of state-of-the-art computing solutions is that they ignore the robot’s operating environment heterogeneity and make static, worst-case assumptions. As this heterogeneity impacts the system’s computing payload, an optimal system must dynamically capture these changes in the environment and adjust its computational resources accordingly. This paper introduces RoboRun, a mobile-robot runtime that dynamically exploits the compute-environment synergy to improve performance and energy. We implement RoboRun in the Robot Operating System (ROS) and evaluate it on autonomous drones. We compare RoboRun against a state-of-the-art static design and show 4.5X and 4X improvements in mission time and energy, respectively, as well as a 36% reduction in CPU utilization.
A common framework for image segmentation

We attempt to unify several approaches to image segmentation in early vision under a common framework. The Bayesian approach is very attractive since: (i) it enables the assumptions used to be explicitly stated in the probability distributions, and (ii) it can be extended to deal with most other problems in early vision. Here, we consider the Markov random field formalism, a special case of the Bayesian approach, in which the probability distributions are specified by an energy function. We show that: (i) our discrete formulations for the energy function is closely related to the continuous formulation; (ii) by using the mean field (MF) theory approach, introduced by Geiger and Girosi [1991], several previous attempts to solve these energy functions are effectively equivalent; (iii) by varying the parameters of the energy functions we can obtain connections to nonlinear diffusion and minimal description length approaches to image segmentation; and (iv) simple modifications to the energy can give a direct relation to robust statistics or can encourage hysteresis and nonmaximum suppression.