cssi/SciSciGPT-SciSciNet huggingface dataset:
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series.

Many real-life applications involve simultaneously forecasting multiple time series that are hierarchically related via aggregation or disaggregation operations. For instance, commercial organizations often want to forecast inventories simultaneously at store, city, and state levels for resource planning purposes. In such applications, it is important that the forecasts, in addition to being reasonably accurate, are also consistent w.r.t one another. Although forecasting such hierarchical time series has been pursued by economists and data scientists, the current state-of-the-art models use strong assumptions, e.g., all forecasts being unbiased estimates, noise distribution being Gaussian. Besides, state-of-the-art models have not harnessed the power of modern nonlinear models, especially ones based on deep learning. In this paper, we propose using a flexible nonlinear model that optimizes quantile regression loss coupled with suitable regularization terms to maintain the consistency of forecasts across hierarchies. The theoretical framework introduced herein can be applied to any forecasting model with an underlying differentiable loss function. A proof of optimality of our proposed method is also provided. Simulation studies over a range of datasets highlight the efficacy of our approach.
Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data

The design space for inertial confinement fusion (ICF) experiments is vast, and experiments are extremely expensive. Researchers rely heavily on computer simulations to explore the design space in search of high-performing implosions. However, ICF multiphysics codes must make simplifying assumptions, and thus deviate from experimental measurements for complex implosions. For more effective design and investigation, simulations require input from past experimental data to better predict future performance. In this work, we describe a cognitive simulation method for combining simulation and experimental data into a common, predictive model. This method leverages a machine learning technique called “transfer learning,” the process of taking a model trained to solve one task, and partially retraining it on a sparse dataset to solve a different, but related task. In the context of ICF design, neural network models are trained on large simulation databases and partially retrained on experimental data, producing models that are far more accurate than simulations alone. We demonstrate improved model performance for a range of ICF experiments at the National Ignition Facility and predict the outcome of recent experiments with less than 10% error for several key observables. We discuss how the methods might be used to carry out a data-driven experimental campaign to optimize performance, illustrating the key product—models that become increasingly accurate as data are acquired.
Analytical Investigation of Propellant Slosh Stability Boundary on a Space Vehicle

The effect of propellant sloshing upon the stability of a liquid-propelled space vehicle in a high-g environment has been studied extensively. For a typical space vehicle with thrust vector control...
Online Model Selection for Reinforcement Learning with Function Approximation

Deep reinforcement learning has achieved impressive successes yet often requires a very large amount of interaction data. This result is perhaps unsurprising, as using complicated function approximation often requires more data to fit, and early theoretical results on linear Markov decision processes provide regret bounds that scale with the dimension of the linear approximation. Ideally, we would like to automatically identify the minimal dimension of the approximation that is sufficient to encode an optimal policy. Towards this end, we consider the problem of model selection in RL with function approximation, given a set of candidate RL algorithms with known regret guarantees. The learner's goal is to adapt to the complexity of the optimal algorithm without knowing it \textit{a priori}. We present a meta-algorithm that successively rejects increasingly complex models using a simple statistical test. Given at least one candidate that satisfies realizability, we prove the meta-algorithm adapts to the optimal complexity with $\tilde{O}(L^{5/6} T^{2/3})$ regret compared to the optimal candidate's $\tilde{O}(\sqrt T)$ regret, where $T$ is the number of episodes and $L$ is the number of algorithms. The dimension and horizon dependencies remain optimal with respect to the best candidate, and our meta-algorithmic approach is flexible to incorporate multiple candidate algorithms and models. Finally, we show that the meta-algorithm automatically admits significantly improved instance-dependent regret bounds that depend on the gaps between the maximal values attainable by the candidates.
Soft topological lattice wheels

Abstract We investigate the large deformation and extreme load-management capabilities of a soft topologically polarized kagome lattice mapped to a cylindrical domain through the problem of a lattice wheel rolling on an irregular surface. We test the surface–lattice interaction experimentally by subjecting a 3D-printed topological lattice wheel prototype to localized and distributed boundary loads. This investigation reveals a dichotomy in the force transfer between the two loading scenarios, whereby localized loads are absorbed with limited stress penetration into the bulk and small force transfer to the wheel axle, compared to distributed loads. Through numerical simulations, we compare the lattice wheel against a baseline solid wheel to highlight the unique stress management opportunities offered by the lattice configuration. These findings promote the design of rolling objects enabled by topological mechanics, in which a surplus of softness, activated by local asperities, can coexist with a globally stiff response to distributed loads that ensures satisfactory load-bearing capabilities.
State-Varying Factor Models of Large Dimensions*

This paper develops an inferential theory for state-varying factor models of large dimensions. Unlike constant factor models, loadings are general functions of some recurrent state process. We deve...
GLORE: Generalized Loop Redundancy Elimination upon LER-Notation

This paper presents GLORE, a novel approach to enabling the detection and removal of large-scoped redundant computations in nested loops. GLORE works on LER-notation, a new representation of computations in both regular and irregular loops. Together with a set of novel algorithms, it makes GLORE able to systematically consider computation reordering at both the expression level and the loop level in a unified manner. GLORE shows an applicability much broader than prior methods have, and frequently lowers the computational complexities of some nested loops that are elusive to prior optimization techniques, producing significantly larger speedups.
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks

Although speech recognition has become a widespread technology, inferring emotion from speech signals remains a challenge. Our paper addresses this problem by proposing a quaternion convolutional neural network (QCNN) based speech emotion recognition (SER) model in which Mel-spectrogram features of speech signals are encoded in an RGB quaternion domain. We demonstrate that our QCNN based SER model outperforms other real-valued methods in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS, 8-classes) dataset, achieving, to the best of our knowledge, state-of-the-art results. The QCNN model also achieves comparable results with state-of-the-art methods in the Interactive Emotional Dyadic Motion Capture (IEMOCAP 4-classes) and Berlin EMO-DB (7-classes) datasets. Specifically, the model achieves an accuracy of 77.87%, 70.46%, and 88.78% for the RAVDESS, IEMOCAP, and EMO-DB datasets, respectively. Additionally, model size results reveal that the quaternion unit structure is significantly better able to encode internal dependencies than real-valued structures.
Material capture by double lunar gravity assist

The equations yielding the performance of a single lunar flyby in removing incoming hyperbolic excess velocity to capture payloads on interplanetary trajectories are briefly derived. The impossibility of using a single lunar flyby to capture a body entering the earth-moon system with a hyperbolic velocity in excess of about 1.9 km/s is discussed, and a method of using a double flyby of the moon to significantly improve this performance is developed. The equations for achieving a double lunar flyby are derived by solving the orbital equations and Lambert's problem both for the incoming trajectory in the plane of the moon's orbit and for arbitrary declination. For the in-plane case it is shown that the maximum removable hyperbolic excess velocity is 2.2687 km/s. For the inclined case, it is shown that the use of a double lunar flyby allows capture for declinations in excess of 54 degrees, and that for declinations less than 38 degrees the double lunar flyby offers better performance than the single lunar flyby.
Did India’s demonetization policy curb stone-pelting in Indian-administered Kashmir

On 9 November 2016, India’s Prime Minister Narendra Modi announced his ‘demonetization’ policy which rendered all Rs. 500 and Rs. 1000 notes null and void. His government claimed that this policy, ...
Geometric deep learning on brain shape predicts sex and age

The complex relationship between the shape and function of the human brain remains elusive despite extensive studies of cortical folding over many decades. The analysis of cortical gyrification presents an opportunity to advance our knowledge about this relationship, and better understand the etiology of a variety of pathologies involving diverse degrees of cortical folding abnormalities. Hypothesis-driven surface-based approaches have been shown to be particularly efficient in their ability to accurately describe unique features of the folded sheet topology of the cortical ribbon. However, the utility of these approaches has been blunted by their reliance on manually defined features aiming to capture the relevant geometric properties of cortical folding. In this paper, we propose an entirely novel, data-driven deep-learning based method to analyze the brain's shape that eliminates this reliance on manual feature definition. This method builds on the emerging field of geometric deep-learning and uses traditional convolutional neural network architecture uniquely adapted to the surface representation of the cortical ribbon. This method is a complete departure from prior brain MRI CNN investigations, all of which have relied on three dimensional MRI data and interpreted features of the MRI signal for prediction. MRI data from 6410 healthy subjects obtained from 11 publicly available data repositories were used for analysis. Ages ranged from 6 to 89 years. Both inner and outer cortical surfaces were extracted using Freesurfer and then registered into MNI space. For purposes of method development, both a classification and regression challenge were introduced for network learning including sex and age prediction, respectively. Two independent graph convolutional neural networks (gCNNs) were trained, the first of which to predict subject's self-identified sex, the second of which to predict subject's age. Class Activation Maps (CAM) and Regression Activation Maps (RAM) were constructed respectively to map the topographic distribution of the most influential brain regions involved in the decision process for each gCNN. Using this approach, the gCNN was able to predict a subject's sex with an average accuracy of 87.99 % and achieved a Person's coefficient of correlation of 0.93 with an average absolute error 4.58 years when predicting a subject's age. We believe this shape-based convolutional classifier offers a novel, data-driven approach to define biomedically relevant features from the brain at both the population and single subject levels and therefore lays a critical foundation for future precision medicine applications.
Adherence to COVID-19 policy measures: Behavioral insights from The Netherlands and Belgium

Background Since the start of the global COVID-19 pandemic, countries have been mirroring each other’s policies to mitigate the spread of the virus. Whether current measures alone will lead to behavioral change such as social distancing, washing hands, and wearing a facemask is not well understood. The objective of this study is to better understand individual variation in behavioral responses to COVID-19 by exploring the influence of beliefs, motivations and policy measures on public health behaviors. We do so by comparing The Netherlands and Flanders, the Dutch speaking part of Belgium. Methods and findings Our final sample included 2,637 respondents from The Netherlands and 1,678 from Flanders. The data was nationally representative along three dimensions: age, gender, and household income in both countries. Our key outcome variables of interest were beliefs about policy effectiveness; stated reasons for complying with public rules; and changes in behavior. For control variables, we included a number of measures of how severe the respondent believed Covid-19 to be and a number of negative side effects that the person may have experienced: loneliness, boredom, anxiety, and conflicts with friends and neighbors. Finally, we controlled for socio-demographic factors: age, gender, income (categorical), education (categorical) and the presence of Covid-19 risk factors (diabetes, high blood pressure, heart disease, asthma, allergies). The dependent variable for each of the estimation models is dichotomous, so we used Probit models to predict the probability of engaging in a given behavior. We found that motivations, beliefs about the effectiveness of measures, and pre-pandemic behavior play an important role. The Dutch were more likely to wash their hands than the Flemish (15.4%, p<0.01), visit family (15.5%, p < .01), run errands (12.0%, p<0.05) or go to large closed spaces such as a shopping mall (21.2%, p<0.01). The Dutch were significantly less likely to wear a mask (87.6%, p<0.01). We also found that beliefs about the virus, psychological effects of the virus, as well as pre-pandemic behavior play a role in adherence to recommendations. Conclusions Our results suggest that policymakers should consider behavioral motivations specific to their country in their COVID-19 strategies. In addition, the belief that a policy is effective significantly increased the probability of the behavior, so policy measures should be accompanied by public health campaigns to increase adherence.
Sampled-Data Stabilization With Control Lyapunov Functions via Quadratically Constrained Quadratic Programs

Controller design for nonlinear systems with Control Lyapunov Function (CLF) based quadratic programs has recently been successfully applied to a diverse set of difficult control tasks. These existing formulations do not address the gap between design with continuous time models and the discrete time sampled implementation of the resulting controllers, often leading to poor performance on hardware platforms. We propose an approach to close this gap by synthesizing sampled-data counterparts to these CLF-based controllers, specified as quadratically constrained quadratic programs (QCQPs). Assuming feedback linearizability and stable zero-dynamics of a system’s continuous time model, we derive practical stability guarantees for the resulting sampled-data system. We demonstrate improved performance of the proposed approach over continuous time counterparts in simulation.
The RNA-binding protein Nab2 regulates the proteome of the developing Drosophila brain.

The human ZC3H14 gene, which encodes a ubiquitously expressed polyadenosine zinc finger RNA-binding protein, is mutated in an inherited form of autosomal recessive, nonsyndromic intellectual disability. To gain insight into neurological functions of ZC3H14, we previously developed a Drosophila melanogaster model of ZC3H14 loss by deleting the fly ortholog, Nab2. Studies in this invertebrate model revealed that Nab2 controls final patterns of neuron projection within fully developed adult brains, but the role of Nab2 during development of the Drosophila brain is not known. Here, we identify roles for Nab2 in controlling the dynamic growth of axons in the developing brain mushroom bodies, which support olfactory learning and memory, and regulating abundance of a small fraction of the total brain proteome. The group of Nab2-regulated brain proteins, identified by quantitative proteomic analysis, includes the microtubule-binding protein Futsch, the neuronal Ig-family transmembrane protein turtle, the glial:neuron adhesion protein contactin, the Rac GTPase-activating protein tumbleweed, and the planar cell polarity factor Van Gogh, which collectively link Nab2 to the processes of brain morphogenesis, neuroblast proliferation, circadian sleep/wake cycles, and synaptic development. Overall, these data indicate that Nab2 controls the abundance of a subset of brain proteins during the active process of wiring the pupal brain mushroom body and thus provide a window into potentially conserved functions of the Nab2/ZC3H14 RNA-binding proteins in neurodevelopment.
GSum: A General Framework for Guided Neural Abstractive Summarization

Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control. While previous studies attempt to provide different types of guidance to control the output and increase faithfulness, it is not clear how these strategies compare and contrast to each other. In this paper, we propose a general and extensible guided summarization framework (GSum) that can effectively take different kinds of external guidance as input, and we perform experiments across several different varieties. Experiments demonstrate that this model is effective, achieving state-of-the-art performance according to ROUGE on 4 popular summarization datasets when using highlighted sentences as guidance. In addition, we show that our guided model can generate more faithful summaries and demonstrate how different types of guidance generate qualitatively different summaries, lending a degree of controllability to the learned models.
Fiber Optic Sensors for Health Monitoring of Morphing Aircraft

Fiber optic sensors are being developed for health monitoring of future aircraft. Aircraft health monitoring involves the use of strain, temperature, vibration and chemical sensors. These sensors will measure load and vibration signatures that will be used to infer structural integrity. Sine the aircraft morphing program assumes that future aircraft will be aerodynamically reconfigurable there is also a requirement for pressure, flow and shape sensors. In some cases a single fiber may be used for measuring several different parameters. The objective of the current program is to develop techniques for using optical fibers to monitor composite cure in real time during manufacture and to monitor in-service structural integrity of the composite structure. Graphite-epoxy panels were fabricated with integrated optical fibers of various types. The panels were mechanically and thermally tested to evaluate composite strength and sensor durability. Finally the performance of the fiber optic sensors was determined. Experimental results are presented evaluating the performance of embedded and surface mounted optical fibers for measuring strain, temperature and chemical composition. The performance of the fiber optic sensors was determined by direct comparison with results from more conventional instrumentation. The facilities for fabricating optical fiber and associated sensors and methods of demodulating Bragg gratings for strain measurement will be described.
To Share, or not to Share Online Event Trend Aggregation Over Bursty Event Streams

Complex event processing (CEP) systems continuously evaluate large workloads of pattern queries under tight time constraints. Event trend aggregation queries with Kleene patterns are commonly used to retrieve summarized insights about the recent trends in event streams. State-of-art methods are limited either due to repetitive computations or unnecessary trend construction. Existing shared approaches are guided by statically selected and hence rigid sharing plans that are often sub-optimal under stream fluctuations. In this work, we propose a novel framework Hamlet that is the first to overcome these limitations. Hamlet introduces two key innovations. First, Hamlet adaptively decides at run time whether to share or not to share computations depending on the current stream properties to harvest the maximum sharing benefit. Second, Hamlet is equipped with a highly efficient shared trend aggregation strategy that avoids trend construction. Our experimental study on both real and synthetic data sets demonstrates that Hamlet consistently reduces query latency by up to five orders of magnitude compared to state-of-the-art approaches.
Belief propagation with quantum messages for quantum-enhanced classical communications

For space-based laser communications, when the mean photon number per received optical pulse is much smaller than one, there is a large gap between communications capacity achievable with a receiver that performs individual pulse-by-pulse detection, and the quantum-optimal “joint-detection receiver” that acts collectively on long codeword-blocks of modulated pulses; an effect often termed “superadditive capacity”. In this paper, we consider the simplest scenario where a large superadditive capacity is known: a pure-loss channel with a coherent-state binary phase-shift keyed (BPSK) modulation. The two BPSK states can be mapped conceptually to two non-orthogonal states of a qubit, described by an inner product that is a function of the mean photon number per pulse. Using this map, we derive an explicit construction of the quantum circuit of a joint-detection receiver based on a recent idea of “belief-propagation with quantum messages” (BPQM). We quantify its performance improvement over the Dolinar receiver that performs optimal pulse-by-pulse detection, which represents the best “classical” approach. We analyze the scheme rigorously and show that it achieves the quantum limit of minimum average error probability in discriminating 8 (BPSK) codewords of a length-5 binary linear code with a tree factor graph. Our result suggests that a BPQM receiver might attain the Holevo capacity of this BPSK-modulated pure-loss channel. Moreover, our receiver circuit provides an alternative proposal for a quantum supremacy experiment, targeted at a specific application that can potentially be implemented on a small, special-purpose, photonic quantum computer capable of performing cat-basis universal qubit logic.
Polaronic Effect and Its Impact on Tc for Novel Layered Superconducting Systems

The crystal lattice of a complex compound may contain a subsystem of ions with each one possessing two close equilibrium positions (double-well structure). For ex- ample, the oxygen ions in the cuprates form such a subsys- tem. In such a situation it is impossible to separate electronic and local vibrational motions. This leads to a large increase in the effective strength of the electron-lattice interaction, which is beneficial for pairing.
Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment

We consider the issue of strategic behaviour in various peer-assessment tasks, including peer grading of exams or homeworks and peer review in hiring or promotions. When a peer-assessment task is competitive (e.g., when students are graded on a curve), agents may be incentivized to misreport evaluations in order to improve their own final standing. Our focus is on designing methods for detection of such manipulations. Specifically, we consider a setting in which agents evaluate a subset of their peers and output rankings that are later aggregated to form a final ordering. In this paper, we investigate a statistical framework for this problem and design a principled test for detecting strategic behaviour. We prove that our test has strong false alarm guarantees and evaluate its detection ability in practical settings. For this, we design and conduct an experiment that elicits strategic behaviour from subjects and release a dataset of patterns of strategic behaviour that may be of independent interest. We use this data to run a series of real and semi-synthetic evaluations that reveal a strong detection power of our test.
Hyponormal Toeplitz operators on weighted Bergman spaces

We consider operators acting on a Hilbert space that can be written as the sum of a shift and a diagonal operator and determine when the operator is hyponormal. The condition is presented in terms ...
Formal Schemes of Rational Degree

Formal schemes in algebraic geometry consist of power series with integer degree, this idea can be naturally carried over to power series with rational degree. In this paper formal schemes with rational degree are constructed. These schemes are non noetherian and thus require slight modification of the standard approach. The first part of the paper constructs non noetherian continuous valuation rings from discrete valuation rings, and these rings are reffered to as ‘eka’ (one in Hindi) rings. These new rings are designed to carry the properties of discrete valuation to continuous valuation faithfully. The second part of the paper constructs non noetherian formal schemes with rational degree and shows their admissibility. The corresponding flatness and coherence is proved. Finally line bundles of rational degree are constructed and their Cech cohomology computed.
Simulation of Turbine Tone Noise Generation Using a Turbomachinery Aerodynamics Solver

As turbofan engine bypass ratios continue to increase, the contribution of the turbine to the engine noise signature is receiving more attention. Understanding the relative importance of the various turbine noise generation mechanisms and the characteristics of the turbine acoustic transmission loss are essential ingredients in developing robust reduced-order models for predicting the turbine noise signature. A computationally based investigation has been undertaken to help guide the development of a turbine noise prediction capability that does not rely on empiricism. As proof-of-concept for this approach, two highly detailed numerical simulations of the unsteady flow field inside the first stage of a modern high-pressure turbine were carried out. The simulations were computed using TURBO, which is an unsteady Reynolds-Averaged Navier-Stokes code capable of multi-stage simulations. Spectral and modal analysis of the unsteady pressure data from the numerical simulation of the turbine stage show a circumferential modal distribution that is consistent with the Tyler-Sofrin rule. Within the high-pressure turbine, the interaction of velocity, pressure and temperature fluctuations with the downstream blade rows are all possible tone noise source mechanisms. We have taken the initial step in determining the source strength hierarchy by artificially reducing the level of temperature fluctuations in the turbine flowfield. This was accomplished by changing the vane cooling flow temperature in order to mitigate the vane thermal wake in the second of the two simulations. The results indicated that, despite a dramatic change in the vane cooling flow, the computed modal levels changed very little indicating that the contribution of temperature fluctuations to the overall pressure field is rather small compared with the viscous and potential field interaction mechanisms.
Deep Learning-Based COVID-19 Pneumonia Classification Using Chest CT Images: Model Generalizability

Since the outbreak of the COVID-19 pandemic, worldwide research efforts have focused on using artificial intelligence (AI) technologies on various medical data of COVID-19-positive patients in order to identify or classify various aspects of the disease, with promising reported results. However, concerns have been raised over their generalizability, given the heterogeneous factors in training datasets. This study aims to examine the severity of this problem by evaluating deep learning (DL) classification models trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We collected one dataset at UT Southwestern (UTSW) and three external datasets from different countries: CC-CCII Dataset (China), COVID-CTset (Iran), and MosMedData (Russia). We divided the data into two classes: COVID-19-positive and COVID-19-negative patients. We trained nine identical DL-based classification models by using combinations of datasets with a 72% train, 8% validation, and 20% test data split. The models trained on a single dataset achieved accuracy/area under the receiver operating characteristic curve (AUC) values of 0.87/0.826 (UTSW), 0.97/0.988 (CC-CCCI), and 0.86/0.873 (COVID-CTset) when evaluated on their own dataset. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better. However, the performance dropped close to an AUC of 0.5 (random guess) for all models when evaluated on a different dataset outside of its training datasets. Including MosMedData, which only contained positive labels, into the training datasets did not necessarily help the performance of other datasets. Multiple factors likely contributed to these results, such as patient demographics and differences in image acquisition or reconstruction, causing a data shift among different study cohorts.
Living in the concrete jungle: carnivore spatial ecology in urban parks

People and wildlife are living in an increasingly urban world, replete with unprecedented human densities, sprawling built environments, and altered landscapes. Such anthropogenic pressures can affect multiple processes within an ecological community, from spatial patterns to interspecific interactions. We tested two competing hypotheses, human shields vs. human competitors, to characterize how humans affect the carnivore community using multispecies occupancy models. From 2017 to 2020, we conducted the first camera survey of city parks in Detroit, Michigan, and collected spatial occurrence data of the local native carnivore community. Our 12,106-trap night survey captured detection data for coyotes (Canis latrans), red foxes (Vulpes vulpes), raccoons (Procyon lotor), and striped skunks (Mephitis mephitis). Overall occupancy varied across species (Ψcoyote = 0.40, Ψraccoon = 0.54, Ψred fox = 0.19, Ψstriped skunk = 0.09). Contrary to expectations, humans did not significantly affect individual occupancy for these urban carnivores. However, co-occurrence between coyote and skunk increased with human activity. The observed positive spatial association between an apex and subordinate pair supports the human shield hypothesis. Our findings demonstrate how urban carnivores can exploit spatial refugia and coexist with humans in the cityscape.
Surface roughness effects on radiant transfer between surfaces

Abstract Radiative heat transfer between opaque interacting surfaces is formulated for equal temperature adjoint plates with a one-dimensional surface roughness profile. The rough surface bidirectional reflectance and directional emittance introduced in the analysis depend on material emittance, roughness element specularity and surface roughness slope. Numerical solutions to the giverning integral equation for radiant intensity yields local and total heat transfer for a range of each of the rough surface parameters. The heat transfer results indicate that roughness effects are relatively unimportant for high emittance materials while for low emittance materials roughness slope can change local flux and total radiant heat transfer by a factor of two. Comparison of rough surface heat transfer to that evaluated with simple surface property models demonstrates the superiority of the diffuse emission-diffuse reflection model employing rough surface apparent hemispherical emittance. The error incurred in local flux using this simple model, however, can be significant.
Better Regularization for Sequential Decision Spaces: Fast Convergence Rates for Nash, Correlated, and Team Equilibria

We study the application of iterative first-order methods to the problem of computing equilibria of large-scale two-player extensive-form games. First-order methods must typically be instantiated with a regularizer that serves as a distance-generating function for the decision sets of the players. For the case of two-player zero-sum games, the state-of-the-art theoretical convergence rate for Nash equilibrium is achieved by using the dilated entropy function. In this paper, we introduce a new entropy-based distance-generating function for two-player zero-sum games, and show that this function achieves significantly better strong convexity properties than the dilated entropy, while maintaining the same easily-implemented closed-form proximal mapping. Extensive numerical simulations show that these superior theoretical properties translate into better numerical performance as well. We then generalize our new entropy distance function, as well as general dilated distance functions, to the scaled extension operator. The scaled extension operator is a way to recursively construct convex sets, which generalizes the decision polytope of extensive-form games, as well as the convex polytopes corresponding to correlated and team equilibria. By instantiating first-order methods with our regularizers, we develop the first accelerated first-order methods for computing correlated equilibra and ex-ante coordinated team equilibria. Our methods have a guaranteed 1/T rate of convergence, along with linear-time proximal updates.
Mechanisms of punctuated vision in fly flight.

Summary To guide locomotion, animals control gaze via movements of their eyes, head, and/or body, but how the nervous system controls gaze during complex motor tasks remains elusive. In many animals, shifts in gaze consist of periods of smooth movement punctuated by rapid eye saccades. Notably, eye movements are constrained by anatomical limits, which requires resetting eye position. By studying tethered, flying fruit flies (Drosophila), we show that flies perform stereotyped head saccades to reset gaze, analogous to optokinetic nystagmus in primates. Head-reset saccades interrupted head smooth movement for as little as 50 ms—representing less than 5% of the total flight time—thereby enabling punctuated gaze stabilization. By revealing the passive mechanics of the neck joint, we show that head-reset saccades leverage the neck’s natural elastic recoil, enabling mechanically assisted redirection of gaze. The consistent head orientation at saccade initiation, the influence of the head’s angular position on saccade rate, the decrease in wing saccade frequency in head-fixed flies, and the decrease in head-reset saccade rate in flies with their head range of motion restricted together implicate proprioception as the primary trigger of head-reset saccades. Wing-reset saccades were influenced by head orientation, establishing a causal link between neck sensory signals and the execution of body saccades. Head-reset saccades were abolished when flies switched to a landing state, demonstrating that head movements are gated by behavioral state. We propose a control architecture for active vision systems with limits in sensor range of motion. Video abstract Download : Download video (56MB)
Biochemical and immuno-histochemical localization of type IIA procollagen in annulus fibrosus of mature bovine intervertebral disc

For next generation tissue-engineered constructs and regenerative medicine to succeed clinically, the basic biology and extracellular matrix composition of tissues that these repair techniques seek to restore have to be fully determined. Using the latest reagents coupled with tried and tested methodologies, we continue to uncover previously undetected structural proteins in mature intervertebral disc. In this study we show that the "embryonic" type IIA procollagen isoform (containing a cysteine-rich amino propeptide) was biochemically detectable in the annulus fibrosus of both calf and mature steer caudal intervertebral discs, but not in the nucleus pulposus where the type IIB isoform was predominantly localized. Specifically, the triple-helical type IIA procollagen isoform immunolocalized in the outer margins of the inner annulus fibrosus. Triple helical processed type II collagen exclusively localized within the inter-lamellae regions and with type IIA procollagen in the intra-lamellae regions. Mass spectrometry of the α1(II) collagen chains from the region where type IIA procollagen localized showed high 3-hydroxylation of Proline-944, a post-translational modification that is correlated with thin collagen fibrils as in the nucleus pulposus. The findings implicate small diameter fibrils of type IIA procollagen in select regions of the annulus fibrosus where it likely contributes to the organization of collagen bundles and structural properties within the type I-type II collagen transition zone.
Domain name encryption is not enough: privacy leakage via IP-based website fingerprinting

Although the security benefits of domain name encryption technologies such as DNS over TLS (DoT), DNS over HTTPS (DoH), and Encrypted Client Hello (ECH) are clear, their positive impact on user privacy is weakened by--the still exposed--IP address information. However, content delivery networks, DNS-based load balancing, co-hosting of different websites on the same server, and IP address churn, all contribute towards making domain-IP mappings unstable, and prevent straightforward IP-based browsing tracking. In this paper, we show that this instability is not a roadblock (assuming a universal DoT/DoH and ECH deployment), by introducing an IP-based website fingerprinting technique that allows a network-level observer to identify at scale the website a user visits. Our technique exploits the complex structure of most websites, which load resources from several domains besides their primary one. Using the generated fingerprints of more than 200K websites studied, we could successfully identify 84% of them when observing solely destination IP addresses. The accuracy rate increases to 92% for popular websites, and 95% for popular and sensitive websites. We also evaluated the robustness of the generated fingerprints over time, and demonstrate that they are still effective at successfully identifying about 70% of the tested websites after two months. We conclude by discussing strategies for website owners and hosting providers towards hindering IP-based website fingerprinting and maximizing the privacy benefits offered by DoT/DoH and ECH.
Do the right firms survive bankruptcy

Abstract In Chapter 11 bankruptcy cases in the United States, firms are either reorganized, acquired, or liquidated. I show that decisions to liquidate often reduce creditor recovery, costing creditors billions of dollars every year. I exploit the within-district random assignment of bankruptcy judges to estimate a structural model of bankruptcy. I estimate that liquidation is frequently chosen when a reorganization would have maximized total creditor recovery. Liquidations involving “363 sales,” in which managers sell assets without creditor approval, are especially harmful for creditors. I estimate that courts could dramatically improve creditor recovery by assigning liquidations using a statistical model.
A retrospective on Isaac Levi: June 30, 1930 – December 25, 2018

Abstract Isaac Levi's philosophy places him squarely within the tradition of American Pragmatism: the noble legacy of Peirce, James, and Dewey, evidently influenced by his teachers and colleagues at Columbia University, e.g., E. Nagel and S. Morgenbesser, and fellow graduate student at Columbia University, e.g., H.E. Kyburg, Jr. and F. Schick. Important for understanding Levi's original perspective on large scale philosophical problems is the theme that decision theory is embedded in them all. Typical of his work, Levi's contributions are grounded on significant distinctions, many of which are cast with the aid of sound decision-theory. In this retrospective I review four salient examples of his interests, spanning Levi's work on (1) belief acceptance, (2) belief revision, (3) social philosophy, and (4) statistical inference.
A study of the generalizability of self-supervised representations

Abstract Recent advancements in self-supervised learning (SSL) made it possible to learn generalizable visual representations from unlabeled data. The performance of Deep Learning models fine-tuned on pretrained SSL representations is on par with models fine-tuned on the state-of-the-art supervised learning (SL) representations. Irrespective of the progress made in SSL, its generalizability has not been studied extensively. In this article, we perform a deeper analysis of the generalizability of pretrained SSL and SL representations by conducting a domain-based study for transfer learning classification tasks. The representations are learned from the ImageNet source data, which are then fine-tuned using two types of target datasets: similar to the source dataset, and significantly different from the source dataset. We study generalizability of the SSL and SL-based models via their prediction accuracy as well as prediction confidence. In addition to this, we analyze the attribution of the final convolutional layer of these models to understand how they reason about the semantic identity of the data. We show that the SSL representations are more generalizable as compared to the SL representations. We explain the generalizability of the SSL representations by investigating its invariance property, which is shown to be better than that observed in the SL representations.
Do tigers hunt during the day? Diel activity of the Asian tiger mosquito, Aedes albopictus (Diptera: Culicidae), in urban and suburban habitats of North America.

Background Aedes (Stegomyia) albopictus (Skuse) impacts human outdoor activity because of its aggressive biting behavior, and as a major vector of mosquito-borne diseases, it is also of public health importance. Although most mosquito species exhibit crepuscular activity by primarily host seeking at dawn and dusk, Ae. albopictus has been traditionally characterized as a diurnal or day-biting mosquito. With the global expansion and increased involvement of Ae. albopictus in mosquito-borne diseases, it is imperative to elucidate the diel activity of this species, particularly in newly invaded areas. Methodology and principal findings Human sweep netting and carbon dioxide-baited rotator traps were used to evaluate the diel activity of Ae. albopictus in two study sites. Both trapping methods were used in New Jersey’s Mercer County, USA (temperate/urban), while only human sweep netting was used in Florida’s Volusia County, USA (subtropical/suburban). Human sweep netting was performed to determine adult mosquito activity at Sunrise, Solar Noon, Sunset, and Lunar Midnight. Because New Jersey is in a temperate area, diel activity was investigated during the early season (3–19 July), peak season (25 July-19 September), and late season (22 September- 22 October). Aedes albopictus showed the highest activity during peak and late seasons at Solar Noon (P < 0.05). At Sunrise and Sunset during the peak season, Ae. albopictus activity was similar. Lunar Midnight activity was significantly lower than Sunrise and Solar Noon (P < 0.05) but was similar to that of Sunset. In the late season, the highest activity was observed during Solar Noon while the least activity was observed during Sunrise and Lunar Midnight (P<0.05). Bottle rotator traps used in conjunction with the human sweep net technique exhibited similar results. Seasonal activity was not differentiated in Florida due to the consistent subtropical climate. The highest adult activity was observed at Sunrise using human sweep netting, but it was not significantly different from Solar Noon and Sunset. The lowest adult activity was observed at Lunar Midnight; however, it was not significantly different from Solar Noon and Sunset. These results provide evidence that the diel activity of Ae. albopictus, contrary to the common perception of its diurnal activity, is much more varied. Conclusion/Significance Involvement of Ae. albopictus in the transmission of debilitating mosquito-borne pathogens such as chikungunya, dengue, and Zika virus, coupled with its affinity to thrive in human peridomestic environments, substantiates that our findings have global implications in areas where Ae. albopictus populations established. It also highlights the importance of behavioral studies of vector species which will not only help mosquito control professionals plan the timing of their control efforts but also provides empirical evidence against conventional wisdoms that may unjustly persist within public health stewards.
Differential Effects of Cerebellar Degeneration on Feedforward versus Feedback Control across Speech and Reaching Movements

Errors that result from a mismatch between predicted movement outcomes and sensory afference are used to correct ongoing movements through feedback control and to adapt feedforward control of future movements. The cerebellum has been identified as a critical part of the neural circuit underlying implicit adaptation across a wide variety of movements (reaching, gait, eye movements, and speech). The contribution of this structure to feedback control is less well understood: although it has recently been shown in the speech domain that individuals with cerebellar degeneration produce even larger online corrections for sensory perturbations than control participants, similar behavior has not been observed in other motor domains. Currently, comparisons across domains are limited by different population samples and potential ceiling effects in existing tasks. To assess the relationship between changes in feedforward and feedback control associated with cerebellar degeneration across motor domains, we evaluated adaptive (feedforward) and compensatory (feedback) responses to sensory perturbations in reaching and speech production in human participants of both sexes with cerebellar degeneration and neurobiologically healthy controls. As expected, the cerebellar group demonstrated impaired adaptation in both reaching and speech. In contrast, the groups did not differ in their compensatory response in either domain. Moreover, compensatory and adaptive responses in the cerebellar group were not correlated within or across motor domains. These results point to a general impairment in feedforward control with spared feedback control in cerebellar degeneration. However, the magnitude of feedforward impairments and potential changes in feedback-based control manifest in a domain-specific manner across individuals. SIGNIFICANCE STATEMENT The cerebellum contributes to feedforward updating of movement in response to sensory errors, but its role in feedback control is less understood. Here, we tested individuals with cerebellar degeneration (CD), using sensory perturbations to assess adaptation of feedforward control and feedback gains during reaching and speech production tasks. The results confirmed that CD leads to reduced adaption in both domains. However, feedback gains were unaffected by CD in either domain. Interestingly, measures of feedforward and feedback control were not correlated across individuals within or across motor domains. Together, these results indicate a general impairment in feedforward control with spared feedback control in CD. However, the magnitude of feedforward impairments manifests in a domain-specific manner across individuals.