cssi/SciSciGPT-SciSciNet huggingface dataset:
Co-circulation and misdiagnosis led to underestimation of the 2015–2017 Zika epidemic in the Americas

During the 2015–2017 Zika epidemic, dengue and chikungunya–two other viral diseases with the same vector as Zika–were also in circulation. Clinical presentation of these diseases can vary from person to person in terms of symptoms and severity, making it difficult to differentially diagnose them. Under these circumstances, it is possible that numerous cases of Zika could have been misdiagnosed as dengue or chikungunya, or vice versa. Given the importance of surveillance data for informing epidemiological analyses, our aim was to quantify the potential extent of misdiagnosis during this epidemic. Using basic principles of probability and empirical estimates of diagnostic sensitivity and specificity, we generated revised estimates of reported cases of Zika that accounted for the accuracy of diagnoses made on the basis of clinical presentation with or without laboratory confirmation. Applying this method to weekly reported case data from 43 countries throughout Latin America and the Caribbean, we estimated that 944,700 (95% CrI: 884,900–996,400) Zika cases occurred when assuming all confirmed cases were diagnosed using molecular methods versus 608,400 (95% CrI: 442,000–821,800) Zika cases that occurred when assuming all confirmed cases were diagnosed using serological methods. Our results imply that misdiagnosis was more common in countries with proportionally higher reported cases of dengue and chikungunya, such as Brazil. Given that Zika, dengue, and chikungunya appear likely to co-circulate in the Americas and elsewhere for years to come, our methodology has the potential to enhance the interpretation of passive surveillance data for these diseases going forward. Likewise, our methodology could also be used to help resolve transmission dynamics of other co-circulating diseases with similarities in symptomatology and potential for misdiagnosis.
Heat treatment – microstructure – hardness relationships of new nickel-rich nickel-titanium-hafnium alloys developed for tribological applications

Abstract The effects of various heat treatments on the microstructure and hardness of new Ni56Ti41Hf3 and Ni56Ti36Hf8 (atomic %) alloys were studied to evaluate the suitability of these materials for tribological applications. A solid-solution strengthening effect due to Hf atoms was observed for the solution annealed (SA) Ni56Ti36Hf8 alloy (716 HV), resulting in a comparable hardness to the Ni56Ti41Hf3 alloy containing 54 vol.% of Ni4Ti3 precipitates (707 HV). In the Ni56Ti41Hf3 alloy, the maximum hardness (752 HV), achieved after aging at 300°C for 12 h, was attributed to dense, semi-coherent precipitation of the Ni4Ti3 phase. Unlike the lenticular morphology usually observed within binary NiTi alloys, a “blocky” Ni4Ti3 morphology formed within Ni56Ti36Hf3 due to a smaller lattice mismatch in the direction normal to the habit plane at the precipitate/matrix interface. The maximum hardness for Ni56Ti36Hf8 (769 HV) was obtained after applying an intermediate aging step (300 °C for 12 h) followed by normal aging (550 °C for 4 h). This two-step aging treatment induces dense nanoscale precipitation of two interspersed precipitate phases, namely H-phase and a new cubic Ni-rich precipitate phase, resulting in the highest hardness exhibited yet by this family of alloys. The composition and structure of this new precipitate phase was characterized using atom probe tomography and transmission electron microscopy techniques to be cubic with a lattice parameter of a = 8.87 A and symmetry belonging to one of the primitive subgroups of m 3 ¯ m with approximately Ni61.5Ti31Hf7.5 composition .
Bridging BAD Islands: Declarative Data Sharing at Scale

In many Big Data applications today, information needs to be actively shared between systems managed by different organizations. To enable sharing Big Data at scale, developers would have to create dedicated server programs and glue together multiple Big Data systems for scalability. Developing and managing such glued data sharing services requires a significant amount of work from developers. In our prior work, we developed a Big Active Data (BAD) system for enabling Big Data subscriptions and analytics with millions of subscribers. Based on that, we introduce a new mechanism for enabling the sharing of Big Data at scale declaratively so that developers can easily create and provide data sharing services using declarative statements and can benefit from an underlying scalable infrastructure. We show our implementation on top of the BAD system, explain the data sharing data flow among multiple systems, and present a prototype system with experimental results.
A Surface Geometry Model for LiDAR Depth Completion

LiDAR depth completion is a task that predicts depth values for every pixel on the corresponding camera frame, although only sparse LiDAR points are available. Most of the existing state-of-the-art solutions are based on deep neural networks, which need a large amount of data and heavy computations for training the models. In this letter, a novel non-learning depth completion method is proposed by exploiting the local surface geometry that is enhanced by an outlier removal algorithm. The proposed surface geometry model is inspired by the observation that most pixels with unknown depth have a nearby LiDAR point. Therefore, it is assumed those pixels share the same surface with the nearest LiDAR point, and their respective depth can be estimated as the nearest LiDAR depth value plus a residual error. The residual error is calculated by using a derived equation with several physical parameters as input, including the known camera intrinsic parameters, estimated normal vector, and offset distance on the image plane. The proposed method is further enhanced by an outlier removal algorithm that is designed to remove incorrectly mapped LiDAR points from occluded regions. On KITTI dataset, the proposed solution achieves the best error performance among all existing non-learning methods and is comparable to the best self-supervised learning method and some supervised learning methods. Moreover, since outlier points from occluded regions is a commonly existing problem, the proposed outlier removal algorithm is a general preprocessing step that is applicable to many robotic systems with both camera and LiDAR sensors. The code has been published at https://github.com/placeforyiming/RAL_Non-Learning_DepthCompletion .
Location, Location, Location: Using Cost of Living to Achieve Tax Equity

All other things being equal, the federal income tax ignores whether the taxpayer lives in a relatively affordable or expensive location. This approach can lead to unfairness; moreover, special deductions for the taxpayer's actual living expenses, such as home mortgage interest and state and local taxes, do not solve the problem. Tax law scholars have generally been quick to dismiss the equity issues based on assumptions about taxpayer mobility. The existing literature would tax comparable workers equally, regardless of salary and living costs. This approach would unfairly equate differently situated workers. This Article questions the assumption of taxpayer mobility, considers the equity issues associated with failure to index the tax system properly, and assesses potential solutions. ABSTRACT 591 59
Markov Models of Coarsening in Two-Dimensional Foams with Edge Rupture

We construct Markov processes for modeling the rupture of edges in a two-dimensional foam. We first describe a network model for tracking topological information of foam networks with a state space of combinatorial embeddings. Through a mean-field rule for randomly selecting neighboring cells of a rupturing edge, we consider a simplified version of the network model in the sequence space $$\ell _1({\mathbb {N}})$$ which counts total numbers of cells with $$n\ge 3$$ sides (n-gons). Under a large cell limit, we show that number densities of n-gons in the mean field model are solutions of an infinite system of nonlinear kinetic equations. This system is comparable to the Smoluchowski coagulation equation for coalescing particles under a multiplicative collision kernel, suggesting gelation behavior. Numerical simulations reveal gelation in the mean-field model, and also comparable statistical behavior between the network and mean-field models.
Eigen Value Analysis in Lower Bounding Uncertainty of Kalman Filter Estimates

Abstract In this paper we are concerned with the error-covariance lower-bounding problem in Kalman filtering: a sensor releases a set of measurements to the data fusion/estimation center, which has a perfect knowledge of the dynamic model, to allow it to estimate the states, while preventing it to estimate the states beyond a given accuracy. We propose a measurement noise manipulation scheme to ensure lower-bound on the estimation accuracy of states. Our proposed method ensures lower-bound on the steady state estimation error of Kalman filter, using mathematical tools from eigen value analysis.
On Microstructure Estimation Using Flatbed Scanners for Paper Surface-Based Authentication

Paper surfaces under the microscopic view are observed to be formed by intertwisted wood fibers. Such structures of paper surfaces are unique from one location to another and are almost impossible to duplicate. Previous work used microscopic surface normals to characterize such intrinsic structures as a “fingerprint” of paper for security and forensic applications. In this work, we examine several key research questions of feature extraction in both scientific and engineering aspects to facilitate the deployment of paper surface-based authentication when flatbed scanners are used as the acquisition device. We analytically show that, under the unique optical setup of flatbed scanners, the specular reflection does not play a role in norm map estimation. We verify, using a larger dataset than prior work, that the scanner-acquired norm maps, although blurred, are consistent with those measured by confocal microscopes. We confirm that, when choosing an authentication feature, high spatial-frequency subbands of the heightmap are more powerful than the norm map. Finally, we show that it is possible to empirically calculate the physical dimensions of the paper patch needed to achieve a certain authentication performance in equal error rate (EER). We analytically show that log(EER) is decreasing linearly in the edge length of a paper patch.
Main Belt Asteroid Science in the Decade 2023-2032: Fundamental Science Questions and Recommendations on behalf of the Small Bodies Assessment Group

Solicited by the Small Bodies Assessment Group, we recommend a balanced program of telescopic observation (ground-based, airborne, and space-based), laboratory studies, theoretical research and missions to Main Belt Asteroids utilizing the full spectral range from ultraviolet to far-infrared to investigate these outstanding fundamental questions in the next decade.
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation

One of the most challenging topics in Natural Language Processing (NLP) is visually-grounded language understanding and reasoning. Outdoor vision-and-language navigation (VLN) is such a task where an agent follows natural language instructions and navigates in real-life urban environments. With the lack of human-annotated instructions that illustrate the intricate urban scenes, outdoor VLN remains a challenging task to solve. In this paper, we introduce a Multimodal Text Style Transfer (MTST) learning approach and leverage external multimodal resources to mitigate data scarcity in outdoor navigation tasks. We first enrich the navigation data by transferring the style of the instructions generated by Google Maps API, then pre-train the navigator with the augmented external outdoor navigation dataset. Experimental results show that our MTST learning approach is model-agnostic, and our MTST approach significantly outperforms the baseline models on the outdoor VLN task, improving task completion rate by 8.7% relatively on the test set.
Deep Learning-based Low-dose Tomography Reconstruction with Hybrid-dose Measurements

Synchrotron-based X-ray computed tomography is widely used for investigating inner structures of specimens at high spatial resolutions. However, potential beam damage to samples often limits the X-ray exposure during tomography experiments. Proposed strategies for eliminating beam damage also decrease reconstruction quality. Here we present a deep learning-based method to enhance low-dose tomography reconstruction via a hybrid-dose acquisition strategy composed of extremely sparse-view normal-dose projections and full-view low-dose projections. Corresponding image pairs are extracted from low-/normal-dose projections to train a deep convolutional neural network, which is then applied to enhance full-view noisy low-dose projections. Evaluation on two experimental datasets under different hybrid-dose acquisition conditions show significantly improved structural details and reduced noise levels compared to uniformly distributed acquisitions with the same number of total dosage. The resulting reconstructions also preserve more structural information than reconstructions processed with traditional analytical and regularization-based iterative reconstruction methods from uniform acquisitions. Our performance comparisons show that our implementation, HDrec, can perform denoising of a real-world experimental data 410x faster than the state-of-the-art X-learn method while providing better quality. This framework can be applied to other tomographic or scanning based X-ray imaging techniques for enhanced analysis of dose-sensitive samples and has great potential for studying fast dynamic processes.
YouTube Recommendations and Effects on Sharing Across Online Social Platforms

In January 2019, YouTube announced its platform would exclude potentially harmful content from video recommendations while allowing such videos to remain on the platform. While this action is intended to reduce YouTube's role in propagating such content, continued availability of these videos via hyperlinks in other online spaces leaves an open question of whether such actions actually impact sharing of these videos in the broader information space. This question is particularly important as other online platforms deploy similar suppressive actions that stop short of deletion despite limited understanding of such actions' impacts. To assess this impact, we apply interrupted time series models to measure whether sharing of potentially harmful YouTube videos in Twitter and Reddit changed significantly in the eight months around YouTube's announcement. We evaluate video sharing across three curated sets of anti-social content: a set of conspiracy videos that have been shown to experience reduced recommendations in YouTube, a larger set of videos posted by conspiracy-oriented channels, and a set of videos posted by alternative influence network (AIN) channels. As a control, we also evaluate these effects on a dataset of videos from mainstream news channels. Results show conspiracy-labeled and AIN videos that have evidence of YouTube's de-recommendation do experience a significant decreasing trend in sharing on both Twitter and Reddit. At the same time, however, videos from conspiracy-oriented channels actually experience a significant increase in sharing on Reddit following YouTube's intervention, suggesting these actions may have unintended consequences in pushing less overtly harmful conspiratorial content. Mainstream news sharing likewise sees increases in trend on both platforms, suggesting YouTube's suppression of particular content types has a targeted effect. In summary, while this work finds evidence that reducing exposure to anti-social videos within YouTube potentially reduces sharing on other platforms, increases in the level of conspiracy-channel sharing raise concerns about how producers -- and consumers -- of harmful content are responding to YouTube's changes. Transparency from YouTube and other platforms implementing similar strategies is needed to evaluate these effects further.
Copyright and the Vagueness Doctrine

The Constitution’s void-for-vagueness doctrine is itself vaguely stated. The law does little to describe at what point vague laws - other than those that are entirely standardless - might be unconstitutionally vague. Rather than explore this territory, the Supreme Court has identified three "collateral factors" that affect its inclination to invalidate a law for vagueness, including (1) whether the law burdens the exercise of constitutional rights, (2) whether the law is punitive in nature, and (3) whether the law overlays a defendant-protective scienter requirement. Against this backdrop, it is fair to say that copyright law, in its current configuration, does not meet the vagueness doctrine’s minimum requirements of fair notice to the public. Copyright by its terms restricts free speech; the law’s prolixity frustrates ex ante assessment of what speech is lawful. The question whether speech infringes copyright requires reference to a multiplicity of top-level interlocking questions or doctrines - each with its own manifold of subsidiary legal issues. Still more troubling is the uncertainty that inheres in "substantial similarity" and fair use, the very copyright doctrines that are generally held to rescue copyright from charges of First Amendment overbreadth. This Article argues that although a case can be made that copyright is unconstitutionally vague, invalidation of all or any portion of the Copyright Act is unlikely and not constructive. Reforms undertaken specifically to cure copyright’s indeterminacy are not likely to be effective, either. However, a consideration of the vagueness doctrine’s collateral factors and how they apply to copyright suggests an appropriate reform of the law. For cases involving expressive use of copyrighted content, lawmakers should adopt a three-tiered system of civil infringement liability by which strict liability is preserved in cases brought for injunctive relief only, while suits for statutory and actual damages require proof of willful and negligent infringement, respectively.
Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering

The 4D scanning transmission electron microscopy (STEM) method maps the structure and functionality of solids on the atomic scale, yielding information-rich data sets describing the interatomic electric and magnetic fields, structural and electronic order parameters, and other symmetry breaking distortions. A critical bottleneck is the dearth of analytical tools that can reduce complex 4D-STEM data to physically relevant descriptors. We propose an approach for the systematic exploration of 4D-STEM data using rotationally invariant variational autoencoders (rrVAE), which disentangle the general rotation of the object from other latent representations. The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures. The rrVAE analysis of experimental 4D-STEM data of defects in graphene is illustrated and compared to the classical center-of-mass analysis. This approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods.
Underlay Radar-Massive MIMO Spectrum Sharing: Modeling Fundamentals and Performance Analysis

Spectrum sharing alleviates the severe shortage of spectrum in sub-6 GHz frequency bands through the harmonious coexistence of two or more wireless technologies on the same frequency resources. In this work, we study underlay radar-massive MIMO cellular coexistence in LoS/near-LoS channels, where both systems have 3D beamforming capabilities. Using mathematical tools from stochastic geometry, we derive an upper bound on the average interference power at the radar due to the 3D massive MIMO cellular downlink under the worst-case ‘cell-edge beamforming’ conditions. To overcome the technical challenges imposed by asymmetric and arbitrarily large cells, we devise a novel construction in which each Poisson Voronoi (PV) cell is bounded by its circumcircle to bound the effect of the random cell shapes on average interference. Since this model is intractable for further analysis due to the correlation between adjacent PV cells’ shapes and sizes, we propose a tractable nominal interference model, where we model each PV cell as a circular disk with an area equal to the average area of the typical cell. We quantify the gap in the average interference power between these two models and show that the upper bound is tight for realistic deployment parameters. We also compare them with a more practical but intractable MU-MIMO scheduling model to show that our worst-case interference models show the same trends and do not deviate significantly from realistic scheduler models. Under the nominal interference model, we characterize the interference distribution using the dominant interferer approximation by deriving the equi-interference contour expression when the typical receiver uses 3D beamforming. Finally, we use tractable expressions for the interference distribution to characterize radar’s spatial probability of false alarm/detection in a quasi-static target tracking scenario. Our results reveal useful trends in the average interference as a function of the deployment parameters (BS density, exclusion zone radius, antenna height, transmit power of each BS, etc.). We also provide useful system design insights using radar receiver operating characteristic (ROC) curves by applying our analytical results to design the minimum exclusion zone radius in current and future radar-cellular spectrum sharing scenarios.
Demonstration of high-efficiency photonic lantern couplers for PolyOculus

The PolyOculus technology produces large-area-equivalent telescopes by using fiber optics to link modules of multiple semi-autonomous, small, inexpensive, commercial-off-the-shelf telescopes. Crucially, this scalable design has construction costs that are >10× lower than equivalent traditional large-area telescopes. We have developed a novel, to the best of our knowledge, photonic lantern approach for the PolyOculus fiber optic linkages that potentially offers substantial advantages over previously considered free-space optical linkages, including much higher coupling efficiencies. We have carried out the first laboratory tests of a photonic lantern prototype developed for PolyOculus, and demonstrated broadband efficiencies of ∼91%, confirming the outstanding performance of this technology.
Throughput Optimal Routing in Blockchain Based Payment Systems

Cryptocurrency networks such as Bitcoin have emerged as a distributed alternative to traditional centralized financial transaction networks. However, there are major challenges in scaling up the throughput of such networks. Lightning network and Spider network are alternates that build bidirectional payment channels on top of cryptocurrency networks using smart contracts, to enable fast transactions that bypass the Blockchain. In this paper, we study the problem of routing transactions in such a payment processing network. We first propose a Stochastic model to study such a system, as opposed to a fluid model that is studied in the literature. Each link in such a model is a two-sided queue, and unlike classical queues, such queues are not stable unless there is an external control. We propose a notion of stability for the payment processing network consisting of such two-sided queues using the notion of on-chain rebalancing. We then characterize the capacity region and propose a throughput optimal algorithm that stabilizes the system under any load within the capacity region. The stochastic model enables us to study closed loop policies, which typically have better queuing/delay pe
Prospects and challenges of quantum emitters in 2D materials

The search for an ideal single-photon source has generated significant interest in discovering emitters in materials as well as developing new manipulation techniques to gain better control over the emitters' properties. Quantum emitters in atomically thin two-dimensional (2D) materials have proven to be very attractive with high brightness, operation under ambient conditions, and the ability to be integrated with a wide range of electronic and photonic platforms. This Perspective highlights some of the recent advances in quantum light generation from 2D materials, focusing on hexagonal boron nitride and transition metal dichalcogenides. Efforts in engineering and deterministically creating arrays of quantum emitters in 2D materials, their electrical excitation, and their integration with photonic devices are discussed. Finally, we address some of the challenges the field is facing and the near-term efforts to tackle them. We provide an outlook toward efficient and scalable quantum light generation from 2D materials to controllable and addressable on-chip quantum sources.
Colorado in context: Congressional redistricting and competing fairness criteria in Colorado

In this paper, we apply techniques of ensemble analysis to understand the political baseline for Congressional representation in Colorado. We generate a large random sample of reasonable redistricting plans and determine the partisan balance of each district using returns from state-wide elections in 2018, and analyze the 2011/2012 enacted districts in this context. Colorado recently adopted a new framework for redistricting, creating an independent commission to draw district boundaries, prohibiting partisan bias and incumbency considerations, requiring that political boundaries (such as counties) be preserved as much as possible, and also requiring that mapmakers maximize the number of competitive districts. We investigate the relationships between partisan outcomes, number of counties which are split, and number of competitive districts in a plan. This paper also features two novel improvements in methodology—a more rigorous statistical framework for understanding necessary sample size, and a weighted-graph method for generating random plans which split approximately as few counties as acceptable human-drawn maps.
Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering

This paper presents new parallel algorithms for generating Euclidean minimum spanning trees and spatial clustering hierarchies (known as HDBSCAN*). Our approach is based on generating a well-separated pair decomposition followed by using Kruskal's minimum spanning tree algorithm and bichromatic closest pair computations. We introduce a new notion of well-separation to reduce the work and space of our algorithm for HDBSCAN*. We also give a new parallel divide-and-conquer algorithm for computing the dendrogram and reachability plots, which are used in visualizing clusters of different scale that arise for both EMST and HDBSCAN*. We show that our algorithms are theoretically efficient: they have work (number of operations) matching their sequential counterparts, and polylogarithmic depth (parallel time). We implement our algorithms and propose a memory optimization that requires only a subset of well-separated pairs to be computed and materialized, leading to savings in both space (up to 10x) and time (up to 8x). Our experiments on large real-world and synthetic data sets using a 48-core machine show that our fastest algorithms outperform the best serial algorithms for the problems by 11.13--55.89x, and existing parallel algorithms by at least an order of magnitude.
Resolvent near zero energy on Riemannian scattering (asymptotically conic) spaces

We give resolvent estimates near zero energy on Riemannian scattering, i.e. asymptotically conic, spaces, and their generalizations, using a uniform microlocal Fredholm analysis framework.
i3dLoc: Image-to-range Cross-domain Localization Robust to Inconsistent Environmental Conditions

We present a method for localizing a single camera with respect to a point cloud map in indoor and outdoor scenes. The problem is challenging because correspondences of local invariant features are inconsistent across the domains between image and 3D. The problem is even more challenging as the method must handle various environmental conditions such as illumination, weather, and seasonal changes. Our method can match equirectangular images to the 3D range projections by extracting cross-domain symmetric place descriptors. Our key insight is to retain condition-invariant 3D geometry features from limited data samples while eliminating the condition-related features by a designed Generative Adversarial Network. Based on such features, we further design a spherical convolution network to learn viewpoint-invariant symmetric place descriptors. We evaluate our method on extensive self-collected datasets, which involve Long-term (variant appearance conditions), Large-scale (up to 2km structure/unstructured environment), and Multistory (four-floor confined space). Our method surpasses other current state-of-the-arts by achieving around 3 times higher place retrievals to inconsistent environments, and above 3 times accuracy on online localization. To highlight our method’s generalization capabilities, we also evaluate the recognition across different datasets. With one single trained model, i3dLoc can achieve reliable visual localization under random conditions and viewpoints.
Xihe: a 3D vision-based lighting estimation framework for mobile augmented reality

Omnidirectional lighting provides the foundation for achieving spatially-variant photorealistic 3D rendering, a desirable property for mobile augmented reality applications. However, in practice, estimating omnidirectional lighting can be challenging due to limitations such as partial panoramas of the rendering positions, and the inherent environment lighting and mobile user dynamics. A new opportunity arises recently with the advancements in mobile 3D vision, including built-in high-accuracy depth sensors and deep learning-powered algorithms, which provide the means to better sense and understand the physical surroundings. Centering the key idea of 3D vision, in this work, we design an edge-assisted framework called Xihe to provide mobile AR applications the ability to obtain accurate omnidirectional lighting estimation in real time. Specifically, we develop a novel sampling technique that efficiently compresses the raw point cloud input generated at the mobile device. This technique is derived based on our empirical analysis of a recent 3D indoor dataset and plays a key role in our 3D vision-based lighting estimator pipeline design. To achieve the realtime goal, we develop a tailored GPU pipeline for on-device point cloud processing and use an encoding technique that reduces network transmitted bytes. Finally, we present an adaptive triggering strategy that allows Xihe to skip unnecessary lighting estimations and a practical way to provide temporal coherent rendering integration with the mobile AR ecosystem. We evaluate both the lighting estimation accuracy and time of Xihe using a reference mobile application developed with Xihe's APIs. Our results show that Xihe takes as fast as 20.67ms per lighting estimation and achieves 9.4% better estimation accuracy than a state-of-the-art neural network.
Cash-Management in Times of Covid-19

The incidence of COVID-19 has systematically decreased households' use of cash as means of payment as well as the average size and frequency of cash withdrawals. We argue that the structure of Baumol-Tobin type inventory theoretical models and their extensions can be used to separate the confounding factors, such as the desired level of consumption and the choice of the fraction of consumption paid in cash, from the cash management behavior, i.e. the size and frequency of cash withdrawals. Using this insight we argue that the observed cash management is consistent with COVID-19 increasing the fixed cost of withdrawing cash. We use detailed data on ATM cash disbursements in Argentina, Chile, and the US to estimate how much the pandemic has changed the transaction cost of using cash. This estimation shows that if the intensity of the virus doubles in a county, cash transaction cost increases by approximately 2%. The results from Argentina, Chile, and the US are remarkably similar and robust to several forms of measurement error and endogeneity. © 2021 Walter de Gruyter GmbH, Berlin/Boston 2021.
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances

Multi-agent reinforcement learning (MARL) has long been a significant research topic in both machine learning and control systems. Recent development of (single-agent) deep reinforcement learning has created a resurgence of interest in developing new MARL algorithms, especially those founded on theoretical analysis. In this paper, we review recent advances on a sub-area of this topic: decentralized MARL with networked agents. In this scenario, multiple agents perform sequential decision-making in a common environment, and without the coordination of any central controller, while being allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and the smart grid. This review covers several of our research endeavors in this direction, as well as progress made by other researchers along the line. We hope that this review promotes additional research efforts in this exciting yet challenging area.
A steerable landing gear for vehicles with main gear skids

Steerable landing gear system consisting of freely castoring corotating wheel nose gear, tiltable axle and main gear skids for lifting body spacecraft
Optimal Investment with Correlated Stochastic Volatility Factors

The problem of portfolio allocation in the context of stocks evolving in random environments, that is with volatility and returns depending on random factors, has attracted a lot of attention. The problem of maximizing a power utility at a terminal time with only one random factor can be linearized thanks to a classical distortion transformation. In the present paper, we address the problem with several factors using a perturbation technique around the case where these factors are perfectly correlated reducing the problem to the case with a single factor. We illustrate our result with a particular model for which we have explicit formulas. A rigorous accuracy result is also derived using sub- and super-solutions of the HJB equation involved. In order to keep the notations as explicit as possible, we treat the case with one stock and two factors and we describe an extension to the case with two stocks and two factors.
99% Revenue with Constant Enhanced Competition

The enhanced competition paradigm is an attempt at bridging the gap between simple and optimal auctions. In this line of work, given an auction setting with m items and n bidders, the goal is to find the smallest n' ≥ n such that selling the items to n' bidders through a simple auction generates (almost) the same revenue as the optimal auction. Recently, Feldman, Friedler, and Rubinstein [EC, 2018] showed that an arbitrarily large constant fraction of the optimal revenue from selling m items to single bidder can be obtained via simple auctions with a constant number of bidders. However, their techniques break down even for two bidders, and can only show a bound of n' = O(n · log m/n). Our main result is that n' = O(n) bidders suffices for all values of m and n. That is, we show that, for all m and n, an arbitrarily large constant fraction of the optimal revenue from selling m items to n bidders can be obtained via simple auctions with O(n) bidders. Moreover, when the items are regular, we can achieve the same result through auctions that are prior-independent, i.e., they do not depend on the distribution from which the bidders' valuations are sampled.
Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods, and Results.

Introduction: Transparency of computation is a requirement for assessing the validity of computed results and research claims based upon them; and it is essential for access to, assessment, and reuse of computational components. These components may be subject to methodological or other challenges over time. While reference to archived software and/or data is increasingly common in publications, a single machine-interpretable, integrative representation of how results were derived, that supports defeasible reasoning, has been absent.
Entrepreneurial Learning and Strategic Foresight

We study how learning by experience across projects affects an entrepreneur's strategic foresight. In a quantitative study of 314 entrepreneurs across 722 crowdfunded projects supplemented with a p...
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data

This article proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of...
Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks

Wide area networking infrastructures (WANs), particularly science and research WANs, are the backbone for moving large volumes of scientific data between experimental facilities and data centers. With demands growing at exponential rates, these networks are struggling to cope with large data volumes, real-time responses, and overall network performance. Network operators are increasingly looking for innovative ways to manage the limited underlying network resources. Forecasting network traffic is a critical capability for proactive resource management, congestion mitigation, and dedicated transfer provisioning. To this end, we propose a nonautoregressive graph-based neural network for multistep network traffic forecasting. Specifically, we develop a dynamic variant of diffusion convolutional recurrent neural networks to forecast traffic in research WANs. We evaluate the efficacy of our approach on real traffic from ESnet, the U.S. Department of Energy's dedicated science network. Our results show that compared to classical forecasting methods, our approach explicitly learns the dynamic nature of spatiotemporal traffic patterns, showing significant improvements in forecasting accuracy. Our technique can surpass existing statistical and deep learning approaches by achieving ≈20% mean absolute percentage error for multiple hours of forecasts despite dynamic network traffic settings.
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy

A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables. Using synthetic data sets, we show that the shift-VAE latent variables closely match the ground truth parameters. The shift VAE is extended towards the analysis of band-excitation piezoresponse force microscopy (BE-PFM) data, disentangling the resonance frequency shifts from the peak shape parameters in a model-free unsupervised manner. The extensions of this approach towards denoising of data and model-free dimensionality reduction in imaging and spectroscopic data are further demonstrated. This approach is universal and can also be extended to analysis of X-ray diffraction, photoluminescence, Raman spectra, and other data sets.
Liquid testing with your smartphone

Surface tension is an important property of liquids. It has diverse uses such as testing water contamination, measuring alcohol concentration in drinks, and identifying the presence of protein in urine to detect the onset of kidney failure. Today, measurements of surface tension are done in a lab environment using costly instruments, making it hard to leverage this property in ubiquitous applications. In contrast, we show how to measure surface tension using only a smartphone. We introduce a new algorithm that uses the small waves on the liquid surface as a series of lenses that focus light and generate a characteristic pattern. We then use the phone camera to capture this pattern and measure the surface tension. Our approach is simple, accurate, and available to anyone with a smartphone. Empirical evaluations show that our mobile app can detect water contamination and measure alcohol concentration. Furthermore, it can track protein concentration in the urine, providing an initial at-home test for proteinuria, a dangerous complication that can lead to kidney failure.
Social media data reveals signal for public consumer perceptions

Researchers have used social media data to estimate various macroeconomic indicators about public behaviors, mostly as a way to reduce surveying costs. One of the most widely cited economic indicator is consumer confidence index (CCI). Numerous studies in the past have focused on using social media, especially Twitter data, to predict CCI. However, the strong correlations disappeared when those models were tested with newer data according to a recent comprehensive survey. In this work, we revisit this problem of assessing the true potential of using social media data to measure CCI, by proposing a robust non-parametric Bayesian modeling framework grounded in Gaussian Process Regression (which provides both an estimate and an uncertainty associated with it). Integral to our framework is a principled experimentation methodology that demonstrates how digital data can be employed to reduce the frequency of surveys, and thus periodic polling would be needed only to calibrate our model. Via extensive experimentation we show how the choice of different micro-decisions, such as the smoothing interval, various types of lags etc. have an important bearing on the results. By using decadal data (2008--2019) from Reddit, we show that both monthly and daily estimates of CCI can, indeed, be reliably estimated at least several months in advance, and that our model estimates are far superior to those generated by the existing methods.
Predicting the impact of formation protocols on battery lifetime immediately after manufacturing

Summary Increasing the speed of battery formation can significantly lower lithium-ion battery manufacturing costs. However, adopting faster formation protocols in practical manufacturing settings is challenging due to a lack of inexpensive, rapid diagnostic signals that can inform possible impacts to long-term battery lifetime. This work identifies the cell resistance measured at low states of charge as an early-life diagnostic feature for screening new formation protocols. We show that this signal correlates to cycle life and improves the accuracy of data-driven battery lifetime prediction models. The signal is obtainable at the end of the manufacturing line, takes seconds to acquire, and does not require specialized test equipment. We explore a physical connection between this resistance signal and the quantity of lithium consumed during formation, suggesting that the signal may be broadly applicable for evaluating any manufacturing process change that could impact the total lithium consumed during formation.
preciseTAD: A transfer learning framework for 3D domain boundary prediction at base-pair resolution.

MOTIVATION Chromosome conformation capture technologies (Hi-C) revealed extensive DNA folding into discrete 3D domains, such as Topologically Associating Domains and chromatin loops. The correct binding of CTCF and cohesin at domain boundaries is integral in maintaining the proper structure and function of these 3D domains. 3D domains have been mapped at the resolutions of 1 kilobase and above. However, it has not been possible to define their boundaries at the resolution of boundary-forming proteins. RESULTS To predict domain boundaries at base-pair resolution, we developed preciseTAD, an optimized transfer learning framework trained on high-resolution genome annotation data. In contrast to current TAD/loop callers, preciseTAD-predicted boundaries are strongly supported by experimental evidence. Importantly, this approach can accurately delineate boundaries in cells without Hi-C data. preciseTAD provides a powerful framework to improve our understanding of how genomic regulators are shaping the 3D structure of the genome at base-pair resolution. AVAILABILITY preciseTAD is an R/Bioconductor package available at https://bioconductor.org/packages/preciseTAD/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Modeling Fuzzy Cluster Transitions for Topic Tracing.

Twitter can be viewed as a data source for Natural Language Processing (NLP) tasks. The continuously updating data streams on Twitter make it challenging to trace real-time topic evolution. In this paper, we propose a framework for modeling fuzzy transitions of topic clusters. We extend our previous work on crisp cluster transitions by incorporating fuzzy logic in order to enrich the underlying structures identified by the framework. We apply the methodology to both computer generated clusters of nouns from tweets and human tweet annotations. The obtained fuzzy transitions are compared with the crisp transitions, on both computer generated clusters and human labeled topic sets.
Exploring the relationship of rush hour period and fatal and non-fatal crash injuries in the U.S.: A systematic review and meta-analysis.

Abstract Road crashes are preventable causes of morbidity and mortality. In the U.S., substantial crashes occur during the rush hour period. The rush hour represents the period of the day during which the density of humans and vehicles in the road environment is highest. In the U.S., the rush hour period is bi-modal, occurring in the morning and the afternoon, at times that vary by state and urban–rural status. This systematic review and meta-analysis aimed to evaluate the association between the rush hour period and fatal and non-fatal crash injuries. Selected articles were limited to peer-reviewed full-text articles that measured crash injury as an outcome and rush hour as either a predictor, covariate, stratification, or a control variable. A total of 17 articles were identified for systematic review and nine articles were included in the meta-analysis. Across the selected studies, the rush-hour period signified the period of “peak traffic flow.” During the rush hour period, aggressive driving behavior, truck driving, bicycle riding, and precipitation were associated with increased crash events or crash injuries. Across the nine studies included in the meta-analysis, the effective sample size was 236,433. The rush-hour period was associated with a 28% increased risk of fatal crash injury (Pooled RR: 1.28; 95% CI: 1.11–1.45) and the morning rush hour period was associated with 36% increased crash injury risk (Pooled RR: 1.36; 95% CI: 1.13–1.59). The rush hour period, though less commonly studied as a predictor of fatal and non-fatal crash injuries, represents an important domain in need of crash injury prevention attention. The knowledge of the pattern of crash injuries, as it varies across countries, states, regions, and county can inform policy and intervention, in the presence of competing public health needs.