cssi/SciSciGPT-SciSciNet huggingface dataset:
The Evolving Ecosystem of COVID-19 Contact Tracing Applications

Since the outbreak of the novel coronavirus, COVID-19, there has been significant interest in the use of digital contact tracing as a means of stopping chains of viral transmission, provoking alarm from privacy advocates. Concerning the ethics of this technology, recent studies have predominantly focused on (1) the formation of guidelines for ethical contact tracing, (2) the analysis of specific implementations, or (3) the review of a select number of contact tracing applications and their relevant privacy or ethical implications. In this study, we provide a comprehensive survey of the evolving ecosystem of COVID-19 tracing applications, examining 152 such applications and assessing the extent to which they comply with existing guidelines for ethical contact tracing. The assessed criteria cover areas including data collection and storage, transparency and consent, and whether the implementation is open source. We find that although many apps released early in the pandemic fell short of best practices, apps released more recently, following the publication of the Apple/Google exposure notification protocol, have tended to align more closely with ethical contact tracing principles. This data set is publicly available at https://www.benjaminlevy.ca/covid-apps and may be updated as the pandemic continues.
Beyond the universal Dyson singularity for 1-D chains with hopping disorder

Abstract We study a simple non-interacting nearest neighbor tight-binding model in one dimension with disorder, where the hopping terms are chosen randomly. This model exhibits a well-known singularity at the band center both in the density of states and localization length. If the probability distribution of the hopping terms is well-behaved, then the singularities exhibit universal behavior, the functional form of which was first discovered by Freeman Dyson in the context of a chain of classical harmonic oscillators. We show here that this universal form can be violated in a tunable manner if the hopping elements are chosen from a divergent probability distribution. We also demonstrate a connection between a breakdown of universality in this quantum problem and an analogous scenario in the classical domain — that of random walks and diffusion with anomalous exponents.
Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks

While backpropagation (BP) has been applied to spiking neural networks (SNNs) achieving encouraging results, a key challenge involved is to backpropagate a continuous-valued loss over layers of spiking neurons exhibiting discontinuous all-or-none firing activities. Existing methods deal with this difficulty by introducing compromises that come with their own limitations, leading to potential performance degradation. We propose a novel BP-like method, called neighborhood aggregation (NA), which computes accurate error gradients guiding weight updates that may lead to discontinuous modifications of firing activities. NA achieves this goal by aggregating finite differences of the loss over multiple perturbed membrane potential waveforms in the neighborhood of the present membrane potential of each neuron while utilizing a new membrane potential distance function. Our experiments show that the proposed NA algorithm delivers the state-of-the-art performance for SNN training on several datasets.
Infection Rate and Outcomes of Watchman Devices: Results from a Single-Center 14-Year Experience

The Watchman device (WD) is a commonly used alternative strategy to oral anticoagulation for stroke risk reduction in patients with atrial fibrillation who have an increased bleeding risk. There are rare case reports of WD-related infection. Currently, there is no formal study that has systematically evaluated the incidence and outcomes WD-related infections. The objective of this study was to evaluate the incidence, risk factors, and outcomes for WD-associated infections in a single-center cohort over a 14-year period. All patients who underwent WD implantation over a 14-year study period (July 2004 through December 2018) comprised our cohort. Baseline characteristics, procedural data, and postimplantation events were identified through a retrospective chart review. Primary study outcomes included WD-related infection, other cardiovascular device-related infection, bacteremia, and mortality. A total of 181 patients (119 males; 65.7%) with a mean age of 75 years at implantation were included in the analysis. A total of 534.7 patient years of follow-up was accrued, with an average of 2.9 years per patient. The most common indications for implantation included gastrointestinal bleeding (56 patients; 30.9%) and intracerebral bleeding (51 patients; 28.2%). During the follow-up period, 37 (20.4%) patients died. Six developed evidence of bacteremia. Only 1 developed an implantable cardioverter defibrillator infection that required a complete system extraction. None of the cohort developed a WD-related device infection during the study period. We concluded that there is a low risk of WD-related infection even in the setting of a blood stream infection.
Metabolomics of aging in primary fibroblasts from small and large breed dogs

Among several animal groups (eutherian mammals, birds, reptiles), lifespan positively correlates with body mass over several orders of magnitude. Contradicting this pattern are domesticated dogs, with small dog breeds exhibiting significantly longer lifespans than large dog breeds. The underlying mechanisms of differing aging rates across body masses are unclear, but it is generally agreed that metabolism is a significant regulator of the aging process. Herein, we performed a targeted metabolomics analysis on primary fibroblasts isolated from small and large breed young and old dogs. Regardless of size, older dogs exhibited lower glutathione and ATP, consistent with a role for oxidative stress and bioenergetic decline in aging. Furthermore, several size-specific metabolic patterns were observed with aging, including the following: (i) An apparent defect in the lower half of glycolysis in large old dogs at the level of pyruvate kinase. (ii) Increased glutamine anaplerosis into the TCA cycle in large old dogs. (iii) A potential defect in coenzyme A biosynthesis in large old dogs. (iv) Low nucleotide levels in small young dogs that corrected with age. (v) An age-dependent increase in carnitine in small dogs that was absent in large dogs. Overall, these data support the hypothesis that alterations in metabolism may underlie the different lifespans of small vs. large breed dogs, and further work in this area may afford potential therapeutic strategies to improve the lifespan of large dogs.
Personalized optimization with user’s feedback

Abstract This paper develops an online algorithm to solve a time-varying optimization problem with an objective that comprises a known time-varying cost and an unknown function. This problem structure arises in a number of engineering systems and cyber–physical systems where the known function captures time-varying engineering costs, and the unknown function models user’s satisfaction; in this context, the objective is to strike a balance between given performance metrics and user’s satisfaction. Key challenges related to the problem at hand are related to (1) the time variability of the problem, and (2) the fact that learning of the user’s utility function is performed concurrently with the execution of the online algorithm. This paper leverages Gaussian processes (GP) to learn the unknown cost function from noisy functional evaluation and build pertinent upper confidence bounds. Using the GP formalism, the paper then advocates time-varying optimization tools to design an online algorithm that exhibits tracking of the oracle-based optimal trajectory within an error ball, while learning the user’s satisfaction function with no-regret. The algorithmic steps are inexact, to account for possible limited computational budgets or real-time implementation considerations. Numerical examples are illustrated based on a problem related to vehicle control.
Framework for an Intelligent Affect Aware Smart Home Environment for Elderly People

The population of elderly people has been increasing at a rapid rate over the last few decades and their population is expected to further increase in the upcoming future. Their increasing population is associated with their increasing needs due to problems like physical disabilities, cognitive issues, weakened memory and disorganized behavior, that elderly people face with increasing age. To reduce their financial burden on the world economy and to enhance their quality of life, it is essential to develop technology-based solutions that are adaptive, assistive and intelligent in nature. Intelligent Affect Aware Systems that can not only analyze but also predict the behavior of elderly people in the context of their day to day interactions with technology in an IoT-based environment, holds immense potential for serving as a long-term solution for improving the user experience of elderly in smart homes. This work therefore proposes the framework for an Intelligent Affect Aware environment for elderly people that can not only analyze the affective components of their interactions but also predict their likely user experience even before they start engaging in any activity in the given smart home environment. This forecasting of user experience would provide scope for enhancing the same, thereby increasing the assistive and adaptive nature of such intelligent systems. To uphold the efficacy of this proposed framework for improving the quality of life of elderly people in smart homes, it has been tested on three datasets and the results are presented and discussed.
Modeling the heterogeneity in COVID-19's reproductive number and its impact on predictive scenarios

The correct evaluation of the reproductive number R for COVID-19 is central in the quantification of the potential scope of the pandemic and the selection of an appropriate course of action. In mos...
A non-iterative method for computing the infimum in H(infinity)-optimization

This paper presents a simple and non-iterative procedure for the computation of the exact value of the infimum in the singular H(infinity)-optimization problem, and is an extension of our earlier work. The problem formulation is general and does not place any restriction on the direct feedthrough terms between the control input and the controlled output variables, and between the disturbance input and the measurement output variables. Our method is applicable to a class of singular H(infinity)-optimization problems for which the transfer functions from the control input to the controlled output and from the disturbance input to the measurement output have no invariant zeros on the j-omega axis and also satisfy certain geometric conditions. The computation of the infimum in our method involves solving two well-defined Riccati and two Liapunov equations.
AOMD: An analogy-aware approach to offensive meme detection on social media

Abstract This paper focuses on an important problem of detecting offensive analogy meme on online social media where the visual content and the texts/captions of the meme together make an analogy to convey the offensive information. Existing offensive meme detection solutions often ignore the implicit relation between the visual and textual contents of the meme and are insufficient to identify the offensive analogy memes. Two important challenges exist in accurately detecting the offensive analogy memes: i) it is not trivial to capture the analogy that is often implicitly conveyed by a meme; ii) it is also challenging to effectively align the complex analogy across different data modalities in a meme. To address the above challenges, we develop a deep learning based Analogy-aware Offensive Meme Detection (AOMD) framework to learn the implicit analogy from the multi-modal contents of the meme and effectively detect offensive analogy memes. We evaluate AOMD on two real-world datasets from online social media. Evaluation results show that AOMD achieves significant performance gains compared to state-of-the-art baselines by detecting offensive analogy memes more accurately.
Biogeochemical cycling and remote sensing

Research is underway at the NASA Ames Research Center that is concerned with aspects of the nitrogen cycle in terrestrial ecosystems. An interdisciplinary research group is attempting to correlate nitrogen transformations, processes, and productivity with variables that can be remotely sensed. Recent NASA and other publications concerning biogeochemical cycling at global scales identify attributes of vegetation that could be related or explain the spatial variation in biologically functional variables. These functional variables include net primary productivity, annual nitrogen mineralization, and possibly the emission rate of nitrous oxide from soils.
Pain and itch processing by subpopulations of molecularly diverse spinal and trigeminal projection neurons

A remarkable molecular and functional heterogeneity of the primary sensory neurons and dorsal horn interneurons transmits pain- and or itch-relevant information, but the molecular signature of the projection neurons that convey the messages to the brain is unclear. Here, using retro-TRAP (translating ribosome affinity purification) and RNA sequencing, we reveal extensive molecular diversity of spino- and trigeminoparabrachial projection neurons. Among the many genes identified, we highlight distinct subsets of Cck + -, Nptx2 + -, Nmb + -, and Crh + -expressing projection neurons. By combining in situ hybridization of retrogradely labeled neurons with Fos-based assays, we also demonstrate significant functional heterogeneity, including both convergence and segregation of pain- and itch-provoking inputs into molecularly diverse subsets of NK1R- and non-NK1R-expressing projection neurons.
Relationship between changing malaria burden and low birth weight in sub-Saharan Africa: A difference-in-differences study via a pair-of-pairs approach.

Malaria infects around 230 million people each year, mostly in sub-Saharan Africa, and causes more than 400,000 deaths. Pregnant women are particularly susceptible to malaria. The parasite that causes malaria can sap the mother’s iron stores and may starve the baby of nutrients. Babies born to infected mothers often have low birth weights, which can have lasting effects on their health and brain development. Previous studies suggest that preventing malaria in pregnant women using insecticide-treated bed nets or medications may improve birth outcomes. Successful efforts to prevent malaria have led to substantially fewer infections in sub-Saharan Africa. But success has been uneven with some communities continuing to have high rates of infection. These differences may allow scientists to better understand the community-level impact of falling rates of malaria on pregnancy outcomes in the real world. Heng et al. estimated that reducing malaria transmission minimises the number of infants born with low birth weights in communities in sub-Saharan Africa. In an observational study, they used data on more than 18,000 births in 19 countries in this region between 2000 and 2015 to assess the effects of declining malaria rates on birth weights. They found that a decrease of malaria prevalence is estimated to reduce the rate of low birth weight by 1.48%, which is a 17% reduction in the number of observed newborns with low birth weight in the study population. First-born infants appeared to benefit the most. This highlights that malaria interventions are beneficial for pregnant women and their newborns. Most analyses of the impact and cost-benefit of malaria control have ignored the potential advantages of malaria control on birth weight, and may thus undermine the benefits of malaria control. The approach used by Heng et al. may further be useful for other epidemiologists studying global health.
Near-optimal Moving Average Estimation at Characteristic Timescales: An Allan Variance Approach

A major challenge in moving average (MA) estimation is the selection of an appropriate averaging window length or timescale over which measurements remain relevant to the estimation task. Prior works typically perform timescale selection by examining multiple window lengths (or models) before selecting the ‘optimal’ one using heuristics, domain knowledge expertise, goodness-of-fit, or information criterion (e.g. AIC, BIC etc.). In the presented work, we propose an alternative mechanism based on Allan Variance (AVAR) that obviates the need for assessing multiple models and systematically reduces reliance on heuristics or rules-of-thumb. The Allan Variance approach is used to identify the timescale that minimizes bias, thus determining the timescale over which past information remains most relevant. We also introduce an alternative method to obtain AVAR for unevenly spaced timeseries. The results from moving average estimation using an Allan Variance-determined window length are compared to the optimal moving average estimator that minimizes mean square error (MSE) for a variety of signals corrupted with Gaussian white noise. While the relevant timescales determined through AVAR tend to be longer than those associated with minimum MSE (i.e. AVAR-based MA estimation requires more measurements spread over a longer period of time), the AVAR-based moving average approach provides a valuable, systematic technique for near-optimal simple moving average estimation.
Biologically-Inspired Impedance Control With Hysteretic Damping

Recent experiments have shown that human joints can maintain a constant damping ratio across a wide range of external loads. This behavior can be explained by the use of a “complex stiffness” frequency-domain model approximating the impedance of the human joint. However, for a robot to replicate this naturally beneficial human behavior would require a time-domain model of this nonlinear joint impedance. This paper demonstrates that there exists a nonlinear time-domain model (originally from the structural mechanics community) that has a frequency-domain “describing function” that matches the complex stiffness model observed in humans. We provide an extension of this nonlinear time-domain model that removes the need to implement hard-switching control input. In addition, we demonstrate that this proportional-and-hysteretic-damping controller has inertia-invariant overshoot and therefore offers an advantage over the more common proportional-derivative control approach. Implementing the proposed proportional-and-hysteretic-damping control in a single-joint test-robot, we demonstrate for the first time that the desired frequency domain behavior can be reproduced in practice.
Axisymmetric single shear element combustion instability experiment

The combustion stability characteristics of a combustor consisting of a single shear element and a cylindrical chamber utilizing LOX and gaseous hydrogen as propellants are presented. The combustor geometry and the resulting longitudinal mode instability are axisymmetric. Hydrogen injection temperature and pyrotechnic pulsing were used to determine stability boundaries. Mixture ratio, fuel annulus gap, and LOX post configuration were varied. Performance and stability data are presented for chamber pressures of 300 and 1000 psia.
Iterated monodromy groups of Chebyshev-like maps on $\mathbb{C}^n$

Every affine Weyl group appears as the iterated monodromy group of a Chebyshev-like polynomial self-map of $\mathbb{C}^n$.
Directional Camera Control on High Altitude Balloons

The research reported in this paper examined the design and control of a gimbal for solar eclipse tracking and video recording. The gimbal design required 3 axes of rotation to allow for a full range of motion. Utilizing individual brushless motors for each of the axes ensures minimum rotational requirements on each axes. In controlling the gimbal, both a mathematical and visual method was utilized. The mathematical method is a modeled version of what is currently used for solar array pointing. The visual method looks at where the position of the sun is within the image and determines what angle changes are required. Utilizing a combination of these methods helps to eliminate error that accumulates within the onboard gyros due to the erratic behavior of balloon motion during flight. Elimination of this error ensures accurate video recording of the solar eclipse.
Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards

Electronic Health Record (EHR) systems provide critical, rich and valuable information at high frequency. One of the most exciting applications of EHR data is in developing a real-time mortality warning system with tools from survival analysis. However, most of the survival analysis methods used recently are based on (semi)parametric models using static covariates. These models do not take advantage of the information conveyed by the time-varying EHR data. In this work we present an application of a highly scalable survival analysis method, BoXHED 2.0 [1], to develop a real-time in-ICU mortality warning indicator based on the MIMIC IV data set [2]. Importantly, BoXHED can incorporate time-dependent covariates in a fully nonparametric manner and is backed by theory [3]. Our in-ICU mortality model achieves an AUC-PRC of 0.41 and AUC-ROC of 0.83 out of sample, demonstrating the benefit of real-time monitoring.
SimJEB: Simulated Jet Engine Bracket Dataset

Recent advancements in geometric deep learning have enabled a new class of engineering surrogate models; however, few existing shape datasets are well-suited to evaluate them. This paper introduces the Simulated Jet Engine Bracket Dataset (SimJEB): a new, public collection of crowdsourced mechanical brackets and high-fidelity structural simulations designed specifically for surrogate modeling. SimJEB models are more complex, diverse, and realistic than the synthetically generated datasets commonly used in parametric surrogate model evaluation. In contrast to existing engineering shape collections, SimJEB's models are all designed for the same engineering function and thus have consistent structural loads and support conditions. The models in SimJEB were collected from the original submissions to the GrabCAD Jet Engine Bracket Challenge: an open engineering design competition with over 700 hand-designed CAD entries from 320 designers representing 56 countries. Each model has been cleaned, categorized, meshed, and simulated with finite element analysis according to the original competition specifications. The result is a collection of diverse, high-quality and application-focused designs for advancing geometric deep learning and engineering surrogate models.
Vulnerability detection with fine-grained interpretations

Despite the successes of machine learning (ML) and deep learning (DL)-based vulnerability detectors (VD), they are limited to providing only the decision on whether a given code is vulnerable or not, without details on what part of the code is relevant to the detected vulnerability. We present IVDetect, an interpretable vulnerability detector with the philosophy of using Artificial Intelligence (AI) to detect vulnerabilities, while using Intelligence Assistant (IA) to provide VD interpretations in terms of vulnerable statements. For vulnerability detection, we separately consider the vulnerable statements and their surrounding contexts via data and control dependencies. This allows our model better discriminate vulnerable statements than using the mixture of vulnerable code and contextual code as in existing approaches. In addition to the coarse-grained vulnerability detection result, we leverage interpretable AI to provide users with fine-grained interpretations that include the sub-graph in the Program Dependency Graph (PDG) with the crucial statements that are relevant to the detected vulnerability. Our empirical evaluation on vulnerability databases shows that IVDetect outperforms the existing DL-based approaches by 43%–84% and 105%–255% in top-10 nDCG and MAP ranking scores. IVDetect correctly points out the vulnerable statements relevant to the vulnerability via its interpretation in 67% of the cases with a top-5 ranked list. IVDetect improves over the baseline interpretation models by 12.3%–400% and 9%–400% in accuracy.
Soft Information in the Financial Press and Analyst Revisions

Both sell-side analysts and the media are information intermediaries in capital markets. This study investigates the association between sell-side analyst research and information in firm-specific news coverage. More frequent recent news coverage is associated with stronger market reactions to analysts’ research revisions, and primarily explained by soft information in news coverage. The primary result is robust to using both an instrumental variable and a quasi-natural experimental setting to generate exogenous variation in media coverage, alleviating concerns about endogeneity. In addition, using textual analysis, we document that explicit media references in analyst research reports are significantly associated with more frequent analyst revisions and stronger market reactions to revisions. Our study provides empirical evidence of analysts’ assimilation of information from the financial press and their role in the efficiency of capital markets.
Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

We propose an interpretable model that combines the simplicity of matrix factorization with the flexibility of neural networks to model evolving user interests by efficiently extracting nonlinear p...
A zero-one law for Markov chains

We prove an analogue of the classical zero-one law for both homogeneous and nonhomogeneous Markov chains (MC). Its almost precise formulation is simple: given any event A from the tail σ-algebra of...
Show Me the Money: The Economic Impact of Membership-Based Free Shipping Programs on E-Tailers

This research examines the economic impact of membership-based free-shipping (MFS) programs when introduced as an augmentation to widely accepted contingent free-shipping (CFS) policies. Under CFS,...
Navigating the dynamics of financial embeddings over time

Financial transactions constitute connections between entities and through these connections a large scale heterogeneous weighted graph is formulated. In this labyrinth of interactions that are continuously updated, there exists a variety of similarity-based patterns that can provide insights into the dynamics of the financial system. With the current work, we propose the application of Graph Representation Learning in a scalable dynamic setting as a means of capturing these patterns in a meaningful and robust way. We proceed to perform a rigorous qualitative analysis of the latent trajectories to extract real world insights from the proposed representations and their evolution over time that is to our knowledge the first of its kind in the financial sector. Shifts in the latent space are associated with known economic events and in particular the impact of the recent Covid-19 pandemic to consumer patterns. Capturing such patterns indicates the value added to financial modeling through the incorporation of latent graph representations.
Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance

Predicting agents’ future trajectories plays a crucial role in modern AI systems, yet it is challenging due to intricate interactions exhibited in multi-agent systems, especially when it comes to collision avoidance. To address this challenge, we propose to learn congestion patterns as contextual cues explicitly and devise a novel "Sense–Learn–Reason–Predict" framework by exploiting advantages of three different doctrines of thought, which yields the following desirable benefits: (i) Representing congestion as contextual cues via latent factors subsumes the concept of social force commonly used in physics- based approaches and implicitly encodes the distance as a cost, similar to the way a planning-based method models the environment. (ii) By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories. To make the framework computationally tractable, we formulate it as an optimization problem and derive an upper bound by leveraging the variational parametrization. In experiments, we demonstrate that the proposed model is able to generate collision- free trajectory predictions in a synthetic dataset designed for collision avoidance evaluation and remains competitive on the commonly used NGSIM US-101 highway dataset. Source code and dataset tools can be accessed via Github.
A general model of multivalent binding with ligands of heterotypic subunits and multiple surface receptors.

Abstract Multivalent cell surface receptor binding is a ubiquitous biological phenomenon with functional and therapeutic significance. Predicting the amount of ligand binding for a cell remains an important question in computational biology as it can provide great insight into cell-to-cell communication and rational drug design toward specific targets. In this study, we extend a mechanistic, two-step multivalent binding model. This model predicts the behavior of a mixture of different multivalent ligand complexes binding to cells expressing various types of receptors. It accounts for the combinatorially large number of interactions between multiple ligands and receptors, optionally allowing a mixture of complexes with different valencies and complexes that contain heterogeneous ligand units. We derive the macroscopic predictions and demonstrate how this model enables large-scale predictions on mixture binding and the binding space of a ligand. This model thus provides an elegant and computationally efficient framework for analyzing multivalent binding.
Data-driven Actuator Selection for Artificial Muscle-Powered Robots

Even though artificial muscles have gained popularity due to their compliant, flexible and compact properties, there currently does not exist an easy way of making informed decisions on the appropriate actuation strategy when designing a muscle-powered robot; thus limiting the transition of such technologies into broader applications. What’s more, when a new muscle actuation technology is developed, it is difficult to compare it against existing robot muscles. To accelerate the development of artificial muscle applications, we propose a data-driven approach for robot muscle actuator selection using Support Vector Machines (SVM). This first-of-its-kind method gives users insight into which actuators fit their specific needs and actuation performance criteria, making it possible for researchers and engineers with little to no prior knowledge of artificial muscles to focus on application design. It also provides a platform to benchmark existing, new or yet-to-be discovered artificial muscle technologies. We test our method on unseen existing robot muscle designs to prove its usability on real-world applications. We provide an open-access, web-searchable interface for easy access to our models that will additionally allow for the continuous contribution of new actuator data from groups around the world to enhance and expand these models.
Risk-informed participation in T&D markets

Abstract Power producers can exhibit strategic behavior in electricity markets to maximize their profits. This behavior is more pronounced with the deregulation of distribution markets, which offers an opportunity for profit arbitrage between transmission and distribution (T&D) markets. However, the temporally distinct nature of these two markets introduces a significant risk in profit for such producers. This paper finds its motivation from the perspective of a strategic producer and develops a Single Leader Multi-Follower (SLMF) game for deriving its participation strategies in T&D markets, while accounting for different T&D coordination schemes based on the individual market Gate Closure Times (GCT). We compare and contrast joint and sequential market-clearing models with regulated and deregulated distribution environments and evaluate the risk of producer by leveraging consistent and coherent risk measures. SLMF game is reformulated as a Mathematical Program with Equilibrium Constraints (MPEC) and is solved using the seminal Scholtes’s relaxation scheme. We validate the efficacy of our model and solution approach via the case study carried out on the 11-zone New York ISO, and 7-bus Manhattan power networks, used as T&D markets, respectively.
This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News.

The problem of fake news has gained a lot of attention as it is claimed to have had a significant impact on 2016 US Presidential Elections. Fake news is not a new problem and its spread in social networks is well-studied. Often an underlying assumption in fake news discussion is that it is written to look like real news, fooling the reader who does not check for reliability of the sources or the arguments in its content. Through a unique study of three data sets and features that capture the style and the language of articles, we show that this assumption is not true. Fake news in most cases is more similar to satire than to real news, leading us to conclude that persuasion in fake news is achieved through heuristics rather than the strength of arguments. We show overall title structure and the use of proper nouns in titles are very significant in differentiating fake from real. This leads us to conclude that fake news is targeted for audiences who are not likely to read beyond titles and is aimed at creating mental associations between entities and claims.
The Spatial Relationship Between Contemporaneous Tremor Detections in Relatively Low- and High-Frequency Bands

Although tremor is believed to consist of myriad Low-frequency Earthquakes (LFEs), it also contains longer-period signals of unknown origin. We investigate the source of some of the longer-period s...
COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches

One of the most serious global health threats is COVID-19 pandemic. The emphasis on increasing the diagnostic capability helps stopping its spread significantly. Therefore, to assist the radiologist or other medical professional to detect and identify the COVID-19 cases in the shortest possible time, we propose a computer-aided detection (CADe) system that uses the computed tomography (CT) scan images. This proposed boosted deep learning network (CLNet) is based on the implementation of Deep Learning (DL) networks as a complementary to the Compressive Learning (CL). We utilize our inception feature extraction technique in the measurement domain using CL to represent the data features into a new space with less dimensionality before accessing the Convolutional Neural Network. All original features have been contributed equally to the new space using a sensing matrix. Experiments performed on different compressed methods show promising results for COVID-19 detection.
Subgame solving without common knowledge

In imperfect-information games, subgame solving is significantly more challenging than in perfect-information games, but in the last few years, such techniques have been developed. They were the key ingredient to the milestone of superhuman play in no-limit Texas hold'em poker. Current subgame-solving techniques analyze the entire common-knowledge closure of the player's current information set, that is, the smallest set of nodes within which it is common knowledge that the current node lies. However, this set is too large to handle in many games. We introduce an approach that overcomes this obstacle, by instead working with only low-order knowledge. Our approach allows an agent, upon arriving at an infoset, to basically prune any node that is no longer reachable, thereby massively reducing the game tree size relative to the common-knowledge subgame. We prove that, as is, our approach can increase exploitability compared to the blueprint strategy. However, we develop three avenues by which safety can be guaranteed. First, safety is guaranteed if the results of subgame solves are incorporated back into the blueprint. Second, we provide a method where safety is achieved by limiting the infosets at which subgame solving is performed. Third, we prove that our approach, when applied at every infoset reached during play, achieves a weaker notion of equilibrium, which we coin affine equilibrium, and which may be of independent interest. We show that affine equilibria cannot be exploited by any Nash strategy of the opponent, so an opponent who wishes to exploit must open herself to counter-exploitation. Even without the safety-guaranteeing additions, experiments on medium-sized games show that our approach always reduced exploitability even when applied at every infoset, and a depth-limited version of it led to--to our knowledge--the first strong AI for the massive challenge problem dark chess.
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem

In this paper, we study the problem of fair sparse regression on a biased dataset where bias depends upon a hidden binary attribute. The presence of a hidden attribute adds an extra layer of complexity to the problem by combining sparse regression and clustering with unknown binary labels. The corresponding optimization problem is combinatorial, but we propose a novel relaxation of it as an \emph{invex} optimization problem. To the best of our knowledge, this is the first invex relaxation for a combinatorial problem. We show that the inclusion of the debiasing/fairness constraint in our model has no adverse effect on the performance. Rather, it enables the recovery of the hidden attribute. The support of our recovered regression parameter vector matches exactly with the true parameter vector. Moreover, we simultaneously solve the clustering problem by recovering the exact value of the hidden attribute for each sample. Our method uses carefully constructed primal dual witnesses to provide theoretical guarantees for the combinatorial problem. To that end, we show that the sample complexity of our method is logarithmic in terms of the dimension of the regression parameter vector.
Magnetic dichroism in few-photon ionization of polarized atoms

We consider few-photon ionization of atomic lithium by linearly polarized femtosecond laser pulses and demonstrate that asymmetries of the electron angular distribution can occur for initially polarized $(2p,$ $m$=$+1)$ target atoms. The dependence of the photoelectron emission angle relative to the electric field direction is investigated at different laser intensities and wavelengths. The experimental spectra show excellent agreement with numerical solutions of the time-dependent Schr\"odinger equation. In the perturbative picture, the angular shift is traced back to interferences between partial waves with mean magnetic quantum number $\ensuremath{\langle}m\ensuremath{\rangle}\ensuremath{\ne}0$. This observation allows us to obtain quantum mechanical information on the final electronic state.
Ensemble Models for Neural Source Code Summarization of Subroutines.

A source code summary of a subroutine is a brief description of that subroutine. Summaries underpin a majority of documentation consumed by programmers, such as the method summaries in JavaDocs. Source code summarization is the task of writing these summaries. At present, most state-of-the-art approaches for code summarization are neural network-based solutions akin to seq2seq, graph2seq, and other encoder-decoder architectures. The input to the encoder is source code, while the decoder helps predict the natural language summary. While these models tend to be similar in structure, evidence is emerging that different models make different contributions to prediction quality -- differences in model performance are orthogonal and complementary rather than uniform over the entire dataset. In this paper, we explore the orthogonal nature of different neural code summarization approaches and propose ensemble models to exploit this orthogonality for better overall performance. We demonstrate that a simple ensemble strategy boosts performance by up to 14.8%, and provide an explanation for this boost. The takeaway from this work is that a relatively small change to the inference procedure in most neural code summarization techniques leads to outsized improvements in prediction quality.
Diminished alternative reinforcement as a mechanism underlying socioeconomic disparities in adolescent substance use

OBJECTIVE: This study examined socioeconomic disparities in adolescent substance use utilizing a behavioral economic theoretical framework. We tested the hypothesis that teens of lower (vs. higher) socioeconomic status (SES) are vulnerable to substance use because they engage in fewer pleasurable substance-free activities that provide reinforcement and may deter substance use. METHOD: In a cross-sectional correlational design, 9th grade students (N=2,839; mean age=14.1 years) in Los Angeles, California, USA completed surveys in Fall 2013 measuring SES (i.e., parental education), alternative reinforcement (engagement in pleasurable substance-free activities, e.g., hobbies), substance use susceptibility, initiation, and frequency, and other factors. RESULTS: For multi-substance composite outcomes, lower parental education was associated with greater likelihood of substance use initiation in the overall sample, frequency of use among lifetime substance users, and susceptibility to substance use in never users. Substance-specific analyses revealed that lower parental education was associated with higher likelihood of initiating cigarettes, alcohol, and marijuana use as well as greater susceptibility to use cigarettes in never smokers. Each inverse association between parental education and substance-related outcomes was statistically mediated by diminished alternative reinforcement; lower parental education was associated with lower engagement in alternative reinforcers, which, in turn, was associated with greater substance use susceptibility, initiation, and frequency. CONCLUSION: These results point to a behavioral economic interpretation for socioeconomic disparities in adolescent substance use. Replication and extension of these findings would suggest that prevention programs that increase access to and engagement in healthy and fun activities may reduce youth socioeconomic health disparities related to substance use. Language: en
Neither DoG nor LoG fits the receptive field of the vertebrate cone

For a given light intensity distribution I = f(x,y)representing a scene, the visual system seems to encode the point information in terms of intensity I and in terms of the second derivative of intensity (Mach 1868). The question of where in the visual systems and in what form this encoding first takes place and what are the responsible synaptic pathways and mechanisms remains unclear. In this paper we focus on physiological mechanism underlying receptive field of a cone in the vertebrate retina and we test the data with analytical model using UCLA SFINX [1] simulation environment.
Thrombolytic Therapy in Acute Myocardial Infarction

This paper deals with the history of thrombolytic therapy from its inception to its application in acute myocardial infarction. It describes the discovery of streptococcal fibrinolysin, followed by the elucidation of the plasma proteolytic enzyme system concerned with fibrinolysis. An outline is given of the therapeutic basis for the decision to concentrate on the development of activators of the enzyme, rather than the enzyme itself. Early attempts to demonstrate the value of streptokinase and urokinase in the treatment of myocardial infarction are examined. Finally, the more encouraging approaches in current use, especially the early application of thrombolytic therapy after the onset of the morbid event, are discussed.
Development productivity for commercial software using object-oriented methods

Object-oriented software development is widely believed to improve programmer productivity, however, surprisingly little evidence of this has been published. This paper presents an initial analysis of the productivity measured for 19 software products developed at IBM Programming Laboratories. Half of the products were developed using object-oriented methods, while the other half were developed using traditional "procedural" methods. Our study indicates that first generation object-oriented projects achieve the same productivity rates as follow-on releases of procedural projects. There does not appear to be a productivity penalty for moving to object-oriented technology, but there is apparently no immediate productivity benefit either. Furthermore, for this data, productivity rates increase as project size increases. This runs against the "conventional wisdom" about large projects, and is more surprising given that smaller projects had proportionately larger development teams than larger projects. An initial study of this phenomenon indicates that scheduling effects, such as the Parkinson effect and the Deadline effect, may be responsible for this economy of scale.
Occurrence of fungal-inhibiting Pseudomonas on caryopses of Tripsacum dactyloides L. and its implication for seed survival and agriculture application

Pseudomonas bacteria capable of inhibiting maize fungal pathogens were isolated from caryopses of three of 11 populations of eastern gamagrass (Tripsacum dactyloides) collected from eastern and central United States. The bacteria inhibited fungal pathogens in pure cultures and a zone of fungal inhibition 1–2 cm wide occurred around surface sterilized maize kernels that were treated with the bacteria and placed in culture dishes containing 2% maize meal agar. In cold-soil germination tests, there were no significant (P < 0.05) differences in germination of scarified maize kernels that were treated with gum arabic (sticking agent) and one of the bacterial strains (98%) or the commercial fungicide, Captan (96%). Both of these treatments resulted in significantly higher germination than scarification (50%) or scarification and gum arabic alone (58%). Non-scarified controls had 85% germination. The results indicate that Pseudomonas may be effective in preventing invasion of damaged seeds by pathogens capable of inhibiting germination and destroying seed embryos.
Microstructure and Cs Behavior of Ba-Doped Aluminosilicate Pollucite Irradiated with F+ Ions

Radionuclide 137Cs is one of the major fission products that dominate heat generation in spent fuels over the first 300 years. A durable waste form for 137Cs that decays to 137Ba is needed to minimize its environmental impact. Aluminosilicate pollucite CsAlSi2O6 is selected as a model waste form to study the decay-induced structural effects. Whereas Ba-containing precipitates are not present in charge-balanced Cs0.9Ba0.05AlSi2O6, they are found in Cs0.9Ba0.1AlSi2O6 and identified as monoclinic Ba2Si3O8. Pollucite is susceptible to electron-irradiation-induced amorphization. The threshold density of electronic energy deposition for amorphization was determined to be ∼235 keV/nm3. Pollucite can be readily amorphized under F+ ion irradiation at 673 K. A significant amount of Cs diffusion and release from the amorphized pollucite occurs during the irradiation. However, cesium is immobile in the crystalline structure under He+ ion irradiation at room temperature. The critical temperature for amorphization is n...
Technology scaling and soft error reliability

This paper discusses several attributes of integrated circuit scaling in relation to radiation soft error failure modes and vulnerability.