Miracle-dz/UniFAR-FTUs huggingface dataset:
Interventions to Mitigate Vaping Misinformation: A Meta-Analysis

The impact of misinformation about vapes’ relative harms compared with smoking may lead to increased tobacco-related burden of disease and youth vaping. Unfortunately, vaping misinformation has proliferated. Despite growing attempts to mitigate vaping misinformation, there is still considerable ambiguity regarding the ability to effectively curb the negative impact of misinformation. To address this gap, we use a meta-analysis to evaluate the relative impact of interventions designed to mitigate vaping-related misinformation. We searched (from January 2020 till August 2021) various databases and gray literature. Only English language, original studies that employed experimental designs where participants were randomly assigned either to receive mitigating information or to a no-mitigation condition (either misinformation-only or neutral control) were included. Meta-analysis was conducted for the four eligible studies. The mean effect size of attempts to mitigate vaping misinformation was positive but not statistically significant (d = 0.383, 95% CI [−0.029, 0.796], p = .061, k = 5) with lack of evidence for publication bias. Given limited studies included, we were unable to determine factors affecting the efficacy of interventions. The limited focus on non-US studies and youth populations is concerning given the popularity of vaping in low- to middle-income countries (LMICs) and among youth. The findings of this meta-analysis describe the current state of the literature and prescribe specific recommendations to better address the proliferation of vaping misinformation, providing insights helpful in limiting the tobacco mortality burden and curtailing youth vaping.
Temporal dynamics of auditory bistable perception correlated with fluctuation of baseline pupil size

Abstract A dynamic neural network change, accompanied by cognitive shifts such as internal perceptual alternation in bistable stimuli, is reconciled by the discharge of noradrenergic locus coeruleus neurons. Transient pupil dilation as a consequence of the reconciliation with the neural network in bistable perception has been reported to precede the reported perceptual alternation. Here, we found that baseline pupil size, an index of temporal fluctuation of arousal level over a longer range of timescales than that for the transient pupil changes, relates to the frequency of perceptual alternation in auditory bistability. Baseline pupil size was defined as the mean pupil diameter over a period of 1 s prior to the task requirement (i.e., before the observation period for counting the perceptual alternations in Experiment 1 and reporting whether participants experienced the perceptual alternations in Experiment 2). The results showed that the baseline pupil size monotonically increased with an increasing number of perceptual alternations and its occurrence probability. Furthermore, a cross‐correlation analysis indicates that baseline pupil size predicted perceptual alternation at least 35 s before the behavioral response and that the overall correspondence between pupil size and perceptual alternation was maintained over a sustained time window of 45 s at minimum. The overall results suggest that variability of baseline pupil size reflects the stochastic dynamics of arousal fluctuation in the brain related to bistable perception.
Genomic Epidemiology Insights on NDM-Producing Pathogens Revealed the Pivotal Role of Plasmids on blaNDM Transmission

To control the emergence and transmission of CRE, it is important to perform retrospective genomic investigations. It is important to evaluate the plasmid diversity, genetic environment, and evolutionary relationships of the blaNDM-positive clinical strains in the early transmission stages. ABSTRACT Incidences of nosocomial infections mediated by New Delhi metallo-β-lactamase (NDM) enzyme-producing Enterobacterales are increasing globally, resulting in a great burden to public health. The carbapenem-resistant Enterobacterales (CRE) were collected from Henan, China during 2013–2016. The blaNDM-positive strains were characterized using PCR, antimicrobial susceptibility testing, conjugation assay, S1 nuclease pulsed-field gel electrophoresis (S1-PFGE), Southern blot, whole-genome sequencing (WGS), and bioinformatics analysis. Eighty-one NDM-producing strains were identified among 391 nonduplicate CRE strains. Among them, four strains cocarried mcr and blaNDM genes, and two carried blaIMP-4 and blaNDM genes. The coexistence of blaNDM-5 and mcr-9 in Enterobacter hormaechei was found for the first time. In total, four blaNDM subtypes were identified. Among them, blaNDM-1 and blaNDM-5 were predominant. There was an obvious increasing trend in blaNDM-5 from 2013 to 2016. Thirteen different bacterial species were found among the 81 strains, and Escherichia coli was the dominant strain. blaNDM genes were located on nine different Inc-type plasmids, most of them on the IncX3 plasmids, except for the Pr-15-2-50 strain, which was located on the chromosome. We characterized two novel plasmids: the IncHI5-like plasmid carrying blaNDM-9 found in K. pneumonia, and the IncI1 blaNDM-5-positive plasmid. These findings provide the genomic basis for the widespread transmission of blaNDM and pave the way for the formulation of more effective monitoring and control methods. IMPORTANCE To control the emergence and transmission of CRE, it is important to perform retrospective genomic investigations. It is important to evaluate the plasmid diversity, genetic environment, and evolutionary relationships of the blaNDM-positive clinical strains in the early transmission stages. This study conducted an in-depth analysis of blaNDM-positive pathogens during a 4-year period using different methods for observing the high prevalence and active transmission of blaNDM-positive CRE. Moreover, we also explored the coexistence of the blaNDM and mcr, a clinically important mobile colistin resistance gene. This study shows that the prevalence of blaNDM-positive pathogens in Henan is high and the isolation rates increase each year. Moreover, plasmid-mediated horizontal transfer plays an important role in blaNDM dissemination. The co-occurrence of multiple resistance genes highlighted a long-lasting evolutionary pathway. Therefore, we have suggested the long-term continuous surveillance of clinical pathogens carrying blaNDM to learn the future transmission trend and curb the public health risk caused by CRE.
Efficacy of modified active packaging with oxygen scavengers for the preservation of sliced Iberian dry-cured shoulder

The effectiveness of commercial oxygen scavengers was investigated in order to increase the shelf-life of sliced dry-cured Iberian shoulder in MAP (modified atmosphere packaging) for up to 150 days. Five dry-cured shoulders from Iberian pigs were used. Slices of these dry-cured shoulders were randomly packaged in MAP conditions. An active packaging (AP) with oxygen scavengers was evaluated to reduce the level of oxygen within the headspace as close to 0% as possible. AP was compared to a Control Treatment (C) (without scavenger). Sliced dry-cured Iberian shoulder in AP showed lower thiobarbituric acid reactive substances values (TBARS) than control packages after 150 days of storage, and in general, volatile compounds derived from lipid oxidation, increased in C packages, whereas these remained steady in AP. Therefore, AP was effective to decrease the development of lipid oxidation during storage. In contrast, AP was not effective in preserving color changes, although no sensory differences between treatments were appreciated by the panelists.
1 Title 1 OLFACTORY TRAINING EFFICIENCY IN POST-COVID-19 PERSISTENT OLFACTORY DISORDERS 2 3 Running title 4 OLFACTORY TRAINING EFFICIENCY IN COVID-19 SMELL LOSS 5 6

Title 1 OLFACTORY TRAINING EFFICIENCY IN POST-COVID-19 PERSISTENT OLFACTORY DISORDERS 2 3 Running title 4 OLFACTORY TRAINING EFFICIENCY IN COVID-19 SMELL LOSS 5 6 Authors 7 Clair Vandersteen, Magali Payne, Louise-Émilie Dumas, Élisa Cancian, Alexandra 8 Plonka, Grégoire D’Andrea, David Chirio, Élisa Demonchy, Karine Risso, Florence 9 Askenazy-Gittard, Charles Savoldelli, Nicolas Guevara, Philippe Robert, Laurent 10 Castillo, Valeria Manera, Auriane Gros 11 12 Affiliation 13
Quantum Monte Carlo Study of Positron Lifetimes in Solids.

We present an analysis of positron lifetimes in solids with unprecedented depth. Instead of modeling correlation effects with density functionals, we study positron-electron wave functions with long-range correlations included. This gives new insight in understanding positron annihilation in metals, insulators, and semiconductors. By using a new quantum Monte Carlo approach for computation of positron lifetimes, an improved accuracy compared to previous computations is obtained for a representative set of materials when compared with experiment. Thus, we present a method without free parameters as a useful alternative to the already existing methods for modeling positrons in solids.
An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload

Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable of discriminating self-reported perceptions of mental workload. A dataset of raw EEG data was utilized in which 48 subjects performed a resting activity and an induced task demanding exercise in the form of a multitasking SIMKAP test. Band ratios were devised from frontal and parietal electrode clusters. Building and model testing was done with high-level independent features from the frequency and temporal domains extracted from the computed ratios over time. Target features for model training were extracted from the subjective ratings collected after resting and task demand activities. Models were built by employing Logistic Regression, Support Vector Machines and Decision Trees and were evaluated with performance measures including accuracy, recall, precision and f1-score. The results indicate high classification accuracy of those models trained with the high-level features extracted from the alpha-to-theta ratios and theta-to-alpha ratios. Preliminary results also show that models trained with logistic regression and support vector machines can accurately classify self-reported perceptions of mental workload. This research contributes to the body of knowledge by demonstrating the richness of the information in the temporal, spectral and statistical domains extracted from the alpha-to-theta and theta-to-alpha EEG band ratios for the discrimination of self-reported perceptions of mental workload.
Focusing nonlocal nonlinear Schr\"odinger equation with asymmetric boundary conditions: large-time behavior

We consider the focusing integrable nonlocal nonlinear Schrödinger equation iqt(x, t) + qxx(x, t) + 2q (x, t)q̄(−x, t) = 0 with asymmetric nonzero boundary conditions: q(x, t)→ ±Ae−2iA2t as x→ ±∞, where A > 0 is an arbitrary constant. The goal of this work is to study the asymptotics of the solution of the initial value problem for this equation as t→ +∞. For a class of initial values we show that there exist three qualitatively different asymptotic zones in the (x, t) plane. Namely, there are regions where the parameters are modulated (being dependent on the ratio x/t) and a central region, where the parameters are unmodulated. This asymptotic picture is reminiscent of that for the defocusing classical nonlinear Schrödinger equation, but with some important differences. In particular, the absolute value of the solution in all three regions depends on details of the initial data.
Doppler-Shifted Alkali D Absorption as Indirect Evidence for Exomoons

Sodium and potassium signatures in transiting exoplanets can be challenging to isolate from the stellar absorption lines. Here, these challenges are discussed in the framework of Solar System observations, and transits of Mercury in particular. Radiation pressure is important for alkali gas dynamics in close-orbiting exoplanets since the D lines are efficient at resonant scattering. When the star-planet velocity is ≳10 km/s, eccentric exoplanets experience more than an order of magnitude higher radiation pressures, aiding atmospheric escape and producing a larger effective cross-section for absorbing starlight at the phase of transit. The Doppler shift also aids in isolating the planetary signature from the stellar photosphere’s absorption. Only one transiting exoplanet, HD 80606b, is presently thought to have both this requisite Doppler shift and alkali absorption. Radiation pressure on a planetary exosphere naturally produces blue-shifted absorption, but at levels insufficient to account for the extreme Doppler shifts that have been inferred from potassium transit measurements of this system. In the absence of clear mechanisms to generate such a strong wind, it is described how this characteristic could arise from an exomoon-magnetosphere interaction, analogous to Io-Jupiter. At low contrasts presented here, follow-up transit spectra of HD 80606b cannot rule out a potassium jet or atmospheric species with a broad absorption structure. However, it is evident that line absorption within the imaging passbands fails to explain the narrow-band photometry that has been reported in-transit. New observations of energetic alkalis produced by the Io-Jupiter interaction are also presented, which illustrate that energetic sodium Doppler structure offers a more valuable marker for the presence of an exomoon than potassium.
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms

We study a sequential decision problem where the learner faces a sequence of K -armed bandit tasks. The task boundaries might be known (the bandit meta-learning setting), or unknown (the non-stationary bandit setting). For a given integer M ≤ K , the learner aims to compete with the best subset of arms of size M . We design an algorithm based on a reduction to bandit submodular maximization, and show that, for T rounds comprised of N tasks, in the regime of large number of tasks and small number of optimal arms M , its regret in both settings is smaller than the simple baseline of ˜ O ( √ KNT ) that can be obtained by using standard algorithms designed for non-stationary bandit problems. For the bandit meta-learning problem with ﬁxed task length τ , we show that the regret of the algorithm is bounded as ˜ O ( NM √ Mτ + N 2 / 3 Mτ ). Under additional assumptions on the identiﬁability of the optimal arms in each task, we show a bandit meta-learning algorithm with an improved ˜ O ( N √ Mτ + N 1 / 2 √ MKτ ) regret.
Recent Advances and Challenges in Deep Audio-Visual Correlation Learning

Audio-visual correlation learning aims to capture essential correspondences and understand natural phenomena between audio and video. With the rapid growth of deep learning, an increasing amount of attention has been paid to this emerging research issue. Through the past few years, various methods and datasets have been proposed for audio-visual correlation learning, which motivate us to conclude a comprehensive survey. This survey paper focuses on state-of-the-art (SOTA) models used to learn correlations between audio and video, but also discusses some tasks of definition and paradigm applied in AI multimedia. In addition, we investigate some objective functions frequently used for optimizing audio-visual correlation learning models and discuss how audio-visual data is exploited in the optimization process. Most importantly, we provide an extensive comparison and summarization of the recent progress of SOTA audio-visual correlation learning and discuss future research directions.
Adversarial robustness of sparse local Lipschitz predictors

This work studies the adversarial robustness of parametric functions composed of a linear predictor and a non-linear representation map. % that satisfies certain stability condition. Our analysis relies on \emph{sparse local Lipschitzness} (SLL), an extension of local Lipschitz continuity that better captures the stability and reduced effective dimensionality of predictors upon local perturbations. SLL functions preserve a certain degree of structure, given by the sparsity pattern in the representation map, and include several popular hypothesis classes, such as piece-wise linear models, Lasso and its variants, and deep feed-forward \relu networks. % are sparse local Lipschitz. We provide a tighter robustness certificate on the minimal energy of an adversarial example, as well as tighter data-dependent non-uniform bounds on the robust generalization error of these predictors. We instantiate these results for the case of deep neural networks and provide numerical evidence that supports our results, shedding new insights into natural regularization strategies to increase the robustness of these models.
A blob method method for inhomogeneous diffusion with applications to multi-agent control and sampling

As a counterpoint to classical stochastic particle methods for linear diffusion equations, such as Langevin dynamics for the Fokker-Planck equation, we develop a deterministic particle method for the weighted porous medium equation and prove its convergence on bounded time intervals. This generalizes related work on blob methods for unweighted porous medium equations. From a numerical analysis perspective, our method has several advantages: it is meshfree, preserves the gradient flow structure of the underlying PDE, converges in arbitrary dimension, and captures the correct asymptotic behavior in simulations. The fact that our method succeeds in capturing the long time behavior of the weighted porous medium equation is significant from the perspective of related problems in quantization. Just as the Fokker-Planck equation provides a way to quantize a probability measure ρ̄ by evolving an empirical measure ρN (t) = 1 N ∑N i=1 δXi(t) according to stochastic Langevin dynamics so that ρ N (t) flows toward ρ̄, our particle method provides a way to quantize ρ̄ according to deterministic particle dynamics approximating the weighted porous medium equation. In this way, our method has natural applications to multi-agent coverage algorithms and sampling probability measures. A specific case of our method corresponds exactly to the mean-field dynamics of training a two-layer neural network for a radial basis function activation function. From this perspective, our convergence result shows that, in the over parametrized regime and as the variance of the radial basis functions goes to zero, the continuum limit is given by the weighted porous medium equation. This generalizes previous results, which considered the case of a uniform data distribution, to the more general inhomogeneous setting. As a consequence of our convergence result, we identify conditions on the target function and data distribution for which convexity of the energy landscape emerges in the continuum limit.
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning

Recently, finetuning a pretrained language model to capture the similarity between sentence embeddings has shown the state-of-the-art performance on the semantic textual similarity (STS) task. However, the absence of an interpretation method for the sentence similarity makes it difficult to explain the model output. In this work, we explicitly describe the sentence distance as the weighted sum of contextualized token distances on the basis of a transportation problem, and then present the optimal transport-based distance measure, named RCMD; it identifies and leverages semantically-aligned token pairs. In the end, we propose CLRCMD, a contrastive learning framework that optimizes RCMD of sentence pairs, which enhances the quality of sentence similarity and their interpretation. Extensive experiments demonstrate that our learning framework outperforms other baselines on both STS and interpretable-STS benchmarks, indicating that it computes effective sentence similarity and also provides interpretation consistent with human judgement.
Non-isothermal non-Newtonian fluids: the stationary case

The stationary Navier-Stokes equations for a non-Newtonian incompressible fluid are coupled with the stationary heat equation and subject to Dirichlet type boundary conditions. The viscosity is supposed to depend on the temperature and the stress depends on the strain through a suit-able power law depending on $p \in (1,2)$ (shear thinning case). For this problem we establish the existence of a weak solution as well as we prove some regularity results both for the Navier-Stokes and the Stokes cases.Then, the latter case with the Carreau power law is approximated through a FEM scheme and some error estimates are obtained. Such estimates are then validated through some two-dimensional numerical experiments.
Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction

Spatio-temporal graphs (ST-graphs) have been used to model time series tasks such as traffic forecasting, human motion modeling, and action recognition. The high-level structure and corresponding features from ST-graphs have led to improved performance over traditional architectures. However, current methods tend to be limited by simple features, despite the rich information provided by the full graph structure, which leads to inefficiencies and suboptimal performance in downstream tasks. We propose the use of features derived from meta-paths, walks across different types of edges, in ST-graphs to improve the performance of Structural Recurrent Neural Network. In this paper, we present the Meta-path Enhanced Structural Recurrent Neural Network (MESRNN), a generic framework that can be applied to any spatio-temporal task in a simple and scalable manner. We employ MESRNN for pedestrian trajectory prediction, utilizing these meta-path based features to capture the relationships between the trajectories of pedestrians at different points in time and space. We compare our MESRNN against state-of-the-art ST-graph methods on standard datasets to show the performance boost provided by meta-path information. The proposed model consistently outperforms the baselines in trajectory prediction over long time horizons by over 32%, and produces more socially compliant trajectories in dense crowds. For more information please refer to the project website at https://sites.google. com/illinois.edu/mesrnn/home.
Robust Training under Label Noise by Over-parameterization

Recently, over-parameterized deep networks, with increasingly more network parameters than training samples, have dominated the performances of modern machine learning. However, when the training data is corrupted, it has been well-known that over-parameterized networks tend to overfit and do not generalize. In this work, we propose a principled approach for robust training of over-parameterized deep networks in classification tasks where a proportion of training labels are corrupted. The main idea is yet very simple: label noise is sparse and incoherent with the network learned from clean data, so we model the noise and learn to separate it from the data. Specifically, we model the label noise via another sparse over-parameterization term, and exploit implicit algorithmic regularizations to recover and separate the underlying corruptions. Remarkably, when trained using such a simple method in practice, we demonstrate state-of-the-art test accuracy against label noise on a variety of real datasets. Furthermore, our experimental results are corroborated by theory on simplified linear models, showing that exact separation between sparse noise and low-rank data can be achieved under incoherent conditions. The work opens many interesting directions for improving over-parameterized models by using sparse over-parameterization and implicit regularization.