Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Pod-Based Electronic Cigarette Use on Vascular Health and Relation to Volatile Organic Compound Exposure in Young Adults

Background: Pod-based electronic cigarettes (e-cigarettes) use that contain nicotine salts is frequent among youth and young adults; thus, we compared the vascular health effects of pod-based e-cigarette use to combustible cigarette use. Methods and Results We performed a two center observational, cross-sectional study of healthy adults recruited from the community (aged 18-45, N=106) in 3 groups: pod-based e-cigarette users (N=48); combustible cigarette users (N=21); and tobacco nonusers (N=37) and assessed the acute (following structured use) and chronic (resting state after 6 hour tobacco abstinence) effects of pod-based e-cigarette use on endothelial function (brachial artery flow-mediated dilation), blood pressure, and heart rate. Among the pod-based e-cigarette users, 64% were exclusive users including 37% who had never used combustible cigarettes. Pod-based e-cigarette users and combustible cigarette users had higher systolic blood pressure compared to non-users (121{+/-}11mmHg, 121{+/-}13mmHg, 112{+/-}10 mmHg, P=0.0004). Structured pod-based e-cigarette use acutely decreased flow-mediated dilation (-3.2{+/-}2.7%), raised systolic and diastolic blood pressure (6{+/-}8mmHg, 4{+/-}5mmHg) and heart rate (5{+/-}7bpm) , similar to combustible cigarette use (-2.6{+/-}2.6%, 9{+/-}8mmHg, 6{+/-}5mmHg, 6{+/-}6bpm P=0.83, 0.3, 0.4, 0.56 vs pod-based), and to a greater extent than nonuse (0.3{+/-}4.1%, 0.7{+/-}5mmHg, 0.3{+/-}3mmHg, -3{+/-}4bpm, P=1.0x10-7, 0.002, 0.003, 2.6x10-7). Differences remained robust in models adjusted for age, sex, and race. The effect of pod-based cigarette use was similar in adults who had never used combustible cigarettes. Levels of acrolein, acrylamide, acrylonitrile, and crotonaldehyde were associated with the changes in vascular health measures. Conclusions: Overall, our findings suggest that pod-based e-cigarette use has acute and chronic vascular effects in healthy young adults including those who never used combustible cigarettes. Select metabolites derived from volatile organic compounds were associated with the vascular changes suggesting relevance to vascular health.
One-Stage Cascade Refinement Networks for Infrared Small Target Detection

Single-frame infrared small target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this article, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudobox-based label assignment scheme that relaxes the constraints on the scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposal for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model- and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared.
Entanglement-assisted detection of fading targets via correlation-to-displacement conversion

Quantum illumination utilizes an entanglement-enhanced sensing system to outperform classical illumination in detecting a suspected target, despite the entanglement-breaking loss and noise. However, practical and optimal receiver design to fulfil the quantum advantage has been a long open problem. Recently, [arXiv:2207.06609] proposed the correlation-to-displacement (`C$\rightarrow$D') conversion module to enable an optimal receiver design that greatly reduces the complexity of the previous known optimal receiver [Phys. Rev. Lett. {\bf 118}, 040801 (2017)]. There, the analyses of the conversion module assume an ideal target with a known reflectivity and a fixed return phase. In practical applications, however, targets often induce a random return phase; moreover, their reflectivities can have fluctuations obeying a Rayleigh-distribution. In this work, we extend the analyses of the C$\rightarrow$D module to realistic targets and show that the entanglement advantage is maintained albeit reduced. In particular, the conversion module allows exact and efficient performance evaluation despite the non-Gaussian nature of the quantum channel involved.
MM-SHAP: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models & Tasks

Vision and language models (VL) are known to exploit unrobust indicators in individual modalities (e.g., introduced by distributional biases) instead of focusing on relevant information in each modality. That a unimodal model achieves similar accuracy on a VL task to a multimodal one, indicates that so-called unimodal collapse occurred. However, accuracy-based tests fail to detect e.g., when the model prediction is wrong, while the model used relevant information from a modality.Instead, we propose MM-SHAP, a performance-agnostic multimodality score based on Shapley values that reliably quantifies in which proportions a multimodal model uses individual modalities. We apply MM-SHAP in two ways: (1) to compare models for their average degree of multimodality, and (2) to measure for individual models the contribution of individual modalities for different tasks and datasets.Experiments with six VL models – LXMERT, CLIP and four ALBEF variants – on four VL tasks highlight that unimodal collapse can occur to different degrees and in different directions, contradicting the wide-spread assumption that unimodal collapse is one-sided. Based on our results, we recommend MM-SHAP for analysing multimodal tasks, to diagnose and guide progress towards multimodal integration. Code available at https://github.com/Heidelberg-NLP/MM-SHAP.
Effect of a high-precision semianalytical mass function on the merger rate of primordial black holes in dark matter halos

In this work, we study the effect of a high-precision semianalytical mass function on the merger rate of primordial black holes (PBHs) in dark matter halos. For this purpose, we first explain a theoretical framework for dark matter halo models and introduce relevant quantities such as halo density profile, concentration parameter, and a high-precision semianalytical function namely Del Popolo (DP) mass function. In the following, we calculate the merger rate of PBHs in the framework of ellipsoidal-collapse dark matter halo models while considering the DP mass function, and compare it with our previous study for the Sheth-Tormen (ST) mass function. The results show that by taking the mass of PBHs as $M_{\rm PBH} = 30M_{\odot}$, the DP mass function can potentially amplify the merger rate of PBHs. Moreover, we calculate the merger rate of PBHs for the DP mass function as a function of their mass and fraction and compare it with the black hole mergers recorded by the LIGO-Virgo detectors during the latest observing run. Our findings show that the merger rate of PBHs will fall within the LIGO-Virgo band if $f_{\rm PBH} \gtrsim \mathcal{O}(10^{-1})$. This implies that the DP mass function can be used to strengthen constraints on the fraction of PBHs.
Joint processing of linguistic properties in brains and language models

Language models have been shown to be very effective in predicting brain recordings of subjects experiencing complex language stimuli. For a deeper understanding of this alignment, it is important to understand the alignment between the detailed processing of linguistic information by the human brain versus language models. In NLP, linguistic probing tasks have revealed a hierarchy of information processing in neural language models that progresses from simple to complex with an increase in depth. On the other hand, in neuroscience, the strongest alignment with high-level language brain regions has consistently been observed in the middle layers. These findings leave an open question as to what linguistic information actually underlies the observed alignment between brains and language models. We investigate this question via a direct approach, in which we eliminate information related to specific linguistic properties in the language model representations and observe how this intervention affects the alignment with fMRI brain recordings obtained while participants listened to a story. We investigate a range of linguistic properties (surface, syntactic and semantic) and find that the elimination of each one results in a significant decrease in brain alignment across all layers of a language model. These findings provide direct evidence for the role of specific linguistic information in the alignment between brain and language models, and opens new avenues for mapping the joint information processing in both systems.
Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better

While the problem of hallucinations in neural machine translation has long been recognized, so far the progress on its alleviation is very little. Indeed, recently it turned out that without artificially encouraging models to hallucinate, previously existing methods fall short and even the standard sequence log-probability is more informative. It means that internal characteristics of the model can give much more information than we expect, and before using external models and measures, we first need to ask: how far can we go if we use nothing but the translation model itself ? We propose to use a method that evaluates the percentage of the source contribution to a generated translation. Intuitively, hallucinations are translations “detached” from the source, hence they can be identified by low source contribution. This method improves detection accuracy for the most severe hallucinations by a factor of 2 and is able to alleviate hallucinations at test time on par with the previous best approach that relies on external models. Next, if we move away from internal model characteristics and allow external tools, we show that using sentence similarity from cross-lingual embeddings further improves these results. We release the code of our experiments.
Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19

Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings. However, existing studies have limitations in terms of data collection and of the assessment of the performances of the proposed predictive models. This paper rigorously assesses state-of-the-art machine learning techniques used to predict COVID-19 infection status based on vocal audio signals, using a dataset collected by the UK Health Security Agency. This dataset includes acoustic recordings and extensive study participant meta-data. We provide guidelines on testing the performance of methods to classify COVID-19 infection status based on acoustic features and we discuss how these can be extended more generally to the development and assessment of predictive methods based on public health datasets.
Numerical Simulations of the Barrier Jet over Northwestern Taiwan during the Mei-Yu Season

Orographic effects on the airflow over Taiwan under the prevailing southwesterly monsoon flow are studied using the fifth-generation Pennsylvania State University‐National Center for Atmospheric Research (PSU‐ NCAR) Mesoscale Model (MM5). In particular, the orographically induced low-level strong winds along the northwestern coast of Taiwan are simulated in the absence of radiative forcing, thermal forcing from the surface, and synoptic forcing. The momentum balance for the low-level strong winds along the northwest coast of Taiwan shows that, above the mixed layer, the alongshore force balance is dominated by the inertial advection term and the pressure gradient force term. The significant Lagrangian flow acceleration of the northern branch of the deflected airflow along the northwest coast is mainly related to the pressure gradient force as the airflow moves down the orographically induced pressure ridge along the windward coast. Even though the alongshore pressure gradient force along the northwest coast is largest at the surface, the observed and simulated wind profiles show that the wind speed maximum in the vertical of the barrier jet occurs at a height of ;800 m primarily due to frictional retardation in the lowest levels. Within the well-mixed layer, the alongshore force balance is a threeway balance between inertial advection, pressure gradient force, and frictional force. Normal to the northwest coast, the force balance is approximately geostrophic. In addition, the location and the strength of the orographically induced low-level strong winds along the northwest coast of Taiwan also depend on the impinging angle and the wind speed of the prevailing winds.
Enhanced-Rate Iterative Beamformers for Active IRS-Assisted Wireless Communications

Compared to passive intelligent reflecting surface (IRS), active IRS is viewed as a more efficient promising technique to combat the double-fading impact in IRS-aided wireless network. In this letter, in order to boost the achievable rate of user in such a wireless network, three enhanced-rate iterative beamforming methods are proposed by designing the amplifying factors and the corresponding phases at active IRS. The first method, maximizing the simplified signal-to-noise ratio (Max-SSNR) is designed by omitting the cross-term in the definition of rate. Using the Rayleigh-Ritz (RR) theorem, Max-SSNR-RR is proposed to iteratively optimize the norm of beamforming vector and its associated normalized vector. In addition, generalized maximum ratio reflection (GMRR) is presented with a closed-form expression, which is motivated by the maximum ratio combining. To further improve rate, maximizing SNR (Max-SNR) is designed by fractional programming (FP), which is called Max-SNR-FP. Simulation results show that the proposed three methods make an obvious rate enhancement over Max-reflecting signal-to-noise ratio (Max-RSNR), maximum ratio reflection (MRR), selective ratio reflecting (SRR), equal gain reflection (EGR) and passive IRS, and are in increasing order of rate performance as follows: Max-SSNR-RR, GMRR, and Max-SNR-FP.
Automatic Identification of Motivation for Code-Switching in Speech Transcripts

Code-switching, or switching between languages, occurs for many reasons and has important linguistic, sociological, and cultural implications. Multilingual speakers code-switch for a variety of purposes, such as expressing emotions, borrowing terms, making jokes, introducing a new topic, etc. The reason for code-switching may be quite useful for analysis, but is not readily apparent. To remedy this situation, we annotate a new dataset of motivations for code-switching in Spanish-English. We build the first system (to our knowledge) to automatically identify a wide range of motivations that speakers code-switch in everyday speech, achieving an accuracy of 75% across all motivations. Additionally, we show that the system can be adapted to new language pairs, achieving 66% accuracy on a new language pair (Hindi-English), demonstrating the cross-lingual applicability of our annotation scheme
Ghost in the Shell: Evidence for Past Active Galactic Nucleus Activities in NGC 5195 from a Newly Discovered Large-scale Ionized Structure

The early-type galaxy NGC 5195 (alternatively known as M51b) possesses extended gas features detected in the multiwavelength, postulated to be associated with previous activities of the central supermassive black hole. Using integral field spectroscopic observations from the Canada–France–Hawaii Telescope/SITELLE, we report on the discovery of a new large-scale ionized gas structure traced by [O iii], [N ii], and Hα line emission, extending to ∼10 kpc from the nucleus of NGC 5195. Its bipolar morphology, emission-line ratio diagnostics, and comparison with the X-ray image from Chandra and low-frequency radio data from LOFAR all indicate that it is likely an outflow inflated by a past episode of elevated active galactic nucleus (AGN) activity. Assuming the ionized gas is outflowing from the central region of NGC 5195, the estimated mass and energy outflow rates are Ṁout=3.5 –27.9 M ☉ yr−1 and Ėout=0.98 –7.9 × 1040 erg s−1, respectively, which cannot be provided by current star formation and the low-luminosity nucleus. Alternatively, considering the history of gravitational interaction between the M51 pair and the presence of the H i tidal tail, the northern large-scale ionized gas could very likely be associated with tidally stripped material illuminated by a luminous AGN in the past.
HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval

Image-text retrieval (ITR) is a challenging task in the field of multimodal information processing due to the semantic gap between different modalities. In recent years, researchers have made great progress in exploring the accurate alignment between image and text. However, existing works mainly focus on the fine-grained alignment between image regions and sentence fragments, which ignores the guiding significance of context background information. Actually, integrating the local fine-grained information and global context background information can provide more semantic clues for retrieval. In this paper, we propose a novel Hierarchical Graph Alignment Network (HGAN) for image-text retrieval. First, to capture the comprehensive multimodal features, we construct the feature graphs for the image and text modality respectively. Then, a multi-granularity shared space is established with a designed Multi-granularity Feature Aggregation and Rearrangement (MFAR) module, which enhances the semantic corresponding relations between the local and global information, and obtains more accurate feature representations for the image and text modalities. Finally, the ultimate image and text features are further refined through three-level similarity functions to achieve the hierarchical alignment. To justify the proposed model, we perform extensive experiments on MS-COCO and Flickr30K datasets. Experimental results show that the proposed HGAN outperforms the state-of-the-art methods on both datasets, which demonstrates the effectiveness and superiority of our model.
A New Berry-Esseen Theorem for Expander Walks

We prove that the sum of t boolean-valued random variables sampled by a random walk on a regular expander converges in total variation distance to a discrete normal distribution at a rate of O(λ/t1/2−o(1)), where λ is the second largest eigenvalue of the random walk matrix in absolute value. To the best of our knowledge, among known Berry-Esseen bounds for Markov chains, our result is the first to show convergence in total variation distance, and is also the first to incorporate a linear dependence on expansion λ. In contrast, prior Markov chain Berry-Esseen bounds showed a convergence rate of O(1/√t) in weaker metrics such as Kolmogorov distance. Our result also improves upon prior work in the pseudorandomness literature, which showed that the total variation distance is O(λ) when the approximating distribution is taken to be a binomial distribution. We achieve the faster O(λ/t1/2−o(1)) convergence rate by generalizing the binomial distribution to discrete normals of arbitrary variance. We specifically construct discrete normals using a random walk on an appropriate 2-state Markov chain. Our bound can therefore be viewed as a regularity lemma that reduces the study of arbitrary expanders to a small class of particularly simple expanders.
Generalization Bounds for Inductive Matrix Completion in Low-noise Settings

We study inductive matrix completion (matrix completion with side information) under an i.i. d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entries. We obtain for the first time generalization bounds with the following three properties: (1) they scale like the standard deviation of the noise and in particular approach zero in the exact recovery case; (2) even in the presence of noise, they converge to zero when the sample size approaches infinity; and (3) for a fixed dimension of the side information, they only have a logarithmic dependence on the size of the matrix. Differently from many works in approximate recovery, we present results both for bounded Lipschitz losses and for the absolute loss, with the latter relying on Talagrand-type inequalities. The proofs create a bridge between two approaches to the theoretical analysis of matrix completion, since they consist in a combination of techniques from both the exact recovery literature and the approximate recovery literature.
Reinforcement Learning for Agile Active Target Sensing with a UAV

Active target sensing is the task of discovering and classifying an unknown number of targets in an environment and is critical in search-and-rescue missions. This paper develops a deep reinforcement learning approach to plan informative trajectories that increase the likelihood for an uncrewed aerial vehicle (UAV) to discover missing targets. Our approach efficiently (1) explores the environment to discover new targets, (2) exploits its current belief of the target states and incorporates inaccurate sensor models for high-fidelity classification, and (3) generates dynamically feasible trajectories for an agile UAV by employing a motion primitive library. Extensive simulations on randomly generated environments show that our approach is more efficient in discovering and classifying targets than several other baselines. A unique characteristic of our approach, in contrast to heuristic informative path planning approaches, is that it is robust to varying amounts of deviations of the prior belief from the true target distribution, thereby alleviating the challenge of designing heuristics specific to the application conditions.
Can We Find Strong Lottery Tickets in Generative Models?

Yes. In this paper, we investigate strong lottery tickets in generative models, the subnetworks that achieve good generative performance without any weight update. Neural network pruning is considered the main cornerstone of model compression for reducing the costs of computation and memory. Unfortunately, pruning a generative model has not been extensively explored, and all existing pruning algorithms suffer from excessive weight-training costs, performance degradation, limited generalizability, or complicated training. To address these problems, we propose to find a strong lottery ticket via moment-matching scores. Our experimental results show that the discovered subnetwork can perform similarly or better than the trained dense model even when only 10% of the weights remain. To the best of our knowledge, we are the first to show the existence of strong lottery tickets in generative models and provide an algorithm to find it stably. Our code and supplementary materials are publicly available at https://lait-cvlab.github. io/SLT-in-Generative-Models/.