Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focusing on diagnosis and stratification of COVID-19 from medical images. Despite this large-scale research effort, these models have found limited practical application due in part to unproven generalization of these models beyond their source study. This study investigates the generalizability of key published models using the publicly available COVID-19 Computed Tomography data through cross dataset validation. We then assess the predictive ability of these models for COVID-19 severity using an independent new dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. The study shows high variability in the generalization of models trained on these datasets due to varied sample image provenances and acquisition processes amongst other factors. We show that under certain conditions, an internally consistent dataset can generalize well to an external dataset despite structural differences between these datasets with f1 scores up to 86%. Our best performing model shows high predictive accuracy for lung involvement score for an independent dataset for which expertly labelled lung involvement stratification is available. Creating an ensemble of our best model for disease positive prediction with our best model for disease negative prediction using a min-max function resulted in a superior model for lung involvement prediction with average predictive accuracy of 75% for zero lung involvement and 96% for 75-100% lung involvement with almost linear relationship between these stratifications.
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness

Most studies on learning from noisy labels rely on unrealistic models of i.i. d. label noise, such as class-conditional transition matrices. More recent work on instance-dependent noise models are more realistic, but assume a single generative process for label noise across the entire dataset. We propose a more principled model of label noise that generalizes instance-dependent noise to multiple labelers, based on the observation that modern datasets are typically annotated using distributed crowdsourcing methods. Under our labeler-dependent model, label noise manifests itself under two modalities: natural error of good-faith labelers, and adversarial labels provided by malicious actors. We present two adversarial attack vectors that more accurately reflect the label noise that may be encountered in real-world settings, and demonstrate that under our multimodal noisy labels model, state-of-the-art approaches for learning from noisy labels are defeated by adversarial label attacks. Finally, we propose a multi-stage, labeler-aware, model-agnostic framework that reliably filters noisy labels by leveraging knowledge about which data partitions were labeled by which labeler, and show that our proposed framework remains robust even in the presence of extreme adversarial label noise.
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding, thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution differs from training. 2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from different layers, and thus combining both local attention and global attention to render powerful representations. We show that this simple and lightweight design is the key to efficient segmentation on Transformers. We scale our approach up to obtain a series of models from SegFormer-B0 to SegFormer-B5, reaching significantly better performance and efficiency than previous counterparts. For example, SegFormer-B4 achieves 50.3% mIoU on ADE20K with 64M parameters, being 5x smaller and 2.2% better than the previous best method. Our best model, SegFormer-B5, achieves 84.0% mIoU on Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes-C. Code will be released at: github.com/NVlabs/SegFormer.
Diversifying Dialog Generation via Adaptive Label Smoothing

Neural dialogue generation models trained with the one-hot target distribution suffer from the over-confidence issue, which leads to poor generation diversity as widely reported in the literature. Although existing approaches such as label smoothing can alleviate this issue, they fail to adapt to diverse dialog contexts. In this paper, we propose an Adaptive Label Smoothing (AdaLabel) approach that can adaptively estimate a target label distribution at each time step for different contexts. The maximum probability in the predicted distribution is used to modify the soft target distribution produced by a novel light-weight bi-directional decoder module. The resulting target distribution is aware of both previous and future contexts and is adjusted to avoid over-training the dialogue model. Our model can be trained in an endto-end manner. Extensive experiments on two benchmark datasets show that our approach outperforms various competitive baselines in producing diverse responses.
Reinforced Generative Adversarial Network for Abstractive Text Summarization

Sequence-to-sequence models provide a viable new approach to generative summarization, allowing models that are no longer limited to simply selecting and recombining sentences from the original text. However, these models have three drawbacks: their grasp of the details of the original text is often inaccurate, and the text generated by such models often has repetitions, while it is difficult to handle words that are beyond the word list. In this paper, we propose a new architecture that combines reinforcement learning and adversarial generative networks to enhance the sequence-to-sequence attention model. First, we use a hybrid pointer-generator network that copies words directly from the source text, contributing to accurate reproduction of information without sacrificing the ability of generators to generate new words. Second, we use both intra-temporal and intra-decoder attention to penalize summarized content and thus discourage repetition. We apply our model to our own proposed COVID-19 paper title summarization task and achieve close approximations to the current model on ROUEG, while bringing better readability.
Fully Hyperbolic Neural Networks

Hyperbolic neural networks have shown great potential for modeling complex data. However, existing hyperbolic networks are not completely hyperbolic, as they encode features in the hyperbolic space yet formalize most of their operations in the tangent space (a Euclidean subspace) at the origin of the hyperbolic model. This hybrid method greatly limits the modeling ability of networks. In this paper, we propose a fully hyperbolic framework to build hyperbolic networks based on the Lorentz model by adapting the Lorentz transformations (including boost and rotation) to formalize essential operations of neural networks. Moreover, we also prove that linear transformation in tangent spaces used by existing hyperbolic networks is a relaxation of the Lorentz rotation and does not include the boost, implicitly limiting the capabilities of existing hyperbolic networks. The experimental results on four NLP tasks show that our method has better performance for building both shallow and deep networks. Our code will be released to facilitate follow-up research.
Please Repeat: Strong Lensing of Gravitational Waves as a Probe of Compact Binary and Galaxy Populations

Strong gravitational lensing of gravitational wave sources offers a novel probe of both the lens galaxy and the binary source population. In particular, the strong lensing event rate and the time-delay distribution of multiply imaged gravitational-wave binary coalescence events can be used to constrain the mass distribution of the lenses as well as the intrinsic properties of the source population. We calculate the strong lensing event rate for a range of second- (2G) and third-generation (3G) detectors, including Advanced LIGO/Virgo, A+, Einstein Telescope (ET), and Cosmic Explorer (CE). For 3G detectors, we find that ∼0.1% of observed events are expected to be strongly lensed. We predict detections of ∼1 lensing pair per year with A+, and ∼50 pairs per year with ET/CE. These rates are highly sensitive to the characteristic galaxy velocity dispersion, σ *, implying that observations of the rates will be a sensitive probe of lens properties. We explore using the time-delay distribution between multiply imaged gravitational-wave sources to constrain properties of the lenses. We find that 3G detectors would constrain σ * to ∼21% after 5 yr. Finally, we show that the presence or absence of strong lensing within the detected population provides useful insights into the source redshift and mass distribution out to redshifts beyond the peak of the star formation rate, which can be used to constrain formation channels and their relation to the star formation rate and delay-time distributions for these systems.
Prediction of Soft Proton Intensities in the Near-Earth Space Using Machine Learning

The spatial distribution of energetic protons contributes to the understanding of magnetospheric dynamics. Based upon 17 yr of the Cluster/RAPID observations, we have derived machine-learning-based models to predict the proton intensities at energies from 28 to 962 keV in the 3D terrestrial magnetosphere at radial distances between 6 and 22 RE. We used the satellite location and indices for solar, solar wind, and geomagnetic activity as predictors. The results demonstrate that the neural network (multi-layer perceptron regressor) outperforms baseline models based on the k-nearest neighbors and historical binning on average by ∼80% and ∼33%, respectively. The average correlation between the observed and predicted data is about 56%, which is reasonable in light of the complex dynamics of fast-moving energetic protons in the magnetosphere. In addition to a quantitative analysis of the prediction results, we also investigate parameter importance in our model. The most decisive parameters for predicting proton intensities are related to the location—Z geocentric solar ecliptic direction—and the radial distance. Among the activity indices, the solar wind dynamic pressure is the most important. The results have a direct practical application, for instance, for assessing the contamination particle background in the X-ray telescopes for X-ray astronomy orbiting above the radiation belts. To foster reproducible research and to enable the community to build upon our work we publish our complete code, the data, and the weights of trained models. Further description can be found in the GitHub project at https://github.com/Tanveer81/deep_horizon.
Zero-shot Fact Verification by Claim Generation

Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive. We develop QACG, a framework for training a robust fact verification model by using automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. QACG generates question-answer pairs from the evidence and then converts them into different types of claims. Experiments on the FEVER dataset show that our QACG framework significantly reduces the demand for human-annotated training data. In a zero-shot scenario, QACG improves a RoBERTa model’s F1 from 50% to 77%, equivalent in performance to 2K+ manually-curated examples. Our QACG code is publicly available.
Not All Images are Worth 16x16 Words: Dynamic Vision Transformers with Adaptive Sequence Length

Vision Transformers (ViT) have achieved remarkable success in large-scale image recognition. They split every 2D image into a fixed number of patches, each of which is treated as a token. Generally, representing an image with more tokens would lead to higher prediction accuracy, while it also results in drastically increased computational cost. To achieve a decent trade-off between accuracy and speed, the number of tokens is empirically set to 16x16. In this paper, we argue that every image has its own characteristics, and ideally the token number should be conditioned on each individual input. In fact, we have observed that there exist a considerable number of “easy” images which can be accurately predicted with a mere number of 4x4 tokens, while only a small fraction of “hard” ones need a finer representation. Inspired by this phenomenon, we propose a Dynamic Transformer to automatically configure a proper number of tokens for each input image. This is achieved by cascading multiple Transformers with increasing numbers of tokens, which are sequentially activated in an adaptive fashion at test time, i.e., the inference is terminated once a sufficiently confident prediction is produced. We further design efficient feature reuse and relationship reuse mechanisms across different components of the Dynamic Transformer to reduce redundant computations. Extensive empirical results on ImageNet, CIFAR-10, and CIFAR-100 demonstrate that our method significantly outperforms the competitive baselines in terms of both theoretical computational efficiency and practical inference speed.
Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation

A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information. Many recent studies report improvements when equipping their models with the multimodal module, despite the controversy of whether such improvements indeed come from the multimodal part. We revisit the contribution of multimodal information in MMT by devising two interpretable MMT models. To our surprise, although our models replicate similar gains as recently developed multimodal-integrated systems achieved, our models learn to ignore the multimodal information. Upon further investigation, we discover that the improvements achieved by the multimodal models over text-only counterparts are in fact results of the regularization effect. We report empirical findings that highlight the importance of MMT models’ interpretability, and discuss how our findings will benefit future research.
Theorizing disaster Analogy , historical ethnography , and the Challenger accident

usually invisible process behind all published research. Here I reconstruct my theorizing for an historical ethnography of the 1986 Space Shuttle Challenger disaster and the NASA (National Aeronautical and Space Administration) decisions that produced that accident. I show how analogical theorizing, a method that compares similar events or activities across different social settings, leads to more refined and generalizable theoretical explanations. Revealing the utility of mistakes in theorizing, I show how my mistakes uncovered mistakes in the documentary record, converting my analysis to a revisionist account that contradicted the conventional explanation accepted at the time. Retracing how I developed the concepts and theory that explained the case demonstrates the connection between historic political and economic forces, organization structure and processes, and cultural understandings and actions at NASA. Finally, these analytic reflections show how analysis, writing, and theorizing are integrated throughout the research process. K E Y W O R D S ■ disaster, space shuttle, deviance, technology, culture, habitus, ethnography, analogy, institutional theory When NASA’s Space Shuttle Challenger disintegrated in a ball of fire 73 seconds after launch on 28 January 1986, the world learned that NASA was graphy Copyright © 2004 SAGE Publications (London, Thousand Oaks, CA and New Delhi) www.sagepublications. com Vol 5(3): 315–347[DOI: 10.1177/1466138104045659] A R T I C L E 03 vaughn(mrw/d) 17/8/04 1:34 pm Page 315
Sleep: How is it affected in patients with DMD and their mothers?

BACKGROUND To investigate whether children with Duchenne muscular dystrophy (DMD) have sleep disorders, and the sleep quality and daytime sleepiness of mothers who are primary caregivers. METHODS Clinical data and gross motor functional status of 24 patients with DMD were measured using the gross motor function classification system (GMFCS). Sleep Disturbance Scale for Children scores were evaluated to determine their sleep features. The Pittsburgh Sleep Quality Index scores were used to measure sleep quality, and Epworth Sleepiness Scale scores were used to measure daytime sleepiness of 24 mothers. RESULTS Sleep disturbances were observed in 62.5% (n = 15) of patients, 41.6% (n = 5) of those who were GMFCS I-II-III and in 83.3% (n = 10) who were IV-V level. A disturbed sleep pattern was observed in 3 (33.3%) of 9 patients who were younger than 10 and in 12 (80.0%) of 15 patients who were older than 10. Of mothers, 54.2% had low sleep quality, which was present in 83.3% of mothers with GMFCS IV-V children and 54.2% of those with GMFCS I-II-III children. Ten (41.6%) mothers had increased daytime sleepiness, which was present in 66.6% of mothers with GMFCS IV-V children and 16.6% of mothers with GMFCS I-II-III children. CONCLUSIONS Sleep disturbances increase in parallel to loss of gross motor functions in patients with DMD, which has had negative impact on the sleep quality and daytime sleepiness of the caregiver.
Evaluating firearm examiner conclusion variability using cartridge case reproductions

The forensic science pattern comparison areas, including fingerprints, footwear, and firearms, have been criticized for their subjective nature. While much research has attempted to move these disciplines to more objective methods, examiners are still coming to conclusions based on their own training and experience. To complement this subjectivity, black box studies are necessary to establish the accuracy of these feature‐comparison methods. However, when cartridges are fired by a firearm to create cartridge case test sets there may be significant variability within the resulting impressions. This can result in different participants receiving test sets with varying levels of difficulty based on differences in impression quality. Therefore, comparison of accuracy between examiners is not straightforward. To compare accuracy between examiners, a method called double‐casting was used to create plastic cartridge case reproductions. Double‐casts of twenty‐one test sets of master cartridge cases were created and mailed to firearm examiners. The double‐casts ensured that all participants were comparing exhibits with the same level of detail. The examiners were tasked with determining if the unknown cartridge case in each set was fired by the same firearm as the three knowns. Automated comparisons were also used to compare the cartridge cases within each set. The results from this study showed that there are differences in examiner conclusions when examining the same evidence. Furthermore, it was shown that automated comparison metrics would benefit examiners as a quality control measure to correct any potential errors and strengthen conclusions.
Frequency and predictors of no-reflow phenomenon in patients with COVID-19 presenting with ST-segment elevation myocardial infarction

Abstract Objectives Thrombotic process is triggered in the course of Coronavirus disease-2019 (COVID-19), which is a global pandemic, and both arterial and venous systems are affected. ST-elevation myocardial infarction (STEMI) that may develop in these patients may cause more complicated results with the effect of thrombosis burden. Our aim in this study is to determine the frequency of no-reflow phenomenon in COVID-19 patients with STEMI and to determine the factors that predict this complication. Methods In this study, which is a single-centre, retrospective and observational, a total of 126 patients who underwent primary percutaneous coronary intervention (pPCI) in our centre due to STEMI between 11 March 2020 and 10 January 2021 were evaluated. Patients were divided into two groups according to the presence of COVID-19 infection. Results While 62 patients were in the COVID-19 (+) group, 64 patients were evaluated in the COVID-19 (-) group. When the two groups are compared, C-reactive protein, D-dimer, ferritin and neutrophil-lymphocyte ratio (NLR) were significantly higher, and the lymphocyte count was significantly lower in the COVID-19 (+) group. No-reflow was numerically higher in patients with COVID-19. In multivariable analysis, D-dimer and NLR were found to be independent predictors of no-reflow phenomenon in COVID-19 patients. Conclusions Although the no-reflow phenomenon was numerically higher in COVID-19 patients who underwent pPCI due to STEMI compared to the non-COVID group, no statistical difference was found in our study. However, NLR and D-dimer have been identified as independent predictors of no-reflow development risk in COVID-19 patients.
Trace elements in skin-contact clothes and migration to artificial sweat: Risk assessment of human dermal exposure

The concentrations of a considerable number of trace elements (Ag, Al, As, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Fe, Hg, Mg, Mn, Mo, Ni, Pb, Sb, Sc, Se, Sm, Sn, Sr, Ti, Tl, V and Zn) were determined in various skin-contact clothes (T-shirts, blouses, socks, baby pajamas and bodies) from the Catalan (Spain) market. In addition, migration experiments with artificial acidic sweat were conducted in order to establish the migration rates of these elements. High levels of Zn (186–5749 mg/kg) were found in zinc pyrithione labeled T-shirts, while high concentrations of Sb and Cr were found in polyester and black polyamide fabrics, respectively. An environmental scanning electron microscope (ESEM) confirmed the presence of Ag and Ti particles and aggregates in several clothing items. The use of the ESEM complemented the results of the elemental analysis and migration experiments. Dermal exposure to trace elements was subsequently calculated, and the human health risks were assessed. Antimony showed the highest mean hazard quotient (HQ = 0.4) for male and female adults wearing polyester clothes; for one of the examined items (polyester T-shirt) the HQ was even above the safety limit (HQ > 1). Exposure to Sb from polyester textile could mean potential health risks in subpopulation groups who frequently wear these clothes, and for long time periods. The migration experiments with artificial sweat showed to be essential for establishing the exposure to trace elements through cloth with direct contact with skin.