Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomarker in Suspected Acute Stroke Study: A Diagnostic and Prognostic Imaging COVID-19 Stroke Apical Lung Examination

BACKGROUND AND PURPOSE: Diagnosis of coronavirus disease 2019 (COVID-19) relies on clinical features and reverse-transcriptase polymerase chain reaction testing, but the sensitivity is limited. Carotid CTA is a routine acute stroke investigation and includes the lung apices. We evaluated CTA as a potential COVID-19 diagnostic imaging biomarker. MATERIALS AND METHODS: This was a multicenter, retrospective study ( n ¼ 225) including CTAs of patients with suspected acute stroke from 3 hyperacute stroke units (March-April 2020). We evaluated the reliability and accuracy of candidate diagnostic imaging biomarkers. Demographics, clinical features, and risk factors for COVID-19 and stroke were analyzed using univariate and multivariate statistics. RESULTS: Apical ground-glass opaci ﬁ cation was present in 22.2% (50/225) of patients. Ground-glass opaci ﬁ cation had high interrater reliability (Fleiss k ¼ 0.81; 95% CI, 0.68 – 0.95) and, compared with reverse-transcriptase polymerase chain reaction, had good diagnostic performance (sensitivity, 75% [95% CI, 56 – 87]; speci ﬁ city, 81% [95% CI, 71 – 88]; OR ¼ 11.65 [95% CI, 4.14 – 32.78]; P , .001) on multivariate analysis. In contrast, all other contemporaneous demographic, clinical, and imaging features available at CTA were not diagnostic for COVID-19. The presence of apical ground-glass opaci ﬁ cation was an independent predictor of increased 30-day mortality (18.0% versus 5.7%, P ¼ .017; hazard ratio ¼ 3.51; 95% CI, 1.42 – 8.66; P ¼ .006). CONCLUSIONS: We identi ﬁ ed a simple, reliable, and accurate COVID-19 diagnostic and prognostic imaging biomarker obtained from CTA lung apices: the presence or absence of ground-glass opaci ﬁ cation. Our ﬁ ndings have important implications in the management of patients presenting with suspected stroke through early identi ﬁ cation of COVID-19 and the subsequent limitation of disease transmission
BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion

We show, for the first time, that neural networks trained only on synthetic data achieve state-of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real images. Previous synthetic datasets have been small, unrealistic, or lacked realistic clothing. Achieving sufficient realism is non-trivial and we show how to do this for full bodies in motion. Specifically, our BEDLAM dataset contains monocular RGB videos with ground-truth 3D bodies in SMPL-X format. It includes a diversity of body shapes, motions, skin tones, hair, and clothing. The clothing is realistically simulated on the moving bodies using commercial clothing physics simulation. We render varying numbers of people in realistic scenes with varied lighting and camera motions. We then train various HPS regressors using BEDLAM and achieve state-of-the-art accuracy on real-image benchmarks despite training with synthetic data. We use BEDLAM to gain insights into what model design choices are important for accuracy. With good synthetic training data, we find that a basic method like HMR approaches the accuracy of the current SOTA method (CLIFF). BEDLAM is useful for a variety of tasks and all images, ground truth bodies, 3D clothing, support code, and more are available for research purposes. Additionally, we provide detailed information about our synthetic data generation pipeline, enabling others to generate their own datasets. See the project page: https://bedlam.is. tue.mpg. de/.
Parametric Weight-change Reshaping for Portrait Images

We present an easy-to-use parametric image retouching method for thinning or fattening a face in a single portrait image while maintaining a close similarity to the source image. First, our method re-constructs a 3D face from the input face image using a morphable model. Second, according to the linear regression equation derived from the depth statistics of the soft tissue in the face and the user-set parameters of reshaping degree, we calculate the new positions of the feature points. Third, the Laplacian deformation method is employed to calculate the deformed positions of non-feature points in the 3D face model. Finally, we seamlessly blend the projected reshaped face region in 2D image with the background using image retargeting method based on mesh parametrization. Our model-based reshaping process can achieve globally consistent editing effects without noticeable artifacts. The effectiveness of our algorith-m is demonstrated by experiments and user study.
Face Detection Using Support Vector Domain Description in Color Images

In this paper, we present a face detection system using the Support Vector Domain Description (SVDD) in color images. Conventional face detection algorithms require a training procedure using both face and non-face images. In SVDD however we employ only face images for training. We can detect faces in color images from the radius and center pairs of SVDD. We also use Entropic Threshold for extracting the facial feature and sliding window for improved performance while saving processing time. The experimental results indicate the effectiveness and efficiency of the proposed algorithm compared to conventional PCA (Principal Component Analysis)-based methods.
Understanding the Contextual Privacy Comprehension, Attitudes, and Behaviours of Users

To understand and protect their privacy in a world of pervasive data collection, users must understand not only disclosure, but also the transmission and third-party use of their data both in the present and future. Researchers label this phenomenon the contextual integrity of information. While many security and privacy researchers seek to design automated tools to enforce contextual integrity through formally speciﬁed policies, this paper explores users’ understanding of contextual integrity and their ability to inﬂuence it as they learn. We explore whether, by educating participants, participants are more likely to understand and control for the long-term implications of information disclosure. We ﬁnd that participants do develop an understanding of contextual integrity, but continue to struggle with effective methods to protect the contextual integrity of their data. Together, these results provide guidelines for the design of privacy-preserving technologies with learning and comprehension as guiding principles.
Feeling the Heat: Heterogeneous Treatment Effects of Heat on Human Cooperation

The goal of this paper is to examine how increased temperatures affect human behavior in terms of cooperation. Currently, there is an abundance of literature regarding increased aggression in humans as temperatures rise. However, there is ambiguity concerning whether a similar effect exists in altering levels of cooperation in human interactions. Additionally, people of different characteristics may be influenced by heat at varying levels. First, we run a baseline model of prosociality on different heat measurements, such as standard and wet-bulb temperatures. Included in this model are experimental specification variables, a “trigger”, and characteristics. The trigger is defined as losing a competition under heat stress before prosociality is measured to see if this alters the effect of heat on prosociality. Next, In order to find out how heat alters cooperation among individuals of varying backgrounds, we apply three methods of heterogeneous treatment effect estimation: OLS estimation, causal forests, and “generic” machine learning with LASSO. These three methods are employed for comparison purposes and allow us to examine possible heterogeneity in different ways. Our results show the heat treatment to have overall slight heterogeneous treatment effects on human cooperation across OLS and Causal Forests; specifically, we find heterogeneity in treatment responsiveness when examining the location of Davis, gender, and in stress response measurements derived from various Likert scale survey questions. This heterogeneity is present in those with “Tend” or “Befriend” responses to stress. Acknowledgements: I wish to thank my advisor, Professor Bruce Wydick, for pushing me to give my best effort and setting me up for success. I also want to thank Professor Andrew Hobbs for giving me detailed help during my data analysis. Lastly, I want to thank every person who helped with data collection in every experimental location.
New Properties of Intrinsic Information and Their Relation to Bound Secrecy

The secret-key rate measures the rate at which Alice and Bob can extract secret bits from sampling a joint probability distribution, unknown to an eavesdropper Eve. The secret-key rate has been bounded above by the intrinsic information and reduced intrinsic information. However, we prove that the reduced intrinsic information is 0 if and only if the intrinsic information is 0. This result implies that at least one of the following two conjectures is false: bound secrecy exists, or the reduced intrinsic information equals the secret-key rate. We give an explicit construction of an information-erasing binarization for a candidate for bound secrecy. We then introduce some approaches for proving the existence of bound secrecy, such as reducing the channel space, linearly transforming Bob's map, and perturbing a channel for Eve.
Climate change impacts on Robusta coffee production over Vietnam

The Central Highlands of Vietnam is the biggest Robusta coffee (Coffea canephora Pierre ex A.Froehner) growing region in the world. This study aims to identify the most important climatic variables that determine the current distribution of coffee in the Central Highlands and build a “coffee suitability” model to assess changes in this distribution due to climate change scenarios. A suitability model based on neural networks was trained on coffee occurrence data derived from national statistics on coffee-growing areas. Bias-corrected regional climate models were used for two climate change scenarios (RCP8.5 and RCP2.6) to assess changes in suitability for three future time periods (i.e., 2038-2048, 2059-2069, 2060-2070) relative to the 2009-2019 baseline. Average expected losses in suitable areas were 62% and 27% for RCP8.5 and RCP2.6, respectively. The loss in suitability due to RCP8.5 is particularly pronounced after 2060. Increasing mean minimum temperature during harvest (October-November) and growing season (March-September) and decreasing precipitation during late growing season (July-September) mainly determined the loss in suitable areas. If the policy commitments made at the Paris agreement are met, the loss in coffee suitability could potentially be compensated by climate change adaptation measures such as making use of shade trees and adapted clones.
Around the BERT model: from a basic implementation to advanced optimizations and Multi-Task Learning

This report introduces a simple and efficient architecture for Multitask learning in natural language processing, based on a pretrained BERT model. Using BERT contextualized embeddings and making them go through a single additional layer per task, the Multitask BERT consistently achieves decent scores on three target language problems: sentiment analysis, paraphrase detection and semantic textual similarity (less than 20% below state-of-the-art performances). During training, one could choose to freeze BERT weights and only update additional parameters, but we found that fine-tuning the BERT block to fit the underlying distribution yielded better results. The use of PCGrad to get rid of conflicting gradients, as well as gradient accumulation to boost training and additional datasets to better understand diverse language styles are a few examples of ideas leveraged by our model. Our main contribution is the simplicity of the model, which requires less that a single specific dense layer per task, contrary to many state-of-the-art papers in existing literature. This architecture is thus particularly adapted to end users with limited resources willing to simultaneously achieve acceptable baseline results on several downstream language tasks.
Demography, hygiene and previous disease prevalence as plausible risk factors associated with Covid-19 deaths across Indian states

Severity of Covid-19 diseases has been disproportionate with higher case-fatality ratio affecting developed nations. In India, states with higher income have reported more number of deaths compared to lower income states. The global burden of diseases India 2019 and the National Health Profile 2019 data was used to draw correlations with Covid-19 mortality at two different dates of peak Covid-19 cases in India. We explored correlation of mortality in different states of India with prevalence of different diseases, demography, development, sanitation etc. The study found a positive correlation with known demographic parameters such as percentage of elderly population(spearman correlation coefficient(rho) =0.44 and 0.46 with 1st and 2nd peak respectively). Similarly, percentage urbanization was seen to correlate well with mortality(rho=0.71 and 0.57) suggesting Covid-19 to be a predominantly urban disease. Prevalence of Autoimmune diseases, and Cancer show higher correlation with deaths. A surprising positive correlation emerged between improved sanitation parameters, such as closed drainage and indoor toilets, with COVID-19 deaths. Overall the multivariate regression model achieved by combining demography, sanitation, autoimmune diseases and cancer gave us the best prediction for Covid-19 mortality(adjusted R square value of 0.71 with peak 1 and 0.85 with peak 2). Analysis of the Covid-19 related data seems to indicate that as the wealth of a state increases, the state urban landscape changes often leading to better sanitation facilities. The lifestyle and prevalence to autoimmune diseases as well as cancer also increases. However, this may affect the ability of state to fight pandemics due to lower exposure to pathogens and immune training.
Semigroups generated by a group and an idempotent

It is well known that the semigroup of all transformations on a finite set X of order n is generated by its group of units, the symmetric group, and any idempotent of rank n − 1. Similarly, the symmetric inverse semigroup on X is generated by its group of units and any idempotent of rank n − 1 while the analogous result is true for the semigroup of all n × n matrices over a field. In this paper we begin a systematic study of the structure of a semigroup S generated by its group G of units and an idempotent ϵ . The first section consists of preliminaries while the second contains some general results which provide the setting for those which follow. In the third section we shall investigate the situation where G is a permutation group on a set X of order n and ϵ is an idempotent of rank n − 1. In particular, we shall show that any such semigroup S is regular. Furthermore we shall determine when S is an inverse or orthodox semigroup or completely regular semigroup. The fourth section deals with a special ca.
Rare Earth Magnetism Structures And Excitations

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Single image dehazing using multiple transmission layer fusion

Methods for single image dehazing have been widely studied based on the atmospheric scattering model and dark channel prior (DCP); they usually adopt an additional refinement procedure such as guide filtering to restrain the halo artefacts, but it easily induces undesirable textures in the final transmission map, and further leads to an overall contrast reduction and detail blur. In this paper, an efficient approach was proposed to enhance single hazy images without any refined post-process, which is based on the strategy of multiple transmission layers fusion. In order to estimate the final transmission map adapting to different scenes reasonably, the multiple transmission layers were derived based on DCP with different kinds of adaptive local watch windows. To make sure the atmospheric light is estimated in the most haze-opaque region, the corresponding region was searched hierarchically with the quadtree subdivision method in the top part of the minimal channel of the input image. Finally, the hazy image was restored through solving the scattering model. Comparison experiments verify that the proposed method is straightforward and efficient, which can reduce the halo artefacts significantly, yielding satisfactory contrast and colour for varied hazy images.
Refining the impact of genetic evidence on clinical success

The cost of drug discovery and development is driven primarily by failure, with just ~10% of clinical programs eventually receiving approval. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. Relative success (RS) between drug mechanisms with genetic support and those without varies significantly among therapy areas and development phases and improves with increasing confidence in the causal gene. RS is largely unaffected by genetic effect size, minor allele frequency, or year of discovery. Our findings suggest that we are far from reaching peak genetic insights to aid the discovery of targets for more effective drug therapies.
Concrete Crack Detection and Monitoring Using a Capacitive Dense Sensor Array

Cracks in concrete structures can be indicators of important damage and may significantly affect durability. Their timely identification can be used to ensure structural safety and guide on-time maintenance operations. Structural health monitoring solutions, such as strain gauges and fiber optics systems, have been proposed for the automatic monitoring of such cracks. However, these solutions become economically difficult to deploy when the surface under investigation is very large. This paper proposes to leverage a novel sensing skin for monitoring cracks in concrete structures. This sensing skin is constituted of a flexible electronic termed soft elastomeric capacitor, which detects a change in strain through changes in measured capacitance. The SEC is a low-cost, durable, and robust sensing technology that has previously been studied for the monitoring of fatigue cracks in steel components. In this study, the sensing skin is introduced and preliminary validation results on a small-scale reinforced concrete beam are presented. The technology is verified on a full-scale post-tensioned concrete beam. Results show that the sensing skin is capable of detecting, localizing, and quantifying cracks that formed in both the reinforced and post-tensioned concrete specimens.
Identifying Optimal Parameters for Neuroscience-Informed Interventions for Misophonia

Background: Misophonia is the inability to tolerate certain aversive, repetitive common sounds. Methods: Using a within-subjects experimental design, twenty-nine participants with misophonia and thirty clinical controls with high emotion dysregulation received inhibitory neurostimulation (1Hz) over a personalized medial prefrontal cortex (mPFC) target functionally connected to the left insula; excitatory neurostimulation (10Hz) over a personalized dorsolateral PFC (dlPFC) target; and sham stimulation over either target. Stimulations were applied while participants were either listening or cognitively downregulating emotions associated with personalized aversive, misophonic, or neutral sounds. Subjective units of distress (SUDS) and psychophysiological measurements (skin conductance response[SCR] and level [SCL], and high-frequency heart rate variability [HF-HRV]) were collected. Results: Compared to controls, participants with misophonia reported higher distress (delta_SUDS = 1.91-1.93, ps <.001) when listening to and when downregulating misophonic distress, although no psychophysiological differences were found. Both types of neurostimulation reduced distress significantly more than sham, with excitatory rTMS providing the most benefit (Cohen d_SUDS= 0.53; d_SCL= 0.14). Excitatory rTMS also enhanced the regulation of emotions associated with misophonic sounds in both groups when measured by SUDS (d_control= 1.28; d_Misophonia= 0.94), and in the misophonia group alone when measured with SCL (d= 0.20). Both types of neurostimulation were well tolerated and feasible to administer. Discussion: Clinical controls and misophonic participants were different in their self-report but not in psychophysiological measures of distress and regulations. Participants reported the lowest misophonic distress when engaging in cognitive restructuring enhanced with high-frequency neurostimulation, a finding that offers insight into the best path forward for misophonia interventions.
Drug delivery systems designed to overcome antimicrobial resistance

Antimicrobial resistance has emerged as a huge challenge to the effective treatment of infectious diseases. Aside from a modest number of novel anti‐infective agents, very few new classes of antibiotics have been successfully developed for therapeutic use. Despite the research efforts of numerous scientists, the fight against antimicrobial (ATB) resistance has been a longstanding continued effort, as pathogens rapidly adapt and evolve through various strategies, to escape the action of ATBs. Among other mechanisms of resistance to antibiotics, the sophisticated envelopes surrounding microbes especially form a major barrier for almost all anti‐infective agents. In addition, the mammalian cell membrane presents another obstacle to the ATBs that target intracellular pathogens. To negotiate these biological membranes, scientists have developed drug delivery systems to help drugs traverse the cell wall; these are called “Trojan horse” strategies. Within these delivery systems, ATB molecules can be conjugated with one of many different types of carriers. These carriers could include any of the following: siderophores, antimicrobial peptides, cell‐penetrating peptides, antibodies, or even nanoparticles. In recent years, the Trojan horse‐inspired delivery systems have been increasingly reported as efficient strategies to expand the arsenal of therapeutic solutions and/or reinforce the effectiveness of conventional ATBs against drug‐resistant microbes, while also minimizing the side effects of these drugs. In this paper, we aim to review and report on the recent progress made in these newly prevalent ATB delivery strategies, within the current context of increasing ATB resistance.