Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles

Solar radio flux along with geomagnetic indices are important indicators of solar activity and its effects. Extreme solar events such as flares and geomagnetic storms can negatively affect the space environment including satellites in low-Earth orbit. Therefore, forecasting these space weather indices is of great importance in space operations and science. In this study, we propose a model based on long shortterm memory neural networks to learn the distribution of time series data with the capability to provide a simultaneous multivariate 27-day forecast of the space weather indices using time series as well as solar image data. We show a 30–40% improvement of the root mean-square error while including solar image data with time series data compared to using time series data alone. Simple baselines such as a persistence and running average forecasts are also compared with the trained deep neural network models. We also quantify the uncertainty in our prediction using a model ensemble.
A non conventional power converter for plasma stabilization

The design and control of a nonconventional converter with a peak power of 3.6 MVA and a bandwidth ranging from DC up to 10 kHz are presented. The converter has been designed for an experiment that has recently been proposed within the framework of the JET project. The basic conformation of the converter is described. The novel control technique, which is based on a comparison between the current reference and the load current, through a system of hysteresis comparators and a simple combinatorial network, is presented. Simulation results are given. <<ETX>>
Air infiltration through building envelopes: A review

Air leakage through the building envelope into the building interiors has a considerable impact on the energy loads and consequently energy demand and energy costs of buildings. This phenomenon known as infiltration happens through various openings and venues in the building envelope varying from large openings such as doors and windows to minute cracks and crevices. In addition to impacting building energy loads, infiltration impacts indoor air quality and can result in moisture accumulation problems in the building envelope. A generalized review of infiltration that includes evaluation techniques and models, quantification, and interaction with other heat transfer phenomena is presented in this article.
A Minimalistic Stochastic Dynamics Model of Cluttered Obstacle Traversal

Robots are still poor at traversing cluttered large obstacles required for important applications like search and rescue. By contrast, animals are excellent at doing so, often using direct physical interaction with obstacles rather than avoiding them. Here, towards understanding the dynamics of cluttered obstacle traversal, we developed a minimalistic stochastic dynamics simulation inspired by our recent study of insects traversing grass-like beams. The 2-D model system consists of a forward self-propelled circular locomotor translating on a frictionless level plane with a lateral random force and interacting with two adjacent horizontal beams that form a gate. We found that traversal probability increases monotonically with propulsive force, but first increases then decreases with random force magnitude. For asymmetric beams with different stiffness, traversal is more likely towards the side of the less stiff beam. These observations are in accord with those expected from a potential energy landscape approach. Furthermore, we extended the single gate in a lattice configuration to form a large cluttered obstacle field. A Markov chain Monte Carlo method was applied to predict traversal in the large field, using the input-output probability map obtained from single gate simulations. This method achieved high accuracy in predicting the statistical distribution of the final location of the body within the obstacle field, while saving computation time by a factor of $10^5$ over our dynamic simulation.
FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection

The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high-fire-risk days, a small fire ignition can rapidly grow and become out of control. Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios. In this work, we present the Fire Ignition Library (FIgLib), a publicly available dataset of nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras deployed in Southern California. We also introduce SmokeyNet, a novel deep learning architecture using spatiotemporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines and rivals human performance. We hope that the availability of the FIgLib dataset and the SmokeyNet architecture will inspire further research into deep learning methods for wildfire smoke detection, leading to automated notification systems that reduce the time to wildfire response.
Learning To Retrieve Prompts for In-Context Learning

In-context learning is a recent paradigm in natural language understanding, where a large pre-trained language model (LM) observes a test instance and a few training examples as its input, and directly decodes the output without any update to its parameters. However, performance has been shown to strongly depend on the selected training examples (termed prompts). In this work, we propose an efficient method for retrieving prompts for in-context learning using annotated data and an LM. Given an input-output pair, we estimate the probability of the output given the input and a candidate training example as the prompt, and label training examples as positive or negative based on this probability. We then train an efficient dense retriever from this data, which is used to retrieve training examples as prompts at test time. We evaluate our approach on three sequence-to-sequence tasks where language utterances are mapped to meaning representations, and find that it substantially outperforms prior work and multiple baselines across the board.
Infection-induced chromatin modifications facilitate translocation of herpes simplex virus capsids to the inner nuclear membrane

Herpes simplex virus capsids are assembled and packaged in the nucleus and move by diffusion through the nucleoplasm to the nuclear envelope for egress. Analyzing their motion provides conclusions not only on capsid transport but also on the properties of the nuclear environment during infection. We utilized live-cell imaging and single-particle tracking to characterize capsid motion relative to the host chromatin. The data indicate that as the chromatin was marginalized toward the nuclear envelope it presented a restrictive barrier to the capsids. However, later in infection this barrier became more permissive and the probability of capsids to enter the chromatin increased. Thus, although chromatin marginalization initially restricted capsid transport to the nuclear envelope, a structural reorganization of the chromatin counteracted that to promote capsid transport later. Analyses of capsid motion revealed that it was subdiffusive, and that the diffusion coefficients were lower in the chromatin than in regions lacking chromatin. In addition, the diffusion coefficient in both regions increased during infection. Throughout the infection, the capsids were never enriched at the nuclear envelope, which suggests that instead of nuclear export the transport through the chromatin is the rate-limiting step for the nuclear egress of capsids. This provides motivation for further studies by validating the importance of intranuclear transport to the life cycle of HSV-1.
Power conversion distribution system using a high-frequency AC link

A power conversion system configuration based on a 20 kHz single-phase voltage link is proposed for systems having distributed power requirements. The proposed configuration permits flexibility in the choice of voltage levels in the system without the penalty of bulky magnetic components, has fast system response, high efficiency, and freedom from acoustic noise, and provides a high degree of uniformity and ease of implementation. A zero-voltage switching pulse-density-modulated converter is proposed as the basic interface converter. It is shown that the technique of pulse-density-modulation allows low distortion synthesis of fixed- or variable-amplitude DC and fixed- or variable-frequency, single- or three-phase AC from the half-cycle pulses of the link voltage. A breadboard system has been built to experimentally verify the models and to complement the theoretical work. >
Clickbait in YouTube Prevention, Detection and Analysis of the Bait using Ensemble Learning

Unscrupulous content creators on YouTube employ deceptive techniques such as spam and clickbait to reach a broad audience and trick users into clicking on their videos to increase their advertisement revenue. Clickbait detection on YouTube requires an in depth examination and analysis of the intricate relationship between the video content and video descriptors title and thumbnail. However, the current solutions are mostly centred around the study of video descriptors and other metadata such as likes, tags, comments, etc and fail to utilize the video content, both video and audio. Therefore, we introduce a novel model to detect clickbaits on YouTube that consider the relationship between video content and title or thumbnail. The proposed model consists of a stacking classifier framework composed of six base models (K Nearest Neighbours, Support Vector Machine, XGBoost, Naive Bayes, Logistic Regression, and Multilayer Perceptron) and a meta classifier. The developed clickbait detection model achieved a high accuracy of 92.89% for the novel BollyBAIT dataset and 95.38% for Misleading Video Dataset. Additionally, the stated classifier does not use meta features or other statistics dependent on user interaction with the video (the number of likes, followers, or comments) for classification, and thus, can be used to detect potential clickbait videos before they are uploaded, thereby preventing the nuisance of clickbaits altogether and improving the users streaming experience.
Search for temporal cell segmentation robustness in phase-contrast microscopy videos

Studying cell morphology changes in time is critical to understanding cell migration mechanisms. In this work, we present a deep learning-based workflow to segment cancer cells embedded in 3D collagen matrices and imaged with phase-contrast microscopy. Our approach uses transfer learning and recurrent convolutional long-short term memory units to exploit the temporal information from the past and provide a consistent segmentation result. Lastly, we propose a geometrical-characterization approach to studying cancer cell morphology. Our approach provides stable results in time, and it is robust to the different weight initialization or training data sampling. We introduce a new annotated dataset for 2D cell segmentation and tracking, and an open-source implementation to replicate the experiments or adapt them to new image processing problems.
Unleashing Argumentation Support Systems on the Web: The case of CoPe_it!

Argumentation support systems have a long history. Generally speaking, they offer sophisticated support for sense- and/or decision-making, and have been proven effective in addressing a wide range of concerns in various domains, such as engineering, law and medicine. In the majority of cases, these systems have largely remained within the communities in which they originated, thus failing to reach a wider audience. When investigating how the advent of the World Wide Web affected them, the results are rather disappointing: only Web-based discussion forums, with rather primitive support when compared to argumentation support systems, have successfully migrated to the Web. One key factor contributing to the wide adoption of these forums is their emphasis on simplicity. On the other hand, the formal nature of sophisticated argumentation systems has been pointed out as an important barrier to their wide adoption, and as one factor that hinders them to make the step towards the World Wide Web. The new landscape of the Web, as shaped by the so called Web 2.0 move, is considered as a rather hostile environment for traditional argumentation systems. In this new environment, users are moving away from formal systems that prescribe their interactions, and favor applications that place the control of the formalization process in their hands. Nevertheless, attempts to bring advanced argumentation support systems into this new Web environment have already begun to appear [Buckingham Shum, 2008]. In this paper, we use the Walker’s concepts of ‘domesticated’ and ‘feral’ technology [Walker, 2005] to describe the current contradicting environments in which applications must operate, and present how CoPe_it! , an innovative tool that supports argumentative collaboration on the Web, attempts to bridge this gap in the context of argumentation support systems. We show among others how CoPe_it! exploits interlinked data and the Semantic Web in order to bring them into argumentative discourses in ways that are suitable for the user’s needs. The overall aim is to equip the Web with more powerful discussion tools, with which the complex problems of our society can be better addressed.
Sleep Characteristics in the Italian Pediatric Population: A Systematic Review

Objective: During childhood sleep duration, quality and patterns evolve and change greatly and relate strongly to healthy development. This systematic review aims to summarize the literature on sleep characteristics in the Italian pediatric population, adopting a cultural perspective. Method: Pubmed, PsycINFO and Medline databases were systematically searched. Eligible studies had to: include Italian children and adolescents; report data for one or more sleep-related variables; be published in English or Italian. Results: Twenty-nine studies were selected including 18551 Italian children or adolescents. Studies were categorized by age group: infancy and toddlerhood (0-3 years); preschool and school age (3-12 years); adolescence (12-18 years) and mixed age groups. Overall, studies showed that the Italian pediatric population present shorter sleep duration and longer sleep onset latency compared to international recommendations. Furthermore, data indicate high prevalence of dysfunctional sleep habits, such as late bed-time (all age groups), involvement of parents during bed-time (infancy and toddlerhood), and high variability between sleep times on week-days vs. weekends (adolescence). Nevertheless, most studies lacked comprehensive data on sleep patterns, focusing instead on isolated variables. Conclusion: These results suggest a strong trend among Italian children and adolescents towards unhealthy sleep patterns. Comprehensive data are still lacking and large studies evaluating a broad range of sleep characteristics in Italian pediatric populations are needed. Data strongly suggest that Italian Pediatric Primary Care should place higher focus on sleep problems and implement clinical protocols directed towards improving sleep patterns in children and adolescents.
Prevalence of hypertension, and related factors among adults in Wolaita, southern Ethiopia: A community-based cross-sectional study

Introduction Hypertension is a global public health challenge. There is a lack of evidence on the prevalence of hypertension, prehypertension, and related factors among adult populations of Wolaita, southern Ethiopia. Aim To assess the prevalence of hypertension, prehypertension, and related factors among adult populations of Wolaita, southern Ethiopia. Methods A community-based cross-sectional study was conducted on 2483 adult residents, selected using a two-stage random sampling technique. The quantitative data collected from structured questionnaires; anthropometric and biochemical measurements were entered into EpiData version 3.1 using double-entry systems. We determined the weighted prevalence of hypertension and pre-hypertension for the two-stage survey. The multivariate logistic regression analysis was used to assess factors associated with hypertension and carried out after declaring the data set as survey data to account for the effect of clustering. An adjusted coefficient with 95% CI was used to ascertain the significance of the association. Results The weighted prevalence of hypertension and prehypertension in the Wolaita area was 31.3% (27.7%-35.1%) and 46.4% (42.9%-50.0%) respectively. The weighted prevalence of hypertension of those who were not aware of their hypertension until the time of the survey was 29.8%% (26.5%-33.3%). Where the weighted prevalence of self-reported cases of hypertension was 2.2% (1.2%-3.8%). Obesity, sugar-sweetened food consumption, male sex, elevated total cholesterol, raised fasting blood sugar, and advancing age were positively associated with hypertension. Conclusion The prevalence of hypertension among adults in Wolaita was high. A small proportion of the affected people are aware of their high blood pressure. This study reported a high prevalence of pre-hypertension; which indicates a high percentage of people at risk of hypertension. It is essential to develop periodic screening programs, and primary intervention strategies such as the prevention of obesity, and reduction of sugar-sweetened food consumption.
Amortized Noisy Channel Neural Machine Translation

Noisy channel models have been especially effective in neural machine translation (NMT). However, recent approaches like"beam search and rerank"(BSR) incur significant computation overhead during inference, making real-world application infeasible. We aim to study if it is possible to build an amortized noisy channel NMT model such that when we do greedy decoding during inference, the translation accuracy matches that of BSR in terms of reward (based on the source-to-target log probability and the target-to-source log probability) and quality (based on BLEU and BLEURT). We attempt three approaches to train the new model: knowledge distillation, one-step-deviation imitation learning, and Q learning. The first approach obtains the noisy channel signal from a pseudo-corpus, and the latter two approaches aim to optimize toward a noisy-channel MT reward directly. For all three approaches, the generated translations fail to achieve rewards comparable to BSR, but the translation quality approximated by BLEU and BLEURT is similar to the quality of BSR-produced translations. Additionally, all three approaches speed up inference by 1-2 orders of magnitude.
Lacuna Reconstruction: Self-supervised Pre-training for Low-Resource Historical Document Transcription

We present a self-supervised pre-training approach for learning rich visual language representations for both handwritten and printed historical document transcription. After supervised fine-tuning of our pre-trained encoder representations for low-resource document transcription on two languages, (1) a heterogeneous set of handwritten Islamicate manuscript images and (2) early modern English printed documents, we show a meaningful improvement in recognition accuracy over the same supervised model trained from scratch with as few as 30 line image transcriptions for training. Our masked language model-style pre-training strategy, where the model is trained to be able to identify the true masked visual representation from distractors sampled from within the same line, encourages learning robust contextualized language representations invariant to scribal writing style and printing noise present across documents.
Infodemiology of flu: Google trends-based analysis of Italians’ digital behavior and a focus on SARS-CoV-2, Italy

Summary Introduction The aim of the current study was to assess if the frequency of internet searches for influenza are aligned with Italian National Institute of Health (ISS) cases and deaths. Also, we evaluate the distribution over time and the correlation between search volume of flu and flu symptoms with reported new cases of SARS-CoV-2. Materials and methods The reported cases and deaths of flu and the reported cases of SARS-CoV-2 were selected from the reports of ISS, the data have been aggregated by week. The search volume provided by Google Trends (GT) has a relative nature and is calculated as a percentage of query related to a specific term in connection with a determined place and time-frame. Results The strongest correlation between GT search and influenza cases was found at a lag of +1 week particularly for the period 2015-2019. A strong correlation was also found at a lag of +1 week between influenza death and GT search. About the correlation between GT search and SARS-CoV-2 new cases the strongest correlation was found at a lag of +3 weeks for the term flu. Conclusion In the last years research in health care has used GT data to explore public interest in various fields of medicine. Caution should be used when interpreting the findings of digital surveillance.
Depressive Syndromes Associated with Alcohol Dependence

Objective Depressive syndromes (DSs) are some of the most common mental disorders in individuals suffering from alcohol dependence (AD). The aim of the study was to investigate the characteristics of DSs associated with AD in a group of inpatients admitted in a psychiatric hospital. Method One hundred sixty inpatients between 25 and 58 years of age (mean ± SD: 37.30 ± 7.97), suffering from AD and DSs and recruited from a larger clinical sample, were included. They were evaluated by means of a battery of diagnostic/rating scales for assessment of both diagnosis and symptoms severity. Results Complete physical and psychiatric examinations of AD patients showed that DSs represent a very heterogeneous group that can be divided in: psychogenic (66.3%), endogenous (11.3%), organic (22.4%), and mixed. The following clinical depressive subtypes could be identified: hypochondriac (42.5%), asthenic (20.6%), agitated (19.4%), dysphoric (8.8%), simple (4.35%), and apathetic (4.35). Conclusions Our study indicates that DSs during AD represent a constant association that frequently complicates the clinical pictures, induces low quality of life and personal adjustment, and impairs remission. Investigation of the casual and intertwined factors, developmental patterns and clinical structure of the AD-associated DSs should allow optimizing a tailored and integrated system of medical rehabilitation help.
Road-aware Monocular Structure from Motion and Homography Estimation

Structure from motion (SFM) and ground plane homography estimation are critical to autonomous driving and other robotics applications. Recently, much progress has been made in using deep neural networks for SFM and homography estimation respectively. However, directly applying existing methods for ground plane homography estimation may fail because the road is often a small part of the scene. Besides, the performances of deep SFM approaches are still inferior to traditional methods. In this paper, we propose a method that learns to solve both problems in an end-to-end manner, improving performance on both. The proposed networks consist of a Depth-CNN, a Pose-CNN and a Ground-CNN. The Depth-CNN and Pose-CNN estimate dense depth map and ego-motion respectively, solving SFM, while the Pose-CNN and Ground-CNN followed by a homography layer solve the ground plane estimation problem. By enforcing coherency between SFM and homography estimation results, the whole network can be trained end to end using photometric loss and homography loss without any groundtruth except the road segmentation provided by an off-the-shelf segmenter. Comprehensive experiments are conducted on KITTI benchmark to demonstrate promising results compared with various state-of-the-art approaches.