Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacological approaches to prevent vertical transmission of HIV and HBV

ABSTRACT Introduction Mother-to-child transmission (MTCT) is mainly responsible for the global pediatric HIV and HBV epidemic. Vertical transmission can be prevented and reduced through a series of interventions at the primary healthcare level, including extensive screening of pregnant women, administration of antivirals or immune-based treatments, counselling on type of delivery and breastfeeding. Areas covered In this narrative review, approved therapeutic options for the treatment of pregnant women living with HIV or HBV are discussed with special focus on efficacy and safety profiles of each agent or drug class examined. The search was performed using Medline (via PubMed), Web of Science, and Google Scholar to identify studies assessing vertical transmission of both HIV and HBV. Expert Opinion Elimination of MTCT of both infections is firmly endorsed by major global commitments and the integration of tailored preventive interventions into maternal and newborn health services is of strategical importance to achieve this critical target. However, further research centered on antiviral-based and immunization trials among pregnant women is urgently needed to mitigate the risk of maternal and neonatal adverse outcomes, effectively prevent transmission to the offspring and finally eliminate the pediatric HIV and HBV epidemic, one of the key global health challenges of our time.
Management of pediatric pemphigus vulgaris with rituximab: A case report and review of the literature

Pemphigus vulgaris (PV), an acquired autoimmune bullous disease, is caused by autoantibodies targeting desmosomal proteins in the skin and mucous membranes. Recent data from the adult PV population supports the use of rituximab, a chimeric anti‐CD20 IgG1 antibody, as a primary treatment strategy, but limited data exist regarding treatment in the pediatric population. We report the case of a 13‐year‐old male with PV treated successfully with systemic corticosteroids and rituximab, and review the literature supporting the treatment of pediatric PV with rituximab.
Relationship between Nomophobia, Various Emotional Difficulties, and Distress Factors among Students

The concept of nomophobia is a relatively new and is defined as the fear of not being able to use all the features and benefits provided by smartphones. The main goal of this research was to examine the relationship between nomophobia and various emotional difficulties and distress factors. The following measuring instruments were used on a sample of 257 Croatian students whose average age was 22 years: Nomophobia Questionnaire (NMP-Q); Scales of Depression, Anxiety, and Stress (DASS); Social and Emotional Loneliness Scale (SELSA); and Emotional Skills and Competences Questionnaire (ESCQ-15). Significant correlations between nomophobia and all examined variables were determined. It was found that nomophobia and emotional skills and competence act as significant predictors in expression of distress factors: 30% of the variance in the severity of depression symptoms, 24% of the variance in the severity of anxiety symptoms, and 26% of the variance in the severity of stress symptoms were explained.
A Novel Bidirectional T-Type Multilevel Inverter for Electric Vehicle Applications

This paper introduces a new configuration of bidirectional multilevel converter in electric vehicle (EV) applications. It has multilevel dc–dc converter with a dc link capacitor voltage balance feature. The multilevel dc–dc converter operates in a bidirectional manner, which is a fundamental requirement in EVs. Compared to the conventional configurations, the proposed one only implements two extra power switches and a capacitor to balance the voltage of the T-type multilevel inverter (MLI) capacitor over a complete drive cycle or at fault conditions. Therefore, no extra isolated sensor, control loops, and/or special switching pattern are required. Moreover, the proposed configuration due to the high-frequency cycle-by-cycle voltage balance between <inline-formula><tex-math notation="LaTeX">${C_N}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${C_P}$</tex-math></inline-formula>, the bulky electrolytic capacitors used in T-type MLI, are replaced with more reliable longer life film capacitors. This will result in a size and weight reduction of the converter by 20%. This allows more real estate for the EV battery in the chassis’ space envelope to increase its capacity. The proposed configuration is tested and validated using a <sc>matlab</sc>/Simulink simulation model. A laboratory prototype of 1 kW is built to provide the proof of concept results as well.
Building Concept of High School Information Technology Based

The purpose of this research is to apply or deliver the study to the schools that have not used the concepts of information technology-based education system in order to become an information technology-based school to create and promote every school in Indonesia. The method used in this research was descriptive method and action method (action). Descriptive method used to test the existing problems by collecting related data, and describe the analysis or reveal the analysis that has been done, while the action method to follow up from the results of the descriptive analysis then implemented the theory in a good system design. The result of this research is that the school with the media-based information technology results Effectiveness, Productivity, Efficiency, increasing the education Quality that is efficient and able to teach the learners in a sustainable manner and resulting high performance in learners academic achievement.
eyeSay: Brain Visual Dynamics Decoding With Deep Learning & Edge Computing

Brain visual dynamics encode rich functional and biological patterns of the neural system, and if decoded, are of great promise for many applications such as intention understanding, cognitive load quantization and neural disorder measurement. We here focus on the understanding of the brain visual dynamics for the Amyotrophic lateral sclerosis (ALS) population, and propose a novel system that allows these so- called ‘lock-in’ patients to ‘speak’ with their brain visual movements. More specifically, we propose an intelligent system to decode the eye bio-potential signal, Electrooculogram (EOG), thereby understanding the patients’ intention. We first propose to leverage a deep learning framework for automatic feature learning and classification of the brain visual dynamics, aiming to translate the EOG to meaningful words. We afterwards design and develop an edge computing platform on the smart phone, which can execute the deep learning algorithm, visualize the brain visual dynamics, and demonstrate the edge inference results, all in real-time. Evaluated on 4,500 trials of brain visual movements performed by multiple users, our novel system has demonstrated a high eye-word recognition rate up to 90.47%. The system is demonstrated to be intelligent, effective and convenient for decoding brain visual dynamics for ALS patients. This research thus is expected to greatly advance the decoding and understanding of brain visual dynamics, by leveraging machine learning and edge computing innovations.
Health from Brazilian Amazon food wastes: Bioactive compounds, antioxidants, antimicrobials, and potentials against cancer and oral diseases

Abstract Brazilian Amazon contains over 30,000 plant species and foods rich in bioactive compounds such as terpenes, phenolic acids, alkaloids, and flavonoids, of potential health benefits (antioxidant, antimicrobial, antiparasitic, anticancer, gastroprotection, prebiotic effects, among others). The existence of residues from non-edible parts of plants (leaves, roots, stems, branches, barks) or fruit wastes (peel, bagasse, seeds) in the agri-food industry and its supply chain is an important challenge in food loss and waste management. In this critical review several Amazon species, focusing on extracts/essential oils from nonedible parts or wastes, were analyzed in terms of phytochemicals, biological activity, and underlying mechanisms. We hope this review emphasizes the importance of Amazon’s sustainability initiatives on population health due to the potential shown against cancer, infectious diseases, and prevention of oral diseases. It is urgent to think about the conversion of amazon food wastes and co-products into high-added-value raw materials to develop novel drugs, food packaging systems, or nutraceutical foods.
Winds of hot stars in the Magellanic Clouds

The Magellanic Qouds provide a unique laboratory in which to study the metallicity dependence of stellar winds. If proved by observations, such a dependence would have important consequences for stellar evolution, nucleosynthesis, energy balance of the ISM, star formation and so on. In addition, the diagnostics of emitting gaseous nebulae in metal-poor galaxies would be affected, since the photospheric ionizing radiation of massive hot stars is modified by metal absorption in the surrounding stellar winds (see recent review by Kudritzki et al. 1990). Moreover, the method of using mass-loss rates M and terminal velocities to determine stellar distances, radii and masses (Kudritzki & Hummer 1990) would need further modification.
A method to calculate the conversion efficiency of a rectenna

This article studies the working principle of a rectenna which is a combination of an antenna and a rectifying circuit. In the simulation, the working properties of the rectifying circuit can be represented by two characteristic curves which are extracted from it, and these two curves are independent of the device which is connected with it. The measured results demonstrate that the data calculated by the two curves can be used to accurately describe the conversion efficiency of the rectifying circuit. Furthermore, the two curves are used to represent the rectifying circuit in the calculation of the rectenna conversion efficiency. In addition, with the characteristic impedance of the antenna, the calculation equation is given. In the experiment of the rectenna, the measured conversion efficiency of the rectenna is in agreement with the data calculated by the proposed equation, which proves the accuracy of this method.
Strong Coulomb coupling influences ion and neutral temperatures in atmospheric pressure plasmas

Molecular dynamics simulations are used to model ion and neutral temperature evolution in partially-ionized atmospheric pressure plasma at different ionization fractions. Results show that ion–ion interactions are strongly coupled at ionization fractions as low as 10−5 and that the temperature evolution is influenced by effects associated with the strong coupling. Specifically, disorder-induced heating is found to rapidly heat ions on a timescale of the ion plasma period (∼10 s ps) after an ionization pulse. This is followed by the collisional relaxation of ions and neutrals, which cools ions and heats neutrals on a longer (∼ns) timescale. Slight heating then occurs over a much longer (∼100 s ns) timescale due to ion-neutral three-body recombination. An analytic model of the temperature evolution is developed that agrees with the simulation results. A conclusion is that strong coupling effects are important in atmospheric pressure plasmas.
GE-Grasp: Efficient Target-Oriented Grasping in Dense Clutter

Grasping in dense clutter is a fundamental skill for autonomous robots. However, the crowdedness and oc-clusions in the cluttered scenario cause significant difficul-ties to generate valid grasp poses without collisions, which results in low efficiency and high failure rates. To address these, we present a generic framework called GE-Grasp for robotic motion planning in dense clutter, where we leverage diverse action primitives for occluded object removal and present the generator-evaluator architecture to avoid spatial collisions. Therefore, our GE-Grasp is capable of grasping objects in dense clutter efficiently with promising success rates. Specifically, we define three action primitives: target-oriented grasping for target capturing, pushing, and nontarget-oriented grasping to reduce the crowdedness and occlusions. The gen-erators effectively provide various action candidates referring to the spatial information. Meanwhile, the evaluators assess the selected action primitive candidates, where the optimal action is implemented by the robot. Extensive experiments in simulated and real-world environments show that our approach outperforms the state-of-the-art methods of grasping in clutter with respect to motion efficiency and success rates. Moreover, we achieve comparable performance in the real world as that in the simulation environment, which indicates the strong gen-eralization ability of our GE-Grasp. Supplementary material is available at: https://github.com/CaptainWuDaoKou/GE-Grasp.
Beamforming Analysis and Design for Wideband THz Reconfigurable Intelligent Surface Communications

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) communications have been regarded as a promising candidate for future 6G networks because of its ultra-wide bandwidth and ultra-low power consumption. However, there exists the beam split problem, especially when the base station (BS) or RIS owns the large-scale antennas, which may lead to serious array gain loss. Therefore, in this paper, we investigate the beam split and beamforming design problems in the THz RIS communications. Specifically, we first analyze the beam split effect caused by different RIS sizes, shapes and deployments. On this basis, we apply the fully connected time delayer phase shifter hybrid beamforming (FC-TD-PS-HB) architecture at the BS and deploy distributed RISs to cooperatively mitigate the beam split effect. We aim to maximize the achievable sum rate by jointly optimizing the hybrid analog/digital beamforming, time delays at the BS and reflection coefficients at the RISs. To solve the formulated problem, we first design the analog beamforming and time delays based on different RISs’ physical directions, and then it is transformed into an optimization problem by jointly optimizing the digital beamforming and reflection coefficients. Next, we propose an alternatively iterative optimization algorithm to deal with it. Specifically, for given the reflection coefficients, we propose an iterative algorithm based on the minimum mean square error technique to obtain the digital beamforming. After, we apply Lagrangian dual reformulation (LDR) and multidimensional complex quadratic transform (MCQT) methods to transform the original problem to a quadratically constrained quadratic program, which can be solved by alternating direction method of multipliers (ADMM) technique to obtain the reflection coefficients. Finally, the digital beamforming and reflection coefficients are obtained via repeating the above processes until convergence. Simulation results verify that the proposed scheme can effectively alleviate the beam split effect and improve the system capacity.
Patent Search Using Triplet Networks Based Fine-Tuned SciBERT

In this paper, we propose a novel method for the prior-art search task. We ﬁne-tune SciBERT transformer model using Triplet Network approach, allowing us to represent each patent with a ﬁxed-size vector. This also enables us to conduct eﬃcient vector similarity computations to rank patents in query time. In ourexperiments, we show that our proposed method outperforms baseline methods.
Riemannian Geometry Approach for Minimizing Distortion and its Applications

Given an affine transformation $T$, we define its Fisher distortion $Dist_F(T)$. We show that the Fisher distortion has Riemannian metric structure and provide an algorithm for finding mean distorting transformation -- namely -- for a given set $\{T_{i}\}_{i=1}^N$ of affine transformations, find an affine transformation $T$ that minimize the overall distortion $\sum_{i=1}^NDist_F^{2}(T^{-1}T_{i}). $ The mean distorting transformation can be useful in some fields -- in particular, we apply it for rendering affine panoramas.
Techniques for Continuous Touch-Based Authentication Modeling

The field of touch-based authentication has been rapidly developing over the last decade, creating a fragmented and difficult-to-navigate area for researchers and application developers alike due to the variety of methods investigated. In this study, we perform a systematic literature analysis of 30 studies on the techniques used for feature extraction, classification, and aggregation in touch-based authentication systems as well as the performance metrics reported by each study. Based on our findings, we design a set of experiments to compare the performance of the most frequently used techniques in the field under clearly defined conditions. In addition, we introduce three new techniques for touch-based authentication: an expanded feature set (consisting of 149 unique features), a multi-algorithm ensemble-based classifier, and a Recurrent Neural Network based stacking aggregation method. The comparison includes 14 feature sets, 11 classifiers, and 5 aggregation methods. In total, 219 model configurations are examined and we show that our novel techniques outperform the current state-of-the-art in each category. The results are also validated across three different publicly available datasets. Finally, we discuss the findings of our investigation with the aim of making the field more understandable and accessible for researchers and practitioners.
Egocentric scene context for human-centric environment understanding from video

First-person video highlights a camera-wearer’s activities in the context of their persistent environment . However, current video understanding approaches reason over visual features from short video clips that are detached from the underlying physical space and only capture what is directly seen. We present an approach that links egocentric video and camera pose over time by learning representations that are predictive of the camera-wearer’s (potentially unseen) local surroundings to facilitate human-centric environment understanding. We train such models using videos from agents in simulated 3D environments where the environment is fully observable, and test them on real-world videos of house tours from unseen environments. We show that by grounding videos in their physical environment, our models surpass traditional scene classification models at predicting which room a camera-wearer is in (where frame-level information is insufficient), and can leverage this grounding to localize video moments corresponding to environment-centric queries, outperforming prior methods. Project page: http://vision.cs. utexas. edu/projects/ego-scene-context/
Critical phenomena with renormalization group analysis of a hierarchical model of financial crashes

Financial market models are able to help the investors foresee the risk of a financial market crash and reduce the probability of its occurrence. Modelling in financial markets is categorized into microscopic models and macroscopic models. The microscopic models study the mechanisms behind the market and their behaviour. These models assist in an understanding of the causes of financial market crashes. Macroscopic models find the disciplines and rules from the historical macroscopic data for the prediction of market trends, directions and crashes. They aim to provide forecasts of when the crashes should occur. Except for large fluctuations, the stock market price or index movements can be characterized by a random walk. The stock price trajectory possesses the properties of self-similarity and scale invariance, and hence mimics fractals. Furthermore, the stock price movement is a manifestation of the actions and interactions of stock traders. The hierarchical model with fractal structures, representing the interaction structure of the stock traders, is applicable to the study of the microscopic mechanisms of stock price movements. In the language of statistical mechanics, stock market crashes are viewed as critical phenomena where a crash occurs only at the critical point with a phase transition. The renormalization group is a mathematical apparatus that allows the decomposition of a macroscopic problem viewed at different scales. The renormalization group analysis of the hierarchical model finds that the time to reach the critical point of a system is a function of the interaction degree of stock traders in a power law. Moreover, the hierarchical model with the renormalization group formalism shows that the behavior of the fraction of all stock traders putting buy orders over time follows a power law coupled with log-periodic oscillations. Based on the renormalization group analysis results of the hierarchical model, a log-periodic power law model is derived by constructing an renormalization group formalism from the risk-driven model. In the log-periodic power law model, the expected time to crash is always finite. Therefore, market crashes are inevitable, but the existence of bubbles and crash risks is foreseen by the model.
Self-Support Few-Shot Semantic Segmentation

Existing few-shot segmentation methods have achieved great progress based on the support-query matching framework. But they still heavily suffer from the limited coverage of intra-class variations from the few-shot supports provided. Motivated by the simple Gestalt principle that pixels belonging to the same object are more similar than those to different objects of same class, we propose a novel self-support matching strategy to alleviate this problem, which uses query prototypes to match query features, where the query prototypes are collected from high-confidence query predictions. This strategy can effectively capture the consistent underlying characteristics of the query objects, and thus fittingly match query features. We also propose an adaptive self-support background prototype generation module and self-support loss to further facilitate the self-support matching procedure. Our self-support network substantially improves the prototype quality, benefits more improvement from stronger backbones and more supports, and achieves SOTA on multiple datasets. Codes are at \url{https://github.com/fanq15/SSP}.
Low-Complexity CNNs for Acoustic Scene Classification

This paper presents a low-complexity framework for acoustic scene classification (ASC). Most of the frameworks designed for ASC use convolutional neural networks (CNNs) due to their learning ability and improved performance compared to hand-engineered features. However, CNNs are resource hungry due to their large size and high computational complexity. Therefore, CNNs are difficult to deploy on resource constrained devices. This paper addresses the problem of reducing the computational complexity and memory requirement in CNNs. We propose a low-complexity CNN architecture, and apply pruning and quantization to further reduce the parameters and memory. We then propose an ensemble framework that combines various low-complexity CNNs to improve the overall performance. An experimental evaluation of the proposed framework is performed on the publicly available DCASE 2022 Task 1 that focuses on ASC. The proposed ensemble framework has approximately 60K parameters, requires 19M multiply-accumulate operations and improves the performance by approximately 2-4 percentage points compared to the DCASE 2022 Task 1 baseline network.