Miracle-dz/UniFAR-FTUs huggingface dataset:
Gene Tree Construction and Correction Using SuperTree and Reconciliation

The supertree problem asking for a tree displaying a set of consistent input trees has been largely considered for the reconstruction of species trees. Here, we rather explore this framework for the sake of reconstructing a gene tree from a set of input gene trees on partial data. In this perspective, the phylogenetic tree for the species containing the genes of interest can be used to choose among the many possible compatible “supergenetrees”, the most natural criteria being to minimize a reconciliation cost. We develop a variety of algorithmic solutions for the construction and correction of gene trees using the supertree framework. A dynamic programming supertree algorithm for constructing or correcting gene trees, exponential in the number of input trees, is first developed for the less constrained version of the problem. It is then adapted to gene trees with nodes labeled as duplication or speciation, the additional constraint being to preserve the orthology and paralogy relations between genes. Then, a quadratic time algorithm is developed for efficiently correcting an initial gene tree while preserving a set of “trusted” subtrees, as well as the relative phylogenetic distance between them, in both cases of labeled or unlabeled input trees. By applying these algorithms to the set of Ensembl gene trees, we show that this new correction framework is particularly useful to correct weakly-supported duplication nodes. The C++ source code for the algorithms and simulations described in the paper are available at https://github.com/UdeM-LBIT/SuGeT.
Investigating Questioner’s Explicit Information Influences in Transformer-based Community Question Answering

Web Q&A forums provide a collaborative environment where users can post subjective questions and answers based on their life experiences or knowledge of some specific domains or topics. The questions and answers relations help Learning-to-Rank models for Community Question Answering (CQA) tasks to sort user comments to answer questions by relevance. These learning models automatically rank comments as potential answer candidates for new questions. However, finding the proper models for CQA models also depends on the information used to match question and answer pairs. This paper aims to summarise a study about Community Question-Answering (CQA) models in two specific domains: Personal Finance &Money (PF&M) and Home Improvements (HI). We built CQA datasets, where all the data are scraped from user posts in a web community. This dataset provides further explicit user information to build and train our CQA baselines, such as question descriptions and multiple questioner tags to expand the information in short user questions. We offer different evaluation scenarios to assess the baseline performance associated with distinct text inputs by considering two measures: MAP and MRR. CQA models propose answer candidates ranked list based on the QA pairs relevance. We also offer an automatic tag extractor to obtain tags from question descriptions to evaluate the importance of user tags for CQA model performance. In web forums like StackExchange, questioners use multiple tags as keywords to refer to the question context (e.g., investment, tax, health insurance and so on). Our experiments show that incorporating multiple tags and question descriptions to expand user question information improves the performance of CQA for different transformer-based baselines. Our results show that our tag extraction method, which condenses question descriptions into tags, delivered a compelling outcome when we compared the effectiveness of user- and machine-generated tags as model inputs in CQA baselines.
Accelerating Bag-of-Features SIFT Algorithm for 3 D Model Retrieval

We have previously proposed a shape-based 3D model retrieval algorithm that compares 3D shape based on local visual features. The method first computes a set of multi-scale local visual features from a set of depth images rendered from multiple view orientations about the 3D model. Thousands of visual features per model are integrated into a feature vector for the model by using so-called bag-of-features approach. The algorithm performed very well, especially for models having articulation and/or global deformation. However, the method was computationally expensive; retrieving a model from a 1,000 model database took more than 10s. This because the costs of rendering depth images, extracting local visual features, quantizing these features, and computing distance among the pairs of integrated features can be quite expensive. In this paper, we significantly accelerated the algorithm by using a Graphics Processing Unit (GPU).
High Performance DPI Engine Design for Network Traffic Classification, Metadata Extraction and Data Visualization

The increasing volume and complexity of network traffic pose a significant challenge in today’s interconnected world. Internet service providers (ISPs) and telecommunication companies rely on traffic monitoring using Deep Packet Inspection (DPI) frameworks to monitor network traffic, including the detection of anomalous behavior. This paper presents the development of a high-speed and scalable DPI engine. This engine is designed to capture packets at high speeds, extracting data and metadata from these packets, ultimately providing profound insights into internet traffic patterns. The developed DPI engine comprises essential components, such as packet capture, time stamping, flow processing, traffic classification, and semantic analysis. It exhibits the capacity to process data at remarkable speeds, reaching multiple 10 GB/s, which is crucial in this era of rapidly evolving network traffic. Moreover, the paper demonstrates the creation of interactive dashboards that enable effective visualization of the extracted information, making it readily interpretable for users. Perhaps most significantly, the DPI engine is remarkably lightweight and easily deployable on standard hardware, requiring no specialized equipment. Its versatility allows ISPs to employ it for a variety of purposes, including traffic shaping, traffic charging, and ensuring high-quality service (QOS) for their customers.
An approximation method for modeling a CMOS bit-level product cell

An approximation method for a CMOS bit-level product cell used for vector-matrix multiplications is presented. The quantitative mathematical model of the cell is obtained by using CID/CCD principles to understand the device function and employing MOS structure equations to describe the cell operation. Due to the resultant equation cannot be solved explicitly, a multilayer artificial neural network is proposed to do functions approximations. The resultant algorithm is used to create an electrical device model for PSPICE simulations.
High-Efficiency Reconfigurable Cavity-Backed Slot Antenna Array with Simplified Feeding Structure

Reconfigurable cavity-backed slot antenna array using tunable metal post is proposed in this paper. The TE101 mode with a singly-directional electric field will be influenced by metal post placed parallel to its electric field. Therefore, the reconfigurable frequency can be achieved by adjusting metal post’s length. The long side of the radiation slot is placed perpendicular to the electric field of the TE101 mode, and then the radiation slot can be excited to radiate the energy. By utilizing the characteristics of TE101 mode, i.e., the pure directional electric field, multiple radiation slots is directly fed without using a power dividing network. Thus, a simple reconfigurable slot array is realized. Design and optimization of a reconfigurable $3\times 3$ slot antenna array is carried out to prove the feasibility of the proposed design idea.
Access to Personal Data is Still Tempting for Mobile Apps Even after the GDPR Implementation

Mobile phones have become a vital part of our daily lives with users relying on them for a variety of activities. Users don't hesitate to provide their personal data to free or paid mobile applications, in order to use them with no interruption. However, the amount of personal data being exchanged has raised concerns, resulting in the introduction of regulations for protecting personal data at many countries. A regulation in this direction that was enacted in Europe is the General Data Protection Regulation known as GDPR that came into effect in May 2018. This study examines the impact of GDPR on Google Play applications by comparing a dataset of metrics collected in 2017 to a post-GDPR dataset gathered in 2022. The focus of this research is to analyze the effect of GDPR on mobile app usage and its associated metrics by comparing the datasets of the two above periods. Our analysis shows that progress has been made towards achieving GDPR compliance, but there is still a considerable distance to be covered before reaching full compliance. A key observation is that the number of permissions used by apps has not been reduced even after the GDPR implementation.
Conduction velocities and nerve fibre diameters of touch, pain, urinary bladder and anal canal afferents and alpha and gamma-motoneurons in human dorsal sacral roots.

1. Single action potentials were recorded extracellularly at 2 sites from human sacral dorsal nerve roots, and their conduction time measured. Conduction velocity frequency distribution histograms were constructed from afferent and efferent nerve fibres. Conduction velocity distribution peaks could be identified from afferents of mechanoreceptors of the skin, the bladder, the anal canal, of stretch and flow receptors of the urinary bladder, from spindle afferents and from the 3 alpha (extrafusal) and 3 gamma-motoneuron (intrafusal) classes. The electrophysiologically measured roots were removed and morphometrically analysed. Nerve fibre diameter frequency distribution histograms were constructed with respect to 3 myelin sheath thickness ranges. Nerve fibre diameter distribution peaks could partly be correlated to the corresponding conduction velocity distribution peaks. 2. Identified nerve fibre classes, characterized by their group peak values of conduction velocity and fibre diameter were at about 36 degrees C (age 30 years): Spindle afferents: SP1 (60 msec-1/13.2 microns) SP2 (50/12.0? ); touch afferents: TO (49 msec-1/13.0 microns), T1 (44/11.2), T2 (39/10.1), T3 (31/9.1), T4 (20/8.3); pain afferents: P (13 msec-1/? ); mucosa touch afferents from bladder and anal canal: M (12.5 msec-1/? ); bladder afferents from stretch receptors measuring probably mural tension: S1 (42.5 msec-1/? ), ST (38/? ) and from flow receptors: S2 (12.5/? ); alpha-motoneurons: alpha 1 (60 msec-1/13.1 microns) [FF], alpha 11 (? /12.0) [F (int)], alpha 2 (50/10.2) [FR], alpha 3 (37/8.3) [S]; gamma-motoneurons: gamma beta (27 msec-1/7.2 microns), gamma 1 (20/6.7), gamma 2 (15/6.2). 3. Because of the strong temperature dependence of the conduction velocities a calibration of the structure of the velocities, which was almost independent of temperature, is defined by the condition that the alpha 2-motoneurons have the same peak conduction velocity then the secondary spindle afferents (SP2); the T1 touch afferents have about 10% lower velocity values. 4. Touch afferents and alpha-motoneurons had a different velocity-diameter relation, which indicated differences in the myelin sheath thickness and/or membrane properties. The conduction velocities of the touch afferents were more temperature-dependent than those of the alpha-motoneurons. Within the classes of touch afferents and alpha-motoneurons the temperature dependence increased towards smaller class peak values. 5. Through the use of action potential wave form comparisons it was possible to identify action potentials from single nerve fibres in each class of touch afferents (T0 to T4). Activity patterns of single touch units could partly be analysed. (ABSTRACT TRUNCATED AT 400 WORDS)
Lessons from applying SRGAN on Sentinel-2 images for LULC classification

Satellite images are commonly used to monitor land use land cover (LULC) changes. Unfortunately, publicly available images often lack the resolution required for detailed urban studies. In this study, we enhanced the resolution of Sentinel-2 (S2) satellite images from 10 meters to 2.5 meters using two super-resolution models: Real-SR and Real-ESRGAN. We tested the suitability of the enhanced images for LULC classification of an urban city, Singapore. From our results, colors have mostly been preserved and man-made objects have become sharper. However, the enhanced images also exhibit colour change, darkening, and salt-and-pepper effects. At this stage, there is no conclusive evidence that enhanced images can improve LULC classification. In fact, they have worsened classification accuracy by 17 - 30%, and the Kappa coefficient by 0.2 - 0.4. Although our application of super-resolution on LULC classification is not successful, it is a first attempt and could be further improved.
Dataset Creation for Segmentation to Enhance Visual Navigation in a Targeted Indoor Environment

To show the effectiveness of visual navigation based on the results of semantic segmentation, the authors participating in the Tsukuba Challenge, a well-known autonomous robot motion competition in Japan. The Tsukuba Challenge, which normally includes events only in outdoor environments, included in 2023 a new indoor environmental challenge, in which a robot must navigate a crowded shopping mall. In order to achieve visual navigation in an indoor environment, based on the results of segmentation, we needed to train a classifier suitable to navigate many simultaneously moving obstacles such as crowded people. For this purpose, this paper proposes a method for creating an appropriate dataset using a public dataset and modifying an existing dataset for an outdoor scenario: class labels are re-assigned considering the target environment. Experimental results using images captured at the course of the Extra Challenge in the Tsukuba Challenge 2023 showed that the proposed dataset enables the construction of a better classifier for semantic segmentation than only a public dataset.
Leucine metabolism in chronically hypoglycemic hypoinsulinemic growth-restricted fetal sheep.

We measured leucine flux rates during infusions of L-[1-14C]- and L-[1-1C]leucine in fetal sheep exposed to maternal insulin-induced hypoglycemia over the last 8 wk (40%) of gestation to determine effects of chronic glucose deficiency and hypoglycemia on fetal leucine metabolism. Compared with control fetuses (C, n = 5), hypoglycemic fetuses (HG, n = 8) weighed less (C, 3.43 +/- 0.07 kg; HG, 2.32 +/- 0.24 kg), had lower plasma glucose (C, 1.04 +/- 0.02 mM; HG, 0.59 +/- 0.01 mM), insulin (C, 48 +/- 6 pM; HG, 12 +/- 6 pM), and leucine concentrations (C, 195.6 +/- 8.3 microM; HG, 140.8 +/- 15.0 microM), lower rates of net leucine uptake (C, 4.2 +/- 0.6 mumol.min-1.kg-1; HG, 2.1 +/- 0.4 mumol.min-1.kg-1) and leucine flux into protein accretion (C, 2.8 +/- 0.2 mumol.min-1.kg-1; HG, 0.6 +/- 0.1 mumol.min-1.kg-1), and an increased rate of leucine release from protein breakdown (C, 1.1 +/- 0.1 mumol.min-1.kg-1; HG, 3.3 +/- 0.2 mumol.min-1.kg-1) (P < 0.05 for all). Plasma leucine disposal, flux into protein synthesis, and oxidation were not different between groups. We conclude that adaptations of fetal leucine metabolism to long-term hypoglycemia and decreased glucose apply represent diminished leucine uptake and increased leucine release from protein breakdown, which are associated with decreased incorporation of leucine into protein accretion and a slower rate of fetal growth.
Enhancing AI - Powered Malware Detection by Parallel Ensemble Learning

The efficacy of signature-based malware detection is limited in detecting novel forms of malware that are created through obfuscation and deformation methods. AI-powered techniques are extensively utilized to detect malware by employing classification models that analyze its behavioral patterns. This research presents a novel malware detection approach, referred to as PEMA, which integrates multiple effective AI models that are carefully chosen through an automated machine learning framework. Parallel ensemble learning with XGBoost, CatBoost, and LightGBM algorithms is employed by PEMA to make deep malware analysis faster and more accurate. Since PEMA was put together using three separate models, it can lessen the likelihood that it will be susceptible to poisoning and adversarial attacks. The performance of PEMA is verified by experimenting with well-known datasets, such as the EMBER 2017 and 2018, for evaluation purposes. As a result, PEMA attained Fl scores of 99.41% and 97.64%, respectively. Furthermore, it exhibits superior performance compared to other recent methods utilizing the same datasets and improves significant detection time.
Finding the Sweet Spot: How Human Fungal Pathogens Acquire and Turn the Sugar Inositol against Their Hosts

ABSTRACT Inositol is an essential nutrient with important structural and signaling functions in eukaryotes. Its role in microbial pathogenesis has been reported in fungi, protozoans, and eubacteria. In a recent article, Porollo et al. [mBio 5(6):e01834-14, 2014, doi:10.1128/mBio.01834-14] demonstrated the importance of inositol metabolism in the development and viability of Pneumocystis species—obligate fungal pathogens that remain unculturable in vitro. To understand their obligate nature, the authors used innovative comparative genomic approaches and discovered that Pneumocystis spp. are inositol auxotrophs due to the lack of inositol biosynthetic enzymes and that inositol insufficiency is a contributing factor preventing fungal growth in vitro. This work is in accord with other studies suggesting that inositol plays a conserved role in microbial pathogenesis. Inositol uptake and metabolism therefore may represent novel antimicrobial drug targets. Using comparative genomics to analyze metabolic pathways offers a powerful tool to gain new insights into nutrient utilization in microbes, especially obligate pathogens.
Pulmonary hypertension and right ventricular function in patients with COPD.

In 100 patients with chronic obstructive pulmonary disease (COPD), we found no significant correlation between simultaneous measurements of right ventricular ejection fraction, using radionuclide ventriculography, and pulmonary arterial pressure. There was, however, a weak but significant correlation between right ventricular ejection fraction and the pulmonary vascular resistance (r = 0.40, p less than 0.005). In 52 of these patients, 37 with pulmonary hypertension, right ventricular end-systolic volume index was 53 +/- 21 ml.m-2 and end-diastolic volume index was 86 +/- 27 ml.m-2, compared with a calculated mean of 33 ml.m-2 and 79 ml.m-2, respectively, for normal subjects. In 24 of these patients where the measurements were made at rest and on exercise, the mean right ventricular end-systolic volume increased from 66 +/- 20 ml.m-2 to 87 +/- 32 ml.m-2, with an increase in right ventricular systolic pressure from 28 +/- 9 mm Hg to 55 +/- 15 mm Hg. Analysis of the slope of the right ventricular end-systolic pressure volume relationship at rest and on exercise suggested relatively normal right ventricular contractility in the majority of patients. Thus, in these patients with stable COPD, despite the presence of pulmonary hypertension, right ventricular contractility remained relatively normal.
Sleep Apnea Detection: From Signal Processing to Deep Learning

One of the most widespread sleeping disorders is sleep apnea, which if left untreated, can lead to serious health issues, from hypertension to heart functioning. However, many people are not aware of their condition. The gold standard for identifying sleep apnea is overnight polysomnography at a specialized sleep laboratory. However, because experienced personnel must examine the entire data, these examinations are costly and have a limited number of beds. More patients could be examined if a more rapid diagnosis were made available by an automated detection system. Most autonomous sleep apnea detection algorithms use a collection of human-engineered features, which may miss crucial symptoms. In this study, we present an approach for automatically extracting features from respiratory data and detecting sleep apnea events based on an innovative CNN deep learning model. The algorithm is evaluated on the Apnea-ECG Database, and its results demonstrate sensitivity and accuracy per epoch. This study offers a powerful tool to help medical professionals detect sleep apnea more quickly.
Eﬀect of red clover undersowing on foliage, leaf gas exchange, and yield of spring wheat

e use of undersown plants, thanks to their favorable eﬀect on multiple environmental characteristics, is an environmentally-friendly crop cultivation method. A valuable undersown crop is red clover, which is oen grown with spring cereals. e aim of the study was to investigate the following issues: how will the cultivation of the spring wheat with undersown red clover (called intercropping) or growing spring wheat in a mixture with red clover aﬀect the leaf gas exchange in this cereal? Will the process be modified by plant densities? Will the cultivation of the spring wheat mixture change the foliage characteristics and the wheat yield? e following parameters were studied: stomatal conductance, intercellular CO 2 concentration, assimilation rate, transpiration rate, limitation value, photosynthetic water use eﬃciency, leaf area, leaf area index, leaf mass per area, chlorophyll content, N area , P area , leaf temperature, and grain yield. It was demonstrated that the mixed sowing variant reduced the photosynthesis parameters in the leaves of wheat. In this species, a significant reduction in the stomatal conductance, intercellular CO 2 concentration, net assimilation rate, and transpiration rate and an increase in the limitation value and water use eﬃciency were noted. e wheat leaves were characterized by a higher temperature in the object with the mixed sowing variant. e wheat grain yield in the mixture was 12.6–17.5% lower than in the sole crop.
A Framework for Abstractive Text Summarization Using Hugging Face Transformers

Discovering information on the internet is now simple, but finding the specific information you're looking for is now more challenging. It can be challenging to read all of the news articles and other texts that are available online. Thus, the need for effective text summarization has never been more important than it is in today's age of information abundance. Text summarization can be useful in reducing the length of these texts. Finding the information needed and comprehending the main ideas of a document are made simpler as a result. However, humans are unable to, or lack the resources to manually read and summarize the enormous number of documents, reports, or news articles that are generated each and every day. Thus, Automated, or Autonomous Text Summarization backed by Natural Language Processing (NLP) can significantly help in this regard. This model, Autonomous Document Summarization is one such model that uses Google's T5-Small to train a Sequence-to-Sequence (RNN) based model that can generate abstractive text summaries.
Efficient Optimization and Compression of Autonomous Vehicle Target Detection Models

Target detection is a crucial element of computer vision and often requires complex models and extensive computing resources. This inherent complexity poses significant challenges for use especially in the demanding field of autonomous vehicles. Thus, for the urgent need to reduce resource consumption while preserving the accuracy of target detection, we optimized the YOLO model by utilizing depthwise separable convolution. The model reduced resource usage without compromising detection performance. Additionally, quantitative compression techniques were incorporated to decrease the model size for easy deployment. The optimization strategy better-tailored target detection solutions to the specific requirements of autonomous vehicle applications.