Miracle-dz/UniFAR-FTUs huggingface dataset:
N EURAL A LL -P AIRS S HORTEST P ATH FOR R EINFORCEMENT L EARNING

Having an informative and dense reward function is an important requirement to efficiently solve goal-reaching tasks. While the natural reward for such tasks is a binary signal indicating success or failure, providing only a binary reward makes learning very challenging given the sparsity of the feedback. Hence, introducing dense rewards helps to provide smooth gradients. However, these functions are not readily available, and constructing them is difficult, as it often requires a lot of time and domain-specific knowledge, and can unintentionally create spurious local minima. We propose a method that learns neural all-pairs shortest paths, used as a distance function to learn a policy for goal-reaching tasks, requiring zero domainspecific knowledge. In particular, our approach includes both a self-supervised signal from the temporal distance between state pairs of an episode, and a metricbased regularizer that leverages the triangle inequality for an additional connectivity information between state triples. This dynamical distance can be either used as a cost function, or reshaped as a reward, and, differently from previous work, is fully self-supervised, compatible with off-policy learning and robust to local minima.
Exploiting Multiple Guidance from 3DMM for Face Reenactment

Face reenactment aims to transfer the expression and pose from a driving face to a source face. Great progress has been made on this task with the recent success of deep generative models. However, it remains challenging when the two faces hold a significant pose discrepancy. Identity change and image distortion arise as pose discrepancy increases. To tackle these problems, we propose to exploit multiple guidance derived from the 3D morphable face model (3DMM). Firstly, a precomputed optical flow is utilized to guide the estimation of motion fields. Secondly, a precomputed occlusion map is utilized to guide the perception of occluded areas. Finally, a rendered image is utilized to guide the restoration of missing contents. We present a new reenactment framework to integrate the above guidance and generate high-quality results. Extensive experiments show the superior performance of our framework compared with several state-of-the-art methods. Ablation studies demonstrate the effectiveness of exploiting multiple guidance from the 3DMM.
Microbial Biomarkers Identification for Human Gut Disease Prediction using Microbial Interaction Network Embedded Deep Learning

—Human gut microorganisms are crucial in regulating the immune system. Disruption of the healthy relationship between the gut microbiota and gut epithelial cells leads to the development of diseases. Inflammatory Bowel Disease (IBD) and Colorectal Cancer (CRC) are gut-related disorders with complex pathophysiological mechanisms. With the massive availability of microbiome data, computer-aided microbial biomarker discovery for IBD and CRC is becoming common. However, microbial interactions were not considered by many of the existing biomarker identification methods. Hence, in this study, we aim to construct a microbial interaction network (MIN). The MIN accounts for the associations formed and interactions among microbes and hosts. This work explores graph embedding feature selection through the construction of a sparse MIN using MAGMA embedded into a deep feedforward neural network (DFNN). This aims to reduce dimensionality and select prominent features that form the disease biomarkers. The selected features are passed through a deep forest classifier for disease prediction. The proposed methodology is experimentally cross-validated (5-fold) with different classifiers, existing works, and different models of MIN embedded in DFNN for the IBD and CRC datasets. Also, the selected biomarkers are verified against biological studies for the IBD and CRC datasets. The highest achieved AUC, accuracy, and f1-score are 0.863, 0.839, and 0.897, respectively, for the IBD dataset and 0.837, 0.768, and 0.757, respectively, for the CRC dataset. As observed, the proposed method is successful in selecting a subset of informative and prominent biomarkers for IBD and CRC.
Sweet Potato as a Key Crop for Food Security under the Conditions of Global Climate Change: A Review

Sweet potato is one of the most economically important crops for addressing global food security and climate change issues, especially under conditions of extensive agriculture, such as those found in developing countries. However, osmotic stress negatively impacts the agronomic and economic productivity of sweet potato cultivation by inducing several morphological, physiological, and biochemical changes. Plants employ many signaling pathways to respond to water stress by modifying their growth patterns, activating antioxidants, accumulating suitable solutes and chaperones, and making stress proteins. These physiological, metabolic, and genetic modifications can be employed as the best indicators for choosing drought-tolerant genotypes. The main objective of sweet potato breeding in many regions of the world, especially those affected by drought, is to obtain varieties that combine drought tolerance with high yields. In this regard, the study of the physiological and biochemical features of certain varieties is important for the implementation of drought resistance measures. Adapted genotypes can be selected and improved for particular growing conditions by using suitable tools and drought tolerance-related selection criteria. By regulating genetics in this way, the creation of drought-resistant varieties may become cost-effective for smallholder farmers. This review focuses on the drought tolerance mechanisms of sweet potato, the effects of drought stress on its productivity, its crop management strategies for drought mitigation, traditional and molecular sweet potato breeding methods for drought tolerance, and the use of biotechnological methods to increase the tolerance of sweet potato to drought.
AGE-RELATED CHANGES IN THE HOFFMANN-REFLEX PATHWAY OF THE FLEXOR CARPI RADIALIS

Ageing is accompanied by numerous changes within the sensory and motor component of the muscle spindle pathway. To further document these age-related changes, this study compared the characteristics of the Hoffmann (H) reflex and M wave, evoked with several pulse durations, between young and old adults. The H-reflex and M-wave recruitment curves were analysed from recordings performed at rest in the flexor carpi radialis of 12 young (21-36 yrs) and 12 old adults (62-80 yrs). For each pulse duration (0.05-ms, 0.2-ms, and 1-ms), the maximal H-reflex amplitude (HMAX), the associated M wave (MHmax), and the H-reflex amplitude for a stimulus intensity evoking an M-wave of 5% MMAX (HM5%) were measured. The strength-duration time constant and response threshold were estimated from the charge/stimulus-duration relation for H reflex and M wave. The main results indicate that varying pulse duration mainly induces similar effect on H-reflex and M-wave recruitment curves between young and old adults. However, regardless of pulse duration, old adults had lesser HMAX (p = 0.029) but greater HM5% (p<0.001) and MHmax (p<0.001). The strength-duration time constant was lesser in old than young adults for the H reflex (p=0.048) but not the M wave (p=0.21). The H-reflex and M-wave response thresholds were greater in old than young adults (p=0.003). These results suggest greater age-related changes in the sensory than motor component of the H-reflex pathway, which may be indicative of a greater loss of sensory than motor axons or alterations of synapses between Ia afferents and motor neurones. Submitted to: European Journal of Neuroscience December 15th 2022 Age-related changes in the Hoffmann-reflex pathway of the flexor carpi radialis Mélanie Henry, Jacques Duchateau & Stéphane Baudry Laboratory of Applied Biology, Research Unit in Applied Neurophysiology (LABNeuro), ULB-Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), 808 route de Lennik, 1070 Brussels, Belgium Running title: Hoffmann reflex and ageing Corresponding author: Mélanie Henry, Ph.D. Laboratory of Applied Biology, Research Unit in Applied Neurophysiology (LABNeuro) Faculty for Motor Sciences Université Libre de Bruxelles 808, route de Lennik, CP 640, 1070 Brussels, Belgium Email : Melanie.Henry@ulb.be
New Insights in Phenothiazinium-Mediated Photodynamic Inactivation of Candida Auris

In recent years, Candida auris has emerged as a hazardous hospital-acquired pathogen. Its resistance to antifungal treatments makes it challenging, requiring new approaches to manage it effectively. Herein, we aimed to assess the impact of photodynamic inactivation mediated by methylene blue (MB-PDI) or 1,9-dimethyl MB (DMMB-PDI) combined with a red LED against C. auris. To evaluate the photoinactivation of yeasts, we quantified colony-forming units and monitored ROS production. To gain some insights into the differences between MB and DMMB, we assessed lipid peroxidation (LPO) and mitochondrial membrane potential (ΔΨm). After, we verified the effectiveness of DMMB against biofilms by measuring metabolic activity and biomass, and the structures were analyzed through scanning electron microscopy and optical coherence tomography. We also evaluated the cytotoxicity in mammalian cells. DMMB-PDI successfully eradicated C. auris yeasts at 3 μM regardless of the light dose. In contrast, MB (100 μM) killed cells only when exposed to the highest dose of light. DMMB-PDI promoted higher ROS, LPO and ΔΨm levels than those of MB. Furthermore, DMMB-PDI was able to inhibit biofilm formation and destroy mature biofilms, with no observed toxicity in fibroblasts. We conclude that DMMB-PDI holds great potential to combat the global threat posed by C. auris.
Foundation for the redefinition of the kilogram

The redefinition of the kilogram, expected to be approved in the autumn of 2018, will replace the artefact definition of the kilogram by assigning a fixed numerical value to a fundamental constant of physics. While the concept of such a change is pleasing, the mass community as represented by the Consultative Committee for Mass and Related Quantities (CCM) was faced with a number of technical and procedural challenges that needed to be met in order to profit in any meaningful way from the proposed change. In the following, we outline these challenges and how the CCM has met and is meeting them. We focus especially on what the mass community requires of the new definition and the process by which the CCM has sought to ensure that these needs will be met.
Multi-modal Cell Segmentation based on U-Net++ and Attention Gate

Cell segmentation is one of the most fundamental tasks in the areas of medical image analysis, which assists in cell recognition and number counting. The segmentation results obtained will be poor due to the diverse cell morphology and the frequent presence of impurities in the cell pictures. In order to solve the cell segmentation which are from a competition held by Neural Information Processing Systems(NIPS), we present a network that combines attention gates with U-Net++ to segment varied sizes of cells. Using the feature filtering of the attention gate can adjust the convolution block’s output, so as to improve the segmentation effect. The F1 score of our method reached 0.5874, Rank Running Time get 2.5431 seconds.
Satisfying strict deadlines for cellular Internet of Things through hybrid multiple access

Latency-constrained aspects of cellular Internet of Things (IoT) applications rely on Ultra-Reliable and Low Latency Communications (URLLC), which highlight research on satisfying strict deadlines. In this study, we address the problem of latency-constrained communications with strict deadlines under average power constraint using Hybrid Multiple Access (MA), which consists of both Orthogonal MA (OMA) and power domain Non-Orthogonal MA (NOMA) as transmission scheme options. We aim to maximize the timely throughput, which represents the average number of successfully transmitted packets before dead-line expiration, where expired packets are dropped from the buffer. We use Lyapunov stochastic optimization methods to develop a dynamic power assignment algorithm for minimizing the packet drop rate while satisfying time average power constraints. Moreover, we propose a flexible packet dropping mechanism called Early Packet Dropping (EPD) to detect likely to become expired packets and drop them immediately. Numerical results show that Hybrid MA improves the timely throughput compared to conventional OMA by up to 46 % and on average by more than 21 % . With EPD, these timely throughput gains improve to 53 % and 24 . 5 % , respectively.
Emergence of Carbapenem-Resistant Gram-Negative Isolates in Hospital Settings in Djibouti

Introduction: The antimicrobial resistance (AMR) of bacteria is increasing rapidly against all classes of antibiotics, with the increasing detection of carbapenem-resistant isolates. However, while growing prevalence has been reported around the world, data on the prevalence of carbapenem resistance in developing countries are fairly limited. In this study, we investigated and determined the resistance rate to carbapenems among multidrug-resistant Gram-negative bacteria (MDR-GNB) isolated in Djibouti and characterized their resistance mechanisms. Results: Of the 256 isolates, 235 (91.8%) were identified as Gram-negative bacteria (GNB). Of these GNBs, 225 (95.7%) isolates exhibited a multidrug resistance phenotype, and 20 (8.5%) isolates were resistant to carbapenems, including 13 Escherichia coli, 4 Acinetobacter baumannii, 2 Klebsiella pneumoniae and 1 Proteus mirabilis. The most predominant GNB in this hospital setting were E. coli and K. pneumoniae species. Carbapenemase genes such as blaOXA-48 and blaNDM-5 were identified, respectively, in six and four E. coli isolates, whereas the carbapenemase blaNDM-1 was identified in three E. coli, two K. pneumoniae, one P. mirabilis and one A. baumannii. Moreover, three A. baumannii isolates co-hosted blaOXA-23 and blaNDM-1. Materials and Methods: A total of 256 clinical strains collected between 2019 and 2020 were identified using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF). Antibiotic susceptibility testing was performed using disk diffusion and E-test methods. Real-time polymerase chain reaction (RT-PCR), standard PCR and sequencing were used to investigate genes encoding for extended-spectrum-β-lactamases, carbapenemases and colistin resistance genes. Conclusions: We report, for the first time, the presence of MDR-GNB clinical isolates and the emergence of carbapenem-resistant isolates in Djibouti. In addition to performing antimicrobial susceptibility testing, we recommend phenotypic and molecular screening to track the spread of carbapenemase genes among clinical GNB isolates.
Enhancing IoT Security with Deep Stack Encoder using Various Optimizers for Botnet Attack Prediction

—The Internet of Things (IoT) connects different sensors, devices, applications, databases, services, and people, bringing improvements to various aspects of our lives, such as cities, agriculture, finance, and healthcare. However, guaranteeing the safety and confidentiality of IoT data which has become rich in its quality requires careful preparation and awareness. Machine learning techniques are used to predict different types of cyber-attacks, including denial of service (DoS), botnet attacks, malicious operations, unauthorized control, data probing, surveillance, scanning, and incorrect setups. In this study, for improving security of IoT data, a method called Deep Stack Encoder Neural Network to predict botnet attacks by using N-BaIoT bench mark dataset is employed. In this study a new framework is introduced which will improve the performance of prediction rate to 94.5%. To evaluate the performance of this method assessment criteria are adopted like accuracy, precision, recall, and F1 score, comparing it with other models. From the optimizers of Adam, Adagrad and Adadelta, Adam optimizer gave the highest accuracy with relu activation function.
A method to identify the weakest link in urban water systems

Urban water systems are composed of subsystems (gully pots, storm sewers and surface water), each with its own system dynamics. Engineers balance the functioning of the systems based on storage and discharge capacity of the subsystems. The load on, and capacity are influenced by e.g. ageing, urbanization, climate change. Consequently, the performance of and demand put on subsystems varies over time, potentially resulting in disturbances in the balance between the storage and discharge capacity of the subsystems. The Graph Based Weakest Link Method (GBWLM) is developed to analyse the behaviour of urban water systems to identify potential limitations due to deterioration, and/or changes in load. The proposed GBWLM is based on the structure of the networks. In addition, Graph theory is applied as alternative for series of hydraulic calculations. The GBWLM allows for an integrated performance assessments of urban water systems using multi-decades rainfall series. The results are sufficiently accurate to be able to determine the extent and frequency of urban flooding in order to compare the performance of the subsystems for various degrees of available discharge capacity.
Probing Representations of Numbers in Vision and Language Models

The ability to represent and reason about numbers in different contexts is an important aspect of human and animal cognition. Literature in numerical cognition posits the existence of two number representation systems: one for representing small, exact numbers, which is largely based on visual processing, and another system for representing larger, approximate quantities. In this work, we investigate number sense in vision and language models by examining learned representations and asking: What is the structure of the space representing numbers? Which modality contributes mostly to the representation of a number? While our analyses reveal that small numbers are processed differently from large numbers, as in biological systems, we also found a strong linguistic contribution in the structure of number representations in vision and language models, highlighting a difference between representations in biology and artificial systems
Training higher education students for employability skills: Is it worth it?

This paper reflects on the importance of employability skills for higher education students in the present and future working environment to determine the impact these skills may have on student competitiveness and satisfaction. The study focused on university students in the Madrid region during their final academic year who participated in a teaching activity that launched real business challenges. We created a model that analyzed the impact of employability skills on student competitiveness and satisfaction and the mediating role of competitiveness in two different moments (before and during the COVID-19 pandemic). Empirical findings suggest that students' improved employability skills increased their competitiveness and satisfaction, with competitiveness as a mediating factor. Thus, academic authorities must focus on students acquiring employability skills, which are among the most valuable in the labour market and positively impact student competitiveness and satisfaction. This way, future graduates can work successfully in a changing and demanding world and find positions aligned with their training. In contrast to some studies that place the teacher as the research target, ours focused on the student and the impact these skills have on the variables mentioned.
Anti-windup design with guaranteed regions of stability: an LMI-based approach

This paper addresses the design of anti-windup gains for obtaining larger regions of stability for linear systems with saturating inputs. Considering that a linear dynamic output feedback has been designed to stabilize the linear system (without saturation), a method is proposed for designing an anti-windup gain that maximizes the estimation of the basin of attraction of the closed-loop system. It is shown that the closed-loop system obtained from the controller plus the anti-windup gain can be modeled by a linear system with a deadzone nonlinearity. A modified sector condition is then used to obtain stability conditions based on quadratic Lyapunov functions. Different from previous works, these conditions are directly in LMI form. Several examples illustrate the effectiveness of the proposed design technique when compared with the previous ones.
A Music Similarit y Function Based on the Fisher Kernels

Music-similarity computation is an essential building block for browsing, retrieval, and indexing of digital music archives. This pa- per presents a music similarity function based on the Fisher-vector rep- resentation of the spectral features extracted from a song. The distance between the Fisher vectors of two songs is used as the similarity of the two songs. The Fisher vector has a closed-form representation and can be readily incorporated with simple vector distance measures. Experimen- tal results show that the Fisher-vector representation of the auditory features is promising for the music-similarity computation.
Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory

This paper proposes a practical solution to address the energy management issue in fuel cell hybrid electric vehicles (FCHEVs). This solution revolves around a powertrain system that contains a fuel cell (FC) as the main supply, a photovoltaic cell (PC) as the secondary energy source, and a battery bank (Batt) as backup storage to compensate for the FC’s low response rate. The energy in this hybrid powertrain system alternated between the designated elements and the load via a DC bus, and to maintain a stable output voltage, the DC link was adjusted using a nonlinear approach that is based on the flatness theory and the nonlinear autoregressive moving average (NARMA-L2) neuro-controller. As for the current regulation loops, the sliding mode technique was employed to attain the high dynamic of the reference signals produced by the energy manager loop. To validate the accuracy of the proposed energy management approach (EMA), a test bench was equipped with digital, electronic circuits and a dSPACE DS-1104 unit. This experimental bench contained a fuel cell emulator FC of 1200 W and 46 A, lithium-ion batteries of 24 V, and a solar source capable of 400 W. The obtained results, indeed, attested to the validity of the approach used, yielding a notable performance during multiple charge variations. This ultimately demonstrated that the management approach enhanced the efficiency of the hybrid powertrain.
A Nonparametric Approach Using Dirichlet Process for Hierarchical Generalized Linear Mixed Models

In this paper, we propose a nonparametric approach using the Dirichlet processes (DP) as a class of prior distributions for the distribution G of the random effects in the hierarchical generalized linear mixed model (GLMM). The support of the prior distribution (and the posterior distribution) is large, allowing for a wide range of shapes for G. This provides great flexibility in estimating G and therefore produces a more flexible estimator than does the parametric analysis. We present some computation strategies for posterior computations involved in DP modeling. The proposed method is illustrated with real examples as well as simulations.