Miracle-dz/UniFAR-FTUs huggingface dataset:
Sélection des rhizobactéries phytostimulatrices par la plante : impact sur la distribution des propriétés phytobénéfiques chez les Pseudomonas fluorescents

Les plantes interagissent en permanence avec une grande diversite de microorganismes qu'elles selectionnent entre autre au niveau de leurs racines. Certaines bacteries, qualifiees de PGPR (Plant Growth-Promoting Rhizobacteria), sont capables de stimuler la croissance et la sante de la plante, grâce a l'expression d'une large panoplie de proprietes phytobenefiques. L'hypothese actuelle serait que les PGPR possedant un nombre maximal de ces fonctions auraient un plus fort impact benefique sur le vegetal. Toutefois, l'occurrence de ces PGPR multifonctions dans la rhizosphere n'est pas connue. De plus, les possibles interactions entre proprietes co-occurrentes au sein d'une meme PGPR et la resultante de ces interactions sur la plante sont relativement peu documentees. Dans ce contexte, ce projet de these a eu comme objectif general de mieux comprendre la distribution des proprietes phytobenefiques chez un groupe bacterien possedant un large eventail de ses proprietes, celui des Pseudomonas fluorescents et d'evaluer chez une PGPR modele multifonction, Pseudomonas fluorescens F113, si des interactions fonctionnelles existent entre ces proprietes et determiner leurs contributions respectives a l'effet phytostimulateur. L'etude d'environ 700 isolats de Pseudomonas provenant de la rhizosphere de deux cultivars de mais et de sol nonrhizospherique, a mis en evidence que ces cultivars selectionnent majoritairement des Pseudomonas fluorescents (i) arborant un nombre reduit de proprietes phytobenefiques (1 a 5 proprietes) et (ii) appartenant a des sous-groupes taxonomiques particuliers. Par ailleurs, l'etude de genomique comparative que nous avons menee au sein du groupe des Pseudomonas fluorescents souligne le lien entre phylogenie et les profils de proprietes phytobenefiques possedees par les Pseudomonas. Enfin, chez P. fluorescens F113, les proprietes co-occurrentes ne contribuent pas de facon egale a l'effet observe sur la plante. L existence d'interactions entre ces proprietes co-occurrentes a egalement ete mise en evidence. De ce reseau d'interactions, resultera un effet benefique observe sur la plante particulier. Ces resultats sont importants pour mieux comprendre la place et le role de ces PGPR multifonctions dans la rhizosphere. Ils permettent d'approfondir nos connaissances a propos du fonctionnement ecologique des Pseudomonas fluorescents au sein du rhizomicrobiote
Robust Motion Planning for Uncertain Systems With Disturbances Using the Invariant-Set Motion Planner

The invariant-set motion planner uses a collection of safe sets to find a collision-free path through an obstacle-filled environment [1]–[4]. This article extends the invariant-set motion planner to systems with persistently varying disturbances and parametric model uncertainty. This is accomplished by replacing the previously used positive invariant sets with robust positive invariant sets. Since the model uncertainty obfuscates the relationship between the invariant sets in the state space, and the references and obstacles in the output space, we reformulate the dynamics in velocity form so that the system output appears directly in the modified system state. Since the persistently varying disturbances will prevent the closed-loop system from converging to the desired reference, we introduce a new robust connection rule where references are connected when the invariant set of one reference contains the minimal volume robust invariant-set of another. In addition, we bound the time required to transition between invariant sets to ensure safety when the obstacles are moving. By parameterizing the invariant sets using a precomputed input-to-state Lyapunov function, we reduce the real-time computational complexity of our motion planner. The robust invariant-set motion planner is demonstrated for an automated highway driving case study.
Leakage Current in Fast Recovery Diode Suppressed by Low Temperature Supercritical Fluid Treatment Process

Fast recovery diodes (FRD) are widely used in transformer circuits for fast switching. Generally, in order to reduce forward voltage to achieve fast switching, the doping concentration is increased to reduce on-state resistance (Ron). This, however, will also increase reverse bias leakage as well as recovery time, resulting in a large amount of excess power consumption. In this letter, we use a unique low temperature supercritical fluid treatment process (LTSCF) to reduce this reverse bias leakage current, without affecting the on-state characteristics or switching characteristics. Experimental results show that the device’s average reverse bias leakage was reduced by 65% after LTSCF.
Adaptive Constrained Independent Vector Analysis: An Effective Solution for Analysis of Large-Scale Medical Imaging Data

There is a growing need for flexible methods for the analysis of large-scale functional magnetic resonance imaging (fMRI) data for the estimation of global signatures that summarize the population while preserving individual-specific traits. Independent vector analysis (IVA) is a data-driven method that jointly estimates global spatio-temporal patterns from multi-subject fMRI data, and effectively preserves subject variability. However, as we show, IVA performance is negatively affected when the number of datasets and components increases especially when there is low component correlation across the datasets. In this article, we study the problem and its relationship with respect to correlation across the datasets, and propose an effective method for addressing the issue by incorporating reference information of the estimation patterns into the formulation, as a guidance in high dimensional scenarios. Constrained IVA (cIVA) provides an efficient framework for incorporating references, however its performance depends on a user-defined constraint parameter, which enforces the association between the reference signals and estimation patterns to a fixed level. We propose adaptive cIVA (acIVA) that tunes the constraint parameter to allow flexible associations between the references and estimation patterns, and enables incorporating multiple reference signals, without enforcing inaccurate conditions. Our results indicate that acIVA can reliably estimate high-dimensional multivariate sources from large-scale simulated datasets, when compared with standard IVA. It also successfully extracts meaningful functional networks from a large-scale fMRI dataset for which standard IVA did not converge. The method also efficiently captures subject-specific information, which is demonstrated through observed gender differences in spectral power, higher spectral power in males at low frequencies and in females at high frequencies, within the motor, attention, visual and default mode networks.
DC Characterization and Fast Small-Signal Parameter Extraction of Organic Thin Film Transistors With Different Geometries

Organic Devices offer low-cost manufacturing and better flexibility, sustainability and solution-processability than their Si-based MOS counterparts, which make them suitable for new applications where those characteristics are an advantage. However, organic device performance is still far from that provided by CMOS technology and many issues are still unclear. In this work, a performance comparison is made between Interdigitated and Corbino geometries of Organic Thin Film Transistors (OTFT), with different areas but fabricated with identical stack materials and techniques. To this end, I-V characteristics and C-V curves of the OTFTs were measured and a Common-Source circuit was proposed and implemented for extracting relevant electrical parameters of the devices, through a standard small signal analysis. The parameter extraction methodology in the frequency domain proposed allows rapid testing of the device/circuit performance of this technology, which for the first time is applied to organic devices. Results show that despite the large voltages, organic transistors exhibit similar channel dimension dependencies to MOS devices and can be also described by the same small signal model.
User-Friendly Privacy-Preserving Photo Sharing on Online Social Networks

A variety of online social networking (OSNs) services facilitates users to share a huge amount of their personal information such as photos. On the other hand, users may unintentionally reveal their photos to other audiences. Therefore, users of OSNs strive to protect their photos with existing privacy management mechanisms. Moreover, users need adequate feedback to understand their privacy preferences in photo sharing services. In this paper, the authors present a new visualization mechanism called Visual Privacy Management Policy (VPMP) in order to simplify the configuring privacy settings when users of OSNs share their photos with others. Also, this solution aims to address the shortcomings of privacy settings policies of photo sharing in OSNs. Furthermore, the solution employs a social graph and circles for visualizing when, where, how and with whom users will share their photos. So, this solution provides users a comprehensible and effective way to determine their privacy settings. The authors evaluate the visual privacy management policy (VPMP) for photo sharing with a user study that compared our solution with the user interface of photo-sharing on Facebook. The obtained findings provide quantitative evidence regarding the applicability of VPMP in terms of usability and privacy protection.
Modelling a Racetrack-Shaped Microring-Resonator-Based Add-Drop Filter in GaN/Sapphire

We proposed a microring-resonator-based add-drop filter design in a Gallium Nitride/Sapphire. The structure arranged into a single racetrack-shaped microring resonator and two bus waveguides. Simulation results showed that the proposed structure exhibited an FSR of 25 nm, FWHM of 0.2 nm, and resonance loss of 1.6 dB at optical communication Cband wavelength. It also has an on-off ratio of 35 dB, which is enough to suppress crosstalk from other channels in the WDM system.
Research on Computer Aided Detection and Segmentation of Pulmonary Nodules Based on Deep Learning and Generative Adversarial Networks

Pulmonary tuberculosis assisted diagnosis technology mainly faces the problems of poor resistance to overfitting due to the small training dataset and the low accuracy of lung tissue interference detection. This paper mainly solves the current problems from two aspects. First, this paper uses the RetinaNet[l] network structure combined with the DenseNet [2] feature extraction network to perform image detection on lung CT targets. Second, this paper enhance the training dataset by increasing the size of the dataset with the method of generative adversarial network to reduce the phenomena such as overfitting in experimental results. After testing on the existing dataset, it was found that the accuracy rate of target detection based on the improved model is 87.32%, which is 70.1% on the original model. Besides, the accuracy rate can be increased to 90.1% by the enhancement of the dataset using generative adversarial network.
Recommendation System Based on Heterogeneous Feature: A Survey

Recommendation systems have become an important field of research in computer science and physics. In recent years, breakthroughs have been achieved in social, biological, and research cooperation networks. With the popularization of big data and deep learning technology development, graph structures are increasingly being used to represent large-scale and complex data in the real world. In this paper, we reviewed the progress made in recommendation systems research in the past 20 years and comprehensively classified recommender systems based on the heterogeneous input features. We introduced layering in the classification of recommendation systems. Furthermore, we proposed a new hierarchical classification model of recommendation systems divided into three layers: feature input, feature learning, and output layers. In the feature learning layer, existing recommendation systems were divided into graph-based, text-based, behavior-based, spatiotemporal-based, and hybrid recommendation systems. Additionally, we provided evaluation index, open-source implementation, experimental comparison and the relative merits for each recommendation method. Subsequently, future development directions of recommendation systems are discussed.
A Bayesian Probabilistic AOA Localization Algorithm

In the application of fifth-generation (5G) technology, the research of large-scale antenna technology and millimeter-wave beamforming technology has promoted the improvement of positioning accuracy based on the angle of arrival (AOA) method. The AOA positioning only needs to measure the incoming wave direction of the transmitter, eliminating the dependence on signal time synchronization. Due to multipath and non-line-of-sight (NLOS) propagation, the accuracy of AOA measurements is severely affected. Therefore, in most cellular systems, AOA is often not used as the main positioning parameter, but as an auxiliary positioning parameter. In this paper, we propose a positioning algorithm based on Bayesian probability. This method not only solves the multi-value problem in AOA positioning but also further eliminates the AOA deviation caused by NLOS. The method proposed in this paper can adaptively learn channel bias and modify the AOA measurement. Experiments show that our proposed method significantly improves the positioning accuracy, even if NLOS errors occur.
Multi-model Fusion on Real-time Drowsiness Detection for Telemetric Robotics Tracking Applications

Drowsiness of driver is one of the common causes resulting in road crashes. According to the research, there have been twenty percent of the road accidents which are related to the drowsiness of drivers. Nowadays, with the development technology, various approaches are introduced to detect the drowsiness of drivers. In this paper, we propose a multi-model fusion system which is composed of the three models to capture driver’s face and detect drowsiness in the real-time for telemetric robotics tracking applications. The sensor device we used is an RGB camera which is mounted in front of driver to obtain the facial image. Then, we combine the results based on the state of the eye blink, yawn and head deviation to determine whether the driver is drowsy. We test our models to obtain the weighting factors in drowsy value. In the experiment, we show that our system has the high accuracy of detection.
Anti-D chain: A lightweight DDoS attack detection scheme based on heterogeneous ensemble learning in blockchain

With rapid development of blockchain technology, blockchain and its security theory research and practical application have become crucial. At present, a new DDoS attack has arisen, and it is the DDoS attack in blockchain network. The attack is harmful for blockchain technology and many application scenarios. However, the traditional and existing DDoS attack detection and defense means mainly come from the centralized tactics and solution. Aiming at the above problem, the paper proposes the virtual reality parallel an-ti-DDoS chain design philosophy and distributed anti-D Chain detection framework based on hybrid ensemble learning. Here, AdaBoost and Random Forest are used as our ensemble learning strategy, and some different lightweight classifiers are integrated into the same ensemble learning algorithm, such as CART and ID3. Our detection framework in block-chain scene has much stronger generalization performance, universality and complementarity to identify accurately the onslaught features for DDoS attack in P2P network. Extensive experimental results confirm that our distributed heterogeneous anti-D chain detection method has better performance in six important indicators (such as Precision, Recall, F-Score, True Positive Rate, False Positive Rate, and ROC curve).
1.48 MV⋅cmˉ¹/0.2 mΩ⋅cm² GaN Quasi-Vertical Schottky Diode via Oxygen Plasma Termination

In this letter, a high-performance GaN quasi-vertical Schottky barrier diode (SBD) is demonstrated via oxygen plasma termination. A specific on-resistance of 0.2 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot $ </tex-math></inline-formula>cm<sup>2</sup>, turn-on voltage of 0.71 V, and breakdown voltage of 193 V are achieved with a GaN SBD of <inline-formula> <tex-math notation="LaTeX">$1.3~\mu \text{m}$ </tex-math></inline-formula> drift layer. The average breakdown electric field is calculated to be about 1.48 MV/cm, which is much higher than the state-of-art value for GaN vertical SBDs. The temperature-dependent forward and reverse <italic>I-V</italic> characteristics confirm the high-temperature stability of the oxygen plasma treated SBDs. X-ray spectroscopy and Kelvin Probe Force Microscopy reveal that the surface potential is increased after the oxygen plasma treatment, which results in the suppression of the leakage current and improvement of the breakdown electric field.
Chromosome-level genome of Poropuntius huangchuchieni provides a diploid progenitor-like reference genome for the allotetraploid Cyprinus carpio

The diploid Poropuntius huangchuchieni in the cyprinid family, which is distributed in the Mekong and Red River basins, is one of the most closely related diploid progenitor-like species of allotetraploid common carp. Therefore, the P. huangchuchieni genome is essential for polyploidy evolution studies in Cyprinidae. Here, we report a chromosome-level genome assembly of P. huangchuchieni by integrating Oxford Nanopore and Hi-C technology. The assembled genome size was 1021.38 Mb, 895.66 Mb of which was anchored onto 25 chromosomes with a N50 of 32.93 Mb. The genome contained 486.28 Mb repetitive elements and 24,099 protein-coding genes. Approximately 95.9% of the complete BUSCOs were detected, suggesting a high completeness of the genome. Evolutionary analysis revealed that P. huangchuchieni diverged from Cyprinus carpio at approximately 12 Mya. Genome comparison between P. huangchuchieni and the B subgenome of C. carpio provided insights into chromosomal rearrangements during the allotetraploid speciation. With the complete gene set, 17,474 orthologous genes were identified between P. huangchuchieni and C. carpio, providing a broad view of the gene component in the allotetraploid genome, which is critical for future genomic and genetic analyses. The high-quality genomic dataset created for P. huangchuchieni provides a diploid progenitor-like reference for the evolution and adaptation of allotetraploid carps.
A distributed computing framework based on lightweight variance reduction method to accelerate machine learning training on blockchain

To security support large-scale intelligent applications, distributed machine learning based on blockchain is an intuitive solution scheme. However, the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly, which highly demand on computing and memory resources. To overcome the challenges, we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method, which is a lightweight, few additional cost and parallelized scheme for the model training process. To validate the claims, we have conducted several experiments on multiple classical datasets. Results show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.
Performance Evaluation of Model-Based Gait on Multi-View Very Large Population Database With Pose Sequences

Model-based gait recognition is considered to be promising due to the robustness against some variations, such as clothing and baggage carried. Although model-based gait recognition has not been fully explored due to the difficulty of human body model fitting and the lack of a large-scale gait database, recent progress in deep learning-based approaches to human body model fitting and human pose estimation is mitigating the difficulty. In this paper, we, therefore, address the remaining issue by presenting a large-scale human pose-based gait database, OUMVLP-Pose, which is based on a publicly available multi-view large-scale gait database, OUMVLP. OUMVLP-Pose has many unique advantages compared with other public databases. First, OUMVLP-Pose is the first gait database that provides two datasets of human pose sequences extracted by two standard deep learning-based pose estimation algorithms, OpenPose and AlphaPose. Second, it contains multi-view large-scale data, i.e., over 10,000 subjects and 14 views for each subject. In addition, we also provide benchmarks in which different kinds of gait recognition methods, including model-based methods and appearance-based methods, have been evaluated comprehensively. The model-based gait recognition methods have shown promising performances. We believe this database, OUMVLP-Pose, will greatly promote model-based gait recognition in the next few years.
Sensitivity Analysis Based on Neural Network for Optimizing Device Characteristics

This letter presents a novel method for the sensitivity analysis between a process parameter and an electrical characteristic using the gradient of a neural network (NN). As devices become scaled and new emerging devices appear, it becomes more complex and the development of a SPICE model takes considerable time. Sensitivity analysis based on NN can accurately obtain the sensitivity even if the data are correlated with each other and have a non-linear relationship. The proposed method can be used to model the device characteristics and optimize process control through component analysis. It is verified using a feedback field-effect transistor (FBFET), one of the emerging neuromorphic devices. We execute experiments with 1055 TCAD simulations calibrated based on 33 measurement data for various process parameters and bias combinations and compare the results with a general linear model (GLM). In this work, we select 7 input parameters and extract voltage threshold ( ${V} _{\text {th}}$ ) and on-current ( ${I} _{\text {ON}}$ ), which are key characteristics of FBFET, as output parameters, and analyze the sensitivity with our method and provide a process control solution. This letter shows an important role in filling the gap between the emerging device proposal and the development of the SPICE model.
High-Gain Transparent Inverters Based on Deuterated ZnO TFTs Fabricated by Atomic Layer Deposition

Three types of highly transparent inverter circuits, enhancement/depletion, pseudo-CMOS and feedback structures, are fabricated by using zinc-oxide thin film transistors (TFTs) with deuterium plasma immersion to adjust the threshold voltage of the depletion-mode TFTs. Their DC and AC performance are investigated and compared systematically. Especially, the feedback structure inverters demonstrate a maximum gain of −310V/V at ${V}_{DD} =10$ V, which is the highest DC gain ever to be reported for oxide TFTs. Furthermore, the frequency responses of these three inverter types are examined, showing a maximum AC gain of 210 V/V at a 10 mV amplitude input in the pseudo-CMOS inverters. These high-gain transparent inverters have great potential to be used as the common-source amplifiers in the measurement of photo-stimulated electroencephalogram signals.