Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of drought tolerant maize genotypes and seedling based morpho-physiological selection indices for crop improvement

Maize is an imperative grain crop used as a staple food in several countries around the world. Water deficiency is a serious problem limiting its growing area and production. Identification of drought tolerant maize germplasm is comparatively easy and sustainable approach to combat this issue. Present research was conducted to evaluate 50 maize genotypes for drought tolerance at early growth stage. Drought tolerance was assessed on the basis of several morphological and physiological parameters. Analysis of variance showed significant variation among the tested maize genotypes for recorded parameters. Principal component analysis revealed important morpho-physiological traits that were playing key role in drought tolerance. Correlation studies depicted significant positive correlation among the attributes such as fresh shoot length (FSL), fresh root length (FRL), dry shoot weight (DSW), dry root weight (DRW), relative water contents (RWC) and total dry matter (TDM) while a strongly negative correlation was observed among RWC and excised leaf water loss. Results concluded that the parameters fresh shoot weight, fresh root weight, FRL, DRW, TDM, cell membrane thermo stability (CMT) and RWC can be useful for rapid screening of maize germplasm for drought tolerance at early growth stages. Furthermore, the genotypes 6, 16, 18, 40, 45 and 50 can be used as a drought tolerant check in breeding programs. Moreover, biplot analysis along with other indices was proved to be a useful approach for rapid and cost efficient screening of large number of genotypes against drought stress condition.
Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method

Background/Aims: One of the most important impacts of personalized medicine is the connection between patients’ genotypes and their drug responses. Despite a series of studies exploring this relationship, the predictive ability of such analyses still needs to be strengthened. Methods: Here we present the Lq penalized network-constrained logistic regression (Lq-NLR) method to meet this need, in which the predictors are integrated into the gene expression data and biological network knowledge and are combined with a more aggressive penalty function. Response prediction models for two cancer targeting drugs (erlotinib and sorafenib) were developed from gene expression data and IC50 values from a large panel of cancer cell lines by utilizing the proposed approach. Then the drug responders were tested with the baseline tumor gene expression data, yielding an in vivo drug sensitivity prediction. Results: These results demonstrated the high effectiveness of this approach. One of the best results achieved by our method was a correlation of 0.841 between the cell line in vitro drug response and patient’s in vivo drug response. We then applied these two drug prediction models to develop a personalized medicine approach in which the subsequent treatment depends on each patient’s gene-expression profile. Conclusion: The proposed method is much better than the existing approach and can capture a more accurate reflection of the relationship between genotypes and phenotypes.
Etude d'un traitement combiné bio-physico-chimique pour la décontamination des eaux polluées en atrazine

Ce travail concerne l'etude d'un procede bio-physico-chimique pour le traitement d'eaux polluees par de l'atrazine. Le procede etudie consiste en une biofiltration, utilisant l'ecorce de pin comme support d'adsorption et comme agent de biodegradation la souche bacterienne Pseudomonas ADP sp. , connue pour sa capacite a mineraliser l'atrazine en tant que source d'azote. Cette technique vise a eliminer totalement le polluant par mineralisation, valorisant l'ecorce de pin, un dechet de l'industrie du bois, le tout a faible cout. Dans un premier temps, les essais d'adsorption ont montre que l'atrazine etait retenue en faible quantite sur l'ecorce de pin, compare a la capacite d'adsorption d'un charbon actif, rendant le pesticide disponible pour les micro-organismes engages dans le traitement. L'approche biologique en milieu liquide a montre que la biodegradation de l'atrazine pouvait avoir lieu a pH acide (jusqu'a pH 4,4) impose par la presence de l'ecorce en solution aqueuse et a des temperatures relativement basses (jusqu'a 12ʿC) permettant d'exploiter ce procede de maniere rustique : pas de chauffage ni besoin de tamponner l'eau a traiter. Pseudomonas ADP sp. A prouve qu'elle etait capable de survivre en presence des composes organiques de l'ecorce de pin et de les consommer en tant que source unique de carbone. D'autre part, aucune source d'azote supplementaire a l'atrazine ne doit etre apportee au cours du traitement sous peine de voir la mineralisation de celle-ci diminuer. La microflore indigene de l'ecorce de pin a montre une legere capacite a mineraliser l'atrazine pour des concentrations en atrazine comprises entre 45 aeg. L-1 et 20 mg. L-1 permettant, a forte concentration uniquement, d'augmenter le rendement global du traitement. D'une maniere generale, la presence de cette microflore n'aurait qu'un tres leger effet inhibiteur vis a vis de la croissance de Pseudomonas ADP sp. Et de sa capacite a mineraliser l'atrazine. L'etude du traitement combinant adsorption et biodegradation a ete realisee en essais batch et en colonnes. En batch, 50% de l'atrazine sont mineralises en 4 jours ; 50% restent adsorbes irreversiblement a l'ecorce et donc non disponibles pour les bacteries. En colonne, l'apport de Pseudomonas ADP sp. Ameliore l'efficacite du traitement de 50% a 100% par rapport au procede d'adsorption seul. Le traitement etudie semble inexploitable pour traiter des eaux contenant une faible concentration en atrazine (quelques aeg. L-1). En revanche, ce procede combine pourrait etre envisage comme pre-traitement pour des eaux fortement polluees (quelques mg. L-1).
Ultrasonic guided wave-based detection of composite insulator debonding

Based on the theory of ultrasonic guided wave propagation, a simplified model of bilayer composite insulator was constructed, and the dispersion curves of longitudinal (L) and torsional (T) guided wave modes were obtained. To address the problem of composite insulator debonding, the propagation velocity and energy decay of longitudinal and torsional ultrasonic guided waves in the insulator bilayer model were studied, and a method for detecting the axial and circumferential debonding of composite insulator was proposed based on the ultrasonic guided wave attenuation characteristics. On the basis of the theoretical analysis, experiments were conducted to detect the debonding of composite insulator. The experimental results showed that: the same guided wave modes propagated at the same speed on the silicone rubber and rod, but differed in energy distribution; as the guided waves attenuated excessively during propagation in the silicone rubber, only segmented detection was possible for long composite insulator; when there was debonding within the detection range, the energy attenuation of guided waves would increase. Debonding size would also influence the attenuation coefficient, therefore, we could determine whether there was debonding defects as well as the size of defects by detecting the propagation attenuation coefficient of guided waves. L-mode guided waves could detect whether there was debonding defects, but could not determine the size of debonding. T mode guided waves could be used to estimate the size of debonding defects.
Improved Face Recognition Across Poses using Fusion of Probabilistic Latent Variable Models

Uncontrolled environments have often required face recognition systems to identify faces appearing in poses that are different from those of the enrolled samples. To address this problem, probabilistic latent variable models have been used to perform face recognition across poses. Although these models have demonstrated outstanding performance, it is not clear whether richer parameters always lead to performance improvement. This work investigates this issue by comparing performance of three probabilistic latent variable models, namely PLDA, TFA, and TPLDA, as well as the fusion of these classifiers on collections of video data. Experiments on the VidTIMIT+UMIST and the FERET datasets have shown that fusion of multiple classifiers improves face recognition across poses, given that the individual classifiers have similar performance. This proves that different probabilistic latent variable models learn statistical properties of the data that are complementary (not redundant). Furthermore, fusion across multiple images has also been shown to produce better perfomance than recogition using single still image.
Potency of Endophyte Microbes as an Antagonistic Agent for Colletotrichum capsici Causes Anthracnose Disease in Cayenne pepper (Capsicum frustescens)

Background: An anthracnose disease caused by pathogenic fungal Colletotrichum capsici has been attacking the cayenne plants either harvested or has not been harvested. This disease must be handled appropriately and quickly because it can reduce the production of chili up to 90%. Recently, anthracnose disease prevention still use chemical fungicide that if applied for long time will cause new impact for environment. Objective: Trichoderma and Bacillus cereus endophytes may be used as antagonistic agents for C. capsici pathogens because they have various antibiotic compounds. Methods: This research uses experimental method. The stages of this study include sterilization of tools and materials, preparation of culture media of fungal and bacteria, rejuvenation of endophytic microbe culture Trichoderma sp. and Bacillus cereus, rejuvenation of C. capsici pathogen, antagonistic test in vitro using dual culture method. Result and conclusion: The results of in vitro antagonistic tests showed that inhibition percentage of Trichoderma treatment (96%) and combination treatment Trichoderma and B. cereus (97%) is not significantly different. While in B. cereus treatment (11, 88%) significantly different with all of treatments. Endophytes are shown by its dominating growth in petri dishes than C. capsici pathogen or B. cereus endophytes. Furthermore,for endophytes Trichoderma sp continued on in vivo test because it was most effective.The result for incubation period is 3 days after inoculation compared with negative control 2 days. For disease incidence 100%, and for disease intensity that is 61,25% compared with negative control equal to 88,75%.
A Literature Survey on Resource Management Techniques, Issues and Challenges in Cloud Computing

Cloud computing is a large scale distributed computing which provides on demand services for clients. Cloud Clients use web browsers, mobile apps, thin clients, or terminal emulators to request and control their cloud resources at any time and anywhere through the network. As many companies are shifting their data to cloud and as many people are being aware of the advantages of storing data to cloud, there is increasing number of cloud computing infrastructure and large amount of data which lead to the complexity management for cloud providers. We surveyed the state-of-the-art resource management techniques for IaaS (infrastructure as a service) in cloud computing. Then we put forward different major issues in the deployment of the cloud infrastructure in order to avoid poor service delivery in cloud computing.
GRWOTH PERFORMANCE AND IMMUNE RESPONSE OF BROILER CHICKENS REARED UNDER HIGH STOCKING DENSITY AND VITAMIN E SUPPLEMENTATION

The aim of this study was to evaluate the effects of bird density and the consequences of adding dietary vitamin E on broiler performance, blood metabolites, gastric bacterial enumeration, bone contents and the humeral immunity. A total number of 180 one day-old Arbor Acres broiler chickens were randomly divided into 4 equal groups. Each group was subdivided into 3 replicates containing 15 chicks each. The first group was housed at density of 10 birds/m and served as the positive control. The second group was housed at density of 12 birds/mand served as the negative control. The first and second groups were fed the basal diet. The third and fourth groups were housed at density of 12 birds/m, and were fed the basal diet supplemented with vitamin E at either 100mg/kg diet (third group) or 200mg/kg diet (fourth group). The results indicated that birds kept under high stocking density showed reduction of growth performance, plasma metabolites and immune response of broilers. While, the immune response of broilers was improved by dietary vitamin E supplementation under the intensive density. In ileum and ceca, the bacterial count was improved when all pathogens counts (E.coli, Salmonella and Clostridium) were decreased in birds reared at density of 12 birds/m but fed diets supplemented with vitamin E even 100 and 200 mg/kg. It can be concluded that, under high stoking density condition, vitamin E supplementation can improve the production performance and the immune response of broiler chickens.
Multi-Modal Emotion Detection with Transfer Learning

Automated emotion detection in speech is a challenging task due to the complex interdependence between words and the manner in which they are spoken. It is made more difficult by the available datasets; their small size and incompatible labeling idiosyncrasies make it hard to build generalizable emotion detection systems. To address these two challenges, we present a multi-modal approach that first transfers learning from related tasks in speech and text to produce robust neural embeddings and then uses these embeddings to train a pLDA classifier that is able to adapt to previously unseen emotions and domains. We begin by training a multilayer TDNN on the task of speaker identification with the VoxCeleb corpora and then fine-tune it on the task of emotion identification with the Crema-D corpus. Using this network, we extract speech embeddings for Crema-D from each of its layers, generate and concatenate text embeddings for the accompanying transcripts using a fine-tuned BERT model and then train an LDA - pLDA classifier on the resulting dense representations. We exhaustively evaluate the predictive power of every component: the TDNN alone, speech embeddings from each of its layers alone, text embeddings alone and every combination thereof. Our best variant, trained on only VoxCeleb and Crema-D and evaluated on IEMOCAP, achieves an EER of 38.05%. Including a portion of IEMOCAP during training produces a 5-fold averaged EER of 25.72% (For comparison, 44.71% of the gold-label annotations include at least one annotator who disagrees).
Multi-layered tensor networks for image classification

The recently introduced locally orderless tensor network (LoTeNet) for supervised image classification uses matrix product state (MPS) operations on grids of transformed image patches. The resulting patch representations are combined back together into the image space and aggregated hierarchically using multiple MPS blocks per layer to obtain the final decision rules. In this work, we propose a non-patch based modification to LoTeNet that performs one MPS operation per layer, instead of several patch-level operations. The spatial information in the input images to MPS blocks at each layer is squeezed into the feature dimension, similar to LoTeNet, to maximise retained spatial correlation between pixels when images are flattened into 1D vectors. The proposed multi-layered tensor network (MLTN) is capable of learning linear decision boundaries in high dimensional spaces in a multi-layered setting, which results in a reduction in the computation cost compared to LoTeNet without any degradation in performance.
Deep Reinforcement Learning of Transition States

Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach, called RL‡, to automatically unravel chemical reaction mechanisms. In RL‡, locating the transition state of a chemical reaction is formulated as a game, and two functions are optimized, one for value estimation and the other for policy making, to iteratively improve our chance of winning this game. Both functions can be approximated by deep neural networks. By virtue of RL‡, one can directly interpret the reaction mechanism according to the value function. Meanwhile, the policy function allows efficient sampling of the transition path ensemble, which can be further used to analyze reaction dynamics and kinetics. Through multiple experiments, we show that RL‡ can be trained tabula rasa hence allowing us to reveal chemical reaction mechanisms with minimal subjective biases.
Main properties of new heavy hadrons and the luminosity of hadronic dark matter

The origin and main properties of new heavy hadrons as dark matter candidates, are represented. Low-energy interactions of new hadrons with leptons and nucleons are described in the terms of effective vertexes. We consider the lowest excited levels of new mesons in the frame-work of the Heavy Quark Effective Theory. The effect of fine and hyper-fine splitting of excited states follows directly from this theory. We analyze phenomenological consequences of this effect as manifestation of dark matter particles.
Detection of Spatially Modulated Signals via RLS: Theoretical Bounds and Applications

This paper characterizes the performance of massive multiuser spatial modulation MIMO systems, when a regularized form of the least-squares method is used for detection. For a generic distortion function and right unitarily invariant channel matrices, the per-antenna transmit rate and the asymptotic distortion achieved by this class of detectors are derived. Invoking an asymptotic characterization, we address two particular applications. Namely, we derive the error rate achieved by the computationally-intractable optimal Bayesian detector, and we propose an efficient approach to tune LASSO-type detectors. We further validate our derivations through various numerical experiments.
Dependency-based Anomaly Detection: Framework, Methods and Benchmark

Anomaly detection is an important research problem because anomalies often contain critical insights for understanding the unusual behavior in data. One type of anomaly detection approach is dependency-based, which identifies anomalies by examining the violations of the normal dependency among variables. These methods can discover subtle and meaningful anomalies with better interpretation. Existing dependency-based methods adopt different implementations and show different strengths and weaknesses. However, the theoretical fundamentals and the general process behind them have not been well studied. This paper proposes a general framework, DepAD, to provide a unified process for dependency-based anomaly detection. DepAD decomposes unsupervised anomaly detection tasks into feature selection and prediction problems. Utilizing off-the-shelf techniques, the DepAD framework can have various instantiations to suit different application domains. Comprehensive experiments have been conducted over one hundred instantiated DepAD methods with 32 real-world datasets to evaluate the performance of representative techniques in DepAD. To show the effectiveness of DepAD, we compare two DepAD methods with nine state-of-the-art anomaly detection methods, and the results show that DepAD methods outperform comparison methods in most cases. Through the DepAD framework, this paper gives guidance and inspiration for future research of dependency-based anomaly detection and provides a benchmark for its evaluation.
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models

Labeling real-world datasets is time consuming but indispensable for supervised machine learning models. A common solution is to distribute the labeling task across a large number of non-expert workers via crowd-sourcing. Due to the varying background and experience of crowd workers, the obtained labels are highly prone to errors and even detrimental to the learning models. In this paper, we advocate using hybrid intelligence, i.e., combining deep models and human experts, to design an end-to-end learning framework from noisy crowd-sourced data, especially in an on-line scenario. We first summarize the state-of-the-art solutions that address the challenges of noisy labels from non-expert crowd and learn from multiple annotators. We show how label aggregation can benefit from estimating the annotators' confusion matrices to improve the learning process. Moreover, with the help of an expert labeler as well as classifiers, we cleanse aggregated labels of highly informative samples to enhance the final classification accuracy. We demonstrate the effectiveness of our strategies on several image datasets, i.e. UCI and CIFAR-10, using SVM and deep neural networks. Our evaluation shows that our on-line label aggregation with confusion matrix estimation reduces the error rate of labels by over 30%. Furthermore, relabeling only 10% of the data using the expert's results in over 90% classification accuracy with SVM.
Properties of the Sheath Regions of Coronal Mass Ejections with or without Shocks from STEREO in situ Observations near 1 au

We examine 188 coronal mass ejections (CMEs) measured by the twin Solar Terrestrial Relations Observatory spacecraft during 2007–2016 to investigate the generic features of the CME sheath and the magnetic ejecta (ME) and dependencies of average physical parameters of the sheath on the ME. We classify the CMEs into three categories, focusing on whether the ME drives both a shock and sheath, or only a sheath, or neither, near 1 au. We also re-evaluate our initial classification through an automated algorithm and visual inspection. We observe that even for leading-edge speeds greater than 500 km s−1, 1 out of 4 MEs do not drive shocks near 1 au. MEs driving both shocks and sheaths are the fastest and propagate in high magnetosonic solar wind, whereas MEs driving only sheaths are the slowest and propagate in low magnetosonic solar wind. Our statistical and superposed epoch analyses indicate that all parameters are more enhanced in the sheath regions following shocks than in sheaths without shocks. However, differences within sheaths become statistically less significant for similar driving MEs. We also find that the radial thickness of ME-driven sheaths apparently has no clear linear correlation with the speed profile and associated Mach numbers of the driver.
Excitation of PAH Emission: Dependence on Starlight Spectrum, Intensity, PAH Size Distribution, and PAH Ionization

Using physical models, we study the sensitivity of polycyclic aromatic hydrocarbon (PAH) emission spectra to the character of the illuminating starlight, to the PAH size distribution, and to the PAH charge distribution. Starlight models considered range from the emission from a 3 Myr-old starburst, rich in far-ultraviolet (FUV) radiation, to the FUV-poor spectrum of the very old population of the M31 bulge. A wide range of starlight intensities is considered. The effects of reddening in dusty clouds are investigated for different starlight spectra. For a fixed PAH abundance parameter $q_{\rm pah}$, the fraction of the IR power appearing in the PAH emission features can vary by a factor of two as the starlight spectrum varies from FUV-poor (M31 bulge) to FUV-rich (young starburst)