Miracle-dz/UniFAR-FTUs huggingface dataset:
Biosynthesis, optimization, purification and characterization of gold nanoparticles

Many microorganisms produce intracellular metal nanoparticles. Aqueous gold, when exposed to several actinomycetal strains, become thereby, leading to the formation of gold nanoparticles. The use of microorganisms in the synthesis of nanoparticles is emerging as an eco-friendly and exciting approach gold for recovery. Streptomyces hygroscopicus was used for the biosynthesis of gold nanoparticles. UV and visible spectroscopic studies of biofilms revealed better synthesis of nanoparticles. It was observed that better biosynthesis of gold nanoparticles occurred when cell biomass treated with 10-3 and 10-4 mM HAuCl4 solution as compared to other dilutions. The pH 7.0 was found to be optimum for the biosynthesis of gold nanoparticles. The TEM study revealS that there was evidence of gold nanoparticles synthesized by S. hygroscopicus. The results demonstrate that spherical gold nanoparticles in the range of 10 to 20 nm were observed at pH value of 7.0. The actinomycetal biomass and various concentration of aqueous HAuCl4 solution were incubated, it was found that 10-4 concentration shows excellent colour of the actinomycetal biomass. Key words: Biosynthesis, optimization, gold nanoparticles, purification, characterization.
Data augmentation for deep-learning-based electroencephalography

BACKGROUND Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. NEW METHOD: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? RESULTS DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. COMPARING WITH EXISTING METHODS Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload-29% on average. CONCLUSIONS DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications-if adhering to our reporting guidelines-will facilitate more detailed analysis.
Aggressive Action Estimation: A Comprehensive Review on Neural Network Based Human Segmentation and Action Recognition

Human action recognition has been a talked topic since machine vision was coined. With the advent of neural networks and deep learning methods, various architectures were suggested to address the problems within a context. Convolutional neural network has been the primary go-to architecture for image segmentation, flow estimation and action recognition in recent days. As the problem itself is an extended version of various subproblems, such as frame segmentation, spatial and temporal feature extraction, motion modeling and action classification as a whole, some methods reviewed in this paper addressed sub-problems and some tried to address a single architecture to the action recognition problem. While being a success, convolution neural networks have drawbacks in its pooling methods. CapsNet, on the other hand, uses squashing function to determine the activation. Also it addresses spatiotemporal information with the normalized vector maps while CNN-based methods extracts feature map for spatial and temporal information and later augment them in a fusion layer for combining two separate feature maps. Critical review of papers provided in this work can contribute significantly in addressing human action recognition problem as a whole.
On the Marcum Q-Function Behavior of the Left Tail Probability of the Lognormal Sum Distribution

Sums of lognormal random variables can be used to model a wide range of real-life phenomena, and their probability distribution left tails are of crucial interest. Meanwhile, the probability distribution left tail of the sum of lognormal random variables has no closed-form expressions. A closed-form first-order Marcum Q-function lower bound for the probability distribution left tail of the lognormal two-sum is derived. The derivation of the lower bound is based on studying the geometry of the nonzero probability mass region in the Gaussian plane, rather than in the lognormal plane. It is proved that the boundary of the nonzero probability mass region in the Gaussian plane does not change shape and is identical for different lognormal two-sum cumulative distribution function (CDF) arguments, other than for a translation of the boundary curve that is determined by the CDF argument. The first-order Marcum Q-function lower bound becomes an increasingly good approximation for the probability distribution left tail of the lognormal two-sum, as the sum CDF argument approaches zero. A recently reported empirical observation that the Marcum Q-function can be used as an approximation of the left tail probability of the lognormal sum distribution is explained and partially quantified. Simulation results show that the new first-order Marcum Q-function lower bound is tighter than an existing lognormal sum distribution left tail lower bound when the sum CDF argument is sufficiently small.
MISO Cache-Aided Communication with Reduced Subpacketization

We present a novel low complexity scheme for cache aided communication, where a multi-antenna base station serves multiple single-antenna mobiles. The scheme is based on transmission of coded messages to disjoint groups of users simultaneously. Compared to the state-of-the-art, the proposed scheme significantly reduces the transmission and decoding complexity. Furthermore, it substantially relaxes the subpacketization level, and involves a transmission of much smaller number of packets in each time block. The proposed scheme achieves the same degrees of freedom (DoF) as the best known scheme, but, it suffers from a performance degradation of about 1.5dB due to a loss of diversity. Nevertheless, the loss is acceptable as the reduction in complexity allows a practical implementation of this scheme even for a large number of users.
Microsatellites loci isolated in the freshwater fish Brycon hilarii

AbstractWe describe seven microsatellite loci, including tetra-, tri- and dinucleotides, isolated from Brycon hilarii , which is a migratory fish inhabiting the Paraguay River basin (Brazil) andis highly valued for its meat quality. Three to eight alleles per locus were detected and theexpected heterozygosity ranged from 0.31 to 0.81. Positive results were obtained with cross-amplification in five other species of Brycon . These microsatellites provide a potential toolfor wild population and aquaculture studies of B. hilarii and other species of the genus. Keywords : Bryconinae, Characidae, microsatellites, population genetics Received 7 March 2006; revision accepted 4 April 2006Brycon comprises a fish group of great importance forfishing and aquaculture (Mendonca 1994). It is the mainrepresentative of the subfamily Bryconinae (Characidae)and occurs in the large hydrographic basins of theNeotropical region (Lima 2003). It is valued for its superiormeat quality. Some species of this group are suffering apopulational decline mostly related to unregulated humanactivities. Six species are endangered in various Brazilianhydrographic basins (MMA 2004).
Exploring Phrase Grounding without Training: Contextualisation and Extension to Text-Based Image Retrieval

Grounding phrases in images links the visual and the textual modalities and is useful for many image understanding and multimodal tasks. All known models heavily rely on annotated data and complex trainable systems to perform phrase grounding – except for a recent work [38] that proposes a system requiring no training nor aligned data, yet is able to compete with (weakly) supervised systems on popular phrase grounding datasets. We explore and expand the upper bound of such a system, by contextualising both the image and language representation with structured representations. We show that our extensions benefit the model and establish a harder, but fairer baseline for (weakly) supervised models. We also perform a stress test to assess the further applicability of such a system for creating a sentence retrieval system requiring no training nor annotated data. We show that such models have a difficult start and a long way to go and that more research is needed.
CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time.
Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growing demand driven by the applications, predicting 3D pose from a 2D image is a challenging and ill-posed problem due to the loss of depth information during projection from 3D to 2D. Although there have been years of research on 3D pose estimation problem, it still remains unsolved. In this thesis, we propose a variety of ways to tackle the 3D pose estimation problem both for articulated human bodies and rigid object bodies by learning robust features and latent representations. First, we present a novel video-based approach that exploits spatiotemporal features for 3D human pose estimation in a discriminative regression scheme. While early approaches typically account for the motion information by temporally regularizing noisy pose estimates in individual frames, we demonstrate that taking into account motion information very early in the modeling process with spatiotemporal features yields significant performance improvements. We further propose a CNN-based motion compensation approach that stabilizes and centralizes the human body in the bounding boxes of consecutive frames to increase the reliability of spatiotemporal features. This then allows us to effectively overcome ambiguities and improve pose estimation accuracy. Second, we develop a novel Deep Learning framework for structured prediction of 3D human pose. Our approach relies on an auto-encoder to learn a high-dimensional latent pose representation that accounts for joint dependencies. We combine traditional CNNs for supervised learning with auto-encoders for structured learning and demonstrate that our approach outperforms the existing ones both in terms of structure preservation and prediction accuracy. Third, we propose a 3D human pose estimation approach that relies on a two-stream neural network architecture to simultaneously exploit 2D joint location heatmaps and image features. We show that 2D pose of a person, predicted in terms of heatmaps by a fully convolutional network, provides valuable cues to disambiguate challenging poses and results in increased pose estimation accuracy. We further introduce a novel and generic trainable fusion scheme, which automatically learns where and how to fuse the features extracted from two different input modalities that a two-stream neural network operates on. Our trainable fusion framework selects
In vitro tuberization in potato (Solanum tuberosum L.)

In vitro microtuber formation potentiality of potato was investigated to establish a rapid disease free seed production system in potato. MS medium supplemented with 4 mg/L of KIN showed best performance in respect of multiple shoot regeneration and microtuber formation. Simple MS medium was not able to produce any micro tuber under in vitro condition. Dark condition better responded to tuberization than light condition. Among the three different explants (nodal segment, sprout and shoot apex) nodal cutting showed the best performance on days to microtuber formation and average weight of microtuber. MS + 6% sucrose + 4 mg/L KIN combination of treatment was best for in vitro tuberization among the parameters under study.
Anuran abundance and persistence in agricultural landscapes during a climatic extreme

Climate change in concert with habitat loss and degradation are major threats to global biodiversity. As part of climate change, the occurrence of extreme climatic events is expected to increase. Agricultural intensification has led to the increased homogeneity of agricultural habitats and declines in farmland species diversity. We investigated the abundance of common frog Rana temporaria populations in boreal farmlands in relation to multiple scales of landscape structure during years 2002–2003, and the effect of habitat structure on R. temporaria population persistence during a severe drought. On average, 113 and 24 egg clutches were observed per site in years 2002 and 2003, respectively. This dramatic decline was synchronized over the entire study area (ca. 320 km2), with some local variations. Population persistence was higher in sites where ground water levels decreased less but it was also positively correlated with the heterogeneity of the adjacent landscape. Under normal weather conditions, local habitat characteristics had a dominating role on R. temporaria population abundance, as it was positively correlated with the amount of ditches and ponds within the study sites. After the drought, however, population abundance was related to landscape and regional level factors. The results indicate that landscape homogenization can have negative effects on population persistence during climate change. They also show that the scale at which landscape characteristics affect populations can be strongly dependent of processes functioning at large scales, such as weather. In summary, heterogeneous landscapes may lower the risk of regional amphibian population declines under extreme weather perturbations and serve as sources of recovery in postdisturbance recovery phases. Thus, maintaining such areas should be promoted in long‐term biodiversity conservation.
SessionNet: Feature Similarity-Based Weighted Ensemble Learning for Motor Imagery Classification

A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Motor imagery (MI) paradigm is widely used in non-invasive BCI to control external devices by decoding user intentions. The traditional MI-BCI problem is to obtain enough EEG data samples for adopting deep learning techniques, as electroencephalography (EEG) data have intricate and non-stationary properties that can cause a discrepancy between different sessions of data. Because of the discrepancy, the recorded EEG data with different sessions cannot be treated as the same. In this study, we recorded a large intuitive EEG dataset that contained nine types of movements of a single-arm across 12 subjects. We proposed a SessionNet that learns generality with EEG data recorded over multiple sessions using feature similarity to improve classification performance. Additionally, the SessionNet adopts the principle of a hierarchical convolutional neural network that shows robust classification performance regardless of the number of classes. The SessionNet outperforms conventional methods on 3-class, 5-class, and two types of 7-class and 9-class of a single-arm task. Hence, our approach could demonstrate the possibility of using feature similarity based on a novel ensemble learning method to train generality from multiple session data for better MI classification performance.
Z-IoT: Passive Device-class Fingerprinting of ZigBee and Z-Wave IoT Devices

I.In addition to traditional networking devices (e.g., gateways, firewalls), current corporate and industrial networks integrate resource-limited Internet of Things (IoT) devices like smart outlets and smart sensors. In these settings, cyber attackers can bypass traditional security solutions and spoof legitimate IoT devices to gain illegal access to the systems. Thus, IoT device-class identification is crucial to protect critical networks from unauthorized access. In this paper, we propose Z-IoT, the first fingerprinting framework used to identify IoT device classes that utilize ZigBee and Z-Wave protocols. Z-IoT monitors idle network traffic among IoT devices to implement signature-based device-class fingerprinting mechanisms. Utilizing passive packet capturing techniques and optimal selection of filtering criteria and machine learning algorithms, Z-IoT identifies different types of IoT devices while guaranteeing the anonymity of the network data. To test Z-IoT's efficacy, we implemented several testbeds, including a total of 39 commodity IoT devices that communicate over ZigBee and Z-Wave protocols. Our experimental results showed an excellent performance in identifying different classes of IoT devices with average precision and recall of over 91%. Finally, the proposed framework yields no overhead to the IoT devices or the network traffic.
Impulsive Feedback Modeling of Levodopa Pharmacokinetics Subject to Intermittently Interrupted Gastric Emptying*

A novel modeling approach capturing the multiple peak phenomenon in oral levodopa administration is proposed. Multiple peaks in the blood plasma concentration of the drug are attributed to the effects caused by gastric emptying. The developed model describes the instances of interrupted gastric emptying by an impulsive feedback of the dopamine concentration in the brain acting on the pyloric sphincter. A combination of the continuous levodopa clearing dynamics and the impulsive feedback results in a hybrid model, whose solutions are positive and bounded. The stability properties of the model are studied by means of a Poincaré map describing the propagation of the continuous model states through the firings of the impulsive feedback. Model feasibility is illustrated on data sets obtained in clinical experiments.
PERENNIAL GRASSES OF DIFFERENT SUCCESSIONAL STAGES UNDER VARIOUS SOIL WATER INPUTS: DO THEY DIFFER IN ROOT LENGTH DENSITY?

La informacion sobre densidad de longitud de raices (DLR) es escasa en gramineas perennes de diferentes estados sucesionales expuestas a varios niveles hidricos del suelo. Los efectos de distintos niveles hidricos sobre la DLR fueron evaluados en gramineas perennes de etapas sucesionales tardias (Stipa clarazii Ball.), intermedias (S. tenuis Phil.), y tempranas (S. gynerioides Phil.). Se condujeron estudios de campo en 1996 y principios de 1997, aunque los tratamientos fueron impuestos desde 1995. S. clarazii y S. tenuis son importantes gramineas perennes cespitosas palatables en pastizales templados semiaridos del centro de Argentina, donde S. gynerioides es una de las especies de gramineas perennes no palatables mas abundantes. Se probaron las siguientes hipotesis: 1) S. clarazii y S. tenuis tienen menor DLR bajo condiciones de riego que bajo condiciones naturales o estres hidrico, 2) S. clarazii tiene mayor DLR que S. gynerioides y S. tenuis bajo todos los niveles hidricos y fechas de muestreo, y 3) la DLR de las tres especies varia con la fecha de muestreo, dentro de cada especie y nivel hidrico del suelo. Los resultados condujeron a rechazar la hipotesis 1 y aceptar las hipotesis 2 y 3. El mantenimiento del crecimiento radical en todos los niveles hidricos les permitiria a estas especies una mayor exploracion del volumen del suelo y adquisicion de recursos que mantengan el rebrote en sus ambientes nativos, semiaridos. El estudio tambien demostro que las gramineas perennes de estados sucesionales tardios (S. clarazii) deberian tener mayor capacidad competitiva que especies mas tempranas en la sucesion vegetal (S. tenuis y S. gynerioides) debido, al menos en parte, a su mayor DLR promedio bajo condiciones de estres hidrico, naturales y de riego.
Nodule gas exchange and water potential response to rapid imposition of water deficit

The permeability (P) of the gaseous diffusion barrier in the nodules of soybean [Glycine max (L.) Merr. ] decreases when water deficits are extended over a 7 to 10 d period. The mechanism controlling P changes is unclear, but may result from the release of water to intercellular pathways, and an associated change in the nodule water potential. The purpose of these experiments was to impose water deficit treatments rapidly in order to determine the early sequence of the responses of nodule water potential and nodule gas exchange without the complications that arise from long-term water deficit treatments. A vertical, split-root system was used to separate nodule drying effects from plant water deficits by replacing humidified air that was passed over upper root nodules in well-watered plants with dry air, or by replacing the nutrient solution that surrounded lower roots with -1.0 MPa polyethylene glycol (PEG) solution, or by a combination of the dry air and PEG treatments. The PEG treatment caused large decreases in both the components of nodule water potential and nodule relative water content, but there was no indication that these factors had immediate, direct effects on either nitrogenase activity or P. After 7 h of the PEG treatment a significant decrease in nitrogenase activity was found but no decrease in P was detected. These results indicate that changes in nitrogenase activity in response to water deficits precede decreases in P. Exposure of nodules to dry air in well-watered plants had no significant effect on either nitrogenase activity or P during the 7 h treatment.