Miracle-dz/UniFAR-FTUs huggingface dataset:
A Biochemichal and Ultrastructural Study on the Effect of Toluene on the Pars Distalis of Anterior Pituitary Glands of Adult Male Albino Rats

Background: Toluene is an environmental chemical pollutant and widely used as organic solvent and in production of many industrial products. It has systemic toxic effects on many organs as liver, kidney and the central nervous system and cause immune dysfunction. It can affect nerve myelination and cross the blood-brain barrier. Little is known about the exact effect of toluene exposure on the cells of anterior pituitary and no adequate studies were found about the effect of toluene on the morphological and histological structure of the pituitary cells.Aim: To assess the ultra structural of the pars distalis after toluene exposure. And to correlate any biochemical changes with the histological alteration as a trial to know the possible mechanism behind these changes.Materials and Methods: 20 adult male albino rats were divided into control group consisted of 10 rats and toluene exposed group 10 rats were exposed once a day to vapors of toluene (1000 ppm) for 15 minutes for 45 days. Blood samples were taken from the heart chambers of for assessment of the thyroid stimulating hormone (TSH), follicle stimulating hormone (FSH), luteinizing hormone (LH), adrencorticotrophic hormone (ACTH), growth hormone (GH) and prolactin. The anterior lobes of pituitary glands were immediately dissected out. Each specimen was separated into two parts; one processed and stained with hematoxylin & eosin and the other part was examined by electron microscope.Results: Biochemical: in the toluene-exposed group there was significant reduction in plasma level of GH, TSH, FSH and LH hormones and non-significant reduction in ACTH prolactin.Microscopic: the exposed group cells show complete loss of normal architecture in many areas and had vacuolated cytoplasm, shrunken asymmetrical nucleus with more thickened chromatin and few secretory granules.Conclusion: The toluene exposure induced morphological changes in most of cells of pars distalis and their secreted hormones
Stop-mass prediction in naturalness scenarios within MSSM-25

The "top-down" approach to minimal supersymmetric standard model (MSSM) phenomenology provides model-dependent indications for a natural stop mass. The approach is based on specific assumptions about the supersymmetry-breaking energy scale and parameters degeneracies. In order to determine robust predictions, we update the stop-mass prediction within the MSSM with 25 parameters (MSSM-25) by including electroweak fine-tuning as "naturalness data" during the Bayesian fits of the parameters to experimental data. The approximately prior-independent results show that imposing naturalness, taken here to mean a 25% to 100% fine-tuning, predicts a 1–2 TeV stop mass. The posterior distributions for the neutralino–proton cross-sections indicate better prospects for probing the associated neutralino cold dark matter (CDM) with future upgrades of the detection facilities.
FaceWallGrap: Machine Learning in Detecting user’s suspicious behaviour through Facebook wall

Facebook represents the current de-facto choice for social media, changing the nature of social relationships. The increasing amount of personal information that runs through this platform publicly exposes user behaviour and social trends, allowing aggregation of data through conventional intelligence collection techniques such as OSINT (Open Source Intelligence). In this paper, we propose a new method to detect and diagnose variations in overall Facebook user psychology through Open Source Intelligence (OSINT) and machine learning techniques. We are aggregating the spectrum of user sentiments and views by using N-Games charts, which exhibit noticeable variations over time,validated through long term collection. We postulate that the proposed approach can be used by security organisations to understand and evaluate the user psychology, then use the information to predict insider threats or prevent insider attacks.
Machine Learning Based Approach for Virtual Machine Migration

Nowadays the utilization of Cloud computing in industry, government and academia are growing substantially, this is due to their ability to deliver resilient, robust, and scalable computational power. In cloud computing data centers, high-speed networks interconnect both virtual and physical computers. The dynamic provisioning of these systems is based on end-user computing resources requirements. However, high efficient resource utilization is yet far to reach, therefore the operational costs of these data centers are considerably high. Currently, systems allocate a maximum number of resources in a manner that attempts to ensure that all the Service Level Agreements (SLA) are satisfied. Virtualizations represents the core technology of cloud computing. By creating multiple Virtual Machine (VMs) instances, it allows Cloud providers to manage its data center resources more efficiently and accordingly improving the utilization of resources. In addition to that, by using live migration the VMs can be dynamically consolidated on the minimal number of physical nodes according to their current resource requirements and maintaining SLAs. Therefore, non-optimized and inefficient VMs consolidation may cause performance degradation when an application encounters variable workloads. Thus, to ensure acceptable Quality of Service (QoS) and Service Level Agreements (SLA) a novel machine learning based technique for dynamic consolidation of VMs based on an adaptive prediction of utilization thresholds is proposed to satisfy the Service Level Agreements (SLA). Workload traces from Planet Lab servers have been utilized to validate the efficiency of the proposed technique with different of workload patterns.
Parallel and Distributed Collaborative Filtering: A Survey

Collaborative filtering is amongst the most preferred techniques when implementing recommender systems. Recently, great interest has turned towards parallel and distributed implementations of collaborative filtering algorithms. This work is a survey of the parallel and distributed collaborative filtering implementations, aiming not only to provide a comprehensive presentation of the field's development, but also to offer future research orientation by highlighting the issues that need to be further developed.
Elucidation of the 1,4-Dioxane Decomposition Pathway at Discrete Ultrasonic Frequencies

The sonolytic decomposition chemistry of the refractory compound 1,4-dioxane in aqueous solution has been investigated at four ultrasonic frequencies (205, 358, 618, and 1071 kHz). To maintain fully saturated solutions, argon and oxygen were used as sparge gases. Using a frequency of 358 kHz, the observed first-order kinetic rate constants for 1,4-dioxane destruction were highest with a sparge gas ratio of 75% Ar/25% O2 (k = 4.32 ± 0.31 × 10-4 s-1) and lowest in the presence of pure argon (k = 8.67 ± 0.47 × 10-5 s-1). Ethylene glycol diformate, methoxyacetic acid, formaldehyde, glycolic acid, and formic acid were found to be the major intermediates of 1,4-dioxane degradation. A reaction mechanism involving these byproducts was proposed concerning primarily reactions with oxidizing species (•OH, •OOH, •O) in and near the interfacial region of the cavitation bubble. The highest observed first-order 1,4-dioxane decomposition rate occurred at 358 followed by 618, 1071, and 205 kHz. At each frequency, approxim.
Adsorption des métaux lourds contenus dans l'acide phosphorique industriel par une bentonite algérienne activée. Modélisation

Dans le present travail, nous nous interessons a la caracterisation physico-chimique de l'acide phosphorique pretraite produit a l'unite des engrais phosphates d'Annaba (Est-Algerie) et a l'activation de la bentonite brute, utilisee pour l'elimination des metaux lourds contenus dans l'acide phosphorique pretraite. Le pretraitement de l'acide phosphorique industriel est realise au moyen de charbon actif pour eliminer les matieres organiques dissoutes successibles de gener la recuperation des metaux lourds. L'activation de la bentonite est effectuee par voie chimique, en utilisant l'acide sulfurique H 2 SO 4 . Deux parametres importants ont ete examines, la concentration de l'acide et la temperature. Des tests d'adsorption de zinc (II), de cadmium (II) et de chrome (III), contenus dans H 3 PO 4 5,5 M pretraite (30% P 2 O s ), ont ete realises dans differentes conditions de temperature, de pH et de quantites de bentonite, dans le but de developper un procede de fixation a l'echelle du laboratoire. Une modelisation du procede a ete egalement realisee. L'application du modele, defini dans le domaine experimental 1,25≤pH ≤2,50; 0,50g≤ masse de bentonite ≤2,5 g; 20°C ≤ T ≤ 70°C, a permis de donner un rendement moyen de 88 % pour l'adsorption du zinc (II) et 89 % pour le cadmium (II) et le chrome (III).
On the empirical variance of a fractionator estimate

The fractionator consists of several sampling stages with systematic sampling at each stage; data are collected only at the last stage. Therefore, predicting the error variance of a fractionator estimator is a non‐trivial problem, because the observations are correlated in a complicated, unknown way. Gundersen proposed to split the material sampled at the first stage into two subsets, and to compute the variance of the pooled estimate empirically using the corresponding pair of observations made in these two subsets. The idea is very effective, but the estimator thus proposed needed some corrections. The purpose of this paper is to present an improved estimator of the coefficient of error of a fractionator estimator using Gundersen's design.
Convection Patterns Trapped in the Solid State by UV-Induced Polymerization

A novel approach to producing ordered patterns in polymeric films was developed, which employs (i) the generation of nonequilibrium periodic convection patterns in thin liquid layers of monomers and (ii) UV-induced polymerization of convecting fluids under controlled conditions that preserve the periodicity of the pattern. We established the conditions under which an ordered structure induced by buoyancy-driven convection in a layer of methacryloxypropyl-terminated dimethylsiloxane can be trapped in the solid state and analyzed the characteristic length scales of the solidified film.
Correspondence Learning for Controllable Person Image Generation

We present a generative model for controllable person image synthesis,as shown in Figure 1 , which can be applied to pose-guided person image synthesis, i.e., converting the pose of a source person image to the target pose while preserving the texture of that source person image, and clothing-guided person image synthesis, i.e., changing the clothing texture of a source person image to the desired clothing texture. By explicitly establishing the dense correspondence between the target pose and the source image, we can effectively address the misalignment introduced by pose tranfer and generate high-quality images. Specifically, we first generate the target semantic map under the guidence of the target pose, which can provide more accurate pose representation and structural constraints during the generation process. Then, decomposed attribute encoder is used to extract the component features, which not only helps to establish a more accurate dense correspondence, but also realizes the clothing-guided person generation. After that, we will establish a dense correspondence between the target pose and the source image within the sharded domain. The source image feature is warped according to the dense correspondence to flexibly account for deformations. Finally, the network renders image based on the warped source image feature and the target pose. Experimental results show that our method is superior to state-of-the-art methods in pose-guided person generation and its effectiveness in clothing-guided person generation.
Millimeter-Wave Circular Synthetic Aperture Radar Imaging

In this paper, we present a high resolution microwave imaging technique using a compact and low cost single channel Frequency Modulated Continuous Wave (FMCW) radar based on Circular Synthetic Aperture Radar (CSAR) technique. We develop an algorithm to reconstruct the image from the raw data and analyze different aspects of the system analytically. Furthermore, we discuss the differences between the proposed systems in the literature and the one presented in this work. Finally, we apply the proposed approach to the experimental data collected from a single channel FMCW radar operating at 79 G Hz and present the results.
Generating Comprehensive Data with Protocol Fuzzing for Applying Deep Learning to Detect Network Attacks

Network attacks have become a major security concern for organizations worldwide and have also drawn attention in the academics. Recently, researchers have applied neural networks to detect network attacks with network logs. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new approach, protocol fuzzing, to automatically generate high-quality network data, on which deep learning models can be trained. Our findings show that fuzzing generates data samples that cover real-world data and deep learning models trained with fuzzed data can successfully detect real network attacks.
Fractal Dimension Generalization Measure

Developing a robust generalization measure for the performance of machine learning models is an important and challenging task. A lot of recent research in the area focuses on the model decision boundary when predicting generalization. In this paper, as part of the"Predicting Generalization in Deep Learning"competition, we analyse the complexity of decision boundaries using the concept of fractal dimension and develop a generalization measure based on that technique.
Short-term Operational Planning Problem of the Multiple-Energy Carrier Hybrid AC/DC Microgrids

In this paper, the short-term operational planning problem for a multiple energy carrier hybrid AC/DC microgrid is discussed. The hybrid microgrid consists of AC and DC parts, which are connected by means of inverters as well as natural gas network. The microgrid includes photovoltaic (PV) unit, wind turbine (WT), battery storage unit and gas-fired microturbines. A mixed integer linear programming is formed to minimize the overall cost of the microgrid including cost of natural gas supply, the value of lost load and battery degradation cost. The presented case study explored the importance of inverter characteristics and pipeline capacity.
Evolutionary Variational Optimization of Generative Models

We combine two popular optimization approaches to derive learning algorithms for generative models: variational optimization and evolutionary algorithms. The combination is realized for generative models with discrete latents by using truncated posteriors as the family of variational distributions. The variational parameters of truncated posteriors are sets of latent states. By interpreting these states as genomes of individuals and by using the variational lower bound to define a fitness, we can apply evolutionary algorithms to realize the variational loop. The used variational distributions are very flexible and we show that evolutionary algorithms can effectively and efficiently optimize the variational bound. Furthermore, the variational loop is generally applicable ("black box") with no analytical derivations required. To show general applicability, we apply the approach to three generative models (we use noisy-OR Bayes Nets, Binary Sparse Coding, and Spike-and-Slab Sparse Coding). To demonstrate effectiveness and efficiency of the novel variational approach, we use the standard competitive benchmarks of image denoising and inpainting. The benchmarks allow quantitative comparisons to a wide range of methods including probabilistic approaches, deep deterministic and generative networks, and non-local image processing methods. In the category of"zero-shot"learning (when only the corrupted image is used for training), we observed the evolutionary variational algorithm to significantly improve the state-of-the-art in many benchmark settings. For one well-known inpainting benchmark, we also observed state-of-the-art performance across all categories of algorithms although we only train on the corrupted image. In general, our investigations highlight the importance of research on optimization methods for generative models to achieve performance improvements.
Focal Frequency Loss for Generative Models

Despite the remarkable success of generative models in creating photorealistic images using deep neural networks, gaps could still exist between the real and generated images, especially in the frequency domain. In this study, we ﬁnd that narrowing the frequency domain gap can ameliorate the image synthesis quality further. To this end, we pro-pose the focal frequency loss, a novel objective function that brings optimization of generative models into the frequency domain. The proposed loss allows the model to dynamically focus on the frequency components that are hard to synthe-size by down-weighting the easy frequencies. This objective function is complementary to existing spatial losses, offering great impedance against the loss of important frequency information due to the inherent crux of neural networks. We demonstrate the versatility and effectiveness of focal frequency loss to improve various baselines in both perceptual quality and quantitative performance. 1
Autonomous Outdoor Scanning via Online Topological and Geometric Path Optimization

Autonomous 3D acquisition of outdoor environments poses special challenges. Different from indoor scenes, where the room space is delineated by clear boundaries and separations (e.g., walls and furniture), an outdoor environment is spacious and unbounded (thinking of a campus). Therefore, unlike for indoor scenes where the scanning effort is mainly devoted to the discovery of boundary surfaces, scanning an open and unbounded area requires actively delimiting the extent of scanning region and dynamically planning a traverse path within that region. Thus, for outdoor scenes, we formulate the planning of an energy-efficient autonomous scanning through a discrete-continuous optimization of robot scanning paths. The discrete optimization computes a topological map, through solving an online traveling sales problem (Online TSP), which determines the scanning goals and paths on-the-fly. The dynamic goals are determined as a collection of visit sites with high reward of visibility-to-unknown. A visit graph is constructed via connecting the visit sites with edges weighted by traversing cost. This topological map evolves as the robot scans via deleting outdated sites that are either visited or become rewardless and inserting newly discovered ones. The continuous part optimizes the traverse paths geometrically between two neighboring visit sites via maximizing the information gain of scanning along the paths. The discrete and continuous processes alternate until the traverse cost of the current graph exceeds the remaining energy capacity of the robot. Our approach is evaluated with both synthetic and field tests, demonstrating its effectiveness and advantages over alternatives.
Shaping transient response of nonlinear systems to satisfy a class of integral constraints

We consider the problem of shaping the transient step response of nonlinear systems to satisfy a class of integral constraints. Such constraints are inherent in hybrid energy systems consisting of energy sources and storage elements. While typical transient specifications aim to minimize overshoot, this problem is unique in that it requires the presence of an appreciable overshoot to satisfy the foregoing constraints. The problem was previously studied in the context of linear systems and this article extends that work to nonlinear systems. A combined integral and feedforward control, that requires minimal knowledge of the plant model, is shown to make the system amenable to meeting such constraints. Broadly, the compensation is effective for nonlinear plants with stable open‐loop step response and a positive DC gain. However, stability of the resulting closed‐loop system mandates bounds on the integral gain. In this regard, we state and prove generalized stability theorems for first and higher‐order nonlinear plants.
Attention and misinformation sharing on social media

The behavior of sharing information on social media should be fulfilled when the user is exhibiting attentive behavior. Useful information can be consumed constructively, and misinformation should not incline forwarded. Attentive behavior is related to users' cognitive abilities in the processing of set information. The work described in this paper examines the issue of attentive factors that affects the misinformation-sharing behavior of the user's on social media. This research aims to identify the significance of the prevailing attention factor towards sharing misinformation on social media. Our closed-ended questionnaire consisted of a psychometric scale to measure attention behavior with participants in the survey (n = 112). The results coefficient value of -0.390 from a set of regression analyses reports that attention factors have a significant negative correlation effect for users to share misinformation on social media. Along with the findings of the analysis results, we propose the attentive factors incorporated in an application's future design that could intervene in user attention and avoid potential harm caused by the spread of misinformation.
Adaptive Precision Training for Resource Constrained Devices

Learn in-situ is a growing trend for Edge AI. Training deep neural network (DNN) on edge devices is challenging because both energy and memory are constrained. Low precision training helps to reduce the energy cost of a single training iteration, but that does not necessarily translate to energy savings for the whole training process, because low precision could slows down the convergence rate. One evidence is that most works for low precision training keep an fp32 copy of the model during training, which in turn imposes memory requirements on edge devices. In this work we propose Adaptive Precision Training. It is able to save both total training energy cost and memory usage at the same time. We use model of the same precision for both forward and backward pass in order to reduce memory usage for training. Through evaluating the progress of training, APT allocates layer-wise precision dynamically so that the model learns quicker for longer time. APT provides an application specific hyper-parameter for users to play trade-off between training energy cost, memory usage and accuracy. Experiment shows that APT achieves more than 50% saving on training energy and memory usage with limited accuracy loss. 20% more savings of training energy and memory usage can be achieved in return for a 1% sacrifice in accuracy loss.