Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning Issues and Approaches for The Multi-Agent Centric Problems

Reinforcement learning is a subfield of machine learning which is similar to human learning. In recent years, it has drawn a considerable portion of researchers' attention and it has been revolutionized; such as its integration with deep learning. This integration has created a better understanding of the visual environments and end-to-end direct learning from pixels to solve problems that have previously been intractable. This improvement has led to the creation of various deep reinforcement learning algorithms with different goals. In this paper, deep reinforcement learning algorithms and their applications are reviewed and categorized. This work also addresses the advantages and disadvantages of algorithms and the challenges that are solved with appearance of deep reinforcement learning. In order to use these algorithms, there are important considerations that need to be addressed in each problem. These considerations are about the most important components of reinforcement learning, which has been analyzed and categorized as the important achievement of this paper.
A Knowledge-Based Method for Rapid Design Concept Evaluation

Concept generation is one of the most critical steps in product design process. Recently, several computational tools for automatically generating design concepts were developed, which can generate a big number of design concepts. This brings a new challenge to traditional expert-based design concept evaluation methods since experts are not capable of evaluating a large number of design concept in a short time. Therefore, this work develops a knowledge-based method to roughly evaluation design concepts and elect a small number of design concepts for expert-based evaluation. In the proposed method, a knowledge base containing 100 design concepts extracted from existing products is constructed, and four features, including Number of Function (#F), Function Compatibility Index (FCI), Function Component Mapping Index (FCMI) and Component Compatibility Index (CCI) are defined for building evaluation models. Based on the knowledge base and features, several computational evaluation models are developed including novelty evaluation model (NEM), feasibility discrimination model (FDM) and feasibility evaluation model (FEM). Empirical results show the proposed method is capable of evaluating design concepts. This work makes two-fold contributes to the research community, the first is a manually constructed knowledge base is published, while the second is four features are defined and used to define design concept evaluation models.
Combined Static and Dynamic Antiwindup Architecture with Application to Quadcopters Experiencing Large Disturbances

This paper proposes a composite anti-windup design approach, featuring both dynamic and static components. The extra static component is specially structured to limit classical integrator wind-up, and is useful in nonlinear systems which operate far from their design operating point. An approach for stability analysis and design of the combined scheme is also provided. The work was motivated by the need to stabilise quadcopters subject to large disturbances, and excerpts from an extensive simulation campaign are reported. The simulations indicate that the combination of dynamic and static terms provide stability for significantly larger disturbances than either static or dynamic anti-windup alone.
Thermoelastic Damping in Full Clamped Rectangular Microplate Resonator Based on the Modified Couple Stress Theory with Three-Dimensional Heat Conduction

Due to the excellent properties of microplates, microplates are widely employed in the microelectromechanical system (MEMS) and nanoelectromechanical system (NEMS). Thermoelastic damping (TED), which has been identified as an important mechanism of energy dissipation in MEMS and NEMS in a vacuum environment and determines the quality factor in the MEMS, is not able to be eliminated by design and manufacturing. In the meantime, the size dependence of deformation behavior has been verified experimentally in the microstructure, single crystal silicon, which is the inherent property. Zhong et al derived a closed-form model for TED in rectangular microplates considering the bending and axial deformations. It is worth noting that the developed analytical models based on the MCST only took the heat conduction along the thickness into the coupled thermoelasticity governing equations, and ignored the heat conduction from the other axial coordinates, which would lead to the underestimation of the TED in micro-resonators. This paper intent to present an analytical model for rectangular microplate resonator based on the modified couple stress theory with three-dimensional (the thickness direction, the length direction and width direction) heat conduction.
Relay-Assisted D2D Underlay Cellular Network Analysis Using Stochastic Geometry: Overview and Future Directions

Device-to-Device (D2D) communication is one of the enabling technologies for meeting the capacity requirements of the fifth-generation wireless systems (5G). It has diverse applications in traffic offloading, disaster management and content sharing, to mention a few. The network coverage and capacity further improve when relays are introduced to D2D communication. However, the interference becomes more severe since these devices also share resources with the traditional cellular users in the underlay. To take the benefits and avert the drawbacks of this spectrum sharing scenario, analytical tools capable of revealing the mathematical relationships among pertinent network design parameters are needed. This brings stochastic geometry (SG) into the picture. With SG-based analyses, designers can model concepts to understand, provide insights, and address the problems of spectrum sharing in relay-assisted D2D communication. Some of the key metrics of particular interest to network designers are the transmission capacity and spectral efficiency of D2D communication, as they reveal the performance gains and quantify the level of interference within the network. These enable them to properly correlate relevant cause-and-effect relationships before wealth and time are invested in network implementation. Despite the studies on the analysis of relay-assisted D2D underlay cellular networks using SG in recent years, there is no available survey material where researchers can find models, assumptions, key results and derived lessons to further comprehend this area and open up new research lines. This motivates the presentation of this paper which in addition to the aforementioned, gives elaborate discussions on promising areas for future research with respect to the recent advancements in D2D communication and SG research.
Control System Design for Electromagnetic Driving Robot Used for Vehicle Test

It is necessary to conduct repeatable and precise process for a lot of vehicle tests. However, the data obtained from human driver’s vehicle tests can’t meet the precision of product development. To solve the uncertainty of vehicle test results from human drivers, an electromagnetic driving robot used for vehicle test control system instead of human drivers is developed. The vehicle structure parameters self-learning and the vehicle dynamics performance self-learning algorithms are given. The multiple manipulators collaborative control method of electromagnetic driving robot is proposed. Experimental results shows that the electromagnetic driving robot can realize the collaborative control of the shift manipulator, throttle mechanical leg, clutch mechanical leg and brake mechanical leg, and can complete vehicle test tasks, which meets the requirements of national standards about vehicle test.
A Semantic Approach for Cyber Threat Prediction Using Machine Learning

Computers that are connected to the smart systems or Internet infrastructure is incited with various security threats, ranging from Computer infection to the drive-by downloads and botnets. The decent variety and measure of security threats in the system has fundamentally expanded. These threats are utilized by an underground economy for illicit exercises, for example, circulation of spam messages, refusal of-benefit assaults and robbery of MasterCard information. Using such calculations the higher level of security and respectability can be acted efficiently in the client level system framework. This issue of unprotected Computer framework can be analyzed by machine learning models. Machine learning is an order of science in which we are worried about the plan and extension of calculations that enable PCs or any machine to learn the required information, for instance, from sensor information or databases. The main aim of this research work is to utilize machine learning idea for the acknowledgment of complex examples and expectation of threats dependent on the information. Here in this paper, machine learning is applied to cyber security, with a reason to predict, identify and avert the complex cyber-security threats.
Generative Adversarial Network Based Anomaly Detection on the Benchmark Tennessee Eastman Process

Anomaly detection has been of practical interest to many fields of engineering. Generative Adversarial Networks (GAN), which have excellent performance in modelling the complex distributions of real-world data, shows its effectiveness in anomaly detection. In this paper, the application of Bidirectional Generative Adversarial Network (BiGAN) model for anomaly detection has been introduced and investigated. Besides, scoring functions has also been applied to estimate the anomaly of new data samples. The proposed Bidirectional Generative Adversarial Network based anomaly detection method demonstrates a competitive performance on Tennessee Eastman (TE) datasets, while being several hundred-fold faster at test time than other published GAN-based method.
Channel Estimation for Millimeter Wave Massive MIMO Systems with Low-Resolution ADCs

Channel estimation is very challenging for millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems with low-resolution analog-to-digital converters (ADCs) owing to the severe nonlinear distortion introduced by quantization. In this paper, we develop an advanced channel estimation method based on generalized expectation consistent signal recovery (GEC-SR) algorithm, which can obtain accurate channel state information from quantized signal. Specifically, we consider Laplacian prior to model angular domain mmWave channel coefficients and embed the expectation-maximization (EM) principle into GEC-SR algorithm to learn the unknown prior parameters. Numerical results show that our method outperforms state-of-the-art generalized-approximate-message-passing-based channel estimator, and can reduce the required pilot overhead significantly by using orthogonal pilot.
Vibration feature extraction using local temporal self-similarity for rolling bearing fault diagnosis

This paper presents a new method for rolling bearing fault diagnosis. The novel vibration feature extraction is learned with local temporal self-similarities (TSS) continuously from collected vibration signals. The bag-of-words (BoW) scheme is then employed for fault classification taking advantages of these features. We investigated the effectiveness of the framework on the publicly-available Case Western Reserve University (CWRU) data set. We also compare the method with state-of-the-art approaches. The result demonstrates excellent performance of the proposed method, outperforming those compared state-of-the-art approaches.
Research of the Dual-rotor System with Spring Characteristics

The dual-rotor system is the core component of the aero-engine, and it is very important to study dynamics characteristics for the aero-engine’s development. Based on analyzing structural characteristics of aero-engine’s rotors and considering spring characteristics of high and low pressure rotors, a novel dual-rotor dynamic coupling model is first proposed. Based on the dynamics model, natural frequencies of the low pressure rotor are analyzed by Campbell diagrams, and the coupling critical rotational speed of the low pressure rotor is obtained. Through numerical simulations, resonance curves of the dual-rotor system are obtained. It is found that the counter rotation between the low and high pressure rotors leads to the difference between amplitudes of the low pressure rotor in x and y directions, which consists with the results of mathematical analysis. The results of this study put forward new research directions to analyze complex dynamics characteristics of the dual-rotor system.
Learning Radio Maps for Physical-Layer Security in the Radio Access

While there is an evident trend towards “strengthening” the notion of secrecy in information-theoretic analyses, there remains the question of the assumptions under which the promises of physical-layer security may be delivered. A critical aspect here is the level of knowledge of the inherently uncertain wireless environment, required in order to be able to guarantee a certain security performance. A general observation is that the strictness of the notion of physical-layer security increases the focus on the design assumptions of the physical layer and the adequacy of secure transmission strategies. Guided by these insights, we propose a probabilistic secrecy characterization that captures the uncertainty in the communication model coming from the unknown channel state information of the eavesdropper's channel (e.g. due to Eve's unknown position in the wireless environment). Integral to our concept is the use of any-to-any radio maps as underlying context information for the probabilistic secrecy characterization. The radio maps are learned by using recent machine-learning advances in network channel gain cartography. The resulting secrecy maps provide the key ingredient in the integration of physical-layer security in the radio access, and represent a valuable tool for secure radio access system design. The statistical characterization relates to the concept of spatial availability of services in wireless networks, by indicating the locations in a given area where the secrecy-related performance would surmount a given threshold with a guaranteed level of confidence. In this sense, it provides a quality-of-service (QoS) notion for physical-layer security in a spatial context.
Ponatinib modulates the metabolic profile of obese mice by inhibiting adipose tissue macrophage inflammation

Obesity-induced metabolic syndrome is a rapidly growing conundrum, reaching epidemic proportions globally. Chronic inflammation in obese adipose tissue plays a key role in metabolic syndrome with a series of local and systemic effects such as inflammatory cell infiltration and inflammatory cytokine secretion. Adipose tissue macrophages (ATM), as one of the main regulators in this process, are particularly crucial for pharmacological studies on obesity-related metabolic syndrome. Ponatinib, a multi-targeted tyrosine kinase inhibitor originally used to treat leukemia, has recently been found to improve dyslipidemia and atherosclerosis, suggesting that it may have profound effect on metabolic syndrome, although the mechanisms underlying have not yet been revealed. Here we discovered that ponatinib significantly improved insulin sensitivity in leptin deficient obese mice. In addition to that, ponatinib treatment remarkably ameliorated high fat diet-induced hyperlipidemia and inhibited ectopic lipid deposition in the liver. Interestingly, although ponatinib did not reduce but increase the weight of white adipose tissue (WAT), it remarkably suppressed the inflammatory response in WAT and preserved its function. Mechanistically, we showed that ponatinib had no direct effect on hepatocyte or adipocyte but attenuated free fatty acid (FFA) induced macrophage transformation from pro-inflammatory to anti-inflammatory phenotype. Moreover, adipocytes co-cultured with FFA-treated macrophages exhibited insulin resistance, while pre-treat these macrophages with ponatinib can ameliorate this process. These results suggested that the beneficial effects of ponatinib on metabolic disorders are achieved by inhibiting the inflammatory phenotypic transformation of ATMs, thereby maintaining the physiological function of adipose tissue under excessive obesity. The data here not only revealed the novel therapeutic function of ponatinib, but also provided a theoretical basis for the application of multi-target tyrosine kinase inhibitors in metabolic diseases.
Performance Evaluation of Applying N-Gram Based Naïve Bayes Classifier for Hierarchical Classification

Text classification is a process of allocating one or more class label to a text document. If the text classification problem has too many categories, and there are certain categories with less number of training documents, text classification task becomes difficult. Recall will be less for categories with less number of training documents. To handle text classification problem with too many categories and to take into consideration parent-child/sibling relationships between categories in user profile and document profile for content-based filtering, hierarchical classification is better approach. The main issue with hierarchical classification is error propagation. The error that occurs at early level in hierarchy will carry forward to all the levels below it. So, misclassification at early level in hierarchy needs to be reduced. Term ambiguity may be one of the reasons for classification error. Naïve Bayes classification method is mostly used in text classification problem as it takes less time for training and testing. Naïve Bayes model considers that terms are not dependent on each other for a given class. For data where terms are dependent on each other, performance of naïve Bayes is degraded. In this paper, word-level n-gram based Multinomial Naïve Bayes classification method is combined with hierarchical classification to reduce misclassification that occur at early level in hierarchy & improve content-based filtering. Proposed algorithm also suggests a way to reduce execution time requirements for calculating probabilities of terms for n-gram naïve bayes model.
Pharmacological effects of natural medicine ginsenosides against Alzheimer’s disease

Ginsenosides are the most important pharmacological active ingredient of ginseng, with multiple biological therapeutic targets, mild action and no side effects. It is having shown beneficial effects in vitro and in vivo models of AD. In this review, we analyze large literature, summarize the inhibition of ginsenosides fibrous extracellular deposition of β-amyloid (Aβ) and neurofibrillary tangles (NFTs) of possible mechanisms, and explain the effects of ginsenosides on AD neuroprotection from the aspects of antioxidant, anti-inflammatory, and anti-apoptosis, prove the potential of ginsenosides as a new class of drugs for the treatment of AD. In addition, according to the current clinical application status of natural drugs, this paper analysis the delivery route and delivery mode of ginsenosides from the perspective of pharmacokinetics, providing a deeper insight into the clinical application of ginsenosides in the treatment of AD.
Proteomic responses of two spring wheat cultivars to the combined water deficit and aphid (Metopolophium dirhodum) treatments

In the field, plants usually have to face the combined effects of abiotic and biotic stresses. In our study, two spring wheat cultivars—Septima and Quintus—were subjected to three water regimes [70%, 50%, and 40% soil water capacity (SWC)], aphid (Metopolophium dirhodum) infestation, or the combination of both stresses, i.e., water deficit (50%, 40% SWC) and aphids. The study has a 2 × 3 × 2 factorial design with three biological replicates. In the present study, the results of proteomic analysis using 2D-DIGE followed by MALDI-TOF/TOF protein identification are presented. Water deficit but also aphid infestation led to alterations in 113 protein spots including proteins assigned to a variety of biological processes ranging from signaling via energy metabolism, redox regulation, and stress and defense responses to secondary metabolism indicating a long-term adaptation to adverse conditions. The absence of specific proteins involved in plant response to herbivorous insects indicates a loss of resistance to aphids in modern wheat cultivars during the breeding process and is in accordance with the “plant vigor hypothesis. ” Septima revealed enhanced tolerance with respect to Quintus as indicated by higher values of morphophysiological characteristics (fresh aboveground biomass, leaf length, osmotic potential per full water saturation) and relative abundance of proteins involved in mitochondrial respiration and ATP biosynthesis.
Parallel Trajectory-to-Location Join

The matching between trajectories and locations, called Trajectory-to-Location join (TL-Join), is fundamental functionality in spatiotemporal data management. Given a set of trajectories, a set of locations, and a threshold $\theta$θ, the TL-Join finds all (trajectory, location) pairs from the two sets with spatiotemporal correlation above $\theta$θ. This join targets diverse applications, including location recommendation, event tracking, and trajectory activity analyses. We address three challenges in relation to the TL-Join: how to define the spatiotemporal correlation between trajectories and locations, how to prune the search space effectively when computing the join, and how to perform the computation in parallel. Specifically, we define new metrics to measure the spatiotemporal correlation between trajectories and locations. We develop a novel parallel collaborative (PCol) search method based on a divide-and-conquer strategy. For each location $o$o, we retrieve the trajectories with high spatiotemporal correlation to $o$o, and then we merge the results. An upper bound on the spatiotemporal correlation and a heuristic scheduling strategy are developed to prune the search space. The trajectory searches from different locations are independent and are performed in parallel, and the result merging cost is independent of the degree of parallelism. Studies of the performance of the developed algorithms using large spatiotemporal data sets are reported.