Miracle-dz/UniFAR-FTUs huggingface dataset:
Biogenesis and Function of Multivesicular Bodies in Plant Immunity

Multivesicular bodies (MVBs) are specialized endosomes that contain intraluminal vesicles generated from invagination and budding of the limiting membrane. In the endocytic pathway, MVBs are late endosomes whose content can be degraded through fusion with lysosomes/vacuoles or released into the extracellular space after fusion with the plasma membrane (PM). The proteins retained on the limiting membrane of MVBs are translocated to the membrane of lysosomes/vacuoles or delivered back to the PM. It has been long suspected that MVBs might fuse with the PM to form paramural bodies in plant cells, possibly leading to release of building blocks for deposition of papillae and antimicrobial molecules against invading pathogens. Over the past decade or so, major progress has been made in establishing the critical roles of MVBs and associated membrane trafficking in pathogen recognition, defense signaling, and deployment of defense-related molecules during plant immune responses. Regulatory proteins and signaling pathways associated with induced biogenesis and trafficking of MVBs during plant immune responses have also been identified and characterized. Recent successful isolation of plant extracellular vesicles and proteomic profiling of their content have provided additional support for the roles of MVBs in plant–pathogen interactions. In this review, we summarize the important progress and discuss how MVBs, particularly through routing of cellular components to different destinations, contribute to the complex network of plant immune system.
Aircraft recognition in remote sensing images based on deep learning

Object recognition is one of the fundamental issues in the field of computer vision. In traditional methods, invariant features are extracted from segmented targets for recognition. However, there is no common method for segmentation of aircraft targets so far due to the complex backgrounds, illuminations, noise and other practical factors. Therefore, in this paper, we propose a method for aircraft identification in remote sensing images based on HOG and deep learning features. We train two classifiers, one is the SVM classifier based on HOG feature, and the other is a classifier based on deep convolutional neural network VGGNet. First, we use the SVM classifier to identify the aircraft in the picture roughly, then we use the deep learning classifier to exclude misidentified targets. In this way, this coarse to fine framework can significantly improve the speed and accuracy of aircraft recognition in remote sensing images. At the same time, our method has a better generalization capability than the traditional methods. Experimental results demonstrate the robustness of our method.
Model-free semi-global output regulation for discrete-time linear systems subject to input amplitude saturation

In this paper, a data-driven method is developed based on off-policy reinforcement learning to solve the semi-global output regulation of discrete-time linear systems with input saturation. Algebraic Riccati equation based method is used to design a family of state feedback laws for the constrained output regulation problem. In contrast to the existing methods, complete knowledge of the system dynamics is no longer required in this paper. Instead, the data collected from on-line is efficiently utilized to obtain the adaptive optimal control policy. It is shown that the presented method can find feedback control inputs with constraint of amplitude saturation and the ability to stabilize a given linear system with all its poles inside or on the unit circle. Finally, a simulation example is carried out to demonstrate the conclusions of the whole paper.
Stabilization of sampled-data control system via mode-dependent average dwell time

The issue of stabilization for sampled-data control system is studied in this paper. The sampling control system is modeled as a switching system based on whether the control input is missing or not. Then the mode-dependent average dwell time method composed of slow switching and fast switching is used to derive the relevant conclusion. Finally, an example is presented to show the effectiveness of the result.
Salient point region Local Binary Patterns descriptor for target tracking

In this paper, in order to track UAV's and maritime targets an algorithm which is based on extraction of Local Binary Patterns (LBP) near salient points is proposed. The method extracts the LBP histograms from the regions near the detected salient points. Later these histograms are concatenated and used for object representation. The tracking is achieved by comparing the main histogram with the histograms extracted inside the search region. The proposed method is tested for both UAV and sea surface targets and experimental results are presented.
Molecular Characterization of Trypanosoma evansi Mevalonate Kinase (TeMVK)

The mevalonate pathway is an essential part of isoprenoid biosynthesis leading to production of a diverse class of >30,000 biomolecules including cholesterol, heme, and all steroid hormones. In trypanosomatids, the mevalonate pathway also generates dolichols, which play an essential role in construction of glycosylphosphatidylinositol (GPI) molecules that anchor variable surface proteins (VSGs) to the plasma membrane. Isoprenoid biosynthesis involves one of the most highly regulated enzymes in nature, 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), which catalyzes the conversion of HMG-CoA to mevalonic acid. The enzyme mevalonate kinase (MVK) subsequently converts mevalonic acid to 5-phosphomevalonic acid. Trypanosoma evansi is a flagellate protozoan parasite that causes the disease “Surra” in domesticated large mammals, with great economic impact. T. evansi has only a trypomastigote bloodstream form and requires constant modification of the variant surface glycoprotein (VSG) coat for protection against the host immune system. We identified MVK of T. evansi (termed TeMVK) and performed a preliminary characterization at molecular, biochemical, and cellular levels. TeMVK from parasite extract displayed molecular weight ~36 kDa, colocalized with aldolase (a glycosomal marker enzyme) in glycosomes, and is structurally similar to Leishmania major MVK. Interestingly, the active form of TeMVK is the tetrameric oligomer form, in contrast to other MVKs in which the dimeric form is active. Despite lacking organized mitochondria, T. evansi synthesizes both HMGCR transcripts and protein. Both MVK and HMGCR are expressed in T. evansi during the course of infection in animals, and therefore are potential targets for therapeutic drug design.
Study on evacuation simulation of medical pension building based on anylogic

In order to shorten evacuation time and improve evacuation efficiency, evacuation model for a medical pension building based on social force model and agent technology in Anylogic simulation platform is built. In this Evacuation model, physiological and psychological characteristics of evacuees are considered. By comparing the evacuation results with different evacuation numbers, evacuation patterns and mental parameters of evacuees, it conclude that the evacuation time is the longest when the psychological parameters are lower under the same evacuation number. This paper proved the important influence of mental parameters on evacuation through the complicated, diverse and truthful evacuation simulation.
A new approach for machine learning-based fault detection and classification in power systems

The detection and classification of faulty conditions in power systems is a task of crucial importance for a reliable operation. Recently, the use of high-resolution synchronized phasor measurements has been proposed by several researchers for fault detection and classification. Unlike the proposed approaches available in the literature, the central idea in this work is to leverage the delay information of phasor measurement streams to enable a faster recognition of faulty operation. In this work, therefore, we focus on the effect of the communication network delays on the fault detection time, and propose a novel training technique for fault detection and classification which takes delayed measurements into consideration. The performance of the proposed approach is verified using simulated power system data, where artificial neural networks are used for fault detection and classification.
Suppression of Sensorimotor Alpha Power Associated With Pain Expressed by an Avatar: A Preliminary EEG Study

Several studies using functional magnetic resonance imaging (fMRI) showed that empathic capabilities are associated with the activation (and deactivation) of relatively specific neural circuits. A growing number of electroencephalography studies also suggest that it might be useful to assess empathy. The main goal of this study was to use quantitative electroencephalography (qEEG) to test whether observation of pain expressed by an avatar (virtual reality) induces a suppression of alpha waves over sensorimotor cortical areas, as it is observed with human stimuli. Not only was it the case, but also the magnitude of alpha suppression was correlated with perspective-taking capacity of participants. Both empathy levels and magnitude of sensorimotor alpha suppression (SAS) were significantly higher in women than men. Interestingly, a significant interaction emerged between levels of individual empathy and specificity of experimental instructions, where SAS in participants with good perspective-taking was higher during passive observation of the distressed avatar, while the opposite was true during an active (trying to understand) condition. These results suggest that: (1) synthetic characters are able to elicit SAS; (2) SAS is indeed associated with perspective-taking capacities; (3) Persons with poorer perspective-taking capacities can show significant SAS when proper instructions are provided. Therefore, qEEG represents a low-cost objective approach to measure perspective-taking abilities.
Exploration on the Interaction Ability of Antitumor Compound Bis-[2,6-difluoro-N-(hydroxyl-O)benzamidato-O]dibutylitin(IV) with Human Peroxisome Proliferator-Activated Receptor hPPARγ

Diorganotin(IV) antitumor compound bis-[2,6-difluoro-N-(hydroxyl-O)benzamidato-O] (DBDF2,6T) was one of the novel patent organotin compounds with high antitumor activity and relatively low toxicity. In this study, several methods were used to study the interaction between DBDF2,6T and hPPARγ protein, including fluorescence quenching, three-dimensional (3D) fluorescence, drug affinity responsive target stability (DARTS), ultrafiltration-LC, and molecular docking. According to the experimental results, the quenching process of the hPPARγ protein was induced by static quenching mode to form a nonradiative ground-state complex with DBDF2,6T spontaneously, mainly through the hydrophobic force. DBDF2,6T could bind to the hPPARγ protein directly and give the protein the ability of antienzymatic hydrolysis. And the binding mode of DBDF2,6T into hPPARγ protein appeared to have an orientation towards residues of SER342 and GLY284. In conclusion, these methods could comprehensively reveal the interaction details of DBDF2,6T and the hPPARγ protein and established a feasible way to preliminarily identify the agonist compounds for the hPPARγ protein.
A comparative study of machine learning techniques used in non-clinical systems for continuous healthcare of independent livings

New technologies are being adapted to made progress in healthcare especially for independent livings. Medication at distance is leading to integrate technologies with medical. Machine learning methods in collaboration with wearable sensor network technology are used to find hidden patterns in data, detect patient movements, observe habits of patient, analyze clinical data of patient, find intention of patients and make decision on the bases of gathered data. This research performs comparative study on non-clinical systems in healthcare for independent livings. In this study, these systems are sub-divided w.r. t their working into two types: single purpose systems and multi-purpose systems. Systems that are built for single specific purpose (e.g. detect fall, detect emergent state of chronic disease patient) and cannot support healthcare generically are known as single purpose systems, where multi-purpose systems are built to serve for multiple problems (e.g. heart attack, fall detection etc.) by using single system. This study analyzes usages of machine learning techniques in healthcare systems for independent livings. Answer Set Programming (ASP), Artificial Neural Networks, Classification, Sampling and Rule Based Reasoning etc. are some state of art techniques used to determine emergent situations and observe changes in patient data. Among all methods, ASP logic is used most widely, it is due to its feature to deal with incomplete data. It is also observed that system using ANN shows better accuracy than other systems. It is observed that most of the systems created are for single purpose. In this work, 10 single purpose systems and 5 multi-purpose systems are studied. There is need to create more generic systems that can be used for patients with multiple diseases. Furthermore, most of the systems created are prototypical. There is need to create systems that can serve healthcare services in real world. Some systems are hard to be used in real life due to large hardware requirements. Although, good systems are created but still there is need to build more efficient, affordable, adoptive and generic systems.
NAPS: Natural Program Synthesis Dataset

We present a program synthesis-oriented dataset consisting of human written problem statements and solutions for these problems. The problem statements were collected via crowdsourcing and the program solutions were extracted from human-written solutions in programming competitions, accompanied by input/output examples. We propose using this dataset for the program synthesis tasks aimed for working with real user-generated data. As a baseline we present few models, with the best model achieving 8.8% accuracy, showcasing both the complexity of the dataset and large room for future research.
A local multi-robot cooperative formation control

An advanced algorithm for local sensing multi-robot cooperative formation control in uncertain environments is presented in this paper. In this proposed algorithm, the global-level multi-robot cooperative formation control problem is decomposed into a tracking problem of some subsystems each of which only includes one follower and one lead robot. A multi-step predictive strategy is adopted, in which each follower predicts the motion of the leading robot and estimates the relevant information based on the history motion information. The follower keeps formation by observing its leader on the local coordinate system of the following robot. The simulation results are given to demonstrate the feasibility and effectiveness of the approach.
Scalable Multiplex Network Embedding

Network embedding has been proven to be helpful for many real-world problems. In this paper, we present a scalable multiplex network embedding model to represent information of multi-type relations into a unified embedding space. To combine information of different types of relations while maintaining their distinctive properties, for each node, we propose one high-dimensional common embedding and a lower-dimensional additional embedding for each type of relation. Then multiple relations can be learned jointly based on a unified network embedding model. We conduct experiments on two tasks: link prediction and node classification using six different multiplex networks. On both tasks, our model achieved better or comparable performance compared to current state-of-the-art models with less memory use.
FCBF3Rules: A feature selection method for multi-label datasets

In this paper, a novel multi-label feature selection algorithm is introduced based on fast correlation-based filter (FCBF) feature selection method, which is a filter approach for single-label datasets. The strategy of FCBF is that first, it eliminates the features that are irrelevant to classes. Unlike many filter methods which stop on this step, FCBF finds redundant features among relevant features that are remained from the previous step and eliminates them. Therefore, this is one of the most successful single-labels methods in finding the most effective and the smallest feature subset. Extending the step of finding the relevant features in multi-label datasets is not a difficult task. However, in the step of eliminating redundant features, FCBF may removes many effective features. It should be noticed that in multi-label datasets, one feature may be able to distinguish samples that are relevant to a label while another feature is suitable for another label. Hence, these two features cannot be considered to be redundant and one of them cannot be removed. The main contribution of this paper corresponds to the step in which effective and useful features are distinguished from redundant ones in FCBF method. To do so, three rules are implemented and when even one of these rules is not fulfilled, the feature is not removed. The proposed method along with three recently proposed multi-label feature selection methods are applied on 6 standard multi-label datasets for evaluation. The obtained results indicate the strong capability of the proposed algorithm to find the best feature subset, compared to other algorithms.
Requirements for smart cities: Results from a systematic review of literature

Smart cities are gaining increasingly more importance in both research and business circles. Much effort is spent to defining what a smart city is and how it could be realized with today's or future technologies. However, from requirements engineering perspective, our knowledge of smart cities is fragmented; little is known about the requirements for smart cities as complex systems, or as systems of systems, in specific application domains. In this paper, we elicit requirements for smart city systems by carrying out a systematic review of scientific literature focused on the so-called “hard” domains of smart cities. Based on 32 selected publications, we gathered and classified requirements in respect to three types of smart city systems (instrumented, interconnected, and intelligent systems) and four classes of requirements: end-to-end experience, architectural, security, and infrastructure requirements. Our most important findings are that: (1) most authors took a bottom-up approach to defining requirements for smart cities; their efforts focused mainly on requirements important for designing an architecture that could scale up to any size and include any device or system; and (2) very little is mentioned on the newly emerging security and privacy challenges that are critical to gain the citizens' acceptance of smart city apps.
A soft robotic exo-sheath using fabric EMG sensing for hand rehabilitation and assistance

This paper presents the design and evaluation of a soft hand exo-sheath integrated with a soft fabric electromyography (EMG) sensor for rehabilitation and activities of daily living (ADL) assistance of stroke and spinal cord injury (SCI) patients. This wearable robot addresses the limitations of the soft robot gloves with design considerations in terms of ergonomics and clinical practice. Its features include: this exo-sheath is based on electric actuation and has been designed to be compact and portable. It reduces the shear force and avoids kinematic singularity comparing with tendon-driven soft gloves as their tendon routings are typically in parallel with individual fingers. Disparate from conventional robotic gloves, this design optimizes a bio-inspired fin-ray structure to enhance the hand proprioception as the palm is not covered by wearable structures. With a novel self-fastening finger clasp design, wearers can self-don/doff the exoskeleton device simplifying ADL assistance. To develop more intuitive control interface, a soft fabric EMG sensor has been developed to understand human intentions. The functionality of this soft robot has been demonstrated with experimental results using the low-level position control, kinematics evaluation and reliable EMG measurements.
High-precision analysis of discrete-time fractional-order sliding mode control

This paper proposes a discrete-time fractional-order sliding mode (DFSM) control scheme based on the single-input second-order system. The general methods of designing a fractional-order sliding surface and the DFSM controller are introduced. Specifically, a series of researches on the performance of the sliding surface, the dynamics of the sliding variable and the results of simulations and experiments are conduced in this paper. Moreover, during the researches, the parametric uncertainties of the system are taken into consideration, which makes the conclusion properer to the cases in practical industrial applications.
Centralized dynamic event-triggered control for multi-agent systems

This paper studies the consensus problems of multi-agent systems with new dynamic centralized event-triggered mechanism. Event-triggered control laws are adopted so as to reduce the frequency of individual control updating since the agents may be resource-limited in many real systems. The discrete time instants where the events are triggered are determined by a trigger function with respect to a certain measurement error. And the new dynamic centralized event-triggered mechanism is featured by introducing an internal dynamic variable, which is the engine of the of dynamic event-triggered algorithm. The triggering conditions of the agents are not only affected by the current states and the state measurement error of the agents but also those previous ones. And the event-triggered condition is more maneuverability. A Lyapunov function is used to prove the consensus of the multi-agent system under the dynamic event-triggered control. Finally, the effectiveness of the proposed protocol is illustrated by simulations.
3D concave defect measurement system of the cryogenic insulated cylinder based on linear structured light

Cryogenic insulated cylinders' features of the large size and reflecting make the geometric size measurement a challenge work. A new system for 3D visualization measurement of cryogenic insulated cylinders is designed in this paper. The system consists of three CCD cameras and three laser projectors. The measurement method is mainly based on monocular vision, linear structured light extraction and 3D reconstruction. Cylinder fitting and connected component labeling methods are adopted to process the point cloud data after 3D reconstruction. At the same time, approximate voxel grid filter based on Point Cloud Library (PCL) is applied to simplify the processing. Accordingly, the concave depth and position of the cylinder can be obtained. Through the experimental verification, the system can achieve the actual measurement demands of the measurement standard and the error of the depth measurement is lower than 1 mm.