Miracle-dz/UniFAR-FTUs huggingface dataset:
Antigen Extraction and B Cell Activation Enable Identification of Rare Membrane Antigen Specific Human B Cells

Determining antigen specificity is vital for understanding B cell biology and for producing human monoclonal antibodies. We describe here a powerful method for identifying B cells that recognize membrane antigens expressed on cells. The technique depends on two characteristics of the interaction between a B cell and an antigen-expressing cell: antigen-receptor-mediated extraction of antigen from the membrane of the target cell, and B cell activation. We developed the method using influenza hemagglutinin as a model viral membrane antigen, and tested it using acetylcholine receptor (AChR) as a model membrane autoantigen. The technique involves co-culturing B cells with adherent, bioorthogonally labeled cells expressing GFP-tagged antigen, and sorting GFP-capturing, newly activated B cells. Hemagglutinin-specific B cells isolated this way from vaccinated human donors expressed elevated CD20, CD27, CD71, and CD11c, and reduced CD21, and their secreted antibodies blocked hemagglutination and neutralized viral infection. Antibodies cloned from AChR-capturing B cells derived from patients with myasthenia gravis bound specifically to the receptor on cell membrane. The approach is sensitive enough to detect antigen-specific B cells at steady state, and can be adapted for any membrane antigen.
Reconstruction of Fine Grayscale Image with Low Sampling Rate Based on the Salience Analysis

This paper introduces a new method for image reconstruction, which mainly aims at fine grayscale image reconstruction and it is based on visual salience analysis. Considering there is much difference of the texture and details in one image, so the feature of each pixel should be employed. In this paper, an algorithm is proposed to seize this kind of feature and use it to help image reconstruction. For two-dimensional fine grayscale image reconstruction, LC+ASPL algorithm can get better results. LC means Luminance contrast, which is used as a criterion to describe salience of single pixel. ASPL is a weighted image reconstruction algorithm, it is obtained from the method of Smoothed Projected Landweber. For data sampling, analyze the LC feature of the image in advance, then according to the analysis results and Block Compressed Sensing (BCS)theory, choose the sampling data selectively. Give higher weight to high salient area and lower weight to matching areas. For the process of image reconstruction, lock the salient regions of the image for further analysis and processing. It has been proved that this method of image measurement and reconstruction can help to reconstruct the 2D fine grayscale image more accurately and reproduce the details more clearly with fewer data. This method can be applied to medical image reconstruction, like CT and MRI reconstruction. For one thing, reduce the sampling data can reduce the scanning time for the patient; for another thing, finely reconstruction can help the doctors diagnose disease. This method can also give some theoretical guidance for fine image reconstruction.
Reliability Impacts of the Dynamic Thermal Rating System on Smart Grids Considering Wireless Communications

The dynamic thermal rating (DTR) system is one of most widely used state-of-the-art of smart grids owing to its proven ability to improve the existing line ratings at a fraction of the cost of most extant methods without infringing line safety requirements. However, the successful implementation of this system depends on the reliability of communication networks, especially wireless communication systems, in transmitting line rating information back to the utility company so that an appropriate line flow control can be executed. Therefore, the efficient, continuous, and secure operations of smart grids are equally affected by the reliability of the DTR and wireless communication systems. Despite this evidence, the reliability impacts of these systems on power grids are yet to be examined in a single framework. In this paper, a new model for investigating the reliability impacts of the DTR system on power grids considering the wireless communication network is proposed. The inability of the DTR system to perform, either due to its unavailability or communication failure, is taken as a function of line ratings that affect the transmission capacity and reliability of power grids. When the DTR system service is unavailable, the line rating is either reverted to the static thermal rating or estimated via the multi-linear regression (MLR) model based on information from neighboring regions. The comparison of these two models on the IEEE 24-bus reliability test system reveals that the MLR model is more beneficial in mitigating DTR and communication systems failures. This paper also explores various communication system failure scenarios and the reliability impacts of redundancy designs. The findings reveal that the communication system with moderate reliability levels yield the most benefits when the redundancy number increases from 1 to 4. Overall, the communication system significantly affects the availability of the DTR system.
Sign Language Static Gestures Recognition Tool Prototype

this article is aimed at illustrating the approach of detecting and recognizing Kazakh Sign Language static gestures without using expensive depth cameras or sensor gloves. For this research a simple RGB mono camera had been used. The main distinguishing feature of our approach is that, unlike most of the existing approaches, it performs hand detection and gesture recognition jointly, i.e. at the very moment when the shape of a static gesture is detected its specific label is automatically recognized. The approach was tested on a range of datasets, and encouraging results were achieved.
Comparative Analysis of Distributed Ledger Technologies

Distributed Ledger Technology (DLT) refers to a digital method for registering virtual transactions and other similar data in multiple locations simultaneously. The main characteristic of distributed ledgers is that they do not have a central administration component, due to advanced algorithms and methods used for record-keeping. Thus, transactions are faster and reasonable, as they do not require a central authority to validate them. The network is formed by multiple nodes through which the participant can access recordings, making the network become less vulnerable when referring to cyberattacks. After several years of technological development in this area, different distributed ledger technologies are available. This article presents Blockchain and Tangle technologies along with their main characteristics, in order to make an extended comparison of their approaches. In addition to this, the paper contains experimentation details and results for each technology presented.
Machine-Learning-Based Predictor of Human-Bacteria Protein-Protein Interactions by Incorporating Comprehensive Host-Network Properties.

The large-scale identification of protein-protein interactions (PPIs) between humans and bacteria remains a crucial step in systematically understanding the underlying molecular mechanisms of bacterial infection. Computational prediction approaches are playing an increasingly important role in accelerating the identification of PPIs. Here, we developed a new machine-learning-based predictor of human- Yersinia pestis PPIs. First, three conventional sequence-based encoding schemes and two host network-property-related encoding schemes (i.e., NetTP and NetSS) were introduced. Motivated by previous human-pathogen PPI network analyses, we designed NetTP to systematically characterize the host proteins' network topology properties and designed NetSS to reflect the molecular mimicry strategy used by pathogen proteins. Subsequently, individual predictive models for each encoding scheme were inferred by Random Forest. Finally, through the noisy-OR algorithm, 5 individual models were integrated into a final powerful model with an AUC value of 0.922 in the 5-fold cross-validation. Stringent benchmark experiments further revealed that our model could achieve a better performance than two state-of-the-art human-bacteria PPI predictors. In addition to the selection of a suitable computational framework, the success of our proposed approach could be largely attributed to the introduction of two comprehensive host network-property-related feature sets. To facilitate the community, a web server implementing our proposed method has been made freely accessible at http://systbio.cau. edu.cn/intersppiv2/ or http://zzdlab.com/intersppiv2/ .
Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning

Although crack inspection is a routine practice in civil infrastructure management (especially for highway bridge structures), it is time‐consuming and safety‐concerning to trained engineers and costly to the stakeholders. To automate this in the near future, the algorithmic challenge at the onset is to detect and localize cracks in imagery data with complex scenes. The rise of deep learning (DL) sheds light on overcoming this challenge through learning from imagery big data. However, how to exploit DL techniques is yet to be fully explored. One primary component of practical crack inspection is that it is not merely detection via visual recognition. To evaluate the potential risk of structural failure, it entails quantitative characterization, which usually includes crack width measurement. To further facilitate the automation of machine‐vision‐based concrete crack inspection, this article proposes a DL‐enabled quantitative crack width measurement method. In the detection and mapping phase, dual‐scale convolutional neural networks are designed to detect cracks in complex scene images with validated high accuracy. Subsequently, a novel crack width estimation method based on the use of Zernike moment operator is further developed for thin cracks. The experimental results based on a laboratory loading test agree well with the direct measurements, which substantiates the effectiveness of the proposed method for quantitative crack detection.
Nonlinear Robust Control of Antilock Braking Systems Assisted by Active Suspensions for Automobile

In this brief, a novel robust control of an antilock braking system (ABS) with the assistance of an active suspension system (ASS) is proposed for automobile to improve the braking performance. Different from other ABS, not the reference wheel slip ratio but the peak tire–road adhesion coefficient is taken as the control object, and the barrier Lyapunov function control method is adopted to confine the control object within a smaller and more stable area. Besides, the rolling resistance is taken as an external disturbance and counteracted by a modified controller. Furthermore, the increase of a tire vertical load via the ASS is explored to further shorten the braking distance under the condition that the trajectory of the sprung mass and suspension space is bounded to a predetermined limitation. Finally, a favorable control simulation consequence is obtained, validating the effectiveness of the proposed control scheme.
Term Extraction from Chinese Texts Without Word Segmentation

The paper is dedicated to the problem of automatic term extraction from natural language texts. One of the first steps in this topic is building a domain thesaurus. Well approved methods of terms extraction based on word frequencies exist for alphabetic languages. Direct application of these methods for hieroglyphic texts is challenged because of missing spaces between words. The sentence segmentation task in hieroglyphic languages is usually solved by dictionaries or by statistical methods, particularly, by means of a mutual information approach. Sentence segmentation methods, as well as methods of terms extraction, separately, do not reach 100 percent precision and recall, and their combination just increases the number of errors. The aim of this work is to improve recall and precision of domain terms extraction from hieroglyphic texts. The proposed method is to identify repetitions of the two, three or four symbol sequences in each sentence and correlation of occurrence frequencies for these sequences in the target domain and contrast documents collection. According to the research, it was stated that a trivial ranking of all possible symbol sequences allows extracting only frequently used terms. Filtering of symbol sequences by their ratio of frequencies in the domain and in the contrast collection gave the possibility to extract reliably frequently used terms and to find satisfactory rare domain terms. Some results of terms extraction for the “Geology” domain from a Chinese text are presented in this paper. A set of articles from the newspaper “Renmin Ribao” was used as a contrast collection and some satisfactory results were obtained.
Reinforcement Learning for Online Learning Recommendation System

In the learning environment, individual learner requires flexible and suitable learning processes. Online learning should be able to recommend appropriate learning objects (LOs) to an individual in real-time. Most of the existing approaches of online learning recommendation systems are based on collaborative filtering methods. Such methods have a limitation on realtime adaption and require the prior knowledge of students and LOs. Therefore, this study proposes a real-time recommendation method which is suitable for flexible and complex environments. The proposed method is based on Reinforcement Learning problem. The method is able to explore the environment to get information and exploit the information to make a decision. We evaluate the proposed method with the real world data. We vary e-greedy, the learning rate, and the discount rate for a tradeoff between the exploration and exploitation.
Flow Simulation on Multilayer Networks: a New Approach to Community Detection in Complex Systems

A multilayer network, in which each layer represents one type of interactions between nodes in the network, has emerged as a new paradigm in net-work science to study complex systems arising from diverse disciplines including biology, physics and social science. Given that the connectivity in each layer encodes different modalities in a re-al-world system, extracting composite communities shared by layers in a multilayer network has become a new challenge. Based on the introduction of a transition probability for switching be-tween neighboring layers, this paper proposed a new flow simulation based community detection method. It extends the Markov Cluster Algorithm (MCL) to the analysis of multilayer networks. Tested on three synthetic datasets with different levels of noise within and between communities and one real dataset having no ground truth, the proposed method has shown a great potential in combining complementary information contained in each layer and revealing true community structure of a system represented by a multilayer network.
Reversible graphene decoupling by NaCl photo-dissociation

We describe the reversible intercalation of Na under graphene on Ir(1 1 1) by photo-dissociation of a previously adsorbed NaCl overlayer. After room temperature evaporation, NaCl adsorbs on top of graphene forming a bilayer. With a combination of electron diffraction and photoemission techniques we demonstrate that the NaCl overlayer dissociates upon a short exposure to an x-ray beam. As a result, chlorine desorbs while sodium intercalates under the graphene, inducing an electronic decoupling from the underlying metal. Low energy electron diffraction shows the disappearance of the moiré pattern when Na intercalates between graphene and iridium. Analysis of the Na 2p core-level by x-ray photoelectron spectroscopy shows a chemical change from NaCl to metallic buried Na at the graphene/Ir interface. The intercalation-decoupling process leads to a n-doped graphene due to the charge transfer from the Na, as revealed by constant energy angle resolved x-ray photoemission maps. Moreover, the process is reversible by a mild annealing of the samples without damaging the graphene.
Circle of Trust: A High Volume, Energy Efficient, Stake Blind and High Attack Tolerant Blockchain Consensus Protocol

Popular cryptocurrencies like Bitcoin, NEO and Ripple have their own advantages and disadvantage. Bitcoin causes massive depletion of energy and computational power. NEO rewards rich miners with more blocks to validate. Ripple can only process a mere 1500 transactions per second[1]with a block time of 3.5 seconds[2]. All these major blockchains have major flaws. My Consensus Protocol “Circle of Trust” overcomes the drawbacks of other consensus protocols such as PoW, PoS and DPos. Apart from this, Circle of Trust also has an architecture capable of scaling the transactions per second, as the network of miners grow, which no other blockchain on the market is capable of doing. This consensus protocol will be able to beat VISAs 56,000 transactions per second[3]while maintaining the sequence of blocks. All this can be done because, the concept of trust score is at the heart of this consensus protocol.
Using the Total Cost of Ownership to Decide Resource Adjustment in Virtual Networks

The ultimate goal of resource management in virtual computer and network systems is to reduce the amount of used resources to minimize the total cost of ownership (TCO). However, current methods to perform the adjusting of resource assignation are based solely on the usage level of resources themselves. They do not contemplate that there are other conditions that affect TCO, such as making changes in the system and the possibility of degrading the service (rejecting traffic). Those other conditions must be considered when deciding what resources must be adjusted and how in order to actually minimize the TCO. In this paper we propose a decision algorithm that considers, in addition to the cost of using resources, the additional costs of requesting the underlying provider to adjust the resources and the cost of degrading the service level of the system. The resource costs and service degradation are somewhat opposite, so avoiding resource costs will incur in service degradation and avoiding service degradation requires to increase resource costs. Thus, deciding the resource assignation is not a direct answer and depends on the running conditions of the controlled system. After each specific situation, our algorithm evaluates different options to adjust resources and chooses the one that results in the lower TCO. Therefore, our approach is evolutionary, as the system evolves gradually towards the optimum, even when considering the changing requirements of the running system. We demonstrate that a TCO reduction of more than 12 % can be achieved by using our algorithm in comparison to a traditional method that only considers resource utilization.
Deadbeat Control for Hybrid Energy Storage Systems in DC Microgrids

This paper presents a simple and effective deadbeat-based method for hybrid energy storage systems, including batteries and supercapacitors, in dc microgrids. Inheriting from the nature of deadbeat control strategies, the proposed method can generate the optimal duty ratio in one control cycle, so that it can respond faster to the disturbance than various proportional-integral-based methods in previous works. With an appropriate reference assignment based on the characteristics of batteries and supercapacitors, this method can compensate the transient and steady-state power mismatches in dc microgrids while maintaining an excellent dc bus voltage regulation. Specifically, the high power density feature of the supercapacitor is fully utilized by the proposed method, and the burden on the battery can be relieved for a longer lifespan. A current observer is employed in the proposed method to eliminate the requirement of photovoltaic (PV) and load current sensors to reduce the system cost. A laboratorial setup of a dc microgrid, including the HESS and PV emulator, is built to validate the effectiveness of the proposed deadbeat-based method under various varying conditions in load power consumption, PV power generation, and bus voltage reference. The performance of the proposed method is compared with that of the previous works to show its superiority in dynamic and steady-state responses.
Main Direction—An Effective Statistic for Hyperspectral Image Classification

In the hyperspectral image classification literature, mean and covariance are prominently used statistics. The first order statistics, i.e., mean, although easily estimated, can have low discriminative property. However, the discrimination power of the second order statistic, i.e., covariance, is strong. The downside of the estimation of covariance is the large sample size. In this paper, we propose a effective statistic named main direction. The main direction is defined as a direction in which the variation or the distance of the samples becomes the largest. Besides, the estimation of the main direction is stable even when the size of the sample set is very small. Based on the main direction, we propose a modified spectral angle mapper classifier and demonstrate the effectiveness of the modified classifier with experimental results.
A Radial Zoom Motion-Based Paradigm for Steady State Motion Visual Evoked Potentials

Background: In steady state visual evoked potential (SSVEP)-based brain-computer interfaces, prolonged repeated flicker stimulation would reduce the system performance. To reduce the visual discomfort and fatigue, while ensuring recognition accuracy, and information transmission rate (ITR), a novel motion paradigm based on the steady-state motion visual evoked potentials (SSMVEPs) is proposed. Methods: The novel SSMVEP paradigm of the radial zoom motion was realized using the sinusoidal form to modulate the size of the stimuli. The radial zoom motion-based SSMVEP paradigm was compared with the flicker-based SSVEP paradigm and the SSMVEP paradigm based on Newton's ring motion. The canonical correlation analysis was used to identify the frequency of the eight targets, the recognition accuracy of different paradigms with different stimulation frequencies, and the ITR under different stimulation durations were calculated. The subjective comfort scores and fatigue scores, and decrease in the accuracy due to fatigue was evaluated. Results: The average recognition accuracy of the novel radial zoom motion-based SSMVEP paradigm was 93.4%, and its ITR reached 42.5 bit/min, which was greater than the average recognition accuracy of the SSMVEP paradigm based on Newton's ring motion. The comfort score of the novel paradigm was greater than both the flicker-based SSVEP paradigm and SSMVEP paradigm based on Newton's ring motion. The decrease in the recognition accuracy due to fatigue was less than that of the SSSMVEP paradigm based on Newton's ring motion. Conclusion: The SSMVEP paradigm based on radial zoom motion has high recognition accuracy and ITR with low visual discomfort and fatigue scores. The method has potential advantages in overcoming the performance decline caused by fatigue.