Miracle-dz/UniFAR-FTUs huggingface dataset:
Angiotensin II Exaggerates SARS-CoV-2 Specific T-Cell Response in Convalescent Individuals following COVID-19

Dysregulation of renin−angiotensin systems during coronavirus disease 2019 (COVID-19) infection worsens the symptoms and contributes to COVID-19 severity and mortality. This study sought to investigate the effect of exogenous angiotensin II (Ang-II) on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T-cells response in recovered COVID-19 patients. Human peripheral blood mononuclear cells (PBMCs) were treated with Ang II and then stimulated with a SARS-CoV-2 peptide pool. T-cell responses were measured using flow cytometry, while enzyme-linked immunosorbent assay (ELISA) and intracellular cytokine staining (ICS) assays determined functional capability and polarization. Additionally, the relative level of protein phosphorylation was measured using a phosphokinase array. Our results showed that Ang II treatment significantly increased the magnitude of SARS-CoV-2-specific T-cell response in stimulated PBMCs with a SARS-CoV-2 peptide pool. Moreover, the phosphorylation levels of numerous proteins implicated in cardiovascular diseases, inflammation, and viral infection showed significant increases in the presence of Ang II. The mitogenic stimulation of PBMCs after Ang II and SARS-CoV-2 peptide pool stimulation showed functional polarization of T-cells toward Th1/Th17 and Th17 phenotypes, respectively. Meanwhile, ELISA showed increased productions of IL-1β and IL-6 in Ang II-stimulated PBMCs without affecting the IL-10 level. To our knowledge, this study is the first to demonstrate that Ang II exaggerates SARS-CoV-2-specific T-cells response. Therefore, during COVID-19 infection, Ang II may aggravate the inflammatory response and change the immune response toward a more inflammatory profile against SARS-CoV-2 infection.
Carbon deposition fault diagnosis of small piston engine based on optimized VMD

Aiming at the small piston engine carbon deposition fault in the process of running, based on the cylinder pressure and cylinder head vibration signal of the engine, a fault diagnosis method combining variational mode decomposition and support vector machine is used to diagnose the engine carbon deposition fault. Firstly, particle swarm optimization algorithm is used to optimize the parameters of the variational mode decomposition. Then, the intrinsic mode function is obtained by processing the pressure signal and cylinder head vibration signal of the engine. Then, the singular spectrum entropy is calculated by singular value decomposition of the intrinsic mode function. Finally, the singular spectrum entropy is input into the support vector machine classifier as the feature data set for training and testing. The results show that this method can identify the carbon deposition fault of the starting motor well, and the accuracy of fault identification and classification of cylinder pressure and cylinder head vibration signal is 98.33 % and 99.17 % respectively, which verifies the effectiveness of this method.
Semantic-based Self-Critical Training For Question Generation

Question generation is a conditioned language generation task that consists in generating a context-aware question given a context and the targeted answer. Train language modelling with a mere likelihood maximization has been widely used while suffering from exposure bias and the discordance between the training and the test metrics. In the way of addressing this issue, The presented work portrays a fully Transformer-based reinforcement learning generator-evaluation architecture for neural question generation. To edge the flexibility of the generation, a semantic-based reward score was externally infused during the training to drive the training of the language model. The global architecture is laid out in a generator-evaluator fashion optimized directly to n-gram and semantic-based metrics. Evaluation metrics for language modelling only based on n-gram overlapping do not consider semantic relations between reference and candidate sequences. To improve the evaluation step, a two-fold evaluation was carried out. On the one side, an n-gram overlapping evaluation using the BLEU score. On the other side, a semantic-based assessment using BERTScore and NUBIA. The results were corroborated by a binary human evaluation of the semantic relatedness of the generated question and the ground truth. The results obtained showed that use a semantic-based REINFORCE algorithm for the question generation syntactically reshapes the generated questions while preserving their underlying semantic meaning. Many downstream applications can be drawn from a successful question generation including the enlargement of question answering datasets, the improvement of conversational systems, the enhancement of autonomous educational assessment systems, and so forth.
Learning Task-Oriented Channel Allocation for Multi-Agent Communication

Benefiting from the rapid progress of wireless communication and artificial intelligence, multi-agent collaboration opens up new opportunities for various fields. To facilitate multi-agent acting as a group, effective communication plays a crucial role. Recently, many efforts based on multi-agent reinforcement learning have been made to enable effective multi-agent communication under limited bandwidth or noisy channel. However, current methods do not explore wireless resource allocation strategy explicitly. Moreover, due to ignoring task-relevant significance of information, traditional wireless resource allocation schemes may fail to guarantee the transmission efficiency and reliability for multi-agent communication. To this end, in this paper, we propose a task-oriented communication principle for multi-agent communication. We model the task-oriented channel allocation problem as a decentralized partially observable Markov decision process and propose a multi-agent reinforcement learning framework as a solution. Specifically, we design a novel variational information bottleneck to extract task-relevant information from local observation. Furthermore, a task-oriented channel allocation mechanism is developed to choose the allocation pattern with maximum expected gain. Finally, a double attention mechanism is developed to motivate the efficient utilization of task-relevant information. Experimental results show that our method can improve the effectiveness and efficiency of multi-agent communication, enhancing collaboration performance compared to baselines.
Impact of Catheter Ablation for Atrial Fibrillation on Quality of Life

Atrial fibrillation is the most common sustained cardiac arrhythmia in adults. It is a complex arrhythmia leading to increased morbidity and mortality requiring thorough assessment and classification to guide therapy and to assess whether to pursue rate or rhythm control therapy. To obtain rhythm control, several strategies are available with different advantages and disadvantages concerning success rate and safety. Apart from antiarrhythmic drugs, catheter ablation is a well-established invasive therapy to treat atrial fibrillation. As quality of life is a very important factor to pursue rhythm control, several studies investigated on the specific impact of catheter ablation on quality of life. Catheter ablation shows a beneficial effect on quality of life in paroxysmal and persistent atrial fibrillation independent of the timepoint and strategy of catheter ablation.
Sharp nonasymptotic bounds on the norm of random matrices with independent entries

We obtain nonasymptotic bounds on the spectral norm of random matrices with independent entries that improve significantly on earlier results. If $X$ is the $n\times n$ symmetric matrix with $X_{ij}\sim N(0,b_{ij}^2)$, we show that \[\mathbf{E}\Vert X\Vert \lesssim\max_i\sqrt{\sum_jb_{ij}^2}+\max _{ij}\vert b_{ij}\vert \sqrt{\log n}. \] This bound is optimal in the sense that a matching lower bound holds under mild assumptions, and the constants are sufficiently sharp that we can often capture the precise edge of the spectrum. Analogous results are obtained for rectangular matrices and for more general sub-Gaussian or heavy-tailed distributions of the entries, and we derive tail bounds in addition to bounds on the expected norm. The proofs are based on a combination of the moment method and geometric functional analysis techniques. As an application, we show that our bounds immediately yield the correct phase transition behavior of the spectral edge of random band matrices and of sparse Wigner matrices. We also recover a result of Seginer on the norm of Rademacher matrices.
Quantum Dissension for Three-Qubit States

We introduce the notion of quantum dissension for a three-qubit system as a measure of quantum correlations. It generalizes the notion of quantum discord when we deal with a multipartite system. There can be multiple definitions of the dissension depending on the nature of projective measurements done on the subsystems. As an illustration, we explore the consequences of these multiple definitions and compare them for three-qubit pure and mixed GHZ and W states.
The Moderating Role of Psychological Flexibility on the Association between Distress-Driven Impulsivity and Problematic Internet Use

Background: Problematic internet use is receiving increasing attention in the addiction field, yet the mechanisms driving such behaviours remain unclear. Previous research has shown that impulsivity- and compulsivity-related constructs may interactively contribute to a range of problematic behaviours. The current study examined whether distress-driven impulsivity and psychological flexibility may interactively contribute to problematic internet use, which has not been addressed in prior literature. Method: Two hundred and one participants completed an online survey. Bootstrapped moderation analysis was conducted to examine the collected data on distress-driven impulsivity, psychological flexibility, and their interaction in relation to problematic internet use. Results: The interaction between distress-driven impulsivity and psychological flexibility was significantly related to problematic internet use. Simple slope tests confirmed that distress-driven impulsivity was associated with problematic internet use among individuals with low flexibility levels. Conclusions: Our findings highlight the moderating role of psychological inflexibility in the association between distress-driven impulsivity and problematic internet use. Prevention and/or early interventions for problematic internet use should consider targeting psychological inflexibility and distress-driven impulsivity.
Deformed Hamilton-Jacobi Method in Covariant Quantum Gravity Effective Models

We first briefly revisit the original Hamilton-Jacobi method and show that the Hamilton-Jacobi equation for the action $I$ of tunnelings of a fermionic particle from a charged black hole can be written in the same form as that of a scalar particle. For the low energy quantum gravity effective models which respect covariance of the curved spacetime, we derive the deformed model-independent KG/Dirac and Hamilton-Jacobi equations using the methods of effective field theory. We then find that, to all orders of the effective theories, the deformed Hamilton-Jacobi equations can be obtained from the original ones by simply replacing the mass of emitted particles $m$ with a parameter $m_{eff}$ that includes all the quantum gravity corrections. Therefore, in this scenario, there will be no corrections to the Hawking temperature of a black hole from the quantum gravity effects if its original Hawking temperature is independent of the mass of emitted particles. As a consequence, our results show that breaking covariance in quantum gravity effective models is a key for a black hole to have the remnant left in the evaporation.
The New Definitions of Loss Function for the Model-Based Parameter Identification Method in Power Distribution Network

Accurate device parameters play critical roles in calculation and analysis of power distribution network (PDN). However, device parameters are always affected by the operating status and influenced by manual entry; besides, the distribution area of PDN is very wide with many points, which brings more challenges to PDN parameter identification. Most of the proposed algorithms recently assume that the parameters of PDN contribute in a nonlinear probability space and optimize parameters by the power flow model with a loss function. Although these algorithms can achieve satisfying results in PDN analysis, the relationship between the power flow model and loss functions remains unclear. In this paper, the outputs of the power flow model have been analyzed firstly by experimental data, which includes the head and end voltages, as well as active and reactive power on the low-voltage side. It is revealed that the loss functions used by current algorithms are not suitable and reasonable for power flow model in PDN calculation, which constitutes one of the main findings of this work. Subsequently, this work proposes four novel loss functions combined with genetic algorithm (GA) and Markov Chain Monte Carlo (MCMC) to identify PDN parameters. Compared with the published algorithms, our experimental results show that the loss function defined in this paper can achieve better and more stable performance with about two times lower in MAE, RMSE, and RMPE evaluation functions to identify PDN parameters.
Tourist-resident interaction affects mutual understanding but defined by social distance

ABSTRACT This paper examines the interrelationships of social distance, tourist-resident interaction and mutual understanding between mainland Chinese tourists and residents in an urban destination of Hong Kong. Social distance affects tourist-resident interaction that predicts their mutual understanding for both tourists and residents are tested. A total of 416 tourist questionnaires and 315 resident questionnaires were obtained. The results show that quality of interaction is a major factor in predicting mutual understanding but negatively affected by social distance for both tourists and residents. From tourist perspective, only quality of interaction predicts the understanding but negatively affected by their social distances. From resident perspective, both quality of interaction and focused interaction positively affect the understanding but defined by their social distances. Co-presence does not affect residents’ understanding but is positively related to their social distance. Overall, tourist-resident interaction may contribute to mutual understanding, but only when the social distance is small to start with. The research findings have significant implications for sustainable development of tourism destinations.
Does Queue Correlation Matter in 5G Multi-Connectivity With Packet Duplication?

Multi-connectivity (MC) with packet duplication allows users to connect to multiple base stations for ultra-reliability. While related work has investigated the impact of path correlation in terms of correlated channel fading or shadowing correlation, we argue in this letter that another source of correlation referred to as “queue correlation” needs to be considered to better profile the performance of MC. To proceed, we build a queue-theoretic framework to analyze the origin and impact of queue correlation. Evaluation results show that queue correlation could increase service outage by an order of magnitude compared to the baseline without considering such an effect under a standard 5G urban macro-cell environment.
Modeling of surface roughness in electro-discharge machining using artificial neural networks

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism. This method works by forming of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena can arise and adversely affect the surface integrity of EDMed workpieces. These have to be taken into account and studied in order to optimize the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique that can provide reliable results and readily, be integrated into several technological areas. In this paper, we use an ANN, namely, the multi-layer perceptron and the back propagation network (BPNN) to predict the mean surface roughness of electro-discharge machined surfaces. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for getting a reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.
Solar Wind Energy Input: The Primary Control Factor of Magnetotail Reconnection Site

In this paper, we examine the solar wind energy input (expressed by −Vx × Bs, where Vx is the x component of the solar wind velocity and Bs is the southward component of the interplanetary magnetic field IMF Bz) for an onset of magnetic reconnection in the near‐Earth magnetotail. There are 41 events in which in situ observations of magnetic reconnection were made by Geotail. Magnetic reconnection in the postmidnight (premidnight) sector of the plasma sheet occurred under strong (weak) solar wind energy input conditions. Furthermore, we study temporal variations in the solar wind energy input with two different approaches using ground magnetic field observations and proton injections at geosynchronous altitude. These two analyses confirmed the preference of the postmidnight sector for the onset of magnetic reconnection under the strong solar wind energy input conditions. It is also found that the medium and weak solar wind energy input may move the onset location to earlier magnetic local times. The onset location of magnetic reconnection in the near‐Earth magnetotail is controlled by the solar wind energy input through the global magnetospheric dynamics during the loading period.
An Improved Deep Learning Model of Chili Disease Recognition with Small Dataset

—Due to its tasty and spicy fruit with nutritional qualities, chili is a demanding crop widely farmed around the world. Hence, it is essential to accurately determine the health status of chili for agricultural productivity. Recent years have seen impressive results in recognition fields due to deep learning approaches. However, deep learning models’ networks need an abundant data to perform well and collecting enormous data for the networks is time-consuming and resource-intensive. A data augmentation method is proposed to overcome this problem. It was applied to a small dataset of healthy and diseased chili leaf by utilizing geometric transformation method. Eventually, two deep learning models of CNN and ResNet-18 were evaluated using augmented and original datasets. From a series of experiment, it can be concluded that the trained deep learning models using original and augmented datasets perform better with an average accuracy performance of 97%.
Why the Many-Worlds Interpretation?

A brief (subjective) description of the state of the art of the many-worlds interpretation of quantum mechanics (MWI) is presented. It is argued that the MWI is the only interpretation which removes action at a distance and randomness from quantum theory. Limitations of the MWI regarding questions of probability which can be legitimately asked are specified. The ontological picture of the MWI as a theory of the universal wave function decomposed into a superposition of world wave functions, the important parts of which are defined in three-dimensional space, is presented from the point of view of our particular branch. Some speculations about misconceptions, which apparently prevent the MWI from being in the consensus, are mentioned.
Uncovering New Diversity of Photosynthetic Microorganisms from the Mediterranean Region

In the large and morphologically diverse phylum of Chlorophyta, new taxa are discovered every year and their phylogenetic relationships are reconstructed by the incorporation of molecular phylogenetic methods into traditional taxonomy. Herein, we aim to contribute to the photosynthetic microorganisms’ diversity knowledge in the Mediterranean area, a relatively unexplored ecoregion with high diversity. Based on a polyphasic approach, 18 Chlorophyta isolates were investigated and characterized. Morphological characteristics and ultrastructure, the phylogeny based on 18S rRNA gene (small subunit ribosomal RNA), 18S–28S internal transcribed spacer (ITS region), and the ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit region (rbcL gene), support establishing four new genera (Nomia, Ava, Akraea, Lilaea) and five new species (Spongiosarcinopsis limneus, N. picochloropsia, Av. limnothalassea, Ak. chliaropsychia, and L. pamvotia) belonging to orders Sphaeropleales, Chlorellales, and Chlamydomonadales. For some of them, this is the first report of their occurrence in specific aquatic environments.
A negative gyrator-C for purification of inductors in a differential LC-VCO

ABSTRACTBased on the proposed technique, the negative resistor in a differential LC-VCO is configured to impose a negative gyrator-C across the output. This resolves the need for the inductive source degeneration already used for purification of inductors in a differential LC-VCO. In comparison, while the chip area is almost halved, phase noise at 100 kHz offset is noticeably improved from −95.7 to −100 dBc/Hz at no extra power consumption cost. Moreover, in the curve of phase noise versus frequency an inflection point shows up at the minimal. This feature prevents the conventional sharp degradation of the phase noise while the frequency is tuned and extends its minimum for hundreds of MHz to improve the reliability of design. The application of the proposed technique in design of a low power and low phase noise Giga Hertz-range CMOS differential LC-VCO around 2.9 GHz is illustrated and analysed. Post-layout simulations using CMOS 0.18 µm TSMC RF design kit are used for evaluation and comparison. Using 1.
Development of a Hardware-In-Loop (HIL) Simulator for Spacecraft Attitude Control Using Momentum Wheels

In this paper, a Hardware-In-the-Loop simulator to simulate attitude control of space craft using momentum wheels is developed. The simulator consists of a spherical air bearing system allowing rotation and tilt in all three axes, three momentum wheels for actuation, and an AHRS (Attitude Heading Reference System). The simulator processes various types of data in PC104 and wirelessly communicates with a host PC using TCP/IP protocol. A simple low-cost momentum wheel assembly set and its drive electronics are also developed. Several experiments are performed to test the performance of the momentum wheels. For the control performance test of the simulator, a PID controller is implemented. The results of experimental demonstrations confirm the feasibility and validity of the Hardware-In-the-Loop simulator developed in the current study.