Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrinsic spin currents in ferromagnets

First principles calculations show that electric fields applied to ferromagnets generate spin currents flowing perpendicularly to the electric field. Reduced symmetry in these ferromagnets enables a wide variety of such spin currents. However, the total spin current is approximately the sum of a magnetization-independent spin Hall current and an anisotropic spin anomalous Hall current. Intrinsic spin currents are not subject to dephasing, enabling their spin polarizations to be misaligned with the magnetization, which enables the magnetization-independent spin Hall effect. The spin Hall conductivity and spin anomalous Hall conductivities of transition metal ferromagnets are comparable to those found in heavy metals, opening new avenues for efficient spin current generation in spintronic devices.
DeepD_DrugC: Deep and distributed workflow to predict drug- candidates

The applications of computational tools at various stages of drug discovery is one of the most active axes of research. Virtual Screening (VS) is a very common application, which aims to screen and analyze large chemical libraries using algorithms and models to extract drug-candidates that can bind to therapeutic targets. Machine learning (ML) techniques are widely applied as a tool to analyze the chemical libraries in ligand-based virtual screening (LBVS). Deep learning (DL) is a novel mode of machine learning that provides several new architectures primarily based on classical Artificial Neural Network algorithms, but with many hidden layers to learn features with multiple levels of abstraction. Recently, chemical libraries are identified as Big Data, due to their huge size, the variety of data, and the speed at which they are created, streamed and aggregated. In this context, we need advanced tools to handle and treat this type of data. Apache Spark is the most widely used engine for big data processing, with many improvements that make it more suitable for virtual screening analysis. In this work, we propose a novel workflow named DeepD_ DrugC based on Spark and Deep Neural Network model implemented with Deeplearning4j (DL4J) to improve the prediction results in LBVS. To evaluate the workflow, we suggest a process to create training datasets using the PubChem Bioassay database for cancer disease. The evaluation results show a good precision more than 93%, with acceptable scaling behavior.
A review of the relationship between peak currents of the first and subsequent strokes in the same flash

Three distinct data sets are analyzed in terms of probability of flashes having subsequent strokes with higher peak currents than the first stroke in the flash. This type of flashes may have some implication in the shielding failure rate of transmission lines. For three distinct data sets we obtained about 30 % of flashes with a ratio Isub/I1>1. For about 10 % of the flashes the peak current of a subsequent stroke is more the two times the peak of the first stroke, and for about 3 to 4 % of the flashes we obtained a ratio Isub/Il is grater then 3.
Application of Histogram of Oriented Gradients and Support Vector Machine on Detection of Far-side Corrosion

Nondestructive testing (NDT) of far-side corrosion in a multilayer structure of aircraft is an important task to ensure the integrity and safety of the aircraft. Among the NDT methods, electromagnetic testing (ET) is powerful in detecting far-side corrosion. However, the far-side corrosion usually appears at the riveting site, making the ET signal complicated and challenging to recognize the presence of the small corrosion. In this paper, a histogram of gradients will be applied to extract features of the magnetic image, and a support vector machine will be used to detect the far-side corrosion. The proposed method helps to improve the accuracy of the detection significantly.
A Novel Conformal MIMO Antenna Array based a Cylindrical Configuration for 5G Applications

The proposed antenna array is developed based on four printed monopole antennas for multi input multi output (MIMO) 5G systems at Sub-6 GHz bands. The antenna design is structured from Moore fractal geometry connected to T-stub structure. The main objective of introducing the Moore fractal is to improve the antenna bandwidth through accumulating the electric charges in the air gap between the T-stub and the proposed fractal. Therefore, the resulted antenna when excited with a coplanar waveguide (CPW) shows several operating bands at 2.3GHz, 4GHz, 4.7GHz, and 5.4GHz. The generated modes are attributed to the use of the proposed fractal in which the radiation properties of the antenna are almost broadside radiations over the frequency bands of interest. The antenna array is folded on a cylindrical substrate with a radius of 15mm and hight of 34mm of 1mm thickness. The proposed antenna performance is optimized parametrically using CST software packages. Therefore, the proposed antenna design is developed to constructed the array for MIMO applications. The proposed array is oriented on a cylindrical substrate with a separation distance below λ/21, where, the separation measured in terms of the closes gab between adjacent patches edges. The antenna array is found to show a bandwidth from 5GHz to 5.9GHz is achieved with S11 below −10dB. The proposed antenna array shows a coupling effect below −10 dB. The antenna shows a gain of 2.5dBi at 5.4GHz. Nevertheless, the antenna gains at 2.3GHz, 4GHz, and 4.7GHz are found to be 4.2dBi, 1dBi, and 3.5dBi. Finally, it is found that the proposed antenna array shows high diversity with a minimum correlation factor.
Stock Prediction of Multivariable Using Bi-Long Short Term Memory and Capsule Neural Network

Rapid and volatile stock price movements make it difficult for people to predict a stock price, often going up or down. Two approaches are often used in analyzing stocks that is the method of fundamental and technical analysis. Both analyzes are critical to the prediction of stock price. This study proposed stock prediction using fundamental analysis and technical analysis. Fundamental analysis is based on news headlines. Technical data used opening prices, closing prices, highest prices, lowest prices, and volume. In revealing the text of fundamental parameters, where the information in the text is interrelated, it is necessary to pay attention to the relationship between words. Stock price data needs to consider the position and direction of data without eliminating the variables, time, and the relationship between time and existing variables. Therefore, fundamental data are classified through two-way Long Short Term Memory (LSTM) or BiLSTM, producing positive, neutral, or negative sentiments. The sentiment score is the output of BILSTM, and technical variables are used as a feature in predicting the next stock trend using the Capsule Neural Network (CapsNet). The sentiment analysis results using the two-way LSTM produced an accuracy of 92.03%. The stock prediction results also show that the proposed method improved by increasing the accuracy by 7% to 68.79% compared to the Convolutional Neural Network.
Lightning Warning Prediction with Multi-source Data

In this paper we describe a new methodology for generating real-time lightning warning prediction by using a reliable multi-source data. To do so, it was used two years of data covering 50km radius over three regions in Brazil. For 5-minutes intervals, it was evaluated three approaches: a rule-based model that monitors an area of radius greater than the protected area, a machine learning model that considers the amount of lightning that hit small nearby regions, and an integrated approach that combines the two above. The results achieved, on average, about 80% of false alarm ratio, when the model generated an alert but no lightning strikes the area, 14% of failures, opposite to the previous one, had lightning without alert, 1% of the total time operations had to be stopped because of alerts, and 9 minutes of lead time between the generation of the alert and there is a lightning strike. A multi-criteria decision method was used to rank the best method for each location. Rule-based and Integrated models were preferred according to the importance of each criterion for stakeholders. Each methodology has its advantages and they can be extended to other areas according to business needs.
On the Performance of a Composite Right Left Hand Electromagnetic Bandgap Structure

This paper presents the design and analysis of composite right left hand (CRLH) electromagnetic bandgap (EBG) structure. The proposed unit cell is consistent of a dielectric substrate with dimensions of 5×5×1 mm3 made of FR4-Epoxy with εr = 4.4 underneath of a conductive patch with dimensions of 4.4×4.4mm2. The unit cell is structured to perform a negative permittivity (ε) and negative permeability (µ) in different bands. The proposed unit cell is developed to 5G systems in the sub-6GHz bands. In this work, a complete analysis of the unit cell in terms of Sparameters, constitutive parameters and refraction index are evaluated using HFSS simulation package based on Finite Element Method (FEM).
Efficient UAV/Satellite-assisted IoT Task Offloading: A Multi-agent Reinforcement Learning Solution

In the future mobile edge networks, the Internet of things (IoT) applications will be latency-sensitive and computationally intensive. Given the resource limitation of IoT devices, mobile edge computing (MEC) servers are critical to support the efficient processing of IoT tasks. Since MEC servers attached to the ground base stations are generally deployed in fixed locations and vulnerable to physical damage, the unmanned aerial vehicle (UAV) and satellite-assisted MEC framework has been proposed to leverage the flexibility of UAVs and the broad coverage of satellites. However, efficient utilization of the UAV/satellite resources is challenging for the static ground IoT devices because of the dynamic in terms of aerial and space network topology and IoT task arrival rates. To adapt to the changing environment and utilize the interaction among multiple UAVs, we propose a multi-agent deep deterministic policy gradient (MADDPG) framework to jointly optimize the traveling routes of multi-UAVs and the offloading decision of IoT devices. To minimize the processing cost in terms of task processing latency and energy consumption of IoT devices, cooperative UAVs can help find the optimal task offloading location for each IoT device. Simulation results show the proposed algorithm based on MADDPG can averagely decrease 20% of the above processing cost compared with the benchmark approach.
X-Ray Observations of Stellar Clusters Near the Galactic Center

We report the first detection of X-ray emission from the Quintuplet star cluster and compare its X-ray emission to that of the Arches star cluster. Four point sources are significantly detected near the core of the Quintuplet cluster with a total absorption-corrected luminosity of ~1 × 1033 ergs s-1. Diffuse, thermal emission is also detected near the core of the Quintuplet cluster with an absorption-corrected luminosity of ~1 × 1034 ergs s-1. We analyze the diffuse and pointlike emission from the Arches and Quintuplet clusters and discuss the possibility that they are host to cluster wind outflows. We also present the results of our search for X-ray emission from candidate star clusters in the Galactic center (GC) region. We use extinction estimated by near-IR colors and X-ray spectral fits, as well as other IR properties, to determine if the candidate clusters are new GC star clusters. We find that three of the six candidate clusters toward the GC are likely foreground clusters, two of the candidate clusters are not detected in the X-ray data but have near-IR extinctions consistent with a GC location, and one of the candidate clusters has X-ray and near-IR extinctions consistent with being in the GC. The X-ray and IR emission from the candidate clusters is compared to the known massive GC star clusters.
Dual-site Photoplethysmography Sensing for Noninvasive Continuous-time Blood Pressure Monitoring Using Artificial Neural Network

In this work, we propose dual site Photoplethysmography (PPG) sensing for blood pressure monitoring using Artificial Neural Network (ANN). The method was implemented on a microcontroller for real-time BP monitoring. The models were evaluated on 15 volunteers and the ANN model achieved a MAE±SD 0.29 ± 4.49 mmHg for SBP and 0.5±2.4 mmHg for DBP. The proposed dual PPG site ANN model exhibited superior performance and robustness in real-time tests compared to the classical ANN single-site PPG model.
Differential-phase-shift quantum-key-distribution protocol with a small number of random delays

The differential-phase-shift (DPS) quantum key distribution (QKD) protocol was proposed aiming at simple implementation, but it can tolerate only a small disturbance in a quantum channel. The round-robin DPS (RRDPS) protocol could be a good solution for this problem, which in fact can tolerate even up to $50\%$ of a bit error rate. Unfortunately, however, such a high tolerance can be achieved only when we compromise the simplicity, i.e., Bob's measurement must involve a large number of random delays ($|\mathcal{R}|$ denotes its number), and in a practical regime of $|\mathcal{R}|$ being small, the tolerance is low. In this paper, we propose a new DPS protocol to achieve a higher tolerance than the one in the original DPS protocol, in which the measurement setup is less demanding than the one of the RRDPS protocol for the high tolerance regime. We call the new protocol the small-number-random DPS (SNRDPS) protocol, and in this protocol, we add only a small amount of randomness to the original DPS protocol, i.e., $2\leq|\mathcal{R}|\leq10$. In fact, we found that the performance of the SNRDPS protocol is significantly enhanced over the original DPS protocol only by employing a few additional delays such as $|\mathcal{R}|=2$. Also, we found that the key generation rate of the SNRDPS protocol outperforms the RRDPS protocol without monitoring the bit error rate when it is less than $5\%$ and $|\mathcal{R}|\leq10$. Our protocol is an intermediate protocol between the original DPS protocol and the RRDPS protocol, and it increases the variety of the DPS-type protocols with quantified security.
Bayesian Matching Pursuit Based Estimation of Off-Grid Channel for Millimeter Wave Massive MIMO System

Millimeter wave (mmWave) frequency spectrum offers orders of magnitude greater spectrum to mitigate the severe spectrum shortage in conventional cellular bands. To overcome the high propagation loss in the mmWave band, massive multiple-input and multiple-output (MIMO) can be adopted at both transmitter and receiver to provide large beamforming gains. At the same time, hybrid architecture is applied to reduce the huge power consumption caused by devices operating at radio frequency (RF). However, because of the hybrid architecture and large number of antennas, it is hard to obtain the channel state information (CSI) which is crucial for obtaining desirable beamforming gains. Off-grid error and sparsity pattern (SP) estimation error are two main limiting factors of the performance of most existing compressive sensing (CS) based channel estimation (CE) algorithms. Off-grid error presents when the true angle does not lie on the discretized angle grid of mmWave channel in the spatial domain. In this paper, we first propose a fast Bayesian matching pursuit method with ‘virtual sparsity’ to improve the accuracy of SP estimation and name it as the improved Bayesian matching pursuit (IBMP). Then an enhanced algorithm, named off-grid IBMP (OG-IBMP), is developed to mitigate the off-grid problem, followed by a theoretical analysis of OG-IBMP. This method iteratively updates the selected grid points and updates the corresponding parameters based on the maximum a posteriori (MAP) criterion. Numerical simulations are performed to validate our theoretical analysis and evaluate the performance of the proposed method. Compared to other existing methods, the results show that our proposed OG-IBMP algorithm greatly reduces the off-grid error and significantly enhances the accuracy of the SP estimation with low computational complexity.
Physical Layer Security for UAV-Based Full-Duplex Relay NOMA System

In this paper, we investigate the secrecy performance of an unmanned aerial vehicle (UAV)-based full-duplex (FD) relay non-orthogonal multiple access (NOMA) system over the Nakagami-m fading channel. A mobile UAV acts as an aerial relay station (ARS) to assist transmission from a source (S) to two users (D1, D2), in the presence of a ground internal eavesdropper (E). To ensure secure communication at users, we propose to insert an artificial noise (AN) component into the transmit signal at the full-duplex ARS (FD-ARS) with power allocated using the NOMA protocol. We analyze the secrecy outage performance of the system and the users and obtain the approximate secrecy outage probability (SOP) in various propagation environments, i.e., the dense urban, urban and suburban environments. It is demonstrated that using AN allows the users to achieve the secrecy over the signal-to-noise ratio. Also, the system achieves the best secrecy performance when the FD-ARS is closer to the further user while losing the secrecy when closer to the source. The FD-ARS system is shown to achieve better secrecy performance than the respective half-duplex (HD) ARS system.
Collective mode mining from molecular dynamics simulations: a comparative approach

The evaluation of collective modes is fundamental in the analysis of molecular dynamics simulations. Several methods are available to extract that information, i.e normal mode analysis, principal component and spectral analysis of trajectories, basically differing by the quantity considered as the nodal one (frequency, amplitude, or pattern of displacement) and leading to the definition of different kinds of collective excitations and physical spectral observables. Different views converge in the harmonic regime and/or for homo-atomic systems. However, for anharmonic and out of equilibrium dynamics different quantities bring different information and only their comparison can give a complete view of the system behavior. To allow such a comparative analysis, we review and compare the different approaches, applying them in different combination to two examples of physical relevance: graphene and fullerene C60.
A Study on Cluster-Centric Cell-Free Massive MIMO System

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems have been attracting a great attention as a promising architecture for the beyond fifth-generation (B5G) systems. CF-mMIMO is designed to deploy a large number of base station (BS) antennas to accommodate a relatively small number of mobile users. Considering that the computational complexity and the fronthaul capacity are limited in any practical system, scalability is the key to making CF-mMIMO practical. Thus, a user-centric (UC) approach is taken, i.e., each user is served by a certain number of antennas with high channel gain (called the antenna-set). Such a CF-mMIMO is called UC CF-mMIMO in this paper. However, as the user density becomes higher, the antenna-sets tend to overlap at a higher probability, and accordingly, severer interference will occur among the neighborhood users, thereby degrading the transmission performance of UC CF-mMIMO. To mitigate this severer interference problem, in this paper, we propose to group the neighborhood users as the user-cluster and to apply multi-user MIMO (MU-MIMO) technology to each user-cluster. This CF-mMIMO is called cluster-centric (CC) CF-mMIMO. In CC CF-mMIMO, user-clusters are first formed and a set of antennas is associated with each user-cluster for performing MU-MIMO communication in parallel. We evaluate the link capacity and the user fairness by computer simulation to reveal that the proposed CC CF-mMIMO outperforms the UC CF-mMIMO.
Bridge Crack Detection Based on Image Segmentation

The detection of bridge cracks is related to the life of the bridge. Manual detection is time-consuming and laborious. Contact sensors exposed to air are susceptible to weather damage. In practice, the bridge cracks with low contrast and blurred edge features is the difficulty of crack detection based on image segmentation. To this end, this paper proposes a deep learning based image segmentation detection network. In order to reduce the size of the network model, we modify the backbone network of Segnet. The feature extraction network is modified to the structure of mobilenet and improved. Cracks belong to small targets and easily missed in the detection process. In order to improve the detection accuracy of small targets, a multi-scale feature fusion operation is adopted in this paper. The network training uses public datasets. In some images, the contrast between the crack and the background is low, so this paper binarization is used to strengthen the crack structure. The experimental results verify the effectiveness of image segmentation.
Spatially resolved photoionization of ultracold atoms on an atom chip

We report on photoionization of ultracold magnetically trapped Rb atoms on an atom chip. The atoms are trapped at $5\phantom{\rule{0.3em}{0ex}}\ensuremath{\mu}\mathrm{K}$ in a strongly anisotropic trap. Through a hole in the chip with a diameter of $150\phantom{\rule{0.3em}{0ex}}\ensuremath{\mu}\mathrm{m}$, two laser beams are focused onto a fraction of the atomic cloud. A first laser beam with a wavelength of $778\phantom{\rule{0.3em}{0ex}}\mathrm{nm}$ excites the atoms via a two-photon transition to the $5D$ level. With a fiber laser at $1080\phantom{\rule{0.3em}{0ex}}\mathrm{nm}$ the excited atoms are photoionized. Ionization leads to depletion of the atomic density distribution observed by absorption imaging. The resonant ionization spectrum is reported. The setup used in this experiment is suitable not only to investigate mixtures of Bose-Einstein condensates and ions but also for single-atom detection on an atom chip.
Textual Plagiarism Detection Using Embedding Models and Siamese LSTM

Plagiarism is a severe issue that puts academic integrity and intellectual property rights in trouble. Plagiarism occurs in a variety of ways in scientific institutes, the most common of which being direct plagiarism. Manually inspecting plagiarism activities in big volumes of texts is a difficult task. As a result, numerous detection algorithms have been proposed in order to avoid lengthy manual screening. In this paper, we describe a deep learning framework for detecting text plagiarism that uses a network of Siamese LSTM and embeddings based on word. The purpose of this study is to figure out which word-based embedding architecture yields the best accurate plagiarism detection results. To prepare input of the network, which must be multiple pre-trained sequences of text embedding, the proposed framework employs Word2Vec and Glove models. The ratio of plagiarism in the network's two outputs are then measured using a hybrid of Manhattan distance and cosine similarity measures. To the best of our knowledge, this is one of the few studies that attempts to detect plagiarism using several embedding models and hybrid similarity measures. The embeddings from the Word2Vec architecture produce the best accurate detection scores, according to experimental findings on the PAN-PC-11 and Webis-CPC-11 datasets. The method attained F1-measure of 0.816, recall of 0.91, and precision of 0.924for PAN-PC-11 corpus and F1-measure of 0.793, a recall of 0.852, and precision of 0.902for Webis-CPC-11 corpus using word2vec embeddings.