Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Decision Making for Power System via Imitation Learning and Reinforcement Learning

With the development of intermittent renewable energy sources, modern power systems are facing significant uncertainties as well as voltage deviations. In order to respond quickly to power fluctuations and contingencies such as transmission line tripping due to faults, real-time optimal grid control is required. A real-time alternating current (AC) optimal power flow (OPF) method through deep reinforcement learning (DRL) combined with imitation learning (IL) in continuous action space is proposed in this paper. A Markov decision process (MDP) is constructed to describe the real-time AC OPF problem. This proposed method is tested on the IEEE 30-bus system and the results show the significant potential for the optimal online control for power systems compared with the state-of-the-art techniques.
Hybrid Network Intrusion Detection with Stacked Sparse Contractive Autoencoders and Attention-based Bidirectional LSTM

Accurately identifying network intrusion cannot only help individuals and enterprises better deal with network security problems, but also maintain the Internet environment. Currently, classification methods with autoencoders for feature learning have been proved to be suitable for the network intrusion detection. This work proposes a new hybrid classification method named SABD for network intrusion detection. SABD integrates Stacked sparse contractive autoencoders, Attention-based Bidirectional long-term and short-term memory (LSTM), and Decision fusion. SABD integrates the feature extraction of stacked sparse contractive autoencoders with the classification ability of attention-based bidirectional LSTM. Specifically, stacked sparse contractive autoencoders are used for extracting features, which are sent to the attention-based bidirectional LSTM for the classification. Finally, the decision fusion algorithm is adopted to integrate classification results of multiple classifiers and yield the final results. Experimental results based on real-life UNSW-NB15 data demonstrate that the proposed SABD outperforms its state-of-the-art peers in terms of classification accuracy.
A Q-Learning-based Selective Disassembly Sequence Planning Method

Disassembly planning and sequencing play an important role in recycling a fast-growing number of end-of-life products. Optimal sequences can effectively reduce carbon emissions and save natural resources in the remanufacturing industry. Considering the development of intelligent manufacturing technology, this work deals with the optimization problem of selective disassembly sequences with an objective of maximizing disassembly profit. Disassembly sequences are generated based on AND/OR graphs. After setting up an environment matrix based on such graphs, this proposes a Q-learning technique to find an selective optimal disassembly sequence. The algorithm is applied to real-life disassembly cases. Experimental results show that the algorithm is superior a popularly-used genetic algorithm (GA) in both computing speed and solution quality through their various comparisons.
Regional PV Output Power Forecasting Based on Optimized Artificial Neural Network

Due to the characteristics of intermittent, fluctuating and random, the large-scale grid connected photovoltaic plants will seriously affect the stability and economic operation of power system. Carrying out regional PV output power forecasting could provide the dispatching department with reference information of power output, which could help make the dispatching plan and arrange the standby capacity. Therefore, this paper proposed a regional PV output power forecasting method based on artificial neural network, which is optimized by particle swam optimization algorithm. The forecasting accuracy of the proposed method is verified with the historical data of regional PV output.
A Framework for Extracting Abstracted Route Graphs Toward Air Traffic Flow Modeling

In this paper, we study modeling of air traffic flow from flight trajectory data in the airspace. Compared to road/train traffic, modeling of air traffic flow is difficult because trajectory of each aircraft fluctuates in the 3-dimensional space due to various factors such as weather and congestion level. By this reason, finding important routes on which aircrafts frequently pass is necessary for building air traffic flow models. We propose a framework for finding important routes in the form of graphs based on combination of various technologies such as space partition, trajectory clustering, and skeleton extraction.
Optimal Robust Control For Tremor Suppression in Parkinson’s Disease

Deep brain stimulation (DBS) is an effective and promising therapy to control Parkinson’s tremor movement in patients with advanced Parkinson’s disease (PD). This paper proposes a new alternative medication that has several advantages, including compatibility with individual needs and low side effects. There has been a rapid improvement in the literature on the development of the dynamic computational model of neuroscience, alongside the development of DBS. A combination of DBS and model-based control strategies opens up a new vision for Parkinson’s disease treatment. Despite the numerous studies on basal ganglia (BG) modeling, researchers are required to employ adaptive and robust strategies to eliminate Parkinson’s patients’ tremors. This paper proposes a new adaptive optimal fast terminal sliding mode control (AOFTSMC) method to mitigate tremors by tuning GABA thorough DBS. This approach represents finite-time convergence law, a new method to stimulate the inner nuclei of BG in a robust and optimum manner that leads to removing tremors of PD fluctuation signal in the presence of uncertainties. Finally, simulation results of the basal ganglia model under the addressed approach are adopted to demonstrate the effectiveness of the proposed method.
Modeling and SoC Estimation of Li-ion Batteries with an Improved Variable Forgetting Factor RLS Method Augmented with Extended Kalman Filter

This paper presents an improved variable forgetting factor recursive least square (IVFF-RLS) and extended Kalman filter (EKF) based technique for accurate modeling and real-time state of charge (SoC) estimation of Li-ion batteries. In the proposed approach, the IVFF-RLS is used for an accurate estimation of varying battery parameters under abnormal change of operating states such as an abrupt shifting of the battery from charging to discharging state, data loss, etc. The IVFF-RLS is augmented with the extended Kalman filter (EKF) for real-time and improved SoC estimation of Li-ion batteries. Extensive validation studies are performed in the Matlab environment and then experimental studies have been carried out in the LabVIEW platform to validate the proposed IVFF-RLS-EKF technique. The outcomes of the experimental studies validate the higher accuracy and robustness of the proposed approach under a broad spectrum of operating temperature and system disturbances such as abrupt shifting from charging to discharging state and vice versa. The efficacy of the proposed approach has been compared against the coulomb counting technique (CCT) and traditional VFF-RLS-EKF approaches through experimental studies. The results show that the proposed IVFF-RLS-EKF technique outperforms the existing techniques ensuring highly accurate battery model parameters and SoC.
Distributed Photovoltaic Scenario Generation Based on Edge-Cloud Collaborative Architecture

With the expansion of the power system and the growth in renewable energy penetration, the installed capacity of distributed photovoltaics in the power system will increase significantly in the future, and the amount of related data will increase sharply in the future. Traditional centralized cloud management is difficult to afford the massive data processing business to guarantee service quality. This paper proposes an edge-cloud collaboration architecture based on Kubernetes. At the edge, we cluster the distributed photovoltaics to the nearby centralized photovoltaic power plants with strong correlation and synthesize as a virtual station. Subsequently, we use historical meteorological and power data to predict the next day power curve. At the cloud, we generate the scenarios for the centralized station based on the day-ahead power data uploaded by the edge, then we obtain the scenarios for distributed photovoltaic cluster by converting the scenarios of centralized station with the capacity conversion formula.
A review on the design of assistive cable-driven upper-limb exoskeletons and their experimental evaluation

Upper-limb exoskeletons (ULEs) have the potential to assist severely paralyzed individuals. Many rehabilitative upper-limb exoskeletons have been developed in the recent years, where the prominent issue of bulkiness and actuator placement has been effectively resolved by the cable-transmission systems, allowing the actuators to be mounted away from the joints. This results in increasing the compactness and wearability of the exoskeleton. Thus, this article summarizes twenty-five cable-driven upper-limb exoskeletons designed for rehabilitating disabled individuals. This paper also presents a comparison based on usability of ULEs by end users considering the design characteristics and user-based experiments for real-time applications of the arm exoskeletons.
Online Learning of Wearable Sensing for Human Activity Recognition

This article presents a novel semisupervised learning method for wearable sensors to recognize human activities. The proposed method is termed a tri-very fast decision tree (VFDT). The proposed method is a more efficient version of the Hoeffding tree and three VFDTs are generated from the original labeled example set and refined using unlabeled examples. Based on the heuristic growth characteristics of VFDT, a tri-training framework is proposed which uses unlabeled data to update the model without labeled data. This significantly reduces the computational time and storage of the data processing. In addition, the proposed method is embedded into wearable devices for online learning, while the test data flow is regarded as the unlabeled data to update the model. The experiment collects data stream of 16 min with motion state switching frequently while the wearable devices recognize motions in real time. An experimental comparison has also been undertaken for performance evaluation between the wearable and computation using a desktop computer. The obtained results show that only minor difference in terms of the $f1$ -score rendered by the proposed method online or offline. This is a prominent characteristic for wearable computing within the Internet of Things (IoT). Data set can be linked as https://faculty.uestc. edu.cn/gaobin/zh_CN/lwcg/153392/list/index.htm.
An Algorithmic View of Streaming Submodular Data Summarization with A Knapsack Constraint

Data summarization, in the form of extracting a representative subset (i.e, a data summary) from a massive data set, is often used for big data processing. A good summary can not only significantly reduce the information redundancy, but also provide a better understanding of the original data. The utility function we use to evaluate the quality of a summary usually has a natrual diminishing returns property, also known as submodularity. Due to the rapid growth of data scale, traditional offline data processing has become more and more difficult to deal with massive data, and streaming data processing methods that require less space start to attract attention, leading to the emergence of many related studies. In this paper, we first make an algorithmic view of methods widely used in streaming submodu-lar maximization with knapsack constraint. After analyzing the ideas behind them, we further propose a new algorithm, called RSStream, for the same problem. RSStream is an innovative combination of traditional sieve approach, multi-cadidate set method and augmentation strategy with data sampling. It can achieve the state-of-the-art approximation ratio within a near-linear time and space complexity. At the end, we execute our algorithm on two real data summarization applications to demonstrate the effectiveness and efficiency of it.
Fast partial decoherence of a superconducting flux qubit in a spin bath

The superconducting flux qubit has two quantum states with opposite currents which generate different magnetic fields. Environment of nuclear spins can find out the magnetic field after a decoherence time ${\ensuremath{\tau}}_{0}$ inversely proportional to the magnetic field and the square root of the number of spins. When the Hamiltonian of the qubit drives fast coherent Rabi oscillations between the states with opposite current, then the magnetic field is flipped at a constant rate $\ensuremath{\omega}$ and the decoherence time $\ensuremath{\tau}=\ensuremath{\omega}{\ensuremath{\tau}}_{0}^{2}$ is much longer than ${\ensuremath{\tau}}_{0}$. However, on closer inspection decoherence actually takes place on two time scales. The long time $\ensuremath{\tau}$ is a time of full decoherence but a part of quantum coherence is lost already after the much shorter time $\ensuremath{\pi}∕\ensuremath{\omega}$. This fast partial decoherence biases coherent flux oscillations towards the initial flux direction and it can affect performance of the superconducting devices as qubits.
A moving mesh finite element method for the two-dimensional Stefan problems

An r -adaptive moving mesh method is developed for the numerical solution of an enthalpy formulation of two-dimensional heat conduction problems with a phase change. The grid is obtained from a global mapping of the physical to the computational domain which is designed to cluster mesh points around the interface between the two phases of the material. The enthalpy equation is discretised using a semiimplicit Galerkin finite element method using linear basis functions. The moving finite element method is applied to problems where the phase front is cusp shaped and where the interface changes topology.
Ferromagnetism in one dimension: Critical Temperature

Ferromagnetism in one dimension is a novel observation which has been reported in a recent work (P. Gambardella et.al., Nature {\bf 416}, 301 (2002)), anisotropies are responsibles in that relevant effect. In the present work, another approach is used to obtain transition between two different magnetic ordering phases. Critical temperature has been estimated by Binder method. Ferromagnetic long range interactions have been included in a special Hamiltonian through a power law that decays at large inter-particle distance $r$ as $r^{-\alpha}$, where $\alpha\geq0$. For the present model, we have found that the trend of the critical temperature vanishes when the range of interactions decreases ($\alpha\to\infty$) and close to mean field approximation when the range of interactions increases ($\alpha\to0$). The crossover between two these limit situations is discussed
The Fraction of Stars That Form in Clusters in Different Galaxies

We estimate the fraction of stars that form in compact clusters (bound and unbound), , in a diverse sample of eight star-forming galaxies, including two irregulars, two dwarf starbursts, two spirals, and two mergers. The average value for our sample is . We also calculate the fraction of stars in clusters that survive to ages between and , denoted by , and find and , significantly lower than for the same galaxies. We do not find any systematic trends in or with the star formation rate (SFR), the SFR per unit area ( ), or the surface density of molecular gas ( ) within the host galaxy. Our results are consistent with those found previously from the CMF/SFR statistic (where CMF is the cluster mass function), and with the quasi-universal model in which clusters in different galaxies form and disrupt in similar ways. Our results, however, contradict many previous claims that the fraction of stars in bound clusters increases strongly with and . We find that the previously reported trends are largely driven by comparisons that mixed and , where was systematically used for galaxies with higher and , and for galaxies with lower and .
Numerical Algorithm for Exact Finite Temperature Spectra and Its Application to Frustrated Quantum Spin Systems

A numerical algorithm to calculate exact finite-temperature spectra of many-body lattice Hamiltonians is formulated by combining the typicality approach and the shifted Krylov subspace method. The combined algorithm, which we name finite-temperature shifted Krylov subspace method for simulating spectra (FTK$\omega$), efficiently reproduces the canonical-ensemble probability distribution at finite temperatures with the computational cost proportional to the Fock space dimension. The present FTK$\omega$ enables us to exactly calculate finite-temperature spectra of many-body systems whose system sizes are twice larger than those handled by the canonical ensemble average and allows us to access the frequency domain without sequential real-time evolution often used in previous studies. By employing the reweighting method with the present algorithm, we obtain significant reduction of the numerical costs for temperature sweeps. Application to the Kiteav-Heisenberg model (KHM) on a honeycomb lattice demonstrates the capability of the FTK$\omega$. The KHM shows quantum phase transitions from the quantum spin liquid (QSL) phase to magnetically ordered phases when the finite Heisenberg exchange coupling is introduced. We examine temperature dependence of dynamical spin structure factors of the KHM in proximity to the QSL. It is clarified that the crossover from a spin-excitation continuum, which is a characteristic of the QSL, to a damped high-energy magnon mode occurs at temperatures higher than the energy scale of the Heisenberg couplings or the spin gap that is a signature of the QSL at zero temperature. The crossover and the closeness to the Kitaev's QSL are quantitatively measured by the width of the excitation continuum or the magnon spectrum. The present results shed new light on analysis of neutron scattering and other spectroscopy measurements on QSL candidates.
Cross-spectral modelling of the black hole X-ray binary XTE J1550-564 : challenges to the propagating fluctuations paradigm

Timing properties of black hole X-ray binaries in outburst can be modelled with mass accretion rate fluctuations propagating towards the black hole. Such models predict time lags between energy bands due to propagation delays. First application of a propagating fluctuations model to black hole power spectra showed good agreement with the data. Indeed, hard lags observed from these systems appear to be in agreement with this generic prediction. Our propfluc code allows us to simultaneously predict power spectra, time lags and coherence of the variability as a function of energy. This was successfully applied to the Swift data on the black hole MAXI J1659-152, fitting jointly the power spectra in two energy bands and the cross-spectrum between these two bands. In this work, we attempt to model two high signal-to-noise Rossi X-ray Timing Explorer (RXTE) observations of the black hole XTE J1550-564. We find that neither observation can be adequately explained by the model even when considering, additionally to previous propfluc versions, different propagation speeds of the fluctuations. After extensive exploration of model extensions, we tentatively conclude that the quantitative and qualitative discrepancy between model predictions and data is generic to the propagating fluctuations paradigm. This result encourages further investigation of the fundamental hypotheses of the propagating fluctuations model. We discuss some of these hypotheses with an eye to future works.
"Charged" phonons in an external field: a QED analog with Bose-Einstein condensates

We propose a method for using ultracold atomic Bose-Einstein condensates, to form an analog model of a relativistic massive field that carries "charge" and interacts with an external nondynamical gauge field. Such a "scalar QED" analog model, may be useful for simulating various effects of QFT involving charged particles, such as the Schwinger pair-creation of charged phonons in a constant external field, and vacuum instability.
Modelling the solar twin 18 Scorpii

Context. Solar twins are objects of great interest in that they allow us to understand better how stellar evolution and structure are affected by variations of the stellar mass, age and chemical composition in the vicinity of the commonly accepted solar values. Aims. We aim to use the existing spectrophotometric, interferometric and asteroseismic data for the solar twin 18 Sco to constrain stellar evolution models. 18 Sco is the brightest solar twin and is a good benchmark for the study of solar twins. The goal is to obtain realistic estimates of its physical characteristics (mass, age, initial chemical composition, mixing-length parameter) and realistic associated uncertainties using stellar models. Methods. We set up a Bayesian model that relates the statistical properties of the data to the probability density of the stellar parameters. Special care is given to the modelling of the likelihood for the seismic data, using Gaussian mixture models. The probability densities of the stellar parameters are approximated numerically using an adaptive MCMC algorithm. From these approximate distributions we proceeded to a statistical analysis. We also performed the same exercise using local optimisation. Results. The precision on the mass is approximately 6%. The precision reached on X0 and Z0 and the mixing-length parameter are respectively 6%, 9%, and 35%. The posterior density for the age is bimodal, with modes at 4.67 Gyr and 6.95 Gyr, the first one being slightly more likely. We show that this bimodality is directly related to the structure of the seismic data. When asteroseismic data or interferometric data are excluded, we find significant losses of precision for the mass and the initial hydrogen-mass fraction. Our final estimates of the uncertainties from the Bayesian analysis are significantly larger than values inferred from local optimization. This also holds true for several estimates of the age encountered in the literature.
Statistical Inferences For Interobserver Agreement Studies With Nominal Outcome Data

Most statistical methods for interobserver agreement studies involving categorical data focus on dichotomous outcome variables, whereas only a limited number of methods have been developed for nominal outcome data. As a consequence, researchers may resort to dichotomization simply to facilitate the analysis of data, hence discarding potentially valuable information. We present three inference procedures for hypothesis testing concerning interobserver agreement studies with nominal data. These procedures may be applied to studies characterized by two observers and three or more outcome categories. We illustrate these methods by using previously published data sets.
Glassy behaviour in disordered systems with nonrelaxational dynamics

We show that a family of disordered systems with non-relaxational dynamics may exhibit ``glassy'' behavior at nonzero temperature, although such a behavior appears to be ruled out by a face-value application of mean-field theory. Nevertheless, the roots of this behavior can be understood within mean-field theory itself, properly interpreted. Finite systems belonging to this family have a dynamical regime with a self-similar pattern of alternating periods of fast motion and trapping.
Angle-resolved photoemission spectroscopy of band tails in lightly doped cuprates

We amend ab initio strongly correlated band structures by taking into account the band-tailing phenomenon in doped charge-transfer Mott-Hubbard insulators. We show that the photoemission from band tails accounts for sharp ``quasiparticle'' peaks, rapid loss of their intensities in some directions of the Brillouin zone (``Fermi arcs''), and high-energy ``waterfall'' anomalies as a consequence of matrix-element effects of disorder-localized states in the charge-transfer gap of doped cuprates.