Miracle-dz/UniFAR-FTUs huggingface dataset:
Flexibility Oriented Combined Heat and Power Dispatch under Uncertainties

The flexible resources of district heating system (DHS) can effectively cope with the fluctuation of photovoltaic (PV) output and improve the security of electric power network. A stochastic optimization method is proposed in this paper for power distribution network with high penetration of PV based on probabilistic forecasting and aggregate flexibility of DHS. Firstly, an adaptive robust optimization framework is used to aggregate the flexible resources of DHS, and the heat output interval is derived to achieve the decoupling of integrated electric-heating system. Then, the PV forecasting result is obtained by nonparametric probabilistic forecasting. Accordingly, PV scenarios with temporal correlation are acquired using Copula formulations. Based on the typical PV scenarios and aggregate flexibility of DHS, the stochastic optimization for combined heat and power dispatch is established. The case study demonstrates the effectiveness and feasibility of the proposed optimization method.
Deep Reinforcement-Learning-Based Adaptive Traffic Signal Control with Real-Time Queue Lengths

The reinforcement learning (RL) with deep neural network, as a data-driven approach, is promising for adaptive traffic signal control (ATSC) in traffic scenarios. The majority of the existing studies focus on designing efficient agents and policy optimization for ATSC, but neglect to observe more detailed states of the environment. In this paper, an adaptive traffic signal control strategy, named as A2C RTQL, is proposed for scheduling the traffic signal in an intersection, by combining the real-time lane-based queue lengths with deep RL agent. First, the Lighthill-Whitham-Richards (LWR) shockwave theory is employed for obtaining the real-time queue lengths in each lane. After that, by defining the obtained queue lengths as the inputs, A2C RTQL strategy is designed for traffic signal control based on the advanced actor-critic (A2C) agent, where the lanes are divided into multiple parallel environments based on the phases of traffic signal. Simulation results demonstrate the optimality and efficiency of the proposed strategy compared with other methods in SUMO under simulated peak-hour traffic dynamics.
Griffiths physics in an ultracold Bose gas

Coupled XY model systems consisting of three-dimensional (3D) systems with disordered interlayer physics are of significant theoretical interest. We realize a set of coupled quasi-2D layers of $^{87}$Rb in the presence of disordered inter-layer coupling. This is achieved with our high bandwidth arbitrary optical lattice to obviate restrictions on the dimensionality of disorder with speckle-generated optical fields. We identify phase crossover regions compatible with the existence of a pair of intermediate Griffiths phases between a thermal state and the emergence of bulk 3D superfluidity.
An Adam-adjusting-antennae BAS Algorithm for Refining Latent Factor Analysis Model

Extracting the latent information in high-dimensional and incomplete matrices is an important and challenging issue. The Latent Factor Analysis (LFA) model can well handle the high-dimensional matrices analysis. Recently, Particle Swarm optimization (PSO)-incorporated LFA models have been proposed to tune the hyper-parameters adaptively with high efficiency. However, the incorporation of PSO causes the premature problem. To address this issue, we propose a sequential Adam-adjusting-antennae BAS (A2 BAS) optimization algorithm, which refines the latent factors obtained by the PSO-incorporated LFA model. The A2 BAS algorithm consists of two sub-algorithms. First, we design an improved BAS algorithm which adjusts beetles’ antennae and step-size with Adam; Second, we implement the improved BAS algorithm to optimize all the row and column latent factors sequentially. With experimental results on two real high-dimensional matrices, we demonstrate that our algorithm can effectively solve the premature convergence issue.
Polarization switching and AC small-signal capacitance in Ferroelectric Tunnel Junctions

We here report a joint experimental and simulation analysis for large signal P-V and AC small-signal C-V curves in ferroelectric tunnel junctions. The attempt to reproduce both experimental data sets with the same model and material parameters challenges our understanding of the underlying physics, but it also helps develop a sound background for the device design.
Graph Network based Approaches for Multi-modal Movie Recommendation System

The three times increase of SonyLiv viewers during the Tokyo Olympic, the 10% hike of YouTube users during the isolation era of covid-pandemic, and the 19% growth in Netflix user count due to the fastest growth of OTT, etc. have made the digital platform’s mode all-time active and specific. The hourly increase of users’ interactions and the e-commerce platform’s desire of letting users engage on their sites are pushing researchers to shape the virtual digital web as user specific and revenue-oriented. This paper develops a deep learning-based approach for building a movie recommendation system with three main aspects: (a) using a knowledge graph to embed text and meta information of movies, (b) using multi-modal information of movies like audio, visual frames, text summary, meta data information to generate movie/user representations without directly using rating information; this multi-modal representation can help in coping up with cold-start problem of recommendation system (c) a graph attention network based approach for developing regression system. For meta encoding, we have built knowledge graph from the meta information of the movies directly. For movie-summary embedding, we extracted nouns, verbs, and object to build a knowledge graph with head-relation-tail relationships. A deep neural network, as well as Graph attention networks, are utilized for measuring performance in terms of RMSE score. The proposed system is tested on an extended MovieLens-100K data-set having multi-modal information. Experimental results establish that only rating-based embeddings in the current setup outperform the state-of-the-art techniques but usage of multi-modal information in embedding generation performs better than its single-modal counterparts. 1.
Recent Progress on the Fabrication of Silver Nanowire-Based Transparent Electrodes

Transparent electrodes (TEs) made of metallic nanowires, such as Ag, Au, Cu, and Ni, have attracted rising attention for several reasons: 1) they can act as a substitute for tin oxide-based TEs such as indium-tin oxide (ITO) and fluorine-doped tin oxide (FTO); 2) various methods exist for fabricating such TEs such as filtration, spraying and meyer bar coating; 3) greater compatibility with different substrates can be achieved due to the variety of fabrication methods; and 4) extra functions in addition to serving as electrodes, such as catalytic abilities, can be obtained due to the metals that compose the TEs. There are a large number of applications for TEs, ranging from electronics and sensors to biomedical devices. This short review is a summary of recent progress, mainly during the past five years, on silver nanowire-based TEs. The focus of the review will be on theory development, mechanical, chemical and thermal stability and optical properties. The many applications of TEs are outside the scope of this review.
Dynamic Algorithm for on-the-Fly Work and Break Balancing in Emergency Fleets

Quality of emergency services depend on the effectiveness of emergency vehicle fleets (e.g., police, fire trucks, and ambulances). Traditionally, these fleets are composed of emergency crews that must attend highly stochastic incident demand within a short target arrival time. Incidents must be attended immediately, which prevents that an a priori computed work break schedule is executed as planned and many crews may be left without a break during prolonged periods of time. Extended focused work periods decrease efficiency with related decline of attention and performance. Therefore, break schedule should be regularly updated on the fly to allow frequent and sufficiently long time for rest. In this paper, we propose a dynamic algorithm for on the fly work and break balancing for crews in emergency fleets. Based on the historical intervention data, the algorithm (re)arranges vehicles’ crews’ work breaks as the time evolves considering individual crews’ preferences. Moreover, it dynamically reallocates stand-by vehicles for improved coverage of a region of interest. We show the performance of the proposed algorithm on two simple functional examples.
Disturbance-adaptive Short Term Frequency Support Method of Wind Turbine Generators

For short term frequency support (STFS) methods of wind turbine generators (WTGs) adopting extra inertia loop and droop loop, the tuning of the inertia gain for inertia loop and the droop gain for droop loop is crucial for these methods. In this paper, a disturbance-adaptive STFS method of WTGs is proposed, which tunes the inertia gain and droop gain based on the stages of a frequency disturbance, with considering both active frequency support and rotor speed recovery. The inertia gain changes based on the dynamic relation between the rotor speed and the system frequency, and it will remain zero after the frequency nadir (FN). The droop gain includes two variable components, one varies with the stages of a frequency disturbance considering the disturbance severity and the system composition. The other is related to the releasable kinetic energy of WTGs to ensure a safe operation. Besides, in order to restore the rotor speed and prevent an aggravated quasi-steady-state frequency deviation, the droop gain will decrease to zero before primary control finishes. Finally, to validate the performance of the proposed method, the simulations based on EMTP-RV simulator are carried out by considering different wind speeds, disturbance severities, and wind power penetration levels. The results verify that the proposed method can adapt to different scenarios, and is superior to conventional methods in terms of the FN, the second frequency dip and the quasi-steady-state frequency deviation.
Multi-task Facial Expression Recognition With Joint Gender Learning

Facial Expression Recognition (FER) in the wild is a significant yet challenging topic in computer vision due to the feature inconsistency caused by the individual specificity of facial expressions. In addition to variations of facial expressions caused by identity, pose, and occlusion, gender also affects the face of human emotions. Even though males, females, and infants share the same facial expressions, their characteristics are vastly different. To capture the effect of gender on facial expressions, we propose a novel multi-task FER method with joint gender learning. First, in addition to the original emotion labels of face images, we annotate gender labels, including male, female, and infant. Second, we introduce a gender-aware multi-task convolutional neural network for FER, which can learn the emotion and gender features of faces. Compared with single-task expression recognition methods, our proposed framework for introducing gender feature learning can significantly achieve higher performance on FER in the wild. Finally, we verify the effectiveness of our framework on two public wild FER datasets, RAF-DB and FER2013. And the results show that the gender learning auxiliary task is beneficial to the improvement of the performance of FER.
Lightweight High-Resolution Remote Sensing Scene Classification via Adaptive Enhanced Knowledge Distillation

Scene classification is a key step in intelligent information processing of high-resolution remote sensing, which aims to identify the land-use types of remote sensing image blocks. In recent years, the models based on deep convolution neural networks (CNN) have made outstanding achievements in the field of remote sensing, but these models are computationally expensive and time-consuming, while the lightweight shallow network models have few parameters, fast speed, but low accuracy. Both the deep CNN models and the lightweight shallow network models could not be directly applied to embedded devices. Therefore, we propose an Adaptive Enhanced Knowledge Distillation (AE-KD) to deeply mine the output and feature information from teacher model and transfer them to student model, so as to improve the performance of lightweight model. Firstly, aiming at the uneven degree of difference among remote sensing image categories, an adaptive temperature mechanism is proposed by improving the temperature mechanism in the traditional knowledge distillation, which promotes the student model to better learn the probability distribution knowledge from the output layer of the large and deep teacher model. And then, the spatial attention and inter-channel correlation of features are added as constraints in order to make the student model learn the multi-level knowledge from teacher model. The experimental results on UC Merced Land-Use and AID public datasets show that the proposed method reduces 91 % parameters of the teacher model and improves the prediction speed by 22 times, where it has only a small loss of classification accuracy, which is effective for the lightweight model. ablation study also further analyzes and discusses the performance improvement of the student model under different levels of knowledge distillation.
Meditation and Cognitive Enhancement: A Machine Learning Based Classification Using EEG

Meditation methods, which have their origins in ancient traditions are gaining popularity as a result of their potential mental and physical health advantages. EEG neural correlates underlying enhanced cognitive abilities such as sustained attention and working memory need to be analyzed scrutinizingly to evaluate the effects of meditation practices. In this article, we thus provide an analysis of EEG features such as various band powers and connectivity based features to evaluate the meditation effects. Also, we provide a classification framework to classify the meditation states from the baseline EEG states. We report our results on an in house dataset of 20 participants(10 experienced and 10 novice) who underwent a two-week long mantra meditation practice. Strikingly we have found out that, as the novice participants practice meditation overtime, the accuracies of machine learning classification between the baseline EEG versus meditative EEG of the novice increase significantly, as an indication of their enhanced meditation experiences. Also, we found out that even such short and regular meditation practices, the cognitive abilities of novice meditators get enhanced which are evaluated through Brain-Based Intelligence Test (BBIT) psychometric tests and the results that got reflected in their EEG correlates.
Predictive Analytic Healthcare Sector Using Classification Machine Learning Algorithm

There is a wealth of data that can be used to improve public health services by leveraging machine intelligence technology. The problem that arises is the distribution of unstructured data, not detailed and incomplete. In this study, data is processed with the machine learning algorithm classification stages that are tailored to the case and the objectives to be achieved. This Big Data Analytics model uses Descriptive analytics in combination with Predictive Analytics to work by looking at future situations in relation to past data. Prescriptive Analytics tries to analyze decision making to deal with this condition. Predictive analytics aims to analyze what patterns emerge in the health sector to improve health services. The Naive Bayes classifier is used to classify whether a patient will return for treatment or not, based on the village area the probability of returning to treatment is 0.40 and not returning to treatment is 0.60. Of the 13 health indicators, the data are grouped into 2 classes, namely fulfilled and unfulfilled by using a type of algorithm that includes the SVM Classifier. The model is same to have perfect prediction accuracy if the AUC value is 0.90. The best test model is Coarse Gaussian SVM result 90.1%. Grouping through decision trees aims to inform the adequacy of tree forecast accuracy = 97.9%, Linear Regression = 96.6%.
Analysis of Providing Spinning Reserve from Renewable Energy Sources in Renewable Generation Expansion Planning

Usually, reducing the high emission power generation is a useful way to achieve a low-carbon power system. And this paper focuses on an attention-worthy viewpoint to realize it, the spinning reserve. Traditionally, the spinning reserve is supported by conventional generators such as coal-fired generators especially. And the increasing load demand however requires a higher spinning reserve. That seems to mean the spinning reserve result from the load demand growth contradicts achieving the low-carbon power system. Based on this issue, this paper proposes a generation expansion planning method with the spinning reserve considering renewable energy sources to relieve the contradiction and achieve a low-carbon power system. To illustrate the effectiveness of the proposed method, we choose the ROTS case, a high renewable energy penetration power system, as our test case to develop a renewable generation expansion plan for 15 planning years. And the planning results reveal the prospects and potential of this model in achieving a low-carbon power system.
Obfuscated malware detection using dilated convolutional network

Nowadays, information security is a critical field of research since information technologies develop rapidly. Consequently, the possible attacks are also evolving. One of the problems is malware detection. There is no doubt that many antivirus software can catch most cases. However, it is important to remember that such software is one step behind the malware. Here we introduce artificial intelligence that can help to detect obfuscated malware in memory. Modern architectures of a neural network can detect even unknown malware and distinguish whether there is something malicious or not. This paper deals with the problem of the detection of obfuscated malware in memory. Most existing approaches use custom datasets or Microsoft Malware Classification Challenge dataset (BIG2015). However, we applied the latest dataset CIC-MalMem-2022, which reflects the current state of technologies. This dataset contains samples with benign and malware cases. Additionally, the authors provided the family and type of malware, so it is possible to perform advanced experiments. This paper provides techniques for the detection and classification of malware from given memory information. Firstly, the traditional machine learning methods are tested with optimisation techniques; secondly, the dilated convolutional network is proposed. According to the results, the detection by all methods has an accuracy of 0.99. However, the most accurate is a random forest. On the other hand, the proposed neural network architecture is the best for classifying the malware family and has achieved an accuracy of 0.83.
Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain

Most consumers depend on online reviews posted on e-commerce websites when determining whether or not to buy a service or a product. Moreover, due to the presence of fraudulent (deceptive) reviews, the fundamental problem in such reviews is not fully addressed. Thus, deceptive reviews present wrong and misguiding opinions that are harmful to consumers and e-commerce. People called fraudsters who intentionally write deceptive reviews to target and deceive potential consumers, as they target businesses that have a well-built reputation or fame for their personal promotion, create such reviews. Therefore, developing a deceptive review detection system is essential for identifying and classifying online product reviews as truthful or fake/deceptive reviews. The main objective of this research work is to analyze and identify online deceptive reviews in electronic product reviews in the Amazon and Yelp domains. For this purpose, two experiments were conducted individually. The first was executed on standard Yelp product reviews. The second was performed on Amazon product review datasets. For this dataset, we created and labeled it using a deceptiveness score calculated based on features extracted from the review text using the linguistic inquiry and word count (LIWC) tool. These features were authenticity, negative words, comparing words negation words, analytical thinking, and positive words as well as the given rating value by a user. The recurrent neural network, bidirectional long short-term memory (RNN-BLSTM) model, was used to both datasets in order to conduct the evaluation. The application of this model was contingent upon the learning of words embedding of the review text. Finally, we evaluated the RNN-BLSTM model's performance using the Yelp and Amazon datasets and compared the results. The results were 89.6% regarding testing accuracy for both datasets. From our experimental results, we observed that the LIWC feature with word embedding in the review text provided better accuracy performance compared with other existing methods.
Cosmic ray Nickel nuclei spectrum by the NUCLEON experiment

The NUCLEON experiment is designed to measure chemical composition of cosmic rays with charges from Z=1 to 30 in an energy region from 5*10^11 to 10^15 eV. In this article the data analysis algorithm and spectra of Ni and Fe nuclei, measured in the NUCLEON experiment, are presented.
An extended multi-zone model for the MCG-6-30-15 warm absorber

The variable warm absorber seen with ASCA in the X-ray spectrum of MCG–6-30-15 shows complex time behaviour in which the optical depth of O viii anticorrelates with the flux whereas that of O vii is unchanging. The explanation in terms of a two-zone absorber has since been challenged by BeppoSAX observations. These present a more complicated behaviour for the O viii edge. We demonstrate here that the presence of a third, intermediate, zone can explain all the observations. In practice, warm absorbers are likely to be extended, multi-zone regions of which only part causes directly observable absorption edges at any given time.
A quasi-particle model with a phenomenological critical point

A hybrid parameterization of a quasiparticle equation of state is proposed, with a critical point implemented phenomenologically. On the one hand, a quasiparticle model with finite chemical potential is employed for the quark-gluon plasma phase, calibrated to the lattice quantum chromodynamics data. On the other hand, the low-temperature region for the hadronic phase of the matter is described by the hadronic resonance gas model with excluded volume correction. A particular interpolation scheme is adopted so that the phase transition is a smooth crossover for small chemical potential. A phenomenological critical pointed is implemented beyond which the phase transition becomes that of the first order.