Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical algorithm for traffic decorrelation in Fog computing

Given that the presence of correlations in traffic can have a significant impact on the values of service quality parameters, which must be taken into account in the design of services, especially services that are critically dependent on these parameters, the need to develop methods and algorithms for processing correlated traffic increases. This paper presents a theoretical method for decorrelation of network traffic for the purpose of reducing latency in the transmission and processing of video traffic in fog computing. An algorithm and implementation concept for implementing the decorrelation is also given. The method is based on the use of Haar wavelets.
Hydrophobicity Classification of Composite Insulators Based on Image Enhancement and Deep Learning

Accurately judging the hydrophobicity classes (HCs) of the composite insulator helps to grasp the antipollution flashover performance of the insulator and prevent accidents in time. Aiming at the problem of low HC recognition accuracy of hydrophobic images and interference of operators’ subjectivity in the feature extraction process, an intelligent HC recognition model of composite insulators based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) image enhancement method and ResNet deep learning network is proposed in this paper. Firstly, composite insulators under varying HCs were simulated by spraying ethanol solutions with different volume fractions, and a series of hydrophobic images under experimental conditions was obtained. To improve the generalization ability of the model on photographic factors (photographing distance, light intensity, insulator color), images were preprocessed with gray processing, cropping, enhancement, and so on. The transfer learning strategy was used for training and verifying the pre-trained ResNet network by applying the preprocessed image dataset so that it is suitable for recognizing the HC of composite insulators. The results show that the CLAHE-ResNet18 model proposed in this paper can still effectively identify each HC under varying photographic factors and complex conditions. This model can maintain an accuracy above 96.74% in the testing dataset, with good generalization ability and application value.
A Robustly Real-Time Economic Dispatch Strategy of Microgrids Considering Uncertainties

The microgrid is considered to have promising development and practical application due to its characteristics of efficiency, economy and promoting the accommodation for renewable distributed generation (RDG). However, the uncertainties originated from the RDG pose challenges to the reliable operation and real-time management of microgrid. To cope with this problem, this paper proposes a real-time economic dispatch strategy of the microgrid by combining the model predictive control (MPC) and robust optimization (RO). The proposed robust MPC based strategy formulates the MPC optimization as a robust problem, which enables the real-time energy dispatch of the microgrid while ensuring its reliable operation. Meanwhile, the feedback update mechanism of MPC contributes to reduce the conservativeness of RO. In addition, the proposed solution considers the demand response (DR) of electricity load to further reduce operating cost. The effectiveness of the proposed solution is demonstrated by simulation experiments and comparison with the benchmark.
The Influence of Uneven Pollution Distribution on the Electrical and Thermal Characteristics of Insulators

To study the temperature variation of DC insulator string under uneven pollution degrees, a 70kV DC double-enveloped porcelain insulator string model was established. The electrical and thermal field of the insulator string was simulated by the finite element method, and the influence of uneven pollution conditions was analyzed. The results show that under the condition of uniform saturated wet pollution, the temperature of insulators increases with different amplitude, The overall temperature of the insulator increases as the total pollution degree increases. In addition, it is found that wet pollution tends to flatten the potential distribution near the high-voltage electrode, and the effect is more obvious when the potential distribution is uniform and the pollution is uniform. When the salt density on the upper and lower surfaces of an insulator is inconsistent, the pollution on the lower surface of an insulator has a greater impact on the temperature rise of the insulator string than that on the lower surface. As the contamination on the bottom surface of the insulator increases, the whole temperature of the insulator string increases. When the salt density ratio on the upper and lower surfaces changes from 7:1 to 1:7, The insulator string temperature rises first and then drops, reaching a maximum temperature of 1:
Mapping System to Interprete a Fracture Zones in Seismic Data Based on an Autoencoder and Generative Adversarial Networks

Our research was aimed at developing methods and algorithms for identifying and mapping fracture zones of the Jurassic and pre-Jurassic complexes based on seismic data and methods of machine learning and artificial intelligence. In this paper it were developed and tested: the feature selection algorithm based on an autoencoder for interpreting data on seismic slices with low intraclass and high interclass variability, as well as the algorithm for synthesizing new images based on generative adversarial networks and a seismic database. The experiments carried out have shown that the algorithms have good accuracy and speed of operation, which makes them potentially suitable for processing large volumes of seismic information. The proposed autoencoder for candidate images with a high recall value both for the search variant with the micro-averaging metric and for the search with the macroaveraging metric. The automatic interpretation of seismic data on seismic slices of the Salymskaya well was carried out. The system for processing, analyzing and classifying 3D seismic information was developed based on the proposed methods and algorithms. The optimal predictive model was tuned based on machine learning and artificial intelligence methods.
Computer-aided diagnosis Application by using Nonlinear Dynamics of EEG Signals to detect Depression State

In neurophysiology, nonlinear dynamics has been utilised to decode the intricate EEG brainwave patterns. In order to better understand the nonlinear nature of EEG data, numerous nonlinear approaches have been suggested and implemented recently. Nonlinear dynamics, a key mathematical subject that characterises the complex real-world phenomena characterised by nonlinear dynamics, has attracted significant interest in a wide range of scientific disciplines, including physics, chemistry, economics, electronics, and biomedical engineering, to name just a few. In this study, the authors explored how EEG waves may be detected using a computer to help in the diagnosis of depression. Depression is a mental illness that interferes with a person's ability to carry out normal, daily tasks. To lessen the burden on the sufferer and their loved ones, an accurate diagnosis of depression is crucial. Psychiatric disorders can be detected by the EEG's signal changes. As an objective, non-invasive method, the EEG has found widespread application in the study of numerous neurological conditions. But a CAD system is built to identify and analyse the subtle alterations in the signals. EEG signals, both normal and depressed, can be broken down into their component frequencies using the discrete cosine transform (DCT). DCT coefficients are used to assess the efficacy of nonlinear techniques such sample entropy, correlation dimension, fractal dimension, greatest Lyapunov exponent, Hurst exponent, and detrended fluctuation analysis. The t-value is used to rank the extracted characteristics. Classifiers are fed these relevant features. Accuracy achieved is 94.2 percent in classification together with 93.4 percent sensitivity and 95.6 percent specificity.
Multi-Head Multi-Scale Feature Fusion Network for Semantic Scene Completion

In recent years, 3D semantic scene completion has achieved rich development and has been widely used. Part of the work is to study the fusion mode of multimodal features under a basic completion framework in order to obtain better geometric or semantic features. Part of the work is to study the improvement of the completion framework under the condition of using a single depth or RGB-D multimodal input. The semantic scene completion network proposed by us is based on the basic framework, adds attention mechanism, and creatively uses multi-scale method to improve the network, so as to obtain better 3D semantic scene completion effect.
Application of R-CED Model to Remove Noise from Recordings of Industrial Machine Sound

This study approaches the problem of removing background noise from industrial machine sounds. Most existing studies on removing noise from sound focus on speech denoising, and many different methods of noise removal were developed for that task. This paper describes how a speech denoising method can be repurposed for the task of industrial sound cleaning. The method was used to remove factory noise from the recordings of an electric engine. The results show that the method can be successfully used to remove background noise from industrial machine sounds. However, further research is required to determine if the method is applicable to real-life tasks of machine condition monitoring.
‘Ultraslow-Light’ and Flipping Property of the Crisscrossed-assisted Coupled-Ring Reflector

We report two new behaviors of the recently reported crisscross-coupled microring resonator structure, namely (i) ‘ultraslow light’ propagation under symmetric parameter conditions, and (ii) a “flipping property” in the group delay under asymmetric parameter conditions. These properties can be utilized in numerous optical-based applications.
Low Complexity AoA Estimation with Planar Electromagnetic Lens Arrays

This paper describes the problem of angle of arrival (AoA) estimation with planar electromagnetic (EM) lens arrays. Planar EM lens arrays exhibit some implementation advantages if compared with dielectric lenses such as light weight and low cost, making them a very attractive solution for different applications. Although several AoA estimation algorithms and methods have been recently proposed, most part of the literature does not deal with the case of planar lens, and an analysis of EM lens array signal models reveals the impossibility to apply methodologies developed for other kind of EM lenses to this case. Therefore, this work develops novel algorithms for AoA estimation with planar EM lens arrays. Moreover, the proposed methods not only have a very low computational complexity, but also employ only signal energies, leading to a great implementation simplification. Numerical results are also shown to demonstrate the accuracy of the proposed methods.
RANS-based aerodynamic shape optimization investigations of the common research modelwing

The aerodynamic shape optimization of transonic wings requires Reynolds-averaged Navier–Stokes (RANS) modeling due to the strong nonlinear coupling between airfoil shape, wave drag, and viscous effects. While there has been some research dedicated to RANS-based aerodynamic shape optimization, there has not been an benchmark case for researchers to compare their results. In this investigations, a series of aerodynamic shape optimizations of the Common Research Model wing defined for the Aerodynamic Design Optimization Workshop are presented. The computational fluid dynamics solves Reynolds-averaged Navier–Stokes equations with a Spalart–Allmaras turbulence model. A gradient-based optimization algorithm is used in conjunction with a discrete adjoint method that computes the derivatives of the aerodynamic forces. The drag coefficient at the nominal flight condition is minimized subject to lift, pitching moment and geometric constraints. A multilevel acceleration technique is used to reduce the computational cost. A total of 768 shape design variables are considered, together with a grid with 28.8 million cells. The drag coefficient of the optimized wing is reduced by 8.5% relative to the baseline. The single-point design has a sharp leading edge that is prone to flow separation at off-design conditions. A more robust design is achieved through a multipoint optimization, which achieves more reliable performance when lift coefficient and Mach number are varied about the nominal flight condition. To test the design space for local minima, randomly generated initial geometries are optimized, and a flat design space with multiple local minima was observed.
Cascading Modular Multimodal Cross-attention Network for Rumor Detection

In recent years, the task of rumor detection has progressed from its initial focus on textual content to a multimodal approach incorporating both text and images. This means that images and videos within rumors are considered as sources of information and are more likely to capture people's visual attention, thus making the spread of rumors easier. Failure to control rumors on the Internet could lead to negative consequences such as endangering public safety. In this context, multimodal rumor detection has emerged. However, current multimodal rumor detection is unable to capture deep-level textual semantic information and integrate it with key objects in images. So, we introduce the Cascading Modular Multimodal Cross-Attention Network (CMMCN), which has been specially crafted for the purpose of detecting rumors. By facilitating sophisticated interactions between information at both the word and visual-object levels, our network leverages modularized cross-attention units to extract deep textual features in a cascading fashion. The network also facilitates layer-by-layer interaction between textual and visual key objects to capture deep-level fusion features across modalities. Two multimodal rumor datasets (Weibo and Twitter) were used for comparison and ablation experiments. According to the results of the experiment, CMMCN can effectively integrate modal information and enhance the ability to detect rumors through multiple modes.
Holography for non-relativistic CFTs

The AdS/CFT correspondence [1] is a remarkable framework for studying the dynamics of quantum field theories at strong coupling, in addition to providing an in-principle answer to the question: “What is quantum gravity? ” The correspondence was originally proposed based on studies of D-brane dynamics in flat space. By taking an appropriate decoupling limit to focus on the dynamics of the light open string degrees of freedom, one ends up with an isomorphism between the Hilbert space of open string degrees of freedom and a closed string system on a curved background. One natural way to motivate the correspondence is to start from the open string description and ‘integrate out’ the holes in the world-sheet. Over the years the correspondence has been generalized and has yielded many interesting insights into both the dynamics of field theories at strong coupling and into the nature of quantum gravity. One way to view the AdS/CFT correspondence is entirely at a kinematical level based on symmetries. While this does not capture the entire beauty and the details of the correspondence, it nonetheless provides an entry point towards discussing generalizations. In order to do this one has to distill the essentials of the correspondence to asking the following question: “What is the geometric description of a fixed point of the Wilsonian renormalization group flow? ” For instance, one could ask for a geometry that has the symmetries of a conformally invariant fixed point. In d > 1 spatial dimensions
CONSTRAINING THE ANGULAR MOMENTUM EVOLUTION OF V455 ANDROMEDAE

Time-series photometry on the cataclysmic variable V455 Andromedae (hereafter V455 And, HS 2331+3905) reveals a rotation period shorter than the orbital period, implying the presence of a magnetic field. We expect that this magnetic field channels the accreted matter from the disk toward the white dwarf poles, classifying it as an Intermediate Polar. The two polar spinning emission areas are visible in the lightcurves at the rotation period of 67.61970396 ± 0.00000072 s, and its harmonic. Using photometric observations of V455 And obtained from 2007 October to 2015, we derive 3σ upper limits to the rate of change of the spin harmonic (SH) with time to be dPSH/dt ≤ −7.5 × 10−15 s s−1 employing the O–C method, and −5.4 × 10−15 s s−1 with a direct nonlinear least squares fit. There is no significant detection of a changing spin period for the duration of 2007 October–2015. The 3σ upper limit for the rate of change of spin period with time is dPspin/dt ≤ −10.8 × 10−15 s s−1 or −0.34 μs yr−1. V455 And underwent a large-amplitude dwarf nova outburst in 2007 September. The pre-outburst data reflect a period 4.8 ± 2.2 μs longer than the best-fit post-outburst spin period. The angular momentum gained by the white dwarf from matter accreted during outburst and its slight subsequent shrinking should both cause the star to spin slightly faster after the outburst. We estimate that the change in spin period due to the outburst should be 5 μs, consistent with the empirical determination of 4.8 ± 2.2 μs (3σ upper limit of 11.4 μs).
SAVIOR: A Sustainable Network of Vehicles with Near-Perpetual Mobility

Switching to Battery Electric Vehicles (BEV) can have a significant positive impact on our environment. However, the adoption of BEVs is vastly impeded by battery-related concerns, such as limited travel range, long charging time, high purchasing cost (battery-induced) and lack of charging stations. Additionally, it is very expensive to build a large infrastructure of fast charging stations that can cater to a full-scale BEV fleet. Alternative solutions, such as charging from the road and BEV-to-BEV stationary charge sharing, have been proposed to counteract range anxiety, but they are mostly ineffective and suffer from scalability issues. In this article, we present SAVIOR, an innovative Internet-of-Things (IoT) framework for replenishing BEV batteries on-the-go with the help of unmanned aerial vehicles (UAVs) and mobile charging stations (MoCS). This will allow rapid BEV battery replenishment, eliminating the need for BEVs to make prolonged and pre-planned halts for re-charging. We also observe that package delivery UAVs can utilize this framework to make long-distance trips with the help of mobile charging stations and BEVs. We quantitatively analyze the effectiveness of such a framework through a simulation platform that we have developed. There is a drastic improvement in the mobility of BEVs and UAVs. Through statistical analysis, we also observe that greenhouse gas emissions (even for BEVs and UAVs) can be significantly reduced by SAVIOR if the MoCS are powered by renewable energy sources (e.g., solar).
Prototypical Concept Representation

Concepts are building blocks of human thinking. For machines, concept understanding has also been increasingly important, which makes concept representation a fundamental problem in artificial intelligence. While many concepts have their instances, the massive amount of information carried by instances has long been ignored in current concept representation, which limits the usage of these concepts in applications. In this paper, inspired by prototype theory in cognitive science, we propose prototypical concept representation for machines, which represents each concept with a distributed prototype derived from representations of its instances. For prototypical representation learning, we further introduce a novel model named Prototypical Siamese Network (PSN). PSN is trained under the supervision of isA determination, one of the most important concept-related applications. Results of extensive experiments demonstrate that, our method achieves state-of-the-art performance, thus validating the effectiveness of prototypical concept representation.
Aggregation and constraint processing in lifted probabilistic inference

Representations that mix graphical models and first-order logic—called either firstorder or relational probabilistic models—were proposed nearly twenty years ago and many more have since emerged. In these models, random variables are parameterized by logical variables. One way to perform inference in first-order models is to propositionalize the model, that is, to explicitly consider every element from the domains of logical variables. This approach might be intractable even for simple first-order models. The idea behind lifted inference is to carry out as much inference as possible without propositionalizing. An exact lifted inference procedure for first-order probabilistic models was developed by Poole [2003] and later extended to a broader range of problems by de Salvo Braz et al. [2007]. The C-FOVE algorithm by Milch et al. [2008] expanded the scope of lifted inference and is currently the state of the art in exact lifted inference. In this thesis we address two problems related to lifted inference: aggregation in directed first-order probabilistic models and constraint processing during lifted inference. Recent work on exact lifted inference focused on undirected models. Directed first-order probabilistic models require an aggregation operator when a parent random variable is parameterized by logical variables that are not present in a child random variable. We introduce a new data structure, aggregation parfactors, to describe aggregation in directed first-order models. We show how to extend the C-FOVE algorithm to perform lifted inference in the presence of aggregation parfactors. There are cases where the polynomial time complexity (in the domain size
Detection of Different Cable Defects Simulated by Impedance Box Based on FDR

In order to better explore the characteristics of different types of underground cable defects, this paper proposes a method based on frequency domain reflection (FDR) method to detect different defects of underground cable simulated by impedance box, so as to solve the problem that it is difficult to obtain the characteristics of cable defects in practical application. In this paper, the principle of cable local defect location based on FDR and the principle of cable defect diagnosis using time domain pulse waveform based on FDR method are described. Secondly, a system model based on frequency domain reflection method was built to detect different defects of underground cables simulated by impedance box, and the characteristic parameters were calculated according to the actual cable model and structure size, and the cable and capacitor parameters in the impedance box were set. Finally, a laboratory 10kV XLPE power cable is taken as the object to verify the effectiveness of impedance box in simulating cable defects.
Modification of transparent materials with ultrashort laser pulses: What is energetically and mechanically meaningful?

A comprehensive analysis of laser-induced modification of bulk glass by single ultrashort laser pulses is presented which is based on combination of optical Maxwell-based modeling with thermoelastoplastic simulations of post-irradiation behavior of matter. A controversial question on free electron density generated inside bulk glass by ultrashort laser pulses in modification regimes is addressed on energy balance grounds. Spatiotemporal dynamics of laser beam propagation in fused silica have been elucidated for the regimes used for direct laser writing in bulk glass. 3D thermoelastoplastic modeling of material relocation dynamics under laser-induced stresses has been performed up to the microsecond timescale when all motions in the material decay. The final modification structure is found to be imprinted into material matrix already at sub-nanosecond timescale. Modeling results agree well with available experimental data on laser light transmission through the sample and the final modification structure.