Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Variations in Water Soluble Inorganic Ions, OC and EC in PM10 and PM>10 Aerosols over Delhi: Influence of Sources and Meteorological Factors

ABSTRACTThe PM10 and particles of greater than 10 µm aerodynamic diameter (hence forth referred as PM>10) collected over Delhi exceeded the National Ambient Air Quality Standards (100 µg m–3) with an annual average of 215 µg m–3 and 495 µg m–3, respectively. The water soluble inorganic ions (WSII) were higher throughout the year in PM10 (13.5%) than in PM>10 (5%). SO42– dominated over NO3– ions and contributed 54% to the total WSII in PM10. NO3– and Ca2+ were dominant ions in PM>10. NH4+ during winter and autumn, and Ca2+ in summer PM10 samples were the major acid neutralizing species. The WSII showed monthly and seasonal changes. The total carbon (TC = EC + OC) constituted 8.8%–47.8% of total PM10 mass, and OC and EC varied from 9.91 to 37.06 µg m–3 and 5.42 to 22.23 µg m–3 during the year long sampling period. The SOC contributed more to OC in summers and the char (EC1) dominated over soot (EC2 + EC3) in EC fractions throughout the year. In summer samples, secondary OC (SOC) contributed 81% to OC and the OC/EC ratio was 3.61 suggesting the possible role of mineral dust and high photochemical activity in SOC production. For the eight different fractions, three dominant sources were identified, coal combustion, biomass burning, and motor vehicle exhaust for OC1, OC2 and OC3; vehicle exhaust for OC4, OP, EC2 and EC3; and biomass and coal combustion for EC1. The seasonal changes in the WSII and carbon concentrations in PM10 and PM>10 are attributed to both the sources and the meteorological conditions in and around the study area.
Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks

In finance, the momentum spillovers of listed firms is well acknowledged. Only few studies predicted the trend of one firm in terms of its relevant firms. A common strategy of the pilot work is to adopt graph convolution networks (GCNs) with some predefined firm relations. However, momentum spillovers are propagated via a variety of firm relations, of which the bridging importance varies with time. Restricting to several predefined relations inevitably makes noise and thus misleads stock predictions. In addition, traditional GCNs transfer and aggregate the peer influences without considering the states of both connected firms once a connection is built. Such non-attribute sensibility makes traditional GCNs inappropriate to deal with the attribute-sensitive momentum spillovers of listed firms wherein the abnormal price drop of one firm may not spill over if the trade volume of this decreasing price is small or the prices of the linked firms are undervalued. In this study, we propose an attribute-driven graph attention network (AD-GAT) to address both problems in modeling momentum spillovers. This is achieved by element-wisely multiplying the nonlinear transformation of the attributes of the connected firms with the attributes of the source firm to consider its attribute-sensitive momentum spillovers, and applying the unmasked attention mechanism to infer the general dynamic firm relation from observed market signals fused by a novel tensor-based feature extractor. Experiments on the three-year data of the S&P 500 demonstrate the superiority of the proposed framework over stateof-the-art algorithms, including GCN, eLSTM, and TGC.
Efficient Truthful Scheduling and Resource Allocation through Monitoring

We study the power and limitations of the Vickrey-Clarke-Groves mechanism with monitoring (VCGmon) for cost minimization problems with objective functions that are more general than the social cost. We identify a simple and natural sufficient condition for VCGmon to be truthful for general objectives. As a consequence, we obtain that for any cost minimization problem with non-decreasing objective μ, VCGmon is truthful, if the allocation is Maximal-in-Range and μ is 1-Lipschitz (e.g., μ can be the Lp-norm of the agents’ costs, for any p ≥ 1 or p = ∞). We apply VCGmon to scheduling on restricted-related machines and obtain a polynomial-time truthful-in-expectation 2-approximate (resp. O(1)-approximate) mechanism for makespan (resp. Lp- norm) minimization. Moreover, applying VCGmon, we obtain polynomial-time truthful O(1)-approximate mechanisms for some fundamental bottleneck network optimization problems with single-parameter agents. On the negative side, we provide strong evidence that VCGmon could not lead to computationally efficient truthful mechanisms with reasonable approximation ratios for binary covering social cost minimization problems. However, we show that VCGmon results in computationally efficient approximately truthful mechanisms for binary covering problems.
Community-Aware Multi-Task Transportation Demand Prediction

Transportation demand prediction is of great importance to urban governance and has become an essential function in many online applications. While many efforts have been made for regional transportation demand prediction, predicting the diversified transportation demand for different communities (e.g., the aged, the juveniles) remains an unexplored problem. However, this task is challenging because of the joint influence of spatio-temporal correlation among regions and implicit correlation among different communities. To this end, in this paper, we propose the Multi-task Spatio-Temporal Network with Mutually-supervised Adaptive task grouping (Ada-MSTNet) for community-aware transportation demand prediction. Specifically, we first construct a sequence of multi-view graphs from both spatial and community perspectives, and devise a spatio-temporal neural network to simultaneously capture the sophisticated correlations between regions and communities, respectively. Then, we propose an adaptively clustered multi-task learning module, where the prediction of each region-community specific transportation demand is regarded as distinct task. Moreover, a mutually supervised adaptive task grouping strategy is introduced to softly cluster each task into different task groups, by leveraging the supervision signal from one another graph view. In such a way, Ada-MSTNet is not only able to share common knowledge among highly related communities and regions, but also shield the noise from unrelated tasks in an end-to-end fashion. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness of our approach compared with seven baselines.
Riemannian Embedding Banks for Common Spatial Patterns with EEG-based SPD Neural Networks

Modeling non-linear data as symmetric positive definite (SPD) matrices on Riemannian manifolds has attracted much attention for various classification tasks. In the context of deep learning, SPD matrix-based Riemannian networks have been shown to be a promising solution for classifying electroencephalogram (EEG) signals, capturing the Riemannian geometry within their structured 2D feature representation. However, existing approaches usually learn spatial-temporal structures in an embedding space for all available EEG signals, and their optimization procedures rely on computationally expensive iterations. Furthermore, these approaches often struggle to encode all of the various types of relationships into a single distance metric, resulting in a loss of generality. To address the above limitations, we propose a Riemannian Embedding Banks method, which divides the problem of common spatial patterns learning in an entire embedding space into K-subproblems and builds one model for each subproblem, to be combined with SPD neural networks. By leveraging the concept of the "separate to learn" technology on a Riemannian manifold, REB divides the data and the embedding space into K non-overlapping subsets and learns K separate distance metrics in a Riemannian geometric space instead of the vector space. Then, the learned K non-overlapping subsets are grouped into neurons in the SPD neural network's embedding layer. Experimental results on public EEG datasets demonstrate the superiority of the proposed approach for learning common spatial patterns of EEG signals despite their non-stationary nature, increasing the convergence speed while maintaining generalization.
Prediction of advanced oxidation performance in UV/H2O2 reactor systems with LP-UV lamps

Advanced oxidation processes, like UV/H2O2 oxidation, are important barriers against organic micro pollutants in drinking water treatment. In order to guarantee safe drinking water, it is important to be able to predict the reactors' performance to adjust the operating conditions to the actual influent water characteristics (like UV transmission) and lamp performance. Therefore, a design tool was developed, which is based on a kinetic model that describes and predicts the direct photolysis and oxidation of organic compounds in pilot experiments, using Low Pressure (LP) UV-lamps. This model has been combined with computational fluid dynamics (CFD), in order to be able to accurately predict the results of pilot and full scale installations, and also to design reactor systems. The model was applied to three model compounds (atrazine, ibuprofen and NDMA) in two different pilot reactors, and it has been shown that reactor performance can be fairly predicted by applying this 'UVPerox' model. The model takes into account the water quality and power of the lamps, and the properties of the compounds involved. © IWA Publishing 2011.
Generalized Polynomial Chaos-Based Fault Detection and Classification for Nonlinear Dynamic Processes

This paper deals with detection and classification of intermittent stochastic faults by combining a generalized polynomial chaos (gPC) representation with either Maximum Likelihood or Bayesian estimators. The gPC is used to propagate stochastic changes in an input variable to measured quantities from which the fault is to be inferred. The fault detection and classification problem is formulated as an inverse problem of identifying the unknown input based on the Maximum Likelihood of fit between predicted and measured output variables, or on a Bayesian inference based estimator which recursively updates the gPC coefficients. Simulation studies compare the proposed methods with a particle filter (PF) to estimate the value of an unknown feed mass fraction of a chemical process. The proposed method is shown to be significantly more efficient in terms of computational effort and less sensitive to user defined tuning parameters than a PF.
Modeling the Impact of Surrounding Dielectric Width on the Range and Shape of the Recording Volume of Disc Microelectrodes

Efforts to reduce the size of implanted microelectrodes and their supporting dielectric material are being driven by demands to reduce the foreign-body tissue response and increase spatial resolution. Although the impact on recording performance of electrode size and shape have been analyzed in both prior and on-going work, the impact of the width of the surrounding dielectric has not. In this work we use the FEM-reciprocity method, informed by NEURON, to examine the impact of dielectric widths, ranging from 1 to $256 \mu \mathrm{m}$, on the range and shape of the recording volume around $8- \mu \mathrm{m}$-diameter disc electrodes located inside a $700-\mu \mathrm{m}$-diameter fascicle. We observe that reducing the width of the dielectric not only can reduce the recording range by up to 1/3 but also changes the region of tissue recorded from only above the electrode to equally above and below the electrode. As the width of the dielectric is scaled down, these observed impacts occur at different levels for different diameters fibers.
Mathematical model construction of DC Motor by closed-loop system Identification technique Using Matlab/Simulink

Process Identification and PID Control enables students and engineers to understand the essential concepts of feedback control, process identification, auto-tuning, and design of real feedback controllers, especially PID controllers. And related to research, in this paper, the DC-Motor has a speed of 3000 rpm. This paper shows how to design speed 800-1000 rpm. by collect input-output signal information to be analyzed for mathematical equations (Plant-DC Motor) in a system identification toolbox MATLAB/SIMULINK to aid the reader in solving real problems. Readers will be able to design their own controllers, implement them, and confirm performance in real-time using real-time virtual processes. The system of interest is a closed-loop control system with a feedback controller and displays the results with a mathematical model. (Mathematical model identification) of a system that can be designed and implemented.
A Novel Method for Resource Efficient Security Service Chain Embedding Oriented to Cloud Datacenter Networks

An important and promising application for the network function virtualization (NFV) technology is in network security, where can dynamically and flexibly accomplish the chaining virtualized security network functions (VSNFs), e.g., network address translation, antispam and packet filter firewall, etc., and thus inspect, monitor or filter traffic flows in the cloud datacenter networks. However, the traffic flows addressed by the VSNFs mainly depend on the security service requirements from mobile users, such as the network security level, end-to-end latency, and security resource, etc. Considering the dynamic nature of cloud datacenter networks, determining the embedding of VSNFs and routing security service paths that optimizes the security resource utilization is a challenging problem, particularly without violating the end-to-end delay constraints and security service requirements. This can also be called security service chain dynamic embedding problem (SSC-DMP). In this paper, we present an NFV-enabled framework for a system that achieves the SSC dynamic embedding in the cloud datacenter networks. We first formulate an integer linear programming (ILP) model to solve the SSC-DMP exactly in small-scale network topology. Then, in order to reduce the time complexity when applying the large-scale network topology, we propose an efficient SSC dynamic embedding solution that is based on the particle swarm optimization. Extensive simulation results show that the proposed algorithm could significantly outperform the current benchmarks at least 35.2% and 23.1% in terms of resource consumption and end-to-end delay, respectively.
Novel Partitioned Primary Linear Hybrid Excited Flux Switching Machine with Segmented Secondary

This paper presents a novel segmented secondary partitioned primary hybrid excited linear flux switching machine (PP-HELFSM) for long stroke applications. In recent years, the price of rare earth permanent magnets (such as NdFeB) has risen rapidly. Therefore, great attention is paid to substitute the existing magnet materials. This article compares the proposed machine for different magnet materials; one uses NdFeB and other uses a less expensive ferrite magnets. The proposed machine is a double-mover with partitioned primary structure. PM and the field winding are placed in a lower primary and armature winding is placed on the upper primary, to avoid the problem of crowded primary in single sided machines. PMs are placed between the E shaped cores of the lower primary. The electromagnetic properties like flux linkages, back EMF, detent force and thrust force of the proposed machine is investigated. The influence of both magnets ferrite and NdFeB are analysed and compared. The NdFeB indicates higher thrust force at the same electric loading but have high thrust ripples.
Production and diffusion model of Nitric Oxide for bioinspired spiking neural networks

Nitric Oxide (NO) is an intracellular messenger whose diffusive properties enable an unconventional type of communication between neurons in the central nervous system that bypasses their anatomical connectivity. In this work, we modeled NO production and diffusion from a single source and investigated the range of action of the NO signal within a bioinspired spiking neural network. We found that a single active source will produce only a local effect on the individual synapse. While if multiple closely-located sources are active at the same time, NO will act more like a volume transmitter and influence even inactive synapses within that area. We focused our attention on the cerebellum's input layer, where NO is produced by the granule cells. In the granular layer, NO acts as a retrograde second messenger able to enhance presynaptic currents in the mossy fiber - granule cell synapses, thus potentiating them with long-term effects (LTP).
Machine Learning for Performance Enhancement in Fronthaul Links for IOT Applications

We present A-typical machine learning (ML) based digital predistortion (DPD) solution for performance enhancement in analog optical front-hauls (OFH) for internet of things (IoT) based applications. Volterra based DPD has been realized in the past which becomes quite cumbersome due to complexity and choice of coefficients. Whereas the traditional Artificial Neural Networks techniques require time and optimization to determine the best model configuration. The proposed support vector regression (SVR) method is used that alleviates the nonlinearities and uplifts the OFH performance optimally. In this work, the experimental evaluation is made for 5G new radio (NR) signal having 256 quadrature amplitude modulation using 1550 nm Mach Zehnder Modulator and dispersion compensation fiber having 1 km link length. The experimental results suggest that SVR-DPD results in performance enhancement as compared to traditional volterra methods such as generalized memory polynomial, hence proving to be exceptionally operational.
A Comprehensive Survey on Computational Aesthetic Evaluation of Visual Art Images: Metrics and Challenges

Computational image aesthetic evaluation is a computable human aesthetic perception and judgment realized by machines, which has a significant impact on a variety of applications such as image advanced search and promotional exhibition of painting arts. Various approaches have been proposed in copious literature trying to solve this challenging problem. However, there have been few attempts in reviewing works from different types of visual arts, due to their significant differences in visual features and aesthetic principles. In this survey, we present a comprehensive listing of the reviewed works on aesthetic assessment of photographs and paintings, mainly highlighting the contributions and innovations of the existing approaches. We firstly introduce aesthetic assessment benchmark datasets in different categories. Then, conventional aesthetic evaluation approaches based on handcrafted features are reviewed. Besides, we systematically evaluate recent deep learning techniques that are useful for developing robust models for aesthetic prediction tasks in scoring, distribution, attribute, and description. Moreover, the possibility of aesthetic-aware color enhancement, recomposition of photo images, and automatic generation of aesthetic-guided art paintings through computational approaches are summarized. Finally, challenges and potential future directions for this field are discussed. We hope that our survey could serve as a comprehensive reference source for future research on computational aesthetics in visual media.
A Hierarchical Approach for Point Cloud Classification With 3D Contextual Features

Classifying point cloud of urban landscapes plays essential roles in many urban applications. However, automating such a task is challenging due to irregular point distribution and complex urban scenes. Incorporating contextual information is crucial in improving classification accuracy of point clouds. In this article, we propose a hierarchical approach for point cloud classification with 3-D contextual features, which comprises three steps:segment-based classification with primitive features and a random forest classifier; extracting novel 3-D contextual features from the initial labels considering spatial relationships between neighboring segments and semantic dependencies; and refining classification with a combination of primitive features and spatial contextual features, and a hierarchical multilayer perceptron classifier that considers primitive features and spatial contextual features at different levels. The proposed method was tested on two point cloud datasets:the National University of Singapore (NUS) dataset and the Vaihingen benchmark dataset of the International Society of Photogrammetry and Remote Sensing. The evaluation results showed that the proposed method achieved an overall accuracy of 92.51% and 82.34% for the NUS dataset and Vaihingen dataset, respectively. The feature importance evaluation showed that 3-D spatial contextual features contributed useful information for discriminating different classes, such as roof, facade, grassland, tree, and ground. Quantitative comparisons further showed that the proposed method is more advantageous, especially in the detection of class roof and facade.
Finite Time H∞ Filter-based Controller Design for Discrete-Time Systems

This paper presents a new design approach for a discrete-time system affected by norm bounded disturbance. To guarantee finite time H∞ boundedness (FTB-H∞) of the closed-loop system, the determination of reduced order H∞ controller parameters over finite time interval is discussed. Based on the solution of algebraic equations obtained from the unbiasedness conditions of the estimation error, sufficient conditions are given in this paper to design H∞ reduced order controller. At the end of this paper, there is a numerical example to show the effectiveness of the proposed approach.
Biometric Signature based Public Key Security System

Digital signature is a technique that is utilized to check the authenticity of a message transmitted electronically. The digital signature technique is based on a public key method. The meant transmitter signs his / her message of his / her private key and the meant receiver checks that with the public key of the transmitter. Digital signatures also ensure authentication of messages, integrity verification, and nonrepudiation systems. This paper presents the concept of Biometric Signature: a recent technique to digitally sign a message utilizing biometrics- associated with digital signature key creation, so, merging the benefits of Public Key Infrastructure (PKI), through the utilize of biometric-based digital signature creation that is secure, reliable, quickly-comfortable, non-invasive, and clearly describes the transaction creator. It also proposes biometric signature algorithms utilizing the commonly utilized RSA digital signature technique and shows the problems related to them. This paper analyses the security of the modulus and the size of the keys used and computes the strength of RSA based on the number of bits in the key (iris template). The time of the scheme is calculated and it is extremely small (in sec).
Quantization of Impedance Stability in Frequency Domain

A method based on Sample Standard Deviation (SSD) is suggested here to quantify impedance stability in frequency domain. Firstly, impedance instability factor, δT, is introduced as an indicator of that could be used for impedance stability quantifications of measured or simulated impedance data of Radio Frequency (RF) and high-power devices. The method is used then for a generic real/imaginary impedance trajectory as an example to clarify calculation process. Finally, the introduced approach has been used to quantify impedance stabilities in some RF devices such as stable impedance antenna of 5G in-band full duplex (IBFD) transceivers, microstrip low-pass filter and hybrid coupler. Also, suitable efficiency of the method to follow up correct design direction to smoothen impedance of an antenna is realized.