Miracle-dz/UniFAR-FTUs huggingface dataset:
Topic Influence Analysis Based on User Intimacy and Social Circle Difference

Analyzing topical user influence in online social networks is conducive to better advertisement injection, information dissemination, and user behavior analysis. In this paper, we propose a new approach to measure topical user influence in online social networks. Specifically, by comprehensively considering users’ social relationships, posting and forwarding behaviors, and posts content, we define two metrics of user intimacy and social circle difference to measure how influential users rank on different topics. The dataset obtained from Sina Weibo is utilized to evaluate the effects of our proposed approach and several comparison methods. Specifically, the advantages of our approach are evaluated from several aspects, including the correlations between influence rankings on different topics, the spread ability of high influential users, and the spread balance of high influential users. The extensive experimental results show that our approach is superior in mining influential users on different topics. Specifically, in our approach, the user influence rankings on different topics are less correlated, the users with high influence rankings achieve higher spread scope, and the higher a user’s influence ranking, the more evenly the user’s posts being spread among different communities.
Comprehensive Analysis of Electric Properties of Oxide-Confined Vertical-Cavity Surface-Emitting Lasers

We present a comprehensive description of electric properties of vertical–cavity surface–emitting lasers (VCSELs). A complete drift–diffusion model is developed and applied for carrier transport in a multilayer semiconductor laser heterostructure with a p–n junction. We address the impact of interface grading in distributed Bragg reflectors (DBRs), modulation doping of the DBRs and surrounding layers of the quantum well as well as drastically material–dependent carrier mobilities and recombination constants. Solution of the drift–diffusion model yields spatial profiles of the nonequilibrium carrier concentrations and current. The focus is made on oxide–confined GaAs/AlGaAs VCSELs. We evaluate both depletion and diffusion capacitance of the device and show that both contributions to the capacitance as well as the differential series resistance critically depend on the injection current and VCSEL chip design such that, in general, VCSEL cannot be properly modeled by an equivalent circuit approximation. Solution for the current profile demonstrates significant enhancement of the current density at the edges of the oxide–confined aperture (current crowding). We show that, as long as the mesa diameter is small, the effective RC–product can be kept low at small aperture diameters.
A Compact 40-GHz Doherty Power Amplifier With 21% PAE at 6-dB Power Back Off in 0.1- $\mu$ m GaAs pHEMT Process

This letter presents a monolithic 40-GHz Doherty power amplifier (PA) fabricated in 0.1-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> depletion mode (D-mode) gallium arsenide (GaAs) pseudomorphic high electron mobility transistor process. Different from most Doherty PAs, this PA occupies a compact chip size of 2.25 mm<sup>2</sup> because of the power-combining techniques. The measured results show a small signal gain of 12.7 dB, an output 1-dB compression point (<inline-formula> <tex-math notation="LaTeX">$\text{OP}_{\mathrm {1\,dB}}$ </tex-math></inline-formula>) of 19.2 dBm and saturated output power (<inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {sat}}$ </tex-math></inline-formula>) of 23.1 dBm at 40 GHz. Meanwhile, this PA achieves peak power added efficiency (PAE) of 28.5% and PAE of 21.1% at 6-dB power back off (PBO) from <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {sat}}$ </tex-math></inline-formula>. Finally, the bandwidth of this well-improved backed-off efficiency is obtained from 39 to 41 GHz with an output third-order intercept point (OIP<sub>3</sub>) of 20 dBm. Among published <italic>Ka</italic>-band GaAs Doherty PAs, this work exhibits the best 6-dB backed-off efficiency around 38 GHz, supporting the upper-frequency band for 5G wireless systems.
Temporal Graph Convolutional Networks for Traffic Speed Prediction Considering External Factors

Traffic speed prediction is an important part of intelligent transportation systems (ITS). If road traffic speed is predicted accurately, we can provide not only evidence for urban traffic managers, but also support for other road services such as path planning. Traditional prediction models usually ignore the spatio-temporal dependencies of the traffic dynamics and influences of external factors. This paper proposes a Temporal Graph Convolutional Networks (GTCN) which is composed of spatio-temporal component and external component to solving the traffic speed prediction problem. The spatio-temporal component integrates k-order spectral graph convolution and dilated casual convolution to capture the spatio-temporal dependencies. The external component takes social factors such as day of the week into account. To further improve the prediction accuracy, we consider the road structure features and point of interest (POI) during the construction of the sensor station graph. We evaluate the prediction model on two datasets from the Caltrans Performance Measurement System (CalTrans PeMS). Experiments show that the proposed GTCN model obtains high accuracy and outperforms state-of-the-art baselines.
The Competing Two-Strain SIS Pairwise Approximation Model With Infection Age

This paper studies a two-strain SIS pairwise approximation model with a competing mechanism and non-Markovian recovery process in which the recovery rate depends on infection age. The model is a hyperbolic system, which can be transformed into a system of integro-differential equations by using the method of characteristics. We obtain the associated reproduction numbers for each of the two strains, which determine the existence of the boundary equilibria. Besides, we also show that the two competing strains may coexist.
A New Selection Strategy for Decomposition-based Evolutionary Many-Objective Optimization

It is challenging for optimization algorithms to obtain well converged and diverse optimal solutions in solving many-objective optimization problems, especially when the Pareto front is complex. In this paper, a new selection strategy is proposed for decomposition based evolutionary algorithms for solving many-objective optimization problems. In the proposed method, each individual in a population is assigned to a reference vector at first according to the angle between the objective vector of this individual and the reference vectors to divide the population into some subpopulations. Then for each subpopulation, an ideal point will be specified according to the minimum value of each objective among all solutions in the corresponding subpopulation. The individual in each subpopulation with the maximum ratio of the cosine of the angle between its objective vector and the corresponding reference vector to the distance from the individual to the corresponding ideal point will be selected to be passed to the next generation. The proposed algorithm is compared with five state-of-the-art algorithms on the DTLZ1-7 and WFG3-4 test problems with up to 15 objectives. The experimental results showed the competitiveness of the proposed method.
Analysis of Vehicle Network Architecture and Performance Optimization Based on Soft Definition of Integration of Cloud and Fog

In order to solve a series of problems such as the high degree of heterogeneity and the lack of scalability in the traditional vehicle networking due to the increase of the scale of vehicles and the variety of vehicle applications, this study proposes to research and develop an innovative vehicle network; first, by introducing the architecture, technology, and problems encountered in the traditional car networking, the detailed analysis of the cloud and fog-based software-defined vehicles networking is carried out, and the problem of performance optimization of the vehicle’s network is discussed and studied to establish a fog server to make it play a coordinating role, so that the central fog servers can complete the load and deliver them to each other, and then load and unload based on the cloud computing service. Finally, based on the technologies of controller and virtualization, the quality of service and the consumption of energy are optimized from the aspects of system and equipment. Based on this, a two-way constrained particle swarm optimization algorithm can be adopted to mitigate these difficulties. Finally, the experiment and simulation analysis of the proposed algorithm shows that the proposed algorithm has the performance advantages and can help improve the communication delay and energy consumption. Therefore, research and discussion on this aspect are of great significance to the development of future vehicle networking technologies.
Interactional Moral Systems: A Model of Social Mechanisms for the Moral Regulation of Exchange Processes in Agent Societies

In this paper, we first introduce the concepts of moral agent and moral system of agent society and, in particular, the central concept of moral agent sensible to moral sanction. Next, we introduce the concepts of moral gain and moral loss in social exchanges and of reputation-based persistence of exchange processes. Following, we elaborate the notion of negotiated moral regulation of reputation-based persistent exchange processes. Finally, we combine those concepts in the notion of a reputation-based mechanism for the negotiation-driven moral regulation of exchange processes of agents that are sensible to moral sanctions.
Speech Enhancement Algorithm Based on Super-Gaussian Modeling and Orthogonal Polynomials

Different types of noise from the surrounding always interfere with speech and produce annoying signals for the human auditory system. To exchange speech information in a noisy environment, speech quality and intelligibility must be maintained, which is a challenging task. In most speech enhancement algorithms, the speech signal is characterized by Gaussian or super-Gaussian models, and noise is characterized by a Gaussian prior. However, these assumptions do not always hold in real-life situations, thereby negatively affecting the estimation, and eventually, the performance of the enhancement algorithm. Accordingly, this paper focuses on deriving an optimum low-distortion estimator with models that fit well with speech and noise data signals. This estimator provides minimum levels of speech distortion and residual noise with additional improvements in speech perceptual aspects via four key steps. First, a recent transform based on an orthogonal polynomial is used to transform the observation signal into a transform domain. Second, the noise classification based on feature extraction is adopted to find accurate and mutable models for noise signals. Third, two stages of nonlinear and linear estimators based on the minimum mean square error (MMSE) and new models for speech and noise are derived to estimate a clean speech signal. Finally, the estimated speech signal in the time domain is determined by considering the inverse of the orthogonal transform. The results show that the average classification accuracy of the proposed approach is 99.43%. In addition, the proposed algorithm significantly outperforms existing speech estimators in terms of quality and intelligibility measures.
Attributes Assessing the Quality of Microservices Automatically Decomposed from Monolithic Applications

The architectural styles in the world of software development are constantly evolving. Recently the microservice architecture is gaining more and more traction, building on concepts of Service Oriented Architecture (SOA) and steering further away from monolithic architectures. Emerged from agile communities, the microservice oriented architecture implies a number of small-sized microservices independently deployable. The adoption of microservices as the base for creating enterprise applications is certain, yet many companies intend to migrate from the old monolithic style instead of creating new products mainly due to cost related implications as well as challenging and complex tasks. Several tools and approaches for the semi-automatic decomposition of monolithic applications to microservices have emerged, yet many of them still struggle to verify the result of such process, the architect being indispensable for assessing the output microservices. Although this area is intensely studied, no unanimously accepted and clear guidelines for defining a good microservice exist. This survey focuses on providing a comprehensive and broadly applicable set of quality assessment criteria for microservices resulted from semi-automatic migration tools or techniques. Our study aligns with industry requirements, including a case study which further validates our set of quality attributes. In the refinement step of the quality attributes set, the prospect of automating the process of validation is also discussed.
Leveraging InfiniBand for Highly Concurrent Messaging in Java Applications

In this paper, we describe the design and implementation of Ibdxnet, an InfiniBand transport to enable high throughput and low-latency messaging for concurrent Java applications with transparent serialization of Java objects using DXNet. Ibdxnet applies best practices by implementing a dynamic and scalable pipeline with RC QPs and messaging verbs using the ibverbs library. A carefully designed JNI layer ensures minimal overhead to connect the native Ibdxnet library to the Java counterpart without impacting performance. Existing as well as new multi-threaded Java applications can use DXNet's event-based architecture concurrently to send and receive messages and requests transparently over InfiniBand with the Ibdxnet transport. We compared DXNet with Ibdxnet to the InfiniBand supporting MPI implementations FastMPJ and MVAPICH2. DXNet's performance for middle sized and large messages keeps up with FastMPJ's and MVAPICH2's. For small messages, DXNet clearly outperforms both systems especially in a multi-threaded environment. Furthermore, we compared the two key-value storages DXRAM, which uses DXNet with Ibdxnet, and RAMCloud, which uses a custom network subsystem based on ibverbs, using the YCSB with two workloads. On a graph data workload, DXRAM outperforms RAMCloud with a five times higher throughput of 7.96 mops on 40 nodes.
Computational prediction of Greek Nominal Allomorphy

In Theoretical Morphology, an interest in the topic of stem allomorphy has been renewed the last two decades. On the other hand, the computational treatment of allomorphy is still a huge challenge since the first systematic attempts on predicting allomorphy with machine learning techniques. The goals of this paper are to predict the allomorphic changes and to show the essential contribution of various morphological, phonological and semantic characteristics. Therefore, we use a MaxEnt model to identify the weights of these characteristics that are directly dependent on allomorphy and help design a predictive model. Our model is based on AMIS and its overall accuracy was 86.49%. To improve the system, we tried a more rational approach to achieve a better performance (the correct prediction was raised to 91.43%).
An Approximation Model Based on Kernel Ridge Regression for Robot Kinematics Simulation

Cloud computing technologies have enabled a new paradigm for intelligent manufacturing system which is powered by utilizing distributed resources, such as collaborative robots, simulation engines, advanced algorithms and human resources. As one of the key issues, the mechanism for online kinematics control of serial robotic manipulator presents speed challenge in the cloud-based system. In this research, a kinematics approximation model based on kernel ridge regression is developed for cloud manufacturing environment. To begin with, the model input is generated using trigonometric functions of rotation angles with permutation tricks which significantly reduces statistical error. Then, the approximation model is trained using kernel ridge regression with radial basis function, where both regularization and bandwidth of kernel have been optimized using grid-search. In addition, Universal Robot 10 is adapted as a collaborative robot example in simulation comparison experiments in order to evaluate the performance of the kinematics approximation model. As demonstrated in the experiment results, the proposed modelling approach can effectively support the cloud simulation paradigm and efficiently meet the real-time speed requirement in a distributed manufacturing environment.
Feature Engineering for Recycled FPGA Detection Based on WID Variation Modeling

We propose a novel recycled field programmable gate array (FPGA) detection method based on with-in die (WID) variation modeling. Model parameters extracted by the WID modeling simply but accurately represent the process variation of each FPGA, and thus, by using the model parameters as feature vector, machine learning effectively classifies target FPGAs into fresh or aged. Through experiment on commercial circuit simulator, the proposed method achieves 99.6% feature vector size reduction per FPGA with showing 96.0% detection accuracy; while our silicon result using commercial FPGA demonstrates the model parameters have a good distance properties between fresh and aged.
Design of Wireless Monitoring System for Greenhouse Environmental Parameters Based on Fuzzy Control

According to a large dead time and inertia of the control object of indoor temperature and humidity, an automatic monitoring system of room environment based on wireless sensor network is designed. The star-type structures are adopted for the wireless sensor network. By placing sensors network nodes in monitoring area, the system collects monitoring information in center measure and control system to realize unified data management and routing monitoring function of network. The collection part digitalizes the signal from the sensor and sends it out using wireless digital transmission technology. At the reception part, the data is received, saved and displayed. The real-time fuzzy control of parameter is realized, such as temperature and humidity, and make environmental factors stabilize in its optimum. Monitoring experimental result indicates that, this system is reliable and suitable, provides a practicable measures, and high in the ratio of performance to price. The control system not only has a high controlling accuracy, but also has the characteristic of rapid adjustment with small overshoot.
In SILICO Simulations Predict a Causative Link Between Increased Glycolysis and Metabolic Reprogramming in Autosomal Dominant Polycystic Kidney Disease

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder characterized by bilateral renal cyst formation due to loss-of-functions mutations in either PKD1 or PKD2 genes. Metabolic rewiring is an important feature of the disease. In this work, we merged biological knowledge and computational modelling to simulate the metabolic changes of bioenergetic pathways that we observed in ADPKD in a previous study, i.e. increased glucose uptake, increased mitochondrial glutamine uptake, increased fatty acids synthesis and reduced fatty acids oxidation. We found that the simulations of each of these alterations result in shared important overall metabolic changes in similar pathways. However, only increased glycolysis recapitulates simultaneously all changes observed in $Pkd1^{-/-}$ cells, while each of the other simulations caused multiple changes, but did not recapitulate the entire picture. Finally, we proved that citrate has a central role in modulating the switch toward alternative bioenergetic pathways, and we speculate that this metabolite is one of the key factors for the metabolic alterations observed in ADPKD.
A Practically Applicable Performance Prediction Model Based on Capabilities of Texture Mapping Units for Mobile GPUs

The power consumption models of mobile application processors have emerged as key objects of interest following the tremendous growth in mobile device production given that such consumption is an important factor in the graphics performance of mobile technologies. Conventionally, the performance of the graphics processing units (GPUs) depends critically on texture mapping units, which is why the number of such GPU components and texture fill rates value prominently whenever the GPU performance is evaluated. Our previous work has established a model to predict maximum performance based on unified shaders. By extending the work, this paper developed a practically applicable GPU performance prediction model on the basis of texture mapping performance. The effects of increased texture mapping units on unified shader performance and GPU efficiency were examined, and a performance prediction model based on the number of frames per second (FPS) was constructed. For these purposes, a benchmark related to texture mapping units was formulated and the experiments were conducted to determine utilization factors that are relevant to GPU performance and efficiency. The final stage in model construction involved establishing a relationship between the previously investigated utilization factors and relevant resources that are consumed during graphics processing. The experimental results showed that the proposed prediction model produced an average error rate of 5.77%.
Information content analysis reveals desirable aspects of in vivo experiments of a synthetic circuit

Synthetic biology is predicated upon the paradigm of rationally engineering biological systems to enrich them with new functions. Departing from the path outlined by other engineering disciplines, synthetic biology has made limited use of mathematical models so far. Indeed, their laborious inference leverages on noisy data that often provide partial insights into the system behaviour. If the quality of data generated via a perturbation is a road-block to inference, how should we analyse the informativeness of a stimulus? Which are the factors that contribute to it? Here we combine ideas from System Identification and Bayesian inference to quantify the information content of in vivo experiments for the calibration of a deterministic model of the genetic toggle switch. Beyond establishing a link between Bayes- and Fisher Information-based inference approaches, we find a $\sim 40\%$ gain can be ascribed to the dynamical properties of the perturbation scheme. Our results hint at the importance of including Bayesian experimental design in the characterisation of synthetic circuits.
An Accurate False Data Detection in Smart Grid Based on Residual Recurrent Neural Network and Adaptive threshold

Smart grids are vulnerable to cyber-attacks, which can cause significant damage and huge economic losses. Generally, state estimation (SE) is used to observe the operation of the grid. State estimation of the grid is vulnerable to false data injection attack (FDIA), so diagnosing this type of malicious attack has a major impact on ensuring reliable operation of the power system. In this paper, we present an effective FDIA detection method based on residual recurrent neural network (R2N2) prediction model and adaptive judgment threshold. Specifically, considering the data contains both linear and nonlinear components, the R2N2 model divides the prediction process into two parts: the first part uses the linear model to fit the state data; the second part predicts the nonlinearity of the residuals of the linear prediction model. The adaptive judgment threshold is inferred through fitting the Weibull distribution with the sum of squared errors between the predicted values and observed values. The thorough simulation results demonstrate that our scheme performs better than other prediction based FDIA detection schemes.