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Virtual metrology (VM) is crucial for improving process capability and production yield during semiconductor manufacturing processes. However, the performance of VM deteriorates owing to the variable operating regime and the nonlinear characteristics of the process. Herein, Variational inference Gaussian mixture model (VIGMM) and extreme learning machine (ELM) are combined to solve these issues. First, variational inference is conducted on a Gaussian mixture model to determine the number of Gaussian components automatically and the corresponding operating regimes are identified. Subsequently, an extreme learning machine is developed for each operating regime to investigate the nonlinear relationship between process inputs and outputs. Finally, VM is implemented using the corresponding local ELM, which is determined based on the responsibility of the Gaussian components. The feasibility and effectiveness of the proposed methods are validated based on a numerical case and the plasma sputtering process for fabricating thin-film transistor liquid-crystal displays. The proposed VIGMM-ELM can serve as a VM algorithm for manufacturing processes with multiple stages. [ABSTRACT FROM AUTHOR]

The next generation architectures of computer networks and systems and commercial technologies such as Big Data, Decentralized Storage, and 6G require novel approaches to prevent cybersecurity breaches which can negatively affect organizations, operations, and individual customers and stakeholders. In this proposal, we present an approach for identifying transformed features via statistical analysis which can be used in Artificial Intelligence (AI) and machine learning (ML) based systems. We also present a deep learning framework, Small Set of Linearized Variables (SSOLV), for training neural networks based on labeled Zeek datasets containing both malicious and benign activity in real or near-real time. In addition, we present a mechanism for transfer learning using domain adaptation techniques to adapt neural networks trained on one labeled Zeek dataset to another neural network trained on a different labeled Zeek dataset. This research uses a combination of 3 techniques commonly used in network traffic flow analysis: deep neural networks, linear regression, and ANOVA. Our results show that we can classify malicious activity with up to 97-99% accuracy in select cases and high precision (>95%) and recall (>90%) rate. This framework demonstrates a mechanism that stand-alone systems disconnected from larger networks can use to recognize adversarial activity in real time and is transferrable to other stand-alone systems. This work is patent pending under U.S. Patent App. Ser. No. 18/121,716 “Linearized Real-Time Network Intrusion Detection System” and U.S. Patent App. Ser. No. 17/900,982 “Real-Time Network Intrusion Detection System.”

The performance of the metamaterial-inspired substrate-integrated waveguide is discussed in this work, concerning a resonator misalignment potentially caused by the fabrication process. Initially, the design parameters of the aforementioned waveguide at the X-band are presented, while its optimal operation is validated to prove the effectiveness of the apparatus. Then, various significant aspects of the polynomial chaos expansion theory are briefly introduced to facilitate the sensitivity analysis due to fabrication errors. The direction of misalignment is, firstly, investigated, while the general case is, also, considered, highlighting a notable immunity, especially at lower frequencies. Additionally, a parametric examination is conducted in terms of the fabrication tolerance, measured as a percentage of the resonator unit cell. All the required simulations are conducted utilizing the non-intrusive approach of the polynomial chaos methodology via the popular Finite-Difference Time-Domain scheme.

Distributed energy generation systems, key for producing electricity near usage points, are essential to meet theglobal electricity demand, leveraging diverse sources likerenewables, traditional fuels, and industrial waste heat. Despite theirhigh reliability, these systemsare not immune to faults and failures. Such incidents can result in considerable downtime and reduced efficiency, underlining the need for effectivefault detection and diagnosis techniques. Implementing these strategies is crucial not just for mitigating damage and preventing potential disasters, but also to maintain optimal performance levels. This paper introduces a novel methodology based on Bayesian graphical modeling for unsupervised fault diagnosis, focusing on organic Rankine cycle case study. It employsstructural learning to discern unknown intervention points within a directed acyclic graph that models the power plant’s operations. By analyzing real-world data, the study demonstrates the effectiveness of this approach, pinpointing a subset of variables that could be implicated in specific faults.

Forecasting Spatio-Temporal processes has been attracting a great deal of interest within the research community. In this context, there is an increasing trend of proposing and improving methodologies to gather and use vast amounts of Spatio-Temporal data. Spatio Temporal Forecasting (STF) problems present complex interactions and non-linearities as the temporal dimension and the spatial one are usually entangled. To address these problems, statistical, Machine Learning based, and Deep Learning based models are introduced and developed. The use of exogenous data has proven to be beneficial in many STF models. Various techniques of incorporating exogenous data in STF problems have been proposed in the literature. This survey aims at providing a systematic review of the data-driven STF models, with a focus on those that incorporate exogenous data. We first investigate the data properties, including their dynamics, types and representations. Next, we propose a new taxonomy of the reviewed models and inspect the different complexities of STF problems. Exogenous data incorporation techniques are then presented and analyzed. We conclude our paper by highlighting the current open challenges and future research directions.

Measurement of e-commerce usability based on static quantities variable is state-of-the-art because of the adoption of sequential tracing of the next phase in the categorical data. The global COVID-19 outbreak has completely disrupted society and drastically altered daily life. The concept refers to an electronic commerce network that appears with thorough, understandable conviction, demand, and rapid confirmation as a replacement for the economical market’s “brick-and-mortar” model, which replaces how we do everything, including business strategy, and provides a better understanding with the interpretation of e-commerce features. This study was supervised to analyze usability assessments using statistical methods and security assessments using online e-commerce security scanner tools to investigate e-business standards that consider the caliber of e-services in e-commerce websites across Asian nations. The method was developed to optimize complex systems based on multiple criteria. The initial (supplied) weights are used to determine the compromise ranking list and compromise solution. This paper examines the usability of e-commerce in rural areas using a new data set from the Jharkhand region. On the e-commerce websites of Jharkhand, India, usability is commonly considered in conjunction with learnability, memorability, effectiveness, engagement, efficiency, and completeness. Using a user-oriented questionnaire testing method, this survey attempts to close the gaps mentioned above. Then, across each column, divide each value by the column-wise sum that is created using their corresponding value, whichever produces a new matrix B. Finally, determine the row-wise sum of matrix B representing the ( $3\times1$ ) matrix. Using model trees and bagging, this study addresses classification-related issues. This regression technique is useful for problems involving classification. The model is trained using secondary data from the MBTI 16 personality factors affecting personality category.

It is common for older people to live alone, which can have tragic consequences if they have an accident and can’t call for help in time. This is particularly acute in an aging society where falling is one of the most common accidents. According to the CDC, 1/4 of people over the age of 65 in the United States fall each year. The development of IoT and MEMS has made it possible to detect falls in time and automatically call for help. The presented fall detection system focuses on the walk-fall-still pattern, collects accelerations through the wrist-worn M5StickC-Plus watch, analyses the data locally in the watch, detects falls using an algorithm based on observations in the statistics of acceleration in one second, and then transmits the alarm signal to a remote healthcare system in real-time via WIFI. The lightweight algorithm has been proven to be 90% accurate in detecting falls, and the system can notify service staff of accidents within 1 second. The features of comfort, lightness, and timeliness make the device more practical than similar products. The low-cost, non-intrusive device can be used in care homes and is also suitable for elderly people living alone.

There are three common beliefs about the labor market: first, that increased use of artificial intelligence (AI) is going to cause large-scale unemployment in the future; second, that the postpandemic revitalization of mobility and migration would resupply the markets affected currently by the workforce shortage; and third, that job postings, under existing employment laws, do not use biased language and offer equal opportunities to job seekers. All of them are wrong. [ABSTRACT FROM AUTHOR]

During the semiconductor manufacturing process, predicting the yield of the semiconductor is an important problem. Early detection of defective product production in the manufacturing process can save huge production cost. The data generated from the semiconductor manufacturing process have characteristics of non-normal distributions, random missing patterns and high missing rate, which complicate the prediction of the yield. We propose the Dirichlet Process - Naive Bayes model (DPNB) that can simultaneously impute missing values and address classification problems. Since the DPNB is based on the infinite Gaussian mixture model, it can estimate complex data distributions and make predictions for missing datasets with some missing patterns due to nice properties of the Gaussian distribution. The DPNB also performs well for high missing rates since it uses all information of observed components. Experiments on various real datasets including semiconductor manufacturing data show that the DPNB has better performance than state-of-the-art methods in terms of predicting missing values and target variables as percentage of missing values increases. [ABSTRACT FROM AUTHOR]

This study focuses on the quasisynchronization problem for reaction–diffusion neural networks (RDNNs) in the presence of deception attacks. Under deception attacks, a time–space sampled-data (TSSD) control mechanism is proposed for RDNNs. Compared with traditional control strategies, the proposed control mechanism can not only save network bandwidth but also improve the cybersecurity of communications. Inspired by Halanay’s inequality, a new inequality is proposed, which can be effectively applied to the quasisynchronization problem for dynamical systems. Then, by using this inequality and the Lyapunov functional approach, quasisynchronization criteria are set for RDNNs. The desired control gain is gained from solving a group of linear matrix inequalities. Moreover, in the absence of deception attacks, the exponential synchronization problem is studied for RDNNs. In the end, simulation results are given to demonstrate the usefulness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

It is known that many-objective optimization problems (MaOPs) often face the difficulty of maintaining good diversity and convergence in the search process due to the high-dimensional objective space. To address this issue, this article proposes a novel multiobjective framework for many-objective optimization (Mo4Ma), which transforms the many-objective space into multiobjective space. First, the many objectives are transformed into two indicative objectives of convergence and diversity. Second, a clustering-based sequential selection strategy is put forward in the transformed multiobjective space to guide the evolutionary search process. Specifically, the selection is circularly performed on the clustered subpopulations to maintain population diversity. In each round of selection, solutions with good performance in the transformed multiobjective space will be chosen to improve the overall convergence. The Mo4Ma is a generic framework that any type of evolutionary computation algorithm can incorporate compatibly. In this article, the differential evolution (DE) is adopted as the optimizer in the Mo4Ma framework, thus resulting in an Mo4Ma-DE algorithm. Experimental results show that the Mo4Ma-DE algorithm can obtain well-converged and widely distributed Pareto solutions along with the many-objective Pareto sets of the original MaOPs. Compared with seven state-of-the-art MaOP algorithms, the proposed Mo4Ma-DE algorithm shows strong competitiveness and general better performance. [ABSTRACT FROM AUTHOR]

This article presents a rough-to-fine evolutionary multiobjective optimization algorithm based on the decomposition for solving problems in which the solutions are initially far from the Pareto-optimal set. Subsequently, a tree is constructed by a modified $k$ -means algorithm on $N$ uniform weight vectors, and each node of the tree contains a weight vector. Each node is associated with a subproblem with the help of its weight vector. Consequently, a subproblem tree can be established. It is easy to find that the descendant subproblems are refinements of their ancestor subproblems. The proposed algorithm approaches the Pareto front (PF) by solving a few subproblems in the first few levels to obtain a rough PF and gradually refining the PF by involving the subproblems level-by-level. This strategy is highly favorable for solving problems in which the solutions are initially far from the Pareto set. Moreover, the proposed algorithm has lower time complexity. Theoretical analysis shows the complexity of dealing with a new candidate solution is $\mathcal {O}(M \log N)$ , where $M$ is the number of objectives. Empirical studies demonstrate the efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Retail sector around the world is going through a transition due to digitalization. Customers now demand convenience of online channel with instant fulfillment of offline channel. As a result, the concept of omnichannel retailing has come into existence. In omnichannel retailing, both the channels of a firm are fully or partially integrated. There are many strategies that may be developed by retailers for efficient functioning of the omnichannel retail chain. The choice of such a strategy depends upon the comparative benefits that can be derived from them. One such strategy gaining immense attention of retailers is the Buy Online Pickup in Store (BOPS) strategy. In order to understand this BOPS, we conducted a detail case study of an Indian multichannel retail chain who is considering the option of launching BOPS option in few of its retail stores. The focus is to evaluate and select a fixed number of operational retail stores to also act as pick-up points for online deliveries. K-means clustering is utilized for the formation of retail zones as per the number of retail stores to be selected. The retail stores in each zone are evaluated on the basis of a number of criteria such as demand, inventory carrying capacity, cost, and population characteristics using analytical hierarchy process and complex proportional assessment-grey. Based on these evaluations, the best suitable retail store is selected for expansion from each zone. The result implications drawn in the study can provide an understanding of the future strategies that can be developed by the decision makers of the firm for launching the BOPS option. This article can also be beneficial to other Indian retail firms who can derive meaningful insights from the findings. [ABSTRACT FROM AUTHOR]

The developing optimization algorithms provide promising solutions for speeding up analog integrated circuit sizing. However, the optimization of complicated circuits whose solution regions are narrow remains to be a challenge. With a limited number of sampling points due to the restriction of computational resources, it is difficult for traditional algorithms to achieve satisfactory results for such circuits. To solve this problem, this article proposes a rule-guided genetic algorithm (RG-GA) for analog circuit optimization. Different from the random mutation approach in the traditional genetic algorithm (GA), the RG-GA introduces a design rule-guided mutation (RGM) mechanism which helps to find the solution region in a more straightforward fashion. Instead of handing over circuit optimization tasks to pure mathematical algorithms, the proposed method takes advantages of valuable design knowledge to improve searching efficiency. This novel algorithm is implemented and deployed to design a two-stage rail-to-rail operational amplifier (OPA), an LC voltage controlled oscillator (LC-VCO) and a four-stage OPA. Experimental results show that compared to the traditional GA method, the RG-GA achieves about 1.5 and 3.3 times speed enhancement for the two-stage rail-to-rail OPA and the LC-VCO, respectively. For the four-stage OPA, the RG-GA method can find an acceptable point within the given number of iterations while the traditional GA could not. [ABSTRACT FROM AUTHOR]

Nonlinear equation systems (NESs) are ubiquitous, and solving them is an important yet challenging task in numerical computation. Most of the NESs usually contain multiple roots. To locate multiple roots, the use of evolutionary algorithms attracts more attention recently. For the purpose of finding different roots within a limited computational budget in a single run simultaneously, in this article, a hybrid niching-based differential evolution with two archives, referred to as HNDE/2A, is proposed. It can be featured as: 1) two niching techniques, i.e., crowding and speciation, are combined to balance the diversity and convergence; 2) a root archive is used to save the found roots during the run. Additionally, if a root is saved into the root archive, it will be reinitialized immediately to further promote the population diversity; and 3) an inferior offspring archive is presented to utilize the useful information of the inferior offspring. To evaluate the performance of our proposal, 30 problems are chosen as the test suite. Moreover, HNDE/2A is also used to solve two real-world problems. Compared with other algorithms, HNDE/2A yields better results in terms of the root ratio and the success rate with a less computational budget. [ABSTRACT FROM AUTHOR]

Let $\mathrm {W}\in \mathbb {C}^{n\times n}$ be a single-spiked Wishart matrix in the class $\mathrm {W}\sim \mathcal {CW}_{n}(m,\mathrm {I}_{n}+ \theta \mathrm {v}\mathrm {v}^{\dagger}) $ with $m\geq n$ , where ${\mathrm {I}}_{n}$ is the $n\times n$ identity matrix, $\mathrm {v}\in \mathbb {C}^{n\times 1}$ is an arbitrary vector with unit Euclidean norm, $\theta \geq 0$ is a non-random parameter, and $(\cdot)^{\dagger} $ represents the conjugate-transpose operator. Let u1 and ${\mathrm {u}}_{n}$ denote the eigenvectors corresponding to the smallest and the largest eigenvalues of W, respectively. This paper investigates the probability density function (p.d.f.) of the random quantity $Z_{\ell }^{(n)}=\left |{\mathrm {v}^{\dagger} \mathrm {u}_\ell }\right |^{2}\in (0,1)$ for $\ell =1,n$. In particular, we derive a finite dimensional closed-form p.d.f. for $Z_{1}^{(n)}$ which is amenable to asymptotic analysis as $m,n$ diverges with $m-n$ fixed. It turns out that, in this asymptotic regime, the scaled random variable $nZ_{1}^{(n)}$ converges in distribution to $\chi ^{2}_{2}/2(1+\theta)$ , where $\chi _{2}^{2}$ denotes a chi-squared random variable with two degrees of freedom. This reveals that u1 can be used to infer information about the spike. On the other hand, the finite dimensional p.d.f. of $Z_{n}^{(n)}$ is expressed as a double integral in which the integrand contains a determinant of a square matrix of dimension $(n-2)$. Although a simple solution to this double integral seems intractable, for special configurations of $n=2,3$ , and 4, we obtain closed-form expressions. [ABSTRACT FROM AUTHOR]

A mixed-integer programming (MIP) problem contains both constraints and integer restrictions. Integer restrictions divide the feasible region defined by constraints into multiple discontinuous feasible parts. In particular, the number of discontinuous feasible parts will drastically increase with the increase of the number of integer decision variables and/or the size of the candidate set of each integer decision variable. Due to the fact that the optimal solution is located in one of the discontinuous feasible parts, it is a challenging task to solve a MIP problem. This article presents a cutting and repulsion-based evolutionary framework (called CaR) to solve MIP problems. CaR includes two main strategies: 1) the cutting strategy and 2) the repulsion strategy. In the cutting strategy, an additional constraint is constructed based on the objective function value of the best individual found so far, the aim of which is to continuously cut unpromising discontinuous feasible parts. As a result, the probability of the population entering a wrong discontinuous feasible part can be decreased. In addition, in the repulsion strategy, once it has been detected that the population has converged to a discontinuous feasible part, the population will be reinitialized. Moreover, a repulsion function is designed to repulse the previously explored discontinuous feasible parts. Overall, the cutting strategy can significantly reduce the number of discontinuous feasible parts and the repulsion strategy can probe the remaining discontinuous feasible parts. Sixteen test problems developed in this article and two real-world cases are used to verify the effectiveness of CaR. The results demonstrate that CaR performs well in solving MIP problems. [ABSTRACT FROM AUTHOR]

Distributed population dynamics offer a game-theoretical framework for distributed decision making. As such, the application of these methods to the control of networked systems has been widely studied in the literature. The theoretical analyses available in the literature have only considered continuous-time formulations of distributed population dynamics. However, given that modern computers can only process information in a discrete-time fashion, the practical implementation of distributed population dynamics-based methods is inevitably discrete. In consequence, it is paramount to extend the available theory to a more implementable discrete-time approach. For that reason, in this article, we provide a discrete-time analysis of a general class of distributed population dynamics, and we derive sufficient conditions on the discretization time to ensure the asymptotic stability of the discretized dynamics. To illustrate the relevance and performance of the proposed methods, we apply the developed theory to distributed optimization and control problems, including a real multirobot platform, which consider noncomplete communication networks and coupled constraints. [ABSTRACT FROM AUTHOR]

The widespread existence of uncertainty makes power system control and decision-making face various risks, especially the interconnection of modern large-scale power systems, which makes its influence range more extensive. In smart grid, the renewable energy prediction and uncertainty analysis methods represented by wind power generation have attracted extensive attention. In this study, an adaptive parameter selection method based on Kullback−Leibler divergence is designed to fully extract the potential information of data. The grey wolf optimization based on reverse learning is used for prediction optimization, identifying historical data, processing dynamic information, and improving the accuracy of the model. In interval prediction, an interval prediction method combining empirical error, quantile statistics, and Markov Chain Monte Carlo is creatively proposed to generate a large number of independent error cases, and form a series of interval predictions with given confidence according to the quantile of random simulation results. Taking wind speed forecasting as an example, it is verified that the method proposed in this article has good performance. Using interval analysis to quantify the volatility and randomness can provide new ideas for load forecasting in power system and renewable energy output forecasting. [ABSTRACT FROM AUTHOR]

RRAM-based in-memory computing (IMC) effectively accelerates deep neural networks (DNNs). Furthermore, model compression techniques, such as quantization and pruning, are necessary to improve algorithm mapping and hardware performance. However, in the presence of RRAM device variations, low-precision and sparse DNNs suffer from severe post-mapping accuracy loss. To address this, in this work, we investigate a new metric, model stability, from the loss landscape to help shed light on accuracy loss under variations and model compression, which guides an algorithmic solution to maximize model stability and mitigate accuracy loss. Based on statistical data from a CMOS/RRAM 1T1R test chip at 65nm, we characterize wafer-level RRAM variations and develop a cross-layer benchmark tool that incorporates quantization, pruning, device variations, model stability, and IMC architecture parameters to assess post-mapping accuracy and hardware performance. Leveraging this tool, we show that a loss-landscape-based DNN model selection for stability effectively tolerates device variations and achieves a post-mapping accuracy higher than that with 50% lower RRAM variations. Moreover, we quantitatively interpret why model pruning increases the sensitivity to variations, while a lower-precision model has better tolerance to variations. Finally, we propose a novel variation-aware training method to improve model stability, in which there exists the most stable model for the best post-mapping accuracy of compressed DNNs. Experimental evaluation of the method shows up to 19%, 21%, and 11% post-mapping accuracy improvement for our 65nm RRAM device, across various precision and sparsity, on CIFAR-10, CIFAR-100, and SVHN datasets, respectively. [ABSTRACT FROM AUTHOR]

We study the problem of estimating the total number of searches (volume) of queries in a specific domain, which were submitted to a search engine in a given time period. Our statistical model assumes that the distribution of searches follows a Zipf’s law, and that the observed sample volumes are biased accordingly to three possible scenarios. These assumptions are consistent with empirical data, with keyword research practices, and with approximate algorithms used to take counts of query frequencies. A few estimators of the parameters of the distribution are devised and experimented, based on the nature of the empirical/simulated data. For continuous data, we recommend using nonlinear least square regression (NLS) on the top-volume queries, where the bound on the volume is obtained from the well-known Clauset, Shalizi and Newman (CSN) estimation of power-law parameters. For binned data, we propose using a Chi-square minimization approach restricted to the top-volume queries, where the bound is obtained by the binned version of the CSN method. Estimations are then derived for the total number of queries and for the total volume of the population, including statistical error bounds. We apply the methods on the domain of recipes and cooking queries searched in Italian in 2017. The observed volumes of sample queries are collected from Google Trends (continuous data) and SearchVolume (binned data). The estimated total number of queries and total volume are computed for the two cases, and the results are compared and discussed. [ABSTRACT FROM AUTHOR]

Triangle count is a critical parameter in mining relationships among people in social networks. However, directly publishing the findings obtained from triangle counts may bring potential privacy concern, which raises great challenges and opportunities for privacy-preserving triangle counting. In this paper, we choose to use differential privacy to protect triangle counting for large scale graphs. To reduce the large sensitivity caused in large graphs, we propose a novel graph projection method that can be used to obtain an upper bound for sensitivity in different distributions. In particular, we publish the triangle counts satisfying the node-differential privacy with two kinds of histograms: the triangle count distribution and the cumulative distribution. Moreover, we extend the research on privacy preserving triangle counting to one of its applications, the local clustering coefficient. Experimental results show that the cumulative distribution can fit the real statistical information better, and our proposed mechanism has achieved better accuracy for triangle counts while maintaining the requirement of differential privacy. [ABSTRACT FROM AUTHOR]

Experimental demonstration of superdirectivity and superscattering phenomena is among the long-standing challenges in electromagnetic theory. Efficient computational algorithms can contribute to this endeavor by suggesting new designs bypassing commonly accepted limitations. Here, we demonstrate a rectangular wire bundle superscatterer designed using a stochastic optimization algorithm. The structure encompassing wires of different lengths demonstrates superior scattering capabilities, bypassing the single channel dipole limit by an order of magnitude. The subwavelength wire bundle supports several resonant higher-order multipoles, which constructively contribute to the scattering, as we demonstrate experimentally. A new generation of genetically designed superscatterers may be used in a range of wireless applications, including point-to-point communications, smart beacons, and radar targets. [ABSTRACT FROM AUTHOR]

This article takes a step to provide humanoid robots with adaptive morphology abilities. We present a systematic approach for enabling robotic covers to morph their shape, with an overall size fitting the anthropometric dimensions of a humanoid robot. More precisely, we present a cover concept consisting of two main components: a skeleton, which is a repetition of a basic element called node, and a soft membrane, which encloses the cover and deforms with its motion. This article focuses on the cover skeleton and addresses the challenging problems of node design, system modeling, motor positioning, and control design of the morphing system. The cover modeling focuses on kinematics, and a systematic approach for defining the system kinematic constraints is presented. Then, we apply genetic algorithms to find the motor locations so that the morphing cover is fully actuated. Finally, we present control algorithms that allow the cover to morph into a time-varying shape. The entire approach is validated by performing kinematic simulations with four different covers of square dimensions and having $3\times 3$ , $4\times 8$ , $8\times 8$ , and $20\times 20$ nodes, respectively. For each cover, we apply the genetic algorithms to choose the motor locations and perform simulations for tracking a desired shape. The simulation results show that the presented approach ensures the covers to track a desired shape with good tracking performances. [ABSTRACT FROM AUTHOR]

We study generalization under labeled shift for categorical and general normed label spaces. We propose a series of methods to estimate the importance weights from labeled source to unlabeled target domain and provide confidence bounds for these estimators. We deploy these estimators and provide generalization bounds in the unlabeled target domain. [ABSTRACT FROM AUTHOR]

The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically. We believe this problem is rooted in the mismatch between existing statistical techniques and commonly encountered data; object representations are typically high dimensional but statistical techniques tend to treat high dimensions a degenerate case. To address this problem, we develop a dedicated statistical framework for machine learning in high dimensions. The framework derives from the observation that object relations form a natural hierarchy; this leads us to model objects as instances of a high dimensional, hierarchal generative processes. Using a distance based statistical technique, also developed in this paper, we show that in such generative processes, instances of each process in the hierarchy, are almost-always encapsulated by a distinctive-shell that excludes almost-all other instances. The result is shell theory, a statistical machine learning framework in which separability constraints (distinctive-shells) are formally derived from the assumed generative process. [ABSTRACT FROM AUTHOR]

An effective electromagnetic interference (EMI) source reconstruction method based on dynamic differential evolution (DDE) is proposed in this article. In this method, an unknown EMI source is represented by an equivalent dipole array by using the scanned phaseless magnetic near fields. Different from the conventional differential evolution (CDE) method, DDE individually updates every equivalent dipole instead of the whole dipole array for each iteration. It converges faster and takes less computing time than CDE. The equivalent dipole model reconstructed by DDE can thereafter predict the near field and 3-m far field efficiently. The results of numerical and measurement examples have verified the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Uncertainties in power system studies are typically managed by Monte Carlo methods or by applying some worst case assumptions. As the number ofuncertainties increases, there is a need for methods that can estimate statistical parameters from a limited number of calculations. This paper utilizes a method called the Unscented Transform together with Cornish-Fisher expansion to estimate the 2%-value of switching overvoltages using only about one tenth the number of calculations as a Monte Carlo method, with similar accuracy. The uncertainties considered are the energizing instants of the three phases. The method is illustrated through EMT simulations in PSCAD and it is shown to provide a good approximation of the 2%-value in most of the studied cases. [ABSTRACT FROM AUTHOR]

Disasters and the resulting emergencies are increasingly presenting situations where tele-communications infrastructure (e.g., functioning base stations) is often compromised, leading to diminished network capacity. In these situations, providing trapped populations in isolated areas with network access to be able to communicate with rescue and recovery personnel or volunteer teams is critical. Further, when network capacity is diminished, it is essential that mobile network users be alerted to “comply” and not overload the network with non-essential bandwidth-intensive traffic unrelated to the emergency unfolding. While the current evolution of the Integrated Public Alert and Warning System (IPAWS) is able to generate and disseminate such emergency alerts, there has been no analysis or evaluation of the effectiveness of such alerts as well as the design of mechanisms that can ensure such compliance to prevent network outage. In this work, we present a framework for Cognitive Public Alerts to Wireless Subscribers (CPAWS) that is able to predict and prevent mobile network overload/outage during emergencies using a mix of behavioral studies and machine learning tools that consider real-time monitoring information of the environmental status, network situation, and user compliance. Specifically, we study the efficacy of wireless emergency alerts (WEAs) in terms of saving network bandwidth by using surveys on Amazon MTurk to test the effectiveness of seven designed WEAs on reducing users' non-essential traffic across 12 designated cell phone applications. Additionally, we design complementary access and alert control strategies to ensure that the network load remains below the diminished available capacity during emergencies. CPAWS is also backward-compatible since it extends IPAWS by adding a feedback loop for real-time monitoring and control. [ABSTRACT FROM AUTHOR]

Causal classification concerns the estimation of the net effect of a treatment on an outcome of interest at the instance level, i.e., of the individual treatment effect (ITE). For binary treatment and outcome variables, causal classification models produce ITE estimates that essentially allow one to rank instances from a large positive effect to a large negative effect. Often, as in uplift modeling (UM), one is merely interested in this ranking, rather than in the ITE estimates themselves. In this regard, we investigate the potential of learning to rank (L2R) techniques to learn a ranking of the instances directly. We propose a unified formalization of different binary causal classification performance measures from the UM literature and explore how these can be integrated into the L2R framework. Additionally, we introduce a new metric for UM with L2R called the promoted cumulative gain (PCG). We employ the L2R technique LambdaMART to optimize the ranking according to PCG and show improved results over the use of standard L2R metrics and equal to improved results when compared with state-of-the-art UM. Finally, we show how L2R techniques can be used to specifically optimize for the top- $k$ k fraction of the ranking in a UM context, however, these results do not generalize to the test set. [ABSTRACT FROM AUTHOR]

Understanding intra-urban human mobility patterns and their potential driving forces are vital to city planning and commercial site selection. In this paper, we first investigate the functions of urban regions and how different region types dynamically influence people’s trip decisions. Furthermore, we characterize urban circadian rhythms by time-vary inter-regional transition probabilities between these regions with different functions, and integrate them into intervening opportunity model to predict human mobility. Public transportation card data in Shanghai are used to demonstrate the effectiveness of the model in terms of station passenger flows, travel time and trip flux. By taking regional function into consideration, the proposed model significantly improved the prediction accuracy. Quantitative analysis ulteriorly indicates that trip intentions and regional features are critical elements in trip flux prediction, especially in the afternoon and evening when people have an abundance of opportunities to travel by their own volition. When the function of a certain region changes, our model is able to make reasonable predictions accordingly. The results indicate the importance of considering individual travel motivation and regional function in modeling human mobility. The proposed model could serve as a guide for popularity and trip flux prediction in urban planning and reconstruction. [ABSTRACT FROM AUTHOR]

This article surveys the interdisciplinary research of neuroscience, network science, and dynamic systems, with emphasis on the emergence of brain-inspired intelligence. To replicate brain intelligence, a practical way is to reconstruct cortical networks with dynamic activities that nourish the brain functions, instead of using only artificial computing networks. The survey provides a complex network and spatiotemporal dynamics (abbr. network dynamics) perspective for understanding the brain and cortical networks and, furthermore, develops integrated approaches of neuroscience and network dynamics toward building brain-inspired intelligence with learning and resilience functions. Presented are fundamental concepts and principles of complex networks, neuroscience, and hybrid dynamic systems, as well as relevant studies about the brain and intelligence. Other promising research directions, such as brain science, data science, quantum information science, and machine behavior are also briefly discussed toward future applications. [ABSTRACT FROM AUTHOR]

Constrained multiobjective optimization problems (CMOPs) involve both conflicting objective functions and various constraints. Due to the presence of constraints, CMOPs’ Pareto-optimal solutions are very likely lying on constraint boundaries. The experience from the constrained single-objective optimization has shown that to quickly obtain such an optimal solution, the search should surround the boundary of the feasible region from both the feasible and infeasible sides. In this article, we extend this idea to cope with CMOPs and, accordingly, we propose a novel constrained multiobjective evolutionary algorithm with bidirectional coevolution, called BiCo. BiCo maintains two populations, that is: 1) the main population and 2) the archive population. To update the main population, the constraint-domination principle is equipped with an NSGA-II variant to move the population into the feasible region and then to guide the population toward the Pareto front (PF) from the feasible side of the search space. While for updating the archive population, a nondominated sorting procedure and an angle-based selection scheme are conducted in sequence to drive the population toward the PF within the infeasible region while maintaining good diversity. As a result, BiCo can get close to the PF from two complementary directions. In addition, to coordinate the interaction between the main and archive populations, in BiCo, a restricted mating selection mechanism is developed to choose appropriate mating parents. Comprehensive experiments have been conducted on three sets of CMOP benchmark functions and six real-world CMOPs. The experimental results suggest that BiCo can obtain quite competitive performance in comparison to eight state-of-the-art-constrained multiobjective evolutionary optimizers. [ABSTRACT FROM AUTHOR]

Evolutionary multitask learning has achieved great success due to its ability to handle multiple tasks simultaneously. However, it is rarely used in the hyperheuristic domain, which aims at generating a heuristic for a class of problems rather than solving one specific problem. The existing multitask hyperheuristic studies only focus on heuristic selection, which is not applicable to heuristic generation. To fill the gap, we propose a novel multitask generative hyperheuristic approach based on genetic programming (GP) in this article. Specifically, we introduce the idea in evolutionary multitask learning to GP hyperheuristics with a suitable evolutionary framework and individual selection pressure. In addition, an origin-based offspring reservation strategy is developed to maintain the quality of individuals for each task. To verify the effectiveness of the proposed approach, comprehensive empirical studies have been conducted on the homogeneous and heterogeneous multitask dynamic flexible job shop scheduling. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for each task in all the examined scenarios. In addition, the evolved scheduling heuristics verify the mutual help among the tasks in a multitask scenario. [ABSTRACT FROM AUTHOR]

Few-mode erbium-doped fiber amplifier (FM-EDFA) is a key device to achieve power compensation in long-haul mode division multiplexing (MDM) optical system. The gain characteristics of FM-EDFA are related to the number of modes and multiplexing mode selection. In this paper, the gain characteristics of four modes and six modes FM-EDFA with various mode combinations and different wavelengths are analyzed in single and dual-pump cases. The results show that when the azimuthal-dependent pump is selected, different degenerate mode (odd and even mode) multiplexing will arise DMG. In the replacement of different degenerate modes at the same modal order, the DMG of signal modes LP11o,s and LP21o,s is 1 dB (single-pump: LP01,p) while the DMG between signal modes LP11e,s and LP21e,s is 8 dB (dual-pump: LP01,p+LP11e,p). Compared with single-wavelength amplification, both the overall gain and the DMG decrease in the multi-wavelength case. The above conclusions can provide a theoretical basis for the selection of FM-EDFA in the wavelength division multiplexing (WDM)-MDM system. [ABSTRACT FROM AUTHOR]

E-learning is an evolutionary concept, not a revolutionary concept since it has been introduced and practiced in previous decades as well. The current research paper contributes to educational research based on the investigation of holistic perspectives of the ongoing online teaching activities during the lockdown period. The article is built on a literature review. The methodologies are described and the results are presented. There have been research on distant learning and digitalization, but mostly in terms of potential, obstacles, and student assessments, as well as their influence on education. There are no empirical research on the worldwide trend of how employees utilize e-learning resources to determine instructors’ interest in and attitudes about using them. This chasm is also regarded a new angle of empirical research that uses the Chi-square test of independence to find patterns. The present research aims to build a realistic instrument that can assess instructors’ attitudes about e-learning during COVID-19 with this purpose in mind.The paper consists of two parts: a literature review and statistical analysis. The research was conducted by means of a diagnostic opinion CAWI (Computer Assisted Web Interview) questionnaire and statistical analysis. This study was conducted among 342 teachers working in various universities located in the following countries: Poland, Pakistan, Iraq, USA, UK, Germany, and Austria. The questionnaire consisted of 20 questions. The researchers provided a detailed overview of e-learning practices during the pandemic and its future prospects from the teacher’s point of view. The goals of this article and the research problems are to learn about the worldwide trend of how employees utilize e-learning resources, to identify teachers’ interests in and attitudes towards using e-learning resources throughout the globe, and to recommend opportunities for workers to use e-learning resources around the world, which is a very important issue, especially in the era of globalization, society 5.0, and industry 4.0. The authors also proposed a Chi-square statistical analysis to check whether there exists a relationship between two variables based on which the hypotheses were formulated. The results of the study clearly show that the respondents approach to e-learning was very responsible because they realized that it was up to them to properly convey knowledge. The results of empirical research show that the digitalization of education is no longer a future trend, but today’s trend or even a university standard in the education improvement direction.

This paper applies the principles of distribution locational marginal pricing (DLMP) to unbalanced three-phase distribution networks. We first propose a linear model for AC optimal power flow derived through a series of approximation and reformulation techniques. Then a scenario-generation algorithm is proposed to properly model the uncertain parameters in the linear model. Through a proposed No U-Turn sampler (NUTS) based algorithm, probability density functions (PDFs) of DLMPs are calculated. These PDFs provide statistical information about the locational and temporal price risks. By means of applications to two IEEE unbalanced test networks, the numerical results show promising performance for the proposed linear model and the NUTS-based algorithm in creating PDFs of DLMPs. DLMP price densities will be increasingly useful as distribution system operators seek flexible, low risk solutions from embedded generators and aggregators of distributed energy resources. [ABSTRACT FROM AUTHOR]

Long-term electricity consumption forecasting has been a hot issue due to its importance in power systems planning. Recently, system dynamics (SD) have been considered a potential method for electricity consumption forecasting. However, traditional SD methods are failed in forecasting future power systems toward low-carbon, due to the complex influencing factors and the simple SD model structure. In this study, a novel method combining SD and the ImPACT equation is proposed to solve the above problem. First, a new influencing factor system including emerging factors is established to consider the development of future power system. Subsequently, the ImPACT equation is employed to decompose the electricity consumption into four subsystems. By doing this, the linkage between factors and electricity consumption can be better revealed, which helps to improve the forecasting accuracy. Next, models for quantifying the correlation between subsystems and factors are established using SD. Finally, the proposed method is verified by forecasting the power consumption of China in the future year 2030 to 2050. Furthermore, various scenarios are set to analyze the changing trend of future power consumption by different influencing factors. The results of this article can provide important basic information for power supply and power grid planning. [ABSTRACT FROM AUTHOR]

With the abundance of data, machine learning applications engaged increased attention in the last decade. An attractive approach to robustify the statistical analysis is to preprocess the data through clustering. This paper develops a low-complexity Riemannian optimization framework for solving optimization problems on the set of positive semidefinite stochastic matrices. The low-complexity feature of the proposed algorithms stems from the factorization of the optimization variable $\mathbf {X}=\mathbf {Y}\mathbf {Y}^{\mathrm{T}}$ X = Y Y T and deriving conditions on the number of columns of $\mathbf {Y}$ Y under which the factorization yields a satisfactory solution. The paper further investigates the embedded and quotient geometries of the resulting Riemannian manifolds. In particular, the paper explicitly derives the tangent space, Riemannian gradients and Hessians, and a retraction operator allowing the design of efficient first and second-order optimization methods for the graph-based clustering applications of interest. The numerical results reveal that the resulting algorithms present a clear complexity advantage as compared with state-of-the-art euclidean and Riemannian approaches for graph clustering applications. [ABSTRACT FROM AUTHOR]

Online atlasing, i.e., incrementing an atlas with new images as they are acquired, is key when performing studies on very large, or still being gathered, databases. Regular approaches to atlasing however do not focus on this aspect and impose a complete reconstruction of the atlas when adding images. We propose instead a diffeomorphic online atlasing method that allows gradual updates to an atlas. In this iterative centroid approach, we integrate new subjects in the atlas in an iterative manner, gradually moving the centroid of the images towards its final position. This leads to a computationally cheap approach since it only necessitates one additional registration per new subject added. We validate our approach on several experiments with three main goals: 1- to evaluate atlas image quality of the obtained atlases with sharpness and overlap measures, 2- to assess the deviation in terms of transformations with respect to a conventional atlasing method and 3- to compare its computational time with regular approaches of the literature. We demonstrate that the transformations divergence with respect to a state-of-the-art atlas construction method is small and reaches a plateau, that the two construction methods have the same ability to map subject homologous regions onto a common space and produce images of equivalent quality. The computational time of our approach is also drastically reduced for regular updates. Finally, we also present a direct extension of our method to update spatio-temporal atlases, especially useful for developmental studies. [ABSTRACT FROM AUTHOR]

Objective: The bifurcation of neuronal firing is one of important nonlinear phenomena in the nervous system and is characterized by a significant change in the rate or temporal pattern of neuronal firing on responding to a small disturbance from external inputs. Previous studies have reported firing bifurcations for individual neurons, not for a population of neurons. We hypothesized that the integrated firing of a neuronal population could also show a bifurcation behavior that should be important in certain situations such as deep brain stimulations. The hypothesis was verified by experiments of rat hippocampus in vivo. Methods: Stimulation sequences of paired-pulses with two different inter-pulse-intervals (IPIs) or with two different pulse intensities were applied on the alveus of hippocampal CA1 region in anaesthetized rats. The amplitude and area of antidromic population spike (APS) were used as indices to evaluate the differences in the responses of neuronal population to the different pulses in stimulations. Results: During sustained paired-pulse stimulations with a high mean pulse frequency such as ∼130 Hz, a small difference of only a few percent in the two IPIs or in the two intensities was able to generate a sequence of evoked APSs with a substantial bifurcation in their amplitudes and areas. Conclusion: Small differences in the excitatory inputs can cause nonlinearly enlarged differences in the induced firing of neuronal populations. Significance: The novel dynamics and bifurcation of neuronal responses to electrical stimulations provide important clues for developing new paradigms to extend neural stimulations to treat more diseases. [ABSTRACT FROM AUTHOR]

Objective: Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs are based on shape coefficients and lack an explicit one-to-one relationship with anatomical measures of clinical relevance. While a shape coefficient embeds a combination of anatomical measures, a formalized approach to find the relationship between them remains elusive in the literature. This limits the use of SSMs to subjective evaluations in clinical practices. We propose a novel SSM controlled by anatomical parameters derived from morphometric analysis. Methods: The proposed anatomically parameterized SSM (ANAT $_{\text{SSM}}$) is based on learning a linear mapping between shape coefficients (latent space) and selected anatomical parameters (anatomical space). This mapping is learned from a synthetic population generated by the standard SSM. Determining the pseudo-inverse of the mapping allows us to build the ANAT $_{\text{SSM}}$. We further impose orthogonality constraints to the anatomical parameterization (OC-ANAT $_{\text{SSM}}$) to obtain independent shape variation patterns. The proposed contribution was evaluated on two skeletal databases of femoral and scapular bone shapes using clinically relevant anatomical parameters within each (five for femoral and six for scapular bone). Results: Anatomical measures of the synthetically generated shapes exhibited realistic statistics. The learned matrices corroborated well with the obtained statistical relationship, while the two SSMs achieved moderate to excellent performance in predicting anatomical parameters on unseen shapes. Conclusion: This study demonstrates the use of anatomical representation for creating anatomically parameterized SSMs and as a result, removes the limited clinical interpretability of standard SSMs. Significance: The proposed models could help analyze differences in relevant bone morphometry between populations, and be integrated in patient-specific pre-surgery planning or in-surgery assessment. [ABSTRACT FROM AUTHOR]

In recent years, NAND flash memory has been widely used in mobile devices, laptops, desktops, and data center storage systems due to its low-power consumption, high performance, high density, lightweight, shock resistance, and high-reliability natures. However, as the stacked layers and the storage density increase, flash memory also suffers from a higher raw bit error rate (RBER) and shorter lifetime. Observing that reliable cell states suffer from less data retention errors and program disturbance, we propose a differential evolution coding scheme to increase the probability of storing data in more reliable cell states, thereby reducing the RBER. We conducted the experiments over a development platform of SSD storage device with 3D-TLC charge trap (CT) NAND flash memory. The experimental results showed that the proposed differential evolution algorithm with asymmetric coding scheme could averagely reduce RBER by 48.88%, 65.45%, 52.61%, 61.99%, 80.19%, and 33.18% compared with baseline, asymmetric coding algorithm, asymmetric coding scheme with stripe-pattern elimination algorithm, UAC- $n$ LC, word-line batch score modulation programming, and SCB schemes. [ABSTRACT FROM AUTHOR]

In-situ tool condition monitoring (in-situ TCM) is vital for metal removal manufacturing which realizes on-machine diagnosis in a real-time manner. The limitation of in-situ TCM based on traditional deep learning lies in several aspects: the requirement of sufficient labeled data of health conditions, the empirically manual designed architecture, and the labor-intensive tuning of hyperparameters. Network-based deep transfer learning (NDTL) partially solves the problem of limited labeled data. However, the time-consuming architecture design and the tuning of hyperparameters also pose a great impact on the schedule of real in-situ TCM projects. In this article, a new NDTL method is proposed, which is automatically designed by the genetic algorithm. A degradation monitoring experiment is conducted employing edge computing devices under multiple working conditions, where the texture of the machined surface is collected during the whole life cycle of milling cutters. The experimental results suggest that the proposed method possesses competitive performance evaluated by both the robustness metrics (e.g., the area under the curve of precision-recall) and the efficiency metrics (e.g., multiply–accumulates), where the trial-and-errors are reduced significantly by the automatic architecture design and the selection of hyperparameters. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are being actively adopted as a solution to sustainable transportation. However, a bottleneck remains with charging, where two of the main problems are the long charging time and the range anxiety of EV drivers. In this research, we investigate the deployment of dynamic charging systems, i.e., electrified roads that wirelessly charge EVs on the go, with a view to accelerating EV adoption rate. We propose a traffic-based deployment strategy, statistically quantify its impact, and apply the strategy to two case studies of real traffic in New York City (USA) and Xi’an (China). We find that our analytical estimates not only closely match the real data, but they also suggest that dynamic charging considerably extends the driving range of popular EV models in urban mobility. For example, when only 5% of the existing roads in New York City are equipped with this technology, an EV model such as the Nissan Leaf will approximately maintain its battery level without stopping to recharge. If the percentage of charging roads is increased to 10%, then the Leaf will gain nearly 10% of its battery after every 40 kilometers of driving. Our framework provides a solution to public and private organizations that support and facilitate vehicle electrification through charging infrastructure. [ABSTRACT FROM AUTHOR]

This article presents a new variant of particle swarm optimization (PSO) algorithm, which was developed for a reliable parameter estimation in thermal analysis of electrical machines. The proposed algorithm uses a varied social network, where both number and size of the network (local neighborhoods) are randomly adjusted during the optimization process. Such approach has been introduced here to assure improved diversity of the PSO and consequently a more reliable and robust search of the solution space. A case study parameter estimation for a reduced-order thermal-equivalent-circuit (TEC) of an electrical machine has been used to demonstrate effectiveness of the proposed method. The analyzed black-box parameter estimation relies on the input and output data (demand data) from a short-transient finite-element-analysis (FEA) of a complete machine assembly. The proposed PSO variant has been benchmarked with a selection of the existing state-of-the-art PSO algorithms, which employ alternative social network schemes with the network parameters dynamically varied. The statistical data gathered from multiple runs of the PSO-based estimation suggests that the proposed new approach offers considerable improvements in terms of accuracy, efficiency, reliability, and robustness as compared with the alternative PSO algorithms. [ABSTRACT FROM AUTHOR]

In this work, a novel general pattern of assisted flux barriers in an asymmetric V-shape interior permanent magnet (AVIPM) machine is presented. The AVIPM machine has a symmetric permanent magnet (PM) structure and an asymmetric rotor core structure to realize the magnetic-field-shifting (MFS) effect. The general pattern can represent four possible types of assisted flux barriers at different positions on the rotor core and the final structure can be automatically determined by using optimization method. The advantage of the proposed optimization pattern is that the optimal design of assisted flux barriers in V-shape interior permanent magnet (VIPM) machines with high torque and low torque ripple can be generated within a short computing time. The proposed optimization method is applied to improve the structure of a conventional 8-pole 48-slot VIPM machine, which is commonly used for driving electric vehicles (EVs). A non-dominated sorting genetic algorithm II (NSGA-II) is used for the global optimization of both a VIPM machine and an AVIPM machine. The electromagnetic performance is computed using a finite element analysis (FEA). Moreover, a frozen permeability (FP) method is applied to perform an accurate separation of PM torque and reluctance torque. The results exhibit that the peak output torque of the AVIPM machine is increased by 8.7% compared to a conventional VIPM machine with the same PM volume due to the notable MFS effect. In the meantime, the AVIPM machine has a better overload capability due to the larger contribution of reluctance torque. Moreover, the proposed machine has high efficiency of over 97%. [ABSTRACT FROM AUTHOR]

Optimization modeling of real-world application problems usually involves noise from various sources. Noisy optimization imposes challenges to optimization methods since the objective values can be different for multiple evaluations. In this article, we propose a novel online population size learning (OPL) technique of evolution strategies for handling noisy optimization problems. By re-evaluating a fraction of the candidates, we measure the strength of noise level of the re-evaluated candidate solutions and adapt the population size according to the noise level. The proposed OPL combines the advantages of both explicit averaging by re-evaluations and the implicit averaging by large population size and overcomes their limitations. We incorporate it with the covariance matrix adaptation evolution strategy (CMA-ES) and obtain OPL-CMA-ES. Compared with the existing noise handling technique, the proposed OPL is much simpler in both concepts and computation. We conduct comprehensive experiments to evaluate the algorithm’s performance on standard problems with Gaussian noise. We further evaluate the performance of OPL-CMA-ES on the black-box optimization benchmarks (BBOBs) noisy testbed, which is a standard platform for comparing black-box optimization algorithms, compared with the state-of-the-art noise-handling algorithms. The experimental results show that OPL-CMA-ES achieves remarkable performance and outperforms the compared variants. [ABSTRACT FROM AUTHOR]

The article views the forthcoming virtual societies, decentralized society (DeSoc) or metaverse, as cyber-physical social systems, and discusses the key issue of management for such human-centered hybrid systems—the prescription of human behaviors. We build parallel humans with its aggregation—parallel population—to complete that task, where each individual's mental knowledge is cognitively modeled by heterogeneous learning, analyzed by generative big data with deep evolutionary reasoning, and prescribed by knowledge convergence with an active recommendation. A case study from social security has validated that our parallel population and parallel human are a feasible and effective way to construct DeSoc or metaverse. [ABSTRACT FROM AUTHOR]

Both objective optimization and constraint satisfaction are crucial for solving constrained multiobjective optimization problems, but the existing evolutionary algorithms encounter difficulties in striking a good balance between them when tackling complex feasible regions. To address this issue, this article proposes a two-stage evolutionary algorithm, which adjusts the fitness evaluation strategies during the evolutionary process to adaptively balance objective optimization and constraint satisfaction. The proposed algorithm can switch between the two stages according to the status of the current population, enabling the population to cross the infeasible region and reach the feasible regions in one stage, and to spread along the feasible boundaries in the other stage. Experimental studies on four benchmark suites and three real-world applications demonstrate the superiority of the proposed algorithm over the state-of-the-art algorithms, especially on problems with complex feasible regions. [ABSTRACT FROM AUTHOR]