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High-level synthesis (HLS) has democratized field programmable gate arrays (FPGAs) by enabling high-level device programmability and rapid microarchitecture customization through the use of directives. Nevertheless, the manual selection of the appropriate directives, i.e., the annotations included in the high-level source code to instruct the synthesis process, is a difficult task for programmers without a hardware background. In this letter, we present CollectiveHLS, an ultrafast knowledge-based HLS design optimization method that automatically extracts the most promising directive configurations and applies them to the original source code. The proposed optimization scheme is a fully data-driven approach for generalized HLS tuning, as it is not based on quality of result models or meta-heuristics. We design, implement, and evaluate our method with more than 100 applications of Machsuite, Rodinia, and GitHub on a ZCU104 FPGA. We achieve an average geometric mean speedup of x14.1 and x10.5 compared to the unoptimized, i.e., without HLS directives and optimized designs, a high design feasibility score, and an average inference latency of 38 ms. [ABSTRACT FROM AUTHOR]

Nowadays, metaheuristic algorithms have been emerged as a potential solution in adaptive filtering applications since they offer good convergence properties. Nonetheless, most of them fall into a local minimum since their optimization is based on a single-solution technique. As a consequence, these algorithms present a high misadjustment level and require a large population to find the optimal solution. Recently, the grey wolf optimization (GWO) algorithm has emerged as a potential solution since it requires a smaller population and possesses a stronger global optimization ability with lesser control parameters. From an engineering perspective, its compactness is an attractive feature. Therefore, this opens new horizons in the implementation of this algorithm in resource-constrained devices. In this letter, we present for the first time the use of the GWO algorithm for system identification and acoustic echo canceller (AEC) and its implementation in a field programmable gate array (FPGA) device to validate its effectiveness. Our results show that the use of the GWO algorithm achieves lower steady-state mean square error (MSE) and requires less computational resources when compared with one of the most used metaheuristic algorithm. [ABSTRACT FROM AUTHOR]

Target 3.4 of the third Sustainable Development Goal (SDG) of the United Nations (UN) General Assembly proposes to reduce premature mortality from non-communicable diseases (NCDs) by one-third. Epidemiological data presented by the World Health Organization (WHO) in 2016 show that out of a total of 57 million deaths worldwide, approximately 41 million deaths occurred due to NCDs, with 78% of such deaths occurring in low-and-middle-income countries (LMICs). The majority of investigations on NCDs agree that the leading risk factor for mortality worldwide is hypertension. Over 75% of the world's mobile phone subscriptions reside in LMICs, hence making the mobile phone particularly relevant to mHealth deployment in Africa. This study is aimed at determining the scope of the literature available on hypertension diagnosis and management in Africa, with particular emphasis on determining the feasibility, acceptability and effectiveness of interventions based on the use of mobile phones. The bulk of the evidence considered overwhelmingly shows that SMS technology is yet the most used medium for executing interventions in Africa. Consequently, the need to define novel and superior ways of providing effective and low-cost monitoring, diagnosis, and management of hypertension-related NCDs delivered through artificial intelligence and machine learning techniques is clear. [ABSTRACT FROM AUTHOR]

The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). First, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations. [ABSTRACT FROM AUTHOR]

Across the world, people are living longer. This phenomenon is creating a demographic shift: by 2050, the number of people aged 60 and older is expected to double and the number of people older than 80 is expected to triple. A growing elderly population also means an increase in age-related health concerns. In particular, with age comes normal memory decline as well as an increased risk of Alzheimer’s and other dementias. All this means that now more than ever the world needs interventions to support memory. From developing memory protheses to improving memory consolidation during sleep, neurostimulation is one particularly promising type of intervention for enhancing memory. [ABSTRACT FROM AUTHOR]

Recently, multi-objective evolutionary algorithms (MOEAs) have shown promising performance in terms of community detection in complex networks. However, most studies have focused on designing different strategies to achieve good community detection performance based on a single population. Unlike these studies, this study proposes a macro-micro population-based co-evolutionary multi-objective algorithm called MMCoMO for community detection in complex networks to obtain a better trade-off between exploration and exploitation. This algorithm employs two populations, i.e., macro-population and micro-population, for co-evolution to obtain better community structures. In particular, the macro-population prefers exploration and is responsible for quickly determining approximate partitions of the network to obtain good rough community structures as early as possible, whereas the micro-population favors exploitation and is responsible for searching for good fine community structures through the local search process. Thus, these two populations can be used to improve each other through interactions in the co-evolutionary process. In particular, a guiding strategy is designed using the elite (non-dominated) solutions of the macro-population to guide the micro-population, and an influencing strategy is further designed using the elite solutions of the micro-population to positively influence the macro-population. Experiments on synthetic networks and 14 real-world networks demonstrate the superiority of the proposed algorithm over several state-of-the-art community detection algorithms. [ABSTRACT FROM AUTHOR]

Cooperative co-evolution (CC) is an evolutionary algorithm that adopts the divide-and-conquer strategy to solve large-scale optimization problems. It is difficult for CC to specify a suitable subpopulation size to solve different subproblems. The population diversity may be insufficient to search for the global optimum during subpopulations' evolution. In this paper, an adaptive method for enhancing population diversity is embedded in a contribution-based CC. In CC, there are two kinds of subpopulation: the convergent or stagnant subpopulations and the non-convergent and non-stagnant subpopulations. A method is proposed in the paper to evaluate the convergent and stagnant subpopulations' contributions to improving the best overall objective value, which is different from the contribution evaluation on the non-convergent and non-stagnant subpopulations. In each co-evolutionary cycle, the new CC adaptively determines to select a subpopulation, which can make a greater contribution to improving the best overall objective value, between the above two kinds of subpopulation to undergo evolution. When a convergent or stagnant subpopulation is selected to undergo evolution, the subpopulation is re-diversified to enhance its global search capability. Our experimental results and analysis suggest that the new CC algorithm can improve the performance of CC and serves as a competitive solver for large-scale optimization problems. [ABSTRACT FROM AUTHOR]

Researchers analyze underground forums to study abuse and cybercrime activities. Due to the size of the forums and the domain expertise required to identify criminal discussions, most approaches employ supervised machine learning techniques to automatically classify the posts of interest. Human annotation is costly. How to select samples to annotate that account for the structure of the forum? We present a methodology to generate stratified samples based on information about the centrality properties of the population and evaluate classifier performance. We observe that by employing a sample obtained from a uniform distribution of the post degree centrality metric, we maintain the same level of precision but significantly increase the recall (+30%) compared to a sample whose distribution is respecting the population stratification. We find that classifiers trained with similar samples disagree on the classification of criminal activities up to 33% of the time when deployed on the entire forum. [ABSTRACT FROM AUTHOR]

Activation of the autonomic nervous system is a primary characteristic of human hedonic responses to sensory stimuli. For smells, general tendencies of physiological reactions have been described using classical statistics. However, these physiological variations are generally not quantified precisely; each psychophysiological parameter has very often been studied separately and individual variability was not systematically considered. The current study presents an innovative approach based on data mining, whose goal is to extract knowledge from a dataset. This approach uses a subgroup discovery algorithm which allows extraction of rules that apply to as many olfactory stimuli and individuals as possible. These rules are described by intervals on a set of physiological attributes. Results allowed both quantifying how each physiological parameter relates to odor pleasantness and perceived intensity but also describing the participation of each individual to these rules. This approach can be applied to other fields of affective sciences characterized by complex and heterogeneous datasets. [ABSTRACT FROM AUTHOR]

The effects of cognitive stress are complex and multi-dimensional with nuanced neural and physiological representations across our lifespan. Chronic and instantaneous stressors are known to alter both executive function and motor performance — a particularly challenging prospect for older adults. Age, sex, and motor activity are critical yet under-represented dimensions in the domain of stress detection. Through the present work, we explore a subset of these variables and the relevance of brain hemodynamics and heart rate variability (HR/V) as biomarkers of stress in an aging population. We rely on a multimodal, sex-balanced, motor-stress data set (N = 59) and an exhaustive machine learning workflow to operationalize the unique neurophysiological states that form the human stress response. We found that a quadratic discriminant was sufficient to separate the two states across feature, demographic, and activity variables. We report a stress detection accuracy between $78-98\%$ 78 - 98 % when using models trained independently on each feature-set. However, these models were highly sensitive to sex, and activity differences — with distinct regions, and features implicated in stress recognition. Both HR/V and fNIRS based features were excellent indices of cognitive stress, however neither generalized to a degree beneficial toward operational use. Our observations underscore the importance of task-context, age, and sex as factors in modeling stress detection tools for older adults. [ABSTRACT FROM AUTHOR]

We construct an improved and distribution-free quantile estimator (called IB quantile (IBQ) estimator) with representation as a linear combination of order statistics (OSs). The IBQ estimator can estimate quantiles of any scale-invariant population by using censored samples. Taking exponential population as an example, we derive the mean square error (mse) of the IBQ estimator under a small sample case. It is found that the IBQ estimator exhibits higher estimation efficiency when estimating large quantiles compared with the other two existing estimators. Applying the IBQ estimator to create the clutter level, we establish the IBQ constant false alarm rate (CFAR) processor. We investigate the performance of IBQ-CFAR in homogeneous background and multiple target scenarios. The theoretical analyses show that the IBQ-CFAR exhibits outstanding detection performance and false alarm control ability in multiple target scenarios. [ABSTRACT FROM AUTHOR]

Remote-sensing nighttime light (NTL) data are widely used for fine-grained estimation of the population due to their effectiveness in monitoring anthropic lights at night. However, there is a lack of research exploring the correlations between population and NTL data at various administrative and grid scales, as well as their related impact factors. Thus, to analyze the spatial effect of NTL on NTL-based population estimation, five models (linear, quadratic, exponential, logarithmic, and power function) were used in this letter to examine the correlations between NTL and population at multiscale by using the Visible Infrared Imaging Radiometer Suite data from the National Polar-orbiting Partnership. Results show that the correlations between NTL and population were significantly related to geographic scales. The power function model had the highest fitting accuracy between NTL and population when the grid scales were less than 5 km. The quadratic model had the highest precision on grid scales larger than 5 km. It showed a sharp increase in $R^{2}$ values between 0.5 and 10 km as the scale increased. At different scales, the correlations between NTL and population were negatively impacted by temperature (TEM), normalized difference vegetation index (NDVI), and road network density (RND), but they were positively impacted by the relief degree of the land surface (RDLS). We also found that RDLS had the greatest impact on model accuracy, followed by NDVI and TEM. In addition, RND had a low impact on the accuracy of population estimation on rough grids. [ABSTRACT FROM AUTHOR]

In recent years, multilayer networks have served as effective models for addressing and analyzing real-world systems with multiple relationships. Among these scenarios, the community detection (CD) problem is one of the most prominent research hotspots. Although some research on multilayer network CD (MCD) has been proposed to address this problem, most studies focus only on topological structures. Therefore, their algorithms cannot extract the most out of complementary network information, such as node similarities and low-rank features, which may lead to unsatisfactory accuracy. To tackle this problem, this paper proposes a novel multi-objective evolutionary algorithm based on consensus prior information (MOEA-CPI). The proposed algorithm takes full advantage of prior information to guide the MOEA with respect to topological structures, initializations, and the optimization process. More specifically, this paper first extracts two kinds of prior information, i.e., graph-level and node-level information, based on Node2vec and Jaccard similarity, respectively. Then, the prior layer and a high-quality initial population are constructed on the basis of the graph-level information. During the optimization process, the genetic operator, which integrates the weighting strategy and node-level information, is applied to guide the algorithm to distribute similar nodes into the same community. Extensive experiments are implemented to prove the superior performance of MOEA-CPI over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Solving constrained multi-objective optimization problems (CMOPs) is one of the most popular research topics in the multi-objective optimization community. Various approaches based on different algorithmic strategies have been proposed for benchmark CMOPs with different features and challenges. Although most of these algorithms employ one or more fixed strategies, determining the most suitable strategy is challenging and problem-dependent. This work presents a general multitasking framework comprising a main task for the original CMOP and an arbitrary number of auxiliary tasks based on different strategies for the auxiliary problems. Reinforcement learning techniques are utilized according to the population state to choose the most suitable auxiliary task during the evolutionary process. As instantiations, Q-Learning and Deep Q-Learning are successfully applied in the framework. Accordingly, two novel algorithms are developed to solve CMOPs. The results demonstrate the effectiveness of the proposed approaches on five benchmark CMOP test suites and real-world problems compared to 11 advanced methods. [ABSTRACT FROM AUTHOR]

High-speed rail station location (HSR-SL) is a complex problem with multiple conflicting factors, such as local transportation network connectivity, regional accessibility, downtown proximity, and feasibility. The proposed novel methodology quantified these factors using customized nonlinear, linear, or integer utility functions. Suitable distance decay models to quantify the local transport access to and from existing bus stops, train stations, and downtown proximity; a gravity model for potential regional accessibility to the residential population or workforce; linear models for connectivity with the existing public transit routes and normalized land cost; and a binary model with a threshold value for geographical feasibility in terms of environmental sensitivity were developed. These models were evaluated using a geospatial and network analysis-based approach, and the overall nonlinear HSR-SL problem was optimized using the particle swarm optimization algorithm. The results of a real-world study area in downtown Tokyo, Japan, revealed that customized utility functions for various factors reduced the possibility of over- or underestimation and the selection of suboptimal SLs. The proposed method improved estimation of the land cost feasibility, access to transfer points, and connectivity by 100%, 185%, and 222%, respectively, for the given case study. It was most sensitive to connectivity and proximity to the downtown area, followed by location cost, transportation access, and population or workforce potential accessibility. [ABSTRACT FROM AUTHOR]

Affect dynamics, the study of how affect develops and manifests over time, has become a popular area of research in affective computing for learning. In this article, we first provide a detailed analysis of prior affect dynamics studies, elaborating both their findings and the contextual and methodological differences between these studies. We then address methodological concerns that have not been previously addressed in the literature, discussing how various edge cases should be treated. Next, we present mathematical evidence that several past studies applied the transition metric (L) incorrectly - leading to invalid conclusions of statistical significance - and provide a corrected method. Using this corrected analysis method, we reanalyze ten past affect datasets collected in diverse contexts and synthesize the results, determining that the findings do not match the most popular theoretical model of affect dynamics. Instead, our results highlight the need to focus on cultural factors in future affect dynamics research. [ABSTRACT FROM AUTHOR]

Several optimization problems have features that hinder the capabilities of searching heuristics. To cope with this issue, different methods have been proposed to manipulate search spaces and improve the optimization process. This paper focuses on Dilation Functions (DFs), which are one of the most promising techniques to manipulate the fitness landscape, by "expanding" or "compressing" specific regions. The definition of appropriate DFs is problem dependent and requires a-priori knowledge of the optimization problem. Therefore, it is essential to introduce an automatic and efficient strategy to identify optimal DFs. With this aim, we propose a novel method based on Genetic Programming, named GP4DFs, which is capable of evolving effective DFs. GP4DFs identifies optimal dilations, where a specific DF is applied to each dimension of the search space. Moreover, thanks to a knowledge-driven initialization strategy, GP4DFs converges to better solutions with a reduced number of fitness evaluations, compared to the state-of-the-art approaches. The performance of GP4DFs is assessed on a set of 43 benchmark functions mimicking several features of real-world optimization problems. The obtained results indicate the suitability of the generated DFs. [ABSTRACT FROM AUTHOR]

Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes. To evaluate the influence spread of a seed set efficiently, existing studies have proposed transformations with lower computational costs to replace the expensive Monte Carlo simulation process. These alternate transformations, based on network prior knowledge, induce different search behaviors with similar characteristics to various perspectives. Specifically, it is difficult for users to determine a suitable transformation a priori. This article proposes a multi-transformation evolutionary framework for influence maximization (MTEFIM) with convergence guarantees to exploit the potential similarities and unique advantages of alternate transformations and to avoid users manually determining the most suitable one. In MTEFIM, multiple transformations are optimized simultaneously as multiple tasks. Each transformation is assigned an evolutionary solver. Three major components of MTEFIM are conducted via: 1) estimating the potential relationship across transformations based on the degree of overlap across individuals of different populations, 2) transferring individuals across populations adaptively according to the inter-transformation relationship, and 3) selecting the final output seed set containing all the transformation's knowledge. The effectiveness of MTEFIM is validated on both benchmarks and real-world social networks. The experimental results show that MTEFIM can efficiently utilize the potentially transferable knowledge across multiple transformations to achieve highly competitive performance compared to several popular IM-specific methods. The implementation of MTEFIM can be accessed through the external link in the footnote. [ABSTRACT FROM AUTHOR]

Objective quantification of the balancing mechanisms in humans is strongly needed in health care of older people, yet is largely missing among current clinical balance assessment methods. Hence, the main goal of this literature review is to identify methods that have the potential to meet that need. We searched in the PubMed and IEEE Xplore databases using predefined criteria, screened 1064 articles, and systematically reviewed and categorized methods from 73 studies that deal with identification of neuromuscular controller models of human upright standing from empirical data. These studies were then analyzed with the particular aim to understand to what degree such methods would be useful solutions for assessing the balance of older individuals aged above 60 years. The 16 studies that included an older subject population were especially examined with this in mind. The majority of the reviewed articles focused on research questions related to the general function of human balance control rather than clinical applicability. Further efforts need to be made to adapt these methods for more accessible and mobile technologies and to ensure that the outcomes are valid for balance assessment of a general older population. [ABSTRACT FROM AUTHOR]

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. [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]

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]

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]

With the emergence of the multicore architecture and the increase of chip operating frequency, power optimization has become a key step of circuit logic synthesis. Aiming at the XNOR/OR circuits, with the goal of minimizing power, construct the optimal polarity fixed-polarity Reed–Muller (FPRM) circuits power optimization scheme. However, the power optimization for FPRM circuits is a multipeak combinatorial optimization problem, we first propose a metaheuristic optimization algorithm (MOA), which includes the global exploration optimizer, local deep exploitation optimizer, and initial population and uses the proposed differential evolution optimization, fierce wolf siege algorithm-based tabu search, and improved skew tent map to make the population evolve. Based on the proposed Huffman tree construction algorithm and MOA, we propose an efficient power optimization approach (EPOA) to find the minimum power FPRM circuit. Experimental results on the benchmark circuits confirm the effectiveness of EPOA. [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]

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]

The performance of decomposition-based algorithms is sensitive to the Pareto front shapes since their reference vectors preset in advance are not always adaptable to various problem characteristics with no a priori knowledge. For this issue, this article proposes an adaptive reference vector reinforcement learning (RVRL) approach to decomposition-based algorithms for industrial copper burdening optimization. The proposed approach involves two main operations, that is: 1) a reinforcement learning (RL) operation and 2) a reference point sampling operation. Given the fact that the states of reference vectors interact with the landscape environment (quite often), the RL operation treats the reference vector adaption process as an RL task, where each reference vector learns from the environmental feedback and selects optimal actions for gradually fitting the problem characteristics. Accordingly, the reference point sampling operation uses estimation-of-distribution learning models to sample new reference points. Finally, the resultant algorithm is applied to handle the proposed industrial copper burdening problem. For this problem, an adaptive penalty function and a soft constraint-based relaxing approach are used to handle complex constraints. Experimental results on both benchmark problems and real-world instances verify the competitiveness and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the field of optimization and machine learning, the statistical assessment of results has played a key role in conducting algorithmic performance comparisons. Classically, null hypothesis statistical tests have been used. However, recently, alternatives based on Bayesian statistics have shown great potential in complex scenarios, especially when quantifying the uncertainty in the comparison. In this work, we delve deep into the Bayesian statistical assessment of experimental results by proposing a framework for the analysis of several algorithms on several problems/instances. To this end, experimental results are transformed to their corresponding rankings of algorithms, assuming that these rankings have been generated by a probability distribution (defined on permutation spaces). From the set of rankings, we estimate the posterior distribution of the parameters of the studied probability models, and several inferences concerning the analysis of the results are examined. Particularly, we study questions related to the probability of having one algorithm in the first position of the ranking or the probability that two algorithms are in the same relative position in the ranking. Not limited to that, the assumptions, strengths, and weaknesses of the models in each case are studied. To help other researchers to make use of this kind of analysis, we provide a Python package and source code implementation at https://zenodo.org/record/6320599. [ABSTRACT FROM AUTHOR]

Recently, large-scale multiobjective optimization has received increasing attention from the evolutionary multiobjective optimization (EMO) community. This has led to the emergence of a specialized research area called evolutionary large-scale multiobjective optimization (ELMO). In general, it is believed that multiobjective optimization problems become more difficult as the number of decision variables increases. However, the following two counterintuitive observations are obtained from careful examinations of recent ELMO studies. One is that experimental results on some large-scale multiobjective test problems were improved by increasing the number of decision variables. The other is that better results were obtained for some other large-scale multiobjective test problems by conventional EMO algorithms (EMOAs) than state-of-the-art ELMO algorithms (ELMOAs). These observations suggest that ELMOAs have not always been evaluated on appropriate test problems. Moreover, their performance is not always better than the performance of conventional EMOAs. In this letter, we first re-examine the performance of ELMOAs and conventional EMOAs on a wide variety of scalable multiobjective test problems. Then, counterintuitive experimental results are analyzed using the anytime performance evaluation scheme and distributions of randomly generated initial solutions. Based on the analysis, suggestions on how to handle large-scale multiobjective test problems with counterintuitive results are proposed. [ABSTRACT FROM AUTHOR]

Band selection (BS) is a widely used dimensionality reduction technique for hyperspectral images. However, most of existing evolutionary algorithms focus on searching a globally optimal band subset under a fixed size, and their obtained band subsets may still contain a large number of redundant bands. In order to simultaneously obtain multiple optimal band subsets with different sizes, this article proposes an unsupervised multitask artificial bee colony (ABC) BS algorithm based on variable-size clustering (MBBS-VC). First, a variable-size band clustering method based on worst class decomposition is developed, based on which the BS problem can be modeled as a multitask optimization problem. Next, a multitask multimicrogroup bee colony algorithm with variable coding length is proposed to simultaneously search multiple optimal band subsets with different sizes. Moreover, several new strategies, including the intergroup collaboration strategy based on bidirectional neighborhood learning and the multimeasure integration judgment (MIJ) mechanism, are designed to improve the performance of MBBS-VC. In this article, the hyperspectral BS problem is transformed into a multitask optimization problem for the first time. Finally, compared with 15 classical BS algorithms on several commonly used datasets, experimental results verify the superiority of the proposed BS algorithm. [ABSTRACT FROM AUTHOR]

This work assesses the efficacy of evolutionary algorithms (EAs) using an intuitive multidimensional scaling (MDS) visualization of the evolution of a population. We propose the use of landmark MDS (LMDS) to overcome computational challenges inherent to visualizing many-objective and complex problems with MDS. For the benchmark problems we tested, LMDS is akin to MDS visually, whilst requiring less than 1% of the time and memory necessary to produce an MDS visualization of the same objective space solutions, leading to the possibility of online visualizations for multi- and many-objective optimization evaluation. Using multi- and many-objective problems from the DTLZ and WFG benchmark test suites, we analyze how Landmark MDS visualizations can offer far greater insight into algorithm performance than using traditional algorithm performance metrics such as hypervolume alone, and can be used to complement explicit performance metrics. Ultimately, this visualization allows the visual identification of problem features and assists the decision maker in making intuitive recommendations for algorithm parameters/operators for creating and testing better EAs to solve multi- and many-objective problems. [ABSTRACT FROM AUTHOR]

Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved breakthrough success over many applications in the past few years. Among different classes of NAS methods, evolutionary computation-based NAS (ENAS) methods have recently gained much attention. Unfortunately, the development of ENAS is hindered by unfair comparison between different ENAS algorithms due to different training conditions and high computational cost caused by expensive performance evaluation. This article develops a platform named BenchENAS, in short for benchmarking evolutionary NAS, to address these issues. BenchENAS makes it easy to achieve fair comparisons between different algorithms by keeping them under the same settings. To accelerate the performance evaluation in a common lab environment, BenchENAS designs a novel and generic efficient evaluation method for the population characteristics of evolutionary computation. This method has greatly improved the efficiency of the evaluation. Furthermore, BenchENAS is easy to install and highly configurable and modular, which brings benefits in good usability and easy extensibility. This article conducts efficient comparison experiments on eight ENAS algorithms with high GPU utilization on this platform. The experiments validate that the fair comparison issue does exist in the current ENAS algorithms, and BenchENAS can alleviate this issue. A Website has been built to promote BenchENAS at https://benchenas.com , where interested researchers can obtain the source code and document of BenchENAS for free. [ABSTRACT FROM AUTHOR]

Conventional multiobjective optimization algorithms (MOEAs) with or without preferences are successful in solving multi- and many-objective optimization problems. However, a strong hypothesis underlying their performance is that MOEAs are able to find a representative solution set to cover the entire Pareto-optimal front (PF) and decision makers are able to conveniently and precisely articulate their preference, which is not always easy to fulfill in practice. Accordingly, it is suggested that representative solutions in the naturally interesting regions of the PF rather than the whole PF should be targeted. A large body of research has been proposed to search or identify the knees or knee regions over the past decades. Therefore, this article aims to provide a comprehensive survey of the research on knee-oriented optimization. We start with a discussion of the importance and basic concepts of the knees, followed by a summary of knee-oriented benchmarks and indicators. After that, knee-oriented frameworks and techniques, and real-world applications are presented. Finally, potential challenges are pointed out and a few promising future lines of research are suggested. The survey offers a new perspective to develop MOEAs for solving multi- and many-objective optimization problems. [ABSTRACT FROM AUTHOR]

Evolutionary sequential transfer optimization is a paradigm that leverages search experience from solved source optimization tasks to accelerate the evolutionary search of a target task. Even though many algorithms have been developed, they mainly focus on objective-homogeneous problems, where the source and target tasks possess a similar number of objectives. In this work, we explore objective-heterogeneous problems, in which knowledge transfers across single-objective optimization problems (SOPs), multiobjective optimization problems (MOPs), and many-objective optimization problems (MaOPs). Objective-heterogeneous problems challenge the existing methods due to the diverse search and objective spaces between the source and the target task. To address this issue, we present a decision variable analysis-based transfer method that can conduct knowledge transfer across problems with the different numbers of objectives. We first separate decision variables of MOPs and MaOPs into convergence-related variables (CVs) and diversity-related variables (DVs), according to their roles while treating variables of SOPs as CVs. Then, we propose a convergence transfer module to transfer knowledge of CVs to speed up the convergence. It aligns both solutions and fitness ranks for preserving fitness rank consistency between the source and target tasks, whereby accelerating search speed. Besides, a diversity transfer module is presented to refine the distribution of DVs to maintain the population diversity. The experimental results on objective-heterogeneous test problems and a real-world case study have demonstrated the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Fall detection and prevention are crucial in elderly healthcare and humanoid robotic research as they help mitigate the damaging after-effects of falls. In this work, we have presented a deep-learning-based preimpact fall detection system (FDS) that detects a fall within 0.5 s of the fall initiation phase, thus providing a sufficient lead time of 0.5 s which is far better than the state-of-the-art. To achieve this, we have developed an automatic feature extraction methodology that can extract temporal features from all types of human fall data collected using wearable sensors. A deep neural classifier based on the ensemble of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) is trained on the extracted temporal features. The classifier has performed exceptionally well in detecting the Fall Initiation phase with a sensitivity of 99.24% and an F1-score of 98.79% for different types of falls. A sensitivity of 99.24% signifies that the model has sufficiently reduced the occurrence of false negatives, which is far more critical for an FDS. A concept of a transitional window is introduced to improve the reaction time of the FDS. We utilized two standard fall datasets, viz. SisFall and KFall, for the experimentation. Dataset fusion is employed to increase the generalizability of the system. This work can be utilized to design and develop fall detection devices for the Internet-of-Healthcare-Things applications (IoHT) and for imparting fall detection capabilities to humanoid robots and gait rehabilitation devices such as exoskeleton robots and smart prosthetic legs. [ABSTRACT FROM AUTHOR]

The job-shop scheduling problem (JSP) is NP hard, which has very important practical significance. Because of many uncontrollable factors, such as machine delay or human factors, it is difficult to use a single real-number to express the processing and completion time of the jobs. JSP with fuzzy processing time and completion time (FJSP) can model the scheduling more comprehensively, which benefits from the developments of fuzzy sets. Fuzzy relative entropy leads to a method that can evaluate the quality of a feasible solution following the comparison between the actual value and the ideal value (the due date). Therefore, the multiobjective FJSP can be transformed into a single-objective optimization problem and solved by a hybrid adaptive differential evolution (HADE) algorithm. The maximum completion time, the total delay time, and the total energy consumption of jobs will be considered. HADE adopts a mutation strategy based on DE-current-to-best. Its parameters (CR and F) are all made adaptive and normally distributed. The new individuals are selected according to the fitness value (FRE) obtained from a population consisting of N parents and N children in HADE. The algorithm is analyzed from different viewpoints. As the experimental results demonstrate, the performance of the HADE algorithm is better than those of some other state-of-the-art algorithms (namely, ant colony optimization, artificial bee colony, and particle swarm optimization). [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]

Human brain is a complex yet economically organized system, where a small portion of critical hub regions support the majority of brain functions. The identification of common hub nodes in a population of networks is often simplified as a voting procedure on the set of identified hub nodes across individual brain networks, which ignores the intrinsic data geometry and partially lacks the reproducible findings in neuroscience. Hence, we propose a first-ever group-wise hub identification method to identify hub nodes that are common across a population of individual brain networks. Specifically, the backbone of our method is to learn common graph embedding that can represent the majority of local topological profiles. By requiring orthogonality among the graph embedding vectors, each graph embedding as a data element is residing on the Grassmannian manifold. We present a novel Grassmannian manifold optimization scheme that allows us to find the common graph embeddings, which not only identify the most reliable hub nodes in each network but also yield a population-based common hub node map. Results of the accuracy and replicability on both synthetic and real network data show that the proposed manifold learning approach outperforms all hub identification methods employed in this evaluation. [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]

Contribution: This article measures the impact of a service-learning project in a first-year introduction to computing course on students’ understanding of engineers’ roles and responsibilities and their attitudes toward plastic pollution. Background: Service learning has been integrated into classrooms for many years, but its utilization in engineering courses is lacking. Furthermore, plastic pollution is a complex and multidisciplinary problem that requires social, political, environmental, and engineering solutions. This work provides a quantitative assessment on whether service learning through a litter pickup project impacts students’ views of the role of engineers in solving complex sociotechnical problems. Research Questions: 1) How does service learning change students’ perception of an engineer’s role in solving sociotechnical problems? and 2) how can service learning be used to increase students’ environmental awareness and action? Methodology: Surveys were administered to students before and after their participation in a service-learning project. The study was conducted across five sections of an introduction to a computer programming course that is required of first-year civil, electrical, and mechanical engineering students. Findings: After the service-learning project, students were more likely to agree that engineers have the skills, knowledge, and responsibility to solve societal problems. Students also demonstrated a more holistic understanding of plastic pollution and reported changes in their waste reduction behaviors. [ABSTRACT FROM AUTHOR]

Robust estimators and Wald-type tests are developed for the multinomial logistic regression based on $\phi $ -divergence measures. We compute the influence function of the proposed estimators and tests and discuss some consequences. Their robustness is illustrated by an extensive simulation study and two real examples. [ABSTRACT FROM AUTHOR]

A cornerstone of clinical medicine is intervening on a continuous exposure, such as titrating the dosage of a pharmaceutical or controlling a laboratory result. In clinical trials, continuous exposures are dichotomized into narrow ranges, excluding large portions of the realistic treatment scenarios. The existing computational methods for estimating the effect of continuous exposure rely on a set of strict assumptions. We introduce new methods that are more robust towards violations of these assumptions. Our methods are based on the key observation that changes of exposure in the clinical setting are often achieved gradually, so effect estimates must be “locally” robust in narrower exposure ranges. We compared our methods with several existing methods on three simulated studies with increasing complexity. We also applied the methods to data from 14 k sepsis patients at M Health Fairview to estimate the effect of antibiotic administration latency on prolonged hospital stay. The proposed methods achieve good performance in all simulation studies. When the assumptions were violated, the proposed methods had estimation errors of one half to one fifth of the state-of-the-art methods. Applying our methods to the sepsis cohort resulted in effect estimates consistent with clinical knowledge. [ABSTRACT FROM AUTHOR]

As an important research topic of machine learning, multiclass classification has wide applications ranging from computer vision to bioinformatics. A variety of multiclass classification algorithms with promising performance have been proposed. Among them, the decomposition-based algorithms have shown their competitiveness, since they transform the original problem into several easily solved binary classification sub-problems. Unlike existing decomposition-based algorithms which tackle each sub-problem independently, this paper suggests an evolutionary multitasking method, named EMT-MC, for multiclass classification, where the concept of multitasking is introduced to achieve the multiclass classifier with better quality. To be specific, in EMT-MC, each binary classification sub-problem is firstly viewed as a task. Then, during the evolution, the tasks with low performance (termed "ill-solved" tasks) are aided by some well-selected "assisting" tasks by using the evolutionary multitasking learning. This not only ensures that the useful information in "assisting" tasks can be transferred into those "ill-solved" tasks, but also helps them to achieve classifiers with higher accuracy. Numerical experiments on different multiclass classification datasets demonstrate the superiority of the proposed method over the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes and implements an asynchronous distributed IoT-enabled customer characterization framework to classify customers’ load consumption behaviors in electric distribution networks. More specifically, the proposed framework enables robust distributed clustering of customers’ electricity consumption habits in a highly scalable and interoperable framework. The proposed clustering method eliminates the need for hefty synchronization efforts typical to other distributed clustering algorithms. The theoretical foundations of designing the proposed framework are introduced. The orchestration of the application layers integrated in the proposed framework is demonstrated in an experimental implementation on a real-time network adopting a data-centric databus architecture. The results of the experimental implementation on IoT development boards demonstrate that the proposed framework can characterize customer categories with a 95% accuracy compared to classical centralized $k$ -means clustering, while ensuring seamless and interoperable peer-to-peer information exchange. This proved to be highly scalable and applicable to the real-world. [ABSTRACT FROM AUTHOR]

For dynamic multiobjective optimization problems (DMOPs), it is challenging to track the varying Pareto-optimal front. Most traditional approaches estimate the Pareto-optimal sets in the decision space. However, the obtained solutions do not necessarily satisfy the desired properties of decision makers in the objective space. Inverse model-based algorithms have a great potential to solve such problems. Nonetheless, the existing ones have low precision for handling DMOPs with nonlinear correlations between the objective and decision vectors, which greatly limits the application of the inverse models. In this article, an inverse Gaussian process (IGP)-based prediction approach for solving DMOPs is proposed. Unlike most traditional approaches, this approach exploits the IGP to construct a predictor that maps the historical optimal solutions from the objective space to the decision space. A sampling mechanism is developed for generating sample points in the objective space. Then, the IGP-based predictor is employed to generate an effective initial population by using these sample points. The proposed method by introducing IGP can obtain solutions with better diversity and convergence in the objective space, which is more responsive to the demand of decision makers than the traditional methods. It also has better performance than other inverse model-based methods in solving nonlinear DMOPs. To investigate the performance of the proposed approach, experiments have been conducted on 23 benchmark problems and a real-world raw ore allocation problem in mineral processing. The experimental results demonstrate that the proposed algorithm can significantly improve the dynamic optimization performance and has certain practical significance for solving real-world DMOPs. [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]