Maximization Approach Research Articles

Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction

The existence of intra-class spectral variability caused by differential scene components and illumination conditions limits the improvement of endmember extraction accuracy, as most endmember extraction algorithms directly find pixels in the hyperspectral image as endmembers. This paper develops a quadratic clustering-based simplex volume maximization (CSVM) approach to effectively alleviate spectral variability and extract endmembers. CSVM first adopts spatial clustering based on simple linear iterative clustering to obtain a set of homogeneous partitions and uses spectral purity analysis to choose pure pixels. The average of the chosen pixels in each partition is taken as a representative endmember, which reduces the effect of local-scope spectral variability. Then an improved spectral clustering based on k-means is implemented to merge homologous representative endmembers to further reduce the effect of large-scope spectral variability, and final endmember collection is determined by the simplex with maximum volume. Experimental results show that CSVM reduces the average spectral angle distance on Samson, Jasper Ridge and Cuprite datasets to below 0.02, 0.06 and 0.09, respectively, provides the root mean square errors of abundance maps on Samson and Jasper Ridge datasets below 0.25 and 0.10, and exhibits good noise robustness. By contrast, CSVM provides better results than other state-of-the-art algorithms.

BatchedGreedy: A batch processing approach for influence maximization with candidate constraint

BatchedGreedy: A batch processing approach for influence maximization with candidate constraint

Neither here nor there: reconciling nominee directors’ dual loyalties under the Indian Companies Act 2013

ABSTRACT The objective of this article is to determine how Indian law can resolve the conflict of dual loyalties faced by nominee directors. We first study nominee directors’ duties towards their nominator in commercial practice, and directors’ duties under the stakeholder-oriented provision of section 166(2) in Indian Companies Act 2013. We then take stock of the judicial approaches towards nominee directors in the United Kingdom and in India. The Companies Act 2013 is presently inadequate to resolve the identified conflict. We suggest codifying a pragmatic approach by allowing nominee directors to pursue their nominator’s interests, subject to the company’s interests. To determine the company’s interests, we refer to the Entity Maximisation and Sustainability Approach proposed by Andrew Keay that allows for a plurality of interests to be considered by a director, while providing a clear objective. In concluding, we call for further deliberation on the enforcement of the nominee directors’ duties.

Nonnegative block-sparse Bayesian learning algorithm for EEG brain source localization

Localizing electrical sources on the cortex surface from scalp recorded electroencephalogram (EEG) is challenging, due to ill-posed problem, noises/artifacts contamination, etc. Applying sparse Bayesian learning (SBL) in this field can automatically give sparse solution to ill-posed problem, while most of the SBL-based algorithms require precise or estimated version of noise statistics information. As EEG signals are more likely to stem from locally synchronized neural masses, modeling source block-sparsity on the cortex surface would bring benefits. In this paper, we develop an EEG brain source localization algorithm in SBL framework, with innovative modeling at sensor level as well as source level. For sensor-level modeling, the distribution of sample covariance matrix of multi-electrode measurements is considered, to circumvent the requirement of noise covariance matrix information. The innovation of source-level modeling is that, with block-sparsity prior used, the block-sparse signal reconstruction problem is transformed to an atom-sparse one, in which variance parameters of brain regions are to be estimated. As these parameters are nonnegative, their priors are modeled by nonnegative Gaussian, which was neglected by previous studies, and ultimately a nonnegative block-SBL (NNBSBL) algorithm is proposed in expectation–maximization (EM) approach. Simulations demonstrate that the proposed NNBSBL algorithm has excellent performance in variations of source number, source locations, homoscedastic/heteroscedastic noises, signal-to-noise ratio (SNR), and number of samples, compared to benchmark and state-of-the-art algorithms. The performance of the proposed algorithm is also evaluated through real P300 EEG data, which is proved consistent with the conclusions of P300 source locations in literature.

Estimating Spatiotemporal Contacts Between Individuals in Underground Shopping Streets Based on Multi-Agent Simulation

Coronavirus disease 2019 (COVID-19) has exposed the public safety issues. Obtaining inter-individual contact and transmission in the underground spaces is an important issue for simulating and mitigating the spread of the pandemic. Taking the underground shopping streets as an example, this study aimed to verify commercial facilities’ influence on the spatiotemporal distribution of inter-individual contact in the underground space. Based on actual surveillance data, machine learning techniques are adopted to obtain utilizers’ dynamics in underground pedestrian system and shops. Firstly, an entropy maximization approach is adopted to estimate pedestrians’ origin-destination (OD) information. Commercial utilization behaviors at different shops are modeled based on utilizers’ entering frequency and staying duration, which are obtained by re-identifying individuals’ disappearances and appearances at storefronts. Based on observed results, a simulation method is proposed to estimate utilizers’ spatiotemporal contact by recreating their space-time paths in the underground system. Inter-individual contact events and exposure duration are obtained in view of their space-time vectors in passages and shops. A social contact network is established to describe the contact relations between all individuals in the whole system. The exposure duration and weighted clustering coefficients were defined as indicators to measure the contact degree of individual and the social contact network. The simulation results show that the individual and contact graph indicators are similar across time, while the spatial distribution of inter-individual contact within shops and passages are time-varying. Through simulation experiments, the study verified the effects of self-protection and commercial type adjustment measures.

Service deployment design in latency-critical multi-cloud environment

The advent of virtualization-based infrastructure needed for 5G brought new challenges in utilizing the existing physical network infrastructure effectively. Designing physical routes between virtual network functions in a multi-cloud environment is usually handled as a maximum flow problem, separately considering latency requirements. This paper presents an algorithm designed for low latency edge-cloud environments, where results meet both bandwidth and latency requirements of the planned traffic in one step. The proposed algorithm is much faster compared to the wide-spread linear program based flow maximization approach as the incorporated filtering of route candidates by latency allows more efficient search. The paper demonstrates the operation of the algorithm by a detailed example.

A Fully Bayesian Inference with Gibbs Sampling for Finite and Infinite Discrete Exponential Mixture Models

ABSTRACT In this paper, we propose clustering algorithms based on finite mixture and infinite mixture models of exponential approximation to the Multinomial Generalized Dirichlet (EMGD), Multinomial Beta-Liouville (EMBL) and Multinomial Shifted-Scaled Dirichlet (EMSSD) with Bayesian inference. The finite mixtures have already shown superior performance in real data sets clustering using the Expectation–Maximization approach. The proposed approaches in this paper are based on a Monte Carlo simulation technique namely Gibbs sampling algorithm including an additional Metropolis–Hastings step, and we utilize exponential family conjugate prior information to construct their posterior relying on Bayesian theory. Furthermore, we also present the infinite models based on Dirichlet processes, which results in clustering algorithms that do not require the specification of the number of mixture components to be given in advance and selects it in a principled manner. The performance of our Bayesian approaches was evaluated in some challenging real-world applications concerning text sentiment analysis, fake news detection, and human face gender recognition.

Fast model‐based clustering of partial records

Partially recorded data are frequently encountered in many applications and usually clustered by first removing incomplete cases or features with missing values, or by imputing missing values, followed by application of a clustering algorithm to the resulting altered dataset. Here, we develop clustering methodology through a model‐based approach using the marginal density for the observed values, assuming a finite mixture model of multivariate t distributions. We compare our approximate algorithm to the corresponding full expectation–maximization (EM) approach that considers missing values in the incomplete dataset and makes a missing at random (MAR) assumption, as well as case deletion and imputation methods. Since only the observed values are utilized, our approach is computationally more efficient than imputation or full EM. Simulation studies demonstrate that our approach has favourable recovery of the true cluster partition compared to case deletion and imputation under various missingness mechanisms and is at least competitive with the full EM approach, even when MAR assumptions are violated. Our methodology is demonstrated on a problem of clustering gamma‐ray bursts and is implemented at https://github.com/emilygoren/MixtClust.

BMRHTA: Balanced Multipath Routing and Hybrid Transmission Approach for Lifecycle Maximization in WSNs

In this article, a balanced multipath routing and hybrid transmission approach (BMRHTA) is proposed to effectively alleviate the imbalance of the forwarding load in a sink connection area (SCA) and prolong the network lifecycle for wireless sensor networks (WSNs). To achieve the energy efficient and balanced WSNs, three design issues, including the multipath, multihop, and single-hop transmissions, are jointly optimized to maximize the overall network lifecycle. First, the path load aggregation phenomenon in the SCA, which makes the forwarding packet load unevenly distributed among hotspots, is examined. In order to achieve the load balance in SCA, multiple shortest balanced paths are generated in the BMRHTA model. In the first stage, two uncorrelated shortest paths are discovered from each node to the sink and the optimal path selection cycle can be determined to achieve the SCA load balance. Afterward, a network equilibrium policy is offered to resolve the optimal transmission period of energy balance via hybrid transmission. As a result, the balanced shortest paths, the path selection cycle and the transmission period can be determined in the network formation phase to avoid the excessive load concentration in the subsequent maintenance phase. Simulation results show that the joint two uncorrelated balanced routing and the proposed network equilibrium policy can nearly quadruple the network lifecycle extension, as compared to a conventional node power policy. Also, the proposed BMRHTA achieves better performance than the current state-of-the-art competitive approaches in terms of energy efficiency and lifecycle.

Social Group Optimization Approach for Maximization of Comfortable Level of Usage and Minimization of Payment Costs in Smart Grid with Optimal Power Flow

Social Group Optimization Approach for Maximization of Comfortable Level of Usage and Minimization of Payment Costs in Smart Grid with Optimal Power Flow

Diversifying Tire-Defect Image Generation Based on Generative Adversarial Network

With the development of data-driven models, deep learning has been increasingly applied in the field of defect detection. However, the performance of deep learning models is greatly restricted by costly labeling and sample scarcity. One of the best approaches to solve the data imbalance problem is increasing the quantity and diversity of defect samples. Meanwhile, the current models based on generative adversarial network (GAN) cannot readily control the category and shape of generated defect samples, which results in inefficient data augmentation. Thus, to simultaneously achieve the category and shape adjustability of defect samples, a novel model named diversifying tire-defect GAN (DTD-GAN) is proposed in this article by integrating the advantages of latent space decomposition and feature decoupling algorithm. In this model, an auxiliary classifier is designed to control the defect category by discrete latent codes, and the mutual information maximization approach is adopted to diversify the tire-defect shape via continuous latent codes that greatly improve the diversity of the generated tire-defect samples. To illustrate the effectiveness of the proposed DTD-GAN model, extensive experiments on image quality and diversity assessment are carried out on the images generated by this model and other state-of-the-art approaches, and the achieved improvement in deep learning model performance in a tire-defect detection task is also discussed.

Crowdsourcing Incentive Mechanisms for Cross-Platform Tasks: A Weighted Average Maximization Approach

Crowdsourcing Incentive Mechanisms for Cross-Platform Tasks: A Weighted Average Maximization Approach

A two-stage VIKOR assisted multi-operator differential evolution approach for Influence Maximization in social networks

The impact of online social networking on daily life is extending beyond personal boundaries, becoming a tool for financial activities and even public well-being. Interactions among users and items, as nodes of such networks, and flows of information among them play a critical role in this regard. Identifying a small subset of such nodes with maximum spreading power that facilitates this process has attracted a lot of attention. The existing solution approaches to this NP-hard combinatorial optimization problem, known as the Influence Maximization (IM) problem, mainly suffer either from poor solution quality or large computational time. This study addresses these issues by proposing a two-stage optimization framework. First, to reduce the computation complexity, a multi-criteria decision making (MCDM) approach is employed to prune insignificant nodes from the candidate nodes. Second, to optimize the solutions, a modified differential evolution (DE) algorithm equipped with multiple search operators is proposed. The proposed algorithm uses the Expected Diffusion Value (EDV) in place of traditional time-consuming simulations to evaluate the fitness of a candidate solution. We proved that EDV is a monotone and submodular function, and showed that it can effectively and efficiently be integrated into a greedy-based algorithmic framework. We also provided the theoretical analysis and evaluated the performance of this proposed algorithm experimentally using two synthetic and eight diverse real-life networks. The experimental results show that our proposed algorithm achieves a better trade-off between solution quality and running time compared to the existing baseline algorithms.

Semiparametric additive time-varying coefficients model for longitudinal data with censored time origin.

Statistical analysis of longitudinal data often involves modeling treatment effects on clinically relevant longitudinal biomarkers since an initial event (the time origin). In some studies including preventive HIV vaccine efficacy trials, some participants have biomarkers measured starting at the time origin, whereas others have biomarkers measured starting later with the time origin unknown. The semiparametric additive time-varying coefficient model is investigated where the effects of some covariates vary nonparametrically with time while the effects of others remain constant. Weighted profile least squares estimators coupled with kernel smoothing are developed. The method uses the expectation maximization approach to deal with the censored time origin. The Kaplan-Meier estimator and other failure time regression models such as the Cox model can be utilized to estimate the distribution and the conditional distribution of left censored event time related to the censored time origin. Asymptotic properties of the parametric and nonparametric estimators and consistent asymptotic variance estimators are derived. A two-stage estimation procedure for choosing weight is proposed to improve estimation efficiency. Numerical simulations are conducted to examine finite sample properties of the proposed estimators. The simulation results show that the theory and methods work well. The efficiency gain of the two-stage estimation procedure depends on the distribution of the longitudinal error processes. The method is applied to analyze data from the Merck 023/HVTN 502 Step HIV vaccinestudy.

ON OPTIMIZATION OF MANUFACTURING OF PRODUCT TO OBTAIN MAXIMAL VALUE OF PROFIT TAKING INTO ACCOUNT CHANGING OF PRICES

In this paper, we introduce a model for the prognosis of profit of an enterprise taking into account several types of products. Also, we introduce an approach for maximization of the considered profit with an analytical approach for estimation of the maximum value of profit of an enterprise taking into account several types of products. Profit maximization gives a possibility to take into account changes in the price of products on the market. As an example, we consider the estimation of profit for the accounting of three types of products. But the introduced approach gives a possibility to change a number of products.

Target Assignment for Cooperative Quadrotors Unmanned Aerial Vehicles

The target assignment for cooperative Unmanned aerial vehicles is a critical topic. Most target assignment approaches yield computational complexity when dealing with multiple UAVs. This work offers target assignment approaches and resolves multiple UAVs problems by introducing a proposal of target assignment approaches. The main contribution of this work is solving the target assignment problem using optimal time approach and gain maximization approach in double stages. At the first stage, the urgently desired target to be fetched from the detected target group is selected by using the proposed objective function based on three attribute values of targets (threating weight - importance weight - separating distance). At the second stage, the proposed objective function is used and assign related UAVs for selected desired targets by considering the coverage factors, adaptive-limitation and constrains.

A circularity-based quality assessment tool to classify the core for recovery businesses

To realise circularity in the management of used materials and associated recovery practices for value creation, a circularity-based quality assessment tool (CQAT) is proposed in this study. The issue is addressed with core classification based on quality factors assessing circularity at the product level. A circularity-based quality indicator (QI) is developed that classifies the core into reusable, remanufacturable, and recyclable (3R) at the acquisition phases of the core retrieval system. For the validation of CQAT, four real cases of small-medium-sized dismantling firms dealing with engines, motor gearboxes, motor pumps, and refrigerators involved in remanufacturing activities are analysed. The study employs an integrative coefficient correlation maximisation approach (CCMA) and whitenization weight functions (WWF) clustering model that estimates the priorities of regenerative and restorative actions. The result reveals that the severe damage with high disassembly processing time due to destructive disassembly in the post-acquisition phase is highly critical to the circularity of the retrieval system. It exhibits that damage, processing time, value retention classify core into lowest grade at acquisition phases. Since possibilities of value recovery are lost, firms are least concerned with social and environmental considerations. Additionally, the design and functionality dimensions in the inspection majorly classify cores into remanufacturable.

ECM algorithm for estimating vector ARMA model with variance gamma distribution and possible unbounded density

SummaryThe simultaneous analysis of several financial time series is salient in portfolio setting and risk management. This paper proposes a novel alternating expectation conditional maximisation (AECM) algorithm to estimate the vector autoregressive moving average (VARMA) model with variance gamma (VG) error distribution in the multivariate skewed setting. We explain why the VARMA‐VG model is suitable for high‐frequency returns (HFRs) because VG distribution provides thick tails to capture the high kurtosis in the data and unbounded central density further captures the majority of near‐zero HFRs. The distribution can also be expressed in normal‐mean‐variance mixtures to facilitate model implementation using the Bayesian or expectation maximisation (EM) approach. We adopt the EM approach to avoid the time‐consuming Markov chain Monto Carlo sampling and solve the unbounded density problem in the classical maximum likelihood estimation. We conduct extensive simulation studies to evaluate the accuracy of the proposed AECM estimator and apply the models to analyse the dependency between two HFR series from the time zones that only differ by one hour.

Characterization of the Driving Style by State–Action Semantic Plane Based on the Bayesian Nonparametric Approach

The quantification and estimation of the driving style are crucial to improve the safety on the road and the acceptance of drivers with level2–level3(L2–L3) intelligent vehicles. Previous studies have focused on identifying the difference in driving style between categories, without further consideration of the driving behavior frequency, duration proportion properties, and the transition properties between driving style and behaviors. In this paper, a novel methodology to characterize the driving style is proposed by using the State–Action semantic plane based on the Bayesian nonparametric approach, i.e., hierarchical Dirichlet process–hidden semi–Markov model (HDP–HSMM). This method segments the time series driving data into fragment clusters with similar characteristics and construct the State–Action semantic plane based on the statistical characteristics of the state and action layer to label and interpret the fragment clusters. This intuitively and simply visualizes the driving performance of individual drivers, while the risk index of the individual drivers can also be obtained through semantic plane. In addition, according to the joint mutual information maximization (JIMI) approach, seven transition probabilities of driving behaviors are extracted from the semantic plane and applied to identify driving styles of drivers. We found that the aggressive drivers prefer high–risk driving behaviors, and the total duration and frequency of high–risk behaviors are greater than those of cautious and normal drivers. The transition probabilities among high–risk driving behaviors are also greater compared with low–risk behaviors. Moreover, the transition probabilities can provide rich information about driving styles and can improve the classification accuracy of driving styles effectively. Our study has practical significance for the regulation of driving behavior and improvement of road safety and the development of advanced driver assistance systems (ADAS).

OPTIMAL DYNAMIC FUTURES PORTFOLIO UNDER A MULTIFACTOR GAUSSIAN FRAMEWORK

We study the problem of dynamically trading futures in continuous time under a multifactor Gaussian framework. We present a utility maximization approach to determine the optimal futures trading strategy. This leads to the explicit solution to the Hamilton–Jacobi–Bellman (HJB) equations. We apply our stochastic framework to two-factor models, namely, the Schwartz model and Central Tendency Ornstein–Uhlenbeck (CTOU) model. We also develop a multiscale CTOU model, which has a fast mean-reverting and a slow mean-reverting factor in the spot asset price dynamics. Numerical examples are provided to illustrate the investor’s optimal positions for different futures portfolios.