Decomposition Scheme Research Articles

Decomposing Two-Stage Stochastic Scheduling Problems on a continuous-time basis via Slot Similarity

Incorporating uncertainty into process scheduling by explicitly considering future forecasts is crucial for achieving optimal and feasible solutions in practice. However, the complexity of two-stage stochastic scheduling formulations increases exponentially with the number of scenarios, making it difficult to solve large-scale problems in real-time using mathematical programming, which is often necessary for production-maintenance scheduling. This is where decomposition strategies on a scenario basis can be advantageous. Recently, the authors developed a method for decomposing two-stage scheduling problems (TSSPs) formulated on a discrete-time basis using the Similarity Index. This paper extends this concept to TSSPs formulated on a continuous-time basis by fuzzifying discrete decisions among slots instead of time periods, and incorporating the progressive-hedging algorithm to manage non-anticipativity in continuous decisions. The proposed algorithm is tested in a case study of a multiproduct plant consisting of a single processing unit from the literature. Results show that the proposed decomposition algorithm solves the problem faster than the monolithic formulation.

Bacteria-targeted delivery of black phosphorus quantum dots facilitates photothermal therapy against hypoxic tumors and complementary low-dose radiotherapy.

Many approaches have been employed to relieve hypoxia in solid tumors to enhance sensitivity to radiotherapy (RT), including O2 delivery or hydrogen peroxide (H2O2) decomposition strategies. To date, however, these modalities have been restricted by poor O2 loading, rapid O2 leakage, and limited endogenous H2O2 levels. To overcome these limitations, we therefore sought to develop an effective approach for the oxygen-independent treatment of hypoxic tumors. In this study, we designed a novel black phosphorus quantum dot (BPQD)/Escherichia coli (E. coli) hybrid system (BE) capable of facilitating the photothermal therapy (PTT) of hypoxic tumors. A simple electrostatic adsorption approach was used to conjugate BPQDs to E. coli. BE is capable of reliably targeting hypoxic tumors and mediating PTT. BPQDs in BE can directly facilitate X-ray-mediated radiosensitization of tumors, thereby achieving significant RT efficacy in response to lower doses of radiation, effectively and specifically damaging hypoxic tumor tissues to suppress the growth of tumors. Our results highlight this BE system as a novel approach to tumor radiosensitization with great potential for clinical application.

An energy decomposition and extrapolation scheme for evaluating electron transfer rate constants: a case study on electron self-exchange reactions of transition metal complexes.

A simple approach to the analysis of electron transfer (ET) reactions based on energy decomposition and extrapolation schemes is proposed. The present energy decomposition and extrapolation-based electron localization (EDEEL) method represents the diabatic energies for the initial and final states using the adiabatic energies of the donor and acceptor species and their complex. A scheme for the efficient estimation of ET rate constants is also proposed. EDEEL is semi-quantitative by directly evaluating the seam-of-crossing region of two diabatic potentials. In a numerical test, EDEEL successfully provided ET rate constants for electron self-exchange reactions of thirteen transition metal complexes with reasonable accuracy. In addition, its energy decomposition and extrapolation schemes provide all the energy values required for activation-strain model (ASM) analysis. The ASM analysis using EDEEL provided rational interpretations of the variation of the ET rate constants as a function of the transition metal complexes. These results suggest that EDEEL is useful for efficiently evaluating ET rate constants and obtaining a rational understanding of their magnitudes.

A Lightweight Hybrid Convolutional Neural Network for Hyperspectral Image Classification

Recent studies have demonstrated the potential of hybrid convolutional models that combine 3D and 2D convolutional neural networks (CNNs) for hyperspectral image (HSI) classification. However, these models do not fully utilize the benefits of hybrid convolution due to inefficient connections between the two types of CNNs. Moreover, most CNNs, including hybrid models, require a significant number of parameters and computational resources for accurate classification, which increases the need for labeled samples and computational cost. Although the common lightweight strategies like depthwise separable convolution (DSC) can reduce parameters and computation compared to normal convolution (NC), they often compromise accuracy. To address these challenges, we propose a lightweight hybrid convolutional neural network (Lite-HCNet) for HSI classification with minimal model parameters and computational effort. Firstly, we design a novel channel attention module (NCAM) and combine it with a convolutional kernel decomposition (CKD) strategy to propose a lightweight and efficient DSC (LE-DSC) deployed in Lite-HCNet. The LE-DSC not only reduces the DSC volume further but also enhances its performance. Secondly, a lightweight and efficient hybrid convolutional layer (LE-HCL) is designed in Lite-HCNet to explore the efficient connection structure between 3D CNNs and 2D CNNs. Experiments show that the Lite-HCNet reduces the required computational cost and practical deployment difficulty while offering advanced performance with a small number of training samples. Furthermore, abundant ablation experiments confirm the superior performance of the designed LE-DSC.

Dark and singular cubic�quartic optical solitons with Lakshmanan�Porsezian�Daniel equation by the improved Adomian decomposition scheme

Dark and singular cubic�quartic optical solitons with Lakshmanan�Porsezian�Daniel equation by the improved Adomian decomposition scheme

Bright and dark optical solitons for the concatenation model by the Laplace-Adomian decomposition scheme

Bright and dark optical solitons for the concatenation model by the Laplace-Adomian decomposition scheme

Dynamic Residual Filtering With Laplacian Pyramid for Instance Segmentation

Various studies have been conducted on instance segmentation and made great strides over the past few years. Most recently, instance-specific mask generation via dynamic kernel predictions has shown the significant performance improvement even without bounding boxes as well as anchors. However, this scheme still does not fully consider dynamic properties since the size of the receptive field is not enough to cover the spatially-meaningful range due to memory limitations. Furthermore, the single-fused feature often fails to grasp complicated boundaries for objects of different sizes. In this paper, we propose the dynamic residual filtering method with the Laplacian pyramid, which separately restores the global layout and local boundaries of instance masks. Specifically, we firstly apply the Laplacian pyramid-based decomposition scheme to features encoded from the backbone and subsequently restore sub-band mask residuals from coarse to fine pyramid levels. To do this, we design spatially-aware convolution filters to progressively capture the residual form of mask features at each level of the Laplacian pyramid while holding deformable receptive fields with dynamic offset information. This is fairly desirable since global and local properties of mask features can be accurately restored with keeping the spatial flexibility through the invertible process of the Laplacian reconstruction. Experimental results on the COCO dataset demonstrate that our proposed method achieves the state-of-the-art performance, i.e., 42.7% AP. The code and model are publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tjqansthd/LapMask</uri> .

Recent advances in fermionic hierarchical equations of motion method for strongly correlated quantum impurity systems

Investigations of strongly correlated quantum impurity systems (QIS), which exhibit diversified novel and intriguing quantum phenomena, have become a highly concerning subject in recent years. The hierarchical equations of motion (HEOM) method is one of the most popular numerical methods to characterize QIS linearly coupled to the environment. This review provides a comprehensive account of a formally rigorous and numerical convergent HEOM method, including a modeling description of the QIS and an overview of the fermionic HEOM formalism. Moreover, a variety of spectrum decomposition schemes and hierarchal terminators have been proposed and developed, which significantly improve the accuracy and efficiency of the HEOM method, especially in cryogenic temperature regimes. The practicality and usefulness of the HEOM method to tackle strongly correlated issues are exemplified by numerical simulations for the characterization of nonequilibrium quantum transport and strongly correlated Kondo states as well as the investigation of nonequilibrium quantum thermodynamics.

Channel Estimation in RIS-Assisted MIMO Systems Operating Under Imperfections

The promising gains of reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems, in terms of extended coverage and enhanced capacity, are critically dependent on the accuracy of the channel state information. However, traditional channel estimation (CE) schemes are not applicable in RIS-assisted MIMO networks, since passive RISs typically lack the signal processing capabilities that are assumed by CE algorithms. This becomes problematic when physical imperfections or electronic impairments affect the RIS due to its exposition to different environmental effects or caused by hardware limitations from the circuitry. While these real-world effects are typically ignored in the literature, in this paper we propose efficient CE schemes for RIS-assisted MIMO systems taking different imperfections into account. Specifically, we propose two sets of tensor-based algorithms, based on the parallel factor analysis decomposition schemes. First, assuming a long-term model-where the RIS imperfections, modeled as unknown phase shifts, are static within the channel coherence time-we formulate an iterative alternating least squares (ALS)- based algorithm for the joint estimation of the unknown phase deviations and the communication channels. Then, we develop the short-term imperfection model, which allows both amplitude and phase RIS imperfections to be non-static with respect to the channel coherence time. We propose two iterative ALS-based and closed-form higher-order singular value decomposition-based algorithms for jointly estimating the channels and the unknown impairments. We also investigate the computational complexity and the identifiability of the proposed algorithms and study the effect of various imperfections on the CE quality. Simulation results show the effectiveness of our proposed tensor-based algorithms in terms of estimation accuracy and computational complexity.

An Efficient Deep Learning-Based Troposphere ZTD Dataset Generation Method for Massive GNSS CORS Stations

Nowadays, a huge amount of GNSS continuously operating reference stations (CORS) have been established around the world, which have already been and will continue providing massive troposphere zenith total delay (ZTD) data. This paper proposes an efficient deep learning-based troposphere ZTD dataset generation method including ZTD series segmentation, addictive and innovational outlier detection, and missing data imputation. The overall standard deviation of first-order ZTD difference series between adjacent epochs of the selected CORS station in January 2018 is reduced from 0.84 to 0.26 mm with the 3-sigma rule and the wavelet decomposition strategy for outlier elimination. A dense neural network (DNN) is subsequently designed to impute missing ZTD data. Only 3.4 minutes are required for the DNN training using a NVIDIA GeForce RTX 3090Ti GPU. The complete ZTD series with missing ZTD data imputed by the well-trained DNN model has a good agreement with the ZTD series before the data imputation in terms of mean value (-0.0184 vs -0.0187 mm) and standard deviation (5.43 vs 5.26 mm). Complete ZTD series of 120 CORS stations are generated to further evaluate the computational efficiency of the proposed method. An average of 2.56/5.38/5.77/4.33 hours are necessary to generate the ZTD dataset for January/April/July/October in 2018, respectively. Our study confirms that the proposed method can efficiently generate CORS-based ZTD dataset, which could be extended to applications including troposphere temporal-spatial pattern exploration and ZTD augmentation on high-precision GNSS positioning.

Towards Enabling Binary Decomposition for Partial Multi-Label Learning.

Partial multi-label learning (PML) is an emerging weakly supervised learning framework, where each training example is associated with multiple candidate labels which are only partially valid. To learn the multi-label predictive model from PML training examples, most existing approaches work by identifying valid labels within candidate label set via label confidence estimation. In this paper, a novel strategy towards partial multi-label learning is proposed by enabling binary decomposition for handling PML training examples. Specifically, the widely used error-correcting output codes (ECOC) techniques are adapted to transform the PML learning problem into a number of binary learning problems, which refrains from using the error-prone procedure of estimating labeling confidence of individual candidate label. In the encoding phase, a ternary encoding scheme is utilized to balance the definiteness and adequacy of the derived binary training set. In the decoding phase, a loss weighted scheme is applied to consider the empirical performance and predictive margin of derived binary classifiers. Extensive comparative studies against state-of-the-art PML learning approaches clearly show the performance advantage of the proposed binary decomposition strategy for partial multi-label learning.

Spatially localized sparse approximations of deep features for breast mass characterization.

We propose a deep feature-based sparse approximation classification technique for classification of breast masses into benign and malignant categories in film screen mammographs. This is a significant application as breast cancer is a leading cause of death in the modern world and improvements in diagnosis may help to decrease rates of mortality for large populations. While deep learning techniques have produced remarkable results in the field of computer-aided diagnosis of breast cancer, there are several aspects of this field that remain under-studied. In this work, we investigate the applicability of deep-feature-generated dictionaries to sparse approximation-based classification. To this end we construct dictionaries from deep features and compute sparse approximations of Regions Of Interest (ROIs) of breast masses for classification. Furthermore, we propose block and patch decomposition methods to construct overcomplete dictionaries suitable for sparse coding. The effectiveness of our deep feature spatially localized ensemble sparse analysis (DF-SLESA) technique is evaluated on a merged dataset of mass ROIs from the CBIS-DDSM and MIAS datasets. Experimental results indicate that dictionaries of deep features yield more discriminative sparse approximations of mass characteristics than dictionaries of imaging patterns and dictionaries learned by unsupervised machine learning techniques such as K-SVD. Of note is that the proposed block and patch decomposition strategies may help to simplify the sparse coding problem and to find tractable solutions. The proposed technique achieves competitive performances with state-of-the-art techniques for benign/malignant breast mass classification, using 10-fold cross-validation in merged datasets of film screen mammograms.

Formation of development programs with multi-purpose projects at ferrous metallurgy enterprises

The improvement of management mechanisms for the formation and calendar planning of development programs is the most important direction for improving the productivity (achievement of goals) and efficiency (reduction of the amount of resources consumed) of the activities of metallurgical companies. Currently, it is necessary to ensure the mobilization of companies’ assets to solve the tasks of their sustainable development. The task of forming a program for the development of a metallurgical enterprise (company) is considered. The program includes several different areas of development: improvement of existing business processes (sales, supply, production, repair of equipment, etc.), production technologies of various stages (production of coke, agglomerate, cast iron, steel, rolled products), implementation of digital transformation tasks, etc. Each of the directions of the development program contains projects described by effect, size of investments, changes in the expenditure items of operating budget related to the costs of operating those systems and processes that the project is aimed at improving, as well as an indicator describing the risk of project implementation. One of the directions of the development program may include multi-purpose projects, the implementation of which leads not only to changes in its own performance indicators, but also to changes in the performance indicators of projects of other (non-multi-purpose) directions of such development program. The case is considered when management of the development program includes the management of the overall budget and achievement of overall goal of the program (the maximum effect from implementation of all projects). At the same time, project risk management and changes in the operating budget are implemented at the level of project portfolio management of individual program areas (there are no restrictions on risks and changes in the operating budget common to the development program). The stated formalizations of the problems, their decomposition and composition schemes, and the developed procedures for solving individual subtasks are based on the provisions and methods of the theories of system analysis and a new section of discrete mathematics (network programming).

Local surrogate responses in the Schwarz alternating method for elastic problems on random voided domains

Imperfections and inaccuracies in real technical products often influence the mechanical behavior and the overall structural reliability. The prediction of real stress states and possibly resulting failure mechanisms is essential and a real challenge, e.g. in the design process. In this contribution, imperfections in elastic materials such as air voids in adhesive bonds between fiber-reinforced composites are investigated. They are modeled as arbitrarily shaped and positioned. The focus is on local displacement values as well as on associated stress concentrations caused by the imperfections. For this purpose, the resulting complex random one-scale finite element model is numerically solved by a new developed surrogate model using an overlapping domain decomposition scheme based on Schwarz alternating method. Here, the actual response of local subproblems associated with isolated material imperfections is determined by a single appropriate surrogate model, that allows for an accelerated propagation of randomness. The efficiency of the method is demonstrated for imperfections with elliptical and ellipsoidal shape in 2D and 3D and extended to arbitrarily shaped voids. For the latter one, a local surrogate model based on artificial neural networks (ANN) is constructed. Finally, a comparison to experimental results validates the numerical predictions for a real engineering problem.

A dual decomposition strategy for large-scale multiobjective evolutionary optimization

A dual decomposition strategy for large-scale multiobjective evolutionary optimization

Absolute income mobility and the effect of parent generation inequality: An extended decomposition approach

We use full-population data to study trends in intergenerational absolute income mobility, measured as the ratio of children earning more than their parents, for 11 Swedish cohorts born 1972–1983. Absolute mobility during this period increases from 72% to 84% for men and from 76% to 86% for women—higher figures than in most other countries studied. To explain these results, we outline a novel decomposition strategy that accounts for cohort variation in parent-generation income inequality. All else equal, if income inequality is higher in the parent generation, more economic growth is required to achieve any given level of absolute mobility. We discuss implications for comparative research in intergenerational income mobility.

Electron Density Analysis of Metal–Metal Bonding in a Ni4 Cluster Featuring Ferromagnetic Exchange

We present a combined experimental and theoretical study of the nature of the proposed metal-metal bonding in the tetranuclear cluster Ni4(NPtBu3)4, which features four nickel(I) centers engaged in strong ferromagnetic coupling. High-resolution single-crystal synchrotron X-ray diffraction data collected at 25 K provide an accurate geometrical structure and a multipole model electron density description. Topological analysis of the electron density in the Ni4N4 core using the quantum theory of atoms in molecules clearly identifies the bonding as an eight-membered ring of type [Ni-N-]4 without direct Ni-Ni bonding, and this result is generally corroborated by an analysis of the energy density distribution. In contrast, the calculated bond delocalization index of ∼0.6 between neighboring Ni atoms is larger than what has been found for other bridged metal-metal bonds and implies direct Ni-Ni bonding. Similar support for the presence of direct Ni-Ni bonding is found in the interacting quantum atom approach, an energy decomposition scheme, which suggests the presence of stabilizing Ni-Ni bonding interactions with an exchange-correlation energy contribution approximately 50% of that of the Ni-N interactions. Altogether, while the direct interactions between neighboring Ni centers are too weak and sterically constrained to bear the signature of a topological bond critical point, other continuous measures clearly indicate significant Ni-Ni bonding. These metal-metal bonding interactions likely mediate direct ferromagnetic exchange, giving rise to the high-spin ground state of the molecule.

A Full Tensor Decomposition Network for Crop Classification with Polarization Extension

The multisource data fusion technique has been proven to perform better in crop classification. However, traditional fusion methods simply stack the original source data and their corresponding features, which can be only regarded as a superficial fusion method rather than deep fusion. This paper proposes a pixel-level fusion method for multispectral data and dual polarimetric synthetic aperture radar (PolSAR) data based on the polarization extension, which yields synthetic quad PolSAR data. Then we can generate high-dimensional features by means of various polarization decomposition schemes. High-dimensional features usually cause the curse of the dimensionality problem. To overcome this drawback in crop classification using the end-to-end network, we propose a simple network, namely the full tensor decomposition network (FTDN), where the feature extraction in the hidden layer is accomplished by tensor transformation. The number of parameters of the FTDN is considerably fewer than that of traditional neural networks. Moreover, the FTDN admits higher classification accuracy by making full use of structural information of PolSAR data. The experimental results demonstrate the effectiveness of the fusion method and the FTDN model.

A novel carbon price forecasting method based on model matching, adaptive decomposition, and reinforcement learning ensemble strategy.

Carbon price is closely related to energy conservation and emission reduction cost, and carbon price forecasting is conducive to improving and stabilizing market trading mechanisms. Considering the matching relationship between the prediction model and the carbon price market, a decomposition ensemble carbon price forecasting method based on reinforcement learning model fusion is designed. Firstly, considering the problem of disharmony between different base models and data patterns, a model matching strategy is developed to match the appropriate prediction base model for each carbon trading market. Secondly, the empirical wavelet transform (EWT) algorithm is used to decompose the carbon price into several subseries, which reduces the complexity of the original data and improves the prediction effect of each base model. Finally, for each carbon price market, the Q-learning algorithm of reinforcement learning is used to integrate the prediction results of the selected multiple base models into the final price forecasting of the corresponding market. The model is verified in the seven carbon trading markets, and experiments show that the model has superior and stable prediction performance compared with other methods. The mean absolute percentage errors of the Hubei, Beijing, Shenzhen, Chongqing, Tianjin, Shanghai, and EU markets are only 0.3515%, 1.2335%, 1.3388%, 1.2128%, 0.3229%, 0.2418%, and 0.4094%, respectively. It shows that the EWT method and reinforcement learning ensemble strategy do improve the prediction performance, and the proposed model can be used as a feasible tool for price assessment and management in carbon price markets.

Intermittent solar power hybrid forecasting system based on pattern recognition and feature extraction

Solar energy, with its abundance and accessibility, occupies an irreplaceable position in the shift in global energy consumption patterns. The difficulties of managing solar energy on the grid, caused by its highly volatile and intermittent nature, necessitate an accurate and stable forecasting system. However, existing studies have focused more on the accuracy of prediction without considering the reactive power caused by intermittency, whose prevalence leads to the solar power series having both discrete and continuous characteristics, making the prediction problem more challenging. To fill this gap, a multistep ahead hybrid forecasting system has been constructed in this study that contains feature extraction, pattern recognition, forecasting, and integrated optimization modules. The system integrates pattern recognition algorithms into the prediction models to appropriately deal with the inconsistent data features caused by intermittency and introduces a data decomposition strategy to achieve feature extraction, such that it could be adapted to forecast situations where relevant weather information is not available. Finally, the multi-objective optimization algorithm allows selecting, weighting, and integrating all submodels and yields a hybrid model with excellent accuracy and stability. The results showed that the hybrid forecasting system achieved the best performance in all aspects of the four forecast scenarios and also performed well in the multistep advance forecast. Taking Site 1 as an example, the mean absolute scale errors of the one-step, two-step, and three-step advance forecasts are 2.9953%, 3.7095%, and 3.4945%, respectively; and the standard deviations of the errors are 3.1086, 3.6210, and 3.2133, respectively. Furthermore, the hybrid forecasting system achieves a performance improvement in accuracy and stability of more than 90%, and this performance improvement is significant at the 10% significance level.