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Multi-source domain generalization for person re-identification, named Domain Generalizable Person Re-Identification (DG-ReID), refers to training on multiple source domains and testing on unseen target domains. However, the performance of the current methods is rather unsatisfactory. In this paper, we propose the Multi-Source Adaptive Meta-Learning (MAM) framework to push the limit of DG-ReID. First, MAM is built on the novel Multi-Source Adaptive Feature Refinement (MAFR) block, which simultaneously acquires domain-generalized features and domain-unique features on different domains. MAFR consists of three key components: the fine-grained feature extraction (FGFE) branch, the refined feature supplementation (RFS) branch, and the feature fusion (FF) module. Through these blocks, the interrelationships between different domains are explored, facilitating the extraction of features that are both generalized and discriminative. To capture domain-adaptive information, MAFR incorporates a meta-weight generator to effectively capture domain-adaptive information. Second, MAM leverages the principles of meta-learning during training to acquire more powerful generalization features. A total of three novel loss functions are proposed to train the proposed MAM including the clustering loss, the unknown domain identification loss, and the non-parametric identification loss. Last, we further design a novel meta-feature sampling method to diversify features used in the meta-testing stage for better generalization capability. The effectiveness of the proposed method is extensively evaluated on multiple benchmarks, and the results provide comprehensive validation. For instance, our MAM framework presents a promising solution to the challenge of domain generalization in person re-identification. Specifically, under evaluation protocol-2, the MAM framework exhibits improvements of 1.0% and 0.9% in mAP and rank-1 metrics, respectively, compared to the previous state-of-the-art method. [ABSTRACT FROM AUTHOR]

Accelerating large-scale linear solvers is always crucial for scientific research and industrial applications. In this regard, preconditioners play a key role in improving the performance of iterative linear solvers. This paper presents a summary and review of our work about the development of parallel ILU preconditioners on GPUs. The mechanisms of ILU(0), ILU(k), ILUT, enhanced ILUT, and block-wise ILU(k) are reviewed and analyzed, which give a clear guidance in the development of iterative linear solvers. ILU(0) is the most commonly used preconditioner, and the nonzero pattern of its matrix is exactly the same as the original matrix to be solved. ILU(k) uses k levels to control the pattern of its preconditioner matrix. ILUT selects entries for its preconditioner matrix by setting thresholds without considering its original matrix pattern. In addition to point-wise ILU preconditioners, a block-wise ILU(k) preconditioner is designed delicately in support of block-wise matrices. In implementation, the RAS (Restricted Additive Schwarz) method is adopted to optimize the parallel structure of a preconditioner matrix. Coupling with the configuration parameters of ILU preconditioners, a complex situation appears in the parallel solution process, so decoupled algorithms are adopted. These algorithms are implemented and tested on NVIDIA GPUs. The experiment results show that a single-GPU implementation can speed up an ILU preconditioner by a factor of 10, compared to traditional CPU implementation. The results also show that the ILU(0) has better speedup than ILU(k) but slower convergence than ILU(k). Level k of ILU(k) and threshold (p, t) of ILUT are effective adjustment factors for controlling the equilibrium point between acceleration and convergence for ILU(k) and ILUT, respectively. All these ILU preconditioners are characterized and compared in this work, which shows a clear picture and numerical insights for practitioners in the ILU family. [ABSTRACT FROM AUTHOR]

Traditionally, solving the parameter identification inverse problems of partial differential equation encountered many difficulties and insufficiency. In this article, we proposed a restricted gene expression programming (GEP) for parameter identification inverse problems of partial differential equation based on the self-adaption, self-organization, and self-learning characters of GEP. The algorithm simulates parametric function itself of partial differential equation directly through the observed values by taking effect to inverse results caused by noise of the measured value into full consideration. Modeling is unnecessary to add regularization in modeling process aiming at special problems again. The experiment results also showed that the algorithm has good noise-immunity. In case there is no noise or noise is very low, the identified parametric function is almost the same as the original accurate value; when noise is very high, accurate result can still be obtained, which successfully realizes automation of parameter modeling process for partial differential equation. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disease that develops in middle-aged and older adults. The development of a gait detection for PD patients to assist doctors in diagnoses is a crucial research target. This work develops an explainable learning architecture that involves deep learning, machine learning, data selection, feature evaluation and data balancing mechanisms, for gait detection in PD patients. The results obtained for these patients and healthy individuals are analyzed with different algorithms. Application of the research architecture that is developed in this study significantly increased the overall rate of identification; specifically, when used proposed architecture with XGBoost and deep learning achieves accuracy rates as high as 97.32% and 98.41%, respectively. The goal is to provide an early detection system that can serve as a reference for doctors when making a diagnosis. [ABSTRACT FROM AUTHOR]

Developing rule extraction algorithms from machine learning techniques such as artificial neural networks and support vector machines (SVMs), which are considered incomprehensible black-box models, is an important topic in current research. This study proposes a rule extraction algorithm from SVMs that uses a kernel-based clustering algorithm to integrate all support vectors and genetic algorithms into extracted rule sets. This study uses measurements of accuracy, sensitivity, specificity, coverage, fidelity and comprehensibility to evaluate the performance of the proposed method on the public credit screening data sets. Results indicate that the proposed method performs better than other rule extraction algorithms. Thus, the proposed algorithm is an essential analysis tool that can be effectively used in data mining fields. [ABSTRACT FROM AUTHOR]

For stomach tissue-related gonorrhea, gastric mucosal tissue lymphoma is one of the most common, the study found that most lymphomas are related to HP infection, HP is also known as Helicobacter pylori, existing studies may not know enough about HP infection and lymphoma, and this article describes signs and treatments for 12 patients with lymphoma. The materials were analyzed for 12 lymphoma patients admitted to the Second Affiliated Hospital of Zhejiang University from March 2016 to April 2020. The study found that most gastric lymphoma will appear spot-like redness, mucosal rupture, erosion in many places, and part of the gastric mucosa will also appear fold thickening and other phenomena; among the 12 cases studied in this paper, 6 cases of white mucosa and 3 cases are exophytic mucosa. After the study of this paper, a series of references can be made for the diagnosis of H. pylori lymphoma, and gastric lymphoma will manifest in the background mucosa. [ABSTRACT FROM AUTHOR]

The problem reliability estimation of multicomponent stress–strength system is studied in this paper, when the strength components and stress component follow generalized inverted exponential distributions under progressive first failure censored data. Point estimate of reliability in a multicomponent stress–strength model is derived by using maximum likelihood and Bayes methods. We construct the asymptotic confidence interval and highest posterior density credible interval. Two bootstrap confidence intervals are proposed. The performances of the different estimation algorithms are assessed by the Monte Carlo simulations. Carbon fiber strength data and data on water capacity of the Shasta reservoir have been analyzed for the purpose of illustration. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) has been widely applied to complex global optimization problems. Different search strategies have been designed to find the optimum conditions in a fitness landscape. However, none of these strategies works well over all possible fitness landscapes. Since the fitness landscape associated with a complex global optimization problem usually consists of various local landscapes, each search strategy is efficient in a particular type of fitness landscape. A reasonable approach is to combine several search strategies and integrate their advantages to solve global optimization problems. This paper presents a new self-feedback strategy differential evolution (SFSDE) algorithm based on fitness landscape analysis of single-objective optimization problem. In the SFSDE algorithm, in the analysis of the fitness landscape features of fitness-distance correlation, a self-feedback operation is used to iteratively select and evaluate the mutation operators of the new SFSDE algorithm. Moreover, mixed strategies and self-feedback transfer are combined to design a more efficient DE algorithm and enhance the search range, convergence rate and solution accuracy. Finally, the proposed SFSDE algorithm is implemented to optimize soil water textures, and the experimental results show that the proposed SFSDE algorithm reduces the difficulty in estimating parameters, simplifies the solution process and provides a novel approach to calculate the parameters of the Van Genuchten equation. In addition, the proposed algorithm exhibits high accuracy and rapid convergence and can be widely used in the parameter estimation of such nonlinear optimization models. [ABSTRACT FROM AUTHOR]