Background: Work characteristics, such as teleworking rate, have been studied in relation to stress. However, the use of work-related data to improve a high-performance stress prediction model that suits an individual...
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Background: Work characteristics, such as teleworking rate, have been studied in relation to stress. However, the use of work-related data to improve a high-performance stress prediction model that suits an individual's lifestyle has not been evaluated. Objective: This study aims to develop a novel, high-performance algorithm to predict an employee's stress among a group of employees with similar working characteristics. Methods: This prospective observational study evaluated participants' responses to web-based questionnaires, including attendance records and data collected using a wearable device. Data spanning 12 weeks (between January 17, 2022, and April 10, 2022) were collected from 194 Shionogi Group employees. Participants wore the Fitbit Charge 4 wearable device, which collected data on daily sleep, activity, and heart rate. Daily work shift data included details of working hours. Weekly questionnaire responses included the K6 questionnaire for depression/anxiety, a behavioral questionnaire, and the number of days lunch was missed. The proposed prediction model used a neighborhood cluster (N=20) with working-style characteristics similar to those of the prediction target person. Data from the previous week predicted stress levels the following week. Three models were compared by selecting appropriate training data: (1) single model, (2) proposed method 1, and (3) proposed method 2. Shapley Additive Explanations (SHAP) were calculated for the top 10 extracted features from the Extreme Gradient Boosting (XGBoost) model to evaluate the amount and contribution direction categorized by teleworking rates (mean): low: <0.2 (more than 4 days/week in office), middle: 0.2 to <0.6 (2 to 4 days/week in office), and high: >= 0.6 (less than 2 days/week in office). Results: Data from 190 participants were used, with a teleworking rate ranging from 0% to 79%. The area under the curve (AUC) of the proposed method 2 was 0.84 (true positive vs false positive: 0.77 vs 0.26). Am
Path planning on a two-dimensional grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid given that some of the grid cells are impassable (occupie...
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Path planning on a two-dimensional grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid given that some of the grid cells are impassable (occupied by obstacles). Single-source path planning finds shortest paths from a given source vertex to all other vertices of the grid. Singles-source path planning enhances robot autonomy by calculating multiple possible paths for various navigation scenarios when the destination state is unknown. A high-performance algorithm for single-source any-angle path planning on a grid called CWave is proposed here. Any-angle attribute implies that the algorithm calculates paths which can include line segments at any angle, as opposed to standard A* that runs on an 8-connected graph, which permits turns with 45 degrees increments only. The key idea of CWave is to abandon the graph model and operate directly on the grid geometry using discrete geometric primitives (instead of individual vertices) to represent the wave front. In its most basic form (CWaveInt), CWave requires only integer arithmetics. CWaveInt, however, can accumulate the distance error at turning points. A modified version of CWave (CWaveFpuSrc) with minimal usage of floating-point calculations is also developed to eliminate any accumulative errors, which is proven mathematically and experimentally on several maps. The performance of the algorithm on most of the tested maps is demonstrated to be significantly faster than that of Theta*, Lazy Theta*, Field A*, ANYA, Block A*, and A* adapted for single-source planning (on maps with lower number of isolated obstacles, CWaveFpuSrc is 2-3 times faster than its fastest tested alternative Block A*). An N-threaded implementation (CWaveN) of CWave is presented and tested to demonstrate an improved performance (multithreaded implementation is 1.5-3 times faster than single-threaded CWave). The paper discusses foundations and experimental validation of CWave, and p
Finding a feasible path subject to multiple constraints in a network is an NP-complete problem and has been extensively studied. However, current algorithms suffer either high computational complexity or low success r...
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Finding a feasible path subject to multiple constraints in a network is an NP-complete problem and has been extensively studied. However, current algorithms suffer either high computational complexity or low success ratio in finding feasible paths. The authors propose a novel extended Bellman–Ford algorithm (EB), from which they present a high-performance algorithm with low computational complexity in finding feasible paths with multiple additive constraints. Through analysis and simulations, it is shown that the algorithm outperforms its contenders in the success rate of finding a feasible path as well as in terms of scalability; the proposed algorithm can achieve almost 100% success ratio as long as a feasible path exists. Furthermore, the worst case computational complexity is only twice that of the Bellman–Ford algorithm.
Medical applications are among the tasks of optical technology. The processing of two-dimensional optical signals and images is an urgent task today. One of the most dangerous eye diseases is diabetic macular retinopa...
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ISBN:
(数字)9781510644250
ISBN:
(纸本)9781510644250
Medical applications are among the tasks of optical technology. The processing of two-dimensional optical signals and images is an urgent task today. One of the most dangerous eye diseases is diabetic macular retinopathy. The first stage in the laser coagulation operation is the stage of fundus image segmentation. The calculation of texture features for solving this problem takes a lot of time. In this paper, we consider the use of a high-performance algorithm for calculating texture features based on distributed computing to speed up the processing and analysis of medical images. Various use cases of the high-performance algorithm on a single node were investigated and compared with sequential and parallel algorithms. The high-performance algorithm achieves a 40x speedup and more under some parameters. Using a high-performance algorithm, analysis and segmentation is performed in less than 1 minute for standard images. The use of a high-performance algorithm for the analysis and segmentation of fundus images avoids the need for a sequential skip-step algorithm, which, due to interpolation, reduces the execution time, but at the same time, accuracy is lost.
Distributed optimization has been well developed in recent years due to its wide applications in machine learning and signal *** this paper,we focus on investigating distributed optimization to minimize a global *** o...
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Distributed optimization has been well developed in recent years due to its wide applications in machine learning and signal *** this paper,we focus on investigating distributed optimization to minimize a global *** objective is a sum of smooth and strongly convex local cost functions which are distributed over an undirected network of n *** contrast to existing works,we apply a distributed heavy-ball term to improve the convergence performance of the proposed *** accelerate the convergence of existing distributed stochastic first-order gradient methods,a momentum term is combined with a gradient-tracking *** is shown that the proposed algorithm has better acceleration ability than GT-SAGA without increasing the *** experiments on real-world datasets verify the effectiveness and correctness of the proposed algorithm.
Clustering very large datasets while preserving cluster quality remains a challenging data-mining task to date. In this paper, we propose an effective scalable clustering algorithm for large datasets that builds upon ...
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Clustering very large datasets while preserving cluster quality remains a challenging data-mining task to date. In this paper, we propose an effective scalable clustering algorithm for large datasets that builds upon the concept of synchronization. Inherited from the powerful concept of synchronization, the proposed algorithm, CIPA (Clustering by Iterative Partitioning and Point Attractor Representations), is capable of handling very large datasets by iteratively partitioning them into thousands of subsets and clustering each subset separately. Using dynamic clustering by synchronization, each subset is then represented by a set of point attractors and outliers. Finally, CIPA identifies the cluster structure of the original dataset by clustering the newly generated dataset consisting of points attractors and outliers from all subsets. We demonstrate that our new scalable clustering approach has several attractive benefits: (a) CIPA faithfully captures the cluster structure of the original data by performing clustering on each separate data iteratively instead of using any sampling or statistical summarization technique. (b) It allows clustering very large datasets efficiently with high cluster quality. (c) CIPA is parallelizable and also suitable for distributed data. Extensive experiments demonstrate the effectiveness and efficiency of our approach.
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