Retentive Time Series: A Scalable Machine Learning Model for Traffic Prediction in Elastic Optical Networks by Faranak Khosravi, Mehdi Shadaram

“…Compared to RNNs, which successively consider data and always suffer from problematic vanishing gradients, Ret-TS can handle long-term dependencies efficiently through parallel computation; hence, it is more suitable for large-scale and real-time applications. …”
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Vectorized Highly Parallel Density-Based Clustering for Applications With Noise by Joseph Arnold Xavier, Juan Pedro Gutierrez Hermosillo Muriedas, Stepan Nassyr, Rocco Sedona, Markus Gotz, Achim Streit, Morris Riedel, Gabriele Cavallaro

“…Our proposed Vectorized HPDBSCAN (VHPDBSCAN) demonstrates a performance improvement of up to two times over the state-of-the-art parallel version, Highly Parallel DBSCAN (HPDBSCAN), on the ARM-based A64FX processor on two different datasets with varying dimensions. We have parallelized computations which are essential for the efficient workload distribution. …”
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An enhanced fruit fly optimization algorithm with random spare and double adaptive weight strategies for oil and gas production optimization by Xu Wang, Jingfu Shan

“…As a promising alternative, evolutionary algorithms—rooted in the principles of natural selection—have demonstrated strong potential for addressing complex optimization problems due to their gradient-free nature and inherent suitability for parallel computation. In this study, we propose an enhanced evolutionary algorithm tailored for global optimization and oil and gas production improvement. …”
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An efficient parallelization technique for the coupled problems of fluid, gas and plasma mechanics in the grid environment by Andrii Zinchenko, Unai Fernandez-Gamiz, Dmytro Redchyts, Olga Gorna, Tetiana Bilousova

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Comparison and general law research of multiple machine-learning models for proton exchange membrane electrolytic cell parameters prediction by Yukun Wang, Hai-Wen Li, Wenhan An, Yudong Mao, Kaimin Yang, Jiying Liu

“…RF algorithm (an ensemble learning framework) and GA-BP (a model integrating global search and parallel computation mechanisms) are identified as the optimal prediction models, with determination coefficients of 0.995, 0.992, 0.992 and 0.996, 0.997, 0.993 for the three indicators, respectively. …”
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Holographic Renyi entropy of 2d CFT in KdV generalized ensemble by Liangyu Chen, Anatoly Dymarsky, Jia Tian, Huajia Wang

“…In this paper, we carry out parallel computations in the context of AdS/CFT. We focus on the high density limit, which is equivalent to thermodynamic limit in conformal theories. …”
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Efficient Representative Volume Element of a Matrix–Precipitate Microstructure—Application on AlSi10Mg Alloy by Chantal Bouffioux, Luc Papeleux, Mathieu Calvat, Hoang-Son Tran, Fan Chen, Jean-Philippe Ponthot, Laurent Duchêne, Anne Marie Habraken

“…Since a 2.5D RVE simplifies one spatial dimension, the simulations with this model are faster than the 3D RVE (factor 2580 in CPU or taking into account an optimal parallel computation, a factor 417 in real time). Such a discrepancy can affect the FEM² multi-scale simulations or the time required to train a neural network, enhancing the interest in a 2.5D RVE model.…”
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Distributed Heterogeneous Spiking Neural Network Simulator Using Sunway Accelerators by Xuelei Li, Zhichao Wang, Yi Pan, Jintao Meng, Shengzhong Feng, Yanjie Wei

“…Besides, SWsnn relies on parallel Compute Processing Elements (CPEs) rather than serial Manage Processing Element (MPE) to control the communicating buffers, using Register-Level Communication (RLC) and Direct Memory Access (DMA). …”
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A Bidirectional Gated Recurrent Unit and Temporal Convolutional Network With a Self-Attention Mechanism to Improve Traffic Flow Prediction Performance by Yingying Liu, Jing Gu, Xiaoxuan Qi

“…The BiGRU captures bidirectional temporal dependencies, while the TCN enhances training efficiency and models long-sequence dependencies through parallel computation. The self-attention mechanism further improves the model’s ability to capture long-term dependencies, enhancing overall prediction performance. …”
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Pseudorandom Function from Learning Burnside Problem by Dhiraj K. Pandey, Antonio R. Nicolosi

“…Additionally, we explore algorithmic improvements and parallel computation strategies to improve efficiency.…”
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Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy Fruits by Yun Zhao, Yang Li, Xing Xu

“…We also introduced a non-maximal suppression algorithm, Cluster-NMF, which accelerates inference speed through matrix parallel computation and merges multiple high-quality target detection frames to generate an optimal detection frame, enhancing the efficiency of blueberry canopy fruit detection without compromising inference speed.…”
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TDNN achitecture with efficient channel attention and improved residual blocks for accurate speaker recognition by Wenzao Li, Sai Yao, Bing Wan, Linsong Xiao, Chengyu Hou, Yanchuan Zhong, Wengang Zhou

“…To further reduce feature dependency and enhance multi-scale information fusion, a Parallel Residual Structure (PRS) is introduced, enabling the independent capture of multi-scale features through parallel computation instead of sequential processing. The ECA_block adopts the output structure of ECAPA-TDNN, Calling it a Tandem Structure (TS). …”
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A wave-resolving two-dimensional vertical Lagrangian approach to model microplastic transport in nearshore waters based on TrackMPD 3.0 by I. Jalón-Rojas, D. Sous, D. Sous, V. Marieu

“…This approach introduces novel features such as coupling with advanced turbulence models, simulating resuspension and bedload processes, implementing advanced settling and rising velocity formulations, and enabling parallel computation. The wave laboratory experiments conducted by <span class="cit" id="xref_text.1"><a href="#bib1.bibx19">Forsberg et al.…”
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Acute ischaemia and gap junction modulation modify propagation patterns across Purkinje-myocardial junctions by Richard J. Jabbour, Elham Behradfar, Elham Behradfar, Michael Debney, Anders Nygren, Anders Nygren, Adam Hartley, Igor Efimov, Mélèze Hocini, Mélèze Hocini, Nicholas S. Peters, Fu Siong Ng, Edward J. Vigmond, Edward J. Vigmond, Edward J. Vigmond

“…Gap junction enhancement with rotigaptide during ischaemia abolished the aforementioned pattern. Parallel computational experiments replicated experimental findings only when the number of functional PMJs was increased during ischaemia. …”
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Embedded Hardware-Efficient FPGA Architecture for SVM Learning and Inference by B. B. Shabarinath, Muralidhar Pullakandam

“…In this paper, we propose Parallel SMO, a new algorithm that selects multiple violating pairs in each iteration, allowing batch-wise updates that enhance convergence speed and optimize parallel computation. By buffering kernel values, it minimizes redundant computations, leading to improved memory efficiency and faster SVM training on FPGA architectures. …”
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High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks by Hong Zhang, Zhikang Lu, Peicong Gong, Shilong Zhang, Xiaoquan Yang, Xiangning Li, Zhao Feng, Anan Li, Chi Xiao

“…Furthermore, we streamline the entire processing workflow by developing an automated pipeline optimized for cluster-based message passing interface(MPI) parallel computation, which reduces the processing time for a mouse brain dataset to a mere 1.1 h, enhancing manual efficiency by 25 times and overall data processing efficiency by 2.4 times, paving the way for enhancing the efficiency of big data processing and parsing for high-throughput mesoscopic optical imaging techniques.…”
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Jacobian-Free Newton Krylov Methods for Steady and Transient Neutron Transport Models by ZHANG Yangyi, ZHANG Tiancheng, ZHOU Xiafeng

“…The comeSn_JFNK solver integrated the parallel discrete ordinate (SN) neutron transport code comeSn into the parallel computational framework comeJFNK of Jacobian-Free Newton Krylov (JFNK). …”