AI-based prediction of academic success: Support for many, disadvantage for some? by Lisa Herrmann, Jonas Weigert

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A gradient-descent-like learning-based framework in surrogate-assisted evolutionary algorithms for expensive many-objective optimization by Chaoyi Sun, Bo Zhang, Hai Sun, Rui Feng

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A Novel Approach to Many-to-Many User Authentication in Different Information Systems by Vitaly Petrov, Maria Komar, Yevgeny Koucheryavy

“…In this paper, we propose a novel approach to many-to-many user authentication in heterogeneous information systems. …”
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How Many People Live in Politically Partisan Online News Echo Chambers in Different Countries? by Richard Fletcher, Craig T. Robertson, Rasmus Kleis Nielsen

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A Gradient-Based Interior-Point Method to Solve the Many-to-Many Assignment Problems by Nitish Das, P. Aruna Priya

“…The many-to-many assignment problem (MMAP) is a recent topic of study in the field of combinatorial optimization. …”
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Optimal Scheduling of Electric Vehicle Integrated Energy Station Using a Novel Many-Objective Stochastic Competitive Optimization Algorithm by Bangli Yin, Xiang Liao, Beibei Qian, Jun Ma, Runjie Lei

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Many-objective Optimization Design of a 2PRU-PRUR Parallel Mechanism Without Parasitic Motion by Che Linxian, Peng Siyang, Huang Xin, He Bing, He Xiaohui, Ao Jin

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Many-Objective Cheetah Optimizer: A Novel Paradigm for Solving Complex Engineering Problems by Pinank Patel, Divya Adalja, Nikunj Mashru, Pradeep Jangir, Arpita, Reena Jangid, G. Gulothungan, Ahmad O. Hourani, Kaznah Alshammari

“…Optimization approaches currently face limitations when trying to balance exploration and exploitation especially when resources become limited. MaOCO represents the Many-Objective Cheetah Optimization Algorithm which draws its concepts from the hunting behavior of cheetahs. …”
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Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis by Jianing Li, Sijia Xu, Jiaming Zheng, Guoqing Jiang, Weichao Ding

“…Large-scale high-dimensional many-objective optimization problems (LaMaOPs) are prevalent in fields such as autonomous driving, cloud resource scheduling, and smart grids. …”
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Analysis of thermoelastic stresses in filament wound composite pressure vessels using evolutionary deep learning and many-objective optimisation by Dominik Vondráček, Zdeněk Padovec, Tomáš Mareš, Nirupam Chakraborti

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Modular quantum signal processing in many variables by Zane M. Rossi, Jack L. Ceroni, Isaac L. Chuang

“…Consequently, as many quantum algorithms have been unified with the advent of quantum signal processing (QSP) and quantum singular value transformation (QSVT), an opportunity has appeared to cast these algorithms as modules that can be combined to constitute complex programs. …”
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A new stochastic multi-objective model for the optimal management of a PV/wind integrated energy system with demand response, P2G, and energy storage devices by Hossein Faramarzi, Navid Ghaffarzadeh, Farhad Shahnia

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Efficient Reasoning upon Fusion of Many Data Sources by Chase Alexander Yakaboski, Eugene Santos

“…With this algorithm, we show how reasoning complexity upon fusion of many sources can be significantly reduced while maintaining the underlying probability semantics of all sources. …”
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Many-body computing on Field Programmable Gate Arrays by Songtai Lv, Yang Liang, Yuchen Meng, Xiaochen Yao, Jincheng Xu, Yang Liu, Qibin Zheng, Haiyuan Zou

“…Here we leverage the capabilities of Field Programmable Gate Arrays for conducting quantum many-body calculations and realize a tenfold speedup over Central Processing Unit-based computation for a Monte Carlo algorithm. …”
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The advantage of quantum control in many-body Hamiltonian learning by Alicja Dutkiewicz, Thomas E. O'Brien, Thomas Schuster

“…With continuous quantum control, we provide an adaptive algorithm for learning a many-body Hamiltonian at the Heisenberg limit: $T = \mathcal{O}(\epsilon^{-1})$, where $T$ is the total amount of time evolution across all experiments and $\epsilon$ is the target precision. …”
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Effective temperature in approximate quantum many-body states by Yu-Qin Chen, Shi-Xin Zhang

“…This study focuses on the spectral decomposition of these approximate quantum many-body states into exact eigenstates of the target Hamiltonian, which reflects the intricate physics at the interplay between quantum systems and numerical algorithms. …”
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Bilateral Matching Decision Model and Calculation of High-Speed Railway Train Crew Members by Wen Li, Yinzhen Li, Rui Xue, Yuxing Jiang, Yu Li

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Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization by Yunfei Yi, ZhiYong Wang, Yunying Shi, Zhengzhuo Song, Binbin Zhao

“…To overcome these challenges, this study proposes a Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization (CDA-MOPSO) algorithm. …”
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An Evolutionary Toolchain for Morphological Filter Mapping on Many-Core Architectures by Emerson C. Pedrino, Denis P. Lima, Igor F. Gallon, Valentin O. Roda, Naijia Liu, Gianluca Tempesti

“…The approach presented in this paper explores a two-phase design-time optimization toolchain based on evolutionary algorithms: a front-end single-objective algorithm decomposes a MM filter using smaller operators, while a back-end multi-objective (fault-tolerance, energy, and communication) algorithm searches for optimal mappings of the filter on a specific many-core system, taking into account the architectural parameters of the hardware. …”
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New Procedures in Pediatric Oncology and Hematology: Too Many Questions by Polina G. Gabay, Sophia A. Dronova

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