A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X) by Liu Yang, Jiang Guofan, Zhang Yixin, Wei Qianze, Zhang Jian, Roohallah Alizadehsani, Pawel Plawiak

“…The scope loss function ensures an optimal balance between utilizing known data and exploring new data, thus maintaining a delicate equilibrium between accuracy and generalizability. …”
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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors by Adonisz Dimitriu, Tamás Vilmos Michaletzky, Viktor Remeli

“…Adopting Unreal Engine 5, TACO integrates differentiable rendering with a Photorealistic Rendering Network to optimize adversarial textures targeted at YOLOv8. To ensure the generated textures are both effective in deceiving detectors and visually plausible, we introduce the Convolutional Smooth Loss function, a generalized smooth loss function. …”
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Improving Airport Flight Prediction System Based on Optimized Regression Vector Machine Algorithm by Baraa Yousif Salman, Jaber Parchami

“…In this research, the optimized support vector regression (SVR) algorithm has been used to improve the accuracy of air delay prediction. …”
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Building Construction Design Based on Particle Swarm Optimization Algorithm by Wenxue Song

“…When the constraint cost was 320,000 yuan, the global optimal risk loss and global optimal control cost were 910,100 yuan and 317,300, yuan respectively. …”
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Topology-optimized silicon-based dual-mode 4 × 4 electro-optic switch by Niu Jiaqi, Yang Shanglin, Zhou Ting, Jia Hao, Fu Xin, Zhao Zhizun, Li Zhen, Zhang Gaolu, Chen Changhua, Yang Lin

“…Also, 50 Gbps data transmission experiments verify the device’s data transmission functionality.…”
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Bayesian optimized deep learning and ensemble classification approach for multiclass plant disease identification by Silpa Padmanabhuni, Pradeepini Gera

“…The architecture involves freezing specific layers within Inception v3 to retain essential low-level features while adapting high-level features for the target domain. Bayesian optimization is used to identify and combine optimal activation functions, enhancing the network's capacity to learn complex disease patterns from tomato leaf images. …”
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The Multiobjective Control Based on Tolerance Optimization in a Multienergy System by Suliang Ma, Yaxin Li, Yuan Jiang, Yiwen Wu, Guanglin Sha

“…In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. …”
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Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization by Patcharin Kamsing, Chunxiang Cao, You Zhao, Wuttichai Boonpook, Lalida Tantiparimongkol, Pisit Boonsrimuang

“…The objective function of PSO is the training function of the implemented network. …”
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Optimization of Intelligent Distribution of Distribution Network in the Presence of Distributed Sources by Youle Song, Ding Nie, Litao Fan

“…Moreover, this study establishes an objective function composed of system network loss expectation, voltage stability index, and reactive power compensation equipment investment. …”
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Tooth position prediction method based on adaptive geometry optimization. by Tian Ma, Yijie Zeng, Wenda Pei, Chao Li, Yuancheng Li

“…Finally, a jointly supervised loss function is constructed, which can simultaneously capture the intrinsic differences, spatial relationships and uncertainties of the tooth position prediction disorder distribution, and can effectively supervise the tooth spatial structure relationships and prevent tooth collisions and misalignments. …”
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Optimal Health Insurance and Trade-Off between Health and Wealth by Yan Zhang, Yonghong Wu

“…First, compared with property insurance, health insurance provides valuable hedge against unpredictable shocks to health status, instead of loss on property. Therefore, a modified utility function that describes the trade-off between health and wealth should be applied in optimal indemnity design. …”
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Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter by Andrew Lowy, Meisam Razaviyayn

“… We study differentially private (DP) stochastic optimization (SO) with loss functions whose worst-case Lipschitz parameter over all data points may be huge or infinite. …”
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Bibliometric Analysis of Optimal Scheme Selection for Water Conservancy Projects by ZHANG Boyang

“…Water conservancy projects are closely related to people's livelihood.This study aims to systematically analyze the current situation of research on optimal scheme selection for water conservancy projects and explore the related research methodology.To achieve these goals,we make a bibliometric analysis based on the literature on optimal scheme selection for water conservancy projects which is included in China National Knowledge Infrastructure (CNKI),a hotspot clustering analysis with the help of the visual analysis function of VOSviewer,and an analysis of the number of published papers depending on the annual loss statistics of flood disasters in China.Cluster analysis includes two aspects:co-authorship in China and keyword co-occurrence. …”
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Optimizing dental implant design: Structure, strength, and bone ingrowth by Jenny Zwei-Chieng Chang, Jui-Ting Hsu, Ming-Jun Li, Hung-Ying Lin, Jason Sun, Nien-Ti Tsou, Jui-Sheng Sun

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Optimizing physical education schedules for long-term health benefits by Liang Tan, Qin Chen, Jianwei Wu, Mingbang Li, Tianyu Liu

“…These features are combined through a fusion layer, and a customized loss function is employed to accurately predict fitness scores.ResultsExtensive experimental evaluation demonstrates that the proposed model consistently outperforms competitive baseline models. …”
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Loss of β2-integrin function results in metabolic reprogramming of dendritic cells, leading to increased dendritic cell functionality and anti-tumor responses by Heidi Harjunpää, Riku Somermäki, Guillem Saldo Rubio, Manlio Fusciello, Sara Feola, Imrul Faisal, Anni I Nieminen, Liang Wang, Marc Llort Asens, Hongxia Zhao, Ove Eriksson, Vincenzo Cerullo, Susanna C Fagerholm

“…DC-based immunotherapies are used in cancer treatment, but their functionality is not optimized and their clinical efficacy is currently limited. …”
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Optimization of Adversarial Reprogramming for Transfer Learning on Closed Box Models by Alexander Bott, Moritz Siems, Alexander Puchta, Jurgen Fleischer

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Multiobjective Optimization of a Rotman Lens through the QLWS Minimization by Daniele Pinchera, Stefano Perna, Marco Donald Migliore

“…We address the multiobjective optimization of a Rotman lens by means of a recently proposed method based on the minimization of a properly defined global cost function named Quantized Lexicographic Weighted Sum (QLWS). …”
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Bridging prediction and decision: Advances and challenges in data-driven optimization by Yanzhi Wang, Jianxiao Wang, Haoran Zhang, Jie Song

“…Notably, we discuss breakthroughs such as implicit differentiation techniques, surrogate loss functions, and perturbation methods, which provide methodological guidance for achieving data-driven decision-making through prediction. …”
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