arXiv:2511.17850v1 Announce Type: cross 
Abstract: We demonstrate that multipoint Bayesian algorithm execution can overcome fundamental computational challenges in storage ring design optimization. Dynamic (DA) and momentum (MA) optimization is a multipoint, multiobjective design task for storage rings, ultimately informing the flux of x-ray sources and luminosity of colliders. Current state-of-art black-box optimization methods require extensive particle-tracking simulations for each trial configuration; the high computational cost restricts the extent of the search to $\sim 10^3$ configurations, and therefore limits the quality of the final design. We remove this bottleneck using multipointBAX, which selects, simulates, and models each trial configuration at the single particle level. We demonstrate our approach on a novel design for a fourth-generation light source, with neural-network powered multipointBAX achieving equivalent Pareto front results using more than two orders of magnitude fewer tracking computations compared to genetic algorithms. The significant reduction in cost positions multipointBAX as a promising alternative to black-box optimization, and we anticipate multipointBAX will be instrumental in the design of future light sources, colliders, and large-scale scientific facilities.

تم إثبات أن تنفيذ الخوارزمية البايزية متعددة النقاط هو حل فعال للتغلب على التحديات الحسابية في تحسين تصميم حلقات التخزين، وخاصة في تحسين فتحة الديناميكية وفتحة الزخم. تتيح هذه الطريقة اختيار ومحاكاة ونمذجة تكوينات التجربة على مستوى الجسيم الفردي، مما يعزز بشكل كبير عملية التصميم لمصادر الضوء من الجيل الرابع.

Se ha demostrado que la ejecución del algoritmo bayesiano multipunto es una solución eficaz para superar los desafíos computacionales en la optimización del diseño de anillos de almacenamiento, especialmente en la optimización de la apertura dinámica y de momento. Este método permite la selección, simulación y modelado de configuraciones de prueba a nivel de una sola partícula, mejorando significativamente el proceso de diseño para fuentes de luz de cuarta generación.

L'exécution de l'algorithme bayésien multipoint a été démontrée comme une solution efficace pour surmonter les défis computationnels dans l'optimisation de la conception des anneaux de stockage, en particulier dans l'optimisation de l'aperture dynamique et de l'aperture de moment. Cette méthode permet la sélection, la simulation et la modélisation des configurations d'essai au niveau d'une seule particule, améliorant ainsi considérablement le processus de conception pour les sources lumineuses de quatrième génération.

The multipoint Bayesian algorithm execution has been demonstrated as an effective solution for overcoming computational challenges in storage ring design optimization, particularly in dynamic and momentum aperture optimization. This method allows for the selection, simulation, and modeling of trial configurations at the single particle level, significantly enhancing the design process for fourth-generation light sources.

Efficient Dynamic and Momentum Aperture Optimization for Lattice Design Using Multipoint Bayesian Algorithm Execution

arXiv:2511.18827v1 Announce Type: new 
Abstract: Despite being among the most common psychological disorders, anxiety-related conditions are still primarily identified through subjective assessments, such as clinical interviews and self-evaluation questionnaires. These conventional methods often require significant time and may vary depending on the evaluator. However, the emergence of advanced artificial intelligence techniques has created new opportunities for detecting anxiety in a more consistent and automated manner. To address the limitations of traditional approaches, this study introduces a comprehensive model that integrates deep learning architectures with optimization strategies inspired by swarm intelligence. Using multimodal and wearable-sensor datasets, the framework analyzes physiological, emotional, and behavioral signals. Swarm intelligence techniques including genetic algorithms and particle swarm optimization are incorporated to refine the feature space and optimize hyperparameters. Meanwhile, deep learning components are tasked with deriving layered and discriminative representations from sequential, multi-source inputs. Our evaluation shows that the fusion of these two computational paradigms significantly enhances detection performance compared with using deep networks alone. The hybrid model achieves notable improvements in accuracy and demonstrates stronger generalization across various individuals. Overall, the results highlight the potential of combining metaheuristic optimization with deep learning to develop scalable, objective, and clinically meaningful solutions for assessing anxiety disorders

قدمت دراسة جديدة نموذجًا يجمع بين أنظمة التعلم العميق والأساليب الميتاهيرستية، مستهدفةً بشكل خاص الكشف عن اضطرابات القلق. يستخدم هذا النموذج تقنيات متقدمة من الذكاء الاصطناعي لتحليل مجموعات بيانات متعددة الوسائط، بما في ذلك الإشارات الفسيولوجية والسلوكية، متجاوزًا بذلك التقييمات الذاتية التقليدية التي يمكن أن تختلف بشكل كبير بين المقيمين.

Un nuevo estudio ha presentado un modelo que combina sistemas de aprendizaje profundo con enfoques metaheurísticos, dirigido específicamente a la detección de trastornos de ansiedad. Este modelo utiliza técnicas avanzadas de inteligencia artificial para analizar conjuntos de datos multimodales, incluidos señales fisiológicas y conductuales, superando así las evaluaciones subjetivas tradicionales que pueden variar significativamente entre evaluadores.

Une nouvelle étude a introduit un modèle qui combine des systèmes d'apprentissage profond avec des approches métaheuristiques, ciblant spécifiquement la détection des troubles anxieux. Ce modèle utilise des techniques avancées d'intelligence artificielle pour analyser des ensembles de données multimodales, y compris des signaux physiologiques et comportementaux, dépassant ainsi les évaluations subjectives traditionnelles qui peuvent varier considérablement d'un évaluateur à l'autre.

A new study has introduced a model that combines deep learning systems with metaheuristic approaches, specifically targeting the detection of anxiety disorders. This model utilizes advanced artificial intelligence techniques to analyze multimodal datasets, including physiological and behavioral signals, thereby moving beyond traditional subjective assessments that can vary significantly between evaluators.

Efficient Dynamic and Momentum Aperture Optimization for Lattice Design Using Multipoint Bayesian Algorithm Execution

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