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Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm

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arXiv 2024年

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real-time. LLMs centralize traditionally disconnected traffic control processes and can integrate traffic data from diverse sources to provide context-aware decisions. LLMs can also delivere tailored outputs using various means such as wireless signals and visuals to drivers, infrastructures, and autonomous vehicles. To evaluate LLMs ability as traffic controllers, this study proposed a four-stage methodology. The methodology includes data creation and environment initialization, prompt engineering, conflict identification, and fine-tuning. We simulated multi-lane four-leg intersection scenarios and generates detailed datasets to enable conflict detection using LLMs and Python simulation as a ground truth. We used chain-of-thought prompts to lead LLMs in understanding the context, detecting conflicts, resolving them using traffic rules, and delivering context-sensitive traffic management solutions. We evaluated the prformance GPT-mini, Gemini, and Llama as traffic controllers. Results showed that the fine-tuned GPT-mini achieved 83% accuracy and an F1-score of 0.84. GPT-mini model exhibited a promising performance in generating actionable traffic management insights, with high ROUGE-L scores across conflict identification of 0.95, decision-making of 0.91, priority assignment of 0.94, and waiting time optimization of 0.92. This methodology confirmed LLMs' benefits as a traffic controller in real-world applications. We demonstrated that LLMs can offer precise recommendations to drivers in real-time including yielding, slowing, or stopping based on vehicle dynamics. This study demonstrates LLMs transformative potential for traffic control, enhancing efficiency and safety a

关键词： Street traffic control

Protocol to explain support vector machine predictions via exact Shapley value computation

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STAR Protocols 2024年第2期5卷 103010页

作者： Mastropietro, Andrea Bajorath, Jürgen Deparment of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy Department of Life Science Informatics and Data Science B-IT LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 5/6 Bonn 53115 Germany Lamarr Institute for Machine Learning and Artificial Intelligence Friedrich-Hirzebruch-Allee 5/6 Bonn 53115 Germany

Shapley values from cooperative game theory are adapted for explaining machine learning predictions. For large feature sets used in machine learning, Shapley values are approximated. We present a protocol for two techniques for explaining support vector machine predictions with exact Shapley value computation. We detail the application of these algorithms and provide ready-to-use Python scripts and custom code. The final output of the protocol includes quantitative feature analysis and mapping of important features for visualization. For complete details on the use and execution of this protocol, please refer to Feldmann and Bajorath¹ and Mastropietro et al.² © 2024 The Author(s)

关键词： Bioinformatics Chemistry Computer sciences

Structural analysis based on unsupervised learning: Search for a characteristic low-dimensional space by local structures in atomistic simulations

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Physical Review B 2022年第7期105卷 075107-075107页

作者： Ryo Tamura Momo Matsuda Jianbo Lin Yasunori Futamura Tetsuya Sakurai Tsuyoshi Miyazaki International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0044 Japan Graduate School of Frontier Sciences The University of Tokyo Chiba 277-8568 Japan Department of Computer Science University of Tsukuba Tsukuba 305-8573 Japan Center for Artificial Intelligence University of Tsukuba Tsukuba 305-8573 Japan Master's/Doctoral Program in Life Science Innovation University of Tsukuba Tsukuba 305-8577 Japan

Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of data points for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system.

关键词： Amorphous materials Machine learning Molecular dynamics

Brain oscillatory activity evoked by chirp modulated tones in healthy children

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Heliyon 2024年第24期10卷 e40599页

作者： Irazabal, Nadia Inés Urrestarazu, Elena Valencia, Miguel Manzanilla, Oscar Crespo-Eguilaz, Nerea Isaguirre, María Belén Sánchez-Carpintero, Rocío Paediatric Neurology Unit. Department of Paediatrics. Clinica Universidad de Navarra Pamplona Spain Department of Clinical Neurophysiology. Clinica Universidad de Navarra Pamplona Spain Biomedical Engineering Program Universidad de Navarra CIMA Pamplona Spain IdiSNA Instituto de Investigación Sanitaria de Navarra Pamplona Spain Institute of Data Science and Artificial Intelligence Universidad de Navarra Pamplona Spain Statistical Consultant Argentina

Aim: To describe the features of cortical oscillatory activity and neuronal synchronization by monitoring responses evoked by chirp-modulated tone (CMT) and look for relationships with cognition in healthy children. Methods: We recruited 23 healthy children, 1–18 years old, assessed their cognitive abilities and recorded the cortical oscillatory activity evoked by CMTs. We obtained descriptive statistics and looked for correlations with cognitive abilities. Results: In the low gamma band, the neuronal synchronization evoked by CMTs increased with age, reaching adult features by 12 years of age (rho = 0.5;p = 0.042). In the high gamma band, neuronal recruitment was greater at younger ages (rho = −0.55;p = 0.029). In four of the six under-6-year-old participants, there was no CMT-evoked response. We found that the greater a child's receptive vocabulary skills, the lower the frequency at which maximal neuronal recruitment occurred (rho = −0.65;p = 0.003). Interpretation: CMT-evoked cortical oscillatory activity is affected by degree of brain maturation and could be a potential biomarker of language-related disability. Our description of the cortical responses evoked by CMTs in healthy children is a step towards recognition of abnormal patterns and the possible use of this approach as a biomarker of brain disorders in children. © 2024 The Authors

关键词： Brain maturation Brain oscillatory activity Chirp Healthy children Neurodevelopment biomarker

State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis

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arXiv 2023年

作者： Islam, Saiful Khanra, Pitambar Nakuci, Johan Muldoon, Sarah F. Watanabe, Takamitsu Masuda, Naoki Department of Mathematics University at Buffalo State University of New York Buffalo United States Institute for Artificial Intelligence and Data Science University at Buffalo State University of New York Buffalo United States Neuroscience Program University at Buffalo State University of New York Buffalo United States School of Psychology Georgia Institute of Technology United States International Research Centre for Neurointelligence The University of Tokyo Institutes for Advanced Study Tokyo Japan

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of *** Codes 92F05 © 2023, CC BY.

关键词： Dynamics

Energy consumption of high-rise double skin façade buildings, a machine learning analysis

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Journal of Building Engineering 2024年 89卷

作者： Rezaie, Maedeh Kariminia, Shahab Band, Shahab S. Ameri, Rasoul Farokhi, Maryam Pai, Hao-Ting Gocer, Ozgur Rismanchi, Behzad Shooshtarian, Salman Department of Architecture Advancement in Architecture and Urban Planning Research Center Najafabad Branch Islamic Azad University Najafabad Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Information Management National Yunlin University of Science and Technology Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan School of Architecture Design and Planning The University of Sydney SydneyNSW Australia Renewable Energy and Energy Efficiency Group Department of Infrastructure Engineering Faculty of Engineering and Information Technology The University of Melbourne Australia School of Property Construction and Project Management RMIT University Melbourne Australia

Most energy exchanges take place through the building skin. The skin characteristics play a decisive role in the extent of these exchanges, but they are somewhat more varied in the double skin façade (DSF). Among these characteristics, cavity segmentation has a noticeable effect on the implementation of the DSF in different directions during the hot and cold seasons. The aim of this study was to investigate the role of DSF segmentation in energy consumption and natural ventilation of high-rise buildings in hot and dry climates. This study used DesignBuilder software to study sixty-four segmentation component scenarios in an eight-story residential building in Isfahan, Iran. Moreover, a proposed hybrid model utilizing the hybridization of the Hunger Game Search and Gradient Boosting (HGS-GB) algorithm was employed to estimate energy consumption in various scenarios involving lighting, heating, cooling, and total scenarios. The available outcomes revealed that the HGS-GB model had a better performance in comparison with other individual models, such as Gradient Boosting (GB), Random Forest (RF), and K-nearest neighbors (KNN). The R2 values for lighting, heating, cooling, and total energy estimation were 0.9993, 0.9958, 0.9991, and 0.9922, respectively. The findings of this study suggest the significance of DSF segmentation in energy consumption and natural ventilation in high-rise buildings in hot and dry climates. © 2024 Elsevier Ltd

关键词： Forestry

Retrieval-Augmented Generation for Generative artificial intelligence in Medicine

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arXiv 2024年

作者： Yang, Rui Ning, Yilin Keppo, Emilia Liu, Mingxuan Hong, Chuan Bitterman, Danielle S. Ong, Jasmine Chiat Ling Ting, Daniel Shu Wei Liu, Nan Center for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Faculty of Arts and Science University of Toronto TorontoON Canada Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Division of Pharmacy Singapore General Hospital Singapore Singapore Singapore Eye Research Institute Singapore National Eye Center Singapore Singapore Byers Eye Institute Stanford University StanfordCA United States Program in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling models to generate more accurate contents by leveraging the retrieval of external knowledge. With the rapid advancement of generative AI, RAG can pave the way for connecting this transformative technology with medical applications and is expected to bring innovations in equity, reliability, and personalization to health care. © 2024, CC BY.

关键词： Medical applications

Temporal networks provide a unifying understanding of the evolution of cooperation

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arXiv 2023年

作者： Li, Aming Meng, Yao Zhou, Lei Masuda, Naoki Wang, Long Center for Systems and Control College of Engineering Peking University Beijing100871 China Center for Multi-Agent Research Institute for Artificial Intelligence Peking University Beijing100871 China School of Automation Beijing Institute of Technology Beijing100081 China Department of Mathematics State University of New York at Buffalo BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260 United States

Understanding the evolution of cooperation in structured populations represented by networks is a problem of long research interest, and a most fundamental and widespread property of social networks related to cooperation phenomena is that the node’s degree (i.e., number of edges connected to the node) is heterogeneously distributed. Previous results indicate that static heterogeneous (i.e., degree-heterogeneous) networks promote cooperation in stationarity compared to static regular (i.e., degree-homogeneous) networks if equilibrium dynamics starting from many cooperators and defectors is employed. However, the above conclusion reverses if we employ non-equilibrium stochastic processes to measure the fixation probability for cooperation, i.e., the probability that a single cooperator successfully invades a population. Here we resolve this conundrum by analyzing the fixation of cooperation on temporal (i.e., time-varying) networks. We theoretically prove and numerically confirm that on both synthetic and empirical networks, contrary to the case of static networks, temporal heterogeneous networks can promote cooperation more than temporal regular networks in terms of the fixation probability of cooperation. Given that the same conclusion is known for the equilibrium fraction of cooperators on temporal networks, the present results provide a unified understanding of the effect of temporal degree heterogeneity on promoting cooperation across two main analytical frameworks, i.e., equilibrium and non-equilibrium ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Heterogeneous networks

Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study

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Journal of medical Internet research 2023年 25卷 e48834页

作者： Chen, Hung-Hsun Lu, Henry Horng-Shing Weng, Wei-Hung Lin, Yu-Hsuan Department of Mathematics Fu Jen Catholic University New Taipei City Taiwan Program of Artificial Intelligence & Information Security Fu-Jen Catholic University New Taipei City Taiwan Institute of Statistics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Statistics and Data Science Cornell University Ithaca NY United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MN United States Institute of Population Health Sciences National Health Research Institutes Miaoli County Taiwan Department of Psychiatry National Taiwan University Hospital Taipei Taiwan Department of Psychiatry College of Medicine National Taiwan University Taipei Taiwan

BACKGROUND: Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work. OBJECTIVE: In this study, we aimed to develop a novel approach called "probability in work mode," which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours. METHODS: To capture human-smartphone interactions and GPS locations, we used the "Staff Hours" app, developed by our team, to passively and continuously record participants' screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work. RESULTS: Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day;P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes. CONCLUSIONS: Our

关键词： deep learning digital phenotyping extreme gradient-boosted trees human-smartphone interaction machine learning one-dimensional convolutional neural network probability in work mode work hours