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arXiv 2025年

作者： Cheng, Fengxiang Li, Haoxuan Liu, Fenrong van Rooij, Robert Zhang, Kun Lin, Zhouchen Institute for Logic Language and Computation University of Amsterdam Netherlands Center for Data Science Peking University China Machine Learning Department MBZUAI Department of Philosophy Tsinghua University China Department of Philosophy CMU United States Institute for Artificial Intelligence Peking University China Peng Cheng Laboratory China National Key Lab of General AI School of Intelligence Science and Technology Peking University China

Large language models (LLMs) have achieved remarkable successes on various natural language tasks. However, recent studies have found that there are still significant challenges to the logical reasoning abilities of LLMs. This paper summarizes and categorizes the main challenges into two aspects: (1) Logical question answering, LLMs often fail to generate the correct answer within complex logical problem which requires sophisticated deductive, inductive or abductive reasoning given a collection of premises and constrains. (2) Logical consistency, LLMs are prone to producing responses contradicting themselves across different questions. For example, a state-of-the-art Macaw question-answering LLM answers Yes to both questions Is a magpie a bird? and Does a bird have wings? but answers No to Does a magpie have wings?. To facilitate this research direction, we comprehensively investigate the most cutting-edge methods and propose detailed taxonomies of these methods. Specifically, to accurately answer complex logic questions, previous methods can be categorized based on reliance on external solvers, prompts, pretraining, and fine-tuning. To avoid logical contradictions, we discuss concepts and solutions of various logical consistencies, including implication, negation, transitivity, factuality consistency, and their composites. In addition, we review commonly used benchmark datasets and evaluation metrics, and discuss promising research directions, such as extensions to modal logic to account for uncertainty, and efficient algorithms satisfying multiple logical consistencies simultaneously. © 2025, CC BY.

关键词： Birds

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Review of machine learning and Deep learning algorithms for Personality traits classification

Review of Machine Learning and Deep Learning algorithms for ...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Satish N Bhadane Prateek Verma Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Nowadays, people deal with major psychological issues on a daily basis, often in the absence of a desired support system. In the field of psychology, focus is on the thoughts, feelings, behaviors, goals, and interests of individuals. The psychologist conventionally considers various types of personality traits, like the Big 5 personality traits, the Myers-Briggs Type Indicator (MBTI), the OCEAN Model (Openness, Conscientiousness, Agreeableness, Extraversion, Neuroticism), and other methods also considered for measuring personality. To forecast personality traits like extraversion, agreeableness, openness, conscientiousness, and neuroticism, consider the name "Big Five personality traits" It is a universal model to forecast these personality traits. Studies on personality prediction that identify personality disorders from user content on social media sites like Facebook, Twitter, and WhatsApp primarily rely on user behavior on these platforms. A number of popular Deep learning (DL) techniques have advanced recently, including Deep Belief Networks (DBNs), Convolution Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Examine these methods in terms of single and multiple modalities, including psychological, audio, video, text, and image information. Researchers currently focus on predicting personality traits using single-modality text features, static facial images, audio, and video clips, including interview recordings and YouTube videos. Therefore, the current study highlights that personality prediction mostly relies on written text, audio and video datasets used in machine learning (ML) and DL models. Additional investigation is needed to resolve ethical concerns and justify the use of psychological datasets and DL models to evaluate personality traits. By combining personality traits with a psychological dataset, this study imagines a time when people everyday struggles can be effectively addressed. The integration of ML and DL in the development of p

关键词： Industries Deep learning Video on demand Recurrent neural networks Social networking (online) Reviews Psychology Sea measurements Predictive models Web sites

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Making all pairwise comparisons in multi-arm clinical trials without control treatment

arXiv

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arXiv 2024年

作者： Burnett, T. Jaki, T. Department of Mathematical Sciences University of Bath BathBA2 7AY United Kingdom MRC Biostatistics Unit University of Cambridge CambridgeCB2 0SR United Kingdom Department of Machine Learning and Data Science University of Regensburg Regensburg Germany

The standard paradigm for confirmatory clinical trials is to compare experimental treatments with a control, for example the standard of care or a placebo. However, it is not always the case that a suitable control exists. Efficient statistical methodology is well studied in the setting of randomised controlled trials. This is not the case if one wishes to compare several experimental with no control arm. We propose hypothesis testing methods suitable for use in such a setting. These methods are efficient, ensuring the error rate is controlled at exactly the desired rate with no conservatism. This in turn yields an improvement in power when compared with standard methods one might otherwise consider using, such as a Bonferroni adjustment. The proposed testing procedure is also highly flexible. We show how it may be extended for use in multi-stage adaptive trials, covering the majority of scenarios in which one might consider the use of such procedures in the clinical trials setting. With such a highly flexible nature, these methods may also be applied more broadly outside of a clinical trials setting. Copyright © 2024, The Authors. All rights reserved.

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The Future of Driving: A Review on the Combination of Artificial Intelligence and Autonomous Vehicles

The Future of Driving: A Review on the Combination of Artifi...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Nikita Zade Mayur Gawande Prateek Verma Swapnil Gundewar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The convergence of Artificial Intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and machine learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability.

关键词： Ethics machine learning algorithms Reviews Navigation Decision making Sensor fusion Safety Artificial intelligence Socioeconomics Automotive engineering

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Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

arXiv

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arXiv 2024年

作者： Liang, Weixin Izzo, Zachary Zhang, Yaohui Lepp, Haley Cao, Hancheng Zhao, Xuandong Chen, Lingjiao Ye, Haotian Liu, Sheng Huang, Zhi McFarland, Daniel A. Zou, James Y. Department of Computer Science Stanford University United States Machine Learning Department NEC Labs America United States Department of Electrical Engineering Stanford University United States Graduate School of Education Stanford University United States Department of Management Science and Engineering Stanford University United States Department of Computer Science UC Santa Barbara United States Department of Biomedical Data Science Stanford University United States Department of Sociology Stanford University United States Graduate School of Business Stanford University United States

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood

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Deep-learning-enabled multimodal data fusion for lung disease classification

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Informatics in Medicine Unlocked 2023年 42卷

作者： Kumar, Sachin Ivanova, Olga Melyokhin, Artyom Tiwari, Prayag Big Data and Machine Learning Laboratory South Ural State University Chelyabinsk Russian Federation Computer Science Department South Ural State University Chelyabinsk Russian Federation Department of Computer Science Aalto University Espoo Finland

The recent pandemic has revealed the urgent need for lung disease diagnosis at early stages in humans. Deep learning-based automatic diagnosis methods typically rely on single-modality data such as medical imaging. However, analysis of single modality data is not so reliable to diagnose the disease at its early stages. Clinical data, blood tests together with imaging methods are very powerful and reliable sources to detect the presence of disease in the human body. This study attempts to use medical imaging data with clinical information to develop a multimodal fusion approach to detect lung disease. Two architectures of multimodal network based on late and intermediate fusion is proposed. Besides, an approach of adapting batch size is also introduced. Experiments show that the performance of intermediate fusion is better than the late fusion model with both direct and adaptive batch size approach. © 2023 The Authors

关键词： Clinical data Deep learning Lung disease Medical imaging Multimodal fusion

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Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis

arXiv

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arXiv 2022年

作者： Herold, Maternus Veselovska, Anna Jehle, Jonas Krahmer, Felix Department of Mathematics Technical University of Munich Germany Bmw Group Germany Munich Data Science Institute Germany Munich Center for Machine Learning Germany

Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthetic and real data obtained from crashworthiness analyses. The results show a superiority of the here described approach over state of the art surrogate modelling techniques, Polynomial Chaos Expansions and Neural Networks. © 2022, CC BY-NC-ND.

关键词： Crashworthiness

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Ai-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management 22

Ai-Driven Automated Tool for Abdominal CT Body Composition A...

引用

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Nan, Xinyu He, Meng Chen, Zifan Dong, Bin Tang, Lei Zhang, Li Peking University Center for Data Science China Peking University Cancer Hospital and Institute Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Department of Radiology Beijing China Peking University Beijing International Center for Mathematical Research (BICMR) Beijing China Peking University Center for Machine Learning Research Beijing China Peking University National Biomedical Imaging Center Beijing China

ISBN: (纸本)9798331520526

The incidence of gastrointestinal cancers remains significantly high, particularly in China, emphasizing the importance of accurate prognostic assessments and effective treatment strategies. Research shows a strong correlation between abdominal muscle and fat tissue composition and patient outcomes. However, existing manual methods for analyzing abdominal tissue composition are time-consuming and costly, limiting clinical research scalability. To address these challenges, we developed an AI-driven tool for automated analysis of abdominal CT scans to effectively identify and segment muscle, subcutaneous fat, and visceral fat. Our tool integrates a multi-view localization model and a high-precision 2D nnUNet-based segmentation model, demonstrating a localization accuracy of 90% and a Dice Score Coefficient of 0.967 for segmentation. Furthermore, it features an interactive interface that allows clinicians to refine the segmentation results, ensuring high-quality outcomes effectively. Our tool offers a standardized method for effectively extracting critical abdominal tissues, potentially enhancing the management and treatment for gastrointestinal cancers. The code is available at https://***/NanXinyu/***. © 2025 IEEE.

关键词： Abdominal CT analysis AI-driven tool body composition analysis gastrointestinal cancer management multi-view localization

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Constraining Protoplanetary Disk Winds from Forbidden Line Profiles with Simulation-based Inference

arXiv

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arXiv 2024年

作者： Nemer, Ahmad Hahn, ChangHoon Li, Jiaxuan Melchior, Peter Goodman, Jeremy Center for Astrophysics and Space Science New York University Abu Dhabi United Arab Emirates Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Center for Statistics and Machine Learning Princeton University PrincetonNJ08544 United States

Protoplanetary disks are the sites of vigorous hydrodynamic processes, such as accretion and outflows, and ultimately establish the conditions for the formation of planets. The properties of disk outflows are often inferred through analysis of forbidden emission lines. These lines contain multiple overlapping components, tracing different emission regions with different processes that excite them: a high-velocity component (tracing a jet), a broad low-velocity component (tracing inner disk wind), and a narrow low-velocity component (tracing outer disk wind). They are also heavily contaminated by background spectral features. All of these challenges call into question the traditional approach of fitting Gaussian components to the line profiles, and cloud the physical interpretation of those components. We introduce a novel statistical technique to analyze emission lines in protoplanetary disks. Simulation-Based Inference is a computationally efficient machine learning technique that produces posterior distributions of the parameters (e.g. magnetic field, radiation sources, geometry) of a representative wind model when given a spectrum, without any prior assumption about line shapes (e.g. symmetry). In this pathfinder study, we demonstrate that this technique indeed accurately recovers the parameters from simulated spectra without noise and background. A following work will deal with the analysis of observed spectra. Copyright © 2024, The Authors. All rights reserved.

关键词： machine learning

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MAKING THERMODYNAMIC MODELS OF MIXTURES PREDICTIVE BY machine learning: MATRIX COMPLETION OF PAIR INTERACTIONS

arXiv

引用

arXiv 2022年

作者： Jirasek, Fabian Bamler, Robert Fellenz, Sophie Bortz, Michael Kloft, Marius Mandt, Stephan Hasse, Hans TU Kaiserslautern Kaiserslautern Germany Data Science and Machine Learning University of Tübingen Tübingen Germany Machine Learning Group TU Kaiserslautern Kaiserslautern Germany Kaiserslautern Germany Department of Computer Science University of California Irvine Irvine United States

Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On the other hand, matrix completion methods (MCMs) from machine learning successfully generalize over (discrete) binary systems;these MCMs can make predictions without any data for a given binary system by implicitly learning commonalities across systems. In the present work, we combine the strengths from both worlds in a hybrid approach. The underlying idea is to predict the pair-interaction energies, as they are used in basically all physical models of liquid mixtures, by an MCM. As an example, we embed an MCM into UNIQUAC, a widely-used physical model for the Gibbs excess energy. We train the resulting hybrid model in a Bayesian machine-learning framework on experimental data for activity coefficients in binary systems of 1,146 components from the Dortmund data Bank. We thereby obtain, for the first time, a complete set of UNIQUAC parameters for all binary systems of these components, which allows us to predict, in principle, activity coefficients at arbitrary temperature and composition for any combination of these components, not only for binary but also for multi-component systems. The hybrid model even outperforms the best available physical model for predicting activity coefficients, the modified UNIFAC (Dortmund) model. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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