Author: Jack Smith

Authors: Pengkun Wei、Shuo Cheng、Dayou Li、Ran Song、Yipeng Zhang、Wei Zhang Paper: https://arxiv.org/abs/2408.10710 Introduction Background The automation of welding processes has become increasingly significant in industrial applications, driven by the need for efficiency and precision. Welding robots, as reported by the International Federation of Robotics (IFR), hold the second-largest market share among industrial robots globally. Despite advancements in in-welding monitoring and post-welding quality inspection, the pre-welding stage, particularly the recognition and localization of weld seams, remains a critical challenge. Traditional methods often focus on detecting single weld seams, which is time-consuming and inefficient for complex workpieces with multiple seams. Problem Statement The primary…

Authors: Yin Jun Phua、Katsumi Inoue Paper: https://arxiv.org/abs/2408.10709 Introduction Dynamic systems are ubiquitous in our world, and understanding them is crucial for predicting and controlling their outcomes. The Learning from Interpretation Transition (LFIT) framework is a powerful tool for automatically constructing models of dynamic systems in the form of logic programs based on observed state transitions. Traditional symbolic algorithms implementing LFIT are interpretable and verifiable but struggle with noisy data and unobserved transitions. Neural networks, on the other hand, can handle noise and generalize better but often suffer from overfitting and lack interpretability. In this paper, the authors introduce δLFIT2, a…

Authors: Hongbang Yuan、Zhuoran Jin、Pengfei Cao、Yubo Chen、Kang Liu、Jun Zhao Paper: https://arxiv.org/abs/2408.10682 Introduction Large Language Models (LLMs) have achieved significant success across various domains by leveraging extensive corpora for training. However, these models often inadvertently learn undesirable behaviors due to problematic content in the training data, such as copyrighted material, private information, and toxic content. This issue poses significant security and ethical concerns, hindering the deployment of LLMs in real-world applications. Machine unlearning has emerged as a promising solution to mitigate these issues by transforming models to behave as if they were never trained on specific data entries. Despite the effectiveness of…

Authors: Anna-Maria Nau、Phillip Ditto、Dawnie Wolfe Steadman、Audris Mockus Paper: https://arxiv.org/abs/2408.10414 Automating Human Stage of Decay Identification Using AI: A Detailed Analysis Introduction Determining the stage of decomposition (SOD) is a critical task in forensic science, essential for estimating the postmortem interval (PMI) and identifying human remains. Traditionally, this task has been performed manually by forensic experts using visual assessments, which are subjective and labor-intensive. This study, titled “Towards Automation of Human Stage of Decay Identification: An Artificial Intelligence Approach,” explores the feasibility of automating this process using artificial intelligence (AI). Related Work Manual Scoring Methods Two prominent methods for scoring human…

Authors: Yilun Kong、Hangyu Mao、Qi Zhao、Bin Zhang、Jingqing Ruan、Li Shen、Yongzhe Chang、Xueqian Wang、Rui Zhao、Dacheng Tao Paper: https://arxiv.org/abs/2408.10504 Introduction Large Language Models (LLMs) have shown remarkable capabilities in various natural language processing (NLP) tasks. Prompt engineering, which involves adding instructions to input queries, has emerged as a promising technique to adapt LLMs to specific tasks without altering their parameters. However, existing prompt optimization methods often focus on task-level performance, neglecting the potential benefits of query-specific prompts. Additionally, these methods typically require frequent interactions with LLMs to obtain feedback, leading to high interaction costs. To address these challenges, the paper introduces Query-dependent Prompt Optimization (QPO),…

Authors: Chaitra Hegde、Yashar Kiarashi、Allan I Levey、Amy D Rodriguez、Hyeokhyen Kwon、Gari D Clifford Paper: https://arxiv.org/abs/2408.10442 Feasibility of Assessing Cognitive Impairment via Distributed Camera Network and Privacy-Preserving Edge Computing Introduction Mild cognitive impairment (MCI) is a condition characterized by a decline in cognitive functions that surpasses typical age and education-related expectations. It is often a precursor to dementia, with over half of those diagnosed progressing within five years. Early diagnosis of MCI is crucial as it allows patients and caregivers to develop coping strategies while the individual still retains significant cognitive function. However, diagnosis is often delayed due to limited access to expert…

Authors: Tao Yang、Yangming Shi、Yunwen Huang、Feng Chen、Yin Zheng、Lei Zhang Paper: https://arxiv.org/abs/2408.10119 Introduction Video generation, particularly text-to-video (T2V) generation, has garnered significant interest due to its wide range of applications, including video generation, editing, enhancement, and translation. However, generating high-quality (HQ) videos remains a challenging task due to the diverse and complex motions present in real-world scenarios. Most existing approaches attempt to address this challenge by collecting large-scale HQ videos, which are often inaccessible to the broader community. In this context, the paper “Factorized-Dreamer: Training A High-Quality Video Generator with Limited and Low-Quality Data” introduces a novel approach that leverages publicly available…

Authors: Elaine Kong、Kuo-Ting、Huang、Aakash Gautam Paper: https://arxiv.org/abs/2408.10108 Introduction Cancer survivors, particularly those from ethnic and racial minority groups, face significant challenges in their recovery journey. These challenges are often exacerbated by disparities in accessing adequate resources for information and care. Research indicates that as many as half of all cancer survivors suffer from mental health issues such as anxiety, depression, and fear of cancer recurrence. Despite the prevalence of these issues, psychosocial needs are often overlooked within oncological care, signaling a critical area for improvement. With the advent of Artificial Intelligence (AI), there is growing interest in exploring AI as a…

Authors: Randy Harsuko、Shijun Cheng、Tariq Alkhalifah Paper: https://arxiv.org/abs/2408.09767 Propagating the Prior from Shallow to Deep with a Pre-trained Velocity-Model Generative Transformer Network Introduction Background Building accurate subsurface velocity models is crucial for seismic data analysis, which is essential for Earth discovery, exploration, and monitoring. Traditional methods for constructing these models often face challenges in capturing the complex spatial dependencies and resolution changes inherent in seismic data. With the advent of machine learning, generative models have shown promise in storing and utilizing velocity model distributions for various applications, including full waveform inversion (FWI). Problem Statement Most existing generative models, such as normalizing…

Authors: Jan Malte Lichtenberg、Giuseppe Di Benedetto、Matteo Ruffini Paper: https://arxiv.org/abs/2408.09168 Introduction In the evolving landscape of media streaming services, platforms are increasingly offering a diverse array of content types. For instance, audio streaming services that initially focused solely on music now also provide podcasts, videos, and merchandise. This diversification presents a significant challenge for traditional learning-to-rank (LTR) algorithms, which struggle to rank items from different content types due to varying user engagement patterns. This paper explores a novel method called multinomial blending (MB) to address this challenge, enhancing ranking quality while maintaining interpretability, ease-of-use, and stability in dynamic environments. Related Work…