Author: Harrison Campbell

Authors: Guofeng Mei、Luigi Riz、Yiming Wang、Fabio Poiesi Paper: https://arxiv.org/abs/2408.10652 Introduction 3D instance segmentation (3DIS) is a crucial task in computer vision, aiming to identify and label individual objects within a 3D scene. Traditional methods often rely on a predefined set of categories, known as a vocabulary, which limits their flexibility and adaptability to new or unseen objects. Recent advancements have introduced open-vocabulary methods, which allow for a broader range of object recognition. However, these methods still depend on a user-specified vocabulary at test time, restricting their ability to operate in truly open-ended scenarios. In this study, we introduce a novel approach…

Authors: Dario Zanca、Andrea Zugarini、Simon Dietz、Thomas R. Altstidl、Mark A. Turban Ndjeuha、Leo Schwinn、Bjoern Eskofier Paper: https://arxiv.org/abs/2408.09948 Introduction Background and Motivation Understanding human attention is a pivotal aspect of vision science and artificial intelligence (AI). Human visual attention allows for selective processing of relevant visual information while ignoring irrelevant details. This cognitive process is essential for various applications, including neuroscience and AI-driven technologies. Traditional models of human attention have primarily focused on free-viewing scenarios, where the observer’s gaze is driven by bottom-up saliency. However, less is known about task-driven image exploration, where the observer’s gaze is influenced by specific tasks, such as image…

Authors: Eduardo Jr Piedad、Christian Ainsley Del Rosario、Eduardo Prieto-Araujo、Oriol Gomis-Bellmunt Paper: https://arxiv.org/abs/2408.09644 Introduction In the realm of industrial operations, the swift and precise identification of mechanical faults is paramount for maintaining efficiency and minimizing production downtime. Traditional methods of fault detection often rely on vibration sensors, which can be costly and intrusive. However, recent advancements in artificial intelligence (AI) and deep learning (DL) have opened new avenues for non-intrusive fault diagnosis by analyzing motor phase current signals. This study delves into the application of Wavelet Transform (WT) to convert these time-series signals into time-frequency 2D representations, which are then used to…

Authors: Zhongjian Zhang、Xiao Wang、Huichi Zhou、Yue Yu、Mengmei Zhang、Cheng Yang、Chuan Shi Paper: https://arxiv.org/abs/2408.08685 Graph Neural Networks (GNNs) have shown remarkable success in various applications by leveraging their message-passing mechanisms to extract useful information from graph data. However, GNNs are highly vulnerable to adversarial attacks, particularly topology attacks, which can significantly degrade their performance. This paper explores the potential of Large Language Models (LLMs) to enhance the adversarial robustness of GNNs. The authors propose a novel framework, LLM4RGNN, which leverages the inference capabilities of LLMs to identify and mitigate adversarial perturbations in graph structures. Introduction Graph Neural Networks (GNNs) are powerful tools for…

Authors: Li Pan、Yupei Zhang、Qiushi Yang、Tan Li、Xiaohan Xing、Maximus C. F. Yeung、Zhen Chen Paper: https://arxiv.org/abs/2408.08527 Introduction Gliomas, the most common type of brain tumors, are classified into Grades II to IV by the World Health Organization (WHO), with each grade correlating with different prognoses and intervention approaches. The gold standard for grading gliomas involves the observation of representative histopathology features in biopsies. However, histopathology slides present a complex milieu of cells, necrosis, and microenvironments, complicating the localization of tumor foci and necessitating the expertise of senior pathologists. Recent advances in computer-assisted cancer grading have shown promising performance in identifying glioma grades from…

Authors: Bhuvanashree Murugadoss、Christian Poelitz、Ian Drosos、Vu Le、Nick McKenna、Carina Suzana Negreanu、Chris Parnin、Advait Sarkar Paper: https://arxiv.org/abs/2408.08781 Introduction The paper “Evaluating the Evaluator: Measuring LLMs’ Adherence to Task Evaluation Instructions” explores the effectiveness of using Large Language Models (LLMs) as judges in automatic evaluation tasks. This method, known as LLMs-as-a-judge, aims to replace human judgments with automated evaluations using LLMs. The study investigates whether LLMs’ assessments are influenced by the instructions provided in prompts or if they reflect a preference for high-quality data similar to their fine-tuning data. The research analyzes prompts with varying levels of instruction and compares them to a prompt-free method…

Authors: Dongyu Ru、Lin Qiu、Xiangkun Hu、Tianhang Zhang、Peng Shi、Shuaichen Chang、Jiayang Cheng、Cunxiang Wang、Shichao Sun、Huanyu Li、Zizhao Zhang、Binjie Wang、Jiarong Jiang、Tong He、Zhiguo Wang、Pengfei Liu、Yue Zhang、Zheng Zhang Paper: https://arxiv.org/abs/2408.08067 Introduction Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by incorporating external knowledge bases, enabling more precise and contextually relevant responses. However, evaluating these systems presents several challenges due to their modular nature, the complexity of long-form responses, and the reliability of existing metrics. To address these challenges, the authors propose RAGChecker, a fine-grained evaluation framework designed to provide comprehensive diagnostics for both the retrieval and generation components of RAG systems. Related Work Retrieval Augmented Generation RAG…

Authors: Andrea Lops、Fedelucio Narducci、Azzurra Ragone、Michelantonio Trizio、Claudio Bartolini Paper: https://arxiv.org/abs/2408.07846 Introduction Software testing is a critical step in the software development lifecycle, essential for ensuring code correctness and reliability. Within it, unit testing is the stage concerned with verifying the proper functioning of individual code units. Designing and building unit tests is a costly and labor-intensive process that requires significant time and specialized skills. Automating this process represents a promising area for research and development. Automated tools for generating unit tests can reduce test engineers’ and software developers’ workload. These tools typically use static code analysis methods to generate test suites.…

Authors: Donghai Fang、Fangfang Zhu、Dongting Xie、Wenwen Min Paper: https://arxiv.org/abs/2408.06377 Introduction In the realm of complex organisms, cells form specialized clusters through dynamic interactions and intricate organizational structures. These clusters coordinate the functions of the organism through mutual influences and tight connections. The latest Spatial Resolved Transcriptomics (SRT) technologies, such as ST, 10x Visium, and Stereo-seq, can comprehensively measure transcriptional expression at specific spatial locations (spots) while preserving the spatial context of the tissue. The key computational tasks in SRT data analysis are identifying shared and specific clusters, known as spatial domains, and addressing data denoising issues. Traditional non-spatial clustering methods have…