Author: Emma Thompson

Authors: Jinze Sun、Yongpan Sheng、Lirong He Paper: https://arxiv.org/abs/2408.07911 Introduction Knowledge graphs (KGs) have become increasingly significant in natural language processing and knowledge engineering tasks due to their ability to model real-world facts using multi-relationship graph structures. However, real-world knowledge is dynamic, leading to the development of temporal knowledge graphs (TKGs) that encode relationships and events over time. Temporal knowledge graph reasoning (TKGR) models aim to extrapolate new facts from historical data, addressing the inherent incompleteness of TKGs. Despite advancements in TKGR models, existing approaches often learn biased data representations and spurious correlations, failing to discern causal relationships between events. This paper…

Authors: Zhiyang Lu、Qinghan Chen、Zhimin Yuan、Ming Cheng Paper: https://arxiv.org/abs/2408.07825 Introduction Scene flow estimation is a critical task in dynamic scene perception, providing 3D motion vectors for each point in a source frame from two consecutive point clouds. This foundational component aids in various downstream tasks such as object tracking, point cloud label propagation, and pose estimation. Traditional methods often rely on stereo or RGB-D images, but recent advances in deep learning have led to end-to-end algorithms specifically designed for scene flow prediction. However, these methods face challenges in global flow embedding, handling non-rigid deformations, and generalizing from synthetic to real-world datasets.…

Authors: Akane Sano、Judith Amores、Mary Czerwinski Paper: https://arxiv.org/abs/2408.07822 Exploration of LLMs, EEG, and Behavioral Data to Measure and Support Attention and Sleep Introduction Human altered states such as attention and sleep play significant roles in health, safety, and productivity. By precisely measuring these states, we can design adaptive tools and interfaces that respond effectively to users and help promote their health. Human attention states have been measured using physiological and behavioral data such as electroencephalogram (EEG), facial expressions, and eye tracking. Measuring human attention states can help design systems that enhance driver alertness, minimize interruptions during focus, or promote relaxation before…

Authors: Yusong Deng、Min Wu、Lina Yu、Jingyi Liu、Shu Wei、Yanjie Li、Weijun Li Paper: https://arxiv.org/abs/2408.07719 Abstract Symbolic regression aims to identify patterns in data and represent them through mathematical expressions. Traditional methods often treat variables and symbols as mere characters without considering their mathematical essence. This paper introduces the Operator Feature Neural Network (OF-Net), which employs operator representation for expressions and proposes an implicit feature encoding method for the intrinsic mathematical operational logic of operators. By substituting operator features for numeric loss, OF-Net predicts the combination of operators of target expressions. Evaluations on public datasets demonstrate superior recovery rates and high R² scores. The…

Authors: Xin Hao、Bahareh Nakisa、Mohmmad Naim Rastgoo、Richard Dazeley Paper: https://arxiv.org/abs/2408.07877 Introduction In human-AI coordination scenarios, human agents often exhibit behaviors that are sparse and unpredictable compared to AI agents. This introduces challenges in obtaining sparse rewards and training AI agents efficiently. To address these challenges, the Intrinsic Reward-enhanced Context-aware (IReCa) reinforcement learning (RL) algorithm is proposed. IReCa leverages intrinsic rewards to facilitate the acquisition of sparse rewards and utilizes environmental context to enhance training efficiency. The algorithm introduces three unique features: 1. Encourages exploration of sparse rewards by incorporating intrinsic rewards. 2. Improves acquisition of sparse rewards by prioritizing corresponding sparse…

Authors: Xiaomin Wu、Rui Xu、Pengchen Wei、Wenkang Qin、Peixiang Huang、Ziheng Li、Lin Luo Paper: https://arxiv.org/abs/2408.07037 Introduction Pathological examination is the gold standard for diagnosing tumors and cancers. The meticulous analysis of tissue samples by experienced pathologists provides critical insights into appropriate therapeutic strategies. However, the scarcity of senior pathologists and the cumbersome processes of accessing and consulting pathological knowledge exacerbate the complexity and workload inherent in the diagnostic process. The advent of digital tools like Whole Slide Imaging (WSI) has facilitated pathological diagnosis, making data storage and transfer easier. Concurrently, large language models (LLMs) are increasingly utilized for their capabilities in machine learning and…

Authors: Angus R. Williams、Liam Burke-Moore、Ryan Sze-Yin Chan、Florence E. Enock、Federico Nanni、Tvesha Sippy、Yi-Ling Chung、Evelina Gabasova、Kobi Hackenburg、Jonathan Bright Paper: https://arxiv.org/abs/2408.06731 Large Language Models and Election Disinformation: An In-Depth Analysis Introduction The advent of large language models (LLMs) has revolutionized natural language generation, making it accessible to a wide range of users, including those with malicious intent. This study investigates the potential of LLMs to generate high-quality content for election disinformation operations. The research is divided into two main parts: the creation and evaluation of DisElect, a novel dataset for election disinformation, and experiments to assess the “humanness” of LLM-generated content. Related Work Disinformation…

Authors: Hao Li、Fabian Deuser、Wenping Yina、Xuanshu Luo、Paul Walther、Gengchen Mai、Wei Huang、Martin Werner Paper: https://arxiv.org/abs/2408.06761 Introduction The rapid advancement in Remote Sensing (RS) technology has significantly enhanced the availability of large-scale, high-quality Earth observation (EO) data, which is crucial for timely humanitarian responses to natural disasters. Street View Imagery (SVI) has also gained momentum in urban studies and computer vision, providing a unique ground-level perspective that complements traditional satellite imagery analysis. In disaster mapping scenarios, two types of information are critical: disaster damage perception and geolocation awareness. This paper introduces a novel disaster mapping framework, CVDisaster, which addresses both geolocalization and damage perception…

Authors: Matthew Barthet、Diogo Branco、Roberto Gallotta、Ahmed Khalifa、Georgios N. Yannakakis Paper: https://arxiv.org/abs/2408.06346 Introduction Affective computing (AC) aims to create systems that can recognize, interpret, and simulate human emotions. One of the most challenging tasks within AC is to autonomously generate new contexts that elicit desired emotional responses from users. This concept is known as the affective loop. The unpredictability and subjectivity of human emotions make this task particularly difficult. In this paper, the authors introduce a novel method for autonomously generating content that elicits a desired sequence of emotional responses. They focus on the domain of racing games, leveraging human arousal demonstrations…