Author: Aiden Green

Authors: Lyberius Ennio F. Taruc、Arvin R. De La Cruz Paper: https://arxiv.org/abs/2408.08694 Introduction Extracurricular activities are essential in enriching students’ educational experiences by providing opportunities for practical and reflective learning, as well as personal growth. These activities, organized by campus clubs, focus groups, and student organizations, offer students avenues to develop leadership skills, build social connections, and explore interests outside the classroom. This study aims to develop a machine learning workflow that quantifies the effectiveness of student-organized activities based on student emotional responses using sentiment analysis. Research Background The Value of Extracurricular Activities and the Role of Student Affairs Extracurricular activities,…

Authors: Hongyu Li、Snehal Dikhale、Jinda Cui、Soshi Iba、Nawid Jamali Paper: https://arxiv.org/abs/2408.08312 Introduction Tactile sensing is essential for robots to interact with objects in a manner similar to humans. Tactile sensors can be broadly categorized into vision-based and taxel-based sensors. Vision-based sensors have gained popularity due to their pixel-based representation, which is conducive to deep learning. However, their size limits their application in multi-fingered hands. Taxel-based sensors, despite their challenges such as low spatial resolution and non-standardized representations, offer unique advantages in robotic manipulation. This paper introduces HyperTaxel, a novel framework for enhancing the spatial resolution of taxel-based tactile signals using contrastive learning.…

Authors: Huajian Xin、Z.Z. Ren、Junxiao Song、Zhihong Shao、Wanjia Zhao、Haocheng Wang、Bo Liu、Liyue Zhang、Xuan Lu、Qiushi Du、Wenjun Gao、Qihao Zhu、Dejian Yang、Zhibin Gou、Z.F. Wu、Fuli Luo、Chong Ruan Paper: https://arxiv.org/abs/2408.08152 Introduction Recent advancements in large language models have significantly influenced mathematical reasoning and theorem proving in artificial intelligence. Despite notable progress in natural language domains, language models still encounter substantial challenges in formal theorem proving, such as using Lean and Isabelle, which require rigorous derivations satisfying formal specifications of the verification system. Even advanced models like GPT-4 struggle with complex formal proofs, underscoring the intricate nature of both the coding and the mathematics involved. Language models in formal theorem…

Authors: Jahir Sadik Monon、Deeparghya Dutta Barua、Md. Mosaddek Khan Paper: https://arxiv.org/abs/2408.06503 Introduction Multi-agent Reinforcement Learning (MARL) is a critical framework for various decision-making and control tasks. Unlike single-agent systems, MARL requires successful cooperation among agents, especially in decentralized settings with partial observability and sparse rewards. This paper introduces CoHet, an algorithm leveraging Graph Neural Network (GNN)-based intrinsic motivation to facilitate learning in heterogeneous multi-agent systems. Related Works Existing literature often addresses either agent heterogeneity or reward sparsity but rarely both. Traditional methods use agent indexing or centralized critics, which are impractical for decentralized settings. CoHet addresses these gaps by using local…

Authors: Vlad Hondru、Florinel Alin Croitoru、Shervin Minaee、Radu Tudor Ionescu、Nicu Sebe Paper: https://arxiv.org/abs/2408.06687 Introduction In recent years, the field of computer vision has seen significant advancements due to the development of self-supervised learning techniques. One such technique is Masked Image Modeling (MIM), which has emerged as a powerful approach for pre-training models without the need for labeled data. This survey paper, authored by Vlad Hondru, Florinel Alin Croitoru, Shervin Minaee, Radu Tudor Ionescu, and Nicu Sebe, provides a comprehensive overview of MIM, categorizing the various approaches and highlighting key contributions in the field. Abstract The abstract introduces the concept of MIM, where…