Author: Lily Parker

Authors: Chiara Stuardi、Claudio Gheller、Franco Vazza、Andrea Botteon Paper: https://arxiv.org/abs/2408.10871 Radio U-Net: A Convolutional Neural Network to Detect Diffuse Radio Sources in Galaxy Clusters and Beyond Introduction The cosmic web, an intricate network of filaments interconnecting galaxy clusters, is a fundamental structure of the universe. The thermal plasma within these filaments and clusters is permeated by weak magnetic fields, which, through processes like shocks and turbulence, accelerate particles to ultra-relativistic energies. These particles emit radio synchrotron radiation, observed as diffuse radio sources in galaxy clusters. Detecting these sources is crucial for understanding cosmic magnetic fields, particle acceleration mechanisms, and the formation of…

Authors: Huy Quoc To、Ming Liu、Guangyan Huang Paper: https://arxiv.org/abs/2408.10729 Introduction The rapid advancement of large language models (LLMs) has revolutionized the ability to process complex information across various fields, including science. The exponential growth of scientific literature, such as the 2.4 million scholarly papers on ArXiv and 36 million publications on PubMed, has enabled these models to effectively learn and understand scientific knowledge. However, the substantial computational resources, data, and training time required for LLMs pose significant challenges. This review aims to summarize the current advances in making LLMs more accessible for scientific applications and to explore cost-effective strategies for their…

Authors: Hong Xie、Jinyu Mo、Defu Lian、Jie Wang、Enhong Chen Paper: https://arxiv.org/abs/2408.10865 Introduction In the realm of distributed selection problems, the multi-player multi-armed bandit (MAB) model has been a cornerstone for addressing issues such as channel access in cognitive radio applications. However, traditional models often fall short when applied to real-world scenarios like ridesharing or food delivery services, where requests arrive stochastically and independently of the number of players. This paper introduces a novel variant of the multi-player MAB model that captures these stochastic arrivals and proposes efficient algorithms to optimize the allocation of requests to players. Related Work Stochastic Multi-player MAB with…

Authors: Yiming Luo、Patrick Cheong-Iao、Shanton Chang Paper: https://arxiv.org/abs/2408.08894 Introduction In the digital age, the sheer volume of information available to learners has grown exponentially, thanks to advancements in information and communications technology (ICT). Digital libraries, Massive Open Online Courses (MOOCs), and other online resources have made it easier for students to access a wealth of information. However, this information explosion has also introduced challenges in how students find, evaluate, and effectively use this information. Exploratory search, a type of information-seeking activity, has emerged as a solution to these challenges. Unlike targeted search, which involves looking for specific goals and expecting specific…

Authors: Xubin Ren、Chao Huang Paper: https://arxiv.org/abs/2408.08821 Introduction In the realm of online recommender systems, deep learning has emerged as a powerful tool for capturing user preferences by analyzing complex user-item interactions. However, many existing methods rely heavily on unique user and item IDs, which limits their performance in zero-shot learning scenarios where training data is scarce. Inspired by the success of language models (LMs) and their strong generalization capabilities, this study introduces EasyRec, a novel approach that integrates text-based semantic understanding with collaborative signals. EasyRec employs a text-behavior alignment framework, combining contrastive learning with collaborative language model tuning, to ensure…

Authors: Kamyar Zeinalipour、Neda Jamshidi、Monica Bianchini、Marco Maggini、Marco Gori Paper: https://arxiv.org/abs/2408.06396 Introduction In recent years, the field of natural language processing (NLP) has achieved remarkable progress, particularly through the development and utilization of large pre-trained language models (LLMs). These sophisticated models represent a significant leap forward, primarily due to their ability to understand and generate human-like text based on training from extensive datasets. Typically, these models are trained using unsupervised learning techniques, where they learn to predict the next word or token in a sequence by examining the tokens that precede it. This method has propelled them to the forefront of various…

Authors: Minh Nguyen、Phuong Le Paper: https://arxiv.org/abs/2408.06618 Generalized Knowledge-Enhanced Framework for Biomedical Entity and Relation Extraction Introduction The rapid increase in biomedical publications has made it challenging for researchers to stay updated with the latest findings. To address this, various frameworks have been developed for automatic extraction of biomedical entities and their relations. This paper introduces a novel framework that leverages external knowledge to construct a reusable background knowledge graph for biomedical entity and relation extraction. The framework is inspired by how humans learn domain-specific topics, starting with foundational knowledge and extending to specialized topics. Related Works Advanced strategies for domain-specific…