Author: Ava Garcia

Authors: Valentin Zech、Niclas Boehmer、Edith Elkind、Nicholas Teh Paper: https://arxiv.org/abs/2408.11017 Multiwinner Temporal Voting with Aversion to Change: An In-Depth Analysis Introduction In the realm of multiwinner voting, the dynamic nature of voter preferences poses significant challenges. This study, titled “Multiwinner Temporal Voting with Aversion to Change,” delves into the complexities of two-stage committee elections where voters’ preferences evolve over time. The primary objective is to identify a winning committee in the second stage that maintains substantial overlap with the first-stage committee, thereby ensuring stability and continuity. Background The study is motivated by practical scenarios such as local town council elections, where maintaining…

Authors: Bei Ouyang、Shengyuan Ye、Liekang Zeng、Tianyi Qian、Jingyi Li、Xu Chen Paper: https://arxiv.org/abs/2408.10746 Introduction Background Large Language Models (LLMs) have revolutionized machine intelligence, enabling a wide range of applications, especially at the network edge. Intelligent personal assistants (IPAs) are a prime example, providing users with high-performance, privacy-preserving intelligent services. However, the fine-tuning of these models on edge devices presents significant challenges due to their computational and memory-intensive nature. Problem Statement While parameter-efficient fine-tuning (PEFT) techniques like Adapters and LoRA have been developed to mitigate resource constraints, they are not sufficiently resource-efficient for edge devices. Additionally, resource management optimization techniques are bottlenecked by the…

Authors: Mohammad Baqar Paper: https://arxiv.org/abs/2408.10252 Introduction Introduction to AI Tools in Software Development AI tools like ChatGPT and GitHub Copilot have revolutionized the software development process by providing developers with advanced capabilities to write, debug, and optimize code. These tools utilize large language models (LLMs) trained on extensive datasets, including code repositories, technical documentation, and natural language text, to assist developers in real-time. GitHub Copilot is an AI-powered code completion tool developed by GitHub in collaboration with OpenAI. It functions as a “pair programmer,” built on the Codex model, a descendant of GPT-3, specifically fine-tuned for programming tasks. GitHub Copilot…

Authors: Manjil Karki、Pratik Shakya、Sandesh Acharya、Ravi Pandit、Dinesh Gothe Paper: https://arxiv.org/abs/2408.10128 Voice cloning technology has seen significant advancements in recent years, driven by the increasing capabilities of AI and deep learning. This blog delves into a recent study titled “Advancing Voice Cloning for Nepali: Leveraging Transfer Learning in a Low-Resource Language,” which explores the development of a voice cloning system tailored for the Nepali language. The study addresses the challenges posed by limited data availability and aims to create a system that produces audio output with a Nepali accent. Introduction Background Voice cloning involves creating artificial replicas of human voices using advanced…

Authors: Tianwei Lin、Jiang Liu、Wenqiao Zhang、Zhaocheng Li、Yang Dai、Haoyuan Li、Zhelun Yu、Wanggui He、Juncheng Li、Hao Jiang、Siliang Tang、Yueting Zhuang Paper: https://arxiv.org/abs/2408.09856 Introduction In the realm of Natural Language Processing (NLP) and multi-modal understanding, fine-tuning large language models (LLMs) has proven to be highly effective. However, the substantial memory and computational resources required for full fine-tuning (FFT) of models with over a billion parameters pose significant challenges. Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), have emerged to address these issues by fine-tuning a small subset of parameters. Despite their efficiency, these methods often fall short in multidimensional task scenarios due to catastrophic forgetting and…

Authors: Yisong Fu、Fei Wang、Zezhi Shao、Chengqing Yu、Yujie Li、Zhao Chen、Zhulin An、Yongjun Xu Paper: https://arxiv.org/abs/2408.09695 Introduction Accurate weather forecasting is crucial for various sectors, including agriculture, transportation, energy, and economics. The proliferation of automatic weather stations has significantly enhanced modern meteorology by providing high-resolution meteorological data. However, the complex spatial-temporal patterns of global weather data pose significant challenges for accurate forecasting. Recent advancements in deep learning (DL) have shown promise in leveraging historical data for improved weather forecasting. Transformers, in particular, have gained popularity due to their ability to capture long-term spatial-temporal correlations. However, their complex architectures result in large parameter counts and…

Authors: Yuhao Jia、Zile Wu、Shengao Yi、Yifei Sun Paper: https://arxiv.org/abs/2408.08852 Introduction Urban forecasting, which involves predicting economic indicators and human mobility, has traditionally relied on low-dimensional numeric data such as Point of Interest (POI) data, survey data, GPS records, demographic census, and geospatial features. Recent advancements have explored using high-dimensional information to capture the complexities of urban dynamics more effectively. This includes utilizing urban imagery for feature extraction and prediction, and encoding urban information into high-dimensional space representations. While these studies have established frameworks for encoding urban information, there has been limited investigation into optimizing these high-dimensional data for urban forecasting. To…

Authors: Bohao Wang、Feng Liu、Jiawei Chen、Yudi Wu、Xingyu Lou、Jun Wang、Yan Feng、Chun Chen、Can Wang Paper: https://arxiv.org/abs/2408.08208 Leveraging Large Language Models for Denoising Sequential Recommendations: An In-Depth Look at LLM4DSR Introduction Large Language Models (LLMs) have revolutionized artificial intelligence with their remarkable capabilities in content comprehension, generation, and semantic reasoning. This paper explores the innovative application of LLMs in denoising sequential recommendations, a task that relies heavily on the accuracy of users’ historical interaction sequences. These sequences often contain noise due to various factors, such as clickbait or accidental interactions, which can significantly degrade the performance of recommendation models. The proposed method, LLM4DSR, aims…

Authors: Michele Fiori、Gabriele Civitarese、Claudio Bettini Paper: https://arxiv.org/abs/2408.06352 Introduction Sensor-based Human Activity Recognition (HAR) is a significant area of research that involves using unobtrusive sensors to identify daily activities performed by humans. This technology is particularly crucial in smart-home environments to recognize Activities of Daily Living (ADLs) such as cooking, eating, and sleeping. Recognizing these activities can be vital for healthcare applications, including early detection of cognitive decline. Deep learning methods are commonly used for HAR, but they are often considered black boxes, making it difficult for non-expert users like clinicians to trust and understand the model outputs. To address this,…