Author: Isabella Peterson

Authors: Ievgeniia A. Tiukova、Daniel Brunnsåker、Erik Y. Bjurström、Alexander H. Gower、Filip Kronström、Gabriel K. Reder、Ronald S. Reiserer、Konstantin Korovin、Larisa B. Soldatova、John P. Wikswo、Ross D. King Paper: https://arxiv.org/abs/2408.10689 Introduction Background The field of artificial intelligence (AI) has made significant strides in automating scientific research through the development of “Robot Scientists.” These systems autonomously generate hypotheses, design and conduct experiments, interpret results, and iterate the process. Previous iterations, such as ‘Adam’ and ‘Eve,’ have demonstrated the potential of AI in functional genomics and early-stage drug development, respectively. The latest advancement in this domain is the Genesis project, which aims to automate systems biology research, particularly focusing…

Authors: Kai Qiu、Xiang Li、Hao Chen、Jie Sun、Jinglu Wang、Zhe Lin、Marios Savvides、Bhiksha Raj Paper: https://arxiv.org/abs/2408.09027 Introduction Background Autoregressive (AR) modeling has been a cornerstone in the field of generative modeling, particularly in text and image generation. However, its application in audio generation has been limited due to the inherent challenges posed by the long sequence lengths and continuity of audio data. Traditional AR models predict tokens sequentially, which can be computationally expensive and time-consuming, especially for audio data with high sampling rates. Problem Statement The primary challenge in AR-based audio generation is the efficiency of token prediction. Given the significant sequence length of…

Authors: Joanito Agili Lopo、Marina Indah Prasasti、Alma Permatasari Paper: https://arxiv.org/abs/2408.08805 Introduction The advent of powerful Large Language Models (LLMs) like ChatGPT has revolutionized various domains, including education. These models have been effectively used in teacher-student collaborations, virtual tutoring, personalized learning experiences, and intelligent tutoring. However, the deployment of such models in educational dialogue systems presents challenges, such as delivering accurate and contextually appropriate responses consistently. Additionally, the large size of these models makes them impractical for many researchers and practitioners due to high memory consumption and slow generation times. To address these challenges, the study introduces CIKMar, an efficient approach to…

Authors: Harsh Kumar、Mohi Reza、Jeb Mitchell、Ilya Musabirov、Lisa Zhang、Michael Liut Paper: https://arxiv.org/abs/2408.08401 Introduction The integration of large language models (LLMs) in programming education is transforming how students approach writing SQL queries. Traditionally, students have relied on web searches for coding assistance, but the advent of LLMs like ChatGPT is changing this dynamic. This study aims to compare the help-seeking behavior of students using traditional web search versus LLMs, including a publicly available LLM (ChatGPT) and an instructor-tuned LLM, in the context of writing SQL queries. Related Work The rise of LLMs has prompted comparative research with traditional web search methods for information…

Authors: Oscar Dilley、Juan Marcelo Parra-Ullauri、Rasheed Hussain、Dimitra Simeonidou Paper: https://arxiv.org/abs/2408.08214 Introduction Federated Learning (FL) is a privacy-enhancing technology that allows distributed machine learning (ML) by training models locally and aggregating updates, thus bypassing centralized data collection. This approach is increasingly popular in sectors like healthcare, finance, and personal computing. However, FL inherits fairness challenges from classical ML and introduces new ones due to differences in data quality, client participation, communication constraints, aggregation methods, and underlying hardware. Addressing these challenges, the paper proposes Federated Fairness Analytics—a methodology for measuring fairness in FL systems. Technical Background To define the fairness problem and the…

Authors: Hongrui Shen、Long Zhao、Kan Zheng、Yuhua Cao、Pingzhi Fan Paper: https://arxiv.org/abs/2408.06359 Introduction Massive multiple-input multiple-output (MIMO) technology is a cornerstone of 5G mobile communication due to its high spectrum efficiency. For effective downlink signal transmission, accurate downlink channel state information (CSI) is crucial. In frequency division duplex (FDD) systems, obtaining downlink CSI from uplink pilots is challenging, making CSI feedback a critical area of research. Traditional methods like compressive sensing (CS) have limitations, especially with the increasing number of antennas in massive MIMO systems. This paper proposes an adaptive bidirectional long short-term memory network (ABLNet) for CSI feedback, designed to handle various…

Authors: Henrik Abgaryan、Ararat Harutyunyan、Tristan Cazenave Paper: https://arxiv.org/abs/2408.06993 Exploring the Potential of Large Language Models in Job Shop Scheduling Introduction The Job Shop Scheduling Problem (JSSP) is a critical challenge in optimizing production processes. It involves allocating a set of jobs to a limited number of machines while minimizing factors like total processing time or job delays. Traditional approaches, such as mathematical programming and heuristic algorithms, often struggle with scalability and complexity. Recent advancements in artificial intelligence (AI) have introduced promising solutions like reinforcement learning and graph neural networks. This paper explores the potential of Large Language Models (LLMs) for JSSP,…