Author: Amelia Rogers

Authors: Kenji Harada、Tsuyoshi Okubo、Naoki Kawashima Paper: https://arxiv.org/abs/2408.10669 Tensor Tree Learns Hidden Relational Structures in Data to Construct Generative Models Introduction In the realm of machine learning, generative models are pivotal for tasks such as data synthesis, anomaly detection, and more. The architecture of these models often needs to be tailored to the specific characteristics of the data. Traditional approaches either manually select the architecture or optimize it through parameter pruning. However, a novel approach leverages the tensor tree network within the Born machine framework to dynamically optimize the network structure, thereby uncovering hidden relational structures in the data. This blog…

Authors: Xiaochen Wang、Jiaqi Wang、Houping Xiao、Jinghui Chen、Fenglong Ma Paper: https://arxiv.org/abs/2408.10276 Introduction The advent of foundation models has revolutionized the field of artificial intelligence (AI), showcasing remarkable capabilities in handling diverse modalities and tasks. However, in the medical domain, the development of comprehensive foundation models faces significant challenges due to limited access to diverse modalities and stringent privacy regulations. This study introduces a novel approach, FEDKIM, designed to scale medical foundation models within a federated learning framework. FEDKIM leverages lightweight local models to extract healthcare knowledge from private data and integrates this knowledge into a centralized foundation model using an adaptive Multitask…

Authors: Chengyu Gong、Gefei Shen、Luanzheng Guo、Nathan Tallent、Dongfang Zhao Paper: https://arxiv.org/abs/2408.10264 Introduction Background and Motivation In the realm of scientific data management, one of the most resource-intensive operations is the search for the k-nearest neighbors (KNN) of a newly introduced item. Traditional indexing techniques like DBSCAN and K-means are often employed to expedite this process. However, these methods fall short when dealing with multimodal scientific data, where different types of data (e.g., images and text) are not directly comparable. Recent advancements in multimodal machine learning offer a promising alternative by generating embedding vectors that semantically represent the original multimodal data. Despite their…

Authors: Johannes K. Fichte、Markus Hecher、Yasir Mahmood、Arne Meier Paper: https://arxiv.org/abs/2408.10683 Introduction Abstract argumentation is a cornerstone of artificial intelligence, providing a framework for modeling, evaluating, and comparing arguments. Traditionally, argumentation frameworks (AFs) focus on the acceptance of arguments based on certain conditions. However, real-world scenarios often require the rejection of arguments under specific conditions. This paper, titled “Rejection in Abstract Argumentation: Harder Than Acceptance?” by Johannes K. Fichte, Markus Hecher, Yasir Mahmood, and Arne Meier, explores the complexity and expressiveness of rejection conditions (RCs) in abstract argumentation frameworks. Related Work The study of argumentation frameworks has evolved significantly since Dung’s seminal…

Authors: Junwei You、Haotian Shi、Zhuoyu Jiang、Zilin Huang、Rui Gan、Keshu Wu、Xi Cheng、Xiaopeng Li、Bin Ran Paper: https://arxiv.org/abs/2408.09251 Introduction Background The field of autonomous driving has seen significant advancements, particularly with the development of end-to-end (E2E) systems. These systems manage the entire driving process, from environmental perception to vehicle control, using integrated machine learning models to process complex environmental data in real-time. The integration of large-scale machine learning models, such as large language models (LLMs) and vision-language models (VLMs), has further enhanced the capabilities of these systems. Problem Statement Despite the progress, there remains a notable gap in the application of foundation models like VLMs…

Authors: Honggen Zhang、Xiangrui Gao、June Zhang、Lipeng Lai Paper: https://arxiv.org/abs/2408.09048 Introduction Messenger RNA (mRNA)-based vaccines and therapeutics have revolutionized the pharmaceutical industry, offering new avenues for treating diseases such as COVID-19 and cancer. However, selecting optimal mRNA sequences from extensive libraries remains a costly and complex task. Effective mRNA therapeutics require sequences with optimized expression levels and stability. This paper introduces mRNA2vec, a novel contextual language model-based embedding method designed to enhance mRNA sequence representation by combining the 5’ untranslated region (UTR) and coding sequence (CDS) regions. Related Work mRNA Sequence-based Translation Prediction The optimization of mRNA sequences to enhance expression and…

Authors: Jamie Deng、Yusen Wu、Yelena Yesha、Phuong Nguyen Paper: https://arxiv.org/abs/2408.09043 Introduction Venous thromboembolism (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolism (PE), is a significant cardiovascular condition that poses serious health risks if not identified promptly. DVT involves the formation of blood clots in deep veins, typically in the lower extremities, while PE occurs when these clots travel to the lungs, potentially causing life-threatening complications. The timely and accurate identification of VTE is crucial for effective medical intervention, especially in postoperative patients where the risk of VTE can increase significantly. The widespread adoption of electronic health records (EHRs) in hospitals…

Authors: Steven P. Reinhardt Paper: https://arxiv.org/abs/2408.08910 Introduction As the world accelerates its shift towards renewable energy, understanding the complexities of energy technologies and markets becomes crucial. One pressing question is why solar and wind energy are expected to dominate future energy supplies despite their intermittency. This blog delves into the reasons behind this preference, highlighting the scalability, availability, and cost-effectiveness of these technologies. Related Work Key Dimensions of Energy Sources The literature identifies several critical dimensions that determine the value of renewable technologies for utility-scale energy generation. These dimensions include: Efficiency: The ratio of energy output to input, crucial in…

Authors: Moshe BenBassat Paper: https://arxiv.org/abs/2408.10040 Introduction In the realm of manufacturing, generating an optimal production schedule is paramount. It significantly impacts the efficiency and productivity of operations, influencing both the top and bottom lines, as well as on-time delivery dates. However, creating a high-quality schedule for large, complex operations is notoriously challenging. The advent of large-scale automation, which enables the automatic generation of optimal schedules for extended time horizons, opens new avenues for AI applications. These include promising delivery dates based on a more accurate assessment of future resource availability and tactical capacity planning. Optimizing Manufacturing Schedule: The Problem At…

Authors: Wei Sun、Xiaosong Zhang、Fang Wan、Yanzhao Zhou、Yuan Li、Qixiang Ye、Jianbin Jiao Paper: https://arxiv.org/abs/2408.08723 Introduction Novel View Synthesis (NVS) is a critical task in computer vision, aiming to generate new views of a scene from a set of input images. Traditional methods like Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) require accurate camera poses, typically obtained through Structure-from-Motion (SfM) techniques. However, SfM is time-consuming and prone to errors, especially in textureless or repetitive regions. This paper introduces a novel approach, Correspondence-Guided SfM-Free 3D Gaussian Splatting (CG-3DGS), which eliminates the need for SfM by integrating pose estimation directly within the NVS framework.…