Author: Jude Ward

Authors: Zili Liu、Hao Chen、Lei Bai、Wenyuan Li、Wanli Ouyang、Zhengxia Zou、Zhenwei Shi Paper: https://arxiv.org/abs/2408.10854 Introduction In recent years, the increasing frequency and intensity of extreme weather events have highlighted the critical need for accurate and reliable weather forecasting. Traditional numerical weather prediction methods, along with rapidly advancing deep learning-based forecasting models, have enabled increasingly accurate global-scale weather predictions. However, due to computational resource constraints, the resolution of global-scale forecasts is limited to tens of kilometers to 100 km. Such coarse spatial resolution is insufficient for the refined forecasting needs of specific regions and related downstream tasks. As a result, using downscaling techniques to…

Authors: Jan Lewen、Max Pargmann、Mehdi Cherti、Jenia Jitsev、Robert Pitz-Paal、Daniel Maldonado Quinto Paper: https://arxiv.org/abs/2408.10802 Introduction Concentrating Solar Power (CSP) plants are pivotal in the transition to sustainable energy. A critical aspect of CSP plant efficiency is the distribution of concentrated flux density on the receiver. However, non-ideal flux density from individual heliostats can compromise plant safety and efficiency. The flux density is influenced by the heliostat’s surface profile, including canting and mirror errors. Measuring these profiles accurately for numerous heliostats is challenging, leading to reliance on ideal surface conditions, which is suboptimal. This study introduces inverse Deep Learning Ray Tracing (iDLR), a novel…

Authors: Matthias Klusch、Jörg Lässig、Daniel Müssig、Antonio Macaluso、Frank K. Wilhelm Paper: https://arxiv.org/abs/2408.10726 Introduction Quantum Artificial Intelligence (QAI) represents the convergence of quantum computing and artificial intelligence (AI), promising significant advancements in both fields. This paper provides an overview of the current state of QAI, highlighting key achievements and identifying open questions for future research. The focus is on the feasibility and potential of using quantum computing to solve complex AI problems and leveraging AI methods to enhance quantum computing. Quantum Computing in a Nutshell Quantum computing leverages the principles of quantum mechanics to process information, potentially surpassing classical computing capabilities. There are…

Authors: Jintao Cheng、Xingming Chen、Jinxin Liang、Xiaoyu Tang、Xieyuanli Chen、Dachuan Li Paper: https://arxiv.org/abs/2408.10602 Introduction The accurate identification of moving objects in 3D point cloud data is a critical task for autonomous driving and robotics. This task, known as Moving Object Segmentation (MOS), involves distinguishing moving objects from static entities in the environment. Traditional methods for MOS can be broadly classified into 3D voxel-based and 2D projection-based approaches. However, these methods face significant challenges, such as high computational demands and information loss during 3D-to-2D projection. To address these issues, the paper proposes a novel multi-view MOS model (MV-MOS) that fuses motion-semantic features from different…

Authors: Ruofan Liang、Zan Gojcic、Merlin Nimier-David、David Acuna、Nandita Vijaykumar、Sanja Fidler、Zian Wang Paper: https://arxiv.org/abs/2408.09702 Introduction Virtual object insertion into real-world scenes is a crucial task in various applications, including virtual production, interactive gaming, and synthetic data generation. Achieving photorealistic insertions requires accurately modeling the interactions between virtual objects and their environments, such as shadows and reflections. Traditional pipelines for virtual object insertion involve three main steps: lighting estimation, 3D proxy geometry creation, and composited image rendering. However, lighting estimation from a single image remains a significant challenge due to the ill-posed nature of inverse rendering. Recent advancements in large-scale diffusion models (DMs) have…

Authors: Jianhao Guo、Zixuan Ni、Yun Zhu、Siliang Tang Paper: https://arxiv.org/abs/2408.09350 Continual learning has become an essential paradigm for learning from sequential data while preserving previously acquired knowledge. In the context of graph learning, where graphs continuously evolve based on streaming data, continual graph learning presents unique challenges. This blog post delves into the paper “E-CGL: An Efficient Continual Graph Learner,” which addresses these challenges by proposing an efficient continual graph learner (E-CGL). Introduction Background Graphs are a ubiquitous data form, representing various real-world applications such as social networks, recommendation systems, and citation networks. These graphs tend to expand over time, introducing new…

Authors: Jie Wang、Jin Wang、Xuejie Zhang Paper: https://arxiv.org/abs/2408.09177 Introduction Metaphors are a fundamental aspect of human language, enabling nuanced and creative expression. Recognizing and understanding metaphors is crucial for machines to achieve human-like language comprehension. Traditional methods for metaphor identification often rely on pre-trained models, which struggle with metaphors where the tenor (subject) or vehicle (comparative element) is not explicitly mentioned. This study introduces a multi-stage generative heuristic-enhanced prompt framework to improve the ability of Large Language Models (LLMs) in recognizing tenors, vehicles, and grounds in Chinese metaphors. Related Work Automated Metaphor Identification Previous research has primarily treated metaphor identification as…

Authors: Wei Sun、Yuan Li、Qixiang Ye、Jianbin Jiao、Yanzhao Zhou Paper: https://arxiv.org/abs/2408.09097 Introduction Background Image semantic segmentation is a fundamental task in computer vision, aiming to partition an image into regions that are meaningful based on visual characteristics. This task is crucial for various applications such as object recognition, scene understanding, and image editing. Traditional methods primarily rely on color, contour, and shape cues from 2D images. However, these methods often struggle to capture the 3D structure of the scene, leading to less accurate results, especially in complex environments. Problem Statement To address the limitations of traditional methods, researchers have integrated depth information…

Authors: Samee Arif、Sualeha Farid、Abdul Hameed Azeemi、Awais Athar、Agha Ali Raza Paper: https://arxiv.org/abs/2408.08688 Introduction Large Language Models (LLMs) have demonstrated significant capabilities in various Natural Language Processing (NLP) tasks, such as text generation, question answering, and language understanding. However, these models sometimes deviate from user instructions and exhibit unintended behaviors. To address this, techniques like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) have been developed to align LLM outputs more closely with human preferences. This paper explores the use of multi-agent workflows to generate synthetic Preference Optimization (PO) datasets. The process is divided into two modules: response evaluation…

Authors: Huang Lei、Jiaming Guo、Guanhua He、Xishan Zhang、Rui Zhang、Shaohui Peng、Shaoli Liu、Tianshi Chen Paper: https://arxiv.org/abs/2408.08506 Introduction The generation of long-form texts such as novels using artificial intelligence has been a persistent challenge. Traditional methods using large language models (LLMs) often result in novels that lack logical coherence and depth in character and event depiction. This paper introduces a novel method named Extracting, Excelsior, and Expanding (Ex3) to address these issues. Ex3 extracts structural information from raw novel data, fine-tunes LLMs with this data, and uses a tree-like expansion method to generate high-quality, long-form novels. Related Work Long Context Transformers LLMs face limitations in…