stp2y

arXiv:2408.02689v1 Announce Type: new Abstract: Traffic forecasting uses recent measurements by sensors installed at chosen locations to forecast the future road traffic. Existing work either assumes all locations are equipped with sensors or focuses on short-term forecast. This paper studies partial sensing traffic forecast of long-term traffic, assuming sensors only at some locations. The study is important in lowering the infrastructure investment cost in traffic management since deploying sensors at all locations could incur prohibitively high cost. However, the problem is challenging due to the unknown distribution at unsensed locations, the intricate spatio-temporal correlation in long-term forecasting, as well as…

arXiv:2408.02788v1 Announce Type: new Abstract: While exploring visual scenes, humans' scanpaths are driven by their underlying attention processes. Understanding visual scanpaths is essential for various applications. Traditional scanpath models predict the where and when of gaze shifts without providing explanations, creating a gap in understanding the rationale behind fixations. To bridge this gap, we introduce GazeXplain, a novel study of visual scanpath prediction and explanation. This involves annotating natural-language explanations for fixations across eye-tracking datasets and proposing a general model with an attention-language decoder that jointly predicts scanpaths and generates explanations. It integrates a unique semantic alignment mechanism to enhance…

arXiv:2408.02970v1 Announce Type: new Abstract: Large language models (LLMs) have attracted considerable attention in various fields for their cost-effective solutions to diverse challenges, especially with advancements in instruction tuning and quantization. E-commerce, with its complex tasks and extensive product-user interactions, presents a promising application area for LLMs. However, the domain-specific concepts and knowledge inherent in e-commerce pose significant challenges for adapting general LLMs. To address this issue, we developed EC-Guide href{https://github.com/fzp0424/EC-Guide-KDDUP-2024}, a comprehensive e-commerce guide for instruction tuning and quantization of LLMs. We also heuristically integrated Chain-of-Thought (CoT) during inference to enhance arithmetic performance. Our approach achieved the 2nd place…