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EPTC 20242024Plastic encapsulation is a key feature for System-in-Package (SiP) technology as it provides robust mechanical protection and structural support for all the electronic components enclosed within the package. This allows a highly compact design with minimal component-to-component spacing without compromising long-term reliability and performance. However, as the density and complexity of SiP modules continue
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Environmental Research: Infrastructure and Sustainability2024Battery electric trucks (BETs) are the most promising option for fast and large-scale CO2 emission reduction in road freight transport. Yet, the limited range and longer charging times compared to diesel trucks make long-haul BET applications challenging, so a comprehensive fast charging network for BETs is required. However, little is known about optimal truck charging locations for long-haul trucking
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2024Online paging is a fundamental problem in the field of online algorithms, in which one maintains a cache of 𝑘 slots as requests for fetching pages arrive online. In the weighted variant of this problem, each page has its own fetching cost; a substantial line of work on this problem culminated in an (optimal) 𝑂(log 𝑘)-competitive randomized algorithm, due to Bansal, Buchbinder and Naor (FOCS’07). Existing
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CIKM 2024 Workshop on Generative AI for E-commerce2024Large language models (LLMs) offer substantial potential for automating labeling tasks, showcasing robust zero-shot performance across diverse classification tasks. The LLM-generated reasons that accompany these classifications contain signals about the quality of the classifications. Estimates of quality of these reasons can, in essence, be used to detect potentially incorrect predictions. Conventional
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CIKM 2024 Workshop on Generative AI for E-commerce2024Large Language Models (LLMs) have been employed as crowd-sourced annotators to alleviate the burden of human labeling. However, the broader adoption of LLM-based automated labeling systems encounters two main challenges: 1) LLMs are prone to producing unexpected and unreliable predictions, and 2) no single LLM excels at all labeling tasks. To address these challenges, we first develop fast and effective
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