Zobrazeno 1 - 10
of 1 153
pro vyhledávání: '"Lu Chenyang"'
Recent advances in machine learning and hardware have produced embedded devices capable of performing real-time object detection with commendable accuracy. We consider a scenario in which embedded devices rely on an onboard object detector, but have
Externí odkaz:
http://arxiv.org/abs/2410.18919
The integration of technology and healthcare has ushered in a new era where software systems, powered by artificial intelligence and machine learning, have become essential components of medical products and services. While these advancements hold gr
Externí odkaz:
http://arxiv.org/abs/2409.07415
Autor:
Wang, Ruiqi, Wang, Zichen, Gao, Peiqi, Li, Mingzhen, Jeong, Jaehwan, Xu, Yihang, Lee, Yejin, Baum, Carolyn M., Connor, Lisa Tabor, Lu, Chenyang
With advancements in computer vision and deep learning, video-based human action recognition (HAR) has become practical. However, due to the complexity of the computation pipeline, running HAR on live video streams incurs excessive delays on embedded
Externí odkaz:
http://arxiv.org/abs/2409.05662
The precise categorization of white blood cell (WBC) is crucial for diagnosing blood-related disorders. However, manual analysis in clinical settings is time-consuming, labor-intensive, and prone to errors. Numerous studies have employed machine lear
Externí odkaz:
http://arxiv.org/abs/2405.16220
Autor:
Guan, Huaqing, Cui, Hanwen, Ding, Ning, Yang, Kuo, Jiang, Siqi, Sui, Yanfei, Wang, Yuanyuan, Tian, Fuyang, Li, Zhe, Wang, Shuai, Zheng, Pengfei, Lu, Chenyang, Xu, Qiu, Vitos, Levente, Huang, Shaosong
Manipulating point defects for tailored macroscopic properties remains a formidable challenge in materials science. This study demonstrates a proof-of-principle for a universal law involving element Mn, significantly enhancing vacancy diffusion throu
Externí odkaz:
http://arxiv.org/abs/2404.03339
Clinical notes recorded during a patient's perioperative journey holds immense informational value. Advances in large language models (LLMs) offer opportunities for bridging this gap. Using 84,875 pre-operative notes and its associated surgical cases
Externí odkaz:
http://arxiv.org/abs/2402.17493
IoT devices are increasingly the source of data for machine learning (ML) applications running on edge servers. Data transmissions from devices to servers are often over local wireless networks whose bandwidth is not just limited but, more importantl
Externí odkaz:
http://arxiv.org/abs/2310.05306
Survey data can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data under these conditions can overfit and result in poor generalizability.
Externí odkaz:
http://arxiv.org/abs/2308.09892
Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation
Autor:
Xue, Bing, Said, Ahmed Sameh, Xu, Ziqi, Liu, Hanyang, Shah, Neel, Yang, Hanqing, Payne, Philip, Lu, Chenyang
Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting modality for COVID-19 patients who are refractory to conventional therapies. However, the proper treatment decision has been the subject of significant debate and it remains c
Externí odkaz:
http://arxiv.org/abs/2307.03315
This paper introduces Content-aware Token Sharing (CTS), a token reduction approach that improves the computational efficiency of semantic segmentation networks that use Vision Transformers (ViTs). Existing works have proposed token reduction approac
Externí odkaz:
http://arxiv.org/abs/2306.02095