Zobrazeno 1 - 10
of 9 479
pro vyhledávání: '"Gao, Shan"'
Image aesthetic evaluation is a highly prominent research domain in the field of computer vision. In recent years, there has been a proliferation of datasets and corresponding evaluation methodologies for assessing the aesthetic quality of photograph
Externí odkaz:
http://arxiv.org/abs/2405.02982
Autor:
Gao, Shan, Albu, Elena, Putter, Hein, Stijnen, Pieter, Rademakers, Frank, Cossey, Veerle, Debaveye, Yves, Janssens, Christel, Van Calster, Ben, Wynants, Laure
Objective Hospitals register information in the electronic health records (EHR) continuously until discharge or death. As such, there is no censoring for in-hospital outcomes. We aimed to compare different dynamic regression modeling approaches to pr
Externí odkaz:
http://arxiv.org/abs/2405.01986
Autor:
Albu, Elena, Gao, Shan, Stijnen, Pieter, Rademakers, Frank, Janssens, Christel, Cossey, Veerle, Debaveye, Yves, Wynants, Laure, Van Calster, Ben
Prognostic outcomes related to hospital admissions typically do not suffer from censoring, and can be modeled either categorically or as time-to-event. Competing events are common but often ignored. We compared the performance of random forest (RF) m
Externí odkaz:
http://arxiv.org/abs/2404.16127
Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management. Here, we develop a general model, with no real-world training data, that accurately forecasts outbreaks and non-outbreaks. We propose a novel frame
Externí odkaz:
http://arxiv.org/abs/2404.08893
Autor:
Chakraborty, Amit K., Gao, Shan, Miry, Reza, Ramazi, Pouria, Greiner, Russell, Lewis, Mark A., Wang, Hao
The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse
Externí odkaz:
http://arxiv.org/abs/2403.16233
Autor:
Klinger, Marc, Rudolph, Annika, Rodrigues, Xavier, Yuan, Chengchao, de Clairfontaine, Gaëtan Fichet, Fedynitch, Anatoli, Winter, Walter, Pohl, Martin, Gao, Shan
We present the AM$^3$ (``Astrophysical Multi-Messenger Modeling'') software, which has been successfully used in the past to simulate the multi-messenger emission, including neutrinos, from active galactic nuclei, including the blazar sub-class, gamm
Externí odkaz:
http://arxiv.org/abs/2312.13371
Autor:
Di, Yan, Zhang, Chenyangguang, Wang, Chaowei, Zhang, Ruida, Zhai, Guangyao, Li, Yanyan, Fu, Bowen, Ji, Xiangyang, Gao, Shan
In this paper, we present ShapeMatcher, a unified self-supervised learning framework for joint shape canonicalization, segmentation, retrieval and deformation. Given a partially-observed object in an arbitrary pose, we first canonicalize the object b
Externí odkaz:
http://arxiv.org/abs/2311.11106
Unsupervised learning is a challenging task due to the lack of labels. Multiple Object Tracking (MOT), which inevitably suffers from mutual object interference, occlusion, etc., is even more difficult without label supervision. In this paper, we expl
Externí odkaz:
http://arxiv.org/abs/2309.00942
Autor:
Gao, Shan
Interconnects in power module result in thermal interfaces. The thermal interfaces degrade under thermal cycling, or chemical loading. Moreover, the reliability of thermal interfaces can be especially problematic when the interconnecting area is larg
Externí odkaz:
http://hdl.handle.net/10919/90770