Results 131 to 140 of about 720,962 (318)

Inference Methods

, 2012
[[abstract]]Overview of Inference ...
王學亮
core  

A scalable tool for analyzing genomic variants of humans using knowledge graphs and graph machine learning

Frontiers in Big Data
Advances in high-throughput genome sequencing have enabled large-scale genome sequencing in clinical practice and research studies. By analyzing genomic variants of humans, scientists can gain better understanding of the risk factors of complex diseases ...
Shivika Prasanna, Ajay Kumar, Deepthi Rao, Eduardo J. Simoes, Praveen Rao   +4 more
doaj   +1 more source

Brainstem and Cerebellar Volume Loss and Associated Clinical Features in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Progressive Supranuclear Palsy (PSP) is a neurodegenerative ‘tauopathy’ with predominating pathology in the basal ganglia and midbrain. Caudal tau spread frequently implicates the cerebellum; however, the pattern of atrophy remains equivocal.
Chloe Spiegel, Timothy P. Siejka, Cassandra Marotta, Josh J. Y. Lee, Kelly Bertram, Terence J. O'Brien, Meng Law, Lucy Vivash, Ian H. Harding   +8 more
wiley   +1 more source

Chapter 2 Inference for Markov Chain Epidemic Models

, 2019
In order to present an overview of the statistical problems, we first detail the statistical inference for Markov chains. Indeed, discrete time Markov chains models are interesting here because many questions that can arise for more complex models can be
Pardoux, Etienne, Etienne Pardoux, Tom Britton, Britton, Tom,   +3 more
core   +1 more source

CX3CL1 in Early Detection of Alzheimer's Disease: Plasma Dynamics Across Age and Disease Stages

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Backgrounds Alzheimer's disease (AD) is characterized by amyloid‐beta plaques, tau tangles, and neuroinflammation. C‐X3‐C motif chemokine ligand 1 (CX3CL1, also known as fractalkine), a neuroimmune chemokine implicated in AD pathogenesis, shows inconsistent alterations in plasma/serum across studies.
Ling Wang, Yujie Liu, Fei Li, Xuelin Li, Lanlan Li, Jie Zhang, Yali Xu   +6 more
wiley   +1 more source

Research on evaluation methods for inference business capability of intelligent computing center

Dianxin kexue
The construction of artificial intelligence inference centers has become a hotspot in the current development of intelligent computing centers. Evaluating the inference business capability of intelligent computing centers solely based on the scale of ...
WU Zhenyu, ZHAO Zhanjun, BU Zhonggui, LIU Peng, TIAN Wen, WANG Yiwei, DONG Jiangfan, WANG Meng, ZHANG Zhi   +8 more
doaj  

ML-NIC: accelerating machine learning inference using smart network interface cards

Frontiers in Computer Science
Low-latency inference for machine learning models is increasingly becoming a necessary requirement, as these models are used in mission-critical applications such as autonomous driving, military defense (e.g., target recognition), and network traffic ...
Raghav Kapoor, David C. Anastasiu, Sean Choi   +2 more
doaj   +1 more source

Pragmatic Inference and Argumentative Inference [PDF]

, 2018
I offer a theoretical discussion of the relationship between pragmatic inference (inference about meaning) and argumentative inference (inference about the acceptability of a premise/conclusion relationship). The discussion (i) compares an argumentative view on meaning construction and an interpretative view on argument evaluation, (ii) argues that ...
openaire  

Impact of Asymptomatic Intracranial Hemorrhage on Outcome After Endovascular Stroke Treatment

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Endovascular treatment (EVT) achieves high rates of recanalization in acute large‐vessel occlusion (LVO) stroke, but functional recovery remains heterogeneous. While symptomatic intracranial hemorrhage (sICH) has been well studied, the prognostic impact of asymptomatic intracranial hemorrhage (aICH) after EVT is less certain ...
Shihai Yang, Chengsong Yue, Zhuang Li, Xiaolei Shi, Haoxuan Zhu, Jie Yang, Linyu Li, Jiaxing Song, Shitao Fan, Gaoming Li, Wenzhe Sun, Zhixian Ye, Jiacheng Huang, Dahong Yang, Jinfu Ma, Xu Xu, Zhixi Wang, Lilan Wang, Guojian Liu, Fengli Li, Changwei Guo, Li Qi, Wenjie Zi   +22 more
wiley   +1 more source

y0-causal-inference/y0: v0.2.6

<h2>What's Changed</h2> <ul> <li>Improve variable sort and CF graph testing by @cthoyt in https://github.com/y0-causal-inference/y0/pull/199</li> <li>Use frozensets for interventions by @cthoyt in https://github.com/y0-
Jeremy Zucker, pnavada, Benjamin M. Gyori, Marc-Antoine Parent, Charles Tapley Hoyt   +4 more
core   +1 more source