Zobrazeno 1 - 10
of 6 504
pro vyhledávání: '"Kang, Jian"'
Autor:
Li, Shu-Ang, Meng, Xiao-Yan, Zhang, Su, Zhang, Ying-Jie, Yang, Run-Zhou, Wang, Dian-Dian, Yang, Yang, Liu, Pei-Pei, Kang, Jian-Sheng
An accurate map of intracellular organelle pH is crucial for comprehending cellular metabolism and organellar functions. However, a unified intracellular pH spectrum using a single probe is still lack. Here, we developed a novel quantum entanglement-
Externí odkaz:
http://arxiv.org/abs/2407.04232
We explore methods to reduce the impact of unobserved confounders on the causal mediation analysis of high-dimensional mediators with spatially smooth structures, such as brain imaging data. The key approach is to incorporate the latent individual ef
Externí odkaz:
http://arxiv.org/abs/2407.04142
Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes domains are often hampered by unreliable predictions. Although numerous uncertainty quantification methods have been proposed to address this limitation, they
Externí odkaz:
http://arxiv.org/abs/2406.18763
The spatial composition and cellular heterogeneity of the tumor microenvironment plays a critical role in cancer development and progression. High-definition pathology imaging of tumor biopsies provide a high-resolution view of the spatial organizati
Externí odkaz:
http://arxiv.org/abs/2406.16721
As online music consumption increasingly shifts towards playlist-based listening, the task of playlist continuation, in which an algorithm suggests songs to extend a playlist in a personalized and musically cohesive manner, has become vital to the su
Externí odkaz:
http://arxiv.org/abs/2406.14333
Autor:
Hou, Yuanbo, Ren, Qiaoqiao, Mitchell, Andrew, Wang, Wenwu, Kang, Jian, Belpaeme, Tony, Botteldooren, Dick
We live in a rich and varied acoustic world, which is experienced by individuals or communities as a soundscape. Computational auditory scene analysis, disentangling acoustic scenes by detecting and classifying events, focuses on objective attributes
Externí odkaz:
http://arxiv.org/abs/2406.05914
Autor:
Herzog-Arbeitman, Jonah, Yu, Jiabin, Călugăru, Dumitru, Hu, Haoyu, Regnault, Nicolas, Vafek, Oskar, Kang, Jian, Bernevig, B. Andrei
Although the strongly interacting flat bands in twisted bilayer graphene (TBG) have been approached using the minimal Bistritzer-MacDonald (BM) Hamiltonian, there is mounting evidence that strain and lattice relaxation are essential in correctly dete
Externí odkaz:
http://arxiv.org/abs/2405.13880
In multivariate spline regression, the number and locations of knots influence the performance and interpretability significantly. However, due to non-differentiability and varying dimensions, there is no desirable frequentist method to make inferenc
Externí odkaz:
http://arxiv.org/abs/2405.13353
We develop scalable manifold learning methods and theory, motivated by the problem of estimating manifold of fMRI activation in the Human Connectome Project (HCP). We propose the Fast Graph Laplacian Estimation for Heat Kernel Gaussian Processes (FLG
Externí odkaz:
http://arxiv.org/abs/2405.13342
Bayesian Image-on-Scalar Regression (ISR) offers significant advantages for neuroimaging data analysis, including flexibility and the ability to quantify uncertainty. However, its application to large-scale imaging datasets, such as found in the UK B
Externí odkaz:
http://arxiv.org/abs/2404.13204