Zobrazeno 1 - 10
of 10 655
pro vyhledávání: '"Chen, Nan"'
We aim to learn the functional co-response group: a group of taxa whose co-response effect (the representative characteristic of the group) associates well statistically with a functional variable. Different from the state-of-the-art method, we model
Externí odkaz:
http://arxiv.org/abs/2407.03897
Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, but it remains a challenging task that requires multiple low-level implementations, such as natural language processing and visualization design.
Externí odkaz:
http://arxiv.org/abs/2407.00981
Autor:
Chen, Nan, Liu, Honghu
Enhancing the sparsity of data-driven reduced-order models (ROMs) has gained increasing attention in recent years. In this work, we analyze an efficient approach to identifying skillful ROMs with a sparse structure using an information-theoretic indi
Externí odkaz:
http://arxiv.org/abs/2407.00271
Mesoscale eddies are critical in ocean circulation and the global climate system. Standard eddy identification methods are usually based on deterministic optimal point estimates of the ocean flow field, which produce a single best estimate without ac
Externí odkaz:
http://arxiv.org/abs/2405.12342
Autor:
Chen, Kuan-Cheng, Li, Tai-Yue, Wang, Yun-Yuan, See, Simon, Wang, Chun-Chieh, Wille, Robert, Chen, Nan-Yow, Yang, An-Cheng, Lin, Chun-Yu
This paper investigates the application of Quantum Support Vector Machines (QSVMs) with an emphasis on the computational advancements enabled by NVIDIA's cuQuantum SDK, especially leveraging the cuTensorNet library. We present a simulation workflow t
Externí odkaz:
http://arxiv.org/abs/2405.02630
A new knowledge-based and machine learning hybrid modeling approach, called conditional Gaussian neural stochastic differential equation (CGNSDE), is developed to facilitate modeling complex dynamical systems and implementing analytic formulae of the
Externí odkaz:
http://arxiv.org/abs/2404.06749
In the era of data-driven decision-making, the complexity of data analysis necessitates advanced expertise and tools of data science, presenting significant challenges even for specialists. Large Language Models (LLMs) have emerged as promising aids
Externí odkaz:
http://arxiv.org/abs/2402.17168
In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently m
Externí odkaz:
http://arxiv.org/abs/2402.12710
Deploying Lagrangian drifters that facilitate the state estimation of the underlying flow field within a future time interval is practically important. However, the uncertainty in estimating the flow field prevents using standard deterministic approa
Externí odkaz:
http://arxiv.org/abs/2402.10034
Understanding ENSO dynamics has tremendously improved over the past decades. However, one aspect still poorly understood or represented in conceptual models is the ENSO diversity in spatial pattern, peak intensity, and temporal evolution. In this pap
Externí odkaz:
http://arxiv.org/abs/2402.04585