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of 228
pro vyhledávání: '"Fushing, Hsieh"'
We study Covid-19 spreading dynamics underlying 84 curves of daily Covid-19 infection rates pertaining to 84 districts belonging to the largest seven cities in Taiwan during her pristine surge period. Our computational developments begin with selecti
Externí odkaz:
http://arxiv.org/abs/2211.10926
We reformulate and reframe a series of increasingly complex parametric statistical topics into a framework of response-vs-covariate (Re-Co) dynamics that is described without any explicit functional structures. Then we resolve these topics' data anal
Externí odkaz:
http://arxiv.org/abs/2209.02629
Based on structured data derived from large complex systems, we computationally further develop and refine a major factor selection protocol by accommodating structural dependency and heterogeneity among many features to unravel data's information co
Externí odkaz:
http://arxiv.org/abs/2209.02623
Autor:
Shuting Liao, Fushing Hsieh
Publikováno v:
IEEE Access, Vol 12, Pp 3292-3314 (2024)
Data analysis is a scientific endeavor of bottom-up data-driven engineering nature. This nature requires all employed conceptual criteria and algorithmic computations equipped with scientific interpretability. It must be free from top-down modeling v
Externí odkaz:
https://doaj.org/article/d627388368f743a5a6fde543eb4c8a48
Publikováno v:
PLoS ONE, Vol 19, Iss 2, p e0298049 (2024)
We investigate the dynamic characteristics of Covid-19 daily infection rates in Taiwan during its initial surge period, focusing on 79 districts within the seven largest cities. By employing computational techniques, we extract 18 features from each
Externí odkaz:
https://doaj.org/article/3249363471a64a76917b9a0aade6a469
Autor:
Xi Yang, Fushing Hsieh
Publikováno v:
IEEE Access, Vol 11, Pp 4517-4536 (2023)
We develop a computational protocol for mimicking personal gait dynamics with 12-dimensional time series derived from 4 accelerometer sensors found in the MAREA database and then explore its utilities in line with precision learning of human activiti
Externí odkaz:
https://doaj.org/article/5219f0b914234509a34f230d35b9b76c
Autor:
Guan, Jiahui, Fushing, Hsieh
Upon a matrix representation of a binary bipartite network, via the permutation invariance, a coupling geometry is computed to approximate the minimum energy macrostate of a network's system. Such a macrostate is supposed to constitute the intrinsic
Externí odkaz:
http://arxiv.org/abs/1802.00032
Phylogenetic trees in genetics and biology in general are all binary. We make an attempt to answer one fundamental question: Is such binary branching from the coarsest to the finest scales sustained by data? We convert this question into an equivalen
Externí odkaz:
http://arxiv.org/abs/1801.08646
Data generated from a system of interest typically consists of measurements from an ensemble of subjects across multiple response and covariate features, and is naturally represented by one response-matrix against one covariate-matrix. Likely each of
Externí odkaz:
http://arxiv.org/abs/1706.00103
Autor:
Emanuela Furfaro, Fushing Hsieh
Publikováno v:
Entropy, Vol 25, Iss 9, p 1311 (2023)
Individual subjects’ ratings neither are metric nor have homogeneous meanings, consequently digital- labeled collections of subjects’ ratings are intrinsically ordinal and categorical. However, in these situations, the literature privileges the u
Externí odkaz:
https://doaj.org/article/77b0ed4296654fcc80fc6d1e665cca09