no code implementations • 29 Apr 2024 • Sing-Yuan Yeh, Hau-Tieng Wu, Ronen Talmon, Mao-Pei Tsui
Alternating Diffusion (AD) is a commonly applied diffusion-based sensor fusion algorithm.
no code implementations • 31 Jan 2024 • Seonghyeon Jeong, Hau-Tieng Wu
We present a theoretical foundation regarding the boundedness of the t-SNE algorithm.
no code implementations • 8 Jan 2024 • Hau-Tieng Wu
In this manuscript, we propose an efficient manifold denoiser based on landmark diffusion and optimal shrinkage under the complicated high dimensional noise and compact manifold setup.
1 code implementation • 27 Oct 2023 • Riki Shimizu, Hau-Tieng Wu
We introduce the novel non-linear time-frequency analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs).
1 code implementation • 8 Sep 2023 • Joaquin Ruiz, Hau-Tieng Wu, Marcelo A. Colominas
In this paper, we introduce a novel algorithm, coined Harmonic Level Interpolation (HaLI), which enhances the performance of existing imputation algorithms for oscillatory time series.
no code implementations • 17 Aug 2022 • Joyce Chew, Matthew Hirn, Smita Krishnaswamy, Deanna Needell, Michael Perlmutter, Holly Steach, Siddharth Viswanath, Hau-Tieng Wu
Our proposed framework includes previous work on geometric scattering as special cases but also applies to more general settings such as directed graphs, signed graphs, and manifolds with boundary.
no code implementations • 12 Aug 2022 • Marcelo A. Colominas, Hau-Tieng Wu
We present an iterative warping and clustering algorithm to estimate $s_1,\ldots, s_K$ from a nonstationary oscillatory signal with time-varying amplitude and frequency, and hence the change points of the WSFs.
1 code implementation • 21 Jun 2022 • Joyce Chew, Holly R. Steach, Siddharth Viswanath, Hau-Tieng Wu, Matthew Hirn, Deanna Needell, Smita Krishnaswamy, Michael Perlmutter
The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold.
no code implementations • 21 Jan 2022 • Tal Shnitzer, Hau-Tieng Wu, Ronen Talmon
Our approach combines three components that are often considered separately: (i) manifold learning for building operators representing the geometry of the variables, (ii) Riemannian geometry of symmetric positive-definite matrices for multiscale composition of operators corresponding to different time samples, and (iii) spectral analysis of the composite operators for extracting different dynamic modes.
no code implementations • 22 Nov 2021 • Xiucai Ding, Hau-Tieng Wu
It turns out that both the asymptotic limits and convergence rates depend on the signal-to-noise ratio (SNR) of each sensor and selected bandwidths.
no code implementations • 21 Sep 2021 • Shen-Chih Wang, Chien-Kun Ting, Cheng-Yen Chen, Chin-Su Liu, Niang-Cheng Lin, Che-Chuan Loon, Hau-Tieng Wu, Yu-Ting Lin
The neohepatic phase variability of morphology was associated with EAF scores as well as postoperative bilirubin levels, international normalized ratio, aspartate aminotransferase levels, and platelet count.
no code implementations • 26 May 2021 • Peter Zimmermann, Marta C. Antonelli, Ritika Sharma, Alexander Müller, Camilla Zelgert, Bibiana Fabre, Natasha Wenzel, Hau-Tieng Wu, Martin G. Frasch, Silvia M. Lobmaier
What is the influence of chronic maternal prenatal stress (PS) on fetal iron homeostasis?
no code implementations • 21 Nov 2020 • Xiucai Ding, Hau-Tieng Wu
We systematically study the spectrum of kernel-based graph Laplacian (GL) constructed from high-dimensional and noisy random point cloud in the nonnull setup.
no code implementations • 21 Nov 2020 • Gi-Ren Liu, Yuan-Chung Sheu, Hau-Tieng Wu
On the whole, NAST is a transform that comprises a sequence of ``neural processing units'', each of which applies a high pass filter to the input from the previous layer followed by a composition with a nonlinear function as the output to the next neuron.
no code implementations • 3 Nov 2020 • Xiuyuan Cheng, Hau-Tieng Wu
This paper proves the convergence of graph Laplacian operator $L_N$ to manifold (weighted-)Laplacian for a new family of kNN self-tuned kernels $W^{(\alpha)}_{ij} = k_0( \frac{ \| x_i - x_j \|^2}{ \epsilon \hat{\rho}(x_i) \hat{\rho}(x_j)})/\hat{\rho}(x_i)^\alpha \hat{\rho}(x_j)^\alpha$, where $\hat{\rho}$ is the estimated bandwidth function {by kNN}, and the limiting operator is also parametrized by $\alpha$.
1 code implementation • 14 Oct 2020 • Marcelo A. Colominas, Hau-Tieng Wu
We propose a novel {\em nonlinear regression scheme} to robustly decompose a signal into its constituting multiple oscillatory components with time-varying frequency, amplitude and wave-shape function.
no code implementations • 14 Oct 2020 • David B Dunson, Hau-Tieng Wu, Nan Wu
The GL is constructed from a kernel which depends only on the Euclidean coordinates of the inputs.
no code implementations • 1 Sep 2020 • Tzu-Chi Liu, Yi-Wen Liu, Hau-Tieng Wu
The sOS achieved an SNR enhancement of 2 to 3 dB in simulation, and demonstrated capability to enhance the SNR in real recordings when the SNR achieved by the BM was below 0 dB.
no code implementations • 11 Aug 2020 • Whitney K. Huang, Yu-Min Chung, Yu-Bo Wang, Jeff E. Mandel, Hau-Tieng Wu
Airflow signal encodes rich information about respiratory system.
no code implementations • 13 Jul 2020 • Hau-Tieng Wu, Nan Wu
When analyzing modern machine learning algorithms, we may need to handle kernel density estimation (KDE) with intricate kernels that are not designed by the user and might even be irregular and asymmetric.
no code implementations • 23 Jun 2020 • Jacob McErlean, John Malik, Yu-Ting Lin, Ronen Talmon, Hau-Tieng Wu
We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms.
1 code implementation • 9 Aug 2019 • Yu-Min Chung, Chuan-Shen Hu, Yu-Lun Lo, Hau-Tieng Wu
The first step is capturing the shapes of time series from two different aspects -- {the PH's and hence persistence diagrams of its} sub-level set and Taken's lag map.
no code implementations • 30 May 2019 • Yi-Wen Liu, Sheng-Lun Kao, Hau-Tieng Wu, Tzu-Chi Liu, Te-Yung Fang, Pa-Chun Wang
Aims/Objectives: To find parameters for predicting MD hearing outcomes.
no code implementations • 21 Apr 2019 • Pei-Chun Su, Stephen Miller, Salim Idriss, Piers Barker, Hau-Tieng Wu
We propose a novel algorithm to recover fetal electrocardiogram (ECG) for both the fetal heart rate analysis and morphological analysis of its waveform from two or three trans-abdominal maternal ECG channels.
1 code implementation • 14 Feb 2019 • Natalia Martinez, Martin Bertran, Guillermo Sapiro, Hau-Tieng Wu
One way to avoid these constraints is using infrared cameras, allowing the monitoring of iHR under low light conditions.
no code implementations • 11 Nov 2018 • Hau-Tieng Wu, Nan Wu
Based on the Riemannian manifold model, we study the asymptotical behavior of a widely applied unsupervised learning algorithm, locally linear embedding (LLE), when the point cloud is sampled from a compact, smooth manifold with boundary.
Statistics Theory Statistics Theory 62-07
3 code implementations • 7 Nov 2018 • Vahan Huroyan, Gilad Lerman, Hau-Tieng Wu
The main contribution of this work is a method for recovering the rotations of the pieces when both shuffles and rotations are unknown.
no code implementations • 27 Aug 2018 • Yu-Ting Lin, Yu-Lun Lo, Chen-Yun Lin, Hau-Tieng Wu, Martin G. Frasch
However, in this setup the data calibration issue is often not discussed and, rather, implicitly assumed, while the clinical monitors might not be designed for the data analysis purpose.
1 code implementation • 24 Aug 2018 • Martin G. Frasch, Chao Shen, Hau-Tieng Wu, Alexander Mueller, Emily Neuhaus, Raphael A. Bernier, Dana Kamara, Theodore P. Beauchaine
High-frequency heart rate variability (HRV) has identified parasympathetic nervous system alterations in autism spectrum disorder (ASD).
Quantitative Methods Neurons and Cognition
no code implementations • 1 Aug 2018 • John Malik, Yu-Lun Lo, Hau-Tieng Wu
This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring.
no code implementations • 17 Mar 2018 • Chunyu Tan, Liming Zhang, Hau-Tieng Wu
The proposed compression algorithm is applied to the electrocardiogram signal.
no code implementations • 7 Feb 2017 • Ruilin Li, Martin G. Frasch, Hau-Tieng Wu
There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery.
no code implementations • 13 Jan 2017 • Ori Katz, Ronen Talmon, Yu-Lun Lo, Hau-Tieng Wu
We show that without prior knowledge on the different modalities and on the measured system, our method gives rise to a data-driven representation that is well correlated with the underlying sleep process and is robust to noise and sensor-specific effects.
no code implementations • 9 Sep 2016 • Su Li, Hau-Tieng Wu
The multiple fundamental frequency detection problem and the source separation problem from a single-channel signal containing multiple oscillatory components and a nonstationary noise are both challenging tasks.
1 code implementation • 30 Jan 2016 • Ronen Talmon, Hau-Tieng Wu
The analysis of data sets arising from multiple sensors has drawn significant research attention over the years.
no code implementations • 23 May 2014 • Noureddine El Karoui, Hau-Tieng Wu
Recently, several data analytic techniques based on connection graph laplacian (CGL) ideas have appeared in the literature.
no code implementations • 1 Oct 2013 • Noureddine El Karoui, Hau-Tieng Wu
Graph connection Laplacian (GCL) is a modern data analysis technique that is starting to be applied for the analysis of high dimensional and massive datasets.
no code implementations • 7 Jun 2013 • Amit Singer, Hau-Tieng Wu
We prove that the eigenvectors and eigenvalues of these Laplacians converge in the limit of infinitely many independent random samples.
no code implementations • 18 May 2013 • Hau-Tieng Wu
Given a class of closed Riemannian manifolds with prescribed geometric conditions, we introduce an embedding of the manifolds into $\ell^2$ based on the heat kernel of the Connection Laplacian associated with the Levi-Civita connection on the tangent bundle.
1 code implementation • 1 Feb 2011 • Amit Singer, Hau-Tieng Wu
We introduce {\em vector diffusion maps} (VDM), a new mathematical framework for organizing and analyzing massive high dimensional data sets, images and shapes.