Statistical Methods for Large Spatial and Spatio-temporal Datasets

Classical statistical models encounter the computational bottleneck for large spatial/spatio-temporal datasets. This dissertation contains three articles describing computationally efficient approximation methods for applying Gaussian process models to large spatial and spatio-temporal datasets. The first article extends the FSA-Block approach in [60] in the sense of preserving more information of the residual covariance matrix. By using a block conditional likelihood approximation to the residual likelihood, the residual covariance of neighboring data blocks can be preserved, which relaxes the conditional independence assumption of the FSA-Block approach. We show that the approximated likelihood by the proposed method is Gaussian with an explicit form of covariance matrix, and the computational complexity is linear with sample size n. We also show that the proposed method can result in a valid Gaussian process so that both the parameter estimation and prediction are consistent in the same model framework. Since neighborhood information are incorporated in approximating the residual covariance function, simulation studies show that the proposed method can further alleviate the mismatch problems in predicting responses on block boundary locations. The second article is the spatio-temporal extension of the FSA-Block approach, where we model the space-time responses as realizations from a Gaussian process model of spatio-temporal covariance functions. Since the knot number and locations are crucial to the model performance, a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points for the proposed method. We show that the proposed knot selection algorithm can result in more robust prediction results. Then the proposed method is compared with weighted composite likelihood method through simulation studies and an ozone dataset. The third article applies the nonseparable auto-covariance function to model the computer code outputs. It proposes a multi-output Gaussian process emulator with a nonseparable auto-covariance function to avoid limitations of using separable emulators. To facilitate the computation of nonseparable emulator, we introduce the FSA-Block approach to approximate the proposed model. Then we compare the proposed method with Gaussian process emulator with separable covariance models through simulated examples and a real computer code

Statistical Methods for Large Spatial and Spatio-temporal Datasets

Classical statistical models encounter the computational bottleneck for large spatial/spatio-temporal datasets. This dissertation contains three articles describing computationally efficient approximation methods for applying Gaussian process models to large spatial and spatio-temporal datasets. The first article extends the FSA-Block approach in [60] in the sense of preserving more information of the residual covariance matrix. By using a block conditional likelihood approximation to the residual likelihood, the residual covariance of neighboring data blocks can be preserved, which relaxes the conditional independence assumption of the FSA-Block approach. We show that the approximated likelihood by the proposed method is Gaussian with an explicit form of covariance matrix, and the computational complexity is linear with sample size n. We also show that the proposed method can result in a valid Gaussian process so that both the parameter estimation and prediction are consistent in the same model framework. Since neighborhood information are incorporated in approximating the residual covariance function, simulation studies show that the proposed method can further alleviate the mismatch problems in predicting responses on block boundary locations. The second article is the spatio-temporal extension of the FSA-Block approach, where we model the space-time responses as realizations from a Gaussian process model of spatio-temporal covariance functions. Since the knot number and locations are crucial to the model performance, a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points for the proposed method. We show that the proposed knot selection algorithm can result in more robust prediction results. Then the proposed method is compared with weighted composite likelihood method through simulation studies and an ozone dataset. The third article applies the nonseparable auto-covariance function to model the computer code outputs. It proposes a multi-output Gaussian process emulator with a nonseparable auto-covariance function to avoid limitations of using separable emulators. To facilitate the computation of nonseparable emulator, we introduce the FSA-Block approach to approximate the proposed model. Then we compare the proposed method with Gaussian process emulator with separable covariance models through simulated examples and a real computer code

Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers

Current arbitrary style transfer models are limited to either image or video domains. In order to achieve satisfying image and video style transfers, two different models are inevitably required with separate training processes on image and video domains, respectively. In this paper, we show that this can be precluded by introducing UniST, a Unified Style Transfer framework for both images and videos. At the core of UniST is a domain interaction transformer (DIT), which first explores context information within the specific domain and then interacts contextualized domain information for joint learning. In particular, DIT enables exploration of temporal information from videos for the image style transfer task and meanwhile allows rich appearance texture from images for video style transfer, thus leading to mutual benefits. Considering heavy computation of traditional multi-head self-attention, we present a simple yet effective axial multi-head self-attention (AMSA) for DIT, which improves computational efficiency while maintains style transfer performance. To verify the effectiveness of UniST, we conduct extensive experiments on both image and video style transfer tasks and show that UniST performs favorably against state-of-the-art approaches on both tasks. Code is available at https://github.com/NevSNev/UniST.Comment: Conference on International Conference on Computer Vision.(ICCV 2023

Flow-Guided Diffusion for Video Inpainting

Video inpainting has been challenged by complex scenarios like large movements and low-light conditions. Current methods, including emerging diffusion models, face limitations in quality and efficiency. This paper introduces the Flow-Guided Diffusion model for Video Inpainting (FGDVI), a novel approach that significantly enhances temporal consistency and inpainting quality via reusing an off-the-shelf image generation diffusion model. We employ optical flow for precise one-step latent propagation and introduces a model-agnostic flow-guided latent interpolation technique. This technique expedites denoising, seamlessly integrating with any Video Diffusion Model (VDM) without additional training. Our FGDVI demonstrates a remarkable 10% improvement in flow warping error E_warp over existing state-of-the-art methods. Our comprehensive experiments validate superior performance of FGDVI, offering a promising direction for advanced video inpainting. The code and detailed results will be publicly available in https://github.com/NevSNev/FGDVI

PEDOT doped with algal, mammalian and synthetic dopants: polymer properties, protein and cell interactions, and influence of electrical stimulation on neuronal cell differentiation

Poly(3,4-ethylenedioxythiophene) (PEDOT) films were electrochemically polymerised with several synthetic (dodecylbenzosulfonic acid (DBSA)) and biological (dextran sulphate (DS), chondroitin sulphate (CS), alginic acid (ALG) and ulvan (ULV)) dopant anions, and their physical, mechanical and electrochemical properties characterised. PEDOT films incorporating the biological dopants ALG and ULV produced films of the greatest surface roughness (46 ± 5.1 and 31 ± 1.9 nm, respectively), and demonstrated significantly lower shear modulus values relative to all other PEDOT films (2.1 ± 0.1 and 1.2 ± 0.2 MPa, respectively). Quartz crystal microgravimetry was used to study the adsorption of the important extracellular matrix protein fibronectin, revealing protein adsorption to be greatest on PEDOT doped with DS, followed by DBSA, ULV, CS and ALG. Electrical stimulation experiments applying a pulsed current using a biphasic waveform (250 Hz) were undertaken using PEDOT doped with either DBSA or ULV. Electrical stimulation had a significant influence on cell morphology and cell differentiation for PEDOT films with either dopant incorporated, with the degree of branching per cell increased by 10.5x on PEDOT-DBSA and 6.5x on PEDOT-ULV relative to unstimulated cells, and mean neurite length per cell increasing 2.6x and 2.2x on stimulated vs. unstimulated PEDOT-DBSA and PEDOT-ULV, respectively. We demonstrate the cytocompatibility of synthetic and biologically doped PEDOT biomaterials, including the new algal derived polysaccharide dopant ulvan, which, along with DBSA doped PEDOT, is shown to significantly enhance the differentiation of PC12 neuronal cells under electrical stimulation

Transient Inhibition of mTORC1 Signaling Ameliorates Irradiation-Induced Liver Damage

Recurrent liver cancer after surgery is often treated with radiotherapy, which induces liver damage. It has been documented that activation of the TGF-β and NF-κB signaling pathways plays important roles in irradiation-induced liver pathologies. However, the significance of mTOR signaling remains undefined after irradiation exposure. In the present study, we investigated the effects of inhibiting mTORC1 signaling on irradiated livers. Male C57BL/6J mice were acutely exposed to 8.0 Gy of X-ray total body irradiation and subsequently treated with rapamycin. The effects of rapamycin treatment on irradiated livers were examined at days 1, 3, and 7 after exposure. The results showed that 8.0 Gy of irradiation resulted in hepatocyte edema, hemorrhage, and sinusoidal congestion along with a decrease of ALB expression. Exposure of mice to irradiation significantly activated the mTORC1 signaling pathway determined by pS6 and p-mTOR expression via western blot and immunostaining. Transient inhibition of mTORC1 signaling by rapamycin treatment consistently accelerated liver recovery from irradiation, which was evidenced by decreasing sinusoidal congestion and increasing ALB expression after irradiation. The protective role of rapamycin on irradiated livers might be mediated by decreasing cellular apoptosis and increasing autophagy. These data suggest that transient inhibition of mTORC1 signaling by rapamycin protects livers against irradiation-induced damage

Bayesian inference of spatio-temporal changes of arctic sea ice

© 2020 International Society for Bayesian Analysis. Arctic sea ice extent has drawn increasing interest and alarm from geoscientists, owing to its rapid decline. In this article, we propose a Bayesian spatio-temporal hierarchical statistical model for binary Arctic sea ice data over two decades, where a latent dynamic spatio-temporal Gaussian process is used to model the data-dependence through a logit link function. Our ultimate goal is to perform inference on the dynamic spatial behavior of Arctic sea ice over a period of two decades. Physically motivated covariates are assessed using autologistic diagnostics. Our Bayesian spatio-temporal model shows how parameter uncertainty in such a complex hierarchical model can influence spatio-temporal prediction. The posterior distributions of new summary statistics are proposed to detect the changing patterns of Arctic sea ice over two decades since 1997

Estimating Spatial Changes Over Time of Arctic Sea Ice using Hidden 2x2 Tables

Arctic sea ice extent has been of considerable interest to scientists in recent years, mainly due to its decreasing temporal trend over the past 20 years. In this article, we propose a hierarchical spatio-temporal generalized linear model for binary Arctic sea-ice-extent data, where statistical dependencies in the data are modeled through a latent spatio-temporal linear mixed effects model. By using a fixed number of spatial basis functions, the resulting model achieves both dimension reduction and non-stationarity for spatial fields at different time points. An EM algorithm is proposed to estimate model parameters, and an empirical-hierarchical-modeling approach is applied to obtain the predictive distribution of the latent spatio-temporal process. We illustrate the accuracy of the parameter estimation through a simulation study. The hierarchical model is applied to spatial Arctic sea-ice-extent data in the month of September for 20 years in the recent past, where several posterior summaries are obtained to detect the changes of Arctic sea ice cover. In particular, we consider a time series of latent 2 x 2 tables to infer the spatial changes of Arctic sea ice over time