Climate Change and Economic Growth : An Empirical Study of Economic Impacts of Climate Change

Doctoral thesis (PhD) – Nord University, 2021publishedVersio

EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography

This paper introduces the Efficient Decoupled Masked Autoencoder (EDMAE), a novel self-supervised method for recognizing standard views in pediatric echocardiography. EDMAE introduces a new proxy task based on the encoder-decoder structure. The EDMAE encoder is composed of a teacher and a student encoder. The teacher encoder extracts the potential representation of the masked image blocks, while the student encoder extracts the potential representation of the visible image blocks. The loss is calculated between the feature maps output by the two encoders to ensure consistency in the latent representations they extract. EDMAE uses pure convolution operations instead of the ViT structure in the MAE encoder. This improves training efficiency and convergence speed. EDMAE is pre-trained on a large-scale private dataset of pediatric echocardiography using self-supervised learning, and then fine-tuned for standard view recognition. The proposed method achieves high classification accuracy in 27 standard views of pediatric echocardiography. To further verify the effectiveness of the proposed method, the authors perform another downstream task of cardiac ultrasound segmentation on the public dataset CAMUS. The experimental results demonstrate that the proposed method outperforms some popular supervised and recent self-supervised methods, and is more competitive on different downstream tasks.Comment: 15 pages, 5 figures, 8 tables, Published in Biomedical Signal Processing and Contro

TaleCrafter: Interactive Story Visualization with Multiple Characters

Accurate Story visualization requires several necessary elements, such as identity consistency across frames, the alignment between plain text and visual content, and a reasonable layout of objects in images. Most previous works endeavor to meet these requirements by fitting a text-to-image (T2I) model on a set of videos in the same style and with the same characters, e.g., the FlintstonesSV dataset. However, the learned T2I models typically struggle to adapt to new characters, scenes, and styles, and often lack the flexibility to revise the layout of the synthesized images. This paper proposes a system for generic interactive story visualization, capable of handling multiple novel characters and supporting the editing of layout and local structure. It is developed by leveraging the prior knowledge of large language and T2I models, trained on massive corpora. The system comprises four interconnected components: story-to-prompt generation (S2P), text-to-layout generation (T2L), controllable text-to-image generation (C-T2I), and image-to-video animation (I2V). First, the S2P module converts concise story information into detailed prompts required for subsequent stages. Next, T2L generates diverse and reasonable layouts based on the prompts, offering users the ability to adjust and refine the layout to their preference. The core component, C-T2I, enables the creation of images guided by layouts, sketches, and actor-specific identifiers to maintain consistency and detail across visualizations. Finally, I2V enriches the visualization process by animating the generated images. Extensive experiments and a user study are conducted to validate the effectiveness and flexibility of interactive editing of the proposed system.Comment: Github repository: https://github.com/VideoCrafter/TaleCrafte

Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation

Generating videos for visual storytelling can be a tedious and complex process that typically requires either live-action filming or graphics animation rendering. To bypass these challenges, our key idea is to utilize the abundance of existing video clips and synthesize a coherent storytelling video by customizing their appearances. We achieve this by developing a framework comprised of two functional modules: (i) Motion Structure Retrieval, which provides video candidates with desired scene or motion context described by query texts, and (ii) Structure-Guided Text-to-Video Synthesis, which generates plot-aligned videos under the guidance of motion structure and text prompts. For the first module, we leverage an off-the-shelf video retrieval system and extract video depths as motion structure. For the second module, we propose a controllable video generation model that offers flexible controls over structure and characters. The videos are synthesized by following the structural guidance and appearance instruction. To ensure visual consistency across clips, we propose an effective concept personalization approach, which allows the specification of the desired character identities through text prompts. Extensive experiments demonstrate that our approach exhibits significant advantages over various existing baselines.Comment: Github: https://github.com/VideoCrafter/Animate-A-Story Project page: https://videocrafter.github.io/Animate-A-Stor

Predictive model for inflammation grades of chronic hepatitis B: Large‐scale analysis of clinical parameters and gene expressions

BackgroundLiver biopsy is the gold standard to assess pathological features (eg inflammation grades) for hepatitis B virus‐infected patients although it is invasive and traumatic; meanwhile, several gene profiles of chronic hepatitis B (CHB) have been separately described in relatively small hepatitis B virus (HBV)‐infected samples. We aimed to analyse correlations among inflammation grades, gene expressions and clinical parameters (serum alanine amino transaminase, aspartate amino transaminase and HBV‐DNA) in large‐scale CHB samples and to predict inflammation grades by using clinical parameters and/or gene expressions.MethodsWe analysed gene expressions with three clinical parameters in 122 CHB samples by an improved regression model. Principal component analysis and machine‐learning methods including Random Forest, K‐nearest neighbour and support vector machine were used for analysis and further diagnosis models. Six normal samples were conducted to validate the predictive model.ResultsSignificant genes related to clinical parameters were found enriching in the immune system, interferon‐stimulated, regulation of cytokine production, anti‐apoptosis, and etc. A panel of these genes with clinical parameters can effectively predict binary classifications of inflammation grade (area under the ROC curve [AUC]: 0.88, 95% confidence interval [CI]: 0.77‐0.93), validated by normal samples. A panel with only clinical parameters was also valuable (AUC: 0.78, 95% CI: 0.65‐0.86), indicating that liquid biopsy method for detecting the pathology of CHB is possible.ConclusionsThis is the first study to systematically elucidate the relationships among gene expressions, clinical parameters and pathological inflammation grades in CHB, and to build models predicting inflammation grades by gene expressions and/or clinical parameters as well.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139116/1/liv13427.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139116/2/liv13427_am.pd

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Climate Change and Economic Growth : An Empirical Study of Economic Impacts of Climate Change

Doctoral thesis (PhD) – Nord University, 2021publishedVersio

Global Trends in Downward Surface Solar Radiation from Spatial Interpolated Ground Observations during 1961 2019

Downward surface solar radiation (SSR) is a crucial component of the global energy balance, affecting temperature and the hydrological cycle profoundly, and it provides crucial information about climate change. Many studies have examined SSR trends; however, they have often concentrated on specific regions due to limited spatial coverage of ground-based observation stations. To overcome this spatial limitation, this study performs a spatial interpolation based on amachine learning method, randomforest, to interpolatemonthly SSR anomalies using a number of climatic variables (various temperature indices, cloud coverage, etc.), time-point indicators (years and months of SSR observations), and geographical characteristics of locations (latitude, longitude, etc.). The predictors that provide the largest explanatory power for interannual variability are diurnal temperature range and cloud coverage. The output of the spatial interpolation is a 0.58 3 0.58 monthly gridded dataset of SSR anomalies with complete land coverage over the period 1961 2019, which is used afterward in a comprehensive trend analysis for (i) each continent separately and (ii) the entire globe. The continental-level analysis reveals the major contributors to the global dimming and brightening. In particular, the global dimming before the 1980s is primarily dominated by negative trends in Asia and North America, whereas Europe and Oceania have been the two largest contributors to the brightening after 1982 and up until 2019.ISSN:0894-8755ISSN:1520-044