Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution

We propose an efficient diffusion-based text-to-video super-resolution (SR) tuning approach that leverages the readily learned capacity of pixel level image diffusion model to capture spatial information for video generation. To accomplish this goal, we design an efficient architecture by inflating the weightings of the text-to-image SR model into our video generation framework. Additionally, we incorporate a temporal adapter to ensure temporal coherence across video frames. We investigate different tuning approaches based on our inflated architecture and report trade-offs between computational costs and super-resolution quality. Empirical evaluation, both quantitative and qualitative, on the Shutterstock video dataset, demonstrates that our approach is able to perform text-to-video SR generation with good visual quality and temporal consistency. To evaluate temporal coherence, we also present visualizations in video format in https://drive.google.com/drive/folders/1YVc-KMSJqOrEUdQWVaI-Yfu8Vsfu_1aO?usp=sharing .Comment: WACV'24 worksho

Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.Comment: Machine Learning for Healthcare 201

Multi-tissue integrative analysis of personal epigenomes

Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome. To address this, we constructed phased, diploid genomes for four cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors. Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele- and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes. These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics

Roles of Identified Long Noncoding RNA in Diabetic Nephropathy

Diabetes mellitus is the leading chronic disease in the world, and diabetic nephropathy (DN) as one of its complications could increase the mortality. The development of DN is associated to abnormal hemodynamic factors like cytokine networks and the intervention of metabolic risk factors like blood pressure, blood glucose, and blood lipid. However, the pathogenesis of DN is still poorly understood. Although glucose-lowering drugs and insulins have significant effects on blood glucose, the fluctuation of blood glucose or other risk factors could continuously damage the kidney. Recent studies reported that the progression of DN is closely related to the expression of long noncoding RNA (lncRNA), which is important for the early diagnosis and targeted intervention of DN. In this review, we briefly summarize the published studies on the functions and potential mechanism of reported lncRNA in the regulation of DN

Multiple UAVs Path Planning Based on Deep Reinforcement Learning in Communication Denial Environment

In this paper, we propose a C51-Duel-IP (C51 Dueling DQN with Independent Policy) dynamic destination path-planning algorithm to solve the problem of autonomous navigation and avoidance of multiple Unmanned Aerial Vehicles (UAVs) in the communication denial environment. Our proposed algorithm expresses the Q function output by the Dueling network as a Q distribution, which improves the fitting ability of the Q value. We also extend the single-step temporal differential (TD) to the N-step timing differential, which solves the problem of inflexible updates of the single-step temporal differential. More importantly, we use an independent policy to achieve autonomous avoidance and navigation of multiple UAVs without any communication with each other. In the case of communication rejection, the independent policy can achieve the consistency of multiple UAVs and avoid the greedy behavior of UAVs. In multiple-UAV dynamic destination scenarios, our work includes path planning, taking off from different initial positions, and dynamic path planning, taking off from the same initial position. The hardware-in-the-loop (HITL) experiment results show that our C51-Duel-IP algorithm is much more robust and effective than the original Dueling-IP and DQN-IP algorithms in an urban simulation environment. Our independent policy algorithm has similar effects as the shared policy but with the significant advantage of running in a communication denial environment

Experimental Study on Unsteady Cavitating Flow and Its Instability in Liquid Rocket Engine Inducer

To study instability in the unsteady cavitating flow in a liquid rocket engine inducer, visualization experiments of non-cavitating and cavitating flows inside a model inducer were carried out at different flow conditions. Visual experiments were carried out to capture the evolution of non-cavitating and cavitating flows in a three-bladed inducer by using a high-speed camera. The external characteristic performance, cavitation performance, and pressure pulsation were analyzed based on the observation of non-cavitation and cavitation development and their instabilities. Under non-cavitation conditions, the change of flow rate has a significant impact on the pressure pulsation characteristics in the inducer. The occurrence of cavitation aggravated the instability of the flow and caused the intensity of pressure pulsation at each measuring point to increase. This cavitation structure has strong instability, and the tail region is often accompanied by shedding cavitation clouds perpendicular to the blade surface

Experimental Study on Unsteady Cavitating Flow and Its Instability in Liquid Rocket Engine Inducer

To study instability in the unsteady cavitating flow in a liquid rocket engine inducer, visualization experiments of non-cavitating and cavitating flows inside a model inducer were carried out at different flow conditions. Visual experiments were carried out to capture the evolution of non-cavitating and cavitating flows in a three-bladed inducer by using a high-speed camera. The external characteristic performance, cavitation performance, and pressure pulsation were analyzed based on the observation of non-cavitation and cavitation development and their instabilities. Under non-cavitation conditions, the change of flow rate has a significant impact on the pressure pulsation characteristics in the inducer. The occurrence of cavitation aggravated the instability of the flow and caused the intensity of pressure pulsation at each measuring point to increase. This cavitation structure has strong instability, and the tail region is often accompanied by shedding cavitation clouds perpendicular to the blade surface

RDVI: A Retrieval–Detection Framework for Verbal Irony Detection

Verbal irony is a common form of expression used in daily communication, where the intended meaning is often opposite to the literal meaning. Accurately recognizing verbal irony is essential for any NLP application for which the understanding of the true user intentions is key to performing the underlying tasks. While existing research has made progress in this area, verbal irony often involves connotative knowledge that cannot be directly inferred from the text or its context, which limits the detection model’s ability to recognize and comprehend verbal irony. To address this issue, we propose a Retrieval–Detection method for Verbal Irony (RDVI). This approach improves the detection model’s ability to recognize and comprehend verbal irony by retrieving the connotative knowledge from the open domain and incorporating it into the model using prompt learning. The experimental results demonstrate that our proposed method outperforms state-of-the-art models