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The ability to host Majorana modes, which are of great interest for more fault-tolerant quantum computation, keeps topological superconductors in the focus of research. Here, we report experimental data revealing 100-nm-wide quantum anomalous Hall insulator (QAHI) nanoribbons as a promising platform for the realization of zero-energy Majorana modes. One part of the nanoribbon is covered with superconducting niobium, while the other part is connected to a gold lead via two-dimensional QAHI regions. Andreev reflection spectroscopy reveals multiple in-gap conductance peaks in different devices. In the presence of an increasing magnetic field perpendicular to the film, the multiple-peak structure evolves into a single zero-bias conductance peak (ZBCP). Theoretical simulations suggest that the measurements are consistent with the scenario that the increasing field drives the nanoribbons from a multi-channel occupied regime to a single-channel regime, which is the necessary condition for the observation of zero-energy Majorana modes. © 2023 The Author(s)

Bacterial photodynamic inactivation based on the combined actions of photosensitizers, light, and oxygen presents a promising alternative for eliminating bacteria compared to conventional water disinfection methods. However, a significant challenge in this approach is the inability to retrieve photosensitizers after phototreatment, posing potential adverse environmental impacts. Additionally, conventional photosensitizers often exhibit limited photostability and photodynamic efficiency. This study addresses these challenges by employing an aggregation-induced emission (AIE) photosensitizer, iron oxide magnetic nanoparticles (Fe3O4 MNPs), and Pluronic F127 to fabricate AIE magnetic nanoparticles (AIE MNPs). AIE MNPs not only exhibit fluorescence imaging capabilities and superior photosensitizing ability but also demonstrate broad-spectrum bactericidal activities against both Gram-positive and Gram-negative bacteria. The controlled release of TPA-Py-PhMe and magnetic characteristics of the AIE MNPs facilitate reuse and recycling for multiple cycles of bacterial inactivation in water. Our findings contribute valuable insights into developing environmentally friendly disinfectants, emphasizing the full potential of AIE photosensitizers in photodynamic inactivation beyond biomedical applications. © 2024 American Chemical Society

While manga is a popular entertainment form, creating manga is tedious, especially adding screentones to the created sketch, namely manga screening. Unfortunately, there is no existing method that tailors for automatic manga screening, probably due to the difficulty of generating high-quality shaded high-frequency screentones. The classic manga screening approaches generally require user input to provide screentone exemplars or a reference manga image. The recent deep learning models enables the automatic generation by learning from a large-scale dataset. However, the state-of-the-art models still fail to generate high-quality shaded screentones due to the lack of a tailored model and high-quality manga training data. In this paper, we propose a novel sketch-to-manga framework that first generates a color illustration from the sketch and then generates a screentoned manga based on the intensity guidance. Our method significantly outperforms existing methods in generating high-quality manga with shaded high-frequency screentones., Comment: 7 pages, 6 figures

We introduce ToonCrafter, a novel approach that transcends traditional correspondence-based cartoon video interpolation, paving the way for generative interpolation. Traditional methods, that implicitly assume linear motion and the absence of complicated phenomena like dis-occlusion, often struggle with the exaggerated non-linear and large motions with occlusion commonly found in cartoons, resulting in implausible or even failed interpolation results. To overcome these limitations, we explore the potential of adapting live-action video priors to better suit cartoon interpolation within a generative framework. ToonCrafter effectively addresses the challenges faced when applying live-action video motion priors to generative cartoon interpolation. First, we design a toon rectification learning strategy that seamlessly adapts live-action video priors to the cartoon domain, resolving the domain gap and content leakage issues. Next, we introduce a dual-reference-based 3D decoder to compensate for lost details due to the highly compressed latent prior spaces, ensuring the preservation of fine details in interpolation results. Finally, we design a flexible sketch encoder that empowers users with interactive control over the interpolation results. Experimental results demonstrate that our proposed method not only produces visually convincing and more natural dynamics, but also effectively handles dis-occlusion. The comparative evaluation demonstrates the notable superiority of our approach over existing competitors., Comment: Project page: https://doubiiu.github.io/projects/ToonCrafter

Identifying robust and accurate correspondences across images is a fundamental problem in computer vision that enables various downstream tasks. Recent semi-dense matching methods emphasize the effectiveness of fusing relevant cross-view information through Transformer. In this paper, we propose several improvements upon this paradigm. Firstly, we introduce affine-based local attention to model cross-view deformations. Secondly, we present selective fusion to merge local and global messages from cross attention. Apart from network structure, we also identify the importance of enforcing spatial smoothness in loss design, which has been omitted by previous works. Based on these augmentations, our network demonstrate strong matching capacity under different settings. The full version of our network achieves state-of-the-art performance among semi-dense matching methods at a similar cost to LoFTR, while the slim version reaches LoFTR baseline's performance with only 15% computation cost and 18% parameters., Comment: Accepted to CVPR2024 Image Matching Workshop

Creating scenes for captured motions that achieve realistic human-scene interaction is crucial for 3D animation in movies or video games. As character motion is often captured in a blue-screened studio without real furniture or objects in place, there may be a discrepancy between the planned motion and the captured one. This gives rise to the need for automatic scene layout generation to relieve the burdens of selecting and positioning furniture and objects. Previous approaches cannot avoid artifacts like penetration and floating due to the lack of physical constraints. Furthermore, some heavily rely on specific data to learn the contact affordances, restricting the generalization ability to different motions. In this work, we present a physics-based approach that simultaneously optimizes a scene layout generator and simulates a moving human in a physics simulator. To attain plausible and realistic interaction motions, our method explicitly introduces physical constraints. To automatically recover and generate the scene layout, we minimize the motion tracking errors to identify the objects that can afford interaction. We use reinforcement learning to perform a dual-optimization of both the character motion imitation controller and the scene layout generator. To facilitate the optimization, we reshape the tracking rewards and devise pose prior guidance obtained from our estimated pseudo-contact labels. We evaluate our method using motions from SAMP and PROX, and demonstrate physically plausible scene layout reconstruction compared with the previous kinematics-based method., Comment: SIGGRAPH conference

We present a novel differentiable rendering framework for joint geometry, material, and lighting estimation from multi-view images. In contrast to previous methods which assume a simplified environment map or co-located flashlights, in this work, we formulate the lighting of a static scene as one neural incident light field (NeILF) and one outgoing neural radiance field (NeRF). The key insight of the proposed method is the union of the incident and outgoing light fields through physically-based rendering and inter-reflections between surfaces, making it possible to disentangle the scene geometry, material, and lighting from image observations in a physically-based manner. The proposed incident light and inter-reflection framework can be easily applied to other NeRF systems. We show that our method can not only decompose the outgoing radiance into incident lights and surface materials, but also serve as a surface refinement module that further improves the reconstruction detail of the neural surface. We demonstrate on several datasets that the proposed method is able to achieve state-of-the-art results in terms of geometry reconstruction quality, material estimation accuracy, and the fidelity of novel view rendering. © 2023 IEEE.

Speech-driven 3D facial animation has been widely studied, yet there is still a gap to achieving realism and vividness due to the highly ill-posed nature and scarcity of audio-visual data. Existing works typically formulate the cross-modal mapping into a regression task, which suffers from the regression-to-mean problem leading to over-smoothed facial motions. In this paper, we propose to cast speech-driven facial animation as a code query task in a finite proxy space of the learned codebook, which effectively promotes the vividness of the generated motions by reducing the cross-modal mapping uncertainty. The codebook is learned by self-reconstruction over real facial motions and thus embedded with realistic facial motion priors. Over the discrete motion space, a temporal autoregressive model is employed to sequentially synthesize facial motions from the input speech signal, which guarantees lip-sync as well as plausible facial expressions. We demonstrate that our approach outperforms current state-of-the-art methods both qualitatively and quantitatively. Also, a user study further justifies our superiority in perceptual quality., Comment: CVPR2023 Camera-Ready. Project Page: https://doubiiu.github.io/projects/codetalker/, Code: https://github.com/Doubiiu/CodeTalker

The process of adapting or repurposing manga pages is a time-consuming task that requires manga artists to manually work on every single screentone region and apply new patterns to create novel screentones across multiple panels. To address this issue, we propose an automatic manga rescreening pipeline that aims to minimize the human effort involved in manga adaptation. Our pipeline automatically recognizes screentone regions and generates novel screentones with newly specified characteristics (e.g., intensity or type). Existing manga generation methods have limitations in understanding and synthesizing complex tone- or intensity-varying regions. To overcome these limitations, we propose a novel interpretable representation of screentones that disentangles their intensity and type features, enabling better recognition and synthesis of screentones. This interpretable screentone representation reduces ambiguity in recognizing intensity-varying regions and provides fine-grained controls during screentone synthesis by decoupling and anchoring the type or the intensity feature. Our proposed method is demonstrated to be effective and convenient through various experiments, showcasing the superiority of the newly proposed pipeline with the interpretable screentone representations., Comment: 10 pages, 11 figures

Video colorization is a challenging task that involves inferring plausible and temporally consistent colors for grayscale frames. In this paper, we present ColorDiffuser, an adaptation of a pre-trained text-to-image latent diffusion model for video colorization. With the proposed adapter-based approach, we repropose the pre-trained text-to-image model to accept input grayscale video frames, with the optional text description, for video colorization. To enhance the temporal coherence and maintain the vividness of colorization across frames, we propose two novel techniques: the Color Propagation Attention and Alternated Sampling Strategy. Color Propagation Attention enables the model to refine its colorization decision based on a reference latent frame, while Alternated Sampling Strategy captures spatiotemporal dependencies by using the next and previous adjacent latent frames alternatively as reference during the generative diffusion sampling steps. This encourages bidirectional color information propagation between adjacent video frames, leading to improved color consistency across frames. We conduct extensive experiments on benchmark datasets, and the results demonstrate the effectiveness of our proposed framework. The evaluations show that ColorDiffuser achieves state-of-the-art performance in video colorization, surpassing existing methods in terms of color fidelity, temporal consistency, and visual quality., Comment: project page: https://colordiffuser.github.io

Creating a vivid video from the event or scenario in our imagination is a truly fascinating experience. Recent advancements in text-to-video synthesis have unveiled the potential to achieve this with prompts only. While text is convenient in conveying the overall scene context, it may be insufficient to control precisely. In this paper, we explore customized video generation by utilizing text as context description and motion structure (e.g. frame-wise depth) as concrete guidance. Our method, dubbed Make-Your-Video, involves joint-conditional video generation using a Latent Diffusion Model that is pre-trained for still image synthesis and then promoted for video generation with the introduction of temporal modules. This two-stage learning scheme not only reduces the computing resources required, but also improves the performance by transferring the rich concepts available in image datasets solely into video generation. Moreover, we use a simple yet effective causal attention mask strategy to enable longer video synthesis, which mitigates the potential quality degradation effectively. Experimental results show the superiority of our method over existing baselines, particularly in terms of temporal coherence and fidelity to users' guidance. In addition, our model enables several intriguing applications that demonstrate potential for practical usage., Comment: 13 pages, 8 figures. Project page: https://doubiiu.github.io/projects/Make-Your-Video

Image colorization has been attracting the research interests of the community for decades. However, existing methods still struggle to provide satisfactory colorized results given grayscale images due to a lack of human-like global understanding of colors. Recently, large-scale Text-to-Image (T2I) models have been exploited to transfer the semantic information from the text prompts to the image domain, where text provides a global control for semantic objects in the image. In this work, we introduce a colorization model piggybacking on the existing powerful T2I diffusion model. Our key idea is to exploit the color prior knowledge in the pre-trained T2I diffusion model for realistic and diverse colorization. A diffusion guider is designed to incorporate the pre-trained weights of the latent diffusion model to output a latent color prior that conforms to the visual semantics of the grayscale input. A lightness-aware VQVAE will then generate the colorized result with pixel-perfect alignment to the given grayscale image. Our model can also achieve conditional colorization with additional inputs (e.g. user hints and texts). Extensive experiments show that our method achieves state-of-the-art performance in terms of perceptual quality., Comment: project page: https://piggyback-color.github.io

We present a novel differentiable rendering framework for joint geometry, material, and lighting estimation from multi-view images. In contrast to previous methods which assume a simplified environment map or co-located flashlights, in this work, we formulate the lighting of a static scene as one neural incident light field (NeILF) and one outgoing neural radiance field (NeRF). The key insight of the proposed method is the union of the incident and outgoing light fields through physically-based rendering and inter-reflections between surfaces, making it possible to disentangle the scene geometry, material, and lighting from image observations in a physically-based manner. The proposed incident light and inter-reflection framework can be easily applied to other NeRF systems. We show that our method can not only decompose the outgoing radiance into incident lights and surface materials, but also serve as a surface refinement module that further improves the reconstruction detail of the neural surface. We demonstrate on several datasets that the proposed method is able to achieve state-of-the-art results in terms of geometry reconstruction quality, material estimation accuracy, and the fidelity of novel view rendering., Comment: Project page: \url{https://yoyo000.github.io/NeILF_pp}

Background: Perioperative anemia has been associated with increased risk of red blood cell transfusion and increased morbidity and mortality following surgery. The optimal approach to the diagnosis and management of perioperative anemia is not fully established. Objective: To develop consensus recommendations for anemia management in surgical patients. Methods: An international expert panel reviewed the current evidence and developed recommendations using modified RAND Delphi methodology. Results: The panel recommends that all patients be screened for anemia prior to surgery. Appropriate therapy for anemia should be guided by an accurate diagnosis of the etiology. The need to proceed with surgery in some patients with anemia is expected to persist. However, early identification and effective treatment of anemia has the potential to reduce the risks associated with surgery and improve clinical outcomes. As with preoperative anemia, postoperative anemia should be treated in the perioperative period. Conclusions: Early identification and effective treatment of anemia has the potential to improve clinical outcomes in surgical patients.

Variational autoencoders (VAEs) have been used extensively to discover low-dimensional latent factors governing neural activity and animal behavior. However, without careful model selection, the uncovered latent factors may reflect noise in the data rather than true underlying features, rendering such representations unsuitable for scientific interpretation. Existing solutions to this problem involve introducing additional measured variables or data augmentations specific to a particular data type. We propose a VAE architecture that predicts the next point in time and show that it mitigates the learning of spurious features. In addition, we introduce a model selection metric based on smoothness over time in the latent space. We show that together these two constraints on VAEs to be smooth over time produce robust latent representations and faithfully recover latent factors on synthetic datasets., Comment: 16 pages, 4 main figures, 4 supplemental figures, accepted for publication at Unireps Workshop in 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

Recent advances in generative AI have unveiled significant potential for the creation of 3D content. However, current methods either apply a pre-trained 2D diffusion model with the time-consuming score distillation sampling (SDS), or a direct 3D diffusion model trained on limited 3D data losing generation diversity. In this work, we approach the problem by employing a multi-view 2.5D diffusion fine-tuned from a pre-trained 2D diffusion model. The multi-view 2.5D diffusion directly models the structural distribution of 3D data, while still maintaining the strong generalization ability of the original 2D diffusion model, filling the gap between 2D diffusion-based and direct 3D diffusion-based methods for 3D content generation. During inference, multi-view normal maps are generated using the 2.5D diffusion, and a novel differentiable rasterization scheme is introduced to fuse the almost consistent multi-view normal maps into a consistent 3D model. We further design a normal-conditioned multi-view image generation module for fast appearance generation given the 3D geometry. Our method is a one-pass diffusion process and does not require any SDS optimization as post-processing. We demonstrate through extensive experiments that, our direct 2.5D generation with the specially-designed fusion scheme can achieve diverse, mode-seeking-free, and high-fidelity 3D content generation in only 10 seconds. Project page: https://nju-3dv.github.io/projects/direct25., Comment: CVPR 2024 camera ready, including more evaluations and discussions. Project webpage: https://nju-3dv.github.io/projects/direct25

Text-guided video-to-video stylization transforms the visual appearance of a source video to a different appearance guided on textual prompts. Existing text-guided image diffusion models can be extended for stylized video synthesis. However, they struggle to generate videos with both highly detailed appearance and temporal consistency. In this paper, we propose a synchronized multi-frame diffusion framework to maintain both the visual details and the temporal consistency. Frames are denoised in a synchronous fashion, and more importantly, information of different frames is shared since the beginning of the denoising process. Such information sharing ensures that a consensus, in terms of the overall structure and color distribution, among frames can be reached in the early stage of the denoising process before it is too late. The optical flow from the original video serves as the connection, and hence the venue for information sharing, among frames. We demonstrate the effectiveness of our method in generating high-quality and diverse results in extensive experiments. Our method shows superior qualitative and quantitative results compared to state-of-the-art video editing methods., Comment: 11 pages, 11 figures

This paper introduces a novel approach to synthesize texture to dress up a given 3D object, given a text prompt. Based on the pretrained text-to-image (T2I) diffusion model, existing methods usually employ a project-and-inpaint approach, in which a view of the given object is first generated and warped to another view for inpainting. But it tends to generate inconsistent texture due to the asynchronous diffusion of multiple views. We believe such asynchronous diffusion and insufficient information sharing among views are the root causes of the inconsistent artifact. In this paper, we propose a synchronized multi-view diffusion approach that allows the diffusion processes from different views to reach a consensus of the generated content early in the process, and hence ensures the texture consistency. To synchronize the diffusion, we share the denoised content among different views in each denoising step, specifically blending the latent content in the texture domain from views with overlap. Our method demonstrates superior performance in generating consistent, seamless, highly detailed textures, comparing to state-of-the-art methods.

Animating a still image offers an engaging visual experience. Traditional image animation techniques mainly focus on animating natural scenes with stochastic dynamics (e.g. clouds and fluid) or domain-specific motions (e.g. human hair or body motions), and thus limits their applicability to more general visual content. To overcome this limitation, we explore the synthesis of dynamic content for open-domain images, converting them into animated videos. The key idea is to utilize the motion prior of text-to-video diffusion models by incorporating the image into the generative process as guidance. Given an image, we first project it into a text-aligned rich context representation space using a query transformer, which facilitates the video model to digest the image content in a compatible fashion. However, some visual details still struggle to be preserved in the resultant videos. To supplement with more precise image information, we further feed the full image to the diffusion model by concatenating it with the initial noises. Experimental results show that our proposed method can produce visually convincing and more logical & natural motions, as well as higher conformity to the input image. Comparative evaluation demonstrates the notable superiority of our approach over existing competitors., Comment: Project page: https://doubiiu.github.io/projects/DynamiCrafter

We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps). JointNet is extended from a pre-trained text-to-image diffusion model, where a copy of the original network is created for the new dense modality branch and is densely connected with the RGB branch. The RGB branch is locked during network fine-tuning, which enables efficient learning of the new modality distribution while maintaining the strong generalization ability of the large-scale pre-trained diffusion model. We demonstrate the effectiveness of JointNet by using RGBD diffusion as an example and through extensive experiments, showcasing its applicability in a variety of applications, including joint RGBD generation, dense depth prediction, depth-conditioned image generation, and coherent tile-based 3D panorama generation.

A linear-time algorithm for generating auxiliary subgraphs for the 3-edge-connected components of a connected multigraph is presented. The algorithm uses an innovative graph contraction operation and makes only one pass over the graph. By contrast, the previously best-known algorithms make multiple passes over the graph to decompose it into its 2-edge-connected components or 2-vertex-connected components, then its 3-edge-connected components or 3-vertex-connected components, and then construct a cactus representation for the 2-cuts to generate the auxiliary subgraphs for the 3-edge-connected components., Comment: 21 pages, 1 figure

Class activation mapping~(CAM), a visualization technique for interpreting deep learning models, is now commonly used for weakly supervised semantic segmentation~(WSSS) and object localization~(WSOL). It is the weighted aggregation of the feature maps by activating the high class-relevance ones. Current CAM methods achieve it relying on the training outcomes, such as predicted scores~(forward information), gradients~(backward information), etc. However, when with small-scale data, unstable training may lead to less effective model outcomes and generate unreliable weights, finally resulting in incorrect activation and noisy CAM seeds. In this paper, we propose an outcome-agnostic CAM approach, called BroadCAM, for small-scale weakly supervised applications. Since broad learning system (BLS) is independent to the model learning, BroadCAM can avoid the weights being affected by the unreliable model outcomes when with small-scale data. By evaluating BroadCAM on VOC2012 (natural images) and BCSS-WSSS (medical images) for WSSS and OpenImages30k for WSOL, BroadCAM demonstrates superior performance than existing CAM methods with small-scale data (less than 5\%) in different CNN architectures. It also achieves SOTA performance with large-scale training data. Extensive qualitative comparisons are conducted to demonstrate how BroadCAM activates the high class-relevance feature maps and generates reliable CAMs when with small-scale training data.

Given a connected undirected multigraph G (a graph that may contain parallel edges), the algorithm of [19] finds the 3-edge-connected components of $G$ in linear time using an innovative graph contraction technique based on a depth-first search. In [21], it was shown that the algorithm can be extended to produce a Mader construction sequence for each 3-edge-connected component, a cactus representation of the 2-cuts (cut-pairs) of each 2-edge-connected component of $G$, and the 1-cuts (bridges) at the same time. In this paper, we further extend the algorithm of [19] to generate the 2-cuts and the 3-edge-connected components of $G$ simultaneously in linear time by performing only one depth-first search over the input graph. Previously known algorithms solve the two problems separately in multiple phases., Comment: 14 pages, 2 figures. arXiv admin note: text overlap with arXiv:2002.04727

Traditional halftoning usually drops colors when dithering images with binary dots, which makes it difficult to recover the original color information. We proposed a novel halftoning technique that converts a color image into a binary halftone with full restorability to its original version. Our novel base halftoning technique consists of two convolutional neural networks (CNNs) to produce the reversible halftone patterns, and a noise incentive block (NIB) to mitigate the flatness degradation issue of CNNs. Furthermore, to tackle the conflicts between the blue-noise quality and restoration accuracy in our novel base method, we proposed a predictor-embedded approach to offload predictable information from the network, which in our case is the luminance information resembling from the halftone pattern. Such an approach allows the network to gain more flexibility to produce halftones with better blue-noise quality without compromising the restoration quality. Detailed studies on the multiple-stage training method and loss weightings have been conducted. We have compared our predictor-embedded method and our novel method regarding spectrum analysis on halftone, halftone accuracy, restoration accuracy, and the data embedding studies. Our entropy evaluation evidences our halftone contains less encoding information than our novel base method. The experiments show our predictor-embedded method gains more flexibility to improve the blue-noise quality of halftones and maintains a comparable restoration quality with a higher tolerance for disturbances., Comment: published in IEEE Transactions on Visualization and Computer Graphics

Background: High-level evidence is limited for antibiotic duration in children hospitalized with community-acquired pneumonia (CAP) from First Nations and other at-risk populations of chronic respiratory disorders. As part of a larger study, we determined whether an extended antibiotic course is superior to a standard course for achieving clinical cure at 4 weeks in children 3 months to ≤5 years old hospitalized with CAP. Methods: In our multinational (Australia, New Zealand, Malaysia), double-blind, superiority randomized controlled trial, children hospitalized with uncomplicated, radiographic-confirmed, CAP received 1-3 days of intravenous antibiotics followed by 3 days of oral amoxicillin-clavulanate (80 mg/kg, amoxicillin component, divided twice daily) and then randomized to extended (13-14 days duration) or standard (5-6 days) antibiotics. The primary outcome was clinical cure (complete resolution of respiratory symptoms/signs) 4 weeks postenrollment. Secondary outcomes included adverse events, nasopharyngeal bacterial pathogens and antimicrobial resistance at 4 weeks. Results: Of 372 children enrolled, 324 fulfilled the inclusion criteria and were randomized. Using intention-to-treat analysis, between-group clinical cure rates were similar (extended course: n = 127/163, 77.9%; standard course: n = 131/161, 81.3%; relative risk = 0.96, 95% confidence interval = 0.86-1.07). There were no significant between-group differences for adverse events (extended course: n = 43/163, 26.4%; standard course, n = 32/161, 19.9%) or nasopharyngeal carriage of Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis and Staphylococcus aureus or antimicrobial resistance. Conclusions: Among children hospitalized with pneumonia and at-risk of chronic respiratory illnesses, an extended antibiotic course was not superior to a standard course at achieving clinical cure at 4 weeks. Additional research will identify if an extended course provides longer-term benefits

Microtask crowdsourcing workers, also known as crowd workers, perform small tasks known as microtasks. These people use crowdsourcing platforms to complete these microtasks. Crowd workers have to work in front of a screen to complete these microtasks, risking musculoskeletal problems and other mental problems. Their working conditions look similar to desk workers, who are people that work remotely or at the office behind a desk. This study aims to find the health differences between crowd workers and desk workers. It will provide a general overview on the subjective well-being, experienced and mental health. In order to analyze the differences in health, a survey will be deployed on a crowdsourcing platform in order to recruit crowd workers and desk workers will be recruited through snowball sampling. The questions of the survey are divided into 5 groups, each representing a health category: general health, workspace quality, physical well-being, social well-being and emotional well-being. For this study 17 crowd workers were recruited and 9 desk workers. From the results, desk workers are healthier in general, have a healthier workspace because some desk workers work in ergonomically good offices, a healthier physical well-being, a healthier social well-being due to them having colleagues and a better emotional well-being. Crowd workers have a lower level of stress, because of the microtasks being mostly very simple, while desk workers have mentally demanding deadlines and projects to work on., CSE3000 Research Project, Computer Science and Engineering

Generating robust and reliable correspondences across images is a fundamental task for a diversity of applications. To capture context at both global and local granularity, we propose ASpanFormer, a Transformer-based detector-free matcher that is built on hierarchical attention structure, adopting a novel attention operation which is capable of adjusting attention span in a self-adaptive manner. To achieve this goal, first, flow maps are regressed in each cross attention phase to locate the center of search region. Next, a sampling grid is generated around the center, whose size, instead of being empirically configured as fixed, is adaptively computed from a pixel uncertainty estimated along with the flow map. Finally, attention is computed across two images within derived regions, referred to as attention span. By these means, we are able to not only maintain long-range dependencies, but also enable fine-grained attention among pixels of high relevance that compensates essential locality and piece-wise smoothness in matching tasks. State-of-the-art accuracy on a wide range of evaluation benchmarks validates the strong matching capability of our method., Comment: Accepted to ECCV2022, project page at https://aspanformer.github.io

Deep learning models were frequently reported to learn from shortcuts like dataset biases. As deep learning is playing an increasingly important role in the modern healthcare system, it is of great need to combat shortcut learning in medical data as well as develop unbiased and trustworthy models. In this paper, we study the problem of developing debiased chest X-ray diagnosis models from the biased training data without knowing exactly the bias labels. We start with the observations that the imbalance of bias distribution is one of the key reasons causing shortcut learning, and the dataset biases are preferred by the model if they were easier to be learned than the intended features. Based on these observations, we proposed a novel algorithm, pseudo bias-balanced learning, which first captures and predicts per-sample bias labels via generalized cross entropy loss and then trains a debiased model using pseudo bias labels and bias-balanced softmax function. We constructed several chest X-ray datasets with various dataset bias situations and demonstrated with extensive experiments that our proposed method achieved consistent improvements over other state-of-the-art approaches., Comment: To appear in MICCAI 2022. Code available at https://github.com/LLYXC/PBBL

We present a differentiable rendering framework for material and lighting estimation from multi-view images and a reconstructed geometry. In the framework, we represent scene lightings as the Neural Incident Light Field (NeILF) and material properties as the surface BRDF modelled by multi-layer perceptrons. Compared with recent approaches that approximate scene lightings as the 2D environment map, NeILF is a fully 5D light field that is capable of modelling illuminations of any static scenes. In addition, occlusions and indirect lights can be handled naturally by the NeILF representation without requiring multiple bounces of ray tracing, making it possible to estimate material properties even for scenes with complex lightings and geometries. We also propose a smoothness regularization and a Lambertian assumption to reduce the material-lighting ambiguity during the optimization. Our method strictly follows the physically-based rendering equation, and jointly optimizes material and lighting through the differentiable rendering process. We have intensively evaluated the proposed method on our in-house synthetic dataset, the DTU MVS dataset, and real-world BlendedMVS scenes. Our method is able to outperform previous methods by a significant margin in terms of novel view rendering quality, setting a new state-of-the-art for image-based material and lighting estimation.

As a popular comic style, manga offers a unique impression by utilizing a rich set of bitonal patterns, or screentones, for illustration. However, screentones can easily be contaminated with visual-unpleasant aliasing and/or blurriness after resampling, which harms its visualization on displays of diverse resolutions. To address this problem, we propose the first manga retargeting method that synthesizes a rescaled manga image while retaining the screentone in each screened region. This is a non-trivial task as accurate region-wise segmentation remains challenging. Fortunately, the rescaled manga shares the same region-wise screentone correspondences with the original manga, which enables us to simplify the screentone synthesis problem as an anchor-based proposals selection and rearrangement problem. Specifically, we design a novel manga sampling strategy to generate aliasing-free screentone proposals, based on hierarchical grid-based anchors that connect the correspondences between the original and the target rescaled manga. Furthermore, a Recurrent Proposal Selection Module (RPSM) is proposed to adaptively integrate these proposals for target screentone synthesis. Besides, to deal with the translation insensitivity nature of screentones, we propose a translation-invariant screentone loss to facilitate the training convergence. Extensive qualitative and quantitative experiments are conducted to verify the effectiveness of our method, and notably compelling results are achieved compared to existing alternative techniques., Comment: 10 pages, 13 figures

This work presents an innovative method for point set self-embedding, that encodes the structural information of a dense point set into its sparser version in a visual but imperceptible form. The self-embedded point set can function as the ordinary downsampled one and be visualized efficiently on mobile devices. Particularly, we can leverage the self-embedded information to fully restore the original point set for detailed analysis on remote servers. This task is challenging since both the self-embedded point set and the restored point set should resemble the original one. To achieve a learnable self-embedding scheme, we design a novel framework with two jointly-trained networks: one to encode the input point set into its self-embedded sparse point set and the other to leverage the embedded information for inverting the original point set back. Further, we develop a pair of up-shuffle and down-shuffle units in the two networks, and formulate loss terms to encourage the shape similarity and point distribution in the results. Extensive qualitative and quantitative results demonstrate the effectiveness of our method on both synthetic and real-scanned datasets., Comment: Accepted by IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG), 2022. All resources can be found at https://liruihui.github.io

Conventional social media platforms usually downscale the HR images to restrict their resolution to a specific size for saving transmission/storage cost, which leads to the super-resolution (SR) being highly ill-posed. Recent invertible image downscaling methods jointly model the downscaling/upscaling problems and achieve significant improvements. However, they only consider fixed integer scale factors that cannot downscale HR images with various resolutions to meet the resolution restriction of social media platforms. In this paper, we propose a scale-Arbitrary Invertible image Downscaling Network (AIDN), to natively downscale HR images with arbitrary scale factors. Meanwhile, the HR information is embedded in the downscaled low-resolution (LR) counterparts in a nearly imperceptible form such that our AIDN can also restore the original HR images solely from the LR images. The key to supporting arbitrary scale factors is our proposed Conditional Resampling Module (CRM) that conditions the downscaling/upscaling kernels and sampling locations on both scale factors and image content. Extensive experimental results demonstrate that our AIDN achieves top performance for invertible downscaling with both arbitrary integer and non-integer scale factors. Code will be released upon publication.

Neural implicit functions have recently shown promising results on surface reconstructions from multiple views. However, current methods still suffer from excessive time complexity and poor robustness when reconstructing unbounded or complex scenes. In this paper, we present RegSDF, which shows that proper point cloud supervisions and geometry regularizations are sufficient to produce high-quality and robust reconstruction results. Specifically, RegSDF takes an additional oriented point cloud as input, and optimizes a signed distance field and a surface light field within a differentiable rendering framework. We also introduce the two critical regularizations for this optimization. The first one is the Hessian regularization that smoothly diffuses the signed distance values to the entire distance field given noisy and incomplete input. And the second one is the minimal surface regularization that compactly interpolates and extrapolates the missing geometry. Extensive experiments are conducted on DTU, BlendedMVS, and Tanks and Temples datasets. Compared with recent neural surface reconstruction approaches, RegSDF is able to reconstruct surfaces with fine details even for open scenes with complex topologies and unstructured camera trajectories., Comment: CVPR 2022

Neural implicit functions have recently shown promising results on surface reconstructions from multiple views. However, current methods still steer from excessive time complexity and poor robustness when reconstructing unbounded or complex scenes. In this paper; we present RegSDF, which shows that proper point cloud supervisions and geometry regularizations are sufficient to produce high-quality and robust reconstruction results. Specifically, RegSDF takes an additional oriented point cloud as input, and optimizes a signed distance field and a surface light field within a differentiable rendering framework. We also introduce the two critical regularizations for this optimization. The first one is the Hessian regularization that smoothly diffuses the signed distance values to the entire distance field given noisy and incomplete input. And the second one is the minimal surface regularization that compactly interpolates and extrapolates the missing geometry. Extensive experiments are conducted on DTU, Blended-MVS, and Tanks and Temples datasets. Compared with recent neural surface reconstruction approaches, RegSDF is able to reconstruct surfaces with fine details even for open scenes with complex topologies and unstructured camera trajectories.

Microtask crowdsourcing workers, also known as crowd workers, perform small tasks known as microtasks. These people use crowdsourcing platforms to complete these microtasks. Crowd workers have to work in front of a screen to complete these microtasks, risking musculoskeletal problems and other mental problems. Their working conditions look similar to desk workers, who are people that work remotely or at the office behind a desk. This study aims to find the health differences between crowd workers and desk workers. It will provide a general overview on the subjective well-being, experienced and mental health. In order to analyze the differences in health, a survey will be deployed on a crowdsourcing platform in order to recruit crowd workers and desk workers will be recruited through snowball sampling. The questions of the survey are divided into 5 groups, each representing a health category: general health, workspace quality, physical well-being, social well-being and emotional well-being. For this study 17 crowd workers were recruited and 9 desk workers. From the results, desk workers are healthier in general, have a healthier workspace because some desk workers work in ergonomically good offices, a healthier physical well-being, a healthier social well-being due to them having colleagues and a better emotional well-being. Crowd workers have a lower level of stress, because of the microtasks being mostly very simple, while desk workers have mentally demanding deadlines and projects to work on., CSE3000 Research Project, Computer Science and Engineering

В статье рассматриваются проблемы воспитания в Китае в разрезе исторической ретроспективы. Представлены подходы китайских философов Конфуция, Чжуан-цзы, Мэн Кэ к роли музыки в воспитании и становлении личности. Проанализированы взгляды педагогов-просветителей – Цай Юаньпэй, Лян Цичао, Хуан Яньпэй, Тао Синчжи, которые являются основоположниками теории эстетического воспитания в Китае XX в. Рассмотрены основы современной концепции музыкального образования в Китае, включающей национальные духовные ценности и традиции китайской педагогики и современное видение художественного образования европейского и российского сообщества.

Chemical synaptic transmission represents the most sophisticated dynamic process and is highly regulated with optimized neurotransmitter balance. Imbalanced transmitters can lead to transmission impairments, for example, intracellular zinc accumulation is a hallmark of degenerating neurons. However, the underlying mechanisms remain elusive. Postsynaptic density protein-95 (PSD-95) is a primary postsynaptic membrane-associated protein and the major scaffolding component in the excitatory postsynaptic densities, which performs substantial functions in synaptic development and maturation. Its membrane association induced by palmitoylation contributes largely to its regulatory functions at postsynaptic sites. Unlike other structural domains in PSD-95, the N-terminal region (PSD-95NT) is flexible and interacts with various targets, which modulates its palmitoylation of two cysteines (C3/C5) and glutamate receptor distributions in postsynaptic densities. PSD-95NT contains a putative zinc-binding motif (C2H2) with undiscovered functions. This study is the first effort to investigate the interaction between Zn2+ and PSD-95NT. The NMR titration of 15 N-labeled PSD-95NT by ZnCl2 was performed and demonstrated Zn2+ binds to PSD-95NT with a binding affinity (Kd ) in the micromolar range. The zinc binding was confirmed by fluorescence and mutagenesis assays, indicating two cysteines and two histidines (H24, H28) are critical residues for the binding. These results suggested the concentration-dependent zinc binding is likely to influence PSD-95 palmitoylation since the binding site overlaps the palmitoylation sites, which was verified by the mimic PSD-95 palmitoyl modification and intact cell palmitoylation assays. This study reveals zinc as a novel modulator for PSD-95 postsynaptic membrane association by chelating its N-terminal region, indicative of its importance in postsynaptic signaling.

Chemical synaptic transmission represents the most sophisticated dynamic process and is highly regulated with optimized neurotransmitter balance. Imbalanced transmitters can lead to transmission impairments, for example, intracellular zinc accumulation is a hallmark of degenerating neurons. However, the underlying mechanisms remain elusive. Postsynaptic density protein-95 (PSD-95) is a primary postsynaptic membrane-associated protein and the major scaffolding component in the excitatory postsynaptic densities, which performs substantial functions in synaptic development and maturation. Its membrane association induced by palmitoylation contributes largely to its regulatory functions at postsynaptic sites. Unlike other structural domains in PSD-95, the N-terminal region (PSD-95NT) is flexible and interacts with various targets, which modulates its palmitoylation of two cysteines (C3/C5) and glutamate receptor distributions in postsynaptic densities. PSD-95NT contains a putative zinc-binding motif (C2H2) with undiscovered functions. This study is the first effort to investigate the interaction between Zn2+ and PSD-95NT. The NMR titration of 15 N-labeled PSD-95NT by ZnCl2 was performed and demonstrated Zn2+ binds to PSD-95NT with a binding affinity (Kd ) in the micromolar range. The zinc binding was confirmed by fluorescence and mutagenesis assays, indicating two cysteines and two histidines (H24, H28) are critical residues for the binding. These results suggested the concentration-dependent zinc binding is likely to influence PSD-95 palmitoylation since the binding site overlaps the palmitoylation sites, which was verified by the mimic PSD-95 palmitoyl modification and intact cell palmitoylation assays. This study reveals zinc as a novel modulator for PSD-95 postsynaptic membrane association by chelating its N-terminal region, indicative of its importance in postsynaptic signaling.

BackgroundCerebral malaria (CM) pathogenesis remains incompletely understood. Having shown low systemic levels of tetrahydrobiopterin (BH4), an enzymatic cofactor for neurotransmitter synthesis, we hypothesized that BH4 and BH4-dependent neurotransmitters would likewise be low in cerebrospinal fluid (CSF) in CM.MethodsWe prospectively enrolled Tanzanian children with CM and children with nonmalaria central nervous system conditions (NMCs). We measured CSF levels of BH4, neopterin, and BH4-dependent neurotransmitter metabolites, 3-O-methyldopa, homovanillic acid, and 5-hydroxyindoleacetate, and we derived age-adjusted z-scores using published reference ranges.ResultsCerebrospinal fluid BH4 was elevated in CM (n = 49) compared with NMC (n = 51) (z-score 0.75 vs -0.08; P < .001). Neopterin was increased in CM (z-score 4.05 vs 0.09; P < .001), and a cutoff at the upper limit of normal (60 nmol/L) was 100% sensitive for CM. Neurotransmitter metabolite levels were overall preserved. A higher CSF BH4/BH2 ratio was associated with increased odds of survival (odds ratio, 2.94; 95% confidence interval, 1.03-8.33; P = .043).ConclusionDespite low systemic BH4, CSF BH4 was elevated and associated with increased odds of survival in CM. Coma in malaria is not explained by deficiency of BH4-dependent neurotransmitters. Elevated CSF neopterin was 100% sensitive for CM diagnosis and warrants further assessment of its clinical utility for ruling out CM in malaria-endemic areas.

Although digital cameras can acquire high-dynamic range (HDR) images, the captured HDR information are mostly quantized to low-dynamic range (LDR) images for display compatibility and compact storage. In this paper, we propose an invertible tone mapping method that converts the multi-exposure HDR to a true LDR (8-bit per color channel) and reserves the capability to accurately restore the original HDR from this {\em invertible LDR}. Our invertible LDR can mimic the appearance of a user-selected tone mapping style. It can be shared over any existing social network platforms that may re-encode or format-convert the uploaded images, without much hurting the accuracy of the restored HDR counterpart. To achieve this, we regard the tone mapping and the restoration as coupled processes, and formulate them as an encoding-and-decoding problem through convolutional neural networks. Particularly, our model supports pluggable style modulators, each of which bakes a specific tone mapping style, and thus favors the application flexibility. Our method is evaluated with a rich variety of HDR images and multiple tone mapping operators, which shows the superiority over relevant state-of-the-art methods. Also, we conduct ablation study to justify our design and discuss the robustness and generality toward real applications.

Graph convolutional networks have significantly improved 3D human pose estimation by representing the human skeleton as an undirected graph. However, this representation fails to reflect the articulated characteristic of human skeletons as the hierarchical orders among the joints are not explicitly presented. In this paper, we propose to represent the human skeleton as a directed graph with the joints as nodes and bones as edges that are directed from parent joints to child joints. By so doing, the directions of edges can explicitly reflect the hierarchical relationships among the nodes. Based on this representation, we further propose a spatial-temporal conditional directed graph convolution to leverage varying non-local dependence for different poses by conditioning the graph topology on input poses. Altogether, we form a U-shaped network, named U-shaped Conditional Directed Graph Convolutional Network, for 3D human pose estimation from monocular videos. To evaluate the effectiveness of our method, we conducted extensive experiments on two challenging large-scale benchmarks: Human3.6M and MPI-INF-3DHP. Both quantitative and qualitative results show that our method achieves top performance. Also, ablation studies show that directed graphs can better exploit the hierarchy of articulated human skeletons than undirected graphs, and the conditional connections can yield adaptive graph topologies for different poses.

As a popular entertainment art form, manga enriches the line drawings details with bitonal screentones. However, manga resources over the Internet usually show screentone artifacts because of inappropriate scanning/rescaling resolution. In this paper, we propose an innovative two-stage method to restore quality bitonal manga from degraded ones. Our key observation is that the aliasing induced by downsampling bitonal screentones can be utilized as informative clues to infer the original resolution and screentones. First, we predict the target resolution from the degraded manga via the Scale Estimation Network (SE-Net) with spatial voting scheme. Then, at the target resolution, we restore the region-wise bitonal screentones via the Manga Restoration Network (MR-Net) discriminatively, depending on the degradation degree. Specifically, the original screentones are directly restored in pattern-identifiable regions, and visually plausible screentones are synthesized in pattern-agnostic regions. Quantitative evaluation on synthetic data and visual assessment on real-world cases illustrate the effectiveness of our method.

2D image representations are in regular grids and can be processed efficiently, whereas 3D point clouds are unordered and scattered in 3D space. The information inside these two visual domains is well complementary, e.g., 2D images have fine-grained texture while 3D point clouds contain plentiful geometry information. However, most current visual recognition systems process them individually. In this paper, we present a \emph{bidirectional projection network (BPNet)} for joint 2D and 3D reasoning in an end-to-end manner. It contains 2D and 3D sub-networks with symmetric architectures, that are connected by our proposed \emph{bidirectional projection module (BPM)}. Via the \emph{BPM}, complementary 2D and 3D information can interact with each other in multiple architectural levels, such that advantages in these two visual domains can be combined for better scene recognition. Extensive quantitative and qualitative experimental evaluations show that joint reasoning over 2D and 3D visual domains can benefit both 2D and 3D scene understanding simultaneously. Our \emph{BPNet} achieves top performance on the ScanNetV2 benchmark for both 2D and 3D semantic segmentation. Code is available at \url{https://github.com/wbhu/BPNet}., Comment: CVPR 2021 (Oral)

Light fields are 4D scene representation typically structured as arrays of views, or several directional samples per pixel in a single view. This highly correlated structure is not very efficient to transmit and manipulate (especially for editing), though. To tackle these problems, we present a novel compact and editable light field representation, consisting of a set of visual channels (i.e. the central RGB view) and a complementary meta channel that encodes the residual geometric and appearance information. The visual channels in this representation can be edited using existing 2D image editing tools, before accurately reconstructing the whole edited light field back. We propose to learn this representation via an autoencoder framework, consisting of an encoder for learning the representation, and a decoder for reconstructing the light field. To handle the challenging occlusions and propagation of edits, we specifically designed an editing-aware decoding network and its associated training strategy, so that the edits to the visual channels can be consistently propagated to the whole light field upon reconstruction.Experimental results show that our proposed method outperforms related existing methods in reconstruction accuracy, and achieves visually pleasant performance in editing propagation., Comment: submitted to TIP since 2020.08.03

Introduction Worldwide, the gap between organ supply and demand has widened over the years. Malaysia has one of the lowest deceased organ donation rates. Success rate of organ or tissue procurement depends on not only the approach rate by health care providers but also the awareness among the public, whereby it can be a platform for family initiation of organ donation. The purpose of this study is to assess the knowledge of and determine the factors influencing attitude toward organ and tissue donation among patients in a primary clinic. Methods A cross-sectional analytical study was carried out. Self-administered questionnaires were given to 400 patients who registered at an outpatient clinic in April 2018. Convenience sampling was applied. Results Monthly income, education level, occupation, and knowledge level are significantly associated with attitude of the respondents toward organ and tissue donation. Occupation influenced attitude toward organ donation. Knowledge of organ donation and brain death both significantly affected attitude toward organ donation. Conclusion The greater the knowledge of organ donation and brain death, the more positive impression or attitude toward organ donation. Education level and income are the main predictors that influence attitude toward organ donation. Hence, it is important for public health units to promote and deliver public education on organ donation, change public misconceptions, and work parallel with hospitals to increase organ donation rates in Sabah. Previous articleNext article Worldwide, the gap between organ supply and demand has widened over the years. Malaysia has one of the lowest deceased organ donation rates (0.7 donations per million population) compared with countries such as Spain, which had the highest with a donation rate of 36 per million population in 2014. Other Asian countries are not far off from Malaysia, such as Myanmar (0.02) and Thailand (1.26) [1]. The World Health Organization defines transpla

Introduction Worldwide, the gap between organ supply and demand has widened over the years. Malaysia has one of the lowest deceased organ donation rates. Success rate of organ or tissue procurement depends on not only the approach rate by health care providers but also the awareness among the public, whereby it can be a platform for family initiation of organ donation. The purpose of this study is to assess the knowledge of and determine the factors influencing attitude toward organ and tissue donation among patients in a primary clinic. Methods A cross-sectional analytical study was carried out. Self-administered questionnaires were given to 400 patients who registered at an outpatient clinic in April 2018. Convenience sampling was applied. Results Monthly income, education level, occupation, and knowledge level are significantly associated with attitude of the respondents toward organ and tissue donation. Occupation influenced attitude toward organ donation. Knowledge of organ donation and brain death both significantly affected attitude toward organ donation. Conclusion The greater the knowledge of organ donation and brain death, the more positive impression or attitude toward organ donation. Education level and income are the main predictors that influence attitude toward organ donation. Hence, it is important for public health units to promote and deliver public education on organ donation, change public misconceptions, and work parallel with hospitals to increase organ donation rates in Sabah. Previous articleNext article Worldwide, the gap between organ supply and demand has widened over the years. Malaysia has one of the lowest deceased organ donation rates (0.7 donations per million population) compared with countries such as Spain, which had the highest with a donation rate of 36 per million population in 2014. Other Asian countries are not far off from Malaysia, such as Myanmar (0.02) and Thailand (1.26) [1]. The World Health Organization defines transpla

Introduction Worldwide, the gap between organ supply and demand has widened over the years. Malaysia has one of the lowest deceased organ donation rates. Success rate of organ or tissue procurement depends on not only the approach rate by health care providers but also the awareness among the public, whereby it can be a platform for family initiation of organ donation. The purpose of this study is to assess the knowledge of and determine the factors influencing attitude toward organ and tissue donation among patients in a primary clinic. Methods A cross-sectional analytical study was carried out. Self-administered questionnaires were given to 400 patients who registered at an outpatient clinic in April 2018. Convenience sampling was applied. Results Monthly income, education level, occupation, and knowledge level are significantly associated with attitude of the respondents toward organ and tissue donation. Occupation influenced attitude toward organ donation. Knowledge of organ donation and brain death both significantly affected attitude toward organ donation. Conclusion The greater the knowledge of organ donation and brain death, the more positive impression or attitude toward organ donation. Education level and income are the main predictors that influence attitude toward organ donation. Hence, it is important for public health units to promote and deliver public education on organ donation, change public misconceptions, and work parallel with hospitals to increase organ donation rates in Sabah. Previous articleNext article Worldwide, the gap between organ supply and demand has widened over the years. Malaysia has one of the lowest deceased organ donation rates (0.7 donations per million population) compared with countries such as Spain, which had the highest with a donation rate of 36 per million population in 2014. Other Asian countries are not far off from Malaysia, such as Myanmar (0.02) and Thailand (1.26) [1]. The World Health Organization defines transpla

Introduction Worldwide, the gap between organ supply and demand has widened over the years. Malaysia has one of the lowest deceased organ donation rates. Success rate of organ or tissue procurement depends on not only the approach rate by health care providers but also the awareness among the public, whereby it can be a platform for family initiation of organ donation. The purpose of this study is to assess the knowledge of and determine the factors influencing attitude toward organ and tissue donation among patients in a primary clinic. Methods A cross-sectional analytical study was carried out. Self-administered questionnaires were given to 400 patients who registered at an outpatient clinic in April 2018. Convenience sampling was applied. Results Monthly income, education level, occupation, and knowledge level are significantly associated with attitude of the respondents toward organ and tissue donation. Occupation influenced attitude toward organ donation. Knowledge of organ donation and brain death both significantly affected attitude toward organ donation. Conclusion The greater the knowledge of organ donation and brain death, the more positive impression or attitude toward organ donation. Education level and income are the main predictors that influence attitude toward organ donation. Hence, it is important for public health units to promote and deliver public education on organ donation, change public misconceptions, and work parallel with hospitals to increase organ donation rates in Sabah. Previous articleNext article Worldwide, the gap between organ supply and demand has widened over the years. Malaysia has one of the lowest deceased organ donation rates (0.7 donations per million population) compared with countries such as Spain, which had the highest with a donation rate of 36 per million population in 2014. Other Asian countries are not far off from Malaysia, such as Myanmar (0.02) and Thailand (1.26) [1]. The World Health Organization defines transpla

Introduction Worldwide, the gap between organ supply and demand has widened over the years. Malaysia has one of the lowest deceased organ donation rates. Success rate of organ or tissue procurement depends on not only the approach rate by health care providers but also the awareness among the public, whereby it can be a platform for family initiation of organ donation. The purpose of this study is to assess the knowledge of and determine the factors influencing attitude toward organ and tissue donation among patients in a primary clinic. Methods A cross-sectional analytical study was carried out. Self-administered questionnaires were given to 400 patients who registered at an outpatient clinic in April 2018. Convenience sampling was applied. Results Monthly income, education level, occupation, and knowledge level are significantly associated with attitude of the respondents toward organ and tissue donation. Occupation influenced attitude toward organ donation. Knowledge of organ donation and brain death both significantly affected attitude toward organ donation. Conclusion The greater the knowledge of organ donation and brain death, the more positive impression or attitude toward organ donation. Education level and income are the main predictors that influence attitude toward organ donation. Hence, it is important for public health units to promote and deliver public education on organ donation, change public misconceptions, and work parallel with hospitals to increase organ donation rates in Sabah. Previous articleNext article Worldwide, the gap between organ supply and demand has widened over the years. Malaysia has one of the lowest deceased organ donation rates (0.7 donations per million population) compared with countries such as Spain, which had the highest with a donation rate of 36 per million population in 2014. Other Asian countries are not far off from Malaysia, such as Myanmar (0.02) and Thailand (1.26) [1]. The World Health Organization defines transpla