RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion

The raw depth image captured by indoor depth sensors usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transparent objects and the limited distance range. The incomplete depth map with missing values burdens many downstream vision tasks, and a rising number of depth completion methods have been proposed to alleviate this issue. While most existing methods can generate accurate dense depth maps from sparse and uniformly sampled depth maps, they are not suitable for complementing large contiguous regions of missing depth values, which is common and critical in images captured in indoor environments. To overcome these challenges, we design a novel two-branch end-to-end fusion network named RDFC-GAN, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map. The first branch employs an encoder-decoder structure, by adhering to the Manhattan world assumption and utilizing normal maps from RGB-D information as guidance, to regress the local dense depth values from the raw depth map. In the other branch, we propose an RGB-depth fusion CycleGAN to transfer the RGB image to the fine-grained textured depth map. We adopt adaptive fusion modules named W-AdaIN to propagate the features across the two branches, and we append a confidence fusion head to fuse the two outputs of the branches for the final depth map. Extensive experiments on NYU-Depth V2 and SUN RGB-D demonstrate that our proposed method clearly improves the depth completion performance, especially in a more realistic setting of indoor environments, with the help of our proposed pseudo depth maps in training.Comment: Haowen Wang and Zhengping Che are with equal contributions. Under review. An earlier version has been accepted by CVPR 2022 (arXiv:2203.10856

DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field

Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric features that correspond to specific templates while disregarding shape variations and pose differences among objects in the same category. As a result, these methods underperform when handling unseen object instances in complex environments. In contrast, other approaches aim to achieve category-level estimation and reconstruction by leveraging normalized geometric structure priors, but the static prior-based reconstruction struggles with substantial intra-class variations. To solve these problems, we propose the DTF-Net, a novel framework for pose estimation and shape reconstruction based on implicit neural fields of object categories. In DTF-Net, we design a deformable template field to represent the general category-wise shape latent features and intra-category geometric deformation features. The field establishes continuous shape correspondences, deforming the category template into arbitrary observed instances to accomplish shape reconstruction. We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene. We integrate a multi-modal representation extraction module to extract object features and semantic masks, enabling end-to-end inference. Moreover, during training, we implement a shape-invariant training strategy and a viewpoint sampling method to further enhance the model's capability to extract object pose features. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superiority of DTF-Net in both synthetic and real scenes. Furthermore, we show that DTF-Net effectively supports grasping tasks with a real robot arm.Comment: The first two authors are with equal contributions. Paper accepted by ACM MM 202

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Prognostic Role of Mucin Antigen MUC4 for Cholangiocarcinoma: A Meta-Analysis.

Surgery carries the best hope for cure in the treatment of cholangiocarcinoma (CC), whereas surgical outcome is not fully satisfactory. Bio-molecular markers have been used to improve tumor staging and prognosis prediction. Mucin antigen MUC4 (MUC4) has been implicated as a marker for poor survival in various tumors. However, prognostic significance of MUC4 for patients with CC remains undefined. The aim of the present meta-analysis was to investigate the association between MUC4 expression and overall survival (OS) of patients with resected CC.The meta-analysis was conducted in adherence to the MOOSE guidelines. PubMed, Embase databases, Cochrane Library and the Chinese SinoMed were systematically searched to identify eligible studies from the initiation of the databases to April, 2016. OSs were pooled by using hazard ratio (HR) with corresponding 95% confidence interval (CI). Random effect models were utilized because of the between-study heterogeneities.Five studies reporting on 249 patients were analyzed: 94 (37.75%) were in positive or high expression group and 155 (62.25%) in negative or low expression group. The pooled HR for positive or high expression group was found to be 3.04 (95% CI 2.25-4.12) when compared with negative or low expression group with slight between-study heterogeneities (I2 3.10%, P = 0.39). The result indicated that a positive or high expression level of MUC4 was significantly related to poor survival in patients with resected CC. A commensurate result was identified by sensitivity analysis. The main limitations of the present meta-analysis were the rather small size of the studies included and relatively narrow geographical distribution of population.The result of this meta-analysis indicated that a positive or high expression level of MUC4 was significantly related to poor survival in patients with resected CC

Path Exploration and Mechanism Analysis of Villagers' Livelihood Transition Intention in the Early Stage of Tourism Development

With rural tourism playing a crucial role in rural revitalization, the transition of villagers' livelihoods in the early stage of tourism development has become the focus of academic circles, and the willingness of villagers to transition their livelihoods is of great significance for local transition and development. However, existing research is usually based on the framework of sustainable livelihoods and involves mainly five major livelihood capitals to explore the interaction between them and livelihood strategies. Thus, it fails to reflect the dynamic effect of the factors contributing to the villagers' willingness for livelihood transition. To solve this problem, this study takes Beigang village in Haikou as a case study and utilizes interviews, text encoding, and a fuzzy-set qualitative comparative analysis method to explore the factors influencing villagers' livelihood transition intentions to form the path and mechanism. The main conclusions are summarized as follows: 1) Policy guidance, tourist entry, human capital, sense of place, livelihood satisfaction, economic capital, and family pressure are the seven factors that mainly influence the intention of villagers' livelihood transition, while other factors include asymmetry, complex heterogeneity, and dynamic process characteristics. 2) Although tourist entry and a sense of place are necessary conditions to develop the willingness for livelihood transition, they are neither necessary nor sufficient. Based on the internal logic of the seven main factors, the formation path of villagers' willingness to transition to a new livelihood can be divided into three types: individual cognition-oriented paths, family responsibility-driven paths, and external environment-driven paths. 3) The development of the intention of villagers to transition to a new livelihood was analyzed for tourism development, in which the individual cognition-oriented path showed the highest degree of explanation, followed by the family responsibility-driven path and the external environment-driven path. The resident villagers who represent the individual cognition-oriented and family responsibility-driven logic want to realize the transition to tourism livelihood through "Part-time tourism," and have a relatively weak intention to transit their livelihood entirely. Non-resident villagers represent the logic of the external environment-driven path. They are eager to participate in tourism through "tourism-oriented" and strongly intend to transit their livelihood entirely. The difference between resident and non-resident villagers in the path of their intention at the subjective level shows a difference in their willingness, which is the main limitation to local livelihood transition. This study expands and enriches the literature on villagers' livelihood transitions, particularly for rural tourism. Moreover, it provides theoretical and practical implications for villagers to achieve sustainable development in tourism and livelihoods

Age model and sediment geochemical data of sediment core ANT34/A2-10, the Amundsen Sea

This data set contains the age model and sediment elemental compositions of core ANT34/A2-10 (125°35'31”W, 67°02'10”S, 4217m water depth) and covering the last 770 kyr, retrieved from the Amundsen Sea in the Southern Ocean. The elemental compositions were measured by X-ray fluorescence (XRF) core-scanning and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) on sediment samples. The original XRF core-scanned results of calcium and titanium are corrected for water content (indicated by the scanning intensity of chlorine) following the method described in the original publicatio

DAMNet: A Dual Adjacent Indexing and Multi-Deraining Network for Real-Time Image Deraining

Image deraining is increasingly critical in the domain of computer vision. However, there is a lack of fast deraining algorithms for multiple images without temporal and spatial features. To fill this gap, an efficient image-deraining algorithm based on dual adjacent indexing and multi-deraining layers is proposed to increase deraining efficiency. The deraining operation is based on two proposals: the dual adjacent method and the joint training method based on multi-deraining layers. The dual adjacent structure indexes pixels from adjacent features of the previous layer to merge with features produced by deraining layers, and the merged features are reshaped to prepare for the loss computation. Joint training method is based on multi-deraining layers, which utilise the pixelshuffle operation to prepare various deraining features for the multi-loss functions. Multi-loss functions jointly compute the structural similarity by loss calculation based on reshaped and deraining features. The features produced by the four deraining layers are concatenated in the channel dimension to obtain the total structural similarity and mean square error. During the experiments, the proposed deraining model is relatively efficient in primary rain datasets, reaching more than 200 fps, and maintains relatively impressive results in single and crossing datasets, demonstrating that our deraining model reaches one of the most advanced ranks in the domain of rain-removing

A New Organic-Inorganic Compound Fertilizer for Improving Growth, Yield, and 2-Acetyl-1-Pyrroline Biosynthesis of Fragrant Rice

Fragrant rice (Oryza sativa L.) is a high-valued rice type and possesses a unique aroma with 2-acetyl-1-pyrroline (2-AP) as the critical component. However, the cultivation measures in fragrant rice production are far from perfect. In this study, a new organic-inorganic compound fertilizer was made with organic matter, urea, superphosphate, potassium chloride, zinc sulfate, and lanthanum chloride. A four-year field experiment was conducted to investigate its effects on fragrant rice growth, yield formation, and 2-acetyl-1-pyrroline biosynthesis. Three treatments, i.e., (CK) no fertilizer was applied, (IF) the urea, superphosphate, and potassium chloride were applied at 234 kg ha−1, 450 kg ha−1 and 108 kg ha−1, and (OICF) this new fertilizer composed of 10% organic matter, 26% urea, 50% superphosphate, 12% potassium chloride, 1.9% zinc sulfate, and 0.1% lanthanum chloride, was applied at 900 kg ha−1, were adopted in the present study. Across four experimental years, the results showed that the grain yield in OICF treatment ranged between 5.86–8.29 t ha−1, and was significantly (p < 0.05) higher than that in IF treatment and CK. The improvement in grain yield due to OICF treatment was explained by increased effective panicle number per m2 and seed-setting rate. The highest or equally highest chlorophyll content and the net photosynthetic rate at 20, 40, 60, and 80 days after transplanting were recorded in OICF treatment among three treatments. OICF treatment also increased the aboveground biomass of fragrant rice compared with IF treatment and CK. Moreover, compared with CK and IF treatment, OICF treatment significantly (p < 0.05) increased grain 2-AP content by 30–38% and 10–21%, respectively. The contents of 2-AP related precursors, including proline and 1-pyrroline, also increased due to OICF treatment. This study provided a new organic-inorganic compound fertilizer and suggested that it could be used to achieve the goals of high yield and high grain 2-AP content in fragrant rice production