Tailored multi-organ segmentation with model adaptation and ensemble.

Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis. Recently, the immense success of deep learning motivated its wide adoption in multi-organ segmentation tasks. However, due to expensive labor costs and expertise, the availability of multi-organ annotations is usually limited and hence poses a challenge in obtaining sufficient training data for deep learning-based methods.

Open-Ended Online Learning for Autonomous Visual Perception.

The visual perception systems aim to autonomously collect consecutive visual data and perceive the relevant information online like human beings. In comparison with the classical static visual systems focusing on fixed tasks (e.g.

InOR-Net: Incremental 3-D Object Recognition Network for Point Cloud Representation.

3-D object recognition has successfully become an appealing research topic in the real world. However, most existing recognition models unreasonably assume that the categories of 3-D objects cannot change over time in the real world. This unrealistic assumption may result in significant performance degradation for them to learn new classes of 3-D objects consecutively due to the catastrophic forgetting on old learned classes.

PFDN: Pyramid Feature Decoupling Network for Single Image Deraining.

Restoring images degraded by rain has attracted more academic attention since rain streaks could reduce the visibility of outdoor scenes. However, most existing deraining methods attempt to remove rain while recovering details in a unified framework, which is an ideal and contradictory target in the image deraining task. Moreover, the relative independence of rain streak features and background features is usually ignored in the feature domain.

Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation.

Unsupervised domain adaptation without accessing expensive annotation processes of target data has achieved remarkable successes in semantic segmentation. However, most existing state-of-the-art methods cannot explore whether semantic representations across domains are transferable or not, which may result in the negative transfer brought by irrelevant knowledge. To tackle this challenge, in this paper, we develop a novel Knowledge Aggregation-induced Transferability Perception (KATP) for unsupervised domain adaptation, which is a pioneering attempt to distinguish transferable or untransferable knowledge across domains.

L3DOC: Lifelong 3D Object Classification.

3D object classification has been widely applied in both academic and industrial scenarios. However, most state-of-the-art algorithms rely on a fixed object classification task set, which cannot tackle the scenario when a new 3D object classification task is coming. Meanwhile, the existing lifelong learning models can easily destroy the learned tasks performance, due to the unordered, large-scale, and irregular 3D geometry data.

Visual-Tactile Fused Graph Learning for Object Clustering.

Object clustering has received considerable research attention most recently. However, 1) most existing object clustering methods utilize visual information while ignoring important tactile modality, which would inevitably lead to model performance degradation and 2) simply concatenating visual and tactile information via multiview clustering method can make complementary information to not be fully explored, since there are many differences between vision and touch. To address these issues, we put forward a graph-based visual-tactile fused object clustering framework with two modules: 1) a modality-specific representation learning module M and 2) a unified affinity graph learning module M .

What and How: Generalized Lifelong Spectral Clustering via Dual Memory.

Spectral clustering has become one of the most effective clustering algorithms. We in this work explore the problem of spectral clustering in a lifelong learning framework termed as Generalized Lifelong Spectral Clustering (GL SC). Different from most current studies, which concentrate on a fixed spectral clustering task set and cannot efficiently incorporate a new clustering task, the goal of our work is to establish a generalized model for new spectral clustering task by What and How to lifelong learn from past tasks.

Nanoparticles: Oral Delivery for Protein and Peptide Drugs.

Protein and peptide drugs have many advantages, such as high bioactivity and specificity, strong solubility, and low toxicity. Therefore, the strategies for improving the bioavailability of protein peptides are reviewed, including chemical modification of nanocarriers, absorption enhancers, and mucous adhesion systems. The status, advantages, and disadvantages of various strategies are systematically analyzed.

[Effect of Iron on the Release of Arsenic in Flooded Paddy Soils].

Amorphous iron oxides in paddy soil are critical adsorbents of arsenic. The flooding period during rice cultivation contributes to the reductive dissolution of these amorphous iron oxides, which releases sorbed arsenic into the paddy soil solution. However, more detailed work should be conducted to evaluate quantitatively arsenic immobilization, release, and transformation regulated by metastable amorphous iron oxides.

Distribution patterns and major sources of dioxins in soils of the Changsha-Zhuzhou-Xiangtan urban agglomeration, China.

Surface soils from an industrial base, the Changsha-Zhuzhou-Xiangtan urban agglomeration in central China were analyzed for 2378-substituted polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). The PCDD/F concentrations ranged from 268 to 7510 pg g(-1) dry weight (dw), 72% of which were above the U.S.

Distribution patterns of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans in sediments of the Xiangjiang River, China.

We investigated the occurrence and distribution patterns of 2,3,7,8-substituted polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in six sediment samples from the Xiangjiang River, Hunan Province, People's Republic of China. Total concentrations of PCDD/Fs ranged from 876 to 497,759 (mean 160,766) ng/kg dw, the highest of which exceeded that have ever been reported for sediment samples. World Health Organization total toxicity equivalent (WHO-TEQ) concentrations in three out of six samples were significantly higher than the guidance level (21.