Unsteady heat transfer of long gas insulated transmission lines in tunnel: Model and analysis.

The temperature of the cable core and cable casing is crucial for the safety and efficiency of the transmission system. Accurately predicting the distribution and variation of the transmission temperature field in the gas insulated transmission lines (GIL) tunnel is essential to ensure the long GIL tunnel transmission system's safe and stable operation. This paper addresses the challenge of calculating unsteady heat transfer flow in extra-long GIL transmission tunnels.

Performance-Aware Approximation of Global Channel Pruning for Multitask CNNs.

Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance. Previous works focus on either single task model pruning or simply adapting it to multitask scenario, and still face the following problems when handling multitask pruning: 1) Due to the task mismatch, a well-pruned backbone for classification task focuses on preserving filters that can extract category-sensitive information, causing filters that may be useful for other tasks to be pruned during the backbone pruning stage; 2) For multitask predictions, different filters within or between layers are more closely related and interacted than that for single task prediction, making multitask pruning more difficult. Therefore, aiming at multitask model compression, we propose a Performance-Aware Global Channel Pruning (PAGCP) framework.

A biochip based on shell-isolated Au@MnO nanoparticle array-enhanced fluorescence effect for simple and sensitive exosome assay.

Exosomes, carrying specific molecular information of their parent cells, have been regarded as a kind of promising noninvasive biomarker for liquid biopsy. Plentiful fluorescence methods have been proposed for exosome assay. However, most of them are dependent on nucleic acid signal amplification strategies, which require complicated sequence design and experimental operation.

Sample-Centric Feature Generation for Semi-Supervised Few-Shot Learning.

Semi-supervised few-shot learning aims to improve the model generalization ability by means of both limited labeled data and widely-available unlabeled data. Previous works attempt to model the relations between the few-shot labeled data and extra unlabeled data, by performing a label propagation or pseudo-labeling process using an episodic training strategy. However, the feature distribution represented by the pseudo-labeled data itself is coarse-grained, meaning that there might be a large distribution gap between the pseudo-labeled data and the real query data.

Point Cloud Instance Segmentation With Semi-Supervised Bounding-Box Mining.

Point cloud instance segmentation has achieved huge progress with the emergence of deep learning. However, these methods are usually data-hungry with expensive and time-consuming dense point cloud annotations. To alleviate the annotation cost, unlabeled or weakly labeled data is still less explored in the task.

Label-free detection of exosomes based on ssDNA-modulated oxidase-mimicking activity of CuCoO nanorods.

A label-free method for exosome detection was proposed. It is based on the target-responsive controllability of oxidase-like activity of Cu/Co bimetallic metal-organic frameworks (CuCoO nanorods). In the absence of exosomes, the oxidase-like activity was inhibited due to the adsorption of CD63 aptamer onto nanorods' surface.

Two D-π-A Schiff-Base-Functionalized Silica Gel Adsorbents for Preconcentration of Copper Ions in Foods and Water for Detection.

Copper is an essential element in many biological processes and plays an important role in carbohydrate and lipid metabolism. Excess or deficiency of Cu ions can cause disturbances in cellular homeostasis and damage the central nervous system. Here, for the first time, two functionalized silica gel (SG-A and SG-B) adsorbents were prepared and tested for copper detection via the reactions of chlorinated silica gel with two novel D-π-A Schiff base compounds: 2-amino-3-(quinolin-2-ylmethyleneamino)maleonitrile (A) and 2-(4-(diethylamino)-2-hydroxybenzylideneamino)-3-aminomaleonitrile (B) in the thionyl chloride solution, respectively.

SS-HCNN: Semi-Supervised Hierarchical Convolutional Neural Network for Image Classification.

The availability of large-scale annotated data and uneven separability of different data categories become two major impediments of deep learning for image classification. In this paper, we present a Semi-Supervised Hierarchical Convolutional Neural Network (SS-HCNN) to address these two challenges. A large-scale unsupervised maximum margin clustering technique is designed, which splits images into a number of hierarchical clusters iteratively to learn cluster-level CNNs at parent nodes and category-level CNNs at leaf nodes.

S-CNN: Subcategory-Aware Convolutional Networks for Object Detection.

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem.