Breakdown Performance of Guard Ring Designs for Pixel Detectors in 150 nm150~\mathrm{nm} CMOS Technology

Silicon pixel sensors manufactured using commercial CMOS processes are promising instruments for high-energy particle physics experiments due to their high yield and proven radiation hardness. As one of the essential factors for the operation of detectors, the breakdown performance of pixel sensors constitutes the upper limit of the operating voltage. Six types of passive CMOS test structures were fabricated on high-resistivity wafers. Each of them features a combination of different inter-pixel designs and sets of floating guard rings, which differ from each other in the geometrical layout, implantation type, and overhang structure. A comparative study based on leakage current measurements in the sensor substrate of unirradiated samples was carried out to identify correlations between guard ring designs and breakdown voltages. TCAD simulations using the design parameters of the test structures were performed to discuss the observations and, together with the measurements, ultimately provide design features targeting higher breakdown voltages

Sentiment Interaction Distillation Network for Image Sentiment Analysis

Sentiment is a high-level abstraction, and it is a challenging task to accurately extract sentimental features from visual contents due to the “affective gap”. Previous works focus on extracting more concrete sentimental features of individual objects by introducing saliency detection or instance segmentation into their models, neglecting the interaction among objects. Inspired by the observation that interaction among objects can impact the sentiment of images, we propose the Sentiment Interaction Distillation (SID) Network, which utilizes object sentimental interaction to guide feature learning. Specifically, we first utilize a panoptic segmentation method to obtain objects in images; then, we propose a sentiment-related edge generation method and employ Graph Convolution Network to aggregate and propagate object relation representation. In addition, we propose a knowledge distillation framework to utilize interaction information guiding global context feature learning, which can avoid noisy features introduced by error propagation and a varying number of objects. Experimental results show that our method outperforms the state-of-the-art algorithm, e.g., about 1.2% improvement on the Flickr dataset and 1.7% on the most challenging subset of Twitter I. It is demonstrated that the reasonable use of interaction features can improve the performance of sentiment analysis

Discovering Sentimental Interaction via Graph Convolutional Network for Visual Sentiment Prediction

With the popularity of online opinion expressing, automatic sentiment analysis of images has gained considerable attention. Most methods focus on effectively extracting the sentimental features of images, such as enhancing local features through saliency detection or instance segmentation tools. However, as a high-level abstraction, the sentiment is difficult to accurately capture with the visual element because of the “affective gap”. Previous works have overlooked the contribution of the interaction among objects to the image sentiment. We aim to utilize interactive characteristics of objects in the sentimental space, inspired by human sentimental principles that each object contributes to the sentiment. To achieve this goal, we propose a framework to leverage the sentimental interaction characteristic based on a Graph Convolutional Network (GCN). We first utilize an off-the-shelf tool to recognize objects and build a graph over them. Visual features represent nodes, and the emotional distances between objects act as edges. Then, we employ GCNs to obtain the interaction features among objects, which are fused with the CNN output of the whole image to predict the final results. Experimental results show that our method exceeds the state-of-the-art algorithm. Demonstrating that the rational use of interaction features can improve performance for sentiment analysis

Discovering Sentimental Interaction via Graph Convolutional Network for Visual Sentiment Prediction

With the popularity of online opinion expressing, automatic sentiment analysis of images has gained considerable attention. Most methods focus on effectively extracting the sentimental features of images, such as enhancing local features through saliency detection or instance segmentation tools. However, as a high-level abstraction, the sentiment is difficult to accurately capture with the visual element because of the “affective gap”. Previous works have overlooked the contribution of the interaction among objects to the image sentiment. We aim to utilize interactive characteristics of objects in the sentimental space, inspired by human sentimental principles that each object contributes to the sentiment. To achieve this goal, we propose a framework to leverage the sentimental interaction characteristic based on a Graph Convolutional Network (GCN). We first utilize an off-the-shelf tool to recognize objects and build a graph over them. Visual features represent nodes, and the emotional distances between objects act as edges. Then, we employ GCNs to obtain the interaction features among objects, which are fused with the CNN output of the whole image to predict the final results. Experimental results show that our method exceeds the state-of-the-art algorithm. Demonstrating that the rational use of interaction features can improve performance for sentiment analysis

Recommendations for Different Tasks Based on the Uniform Multimodal Joint Representation

Content curation social networks (CCSNs), such as Pinterest and Huaban, are interest driven and content centric. On CCSNs, user interests are represented by a set of boards, and a board is composed of various pins. A pin is an image with a description. All entities, such as users, boards, and categories, can be represented as a set of pins. Therefore, it is possible to implement entity representation and the corresponding recommendations on a uniform representation space from pins. Furthermore, lots of pins are re-pinned from others and the pin’s re-pin sequences are recorded on CCSNs. In this paper, a framework which can learn the multimodal joint representation of pins, including text representation, image representation, and multimodal fusion, is proposed. Image representations are extracted from a multilabel convolutional neural network. The multiple labels of pins are automatically obtained by the category distributions in the re-pin sequences, which benefits from the network architecture. Text representations are obtained with the word2vec tool. Two modalities are fused with a multimodal deep Boltzmann machine. On the basis of the pin representation, different recommendation tasks are implemented, including recommending pins or boards to users, recommending thumbnails to boards, and recommending categories to boards. Experimental results on a dataset from Huaban demonstrate that the multimodal joint representation of pins contains the information of user interests. Furthermore, the proposed multimodal joint representation outperformed unimodal representation in different recommendation tasks. Experiments were also performed to validate the effectiveness of the proposed recommendation methods

Transcriptome and Lipidomic Analysis Suggests Lipid Metabolism Reprogramming and Upregulating <i>SPHK1</i> Promotes Stemness in Pancreatic Ductal Adenocarcinoma Stem-like Cells

Cancer stem cells (CSCs) are considered to play a key role in the development and progression of pancreatic ductal adenocarcinoma (PDAC). However, little is known about lipid metabolism reprogramming in PDAC CSCs. Here, we assigned stemness indices, which were used to describe and quantify CSCs, to every patient from the Cancer Genome Atlas (TCGA-PAAD) database and observed differences in lipid metabolism between patients with high and low stemness indices. Then, tumor-repopulating cells (TRCs) cultured in soft 3D (three-dimensional) fibrin gels were demonstrated to be an available PDAC cancer stem-like cell (CSLCs) model. Comprehensive transcriptome and lipidomic analysis results suggested that fatty acid metabolism, glycerophospholipid metabolism, and, especially, the sphingolipid metabolism pathway were mostly associated with CSLCs properties. SPHK1 (sphingosine kinases 1), one of the genes involved in sphingolipid metabolism and encoding the key enzyme to catalyze sphingosine to generate S1P (sphingosine-1-phosphate), was identified to be the key gene in promoting the stemness of PDAC. In summary, we explored the characteristics of lipid metabolism both in patients with high stemness indices and in novel CSLCs models, and unraveled a molecular mechanism via which sphingolipid metabolism maintained tumor stemness. These findings may contribute to the development of a strategy for targeting lipid metabolism to inhibit CSCs in PDAC treatment

Warm Needling Therapy and Acupuncture at Meridian-Sinew Sites Based on the Meridian-Sinew Theory: Hemiplegic Shoulder Pain

This study was performed to evaluate the effectiveness and safety of warm needling acupuncture at meridian-sinew sites based on the meridian-sinew theory in the treatment of hemiplegic shoulder pain (HSP) after stroke. In total, 124 subjects were randomized into a treatment group and control group. In the treatment group, warm needling therapy and acupuncture at meridian-sinew sites based on the meridian-sinew theory were performed. In the control group, usual care therapy was applied. The visual analog scale (VAS) score, range of motion (ROM), and Barthel index (BI) were used to evaluate treatment effectiveness. At 2 weeks of treatment, the VAS score, ROM, and BI had obviously changed from baseline in the two groups (P < 0.01). The changes in the VAS score and ROM in the treatment group were significantly greater than those in the control group (P < 0.01). At the 3-month follow-up after treatment, the changes in the treatment group were significantly greater than those in the control group (P < 0.01). This study indicates that warm needling therapy with acupuncture at meridian-sinew sites based on the meridian-sinew theory is effective for HSP