Understanding Convolution for Semantic Segmentation

Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems. Here we show how to improve pixel-wise semantic segmentation by manipulating convolution-related operations that are of both theoretical and practical value. First, we design dense upsampling convolution (DUC) to generate pixel-level prediction, which is able to capture and decode more detailed information that is generally missing in bilinear upsampling. Second, we propose a hybrid dilated convolution (HDC) framework in the encoding phase. This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation. We evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a state-of-art result of 80.1% mIOU in the test set at the time of submission. We also have achieved state-of-the-art overall on the KITTI road estimation benchmark and the PASCAL VOC2012 segmentation task. Our source code can be found at https://github.com/TuSimple/TuSimple-DUC .Comment: WACV 2018. Updated acknowledgements. Source code: https://github.com/TuSimple/TuSimple-DU

GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state

Extracting summaries from long documents can be regarded as sentence classification using the structural information of the documents. How to use such structural information to summarize a document is challenging. In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-paper summarization. In particular, GoSum encodes sentence states in reinforcement learning by building a heterogeneous graph for each input document at different discourse levels. An edge in the graph reflects the discourse hierarchy of a document for restraining the semantic drifts across section boundaries. We evaluate GoSum on two datasets of scientific articles summarization: PubMed and arXiv. The experimental results have demonstrated that GoSum achieve state-of-the-art results compared with strong baselines of both extractive and abstractive models. The ablation studies further validate that the performance of our GoSum benefits from the use of discourse information

IMPROVEMENT OF WEAR COMPONENT'S PERFORMANCE BY UTILIZING ADVANCED MATERIALS AND NEW MANUFACTURING TECHNOLOGIES: CASTCON PROCESS FOR MINING APPLICATIONS

The project was highlighted by continued fabrication of drill bit inserts and testing them: (1) The inserts were subjected to hammer tests to determine brittleness. Selected inserts experienced multiple blows from a 16 pound sledge hammer. The resulting damage was minimal. (2) Three inserts were placed on three different 16.5 inch diameter rotary drill bits, and the bits drilled taconite rock until the entire bit failed. (3) The inserts had somewhat less wear resistance than current art, and exhibited no brittle failures. (4) More work is needed to produce the inserts at near net shape. The test inserts required too much machining. The project next turned to manufacturing 6.5 inch diameter disc cutters. The cutters will feature a core of tungsten carbide (TC) in a disc body composed of H13 tool steel. The TC inserts are in manufacture and the dies for the disc are being designed. The plan for next quarter: (1) Investigate materials and manufacturing changes for the fibrous monolith drill bit inserts that will increase their wear life. (2) Begin manufacturing disc cutters

On exploiting social relationship and personal background for content discovery in P2P networks

International audienceContent discovery is a critical issue in unstructured Peer-to-Peer (P2P) networks as nodes maintain only local network information. However, similarly without global information about human networks, one still can find specific persons via his/her friends by using social information. Therefore, in this paper, we investigate the problem of how social information (i.e., friends and background information) could benefit content discovery in P2P networks. We collect social information of 384, 494 user profiles from Facebook, and build a social P2P network model based on the empirical analysis. In this model, we enrich nodes in P2P networks with social information and link nodes via their friendships. Each node extracts two types of social features-Knowledge and Similarity-and assigns more weight to the friends that have higher similarity and more knowledge. Furthermore, we present a novel content discovery algorithm which can explore the latent relationships among a node's friends. A node computes stable scores for all its friends regarding their weight and the latent relationships. It then selects the top friends with higher scores to query content. Extensive experiments validate performance of the proposed mechanism. In particular, for personal interests searching, the proposed mechanism can achieve 100% of Search Success Rate by selecting the top 20 friends within two-hop. It also achieves 6.5 Hits on average, which improves 8x the performance of the compared methods

On exploiting social relationship and personal background for content discovery in P2P networks

Content discovery is a critical issue in unstructured Peer-to-Peer (P2P) networks as nodes maintain only local network information. However, similarly without global information about human networks, one still can find specific persons via his/her friends by using social information. Therefore, in this paper, we investigate the problem of how social information (i.e., friends and background information) could benefit content discovery in P2P networks. We collect social information of 384,494 user profiles from Facebook, and build a social P2P network model based on the empirical analysis. In this model, we enrich nodes in P2P networks with social information and link nodes via their friendships. Each node extracts two types of social features – Knowledge and Similarity – and assigns more weight to the friends that have higher similarity and more knowledge. Furthermore, we present a novel content discovery algorithm which can explore the latent relationships among a node’s friends. A node computes stable scores for all its friends regarding their weight and the latent relationships. It then selects the top friends with higher scores to query content. Extensive experiments validate performance of the proposed mechanism. In particular, for personal interests searching, the proposed mechanism can achieve 100% of Search Success Rate by selecting the top 20 friends within two-hop. It also achieves 6.5 Hits on average, which improves 8x the performance of the compared methods.This work has been funded by the European Union under the project eCOUSIN (EU-FP7-318398) and the project SITAC (ITEA2-11020). It also has been partially funded by the Spanish Government through the MINEC eeCONTENT project (TEC2011-29688-C02-02)

Study of subjective and objective quality assessment of night-time videos

With the widespread usage of video capture devices and social media videos, videos are dominating the multimedia landscape. There is an emerging need for video quality assessment (VQA) that forms the backbone of advanced video systems. Night-time videos play an important role in user capturing, hence being able to accurately assess their quality is critical. However, the characteristics of night-time videos differ from those of general in-capture videos; and VQA algorithms that have been developed for general-purpose videos cannot accurately assess the quality of night-time videos. Research is needed to gain a better understanding of how humans perceive the quality of night-time videos, and use this new understanding to develop reliable VQA algorithms. To this end, we construct a large-scale night-time VQA database, namely Mobile In-capture Night-time Database for Video Quality (MIND-VQ), containing 1181 night-time videos, 435 subjects, and over 130000 opinion scores. We perform thorough analyses to reveal subjective quality assessment behaviors of night-time videos. Furthermore, we propose a new VQA model, namely Visibility-based Night-time Video Quality Assessment Network, VINIA. Spatial and temporal visibility-aware components are characterized to reflect properties of human perception of night-time VQA task. A series of experiments are conducted to compare our VINIA with other existing IQA/VQA algorithms using our new MIND-VQ database and other public VQA databases. Experimental results show that our subjective VQA database provides new insights and our new VINIA model achieves superior performance in accessing night-time video quality

SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization

Clinical research on smart healthcare has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. To this end, we have developed SenseCare research platform for smart healthcare, which is designed to boost translational research on intelligent diagnosis and treatment planning in various clinical scenarios. To facilitate clinical research with Artificial Intelligence (AI), SenseCare provides a range of AI toolkits for different tasks, including image segmentation, registration, lesion and landmark detection from various image modalities ranging from radiology to pathology. In addition, SenseCare is clinic-oriented and supports a wide range of clinical applications such as diagnosis and surgical planning for lung cancer, pelvic tumor, coronary artery disease, etc. SenseCare provides several appealing functions and features such as advanced 3D visualization, concurrent and efficient web-based access, fast data synchronization and high data security, multi-center deployment, support for collaborative research, etc. In this paper, we will present an overview of SenseCare as an efficient platform providing comprehensive toolkits and high extensibility for intelligent image analysis and clinical research in different application scenarios.Comment: 11 pages, 10 figure

miRNA-378 reverses chemoresistance to cisplatin in lung adenocarcinoma cells by targeting secreted clusterin

Cisplatin resistance is a major obstacle in the treatment of NSCLC, and its mechanism has not been fully elucidated. The objectives of the study were to determine the role of miR-378 in the sensitivity of lung adenocarcinoma cells to cisplatin (cDDP) and its working mechanism. With TargetScan and luciferase assay, miR-378 was found to directly target sCLU. miR-378 and sCLU were regulated in A549/cDDP and Anip973/cDDP cells to investigate the effect of miR-378 on the sensitivity and apoptotic effects of cDDP. The effect of miR-378 upregulation on tumor growth was analyzed in a nude mouse xenograft model. The correlation between miR-378 and chemoresistance was tested in patient samples. We found that upregulation of miR-378 in A549/cDDP and Anip973/cDDP cells significantly down-regulated sCLU expression, and sensitized these cells to cDDP. miR-378 overexpression inhibited tumor growth and sCLU expression in a xenograft animal model. Analysis of human lung adenocarcinoma tissues revealed that the cDDP sensitive group expressed higher levels of miR-378 and lower levels of sCLU. miR-378 and sCLU were negatively correlated. To conclude, we identified sCLU as a novel miR-378 target, and we showed that targeting sCLU via miR-378 may help disable the chemoresistance against cisplatin in lung adenocarcinoma cells