263 research outputs found
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
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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
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Final Technical Report Microwave Assisted Electrolyte Cell for Primary Aluminum Production
This research addresses the high priority research need for developing inert anode and wetted cathode technology, as defined in the Aluminum Industry Technology Roadmap and Inert Anode Roadmap, with the performance targets: a) significantly reducing the energy intensity of aluminum production, b) ultimately eliminating anode-related CO2 emissions, and c) reducing aluminum production costs. This research intended to develop a new electrometallurgical extraction technology by introducing microwave irradiation into the current electrolytic cells for primary aluminum production. This technology aimed at accelerating the alumina electrolysis reduction rate and lowering the aluminum production temperature, coupled with the uses of nickel based superalloy inert anode, nickel based superalloy wetted cathode, and modified salt electrolyte. Michigan Technological University, collaborating with Cober Electronic and Century Aluminum, conducted bench-scale research for evaluation of this technology. This research included three sub-topics: a) fluoride microwave absorption; b) microwave assisted electrolytic cell design and fabrication; and c) aluminum electrowinning tests using the microwave assisted electrolytic cell. This research concludes that the typically used fluoride compound for aluminum electrowinning is not a good microwave absorbing material at room temperature. However, it becomes an excellent microwave absorbing material above 550°C. The electrowinning tests did not show benefit to introduce microwave irradiation into the electrolytic cell. The experiments revealed that the nickel-based superalloy is not suitable for use as a cathode material; although it wets with molten aluminum, it causes severe reaction with molten aluminum. In the anode experiments, the chosen superalloy did not meet corrosion resistance requirements. A nicked based alloy without iron content could be further investigated
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 â Knowledge and Similarity â 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 nodeâs 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
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