Detecting User-Visible Failures in Web-Sites by Using End-to-End Fine-Grained Monitoring: An Experimental Study

The copper ore, which has fine-grained nature and differences in the degree of mineral dissemination, is a kind of low grade sulfide minerals. Tests indicate that not only the grinding fineness but also the combination mode of depressants is one of the most important factors to improve the concentrate grade index. Additionally, according to tests conducted with dosage of lime, the rougher flotation should be operated at a pH of 11. Furthermore, all the depressants used were effective to increase the concentrate grade. By the closed-circuit micro-flotation experiment, satisfied grade index (18.7%Cu with 81% recovery) of the final concentrate was achieved, which could provide reference in industrial applications.

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A new reconstructive procedure after segmental pancreatectomy: an experimental study of pancreatic end-to-end (duct-to-duct) anastomosis

Journal of Hepato-Biliary-Pancreatic Surgery ◽

10.1007/s005340170006 ◽

2001 ◽

Vol 8 (4) ◽

pp. 342-348 ◽

Cited By ~ 6

Author(s):

Atsushi Ota ◽

Mitsuo Kusano ◽

Hiroshi Ishii ◽

Mitsunori Hoshino ◽

Akio Nakamura ◽

...

Keyword(s):

Experimental Study ◽

Reconstructive Procedure ◽

End To End

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Localizing Unseen Activities in Video via Image Query

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/610 ◽

2019 ◽

Cited By ~ 3

Author(s):

Zhu Zhang ◽

Zhou Zhao ◽

Zhijie Lin ◽

Jingkuan Song ◽

Deng Cai

Keyword(s):

Relative Position ◽

Image Region ◽

Video Understanding ◽

Fine Grained ◽

Target Segment ◽

Action Localization ◽

End To End ◽

Image Query

Action localization in untrimmed videos is an important topic in the field of video understanding. However, existing action localization methods are restricted to a pre-defined set of actions and cannot localize unseen activities. Thus, we consider a new task to localize unseen activities in videos via image queries, named Image-Based Activity Localization. This task faces three inherent challenges: (1) how to eliminate the influence of semantically inessential contents in image queries; (2) how to deal with the fuzzy localization of inaccurate image queries; (3) how to determine the precise boundaries of target segments. We then propose a novel self-attention interaction localizer to retrieve unseen activities in an end-to-end fashion. Specifically, we first devise a region self-attention method with relative position encoding to learn fine-grained image region representations. Then, we employ a local transformer encoder to build multi-step fusion and reasoning of image and video contents. We next adopt an order-sensitive localizer to directly retrieve the target segment. Furthermore, we construct a new dataset ActivityIBAL by reorganizing the ActivityNet dataset. The extensive experiments show the effectiveness of our method.

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