The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge

This article describes the final solution of team monkeytyping, who finished in second place in the YouTube-8M video understanding challenge. The dataset used in this challenge is a large-scale benchmark for multi-label video classification. We extend the work in [1] and propose several improvements for frame sequence modeling. We propose a network structure called Chaining that can better capture the interactions between labels. Also, we report our approaches in dealing with multi-scale information and attention pooling. In addition, We find that using the output of model ensemble as a side target in training can boost single model performance. We report our experiments in bagging, boosting, cascade, and stacking, and propose a stacking algorithm called attention weighted stacking. Our final submission is an ensemble that consists of 74 sub models, all of which are listed in the appendix.Comment: Submitted to the CVPR 2017 Workshop on YouTube-8M Large-Scale Video Understandin

Learning large margin multiple granularity features with an improved siamese network for person re-identification

Person re-identification (Re-ID) is a non-overlapping multi-camera retrieval task to match different images of the same person, and it has become a hot research topic in many fields, such as surveillance security, criminal investigation, and video analysis. As one kind of important architecture for person re-identification, Siamese networks usually adopt standard softmax loss function, and they can only obtain the global features of person images, ignoring the local features and the large margin for classification. In this paper, we design a novel symmetric Siamese network model named Siamese Multiple Granularity Network (SMGN), which can jointly learn the large margin multiple granularity features and similarity metrics for person re-identification. Firstly, two branches for global and local feature extraction are designed in the backbone of the proposed SMGN model, and the extracted features are concatenated together as multiple granularity features of person images. Then, to enhance their discriminating ability, the multiple channel weighted fusion (MCWF) loss function is constructed for the SMGN model, which includes the verification loss and identification loss of the training image pair. Extensive comparative experiments on four benchmark datasets (CUHK01, CUHK03, Market-1501 and DukeMTMC-reID) show the effectiveness of our proposed method and its performance outperforms many state-of-the-art methods

凍結乾燥 Lactobacillus paracasei subsp. paracasei JCM8130Tの常温安定化に関する研究

内容の要約広島大学(Hiroshima University)博士(農学)Doctor of Agriculturedoctora

Active inductor shunt peaking in high-speed VCSEL driver design

An all transistor active inductor shunt peaking structure has been used in a prototype of 8-Gbps high-speed VCSEL driver which is designed for the optical link in ATLAS liquid Argon calorimeter upgrade. The VCSEL driver is fabricated in a commercial 0.25-um Silicon-on-Sapphire (SoS) CMOS process for radiation tolerant purpose. The all transistor active inductor shunt peaking is used to overcome the bandwidth limitation from the CMOS process. The peaking structure has the same peaking effect as the passive one, but takes a small area, does not need linear resistors and can overcome the process variation by adjust the peaking strength via an external control. The design has been tapped out, and the prototype has been proofed by the preliminary electrical test results and bit error ratio test results. The driver achieves 8-Gbps data rate as simulated with the peaking. We present the all transistor active inductor shunt peaking structure, simulation and test results in this paper.Comment: 4 pages, 6 figures and 1 table, Submitted to 'Chinese Physics C

Design of Three-Tiered Sensor Networks with a Mobile Data Collector under Energy and Buffer Constraints

A sensor network consists of a network with a large number of sensor nodes deployed around some phenomenon to gather information. Since the nature of sensor nodes is that their energy is limited, many techniques focus on addressing the problem of minimizing the energy consumption in order to extend the network lifetime. One approach is to deploy relay nodes. However, the requirement to transmit over large distances leads to a high rate of energy dissipation. Therefore, mobile data collectors are introduced to resolve this problem. In this thesis, we present an Integer Linear Programming formulation that takes different parameters into consideration to determine an optimal relay node placement scheme in networks with a mobile data collector, which ensures that there is no data loss and the energy dissipation does not exceed a specified level. The simulation results show that our formulation can significantly extend the network lifetime and provide Quality of Service