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arXiv 2017年

作者： Yang, Bingjia Xie, Pinchen Zhang, Zhongzhi Department of Physics Fudan University Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China School of Computer Science Fudan University Shanghai200433 China

We studied the thermodynamic behaviors of non-interacting bosons and fermions trapped by a scale-invariant branching structure of adjustable degree of heterogeneity. The full energy spectrum in tight-binding approximation was analytically solved . We found that the log-periodic oscillation of the specific heat for Fermi gas depended on the heterogeneity of hopping. Also, low dimensional Bose-Einstein condensation occurred only for non-homogeneous setup. Copyright © 2017, The Authors. All rights reserved.

关键词： Bose-Einstein condensation

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A new chaotic feature for EEG classification based seizure diagnosis

A new chaotic feature for EEG classification based seizure d...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Su Yang Anqin Zhang Jiulong Zhang Weishan Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China School of Computer Science Xi'an University of Technology China Department of Software Engineering China University of Petroleum Qingdao China

ISBN: (纸本)9781509041183

Seeking effective measures to characterize the chaotic patterns of EEG signals for seizure diagnosis is a long-term endeavor in the literature. We propose to count the number of zero-crossing (ZC) points on Poincare surface as a feature when the time series of interest is embedded into the reconstructed state space. The experiments show that Poincare surface can act as a platform to observe the chaotic patterns of EEG signals and the ZC feature on Poincare surface is a promising pattern descriptor to discriminate different categories of EEG signals. When used alone for EEG classification, the ZC feature achieves 100%, 99.27%, and 94.68% accuracy in 2-class, 3-class, and 5-class classification on a widely used benchmark.

关键词： Nonlinear time series analysis feature extraction pattern recognition EEG signal classification Electroencephalography Feature extraction Pattern recognition time series analysis Seizures signal classification signals Phyla state space

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Evolving boxes for fast vehicle detection

Evolving boxes for fast vehicle detection

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Li Wang Yao Lu Hong Wang Yingbin Zheng Hao Ye Xiangyang Xue Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China Chinese Academy of Sciences Shanghai Advanced Research Institute Shanghai China School of Computer Science and Engineering University of Washington Seattle USA

ISBN: (纸本)9781509060689

We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition. We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9-13 FPS detection speed on a moderate commercial GPU.

关键词： Proposals Vehicle detection Machine learning Training Detectors Object detection Surveillance

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From social to individuals: A parsimonious path of multi-level models for crowdsourced preference aggregation

arXiv

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arXiv 2018年

作者： Xu, Qianqian Xiong, Jiechao Cao, Xiaochun Huang, Qingming Yao, Yuan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing1000190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Tencent AI Lab Shenzhen518057 BICMR-LMAM-LMEQF-LMP School of Mathematical Sciences Peking University Beijing100871 China University of Chinese Academy of Sciences Institute of Computing Technology of Chinese Academy of Sciences Beijing100190 China Department of Mathematics and by courtesy Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

In crowdsourced preference aggregation, it is often assumed that all the annotators are subject to a common preference or social utility function which generates their comparison behaviors in experiments. However, in reality annotators are subject to variations due to multi-criteria, abnormal, or a mixture of such behaviors. In this paper, we propose a parsimonious mixed-effects model, which takes into account both the fixed effect that the majority of annotators follows a common linear utility model, and the random effect that some annotators might deviate from the common significantly and exhibit strongly personalized preferences. The key algorithm in this paper establishes a dynamic path from the social utility to individual variations, with different levels of sparsity on personalization. The algorithm is based on the Linearized Bregman Iterations, which leads to easy parallel implementations to meet the need of large-scale data analysis. In this unified framework, three kinds of random utility models are presented, including the basic linear model with L2 loss, Bradley-Terry model, and Thurstone-Mosteller model. The validity of these multi-level models are supported by experiments with both simulated and real-world datasets, which shows that the parsimonious multi-level models exhibit improvements in both interpretability and predictive precision compared with traditional HodgeRank. Copyright © 2018, The Authors. All rights reserved.

关键词： Crowdsourcing

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Multi-scale deep learning architectures for person re-identification

arXiv

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arXiv 2017年

作者： Qian, Xuelin Fu, Yanwei Jiang, Yu-Gang Xiang, Tao Xue, Xiangyang Shanghai Key Lab of Intelligent Info. Processing School of Computer Science Fudan University School of Data Science Fudan University Tencent AI Lab Queen Mary University of London University of Technology Sydney

Person Re-identification (re-id) aims to match people across non-overlapping camera views in a public space. It is a challenging problem because many people captured in surveillance videos wear similar clothes. Consequently, the differences in their appearance are often subtle and only detectable at the right location and scales. Existing re-id models, particularly the recently proposed deep learning based ones match people at a single scale. In contrast, in this paper, a novel multi-scale deep learning model is proposed. Our model is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching. The importance of different spatial locations for extracting discriminative features is also learned explicitly. Experiments are carried out to demonstrate that the proposed model outperforms the state-of-the art on a number of benchmarks. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

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Hashtag recommendation using end-to-end memory networks with hierarchical attention 26

Hashtag recommendation using end-to-end memory networks with...

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26th International Conference on Computational Linguistics, COLING 2016

作者： Huang, Haoran Zhang, Qi Gong, Yeyun Huang, Xuanjing Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

ISBN: (纸本)9784879747020

On microblogging services, people usually use hashtags to mark microblogs, which have a specific theme or content, making them easier for users to find. Hence, how to automatically recommend hashtags for microblogs has received much attention in recent years. Previous deep neural network-based hashtag recommendation approaches converted the task into a multi-class classification problem. However, most of these methods only took the microblog itself into consideration. Motivated by the intuition that the history of users should impact the recommendation procedure, in this work, we extend end-to-end memory networks to perform this task. We incorporate the histories of users into the external memory and introduce a hierarchical attention mechanism to select more appropriate histories. To train and evaluate the proposed method, we also construct a dataset based on microblogs collected from Twitter. Experimental results demonstrate that the proposed methods can significantly outperform state-of-the-art methods. By incorporating the hierarchical attention mechanism, the relative improvement in the proposed method over the state-of-the-art method is around 67.9% in the F1-score. © 1963-2018 ACL.

关键词： Deep neural networks

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Deep User Modeling for Content-based Event Recommendation in Event-based Social Networks

Deep User Modeling for Content-based Event Recommendation in...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Zhibo Wang Yongquan Zhang Honglong Chen Zhetao Li Feng Xia Jiangsu Key Lab. of Big Data Security & Intelligent Processing NJUPT P. R. China School of Cyber Science and Engineering Wuhan University P. R. China College of Information and Control Engineering China University of Petroleum P. R. China College of Information Engineering Xiangtan University P. R. China School of Software Dalian University of Technology P. R. China

ISBN: (纸本)9781538641293

Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, ***, and the experimental results show that DUMER outperforms the compared benchmarks.

关键词： Recommender systems Machine learning Social network services Feature extraction Context modeling Conferences Probabilistic logic

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Attention-based convolutional neural network for semantic relation extraction 26

Attention-based convolutional neural network for semantic re...

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26th International Conference on Computational Linguistics, COLING 2016

作者： Shen, Yatian Huang, Xuanjing Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University 825 Zhangheng Road Shanghai China

ISBN: (纸本)9784879747020

Nowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model makes full use of word embedding, part-of-speech tag embedding and position embedding information. Word level attention mechanism is able to better determine which parts of the sentence are most influential with respect to the two entities of interest. This architecture enables learning some important features from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model achieves better performances than several state-of-the-art neural network models and can achieve a competitive performance just with minimal feature engineering. © 1963-2018 ACL.

关键词： Embeddings

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Recent advances in zero-shot recognition

arXiv

引用

arXiv 2017年

作者： Fu, Yanwei Xiang, Tao Jiang, Yu-Gang Xue, Xiangyang Sigal, Leonid Gong, Shaogang School of Data Science Fudan University Shanghai200433 China School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University Department of Computer Science University of British Columbia BC Canada School of Electronic Engineering and Computer Science Queen Mary University of London E1 4NS United Kingdom

With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samples for each class remains an unsolved problem. One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/learning. This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, and from datasets and evaluation settings. We also overview related recognition tasks including one-shot and open set recognition which can be used as natural extensions of zero-shot recognition when limited number of class samples become available or when zero-shot recognition is implemented in a real-world setting. Importantly, we highlight the limitations of existing approaches and point out future research directions in this existing new research area. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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Leveraging composition of object regions for aesthetic assessment of photographs 1

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Lu, Hong Yao, Zeping Bai, Yunhan Zhu, Zhibin Yang, Bohong Chen, Lukun Zhang, Wenqiang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center for Video Technology and System School of Computer Science Fudan University Shanghai China College of Information Science and Technology Nanjing Agricultural University Nanjing China

ISBN: (数字)9783319488905

ISBN: (纸本)9783319488899

Evaluating the aesthetic quality of photos automatically can be considered as a highly challenging task. In this paper, we propose and investigate a novel method for the aesthetic assessment of photos. We integrate photo composition of salient object regions into the assessment. Specifically, we first evaluate the objectness of regions in photos by considering the spatial location and shape of the image salient object regions. Then, we extract features based on the spatial composition of objects. The proposed features fuse aesthetics rules with composition of semantic regions. The proposed method is evaluated on a large dataset. Experimental results demonstrate the efficacy of the proposed method. © Springer International Publishing AG 2016.

关键词： Photocomposition

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