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Yi Zhen

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7 papers

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YNIMG Journal 2024 Journal Article

The heritability and structural correlates of resting-state fMRI complexity

Yi Zhen
Yaqian Yang
Yi Zheng
Xin Wang
Longzhao Liu
Zhiming Zheng
Hongwei Zheng
Shaoting Tang

The complexity of fMRI signals quantifies temporal dynamics of spontaneous neural activity, which has been increasingly recognized as providing important insights into cognitive functions and psychiatric disorders. However, its heritability and structural underpinnings are not well understood. Here, we utilize multi-scale sample entropy to extract resting-state fMRI complexity in a large healthy adult sample from the Human Connectome Project. We show that fMRI complexity at multiple time scales is heritable in broad brain regions. Heritability estimates are modest and regionally variable. We relate fMRI complexity to brain structure including surface area, cortical myelination, cortical thickness, subcortical volumes, and total brain volume. We find that surface area is negatively correlated with fine-scale complexity and positively correlated with coarse-scale complexity in most cortical regions, especially the association cortex. Most of these correlations are related to common genetic and environmental effects. We also find positive correlations between cortical myelination and fMRI complexity at fine scales and negative correlations at coarse scales in the prefrontal cortex, lateral temporal lobe, precuneus, lateral parietal cortex, and cingulate cortex, with these correlations mainly attributed to common environmental effects. We detect few significant associations between fMRI complexity and cortical thickness. Despite the non-significant association with total brain volume, fMRI complexity exhibits significant correlations with subcortical volumes in the hippocampus, cerebellum, putamen, and pallidum at certain scales. Collectively, our work establishes the genetic basis and structural correlates of resting-state fMRI complexity across multiple scales, supporting its potential application as an endophenotype for psychiatric disorders.

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AAAI Conference 2015 Conference Paper

Cross-Modal Similarity Learning via Pairs, Preferences, and Active Supervision

Yi Zhen
Piyush Rai
Hongyuan Zha
Lawrence Carin

We present a probabilistic framework for learning pairwise similarities between objects belonging to different modalities, such as drugs and proteins, or text and images. Our framework is based on learning a binary code based representation for objects in each modality, and has the following key properties: (i) it can leverage both pairwise as well as easy-to-obtain relative preference based cross-modal constraints, (ii) the probabilistic framework naturally allows querying for the most useful/informative constraints, facilitating an active learning setting (existing methods for cross-modal similarity learning do not have such a mechanism), and (iii) the binary code length is learned from the data. We demonstrate the effectiveness of the proposed approach on two problems that require computing pairwise similarities between cross-modal object pairs: cross-modal link prediction in bipartite graphs, and hashing based cross-modal similarity search.

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TIST Journal 2015 Journal Article

Depth Error Elimination for RGB-D Cameras

Yue Gao
You Yang
Yi Zhen
Qionghai Dai

The rapid spreading of RGB-D cameras has led to wide applications of 3D videos in both academia and industry, such as 3D entertainment and 3D visual understanding. Under these circumstances, extensive research efforts have been dedicated to RGB-D camera--oriented topics. In these topics, quality promotion of depth videos with the temporal characteristic is emerging and important. Due to the limited exposure time of RGB-D cameras, object movement can easily lead to motion blurs in intensive images, which can further result in obvious artifacts (holes or fake boundaries) in the corresponding depth frames. With regard to this problem, we propose a depth error elimination method based on time series analysis to remove the artifacts in depth images. In this method, we first locate the regions with erroneous depths in intensive images by using motion blur detection based on a time series analysis model. This is based on the fact that the depth image is calculated by intensive color images that are captured synchronously by RGB-D cameras. Then, the artifacts, such as holes or fake boundaries, are fixed by a depth error elimination method. To evaluate the performance of the proposed method, we conducted experiments on 250 images. Experimental results demonstrate that the proposed method can locate the error regions correctly and eliminate these artifacts effectively. The quality of depth video can be improved significantly by using the proposed method.

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IJCAI Conference 2015 Conference Paper

Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences

Dixin Luo
Hongteng Xu
Yi Zhen
Xia Ning
Hongyuan Zha
Xiaokang Yang
Wenjun Zhang

We propose a Multi-task Multi-dimensional Hawkes Process (MMHP) for modeling event sequences where there exist multiple triggering patterns within sequences and structures across sequences. MMHP is able to model the dynamics of multiple sequences jointly by imposing structural constraints and thus systematically uncover clustering structure among sequences. We propose an effective and robust optimization algorithm to learn MMHP models, which takes advantage of alternating direction method of multipliers (ADMM), majorization minimization and Euler-Lagrange equations. Our experimental results demonstrate that MMHP performs well on both synthetic and real data.

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IJCAI Conference 2015 Conference Paper

Trailer Generation via a Point Process-Based Visual Attractiveness Model

Hongteng Xu
Yi Zhen
Hongyuan Zha

Producing attractive trailers for videos needs human expertise and creativity, and hence is challenging and costly. Different from video summarization that focuses on capturing storylines or important scenes, trailer generation aims at producing trailers that are attractive so that viewers will be eager to watch the original video. In this work, we study the problem of automatic trailer generation, in which an attractive trailer is produced given a video and a piece of music. We propose a surrogate measure of video attractiveness named fixation variance, and learn a novel self-correcting point process-based attractiveness model that can effectively describe the dynamics of attractiveness of a video. Furthermore, based on the attractiveness model learned from existing training trailers, we propose an efficient graph-based trailer generation algorithm to produce a max-attractiveness trailer. Experiments demonstrate that our approach outperforms the state-of-the-art trailer generators in terms of both quality and efficiency.

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IJCAI Conference 2013 Conference Paper

Parametric Local Multimodal Hashing for Cross-View Similarity Search

Deming Zhai
Hong Chang
Yi Zhen
Xianming Liu
Xilin Chen
Wen Gao

Recent years have witnessed the growing popularity of hashing for efﬁcient large-scale similarity search. It has been shown that the hashing quality could be boosted by hash function learning (HFL). In this paper, we study HFL in the context of multimodal data for cross-view similarity search. We present a novel multimodal HFL method, called Parametric Local Multimodal Hashing (PLMH), which learns a set of hash functions to locally adapt to the data structure of each modality. To balance locality and computational efﬁciency, the hashing projection matrix of each instance is parameterized, with guaranteed approximation error bound, as a linear combination of basis hashing projections of a small set of anchor points. A local optimal conjugate gradient algorithm is designed to learn the hash functions for each bit, and the overall hash codes are learned in a sequential manner to progressively minimize the bias. Experimental evaluations on cross-media retrieval tasks demonstrate that PLMH performs competitively against the state-of-the-art methods.

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NeurIPS Conference 2012 Conference Paper

Co-Regularized Hashing for Multimodal Data

Yi Zhen
Dit-Yan Yeung

Hashing-based methods provide a very promising approach to large-scale similarity search. To obtain compact hash codes, a recent trend seeks to learn the hash functions from data automatically. In this paper, we study hash function learning in the context of multimodal data. We propose a novel multimodal hash function learning method, called Co-Regularized Hashing (CRH), based on a boosted co-regularization framework. The hash functions for each bit of the hash codes are learned by solving DC (difference of convex functions) programs, while the learning for multiple bits proceeds via a boosting procedure so that the bias introduced by the hash functions can be sequentially minimized. We empirically compare CRH with two state-of-the-art multimodal hash function learning methods on two publicly available data sets.

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