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Meng Liao

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5 papers

1 author row

YNICL Journal 2022 Journal Article

Abnormal effective connectivity in visual cortices underlies stereopsis defects in amblyopia

Xia Chen
Meng Liao
Ping Jiang
Huaiqiang Sun
Longqian Liu
Qiyong Gong

The neural basis underlying stereopsis defects in patients with amblyopia remains unclear, which hinders the development of clinical therapy. This study aimed to investigate visual network abnormalities in patients with amblyopia and their associations with stereopsis function. Spectral dynamic causal modeling methods were employed for resting-state functional magnetic resonance imaging data to investigate the effective connectivity (EC) among 14 predefined regions of interest in the dorsal and ventral visual pathways. We adopted two independent datasets, including a cross-sectional and a longitudinal dataset. In the cross-sectional dataset, we compared group differences in EC between 31 patients with amblyopia (mean age: 26.39 years old) and 31 healthy controls (mean age: 25.71 years old) and investigated the association between EC and stereoacuity. In addition, we explored EC changes after perceptual learning in a novel longitudinal dataset including 9 patients with amblyopia (mean age: 15.78 years old). We found consistent evidence from the two datasets indicating that the aberrant EC from V2v to LO2 is crucial for the stereoscopic deficits in the patients with amblyopia: it was weaker in the patients than in the controls, showed a positive linear relationship with the stereoscopic function, and increased after perceptual learning in the patients. In addition, higher-level dorsal (V3d, V3A, and V3B) and ventral areas (LO1 and LO2) were important nodes in the network of abnormal ECs associated with stereoscopic deficits in the patients with amblyopia. Our research provides insights into the neural mechanism underlying stereopsis deficits in patients with amblyopia and provides candidate targets for focused stimulus interventions to enhance the efficacy of clinical treatment for the improvement of stereopsis deficiency.

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AAAI Conference 2022 Conference Paper

Procedural Text Understanding via Scene-Wise Evolution

Jialong Tang
Hongyu Lin
Meng Liao
Yaojie Lu
Xianpei Han
Le Sun
Weijian Xie
Jin Xu

Procedural text understanding requires machines to reason about entity states within the dynamical narratives. Current procedural text understanding approaches are commonly entity-wise, which separately track each entity and independently predict different states of each entity. Such an entity-wise paradigm does not consider the interaction between entities and their states. In this paper, we propose a new scene-wise paradigm for procedural text understanding, which jointly tracks states of all entities in a scene-by-scene manner. Based on this paradigm, we propose Scene Graph Reasoner (SGR), which introduces a series of dynamically evolving scene graphs to jointly formulate the evolution of entities, states and their associations throughout the narrative. In this way, the deep interactions between all entities and states can be jointly captured and simultaneously derived from scene graphs. Experiments show that SGR not only achieves the new state-of-the-art performance but also significantly accelerates the speed of reasoning.

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JAIR Journal 2022 Journal Article

Two-phase Multi-document Event Summarization on Core Event Graphs

Zengjian Chen
Jin Xu
Meng Liao
Tong Xue
Kun He

Succinct event description based on multiple documents is critical to news systems as well as search engines. Different from existing summarization or event tasks, Multi-document Event Summarization (MES) aims at the query-level event sequence generation, which has extra constraints on event expression and conciseness. Identifying and summarizing the key event from a set of related articles is a challenging task that has not been sufficiently studied, mainly because online articles exhibit characteristics of redundancy and sparsity, and a perfect event summarization needs high level information fusion among diverse sentences and articles. To address these challenges, we propose a two-phase framework for the MES task, that first performs event semantic graph construction and dominant event detection via graph-sequence matching, then summarizes the extracted key event by an event-aware pointer generator. For experiments in the new task, we construct two large-scale real-world datasets for training and assessment. Extensive evaluations show that the proposed framework significantly outperforms the related baseline methods, with the most dominant event of the articles effectively identified and correctly summarized.

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IJCAI Conference 2021 Conference Paper

GSPL: A Succinct Kernel Model for Group-Sparse Projections Learning of Multiview Data

Danyang Wu
Jin Xu
Xia Dong
Meng Liao
Rong Wang
Feiping Nie
Xuelong Li

This paper explores a succinct kernel model for Group-Sparse Projections Learning (GSPL), to handle multiview feature selection task completely. Compared to previous works, our model has the following useful properties: 1) Strictness: GSPL innovatively learns group-sparse projections strictly on multiview data via ‘2; 0-norm constraint, which is different with previous works that encourage group-sparse projections softly. 2) Adaptivity: In GSPL model, when the total number of selected features is given, the numbers of selected features of different views can be determined adaptively, which avoids artificial settings. Besides, GSPL can capture the differences among multiple views adaptively, which handles the inconsistent problem among different views. 3) Succinctness: Except for the intrinsic parameters of projection-based feature selection task, GSPL does not bring extra parameters, which guarantees the applicability in practice. To solve the optimization problem involved in GSPL, a novel iterative algorithm is proposed with rigorously theoretical guarantees. Experimental results demonstrate the superb performance of GSPL on synthetic and real datasets.

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AAAI Conference 2020 Conference Paper

Active Learning with Query Generation for Cost-Effective Text Classification

Yi-Fan Yan
Sheng-Jun Huang
Shaoyi Chen
Meng Liao
Jin Xu

Labeling a text document is usually time consuming because it requires the annotator to read the whole document and check its relevance with each possible class label. It thus becomes rather expensive to train an effective model for text classiﬁcation when it involves a large dataset of long documents. In this paper, we propose an active learning approach for text classiﬁcation with lower annotation cost. Instead of scanning all the examples in the unlabeled data pool to select the best one for query, the proposed method automatically generates the most informative examples based on the classiﬁcation model, and thus can be applied to tasks with large scale or even inﬁnite unlabeled data. Furthermore, we propose to approximate the generated example with a few summary words by sparse reconstruction, which allows the annotators to easily assign the class label by reading a few words rather than the long document. Experiments on different datasets demonstrate that the proposed approach can effectively improve the classiﬁcation performance while signiﬁcantly reduce the annotation cost.

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