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Prefrontal Internal Event-Driven Analysis of Dynamical Electroencephalographic Biomarkers in Depression During Emotional Auditory Task

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SSRN

SSRN 2024年

作者： Zhao, Qinglin Cui, Kunbo Jiang, Hua Wu, Zhongqing Zhang, Lixin Zhao, Mingqi Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Medical Technology Beijing Institute of Technology Beijing100081 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai200000 China Joint Research Center for Cognitive Neurosensor Technology Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou730000 China

The prefrontal cortex is considered as one of the key brain regions for the study of affective dysfunction in depression. The study of neural processes in this region during specific emotional tasks may enhance our understanding on the mechanisms of depression. One powerful, direct and convenient tool is electroencephalography (EEG);yet in many experimental paradigms, the absence of external events limits the effectiveness of resolving such emotional task-specific prefrontal and brain-wide electroencephalographic dynamics. To address this issue, we analyzed 64-channel EEG data acquired from 55 healthy and 55 depressed participants during a three-polar (positive, neutral and negative) emotional auditory task in a prefrontal internal event-driven approach. Specifically, we extracted depression-related prefrontal internal events according to signals acquired from Fp1, Fpz, and Fp2 by statistically comparing the microstates identified with an unsupervised Gaussian mixture model. We then examined such event-related neural dynamics in terms of power change in neural oscillations and cross-oscillation interaction in a reconstructed brain source space. Our results suggested that the prefrontal events can be used to resolve abnormal neural dynamics in depression. Our results revealed emotional process-specific and internal event-related abnormalities dynamically in the brain source space, as evaluated by internal event-related changes in power of neural oscillations and in modulations of low frequency phases to high frequency amplitudes. Overall, our findings present new insights for dynamical electroencephalographic biomarkers in depression, which potentially provide EEG signal decoding solutions for EEG feedback-based closed loop intervention of depression. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

Uncertainty calibration for ensemble-based debiasing methods 21

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Uncertainty calibration for ensemble-based debiasing methods

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Ruibin Xiong Yimeng Chen Liang Pang Xueqi Cheng Zhiming Ma Yanyan Lan CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences and Baidu Inc. University of Chinese Academy of Sciences and Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences and Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Institute for AI Industry Research Tsinghua University

ISBN: (纸本)9781713845393

Ensemble-based debiasing methods have been shown effective in mitigating the reliance of classifiers on specific dataset bias, by exploiting the output of a bias-only model to adjust the learning target. In this paper, we focus on the bias-only model in these ensemble-based methods, which plays an important role but has not gained much attention in the existing literature. Theoretically, we prove that the debiasing performance can be damaged by inaccurate uncertainty estimations of the bias-only model. Empirically, we show that existing bias-only models fall short in producing accurate uncertainty estimations. Motivated by these findings, we propose to conduct calibration on the bias-only model, thus achieving a three-stage ensemble-based debiasing framework, including bias modeling, model calibrating, and debiasing. Experimental results on NLI and fact verification tasks show that our proposed three-stage debiasing framework consistently outperforms the traditional two-stage one in out-of-distribution accuracy.

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Learning to truncate ranked lists for information retrieval

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arXiv 2021年

作者： Wu, Chen Zhang, Ruqing Guo, Jiafeng Fan, Yixing Lan, Yanyan Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Ranked list truncation is of critical importance in a variety of professional information retrieval applications such as patent search or legal search. The goal is to dynamically determine the number of returned documents according to some user-defined objectives, in order to reach a balance between the overall utility of the results and user efforts. Existing methods formulate this task as a sequential decision problem and take some pre-defined loss as a proxy objective, which suffers from the limitation of local decision and non-direct optimization. In this work, we propose a global decision based truncation model named AttnCut, which directly optimizes user-defined objectives for the ranked list truncation. Specifically, we take the successful transformer architecture to capture the global dependency within the ranked list for truncation decision, and employ the reward augmented maximum likelihood (RAML) for direct optimization. We consider two types of user-defined objectives which are of practical usage. One is the widely adopted metric such as F1 which acts as a balanced objective, and the other is the best F1 under some minimal recall constraint which represents a typical objective in professional search. Empirical results over the Robust04 and MQ2007 datasets demonstrate the effectiveness of our approach as compared with the state-of-the-art baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Information retrieval

A linguistic study on relevance modeling in information retrieval

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arXiv 2021年

作者： Fan, Yixing Guo, Jiafeng Ma, Xinyu Zhang, Ruqing Lan, Yanyan Cheng, Xueqi University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Relevance plays a central role in information retrieval (IR), which has received extensive studies starting from the 20th century. The definition and the modeling of relevance has always been critical challenges in both information science and computer science research areas. Along with the debate and exploration on relevance, IR has already become a core task in many real-world applications, such as Web search engines, question answering systems, conversational bots, and so on. While relevance acts as a unified concept in all these retrieval tasks, the inherent definitions are quite different due to the heterogeneity of these tasks. This raises a question to us: Do these different forms of relevance really lead to different modeling focuses? To answer this question, in this work, we conduct an empirical study on relevance modeling in three representative IR tasks, i.e., document retrieval, answer retrieval, and response retrieval. Specifically, we attempt to study the following two questions: 1) Does relevance modeling in these tasks really show differences in terms of natural language understanding (NLU)? We employ 16 linguistic tasks to probe a unified retrieval model over these three retrieval tasks to answer this question. 2) If there do exist differences, how can we leverage the findings to enhance the relevance modeling? We proposed three intervention methods to investigate how to leverage different modeling focuses of relevance to improve these IR tasks. We believe the way we study the problem as well as our findings would be beneficial to the IR community. © 2021, CC BY.

关键词： Information retrieval

FedMatch: Federated learning over heterogeneous question answering data

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arXiv 2021年

作者： Chen, Jiangui Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Question Answering (QA), a popular and promising technique for intelligent information access, faces a dilemma about data as most other AI techniques. On one hand, modern QA methods rely on deep learning models which are typically data-hungry. Therefore, it is expected to collect and fuse all the available QA datasets together in a common site for developing a powerful QA model. On the other hand, real-world QA datasets are typically distributed in the form of isolated islands belonging to different parties. Due to the increasing awareness of privacy security, it is almost impossible to integrate the data scattered around, or the cost is prohibited. A possible solution to this dilemma is a new approach known as federated learning, which is a privacy-preserving machine learning technique over distributed datasets. In this work, we propose to adopt federated learning for QA with the special concern on the statistical heterogeneity of the QA data. Here the heterogeneity refers to the fact that annotated QA data are typically with non-identical and independent distribution (non-IID) and unbalanced sizes in practice. Traditional federated learning methods may sacrifice the accuracy of individual models under the heterogeneous situation. To tackle this problem, we propose a novel Federated Matching framework for QA, named FedMatch, with a backbone-patch architecture. The shared backbone is to distill the common knowledge of all the participants while the private patch is a compact and efficient module to retain the domain information for each participant. To facilitate the evaluation, we build a benchmark collection based on several QA datasets from different domains to simulate the heterogeneous situation in practice. Empirical studies demonstrate that our model can achieve significant improvements against the baselines over all the datasets. © 2021, CC BY-NC-ND.

关键词： Deep learning

BiKT: Unleashing the potential of GNNs via Bi-directional Knowledge Transfer

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arXiv 2023年

作者： Zheng, Shuai Liu, Zhizhe Zhu, Zhenfeng Zhang, Xingxing Li, Jianxin Zhao, Yao The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Qiyuan Lab Beijing China The Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing100083 China

Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on feature transformation, another major operation of the message-passing framework. In this paper, we first empirically investigate the performance of the feature transformation operation in several typical GNNs. Unexpectedly, we notice that GNNs do not completely free up the power of the inherent feature transformation operation. By this observation, we propose the Bi-directional Knowledge Transfer (BiKT), a plug-and-play approach to unleash the potential of the feature transformation operations without modifying the original architecture. Taking the feature transformation operation as a derived representation learning model that shares parameters with the original GNN, the direct prediction by this model provides a topological-agnostic knowledge feedback that can further instruct the learning of GNN and the feature transformations therein. On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other. In addition, a theoretical analysis is further provided to demonstrate that BiKT improves the generalization bound of the GNNs from the perspective of domain adaption. An extensive group of experiments on up to 7 datasets with 5 typical GNNs demonstrates that BiKT brings up to 0.5% - 4% performance gain over the original GNN, which means a boosted GNN is obtained. Meanwhile, the derived model also shows a powerful performance to compete with or even surpass the original GNN, enabling us to flexibly apply it independently to some other specific downstream tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

Focus on the Stability of Large Systems: Toward Automatic Prediction and Analysis of Vulnerability Threat Intelligence

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Focus on the Stability of Large Systems: Toward Automatic Pr...

IEEE International Conference on data science in Cyberspace (DSC)

作者： Shiwen Song Qiong Wu Xin Zheng Peng Wang Yuchen Dou Zhongwen Li Lidong Zhai Institute of Information Engineering Chinese Academy of Sciences BeiJing China Bag Data Academy Zhongke Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665418164

With the increase in the number of users and business volume, the business systems of Internet companies are becoming more and more complex, resulting in a surge in the number of alarms. A large number of dirty alarms add a huge workload to security operations, which indirectly pose a large number of threats to business systems. At present, most systems use the method of accessing third-party Threat Intelligence to assist operators to realize automatic handling of alarms. However, this method has lagging and accuracy problems, making this work always difficult to meet the requirements of fast and accurate. This article proposes a new method for gathering vulnerability Threat Intelligence, which can obtain vulnerability information in advance of security announcements issued by security vendors. By analyzing the vulnerability disclosure process, this method obtains vulnerability information from the original source submitted by open source mail group, of developers. We used NLP technology and XGBoost model to automatically analyze the vulnerability information, and finally generate FINTEL. The experimental result shows that this method has an accuracy of 93%, and can obtain vulnerability information 10h to 7 days before security vendors release. The scope of application covers all open source code repositories and some closed source repositories.

关键词： Machine learning algorithms Scalability Prediction methods Stability analysis Security Surges Research and development

B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrieval

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arXiv 2021年

作者： Ma, Xinyu Guo, Jiafeng Zhang, Ruqing Fan, Yixing Li, Yingyan Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Pre-training and fine-tuning have achieved remarkable success in many downstream natural language processing (NLP) tasks. Recently, pre-training methods tailored for information retrieval (IR) have also been explored, and the latest success is the PROP method which has reached new SOTA on a variety of ad-hoc retrieval benchmarks. The basic idea of PROP is to construct the representative words prediction (ROP) task for pre-training inspired by the query likelihood model. Despite its exciting performance, the effectiveness of PROP might be bounded by the classical unigram language model adopted in the ROP task construction process. To tackle this problem, we propose a bootstrapped pre-training method (namely B-PROP) based on BERT for ad-hoc retrieval. The key idea is to use the powerful contextual language model BERT to replace the classical unigram language model for the ROP task construction, and re-train BERT itself towards the tailored objective for IR. Specifically, we introduce a novel contrastive method, inspired by the divergence-from-randomness idea, to leverage BERT's self-attention mechanism to sample representative words from the document. By further fine-tuning on downstream ad-hoc retrieval tasks, our method achieves significant improvements over PROP and other baselines, and further pushes forward the SOTA on a variety of ad-hoc retrieval tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction

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arXiv 2024年

作者： Du, Chenhe Lin, Xiyue Wu, Qing Tian, Xuanyu Su, Ying Luo, Zhe Zheng, Rui Chen, Yang Wei, Hongjiang Zhou, S. Kevin Yu, Jingyi Zhang, Yuyao The School of Information Science and Technology ShanghaiTech University Shanghai China The Department of Critical Care Medicine Zhongshan Hospital Fudan University Shanghai China The Laboratory of Image Science and Technology The School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China The School of Biomedical Engineering Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China

Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data acquisition, resulting in diverse artifacts in the reconstructed CT images. Emerging implicit neural representation (INR) techniques, such as NeRF, NeAT, and NeRP, have shown promise in under-determined CT imaging reconstruction tasks. However, the unsupervised nature of INR architecture imposes limited constraints on the solution space, particularly for the highly ill-posed reconstruction task posed by LACT and ultra-SVCT. In this study, we introduce the Diffusion Prior Driven Neural Representation (DPER), an advanced unsupervised framework designed to address the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into data fidelity and distribution prior sub-problems. The two sub-problems are respectively addressed by INR reconstruction scheme and pre-trained score-based diffusion model. This combination first injects the implicit image local consistency prior from INR. Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions. We conduct comprehensive experiments to evaluate the performance of DPER on LACT and ultra-SVCT reconstruction with two public datasets (AAPM and LIDC), an in-house clinical COVID-19 dataset and a public raw projection dataset created by Mayo Clinic. The results show that our method outperforms the state-of-the-art reconstruction methods on in-domain datasets, while achieving significant performance improvements on out-of-domain (OOD) datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems