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Detecting differential transcript usage across multiple conditions for RNA-seq data based on the smoothed LDA model

Frontiers of Computer Science 2021年第3期15卷 217-219页

作者： Jing LI Xuejun LIU Daoqiang ZHANG College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsMIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China

1 Introduction and main contributions Differential transcript usage(DTU),which refers to the event that the relative transcript abundance within a gene changes between *** detect DTU,various methods have been proposed,which can be classified into exon-based models and gene-based *** approaches either cannot estimate the relative transcript abundance,or they cannot deal properly with the multi-source mapping problems of ***,few methods currently consider sample-to-sample variability under multiple conditions[1].

关键词： conditions. properly differential

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Boundary Data Augmentation for Offline Reinforcement Learning

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ZTE Communications 2023年第3期21卷 29-36页

作者： SHEN Jiahao JIANG Ke TAN Xiaoyang College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing 211106China

Offline reinforcement learning(ORL)aims to learn a rational agent purely from behavior data without any online *** of the major challenges encountered in ORL is the problem of distribution shift,i.e.,the mismatch between the knowledge of the learned policy and the reality of the underlying *** works usually handle this in a too pessimistic manner to avoid out-of-distribution(OOD)queries as much as possible,but this can influence the robustness of the agents at unseen *** this paper,we propose a simple but effective method to address this *** key idea of our method is to enhance the robustness of the new policy learned offline by weakening its confidence in highly uncertain regions,and we propose to find those regions by simulating them with modified Generative Adversarial Nets(GAN)such that the generated data not only follow the same distribution with the old experience but are very difficult to deal with by themselves,with regard to the behavior policy or some other reference *** then use this information to regularize the ORL algorithm to penalize the overconfidence behavior in these *** experiments on several publicly available offline RL benchmarks demonstrate the feasibility and effectiveness of the proposed method.

关键词： offline reinforcement learning out‐of‐distribution state robustness uncertainty

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A Multi-Scale Multi-Hop Graph Convolution Network for Predicting Fluid intelligence via Functional Connectivity

A Multi-Scale Multi-Hop Graph Convolution Network for Predic...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wen, Xuyun Cao, Qumei Zhang, Daoqiang Nanjing University of Aeronautics and Astronautics Nanjing College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Jiangsu China

ISBN: (纸本)9781665468190

Predicting fluid intelligence via neuroimaging data is important to understand neural mechanisms underlying diverse complex cognitive tasks in human brain. Functional connectivity (FC) reflects interactions among brain regions providing rich information of brain organization, which has been widely used in various behavior predictions. With the success of deep neural networks, graph convolutional network (GCN) is regarded as a promising feature learning method in FC networks (FCNs). However, as a challenging task, the existing GCN models cannot achieve a satisfactory performance in fluid intelligence predication due to the insufficient information utilization of brain connectivity and the limitation of graph convolution layer. To tackle these problems, this paper developed a Multi-Scale Multi-Hop GCN (MS-MH-GCN) to estimate fluid intelligence score by using FC. In the proposed method, we considered the hierarchy of brain system and thus utilized FCs from multiple spatial scales as input for the subsequent feature learning to achieve a complete characterize of brain organization for each individual. We also designed a new multi-hop graph convolution layer that uses multi-hop neighbors instead of l-hop neighbor in traditional GCN to guide message passing of nodal feature at every step. The introduction of high-order graph information benefits to the model learning ability improvement. Additionally, it is also worth emphasizing that, during feature learning process, we added contrast constraint to multi-scale FCNs to improve the similarity of feature representations across different spatial scales within a subject. Experimental results showed that our proposed method performed much better than the other four art-of-the-state methods. © 2022 IEEE.

关键词： Brain

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Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic Information

Decoding the Echoes of Vision from fMRI: Memory Disentanglin...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xia, Runze Yin, Congchi Li, Piji College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (纸本)9798891761643

The human visual system is capable of processing continuous streams of visual information, but how the brain encodes and retrieves recent visual memories during continuous visual processing remains unexplored. This study investigates the capacity of working memory to retain past information under continuous visual stimuli. And then we propose a new task Memory Disentangling, which aims to extract and decode past information from fMRI signals. To address the issue of interference from past memory information, we design a disentangled contrastive learning method inspired by the phenomenon of proactive interference. This method separates the information between adjacent fMRI signals into current and past components and decodes them into image descriptions. Experimental results demonstrate that this method effectively disentangles the information within fMRI signals. This research could advance brain-computer interfaces and mitigate the problem of low temporal resolution in fMRI. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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GBCI: Adaptive Frequency Band Learning for Gender Recognition in Brain-Computer Interfaces 1st

GBCI: Adaptive Frequency Band Learning for Gender Recogniti...

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1st CAAI International Conference on Artificial intelligence, CICAI 2021

作者： Wang, Pengpai Zhou, Yueying Li, Zhongnian Zhang, Daoqiang College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9783030930455

In recent years, with the rapid development of brain-computer interface (BCI) technology, various applications based on BCI have generated significant interest. A motivating application of BCI is predicting human gender by electroencephalogram (EEG) analysis. However, most of the recent researches only identify the gender of EEG in the resting state. In real-life practice, it is difficult for the subjects to be at a full resting state when it mixes with motor imagery (MI). Therefore, we acquired two kinds of EEG activities data including 9 subjects (5 males and 4 females) with two state (i.e., resting state and MI) based on Chinese sign language. In this paper, to recognize gender form two state, an improved adaptive variational mode decomposition with long short term memory (AVMD-LSTM) network is developed to construct a hybrid state learning framework. Besides, the sample entropy (SE) is employed to select the channel of EEG data as the input of AVMD-LSTM. The recognition accuracy of gender classification was 89.21 %. Comparing with the task of complete resting state, the model is validated with more robustness. Furthermore, the proposed algorithm has many applications, including biometrics, healthcare, and online entertainment advertising. © 2021, Springer Nature Switzerland AG.

关键词： Variational mode decomposition

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uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers 29

uChecker: Masked Pretrained Language Models as Unsupervised ...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Li, Piji College of Computer Science and Technology Nanjing University of Aeronautics Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Jiangsu Nanjing China

The task of Chinese Spelling Check (CSC) is aiming to detect and correct spelling errors that can be found in the text. While manually annotating a high-quality dataset is expensive and time-consuming, thus the scale of the training dataset is usually very small (e.g., SIGHAN151 only contains 2339 samples for training), therefore supervised-learning based models usually suffer the data sparsity limitation and over-fitting issue, especially in the era of big language models. In this paper, we are dedicated to investigating the unsupervised paradigm to address the CSC problem and we propose a framework named uChecker to conduct unsupervised spelling error detection and correction. Masked pretrained language models such as BERT are introduced as the backbone model considering their powerful language diagnosis capability. Benefiting from the various and flexible MASKing operations, we propose a Confusionset-guided masking strategy to fine-train the masked language model to further improve the performance of unsupervised detection and correction. Experimental results on standard datasets demonstrate the effectiveness of our proposed model uChecker in terms of character-level and sentence-level Accuracy, Precision, Recall, and F1-Measure on tasks of spelling error detection and correction respectively. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Computational linguistics

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DouGNN: An End-to-End Deep Learning Framework for Predicting Individual Behaviors from fMRI Data 2

DouGNN: An End-to-End Deep Learning Framework for Predicting...

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2nd International Conference on Image Processing, Computer Vision and machine Learning, ICICML 2023

作者： Cao, Qumei Wen, Xuyun MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798350331417

Predicting individual behavior from functional connectivity (FC) using machine learning is a critical research topic in neuroscience. While various models have been proposed, they mainly focus on designing behavior prediction methods, overlooking the influence of upstream FC network construction approach on downstream tasks. In this paper, leveraging graph neural networks (GNNs), we introduce an end-to-end deep learning framework named DouGNN, aimed at learning individual behavior from fMRI data. Through joint training of two GNNs, DouGNN innovatively integrates individualized cortical parcellation (for FC network construction) and individual behavior prediction into a unified optimization model. By integrating individualized cortical parcellation, the model gains the ability to dynamically modify the subject's cortical parcellation and the downstream tasks. This enhancement enables the subsequently constructed FC networks to better represent individual-specific features and task-related characteristics, thereby significantly improving the performance of individual behavior prediction. Moreover, the generated individual-specific, task-aware cortical parcellations from this process can also help us in understanding the relationship between FC and behaviors. We conducted three representative behavior prediction experiments on the publicly available HCP dataset. The results demonstrate that DouGNN outperforms existing methods in all behavior prediction tasks, achieving superior performance and generating more functionally homogeneous individualized cortical parcellations. © 2023 IEEE.

关键词： Behavior prediction functional magnetic resonance imaging graph neural network individualized cortical parcellation

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LEMoE: Advanced Mixture of Experts Adaptor for Lifelong Model Editing of Large Language Models

LEMoE: Advanced Mixture of Experts Adaptor for Lifelong Mode...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wang, Renzhi Li, Piji College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (纸本)9798891761643

Large language models (LLMs) require continual knowledge updates to stay abreast of the ever-changing world facts, prompting the formulation of lifelong model editing task. While recent years have witnessed the development of various techniques for single and batch editing, these methods either fail to apply or perform sub-optimally when faced with lifelong editing. In this paper, we introduce LEMoE, an advanced Mixture of Experts (MoE) adaptor for lifelong model editing. We first analyze the factors influencing the effectiveness of conventional MoE adaptor in lifelong editing, including catastrophic forgetting, inconsistent routing and order sensitivity. Based on these insights, we propose a tailored module insertion method to achieve lifelong editing, incorporating a novel KV anchor routing to enhance routing consistency between training and inference stage, along with a concise yet effective clustering-based editing order planning. Experimental results demonstrate the effectiveness of our method in lifelong editing, surpassing previous model editing techniques while maintaining outstanding performance in batch editing task. Our code can be found at: https://***/rzhwang/LEMoE. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks 22

A Systematic Evaluation of Large Language Models for Natural...

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22nd Chinese National Conference on Computational Linguistics, CCL 2023

作者： Ni, Xuanfan Li, Piji College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Jiangsu Nanjing210016 China

ISBN: (纸本)9781713889892

Recent efforts have evaluated large language models (LLMs) in areas such as commonsense reasoning, mathematical reasoning, and code generation. However, to the best of our knowledge, no work has specifically investigated the performance of LLMs in natural language generation (NLG) tasks, a pivotal criterion for determining model excellence. Thus, this paper conducts a comprehensive evaluation of well-known and high-performing LLMs, namely ChatGPT, ChatGLM, T5-based models, LLaMA-based models, and Pythia-based models, in the context of NLG tasks. We select English and Chinese datasets encompassing Dialogue Generation and Text Summarization. Moreover, we propose a common evaluation setting that incorporates input templates and post-processing strategies. Our study reports both automatic results, accompanied by a detailed analysis. © 2023 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization 41

Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimizat...

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41st International Conference on machine Learning, ICML 2024

作者： Huang, Feihu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

Bilevel optimization is widely applied in many machine learning tasks such as hyper-parameter learning, meta learning and reinforcement learning. Although many algorithms recently have been developed to solve the bilevel optimization problems, they generally rely on the (strongly) convex lower-level problems. More recently, some methods have been proposed to solve the nonconvex-PL bilevel optimization problems, where their upper-level problems are possibly nonconvex, and their lower-level problems are also possibly nonconvex while satisfying Polyak-Lojasiewicz (PL) condition. However, these methods still have a high convergence complexity or a high computation complexity such as requiring compute expensive Hessian/Jacobian matrices and its inverses. In the paper, thus, we propose an efficient Hessian/Jacobian-free method (i.e., HJFBiO) with the optimal convergence complexity to solve the nonconvex-PL bilevel problems. Theoretically, under some mild conditions, we prove that our HJFBiO method obtains an optimal convergence rate of O(1/T), where T denotes the number of iterations, and has an optimal gradient complexity of O(ϵ-1) in finding an ϵ-stationary solution. We conduct some numerical experiments on the bilevel PL game and hyper-representation learning task to demonstrate efficiency of our proposed method. Copyright 2024 by the author(s)

关键词： Optimization algorithms

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