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作者： Wong, Chi Man Wan, Feng Wang, Boyu Wang, Ze Nan, Wenya Lao, Ka Fai Mak, Peng Un Vai, Mang I. Rosa, Agostinho Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau China Department of Computer Science University of Western Ontario LondonON Canada Department of Psychology Shanghai Normal University Shanghai China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China LaSEEB-ISR-LARSyS Universidade de Lisboa Lisbon Portugal

Objective. Latest target recognition methods that are equipped with learning from the subject's calibration data, represented by the extended canonical correlation analysis (eCCA) and the ensemble task-related component analysis (eTRCA), can achieve extra high performance in the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This paper develops a new scheme to learn from limited calibration data. Approach. A learning across multiple stimuli scheme is proposed for the target recognition methods, which applies to learning the data corresponding to not only the target stimulus but also the other stimuli. The resulting optimization problems can be simplified and solved utilizing the prior knowledge and properties of SSVEPs across different stimuli. With the new learning scheme, the eCCA and the eTRCA can be extended to the multi-stimulus eCCA (ms-eCCA) and the multi-stimulus eTRCA (ms-eTRCA), respectively, as well as a combination of them (i.e. ms-eCCA+ms-eTRCA) that incorporates their merits. Main results. Evaluation and comparison using an SSVEP-BCI benchmark dataset with 35 subjects show that the ms-eCCA (or ms-eTRCA) performs significantly better than the eCCA (or eTRCA) method while the ms-eCCA+ms-eTRCA performs the best. With the learning across stimuli scheme, the existing target recognition methods can be further improved in terms of the target recognition performance and the ability against insufficient calibration. Significance. A new learning scheme is proposed towards the efficient use of the calibration data, providing enhanced performance and saving calibration time in the SSVEP-based BCIs. © 2020 IOP Publishing Ltd.

关键词： brain computer interface

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SoloGAN: Multi-domain Multimodal Unpaired Image-to-Image Translation via a Single Generative Adversarial Network

arXiv

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arXiv 2020年

作者： Huang, Shihua He, Cheng Cheng, Ran The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China The Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

Despite significant advances in image-to-image (I2I) translation with generative adversarial networks (GANs), it remains challenging to effectively translate an image to a set of diverse images in multiple target domains using a single pair of generator and discriminator. Existing I2I translation methods adopt multiple domain-specific content encoders for different domains, where each domain-specific content encoder is trained with images from the same domain only. Nevertheless, we argue that the content (domain-invariance) features should be learned from images among all of the domains. Consequently, each domain-specific content encoder of existing schemes fails to extract the domain-invariant features efficiently. To address this issue, we present a flexible and general SoloGAN model for efficient multimodal I2I translation among multiple domains with unpaired data. In contrast to existing methods, the SoloGAN algorithm uses a single projection discriminator with an additional auxiliary classifier and shares the encoder and generator for all domains. Consequently, the SoloGAN can be trained effectively with images from all domains such that the domain-invariance content representation can be efficiently extracted. Qualitative and quantitative results over a wide range of datasets against several counterparts and variants of the SoloGAN demonstrate the merits of the method, especially for challenging I2I translation datasets, i.e., datasets involving extreme shape variations or need to keep the complex backgrounds unchanged after translations. Furthermore, we demonstrate the contribution of each component in SoloGAN by ablation studies. Impact Statement-Image-to-image (I2I) translation is a popular technology in image synthesis. Owing to the rapid development of artificial intelligence, particularly deep learning, I2I translation has been applied in a wide range of real-world scenarios such as image super-resolution, style transfer, and face swapping. Nevertheless,

关键词： Generative adversarial networks

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An Investigation on Deep Learning with Beta Stabilizer

An Investigation on Deep Learning with Beta Stabilizer

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Qi Liu Tian Tan Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Speech Lab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University

Artificial neural networks(ANN) have been used in many applications such like handwriting recognition and speech recognition. It is well-known that learning rate is a crucial value in the training procedure for artificial neural networks. It is shown that the initial value of learning rate can confoundedly affect the final result and this value is always set manually in practice. A new parameter called beta stabilizer has been introduced to reduce the sensitivity of the initial learning rate. But this method has only been proposed for deep neural network(DNN) with sigmoid activation function. In this paper we extended beta stabilizer to long short-term memory(LSTM) and investigated the effects of beta stabilizer parameters on different models, including LSTM and DNN with relu activation *** is concluded that beta stabilizer parameters can reduce the sensitivity of learning rate with almost the same performance on DNN with relu activation function and LSTM. However, it is shown that the effects of beta stabilizer on DNN with relu activation function and LSTM are fewer than the effects on DNN with sigmoid activation function.

关键词： DNN An Investigation on Deep Learning with Beta Stabilizer

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation

arXiv

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arXiv 2024年

作者： Zhang, Qingquan Li, Yuchen Lin, Yuhang Wang, Handing Liu, Jialin Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Artificial Intelligence Xidian University Xi’an China

This paper presents an interactive platform to interpret multi-objective evolutionary algorithms. Sokoban level generation is selected as a showcase for its widespread use in procedural content generation. By balancing the emptiness and spatial diversity of Sokoban levels, we illustrate the improved two-archive algorithm, Two_Arch2, a well-known multiobjective evolutionary algorithm. Our web-based platform integrates Two_Arch2 into an interface that visually and interactively demonstrates the evolutionary process in real-time. Designed to bridge theoretical optimisation strategies with practical game generation applications, the interface is also accessible to both researchers and beginners to multi-objective evolutionary algorithms or procedural content generation on a website. Through dynamic visualisations and interactive gameplay demonstrations, this web-based platform also has potential as an educational tool. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Phone-Aware LSTM-RNN for Voice Conversion

Phone-Aware LSTM-RNN for Voice Conversion

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Jiahao Lai Bo Chen Tian Tan Sibo Tong Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Speech Lab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University

This paper investigates a new voice conversion technique using phone-aware Long Short-Term Memory Recurrent Neural Networks（LSTM-RNNs）. Most existing voice conversion methods, including Joint Density Gaussian Mixture Models（JDGMMs）, Deep Neural Networks（DNNs）and Bidirectional Long Short-Term Memory Recurrent Neural Networks（BLSTM-RNNs）, only take acoustic information of speech as features to train models. We propose to incorporate linguistic information to build voice conversion system by using monophones generated by a speech recognizer as linguistic features. The monophones and spectral features are combined together to train LSTM-RNN based voice conversion models,reinforcing the context-dependency modelling of *** results of the 1st voice conversion challenge shows our system achieves significantly higher performance than baseline（GMM method） and was found among the most competitive scores in similarity test. Meanwhile, the experimental results show phone-aware LSTM-RNN method obtains lower Melcepstral distortion and higher MOS scores than the baseline LSTM-RNNs.

关键词： voice conversion voice conversion challenge long short-term memory recurrent neural networks phone

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Encoder-decoder with focus-mechanism for sequence labelling based spoken language understanding

Encoder-decoder with focus-mechanism for sequence labelling ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Su Zhu Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University China

ISBN: (纸本)9781509041183

This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding. We introduce Bidirectional Long Short Term Memory - Long Short Term Memory networks (BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep learning. In the sequence labelling task, the input and output sequences are aligned word by word, while the attention mechanism cannot provide the exact alignment. To address this limitation, we propose a novel focus mechanism for encoder-decoder framework. Experiments on the standard ATIS dataset showed that BLSTM-LSTM with focus mechanism defined the new state-of-the-art by outperforming standard BLSTM and attention based encoder-decoder. Further experiments also show that the proposed model is more robust to speech recognition errors.

关键词： Spoken language understanding encoder-decoder focus-mechanism robustness spoken language understanding Word Packaging Short-Term Memory Robustness Speech recognition

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The influence of physiological environment on the targeting effect of aptamer-guided gold nanoparticles

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Nano Research 2019年第1期12卷 129-135页

作者： Ding Ding Yinling Zhang Edward A.Sykes Long Chen Zhuo Chen Weihong Tan Molecular Science and Biomedicine Laboratory(MBL) State Key Laboratory of Chemo/Bio-Sensing and ChemometricsCollege of Chemistry and Chemical Engineering and College of Life SciencesHunan UniversityChangsha 410082China School of Medicine Queens University81 Barrie StreetKingstonONK7L 3N6Canada Faculty of Science and Technology University of MacaoAvenida da UniversidadeMacao Taipa 999078China Department of Chemistry Departments of Physiology and Functional GenomicsCenter far Research at the Bio/Nano InterfaceUF Health Cancer CenterUF Genetics Institute and McKnight Brain InstituteUniversity of FloridaGainesvilleFlorida 32611USA Institute of Molecular Medicine Renji HospitalShanghai Jiao Tong University School of MedicineShanghai Jiao Tong UniversityShanghai 200240China

Aptamer guided nano medicine shows great promise in targeted cancer *** the loss of targeting capacity duri ng in vivo or clinical trials has largely hindered its popularity and there are no systematic studies to elucidate the ***,we investigated such loss of targeting capacity by examining how the physiological milieu affected targeting *** functionalized gold nanoparticle(AuNP)was chosen as the model and exposed to human blood serum that is used to mimic physiological *** light scattering(DLS),flow cytometry and label-free liquid chromatography tan dem mass spectrometry(LC-MS/MS)were employed to determi ne variations of NPs'surface chemistry and biological identities changes after serum *** showed that the targeting ability loss was caused by protein corona blocking,replacement and enzymatic cleavage of surface aptamer targeting ***,the aggregation issue is critical for the smaller *** of the protein corona profile indicated the accumulation of immune-related proteins on the surface of aptamer-conjugated NPs,which could induce immune response,resulting in rapid clearanee of NPs.

关键词： targeting effect aptamer nanomedicine physiological environment

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Multi-Track Music Generation with WGAN-GP and Attention Mechanisms 12

Multi-Track Music Generation with WGAN-GP and Attention Mech...

引用

12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Chen, Luyu Shen, Lin Yu, Dan Wang, Zhihua Qian, Kun Hu, Bin Schuller, Björn W. Yamamoto, Yoshiharu Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Beijing Institute of Technology Key Laboratory of Brain Health Intelligent Evaluation and Intervention Ministry of Education Beijing100081 China The University of Tokyo Educational Physiology Laboratory Graduate School of Education Tokyo113-0033 Japan Imperial College London Glam - the Group on Language Audio & Music LondonSW7 2AZ United Kingdom

ISBN: (纸本)9798350340181

Music generation with artificial intelligence is a complex and captivating task. The utilisation of generative adversarial networks (GANs) has exhibited promising outcomes in producing realistic and diverse music compositions. In this paper, we propose a model based on Wasserstein GAN with gradient penalty (WGAN-GP) for multi-track music generation. This model incorporates self-attention and introduces a novel cross-attention mechanism in the generator to enhance its expressive capability. Additionally, we transpose all music to C major in training to ensure data consistency and quality. Experimental results demonstrate that our model can produce multi-track music with enhanced rhythm and sound characteristics, accelerate convergence, and improve generation quality. © 2023 IEEE.

关键词： Music

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Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation

Interpreting Multi-objective Evolutionary Algorithms via Sok...

引用

IEEE Symposium on Computational Intelligence and Games, CIG

作者： Qingquan Zhang Yuchen Li Yuhang Lin Handing Wang Jialin Liu Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China School of Artificial Intelligence Xidian University Xi’an China

ISBN: (数字)9798350350678

ISBN: (纸本)9798350350685

This paper presents an interactive platform to interpret multi-objective evolutionary algorithms. Sokoban level generation is selected as a showcase for its widespread use in procedural content generation. By balancing the emptiness and spatial diversity of Sokoban levels, we illustrate the improved two-archive algorithm, Two_Arch2, a well-known multi-objective evolutionary algorithm. Our web-based platform integrates Two_Arch2 into an interface that visually and interactively demonstrates the evolutionary process in real-time. Designed to bridge theoretical optimisation strategies with practical game generation applications, the interface is also accessible to both researchers and beginners to multi-objective evolutionary algorithms or procedural content generation on a website. Through dynamic visualisations and interactive gameplay demonstrations, this web-based platform also has potential as an educational tool.

关键词： Bridges Visualization Procedural generation Spatial diversity Evolutionary computation Real-time systems Optimization

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