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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Sho Inoue Kun Zhou Shuai Wang Haizhou Li School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Speech Lab Institute for Intelligent Computing Alibaba Group Singapore

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

It remains a significant challenge how to quantitatively control the expressiveness of speech emotion in speech generation. In this work, we propose an approach for quantitative manipulation of the emotion rendering for emotion editing in speech generation. We apply a hierarchical emotion distribution extractor, i.e. Hierarchical ED, that quantifies the intensity of emotions at different levels of granularity. Hierarchical ED is subsequently integrated into the FastSpeech2 framework, guiding the model to learn emotion intensity at phoneme, word, and utterance levels. During synthesis, users can manually edit the emotional intensity of the generated voices. Both objective and subjective evaluations demonstrate the effectiveness of the proposed network in terms of fine-grained quantitative emotion editing.

关键词： Asia Information processing Rendering (computer graphics) Text to speech Speech processing

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Evolutionary Dynamic Optimization-Based Calibration Framework for Agent-Based Financial Market Simulators 13

Evolutionary Dynamic Optimization-Based Calibration Framewor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Yang, Zhenhua Zhong, Muyao Yang, Peng Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Statistics and Data Science Shenzhen518055 China

ISBN: (纸本)9798350308365

The agent-based financial market simulators serve as an important validation tool for trading strategies. For high-fidelity simulation, it is pivotal to calibrate the parameters of a simulator so that the generated simulation data resembles the observed real market data of interest. In traditional calibration methods, it is typical that the parameters of the simulator are set to be time-invariant. However, the dynamic nature of the real financial market introduces various variability into the behaviors of the market participants over different time intervals, posing in-herent limitations to the traditional methods. A more reasonable approach might involve employing a simulator with time-variant parameters. This suggests that the model parameters can be dynamically adjusted at different stages of the simulation to adapt to the evolving market. Consequently, the calibration problem of the financial market simulators can be treated as a dynamic optimization problem. To dynamically calibrate the simulators, we introduce an Evolutionary Dynamic Optimization (EDO) framework. By monitoring the changes of the best fitness, the whole simulation time interval is adaptively divided into multiple stages. Then the Negatively Correlated Search (NCS) algorithm is employed to effectively adjust the parameters at different simulation stages to better simulate the real financial market. Empirical results on both synthetic and real data verify that our dynamic calibration framework significantly outperforms traditional calibration methods that fixing a parameter for the whole simulation interval. The proposed strategy of detecting dynamic changes is also shown to be more reliable than the naive method of manually segmenting stages. In terms of calibration time, our proposed method significantly improves by nearly 93% compared to the fixed parameter setting, and approximately 61% compared to manual segmentation calibration. © 2024 IEEE.

关键词： Financial markets

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study 9

Evaluating the Performance to Detecting Topic Shift of Large...

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9th International Conference on intelligent Computing and Signal Processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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Channel Capacity and Power Allocation of MIMO Visible Light Communication System

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China Communications 2023年第2期20卷 122-138页

作者： Shuai Ma Ruixin Yang Guanjie Zhang Hang Li Wen Cao Linqiong Jia Yanyu Zhang Shiyin Li Peng Cheng Laboratory Shenzhen 518055China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Shenzhen Research Institute of Big Data Shenzhen 518172China School of Electronics and Control Engineering Chang’an UniversityXi’an 710064China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China National Digital Switching System Engineering&Technological Research Center Zhengzhou 450000China

In this paper,the channel capacity of the multiple-input multiple-output(MIMO)visible light communication(VLC)system is investigated under the peak,average optical and electrical power *** the channel capacity of MIMO VLC is shown to be a mixed integer programming *** address this open problem,we propose an inexact gradient projection method to find the channel capacity-achieving discrete input distribution and the channel capacity of MIMO *** we derive both upper and lower bounds of the capacity of MIMO VLC with the closed-form ***,by considering practical discrete constellation inputs,we develop the optimal power allocation scheme to maximize transmission rate of MIMO VLC *** results show that more discrete points are needed to achieve the channel capacity as SNR *** the upper and lower bounds of channel capacity are tight at low SNR *** addition,comparing the equal power allocation,the proposed power allocation scheme can significantly increase the rate for the low-order modulation inputs.

关键词： visible light communication MIMO discrete constellation inputs power allocation

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PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Transformer-Inspired Deep Adaptive CSI Sensing With Learnable Quantizer

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IEEE Transactions on Vehicular Technology 2025年

作者： Chen, Qian Li, Lei Luo, Xiaodong Chang, Tsung-Hui The Chinese University of HongKong School of Science and Engineering Shenzhen China Shenzhen Research Institute of Big Data China Shenzhen Research Institute of Big Data Shenzhen International Center For Industrial And Applied Mathematics China Chinese University of HongKong School of Artificial Intelligence Shenzhen China

As a key enabler for 5 G and next-generation communication, multi-antenna techniques usually require accurate channel state information (CSI) to release their potential. However, the corresponding CSI sensing is challenging, especially in frequency division duplex systems under the codebook-based feedback mechanism. While this is conventionally accomplished by solving a phase retrieval (PR) problem, existing methods based on PR usually utilize heuristic sensing precoders that can hardly capture the temporal dependencies among feedbacks effectively and sense adaptively. Inspired by Transformer, in this work, we propose a CSI sensing deep neural network, leveraging multi-head attention to learn the environment knowledge from the feedbacks and adjust the precoders adaptively. Moreover, we further consider the impact of feedback quantization, develop a learnable computation-efficient quantizer, and embed it into our proposed adaptive sensing. Numerical results show that our proposed adaptive sensing greatly outperforms existing approaches, and the learnable quantizer can effectively alleviate the performance loss due to feedback quantization. © 1967-2012 IEEE.

关键词： Channel state information

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MMFNet: A Multi-modal and Multi-attention Fusion Network for Multi-task Skin Disease Classification

MMFNet: A Multi-modal and Multi-attention Fusion Network for...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Xinlei Qian, Pengjiang Wang, Chuang Liu, Li Gao, Ming Cai, Weiwei Ng, Eddie Yin-Kwee Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China Jiangnan University Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Wuxi China Affiliated Hospital Jiangnan University Big Data Center Wuxi China Nanyang Technological University School of Mechanical and Aerospace Engineering Nanyang Singapore

ISBN: (纸本)9798350386226

Skin diseases rank among the most prevalent ailments in humans, which underscores the critical importance of early detection and diagnosis. Considering clinical practice, the integration of information from various modalities holds significant potential to enhance diagnostic accuracy and precision. In this study, we propose a multi-modal and multi-attention fusion network for multi-task skin disease classification. We initially devise the Shifted Window Cross-attention Fusion (SWCF) module based on the shifted window self-attention mechanism, leveraging the advantages of the attention mechanism to learn the correlations between different modalities and fully integrate multi-modal features. Subsequently, taking into account the heterogeneity of the data, we employ the Heterogeneous data Cross-attention Fusion (HDCF) module using the idea of information decoupling and separation processing to integrate image and text data. Additionally, we suggest the Dynamic Loss Weight Allocation (DLWA) method for multi-task learning to refine the training procedure. We confirm the superiority of the proposed method on the publicly available multi-modal skin lesion dataset, Derm7pt. The average accuracy of multi-modal skin disease classification is 79.14%, surpassing the current state-of-the-art methods. © 2024 IEEE.

关键词： Multi-task learning

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Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke:development and validation of a hybrid machine learning model

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Stroke & Vascular Neurology 2024年第6期9卷 631-639页

作者： Ximing Nie Jinxu Yang Xinxin Li Tianming Zhan Dongdong Liu Hongyi Yan Yufei Wei Xiran Liu Jiaping Chen Guoyang Gong Zhenzhou Wu Zhonghua Yang Miao Wen Weibin Gu Yuesong Pan Yong Jiang Xia Meng Tao Liu Jian Cheng Zixiao Li Zhongrong Miao Liping Liu Department of Neurology Capital Medical UniversityBeijingChina China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalBeijingChina School of Computer and Communication Engineering University of Science and TechnologyBeijingChina Department of Radiology Beijing Tiantan HospitalBeijingChina Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationSchool of Biological Science and Medical Engineering International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Computer Science and EngineeringBeihang UniversityBeijingChina Department of Interventional Neuroradiology Beijing Tiantan HospitalCapital Medical UniversityBeijingChina

Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and ***,we present a novel risk stratification system based on hybrid machine learning method for predicting futile *** Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management *** models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following *** optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation *** Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction *** model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

关键词： database prediction optimal

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Forecasting the yield of wafer by using improved genetic algorithm, high dimensional alternating feature selection and SVM with uneven distribution and high-dimensional data

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Autonomous intelligent Systems 2022年第1期2卷 1-16页

作者： Xu, Qiuhao Xu, Chuqiao Wang, Junliang Institute of Artificial Intelligence Donghua University Shanghai201620 China College of Mechanical Engineering Donghua University Shanghai201620 China School of Mechanical Engineering Shanghai Jiaotong University Shanghai200240 China Shanghai Engineering Research Center of Industrial Big Data and Intelligent System Shanghai201512 China

Wafer yield prediction, as the basis of quality control, is dedicated to predicting quality indices of the wafer manufacturing process. In recent years, data-driven machine learning methods have received a lot of attention due to their accuracy, robustness, and convenience for the prediction of quality indices. However, the existing studies mainly focus on the model level to improve the accuracy of yield prediction does not consider the impact of data characteristics on yield prediction. To tackle the above issues, a novel wafer yield prediction method is proposed, in which the improved genetic algorithm (IGA) is an under-sampling method, which is used to solve the problem of data overlap between finished products and defective products caused by the similarity of manufacturing processes between finished products and defective products in the wafer manufacturing process, and the problem of data imbalance caused by too few defective samples, that is, the problem of uneven distribution of data. In addition, the high-dimensional alternating feature selection method (HAFS) is used to select key influencing processes, that is, key parameters to avoid overfitting in the prediction model caused by many input parameters. Finally, SVM is used to predict the yield. Furthermore, experiments are conducted on a public wafer yield prediction dataset collected from an actual wafer manufacturing system. IGA-HAFS-SVM achieves state-of-art results on this dataset, which confirms the effectiveness of IGA-HAFS-SVM. Additionally, on this dataset, the proposed method improves the AUC score, G-Mean and F1-score by 21.6%, 34.6% and 0.6% respectively compared with the conventional method. Moreover, the experimental results prove the influence of data characteristics on wafer yield prediction. © 2022, The Author(s).

关键词： Genetic algorithms

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Flexible Microgrids with Dynamic Boundaries for Voltage Volatility Reduction 7

Flexible Microgrids with Dynamic Boundaries for Voltage Vola...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Lyu, Fengqi Lei, Shunbo Song, Yue Huang, Gang Zhang, Aichao Xu, Yin School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China Tongji University Department of Control Science and Engineering China College of Electrical Engineering Zhejiang University China School of Electrical Engineering Beijing Jiaotong University China

ISBN: (纸本)9798350345094

To improve the utilization of distributed energy resources (DERs), a smart and active distribution network (DN) can be flexibly reorganized into several microgrids (MGs) through the dispatch of smart switches. In this work, we find that the dynamic boundaries of networked MGs can be exploited to reduce the renewable-induced voltage volatility of the whole underlying DN. Specifically, we propose a mixed integer nonlinear programming (MINLP) optimization model to determine the optimal boundary of each MG in the networked MGs at each time step considering hourly line switching and DER dispatch. The model is solved by a linear cut generation algorithm, which sequentially solves a series of associated mixed integer linear programming (MILP) problems to find the original MINLP problem's optimal solution. Results on the modified IEEE 123-node test system show that, with boundaries of MGs coordinately and flexibly reorganized, the voltage volatilities under each scenario are effectively limited in the safe range. © 2023 IEEE.

关键词： Integer programming

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