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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Efficient tensor networks for control-enhanced quantum metrology

arXiv

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arXiv 2024年

作者： Liu, Qiushi Yang, Yuxiang QICI Quantum Information and Computation Initiative Department of Computer Science School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong

Optimized quantum control can enhance the performance and noise resilience of quantum metrology. However, the optimization quickly becomes intractable when multiple control operations are applied sequentially. In this work, we propose efficient tensor network algorithms for optimizing strategies of quantum metrology enhanced by a long sequence of control operations. Our approach covers a general and practical scenario where the experimenter applies N−1 interleaved control operations between N queries of the channel to estimate and uses no or bounded ancilla. Tailored to different experimental capabilities, these control operations can be generic quantum channels or variational unitary gates. Numerical experiments show that our algorithm has a good performance in optimizing the metrological strategy for as many as N = 100 queries. In particular, our algorithm identifies a strategy that can outperform the state-of-the-art strategy when N is finite but large. © 2024, CC BY.

关键词： Tensors

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Analysis of Labanotation generation based on motion capture data 5

Analysis of Labanotation generation based on motion capture ...

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5th International Conference on Artificial Intelligence and computer science, AICS 2023

作者： Chen, Boran Wang, Jiaji Miao, Zhenjiang Li, Jianpo Yang, Shengchun Jilin Electric Power Big Data Intelligent Processing Engineering Technology Research Center School of Computing Northeast Electric Power University Jilin Jilin132012 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510668621

Labanotation is an approach to record human movement with different distributions in space that have different representations of symbols. Motion capture is a technique that records the trajectory of an object in the time domain by tracking the motion of key points, observed using different sensors, and recorded in files. This paper is to manually mark the Labanotation based on 3D human motion capture data, to divide the 3D human motion into gaits and poses, and to record and represent different human movements and positions in space in the notation. This is an exploration of motion capture data to generate Labanotation, while also contributing to the preservation of folk-art forms. © 2023 SPIE.

关键词： Time domain analysis

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An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

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computers, Materials & Continua 2022年第10期73卷 493-511页

作者： Muhammad Zubair Rehman Muhammad Aamir Nazri Mohd.Nawi Abdullah Khan Saima Anwar Lashari Siyab Khan Faculty of Computing and Information Technology Sohar UniversitySohar311Sultanate of Oman Soft Computing&Data Mining Centre(SMC) Faculty of Computer Science&Information TechnologyUniversiti Tun Hussein Onn Malaysia(UTHM)Batu Pahat86400Malaysia School of Electronics Computing and MathematicsUniversity of DerbyDerbyDE221GBUnited Kingdom Institute of Computer Sciences and Information Technology The University of Agriculture25120PeshawarPakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on *** on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.

关键词： DNA sequence classification bat algorithm levy flight back propagation neural network hybrid artificial neural networks(HANN)

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Heisenberg-Limited Quantum Metrology without Ancilla

arXiv

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arXiv 2024年

Extensive research has been dedicated to the asymptotic theory of quantum metrology, where the goal is to determine the ultimate precision limit of quantum channel estimation when many accesses to the channel are allowed. The ultimate limit has been well established, but in general noiseless and controllable ancilla is required for attaining it. Little is known about the metrological performance without noiseless ancilla, which is more relevant in practical circumstances. In this work, we present a novel theoretical framework to address this problem, bridging quantum metrology and the asymptotic theory of quantum channels. Leveraging this framework, we prove sufficient conditions for achieving the Heisenberg limit with repeated applications of the channel to estimate, both with and without applying interleaved unitary control operations. For the latter case, we design an algorithm to identify explicitly the control operation. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum channel

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SelectiveFinetuning: Enhancing Transfer Learning In Sleep Staging Through Selective Domain Alignment

SelectiveFinetuning: Enhancing Transfer Learning In Sleep St...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Siyuan Zhao Chenyu Liu Yi Ding Xinliang Zhou School of Computer and Information Technology Beijing Jiaotong University Beijing China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In practical sleep stage classification, a key challenge is the variability of EEG data across different subjects and environments. Differences in physiology, age, health status, and recording conditions can lead to domain shifts between data. These domain shifts often result in decreased model accuracy and reliability, particularly when the model is applied to new data with characteristics different from those it was originally trained on, which is a typical manifestation of negative transfer. To address this, we propose SelectiveFinetuning in this paper. Our method utilizes a pre-trained Multi-Resolution Convolutional Neural Network (MRCNN) to extract EEG features, capturing the distinctive characteristics of different sleep stages. To mitigate the effect of domain shifts, we introduce a domain aligning mechanism that employs Earth Mover’s Distance (EMD) to evaluate and select source domain data closely matching the target domain. By finetuning the model with selective source data, our SelectiveFinetuning enhances the model’s performance on target domain that exhibits domain shifts compared to the data used for training. Experimental results show that our method outperforms existing baselines, offering greater robustness and adaptability in practical scenarios where data distributions are often unpredictable.

关键词： Training Adaptation models Accuracy Sleep Transfer learning Brain modeling Feature extraction data models Electroencephalography Robustness

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Design of Airborne Intelligent computing System Oriented to Intelligent Task Processing 26

Design of Airborne Intelligent Computing System Oriented to ...

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26th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing, SNPD-Winter 2023

作者： Bai, Linting Wen, Pengcheng Xie, Jianchun Li, Yunxi Lin, Rongchao School of Computer Science Northwestern Polytechnical University Xi'an Aeronautics Computing Technique Research Institute Avic Xi'an China Xi'an Aeronautics Computing Technique Research Institute Avic Aviation Key Laboratory of Science and Technology on Airborne and Missile-borne Computer Xi'an China Xi'an Aeronautics Computing Technique Research Institute Avic Aeronautics Data and Sensor Business Unit Avic Xi'an China

ISBN: (纸本)9798350345865

The rapid development of artificial intelligence technology promotes the gradual evolution of the 'three integration' of mechanization, informatization and intelligence. The mass data characteristics of airborne mission informatization put forward higher demands on the storage, transmission and management capabilities of airborne data. The characteristics of high complexity, strong confrontation, strong real-time, and uncertainty of airborne intelligent tasks pose more challenges to the intelligent processing of airborne data. This paper analyzes the data characteristics and mission characteristics in the process of airborne intelligence missions, and proposes an airborne intelligent computing architecture for airborne data processing of intelligent missions. The composition and function of each technical layer are expounded, and several problems of airborne intelligent computing technology are analyzed. Finally, the development trend of airborne intelligent technology development is prospected. © 2023 IEEE.

关键词： Digital storage

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Practical Applications of ChatGPT and BERT: Long Text Summarization and Model Feature Ablation Study for Stock Comment Topic Identification 24

Practical Applications of ChatGPT and BERT: Long Text Summar...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Xu, Mingyan Chen, Hongyu Deng, Fuwen Liu, Yuan Zhao, Feng Zhang, Yong School of Statistics Shandong Technology and Business University Yantai264000 China School of Hospital Shandong Technology and Business University Yantai264000 China School of Computer Science and Technology Shandong Technology and Business University Yantai264000 China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing School of Computer Science and Technology Shandong Technology and Business University Yantai China School of Information and Electrotic Shandong Technology and Business University Yantai264000 China

ISBN: (纸本)9798400717147

Topic identification of expert stock comments is an important task in the field of finance, which helps investors understand the market dynamics and expert opinions so that they can make more informed investment decisions. The Bidirectional Encoder Representations from Transformers (BERT) model is one of the widely used methods for text topic identification. To improve the identification accuracy and overcome the limitations of the BERT model, two strategies are proposed in this study. First, given the BERT model's input length limitation when dealing with long texts, we used the summaries created by Chat Generative Pre-training Transformer (ChatGPT) as inputs to the model. The generative summarization capability of ChatGPT not only reduces the length of the text effectively but also preserves the key information of the original text, which enables the model to better understand and deal with long texts. Second, we perform multi-layer feature ablation on the BERT model to find the most representative features for text topic identification. The multi-layer features of the BERT model can be more effectively utilized to improve the accuracy of text topic identification. Experiments show that our proposed strategies exhibit excellent performance and provide effective paths to improve the accuracy of stock comment topic identification. © 2024 ACM.

关键词： Ablation

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Towards a Sustainable Future: Analyzing Water Quality Changes for Environmental Sustainability 1

Towards a Sustainable Future: Analyzing Water Quality Change...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Mansor, Nur Suhaili Awang, Hapini Harun, Nor Hazlyna Hassan, Mohamad Ghozali Shehu Malami, Sarkin Tudu Shamsuddin, Noraishah Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia School of Computing Universiti Utara Malaysia Data Science Research Lab Sintok Malaysia School of Technology Management and Logistics Universiti Utara Malaysia Sintok Malaysia Sokoto State University Faculty of Science Department of Computer Science and Information Technology Sokoto Nigeria Ici in Smart Manufacturing Sirim Bhd Shah Alam Malaysia

ISBN: (纸本)9798350306156

The development of industrial areas is frequently thought to have a good impact on society because it helps to meet the population's requirements. However, it also negatively impacts the environment, such as river pollution. If a country experiences rapid economic growth without considering the effects of industrial development on the environment, then human well-being will be affected. In this context, advanced development is attained by a developed nation while ensuring the well-being of society. Uncontrolled and unsustainable development increases environmental pollution, adversely affecting human health. As water is essential for human life, water resources, such as rivers, must be properly managed and protected to ensure human well-being. Thus, the study conducted on the changes in the water quality of the Kim Kim River provides insights into how unintended consequences of urban and industrial development can be identified and addressed using three sets of satellite imagery data from 2015, 2019 and 2020. The study was led to identify research problems and achieve the study objectives to obtain research results. The results of image classification analyses indicate that the development area near Sungai Kim Kim increased to 50%, with 38% from industrial areas. The RMSE for the image classification is 1.51, significantly better than the minimum allowed value of 2.00. It provides an understanding of the change in river water quality because of urban and industrial development. By highlighting the importance of sustainable development and the need to balance economic growth with environmental protection, this study contributes to the development of effective policies and strategies for the sustainable development of industrial areas. © 2023 IEEE.

关键词： Water quality

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation 38

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmenta...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024 Neural information processing systems foundation. All rights reserved.

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