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Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1262-1272页

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA IEEE

The core task of tracking control is to make the controlled plant track a desired *** traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps *** this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control *** the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to *** discounted iterative scheme under the new cost function for the special case of linear systems is ***,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

关键词： Adaptive critic design adaptive dynamic programming(ADP) approximate dynamic programming discrete-time nonlinear systems reinforcement learning stability analysis tracking control value iteration(VI)

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Image Coding For Machine Via Analytics-Driven Appearance Redundancy Reduction

Image Coding For Machine Via Analytics-Driven Appearance Red...

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IEEE International Conference on Image Processing

作者： Xuelin Shen Haoqiao Ou Wenhan Yang Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) College of Computer Science and Software Engineering Shenzhen University PengCheng Laboratory

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Among various technical approaches in machine vision coding, Image Coding for Machine (ICM) stands out for its capability to simultaneously fulfill both human perception and machine vision needs. However, it is often criticized for its lack of efficiency regarding rate-analytics performance. In this paper, we propose an Appearance Redundancy Reduction (ARR) module, designed to function as a plug-in for existing ICM frameworks, aiming to further enhance the coding efficiency regarding rate analytics without any changes to the ICM itself. To be specific, our work pays additional attention to the intrinsic correlation between the low-level image structure and high-level vision analytics, and subsequently proposes a novel colour quantization mechanism to squeeze out the analytics-free redundant appearance information. Moreover, a differentiable soften quantization operation is derived to enable end-to-end training within the ICM framework. Extensive experimental results have shown that integrating the proposed ARR module yields substantial improvements regarding rate-analytic performance, even surpassing the performance of the feature coding paradigm, while maintaining the generalizability across different tasks and acceptable perceptual representation.

关键词： Training Image coding Quantization (signal) Correlation Codecs Image color analysis Machine vision

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Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection - Towards Precise Fish Morphological Assessment in Aquaculture Breeding

arXiv

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arXiv 2024年

作者： Liu, Weizhen Tan, Jiayu Lan, Guangyu Li, Ao Li, Dongye Zhao, Le Yuan, Xiaohui Dong, Nanqing School of Computer Science and Artificial Intelligence Wuhan University of Technology China Shanghai Artificial Intelligence Laboratory China Yazhouwan National Laboratory China Sanya Boruiyuan Technology Co. Ltd China

Accurate phenotypic analysis in aquaculture breeding necessitates the quantification of subtle morphological phenotypes. Existing datasets suffer from limitations such as small scale, limited species coverage, and inadequate annotation of keypoints for measuring refined and complex morphological phenotypes of fish body parts. To address this gap, we introduce FishPhenoKey, a comprehensive dataset comprising 23,331 high-resolution images spanning six fish species. Notably, FishPhenoKey includes 22 phenotype-oriented annotations, enabling the capture of intricate morphological phenotypes. Motivated by the nuanced evaluation of these subtle morphologies, we also propose a new evaluation metric, Percentage of Measured Phenotype (PMP). It is designed to assess the accuracy of individual keypoint positions and is highly sensitive to the phenotypes measured using the corresponding keypoints. To enhance keypoint detection accuracy, we further propose a novel loss, Anatomically-Calibrated Regularization (ACR), that can be integrated into keypoint detection models, leveraging biological insights to refine keypoint localization. Our contributions set a new benchmark in fish phenotype analysis, addressing the challenges of precise morphological quantification and opening new avenues for research in sustainable aquaculture and genetic studies. Our dataset and code are available at https://***/WeizhenLiuBioinform/Fish-Phenotype-Detect. Copyright © 2024, The Authors. All rights reserved.

关键词： Fish

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Contrastive Learning Consistent and Identifiable Latent Embeddings for EEG 19th

Contrastive Learning Consistent and Identifiable Latent Emb...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Liang, Feng Zhang, Zhen Mo, Jiawei Hu, Wenxin Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen China School of Information Science and Engineering Lanzhou University Lanzhou China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9783031651250

Extracting informative EEG data into low-dimension latent embeddings is important for storing and analyzing these neuron signals and applying them to various applications, such as modern human-computer interaction (HCI) techniques. We use the contrastive learning algorithm on time-domain features of EEG in both discovery-driven (self-supervised) and hypothesis (supervised) manners to encode the EEG data into latent embeddings that are proven consistent and identifiable. The self-supervised embeddings have the potential to be used for a range of downstream tasks, while the supervised embeddings have very high decoding accuracy for specific tasks. With embeddings encoded from EEG features collected within every 0.5-s window, the accuracy of recognizing the identities of persons by decoding the self-supervised and supervised embeddings is as high as 96.2% and 99.6%, respectively. Our method and results can promote new HCI techniques, e.g., automatically connecting users to their roles in AR games once they wear EEG-capable devices. The source code is available at: https://***/liangfengsid/timeEegContrastive. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Contrastive Learning

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Magnetic separation, sunlight-driven photocatalytic activity, and antibacterial studies of Sm-doped Co0.33Mg0.33Ni0.33Fe2O4 nanoparticles

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Environmental science and Pollution Research 2024年第24期31卷 35631-35650页

作者： Aridi, Amani Rabaa, Mariam Mezher, Malak Naoufal, Daoud Khalil, Mahmoud I. Awad, Ramadan Chemistry Department Faculty of Science Beirut Arab University Beirut Lebanon Inorganic and Organometallic Coordination Chemistry Laboratory Faculty of Sciences I Lebanese University Hadath Lebanon Department of Biological Sciences Faculty of Science Beirut Arab University Beirut Lebanon Molecular Biology Unit Department of Zoology Faculty of Science Alexandria University Alexandria Egypt Physics Department Faculty of Science Alexandria University Alexandria Egypt Department of Basic Sciences Faculty of Computer Science and Artificial Intelligence Pharos University in Alexandria Alexandria Egypt

Magnetic nanoparticles have emerged as a promising tool for wastewater treatment due to their unique properties. In this regard, Co0.33Mg0.33Ni0.33SmxFe2-xO4 (0.00 ≤x≤ 0.08) nanoparticles were prepared to examine their magnetic separation efficiency (MSE), photocatalytic, antibacterial, and antibiofilm performances. Pure nanoparticles, having the highest saturation magnetization (Ms = 31.87 emu/g), exhibit the highest MSE, where 95.6% of nanoparticles were separated after 20 min of applying a magnetic field of 150 mT. The catalytic performance of the prepared samples is examined by the photodegradation of rhodamine B (RhB) dye exposed to direct sunlight radiation. Improved photocatalytic activity is exhibited by Co0.33Mg0.33Ni0.33Sm0.04Fe1.96O4 nanoparticles, labeled as Sm0.04, where the rate of the degradation reaction is enhanced by 4.1 times compared to pure nanoparticles. Rising the pH and reaction temperature improves the rate of the photodegradation reaction of RhB. The incorporation of 15 wt% reduced graphene oxide (rGO) with Sm0.04 enhanced the rate of the reaction by 1.7 and 2.4 times compared with pure Sm0.04 sample and rGO, respectively. The antibacterial and antibiofilm activities against Escherichia coli, Leclercia adecarboxylata, Staphylococcus aureus, and Enterococcus faecium are assessed by the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC) broth microdilution, the agar well diffusion, the time-kill assays, the biofilm formation, and destruction assays. The bacteria used in these assessments are isolated from wastewater. The nanoparticles exhibit a bacteriostatic activity, with a better effect against the Gram-positive isolates. Co0.33Mg0.33Ni0.33SmxFe2O4 (x = 0.00) nanoparticles have the best effect. The effect is exerted after 2–3 h of incubation. Gram-positive biofilms are more sensitive to nanoparticles. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Iron compounds

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GarFast: Realistic and Fast Garment Transfer with a Simplified Parser-Free Approach 39

GarFast: Realistic and Fast Garment Transfer with a Simplifi...

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39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Du, Chenghu Wang, Junyin Rong, Yi Yu, Feng Xiong, Shengwu School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Sanya Science and Education Innovation Park Wuhan University of Technology Sanya572000 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Interdisciplinary Artificial Intelligence Research Institute Wuhan College Wuhan430212 China School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan430200 China

ISBN: (纸本)157735897X

A good garment try-on model should learn the transfer between different types of garments while satisfying: 1) high fidelity and 2) low inference speed. Existing methods address either of these two issues, limited processing speed or low generation quality. We directly use a lightweight encoder-decoder, ensuring faster speeds. To tackle the problem of lower image quality typically generated by lighter models, we present GarFast, a simplified, parser-free framework that optimizes the same lightweight network through a two-stage transformation of real data roles (from input to supervision), thereby greatly promoting model convergence. Specifically, first, we propose a correction strategy to prevent the difficulty of convergence caused by the lack of ground truth in the first stage. Second, we propose a fine-grained domain consistency to ensure that the results generated in the unsupervised first stage are highly realistic clothed human images. Finally, we propose a skin-variant refinement loss and a skinMix regularization to amplify texture differences and enhance the realism of skin-variant regions, thereby improving the quality of the generated skin. Extensive experiments thoroughly demonstrate that our method achieves high resolution, near real-time performance, and superior reconstruction quality compared to state-of-the-art approaches, with processing times of less than 0.03 seconds on an Nvidia A100. © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

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Confidence-aware Contrastive Learning for Selective Classification 41

Confidence-aware Contrastive Learning for Selective Classifi...

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41st International Conference on Machine Learning, ICML 2024

作者： Wu, Yu-Chang Lyu, Shen-Huan Shang, Haopu Wang, Xiangyu Qian, Chao National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China

Selective classification enables models to make predictions only when they are sufficiently confident, aiming to enhance safety and reliability, which is important in high-stakes scenarios. Previous methods mainly use deep neural networks and focus on modifying the architecture of classification layers to enable the model to estimate the confidence of its prediction. This work provides a generalization bound for selective classification, disclosing that optimizing feature layers helps improve the performance of selective classification. Inspired by this theory, we propose to explicitly improve the selective classification model at the feature level for the first time, leading to a novel Confidence-aware Contrastive Learning method for Selective Classification, CCL-SC, which similarizes the features of homogeneous instances and differentiates the features of heterogeneous instances, with the strength controlled by the model's confidence. The experimental results on typical datasets, i.e., CIFAR-10, CIFAR-100, CelebA, and ImageNet, show that CCL-SC achieves significantly lower selective risk than state-of-the-art methods, across almost all coverage degrees. Moreover, it can be combined with existing methods to bring further improvement. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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OneActor: Consistent Subject Generation via Cluster-Conditioned Guidance 38

OneActor: Consistent Subject Generation via Cluster-Conditio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Jiahao Yan, Caixia Lin, Haonan Zhang, Weizhan Wang, Mengmeng Gong, Tieliang Dai, Guang Sun, Hao School of Computer Science and Technology MOEKLINNS Xi'an Jiaotong University China State Key Laboratory of Communication Content Cognition China College of Computer Science and Technology Zhejiang University of Technology China SGIT AI Lab State Grid Corporation of China Switzerland China Telecom Artificial Intelligence Technology Co.Ltd. China

Text-to-image diffusion models benefit artists with high-quality image generation. Yet their stochastic nature hinders artists from creating consistent images of the same subject. Existing methods try to tackle this challenge and generate consistent content in various ways. However, they either depend on external restricted data or require expensive tuning of the diffusion model. For this issue, we propose a novel one-shot tuning paradigm, termed OneActor. It efficiently performs consistent subject generation solely driven by prompts via a learned semantic guidance to bypass the laborious backbone tuning. We lead the way to formalize the objective of consistent subject generation from a clustering perspective, and thus design a cluster-conditioned model. To mitigate the overfitting challenge shared by one-shot tuning pipelines, we augment the tuning with auxiliary samples and devise two inference strategies: semantic interpolation and cluster guidance. These techniques are later verified to significantly improve the generation quality. Comprehensive experiments show that our method outperforms a variety of baselines with satisfactory subject consistency, superior prompt conformity as well as high image quality. Our method is capable of multi-subject generation and compatible with popular diffusion extensions. Besides, we achieve a 4× faster tuning speed than tuning-based baselines and, if desired, avoid increasing the inference time. Furthermore, our method can be naturally utilized to pre-train a consistent subject generation network from scratch, which will implement this research task into more practical applications. Project page: https://***/OneActor-webpage/. © 2024 Neural information processing systems foundation. All rights reserved.

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Automatic Identification and Extraction of Assumptions on GitHub 29

Automatic Identification and Extraction of Assumptions on Gi...

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29th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2023

作者： Yang, Chen Ma, Zinan Liang, Peng Liu, Xiaohua School of Artificial Intelligence Shenzhen Polytechnic Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)1891706578

In software development, due to the lack of knowledge or information, time pressure, complex context, and many other factors, various uncertainties emerge during the development process, leading to assumptions scattered in projects. Being unaware of certain assumptions can result in critical problems (e.g., system vulnerability and failures). The prerequisite of analyzing and understanding assumptions in software development is to identify and extract those assumptions with acceptable effort. In this paper, we proposed a tool (i.e., Assumption Miner) to automatically identify and extract assumptions on GitHub projects. To evaluate the applicability of Assumption Miner, we first presented an example of using the tool to mine assumptions from one large and popular deep learning framework project: the TensorFlow project on GitHub. We then conducted an evaluation of the tool. The results show that Assumption Miner can effectively identify and extract assumptions from the repositories on GitHub. Copyright © 2023 by KSI Research Inc. and Knowledge Systems Institute, USA.

关键词： Deep learning

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Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback

arXiv

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arXiv 2024年

作者： Gao, Songyang Ge, Qiming Shen, Wei Dou, Shihan Ye, Junjie Wang, Xiao Zheng, Rui Zou, Yicheng Chen, Zhi Yan, Hang Zhang, Qi Lin, Dahua Shanghai Artificial Intelligence Laboratory China School of Computer Science Fudan University China ByteDance Inc China

The success of AI assistants based on Language Models (LLMs) hinges on Reinforcement Learning from Human Feedback (RLHF) to comprehend and align with user intentions. However, traditional alignment algorithms, such as PPO, are hampered by complex annotation and training requirements. This reliance limits the applicability of RLHF and hinders the development of professional assistants tailored to diverse human preferences. In this work, we introduce Linear Alignment, a novel algorithm that aligns language models with human preferences in one single inference step, eliminating the reliance on data annotation and model training. Linear alignment incorporates a new parameterization for policy optimization under divergence constraints, which enables the extraction of optimal policy in a closed-form manner and facilitates the direct estimation of the aligned response. Extensive experiments on both general and personalized preference datasets demonstrate that linear alignment significantly enhances the performance and efficiency of LLM alignment across diverse scenarios. Our code and dataset is published on https://***/Wizardcoast/Linear_***. © 2024, CC BY-NC-SA.

关键词： Human form models

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