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25th International computer science and engineering Conference, ICSEC 2021

作者： Hnoohom, Narit Maitrichit, Nagorn Chotivatunyu, Pitchaya Sornlertlamvanich, Virach Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand Musashino University Faculty of Data Science Tokyo Japan University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

This research aimed to fine-Tune image classification with deep learning techniques to verify the dispensing of prescriptions in hospitals. The proposed approach will be able to help pharmacies reduce the errors that lead to patients receiving the wrong medications. The image classification model uses a double-side transformed image dataset with download from Highlighted Deep Learning (HDL) paper. The dataset collected two-hundred seventy-Two images for types of medicine blister packs, including 72 images of front-side and backside merged with a horizontal cropped background, which were used for training the model. The blister package image dataset uses a deep learning model with a ResNet-101 pre-Trained model from the TensorFlow framework. The experimental results indicated that the TensorFlow framework achieved higher precision, recall, and F1-score than the Caffe framework. A ResNet-101 model with histogram equalization in the front and backside has the highest accuracy at 100 percent. © 2021 IEEE.

关键词： image classification

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Divide-Solve-Combine: An Interpretable and Accurate Prompting Framework for Zero-shot Multi-Intent Detection 39

Divide-Solve-Combine: An Interpretable and Accurate Promptin...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qin, Libo Chen, Qiguang Zhou, Jingxuan Wang, Jin Fei, Hao Che, Wanxiang Li, Min School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Yunnan University China National University of Singapore Singapore

ISBN: (纸本)157735897X

Zero-shot multi-intent detection is capable of capturing multiple intents within a single utterance without any training data, which gains increasing attention. Building on the success of large language models (LLM), dominant approaches in the literature explore prompting techniques to enable zero-shot multi-intent detection. While significant advancements have been witnessed, the existing prompting approaches still face two major issues: lacking explicit reasoning and lacking interpretability. Therefore, in this paper, we introduce a Divide-Solve-Combine Prompting (DSCP) to address the above issues. Specifically, DSCP explicitly decomposes multi-intent detection into three components including (1) single-intent division prompting is utilized to decompose an input query into distinct sub-sentences, each containing a single intent;(2) intent-by-intent solution prompting is applied to solve each sub-sentence recurrently;and (3) multi-intent combination prompting is employed for combining each sub-sentence result to obtain the final multi-intent result. By decomposition, DSCP allows the model to track the explicit reasoning process and improve the interpretability. In addition, we propose an interactive divide-solve-combine prompting (Inter-DSCP) to naturally capture the interaction capabilities of large language models. Experimental results on two standard multi-intent benchmarks (i.e., MIXATIS and MIXSNIPS) reveal that both DSCP and Inter-DSCP obtain substantial improvements over baselines, achieving superior performance and higher interpretability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical image

CM-MLP: Cascade Multi-scale MLP with Axial Context Relation ...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lv, Jinkai Hu, Yuyong Fu, Quanshui Zhang, Zhiwang Hu, Yuqiang Lv, Lin Yang, Guoqing Li, Jinpeng Zhao, Yi Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China Suining Central Hospital Suining China Hwa Mei Hospital University of Chinese Academy of Sciences Ningbo China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Mathematics and Computer Sciences NanChang University Nanchang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665468190

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multiscale Feature Interaction) block and ACRE (axial context relation encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multiscale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our inhouse dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://***/ProgrammerHyy/CM-MLP. © 2022 IEEE.

关键词： Semantic Segmentation

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Efficient and broadband terahertz polarization convertor enabled by an all-metal stereo reflective metasurface

Efficient and broadband terahertz polarization convertor ena...

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International Conference on Infrared and Millimeter Waves

作者： Yuehong Xu Quan Xu Xueqian Zhang Xi Feng Yongchang Lu Xixiang Zhang Jiaguang Han Weili Zhang Center for Terahertz Waves College of Precision Instrument and Optoelectronics Engineering and the Key Laboratory of Optoelectronics Information and Technology (Ministry of Education) Tianjin University China RIKEN Center for Advanced Photonics RIKEN 519-1399 Aramaki-Aoba Aoba-ku Sendai Miyagi Japan Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia School of Electrical and Computer Engineering Oklahoma State University Stillwater Oklahoma USA

The pursuit of novel concepts and design schemes for metasurfaces to develop Terahertz (THz) polarization-related devices with exceptional performance is an ongoing and highly demanding project. Here, A new all-metal stereo U-shaped meta-atom based design scheme for manipulating terahertz polarization is proposed. The design functions as an efficient and broadband THz waveplate with tailorable birefringence by controlling sunken depth. The polarization conversion ratio (PCR) is $\gt90$% with 69% relative bandwidth and 85° angle tolerance. The proposed meta-grating based on Pancharatnam-Berry (PB) phase method is also demonstrated, enriching design degrees of freedom for metasurfaces and offering potential for various functional devices.

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CODH++: Macro-semantic differences oriented instance segmentation network

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Expert Systems with Applications 2022年 202卷

作者： Zhang, Wenchao Fu, Chong Cao, Lin Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China School of Computer Science The University of Auckland New Zealand

With the idea of divide and rule, there exist two different forms of semantic features flowing in the two stage instance segmentation paradigms. They are the global features at the image level and the instance features at the region-wise. The most significant distinction of the two macro-semantic morphological features lies in the different relevance of neighborhood features caused by background noise. Hence, we should consider different situations and make different schemes. Notice that the fields-of-view determines the range of local features that can be perceived in the convolution operation and implies the representation capability of the network. To this end, for FPN and Mask Head in two stage paradigms, we propose a more efficient methodology with Group-Inception and Asymmetric-Inception modules. This proposed methodology can act as a drop-in replacement to upgrade the plain convolution operation, which enables the network to look more via modeling long-range dependencies. Our method is simple yet effective. Quantitatively, we can significantly improve the state-of-the-art frameworks, including Mask R-CNN, Mask Scoring R-CNN, Cascade Mask R-CNN, and HTC by about 1.2%–2.2% AP on MS COCO test-dev yet with fewer parameters and FLOPs. Moreover, the proposed approach achieves competitive performances on the Scapes, KINS and SBD datasets. The source code of our method will be made available. © 2022 Elsevier Ltd

关键词： Semantics

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Federated learning: Applications, challenges and future directions

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International Journal of Hybrid Intelligent Systems 2022年第1-2期18卷 19-35页

作者： Bharati, Subrato Mondal, M. Rubaiyat Hossain Podder, Prajoy Prasath, V.B. Surya Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati OH United States Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH United States Department of Biomedical Informatics College of Medicine University of Cincinnati Cincinnati OH United States Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati OH United States

Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. Inspired by the rapid growth of FL research, this paper examines recent developments and provides a comprehensive list of unresolved issues. Several privacy methods including secure multiparty computation, homomorphic encryption, differential privacy and stochastic gradient descent are described in the context of FL. Moreover, a review is provided for different classes of FL such as horizontal and vertical FL and federated transfer learning. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis system and healthcare, which we review in this paper. We also present a comprehensive review of existing FL challenges for example privacy protection, communication cost, systems heterogeneity, unreliable model upload, followed by future research directions. © 2022 - IOS Press. All rights reserved.

关键词： Artificial intelligence computing methodologies federated learning healthcare machine learning privacy protection

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From Non-Expert to Expert: Recurrent Refined Learning for Medical image Segmentation

From Non-Expert to Expert: Recurrent Refined Learning for Me...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ruohui Jiang Changtai Li Xiaojuan Ban Shihua Yin Chao Yao Yu Guo Mohammad S. Obaidat Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Institute of Materials Intelligent Technology Liaoning Academy of Materials Shenyang China Second Affiliated Hospital of Guangxi Medical University Nanning China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China King Abdullah II School of Information Technology University of Jordan Amman Jordan Amity University Noida UP India

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Precise lesion segmentation is essential in computer-aided diagnosis and treatment. Prevalent deep-learning-based approaches need high-quality annotations to achieve satisfying performance. However, blurring effects and infiltration hamper the accurate delineation of lesions. A common solution is to divide the annotation process into initial annotation by non-specialized personnel and subsequent modification by expert physicians, where the latent correction patterns and non-expert labels are seldom utilized effectively. To explore their helpfulness, we propose ReReNet, a recurrent refined network for lesion segmentation that learns from non-expert to expert. It achieves progressively refined segmentation results through multiple iterations with tailored discrepancy-aware supervision. During training, as the iterative process perceives discrepancies between refining and expert labels, the model gradually grasps the knowledge of turning the barely correct into the clinically accurate. We validate ReReNet’s capability on three medical image segmentation (MIS) datasets, including magnetic resonance (MR) and computed tomography (CT) modalities. Comparison results indicate that the proposed approach achieves superior performance by introducing the designed recurrent mechanism and outperforms mainstream methods, demonstrating the effectiveness of mining hidden correction patterns by utilizing non-expert information.

关键词： Training image segmentation Accuracy Annotations Computed tomography Refining computer architecture Turning Lesions Medical diagnostic imaging

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Control Barrier Functions for Systems with Multiple Control Inputs

arXiv

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arXiv 2022年

作者： Xiao, Wei Cassandras, Christos G. Belta, Calin A. Rus, Daniela The Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology The Division of Systems Engineering Center for Information and Systems Engineering Boston University BrooklineMA02446 United States

Control Barrier Functions (CBFs) are becoming popular tools in guaranteeing safety for nonlinear systems and constraints, and they can reduce a constrained optimal control problem into a sequence of Quadratic Programs (QPs) for affine control systems. The recently proposed High Order Control Barrier Functions (HOCBFs) work for arbitrary relative degree constraints. One of the challenges in a HOCBF is to address the relative degree problem when a system has multiple control inputs, i.e., the relative degree could be defined with respect to different components of the control vector. This paper proposes two methods for HOCBFs to deal with systems with multiple control inputs: a general integral control method and a method which is simpler but limited to specific classes of physical systems. When control bounds are involved, the feasibility of the above mentioned QPs can also be significantly improved with the proposed methods. We illustrate our approaches on a unicyle model with two control inputs, and compare the two proposed methods to demonstrate their effectiveness and performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Quadratic programming

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Robust Co-Design of Canonical Underactuated Systems for Increased Certifiable Stability

Robust Co-Design of Canonical Underactuated Systems for Incr...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Federico Girlanda Lasse Shala Shivesh Kumar Frank Kirchner Robotics Innovation Center German Research Center for Artificial Intelligence (DFKI GmbH) Bremen Germany Department of Information Engineering University of Padova Italy Department of Mechanics & Maritime Sciences Dynamics Division Chalmers University of Technology Gotheburg Sweden Faculty of Mathematics and Computer Science University of Bremen Bremen Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Optimal behaviours of a system to perform a specific task can be achieved by leveraging the coupling between trajectory optimization, stabilization, and design optimization. This approach is particularly advantageous for underactuated systems, which are systems that have fewer actuators than degrees of freedom and thus require for more elaborate control systems. This paper proposes a novel co-design algorithm, namely Robust Trajectory Control with Design optimization (RTC-D). An inner optimization layer (RTC) simultaneously performs direct transcription (DIRTRAN) to find a nominal trajectory while computing optimal hyperparameters for a stabilizing time-varying linear quadratic regulator (TVLQR). RTC-D augments RTC with a design optimization layer, maximizing the system’s robustness through a time-varying Lyapunov-based region of attraction (ROA) analysis. This analysis provides a formal guarantee of stability for a set of off-nominal states. The proposed algorithm has been tested on two different underactuated systems: the torque-limited simple pendulum and the cart-pole. Extensive simulations of off-nominal initial conditions demonstrate improved robustness, while real-system experiments show increased insensitivity to torque disturbances.

关键词： Couplings Torque Regulators Stability analysis Robustness Task analysis Robotics and automation

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Nonequilibrium transport and the fluctuation theorem in the thermodynamic behaviors of nonlinear photonic systems

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Physical Review Research 2025年第1期7卷 013084-013084页

作者： Yang Liu Jincheng Lu Zhongfei Xiong Fan O. Wu Demetrios Christodoulides Yuntian Chen Jian-Hua Jiang School of Physical Science and Technology & Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University 1 Shizi Street Suzhou 215006 China Key Laboratory of Efficient Low-carbon Energy Conversion and Utilization of Jiangsu Provincial Higher Education Institutions School of Physical Science and Technology Suzhou University of Science and Technology Suzhou 215009 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan 430074 China CREOL College of Optics and Photonics University of Central Florida Orlando Florida 32816-2700 USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei 230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou 215123 China School of Physical Sciences University of Science and Technology of China Hefei 230026 China

Nonlinear multimode optical systems have attracted substantial attention due to their rich physical properties. Complex interplay between the nonlinear effects and mode couplings makes it difficult to understand the collective dynamics of photons. Authors of recent studies have shown that such collective phenomena can be effectively described by Rayleigh-Jeans thermodynamics theory, which is a powerful tool for the study of nonlinear multimode photonic systems. These systems, in turn, offer a compelling platform for investigating fundamental issues in statistical physics, attributed to their tunability and the ability to access negative temperature regimes. However, to date, a theory for nonequilibrium transport and fluctuations is yet to be established. Here, we employ full counting statistics theory to study the nonequilibrium transport of particles and energy in nonlinear multimode photonic systems in both positive and negative temperature regimes. Furthermore, we discover that, in situations involving two reservoirs of opposite temperatures and chemical potentials, an intriguing phenomenon known as the loop current effect can arise, wherein the current in the positive energy sector runs counter to that in the negative energy sector. In addition, we numerically confirm that the fluctuation theorem remains applicable in optical thermodynamics systems across all regimes, from positive temperatures to negative ones. Our findings closely align with numerical simulations based on first-principles nonlinear wave equations. In this paper, we seek to deepen the understanding of irreversible nonequilibrium processes and statistical fluctuations in nonlinear many-body photonic systems which will enhance our grasp of collective phenomena of photons and foster a fruitful intersection between optics and statistical physics.

关键词： Chemical potential

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