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MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for Medical Image Segmentation

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MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for M...

New Trends in Computational Intelligence (NTCI), International Conference on

作者： Liangjin Diao Longfeng Shen Jiacong Chen Fangzhen Ge Mengwen Li Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) College of Computer Science and Technology Huaibei Normal University Huaibei Anhui China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei Anhui China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Recent advancements in CNNs for medical image segmentation have focused on the combination of CNN and Transformer architectures to capture both local and global features. However, challenges remain, such as effectively utilizing fused features at the encoder-decoder bottleneck and addressing blurred object edges in datasets. To tackle these issues, we introduce a multi-scale channel adaptive sensing module, enhancing the model's representational and perceptual capabilities by integrating multi-scale and channel-specific information. Additionally, we propose a cross-perception module and multi-scale segmentation head to improve global coordination and multi-scale information gathering. Experiments on the ACDC and Synapse datasets demonstrate our model's superiority.

关键词： Image segmentation Adaptation models Head Image edge detection Computer architecture Transformers Market research Sensors Synapses Biomedical imaging

OmniTokenizer: A Joint Image-Video Tokenizer for Visual Generation

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arXiv 2024年

作者： Wang, Junke Jiang, Yi Yuan, Zehuan Peng, Binyue Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Bytedance Inc China

Tokenizer, serving as a translator to map the intricate visual data into a compact latent space, lies at the core of visual generative models. Based on the finding that existing tokenizers are tailored to image or video inputs, this paper presents OmniTokenizer, a transformer-based tokenizer for joint image and video tokenization. OmniTokenizer is designed with a spatial-temporal decoupled architecture, which integrates window and causal attention for spatial and temporal modeling. To exploit the complementary nature of image and video data, we further propose a progressive training strategy, where OmniTokenizer is first trained on image data on a fixed resolution to develop the spatial encoding capacity and then jointly trained on image and video data on multiple resolutions to learn the temporal dynamics. OmniTokenizer, for the first time, handles both image and video inputs within a unified framework and proves the possibility of realizing their synergy. Extensive experiments demonstrate that OmniTokenizer achieves state-of-the-art (SOTA) reconstruction performance on various image and video datasets, e.g., 1.11 reconstruction FID on ImageNet and 42 reconstruction FVD on UCF-101, beating the previous SOTA methods by 13% and 26%, respectively. Additionally, we also show that when integrated with OmniTokenizer, both language model-based approaches and diffusion models can realize advanced visual synthesis performance, underscoring the superiority and versatility of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Video recording

A novel position-shift method of double-phase fresnel hologram for synthesizing a complex fresnel hologram 2nd

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A novel position-shift method of double-phase fresnel hologr...

2nd International Conference on Sensor Networks and signal processing, SNSP 2019

作者： Shen, Chuan Zhu, QinQin Hong, QingQing Zhang, Cheng Wei, Sui Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Key Laboratory of Modern Imaging and Display Technology of Anhui Province Anhui University Hefei230601 China

ISBN: (纸本)9789811549168

An ideal desired spatial light modulator that is capable of complex amplitude modulation will be one of the ultimate tools for holographic display. In this paper, an analytical method to overlap double-phase Fresnel holograms displayed on the phase-only spatial light modulators for synthesizing a complex Fresnel hologram is proposed. Two $$4f$$ configurations with the phase-only spatial light modulator inserted in the Fourier plane are employed. Different programmable blazed phase gratings can be easily implemented by encoding their phase functions onto the spatial light modulators. We theoretically analyze the implementation of the proposed system based on Fourier optics. The effects introduced by the discrete pixelated nature of the spatial light modulator are assessed. A $$4f$$ configuration of holographic display system based on two liquid crystals on silicons is designed to indicate the free position-shift of the hologram. Optical reconstructions yield satisfactory results. © Springer Nature Singapore Pte Ltd 2021.

关键词： Holographic displays

Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling

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arXiv 2024年

作者： Shi, Yuhui Sheng, Qiang Cao, Juan Mi, Hao Hu, Beizhe Wang, Danding Key Lab of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Xi’an Jiaotong University China

With the rapidly increasing application of large language models (LLMs), their abuse has caused many undesirable societal problems such as fake news, academic dishonesty, and information pollution. This makes AI-generated text (AIGT) detection of great importance. Among existing methods, white-box methods are generally superior to black-box methods in terms of performance and generalizability, but they require access to LLMs’ internal states and are not applicable to black-box settings. In this paper, we propose to estimate word generation probabilities as pseudo white-box features via multiple re-sampling to help improve AIGT detection under the black-box setting. Specifically, we design POGER, a proxy-guided efficient re-sampling method, which selects a small subset of representative words (e.g., 10 words) for performing multiple re-sampling in black-box AIGT detection. Experiments on datasets containing texts from humans and seven LLMs show that POGER outperforms all baselines in macro F1 under black-box, partial white-box, and out-of-distribution settings and maintains lower re-sampling costs than its existing counterparts. © 2024, CC BY-NC-SA.

关键词： Computational linguistics

A Unified Framework Integrating Knowledge and Data for Collaborative Root Cause Identification

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A Unified Framework Integrating Knowledge and Data for Colla...

Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Jiefei Yu Zicheng Cao Siyi He Zuyi Gu Yingcheng Xu Kai Zhong Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Anhui China Data Science and Big Data Technology School of Big Data and Statistics Anhui University Anhui China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Capturing the root cause and propagation path of the fault is critical to ensuring the safety and efficiency of industrial processes, especially those that inadequately utilize process knowledge and data. To address this issue, a unified framework integrating knowledge and data for collaborative root cause identification is proposed. First, the knowledge causal graph (KCG) is constructed using expert knowledge and industrial flow charts, providing a preliminary reference for subsequent causality analysis. Next, by replacing the traditional vector autoregression (VAR) model in Granger Causality (GC) with the gated recurrent unit (GRU), a more reliable causal relationship between variables is obtained. Additionally, a causality fusion propagation path identification method (CF-PPI) is designed to identify the root cause and propagation path of the fault, so that the obtained fault propagation path has less redundancy and higher accuracy. Finally, the method is validated using data from the ASHRAE RP-1043 centrifugal chiller.

关键词： Fault diagnosis Knowledge engineering Reactive power Analytical models Delay effects Redundancy Collaboration Cause effect analysis Vectors Safety

ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data

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arXiv 2024年

作者： Yuan, Doncheng Xue, Jianzhe Su, Jinshan Xu, Wenchao Zhou, Haibo School of Electronic Science and Engineering Nanjing University Nanjing210023 China Key Laboratory of Vibration Signal Capture and Intelligent Processing Yili Normal University Yining835000 China Department of Computing The Hong Kong Polytechnic University Hong Kong

Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems. While traditional on-road detectors are hindered by limited coverage and high costs, cloud computing and data mining of vehicular network data, such as driving speeds and GPS coordinates, present a promising and cost-effective alternative. Furthermore, minimizing data collection can significantly reduce overhead. However, limited data can lead to inaccuracies and instability in TFE. To address this, we introduce the spatial-temporal Mamba (ST-Mamba), a deep learning model combining a convolutional neural network (CNN) with a Mamba framework. ST-Mamba is designed to enhance TFE accuracy and stability by effectively capturing the spatial-temporal patterns within traffic flow. Our model aims to achieve results comparable to those from extensive data sets while only utilizing minimal data. Simulations using real-world datasets have validated our model’s ability to deliver precise and stable TFE across an urban landscape based on limited data, establishing a cost-efficient solution for TFE. © 2024, CC BY.

关键词： Data mining

A Novel Multi-band EBG Structure Using Nested Square Grooves 5

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A Novel Multi-band EBG Structure Using Nested Square Grooves

5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Sun, Guoxing Ren, Xingang Ju, Huping Xueyuan, Cai He, Shuping Xianliang, Wu Huang, Zhixiang Anhui University Key Laboratory of Target Recognition and Feature Extraction of Anhui Province Lu'an China Key Laboratory of Electromagnetic Environmental Sensing of Anhui Province Anqing Normal Unverisity Anqing China Ministry of Education Anhui University Key Laboratory of Intelligent Computing & Signal Processing Hefei China Anhui University Key Laboratory of Electromagnetic Environmental Sensing of Anhui Province Hefei China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A nested rectangle electromagnetic bandgap structure with dual broadband band is designed to enhance the performance of telecom/mobile 5G antennas operating at 3.3GHz and 4.9GHz, which has a short frequency spacing of about 1.6GHz. through loading EBG structure under dipole antenna, directional radiation, low profile and high gain performance have been achieved. The distance between electromagnetic bandgap (EBG) structure and antenna is only 0.067 wavelength, the first band and the second band of EBG are greatly expanded by etching nested square grooves around the EBG (NSGEBG) as compared with traditional etched ring groove EBG (ERGEBG). The bandwidth of the first band of 0.39GHz is twice wider than that of the ring groove of 0.2GHz, the second band has a bandwidth of 0.46GHz, which perfectly solves the problem that the spacing of the two operating frequency is greater than 2GHz. © 2022 IEEE.

关键词： Energy gap

Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion

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arXiv 2024年

作者： Cao, Yice Liu, Chenchen Wu, Zhenhua Yao, Wenxin Xiong, Liu Chen, Jie Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei230601 China The 38th Research Institute China Electronics Technology Group Corporation Hefei230088 China

As remote sensing imaging technology continues to advance and evolve, processing high-resolution and diversified satellite imagery to improve segmentation accuracy and enhance interpretation efficiency emerg as a pivotal area of investigation within the realm of remote sensing. Although segmentation algorithms based on CNNs and Transformers achieve significant progress in performance, balancing segmentation accuracy and computational complexity remains challenging, limiting their wide application in practical tasks. To address this, this paper introduces state space model (SSM) and proposes a novel hybrid semantic segmentation network based on vision Mamba (CVMH-UNet). This method designs a cross-scanning visual state space block (CVSSBlock) that uses cross 2D scanning (CS2D) to fully capture global information from multiple directions, while by incorporating convolutional neural network branches to overcome the constraints of Vision Mamba (VMamba) in acquiring local information, this approach facilitates a comprehensive analysis of both global and local features. Furthermore, to address the issue of limited discriminative power and the difficulty in achieving detailed fusion with direct skip connections, a multi-frequency multi-scale feature fusion block (MFMSBlock) is designed. This module introduces multi-frequency information through 2D discrete cosine transform (2D DCT) to enhance information utilization and provides additional scale local detail information through point-wise convolution branches. Finally, it aggregates multi-scale information along the channel dimension, achieving refined feature fusion. Findings from experiments conducted on renowned datasets of remote sensing imagery demonstrate that proposed CVMH-UNet achieves superior segmentation performance while maintaining low computational complexity, outperforming surpassing current leading-edge segmentation algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

A Time-Aware Dynamic Service Quality Prediction Approach for Services

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Tsinghua Science and Technology 2020年第2期25卷 227-238页

作者： Ying Jin Weiguang Guo Yiwen Zhang Department of Management Hefei UniversityHefei 230601China School of Computer Science and Technology and also with the Key Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui UniversityHefei 230601China

Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and *** paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time *** the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing *** the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.

关键词： dynamic Quality of Service(QoS)prediction time-aware service recommendation