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Ant Colony Optimization based Medical Image Preservation and Segmentation

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Ant Colony Optimization based Medical Image Preservation and...

Advanced Technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), International Conference on

作者： Priya Nandihal Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Madhumala R B Ashish Khanna V Sunil Kumar Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceara Brazil Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru

ISBN: (纸本)9781665493970

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work.

关键词： Integrated optics Image segmentation Ant colony optimization Biomedical optical imaging Optical design Image edge detection Metaheuristics

Structural, optical, and thermal properties of BN thin films grown on diamond via pulsed laser deposition

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Physical Review Materials 2023年第9期7卷 094602-094602页

作者： Abhijit Biswas Gustavo A. Alvarez Tao Li Joyce Christiansen-Salameh Eugene Jeong Anand B. Puthirath Sathvik Ajay Iyengar Chenxi Li Tia Gray Xiang Zhang Tymofii S. Pieshkov Harikishan Kannan Jacob Elkins Robert Vajtai A. Glen Birdwell Mahesh R. Neupane Elias J. Garratt Bradford B. Pate Tony G. Ivanov Yuji Zhao Zhiting Tian Pulickel M. Ajayan Department of Materials Science and Nanoengineering Rice University Houston Texas 77005 USA Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca New York 14853 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA DEVCOM Army Research Laboratory RF Devices and Circuits Adelphi Maryland 20783 USA Naval Research Laboratory Chemistry Division Washington District of Columbia 20375 USA

Heterostructures based on ultrawide-bandgap (UWBG) semiconductors (bandgap>4.0eV), such as BN and diamond, hold significant importance for the development of high-power electronics in the next generation. However, achieving in situ heteroepitaxy of BN/diamond or vice versa remains exceptionally challenging due to the complex growth kinetics involved. In this work, we grew BN thin film on (100) single-crystal diamonds using pulsed laser deposition and investigated its structural, magnetic, optical, and thermal properties. The structural analyses confirmed the growth of BN films, which exhibited diamagnetic behavior at room temperature. Notably, the film demonstrated anisotropic refractive index characteristics within the visible-to-near-infrared wavelength range. The room-temperature cross-plane thermal conductivity of BN is 1.53 ± 0.77 W/mK, while the thermal conductance of the BN/diamond interface is 20±2MW/m2K. These findings have significant implications for a range of device applications based on UWBG BN/diamond heterostructures.

关键词： Growth Thermal conductivity Boron nitride Thin films Wide band gap systems Film deposition Laser ablation Thermoreflectance

High-Frequency Stereo Matching Network

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High-Frequency Stereo Matching Network

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Haoliang Zhao Huizhou Zhou Yongjun Zhang Jie Chen Yitong Yang Yong Zhao Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Artificial Intelligence Guizhou University Guiyang Guizhou China Ghost-Valley AI Technology Shenzhen Guangdong China School of Physics and Optoelectronic Engineering Guangdong University of Technology Guangzhou China The Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University China

In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it difficult to generate more detailed difference maps that take full advantage of high-frequency information. We propose the Decouple module to alleviate the problem of data coupling and allow features containing subtle details to transfer across the iterations which proves to alleviate the problem significantly in the ablations. To further capture high-frequency details, we propose a Normalization Refinement module that unifies the disparities as a proportion of the disparities over the width of the image, which address the problem of module failure in cross-domain scenarios. Further, with the above improvements, the ResNet-like feature extractor that has not been changed for years becomes a bottleneck. Towards this end, we proposed a multi-scale and multi-stage feature extractor that introduces the channel-wise self-attention mechanism which greatly addresses this bottleneck. Our method (DLNR) ranks 1st on the Middlebury leaderboard, significantly outperforming the next best method by 13.04%. Our method also achieves SOTA performance on the KITTI-2015 benchmark for D1-fg. Code and demos are available at: https://***/David-Zhao-1997/High-frequency-Stereo-Matching-Network.

关键词：

Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

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TechRxiv

TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum States

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arXiv 2022年

作者： Tsai, Chung-En Cheng, Hao-Chung Li, Yen-Huan Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Electrical Engineering Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Mathematics National Taiwan University Taiwan Quantum Computing Centre United Kingdom Center for Quantum Science and Engineering National Taiwan University Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan

Consider an online convex optimization problem where the loss functions are self-concordant barriers, smooth relative to a convex function h, and possibly non-Lipschitz. We analyze the regret of online mirror descent with h. Then, based on the result, we prove the following in a unified manner. Denote by T the time horizon and d the parameter dimension. 1. For online portfolio selection, the regret of (Equation), a variant of exponentiated gradient due to Helmbold et al. [1], is (Equation) when T > 4d/logd. This improves on the original Õ(T3/4d1/2) regret bound for (Equation). 2. For online portfolio selection, the regret of online mirror descent with the logarithmic barrier is (Equation). The regret bound is the same as that of Soft-Bayes due to Orseau et al. [2] up to logarithmic terms. 3. For online learning quantum states with the logarithmic loss, the regret of online mirror descent with the log-determinant function is also (Equation). Its per-iteration time is shorter than all existing algorithms we know. Copyright © 2022, The Authors. All rights reserved.

关键词： Mirrors

The Database and Benchmark For the Source Speaker Tracing Challenge 2024

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The Database and Benchmark For the Source Speaker Tracing Ch...

IEEE Spoken Language Technology Workshop

作者： Ze Li Yuke Lin Tian Yao Hongbin Suo Pengyuan Zhang Yanzhen Ren Zexin Cai Hiromitsu Nishizaki Ming Li School of Computer Science Wuhan University Wuhan China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan China AI Center OPPO Beijing China Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics CAS China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Center for Language and Speech Processing Johns Hopkins University USA Integrated Graduate School of Medicine Engineering and Agricultural Sciences University of Yamanashi 4-4-37 Takeda Kofu Yamanashi Japan

ISBN: (数字)9798350392258

ISBN: (纸本)9798350392265

Voice conversion (VC) systems can transform audio to mimic another speaker’s voice, thereby attacking speaker verification (SV) systems. However, ongoing studies on source speaker verification (SSV) are hindered by limited data availability and methodological constraints. This paper presents the Source Speaker Tracking Challenge (SSTC) on STL 2024, which aims to fill the gap in the database and benchmark for the SSV task. In this study, we generate a large-scale converted speech database with 16 common VC methods and train a batch of baseline systems based on the MFA-Conformer architecture. In addition, we introduced a related task called conversion method recognition, with the aim of assisting the SSV task. We expect SSTC to be a platform for advancing the development of the SSV task and provide further insights into the performance and limitations of current SV systems against VC attacks. Further details about SSTC can be found here 1 . 1 https://***/

关键词： Databases Conferences Transforms Contrastive learning Benchmark testing Multitasking Calibration

Optimal targeted advertising strategy for secure wireless edge metaverse

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arXiv 2021年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Kim, Dong In Miao, Chunyan The School of Computer Science and Engineering The Energy Research Institute NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Recently, Metaverse has attracted increasing attention from both industry and academia, because of the significant potential to integrate real and digital worlds ever more seamlessly. By combining advanced wireless communications, edge computing and virtual reality (VR) technologies into Metaverse, a multidimensional, intelligent and powerful wireless edge Metaverse is created for future human society. In this paper, we design a privacy preserving targeted advertising strategy for the wireless edge Metaverse. Specifically, a Metaverse service provider (MSP) allocates bandwidth to the VR users so that the users can access Metaverse from edge access points. To protect users’ privacy, the covert communication technique is used in the downlink. Then, the MSP can offer high-quality access services to earn more profits. Motivated by the concept of "covert", targeted advertising is used to promote the sale of bandwidth and ensure that the advertising strategy cannot be detected by competitors who may make counter-offer and by attackers who want to disrupt the services. We derive the best advertising strategy in terms of budget input, with the help of the Vidale-Wolfe model and Hamiltonian function. Furthermore, we propose a novel metric named Meta-Immersion to represent the user’s experience feelings. The performance evaluation shows that the MSP can boost its revenue with an optimal targeted advertising strategy, especially compared with that without the advertising. © 2021, CC BY.

关键词： Bandwidth

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning