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PRIORPATH: COARSE-TO-FINE APPROACH FOR CONTROLLED DE-NOVO PATHOLOGY SEMANTIC MASKS GENERATION

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arXiv 2024年

作者： Daniel, Nati Nathan, May Azeroual, Eden Fisher, Yael Savir, Yonatan Department of Physiology Biophysics and Systems Biology Faculty of Medicine Technion - IIT Haifa Israel Network Biology Research Lab Faculty of Electrical and Computer Engineering Technion – IIT Haifa Israel Division of Pathology Rambam Health Care Campus Haifa Israel

Incorporating artificial intelligence (AI) into digital pathology offers promising prospects for automating and enhancing tasks such as image analysis and diagnostic processes. However, the diversity of tissue samples and the necessity for meticulous image labeling often result in biased datasets, constraining the applicability of algorithms trained on them. To harness synthetic histopathological images to cope with this challenge, it is essential not only to produce photorealistic images but also to be able to exert control over the cellular characteristics they depict. Previous studies used methods to generate, from random noise, semantic masks that captured the spatial distribution of the tissue. These masks were then used as a prior for conditional generative approaches to produce photorealistic histopathological images. However, as with many other generative models, this solution exhibits mode collapse as the model fails to capture the full diversity of the underlying data distribution. In this work, we present a pipeline, coined PriorPath, that generates detailed, realistic, semantic masks derived from coarse-grained images delineating tissue regions. This approach enables control over the spatial arrangement of the generated masks and, consequently, the resulting synthetic images. We demonstrated the efficacy of our method across three cancer types, skin, prostate, and lung, showcasing PriorPath’s capability to cover the semantic mask space and to provide better similarity to real masks compared to previous methods. Our approach allows for specifying desired tissue distributions and obtaining both photorealistic masks and images within a single platform, thus providing a state-of-the-art, controllable solution for generating histopathological images to facilitate AI for computational pathology. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity

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arXiv 2023年

作者： Nguyen, Loc X. Tun, Ye Lin Tun, Yan Kyaw Nguyen, Minh N.H. Zhang, Chaoning Han, Zhu Hong, Choong Seon The Department of Computer Science and Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of The Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Brinellvägen 8 Stockholm114 28 Sweden The Digital Science and Technology Institute The University of Danang Vietnam-Korea University of Information and Communication Technology Da Nang550000 Viet Nam The Department of Artificial Intelligence School of Computing Kyung Hee University Yongin-si17104 Korea Republic of The Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States

Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there are variations in the model architectures used by users and their computing capacities. To address this issue, we explore a semantic communication system that caters to multiple users with different model architectures by using a multi-purpose transmitter at the base station (BS). Specifically, the BS in the proposed framework employs semantic and channel encoders to encode the image for transmission, while the receiver utilizes its local channel and semantic decoder to reconstruct the original image. Our joint source-channel encoder at the BS can effectively extract and compress semantic features for specific users by considering the signal-to-noise ratio (SNR) and computing capacity of the user. Based on the network status, the joint source-channel encoder at the BS can adaptively adjust the length of the transmitted signal. A longer signal ensures more information for high-quality image reconstruction for the user, while a shorter signal helps avoid network congestion. In addition, we propose a hybrid loss function for training, which enhances the perceptual details of reconstructed images. Finally, we conduct a series of extensive evaluations and ablation studies to validate the effectiveness of the proposed system. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

Intelligent Energy-Efficient and Fair Resource Scheduling for UAV-Assisted Space-Air-Ground Integrated networks Under Jamming Attacks

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Intelligent Energy-Efficient and Fair Resource Scheduling fo...

IEEE Conference on Vehicular Technology (VTC)

作者： Shihao Chen Helin Yang Liang Xiao Changyuan Xu Xianzhong Xie Wanting Yang Zehui Xiong Department of Information & Communication Engineering Xiamen University Xiamen China Chongqing Key Laboratory of Computer Network and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China Pillar of Information System Technology & Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

The space-air-ground integrated network (SAGIN) is a crucial technology for sixth-generation (6G) wireless communication networks to achieve seamless coverage and high throughput. In this paper, we propose an unmanned aerial vehicle (UAV)-assisted SAGIN structure, where the UAV is responsible for collecting data from ground users (GUs) and transmitting it to low-earth orbit (LEO) satellites. This paper also formulates a joint energy-efficient and fair resource scheduling optimization problem under jamming attacks and limited energy constraints, where the line-of-sight (LoS) links between the UAV and GUs are susceptible to being jammed. Due to the non-convex problem and dynamic environments, a deep reinforcement learning (DRL)-based twin delayed deep deterministic policy gradient (TD3) is developed to search optimal UAV trajectory to maximize energy efficiency (EE) and fairness against jamming. Simulation results verify that the proposed intelligent resource scheduling algorithm outperforms the baseline algorithms in terms of EE and fairness index in different settings.

关键词： Wireless communication Vehicular and wireless technologies Simulation Autonomous aerial vehicles Space-air-ground integrated networks Energy efficiency Trajectory

Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

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Robust Generalization Against Photon-Limited Corruptions via...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhuo Huang Miaoxi Zhu Xiaobo Xia Li Shen Jun Yu Chen Gong Bo Han Bo Du Tongliang Liu Sydney AI Centre The University of Sydney National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University JD Explore Academy Department of Automation University of Science and Technology of China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Department of Computer Science Hong Kong Baptist University

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains.

关键词：

A QoE-Aware Adaptive Energy-Efficient Transmission Scheduling Method

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A QoE-Aware Adaptive Energy-Efficient Transmission Schedulin...

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yankun Yuan Lin Wang Chonghui Xiao Zijuan Liu Fan Dang Xu Wang Haitian Zhao Xin Miao School of Information Science and Engineering Yanshan University National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Automation and BNRist Tsinghua University Global Innovation Exchange Tsinghua University School of Software and BNRist Tsinghua University

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In this paper, we propose a dynamic data transmission strategy for smart home environments that aims to optimize the Quality of Experience (QoE) by adaptively adjusting the data upload frequency based on the predicted trends in sensor data. Using the home wireless sensors monitoring dataset, we implement a deep learning model for accurate time series forecasting. In addition, an anomaly detection mechanism is used to identify critical events, requiring more frequent data uploads when important changes are detected. The QoE is quantified through a weighted average of several influencing factors, including data timeliness, timely upload of critical events, and transmission frequency. Our optimization objective is to maximize QoE while minimizing the number of transmissions, with an emphasis on reducing energy consumption through intelligent scheduling. The results demonstrate that our approach effectively balances data timeliness, transmission efficiency, and energy savings, leading to improved user satisfaction in smart home applications.

关键词： Wireless communication Wireless sensor networks Energy consumption Data centers Time-frequency analysis Time series analysis Smart homes Quality of experience Data communication Optimization

A WiFi-aware method for mobile data offloading with deadline constraints

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A WiFi-aware method for mobile data offloading with deadline...

作者： Tang, Wenda Wu, Chaobing Qi, Lianyong Zhang, Xuyun Xu, Xiaolong Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Information Science and Engineering Chinese Academy of Education Big Data Qufu Normal University Rizhao China Department of Electrical and Computer Engineering The University of Auckland Auckland New Zealand Jiangsu Engineering Centre of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

With an increasing number of public WiFi hotspots have been constructed in metropolitan areas, citizens can leverage these WiFi hotspots to surf the Internet with their smart devices almost everywhere. Compared to cellular networks, most people prefer using WiFi because this can not only save money by reducing the cellular data traffic but also save energy to prolong the battery lifetime of mobile devices. Mobile offloading techniques give the opportunity for smart devices to offload cellular data traffic to WiFi networks whenever they are connected WiFi networks. Although offloading to WiFi networks can cause communication delay, many delay-tolerant applications still prefer doing so in order to save the cellular data traffic. Existing offloading approaches only take the application delay tolerance into consideration and therefore easy to induce some tasks missing deadlines. In this paper, we propose a novel WiFi-aware method for mobile data offloading through WiFi networks with deadline constraints. Specifically, it takes the WiFi distribution in city areas into consideration while making offloading decision. The simulation results on real-world data have shown that our method improves the percentage of tasks that can meet their deadlines, and reduces the average task completion time by comparing with other outstanding methods. © 2019 John Wiley & Sons, Ltd.

关键词： Wireless local area networks (WLAN)

Linear-time computation of generalized minimal absent words for multiple strings

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arXiv 2023年

作者： Okabe, Kouta Mieno, Takuya Nakashima, Yuto Inenaga, Shunsuke Bannai, Hideo Department of Information Science and Technology Kyushu University Japan Department of Computer and Network Engineering University of Electro-Communications Japan Department of Informatics Kyushu University Japan M&D Data Science Center Tokyo Medical and Dental University Japan

A string w is called a minimal absent word (MAW) for a string S if w does not occur as a substring in S and all proper substrings of w occur in S. MAWs are well-studied combinatorial string objects that have potential applications in areas including bioinformatics, musicology, and data compression. In this paper, we generalize the notion of MAWs to a set S = {S1, ..., Sk} of multiple strings. We first describe our solution to the case of k = 2 strings, and show how to compute the set M of MAWs in optimal O(n + |M|) time and with O(n) working space, where n denotes the total length of the strings in S. We then move on to the general case of k > 2 strings, and show how to compute the set M of MAWs in O(n⌈k/log n⌉+ |M|) time and with O(n(k + log n)) bits of working space, in the word RAM model with machine word size ω = log n. The latter algorithm runs in optimal O(n + |M|) time for k = O(log n). © 2023, CC BY.

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Characterization of Embedded and Thinned RF Chips

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Characterization of Embedded and Thinned RF Chips

European Microelectronics and Packaging Conference, EMPC

作者： Ran Yin Helmuth P. E. Morath Christian Hoyer Krzysztof Nieweglowski Karsten Meier Jens Wagner Frank Ellinger Karlheinz Bock Centre for Tactile Internet with Human-in-the-Loop (CeTI) Institute of Electronic Packaging Technology Technische Universität Dresden Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI) Chair of Circuit Design and Network Theory Technische Universität Dresden Germany Department of Electrical and Computer Engineering Chair of Circuit Design and Network Theory Technische Universität Dresden Germany Department of Electrical and Computer Engineering Institute of Electronic Packaging Technology Technische Universität Dresden Germany

This work studies the effect of thinning down chips with transmission line structures for flexible embedding technology. Test chips with 90 Ω grounded coplanar waveguide (G-CPW) transmission lines have been designed and manufactured to characterize the high-frequency performance before and after embedding in sheet molding compound (SMC), and thinning down from 300 µm to 20 µm, Molding first technology enables single-die or multiple-die thinning with the embedding material while being held in the exact position, where the subsequent fan-out copper (Cu) redistribution layer (RDL) structuring realizes the direct contacts to the embedded chip, followed by semi-additive manufacturing of traces. The embedded and thinned chips are characterized by S-parameter measurements in the frequency range from 20 MHz to 65 GHz using a vector network analyzer (VNA). The results show the feasibility of the examined embedding and thinning processes which are suitable for millimeter wave (mmWave) components as shown by the example of a G-CPW transmission line without degrading its RF performance in terms of characteristic impedance and attenuation.

关键词：

UAV-Aided Lifelong Learning for AoI and Energy Optimization in Non-Stationary IoT networks

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arXiv 2023年

作者： Gong, Zhenzhen Hashash, Omar Wang, Yingze Cui, Qimei Ni, Wei Saad, Walid Sakaguchi, Kei The National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States Data61 CSIRO MarsfieldNSW2122 Australia Department of Electrical and Electronic Engineering Tokyo Institute of Technology Tokyo152-8550 Japan

In this paper, a novel joint energy and age of information (AoI) optimization framework for IoT devices in a non-stationary environment is presented. In particular, IoT devices that are distributed in the real-world are required to efficiently utilize their computing resources so as to balance the freshness of their data and their energy consumption. To optimize the performance of IoT devices in such a dynamic setting, a novel lifelong reinforcement learning (RL) solution that enables IoT devices to continuously adapt their policies to each newly encountered environment is proposed. Given that IoT devices have limited energy and computing resources, an unmanned aerial vehicle (UAV) is leveraged to visit the IoT devices and update the policy of each device sequentially. As such, the UAV is exploited as a mobile learning agent that can learn a shared knowledge base with a feature base in its training phase, and feature sets of a zero-shot learning method in its testing phase, to generalize between the environments. To optimize the trajectory and flying velocity of the UAV, an actor-critic network is leveraged so as to minimize the UAV energy consumption. Simulation results show that the proposed lifelong RL solution can outperform the state-of-art benchmarks by enhancing the balanced cost of IoT devices by 8.3% when incorporating warm-start policies for unseen environments. In addition, our solution achieves up to 49.38% reduction in terms of energy consumption by the UAV in comparison to the random flying strategy. © 2023, CC BY-NC-SA.

关键词： Internet of things