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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wald, Tassilo Ulrich, Constantin Köhler, Gregor Zimmerer, David Denner, Stefan Baumgartner, Michael Isensee, Fabian Jaini, Priyank Maier-Hein, Klaus H. Heidelberg Germany Helmholtz Imaging DKFZ Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Germany Medical Faculty Heidelberg University of Heidelberg Germany Google Deepmind United Kingdom Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg Germany

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to their internal high dimensionality and complexity. To address this, one approach is to measure the similarity of activation responses to various inputs. Representational Similarity Matrices (RSMs) distill this similarity into scalar values for each input pair. These matrices encapsulate the entire similarity structure of a system, indicating which input leads to similar responses. While the similarity between images is ambiguous, we argue that the spatial location of semantic objects does neither influence human perception nor deep learning classifiers. Thus this should be reflected in the definition of similarity between image responses for computer vision systems. Revisiting the established similarity calculations for RSMs we expose their sensitivity to spatial alignment. In this paper, we propose to solve this through semantic RSMs, which are invariant to spatial permutation. We measure semantic similarity between input responses by formulating it as a set-matching problem. Further, we quantify the superiority of semantic RSMs over spatio-semantic RSMs through image retrieval and by comparing the similarity between representations to the similarity between predicted class probabilities. © 2024 Neural information processing systems foundation. All rights reserved.

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Investigation of the separation zone for split-domain light field visualization 28

Investigation of the separation zone for split-domain light ...

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2024 28th International Conference on Information Technology, IT 2024

作者： Javidi, Kamran Martini, Maria G. Kara, Peter A. School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science Penrhyn Road Campus Kingston upon Thames LondonKT1 2EE United Kingdom Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary

ISBN: (纸本)9798350369618

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution. © 2024 IEEE.

关键词： Visualization

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Pulsed Electromagnetic Field Interaction With a Transmission Line: An Analytical Traveling-Wave Approach Based on Reciprocity

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3783-3791页

作者： Stumpf, Martin Antonini, Giulio Lager, Ioan E. Ekman, Jonas The Czech Republic Lerch Laboratory of em Research Department of Radio Electronics Feec Brno University of Technology Technická Brno3082/12 616 00 Czech Republic Luleå University of Technology Department of Computer Science Electrical and Space Engineering Eislab Luleå971 87 Sweden University of l'Aquila UAq Emc Laboratory Department of Industrial and Information Engineering and Economics L'Aquila671 00 Italy Delft University of Technology Terahertz Sensing Group Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands

Pulsed electromagnetic (EM) field signal transfer from a general EM source distribution to a transmission line (TL) is analyzed with the aid of Lorentz's reciprocity theorem. In this fashion, the transient voltage induced by the impulsive EM source is expressed through the EM fields as radiated by the TL. These transmitted EM fields are expressed in closed form using an analytical procedure that resembles the Cagniard-DeHoop (CdH) technique. The validity of the proposed reciprocity-based methodology is verified with the aid of an alternative analytical solution describing the EM field signal transfer excited by an impulsive vertical electric dipole (VED). Illustrative numerical examples are presented. © 1963-2012 IEEE.

关键词： Time domain analysis

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Tensor Graph Decomposition for Temporal Networks

Tensor Graph Decomposition for Temporal Networks

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bishwadeep Das Elvin Isufi Multimedia Computing Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology The Netherlands

Temporal networks arise due to certain dynamics influencing their connections or due to the change in interactions between the nodes themselves, as seen for example in social networks. Such evolution can be algebraically represented by a three-way tensor, which lends itself to using tensor decompositions to study the underpinning factors driving the network evolution. Low rank tensor decompositions have been used for temporal networks but mostly with a focus on downstream tasks and have been seldom used to study the temporal network itself. Here, we use the tensor decomposition to identify a limited number of key mode graphs that can explain the temporal network, and which linear combination can represent its evolution. For this, we put for a novel graph-based tensor decomposition approach where we impose a graph structure on the two modes of the tensor and a smoothness on the temporal dimension. We use these mode graphs to investigate the temporal network and corroborate their usability for network reconstruction and link prediction.

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Scenario Generation by Physical Model-Free Approaches for Multiple Renewables 18

Scenario Generation by Physical Model-Free Approaches for Mu...

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18th International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES 2023

作者： Chen, Hao Jin, Ailing Zhao, Wei Yi, Haoran Zhang, Qixia IEBIS Department of High-tech Business and Entrepreneurship Faculty of Behavioural Management and Social Sciences University of Twente Netherlands BOE Technology Group Co. Ltd China Faculty of Business Administration Turiba University Latvia Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Faculty of Science and Technology UiT the Arctic University of Norway Norway

The augmentation of renewable energy sources within the global energy portfolio is imperative for mitigating the impacts of climate change. Nonetheless, the inherent variability, intermittency, and unpredictability associated with certain forms of renewable energy present significant challenges. Effective integration of these energy sources into existing grids is contingent upon accurate predictions and robust scenario planning. To address this, we introduce a novel data-driven framework that facilitates the generation of energy scenarios without relying on intricate physical models or extensive assumptions. This framework is underpinned by an innovative combination of a grey neural network, which is fine-tuned using a genetic algorithm, and a Gaussian Copula to enhance the prediction accuracy. Extensive experimental analyses validate the effectiveness and advanced capabilities of our proposed model. Moreover, the adaptable nature of this data-driven approach allows for its potential application across various sectors within the sustainable industry, further underscoring its versatility and utility. © Published under licence by IOP Publishing Ltd.

关键词： Renewable energy

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Ontology-Driven IoT System for Monitoring Hypertension 25

Ontology-Driven IoT System for Monitoring Hypertension

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25th International Conference on Enterprise Information Systems, ICEIS 2023

作者： de Souza, Pedro Lopes de Souza, Wanderley Lopes Pires, Luís Ferreira Moreira, João Luiz Rebelo da Silva Rodrigues, Ronitti Juner Ciferri, Ricardo Rodrigues Semantics Cybersecurity & Services Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede Netherlands Ubiquitous Computing Group Graduate Program in Computer Science Computing Department Federal University of São Carlos P.O. Box 676 São Carlos Brazil

ISBN: (纸本)9789897586484

Hypertension is a noncommunicable disease (NCD) that causes global concern, high costs and a high number of deaths. Internet of Things, Ubiquitous Computing, and Cloud Computing enable the development of systems for remote and real-time monitoring of patients affected with NCDs like hypertension. This paper reports on a system for monitoring hypertension patients that was built by employing these techniques. This system allows the vital signs of a patient (blood pressure, heart rate, body temperature) to be captured via sensors built in a wearable device similar to a wristwatch. These signals are transmitted to the patient's mobile device for processing, and the generated clinical data are sent to the cloud to be properly presented and analysed by the health professionals responsible for the patient. To deal with semantic interoperability issues that arise when multiple different devices and system components must interoperate, a semantic model was conceived for this system in terms of ontologies for diseases and devices. This paper also presents the semantic module that we developed and implemented in the cloud to perform reasoning based on this model, demonstrating the potential benefits of incorporating semantic technologies in our system. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Ontology

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Overcoming Common Flaws in the Evaluation of Selective Classification Systems 38

Overcoming Common Flaws in the Evaluation of Selective Class...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Traub, Jeremias Bungert, Till J. Lüth, Carsten T. Baumgartner, Michael Maier-Hein, Klaus H. Maier-Hein, Lena Jäger, Paul F. Heidelberg Interactive Machine Learning Group Germany Helmholtz Imaging DKFZ Heidelberg Germany Germany Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg69120 Germany Faculty of Mathematics and Computer Science University of Heidelberg Germany Heidelberg Germany

Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenarios such as clinical diagnostics. While current evaluation of these systems typically assumes fixed working points based on pre-defined rejection thresholds, methodological progress requires benchmarking the general performance of systems akin to the AUROC in standard classification. In this work, we define 5 requirements for multi-threshold metrics in selective classification regarding task alignment, interpretability, and flexibility, and show how current approaches fail to meet them. We propose the Area under the Generalized Risk Coverage curve (AUGRC), which meets all requirements and can be directly interpreted as the average risk of undetected failures. We empirically demonstrate the relevance of AUGRC on a comprehensive benchmark spanning 6 data sets and 13 confidence scoring functions. We find that the proposed metric substantially changes metric rankings on 5 out of the 6 data sets. © 2024 Neural information processing systems foundation. All rights reserved.

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Architectural Design Decisions for Data Communication of Cyber-Physical Systems

Architectural Design Decisions for Data Communication of Cyb...

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Asia-Pacific Conference on Software Engineering

作者： Amirali Amiri Evangelos Ntentos Uwe Zdun Research Group Software Architecture Faculty of Computer Science University of Vienna Vienna Austria

Designing Cyber-Physical Systems (CPS) is a complex task involving integrating physical and digital components to achieve specific objectives. This process consolidates data from various Internet of Things (IoT) devices and sources to generate meaningful insights and actionable outcomes. IoT-cloud data communication comprises multiple stages, e.g., data collection, processing, analysis, and visualization. Adopting a comprehensive approach that considers physical and digital aspects is essential to ensure effective data communication in CPS. As a result, architectural design choices are crucial in determining CPS functionality and runtime qualities, e.g., performance, security, and reliability. While numerous CPS architectural patterns and practices have been proposed, much of the relevant knowledge remains scattered across various sources, such as practitioner blogs and system documentation. These sources are often based on personal experiences and lack consistency. To address this gap, our study presents the outcomes of an in-depth qualitative investigation into practitioners' descriptions of the best practices and patterns in CPS architecture. We have developed a formal architectural decision model using a model-based qualitative research method. We aim to bridge the division between scientific understanding and practical use cases, enhance comprehension of practitioners' approaches to CPS, and provide decision-making support for designing CPS applications.

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Efficient Parallel Data Optimization for Homogeneous Diffusion Inpainting of 4K Images

arXiv

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arXiv 2024年

作者： Kämper, Niklas Chizhov, Vassillen Weickert, Joachim Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University Saarbrücken66041 Germany

Homogeneous diffusion inpainting can reconstruct missing image areas with high quality from a sparse subset of known pixels, provided that their location as well as their gray or color values are well optimized. This property is exploited in inpainting-based image compression, which is a promising alternative to classical transform-based codecs such as JPEG and JPEG2000. However, optimizing the inpainting data is a challenging task. Current approaches are either fairly slow or do not produce high quality results. As a remedy we propose fast spatial and tonal optimization algorithms for homogeneous diffusion inpainting that efficiently utilize GPU parallelism, with a careful adaptation of some of the most successful numerical concepts. We propose a densification strategy using ideas from error-map dithering combined with a Delaunay triangulation for the spatial optimization. For the tonal optimization we design a domain decomposition solver that solves the corresponding normal equations in a matrix-free fashion and supplement it with a Voronoi-based initialization strategy. With our proposed methods we are able to generate high quality inpainting masks for homogeneous diffusion and optimized tonal values in a runtime that outperforms prior state-of-the-art by a wide *** Codes 65N55, 65D18, 68U10, 94A08 Copyright © 2024, The Authors. All rights reserved.

关键词： Domain decomposition methods

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