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ETG-Kongress 2021: Von Komponenten bis zum Gesamtsystem fur die Energiewende - ETG Congress 2021: From Components to the Overall System for the Energy Turnaround

作者： Wussow, Jonas Babazadeh, Davood Beutel, Vanessa Buchholz, Sebastian Geissendörfer, Stefan Gerlach, Jana Majumdar, Neelopal von Maydell, Karsten Narayan, Anand Hoffmann, Melanie Kahl, Lily Leveringhaus, Thomas Lotz, Marc René Scheunert, Alexandra Baboli, Payam Teimourzadeh Tiemann, Paul Hendrik Huxoll, Nils Werth, Oliver Agert, Carsten Breitner, Michael H. Engel, Bernd Hofmann, Lutz Könemund, Martin Kurrat, Michael Lehnhoff, Sebastian Nieße, Astrid Weyer, Hartmut Technische Universität Braunschweig Elenia Institute for High Voltage Technology and Power Systems Braunschweig Germany Carl von Ossietzky Universität Oldenburg Department of Computing Science Digitalized Energy Systems Group Oldenburg Germany German Aerospace Center Institute of Networked Energy Systems Oldenburg Germany Leibniz Universität Hannover Institute of Computer Science for Business Administration Hannover Germany Leibniz Universität Hannover Institute of Electric Power Systems Electric Power Engineering Section Hannover Germany OFFIS Institute for Information Technology Oldenburg Germany Ostfalia - University of Applied Science Institute of Electrical Systems and Automation Technology Wolfenbüttel Germany Technische Universität Clausthal Institute of German and International Mining and Energy Law Clausthal-Zellerfeld Germany

ISBN: (纸本)9783800755493

Within SiNED research project, several members of the Energy Research Centre of Lower Saxony (Energieforschungszentrum Niedersachsen, EFZN) are working on various issues relating to the future provision of ancillary services and to future congestion management. The questions include energy technology, economic and energy law aspects as well as information and communications technology (ICT) and data. The investigations are based on Lower Saxony and the framework conditions there. The temporal focus of the investigations is the year 2030. © VDE VERLAG GMBH. Berlin. Offenbach

关键词： Electric power transmission networks

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Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

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Medical Image Analysis 2022年 78卷 102396-102396页

作者： Subramaniam, Pooja Kossen, Tabea Ritter, Kerstin Hennemuth, Anja Hildebrand, Kristian Hilbert, Adam Sobesky, Jan Livne, Michelle Galinovic, Ivana Khalil, Ahmed A. Fiebach, Jochen B. Frey, Dietmar Madai, Vince I. CLAIM - Charité Lab for AI in Medicine Charité Universitätsmedizin Berlin Germany Department of Computer Engineering and Microelectronics Computer Vision & Remote Sensing Technical University Berlin Berlin Germany Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine Charité Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Max-von-Laue-Str. 2 Bremen Germany Department VI Computer Science and Media Beuth University of Applied Sciences Berlin Germany Johanna-Etienne-Hospital Neuss Germany Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Mind Brain Body Institute Berlin School of Mind and Brain Humboldt University Berlin Berlin Germany Berlin Institute of Health Berlin Germany School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom QUEST-Center for Transforming Biomedical Research Berlin Institute of Health Charité Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark

关键词： Generative adversarial networks

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UFA-FastSLAM: The New Hybrid of Firefly Algorithm and FastSLAM Algorithm

UFA-FastSLAM: The New Hybrid of Firefly Algorithm and FastSL...

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International Conference on Green Engineering Technology and applied computing 2019, IConGETech2 019 and International Conference on applied computing 2019, ICAC 2019

作者： Musridho, R.J. Hasan, H. Haron, H. Department of Computer Science School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Johor Bahru Malaysia Applied Industrial Analytics Research Group Johor Johor Bahru Malaysia

Simultaneous Localization and Mapping (SLAM) is a challenging and fundamental issue in autonomous mobile robots. One of two main solutions of SLAM problem is FastSLAM. In terms of accuracy, FastSLAM is known to degenerate over time. This work hybridized Firefly Algorithm (FA) and FastSLAM to optimize FastSLAM. But in FA, there is a ranking process which ranked the fireflies based on their light intensity. It caused the output needed by particles in FastSLAM came in different sequence. Thus, the FA in this work is modified before it is applied to FastSLAM, the ranking process is deleted. The result has shown that the modified FA, that is unranked Firefly Algorithm (uFA) has been successfully hybridized with FastSLAM. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Bioluminescence

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The space of equidistant phylogenetic cactuses

arXiv

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arXiv 2021年

作者： Huber, Katharina T. Moulton, Vincent Owen, Megan Spillner, Andreas St. John, Katherine University of East Anglia School of Computing Sciences NorwichNR4 7TJ United Kingdom Lehman College CUNY Department of Mathematics New YorkNY10468 United States Merseburg University of Applied Sciences Merseburg06217 Germany Hunter College CUNY Department of Computer Science New YorkNY10065 United States

We introduce and investigate the space of equidistant X-cactuses. These are rooted, arc weighted, phylogenetic networks with leaf set X, where X is a finite set of species, and all leaves have the same distance from the root. The space contains as a subset the space of ultrametric trees on X that was introduced by Gavryushkin and Drummond. We show that equidistant-cactus space is a CAT(0)-metric space which implies, for example, that there are unique geodesic paths between points. As a key step to proving this, we present a combinatorial result concerning ranked rooted X-cactuses. In particular, we show that such networks can be encoded in terms of a pairwise compatibility condition arising from a poset of collections of pairs of subsets of X that satisfy certain set-theoretic properties. As a corollary, we also obtain an encoding of ranked, rooted X-trees in terms of partitions of X, which provides an alternative proof that the space of ultrametric trees on X is CAT(0). As with spaces of phylogenetic trees, we expect that our results should provide the basis for and new directions in performing statistical analyses for collections of phylogenetic networks with arc lengths. © 2021, CC BY-NC-ND.

关键词： Topology

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Nonlinear feedforward enabling quantum computation

arXiv

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arXiv 2022年

作者： Sakaguchi, Atsushi Konno, Shunya Hanamura, Fumiya Asavanant, Warit Takase, Kan Ogawa, Hisashi Marek, Petr Filip, Radim Yoshikawa, Jun-Ichi Huntington, Elanor Yonezawa, Hidehiro Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Department of Optics Palacký University 17. listopadu 1192/12 Olomouc77146 Czech Republic Centre for Quantum Computation and Communication Technology and Energy and Materials Engineering College of Engineering and Computer Science Australian National University CanberraACT2600 Australia Centre for Quantum Computation and Communication Technology School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

Measurement-based quantum computation with optical time-domain multiplexing is a promising method to realize a quantum computer from the viewpoint of scalability. Fault tolerance and universality are also realizable by preparing appropriate resource quantum states and electro-optical feedforward that is altered based on measurement results. While a linear feedforward has been realized and become a common experimental technique, nonlinear feedforward was unrealized until now. In this paper, we demonstrate that a fast and flexible nonlinear feedforward realizes the essential measurement required for fault-tolerant and universal quantum computation. Using non-Gaussian ancillary states we observed 10% reduction of the measurement excess noise relative to classical vacuum ancilla. © 2022, CC BY.

关键词： Fault tolerance

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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Enhanced Arithmetic Optimization Algorithm for Intrusion Detection in Wireless Sensor Networks

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International Journal of Communication Systems 2025年第10期38卷

作者： Otair, Mohammed Qawaqzeh, Faten Ali Alomari, Saleh Ali Zitar, Raed Abu Migdady, Hazem Saleem, Kashif Smerat, Aseel Alsoud, Anas Ratib Abualigah, Laith Department of Computer Science Faculty of Information Technology Middle East University Amman Jordan School of Information Engineering Sanming University Sanming China Faculty of Science and Information Technology Jadara University Irbid Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology Barka Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman Jordan Centre for Research Impact & Outcome Institute of Engineering and Technology Chitkara University Punjab Rajpura India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq TECH Harvard John A. Paulson School of Engineering & Applied Sciences Harvard University BostonMA United States Computer Science Department Al al-Bayt University Mafraq Jordan

Wireless sensor networks (WSNs) face significant security challenges because of their resource constraints and exposure to malicious attacks. Traditional intrusion detection systems (IDSs) often suffer from low detection accuracy, high false alarm rates, and long processing times. To address these issues, this paper proposes an enhanced IDS framework based on the arithmetic optimization algorithm (AOA) for feature selection, combined with support vector machine (SVM) for classification. A new cost function is introduced to guide the feature selection process and improve model performance. The approach, named AS_IDS, is evaluated on the NSL-KDD dataset, achieving an accuracy of 96.65%, a detection rate of 98.69%, and a false alarm rate of 0.04% using only 15 features, with a significant reduction in execution time. Comparative results with state-of-the-art methods demonstrate the effectiveness and efficiency of the proposed framework in enhancing intrusion detection in WSNs. © 2025 John Wiley & Sons Ltd.

关键词： Cost functions

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Preface to the joint proceedings of the ComplexRec and ImpactRS workshops at ACM RecSys 2020

Preface to the joint proceedings of the ComplexRec and Impac...

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2020 Workshops on Recommendation in Complex Scenarios and the Impact of Recommender Systems, ComplexRec-ImpactRS 2020

作者： Bogers, Toine Koolen, Marijn Petersen, Casper Mobasher, Bamshad Tuzhilin, Alexander Shalom, Oren Sar Jannach, Dietmar Konstan, Joseph A. Aalborg University Department of Communication and Psychology Copenhagen Denmark Royal Netherlands Academy of Arts and Sciences Amsterdam Netherlands SamPension Copenhagen Denmark School of Computing DePaul University ChicagoIL United States New York University Stern School of Business New York CityNY United States Facebook United States University of Klagenfurt Department of Applied Informatics Austria University of Minnesota Department of Computer Science and Engineering MN United States

Recommender systems are widely used in modern online applications, from e-commerce sites over media streaming services to social networks. In academic research, we however often abstract from the specifics of these applications and rely on simplified assumptions such as the availability of past rating data. Furthermore, we mostly focus on predicting to what extent a user will like a certain item, but do not explicitly consider the long-term effects of recommendations on the users' decision-making processes or the expected impact on orgnizations. The 14th ACM Conference on Recommender Systems hosted two workshops which aim to look beyond our often too simplifying assumptions, the Fourth Workshop on Recommendation in Complex Environments and the Second Workshop on the Impact of Recommender Systems. These proceedings describe the specific goals of the workshops and contain the papers that were presented during the online events. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Recommender systems

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