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Universal structure of objective states in all fundamental causal theories

Physical Review Research 2021年第3期3卷 033148-033148页

作者： Carlo Maria Scandolo Roberto Salazar Jarosław K. Korbicz Paweł Horodecki Department of Mathematics & Statistics University of Calgary T2N 1N4 Calgary Alberta Canada Institute for Quantum Science and Technology University of Calgary T2N 1N4 Calgary Alberta Canada Institute of Informatics National Quantum Information Centre Faculty of Mathematics Physics and Informatics University of Gdańsk 80-308 Gdańsk Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University 30-348 Kraków Poland Center for Theoretical Physics Polish Academy of Sciences 02-668 Warsaw Poland International Centre for Theory of Quantum Technologies University of Gdańsk 80-308 Gdańsk Poland Faculty of Applied Physics and Mathematics National Quantum Information Centre Gdańsk University of Technology 80-233 Gdańsk Poland

A crucial question is how objective and classical behavior arises from a fundamental physical theory. Here we provide a natural definition of a decoherence process valid in all causal theories and show how its behavior can be extremely different from the quantum one. Remarkably, despite this, we prove that the so-called spectrum broadcast structure characterizes all objective states in every fundamental causal theory, exactly as in quantum mechanics. Our results show a stark contrast between the extraordinarily diverse decoherence behavior and the universal features of objectivity.

关键词： Open quantum systems & decoherence Quantum formalism Quantum foundations Quantum theory Quantum-to-classical transition

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Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

SSRN

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SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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An Enhanced Algorithm for Matching Arabic Names Entered by Mobile Phones

An Enhanced Algorithm for Matching Arabic Names Entered by M...

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First International Conference of Intelligent Computing and Engineering (ICOICE)

作者： Salah Al-Hagree Sarah Abdulmalik Muneer Alsurori Maher Al-Sanabani Ibb University Yemen Faculty of Computer Science and Information Systems Thamar University Yemen

ISBN: (数字)9781728144870

ISBN: (纸本)9781728144887

The field of matching Arabic names is a serious challenge when dealing with medical data, especially in decision making through social networking programs while using the mobile phone. Although many algorithms have been proposed in matching Arabic names, most studies focus on errors introduced by computers through the keyboard. To our knowledge, there is no study on matching the Arabic names entered by mobile phones. So in this work, we propose a new algorithm based on the convergence and spacing between the Arabic characters in the keyboard of the mobile phone. The experimental results show that the proposed algorithm give more accurate results than the levenshtein algorithm.

关键词： Mobile handsets Keyboards Classification algorithms Semantics Heuristic algorithms Twitter

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sbi reloaded: a toolkit for simulation-based inference workflows

arXiv

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arXiv 2024年

作者： Boelts, Jan Deistler, Michael Gloeckler, Manuel Tejero-Cantero, Álvaro Lueckmann, Jan-Matthis Moss, Guy Steinbach, Peter Moreau, Thomas Muratore, Fabio Linhart, Julia Durkan, Conor Vetter, Julius Miller, Benjamin Kurt Herold, Maternus Ziaeemehr, Abolfazl Pals, Matthijs Gruner, Theo Bischoff, Sebastian Krouglova, Anastasia N. Gao, Richard Lappalainen, Janne K. Mucsányi, Bálint Pei, Felix Schulz, Auguste Stefanidi, Zinovia Rodrigues, Pedro L.C. Schröder, Cornelius Zaid, Faried Abu Beck, Jonas Kapoor, Jaivardhan Greenberg, David S. Gonçalves, Pedro J. Macke, Jakob H. Machine Learning in Science University of Tübingen Germany Tübingen AI Center Germany TransferLab AppliedAI Institute for Europe Germany ML Colab Cluster ML in Science University of Tübingen Germany Google Research United States Helmholtz-Zentrum Dresden-Rossendorf Germany Université Paris-Saclay INRIA CEA Palaiseau France Robert Bosch GmbH Germany School of Informatics University of Edinburgh United Kingdom University of Amsterdam Netherlands Research and Innovation Center BMW Group Germany Institute for Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Germany Aix Marseille INSERM INS France TU Darmstadt Hessian.AI Germany University Hospital Tübingen M3 Research Center Germany Faculty of Science KU Leuven B-3000 Belgium Imec Belgium Methods of Machine Learning University of Tübingen Germany Neuroscience Institute Carnegie Mellon University United States Université Grenoble Alpes INRIA CNRS Grenoble INP LJK France Hertie Institute for AI in Brain Health University of Tübingen Germany Institute of Coastal Systems - Analysis and Modeling Helmholtz AI Germany Departments of Computer Science Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data. © 2024, CC BY-SA.

关键词： Neural networks

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Group-level selection avoids the tragedy of the commons

arXiv

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arXiv 2020年

作者： Hintze, Arend Staudacher, Jochen Gelhar, Katja Pothmann, Alexander Rasch, Juliana Wildegger, Daniel Dalarna University Institute for Complex Dynamical Systems and MicroData Analytics Sweden BEACON Center for the Study of Evolution in Action Michigan State University United States Kempten University of Applied Sciences Faculty of Computer Science Germany

The public goods game is a famous example illustrating the tragedy of the commons1. In this game cooperating individuals contribute to a pool, which in turn is distributed to all members of the group, including defectors who reap the same rewards as cooperators without having made a contribution before. The question is now, how to incentivize group members to all cooperate as it maximizes the common good. While costly punishment2 presents one such method, the cost of punishment still reduces the common good. Here we show how group-level selection can be such an incentive, and specifically how even fractions of group-level selection can overcome the benefits defectors receive. Further, we show how punishment and group-level selection interact. This work suggests that a redistribution similar to a basic income that is coupled to the economic success of the entire group could overcome the tragedy of the commons. Copyright © 2020, The Authors. All rights reserved.

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Testing Environment for ADAS Software Solutions

Testing Environment for ADAS Software Solutions

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Davor Barić Ratko Grbić Miloš Subotić Velibor Mihić RT-RK Institute for Computer Based Systems Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Kneza Trpimira 2B Osijek Croatia Faculty of Technical Sciences Novi Sad Trg Dositeja Obradovića 6 Novi Sad Serbia RT-RK Institute for Computer Based Systems Narodnog Fronta 23a Novi Sad Serbia

ISBN: (数字)9781728182599

ISBN: (纸本)9781728182605

Nowadays software testing in the automotive industry is a very important step in overall software development. The work in this paper is based on a pre-existing special testing environment for Advanced Driver Assistance systems (ADAS) software solutions which automates the process of testing as much as possible. Within the paper, this environment was analyzed and some of the shortcomings were identified. An alternative solution is proposed which, instead of storing tests in one large dictionary, uses an SQLite database as a storage method. Before integration into the existing environment, a standalone solution was developed. After that, efforts were made to integrate such a solution into the existing environment. While the proposed standalone solution obtains smaller processing time regarding test adding in comparison with existing solution, after integration into the existing environment, the proposed solution obtains slightly higher processing time. However, the proposed approach provides security, robustness and stability of the entire environment with respect to data storage.

关键词： Databases Testing Dictionaries Accidents Autonomous vehicles Python

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Retraction Note: Improving the reconstruction of dental occlusion using a reconstructed-based identical matrix point technique

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 261-261页

作者： Anil, Sukumaran Vellappally, Sajith Al Kheraif, Abdulaziz A. Divakar, Darshan Devang Said, Wael Hassanein, Azza S. Department of Dentistry Oral Health Institute Hamad Medical Corporation Doha Qatar College of Dental Medicine Qatar University Doha Qatar Dental Health Department Dental Biomaterials Research Chair College of Applied Medical Sciences King Saud University Riyadh Saudi Arabia Computer Science Department Faculty of Computers and Information Zagazig University Zagazig Egypt Biomedical Engineering Department Faculty of Engineering Helwan University Cairo Egypt

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SpinalXNet: Transfer Learning with Modified Fully Connected Layer for X-Ray Image Classification

SpinalXNet: Transfer Learning with Modified Fully Connected ...

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Recent Advances in systems science and Engineering (RASSE), IEEE International Conference on

作者： Keshav Kumar Sadia Khanam Md Mahbub Islam Bhuiyan Mohammad Reza Chalak Qazani Subrota Kumar Mondal Houshyar Asadi H M Dipu Kabir Abbas Khorsavi Saeid Nahavandi Centre of Energy Excellence Gyancity Research Lab Motihari India Dhaka Dental College Dhaka Bangladesh Smartify Pty Ltd Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Australia Faculty of Information Technology Macau University of Science and Technology Macao Harvard Paulson School of Engineering and Applied Sciences Harvard University Allston USA

ISBN: (纸本)9781665434423

Over the past year, COVID-19 has become a global pandemic and people across the globe have suffered a lot from this pandemic. The rate of transmitting the coronavirus in people is very quick. A rapid diagnosis can potentially help governments in identifying the pattern of transmission. There are some tests available but those tests take a long time to give the report. So, in this work, we have proposed a model that will distinguish between normal people, COVID affected people, and pneumonia affected people with the help of an X-ray. X-ray images are considered because taking an X-ray image is very little time-consuming. In this work, we have trained the X-ray images with a novel Deep Learning approach with SpinalNet architecture, and that distinguishes normal people, COVID affected people, and pneumonia affected people. After training the model we have achieved a very good accuracy for the SpinalNet architecture that is 96.12% while the traditional model provides 95.50% accuracy. We present precision, recall, and F1 scores of COVID and Pneumonia classes. We also present our results and codes with execution details. This paper contains the link to Kaggle notebooks with execution details. The applied Spinalnet transfer learning code is available in our GitHub repository: https://***/dipuk0506/SpinalNet

关键词： COVID-19 Training Codes Pandemics Pulmonary diseases Transfer learning Government

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Hcarepilot: Healthcare Copilot in the World of Hallucinated Large Language Models

SSRN

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SSRN 2025年

作者： Sachdeva, Shelly Shankar, Saumil Soni, Kanika Wróblewska, Anna Paprzycki, Marcin Ganzha, Maria Department of Computer Science and Engineering National Institute of Technology Delhi Plot No. FA7Zone P1 GT Karnal Road Delhi110036 India Faculty of Mathematics and Information Science Warsaw University of Technology ul. Koszykowa 75 Warsaw00-662 Poland Systems Research Institute Polish Academy of Sciences ul.Newelska 6 Warsaw01-447 Poland

In the evolving landscape of healthcare, there is a growing need for advanced support for medical professionals, to deliver high-quality, personalized care. While traditional medical practices have served well, they are challenged by the complexities of modern medicine, from the vast amounts of medical data to the demand for faster, more accurate diagnoses and treatments. As the healthcare sector looks to innovate, one promising area is application of Large Language Models (LLMs), which have shown conversational capabilities across multiple domains. In this context, LLMs have demonstrated the ability to process complex medical data, generate treatment recommendations, and enhance communication between patients and healthcare providers. However, utilizing LLMs in healthcare confronts important challenges including hallucinations, accuracy of information, and handling of sensitive *** contribution introduces HCarePilot, a novel co-pilot system, designed to bridge the gap between traditional medical practices and the possibilities offered by LLMs. HCarePilot aims at developing comprehensive LLM-based medical support, capable of analyzing complex medical data, offering personalised treatment recommendations, and minimizing *** leverages transformers and attention mechanisms, while incorporating vector databases to enhance the retrieval of the relevant medical ***, the Gale Encyclopedia of Medicine was used as the primary dataset. This dataset served as the foundation for the RAG and for the evaluation of the performance of multiple retrieval and language models. Specifically, after processing 30 prompts of varying complexity, the results of HCarePilot, have been compared with other medical-based LLMs, i.e. *** and Ada Health. The comparisons were made across multiple metrics, including relevance score, F1 score, BLEU score, and METEOR score. Based on the relevance score, HCarePilot demonstrated superior performance, outperforming

关键词： Theranostics

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Urban-rural relations in renewable electric energy supply – The case of a German energy region

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International Journal of Sustainable Energy Planning and Management 2019年 21卷 93-110页

作者： Möller, Caroline Faulstich, Martin Rosenberger, Sandra Faculty of Engineering and Computer Science Osnabrück University of Applied Sciences Albrechtstr. 30 Osnabrück49076 Germany Transformation of Energy Systems Reiner Lemoine Institut gGmbH Rudower Chaussee 12 Berlin12489 Germany Clausthal University of Technology Leibnizstraße 28 Clausthal-Zellerfeld38678 Germany

So-called energy regions are one main driver in the transition towards 100% renewables on a local level. With their ambitious goals they strive for energy self-sufficiency based on their renewables potential. The model region consists of three municipalities (two rural regions and a medium-sized city) with the joint goal of 100% renewable electrical power supply in annual average by 2030. Based on the region’s development path, this study predicts time-resolved renewable production and electrical demand profiles, including a sensitivity analysis on demand and generation profiles. In both rural regions renewable power production will exceed electrical demand while the city can only cover 27% of its power demand in 2030. The transition to renewable electricity supply of the city thus depends on its rural hinterlands. Synergetic cross-linking of urban and rural regions increases the total renewable electricity supply to 60 or 70%, depending on the size of the rural region considered. Seen from the perspective of rural regions cross-linkage to a city decreases the possible self-sufficiency compared to considering them as single regions. They can act as energy suppliers for neighbouring cities in the future. © 2019, Aalborg University press. All rights reserved.

关键词： Sensitivity analysis

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