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Cost-Effective Communication in UDN in Indoor and Outdoor Environment via machine learning

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Cost-Effective Communication in UDN in Indoor and Outdoor En...

Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International Conference on

作者： K Nattar Karman V. Velmurugan Kommisetti Murthy Raju T. Sajana V. Vijayalakshmi JoshuvaArockia Dhanraj Departmeru of Artificial Intelligence and Machine Learning Saveetha School of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala R&D Institute of science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women West Godavari Andhra Pradesh India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra Dense cloud Network (UDN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra Dense cloud Network (UDN). The proposed method achieves 93.28% of Cost-effective data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management.

关键词： Costs Mobility models machine learning Knowledge discovery Scheduling Energy efficiency Concurrent engineering

Accurate machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations

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arXiv 2022年

作者： Unke, Oliver T. Stöhr, Martin Ganscha, Stefan Unterthiner, Thomas Maennel, Hartmut Kashubin, Sergii Ahlin, Daniel Gastegger, Michael Sandonas, Leonardo Medrano Tkatchenko, Alexandre Müller, Klaus-Robert Google Research Brain Team Machine Learning Group Technische Universität Berlin Berlin10587 Germany Technische Universität Berlin Berlin10623 Germany Department of Physics and Materials Science University of Luxembourg Luxembourg CityL-1511 Luxembourg BASLEARN TU Berlin Berlin10587 Germany BASF Joint Lab for Machine Learning Technische Universität Berlin Berlin10587 Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of Max Planck Institute for Informatics Stuhlsatzenhausweg Saarbrücken66123 Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin Germany

Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited to short timescales and few atoms. For larger systems, efficient, but much less reliable empirical force fields are used. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations, offering similar accuracy as ab initio methods at orders-of-magnitude speedup. Until now, MLFFs mainly capture short-range interactions in small molecules or periodic materials, due to the increased complexity of constructing models and obtaining reliable reference data for large molecules, where long-ranged many-body effects become important. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations (GEMS) by training on "bottom-up" and "top-down" molecular fragments of varying size, from which the relevant physicochemical interactions can be learned. GEMS is applied to study the dynamics of alanine-based peptides and the 46-residue protein crambin in aqueous solution, allowing nanosecond-scale MD simulations of >25k atoms at essentially ab initio quality. Our findings suggest that structural motifs in peptides and proteins are more flexible than previously thought, indicating that simulations at ab initio accuracy might be necessary to understand dynamic biomolecular processes such as protein (mis)folding, drug–protein binding, or allosteric regulation. Copyright © 2022, The Authors. All rights reserved.

关键词： Molecular dynamics

machine learning of Thermodynamic Observables in the Presence of Mode Collapse 38

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Machine Learning of Thermodynamic Observables in the Presenc...

38th International Symposium on Lattice Field Theory, LATTICE 2021

作者： Nicoli, Kim A. Anders, Christopher J. Funcke, Lena Hartung, Tobias Jansen, Karl Kessel, Pan Nakajima, Shinichi Stornati, Paolo Technische Universität Berlin Machine Learning Group Marchstrasse 23 Berlin10587 Germany Technische Universität Berlin Berlin Germany Center for Theoretical Physics Co-Design Center for Quantum Advantage NSF AI Institute for Artificial Intelligence and Fundamental Interactions Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Perimeter Institute for Theoretical Physics 31 Caroline Street North WaterlooONN2L 2Y5 Canada Computation-Based Science and Technology Research Center The Cyprus Institute 20 Kavafi Street Nicosia2121 Cyprus Department of Mathematical Sciences University of Bath Bath United Kingdom Deutsches Elektronen-Synchrotron DESY Platanenallee 6 Zeuthen15738 Germany RIKEN Center for AIP 1-4-1 Nihonbashi Chuo-ku Tokyo Japan ICFO The Barcelona Institute of Science and Technology Av. Carl Friedrich Gauss 3 Castelldefels Barcelona08860 Spain

Estimating the free energy, as well as other thermodynamic observables, is a key task in lattice field theories. Recently, it has been pointed out that deep generative models can be used in this context [1]. Crucially, these models allow for the direct estimation of the free energy at a given point in parameter space. This is in contrast to existing methods based on Markov chains which generically require integration through parameter space. In this contribution, we will review this novel machine-learning-based estimation method. We will in detail discuss the issue of mode collapse and outline mitigation techniques which are particularly suited for applications at finite temperature. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Free energy

Software for dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy

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arXiv 2021年

作者： Anders, Christopher J. Neumann, David Samek, Wojciech Müller, Klaus-Robert Lapuschkin, Sebastian Machine Learning Group Department of Electrical Engineering and Computer Science Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany Machine Learning and Communications Group Department of Electrical Engineering and Computer Science Technische Universität Berlin Germany Department of Artificial Intelligence Korea University Seoul Korea Republic of Max Planck Institut für Informatik Saarbrücken Germany

Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood. With recent advances in Explainable Artificial Intelligence (XAI), approaches are available to explore the reasoning behind those complex models’ predictions. Among post-hoc attribution methods, Layer-wise Relevance Propagation (LRP) shows high performance. For deeper quantitative analysis, manual approaches exist, but without the right tools they are unnecessarily labor intensive. In this software paper, we introduce three software packages targeted at scientists to explore model reasoning using attribution approaches and beyond: (1) Zennit – a highly customizable and intuitive attribution framework implementing LRP and related approaches in PyTorch, (2) CoRelAy – a framework to easily and quickly construct quantitative analysis pipelines for dataset-wide analyses of explanations, and (3) ViRelAy – a web-application to interactively explore data, attributions, and analysis results. With this, we provide a standardized implementation solution for XAI, to contribute towards more reproducibility in our field. © 2021, CC BY-SA.

关键词： Deep neural networks

Insightful analysis of historical sources at scales beyond human capabilities using unsupervised machine learning and XAI

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arXiv 2023年

作者： Eberle, Oliver Büttner, Jochen El-Hajj, Hassan Montavon, Grégoire Müller, Klaus-Robert Valleriani, Matteo Machine Learning Group Technische Universität Berlin Marchstr. 23 Berlin10587 Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Max Planck Institute for the History of Science Boltzmannstr. 22 Berlin14195 Germany Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institute for Informatics Stuhlsatzenhausweg 4 Saarbrücken66123 Germany Institute of History and Philosophy of Science Technology and Literature Faculty I - Humanities and Educational Sciences Technische Universität Berlin Straße des 17. Juni 135 Berlin10623 Germany The Cohn Institute for the History and Philosophy of Science and Ideas Faculty of Humanities Tel Aviv University P.O.B. 39040 Ramat Aviv Tel Aviv6139001 Israel

Historical materials are abundant. Yet, piecing together how human knowledge has evolved and spread both diachronically and synchronically remains a challenge that can so far only be very selectively addressed. The vast volume of materials precludes comprehensive studies, given the restricted number of human specialists. However, as large amounts of historical materials are now available in digital form there is a promising opportunity for AI-assisted historical analysis. In this work, we take a pivotal step towards analyzing vast historical corpora by employing innovative machine learning (ML) techniques, enabling in-depth historical insights on a grand scale. Our study centers on the evolution of knowledge within the ‘Sacrobosco Collection’ – a digitized collection of 359 early modern printed editions of textbooks on astronomy used at European universities between 1472 and 1650 – roughly 76,000 pages, many of which contain astronomic, computational tables. An ML based analysis of these tables helps to unveil important facets of the spatio-temporal evolution of knowledge and innovation in the field of mathematical astronomy in the period, as taught at European universities. Copyright © 2023, The Authors. All rights reserved.

关键词： machine learning

Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

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arXiv 2024年

作者： Li, Haopeng Deng, Andong Liu, Jun Rahmani, Hossein Guo, Yulan Schiele, Bernt Bennamoun, Mohammed Ke, Qiuhong School of Computing and Information Systems University of Melbourne Australia Center for Research in Computer Vision University of Central Florida United States Pillar Singapore University of Technology and Design Singapore School of Computing and Communications Lancaster University United Kingdom School of Electronics and Communication Engineering Sun Yat-sen University China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Germany School of Physics Maths and Computing University of Western Australia Australia Department of Data Science & AI Monash University Australia

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials

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arXiv 2023年

作者： Lee, Siwoo Heinen, Stefan Khan, Danish Von Lilienfeld, O. Anatole Department of Chemistry University of Toronto St. George campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Acceleration Consortium University of Toronto 80 St George St TorontoONM5S 3H6 Canada Department of Materials Science and Engineering University of Toronto St. George campus TorontoON Canada Department of Physics University of Toronto St. George campus TorontoON Canada Machine Learning Group Technische Universität Berlin Berlin Institute for the Foundations of Learning and Data Berlin Germany

We present an automated data-collection pipeline involving a convolutional neural network and a large language model to extract user-specified tabular data from peer-reviewed literature. The pipeline is applied to 74 reports published between 1957 and 2014 with experimentally-measured oxidation potentials for 592 organic molecules (-0.75-3.58 V). After data curation (solvents, reference electrodes, and missed data points), we trained multiple supervised machine learning models reaching prediction errors similar to experimental uncertainty (∼0.2 V). For experimental measurements of identical molecules reported in multiple studies, we identified the most likely value based on out-of-sample machine learning predictions. Using the trained machine learning models, we then estimated oxidation potentials of ∼132k small organic molecules from the QM9 data set, with predicted values spanning 0.21-3.46 V. Analysis of the QM9 predictions in terms of plausible descriptor-property trends suggests that aliphaticity increases the oxidation potential of an organic molecule on average from ∼1.5 V to ∼2 V, while an increase in number of heavy atoms lowers it systematically. The pipeline introduced offers significant reductions in human labor otherwise required for conventional manual data collection of experimental results, and exemplifies how to accelerate scientific research through automation. © 2023, CC0.

关键词： Pipelines

Emulation of cosmological mass maps with conditional generative adversarial networks

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arXiv 2020年

作者： Perraudin, Nathanaël Marcon, Sandro Lucchi, Aurelien Kacprzak, Tomasz Swiss Data Science Center ETH Zurich Institute for Machine Learning ETH Zurich Institute for Particle Physics and Astrophysics ETH Zurich

Weak gravitational lensing mass maps play a crucial role in understanding the evolution of structures in the universe and our ability to constrain cosmological models. The prediction of these mass maps is based on expensive N-body simulations, which can create a computational bottleneck for cosmological analyses. Simulation-based emulators of map summary statistics, such as the matter power spectrum and its covariance, are starting to play increasingly important role, as the analytical predictions are expected to reach their precision limits for upcoming experiments. Creating an emulator of the cosmological mass maps themselves, rather than their summary statistics, is a more challenging task. Modern deep generative models, such as Generative Adversarial Networks (GAN), have demonstrated their potential to achieve this goal. Most existing GAN approaches produce simulations for a fixed value of the cosmological parameters, which limits their practical applicability. We propose a novel conditional GAN model that is able to generate mass maps for any pair of matter density Ωm and matter clustering strength σ8, parameters which have the largest impact on the evolution of structures in the universe, for a given source galaxy redshift distribution n(z). Our results show that our conditional GAN can interpolate efficiently within the space of simulated cosmologies, and generate maps anywhere inside this space with good visual quality high statistical accuracy. We perform an extensive quantitative comparison of the N-body and GAN -generated maps using a range of metrics: the pixel histograms, peak counts, power spectra, bispectra, Minkowski functionals, correlation matrices of the power spectra, the Multi-Scale Structural Similarity Index (MS-SSIM) and our equivalent of the Fréchet Inception Distance (FID). We find a very good agreement on these metrics, with typical differences are 1 and the data2 publicly available. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes

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Computers in Biology and Medicine 2024年 173卷 108306-108306页

作者： Kazemi, Azar Rasouli-Saravani, Ashkan Gharib, Masoumeh Albuquerque, Tomé Eslami, Saeid Schüffler, Peter J. Department of Medical Informatics School of Medicine Mashhad University of Medical Sciences Mashhad Iran Institute of General and Surgical Pathology Technical University of Munich Munich Germany Student Research Committee Department of Immunology School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran Department of Pathology Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran INESC TEC -Rua Dr. Roberto Frias Porto Portugal Pharmaceutical Sciences Research Center Institute of Pharmaceutical Technology Mashhad University of Medical Sciences Mashhad Iran Department of Medical Informatics University of Amsterdam Amsterdam Netherlands TUM School of Computation Information and Technology Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Munich Germany

The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for treating patients with CRC. While clinical studies showed that TILs improve the host immune response, leading to a better prognosis, inter-observer agreement for quantifying TILs is not perfect. Incorporating machine learning (ML) based applications in clinical routine may promote diagnosis reliability. Recently, ML has shown potential for making progress in routine clinical procedures. We aim to systematically review the TILs analysis based on ML in CRC histological images. Deep learning (DL) and non-DL techniques can aid pathologists in identifying TILs, and automated TILs are associated with patient outcomes. However, a large multi-institutional CRC dataset with a diverse and multi-ethnic population is necessary to generalize ML methods. © 2024 Elsevier Ltd

关键词： Tumors