Pub Date : 2022-04-14DOI: 10.1007/978-3-031-08754-7_23
Jitendra Kumar Samriya, Mohit Kumar, M. Ganzha, M. Paprzycki, M. Bolanowski, A. Paszkiewicz
{"title":"An Energy Aware Clustering Scheme for 5G-enabled Edge Computing based IoMT Framework","authors":"Jitendra Kumar Samriya, Mohit Kumar, M. Ganzha, M. Paprzycki, M. Bolanowski, A. Paszkiewicz","doi":"10.1007/978-3-031-08754-7_23","DOIUrl":"https://doi.org/10.1007/978-3-031-08754-7_23","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121803538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-12DOI: 10.1007/978-3-031-08760-8_46
D. Bernholdt, M. Doucet, William F. Godoy, Addi Malviya-Thakur, G. R. Watson
{"title":"A Survey on Sustainable Software Ecosystems to Support Experimental and Observational Science at Oak Ridge National Laboratory","authors":"D. Bernholdt, M. Doucet, William F. Godoy, Addi Malviya-Thakur, G. R. Watson","doi":"10.1007/978-3-031-08760-8_46","DOIUrl":"https://doi.org/10.1007/978-3-031-08760-8_46","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115133313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-02DOI: 10.48550/arXiv.2204.00872
Julia Nasiadka, W. Nitka, R. Weron
We employ a recently proposed change-point detection algorithm, the Narrowest-Over-Threshold (NOT) method, to select subperiods of past observations that are similar to the currently recorded values. Then, contrarily to the traditional time series approach in which the most recent $tau$ observations are taken as the calibration sample, we estimate autoregressive models only for data in these subperiods. We illustrate our approach using a challenging dataset - day-ahead electricity prices in the German EPEX SPOT market - and observe a significant improvement in forecasting accuracy compared to commonly used approaches, including the Autoregressive Hybrid Nearest Neighbors (ARHNN) method.
{"title":"Calibration window selection based on change-point detection for forecasting electricity prices","authors":"Julia Nasiadka, W. Nitka, R. Weron","doi":"10.48550/arXiv.2204.00872","DOIUrl":"https://doi.org/10.48550/arXiv.2204.00872","url":null,"abstract":"We employ a recently proposed change-point detection algorithm, the Narrowest-Over-Threshold (NOT) method, to select subperiods of past observations that are similar to the currently recorded values. Then, contrarily to the traditional time series approach in which the most recent $tau$ observations are taken as the calibration sample, we estimate autoregressive models only for data in these subperiods. We illustrate our approach using a challenging dataset - day-ahead electricity prices in the German EPEX SPOT market - and observe a significant improvement in forecasting accuracy compared to commonly used approaches, including the Autoregressive Hybrid Nearest Neighbors (ARHNN) method.","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116271939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1007/978-3-031-08760-8_11
S. Narayanan, T. Propson, Marcelo Bongarti, Jan Hueckelheim, P. Hovland
{"title":"Reducing Memory Requirements of Quantum Optimal Control","authors":"S. Narayanan, T. Propson, Marcelo Bongarti, Jan Hueckelheim, P. Hovland","doi":"10.1007/978-3-031-08760-8_11","DOIUrl":"https://doi.org/10.1007/978-3-031-08760-8_11","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126136095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.48550/arXiv.2203.12730
David Lenz, Raine Yeh, V. Mahadevan, I. Grindeanu, T. Peterka
B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to be approximated are not uniformly distributed. Model regularization (i.e., smoothing) has traditionally been used to minimize these oscillations; unfortunately, it is sometimes impossible to sufficiently remove unwanted artifacts without smoothing away key features of the data set. In this article, we present a method of model regularization that preserves significant features of a data set while minimizing artificial oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations.
{"title":"Adaptive Regularization of B-Spline Models for Scientific Data","authors":"David Lenz, Raine Yeh, V. Mahadevan, I. Grindeanu, T. Peterka","doi":"10.48550/arXiv.2203.12730","DOIUrl":"https://doi.org/10.48550/arXiv.2203.12730","url":null,"abstract":"B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to be approximated are not uniformly distributed. Model regularization (i.e., smoothing) has traditionally been used to minimize these oscillations; unfortunately, it is sometimes impossible to sufficiently remove unwanted artifacts without smoothing away key features of the data set. In this article, we present a method of model regularization that preserves significant features of a data set while minimizing artificial oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations.","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130109425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-03DOI: 10.1007/978-3-031-08760-8_22
Tomasz 'Smierzchalski, Lukasz Pawela, Z. Puchała, Tomasz Trzci'nski, Bartłomiej Gardas
{"title":"Post-error Correction for Quantum Annealing Processor Using Reinforcement Learning","authors":"Tomasz 'Smierzchalski, Lukasz Pawela, Z. Puchała, Tomasz Trzci'nski, Bartłomiej Gardas","doi":"10.1007/978-3-031-08760-8_22","DOIUrl":"https://doi.org/10.1007/978-3-031-08760-8_22","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123010702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-02DOI: 10.48550/arXiv.2203.00980
Grzegorz Dudek
Time series forecasting is a challenging problem particularly when a time series expresses multiple seasonality, nonlinear trend and varying variance. In this work, to forecast complex time series, we propose ensemble learning which is based on randomized neural networks, and boosted in three ways. These comprise ensemble learning based on residuals, corrected targets and opposed response. The latter two methods are employed to ensure similar forecasting tasks are solved by all ensemble members, which justifies the use of exactly the same base models at all stages of ensembling. Unification of the tasks for all members simplifies ensemble learning and leads to increased forecasting accuracy. This was confirmed in an experimental study involving forecasting time series with triple seasonality, in which we compare our three variants of ensemble boosting. The strong points of the proposed ensembles based on RandNNs are extremely rapid training and pattern-based time series representation, which extracts relevant information from time series.
{"title":"Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting","authors":"Grzegorz Dudek","doi":"10.48550/arXiv.2203.00980","DOIUrl":"https://doi.org/10.48550/arXiv.2203.00980","url":null,"abstract":"Time series forecasting is a challenging problem particularly when a time series expresses multiple seasonality, nonlinear trend and varying variance. In this work, to forecast complex time series, we propose ensemble learning which is based on randomized neural networks, and boosted in three ways. These comprise ensemble learning based on residuals, corrected targets and opposed response. The latter two methods are employed to ensure similar forecasting tasks are solved by all ensemble members, which justifies the use of exactly the same base models at all stages of ensembling. Unification of the tasks for all members simplifies ensemble learning and leads to increased forecasting accuracy. This was confirmed in an experimental study involving forecasting time series with triple seasonality, in which we compare our three variants of ensemble boosting. The strong points of the proposed ensembles based on RandNNs are extremely rapid training and pattern-based time series representation, which extracts relevant information from time series.","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132372928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-17DOI: 10.1007/978-3-031-08751-6_32
Richard J Clancy, M. Menickelly, J. Hückelheim, P. Hovland, Prani Nalluri, R. Gjini
{"title":"TROPHY: Trust Region Optimization Using a Precision Hierarchy","authors":"Richard J Clancy, M. Menickelly, J. Hückelheim, P. Hovland, Prani Nalluri, R. Gjini","doi":"10.1007/978-3-031-08751-6_32","DOIUrl":"https://doi.org/10.1007/978-3-031-08751-6_32","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128769810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-01DOI: 10.1007/978-3-030-77961-0_4
K. Małecki, M. Kamiński, Jarosław Wąs
{"title":"A Multi-cell Cellular Automata Model of Traffic Flow with Emergency Vehicles: Effect of a Corridor of Life","authors":"K. Małecki, M. Kamiński, Jarosław Wąs","doi":"10.1007/978-3-030-77961-0_4","DOIUrl":"https://doi.org/10.1007/978-3-030-77961-0_4","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123535876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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