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arXiv 2022年

作者： Saez, Juan Manuel Gorriz Clemente, R. Martin Puntonet, C.G. Ortiz, A. Ramirez, J. Suckling, John Dasci Institute University of Granada Granada Spain Dpt. Signal Theory and Communications University of Seville Seville Spain Dpt. Computer Architecture and Tech. University of Granada Granada Spain Dpt. Communication Engineering University of Malaga Malaga Spain Department of Psychiatry University of Cambridge Cambridge United Kingdom

There remains an open question about the usefulness and the interpretation of Machine learning (MLE) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these approaches have limited their operation to feature extraction and linear classification tasks for between-group inference. In this context, statistical inference is assessed by randomly permuting image labels or by the use of random effect models that consider between-subject variability. These multivariate MLE-based statistical pipelines, whilst potentially more effective for detecting activations than hypotheses-driven methods, have lost their mathematical elegance, ease of interpretation, and spatial localization of the ubiquitous General linear Model (GLM). Recently, the estimation of the conventional GLM has been demonstrated to be connected to an univariate classification task when the design matrix is expressed as a binary indicator matrix. In this paper we explore the complete connection between the univariate GLM and MLE regressions. To this purpose we derive a refined statistical test with the GLM based on the parameters obtained by a linear Support Vector Regression (SVR) in the inverse problem (SVR-iGLM). Subsequently, random field theory (RFT) is employed for assessing statistical significance following a conventional GLM benchmark. Experimental results demonstrate how parameter estimations derived from each model (mainly GLM and SVR) result in different experimental design estimates that are significantly related to the predefined functional task. Moreover, using real data from a multisite initiative the proposed MLE-based inference demonstrates statistical power and the control of false positives, outperforming the regular GLM. © 2022, CC BY.

关键词： Magnetic resonance imaging

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Results From a Large-Scale IoT-based Intervention for Energy Saving and Sustainability Awareness via Behavior Change in 25 K-12 Schools in Europe

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IEEE Internet of Things Journal 2025年

作者： Mylonas, Georgios Amaxilatis, Dimitrios Paganelli, Federica Chatzigiannakis, Ioannis Koulouris, Pavlos Falkouskaya, Yelizaveta Markopoulos, Panos Athena Research Center Industrial Systems Institute Patras26504 Greece Comp. Tech. Institute and Press "Diophantus" 26504 Greece Spark Works Ltd GalwayH91 AHR1 Ireland Univ. of Pisa Dpt. of Computer Science Pisa56127 Italy CNIT Florence50134 Italy Sapienza University of Rome Dpt. of Computer Control and Management Engineering 00185 Italy Ellinogermaniki Agogi Athens Greece Eindhoven University of Technology AI Systems Institute Industrial Design Noord-Brabant Eindhoven5612 AE Netherlands

The unfolding climate crisis has resulted in a rising interest for increasing sustainability awareness and achieving energy savings worldwide. Several interventions within educational environments have been aimed at mobilizing younger audiences towards such goals. However, most of the interventions carried out so far are based on a subset of possible tools (e.g., IoT monitoring, gamification) and offer "all or nothing"approaches, which do not cope well with the uniqueness of schools and the need to adapt interventions to specific contexts (e.g. school size, building, curriculum, teacher involvement). We present a large-scale intervention in 25 school buildings in Europe over the course of two school years. Our intervention was based on the installation of a relatively low-cost IoT infrastructure in schools, that produced real-time energy-related data from the school buildings involved, and the application of a strategy combining, in a flexible and customizable way, educational activities, monitoring tools, gamification, and competition to motivate behavior change and achieve energy savings. Overall, 2983 students and 204 educators were directly involved to a variable degree of participation. Our results indicate a positive overall result in terms of short-term energy savings in most schools involved, with an average reduction of 20-25% in the energy that could be affected by end-users, as well as a 32.9% increase after the intervention in students self-reporting good or very good sustainability awareness. These findings suggest that such interventions can be a valuable step towards educating young students regarding sustainability and towards sustainable schools, if designed and implemented properly. © 2014 IEEE.

关键词： Behavior Change Education for Sustainable Development Energy Literacy Energy Saving Environmental Education Gamification Internet of Things K-12 Sustainability Sustainable Smart Schools

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Patient-specific models from inter-patient biological models and clinical records 14

Patient-specific models from inter-patient biological models...

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Formal Methods in computer Aided Design (FMCAD)

作者： E. Tronci T. Mancini I. Salvo S. Sinisi F. Mari I. Melatti A. Massini F. Davì T. Dierkes R. Ehrig S. Röblitz B. Leeners T.H.C. Krüger M. Egli F Ille Computer Science Department Sapienza University of Rome Computational Systems Biology Group Zuse Institute Berlin Division of Reproductive Endocrinology University Hospital Zurich Dpt. of Psychiatry Social Psychiatry and Psychotherapy Hannover Medical School CC Aerospace Biomedical Science & Tech. Luzern Univ. of Appl. Sciences & Arts

ISBN: (纸本)9780983567844

One of the main goals of systems biology models in a health-care context is to individualise models in order to compute patient-specific predictions for the time evolution of species (e.g., hormones) concentrations. In this paper we present a statistical model checking based approach that, given an inter-patient model and a few clinical measurements, computes a value for the model parameter vector (model individualisation) that, with high confidence, is a global minimum for the function evaluating the mismatch between the model predictions and the available measurements. We evaluate effectiveness of the proposed approach by presenting experimental results on using the GynCycle model (describing the feedback mechanisms regulating a number of reproductive hormones) to compute patient-specific predictions for the time evolution of blood concentrations of E2 (Estradiol), P4 (Progesterone), FSH (Follicle-Stimulating Hormone) and LH (Luteinizing Hormone) after a certain number of clinical measurements.

关键词： Computational modeling Biological system modeling Barium Predictive models Evolution (biology) Biochemistry

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Clustering approach for the classificarion of SPECT images

Clustering approach for the classificarion of SPECT images

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2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008

作者： Lassl, A. Górriz, J.M. Ramírez, J. Salas-Gonzalez, D. Puntonet, C.G. Lang, E.W. Dpt.of Signal Theory and Communications University of Granada Spain Dpt. of Computer Architecture and Tech. University of Granada Spain Computational Intelligence and Machine Learning Group University of Regensburg Germany

ISBN: (纸本)9781424427154

We present a method to cluster the information contained in 3-dimensional brain images where each cluster incorporates a contiguous brain region with similar activation. The grey-level distribution of a brain image is approximated by a sum of Gaussian functions and the parameters of the Gaussian mixture are determined by a maximum likelihood criterion via the expectation maximization (EM) algorithm. Each cluster, therefore, is represented by a multivariate Gaussian function with a definite centre coordinate and a certain shape. This approach leads to a drastic compression of the information contained in the brain image and serves as a starting point for a variety of possible feature extraction methods for the diagnosis of brain diseases. © 2008 IEEE.

关键词： Maximum principle

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Clustering approach for the classificarion of SPECT images

Clustering approach for the classificarion of SPECT images

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： A. Lassl J. M. Gorriz J. Ramirez D. Salas-Gonzalez C. G. Puntonet E. W. Lang Dpt. of Signal Theory and Communications University of Granada Spain Dpt. of Computer Architecture and Tech. University of Granada Spain Computational Intelligence and Machine Learning Group University of Regensburg Germany

关键词： Brain Alzheimer's disease Clustering algorithms Magnetic resonance imaging Positron emission tomography Image reconstruction Electronic mail Probability distribution Gaussian distribution Histograms

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A survey of forecasting preprocessing tech.iques using RNs

A survey of forecasting preprocessing techniques using RNs

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作者： Górriz, J.M. Segura-Luna, J.C. Puntonet, C.G. Salmerón, M. Dpt. Signal Theory Daniel Saucedo s/n E-18071 Dpt. Architecture and Computer Tech. Daniel Saucedo E-18071

In this paper we make a survey of various preprocessing tech.iques including the statistical method for volatile time series forecasting using Regularization Networks (RNs). These methods improve the performance of Regularization Networks i.e. using Independent Component Analysis (ICA) algorithms and filtering as preprocessing tools. The preprocessed data is introduced into a Regularized Artificial Neural Network (ANN) based on radial basis functions (RBFs) and the prediction results are compared with the ones we get without these preprocessing tools, with the high computational effort method based on multidimensional regularization networks (MRN) and with the Principal Component Analysis (PCA) tech.ique.

关键词： Information science

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Maximum Entropy Guide for BSS

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AIP Conference Proceedings 2005年第1期803卷 282-289页

作者： J. M. Górriz C. G. Puntonet E. G. Medialdea F. Rojas *Dpt. Signal theory Fuente nueva s/n 19071 Granada Spain †Dpt. Architecture and Computer Tech. Daniel Saucedo s/n Granada Spain

This paper proposes a novel method for Blindly Separating unobservable independent component (IC) Signals (BSS) based on the use of a maximum entropy guide (MEG). The paper also includes a formal proof on the convergence of the proposed algorithm using the guiding operator, a new concept in the genetic algorithm (GA) scenario. The Guiding GA (GGA) presented in this work, is able to extract IC with faster rate than the previous ICA algorithms, based on maximum entropy contrast functions, as input space dimension increases. It shows significant accuracy and robustness than the previous approaches in any case.

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"Lexifanis" a lexical analyzer of modern greek 2

"Lexifanis" a lexical analyzer of modern greek

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2nd Conference of the European Chapter of the Association for Computational Linguistics, EACL 1985

作者： Kotsanis, Yannis Maistros, Yanis Computer Sc. Dpt. National Tech. University Heroon Polytechniou 9 AthensGR - 157 73 Greece

Lexifanis" is a Software Tool designed and implemented by the authors to analyze Modern Greek Language This system assigns grammatical lasses (parts of speech) to 95-98% of the words of a text which is read and normalized by the computer. By providing the system with the appropriate grammatical knowledge ( i . e . : dictionaries of non-inflected words affixation morphology and limited surface syntax rules ) any "variant" of Modern Greek Language (dialect or idiom) can be processed. In designing the system, special consideration is given to the Greek Language morphological characteristics, primarily to the inflection and the accentuation. In Linguistics, Lexifanis, can assist the generation of indexes or lemmata;on the other hand readability or style analysis can be performed using this software as a basic component. In Word Processing this software may serve as a background to build dictionaries for a spelling checking and error detection package. Through this study our research group has set the basis in designing an expert system " which is intended to "understand" and process Modern Greek texts. Lexifanis is the first working tool for Modern Greek Language. © EACL *** right reserved.

关键词： Expert systems

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