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  1. Article ; Online: Analogies and Relations between Non-Additive Entropy Formulas and Gintropy.

    Biró, Tamás S / Telcs, András / Jakovác, Antal

    Entropy (Basel, Switzerland)

    2024  Volume 26, Issue 3

    Abstract: We explore formal similarities and mathematical transformation formulas between general trace-form entropies and the Gini index, originally used in quantifying income and wealth inequalities. We utilize the notion of gintropy introduced in our earlier ... ...

    Abstract We explore formal similarities and mathematical transformation formulas between general trace-form entropies and the Gini index, originally used in quantifying income and wealth inequalities. We utilize the notion of gintropy introduced in our earlier works as a certain property of the Lorenz curve drawn in the map of the tail-integrated cumulative population and wealth fractions. In particular, we rediscover Tsallis'
    Language English
    Publishing date 2024-02-22
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e26030185
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A Note on Representational Understanding.

    Jakovác, Antal / Telcs, András

    Entropy (Basel, Switzerland)

    2022  Volume 24, Issue 9

    Abstract: In this paper, we explore a new approach in which understanding is interpreted as a set representation. We prove that understanding/representation, finding the appropriate coordination of data, is equivalent to finding the minimum of the representational ...

    Abstract In this paper, we explore a new approach in which understanding is interpreted as a set representation. We prove that understanding/representation, finding the appropriate coordination of data, is equivalent to finding the minimum of the representational entropy. For the control of the search for the correct representation, we propose a loss function as a combination of the representational entropy, type one and type two errors. Computational complexity estimates are presented for the process of understanding and using the representation found.
    Language English
    Publishing date 2022-09-17
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e24091313
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Facilitating time series classification by linear law-based feature space transformation.

    Kurbucz, Marcell T / Pósfay, Péter / Jakovác, Antal

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 18026

    Abstract: The aim of this paper is to perform uni- and multivariate time series classification tasks with linear law-based feature space transformation (LLT). First, LLT is used to separate the training and test sets of instances. Then, it identifies the governing ...

    Abstract The aim of this paper is to perform uni- and multivariate time series classification tasks with linear law-based feature space transformation (LLT). First, LLT is used to separate the training and test sets of instances. Then, it identifies the governing patterns (laws) of each input sequence in the training set by applying time-delay embedding and spectral decomposition. Finally, it uses the laws of the training set to transform the feature space of the test set. These calculation steps have a low computational cost and the potential to form a learning algorithm. For the empirical study of LLT, a widely used human activity recognition database called AReM is employed. Based on the results, LLT vastly increases the accuracy of traditional classifiers, outperforming state-of-the-art methods after the proposed feature space transformation is applied. The fastest error-free classification on the test set is achieved by combining LLT and the k-nearest neighbor (KNN) algorithm while performing fivefold cross-validation.
    Language English
    Publishing date 2022-10-27
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-22829-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: LLT

    Kurbucz, Marcell T. / Pósfay, Péter / Jakovác, Antal

    An R package for Linear Law-based Feature Space Transformation

    2023  

    Abstract: The goal of the linear law-based feature space transformation (LLT) algorithm is to assist with the classification of univariate and multivariate time series. The presented R package, called LLT, implements this algorithm in a flexible yet user-friendly ... ...

    Abstract The goal of the linear law-based feature space transformation (LLT) algorithm is to assist with the classification of univariate and multivariate time series. The presented R package, called LLT, implements this algorithm in a flexible yet user-friendly way. This package first splits the instances into training and test sets. It then utilizes time-delay embedding and spectral decomposition techniques to identify the governing patterns (called linear laws) of each input sequence (initial feature) within the training set. Finally, it applies the linear laws of the training set to transform the initial features of the test set. These steps are performed by three separate functions called trainTest, trainLaw, and testTrans. Their application requires a predefined data structure; however, for fast calculation, they use only built-in functions. The LLT R package and a sample dataset with the appropriate data structure are publicly available on GitHub.

    Comment: 15 pages, 5 figures, 1 table
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Mathematical Software ; Statistics - Machine Learning ; 62H30 ; 68T10 ; 62M10 ; 60-04 ; I.5 ; G.3 ; J.0 ; I.2.0
    Subject code 000
    Publishing date 2023-04-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Predicting the Price Movement of Cryptocurrencies Using Linear Law-based Transformation

    Kurbucz, Marcell T. / Pósfay, Péter / Jakovác, Antal

    2023  

    Abstract: The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies. To do this, the 1-minute interval price data of ... ...

    Abstract The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies. To do this, the 1-minute interval price data of Bitcoin, Ethereum, Binance Coin, and Ripple between 1 January 2019 and 22 October 2022 were collected from the Binance cryptocurrency exchange. Then, 14-hour nonoverlapping time windows were applied to sample the price data. The classification was based on the first 12 hours, and the two classes were determined based on whether the closing price rose or fell after the next 2 hours. These price data were first transformed with the LLT, then they were classified by traditional machine learning algorithms with 10-fold cross-validation. Based on the results, LLT greatly increased the accuracy for all cryptocurrencies, which emphasizes the potential of the LLT algorithm in predicting price movements.

    Comment: Manuscript: 9 pages, 1 figure, 1 table; Supplementary material: 33 pages, 64 figures
    Keywords Quantitative Finance - Statistical Finance ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Quantitative Finance - Computational Finance ; 68T10 ; 91B84 ; 62M10 ; 91-08 ; I.5.4 ; I.2.0 ; G.3 ; J.4
    Publishing date 2023-04-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Transport coefficients in non-quasiparticle systems

    Jakovác A.

    EPJ Web of Conferences, Vol 13, p

    2011  Volume 06004

    Abstract: Transport coefficients, in particular the shear viscosity to entropy density ratio are studied in systems where the small-width quasiparticle assumption is not valid. It is found that η/s has no universal lower bound, the minimal value depends on the ... ...

    Abstract Transport coefficients, in particular the shear viscosity to entropy density ratio are studied in systems where the small-width quasiparticle assumption is not valid. It is found that η/s has no universal lower bound, the minimal value depends on the system and the temperature, and can be even zero. We construct models where the 1/4π conjectured bound is violated.
    Keywords Physics ; QC1-999 ; Science ; Q ; DOAJ:Physics (General) ; DOAJ:Physics and Astronomy
    Language English
    Publishing date 2011-04-01T00:00:00Z
    Publisher EDP Sciences
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Understanding understanding

    Jakovac, A. / Berenyi, D. / Posfay, P.

    a renormalization group inspired model of (artificial) intelligence

    2020  

    Abstract: This paper is about the meaning of understanding in scientific and in artificial intelligent systems. We give a mathematical definition of the understanding, where, contrary to the common wisdom, we define the probability space on the input set, and we ... ...

    Abstract This paper is about the meaning of understanding in scientific and in artificial intelligent systems. We give a mathematical definition of the understanding, where, contrary to the common wisdom, we define the probability space on the input set, and we treat the transformation made by an intelligent actor not as a loss of information, but instead a reorganization of the information in the framework of a new coordinate system. We introduce, following the ideas of physical renormalization group, the notions of relevant and irrelevant parameters, and discuss, how the different AI tasks can be interpreted along these concepts, and how the process of learning can be described. We show, how scientific understanding fits into this framework, and demonstrate, what is the difference between a scientific task and pattern recognition. We also introduce a measure of relevance, which is useful for performing lossy compression.

    Comment: 15 pages, 3 figures
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; High Energy Physics - Theory
    Subject code 501
    Publishing date 2020-10-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Learning ECG signal features without backpropagation

    Pósfay, Péter / Kurbucz, Marcell T. / Kovács, Péter / Jakovác, Antal

    2023  

    Abstract: Representation learning has become a crucial area of research in machine learning, as it aims to discover efficient ways of representing raw data with useful features to increase the effectiveness, scope and applicability of downstream tasks such as ... ...

    Abstract Representation learning has become a crucial area of research in machine learning, as it aims to discover efficient ways of representing raw data with useful features to increase the effectiveness, scope and applicability of downstream tasks such as classification and prediction. In this paper, we propose a novel method to generate representations for time series-type data. This method relies on ideas from theoretical physics to construct a compact representation in a data-driven way, and it can capture both the underlying structure of the data and task-specific information while still remaining intuitive, interpretable and verifiable. This novel methodology aims to identify linear laws that can effectively capture a shared characteristic among samples belonging to a specific class. By subsequently utilizing these laws to generate a classifier-agnostic representation in a forward manner, they become applicable in a generalized setting. We demonstrate the effectiveness of our approach on the task of ECG signal classification, achieving state-of-the-art performance.

    Comment: 28 pages, 1 figure, 1 table
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Statistics - Applications ; Statistics - Machine Learning ; 62H30 ; 68T10 ; 62M10 ; 92C50 ; J.3 ; I.5 ; I.2.0 ; G.3
    Subject code 006
    Publishing date 2023-07-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Reconstruction of observed mechanical motions with Artificial Intelligence tools

    Jakovac, Antal / Kurbucz, Marcell T. / Posfay, Peter

    2022  

    Abstract: The goal of this paper is to determine the laws of observed trajectories assuming that there is a mechanical system in the background and using these laws to continue the observed motion in a plausible way. The laws are represented by neural networks ... ...

    Abstract The goal of this paper is to determine the laws of observed trajectories assuming that there is a mechanical system in the background and using these laws to continue the observed motion in a plausible way. The laws are represented by neural networks with a limited number of parameters. The training of the networks follows the Extreme Learning Machine idea. We determine laws for different levels of embedding, thus we can represent not only the equation of motion but also the symmetries of different kinds. In the recursive numerical evolution of the system, we require the fulfillment of all the observed laws, within the determined numerical precision. In this way, we can successfully reconstruct both integrable and chaotic motions, as we demonstrate in the example of the gravity pendulum and the double pendulum.

    Comment: 19 pages, 8 figures
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 629
    Publishing date 2022-02-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Editorial

    Lohbeck, M.W.M. / Rother, Débora Cristina / Jakovac, A.C.

    Frontiers in Forests and Global Change

    Enhancing Natural Regeneration to Restore Landscapes

    2021  Volume 4

    Keywords assisted natural regeneration ; land use ; management ; natural regeneration ; restoration ; secondary succession
    Language English
    Publishing country nl
    Document type Article ; Online
    ISSN 2624-893X
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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