Ing. Daniel Vašata, Ph.D.

Publikace

Evacuation trials from a double-deck electric train unit: Experimental data and sensitivity analysis

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Najmanová, H.; Kuklík, L.; Pešková, V.; Bukáček, M.; Vašata, D.; Hrabák, P.
Rok
2022
Publikováno
Safety Science. 2022, 146 ISSN 0925-7535.
Typ
Článek
Anotace
Passenger trains represent a challenging environment in emergencies, with specific evacuation conditions resulting from the typical layout and interior design inherent to public transportation vehicles. This paper describes a dataset obtained in a full-scale controlled experiment emulating the emergency evacuation of a double-deck electric unit railcar carried out in Prague in 2018. 15 evacuation trials involving 91 participants were conducted under various evacuation scenarios considering different compositions of passenger crowd, exit widths, and exit types (egress to a high platform, to an open rail line using stairs, and a 750 mm jump without any supporting equipment). The study’s main goals were to collect experimental data on the movement conditions in the railcar and to study the impact of various boundary conditions on evacuation process and total evacuation time. Movement characteristics (exit flows, speeds) and human behaviour (pre-movement activities, exiting behaviours) were also analysed. The data obtained was used to validate and adjust a Pathfinder model to capture important aspects of evacuation from the railcar. Furthermore, a series of simulations using this model was performed to provide sensitivity analysis of the influence of crowd composition, exit width, and exit type on total evacuation time. As a key finding, we can conclude that for the case of a standard exit path (platform or stairs) the width of the main exit had the greatest impact on total evacuation time, however, crowd composition played the prevailing role in evacuation scenarios involving a jump.

Image Inpainting Using Wasserstein Generative Adversarial Imputation Network

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Rok
2021
Publikováno
Artificial Neural Networks and Machine Learning – ICANN 2021. Cham: Springer, 2021. p. 575-586. 1. vol. 12892. ISSN 1611-3349. ISBN 978-3-030-86339-5.
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Stať ve sborníku
Anotace
Image inpainting is one of the important tasks in computer vision which focuses on the reconstruction of missing regions in an image. The aim of this paper is to introduce an image inpainting model based on Wasserstein Generative Adversarial Imputation Network. The generator network of the model uses building blocks of convolutional layers with different dilation rates, together with skip connections that help the model reproduce fine details of the output. This combination yields a universal imputation model that is able to handle various scenarios of missingness with sufficient quality. To show this experimentally, the model is simultaneously trained to deal with three scenarios given by missing pixels at random, missing various smaller square regions, and one missing square placed in the center of the image. It turns out that our model achieves high-quality inpainting results on all scenarios. Performance is evaluated using peak signal-to-noise ratio and structural similarity index on two real-world benchmark datasets, CelebA faces and Paris StreetView. The results of our model are compared to biharmonic imputation and to some of the other state-of-the-art image inpainting methods.

Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network

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Chobola, T.; Vašata, D.; Kordík, P.
Rok
2021
Publikováno
AAAI Workshop on Meta-Learning and MetaDL Challenge. Proceedings of Machine Learning Research, 2021. p. 29-37. vol. 140. ISSN 2640-3498.
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Stať ve sborníku
Anotace
The MetaDL Challenge 2020 focused on image classification tasks in few-shot settings. This paper describes second best submission in the competition. Our meta learning approach modifies the distribution of classes in a latent space produced by a backbone network for each class in order to better follow the Gaussian distribution. After this operation which we call Latent Space Transform algorithm, centers of classes are further aligned in an iterative fashion of the Expectation Maximisation algorithm to utilize information in unlabeled data that are often provided on top of few labelled instances. For this task, we utilize optimal transport mapping using the Sinkhorn algorithm. Our experiments show that this approach outperforms previous works as well as other variants of the algorithm, using K-Nearest Neighbour algorithm, Gaussian Mixture Models, etc.

Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network

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Rok
2020
Publikováno
Computational Science - ICCS 2020. Cham: Springer, 2020. p. 225-239. vol. 12140. ISSN 0302-9743. ISBN 978-3-030-50422-9.
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Stať ve sborníku
Anotace
Missing data is one of the most common preprocessing problems. In this paper, we experimentally research the use of generative and non-generative models for feature reconstruction. Variational Autoencoder with Arbitrary Conditioning (VAEAC) and Generative Adversarial Imputation Network (GAIN) were researched as representatives of generative models, while the denoising autoencoder (DAE) represented non-generative models. Performance of the models is compared to traditional methods k-nearest neighbors (k-NN) and Multiple Imputation by Chained Equations (MICE). Moreover, we introduce WGAIN as the Wasserstein modification of GAIN, which turns out to be the best imputation model when the degree of missingness is less than or equal to 30%. Experiments were performed on real-world and artificial datasets with continuous features where different percentages of features, varying from 10% to 50%, were missing. Evaluation of algorithms was done by measuring the accuracy of the classification model previously trained on the uncorrupted dataset. The results show that GAIN and especially WGAIN are the best imputers regardless of the conditions. In general, they outperform or are comparative to MICE, k-NN, DAE, and VAEAC.

Unsupervised Latent Space Translation Network

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Rok
2020
Publikováno
ESANN 2020 - Proceedings. Louvain la Neuve: Ciaco - i6doc.com, 2020. p. 13-18. ISBN 978-2-87587-074-2.
Typ
Stať ve sborníku
Anotace
One task that is often discussed in a computer vision is the mapping of an image from one domain to a corresponding image in another domain known as image-to-image translation. Currently there are several approaches solving this task. In this paper, we present an enhancement of the UNIT framework that aids in removing its main drawbacks. More specifically, we introduce an additional adversarial discriminator on the latent representation used instead of VAE, which enforces the latent space distributions of both domains to be similar. On MNIST and USPS domain adaptation tasks, this approach greatly outperforms competing approaches.

Missing Features Reconstruction and Its Impact on Classification Accuracy

Rok
2019
Publikováno
Computational Science – ICCS 2019. Springer, Cham, 2019. p. 207-220. vol. 11538. ISBN 978-3-030-22744-9.
Typ
Stať ve sborníku
Anotace
In real-world applications, we can encounter situations when a well-trained model has to be used to predict from a damaged dataset. The damage caused by missing or corrupted values can be either on the level of individual instances or on the level of entire features. Both situations have a negative impact on the usability of the model on such a dataset. This paper focuses on the scenario where entire features are missing which can be understood as a specific case of transfer learning. Our aim is to experimentally research the influence of various imputation methods on the performance of several classification models. The imputation impact is researched on a combination of traditional methods such as k-NN, linear regression, and MICE compared to modern imputation methods such as multi-layer perceptron (MLP) and gradient boosted trees (XGBT). For linear regression, MLP, and XGBT we also propose two approaches to using them for multiple features imputation. The experiments were performed on both real world and artificial datasets with continuous features where different numbers of features, varying from one feature to 50%, were missing. The results show that MICE and linear regression are generally good imputers regardless of the conditions. On the other hand, the performance of MLP and XGBT is strongly dataset dependent. Their performance is the best in some cases, but more often they perform worse than MICE or linear regression.

Cantor spectra of magnetic chain graphs

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Exner, P.; Vašata, D.
Rok
2017
Publikováno
Journal of Physics A: Mathematical and Theoretical. 2017, 50(16), ISSN 1751-8113.
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Článek
Anotace
We demonstrate a one-dimensional magnetic system can exhibit a Cantortype spectrum using an example of a chain graph with δ coupling at the vertices exposed to a magnetic field perpendicular to the graph plane and varying along the chain. If the field grows linearly with an irrational slope, measured in terms of the flux through the loops of the chain, we demonstrate the character of the spectrum relating it to the almost Mathieu operator.

Generalizations of the centroid with an application in stochastic geometry

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Rok
2017
Publikováno
Mathematische Nachrichten. 2017, 290(2-3), 452-473. ISSN 0025-584X.
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Článek
Anotace
The centroid of a subset of math formula with positive volume is a well-known characteristic. An interesting task is to generalize its definition to at least some sets of zero volume. In the presented paper we propose two possible ways how to do that. The first is based on the Hausdorff measure of an appropriate dimension. The second is given by the limit of centroids of ε-neighbourhoods of the particular set when ε goes to 0. For both generalizations we discuss their existence and basic properties. Then we focus on sufficient conditions of existence of the second generalization and on conditions when both generalizations coincide. It turns out that they can be formulated with the help of the Minkowski content, rectifiability, and self-similarity. Since the centroid is often used in stochastic geometry as a centre function for certain particle processes, we present properties that are needed for both generalizations to be valid centre functions. Finally, we also show their continuity on compact convex m-sets with respect to the Hausdorff metric topology.

On long-range dependence of random measures

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Rok
2016
Publikováno
Advances in Applied Probability. 2016, 48(4), 1235-1255. ISSN 0001-8678.
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Článek
Anotace
This paper deals with long-range dependence of random measures on ℝd. By examples, it is demonstrated that one must be careful in order to define it consistently. Therefore, we define long-range dependence by a rather specific second-order condition and provide an equivalent formulation involving the asymptotic behaviour of the Bartlett spectrum near the origin. Then it is shown that the defining condition may be formulated less strictly when the additional isotropy assumption holds. Finally, we present an example of a long-range dependent random measure based on the 0-level excursion set of a Gaussian random field for which the corresponding spectral density and its asymptotics are explicitly derived.