Number of found documents: 1551
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Modeling the spread of loanwords in South-East Asia using sailing navigation software and Bayesian networks
Kratochvíl, F.; Kratochvíl, Václav; Saad, G.; Vomlel, Jiří
2022 - English
A loanword is a word permanently adopted from one language and incorporated into another language without translation. In this paper, we study loanwords in the South-East Asia Archipelago, home to a large number of languages. Our paper is inspired by the works of Hoffmann et al. (2021) Bayesian methods are applied to probabilistic modeling of family trees representing the history of language families and by Haynie et al. (2014) modeling the diffusion of a special class of loanwords, so-called Wanderw ̈orter in languages of Australia, North America, and South America. We assume that in the South-East Asia Archipelago Wanderwörter spread along specific maritime trade routes whose geographical characteristics can help unravel the history of Wanderwörter diffusion in the area. For millennia trade was conducted using sailing ships which were constrained by the monsoon system and in certain areas also by strong sea currents. Therefore rather than the geographical distances, the travel times of sailing ships should be considered as a major factor determining the intensity of contact among cultures. We use sailing navigation software to estimate travel times between different ports and show that the estimated travel times correspond well to the travel times of a Chinese map of the sea trade routes from the early seventeenth century. We model the spread of loanwords using a probabilistic graphical model - a Bayesian network. We design a novel heuristic Bayesian network structure learning algorithm that learns the structure as a union of spanning trees for graphs of all loanwords in the training dataset. We compare this algorithm with BIC optimal Bayesian networks by measuring how well these models predict the true presence/absence of a loanword. Interestingly, Bayesian networks learned by our heuristic spanning tree-based algorithm provide better results than the BIC optimal Bayesian networks. Keywords: loanwords; Bayesian methods; probabilistic graphical model Fulltext is available at external website.
Modeling the spread of loanwords in South-East Asia using sailing navigation software and Bayesian networks

A loanword is a word permanently adopted from one language and incorporated into another language without translation. In this paper, we study loanwords in the South-East Asia Archipelago, home to a ...

Kratochvíl, F.; Kratochvíl, Václav; Saad, G.; Vomlel, Jiří
Ústav teorie informace a automatizace, 2022

Two Composition Operators for Belief Functions Revisited
Jiroušek, Radim; Kratochvíl, Václav; Shenoy, P. P.
2022 - English
In probability theory, compositional models are as powerful as Bayesian networks. However, the relation between belief-function graphical models and the corresponding compositional models is much more complicated due to several reasons. One of them is that there are two composition operators for belief functions. This paper deals with their main properties and presents sufficient conditions under which they yield the same results. Keywords: probability theory; Bayesian networks; belief functions Fulltext is available at external website.
Two Composition Operators for Belief Functions Revisited

In probability theory, compositional models are as powerful as Bayesian networks. However, the relation between belief-function graphical models and the corresponding compositional models is much more ...

Jiroušek, Radim; Kratochvíl, Václav; Shenoy, P. P.
Ústav teorie informace a automatizace, 2022

Modeling COVID Pandemics: Strengths and Weaknesses of Epidemic Models
Šmíd, Martin
2022 - English
We generally discuss modeling the present COVID pandemics. We argue that useful models have to be simple in the first case, yet their uncertainty has to be handled properly. In order to study circumstances of the upcoming wave of infection,\nwe construct a simple stochastic model and present predictions it gives. We conclude that the autumn wave is most likely unavoidable and suggest concentrating to mitigation. Keywords: COVID; Epidemic Models; stochastic model Fulltext is available at external website.
Modeling COVID Pandemics: Strengths and Weaknesses of Epidemic Models

We generally discuss modeling the present COVID pandemics. We argue that useful models have to be simple in the first case, yet their uncertainty has to be handled properly. In order to study ...

Šmíd, Martin
Ústav teorie informace a automatizace, 2022

Analysis of Impact of Covariates Entering Stochastic Optimization Problem
Volf, Petr
2022 - English
In the contribution we study consequences of imperfect information to precision of stochastic optimization solution. In particular, it is assumed that the characteristics of optimization problem are influenced by a set of covariates. This dependence is described via a regression model. Hence, the uncertainty is then caused by statistical estimation of regression parameters. The contribution will analyze several regression model cases, together with their application. Precision of results will be explored, both theoretically as well as with the aid of simulations. Keywords: stochastic optimization; regression model; statistical estimation Fulltext is available at external website.
Analysis of Impact of Covariates Entering Stochastic Optimization Problem

In the contribution we study consequences of imperfect information to precision of stochastic optimization solution. In particular, it is assumed that the characteristics of optimization problem are ...

Volf, Petr
Ústav teorie informace a automatizace, 2022

Jak to v těch datech najít?
Zitová, Barbara; Šorel, Michal
2022 - Czech
Přednáška si klade za cíl seznámit odbornou veřejnost s aktivitami oddělení Zpracování obrazové informace ÚTIA AV ČR v.v.i. v oblasti analýz dat projektu Copernicus. Oddělení se dlouhodobě zabývá vývojem metod digitálního zpracování obrazu a hlubokého učení. Během posledních dvou let vzniklo několik demonstračních studentských prací ve spolupráci s MFF UK a FJFI ČVUT využívajících data z družic Sentinel, jako například rozpoznávání typů plodin z časových řad snímků ze satelitu Sentinel-2, automatická segmentace oblastí podle způsobu využití či typu povrchu pomocí metod strojového učení, přesnější detekce mraků v datech ze Sentinel-2, ve spolupráci s Ústavem pro hydrodynamiku AV ČR postupy pro odhad vlhkosti povrchové vrstvy krajiny z dat družice Sentinel-2 a zvyšování rozlišení tepelných dat Sentinel-3 pomocí metod hlubokého učení. V druhé části budou přiblíženy možnosti aplikace metod vyvinutých pro jiné oblasti (separace zdrojů \ninformace) v DPZ. The lecture aims to introduce the activities of the Image Processing Department of the Institute of Image Information of the CAS in the field of Copernicus data analysis to the professional public. The department has long been involved in the development of digital image processing and deep learning methods. During the last two years, in cooperation with the MFF UK and FJFI CTU, several student demonstration projects using data from Sentinel satellites have been finished, such as crop type recognition from Sentinel-2 time-series images, automatic segmentation of areas by land use or surface type using machine learning methods learning, more accurate cloud detection in Sentinel-2 data, in collaboration with the Institute of Hydrodynamics of the CAS Czech Republic, procedures for estimating landscape surface moisture from Sentinel-2 data and increasing the resolution of Sentinel-3 thermal data using deep learning methods. The second part will present the application of developed methods for other areas in remote sensing. Keywords: remote sensing; satellite imaging; machine learning Fulltext is available at external website.
Jak to v těch datech najít?

Přednáška si klade za cíl seznámit odbornou veřejnost s aktivitami oddělení Zpracování obrazové informace ÚTIA AV ČR v.v.i. v oblasti analýz dat projektu Copernicus. Oddělení se dlouhodobě zabývá ...

Zitová, Barbara; Šorel, Michal
Ústav teorie informace a automatizace, 2022

Diffusion Kalman filtering under unknown process and measurement noise covariance matrices
Vlk, T.; Dedecius, Kamil
2022 - English
The state-of-the-art algorithms for Kalman filtering in agent networks with information diffusion impose the requirement of well-defined state-space models. In particular, they assume that both the process and measurement noise covariance matrices are known and properly set. This is a relatively strong assumption in the signal processing domain. By design, the Kalman filters are rather sensitive to its violation, which may potentially lead to their divergence. In this paper, we propose a novel distributed filtering algorithm with increased robustness under unknown process and measurement noise covariance matrices. It is formulated as a Bayesian variational message passing procedure for simultaneous analytically tractable inference of states and measurement noise covariance matrices. Keywords: Collaborative estimation; State estimation; Variational Bayesian methods Fulltext is available at external website.
Diffusion Kalman filtering under unknown process and measurement noise covariance matrices

The state-of-the-art algorithms for Kalman filtering in agent networks with information diffusion impose the requirement of well-defined state-space models. In particular, they assume that both the ...

Vlk, T.; Dedecius, Kamil
Ústav teorie informace a automatizace, 2022

Recursive mixture estimation with univariate multimodal Poisson variable
Uglickich, Evženie; Nagy, Ivan
2022 - English
Analysis of count variables described by the Poisson distribution is required in many application fields. Examples of the count variables observed per a time unit can be, e.g., number of customers, passengers, road accidents, Internet traffic packet arrivals, bankruptcies, virus attacks, etc. If the behavior of such a variable exhibits a multimodal character, the problem of clustering and classification of incoming count data arises. This issue can touch, for instance, detecting clusters of the different behavior of drivers in traffic flow analysis as well as cyclists or pedestrians. This work focuses on the model-based clustering of Poisson-distributed count data with the help of the recursive Bayesian estimation of the mixture of Poisson components. The aim of the work is to explain the methodology in details with an illustrative simple example, so that the work is limited to the univariate case and static pointer. Keywords: recursive mixture estimation; mixture of Poisson distributions; clustering and classification Fulltext is available at external website.
Recursive mixture estimation with univariate multimodal Poisson variable

Analysis of count variables described by the Poisson distribution is required in many application fields. Examples of the count variables observed per a time unit can be, e.g., number of customers, ...

Uglickich, Evženie; Nagy, Ivan
Ústav teorie informace a automatizace, 2022

On a class of biped underactuated robot models with upper body: Sensitivity analysis of the walking performance
Papáček, Štěpán; Polach, P.; Prokýšek, R.; Anderle, Milan
2022 - English
Biped underactuated robots with an upper body (being a torso) form a subclass of legged robots. This study deals with the walking performance of such class of legged robot models and has been motivated by the need to implement of the previously developed sensor and control algorithms for the real-time movement of the laboratory walking robot, designed and built at the Department of Control Theory of the Institute of Information Theory and Automation (UTIA) of the Czech Academy of Sciences, see Fig. 1 (left). A detailed description of this underactuated walking-like mechanical system (called further UTIA Walking Robot – UWR) is provided in [2] and [5]. The simplest underactuated walking robot hypothetically able to walk is the so-called Compass gait biped walker, alternatively called the Acrobot, see Fig. 1 (right). For a review of underactuated mechanical systems, i.e. systems with fewer actuators than degrees of freedom, which encounter many applications in different fields (e.g., in robotics, in aeronautical and spatial systems, in marine and underwater systems, and in-flexible and mobile systems), see [3]. As follows, we examine the walking performance of parametrized models for different walking regimes and different values of model parameters. More specifically, the sensitivity analysis (i.e., parameter study) of the walking performance with respect to certain design variables (model parameters) is carried out using the software package alaska/MultibodyDynamics. The main attention is attracted to the role of the upper body mass m3 and position lc3, see Fig. 1 (right). Last but not least, having surveyed the mechanics of planar biped robots, our subsequent goal is the analysis of a 3D biped model where lateral balance is either controlled, suppressed or compensated. Keywords: Walking robot; ALASCA; Simulation Fulltext is available at external website.
On a class of biped underactuated robot models with upper body: Sensitivity analysis of the walking performance

Biped underactuated robots with an upper body (being a torso) form a subclass of legged robots. This study deals with the walking performance of such class of legged robot models and has been ...

Papáček, Štěpán; Polach, P.; Prokýšek, R.; Anderle, Milan
Ústav teorie informace a automatizace, 2022

Yield Curve Dynamics and Fiscal Policy Shocks
Kučera, A.; Kočenda, Evžen; Maršál, Aleš
2022 - English
We show that government spending does play a role in shaping the yield curve which has important consequences for the cost of private and government financing. We combine government spending shock identification strategies from the fiscal macro literature with recent advancements in no-arbitrage affine term structure modeling, where we account for time-varying macroeconomic trends in inflation and the equilibrium real interest rate. We stress in our empirical macro-finance framework the importance of timing in the response of yields to government spending. We find that the yield curve responds positively but mildly to a surprise in government spending shocks where the rise in risk-neutral yields is compensated by a drop in nominal term premia. The news shock in expectations about future expenditures decreases yields across all maturities. Complementarily, we also analyze the effect of fiscal policy uncertainty where higher fiscal uncertainty lowers yields. Keywords: Government Expenditures; Fiscal policy; U.S. Treasury Yield Curve; Affine Term Structure Model Fulltext is available at external website.
Yield Curve Dynamics and Fiscal Policy Shocks

We show that government spending does play a role in shaping the yield curve which has important consequences for the cost of private and government financing. We combine government spending shock ...

Kučera, A.; Kočenda, Evžen; Maršál, Aleš
Ústav teorie informace a automatizace, 2022

Characterizing Uncertainty In Decision-Making Models For Maintenance In Industry 4.0
Ahmed, U.; Carpitella, Silvia; Certa, A.
2022 - English
Decision-making involves our daily life at any level, something that entails uncertainty and potential occurrence of risks of varied nature. When dealing with industrial engineering systems, effective decisions are fundamental in terms of maintenance planning and implementation. Specifically, several forms of uncertainty may affect decision-making procedures, for which adopting suitable techniques seems to be a good strategy to attain the main maintenance goals by taking into account system criticality along with decision-maker(s) opinions. A wide variety of factors contributes to uncertainty, being some of them greatly important while other ones less significant. However, all of these factors in synergy can impact the functioning of systems in a positive, neutral, or negative way. In this case, the question is whether obtaining a complete picture of such uncertainty can improve decision-making capabilities and mitigate both through-life costs and unforeseen problems. The fundamental issues include dealing with ambiguity in the maintenance decision-making process by employing numerous evaluation criteria and dealing with real-world scenarios in the maintenance environment. In this study, the Multi-Criteria Decision-Making (MCDM) approach is analysed, with particular reference to the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS), technique capable to effectively rank alternatives while dealing with uncertainty for maintenance decision-making. A final case study is developed to demonstrate the applicability of the method to the field of maintenance in industry 4.0. The proposed study may be useful in supporting intelligent and efficient decisions resulting in favorable maintenance outcomes. Keywords: Decision-Making; Uncertainty; Industry 4.0 Fulltext is available at external website.
Characterizing Uncertainty In Decision-Making Models For Maintenance In Industry 4.0

Decision-making involves our daily life at any level, something that entails uncertainty and potential occurrence of risks of varied nature. When dealing with industrial engineering systems, effective ...

Ahmed, U.; Carpitella, Silvia; Certa, A.
Ústav teorie informace a automatizace, 2022

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