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Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India
Nugent, Sh. ; Quinn, Anthony
Existing frameworks for multi-task learning [1],[2] often rely on completely modelled relationships between tasks, which may not be available. Recent work [3], [4] has been undertaken on approaches to fully probabilistic methods for transfer learning between two Gaussian Process (GP) tasks. There, the target algorithm accepts source knowledge in the form of a probabilistic prior from a source algorithm, without requiring the target to model their interaction with the source. These strategies have offered robust improvements on current state of the art algorithms, such as the Intrinsic Coregionalization Model. The Bayesian Transfer Learning algorithm proposed in [4], was found to provide robust, positive\ntransfer. This algorithm was then extended to accommodate knowledge transfer from multiple source modellers [5]. Improved predictive performance was observed from increases in the number of sources. This report reviews the multi-source transfer findings in [5] and applies it to a real world problem of pollution modelling in India, using public-domain data.
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Distributed Sequential Zero-Inflated Poisson Regression
Žemlička, R. ; Dedecius, Kamil
The zero-inflated Poisson regression model is a generalized linear model (GLM) for non-negative count variables with an excessive number of zeros. This letter proposes its low-cost distributed sequential inference from streaming data in networks with information diffusion. The model is viewed as a probabilistic mixture of a Poisson and a zero-located Dirac component, whose probabilities are estimated using a quasi-Bayesian procedure. The regression coefficients are inferred by means of a weighted Bayesian update. The network nodes share their posterior distributions using the diffusion protocol.\n
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Research Report Influence of Vehicle Assistant System on Track keeping
Nedoma, P. ; Herda, Z. ; Plíhal, Jiří
Presented results describe different methods for the evaluation of car stability in lateral direction. Due to the significant differences between the tests, uniform methodology for recognizing the drives with ESC and without it was not determined. Two different methods for the drive on the circle and VDA were proposed instead. For evaluation criteria of vehicle stability with respect to base measured quantities, was used model with weight functions.
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Variational Analysis and Its Applications
Červinka, Michal
Following a longstanding tradition, the Faculty of Mathematics and Physics, Charles University, in cooperation with Institutes of Information Theory and Automation and of Mathematics, Czech Academy of Sciences, and Faculty of Social Sciences, Charles University will organize the Spring School on Variational Analysis VII. The spring school will be held in Paseky nad Jizerou, in a chalet in the Krkonoše Mountains from May 19 to May 25, 2019. The purpose of this meeting is to bring together researchers with common interest in the field. There will be opportunities for informal discussions and short communications.
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Unsupervised Verification of Fake News by Public Opinion
Grim, Jiří
In this paper we discuss a simple way to evaluate the messages in social networks automatically, without any special content analysis or external intervention. We presume, that a large number of social network participants is capable of a relatively reliable evaluation of materials presented in the network. Considering a simple binary evaluation scheme (like/dislike), we propose a transparent algorithm with the aim to increase the voting power of reliable network members by means of weights. The algorithm supports the votes which correlate with the more reliable weighted majority and, in turn, the modified weights improve the quality of the weighted majority voting. In this sense the weighting is controlled only by a general coincidence of voting members while the specific content of messages is unimportant. The iterative optimization procedure is unsupervised and does not require any external intervention with only one exception, as discussed in Sec. 5.2 .\n\nIn simulation experiments the algorithm nearly exactly identifies the reliable members by means of weights. Using the reinforced weights we can compute for a new message the weighted sum of votes as a quantitative measure of its positive or negative nature. In this way any fake news can be recognized as negative and indicated as controversial. The accuracy of the resulting weighted decision making was essentially higher than a simple majority voting and has been considerably robust with respect to possible external manipulations.\n\nThe main motivation of the proposed algorithm is its application in a large social network. The content of evaluated messages is unimportant, only the related decision making of participants is registered and compared with the weighted vote with the aim to identify the most reliable voters. A large number of participants and communicated messages should enable to design a reliable and robust weighted voting scheme. Ideally the resulting weighted vote should provide a generally acceptable emotional feedback for network participants and could be used to indicate positive or controversial news in a suitably chosen quantitative way. The optimization algorithm has to be simple, transparent and intuitive to make the weighted vote well acceptable as a general evaluation tool.\n
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A NUMERICAL METHOD FOR THE SOLUTION OF THE NONLINEAR OBSERVER PROBLEM
Rehák, Branislav
The central part in the process of solving the observer problem for nonlinear systems is to nd a solution of a partial differential equation of first order. The original method proposed to solve this equation used expansions into Taylor polynomials, however, it suffers from rather restrictive assumptions while the approach proposed here allows to generalize these requirements. Its characteristic feature is that it is based on the application of the Finite Element\nMethod. An illustrating example is provided.
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REGULATORY NETWORK OF DRUG-INDUCED ENZYME PRODUCTION: PARAMETER ESTIMATION BASED ON THE PERIODIC DOSING RESPONSE MEASUREMENT
Papáček, Štěpán ; Lynnyk, Volodymyr ; Rehák, Branislav
The common goal of systems pharmacology, i.e. systems biology applied to the eld of pharmacology, is to rely less on trial and error in designing an input-output systems, e.g. therapeutic schedules. In this paper we present, on the paradigmatic example of a regulatory network of drug-induced enzyme production, the further development of the study published by Duintjer Tebbens et al. (2019) in the Applications of Mathematics. Here, the key feature is that the nonlinear model in form of an ODE system is controlled (or periodically forced) by an input signal being a drug intake. Our aim is to test the model features under both periodic and nonrecurring dosing, and eventually to provide an innovative method for a parameter estimation based on the periodic dosing response measurement.
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DEnFi: Deep Ensemble Filter for Active Learning
Ulrych, Lukáš ; Šmídl, Václav
Deep Ensembles proved to be a one of the most accurate representation of uncertainty for deep neural networks. Their accuracy is beneficial in the task of active learning where unknown samples are selected for labeling based on the uncertainty of their prediction. Underestimation of the predictive uncertainty leads to poor exploration of the method. The main issue of deep ensembles is their computational cost since multiple complex networks have to be computed in parallel. In this paper, we propose to address this issue by taking advantage of the recursive nature of active learning. Specifically, we propose several methods how to generate initial values of an ensemble based of the previous ensemble. We provide comparison of the proposed strategies with existing methods on benchmark problems from Bayesian optimization and active classification. Practical benefits of the approach is demonstrated on example of learning ID of an IoT device from structured data using deep-set based networks.
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ECB monetary policy and commodity prices
Aliyev, S. ; Kočenda, Evžen
We analyze the impact of the ECB monetary policies on global aggregate and sectoral commodity prices using monthly data from January 2001 till August 2019. We employ a SVAR model and assess separately period of conventional monetary policy before global financial crisis (GFC) and unconventional monetary policy during post-crisis period. Our key results indicate that contractionary monetary policy shocks have positive effects on the aggregate and sectoral commodity prices during both conventional and unconvetional monetary policy periods. The effect is statistically significant for aggregate commodity prices during post-crisis period. In terms of sectoral impact, the effect is statistically significant for food prices in both periods and for fuel prices during post-crisis period; other commodities display positive but statistically insignificant responses. Further, we demonstrate that the impact of the ECB monetary policy on commodity prices increased remarkably after the GFC. Our results also suggest that the effect of the ECB monetary policy on commodity prices does not transmit directly through market demand and supply expectations channel, but rather through the exchange rate channel that influences the European market demand directly.
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Potential Radioactive Hot Spots Induced by Radiation Accident Being Underway of Atypical Low Wind Meteorological Episodes
Pecha, Petr ; Tichý, Ondřej ; Pechová, E.
Hypothetical radioactivity release with potentially high variability of the source strength is examined. The interactions of the radioactive cloud with surface and atmospheric precipitation are studied and possible adverse consequences on the environment are estimated. The worst-case scenario is devised in two stages starting with a calm meteorological situation succeeded by wind. At the first stage, the discharges of radionuclides into the motionless ambient atmosphere are assumed. During several hours of this calm meteorological situation, a relatively significant level of radioactivity can be accumulated around the source. At the second stage, the calm is assumed to terminate and convective movement of the air immediately starts. The pack of accumulated radioactivity in the form of multiple Gaussian puffs is drifted by wind and pollution is disseminated over the terrain. The results demonstrate the significant transport of radioactivity even behind the protective zone of a nuclear facility (up to between 15 and 20 km). In the case of rain, the aerosols are heavily washed out and dangerous hot spots of the deposited radioactivity can surprisingly emerge even far from the original source of the pollution.
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