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Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy
Murray, Sean Ernest; Quinn, Anthony
2020 - English
This research report outlines a selective transfer approach for Bayesian estimation of patient-specific levels of radioiodine activity in the thyroid during the treatment of differentiated thyroid carcinoma. The work seeks to address some limitations of previous approaches [4] which involve generic, non-selective transfer of archival data. It is proposed that improvements in patient-specific inferences may be achieved via transferring external population knowledge selectively. This involves matching the patient to a similar sub-population based on available metadata, generating a Gaussian Mixture Model within the partitioned data, and optimally transferring a data predictive distribution from the sub-population to the specific patient. Additionally, a performance evaluation method is proposed and early-stage results presented.
Keywords:
Bayesian estimation; thyroid carcinoma; patient-specific inferences
Fulltext is available at external website.
Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy
This research report outlines a selective transfer approach for Bayesian estimation of patient-specific levels of radioiodine activity in the thyroid during the treatment of differentiated thyroid ...
Bayesian transfer learning between autoregressive inference tasks
Barber, Alec; Quinn, Anthony
2020 - English
Bayesian transfer learning typically relies on a complete stochastic dependence speci cation between source and target learners which allows the opportunity for Bayesian conditioning. We advocate that any requirement for the design or assumption of a full model between target and sources is a restrictive form of transfer learning.
Keywords:
autoregression; transfer learning; Fully Probabilistic Design; FPD; food-commodities price prediction
Fulltext is available at external website.
Bayesian transfer learning between autoregressive inference tasks
Bayesian transfer learning typically relies on a complete stochastic dependence speci cation between source and target learners which allows the opportunity for Bayesian conditioning. We advocate that ...
Two limited-memory optimization methods with minimum violation of the previous quasi-Newton equations
Vlček, Jan; Lukšan, Ladislav
2020 - English
Limited-memory variable metric methods based on the well-known BFGS update are widely used for large scale optimization. The block version of the BFGS update, derived by Schnabel (1983), Hu and Storey (1991) and Vlček and Lukšan (2019), satisfies the quasi-Newton equations with all used difference vectors and for quadratic objective functions gives the best improvement of convergence in some sense, but the corresponding direction vectors are not descent directions generally. To guarantee the descent property of direction vectors and simultaneously violate the quasi-Newton equations as little as possible in some sense, two methods based on the block BFGS update are proposed. They can be advantageously combined with methods based on vector corrections for conjugacy (Vlček and Lukšan, 2015). Global convergence of the proposed algorithm is established for convex and sufficiently smooth functions. Numerical experiments demonstrate the efficiency of the new methods.
Keywords:
unconstrained minimization; variable metric methods; limited-memory methods; variationally derived methods; global convergence; numerical results
Available in a digital repository NRGL
Two limited-memory optimization methods with minimum violation of the previous quasi-Newton equations
Limited-memory variable metric methods based on the well-known BFGS update are widely used for large scale optimization. The block version of the BFGS update, derived by Schnabel (1983), Hu and Storey ...
Linear-time Algorithms for Largest Inscribed Quadrilateral
Keikha, Vahideh
2020 - English
Let P be a convex polygon of n vertices. We present a linear-time algorithm for the problem of computing the largest-area inscribed quadrilateral of P. We also design the parallel version of the algorithm with O(log n) time and O(n) work in CREW PRAM model, which is quite work optimal. Our parallel algorithm also computes all the antipodal pairs of a convex polygon with O(log n) time and O(log2n+s) work, where s is the number of antipodal pairs, that we hope is of independent interest. We also discuss several approximation algorithms (both constant factor and approximation scheme) for computing the largest-inscribed k-gons for constant values of k, in both area and perimeter measures.
Keywords:
Maximum-area quadrilateral; extreme area k-gon
Available in a digital repository NRGL
Linear-time Algorithms for Largest Inscribed Quadrilateral
Let P be a convex polygon of n vertices. We present a linear-time algorithm for the problem of computing the largest-area inscribed quadrilateral of P. We also design the parallel version of the ...
Kombinace prediktorů v odhadování parametrů
Podlesna, Yana; Kárný, Miroslav
2020 - Czech
Tato bakalářská práce se zabývá návrhem metody k řešení prokletí rozměrnosti vznikající v kvantitativním modelování složitých vzájemně propojených systémů. Jedná se o předpovídací modely, které jsou založené na diskrétním markovském rozhodovacím procesu. Předpovídání je založeno na odhadu parametrů modelu pomocí bayesovské statistiky. Tato práce obsahuje návod na zmenšení rozměrnosti dat, potřebných k předpovídání v systémech s velkým počtem stavů a akcí. Místo odhadu prediktoru závislého na všech parametrech metoda předpokládá užití několika prediktorů, které vznikají odhadováním parametrických modelů, předpokládajících závislost na různých regresorech. Vlastnosti chování navržené metody jsou ilustrovány simulačními experimenty. This bachelor thesis deals with the design of the method for solving the curse of dimensionality arising in the quantitative modeling of complex interconnected systems. The employed predictive models are based on a discrete Markov process. Prediction is based on estimating model parameters using Bayesian statistics. This work contains method for reducing the amount of data needed for prediction in systems with a large number of occurring states and actions. Instead of estimating a predictor dependent on all parameters, the method assumes the use of several predictors, which arise from estimating parametric models based on dependences on different regressors. The behavioral properties of the proposed method are illustrated by simulation experiments.
Keywords:
curse of dimensionality; Bayesian estimation; prediction; Markov decision process; decision making
Fulltext is available at external website.
Kombinace prediktorů v odhadování parametrů
Tato bakalářská práce se zabývá návrhem metody k řešení prokletí rozměrnosti vznikající v kvantitativním modelování složitých vzájemně propojených systémů. Jedná se o předpovídací modely, které jsou ...
Aktualizace potenciálu větrné energie v České republice z perspektivy roku 2020
Hanslian, David
2020 - Czech
Studie navazuje na předchozí studie realizovatelného potenciálu větrné energie v České republice z let 2008 a 2012. Předpoklady pro odhad budoucích reálných možností větrné energie byly aktualizovány s ohledem na současný a nyní předpokládaný budoucí technologický a společenský vývoj. Dále byly zohledněny nové poznatky ohledně realizovatelnosti, provozu a výroby větrných elektráren. Nad rámec předchozích studií byly diskutovány širší souvislosti ohledně možností a limitů využití tohoto zdroje energie, zejména v oblasti vlivu na klima, integrace větrné energie do elektrické sítě či povolovacích procesů.\nUplatnění: Jedná se o klíčový podklad pro odhad budoucích možností využití větrné energie v České republice.\n The study updates the preceding analyses of wind energy potential of the Czech Republic published in 2008 and 2012. The assumptions for the estimation of the future possibilities of wind energy development were updated with respect to the current and currently assumed future technological and social development. The new findings on the feasibility of realization of wind turbines, operation issues and energy production were taken into account. Beyond the scope of previous studies, the broader context was discusses, considering various limits of wind energy utilization, such as permitting issues, climate impacts or grid integration.
Keywords:
wind energy; wind energy potential; Czech Republic
Fulltext is available at external website.
Aktualizace potenciálu větrné energie v České republice z perspektivy roku 2020
Studie navazuje na předchozí studie realizovatelného potenciálu větrné energie v České republice z let 2008 a 2012. Předpoklady pro odhad budoucích reálných možností větrné energie byly aktualizovány ...
Potential Radioactive Hot Spots Induced by Radiation Accident Being Underway of Atypical Low Wind Meteorological Episodes
Pecha, Petr; Tichý, Ondřej; Pechová, E.
2020 - English
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.
Keywords:
radioactivity; atmospheric dissemination; deposition hot-spots
Fulltext is available at external website.
Potential Radioactive Hot Spots Induced by Radiation Accident Being Underway of Atypical Low Wind Meteorological Episodes
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 ...
DEnFi: Deep Ensemble Filter for Active Learning
Ulrych, Lukáš; Šmídl, Václav
2020 - English
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.
Keywords:
Deep Ensembles; uncertainty; neural networks
Fulltext is available at external website.
DEnFi: Deep Ensemble Filter for Active Learning
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 ...
Globální implicitní funkce
Rohn, Jiří
2020 - Czech
Tento text pochází z roku 1973 a nebyl dosud zveřejněn. Jeho hlavním výsledkem je věta o existenci a jednoznačnosti globální implicitní funkce v Rn. Tomuto výsledku předchází řada pomocných tvrzení.
Keywords:
silně lokální souvislé množiny; iredundantní pokrytí; pokračování implicitní funkce; existence a jednoznačnost; globální implicitní funkce; inverzní zobrazení
Available in a digital repository NRGL
Globální implicitní funkce
Tento text pochází z roku 1973 a nebyl dosud zveřejněn. Jeho hlavním výsledkem je věta o existenci a jednoznačnosti globální implicitní funkce v Rn. Tomuto výsledku předchází řada pomocných tvrzení.
Macroeconomic Responses of Emerging Market Economies to Oil Price Shocks: Analysis by Region and Resource Profile
Togonidze, S.; Kočenda, Evžen
2020 - English
This study employs a vector autoregressive (VAR) model to analyse how oil price shocks affect macroeconomic fundamentals in emerging economies. Findings from existing literature remain inconclusive how macroeconomic variables fare towards shocks, especially in emerging economies. The objective of our study is to uncover if analysis by region (Latin America and the Caribbean, East Asia and the Pacific, Europe, and Central Asia) and resource intensity of economies (oil exporters, oil importers, minerals exporters, and less resource intensive). Our unique approach forms part of our contribution to the literature. We find that Latin America and the Caribbean are least affected by oil price shocks, while in East Asia and the Pacific the response of inflation and interest rate to oil price shocks is positive, and output growth is negative. Our analysis by resource endowment fails to show oil price shocks’ ability to explain huge variations in macroeconomic variables in oil importing economies. Further sensitivity analysis using US interest rates as an alternative source of external shocks to emerging economies establishes a significant response of interest rate responses to US interest rate in Europe and Central Asia, and in inflation in Latin America and the Caribbean. We also find that regardless of resource endowment, the response of output growth and capital to a positive US interest rate shock is negative and significant in EMs. Our results are persuasive that resource intensity and regional factors impact the responsiveness of emerging economies to oil price shocks, thus laying a basis for policy debate.\n
Keywords:
Emerging market economies; Oil price shocks; Output growth; Panel VAR
Fulltext is available at external website.
Macroeconomic Responses of Emerging Market Economies to Oil Price Shocks: Analysis by Region and Resource Profile
This study employs a vector autoregressive (VAR) model to analyse how oil price shocks affect macroeconomic fundamentals in emerging economies. Findings from existing literature remain inconclusive ...
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