Využití tenchnologie GRID při zpracování medicínské informace

Práce se soustředí na vybrané oblasti biomedicínského výzkumu, které mohou profitovat ze současných výpočetních infrastruktur vybudovaných ve vědecké komunitě v evropském a světovém prostoru. Teorie výpočtu, paralelismu a distribuovaného počítání je stručně uvedena s ohledem na počítání v gridech a cloudech. Práce se zabývá oblastí výměny medicínských snímků a představuje propojení Gridového PACS systému s existujícími distribuovanými systémy pro sdílení DICOM snímků. Práce se dál zaměřuje na studium vědy týkající se lidského hlasu. Práce představuje vzdálený způsob přístupu k aplikaci pro analýzu hlasu v reálném čase pomocí úpravy protokolů pro vzdálenou plochu a pro přenos zvukových nahrávek. Tento dílčí výsledek ukazuje možnost využití stávajících aplikací na dálku specialisty na hlas. Oblast lidské fyziologie a patofyziologie byla studována pomocí přístupu tzv. systémové biologie. Práce přispívá v oblasti metodologie modelování lidské fyziologie pro tvorbu komplexních modelů založených na akauzálním a objektově orientovaném modelovacím přístupu. Metody pro studium parametrů byly představeny pomocí technologie počítání v gridech a v cloudech. Práce ukazuje, že proces identifikaci parametrů středně komplexních modelů kardiovasculárního systému a komplexního modelu lidské fyziologie lze významně zrychlit...This thesis focuses on selected areas of biomedical research in order to benefit from current computational infrastructures established in scientific community in european and global area. The theory of computation, parallelism and distributed computing, with focus on grid computing and cloud computing, is briefly introduced. Exchange of medical images was studied and a seamless integration of grid-based PACS system was established with the current distributed system in order to share DICOM medical images. Voice science was studied and access to real-time voice analysis application via remote desktop technology was introduced using customized protocol to transfer sound recording. This brings a possibility to access current legacy application remotely by voice specialists. The systems biology approach within domain of human physiology and pathophysiology was studied. Modeling methodology of human physiology was improved in order to build complex models based on acausal and object-oriented modeling techniques. Methods for conducting a parameter study (especially parameter estimation and parameter sweep) were introduced using grid computing and cloud computing technology. The identification of parameters gain substantial speedup by utilizing cloud computing deployment when performed on medium complex models of...nezařazení_neaktivníFirst Faculty of Medicine1. lékařská fakult

Structured inference and sequential decision-making with Gaussian processes

Sequential decision-making is a central ability of intelligent agents interacting with an environment, including humans, animals, and animats. When those agents operate in complex systems, they need to be endowed with automatic decision-making frameworks quantifying the system uncertainty and the utility of different actions while allowing them to sequentially update their beliefs about the environment. When agents also aim at manipulating a system, they need to understand the data-generating mechanism. This requires accounting for causality which allows evaluating counterfactual scenarios while increasing interpretability and generalizability of an algorithm. Sequential causal decision making algorithms require an accurate surrogate model for the causal system and an acquisition function that based on its properties allows selecting actions. In this thesis, I tackle both components through the Bayesian framework which enables probabilistic reasoning while handling uncertainty in a principled manner. I consider Gaussian process (gp) models for both inference and causal decision-making as they provide a flexible framework capable of capturing a variety of data distributions. I first focus on developing scalable gp models incorporating structure in the likelihood and accounting for complex dependencies in the posteriors. These are indeed crucial properties of surrogate models used within decision-making algorithms. Particularly, I investigate models for point data as many realworld problems involve events and they present significant computational and methodological challenges. I then study how such models can incorporate causal structure and can be used to select actions based on cause-effect relationships. I focus on multi-task gp models, Bayesian Optimization, and Active Learning and show how they can be generalized to capture causality

Resource-aware motion control:feedforward, learning, and feedback

Controllers with new sampling schemes improve motion systems’ performanc

Dynamics of Long-Life Assets: From Technology Adaptation to Upgrading the Business Model

Knowledge management; Business information system

Meta-Science:Towards a Science of Meaning and Complex Solutions

Science has lost its ethical imperatives as it moved away from a science of ought to a science of is. Subsequently, it might have answers for how we can address global challenges, such as climate change and poverty, but not why we should. This supposedly neutral stance leaves it to politics and religions (in the sense of non-scientific fields of social engagement) to fill in the values. The problem is that through this concession, science implicitly acknowledges that it is not of universal relevance.Objective knowledge, as Karl Popper calls for, might be less easily attainable in the world of ideas and within the confines of scientific idealism. However, if ideas, values and meaning have equal claim to be drivers of change in the sense of causation, aspiring to identify objective knowledge about the world of ideas and of meaning is necessary. If the sciences and disciplines aim to give objectively valid reasons for our actions (and for how to address global challenges), we need to elevate the study of meaning beyond the cultural, disciplinary and ideational delineations. We need to come to a meta understanding of values and meaning equal to objective knowledge about the material world. But differently than in the material world this meta understanding needs to incorporate individual and subjective experiences as cornerstones of objectivity on a meta-level.We need a science of meaning; one that can scientifically answer Kant’s third question of “what may we hope for”

Meta-Science:Towards a Science of Meaning and Complex Solutions

Science has lost its ethical imperatives as it moved away from a science of ought to a science of is. Subsequently, it might have answers for how we can address global challenges, such as climate change and poverty, but not why we should. This supposedly neutral stance leaves it to politics and religions (in the sense of non-scientific fields of social engagement) to fill in the values. The problem is that through this concession, science implicitly acknowledges that it is not of universal relevance.Objective knowledge, as Karl Popper calls for, might be less easily attainable in the world of ideas and within the confines of scientific idealism. However, if ideas, values and meaning have equal claim to be drivers of change in the sense of causation, aspiring to identify objective knowledge about the world of ideas and of meaning is necessary. If the sciences and disciplines aim to give objectively valid reasons for our actions (and for how to address global challenges), we need to elevate the study of meaning beyond the cultural, disciplinary and ideational delineations. We need to come to a meta understanding of values and meaning equal to objective knowledge about the material world. But differently than in the material world this meta understanding needs to incorporate individual and subjective experiences as cornerstones of objectivity on a meta-level.We need a science of meaning; one that can scientifically answer Kant’s third question of “what may we hope for”