Infectious diseases, including influenza, measles, and sexually transmitted diseases, spread from person to person. Different attempts have been made to modify or extend traditional epidemic models to relax homogeneity assumptions, so as to handle more complex and realistic situations. We propose a network-based approach to the modeling and prediction of infectious disease outbreaks. Our focus is on heterogeneous populations where there is variation in individual susceptibility, infectivity, and person-to-person contact patterns. To address the complexity of disease propagation over a contact network, we develop a Bayesian survival model that maps the network onto a latent space and uses latent positions to predict disease transmission. We present an R package (`epinet') implementation of our methods and an application to a high school contact network. The package uses C code to implement an MCMC algorithm to efficiently estimate parameters and predict disease outcomes. Our application involves contact data collected by mobile sensors distributed to individuals, and provides estimates of disease transmission in line with the network structure. In it, we address issues that are of direct interest to public health professionals, such as prediction of future outbreaks of diseases. Questions such as whether quarantine will help mitigate an outbreak can also be explored using our proposed model.

No description available.

Statistical modelling has become a ubiquitous tool for analysing of morphological variation of bone structures in medical images. For radiological images, the shape, relative pose between the bone structures and the intensity distribution are key features often modelled separately. A wide range of research has reported methods that incorporate these features as priors for machine learning purposes. Statistical shape, appearance (intensity profile in images) and pose models are popular priors to explain variability across a sample population of rigid structures. However, a principled and robust way to combine shape, pose and intensity features has been elusive for four main reasons: 1) heterogeneity of the data (data with linear and non-linear natural variation across features); 2) sub-optimal representation of three-dimensional Euclidean motion; 3) artificial discretization of the models; and 4) lack of an efficient transfer learning process to project observations into the latent space. This work proposes a novel statistical modelling framework for multiple bone structures. The framework provides a latent space embedding shape, pose and intensity in a continuous domain allowing for new approaches to skeletal joint analysis from medical images. First, a robust registration method for multi-volumetric shapes is described. Both sampling and parametric based registration algorithms are proposed, which allow the establishment of dense correspondence across volumetric shapes (such as tetrahedral meshes) while preserving the spatial relationship between them. Next, the framework for developing statistical shape-kinematics models from in-correspondence multi-volumetric shapes embedding image intensity distribution, is presented. The framework incorporates principal geodesic analysis and a non-linear metric for modelling the spatial orientation of the structures. More importantly, as all the features are in a joint statistical space and in a continuous domain; this permits on-demand marginalisation to a region or feature of interest without training separate models. Thereafter, an automated prediction of the structures in images is facilitated by a model-fitting method leveraging the models as priors in a Markov chain Monte Carlo approach. The framework is validated using controlled experimental data and the results demonstrate superior performance in comparison with state-of-the-art methods. Finally, the application of the framework for analysing computed tomography images is presented. The analyses include estimation of shape, kinematic and intensity profiles of bone structures in the shoulder and hip joints. For both these datasets, the framework is demonstrated for segmentation, registration and reconstruction, including the recovery of patient-specific intensity profile. The presented framework realises a new paradigm in modelling multi-object shape structures, allowing for probabilistic modelling of not only shape, but also relative pose and intensity as well as the correlations that exist between them. Future work will aim to optimise the framework for clinical use in medical image analysis.

Statistical modelling has become a ubiquitous tool for analysing of morphological variation of bone structures in medical images. For radiological images, the shape, relative pose between the bone structures and the intensity distribution are key features often modelled separately. A wide range of research has reported methods that incorporate these features as priors for machine learning purposes. Statistical shape, appearance (intensity profile in images) and pose models are popular priors to explain variability across a sample population of rigid structures. However, a principled and robust way to combine shape, pose and intensity features has been elusive for four main reasons: 1) heterogeneity of the data (data with linear and non-linear natural variation across features); 2) sub-optimal representation of three-dimensional Euclidean motion; 3) artificial discretization of the models; and 4) lack of an efficient transfer learning process to project observations into the latent space. This work proposes a novel statistical modelling framework for multiple bone structures. The framework provides a latent space embedding shape, pose and intensity in a continuous domain allowing for new approaches to skeletal joint analysis from medical images. First, a robust registration method for multi-volumetric shapes is described. Both sampling and parametric based registration algorithms are proposed, which allow the establishment of dense correspondence across volumetric shapes (such as tetrahedral meshes) while preserving the spatial relationship between them. Next, the framework for developing statistical shape-kinematics models from in-correspondence multi-volumetric shapes embedding image intensity distribution, is presented. The framework incorporates principal geodesic analysis and a non-linear metric for modelling the spatial orientation of the structures. More importantly, as all the features are in a joint statistical space and in a continuous domain; this permits on-demand marginalisation to a region or feature of interest without training separate models. Thereafter, an automated prediction of the structures in images is facilitated by a model-fitting method leveraging the models as priors in a Markov chain Monte Carlo approach. The framework is validated using controlled experimental data and the results demonstrate superior performance in comparison with state-of-the-art methods. Finally, the application of the framework for analysing computed tomography images is presented. The analyses include estimation of shape, kinematic and intensity profiles of bone structures in the shoulder and hip joints. For both these datasets, the framework is demonstrated for segmentation, registration and reconstruction, including the recovery of patient-specific intensity profile. The presented framework realises a new paradigm in modelling multi-object shape structures, allowing for probabilistic modelling of not only shape, but also relative pose and intensity as well as the correlations that exist between them. Future work will aim to optimise the framework for clinical use in medical image analysis.

No description available.

Variational autoencoders (VAEs) are a neural network architecture broadly used in image generation (Doersch 2016). VAEs are neural network models that encode data from some domain and project it into a latent space (Doersch 2016). In doing so, the resulting encoding space goes from being a discrete distribution of vectors to a series of continuous manifolds. The latent space is subject to a Gaussian prior, giving the space some convenient properties for the distribution of said manifolds. Several strategies have been presented to try to disentangle said latent space to force each of their dimensions to have an interpretable meaning, for example, 𝛽-VAE, Factor-VAE, 𝛽-TCVAE. In this thesis, some previous VAE models for NaturalLanguage Processing (like Park and Lee (2021), and Bowman et al. (2015), where they finetune pretrained transformer models so they behave as VAEs, and where they used recurrent neural network language model to create a VAEs model that generates sentences in the continuous latent space, respectively) are combined with these disentangling techniques, to show if we can find any understandable meaning in the associated dimensions. The obtained results indicate that the techniques cannot be applied to text-based data without causing the model to suffer from posterior collapse.

The aim of this thesis is to provide methods for 2D segmentation and 2D/3D tracking, that are both fast and robust to imperfect image information, as caused for example by occlusions, motion blur and cluttered background. We do this by combining high level shape information with simultaneous segmentation and tracking. We base our work on the assumption that the space of possible 2D object shapes can be either generated by projecting down known rigid 3D shapes or learned from 2D shape examples. We minimise the discrimination between statistical foreground and background appearance models with respect to the parameters governing the shape generative process (the 6 degree-of-freedom 3D pose of the 3D shape or the parameters of the learned space). The foreground region is delineated by the zero level set of a signed distance function, and we define an energy over this region and its immediate background surroundings based on pixel-wise posterior membership probabilities. We obtain the differentials of this energy with respect to the parameters governing shape and conduct searches for the correct shape using standard non-linear minimisation techniques. This methodology first leads to a novel rigid 3D object tracker. For a known 3D shape, our optimisation here aims to find the 3D pose that leads to the 2D projection that best segments a given image. We extend our approach to track multiple objects from multiple views and propose novel enhancements at the pixel level based on temporal consistency. Finally, owing to the per pixel nature of much of the algorithm, we support our theoretical approach with a real-time GPU based implementation. We next use our rigid 3D tracker in two applications: (i) a driver assistance system, where the tracker is augmented with 2D traffic sign detections, which, unlike previous work, allows for the relevance of the traffic signs to the driver to be gauged and (ii) a robust, real time 3D hand tracker that uses data from an off-the-shelf accelerometer and articulated pose classification results from a multiclass SVM classifier. Finally, we explore deformable 2D/3D object tracking. Unlike previous works, we use a non-linear and probabilistic dimensionality reduction, called Gaussian Process Latent Variable Models, to learn spaces of shape. Segmentation becomes a minimisation of an image-driven energy function in the learned space. We can represent both 2D and 3D shapes which we compress with Fourier-based transforms, to keep inference tractable. We extend this method by learning joint shape-parameter spaces, which, novel to the literature, enable simultaneous segmentation and generic parameter recovery. These can describe anything from 3D articulated pose to eye gaze. We also propose two novel extensions to standard GP-LVM: a method to explore the multimodality in the joint space efficiently, by learning a mapping from the latent space to a space that encodes the similarity between shapes and a method for obtaining faster convergence and greater accuracy by use of a hierarchy of latent embeddings.

No description available.

This diploma thesis is focusing on description of processing social network analysis, design and implementation of a model that simulates a particular social network and its analysis. Social networks are modern and very used in this time. They are very good point for exploring. This project deal with static analysis social network, where social network is constructed by graph. We nd out di erent properties of single component and than we establish signi cance of them. Relationships between components are important too for us, because they have a big influence on propagation information in network. Structural properties figure out existence of di fferent communities. We simulate social network with multi-agent systems, they are desirable for represent changes in network. Multi-agent systems have implemented a simulation model that represents a particular social network. His behaviour was analyzed and examinated by chosen methods.

Human pose estimation (HPE) constitutes a fundamental problem within the domain of computer vision, finding applications in diverse fields like motion analysis and human-computer interaction. This paper introduces innovative methodologies aimed at enhancing the accuracy and robustness of 3D joint estimation. Through the integration of Variational Autoencoders (VAEs), pertinent information is extracted from depth maps, even in the presence of inevitable image-capturing inconsistencies. This concept is enhanced through the introduction of noise to the body or specific regions surrounding key joints. The deliberate introduction of noise to these areas enables the VAE to acquire a robust representation that captures authentic pose-related patterns. Moreover, the introduction of a localised mask as a constraint in the loss function ensures the model predominantly relies on pose-related cues while disregarding potential confounding factors that may hinder the compact representation of accurate human pose information. Delving into the latent space modulation further, a novel model architecture is devised, joining a VAE and fully connected network into a multi-task joint training objective. In this framework, the VAE and regressor harmoniously influence the latent representations for accurate joint detection and localisation. By combining the multi-task model with the loss function constraint, this study attains results that compete with state-of-the-art techniques. These findings underscore the significance of leveraging latent space modulation and customised loss functions to address challenging human poses. Additionally, these novel methodologies pave the way for future explorations and provide prospects for advancing HPE. Subsequent research endeavours may optimising these techniques, evaluating their performance across diverse datasets, and exploring potential extensions to unravel further insights and advancements in the field. / Human pose estimation (HPE) är ett grundläggande problem inom datorseende och används inom områden som rörelseanalys och människa-datorinteraktion. I detta arbete introduceras innovativa metoder som syftar till att förbättra noggrannheten och robustheten i 3D-leduppskattning. Genom att integrera variationsautokodare (eng. variational autoencoder, VAE) extraheras relevant information från djupkartor, trots närvaro av inkonsekventa avvikelser i bilden. Dessa avvikelser förstärks genom att applicera brus på kroppen eller på specifika regioner som omger viktiga leder. Det avsiktliga införandet av brus i dessa områden gör det möjligt för VAE att lära sig en robust representation som fångar autentiska poseringsrelaterade mönster. Dessutom införs en lokaliserad mask som en begränsning i förlustfunktionen, vilket säkerställer att modellen främst förlitar sig på poseringsrelaterade signaler samtidigt som potentiella störande faktorer som hindrar den kompakta representationen av korrekt mänsklig poseringsinformation bortses ifrån. Genom att fördjupa sig ytterligare i den latenta rumsmoduleringen har en ny modellarkitektur tagits fram som förenar en VAE och ett fullständigt anslutet nätverk i en fleruppgiftsmodell. I detta ramverk påverkar VAE och det fullständigt ansluta nätverket de latenta representationerna på ett harmoniskt sätt för att uppnå korrekt leddetektering och lokalisering. Genom att kombinera fleruppgiftsmodellen med förlustfunktionsbegränsningen uppnår denna studie resultat som konkurrerar med toppmoderna tekniker. Dessa resultat understryker betydelsen av att utnyttja latent rymdmodulering och anpassade förlustfunktioner för att hantera utmanande mänskliga poser. Dessutom banar dessa nya metoder väg för framtida utveckling inom uppskattning av HPE. Efterföljande forskningsinsatser kan optimera dessa tekniker, utvärdera deras prestanda över olika datamängder och utforska potentiella tillägg för att avslöja ytterligare insikter och framsteg inom området.