DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic

Deep Neural Network and Monte Carlo Tree Search applied to Fluid-Structure Topology Optimization

This paper shows the application of Deep Neural Network algorithms for Fluid-Structure Topology Optimization. The strategy offered is a new concept which can be added to the current process used to study Topology Optimization with Cellular Automata, Adjoint and Level-Set methods. The design space is described by a computational grid where every cell can be in two states: fluid or solid. The system does not require human intervention and learns through an algorithm based on Deep Neural Network and Monte Carlo Tree Search. In this work the objective function for the optimization is an incompressible fluid solver but the overall optimization process is independent from the solver. The test case used is a standard duct with back facing step where the optimizer aims at minimizing the pressure losses between inlet and outlet. The results obtained with the proposed approach are compared to the solution via a classical adjoint topology optimization code

An Object-Oriented Model for Extensible Concurrent Systems: the Composition-Filters Approach

Applying the object-oriented paradigm for the development of large and complex software systems offers several advantages, of which increased extensibility and reusability are the most prominent ones. The object-oriented model is also quite suitable for modeling concurrent systems. However, it appears that extensibility and reusability of concurrent applications is far from trivial. The problems that arise, the so-called inheritance anomalies are analyzed and presented in this paper. A set of requirements for extensible concurrent languages is formulated. As a solution to the identified problems, an extension to the object-oriented model is presented; composition filters. Composition filters capture messages and can express certain constraints and operations on these messages, for example buffering. In this paper we explain the composition filters approach, demonstrate its expressive power through a number of examples and show that composition filters do not suffer from the inheritance anomalies and fulfill the requirements that were established

Semantic Segmentation Network Stacking with Genetic Programming

Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2023). Semantic Segmentation Network Stacking with Genetic Programming. Genetic Programming And Evolvable Machines, 24(2 — Special Issue on Highlights of Genetic Programming 2022 Events), 1-37. [15]. https://doi.org/10.1007/s10710-023-09464-0---Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018), AICE (DSAIPA/DS/0113/2019), UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, and by the grant SFRH/BD/137277/2018.Semantic segmentation consists of classifying each pixel of an image and constitutes an essential step towards scene recognition and understanding. Deep convolutional encoder–decoder neural networks now constitute state-of-the-art methods in the field of semantic segmentation. The problem of street scenes’ segmentation for automotive applications constitutes an important application field of such networks and introduces a set of imperative exigencies. Since the models need to be executed on self-driving vehicles to make fast decisions in response to a constantly changing environment, they are not only expected to operate reliably but also to process the input images rapidly. In this paper, we explore genetic programming (GP) as a meta-model that combines four different efficiency-oriented networks for the analysis of urban scenes. Notably, we present and examine two approaches. In the first approach, we represent solutions as GP trees that combine networks’ outputs such that each output class’s prediction is obtained through the same meta-model. In the second approach, we propose representing solutions as lists of GP trees, each designed to provide a unique meta-model for a given target class. The main objective is to develop efficient and accurate combination models that could be easily interpreted, therefore allowing gathering some hints on how to improve the existing networks. The experiments performed on the Cityscapes dataset of urban scene images with semantic pixel-wise annotations confirm the effectiveness of the proposed approach. Specifically, our best-performing models improve systems’ generalization ability by approximately 5% compared to traditional ensembles, 30% for the less performing state-of-the-art CNN and show competitive results with respect to state-of-the-art ensembles. Additionally, they are small in size, allow interpretability, and use fewer features due to GP’s automatic feature selection.publishersversionepub_ahead_of_prin