ConsInstancy: learning instance representations for semi-supervised panoptic segmentation of concrete aggregate particles

We present a semi-supervised method for panoptic segmentation based on ConsInstancy regularisation, a novel strategy for semi-supervised learning. It leverages completely unlabelled data by enforcing consistency between predicted instance representations and semantic segmentations during training in order to improve the segmentation performance. To this end, we also propose new types of instance representations that can be predicted by one simple forward path through a fully convolutional network (FCN), delivering a convenient and simple-to-train framework for panoptic segmentation. More specifically, we propose the prediction of a three-dimensional instance orientation map as intermediate representation and two complementary distance transform maps as final representation, providing unique instance representations for a panoptic segmentation. We test our method on two challenging data sets of both, hardened and fresh concrete, the latter being proposed by the authors in this paper demonstrating the effectiveness of our approach, outperforming the results achieved by state-of-the-art methods for semi-supervised segmentation. In particular, we are able to show that by leveraging completely unlabelled data in our semi-supervised approach the achieved overall accuracy (OA) is increased by up to 5% compared to an entirely supervised training using only labelled data. Furthermore, we exceed the OA achieved by state-of-the-art semi-supervised methods by up to 1.5%

A Nonparametric Examination of Capital-Skill Complementarity

This paper uses nonparametric kernel methods to construct observation-specific elasticities of substitution for a balanced panel of 73 developed and developing countries to examine the capital-skill complementarity hypothesis. The exercise shows some support for capital-skill complementarity, but the strength of the evidence depends upon the definition of skilled labor and the elasticity of substitution measure being used. The added flexibility of the nonparametric procedure is also able to uncover that the elasticities of substitution vary across countries, groups of countries and time periods.capital-skill complementarity, elasticity of substitution, nonparametric kernel, stochastic dominance

Multimodal Data Fusion and Quantitative Analysis for Medical Applications

Medical big data is not only enormous in its size, but also heterogeneous and complex in its data structure, which makes conventional systems or algorithms difficult to process. These heterogeneous medical data include imaging data (e.g., Positron Emission Tomography (PET), Computerized Tomography (CT), Magnetic Resonance Imaging (MRI)), and non-imaging data (e.g., laboratory biomarkers, electronic medical records, and hand-written doctor notes). Multimodal data fusion is an emerging vital field to address this urgent challenge, aiming to process and analyze the complex, diverse and heterogeneous multimodal data. The fusion algorithms bring great potential in medical data analysis, by 1) taking advantage of complementary information from different sources (such as functional-structural complementarity of PET/CT images) and 2) exploiting consensus information that reflects the intrinsic essence (such as the genetic essence underlying medical imaging and clinical symptoms). Thus, multimodal data fusion benefits a wide range of quantitative medical applications, including personalized patient care, more optimal medical operation plan, and preventive public health. Though there has been extensive research on computational approaches for multimodal fusion, there are three major challenges of multimodal data fusion in quantitative medical applications, which are summarized as feature-level fusion, information-level fusion and knowledge-level fusion: • Feature-level fusion. The first challenge is to mine multimodal biomarkers from high-dimensional small-sample multimodal medical datasets, which hinders the effective discovery of informative multimodal biomarkers. Specifically, efficient dimension reduction algorithms are required to alleviate "curse of dimensionality" problem and address the criteria for discovering interpretable, relevant, non-redundant and generalizable multimodal biomarkers. • Information-level fusion. The second challenge is to exploit and interpret inter-modal and intra-modal information for precise clinical decisions. Although radiomics and multi-branch deep learning have been used for implicit information fusion guided with supervision of the labels, there is a lack of methods to explicitly explore inter-modal relationships in medical applications. Unsupervised multimodal learning is able to mine inter-modal relationship as well as reduce the usage of labor-intensive data and explore potential undiscovered biomarkers; however, mining discriminative information without label supervision is an upcoming challenge. Furthermore, the interpretation of complex non-linear cross-modal associations, especially in deep multimodal learning, is another critical challenge in information-level fusion, which hinders the exploration of multimodal interaction in disease mechanism. • Knowledge-level fusion. The third challenge is quantitative knowledge distillation from multi-focus regions on medical imaging. Although characterizing imaging features from single lesions using either feature engineering or deep learning methods have been investigated in recent years, both methods neglect the importance of inter-region spatial relationships. Thus, a topological profiling tool for multi-focus regions is in high demand, which is yet missing in current feature engineering and deep learning methods. Furthermore, incorporating domain knowledge with distilled knowledge from multi-focus regions is another challenge in knowledge-level fusion. To address the three challenges in multimodal data fusion, this thesis provides a multi-level fusion framework for multimodal biomarker mining, multimodal deep learning, and knowledge distillation from multi-focus regions. Specifically, our major contributions in this thesis include: • To address the challenges in feature-level fusion, we propose an Integrative Multimodal Biomarker Mining framework to select interpretable, relevant, non-redundant and generalizable multimodal biomarkers from high-dimensional small-sample imaging and non-imaging data for diagnostic and prognostic applications. The feature selection criteria including representativeness, robustness, discriminability, and non-redundancy are exploited by consensus clustering, Wilcoxon filter, sequential forward selection, and correlation analysis, respectively. SHapley Additive exPlanations (SHAP) method and nomogram are employed to further enhance feature interpretability in machine learning models. • To address the challenges in information-level fusion, we propose an Interpretable Deep Correlational Fusion framework, based on canonical correlation analysis (CCA) for 1) cohesive multimodal fusion of medical imaging and non-imaging data, and 2) interpretation of complex non-linear cross-modal associations. Specifically, two novel loss functions are proposed to optimize the discovery of informative multimodal representations in both supervised and unsupervised deep learning, by jointly learning inter-modal consensus and intra-modal discriminative information. An interpretation module is proposed to decipher the complex non-linear cross-modal association by leveraging interpretation methods in both deep learning and multimodal consensus learning. • To address the challenges in knowledge-level fusion, we proposed a Dynamic Topological Analysis framework, based on persistent homology, for knowledge distillation from inter-connected multi-focus regions in medical imaging and incorporation of domain knowledge. Different from conventional feature engineering and deep learning, our DTA framework is able to explicitly quantify inter-region topological relationships, including global-level geometric structure and community-level clusters. K-simplex Community Graph is proposed to construct the dynamic community graph for representing community-level multi-scale graph structure. The constructed dynamic graph is subsequently tracked with a novel Decomposed Persistence algorithm. Domain knowledge is incorporated into the Adaptive Community Profile, summarizing the tracked multi-scale community topology with additional customizable clinically important factors

Automatic analysis of overnight airflow to help in the diagnosis of pediatric obstructive sleep apnea

La apnea obstructiva del sueño (AOS) pediátrica es una enfermedad respiratoria altamente prevalente e infradiagnosticada que puede afectar negativamente a las funciones fisiológicas y cognitivas de los niños, causándoles graves deficiencias neurocognitivas, cardiometabólicas y endocrinas. El método estándar para su diagnóstico es la polisomnografía nocturna, una prueba compleja, de elevado coste, altamente intrusiva y poco accesible, lo que genera largas listas de espera y retrasos en el diagnóstico. Por ello, es necesario desarrollar pruebas diagnósticas más sencillas. Una de estas alternativas es el análisis automático de señales cardiorrespiratorias. Así, esta tesis doctoral presenta un compendio de cuatro publicaciones que proponen el uso de novedosos métodos de procesado de señal (no lineal, espectral, bispectral, gráficos de recurrencia y wavelet) que permiten caracterizar exhaustivamente el comportamiento del flujo aéreo nocturno de los niños y simplificar el diagnóstico de la apnea obstructiva del sueño pediátrica.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Approaching an investigation of multi-dimensional inequality through the lenses of variety in models of capitalism

After a synthetic presentation of the state of poverty and inequality in the world and the contradictions incurred by economic theory in this field after decades of globalization and in the midst of a persisting global crisis, in paragraphs 2. and 3. we outline the rational for our theoretical analysis, underlining two main aspects. First of all, in paragraph 2. we recall the reasons which makes inequality a multidimensional phenomenon, while in paragraph 3. we explore the reasons why the models of capitalism theory is relevant for studying multidimensional inequality. These paragraphs emphasise that inequality is a multidimensional and cumulative phenomenon and it should not be conceived only as the result of the processes of personal and functional distribution of income and wealth, which even by themselves are intrinsically multidimensional. The basic idea is that institutions, the cobweb of relations among them and their interaction with the economic structure define the model of capitalism which characterises a specific country and this, in turn, affects the level and the dynamics of inequality. This approach is consistent with the sociological approach by Rehbein and Souza (2014), based on the analytical framework developed by Pierre Bourdieu. In paragraph 4. we outline the rational for our empirical analysis, applying the notion of institutional complementarity and examining the relationship between institutional complementarity, models of capitalism and inequality. Besides, refining Amable’s analysis (2003), we provide empirical evidence on the relationship between inequality in income distribution and models of capitalism. Additionally, basing on cluster analysis, we identify six different models of capitalism in a sample of OECD countries, provide preliminary evidence on the different level of inequality which characterises each model and suggest that no evidence supports of the idea that a single model of capitalism is taking shape in this sphere in EU. In paragraph 5. we give some hints about issues in search for a new interpretation capable to fasten together the process of increasing inequality, the notion of symbolic violence and the models of capitalism theory. In the last paragraph we focus on conclusions useful for carrying on our research agenda