Learning Context-sensitive Human Emotions in Categorical and Dimensional Domains

Still image emotion recognition (ER) has been receiving increasing attention in recent years due to the tremendous amount of social media content on the Web. Many works offer both categorical and dimensional methods to detect image sentiments, while others focus on extracting the true social signals, such as happiness and anger. Deep learning architectures have delivered great suc- cess, however, their dependency on large-scale datasets labeled with (1) emotion, and (2) valence, arousal and dominance, in categorical and dimensional domains respectively, introduce challenges the community tries to tackle. Emotions offer dissimilar semantics when aroused in different con- texts, however context-sensitive ER has been by and large discarded in the literature so far. Moreover, while dimensional methods deliver higher accuracy, they have been less attended due to (1) lack of reliable large-scale labeled datasets, and (2) challenges involved in architecting un- supervised solutions to the problem. Owing to the success offered by multi-modal ER, still image ER in the single-modal domain; i.e. using only still images, remains less resorted to. In this work, (1) we first architect a novel fully automated dataset collection pipeline, equipped with a built-in semantic sanitizer, (2) we then build UCF-ER with 50K images, and LUCFER, the largest labeled ER dataset in the literature with more than 3.6M images, both datasets labeled with emotion and context, (3) next, we build a single-modal context-sensitive ER CNN model, fine-tuned on UCF-ER and LUCFER, (4) we then claim and show empirically that infusing context to the unified training process helps achieve a more balanced precision and recall, while boosting performance, yielding an overall classification accuracy of 73.12% compared to the state of the art 58.3%, (5) next, we propose an unsupervised approach for ranking of continuous emotions in images using canonical polyadic (CP) decomposition, providing theoretical proof that rank-1 CP decomposition can be used as a ranking machine, (6) finally, we provide empirical proof that our method generates a Pearson Correlation Coefficient, outperforming the state of the art by a large margin; i.e. 65.13% (difference) in one experiment and 104.08% (difference) in another, when applied to valence rank estimation

Arrhythmogenic cardiomyopathy: electrical instability and intercalated disc abnormalities in transgenic mice

Aims: Mutations in genes encoding desmosomal proteins have been implicated in the pathogenesis of arrhythmogenic right ventricular cardiomyopathy (ARVC). However, the consequences of these mutations in early disease stages are unknown. We investigated whether mutation-induced intercalated disc remodeling impacts on electrophysiological properties before the onset of cell death and replacement fibrosis. Methods and Results: Transgenic mice with cardiac overexpression of mutant Desmoglein2 (Dsg2) Dsg2-N271S (Tg-NS/L) were studied before and after the onset of cell death and replacement fibrosis. Mice with cardiac overexpression of wild-type Dsg2 and wild-type mice served as controls. Assessment by electron microscopy established that intercellular space widening at the desmosomes/adherens junctions occurred in Tg-NS/L mice before the onset of necrosis and fibrosis. At this stage, epicardial mapping in Langendorff-perfused hearts demonstrated prolonged ventricular activation time, reduced longitudinal and transversal conduction velocities, and increased arrhythmia inducibility. A reduced action potential upstroke velocity due to a lower Na+ current density was also observed at this stage of the disease. Furthermore, co-immunoprecipitation demonstrated an in vivo interaction between Dsg2 and the Na+ channel protein NaV1.5. Conclusion: Intercellular space widening at the level of the intercalated disc (desmosomes/fascia adherens junctions) and a concomitant reduction in action potential upstroke velocity, as a consequence of lower Na+ current density, leads to slowed conduction and increased arrhythmia susceptibility at disease stages preceding the onset of necrosis and replacement fibrosis. The demonstration of an in vivo interaction between Dsg2 and NaV1.5 provides a molecular pathway for the observed electrical disturbances during the early ARVC stages

Distributional Semantic Models of Attribute Meaning in Adjectives and Nouns

Hartung M. Distributional Semantic Models of Attribute Meaning in Adjectives and Nouns. Heidelberg: Universität Heidelberg; 2015

Distributional Semantic Models of Attribute Meaning in Adjectives and Nouns

Attributes such as SIZE, WEIGHT or COLOR are at the core of conceptualization, i.e., the formal representation of entities or events in the real world. In natural language, formal attributes find their counterpart in attribute nouns which can be used in order to generalize over individual properties (e.g., 'big' or 'small' in case of SIZE, 'blue' or 'red' in case of COLOR). In order to ascribe such properties to entities or events, adjective-noun phrases are a very frequent linguistic pattern (e.g., 'a blue shirt', 'a big lion'). In these constructions, attribute meaning is conveyed only implicitly, i.e., without being overtly realized at the phrasal surface. This thesis is about modeling attribute meaning in adjectives and nouns in a distributional semantics framework. This implies the acquisition of meaning representations for adjectives, nouns and their phrasal combination from corpora of natural language text in an unsupervised manner, without tedious handcrafting or manual annotation efforts. These phrase representations can be used to predict implicit attribute meaning from adjective-noun phrases -- a problem which will be referred to as attribute selection throughout this thesis. The approach to attribute selection proposed in this thesis is framed in structured distributional models. We model adjective and noun meanings as distinct semantic vectors in the same semantic space spanned by attributes as dimensions of meaning. Based on these word representations, we make use of vector composition operations in order to construct a phrase representation from which the most prominent attribute(s) being expressed in the compositional semantics of the adjective-noun phrase can be selected by means of an unsupervised selection function. This approach not only accounts for the linguistic principle of compositionality that underlies adjective-noun phrases, but also avoids inherent sparsity issues that result from the fact that the relationship between an adjective, a noun and a particular attribute is rarely explicitly observed in corpora. The attribute models developed in this thesis aim at a reconciliation of the conflict between specificity and sparsity in distributional semantic models. For this purpose, we compare various instantiations of attribute models capitalizing on pattern-based and dependency-based distributional information as well as attribute-specific latent topics induced from a weakly supervised adaptation of Latent Dirichlet Allocation. Moreover, we propose a novel framework of distributional enrichment in order to enhance structured vector representations by incorporating additional lexical information from complementary distributional sources. In applying distributional enrichment to distributional attribute models, we follow the idea to augment structured representations of adjectives and nouns to centroids of their nearest neighbours in semantic space, while keeping the principle of meaning representation along structured, interpretable dimensions intact. We evaluate our attribute models in several experiments on the attribute selection task framed for various attribute inventories, ranging from a thoroughly confined set of ten core attributes up to a large-scale set of 260 attributes. Our results show that large-scale attribute selection from distributional vector representations that have been acquired in an unsupervised setting is a challenging endeavor that can be rendered more feasible by restricting the semantic space to confined subsets of attributes. Beyond quantitative evaluation, we also provide a thorough analysis of performance factors (based on linear regression) that influence the effectiveness of a distributional attribute model for attribute selection. This investigation reflects strengths and weaknesses of the model and sheds light on the impact of a variety of linguistic factors involved in attribute selection, e.g., the relative contribution of adjective and noun meaning. In conclusion, we consider our work on attribute selection as an instructive showcase for applying methods from distributional semantics in the broader context of knowledge acquisition from text in order to alleviate issues that are related to implicitness and sparsity

Arrhythmogenic cardiomyopathy - beyond monogenetic disease

Interpreting genetic variants, describing their associated clinical characteristics, and identifying new genetic loci involved in arrhythmogenic cardiomyopathy (ACM) is the focus of this thesis. By investigating various aspects of these genetic variants, we were able to correctly classify two variants occurring in the lamin A/C (LMNA) and titin (TTN) gene. We demonstrated that the reduced force generation seen in cardiomyocytes with the LMNA variant (LMNA c.992G>A) is due to remodelling within the cardiomyocytes and that patients with this specific variant have a milder phenotype compared to what is known from other pathogenic LMNA variants. By extensive phenotyping of carriers of a truncating TTN variant (TTN c.59926+1G>A) we were the first to show that (paroxysmal) atrial fibrillation is an important clinical feature in carriers of truncated TTN variants, even in the absence of dilated cardiomyopathy, atrial enlargement or generally accepted risk factors for atrial fibrillation. Thanks to extensive international collaboration it was possible to compile one of the largest cohorts of patients carrying truncating variants in desmoplakin (DSP). We showed that the location of such a genetic variant within the gene is associated with disease severity. Moreover, these studies show that enrichment of truncating genetic variants in specific regions of DSP variants in ACM patients, when compared to controls, facilitating interpretation of such variants. The multifactorial nature of ACM was underscored in a systematic analysis of the clinical outcome of patients from ACM cohorts carrying multiple variants in ACM related genes, showing that carrying multiple variants influences disease severity. Finally, by analysing genes encoding the sarcomere, the contractile unit of the heart muscle and the plectin (PLEC) gene for rare variants in ACM patients, we showed that these genes do not have a major role in the development of ACM

Re-manufacturing networks for tertiary architectures

This book deals with re-manufacturing, recondition, reuse and repurpose considered as winning strategies for boosting regenerative circular economy in the building sector. It presents many of the outcomes of the research Re-NetTA (Re-manufacturing Networks for Tertiary Architectures). New organisational models and tools for re-manufacturing and re-using short life components coming from tertiary buildings renewal, funded in Italy by Fondazione Cariplo for the period 2019-2021. The field of interest of the book is the building sector, focusing on various categories of tertiary buildings, characterized by short term cycles of use. The book investigates the most promising strategies and organizational models to maintain over time the value of the environmental and economic resources integrated into manufactured products, once they have been removed from buildings, by extending their useful life and their usability with the lower possible consumption of other materials and energy and with the maximum containment of emissions into the environment. The text is articulated into three sections. Part I BACKGROUND introduces the current theoretical background and identifies key strategies about circular economy and re-manufacturing processes within the building sector, focusing on tertiary architectures. It is divided into three chapters. Part II PROMISING MODELS outlines, according to a proposed framework, a set of promising circular organizational models to facilitate re-manufacturing practices and their application to the different categories of the tertiary sectors: exhibition, office and retail. This part also reports the results of active dialogues and roundtables with several categories of operators, adopting a stakeholder perspective. Part III INSIGHTS provides some insights on the issue of re-manufacturing, analyzed from different perspectives with the aim of outlining a comprehensive overview of challenges and opportunities for the application of virtuous circular processes within building sector. Part III is organized in four key topics: A) Design for Re-manufacturing; B) Digital Transformation; C) Environmental Sustainability; D) Stakeholder Management, Regulations & Policies