Constructing correctly in wood: new insights into timber technology approaches through purist and liberalist schools of thought.

Conventionally, technology-based articles focus on methods by which architects and engineers designed and built to present new methods, materials to evidence novelty in technical terms. This paper does not do that. Instead, through a current overview of past and present timber practices, it will present a new cultural perspective by looking at timber technology from purist and liberalist approaches. Indicating a moralistic sensibility of what "constructing correctly" in wood means to them, with these two attitudes implying inherent values, this paper seeks to project a new cultural dimension on technology. More importantly, the approaches convincingly reflects our relationship with digital technology, as timber culture and tradition come to terms with the inevitability of the digital age

RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with poorly defined aetiology characterised by synovial inflammation with variable disease severity and drug responsiveness. To investigate the peripheral blood immune cell landscape of early, drug naive RA, we performed comprehensive clinical and molecular profiling of 267 RA patients and 52 healthy vaccine recipients for up to 18 months to establish a high quality sample biobank including plasma, serum, peripheral blood cells, urine, genomic DNA, RNA from whole blood, lymphocyte and monocyte subsets. We have performed extensive multi-omic immune phenotyping, including genomic, metabolomic, proteomic, transcriptomic and autoantibody profiling. We anticipate that these detailed clinical and molecular data will serve as a fundamental resource offering insights into immune-mediated disease pathogenesis, progression and therapeutic response, ultimately contributing to the development and application of targeted therapies for RA.</p

Input effects across domains:The case of Greek subjects in child heritage language

A recurring question in the literature of heritage language acquisition, and more generally of bilingual acquisition, is whether all linguistic domains are sensitive to input reduction and to cross-linguistic influence and to what extent. According to the Interface Hypothesis, morphosyntactic phenomena regulated by discourse–pragmatic conditions are more likely to lead to non-native outcomes than strictly syntactic aspects of the language (Sorace, 2011). To test this hypothesis, we examined subject realization and placement in Greek–English bilingual children learning Greek as a heritage language in North America and investigated whether the amount of heritage language use can predict their performance in syntax–discourse and narrow syntactic contexts. Results indicated two deviations from the Interface Hypothesis: First, subject realization (a syntax–discourse phenomenon) was found to be largely unproblematic. Second, subject placement was affected not only in syntax–discourse structures but also in narrow syntactic structures, though to a lesser degree, suggesting that the association between the interface status of subject placement and its sensitivity to heritage language use among children heritage speakers is gradient rather than categorical

Deep ensembles and data augmentation for semantic segmentation

The task of classifying each pixel in an image is known as semantic segmentation in the context of computer vision, and it is critical for image analysis in many domains. Semantic segmentation, for example, is required in clinical practice to improve accuracy in identifying potential pathologies, such as polyp segmentation, which provides critical information for detecting colorectal cancer in its early stages. Autoencoder architectures that learn low-level semantical descriptions of an image are commonly used for semantic segmentation. This architecture is made up of an encoder module that generates low-level data representations, which are then used by a second module (the decoder) that learns to rebuild the initial input. We tackle the semantic segmentation process in this chapter by constructing a novel ensemble of convolutional neural networks (CNNs) and transformers. An ensemble is a machine learning method that trains different models to make predictions on a given input, and then aggregates these predictions to compute a final decision. We enforce ensemble diversity by experimenting with various loss functions and data augmentation approaches. We combine DeepLabV3+, HarDNet-MSEG CNN, and Pyramid Vision Transformers to create the proposed ensemble. We present a thorough empirical analysis of our system based on three semantic segmentation problems: polyp detection, skin detection, and leukocyte recognition. Experiments show that our method produces cutting-edge results

Polyp Segmentation with Deep Ensembles and Data Augmentation

Globally, colorectal cancer is one of the leading causes of mortality. Colonoscopies and the early removal of polys significantly increase the survival rate of this cancer, but this intervention depends on the accurate detection of polys in the surrounding tissues. Missing a poly has serious consequences. One way to guard against human error is to develop automatic polyp detection systems. Deep learning semantic segmentation offers one approach to solving the problem of poly detection. In this work, we propose an ensemble of ensembles composed of two deep convolutional neural networks (DCNNs): DeepLabV3+ and HarDNet. Diversity among the single classifiers is enforced on the data level using different data augmentation approaches and on the classifier level with two DCNNs: DeepLabV3+ and HardNet, each using an encoder-decoder unit. In addition, ensembles of DeepLabV3+ are built using fifteen loss functions. Our best ensembles are tested on a large dataset composed of samples taken from five polyp benchmarks. Ensembles are assessed and compared with the best method reported in the literature and shown to produce state-of-the-art results. The source code, the dataset, and the testing protocol used in this study are freely available at https://github.com/LorisNanni