Modelling Word Associations with Word Embeddings for a Guesser Agent in the Taboo City Challenge Competition

In the Taboo City Challenge, artificial agents should guess the names of cities from simple textual hints and are evaluated with games played by humans. Thus, playing the games successfully requires mimicking associations that humans have with geographical locations. In this paper, an architecture is proposed that calculates the associative similarity between a city and a hint from a semantic vector space. The semantic vector space is created using the Skip-gram hierarchical softmax model, from a tailored corpus about travel destinations. We investigate the effect of varying training parameters and introduce a targeted corpus annotation method that significantly improves performance. The results on a dataset of 149 games indicate that the proposed architecture can guess the target city with up to 22.45% accuracy — a substantial improvement over the 4.11% accuracy achieved by the baseline architecture

Threshold Implementations of all 3x3 and 4x4 S-boxes

Side-channel attacks have proven many hardware implementations of cryptographic algorithms to be vulnerable. A recently proposed masking method, based on secret sharing and multi-party computation methods, introduces a set of sufficient requirements for implementations to be provably resistant against first-order DPA with minimal assumptions on the hardware. The original paper doesn\u27t describe how to construct the Boolean functions that are to be used in the implementation. In this paper, we derive the functions for all invertible $3 \times 3$, $4 \times 4$ S-boxes and the $6 \times 4$ DES S-boxes. Our methods and observations can also be used to accelerate the search for sharings of larger (e.g. $8 \times 8$) S-boxes. Finally, we investigate the cost of such protection

The Mechanism of Regulated Release of Lasso/Teneurin-2

Teneurins are large cell-surface receptors involved in axon guidance. Teneurin-2 (also known as latrophilin-1-associated synaptic surface organizer (Lasso)) interacts across the synaptic cleft with presynaptic latrophilin-1, an adhesion G-protein-coupled receptor that participates in regulating neurotransmitter release. Lasso-latrophilin-1 interaction mediates synapse formation and calcium signaling, highlighting the important role of this trans-synaptic receptor pair. However, Lasso is thought to be proteolytically cleaved within its ectodomain and released into the medium, making it unclear whether it acts as a proper cell-surface receptor or a soluble protein. We demonstrate here that during its intracellular processing Lasso is constitutively cleaved at a furin site within its ectodomain. The cleaved fragment, which encompasses almost the entire ectodomain of Lasso, is potentially soluble; however, it remains anchored on the cell surface via its non-covalent interaction with the transmembrane fragment of Lasso. Lasso is also constitutively cleaved within the intracellular domain (ICD). Finally, Lasso can be further proteolytically cleaved within the transmembrane domain. The third cleavage is regulated and releases the entire ectodomain of Lasso into the medium. The released ectodomain of Lasso retains its functional properties and binds latrophilin-1 expressed on other cells; this binding stimulates intracellular Ca2+ signaling in the target cells. Thus, Lasso not only serves as a bona fide cell-surface receptor, but also as a partially released target-derived signaling factor

Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues

A multidisciplinary approach to address climate-resilience, conservation and comfort in traditional architecture: The PROT3CT example

Traditional dwellings despite their environmental credentials, due to age, previous damage, and residents unable to afford even the limited maintenance allowed by restrictive legal framework, may offer poor thermal performance, which is expected to be further exacerbated by changing climate. More than 70% of Turkey’s built heritage stock is composed of traditional dwellings, which makes this stock able to create a major impact nationally on the building-related energy use, carbon emissions and population wellbeing. This research aims to develop an evidence-based multidisciplinary methodology for cost-effective retrofit of the traditional dwellings in Turkey, to improve energy performance, satisfy user expectations of comfort, and protect heritage value

Automatic Tumor-Stroma Separation in Fluorescence TMAs Enables the Quantitative High-Throughput Analysis of Multiple Cancer Biomarkers

The upcoming quantification and automation in biomarker based histological tumor evaluation will require computational methods capable of automatically identifying tumor areas and differentiating them from the stroma. As no single generally applicable tumor biomarker is available, pathology routinely uses morphological criteria as a spatial reference system. We here present and evaluate a method capable of performing the classification in immunofluorescence histological slides solely using a DAPI background stain. Due to the restriction to a single color channel this is inherently challenging. We formed cell graphs based on the topological distribution of the tissue cell nuclei and extracted the corresponding graph features. By using topological, morphological and intensity based features we could systematically quantify and compare the discrimination capability individual features contribute to the overall algorithm. We here show that when classifying fluorescence tissue slides in the DAPI channel, morphological and intensity based features clearly outpace topological ones which have been used exclusively in related previous approaches. We assembled the 15 best features to train a support vector machine based on Keratin stained tumor areas. On a test set of TMAs with 210 cores of triple negative breast cancers our classifier was able to distinguish between tumor and stroma tissue with a total overall accuracy of 88%. Our method yields first results on the discrimination capability of features groups which is essential for an automated tumor diagnostics. Also, it provides an objective spatial reference system for the multiplex analysis of biomarkers in fluorescence immunohistochemistry