The Racing Mind and the Path of Love: automatic extraction of image schematic triggers in knowledge graphs generated from natural language

Embodied Cognition and Cognitive Metaphors Theory take their origin from our use of language: sensorimotor triggers are disseminated in our daily communication, expression and commonsense knowledge. We propose, in this work, a first attempt of image-schematic triggers automatic extraction, starting from knowledge graphs automatically generated from natural language. The methodology proposed here is conceived as a modular addition integrated in the FRED tool, able to generate knowledge graphs from natural language, while it has its foundation in querying ImageSchemaNet, the Image Schematic layer developed on top of FrameNet and integrated in the Framester resource. This methodology allows the extraction of sensorimotor triggers from WordNet, VerbNet, MetaNet, BabelNet and many more

In-vivo X-ray Dark-Field Chest Radiography of a Pig

X-ray chest radiography is an inexpensive and broadly available tool for initial assessment of the lung in clinical routine, but typically lacks diagnostic sensitivity for detection of pulmonary diseases in their early stages. Recent X-ray dark-field (XDF) imaging studies on mice have shown significant improvements in imaging-based lung diagnostics. Especially in the case of early diagnosis of chronic obstructive pulmonary disease (COPD), XDF imaging clearly outperforms conventional radiography. However, a translation of this technique towards the investigation of larger mammals and finally humans has not yet been achieved. In this letter, we present the first in-vivo XDF full-field chest radiographs (32 × 35 cm²) of a living pig, acquired with clinically compatible parameters (40s scan time, approx. 80 μSv dose). For imaging, we developed a novel high-energy XDF system that overcomes the limitations of currently established setups. Our XDF radiographs yield sufficiently high image quality to enable radiographic evaluation of the lungs. We consider this a milestone in the bench-to-bedside translation of XDF imaging and expect XDF imaging to become an invaluable tool in clinical practice, both as a general chest X-ray modality and as a dedicated tool for high-risk patients affected by smoking, industrial work and indoor cooking

The CMS Phase-1 pixel detector upgrade

The CMS detector at the CERN LHC features a silicon pixel detector as its innermost subdetector. The original CMS pixel detector has been replaced with an upgraded pixel system (CMS Phase-1 pixel detector) in the extended year-end technical stop of the LHC in 2016/2017. The upgraded CMS pixel detector is designed to cope with the higher instantaneous luminosities that have been achieved by the LHC after the upgrades to the accelerator during the first long shutdown in 2013–2014. Compared to the original pixel detector, the upgraded detector has a better tracking performance and lower mass with four barrel layers and three endcap disks on each side to provide hit coverage up to an absolute value of pseudorapidity of 2.5. This paper describes the design and construction of the CMS Phase-1 pixel detector as well as its performance from commissioning to early operation in collision data-taking.Peer reviewe

The European language technology landscape in 2020 : language-centric and human-centric AI for cross-cultural communication in multilingual Europe

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions

Analyzing the imagistic foundation of framality via prepositions

Natural language understanding is a vibrant research area in Artificial Intelligence that requires linguistic and commonsense knowledge. To unite both types of knowledge, FrameNet associates words with semantic frames, conceptual structures that describe a type of object, event or situation. Frames are interrelated and feature some image schematic foundations. However, the resource\u2019s usefulness is limited by non-standard semantics. Framester, lying on a solid formal frame semantics, reengineers and links FrameNet to lexical and ontological resources to create one joint, powerful knowledge base. In this paper, we use Framester of FrameNet and of the Preposition Project (TPP) to systematically analyze the image-schematic foundation of frames via preposition senses. Framal knowledge is extracted from TPP, which contains senses for each English preposition, and frame interrelations are analyzed for the imagistic foundation of framality via preposition senses