Baget, Jean-François (Institut National de Recherche en Informatique et en Automatique (INRIA)) | Benferhat, Salem (Université d'Artois) | Bouraoui, Zied (Centre National de la Recherche Scientifique (CNRS), Aix-Marseille Université) | Croitoru, Madalina (Université de Montpellier) | Mugnier, Marie-Laure (Université de Montpellier) | Papini, Odile (Aix-Marseille Université) | Rocher, Swan (Université de Montpellier) | Tabia, Karim (Université d'Artois)
We propose a general framework for inconsistency-tolerant query answering within existential rule setting. This framework unifies the main semantics proposed by the state of art and introduces new ones based on cardinality and majority principles. It relies on two key notions: modifiers and inference strategies. An inconsistency-tolerant semantics is seen as a composite modifier plus an inference strategy. We compare the obtained semantics from a productivity point of view.
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray datasets to develop deep learning models for recognition of a handful of coarse finding classes such as opacities, masses and nodules. In this paper, we focus on extracting and learning fine-grained labels for chest X-ray images. Specifically we develop a new method of extracting fine-grained labels from radiology reports by combining vocabulary-driven concept extraction with phrasal grouping in dependency parse trees for association of modifiers with findings. A total of 457 fine-grained labels depicting the largest spectrum of findings to date were selected and sufficiently large datasets acquired to train a new deep learning model designed for fine-grained classification. We show results that indicate a highly accurate label extraction process and a reliable learning of fine-grained labels. The resulting network, to our knowledge, is the first to recognize fine-grained descriptions of findings in images covering over nine modifiers including laterality, location, severity, size and appearance.
Destiny's Prison of Elders mode -- introduced in 2015's House of Wolves add-on then forgotten with the release of The Taken King -- is making a comeback. A refreshed "PoE" arrives with the game's big spring update on Apr. SEE ALSO: 'Destiny' toys are coming in 2016 from Mega Bloks: Here's your first look A new Strike introduces Malok, a villainous presence that's been referred to in the lore but never seen. He's the Strike's boss, and also the new Taken commander. The old Winter's Run Strike is also making a comeback, refreshed so that the game's current high-level players can face a challenge.
Developer Bungie has revealed the roadmap of its upcoming updates for "Destiny 2." Unfortunately, the team has encountered a setback so one major feature won't be coming with the next update that's scheduled for release in less than two weeks. Late last week, game director Christopher Barrett released a statement about Update 1.1.4 The new feature was supposed to launch with Update 1.1.4, The game director then revealed that the Heroic Modifiers will instead be released alongside Update 1.2.0, which is set for release in May. While this is bad news for fans who are anticipating the new feature, the delay will actually be for the better since Bungie will have more time to polish the Modifiers and do even more.
We present CrossBridge, a practical algorithm for retrieving analogies in large, sparse semantic networks. Other algorithms adopt a generate-and-test approach, retrieving candidate analogies by superficial similarity of concepts, then testing them for the particular relations involved in the analogy. CrossBridge adopts a global approach. It organizes the entire knowledge space at once, as a matrix of small concept-and-relation subgraph patterns versus actual occurrences of subgraphs from the knowledge base. It uses the familiar mathematics of dimensionality reduction to reorganize this space along dimensions representing approximate semantic similarity of these subgraphs. Analogies can then be retrieved by simple nearest-neighbor comparison. CrossBridge also takes into account not only knowledge directly related to the source and target domains, but also a large background Commonsense knowledge base. Commonsense influences the mapping between domains, preserving important relations while ignoring others. This property allows CrossBridge to find more intuitive and extensible analogies. We compare our approach with an implementation of structure mapping and show that our algorithm consistently finds analogies in cases where structure mapping fails. We also present some discovered analogies.