49 resultados para Query languages (Computer science)


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We review our recent work on protein-ligand interactions in vitamin transporters of the Sec-14-like protein. Our studies focused on the cellular-retinaldehyde binding protein (CRALBP) and the alpha-tocopherol transfer protein (alpha-TTP). CRALBP is responsible for mobilisation and photo-protection of short-chain cis-retinoids in the dim-light visual cycle or rod photoreceptors. alpha-TTP is a key protein responsible for selection and retention of RRR-alpha-tocopherol, the most active isoform of vitamin E in superior animals. Our simulation studies evidence how subtle chemical variations in the substrate can lead to significant distortion in the structure of the complex, and how these changes can either lead to new protein function, or be used to model engineered protein variants with tailored properties. Finally, we show how integration of computational and experimental results can contribute in synergy to the understanding of fundamental processes at the biomolecular scale.

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Wie kam das polynesische Wort «Tabu» nach Europa, die Kalaschnikow auf die Flagge Mosambiks? Finden Sie unterhaltsame Antworten im Projekt «KulturTransfer».

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Several western Swiss German dialects roughly grouped around the nation's capital Bern show /l/ > [u] vocalization in various contexts. The spatial boundaries of /l/-vocalization in Swiss German are suspected to have been expanding since being described in the Linguistic Atlas of German-Speaking Switzerland in the middle of the 20th century. The present study assesses the overall expansion of /l/-vocalization by means of a rapid anonymous survey in 20 urban regional centers situated just beyond the traditional boundaries of /l/-vocalization highlighted by the Atlas. Results show that the expansion of /l/-vocalization mainly progresses in southeasterly, southerly, and westerly directions, but with much less success to the north and northwest, where the equally influential dialectal areas of Basel and Zürich seem to exert opposing influences. Further analysis of the data indicates that somewhat differing constraint hierarchies are at work in the different places to which vocalization has diffused.

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Femoroacetabular impingement (FAI) before or after Periacetabular Osteotomy (PAO) is surprisingly frequent and surgeons need to be aware of the risk preoperatively and be able to avoid it intraoperatively. In this paper we present a novel computer assisted planning and navigation system for PAO with impingement analysis and range of motion (ROM) optimization. Our system starts with a fully automatic detection of the acetabular rim, which allows for quantifying the acetabular morphology with parameters such as acetabular version, inclination and femoral head coverage ratio for a computer assisted diagnosis and planning. The planned situation was optimized with impingement simulation by balancing acetabuar coverage with ROM. Intra-operatively navigation was conducted until the optimized planning situation was achieved. Our experimental results demonstrated: 1) The fully automated acetabular rim detection was validated with accuracy 1.1 ± 0.7mm; 2) The optimized PAO planning improved ROM significantly compared to that without ROM optimization; 3) By comparing the pre-operatively planned situation and the intra-operatively achieved situation, sub-degree accuracy was achieved for all directions.

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Software dependencies play a vital role in programme comprehension, change impact analysis and other software maintenance activities. Traditionally, these activities are supported by source code analysis; however, the source code is sometimes inaccessible or difficult to analyse, as in hybrid systems composed of source code in multiple languages using various paradigms (e.g. object-oriented programming and relational databases). Moreover, not all stakeholders have adequate knowledge to perform such analyses. For example, non-technical domain experts and consultants raise most maintenance requests; however, they cannot predict the cost and impact of the requested changes without the support of the developers. We propose a novel approach to predicting software dependencies by exploiting the coupling present in domain-level information. Our approach is independent of the software implementation; hence, it can be used to approximate architectural dependencies without access to the source code or the database. As such, it can be applied to hybrid systems with heterogeneous source code or legacy systems with missing source code. In addition, this approach is based solely on information visible and understandable to domain users; therefore, it can be efficiently used by domain experts without the support of software developers. We evaluate our approach with a case study on a large-scale enterprise system, in which we demonstrate how up to 65 of the source code dependencies and 77% of the database dependencies are predicted solely based on domain information.

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Software corpora facilitate reproducibility of analyses, however, static analysis for an entire corpus still requires considerable effort, often duplicated unnecessarily by multiple users. Moreover, most corpora are designed for single languages increasing the effort for cross-language analysis. To address these aspects we propose Pangea, an infrastructure allowing fast development of static analyses on multi-language corpora. Pangea uses language-independent meta-models stored as object model snapshots that can be directly loaded into memory and queried without any parsing overhead. To reduce the effort of performing static analyses, Pangea provides out-of-the box support for: creating and refining analyses in a dedicated environment, deploying an analysis on an entire corpus, using a runner that supports parallel execution, and exporting results in various formats. In this tool demonstration we introduce Pangea and provide several usage scenarios that illustrate how it reduces the cost of analysis.

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The domain of context-free languages has been extensively explored and there exist numerous techniques for parsing (all or a subset of) context-free languages. Unfortunately, some programming languages are not context-free. Using standard context-free parsing techniques to parse a context-sensitive programming language poses a considerable challenge. Im- plementors of programming language parsers have adopted various techniques, such as hand-written parsers, special lex- ers, or post-processing of an ambiguous parser output to deal with that challenge. In this paper we suggest a simple extension of a top-down parser with contextual information. Contrary to the tradi- tional approach that uses only the input stream as an input to a parsing function, we use a parsing context that provides ac- cess to a stream and possibly to other context-sensitive infor- mation. At a same time we keep the context-free formalism so a grammar definition stays simple without mind-blowing context-sensitive rules. We show that our approach can be used for various purposes such as indent-sensitive parsing, a high-precision island parsing or XML (with arbitrary el- ement names) parsing. We demonstrate our solution with PetitParser, a parsing-expression grammar based, top-down, parser combinator framework written in Smalltalk.

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Polymorphism, along with inheritance, is one of the most important features in object-oriented languages, but it is also one of the biggest obstacles to source code comprehension. Depending on the run-time type of the receiver of a message, any one of a number of possible methods may be invoked. Several algorithms for creating accurate call-graphs using static analysis already exist, however, they consume significant time and memory resources. We propose an approach that will combine static and dynamic analysis and yield the best possible precision with a minimal trade-off between used resources and accuracy.

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Extraction of both pelvic and femoral surface models of a hip joint from CT data for computer-assisted pre-operative planning of hip arthroscopy is addressed. We present a method for a fully automatic image segmentation of a hip joint. Our method works by combining fast random forest (RF) regression based landmark detection, atlas-based segmentation, with articulated statistical shape model (aSSM) based hip joint reconstruction. The two fundamental contributions of our method are: (1) An improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the atlas-based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Validation on 30 hip CT images show that our method achieves high performance in segmenting pelvis, left proximal femur, and right proximal femur surfaces with an average accuracy of 0.59 mm, 0.62 mm, and 0.58 mm, respectively.

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« Les littératures nationales ne signifient plus grand-chose aujourd’hui. Nous entrons dans l’ère de la littérature mondiale et il appartient à chacun de nous de hâter cette évolution » (Goethe). Milan Kundera, écrivain natif de Moravie ayant émigré en France en 1975, reprend à son compte cet idéal formulé en 1827 par Goethe et le réactive : selon lui, en effet, le seul contexte où peuvent véritablement se révéler le sens et la valeur d’une œuvre est le grand contexte de l’histoire supranationale. Dans cet espace de diversité où l’œuvre n’est pas soutenue par son prestige national, mais par son originalité, un roman tchèque (littérature d’une petite nation) aussi bien qu’un roman français (littérature d’une grande nation) peut trouver sa place. Comment faire une histoire de la littérature mondiale sans la présenter comme une juxtaposition d’histoires littéraires nationales ? C’est la question à laquelle cette conférence s’efforcera de répondre en s’appuyant sur les réflexions culturelles aussi bien que politiques de Milan Kundera.

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This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.