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International audience; The High Level Trigger (HLT) of the ATLAS experiment at the Large Hadron Collider receives events which pass the LVL1 trigger at ~75 kHz and has to reduce the rate to ~200 Hz while retaining the most interesting physics. It is a software trigger and performs the reduction in two stages: the LVL2 trigger and the Event Filter (EF). At the heart of the HLT is the Steering software. To minimise processing time and data transfers it implements the novel event selection strategies of seeded, step-wise reconstruction and early rejection. The HLT is seeded by regions of interest identified at LVL1. These and the static configuration determine which algorithms are run to reconstruct event data and test the validity of trigger signatures. The decision to reject the event or continue is based on the valid signatures, taking into account pre-scale and pass-through. After the EF, event classification tags are assigned for streaming purposes. Several powerful new features for commissioning and operation have been added: comprehensive monitoring is now built in to the framework; for validation and debugging, reconstructed data can be written out; the steering is integrated with the new configuration (presented separately), and topological and global triggers have been added. This paper will present details of the final design and its implementation, the principles behind it, and the requirements and constraints it is subject to. The experience gained from technical runs with realistic trigger menus will be described.

We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy.

We present an overview of the CODICES project, an interdisciplinary approach for analysis of pre-Columbian collections of pictorial materials – more specifically, of Maya hieroglyphics. We discuss some of the main scientific and technical challenges that we have found in our work, and present a summary of our current technical achievements. This overview stresses the importance of thinking globally and acting both locally and globally with respect to developing approaches for cultural heritage preservation, research, and education.

This paper focuses on the normative notion of ‘good death’, its practical relevance as a frame of reference for ‘death work’ procedures in institutional elder care in Switzerland and the ways in which it may be challenged within migrant ‘dying trajectories’. In contemporary palliative care, the concept of ‘good death’ focuses on the ideal of an autonomous dying person, cared for under a specialised biomedical authority. Transferred to the nursing home context, characterised by long-term basic care for the very old under conditions of scarce resources, the notion of ‘good death’ is broken down into ready-to-use, pragmatic elements of daily routines. At the same time, nursing homes are increasingly confronted with socially and culturally diversified populations. Based on ethnographic findings, we give insight into current practices of institutional ‘death work’ and tensions arising between contradicting notions of a ‘good death’, by referring to decision-making, life-prolonging measures, notions on food/feeding and the administration of sedative painkillers.

We describe the overall organization of the CLEF 2003 evaluation campaign, with a particular focus on the cross-language ad hoc and domain-specific retrieval tracks. The paper discusses the evaluation approach adopted, describes the tracks and tasks offered and the test collections used, and provides an outline of the guidelines given to the participants. It concludes with an overview of the techniques employed for results calculation and analysis for the monolingual, bilingual and multilingual and GIRT tasks.

We propose an approach to categorize real-world natural scenes based on a semantic typicality measure. The proposed typicality measure allows to grade the similarity of an image with respect to a scene category. We argue that such a graded decision is appropriate and justified both from a human’s perspective as well as from the image-content point of view. The method combines bottom-up information of local semantic concepts with the typical semantic content of an image category. Using this learned category representation the proposed typicality measure also quantifies the semantic transitions between image categories such as coasts, rivers/lakes, forest, plains, mountains or sky/clouds. The method is evaluated quantitatively and qualitatively on a database of natural scenes. The experiments show that the typicality measure well represents the diversity of the given image categories as well as the ambiguity in human judgment of image categorization.

PREMIÈRE APPROCHE TERMINOLOGIQUE Dès le premier cours donné en 1861 par Gaston Paris, alors âge de vingt-deux ans, au quai Malaquais, la typologie de la littérature française du Moyen Âge qui sera celle du philologue pendant plus de quarante ans est fixée : Histoire de la littérature française du moyen âge1[è]re Leçon. — Préliminaires. — Origines, formation et phases successives de la langue.2e Leçon. — Anciennes traditions épiques.3e Leçon. — Les épopées Carlovingiennes. — La...

Ever-increasing amount of multimedia available online necessitates the development of new techniques and methods that can overcome the semantic gap problem. The said problem, encountered due to major disparities between inherent representational characteristics of multimedia and its semantic content sought by the user, has been a prominent research direction addressed by a great number of semantic augmentation approaches originating from such areas as machine learning, statistics, natural language processing, etc. In this paper, we review several of these recently developed techniques that bring together low-level representation of multimedia and its semantics in order to improve the efficiency of access and retrieval. We also present a distance-based discriminant analysis (DDA) method that defines the design of a basic building block classifier for distinguishing among a selected number of semantic categories. In addition to that, we demonstrate how a set of DDA classifiers can be grouped into a hierarchical ensemble for prediction of an arbitrary set of semantic classes.

There is magic (or is it witchcraft?) in a speech recognizer that transcribes continuous radio speech into text with a word accuracy of even not more than 50%. The extreme difficulty of this task, tough, is usually not perceived by the general public. This is because we are almost deaf to the infinite acoustic variations that accompany the production of vocal sounds, which arise from physiological constraints (co-articulation), but also from the acoustic environment (additive or convolutional noise, Lombard effect), or from the emotional state of the speaker (voice quality, speaking rate, hesitations, etc.)46. Our consciousness of speech is indeed not stimulated until after it has been processed by our brain to make it appear as a sequence of meaningful units: phonemes and words. In this Chapter we will see how statistical pattern recognition and statistical sequence recognition techniques are currently used for trying to mimic this extraordinary faculty of our mind (4.1). We will follow, in Section 4.2, with a MATLAB-based proof of concept of word-based automatic speech recognition (ASR) based on Hidden Markov Models (HMM), using a bigram model for modeling (syntactic-semantic) language constraints.

Astrophysics deals with important questions characterizing human nature. Most of our knowledge was obtained in the last century. The data resulting from these explorations, collected in space or on ground, are of great value, encoding our knowledge of the Universe, of our past and of our future. With the emergence of clouds and deep learning, high level interfaces to these data could evolve towards providing direct access to knowledge. The United Nations Office for Outer Space Affairs can play a role towards a consensus in establishing a code of conduct on data handling and promoting the data and knowledge on the Universe as an intangible world cultural heritage.