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Resume Introduction Le present article a pour objectif de presenter le role de l’IDE dans la mise en place de sejours d’hyper-preparation (pre-habilitation) et d’un programme de prostatectomie totale robotique en ambulatoire. Methodes Cet article est base sur l’analyse de la litterature et l’experience de notre centre. Resultats Afin de preparer les patients a l’intervention chirurgicale en ambulatoire, l’IDE a un role primordial a joue lors des sejours d’« hyper-preparation » (ou pre-habilitation). Deux semaines avant l’intervention, les equipes soignantes (infirmiers, anesthesistes, kinesitherapeutes, etc.) organisent ainsi des ateliers pour expliquer aux patients les modalites de l’operation et les soins post-operatoires afin de dissiper d’eventuelles angoisses. Des entretiens individuels et en groupe, au sein d’ateliers specifiques (bas de contention, sonde urinaire, sexologie) sont notamment organises avec les IDE pour repondre a toutes les questions des patients. En association avec les protocoles plus classiques de Recuperation accelere apres chirurgie (RAAC), ces programmes de preparation du patient a la chirurgie ont permis d’ameliorer le vecu patient, les parametres operatoires, et de developper un programme de chirurgie ambulatoire qui represente actuellement environ un tiers des chirurgies. Conclusion L’implementation des protocoles peri-operatoires permettent d’ameliorer les resultats precoces de la prostatectomie totale robotique, mais egalement le vecu patient. L’IDE a un role primordial a jouer dans la mise en place de ces journees d’education patient et dans la preparation, l’information, l’accompagnement et le suivi du patient tout au long de son parcours d’opere, depuis l’accueil en HP jusqu’a la sortie du patient d’ambulatoire.

International audience; Abstract: Tracking data of marine predators are increasingly used in marine spatial management. We developed a spatial dataset with estimates of the monthly distribution of six pelagic seabird species breeding in the Northeast Atlantic. The dataset is based on year-round global location sensor (GLS) tracking data of 2356 adult seabirds from 2006-2019 from a network of seabird colonies, data describing the physical environment, and data on seabird population sizes. Tracking and environmental data were combined in monthly species distribution models (SDMs). Cross-validations were used to assess the transferability of models between years and breeding locations. The analyses showed that birds from colonies close to each other (1000 km) used colony-specific, and in many cases, non-overlapping habitats. Based on these results, the SDM from the nearest model colony was used to predict the distribution of all seabird colonies lying within a species-specific cut-off distance (400-500 km). The uncertainties in predictions were estimated by cluster bootstrap sampling. The resulting dataset consists of 4692 map layers, each layer predicting the densities of birds from a given species, colony and month across the North Atlantic. The dataset represents the annual distribution of 23.5 million adult pelagic seabirds, or 87% of the Northeast Atlantic breeding population of the study species. We show how the dataset can be used in population and spatial management applications, including the detection of population-specific nonbreeding habitats and identifying populations influenced by marine protected areas.

The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. RAATD consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. These datasets and accompanying syntheses provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, support modelling of predator distributions under future climate scenarios and create inputs that can be incorporated into decision making processes by management authorities. In this data paper, we present the compiled tracking data from research groups that have worked in the Antarctic since the 1990s. The data are publicly available through biodiversity.aq and the Ocean Biogeographic Information System. The archive includes tracking data from over 70 contributors across 12 national Antarctic programs, and includes data from 17 predator species, 4060 individual animals, and over 2.9 million observed locations., Measurement(s)geographic locationTechnology Type(s)digital curationFactor Type(s)temporal interval • geographic locationSample Characteristic – OrganismAptenodytes forsteri • Lobodon carcinophaga • Eudyptes chrysolophus • Thalassarche melanophrys • Phoebetria palpebrata • Aptenodytes patagonicus • Pygoscelis adeliae • Leptonychotes weddellii • Megaptera novaeangliae • Mirounga leonina • Arctocephalus gazella • Thalassoica antarctica • Eudyptes schlegeli • Diomedea exulans • Phoebetria fusca • Diomedea chrysostoma • Procellaria aequinoctialisSample Characteristic – Environmentpolar biomeSample Characteristic – LocationAntarctica Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11688876

International audience; Flight is intrinsically an energetically costly way of moving and birds have developed morphological,physiological and behavioural adaptations to minimize these costs. Central-place foraging seabirdscommute regularly between nesting and foraging areas, providing us with opportunities to investigatetheir behavioural response to environmental conditions that may affect flight, such as wind. Here wetested hypotheses on how wind conditions influence flight behaviour in situations devoid of the confoundingeffect that, for instance, active foraging behaviour can have on movement patterns. We studiedthe Antarctic petrel, Thalassoica antarctica, a seabird breeding far inland in Antarctica and commutingthrough vast ice-covered areas characterized by steady and strong winds as well as a strict absence offoraging opportunities. We combined the three-dimensional location data from 79 GPS tracks with atmosphericwind data over three consecutive breeding seasons (2011e2013) in order to assess individualflight responses to wind conditions. Antarctic petrels encountered generally unfavourable winds,particularly during return flights. Despite their capacity to adjust their speed and heading in order tomaintain constant track direction (compensation) in the strongest winds, they generally drifted as windstrengthened. Strong winds induced low-altitude flight. Birds tended to otherwise fly relatively high, butat altitudes with more favourable winds than what they would have encountered if flying higher. Ourresults show that commuting Antarctic petrels: (1) can tolerate a certain amount of drift according towind conditions and (2) might be more limited by their ability to assess drift, rather than compensate forit, at least during returning flights.

Interval constraint propagation (ICP) algorithms allow to solve problems described as constraint satisfaction problems (CSP). ICP has been successfully applied to vehicle localization in the last few years. Once the localization problem has been stated, a large class of ICP solvers can be used. This paper compares a few ICP algorithms, using the same experimental data, in order to rank their performances in terms of accuracy and computing time.

This paper provides a normative appraisal of the Canadian equalization transfers system. For that sake, the two-dimensional dominance criteria introduced by Atkinson and Bourguignon(RES, 1982) are used to compare the distributions of private and public good before and after equalization payments. As the distribution before equalization is not observable, one simulates it on the basis of various scenarios which specify both its financing by the federal government and its utilization by provincial governments. The results show that Canadian equalization payments never improve social welfare for all utilitarian social planners who believe that household convert public and private goods into well-being by a utility function belonging to the classes that corresponds to the Atkinson and Bourguignon criteria. A further parametric restriction on the class of utility functions however enables to get more precise results.

When people scan mental images, their response times increase linearly with increases in the distance to be scanned, which is generally taken as reflecting the fact that their internal representations incorporate the metric properties of the corresponding objects. In view of this finding, we investigated the structural properties of spatial mental images created from nonvisual sources in three groups (blindfolded sighted, late blind, and congenitally blind). In Experiment 1, blindfolded sighted and late blind participants created metrically accurate spatial representations of a small-scale spatial configuration under both verbal and haptic learning conditions. In Experiment 2, late and congenitally blind participants generated accurate spatial mental images after both verbal and locomotor learning of a full-scale navigable space (created by an immersive audio virtual reality system), whereas blindfolded sighted participants were selectively impaired in their ability to generate precise spatial representations from locomotor experience. These results attest that in the context of a permanent lack of sight, encoding spatial information on the basis of the most reliable currently functional system (the sensorimotor system) is crucial for building a metrically accurate representation of a spatial environment. The results also highlight the potential of spatialized audio-rendering technology for exploring the spatial representations of visually impaired participants.

When the environment changes, as is increasingly the case when considering unending streams and long-life learning tasks, it is necessary to rely on on-line learning with the capability to adapt to changing conditions a.k.a. concept drifts. Previous works have focused on means to detect changes and to adapt to them. Ensemble methods relying on committees of base learners have been among the most successful approaches. In this paper, we introduce a new second-order learning mechanism that is able to detect relevant states of the environment in order to recognize recurring contexts and act pro-actively to concepts changes. Empirical comparisons with existing methods on well-known data sets show the advantage of the proposed algorithm.

Learning from data streams in the presence of concept drifts has become an important application area. When the environment changes, it is necessary to rely on on-line learning with the capability to forget outdated information. Ensemble methods have been among the most successful approaches because they do not need hard-coded and difficult to obtain prior knowledge about the changes in the environment. However, the management of the committee of experts which ultimately controls how past data is forgotten has not been thoroughly investigated so far. This paper shows the importance of the forgetting strategy by comparing several approaches. The results lead us to propose a new ensemble method which compares favorably with the well-known CDC system based on the classical “replace the worst experts” forgetting strategy.

Among the existing texture segmentation methods, those relying on Markov random fields have retained substantial interest and have proved to be very efficient in supervised mode. The use of Markov random fields in unsupervised mode is, however, hampered by the parameter estimation problem. The recent solutions proposed to overcome this difficulty rely on assumptions about the shapes of the textured regions or about the number of textures in the input image that may not be satisfied in practice. In this paper, an evolutionary approach, selectionist relaxation, is proposed as a solution to the problem of segmenting Markov random field modeled textures in unsupervised mode. In selectionist relaxation, the computation is distributed among a population of units that iteratively evolves according to simple and local evolutionary rules. A unit is an association between a label and a texture parameter vector. The units whose likelihood is high are allowed to spread over the image and to replace the units that receive lower support from the data. Consequently, some labels are growing while others are eliminated. Starting with an initial random population, this evolutionary process eventually results in a stable labelization of the image, which is taken as the segmentation. In this work, the generalized Ising model is used to represent textured data. Because of the awkward nature of the partition function in this model, a high-temperature approximation is introduced to allow the evaluation of unit likelihoods. Experimental results on images containing various synthetic and natural textures are reported.

International audience; Autonomy and self-improvment capabilities are still challenging in the fields of robotics. Allowing a robot to autonomously navigate wide and unknown environments not only requires a set of robust strategies to cope with miscellaneous situations, but also needs mechanisms of self-assesment for guiding learning and monitoring strategies. Monitoring strategies requires feedbacks on the behavior's quality, from a given fitness system, to take correct decisions. In this work, we focuse on how an emotional controller can be used to modulate robot behaviors. Following an incremental and constructivist approach, we present a generic neural architecture, based on an online novelty detection algorithm, that may be able to self-evaluate any sensori-motor strategies. This architecture learns contingencies between sensations and actions, giving the expected sensation based from the previous perception. Prediction error, coming from surprising events, provides a direct measure of the quality of the underlying sensori-motor contingencies involved. We show how a simple emotional controller based on the prediction progress allows the system to monitor its strategies to solve complex navigation tasks and communicate its disability in deadlock situations. We propose that this model could be a key structure toward self-monitoring. We made several experiments that can account for such properties for different behaviors (road following and Place Cells based navigation).

International audience; Allowing a robot to autonomously navigate wide and unknown environments not only requires a set of robust strategies to cope with miscellaneous situations, but also needs mechanisms of self-assessment for guiding learning and monitoring strategies. Monitoring strategies requires feedbacks on the behavior's quality, from a given fitness system, to take correct decisions. In this work, we focus on how violations of expectations of such fitness system can be detected. Following an incremental and bio-mimetic approach, we first present two different sensorimotor strategies our robot can use to navigate: a Place Cells based strategy and a road following strategy. Then, we present a neural architecture that may be able to evaluate both navigation strategies. This model is based on an online novelty detection algorithm using a neural predictor. This neural predictor learns contingencies between sensations and actions, giving the expected sensation based from the previous perception. Prediction error, coming from surprising events, provides a direct measure of the quality of the underlying sensorimotor contingencies involved. We propose that this model might be a key structure toward self-assessment. We made several experiments that can account for such properties for both strategies.

Vehicle localization can be stated as a Constraint Satisfaction Problem (CSP). Interval Constraint Propagation (ICP) algorithms allow us to solve CSPs. This paper discusses the performance of some ICP algorithms applied to vehicle localization. Each of them is fed with the same data obtained with our experimental vehicle equipped with odometers, a gyro and a GPS receiver. Results present the efficiency of these algorithms in terms of heading and positioning accuracy. It is shown that local consistency algorithms do not correct the heading imprecision.

International audience; Autonomy and self-improvement capabilities are still challenging in the fields of robotics and machine learning. Allowing a robot to autonomously navigate in wide and unknown environments not only requires a repertoire of robust strategies to cope with miscellaneous situations, but also needs mechanisms of self-assessment for guiding learning and for monitoring strategies. Monitoring strategies requires feedbacks on the behavior's quality, from a given fitness system in order to take correct decisions. In this work, we focus on how a second-order controller can be used to (1) manage behaviors according to the situation and (2) seek for human interactions to improve skills. Following an incremental and constructivist approach, we present a generic neural architecture, based on an online novelty detection algorithm that may be able to self-evaluate any sensory-motor strategies. This architecture learns contingencies between sensations and actions, giving the expected sensation from the previous perception. Prediction error, coming from surprising events, provides a measure of the quality of the underlying sensory-motor contingencies. We show how a simple second-order controller (emotional system) based on the prediction progress allows the system to regulate its behavior to solve complex navigation tasks and also succeeds in asking for help if it detects dead-lock situations. We propose that this model could be a key structure toward self-assessment and autonomy. We made several experiments that can account for such properties for two different strategies (road following and place cells based navigation) in different situations.

This work investigates the influence of environmental inducers on the organization of cell regulation networks, using a connectionist approach. Protein interactions are modeled by an asymmetrical recurrent network, the units of which take continuous values. In contrast to classical models, we explicitly introduce a genome to encode the architecture of the system. This feature enables us to introduce an evolution model, in which a genetic algorithm that mimics the effects of evolution on proteins mutual interactions is used. We assume an efficient system to respond to persistent environmental stimuli, independently of their amplitude. Results are presented that show a structuration of the network with the emergence of specialized hierarchical structures. These structures seem to drive the system at the edge of chaos, so that it can present adapted responses to significant environmental changes.

International audience; In the present study, we propose a model of multimodal place cells merging visual and proprioceptive primitives. First we will briefly present our previous sensory-motor architecture, highlighting limitations of a visual-only based system. Then we will introduce a new model of proprioceptive localization, giving rise to the so-called grid cells, wich are congruent with neurobiological studies made on rodent. Finally we will show how a simple conditionning rule between both modalities can outperform visual-only driven models by producing ro- bust multimodal place cells. Experiments show that this model enhances robot localization and also allows to solve some benchmark problems for real life robotics applications.

A new methodological approach to digital image processing applied to the particular case of gray-level image segmentation is introduced. The method is based on a modified and simplified version of classifier systems. The labeling function is implemented as a spatially structured set of binary-coded production rules. The labeling is iteratively modified using a distributed genetic algorithm. Results are presented which illustrate both the mechanisms underlying the functioning of the method and its performance on natural images. The relationships between this approach and other related techniques are discussed and it is shown that it compares favorably with these.

In on-line machine learning, predicting changes is not a trivial task. In this paper, a novel prediction approach is presented, that relies on a committee of experts. Each expert is trained on a specific history of changes and tries to predict future changes. The experts are constantly modified based on their performance and the committee as a whole is thus dynamic and can adapt to a large variety of changes. Experimental results based on synthetic data show three advantages: (a) it can adapt to different types of changes, (b) it can use different types of prediction models and (c) the committee outperforms predictors trained on a priori fixed size history of changes.

International audience; We propose an implementation of covert attention mechanisms with spiking neurons. Spiking neural models describe the activity of a neuron with precise spike-timing rather than firing rate. We investigate the interests offered by such a temporal code for low-level vision and early attentional process. This paper describes a spiking neural network which achieves saliency extraction and stable attentional focus of a moving stimulus. Experimental results obtained using real visual scene illustrate the robustness and the quickness of this approach.

In many species, neurons with highly selective stimulus-response properties characterize higher order sensory areas and/or sensory motor areas of the CNS. In the songbird nuclei, the responses of HVC (used as a proper name) neurons during playback of the bird's own song (BOS) are probably one of the most striking examples of selectivity for natural stimuli. We examined here to what extent spike-timing carries information about natural and time-reversed versions of the BOS. From a heterogenous population of 107 HVC neurons recorded in long-day or short-day conditions, a standard indicator of stimulus preference based on spike-count (the d' index) indicates that a limited proportion of cells can be classified as selective for the BOS (20% with a |d'| > 1). In contrast, quantifying the information conveyed by spike trains with the metric-space of J.D. Victor & K.P Purpura [(1996) J. Neurophysiol., 76, 1310-1326] indicates that 62% of the cells display significant amounts of transmitted information, among which 77% are 'temporal cells'. 'Temporal cells' correspond to cells transmitting significant amounts of information when spike-timing is considered, whereas no information, or lower amounts of transmitted information, is obtained when only spike-count is considered. Computing a correlation index between spike trains [S. Schreiber et al. (2003) Neurocomputing, 52-54,925-931] revealed that spike-timing reliability is higher for the forward than for the reverse BOS, whatever the day length and the cell type are. Cells classified as selective in terms of spike-counts (d' index) had greater amounts of transmitted information, but cells classified as non-selective (d' < 0.5) can also transmit significant amounts of information. Thus, information theory methods demonstrate that a much larger proportion of neurons than expected based on spike-count only participate in the discrimination between stimuli.

Objective To evaluate the safety and effectiveness of surgery using scleral expansion bands. Design Prospective, noncomparative, small case series. Participants Six subjects were enrolled. Four subjects received implants in one eye and two subjects received implants in both eyes. Methods Implantation of the scleral expansion bands (SEB) was performed using Schachar's standard technique. Subjects were observed at six postoperative examination intervals through 1 year. Main outcome measures The key efficacy measures were distance-corrected near visual acuity (at 40 cm and 60 cm) and subjective amplitude of accommodation (push-up, push-down, and minus lens procedures). Results Distance visual acuity was similar before and after surgery. The near visual acuity and the subjective amplitude of accommodation were temporarily improved in three eyes. However, in the other five eyes, we did not note any improvement of accommodation or near vision after the scleral expansion surgery. Conclusions In this study, the outcome of the SEB intervention was characterized by inconsistent and unpredictable results with a low level of patient satisfaction.

This paper presents a visual architecture able to identify salient regions in a visual scene and to use them to focus on interesting locations. It is inspired by the ability of natural vision systems to perform a differential processing of spatial frequencies in both time and space and to focus their attention on a local part of the visual scene. The present paper analyzes how this differential processing of spatial frequencies is able to provide an artificial system with the information required to perform an exploration of its visual world based on a center-surround distinction of the external scene. It shows how the salient locations can be gathered on the basis of their similarities to form a high level representation of the visual scene.

We introduced an unsupervised texture segmentation method, the selectionist relaxation, relying on a Markov Random Field (MRF) texture description and a genetic algorithm based relaxation scheme. It has been shown elsewhere that this method is convenient for achieving a parallel and reliable estimation of MRF parameters and consequently a correct image segmentation. Nevertheless, these results have been obtained with an order 2 model on artificial textures. The purpose of the present work is to extend the use of this technique to higher orders and to show that it is suitable for the segmentation of natural textures, which require orders higher than 2 to be accurately described. The results reported here have been obtained using the generalized Ising model but the method can be easily transposed to other models.

This paper describes various methods suitable for implementation of two-dimensional processing software. The different steps leading to a complete processing are described, from the digitalization of the image to the processing of the resulting data. The characteristics of a convenient digitalization system are discussed. The different software devoted to spot detection is reviewed with respect to the presence or otherwise of a spot model and its characteristics. The major techniques for gel matching are compared as are designs for database structures suitable for tabulation of measurements. Finally, the need for a sophisticated system of data processing is stressed and its main requirements are described.