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Le groupe de travail sur l’enseignement supérieur (GTES) est un réseau d’échanges et de production sur les parcours de formation et d’insertion des étudiant·e·s de l’enseignement supérieur. Il réunit des chargé·e·s d’études du Céreq, des chercheur·e·s et enseignant·e·s chercheur·e·s, des représentant·e·s de l’Observatoire national de la vie étudiante (OVE), des observatoires universitaires et régionaux, des membres de services du MESRI (DGESIP et SIES), de l’agence ERASMUS + France et de l’Institut national de la jeunesse et de l’éducation populaire (INJEP). Les dernières journées ont eu lieu à Marseille les 6 et 7 juin 2019. Quatre projets d’ouvrages y ont été présentés dont celui-ci qui porte sur les reprises d’études à l’université. Qui sont ces publics de plus en plus nombreux ? Comment les repérer ? Quelles sont leurs motivations ? Quelle place prend la reprise d’études dans leurs trajectoires personnelle et professionnelle ? S’inscrit-elle comme une deuxième chance, en continuité d’un parcours antérieur ? Est-elle toujours formalisée par un dispositif de formation professionnelle continue ? Autant d’interrogations que les différentes contributions (construites à partir de sources et de méthodes d’analyses différentes) se proposent d’explorer en dressant un portrait de cette catégorie d’étudiants dont l’hétérogénéité des situations et des profils n’est plus à démontrer.

Cet article rend compte de l’expérimentation conduite au sein d’Aix-Marseille Université (AMU) concernant la mise en place d’un suivi des étudiants en reprise d’études relevant du régime de la formation continue (FC). La première partie précise le contexte institutionnel dans lequel a été conduite cette expérimentation (cadre légal national, complexité de l’identification des publics « formation continue », spécificités des pratiques internes à l’établissement). La deuxième partie présente la...

Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Instituto de Salud Carlos III (ISCIII) (PI10/02763, PI10/02745, PI18/00449, PI19/00907), co-funded by FEDER “Una manera de hacer Europa”, SEPAR, Catalonian Cardiology Society, ResMed Ltd. (Australia), EsteveTeijin (Spain), Oxigen Salud (Spain), Associació Lleidatana de Respiratori (ALLER), CIBERES. MS received financial support from a "Ramón y Cajal" grant (RYC2019-027831-I) from the“Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación” co-funded by the European Social Fund (ESF)/“Investing in your future”. AZ held the predoctoral fellowship “Ajuts 2021 de Promoció de la Recerca en Salut-9ª edició” from IRBLleida/Diputació de Lleida. JdB acknowledges receiving financial support from ISCIII (Miguel Servet 2019: CP19/00108), co-funded by the European Social Fund (ESF), “Investing in your future”. Rationale Obstructive sleep apnea (OSA) is prevalent in acute coronary syndrome (ACS) patients and is a cause of secondary hypertension. Objectives To evaluate the long-term effects of OSA and CPAP treatment on blood pressure (BP) control in patients discharged after an ACS. Methods Post hoc analysis of the ISAACC study included 1803 patients admitted for ACS (NCT01335087). Patients with OSA (apnea-hypopnea index ≥15 events/h) were randomly assigned to receive either CPAP or/and usual care and followed up for one to 5 years. Office BP was determined at each visit. Measurements and Main Results We included 596 patients without OSA, 605 patients in the CPAP group, and 602 patients in the usual care group. 52% of the patients had a diagnosis of hypertension at baseline. Median age and body mass index were 59 [52.0;67.0] years and 28.2 [25.6;31.2] kg/m2, respectively. After a median [25th;75th percentile] follow-up of 41.2 [18.3;59.6] months, BP changes were similar between OSA and non-OSA groups. However, we observed an increase in BP in the third tertile of the AHI (AHI>40 events/h) with a maximum difference in mean BP of +3.3 mmHg at 30 months. OSA patients with good CPAP adherence (≥4 hours/night) reduced mean BP after 18 months compared to non-OSA and poor CPAP adherence patients, maximum mean difference (95% CI) of -4.7 (-6.7,-2.7) mmHg. In patients with severe OSA we observed a maximum mean difference of -7.1 (-10.3,-3.8) mmHg. Conclusions In patients discharged after an ACS, severe OSA is associated with a long-term increase in BP, which is reduced by good CPAP adherence.

Rationale: Obstructive sleep apnea (OSA) is prevalent in patients with acute coronary syndrome (ACS) and is a cause of secondary hypertension. Objectives: To explore the long-term effects of OSA and continuous positive airway pressure (CPAP) treatment on blood pressure (BP) in patients with ACS. Methods: Post hoc analysis of the ISAACC study (Continuous Positive Airway Pressure in Patients with Acute Coronary Syndrome and Obstructive Sleep Apnea; NCT01335087) included 1,803 patients admitted for ACS. Patients with OSA (apnea-hypopnea index [AHI], >= 15 events/h) were randomly assigned to receive either CPAP or usual care and were seen in follow-up for 1-5 years. Office BP was determined at each visit. Results: We included 596 patients without OSA, 978 patients in the usual care or poor CPAP adherence group, and 229 patients in the good CPAP adherence group. At baseline, 52% of the patients were diagnosed with hypertension. Median (25th to 75th percentile) age and body mass index were 59 (52.0 to 67.0) years and 28.2 (25.6 to 31.2) kg/m(2), respectively. After a median (25th to 75th percentile) follow-up of 41.2 (18.3 to 59.6) months, BP changes were similar in the OSA and non-OSA groups. However, we observed an increase in BP in the third tertile of the AHI (AHI, >40 events/h), with a maximum difference in mean BP of +3.3 mm Hg at 30 months. Patients with OSA with good CPAP adherence (>= 4 h/night) reduced mean BP after 18 months compared with patients with usual care/poor CPAP adherence, with a maximum mean difference (95% confidence interval) of -4.7 (-6.7 to -2.7) mm Hg. In patients with severe OSA, we observed a maximum mean difference of -7.1 (-10.3 to -3.8) mm Hg. Conclusions: In patients with ACS, severe OSA is associated with a long-term increase in BP, which is reduced by good CPAP adherence.

Rationale: Obstructive sleep apnea (OSA) is associated with increased cardiovascular disease (CVD) risk. Conversely, OSA has not been shown to increase recurrent cardiovascular events in patients with acute coronary syndrome (ACS). This lack of homogeneity could suggest that the deleterious effect of OSA and its contribution to CVD could depend on specific patient profiles. Objectives: To evaluate the effect of OSA on cardiovascular risk for patients with different ACS phenotypes. Methods: Post hoc analysis of the ISAACC (Continuous Positive Airway Pressure in Patients with ACS and OSA) study, including 1,701 patients admitted for ACS (NCT01335087). To evaluate the presence of OSA (apnea-hypopnea index = 15 event.h(-1)), all patients underwent polygraphy. Patients were followed up for a minimum period of 1 year. We performed nonsupervised clustering using latent class analysis to identify subgroups of patients on the basis of 12 clinical factors associated with cardiovascular risk. The effect of OSA on recurrent cardiovascular event risk was evaluated for each phenotype identified. Measurements and Main Results: Two phenotypes were identified: patients without previous heart disease and without previous ACS ("no-previous-CVD" phenotype; 81%) and patients with previous heart disease and previous ACS ("previous-CVD" phenotype; 19%). The median (interquartile range) at follow-up was 2.67 (3.8) years. For the no-previous-CVD phenotype, the effect of OSA showed an adjusted hazard ratio (95% confidence interval) of 1.54 (1.06-2.24; P value = 0.02), whereas for the previous-CVD phenotype, the effect of OSA showed an adjusted hazard ratio of 0.69 (0.46-1.04; P value = 0.08). Conclusions: For patients with ACS and a specific phenotype, OSA is associated with an increased risk of recurrent cardiovascular events. These patients are mainly characterized by no previous heart disease and admission for a first ACS occurrence.

Background Despite the improvement in the prognosis of acute coronary syndrome (ACS), substantial morbidity and mortality remain. We aimed to evaluate the effect of obstructive sleep apnoea (OSA) and its treatment with continuous positive airway pressure (CPAP) on the clinical evolution of patients with ACS. Methods We designed a multicentre, open-label, parallel-group, randomised controlled trial of patients with ACS at 15 hospitals in Spain. Eligible non-sleepy patients were men and women aged 18 years and older, admitted to hospital for documented symptoms of ACS. All patients underwent respiratory polygraphy during the first 24-72 h after admission. OSA patients were randomly assigned (1:1) to CPAP treatment plus usual care (CPAP group) or usual care alone (UC group) by a computerised system available 24 h a day. A group of patients with ACS but without OSA was also included as a reference group. Because of the nature of the intervention, the trial intervention could not be masked to either investigators or patients. Patients were monitored and followed for a minimum of 1 year. Patients were examined at the time of inclusion; after 1 month, 3 months, 6 months, 12 months, 18 months, 24 months, 30 months, and 36 months; and every 12 months thereafter, if applicable, during the follow-up period. The primary endpoint was the prevalence of a composite of cardiovascular events (cardiovascular death or non-fatal events [Acute myocardial infarction, non-fatal stroke, hospital admission for heart failure, and new hospitalisations for unstable angina or transient ischaemic attack]) in patients followed up for a minimum of 1 year. The primary analysis was done according to the intention-to-treat principle. This study is registered with Clinicaltrials.gov, NCT01335087 and is now closed. Findings Between April 25, 2011, and Feb 2, 2018, a total of 2834 patients with ACS had respiratory polygraphy, of whom 2551 (90 center dot 01%) were recruited. 1264 (49 center dot 55%) patients had OSA and were randomly assigned to the CPAP group (n=633) or the UC group (n=631). 1287 (50 center dot 45%) patients did not have OSA, of whom 603 (46 center dot 85%) were randomly assigned to the reference group. Patients were followed up for a median of 3 center dot 35 years (IQR 1 center dot 50-5 center dot 31). The prevalence of cardiovascular events was similar in the CPAP and UC groups (98 events [16%] vs 108 events [17%]; hazard ratio [HR] 0 center dot 89 [95% CI 0 center dot 68-1 center dot 17]; p=0 center dot 40) during follow-up. Mean time of adherence to CPAP treatment was 2 center dot 78 h/night (SD 2 center dot 73). The prevalence of cardio-vascular events was similar between patients in the reference group (90 [15%] events) and those in the UC group (102 (17%) events) during follow-up (1 center dot 01 [0 center dot 76-1 center dot 35]; p=0 center dot 93). The prevalence of cardiovascular events seem not to be related to CPAP compliance or OSA severity. 464 (74%) of 629 patients in the CPAP group had 1538 serious adverse events and 406 (65%) of 626 patients in the UC group had 1764 serious adverse events. Interpretation Among non-sleepy patients with ACS, the presence of OSA was not associated with an increased prevalence of cardiovascular events and treatment with CPAP did not significantly reduce this prevalence. Copyright (c) 2019 Elsevier Ltd. All rights reserved.

Na América do Sul, historicamente, os projetos de integração regional são acompanhados de discursos desenvolvimentistas que defendem o crescimento econômico. A zona transfronteiriça entre Brasil, Argentina e Paraguai, a região histórica das Missões Jesuíticas, vem sofrendo as consequências de projetos de integração energética que ocorrem desde a década de 1970. No início do século XXI, o advento da IIRSA-COSIPLAN abriu um novo mercado a grandes empresas envolvidas nos projetos de integração de infraestruturas na América do Sul. Não obstante, obras em nome da integração energética geram efeitos negativos, como danos ambientais, remoções, gentrificação e exclusão social. Na defesa de seus direitos, populações atingidas pelas novas hidrelétricas de Panambi e Garabi precisam se organizar por meio de movimentos sociais, como o MAB. In South American history, regional integration projects are commonly followed by developmental speeches advocating economic growth. The Tri-Border Area between Brazil, Argentina and Paraguay, the historical region of the Jesuit reductions, has been suffering the consequences of energy integration projects that take place since the 1970s. The emergence of IIRSA-COSIPLAN initiative in the beginning of the XXI century has opened a new market for large companies involved in integration projects of infrastructure. The current energy integration projects have negative effects, such as environmental damages, removals, gentrification and social exclusion. People affected by projects as the new hydroelectric power stations Panambi and Garabi have been fighting on behalf of their rights. In this struggle they need to organize themselves in social movements, such as MAB.

BackgroundAn analysis of cardiac injury markers in patients with OSA who sustain an episode of acute coronary syndrome (ACS) may contribute to a better understanding of the interactions and impact of OSA in subjects with ACS. We compared peak cardiac troponin I (cTnI) levels in patients with OSA and patients without OSA who were admitted for ACS.MethodsBlood samples were collected every 6 hours from the time of admission until two consecutive assays showed a downward trend in the cTnI assay. The highest value obtained defined the peak cTnI value, which provides an estimate of infarct size.ResultsWe included 89 patients with OSA and 38 patients without OSA with an apnea-hypopnea index of a median of 32 (interquartile range [IQR], 20.8-46.6/h and 4.8 [IQR, 1.6-9.6]/h, respectively. The peak cTnI value was significantly higher in patients without OSA than in patients with OSA (median, 10.7ng/mL [IQR, 1.78-40.1ng/mL] vs3.79ng/mL [IQR, 0.37-24.3ng/mL]; P= .04). The multivariable linear regression analysis of the relationship between peak cTnI value and patient group, age, sex, and type of ACS showed that the presence or absence of OSA significantly contributed to the peak cTnI level, which was 54%lower in patients with OSA than in those without OSA.ConclusionsThe results of this study suggest that OSA has a protective effect in the context of myocardial infarction and that patients with OSA may experience less severe myocardial injury. The possible role of OSA in cardioprotection should be explored in future studies.

IEEE robotics & automation magazine 24(3), 146-156 (2017). doi:10.1109/MRA.2016.2636359, Published by IEEE, New York, NY

Using geographic information systems (GIS) and remote sensing integration to determine runoff caused due to rainfall from watershed has performed increasingly attention in recent years. This study was conducted in the Khartoum State. Curve Number (CN) method was applied for estimating the runoff depth in the watershed. Hydrologic soil group and land use maps were generated in GIS Environment. Hydrologic soil groups and land use maps were used to generate the CN map. Soil Conservation Services-Curve number (SCS-CN) method was followed to estimate runoff for the watershed. It was found that the model can predict runoff reasonably well. It could be concluded that the SCS-CN method can be applied to predict runoff volumes for planning of various conservation measures and for other water resources applications. Key words: Soil Conservation Services-Curve number (SCS-CN), geographic information systems (GIS), remote sensing, runoff estimation.

Pipelines to recognize 3D objects despite clutter and occlusions usually end up with a final verification stage whereby recognition hypotheses are validated or dismissed based on how well they explain sensor measurements. Unlike previous work, we propose a Global Hypothesis Verification (GHV) approach which regards all hypotheses jointly so as to account for mutual interactions. GHV provides a principled framework to tackle the complexity of our visual world by leveraging on a plurality of recognition paradigms and cues. Accordingly, we present a 3D object recognition pipeline deploying both global and local 3D features as well as shape and color. Thereby, and facilitated by the robustness of the verification process, diverse object hypotheses can be gathered and weak hypotheses need not be suppressed too early to trade sensitivity for specificity. Experiments demonstrate the effectiveness of our proposal, which significantly improves over the state-of-art and attains ideal performance (no false negatives, no false positives) on three out of the six most relevant and challenging benchmark datasets.

Eine Hauptaufgabe fur Roboter ist es, Objekte und Objektklassen zu erkennen, um diese zu finden und handzuhaben. Bild-basierte Ansatze lernen aus grosen Datenbanken, haben aber keinen Zugriff auf Kontextwissen zur Verfugung, zum Beispiel, wie Roboter in Zimmern navigieren. Wir schlagen daher den Ansatz des situierten Sehens vor, um kontextuelles Wissen uber die Aufgabe und die Anwendung zur Verbesserung der Objekterkennung zu verwenden. Basierend auf der Bestimmung des freien Bodens vor dem Roboter, der fur die sichere Navigation notwendig ist, zeigen wir, dass dadurch die Lokalisierung, das Erkennung von Flachen und die Kategorisierung von Objekten vereinfacht werden. Wir zeigen ferner, dass Objektklassen effizient aus 3D-Web-Daten gelernt werden konnen, wenn das Lernen virtuelle 2,5D-Ansichten verwendet, um die Sicht der Sensoren des Roboters auf die reale Welt anzunehmen. Mit diesem Ansatz wurden 200 Objektklassen (zu finden unter www.3d-net.org) modelliert und in Szenen erkannt, z. B. Stuhle mit einer Erkennungsrate von 93 Prozent.

This paper proposes an effective algorithm for recognizing objects and accurately estimating their 6DOF pose in scenes acquired by a RGB-D sensor. The proposed method is based on a combination of different recognition pipelines, each exploiting the data in a diverse manner and generating object hypotheses that are ultimately fused together in an Hypothesis Verification stage that globally enforces geometrical consistency between model hypotheses and the scene. Such a scheme boosts the overall recognition performance as it enhances the strength of the different recognition pipelines while diminishing the impact of their specific weaknesses. The proposed method outperforms the state-of-the-art on two challenging benchmark datasets for object recognition comprising 35 object models and, respectively, 176 and 353 scenes.

This work presents a flexible system to reconstruct 3D models of objects captured with an RGB-D sensor. A major advantage of the method is that unlike other modelling tools, our reconstruction pipeline allows the user to acquire a full 3D model of the object. This is achieved by acquiring several partial 3D models in different sessions—each individual session presenting the object of interest in different configurations that reveal occluded parts of the object — that are automatically merged together to reconstruct a full 3D model. In addition, the 3D models acquired by our system can be directly used by state-of-the-art object instance recognition and object tracking modules, providing object-perception capabilities to complex applications requiring these functionalities (e.g. human-object interaction analysis, robot grasping, etc.). The system does not impose constraints in the appearance of objects (textured, untextured) nor in the modelling setup (moving camera with static object or turn-table setups with static camera). The proposed reconstruction system has been used to model a large number of objects resulting in metrically accurate and visually appealing 3D models.

This paper presents a novel method to localize and segment objects on close-range table-top scenarios acquired with a depth sensor. The method is based on a novel objectness measure that evaluates how likely a 3D region in space (defined by an oriented 3D bounding box) could contain an object. Within a parametrized volume of interest placed above the table plane, a set of 3D bounding boxes is generated that exhaustively covers the parameter space. Efficiently evaluating — thanks to integral volumes and parallel computing— the 3D objectness at each sampled bounding box allows defining a set of regions in space with high probability of containing an object. Bounding boxes characterized by high objectness are then processed by means of a global optimization stage aimed at discarding inconsistent object hypotheses with respect to the scene. We evaluate the effectiveness of the method for the task of scene segmentation.

We propose a method for recognizing rigid object instances in RGB-D point clouds by accumulating low-level information from keypoint correspondences over multiple observations. Compared to existing multi-view approaches, we make fewer assumptions on the recognition problem, dealing with cluttered and partially dynamic environments as well as covering a wide range of objects. Evaluation on the publicly available TUW and Willow datasets showed that our method achieves state-of-the-art recognition performance for challenging sequences of static environments and a significant improvement for environments partially changing during the observation.

We propose a novel method to estimate a unique and repeatable reference frame in the context of 3D object recognition from a single viewpoint based on global descriptors. We show that the ability of defining a robust reference frame on both model and scene views allows creating descriptive global representations of the object view, with the beneficial effect of enhancing the spatial descriptiveness of the feature and its ability to recognize objects by means of a simple nearest neighbor classifier computed on the descriptor space. Moreover, the definition of repeatable directions can be deployed to efficiently retrieve the 6DOF pose of the objects in a scene. We experimentally demonstrate the effectiveness of the proposed method on a dataset including 23 scenes acquired with the Microsoft Kinect sensor and 25 full-3D models by comparing the proposed approach with state-of-the-art global descriptors. A substantial improvement is presented regarding accuracy in recognition and 6DOF pose estimation, as well as in terms of computational performance.

The ability to perceive possible interactions with the environment is a key capability of task-guided robotic agents. An important subset of possible interactions depends solely on the objects of interest and their position and orientation in the scene. We call these object-based interactions 0-order affordances and divide them among non-hidden and hidden whether the current configuration of an object in the scene renders its affordance directly usable or not. Conversely to other works, we propose that detecting affordances that are not directly perceivable increase the usefulness of robotic agents with manipulation capabilities, so that by appropriate manipulation they can modify the object configuration until the seeked affordance becomes available. In this paper we show how 0-order affordances depending on the geometry of the objects and their pose can be learned using a supervised learning strategy on 3D mesh representations of the objects allowing the use of the whole object geometry. Moreover, we show how the learned affordances can be detected in real scenes obtained with a low-cost depth sensor like the Microsoft Kinect through object recognition and 6D0F pose estimation and present results for both learning on meshes and detection on real scenes to demonstrate the practical application of the presented approach.

We propose a novel approach for verifying model hypotheses in cluttered and heavily occluded 3D scenes. Instead of verifying one hypothesis at a time, as done by most state-of-the-art 3D object recognition methods, we determine object and pose instances according to a global optimization stage based on a cost function which encompasses geometrical cues. Peculiar to our approach is the inherent ability to detect significantly occluded objects without increasing the amount of false positives, so that the operating point of the object recognition algorithm can nicely move toward a higher recall without sacrificing precision. Our approach outperforms state-of-the-art on a challenging dataset including 35 household models obtained with the Kinect sensor, as well as on the standard 3D object recognition benchmark dataset.