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International audience; In this paper we address the problem of learning robust cross-domain representations for sketch-based image retrieval (SBIR). While most SBIR approaches focus on extracting low-and mid-level descriptors for direct feature matching, recent works have shown the benefit of learning coupled feature representations to describe data from two related sources. However, cross-domain representation learning methods are typically cast into non-convex minimization problems that are difficult to optimize, leading to unsatisfactory performance. Inspired by self-paced learning, a learning methodology designed to overcome convergence issues related to local optima by exploiting the samples in a meaningful order (i.e. easy to hard), we introduce the cross-paced partial curriculum learning (CPPCL) framework. Compared with existing self-paced learning methods which only consider a single modality and cannot deal with prior knowledge, CPPCL is specifically designed to assess the learning pace by jointly handling data from dual sources and modality-specific prior information provided in the form of partial curricula. Additionally, thanks to the learned dictionaries, we demonstrate that the proposed CPPCL embeds robust coupled representations for SBIR. Our approach is extensively evaluated on four publicly available datasets (i.e. CUFS, Flickr15K, QueenMary SBIR and TU-Berlin Extension datasets), showing superior performance over competing SBIR methods.

Palynological studies are considered as an excellent tool for palaeoaltitude estimates as sedimentary basins receive abundant pollen grains from surrounding uplands, especially through riverine detritic inputs. Here, we provide new evidence for estimating the palaeoaltitude of the Northern and Central Apennines (Italy) during the late Neogene (from the late Tortonian to the early Zandean), based on vegetation data derived from pollen analysis of five sections (Trave, Legnagnone, Cava Li Monti, Maccarone and Stirone). The past vegetation changes reflect shifts in the vegetation belts on the nearby massifs. The climate reconstruction realised on these sites, at sea-level, suggests that mean annual temperatures were higher than today at the end of the Neogene. Therefore, the different vegetation belts occurred at higher elevation than at present to compensate for the higher temperatures. The results obtained in our study suggest that the Northern and Central Apennines were uplifted as early as the Tortonian attaining significant minimum altitudes around 1350 m up to 1500 m at the end of the Miocene in the Central part, a minimum altitude around 1900 m during the Messinian and around 2150 m during the early Zandean in the Northern part. (C) 2014 Elsevier B.V. All rights reserved.

Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. However, in many real-world scenarios, labels are partially available, motivating a recent line of work on semi-supervised methods inspired by self-supervised principles. In this paper, we propose a conceptually simple yet empirically powerful approach to turn clustering-based self-supervised methods such as SwAV or DINO into semi-supervised learners. More precisely, we introduce a multi-task framework merging a supervised objective using ground-truth labels and a self-supervised objective relying on clustering assignments with a single cross-entropy loss. This approach may be interpreted as imposing the cluster centroids to be class prototypes. Despite its simplicity, we provide empirical evidence that our approach is highly effective and achieves state-of-the-art performance on CIFAR100 and ImageNet., Comment: CVPR 2023 - Code available at https://github.com/pietroastolfi/suave-daino

International audience; Contagion processes on networks, including disease spreading, information diffusion, or social behaviors propagation, can be modeled as simple contagion, i.e., as a contagion process involving one connection at a time, or as complex contagion, in which multiple interactions are needed for a contagion event. Empirical data on spreading processes, however, even when available, do not easily allow us to uncover which of these underlying contagion mechanisms is at work. We propose a strategy to discriminate between these mechanisms upon the observation of a single instance of a spreading process. The strategy is based on the observation of the order in which network nodes are infected, and on its correlations with their local topology: these correlations differ between processes of simple contagion, processes involving threshold mechanisms, and processes driven by group interactions (i.e., by “higher-order” mechanisms). Our results improve our understanding of contagion processes and provide a method using only limited information to distinguish between several possible contagion mechanisms.

Despite significant advances, the performance of state-of-the-art continual learning approaches hinges on the unrealistic scenario of fully labeled data. In this paper, we tackle this challenge and propose an approach for continual semi-supervised learning--a setting where not all the data samples are labeled. A primary issue in this scenario is the model forgetting representations of unlabeled data and overfitting the labeled samples. We leverage the power of nearest-neighbor classifiers to nonlinearly partition the feature space and flexibly model the underlying data distribution thanks to its non-parametric nature. This enables the model to learn a strong representation for the current task, and distill relevant information from previous tasks. We perform a thorough experimental evaluation and show that our method outperforms all the existing approaches by large margins, setting a solid state of the art on the continual semi-supervised learning paradigm. For example, on CIFAR-100 we surpass several others even when using at least 30 times less supervision (0.8% vs. 25% of annotations). Finally, our method works well on both low and high resolution images and scales seamlessly to more complex datasets such as ImageNet-100. The code is publicly available on https://github.com/kangzhiq/NNCSL, 13 pages

The Tiber Valley Project aims to create a series of digital applications for 3D reconstructing, visualization and real time browsing of the ancient and current Tiber Valley landscape (particularly for the Villa dei Volusii and Lucus Feroniae areas), in four different historical phases. In this perspective, the first problem to face is the need for a valid methodology for ancient landscape ecosystem reconstruction, before dealing with monuments and building. On the basis of an intense multi-disciplinary discussion and the previous VH Lab experience in this field, in this article we are presenting a scheme for a standardized reconstruction procedure, where the landscape is built using all available sources and elevation data obtained by a photogrammetry process on historical pictures. Ecosystem areas are then calculated through GIS elaboration in GRASS-GIS environment, through a procedure which may be shared for any situation of historical landscape reconstruction, allowing the matching and the mathematical processing of geographical data aimed to the definition of different ecological areas (both in terms of natural vegetation and cultivated lands). Maps are then created to be imported in procedural landscape generation engines: the last part of the paper focuses on the lack of effective open source software in this field, and a possible proposal implementation in this sense, 1. Il progetto " Valle del Tevere " Il presente lavoro illustra la sperimentazione di una metodologia per la ricostruzione del paesaggio storico, con particolare riguardo agli aspetti di definizione delle diverse presenze vegetazionali e antropiche del territorio in determinate epoche del passato. Il caso di studio è rappresentato dal lavoro che l'Istituto per le Tecnologie Applicate ai Beni Culturali (ITABC) del CNR sta svolgendo nell'ambito del Programma " Arcus " , finalizzato alla creazione di un sistema integrato di conoscenza, valorizzazione e comunicazione del paesaggio culturale della Valle del Tevere (contesti archeologici, storico-arti-stici, naturalistici, antropici), in particolare dell'area compresa tra il Monte Soratte e Fiano Romano, in direzione N-S, e il tracciato della via Flaminia antica e Palombara Sabina, in direzione E-O. Il progetto prevede una variegata serie di prodotti finali, quali: – un'installazione di realtà virtuale ludico-educativa, caratterizzata da un sistema di natural interaction (interazione attraverso i movimenti del corpo), localizzata all'interno di un museo di Roma, quale porta privilegiata di accesso e promozione del territorio della Valle del Tevere; – una guida multimediale alla Villa dei Volusii e al sito di Lucus Feroniae, da fruire sia su mobile che presso il Museo di Lucus Feroniae; – una guida alla Riserva del Tevere-Farfa, per smartphone o tablet, da fruire durante la visita all'area naturalistica; – un'installazione multimediale-filmica dedicata alla Riserva naturale del Teve-re-Farfa e destinata alle scuole, da fruire nel Museo del Fiume di Nazzano; – un sito web sul paesaggio culturale, basato su un sistema informativo geo-grafico in 3D e dedicato al pubblico di turisti, studiosi, operatori, scuole. Il lavoro comporta una ricostruzione fotorealistica del paesaggio natu-rale e antropico rispetto alle seguenti fasi cronologiche: – fase preistorica (ricostruzione della storia geologica dell'area, formazione della Valle del Tevere e dell'alveo fluviale, tra 3 milioni di anni fa e 12.000 anni fa); – fase pre-romana (focalizzata sull'Età del Ferro e sul periodo Orientaliz-zante); – fase romana (focalizzata, in particolare, sul periodo augusteo); – fase medievale (focalizzata, in particolare, sul XII secolo); – fase contemporanea.

International audience; Modelling languages and frameworks have been the key technology for advancing Model-Driven Engineering (MDE) methods and tools. Many industrial and research tools have been realised and are used across many domains. Hence, we think it is the right time to define what should be the future of modelling technologies, especially the requirements for the next generation of modelling frameworks and languages. In January 2020, the Second Winter Modelling Meeting (WMM2020) was held in San Vigilio di Marebbe (Italy), focusing on the analysis of the state of research, state of practice, and state of the art in MDE. The event brought together experts from industry, academia, and the open-source community to assess (i) what had changed in research on modelling in the last ten years, (ii) which problems are still unsolved, and (iii) which new challenges have arisen. This article presents a set of success stories and driving success factors of modelling and MDE, as well as a set of challenges and corresponding research directions that emerged from the synthesis of the results of our analysis.

Although fully autonomous mapping methods are becoming more and more common and reliable, still the human operator is regularly employed in many 3D surveying missions. In a number of underwater applications, divers or pilots of remotely operated vehicles (ROVs) are still considered irreplaceable, and tools for real-time visualization of the mapped scene are essential to support and maximize the navigation and surveying efforts. For underwater exploration, image mosaicing has proved to be a valid and effective approach to visualize large mapped areas, often employed in conjunction with autonomous underwater vehicles (AUVs) and ROVs. In this work, we propose the use of a modified image mosaicing algorithm that coupled with image-based real-time navigation and mapping algorithms provides two visual navigation aids. The first is a classic image mosaic, where the recorded and processed images are incrementally added, named 2D sequential image mosaicing (2DSIM). The second one geometrically transform the images so that they are projected as planar point clouds in the 3D space providing an incremental point cloud mosaicing, named 3D sequential image plane projection (3DSIP). In the paper, the implemented procedure is detailed, and experiments in different underwater scenarios presented and discussed. Technical considerations about computational efforts, frame rate capabilities and scalability to different and more compact architectures (i.e. embedded systems) is also provided.

International audience; No abstract available

A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e. the tendency of neural networks to fail to preserve the knowledge acquired from old tasks when learning new tasks. This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years. While earlier works in computer vision have mostly focused on image classification and object detection, more recently some IL approaches for semantic segmentation have been introduced. These previous works showed that, despite its simplicity, knowledge distillation can be effectively employed to alleviate catastrophic forgetting. In this paper, we follow this research direction and, inspired by recent literature on contrastive learning, we propose a novel distillation framework, Uncertainty-aware Contrastive Distillation (\method). In a nutshell, \method~is operated by introducing a novel distillation loss that takes into account all the images in a mini-batch, enforcing similarity between features associated to all the pixels from the same classes, and pulling apart those corresponding to pixels from different classes. In order to mitigate catastrophic forgetting, we contrast features of the new model with features extracted by a frozen model learned at the previous incremental step. Our experimental results demonstrate the advantage of the proposed distillation technique, which can be used in synergy with previous IL approaches, and leads to state-of-art performance on three commonly adopted benchmarks for incremental semantic segmentation. The code is available at \url{https://github.com/ygjwd12345/UCD}., Comment: TPAMI

International audience; Microservices are an emerging architectural style advocating for small loosely-coupled services in order to maximize scalability and adaptability. In order to help IT personnel, adaptability can be put (completely or partially) under the responsibility of the system using autonomic techniques, e.g., underpinned by a MAPE-K control loop. This paper discusses possible trade-offs, challenges and support techniques for software architects involved in building autonomic microservicebased systems.

International audience; This preface for the proceedings of the first international workshop on Modular Knowledge (MK2022) presents the content of the workshop and reports on the results of the interactive sessions that took place during the event. The dramatic increase in the amount of open and linked data and the increasing semantification of such data make clear that knowledge is not monolithic, static or uniform. This requires a renewed push for dealing with heterogeneous and distributed knowledge as a constellation of modules. Each module stores a portion of knowledge about one particular subdomain, described in a specific schema, and valid under a set of circumstances. In such a scenario we need well-founded conceptual approaches and practical techniques for modular knowledge management, for example, to recognize relevant partitions of a monolithic knowledge source, but also to define a modularized vision of the domain qualifying the knowledge with a given situation or agent, integrating heterogeneous modules of knowledge, including knowledge represented in sub-symbolic models. The discussion of such modularity notions and techniques, their development and exploitation are the focus of the proposed Modular Knowledge workshop. The Modular Knowledge workshop combines the efforts of previous experiences (like WoMO, ARCOE-Logic and WOMoCoE workshops) into an interdisciplinary venue for discussing and developing solutions for modularity of knowledge. The workshop series aims at covering the use of various approaches (ranging from rich semantic representations, like Knowledge Graphs and formal ontology, to simpler schemas, like RDF and database schemas) for representing knowledge, its context, its evolution, and for making it accessible to automatic reasoning and knowledge management tasks. The Modular Knowledge workshop covers logic-based languages as well as subsymbolic and numerical representations.

Subtitles appear on screen as short pieces of text, segmented based on formal constraints (length) and syntactic/semantic criteria. Subtitle segmentation can be evaluated with sequence segmentation metrics against a human reference. However, standard segmentation metrics cannot be applied when systems generate outputs different than the reference, e.g. with end-to-end subtitling systems. In this paper, we study ways to conduct reference-based evaluations of segmentation accuracy irrespective of the textual content. We first conduct a systematic analysis of existing metrics for evaluating subtitle segmentation. We then introduce $Sigma$, a new Subtitle Segmentation Score derived from an approximate upper-bound of BLEU on segmentation boundaries, which allows us to disentangle the effect of good segmentation from text quality. To compare $Sigma$ with existing metrics, we further propose a boundary projection method from imperfect hypotheses to the true reference. Results show that all metrics are able to reward high quality output but for similar outputs system ranking depends on each metric's sensitivity to error type. Our thorough analyses suggest $Sigma$ is a promising segmentation candidate but its reliability over other segmentation metrics remains to be validated through correlations with human judgements., Comment: Accepted at LREC 2022

International audience; Over the past years, semantic segmentation, as many other tasks in computer vision, benefited from the progress in deep neural networks, resulting in significantly improved performance. However, deep architectures trained with gradient-based techniques suffer from catastrophic forgetting, which is the tendency to forget previously learned knowledge while learning new tasks. Aiming at devising strategies to counteract this effect, incremental learning approaches have gained popularity over the past years. However, the first incremental learning methods for semantic segmentation appeared only recently. While effective, these approaches do not account for a crucial aspect in pixel-level dense prediction problems, i.e. the role of attention mechanisms. To fill this gap, in this paper we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial- and channel-level dependencies. Furthermore, we propose a {continual attentive fusion} structure, which takes advantage of the attention learned from the new and the old tasks while learning features for the new task. Finally, we also introduce a novel strategy to account for the background class in the distillation loss, thus preventing biased predictions. We demonstrate the effectiveness of our approach with an extensive evaluation on Pascal-VOC 2012 and ADE20K, setting a new state of the art.

International audience; The proceedings of the first international workshop on Modular Knowledge (MK2022).

International audience; Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale. However, their efficacy is catastrophically reduced in a Continual Learning (CL) scenario where data is presented to the model sequentially. In this paper, we show that self-supervised loss functions can be seamlessly converted into distillation mechanisms for CL by adding a predictor network that maps the current state of the representations to their past state. This enables us to devise a framework for Continual self-supervised visual representation Learning that (i) significantly improves the quality of the learned representations, (ii) is compatible with several state-of-the-art self-supervised objectives, and (iii) needs little to no hyperparameter tuning. We demonstrate the effectiveness of our approach empirically by training six popular self-supervised models in various CL settings.

In this paper, we are interested in audio-visual speech separation given a single-channel audio recording as well as visual information (lips movements) associated with each speaker. We propose an unsupervised technique based on audio-visual generative modeling of clean speech. More specifically, during training, a latent variable generative model is learned from clean speech spectrograms using a variational auto-encoder (VAE). To better utilize the visual information, the posteriors of the latent variables are inferred from mixed speech (instead of clean speech) as well as the visual data. The visual modality also serves as a prior for latent variables, through a visual network. At test time, the learned generative model (both for speaker-independent and speaker-dependent scenarios) is combined with an unsupervised non-negative matrix factorization (NMF) variance model for background noise. All the latent variables and noise parameters are then estimated by a Monte Carlo expectation-maximization algorithm. Our experiments show that the proposed unsupervised VAE-based method yields better separation performance than NMF-based approaches as well as a supervised deep learning-based technique., Comment: Accepted to the 31st IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Oct. 25-28, 2021, Gold Coast, Queensland, Australia

In this paper we consider the problem of orchestrating video analytics applications over an edge computing infrastructure. Video analytics applications have been traditionally associated to the processing of video streams generated by fixed video cameras. Nowadays, however, the availability of mobile video cameras has become pervasive. We argue that to take advantage of the presence of mobile video cameras-and their informative content-it may be necessary to refactor the edge orchestration logic. We propose a new solution that splits the problem into two connected actions: 1) Placement of processing functions in the infrastructure and 2) Admission of most informative cameras based on their field of view. We hence describe a possible scheme for joint video stream admission and orchestration. Finally, preliminary numerical results are presented, demonstrating that separating the two logic components can improve coverage while reducing the cost of deployment. CCS CONCEPTS • Networks → Network control algorithms; • Information systems → Multimedia streaming.

This paper introduces Click to Move (C2M), a novel framework for video generation where the user can control the motion of the synthesized video through mouse clicks specifying simple object trajectories of the key objects in the scene. Our model receives as input an initial frame, its corresponding segmentation map and the sparse motion vectors encoding the input provided by the user. It outputs a plausible video sequence starting from the given frame and with a motion that is consistent with user input. Notably, our proposed deep architecture incorporates a Graph Convolution Network (GCN) modelling the movements of all the objects in the scene in a holistic manner and effectively combining the sparse user motion information and image features. Experimental results show that C2M outperforms existing methods on two publicly available datasets, thus demonstrating the effectiveness of our GCN framework at modelling object interactions. The source code is publicly available at https://github.com/PierfrancescoArdino/C2M., Comment: Accepted by International Conference on Computer Vision (ICCV 2021)

Context: Feedback from end users of software applications is a valuable resource in understanding what users request, what they value, and what they dislike. Information derived from user-feedback can support software evolution activities, such as requirements prioritization. User-feedback analysis is still mostly performed manually by practitioners, despite growing research in automated analysis. Objective: We address two issues in automated user-feedback analysis: (i) most of the existing automated analysis approaches that exploit linguistic analysis assume that the vocabulary adopted by users (when expressing feedback) and developers (when formulating requirements) are the same; and (ii) user-feedback analysis techniques are usually experimentally evaluated only on some user-feedback dataset, not involving assessment by potential software developers. Method: We propose an approach, ReFeed , that computes, for each requirement, the set of related user-feedback, and from such user-feedback extracts quantifiable properties which are relevant for prioritizing the requirement. The extracted properties are propagated to the related requirements, based on which ranks are computed for each requirement. ReFeed relies on domain knowledge, in the form of an ontology, helping mitigate the gap in the vocabulary of end users and developers. The effectiveness of ReFeed is evaluated on a realistic requirements prioritization scenario in two experiments involving graduate students from two different universities. Results: ReFeed is able to synthesize reasonable priorities for a given set of requirements based on properties derived from user-feedback. The implementation of ReFeed and related resources are publicly available. Conclusion: The results from our studies are encouraging in that using only three properties of user-feedback, ReFeed is able to prioritize requirements with reasonable accuracy. Such automatically determined prioritization could serve as a good starting point for requirements experts involved in the task of prioritizing requirements Future studies could explore additional user-feedback properties to improve the effectiveness of computed priorities.

Modern protocols for wireless sensor networks efficiently support multi-hop upward traffic from many sensors to a collection point, a key functionality enabling monitoring applications. However, the ever-evolving scenarios involving low-power wireless devices increasingly require support also for downward traffic, e.g., enabling a controller to issue actuation commands based on the monitored data. The IETF Routing Protocol for Low-power and Lossy Networks (RPL) is among the few tackling both traffic patterns. Unfortunately, its support for downward traffic is significantly unreliable and inefficient compared to its upward counterpart. We tackle this problem by extending RPL with mechanisms inspired by opposed, yet complementary, principles. At one extreme, we retain the route-based operation of RPL and devise techniques allowed by the standard but commonly neglected by popular implementations. At the other extreme, we rely on flooding as the main networking primitive. Inspired by these principles, we define three base mechanisms, integrate them in a popular RPL implementation, analyze their individual and combined performance, and elicit the resulting tradeoffs in scalability, reliability, and energy consumption. The evaluation relies on simulation, using both real-world topologies from a smart city scenario and synthetic grid ones, as well as on testbed experiments validating our findings from simulation. Results show that the combination of all three mechanisms into a novel protocol, T-RPL (i) yields high reliability, close to the one of flooding, (ii) with a low energy consumption, similar to route-based approaches, and (iii) improves remarkably the scalability of RPL with respect to downward traffic.

Purpose: Old (>64 years) and very old (>79 years) intensive care patients with sepsis have a high mortality. In the very old, the value of critical care has been questioned. We aimed to compare the mortality, rates of organ support, and the length of stay in old vs. very old patients with sepsis and septic shock in intensive care.Methods: This analysis included 9,385 patients, from the multi-center eICU Collaborative Research Database, with sepsis; 6184 were old (aged 65–79 years), and 3,201 were very old patients (aged 80 years and older). A multi-level logistic regression analysis was used to fit three sequential regression models for the binary primary outcome of ICU mortality. A sensitivity analysis in septic shock patients (n = 1054) was also conducted.Results: In the very old patients, the median length of stay was shorter (50 ± 67 vs. 56 ± 72 h; p < 0.001), and the rate of a prolonged ICU stay was lower (>168 h; 9 vs. 12%; p < 0.001) than the old patients. The mortality from sepsis was higher in very old patients (13 vs. 11%; p = 0.005), and after multi-variable adjustment being very old was associated with higher odds for ICU mortality (aOR 1.32, 95% CI 1.09–1.59; p = 0.004). In patients with septic shock, mortality was also higher in the very old patients (38 vs. 36%; aOR 1.50, 95% CI 1.10–2.06; p = 0.01).Conclusion: Very old ICU-patients suffer from a slightly higher ICU mortality compared with old ICU-patients. However, despite the statistically significant differences in mortality, the clinical relevance of such minor differences seems to be negligible.

International audience; In both the industrial and scientific fields, the need for very high-resolution cartographic data is constantly increasing. With the aging of offshore subsea assets, it is very important to plan and maintain the longevity of structures, equipment, and systems. Inspection, maintenance, and repair (IMR) of subsea structures are key components of an overall integrity management system that aims to reduce the risk of failure and extend the life of installations. The acquisition of very detailed data during the inspection phase is a technological challenge, especially since offshore installations are sometimes deployed in extreme conditions (e.g., depth, hydrodynamics, visibility). After a review of high resolution mapping techniques for underwater environment, this article will focus on optical sensors that can satisfy the requirements of the offshore industry by assessing their relevance and degree of maturity. These requirements concern the resolution and accuracy but also cost, ease of implementation, and qualification. With the evolution of embedded computing resources, in-vehicle optical survey solutions are becoming increasingly important in the landscape of large-scale mapping solutions and more and more off-the-shelf systems are now available. The issues raised in this review are mainly related to the qualification of the results produced by optical systems and their limitations to cover all the needs expressed by the oil and gas industry field. Interesting qualification works of these solutions are presented in this paper as well as the use of online processing tools such as visual odometry or VSLAM to guide the data acquisition and pre-qualified survey. Finally, it seems interesting to combine acoustic and optical technologies in order to extend the field of application of these methods to low visibility conditions, which remains one of the main limiting factors in the generalization of the use of optical sensors in high resolution underwater cartography applications.

Photogrammetry needs known geometric elements to provide metric traceable measurements. These known elements can be a distance between two three-dimensional object points or two camera stations, or a combination of known coordinates and/or angles to solve the seven degrees of freedom that lead to rank deficiency of the normal-equation matrix. In this paper we present a novel approach for scaling and levelling to the local vertical direction an underwater photogrammetric survey. The developed methodology is based on a portable low-cost device designed and realized by the authors that uses depth measurements from a high resolution pressure sensor. The prototype consists of a data logger featuring a pressure sensor synchronized with a digital camera in its underwater pressure housing. The modular design, with optical communication and synchronization, provides great flexibility not requiring the camera housing to undergo any hardware modifications. The proposed methodology allows for a full 3D levelling transformation comprising two angles, a vertical translation and a scale factor and can work for surveying scenes extending horizontally, vertically or both. The paper presents the theoretical principles, an overview of the developed system together with preliminary calibration results. Tests in a lake and at sea are reported. An accuracy better than 1:5000 on the length measurement was achieved in calm water conditions.

International audience; With the experiment that we outline in this paper, we have had the ambition to pave the way for addressing the problem of supporting, enhancing and measuring collective AND informal learning, in particular serendipity. We want to support a new type of free navigation on Web resources (Documents, Topics, Events and Agents – human and artificial -) that is driven by the learner’s current needs and the preferences of the community of trust chosen by the learner, not by external actors. The experiment exploits the ViewpointS Web Application (VWA) prototype, that restructures a private version of a subset of the Web according to personalized choices in order to determine distances/proximities among resources. The process allows to enable, empower and measure the influence of members of the community of trust of the learner, on the learner’s choices when navigating in search of THE resources corresponding to THE immediate need, goal, strategy, wish. In the following, we will outline: 1. the rationale of our efforts and 2. the user’s reactions during the phase of -formal and informal- learning the functions and use of the prototypical software environment VWA, i.e.: a proof of concept for VWA.

International audience; The coordination and control of hierarchically organized interacting agents is an important issue in many applications, e.g., harbor or warehouse automation. A formalism of agents as hierarchical input/output automata is proposed. A system of interacting agents is modeled as the parallel composition of their automata. We extend the usual parallel composition operation of I/O automata with a hierarchical composition operation for refining abstract tasks into lower-level subtasks. We provide an algorithm to synthesize hierarchically organized controllers to coordinate the agents' interactions in order to drive the system toward desired states. Our main contribution regards the formal definition, the representation, the theorems about its properties (i.e., the parallel and hierarchical composition are distributive operations), and the synthesis algorithm, proved to be complete and correct.

International audience; Collective learning has been advocated to be at the source for innovation, particularly as serendipity seems historically to have been the driving force not only behind innovation, but also behind scientific discovery and artistic creation. Informal learning is well known to represent the most significant learning effects in humans, far better than its complement: formal learning with predefined objectives. We have designed an approach—ViewpointS—based on a digital medium—the ViewpointS Web Application—that enables and enhances the processes for sharing knowledge within a group and is equipped with metrics aimed at assessing collective and informal learning. In this article, we introduce by giving a brief state of the art about collective and informal learning, then outline our approach and medium, and finally, present and exploit a real-life experiment aimed at evaluating the ViewpointS approach and metrics.

The challenges related to dependable complex systems are heterogeneous and involve different aspects of the system. On one hand, the decision-making processes need to take into account many options. On the other hand, the design of the system's logical architecture must consider various dependability concerns such as safety, reliability, and security. Moreover, in case of high-assurance systems, the analysis of such concerns must be performed with rigorous methods. In this paper, we present the new development of CHESS, a cross-domain, model-driven, component-based, and open-source tool for the development of high-integrity systems. We focus on the new recently distributed version of CHESS, which supports extended model-based development and analyses for safety and security concerns. Finally, we present contributions of CHESS to several international research projects., Comment: 10 pages, 11 figures

In this work, we address the problem of multi-domain image-to-image translation with particular attention paid to computational cost. In particular, current state of the art models require a large and deep model in order to handle the visual diversity of multiple domains. In a context of limited computational resources, increasing the network size may not be possible. Therefore, we propose to increase the network capacity by using an adaptive graph structure. At inference time, the network estimates its own graph by selecting specific sub-networks. Sub-network selection is implemented using Gumbel-Softmax in order to allow end-to-end training. This approach leads to an adjustable increase in number of parameters while preserving an almost constant computational cost. Our evaluation on two publicly available datasets of facial and painting images shows that our adaptive strategy generates better images with fewer artifacts than literature methods, Accepted at ICPR 2020

International audience; Chronic diseases cause many deaths worldwide each year. The most common action to manage this type of disease is to conduct expensive pathological tests whose goal is to assess risks and identify early complications to patients health or to prevent the patient from developing other diseases. The choice of which action/test to perform is therefore important. There are in the literature some guideline-based decision-support systems which help to assist practitioners to choose the appropriate therapeutic action for their patients. The idea of such systems is to structure a guideline as a set of choices to be made by the clinician. However, such a set of actions/tests/decisions can change according to new information obtained concerning the patient state (direct answers from the patient, blood test results, etc.). Because the time factor may be crucial and because executing tests is expensive, it is then important for the practitioners to ask for or to quickly obtain useful information helping take the good decision in order to achieve the goals of excluding the risks as soon as possible, which means with a minor quantity of information/tests required. In this paper, we address the challenges introduced above by proposing an agent-based framework that supports the development of an intelligent goal-driven agent to help practitioners in choosing the most useful action to perform (question to ask a patient, test, etc.) in the case of a chronic disease. The framework supports the selection of the next dialogue action by measuring the usefulness, with respect to a goal, of a piece of information to be obtained. We introduce our framework through the use of a running example, showing how an agent can drive the interaction based on both its background knowledge and the new information it acquires. Experiments performed concerning two chronic diseases, namely asthma and type-2 diabetes, validate our approach. Finally, we discuss further possible scenarios where our framework can be applied in different ways.; Les maladies chroniques causent chaque année de nombreux décès dans le monde. L'action la plus courante pour gérer ce type de maladie consiste à effectuer des tests pathologiques coûteux dont le but est d'évaluer les risques et d'identifier les complications précoces pour la santé des patients ou de prévenir le développement d'autres maladies. Le choix de l'action ou du test à réaliser est donc important. Il existe dans la littérature des systèmes d'aide à la décision basés sur des lignes directrices qui aident les praticiens à choisir l'action thérapeutique appropriée pour leurs patients. L'idée de ces systèmes est de structurer une ligne directrice comme un ensemble de choix à faire par le clinicien. Cependant, cet ensemble d'actions/tests/décisions peut changer en fonction des nouvelles informations obtenues concernant l'état du patient (réponses directes du patient, résultats de tests sanguins, etc.) Comme le facteur temps peut être crucial et que l'exécution de tests est coûteuse, il est alors important pour les praticiens de demander ou d'obtenir rapidement des informations utiles pour prendre la bonne décision afin d'atteindre les objectifs d'exclusion des risques le plus tôt possible, ce qui signifie avec une quantité mineure d'informations/tests requis. Dans cet article, nous relevons les défis présentés ci-dessus en proposant un cadre de travail basé sur les agents qui soutient le développement d'un agent intelligent orienté vers les objectifs pour aider les praticiens à choisir l'action la plus utile à effectuer (question à poser à un patient, test, etc.) dans le cas d'une maladie chronique. Le cadre soutient la sélection de la prochaine action de dialogue en mesurant l'utilité, par rapport à un but, d'un élément d'information à obtenir. Nous présentons notre cadre à l'aide d'un exemple courant, montrant comment un agent peut diriger l'interaction en se basant à la fois sur ses connaissances de base et sur les nouvelles informations qu'il acquiert. Des expériences réalisées sur deux maladies chroniques, à savoir l'asthme et le diabète de type 2, valident notre approche. Enfin, nous discutons d'autres scénarios possibles où notre cadre peut être appliqué de différentes manières.

During underwater investigations, whatever the mission objective and the type of vehicle, obstacles detection and avoidance are essential tasks. They can either represent a target of interest that is the object of the mission or, on the contrary, represent obstacles that can hinder or affect the navigation of the vehicle. The underwater optical cameras that are usually fitted to underwater vehicles only offer a narrow field of view. The absorption of electromagnetic waves in the first few meters and the diffusion of light by the particles limit the use of these sensors to only a few meters range. The use of acoustic sensors, such as the forward looking sonar (FLS), is then necessary to enlarge the volume in which a target can be detected during the progression of the vehicle. Traditionally, sonars featured mechanical rotating parts, but lately bidirectional forward looking sonar, which directly produces a 2D image of the area, are becoming more and more common. Although these sonars can operate at frequency higher than 1MHz, their spatial resolution remains much lower if compared to current optical sensors and can be insufficient to identify and characterize a target. The combination of these two sensors in an operational scenario is essential to take advantage of each technology. In this paper we describe a low cost, multi-sensor, underwater survey solution for the identification, tracking, and 3D mapping of targets. After a description of the architecture of the opto-acoustics data acquisition and processing platform, we will focus on the calibration of the rigid transformation between the two sensors.

Song lyrics contain repeated patterns that have been proven to facilitate automated lyrics segmentation, with the final goal of detecting the building blocks (e.g., chorus, verse) of a song text. Our contribution in this article is twofold. First, we introduce a convolutional neural network (CNN)-based model that learns to segment the lyrics based on their repetitive text structure. We experiment with novel features to reveal different kinds of repetitions in the lyrics, for instance based on phonetical and syntactical properties. Second, using a novel corpus where the song text is synchronized to the audio of the song, we show that the text and audio modalities capture complementary structure of the lyrics and that combining both is beneficial for lyrics segmentation performance. For the purely text-based lyrics segmentation on a dataset of 103k lyrics, we achieve an F-score of 67.4%, improving on the state of the art (59.2% F-score). On the synchronized text–audio dataset of 4.8k songs, we show that the additional audio features improve segmentation performance to 75.3% F-score, significantly outperforming the purely text-based approaches.

In this paper, we study the problem of Novel Class Discovery (NCD). NCD aims at inferring novel object categories in an unlabeled set by leveraging from prior knowledge of a labeled set containing different, but related classes. Existing approaches tackle this problem by considering multiple objective functions, usually involving specialized loss terms for the labeled and the unlabeled samples respectively, and often requiring auxiliary regularization terms. In this paper, we depart from this traditional scheme and introduce a UNified Objective function (UNO) for discovering novel classes, with the explicit purpose of favoring synergy between supervised and unsupervised learning. Using a multi-view self-labeling strategy, we generate pseudo-labels that can be treated homogeneously with ground truth labels. This leads to a single classification objective operating on both known and unknown classes. Despite its simplicity, UNO outperforms the state of the art by a significant margin on several benchmarks (~+10% on CIFAR-100 and +8% on ImageNet). The project page is available at: https://ncd-uno.github.io., ICCV 2021 (Oral)

International audience; Emotion analysis in polarized contexts represents a challenge for Natural Language Processing modeling. As a step in the aforementioned direction, we present a methodology to extend the task of Aspect-based Sentiment Analysis (ABSA) toward the affect and emotion representation in polarized settings. In particular, we adopt the three-dimensional model of affect based on Valence, Arousal, and Dominance (VAD). We then present a Brexit scenario that proves how affect varies toward the same aspect when politically polarized stances are presented. Our approach captures aspect-based polarization from newspapers regarding the Brexit scenario of 1.2m entities at sentence-level. We demonstrate how basic constituents of emotions can be mapped to the VAD model, along with their interactions respecting the polarized context in ABSA settings using biased key-concepts (e.g., "stop Brexit" vs. "support Brexit"). Quite intriguingly, the framework achieves to produce coherent aspect evidences of Brexit's stance from key-concepts, showing that VAD influence the support and opposition aspects.

The unique solution of contractions is a proof technique for bisimilarity that overcomes certain syntactic constraints of Milner's "unique solution of equations" technique. The paper presents an overview of a rather comprehensive formalisation of the core of the theory of CCS in the HOL theorem prover (HOL4), with a focus towards the theory of unique solutions of contractions. (The formalisation consists of about 20,000 lines of proof scripts in Standard ML.) Some refinements of the theory itself are obtained. In particular we remove the constraints on summation, which must be weakly-guarded, by moving to rooted contraction, that is, the coarsest precongruence contained in the contraction preorder., Comment: In Proceedings EXPRESS/SOS 2018, arXiv:1808.08071

International audience; Chronic diseases cause many deaths worldwide each year. The most common action to manage this type of disease is to conduct expensive pathological tests whose goal is to assess risks and identify early complications to patients health or to prevent the patient from developing other diseases. The choice of which action/test to perform is therefore important. There are in the literature some guideline-based decision-support systems which help to assist practitioners to choose the appropriate therapeutic action for their patients. The idea of such systems is to structure a guideline as a set of choices to be made by the clinician. However, such a set of actions/tests/decisions can change according to new information obtained concerning the patient state (direct answers from the patient, blood test results, etc.). Because the time factor may be crucial and because executing tests is expensive, it is then important for the practitioners to ask for or to quickly obtain useful information helping take the good decision in order to achieve the goals of excluding the risks as soon as possible, which means with a minor quantity of information/tests required. In this paper, we address the challenges introduced above by proposing an agent-based framework that supports the development of an intelligent goal-driven agent to help practitioners in choosing the most useful action to perform (question to ask a patient, test, etc.) in the case of a chronic disease. The framework supports the selection of the next dialogue action by measuring the usefulness, with respect to a goal, of a piece of information to be obtained. We introduce our framework through the use of a running example, showing how an agent can drive the interaction based on both its background knowledge and the new information it acquires. Experiments performed concerning two chronic diseases, namely asthma and type-2 diabetes, validate our approach. Finally, we discuss further possible scenarios where our framework can be applied in different ways.; Les maladies chroniques causent chaque année de nombreux décès dans le monde. L'action la plus courante pour gérer ce type de maladie consiste à effectuer des tests pathologiques coûteux dont le but est d'évaluer les risques et d'identifier les complications précoces pour la santé des patients ou de prévenir le développement d'autres maladies. Le choix de l'action ou du test à réaliser est donc important. Il existe dans la littérature des systèmes d'aide à la décision basés sur des lignes directrices qui aident les praticiens à choisir l'action thérapeutique appropriée pour leurs patients. L'idée de ces systèmes est de structurer une ligne directrice comme un ensemble de choix à faire par le clinicien. Cependant, cet ensemble d'actions/tests/décisions peut changer en fonction des nouvelles informations obtenues concernant l'état du patient (réponses directes du patient, résultats de tests sanguins, etc.) Comme le facteur temps peut être crucial et que l'exécution de tests est coûteuse, il est alors important pour les praticiens de demander ou d'obtenir rapidement des informations utiles pour prendre la bonne décision afin d'atteindre les objectifs d'exclusion des risques le plus tôt possible, ce qui signifie avec une quantité mineure d'informations/tests requis. Dans cet article, nous relevons les défis présentés ci-dessus en proposant un cadre de travail basé sur les agents qui soutient le développement d'un agent intelligent orienté vers les objectifs pour aider les praticiens à choisir l'action la plus utile à effectuer (question à poser à un patient, test, etc.) dans le cas d'une maladie chronique. Le cadre soutient la sélection de la prochaine action de dialogue en mesurant l'utilité, par rapport à un but, d'un élément d'information à obtenir. Nous présentons notre cadre à l'aide d'un exemple courant, montrant comment un agent peut diriger l'interaction en se basant à la fois sur ses connaissances de base et sur les nouvelles informations qu'il acquiert. Des expériences réalisées sur deux maladies chroniques, à savoir l'asthme et le diabète de type 2, valident notre approche. Enfin, nous discutons d'autres scénarios possibles où notre cadre peut être appliqué de différentes manières.

We present new planning and learning algorithms for RAE, the Refinement Acting Engine. RAE uses hierarchical operational models to perform tasks in dynamically changing environments. Our planning procedure, UPOM, does a UCT-like search in the space of operational models in order to find a near-optimal method to use for the task and context at hand. Our learning strategies acquire, from online acting experiences and/or simulated planning results, a mapping from decision contexts to method instances as well as a heuristic function to guide UPOM. Our experimental results show that UPOM and our learning strategies significantly improve RAE's performance in four test domains using two different metrics: efficiency and success ratio., Accepted in ICAPS 2020 (30th International Conference on Automated Planning and Scheduling)

International audience; We consider the problem of detecting the activity and counting overlapping speakers in distant-microphone recordings. We treat supervised Voice Activity Detection (VAD), Overlapped Speech Detection (OSD), joint VAD+OSD, and speaker counting as instances of a general Overlapped Speech Detection and Counting (OSDC) task, and we design a Temporal Convolu-tional Network (TCN) based method to address it. We show that TCNs significantly outperform state-of-the-art methods on two real-world distant speech datasets. In particular our best architecture obtains, for OSD, 29.1% and 25.5% absolute improvement in Average Precision over previous techniques on, respectively, the AMI and CHiME-6 datasets. Furthermore, we find that generalization for joint VAD+OSD improves by using a speaker counting objective rather than a VAD+OSD objective. We also study the effectiveness of forced alignment based labeling and data augmentation, and show that both can improve OSD performance.

International audience; Understanding and forecasting brain functions is the major challenge of our times. The focus of this endeavor is understanding and forecasting learning events, such as the dynamic adaptation of beams connecting neuronal cards in Edelman's Theory of Neuronal Group Selection (TNGS). We have conceived, designed and evaluated a new paradigm for constructing and using collective knowledge by Web interactions that we called ViewpointS. By exploiting the similarity with the TNGS we conjecture that it may be metaphorically considered a Collective Brain, especially effective in the case of trans-disciplinary representations. Far from being without doubts, in the paper we present the reasons (and the limits) of our proposal that aims to become a useful integrating tool for future quantitative explorations of individual brain functions as well as of collective wisdom at different degrees of granularity. We are therefore challenging each of the current approaches: the logical one in the semantic Web, the statistical one in mining and deep learning, the social one in recommender systems based on authority and trust; not in each of their own preferred field of operation, rather in their integration weaknesses far from the holistic and dynamic behavior of the human brain.

In AI research, synthesizing a plan of action has typically used descriptive models of the actions that abstractly specify what might happen as a result of an action, and are tailored for efficiently computing state transitions. However, executing the planned actions has needed operational models, in which rich computational control structures and closed-loop online decision-making are used to specify how to perform an action in a nondeterministic execution context, react to events and adapt to an unfolding situation. Deliberative actors, which integrate acting and planning, have typically needed to use both of these models together -- which causes problems when attempting to develop the different models, verify their consistency, and smoothly interleave acting and planning. As an alternative, we define and implement an integrated acting and planning system in which both planning and acting use the same operational models. These rely on hierarchical task-oriented refinement methods offering rich control structures. The acting component, called Reactive Acting Engine (RAE), is inspired by the well-known PRS system. At each decision step, RAE can get advice from a planner for a near-optimal choice with respect to a utility function. The anytime planner uses a UCT-like Monte Carlo Tree Search procedure, called UPOM, whose rollouts are simulations of the actor's operational models. We also present learning strategies for use with RAE and UPOM that acquire, from online acting experiences and/or simulated planning results, a mapping from decision contexts to method instances as well as a heuristic function to guide UPOM. We demonstrate the asymptotic convergence of UPOM towards optimal methods in static domains, and show experimentally that UPOM and the learning strategies significantly improve the acting efficiency and robustness., Published in Artificial Intelligence (AIJ). Please cite as: Sunandita Patra, James Mason, Malik Ghallab, Dana Nau, Paolo Traverso. Deliberative Acting, Planning and Learning with Hierarchical Operational Models. Artificial Intelligence, Elsevier, 2021, 299, pp.103523. 10.1016/j.artint.2021.103523. arXiv admin note: text overlap with arXiv:2003.03932

Invited Editorial; International audience; The two communities of Symbolic Computation and Satisfiability Checking have recently found themselves tackling similar problems and having a growing interest in each other’s technology. This special issue presents articles whose contribution is of interest to, and is influenced by, both communities. Given the context of this journal we start this editorial with a more thorough overview of Satisfiability Checking, and then turn to Symbolic Computation and the potentials and challenges for collaboration. The collection of articles in this issue is evidence of the already existing fruitful work at the intersection of these communities.

The present document presents SAREF4EHAW, an extension of SAREF for the eHealth/Ageing-well Domain.

International audience; Slicing is emerging as a promising technique to support new differentiated services in 5G networks. It provides the necessary flexility and scalability associated with future services. To maintain satisfactory services requirements and high profit for service providers, a slice may be designing according to the varying demands and resource availability. This paper develops a framework for resources allocation between slicing and business layer for multi-tenant slicing, e.g. virtual wireless operators, service providers and smart cities services. This paper proposes a flexible mechanism based on a biding scheme for slicing allocation, which achieve desirable fairness and efficiency among the network slices of the different tenants and their associated users. We then design a practical algorithms to realise the proposed desired solution. We also show through the simulation the efficiency of our approach in term of efficiency and fairness.

International audience; Slicing is emerging as a promising technique to support new differentiated services in 5G networks. It provides the necessary flexility and scalability associated with future services. To maintain satisfactory services requirements and high profit for service providers, a slice may be designing according to the varying demands and resource availability. This paper develops a framework for resources allocation between slicing and business layer for multi-tenant slicing, e.g. virtual wireless operators, service providers and smart cities services. This paper proposes a flexible mechanism based on a biding scheme for slicing allocation, which achieve desirable fairness and efficiency among the network slices of the different tenants and their associated users. We then design a practical algorithms to realise the proposed desired solution. We also show through the simulation the efficiency of our approach in term of efficiency and fairness.

International audience; The increasing popularity of social media platforms like Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech at scale is still an unsolved problem. In this paper, we propose a robust neural architecture which is shown to perform in a satisfactory way across different languages, namely English, Italian and German. We address an extensive analysis of the obtained experimental results over the three languages to gain a better understanding of the contribution of the different components employed in the system, both from the architecture point of view (i.e., Long Short Term Memory, Gated Recurrent Unit, and bidirectional Long Short Term Memory) and from the feature selection point of view (i.e., ngrams, social network specific features, emotion lexica, emojis, word embeddings). To address such in-depth analysis, we use three freely available datasets for hate speech detection on social media on English, Italian and German.

Botvinik-Nezer, Rotem et al., Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., European Union’s Horizon 2020 research and innovation programme (https://ec.europa.eu/programmes/horizon2020/en) under the Marie Skłodowska-Curie grant agreement no. 665501. A. Eed was supported by a predoctoral fellowship La Caixa-Severo Ochoa from Obra Social La Caixa. S.B.E. was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 785907 (HBP SGA2).

International audience; We study the problem of detecting and counting simultaneous, overlapping speakers in a multichannel, distant-microphone scenario. Focusing on a supervised learning approach, we treat Voice Activity Detection (VAD), Overlapped Speech Detection (OSD), joint VAD and OSD (VAD+OSD) and speaker counting in a unified way, as instances of a general Overlapped Speech Detection and Counting (OSDC) multi-class supervised learning problem. We consider a Temporal Convolutional Network (TCN) and a Transformer based architecture for this task, and compare them with previously proposed state-of-the art methods based on Recurrent Neural Networks (RNN) or hybrid Convolutional-Recurrent Neural Networks (CRNN). In addition, we propose ways of exploiting multichannel input by means of early or late fusion of single-channel features with spatial features extracted from one or more microphone pairs. We conduct an extensive experimental evaluation on the AMI and CHiME-6 datasets and on a purposely made multichannel synthetic dataset. We show that the Transformer-based architecture performs best among all architectures and that neural network based spatial localization features outperform signal-based spatial features and significantly improve performance compared to single-channel features only. Finally, we find that training with a speaker counting objective improves OSD compared to training with a VAD+OSD objective.

Nanometer-sized structures, surfaces and sub-surface phenomena have played an enormous role in science and technological applications and represent a driving-force of current interdisciplinary science. Recent developments include the atomic-scale characterization of nanoparticles, molecular reactions at surfaces, magnetism at the atomic scale, photoelectric characterization of nanostructures as well as two-dimensional solids. Research and development of smart nanostructured materials governed by their surface properties is a rapidly growing field. The main challenge is to develop an accurate and robust electronic structure description. The density of surface-related trap states is analyzed by transient UV photoconductivity and temperature-dependent admittance spectroscopy. An advanced application of thin films on shaped substrates is the deposition of catalytic layers on hollow glass microspheres for hydrogen storage controlled exothermal hydrolytic release. Surface properties of thin films including dissolution and corrosion, fouling resistance, and hydrophilicity/hydrophobicity are explored to improve materials response in biological environments and medicine. Trends in surface biofunctionalization routes based on vacuum techniques, together with advances in surface analysis of biomaterials, are discussed. Pioneering advances in the application of X-ray nanodiffraction of thin film cross-sections for characterizing nanostructure and local strain including in-situ experiments during nanoindentation are described. Precise measurements and control of plasma properties are important for fundamental investigations and the development of next generation plasma-based technologies. Critical control parameters are the flux and energy distribution of incident ions at reactive surfaces; it is also crucial to control the dynamics of electrons initiating non-equilibrium chemical reactions. The most promising approach involves the exploitation of complementary advantages in direct measurements combined with specifically designed numerical simulations. Exciting new developments in vacuum science and technology have focused on forward-looking and next generation standards and sensors that take advantage of photonics based measurements. These measurements are inherently fast, frequency based, easily transferrable to sensors based on photonics and hold promise of being disruptive and transformative. Realization of Pascal, the SI unit for pressure, a cold-atom trap based ultra-high and extreme high vacuum (UHV and XHV) standard, dynamic pressure measurements and a photonic based thermometer are three key examples that are presented.

The paper presents a new approach to assess the effecctiveness of rockfall protection barriers, accounting for the wide variety of impact conditions observed on natural sites. This approach makes use of meta-models, considering a widely used rockfall barrier type and was developed from on FE simulation results. Six input parameters relevant to the block impact conditions have been considered. Two meta-models were developed concerning the barrier capability either of stopping the block or in reducing its kinetic energy. The outcome of the parameters range on the meta-model accuracy has been also investigated. The results of the study reveal that the meta-models are effective in reproducing with accuracy the response of the barrier to any impact conditions, providing a formidable tool to support the design of these structures. Furthermore, allowing to accommodate the effects of the impact conditions on the prediction of the block–barrier interaction, the approach can be successfully used in combination with rockfall trajectory simulation tools to improve rockfall quantitative hazard assessment and optimise rockfall mitigation strategies.

International audience; The Material Properties Open Database (MPOD, http//mpod.cimav.edu.mx) is a functional element of the web-based open databases system linked with crystallography. MPOD delivers single-crystal tensor properties in several representations, ranging from numerical matrices to 3D printing. Longitudinal moduli surfaces can be displayed in computers as well as in smart cell phones. Properties are stored as '.mpod' files. IUCr formatting standards (CIF) are followed. The original published paper containing the data is cited. Structural and experimental information is also registered and linked. 'Coupling properties', say piezo-effects and magnetoelectricity, represent interactions linking different subsystems in a material. Currently, piezoelectricity occupies a significant fraction of cases in MPOD. The implications of crystal symmetry in piezoelectricity are systematically taken into account. Matrices' elements and longitudinal moduli surfaces are checked for consistency with the Neumann principle. The inclusion of magnetoelectric axial tensors introduces exciting features into MPOD.