Showing papers by "Mines ParisTech published in 2018"

The industrial management of SMEs in the era of Industry 4.0

Abstract: Industry 4.0 provides new paradigms for the industrial management of SMEs. Supported by a growing number of new technologies, this concept appears more flexible and less expensive than traditional ...

Improving galaxy morphologies for SDSS with Deep Learning

Abstract: We present a morphological catalogue for $\sim$ 670,000 galaxies in the Sloan Digital Sky Survey in two flavours: T-Type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair & Abraham (2010), for training CNNs with colour images in order to obtain T-Types and a series of GZ2 type questions (disk/features, edge-on galaxies, bar signature, bulge prominence, roundness and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-Type model is not so efficient. For the T-Type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (> 97\%), precision and recall values (> 90\%) when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper.

Effects of Selective Laser Melting additive manufacturing parameters of Inconel 718 on porosity, microstructure and mechanical properties

Abstract: The effect of SLM parameters on porosity, microstructure and mechanical properties is studied. To this purpose, the Selective Laser Melting (SLM) technology is applied to manufacture Inconel 718 specimens. The material, the manufacturing process, the Hot Isostatic Pressure (HIP), heat treatment, observation procedures and characterisation of mechanical properties are presented. A columnar-dendritic microstructure was observed on all the SLM specimens and a Volumetric Energy Density (VED) effect on the latter was also noted. The rate of porosity varies in relation to the VED and is considerably reduced after HIP. The heat treatment erases the dendritic microstructure, significantly enhances microhardness and confers on the alloy tensile mechanical properties comparable to forged Inconel 718.

Random Walks in the Quarter Plane: Algebraic Methods, Boundary Value Problems, Applications to Queueing Systems and Analytic Combinatorics

Abstract: This monograph aims to promote original mathematical methods to determine the invariant measure of two-dimensional random walks in domains with boundaries. Such processesarise in numerous applications and are of interest in several areas of mathematical research, such as Stochastic Networks, Analytic Combinatorics, and Quantum Physics. This second edition consists of two parts. Part I is a revised upgrade of the first edition (1999), with additional recent results on the group of a random walk. The theoretical approach given therein has been developed by the authors since the early 1970s. By using Complex Function Theory, Boundary Value Problems, Riemann Surfaces, and Galois Theory, completely new methods are proposed for solving functional equations of two complex variables, which can also be applied to characterize the Transient Behavior of the walks, as well as to find explicit solutions to the one-dimensional Quantum Three-Body Problem, or to tackle a new class of Integrable Systems. Part II borrows special case-studies from queueing theory (in particular, the famous problem of Joining the Shorter of Two Queues) and enumerative combinatorics (Counting, Asymptotics). Researchers and graduate students should find this book very useful.

OZCAR: The French Network of Critical Zone Observatories

Abstract: The French critical zone initiative, called OZCAR (Observatoires de la Zone Critique–Application et Recherche or Critical Zone Observatories–Application and Research) is a National Research Infrastructure (RI). OZCAR-RI is a network of instrumented sites, bringing together 21 pre-existing research observatories monitoring different compartments of the zone situated between “the rock and the sky,” the Earth’s skin or critical zone (CZ), over the long term. These observatories are regionally based and have specific initial scientific questions, monitoring strategies, databases, and modeling activities. The diversity of OZCAR-RI observatories and sites is well representative of the heterogeneity of the CZ and of the scientific communities studying it. Despite this diversity, all OZCAR-RI sites share a main overarching mandate, which is to monitor, understand, and predict (“earthcast”) the fluxes of water and matter of the Earth’s near surface and how they will change in response to the “new climatic regime.” The vision for OZCAR strategic development aims at designing an open infrastructure, building a national CZ community able to share a systemic representation of the CZ , and educating a new generation of scientists more apt to tackle the wicked problem of the Anthropocene. OZCAR articulates around: (i) a set of common scientific questions and cross-cutting scientific activities using the wealth of OZCAR-RI observatories, (ii) an ambitious instrumental development program, and (iii) a better interaction between data and models to integrate the different time and spatial scales. Internationally, OZCAR-RI aims at strengthening the CZ community by providing a model of organization for pre-existing observatories and by offering CZ instrumented sites. OZCAR is one of two French mirrors of the European Strategy Forum on Research Infrastructure (eLTER-ESFRI) project.

Chitosan for wastewater treatment

Abstract: Chitosan (an amino-polysaccharide obtained from deacetylation of chitin, the major constituent of crustaceous shells and insect cuticles) presents a cationic character in acidic media allowing its dissolution, its shaping and possible ion-exchange interactions with anionic compounds (a property applied in adsorption and coagulation–flocculation processes). In neutral media, non-protonated amino groups allow complexation of metal cations or organic chemicals. These different properties explain the interest taken by the scientific community in using this biopolymer. In solution it contributes to complex metals and their recovery by complexation-assisted ultrafiltration. It can also be used to coagulate–flocculate organic compounds (as anionic dyes). In the solid state, it can be used for metal ion adsorption, as well as adsorption of organic compounds (dyes, pesticides, drugs, endocrine disruptors, etc.). The adsorption and coagulation–flocculation processes will be compared and examples considered. Moreover, it is noteworthy that the thermal degradation of this type of material is also more environmentally friendly than that of conventional synthetic resins (production of hazardous by-products, etc.), a supplementary advantage of these biopolymer-based sorbents. Combined with its ability to be chemically or physically modified improving the potential and phase separation of chitosan-based materials, all these properties mean it is an excellent candidate for wastewater treatment. © 2017 Society of Chemical Industry

SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators

Abstract: The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modeling the interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN's Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on \emph{Spatial Data on the Web}. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC's Observations and Measurements (O\&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modeling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at \url{this https URL}.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

Abstract: Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

Abstract: Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

Urban resilience implementation: A policy challenge and research agenda for the 21st century

Abstract: Resilience has risen rapidly over the last decade to become one of the key terms in international policy and academic discussions associated with civil contingencies and crisis management. As governments and institutions confront threats such as environmental hazards, technological accidents, climate change, and terrorist attacks, they recognise that resilience can serve as a key policy response. Many organisations including the United Nations, the European Union, the World Bank, International Monetary Fund, government agencies and departments, international non-governmental organisations and community groups promote resilience. However, with the rapid rise of resilience has come uncertainty as to how it should be built and how different practices and approaches should come together to operationalise it (Chandler & Coaffee, 2016). Whilst there is a variety of different interpretations given to resilience from practitioners and an open debate about resilience principles and characteristics in academia, we adopt the crisis and disaster management definition of “the capacity of a social system to proactively adapt to and recover from disturbances that are perceived within the system to fall outside the range of normal and expected disturbances” (Boin, Comfort, & Demchak, 2010; p. 9). By developing resilience, a system becomes capable of reducing the impact of shocks and resuming normal functioning more quickly following a disaster and better equipped to meet population needs and minimise economic losses caused by crises (Lagadec, 2009; Meerow, Newell, & Stults, 2016). However, it should be noted that this definition fails to capture preexisting socio-economic inequities within society and that in many countries “negotiated resilience” may be desirable (Ziervogel et al., 2017). Moreover, in the rapidly emerging policy discourse of resilience, cities and urban areas have become a key focus of action where rapid urbanisation and greater global connectedness present unprecedented challenges. Such increased urbanisation also concentrates risk in cities making them increasingly vulnerable to an array of shocks and stresses. Under such circumstances, city managers are increasingly seeking to enhance urban resilience by addressing underlying risk factors, and by reducing the exposure and vulnerability of people and assets to a range of current and future threats. In this sense urban resilience provides different frameworks for reducing the multiple risks faced by cities and communities, ensuring there are appropriate levels of resources and capacities to mitigate, prepare for, respond to, and recover from a range of shocks and stresses (Coaffee & Lee, 2016). Many initiatives organised through global governance networks promote the importance of city-based resilience whilst a range of private sector and philanthropic organisations have advanced programmes of work and frameworks by which cities might develop the capacities to become more resilient. Most notably, major cities throughout the world have joined the 100 Resilient Cities programme (http://www. 100resilientcities.org/) (Rockefeller Foundation & Arup, 2015), pioneered by the Rockefeller Foundation, to develop resilience strategies to face disruptive events and address vulnerabilities that amplify crises and erode coping abilities (e.g., inequality, ageing infrastructure, environmental degradation) (100 Resilient Cities, 2016). Organisations of the United Nations are also urging the development of operational frameworks for dealing with integrated risks management, as the UN Habitat City Resilience Profiling Programme, enhancing resilient communities building in relation to Sustainable Development Goals and the Sendai Framework on Disaster Risk Reduction 2015-2030 (UN, 2015) that followed the Hyogo Framework for Action 2005-2015: Building the Resilience of Nations and Communities to Disasters (ISDR, 2005). However, empirical studies show that despite the popularity of resilience, its implementation sometimes lead to business as usual approaches neglecting social justice (Anguelovski et al., 2016; Ziervogel et al., 2017), or lock-in the development path through unsustainable trajectories, and thus resulting in a complex and underestimated set of trade-offs across spatial and temporal scales (Chelleri, Waters, Olazabal, & Minucci, 2015). This implementation gap (Coaffee & Clarke, 2015) remains between resilience as ambitious objective and the “demonstrated capacity to govern resilience in practice” at the urban level (Wagenaar & Wilkinson, 2015; p. 1265). The implementation of resilience challenges the normal functioning of public administrations (Bourgon, 2009; Duit, 2016) by highlighting the need to replace silos with horizontal management (Matyas & Pelling, 2015), take interdependence with external partners into account (Henstra, 2012; McConnell & Drennan, 2006; Valiquette L’Heureux & Therrien, 2013), and encourage flexible and adaptive processes rather than regular routines that maintain the status quo (Pelling & Manuel-Navarrete, 2011; Stark, 2014). Whilst from a governance perspective we can readily acknowledge that “the building of urban resilience will be most effective when it involves a mutual and accountable network of civic institutions, agencies and individual citizens working in partnership towards common goals within a common strategy” (Coaffee, Murakami-Wood, & Rogers, 2008), municipal authorities are undoubtedly struggling to do so. In seeking to identify the different knowledge gaps and future research questions regarding the implementation of urban resilience we ran a 3-day intensive knowledge-brokering workshop on Co-constructing Knowledge for Urban Resilience Implementation at the Ecole nationale DOI: 10.1111/1468-5973.12233

The major β-catenin/E-cadherin junctional binding site is a primary molecular mechano-transductor of differentiation in vivo.

Abstract: In vivo, the primary molecular mechanotransductive events mechanically initiating cell differentiation remain unknown. Here we find the molecular stretching of the highly conserved Y654-β-catenin-D665-E-cadherin binding site as mechanically induced by tissue strain. It triggers the increase of accessibility of the Y654 site, target of the Src42A kinase phosphorylation leading to irreversible unbinding. Molecular dynamics simulations of the β-catenin/E-cadherin complex under a force mimicking a 6 pN physiological mechanical strain predict a local 45% stretching between the two α-helices linked by the site and a 15% increase in accessibility of the phosphorylation site. Both are quantitatively observed using FRET lifetime imaging and non-phospho Y654 specific antibody labelling, in response to the mechanical strains developed by endogenous and magnetically mimicked early mesoderm invagination of gastrulating Drosophila embryos. This is followed by the predicted release of 16% of β-catenin from junctions, observed in FRAP, which initiates the mechanical activation of the β-catenin pathway process.

Durability and Swelling of Solidified/Stabilized Dredged Marine Soils with Class-F Fly Ash, Cement, and Lime

Abstract: Very few studies have been carried out to investigate the durability and swelling behavior of dredged marine materials. To tackle this issue, this study mainly considers the influence of wa...

SNO KARST: a French network of observatories for the multidisciplinary study of critical zone processes in karst watersheds and aquifers

Abstract: Karstaquifers and watersheds represent a major source of drinking water around the world. They are also known as complex and often highly vulnerable hydrosystems due to strong surface-groundwater interactions. Improving the understanding of karst functioning is thus a major issue for the efficient management of karst groundwater resources. A comprehensive understanding of the various processes can be achieved only by studying karst systems across a wide range of spatiotemporal scales under different geological, geomorphological, climatic, and soil cover settings. The objective of the French Karst National Observatory Service (SNO KARST) is to supply the international scientific community with appropriate data and tools, with the ambition of (i) facilitating the collection of long-term observations of hydrogeochemical variables in karst, and (ii) promoting knowledge sharing and developing cross-disciplinary research on karst. This paper provides an overview of the monitoring sites and collective achievements, such as the KarstMod modular modeling platform and the PaPRIKa toolbox, of SNO KARST. It also presents the research questions addressed within the framework of this network, along with major research results regarding (i) the hydrological response of karst to climate and anthropogenic changes, (ii) the influence of karst on geochemical balance of watersheds in the critical zone, and (iii) the relationships between the structure and hydrological functioning of karst aquifers and watersheds.

On Homogeneous Finite-Time Control for Linear Evolution Equation in Hilbert Space

Abstract: Based on the notion of generalized homogeneity, a new algorithm of feedback control design is developed for a plant modeled by a linear evolution equation in a Hilbert space with a possibly unbounded operator The designed control law steers any solution of the closed-loop system to zero in a finite time A method of homogeneous extension is presented in order to make the developed control design principles to be applicable for evolution systems with nonhomogeneous operators The design scheme is demonstrated for the heat equation with the control input distributed on the segment ${[}0,1{]}$

Fifty Years of Kriging

Abstract: Random function models and kriging constitute the core of the geostatistical methods created by Georges Matheron in the 1960s and further developed at the research center he created in 1968 at Ecole des Mines de Paris, Fontainebleau. Initially developed to avoid bias in the estimation of the average grade of mining panels delimited for their exploitation, kriging received progressively applications in all domains of natural resources evaluation and earth sciences, and more recently in completely new domains, for example, the design and analysis of computer experiments (DACE). While the basic theory of kriging is rather straightforward, its application to a large diversity of situations requires extensions of the random function models considered and sound solutions to practical problems. This chapter presents the origins of kriging as well as the development of its theory and its applications along the last fifty years. More details are given for methods presently in development to efficiently handle kriging in situations with a large number of data and a nonstationary behavior, notably the Gaussian Markov random field (GMRF) approximation and the stochastic partial differential (SPDE) approach, with a synthetic case study concerning the latter.

YoTube: Searching Action Proposal Via Recurrent and Static Regression Networks

Abstract: In this paper, we propose YoTube —a novel deep learning framework for generating action proposals in untrimmed videos, where each action proposal corresponds to a spatial-temporal tube that potentially locates one human action. Most of the existing works generate proposals by clustering low-level features or linking image proposals, which ignore the interplay between long-term temporal context and short-term cues. Different from these works, our method considers the interplay by designing a new recurrent YoTube detector and static YoTube detector. The recurrent YoTube detector sequentially regresses candidate bounding boxes using Recurrent Neural Network learned long-term temporal contexts. The static YoTube detector produces bounding boxes using rich appearance cues in every single frame. To fully exploit the complementary appearance, motion, and temporal context, we train the recurrent and static detector using RGB (Color) and flow information. Moreover, we fuse the corresponding outputs of the detectors to produce accurate and robust proposal boxes and obtain the final action proposals by linking the proposal boxes using dynamic programming with a novel path trimming method. Benefiting from the pipeline of our method, the untrimmed video could be effectively and efficiently handled. Extensive experiments on the challenging UCF-101, UCF-Sports, and JHMDB datasets show superior performance of the proposed method compared with the state of the arts.

Transfer of innovation on allergic rhinitis and asthma multimorbidity in the elderly (MACVIA-ARIA) - EIP on AHA Twinning Reference Site (GARD research demonstration project)

Abstract: The overarching goals of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) are to enable European citizens to lead healthy, active and independent lives whilst ageing. The EIP on AHA includes 74 Reference Sites. The aim of this study was to transfer innovation from an app developed by the MACVIA-France EIP on AHA reference site (Allergy Diary) to other reference sites. The phenotypic characteristics of rhinitis and asthma multimorbidity in adults and the elderly will be compared using validated information and communication technology (ICT) tools (i.e. the Allergy Diary and CARAT: Control of Allergic Rhinitis and Asthma Test) in 22 Reference Sites or regions across Europe. This will improve the understanding, assessment of burden, diagnosis and management of rhinitis in the elderly by comparison with an adult population. Specific objectives will be: (i) to assess the percentage of adults and elderly who are able to use the Allergy Diary, (ii) to study the phenotypic characteristics and treatment over a 1-year period of rhinitis and asthma multimorbidity at baseline (cross-sectional study) and (iii) to follow-up using visual analogue scale (VAS). This part of the study may provide some insight into the differences between the elderly and adults in terms of response to treatment and practice. Finally (iv) work productivity will be examined in adults.

E-HEFT: Enhancement Heterogeneous Earliest Finish Time algorithm for Task Scheduling based on Load Balancing in Cloud Computing

Abstract: Cloud computing is one of the most widely spreaded platforms for executing tasks through virtual machines as processing elements. However, there are various issues that need to be addressed in order to be efficiently utilized for workflow applications. One of the fundamental issues in cloud computing is related to task scheduling. Optimal scheduling of tasks in cloud computing is an NP-complete optimization problem, and many algorithms have been proposed to solve it. Furthermore, the existing algorithms fail to either meet the user's Quality of Service (QoS) requirements such as minimizing the makespan and satisfying budget constraints, or to incorporate some basic principles of cloud computing such as elasticity and heterogeneity of computing resources. Among these algorithms, the Heterogeneous Earliest Finish Time (HEFT) heuristic is known to give good results in short time for tasks scheduling in heterogeneous systems. Generally, the HEFT algorithm yields good tasks execution time, but its drawback is that there is no load balancing. In this paper, an enhancement of Heterogeneous Earliest Finish Time (E-HEFT) algorithm under a user-specified financial constraint is proposed to achieve a well balanced load across the virtual machines while trying to minimize the makespan of a given workflow application. To evaluate the performance of the enhancement algorithm, we compare our algorithm with some existing scheduling algorithms. Experimental results show that our algorithm outperforms other algorithms by reducing the makespan and improving load balance among virtual machines.