Institution
Institut Mines-Télécom
Facility•Paris, France•
About: Institut Mines-Télécom is a facility organization based out in Paris, France. It is known for research contribution in the topics: Cloud computing & Wireless sensor network. The organization has 1519 authors who have published 2490 publications receiving 35510 citations. The organization is also known as: IMT & Institut Mines-Telecom.
Papers published on a yearly basis
Papers
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TL;DR: It is illustrated how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps and its application to neuroimaging data provides a versatile tool to study the brain.
Abstract: Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.
1,418 citations
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01 Jan 2015TL;DR: A STAP model is proposed that first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference, and a context-aware fusion framework is put forward to combine the temporal and spatial activity preference models for preferences inference.
Abstract: With the recent surge of location based social networks (LBSNs), activity data of millions of users has become attainable. This data contains not only spatial and temporal stamps of user activity, but also its semantic information. LBSNs can help to understand mobile users’ spatial temporal activity preference (STAP), which can enable a wide range of ubiquitous applications, such as personalized context-aware location recommendation and group-oriented advertisement. However, modeling such user-specific STAP needs to tackle high-dimensional data, i.e., user-location-time-activity quadruples, which is complicated and usually suffers from a data sparsity problem. In order to address this problem, we propose a STAP model. It first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference. In order to characterize the impact of spatial features on user activity preference, we propose the notion of personal functional region and related parameters to model and infer user spatial activity preference. In order to model the user temporal activity preference with sparse user activity data in LBSNs, we propose to exploit the temporal activity similarity among different users and apply nonnegative tensor factorization to collaboratively infer temporal activity preference. Finally, we put forward a context-aware fusion framework to combine the spatial and temporal activity preference models for preference inference. We evaluate our proposed approach on three real-world datasets collected from New York and Tokyo, and show that our STAP model consistently outperforms the baseline approaches in various settings.
548 citations
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TL;DR: The main developments and technical aspects of this ongoing standardization effort for compactly representing 3D point clouds, which are the 3D equivalent of the very well-known 2D pixels are introduced.
Abstract: Due to the increased popularity of augmented and virtual reality experiences, the interest in capturing the real world in multiple dimensions and in presenting it to users in an immersible fashion has never been higher. Distributing such representations enables users to freely navigate in multi-sensory 3D media experiences. Unfortunately, such representations require a large amount of data, not feasible for transmission on today’s networks. Efficient compression technologies well adopted in the content chain are in high demand and are key components to democratize augmented and virtual reality applications. Moving Picture Experts Group, as one of the main standardization groups dealing with multimedia, identified the trend and started recently the process of building an open standard for compactly representing 3D point clouds, which are the 3D equivalent of the very well-known 2D pixels. This paper introduces the main developments and technical aspects of this ongoing standardization effort.
470 citations
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TL;DR: This paper proposes an effective announcement network called CreditCoin, a novel privacy-preserving incentive announcement network based on Blockchain via an efficient anonymous vehicular announcement aggregation protocol, and shows that CreditCoin is efficient and practical in simulations of smart transportation.
Abstract: The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems. In general, there are two major issues in building an effective vehicular announcement network. First, it is difficult to forward reliable announcements without revealing users’ identities. Second, users usually lack the motivation to forward announcements. In this paper, we endeavor to resolve these two issues through proposing an effective announcement network called CreditCoin , a novel privacy-preserving incentive announcement network based on Blockchain via an efficient anonymous vehicular announcement aggregation protocol. On the one hand, CreditCoin allows nondeterministic different signers (i.e., users) to generate the signatures and to send announcements anonymously in the nonfully trusted environment. On the other hand, with Blockchain, CreditCoin motivates users with incentives to share traffic information. In addition, transactions and account information in CreditCoin are tamper-resistant. CreditCoin also achieves conditional privacy since Trace manager in CreditCoin traces malicious users’ identities in anonymous announcements with related transactions. CreditCoin thus is able to motivate users to forward announcements anonymously and reliably. Extensive experimental results show that CreditCoin is efficient and practical in simulations of smart transportation.
441 citations
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TL;DR: A 3D-printed fetal brain undergoes constrained expansion to reproduce the shape of the human cerebral cortex, mimicking cortical growth and revealing the mechanical origin of the brain’s folded geometry.
Abstract: A 3D-printed fetal brain undergoes constrained expansion to reproduce the shape of the human cerebral cortex. The soft gels of the model swell in solvent, mimicking cortical growth and revealing the mechanical origin of the brain’s folded geometry.
429 citations
Authors
Showing all 1537 results
Name | H-index | Papers | Citations |
---|---|---|---|
Eric Guibal | 69 | 294 | 16397 |
Keith W. Ross | 68 | 249 | 17873 |
Daqing Zhang | 67 | 331 | 16675 |
Yuan Yang | 56 | 268 | 28813 |
Isabelle Bloch | 50 | 572 | 13056 |
Ernst W. Biersack | 49 | 175 | 8902 |
Catherine Pelachaud | 48 | 379 | 10057 |
Alexandre Gramfort | 48 | 258 | 74203 |
Olivier Cappé | 47 | 148 | 12119 |
Pascal Felber | 46 | 306 | 12235 |
B. Revenu | 44 | 88 | 7615 |
Gael Richard | 44 | 316 | 7077 |
Emilio Leonardi | 43 | 299 | 6268 |
Gregoire Mercier | 42 | 280 | 6337 |
Dario Rossi | 40 | 257 | 5972 |