Sparse Transfer Learning for Interactive Video Search Reranking

Visual reranking is effective to improve the performance of the text-based video search. However, existing reranking algorithms can only achieve limited improvement because of the well-known semantic gap between low level visual features and high level semantic concepts. In this paper, we adopt interactive video search reranking to bridge the semantic gap by introducing user's labeling effort. We propose a novel dimension reduction tool, termed sparse transfer learning (STL), to effectively and efficiently encode user's labeling information. STL is particularly designed for interactive video search reranking. Technically, it a) considers the pair-wise discriminative information to maximally separate labeled query relevant samples from labeled query irrelevant ones, b) achieves a sparse representation for the subspace to encodes user's intention by applying the elastic net penalty, and c) propagates user's labeling information from labeled samples to unlabeled samples by using the data distribution knowledge. We conducted extensive experiments on the TRECVID 2005, 2006 and 2007 benchmark datasets and compared STL with popular dimension reduction algorithms. We report superior performance by using the proposed STL based interactive video search reranking.Comment: 17 page

Reproducing properties of MW dSphs as descendants of DM-free TDGs

The Milky Way (MW) dwarf spheroidal (dSph) satellites are known to be the most dark-matter (DM) dominated galaxies with estimates of dark to baryonic matter reaching even above one hundred. It comes from the assumption that dwarfs are dynamically supported by their observed velocity dispersions. However their spatial distributions around the MW is not at random and this could challenge their origin, previously assumed to be residues of primordial galaxies accreted by the MW potential. Here we show that alternatively, dSphs could be the residue of tidal dwarf galaxies (TDGs), which would have interacted with the Galactic hot gaseous halo and disk. TDGs are gas-rich and have been formed in a tidal tail produced during an ancient merger event at the M31 location, and expelled towards the MW. Our simulations show that low-mass TDGs are fragile to an interaction with the MW disk and halo hot gas. During the interaction, their stellar content is progressively driven out of equilibrium and strongly expands, leading to low surface brightness feature and mimicking high dynamical M/L ratios. Our modeling can reproduce the properties, including the kinematics, of classical MW dwarfs within the mass range of the Magellanic Clouds to Draco. An ancient gas-rich merger at the M31 location could then challenge the currently assumed high content of dark matter in dwarf galaxies. We propose a simple observational test with the coming GAIA mission, to follow their expected stellar expansion, which should not be observed within the current theoretical framework.Comment: 17 pages, 11 figures, accepted by the Monthly Notices of the Royal Astronomical Society (MNRAS

Kevoree Modeling Framework (KMF): Efficient modeling techniques for runtime use

The creation of Domain Specific Languages(DSL) counts as one of the main goals in the field of Model-Driven Software Engineering (MDSE). The main purpose of these DSLs is to facilitate the manipulation of domain specific concepts, by providing developers with specific tools for their domain of expertise. A natural approach to create DSLs is to reuse existing modeling standards and tools. In this area, the Eclipse Modeling Framework (EMF) has rapidly become the defacto standard in the MDSE for building Domain Specific Languages (DSL) and tools based on generative techniques. However, the use of EMF generated tools in domains like Internet of Things (IoT), Cloud Computing or Models@Runtime reaches several limitations. In this paper, we identify several properties the generated tools must comply with to be usable in other domains than desktop-based software systems. We then challenge EMF on these properties and describe our approach to overcome the limitations. Our approach, implemented in the Kevoree Modeling Framework (KMF), is finally evaluated according to the identified properties and compared to EMF.Comment: ISBN 978-2-87971-131-7; N° TR-SnT-2014-11 (2014

Enabling lock-free concurrent workers over temporal graphs composed of multiple time-series

Time series are commonly used to store temporal data, e.g., sensor measurements. However, when it comes to complex analytics and learning tasks, these measurements have to be combined with structural context data. Temporal graphs, connecting multiple time- series, have proven to be very suitable to organize such data and ultimately empower analytic algorithms. Computationally intensive tasks often need to be distributed and parallelized among different workers. For tasks that cannot be split into independent parts, several workers have to concurrently read and update these shared temporal graphs. This leads to inconsistency risks, especially in the case of frequent updates. Distributed locks can mitigate these risks but come with a very high-performance cost. In this paper, we present a lock-free approach allowing to concurrently modify temporal graphs. Our approach is based on a composition operator able to do online reconciliation of concurrent modifications of temporal graphs. We evaluate the efficiency and scalability of our approach compared to lock-based approaches

Raising Time Awareness in Model-Driven Engineering

International audienceThe conviction that big data analytics is a key for the success of modern businesses is growing deeper, and the mo-bilisation of companies into adopting it becomes increasingly important. Big data integration projects enable companies to capture their relevant data, to efficiently store it, turn it into domain knowledge, and finally monetize it. In this context, historical data, also called temporal data, is becoming increasingly available and delivers means to analyse the history of applications, discover temporal patterns, and predict future trends. Despite the fact that most data that today's applications are dealing with is inherently temporal current approaches, methodologies, and environments for developing these applications don't provide sufficient support for handling time. We envision that Model-Driven Engineering (MDE) would be an appropriate ecosystem for a seamless and orthogonal integration of time into domain modelling and processing. In this paper, we investigate the state-of-the-art in MDE techniques and tools in order to identify the missing bricks for raising time-awareness in MDE and outline research directions in this emerging domain

Reliable and Safe, Gram-Scale Hydrogenation and Hydrogenolysis of O-Benzyl Ether Groups with In Situ Pd-0/C Catalyst

Hydrogenation of alkenes, alkynes, and hydrogenolysis of O-benzyl ethers with Pd-0/C catalyst generated in situ can be readily scaled up under safer conditions than with traditional procedures. The precise control of the palladium loading and the mild conditions developed allow the formation of a very active and reliable Pd-0/C catalyst, leading to highly reproducible results

Crash prediction for a French highway network with an XAI-informed Bayesian hierarchical model

International audienc