L'exploitation romaine du sel dans la region de Sigúenza

Sin resume

Co-Regularized Deep Representations for Video Summarization

Compact keyframe-based video summaries are a popular way of generating viewership on video sharing platforms. Yet, creating relevant and compelling summaries for arbitrarily long videos with a small number of keyframes is a challenging task. We propose a comprehensive keyframe-based summarization framework combining deep convolutional neural networks and restricted Boltzmann machines. An original co-regularization scheme is used to discover meaningful subject-scene associations. The resulting multimodal representations are then used to select highly-relevant keyframes. A comprehensive user study is conducted comparing our proposed method to a variety of schemes, including the summarization currently in use by one of the most popular video sharing websites. The results show that our method consistently outperforms the baseline schemes for any given amount of keyframes both in terms of attractiveness and informativeness. The lead is even more significant for smaller summaries.Comment: Video summarization, deep convolutional neural networks, co-regularized restricted Boltzmann machine

Group Invariant Deep Representations for Image Instance Retrieval

Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in large scale image classification, representations extracted from Convolutional Neural Networks (CNN) are quickly gaining ground on Fisher Vectors (FVs) as state-of-the-art global descriptors for image instance retrieval. While CNN-based descriptors are generally remarked for good retrieval performance at lower bitrates, they nevertheless present a number of drawbacks including the lack of robustness to common object transformations such as rotations compared with their interest point based FV counterparts. In this paper, we propose a method for computing invariant global descriptors from CNNs. Our method implements a recently proposed mathematical theory for invariance in a sensory cortex modeled as a feedforward neural network. The resulting global descriptors can be made invariant to multiple arbitrary transformation groups while retaining good discriminativeness. Based on a thorough empirical evaluation using several publicly available datasets, we show that our method is able to significantly and consistently improve retrieval results every time a new type of invariance is incorporated. We also show that our method which has few parameters is not prone to overfitting: improvements generalize well across datasets with different properties with regard to invariances. Finally, we show that our descriptors are able to compare favourably to other state-of-the-art compact descriptors in similar bitranges, exceeding the highest retrieval results reported in the literature on some datasets. A dedicated dimensionality reduction step --quantization or hashing-- may be able to further improve the competitiveness of the descriptors

Caractérisation du transporteur de nitrate à double affinité, MtNPF6.8 (MtNRT1.3), de Medicago truncatula : rôles dans le transport et la perception du signal nitrate

Nitrate, a major nitrogen source for most plants, is not only a nutrient but also a signaling molecule. However, there are contrasting responses to nitrate between different higher plants. In the model legume Medicago truncatula, nitrate has an inhibitory effect on the primary root growth in post-germination phase. A quantitative genetic study has shown that a nitrate transporter is localized at the peak of a QTL involved in the primary root growth. Functional characterization of the transporter, named MtNRT1.3 and renamed MtNPF6.8, showed that it encodes a dual affinity nitrate transporter. MtNPF6.8 is likely to participate in the nitrate influx in the plant. After obtaining three knockdown lines by RNA interference, experiments using K15NO3 showed that this transporter is effect involved in nitrate influx related to the inducible low affinity transport system (iLATS). However, mutation in MtNPF6.8 does not any effect on nitrogen metabolism. In addition, studies on the primary root growth have confirmed the involvement of the transporter on phenotypic trait. In wild-type plants, cortical cell size decreased after nitrate treatment, showing that primary root growth was due to this reduced cell elongation. The possibility that ABA also plays a role in mediating this nitrate dependent response is heavily favored. All these results, reinforced by a study of mutants expressing this transporter in A. thaliana, indicate that MtNPF6.8 is a nitrate sensor for Medicago in the post-germination phase, independently of its nitrate transport activity

Designing nanocomposites using supercritical CO2 to insert Ni nanoparticles into the pores of nanopatterned BaTiO3 thin films

A new concept to prepare nanocomposite thin films is explored. Two chemical-based bottom-up steps are used to design functional films including: i) block copolymerassisted self-assembly of a porous matrix; and ii) impregnation of nanoparticles from a ferroic phase within the pores by supercritical CO2 deposition. Porous nanopatterned BaTiO3 thin films with ca. 17 nm of thickness are prepared using a cost-effective solgel solution containing a block copolymer and evaporation-induced self-assembly methodology. Hexagonal-arranged pores with diameter of ca. 95 nm, running perpendicularly to the substrate are filled with Ni nanoparticles using the supercritical fluid deposition technique from reduction of hydrated nickel nitrate in a supercritical CO2-ethanol mixture at 250 ºC. Small Ni nanoparticles with 21 ± 5 nm nm are selectively deposited inside the pores of the porous matrix. Structural and magnetic properties prove the coexistence of both phases

Effects of secondary compounds from cactus and acacias trees on rumen microbial profile changes performed by Real-Time PCR

Plant rich secondary compounds had antimicrobial effects by acting against different rumen microbial populations. The current study investigated the influence of spineless cactus (Opuntia ficus indica f. inermis), Acacia nilotica and A. saligna on rumen microbial fermentation, using in vitro gas production technique, and microbial population profile changes, using a molecular-based technique (Real-Time PCR). The acacias and Opuntia reduced significantly total gas production (p<0.01), rumen CH4 production (p?0.01) and ammonia concentration (p<0.001). At 24h of incubation, Fungi population was 0.30- and 0.03 -fold reduced with A.nilotica and Opuntia as compared to 0h, but 2-and 1.24- fold higher with A.cyanophylla .Increases in the abundance of F.succinogenes were observed in all substrates; however, the tanniferous plants and Opuntia reduced the relative abundance of R.flavefaciens. Methanogenic population was increased with all substrates, except for Opuntia (0. 90- fold lower than the control). There was a significant reduction (p<0.05) in rumen protozoa count with A.cyanophylla, Opuntia and A.nilotica (3.68; 5.59 and 5.34 times, respectively). Results suggested that tannin sources from A.nilotica and A.cyanophylla had an indirect effect on methanogenesis. This study showed an antimicrobial activity of oxalates content of O. ficus indica