Synchronization recovery and state model reduction for soft decoding of variable length codes

Variable length codes exhibit de-synchronization problems when transmitted over noisy channels. Trellis decoding techniques based on Maximum A Posteriori (MAP) estimators are often used to minimize the error rate on the estimated sequence. If the number of symbols and/or bits transmitted are known by the decoder, termination constraints can be incorporated in the decoding process. All the paths in the trellis which do not lead to a valid sequence length are suppressed. This paper presents an analytic method to assess the expected error resilience of a VLC when trellis decoding with a sequence length constraint is used. The approach is based on the computation, for a given code, of the amount of information brought by the constraint. It is then shown that this quantity as well as the probability that the VLC decoder does not re-synchronize in a strict sense, are not significantly altered by appropriate trellis states aggregation. This proves that the performance obtained by running a length-constrained Viterbi decoder on aggregated state models approaches the one obtained with the bit/symbol trellis, with a significantly reduced complexity. It is then shown that the complexity can be further decreased by projecting the state model on two state models of reduced size

Particular object retrieval with integral max-pooling of CNN activations

Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations. Yet such models are not compatible with geometry-aware re-ranking methods and still outperformed, on some particular object retrieval benchmarks, by traditional image search systems relying on precise descriptor matching, geometric re-ranking, or query expansion. This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN. We build compact feature vectors that encode several image regions without the need to feed multiple inputs to the network. Furthermore, we extend integral images to handle max-pooling on convolutional layer activations, allowing us to efficiently localize matching objects. The resulting bounding box is finally used for image re-ranking. As a result, this paper significantly improves existing CNN-based recognition pipeline: We report for the first time results competing with traditional methods on the challenging Oxford5k and Paris6k datasets

Balancing clusters to reduce response time variability in large scale image search

Many algorithms for approximate nearest neighbor search in high-dimensional spaces partition the data into clusters. At query time, in order to avoid exhaustive search, an index selects the few (or a single) clusters nearest to the query point. Clusters are often produced by the well-known $k$-means approach since it has several desirable properties. On the downside, it tends to produce clusters having quite different cardinalities. Imbalanced clusters negatively impact both the variance and the expectation of query response times. This paper proposes to modify $k$-means centroids to produce clusters with more comparable sizes without sacrificing the desirable properties. Experiments with a large scale collection of image descriptors show that our algorithm significantly reduces the variance of response times without seriously impacting the search quality

Orientation covariant aggregation of local descriptors with embeddings

Image search systems based on local descriptors typically achieve orientation invariance by aligning the patches on their dominant orientations. Albeit successful, this choice introduces too much invariance because it does not guarantee that the patches are rotated consistently. This paper introduces an aggregation strategy of local descriptors that achieves this covariance property by jointly encoding the angle in the aggregation stage in a continuous manner. It is combined with an efficient monomial embedding to provide a codebook-free method to aggregate local descriptors into a single vector representation. Our strategy is also compatible and employed with several popular encoding methods, in particular bag-of-words, VLAD and the Fisher vector. Our geometric-aware aggregation strategy is effective for image search, as shown by experiments performed on standard benchmarks for image and particular object retrieval, namely Holidays and Oxford buildings.Comment: European Conference on Computer Vision (2014

Predicting soil water and mineral nitrogen contents with the STICS model for estimating nitrate leaching under agricultural fields

The performance of the STICS soil-crop model for the dynamic prediction of soil water content (SWC) and soil mineral nitrogen (SMN) in the root zone (120 cm) of seven agricultural fields was evaluated using field measurements in a coarse-grained alluvial aquifer of the Garonne River floodplain (southwestern France) from 2005 to 2007. The STICS model was used to simulate drainage and nitrate concentration in drainage water in all the agricultural fields of the study area, in order to quantify and assess the temporal and spatial variability of nitrate leaching into groundwater. Simulations of SWC and SMN in the seven monitored fields were found to be satisfactory as indicated by root mean square error (RMSE) and model efficiency being 6.8 and 0.84% for SWC and 22.8 and 0.92% for SMN, respectively. On average, SWC was slightly overestimated by a mean difference of 10 mm (3%) and there was almost no bias in SMN estimations (<0.5%). These satisfactory results demonstrate the potential for using the STICS model to accurately simulate nitrate leaching. Across the study area, simulated drainage and nitrate concentration were extremely variable from one field to another. For some fields, simulated mean annual nitrate concentration in drainage water exceeded 300 mg NO3 − L−1 and predicted nitrate leaching was close to 100 kg N ha−1, while other fields had very low nitrate losses. About 15% of the farmers’ fields were responsible for 60–70% of nitrate leaching. The SMN in late autumn, before winter drainage, was found the main determining factor explaining this variability. This situation may be attributed to unsatisfactory cumulative nitrogen management over the medium term. Ineffective nitrogen management was found to be more detrimental than a single annual incident of overfertilization, particularly in situations of deep soils and in cases of low or highly variable drainage between years

Low-shot learning with large-scale diffusion

This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time. This setup is often referred to as low-shot learning, where a standard approach is to re-train the last few layers of a convolutional neural network learned on separate classes for which training examples are abundant. We consider a semi-supervised setting based on a large collection of images to support label propagation. This is possible by leveraging the recent advances on large-scale similarity graph construction. We show that despite its conceptual simplicity, scaling label propagation up to hundred millions of images leads to state of the art accuracy in the low-shot learning regime

L'infertilité mâle aujourd'hui. [Male infertility today].

International audienceAu siècle dernier, plus exactement en avril 1995, la revue médecine/sciences consacrait pour la première fois un de ses numéros au testicule. Dix sept ans plus tard, le saut du siècle accompli, il est frappant de constater que la publication de ce premier numéro suivait de peu la survenue de trois événements qui allaient totalement bouleverser non seulement le champ de la médecine et des sciences relatif à la fertilité humaine, mais aussi le rapport des humains aux mystères de leur conception, de la détermination du sexe, et à leur environnement..