COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec

We introduce COOL-CHIC, a Coordinate-based Low Complexity Hierarchical Image Codec. It is a learned alternative to autoencoders with approximately 2000 parameters and 2500 multiplications per decoded pixel. Despite its low complexity, COOL-CHIC offers compression performance close to modern conventional MPEG codecs such as HEVC and VVC. This method is inspired by the Coordinate-based Neural Representation, where an image is represented as a learned function which maps pixel coordinates to RGB values. The parameters of the mapping function are then sent using entropy coding. At the receiver side, the compressed image is obtained by evaluating the mapping function for all pixel coordinates. COOL-CHIC implementation is made available upon request

Trim optimisation in waves

The trim optimisation is nowadays a common practice for all ship owners. Reduction o fuel consumption and improvement of ship energy efficiency concern all sectors of the maritime industry. The trim optimisation consists in finding the best trim angle with regards to the lowest power - i.e. fuel consumption - for a given operating condition (loading condition and speed). Although the operational efficiency of trim optimisation based on calm water resistance computations databases could be proven at sea, it is well known that added resistance due to wave can significantly impact fuel consumption. The increase of cluster computing power make it today possible to evaluate the added resistance in waves using state of the art free surface RANSE solvers. This study of the optimal trim depending on the sea- states show that taking into account the waves could have a significant impact on the optimal trim that have been identify on still water. Although the optimal trim angle trends are the same on still water and in waves for low sea-states, for highest sea-states the trends can be the perfect opposite. This study shows that the optimal trim mainly depends on the draft and the sea-state. The speed seems to have an effect on the gains / losses amplitude but not on the best trim value. The in waves databases will enable operators to predict their ship efficiency with better accuracy, taking into account weather predictions. The trim needs to be therefore adapted regularly depending on the weather conditions the ship encounter during her voyage

Multi-Dimensional Atom Optics and Interferometry

We propose new multi-dimensional atom optics that can create coherent superpositions of atomic wavepackets along three spatial directions. These tools can be used to generate light-pulse atom interferometers that are simultaneously sensitive to the three components of acceleration and rotation, and we discuss how to isolate these inertial components in a single experimental shot. We also present a new type of atomic gyroscope that is insensitive to parasitic accelerations and initial velocities. The ability to measure the full acceleration and rotation vectors with a compact, high-precision, low-bias inertial sensor could strongly impact the fields of inertial navigation, gravity gradiometry, and gyroscopy.Comment: 6 pages + 8 supplementary, 6 figure

Low-complexity Overfitted Neural Image Codec

We propose a neural image codec at reduced complexity which overfits the decoder parameters to each input image. While autoencoders perform up to a million multiplications per decoded pixel, the proposed approach only requires 2300 multiplications per pixel. Albeit low-complexity, the method rivals autoencoder performance and surpasses HEVC performance under various coding conditions. Additional lightweight modules and an improved training process provide a 14% rate reduction with respect to previous overfitted codecs, while offering a similar complexity. This work is made open-source at https://orange-opensource.github.io/Cool-Chic/Comment: Accepted at IEEE MMSP 202

An harmonious encoding of instrument values by a 19th century Parisian violin dealer

International audienceThe study of three ledgers from the archives of a prominent Parisian violin maker's workshop (active from 1796 to 1948) reveals that some of their content was encrypted. We present here the deciphering of the code, and a discussion of its use in the context of the workshop. Charles-Adolphe Gand introduced this code around 1847 to encrypt values of antique/used violins he would buy and resell. His successors maintained the use of this code at least until 1921. Taking a few examples of instruments by Stradivari and other violin makers, we illustrate how the decoded ledgers –listing transactions for more than 2,500 instruments– are of high interest as historical sources documenting the margins, rebates, and commercial practices of these violin dealers. More generally, will contribute to better describing the evolution of the market for antique instruments of the violin family

Evaluation of OpenMP Dependent Tasks with the KASTORS Benchmark Suite

International audienceThe recent introduction of task dependencies in the OpenMP specifi-cation provides new ways of synchronizing tasks. Application programmers can now describe the data a task will read as input and write as output, letting the runtime system resolve fine-grain dependencies between tasks to decide which task should execute next. Such an approach should scale better than the excessive global synchronization found in most OpenMP 3.0 applications. As promising as it looks however, any new feature needs proper evaluation to encourage applica-tion programmers to embrace it. This paper introduces the KASTORS benchmark suite designed to evaluate OpenMP tasks dependencies. We modified state-of-the-art OpenMP 3.0 benchmarks and data-flow parallel linear algebra kernels to make use of tasks dependencies. Learning from this experience, we propose extensions to the current OpenMP specification to improve the expressiveness of dependen-cies. We eventually evaluate both the GCC/libGOMP and the CLANG/libIOMP implementations of OpenMP 4.0 on our KASTORS suite, demonstrating the in-terest of task dependencies compared to taskwait-based approaches

Choice and adaptation of statistical models for single channel singing voice separation

The problem of singing voice extraction from mono audio recordings, i.e., one microphone separation of voice and music, is studied. The approach is based on a priori probabilistic models for two sources, more precisely on Gaussian Mixture Models (GMM). A method for model adaptation to the characteristics of the mixed sources is developed and a comparative study of different models and estimators is performed. We show that the adaptation of the model of music from the non-vocal parts of songs yields good results in realistic conditions.Le problème de l’extraction de la voix chantée dans des enregistrements musicaux monophoniques, c’est-à-dire la séparation voix / musique avec un seul capteur, est étudié. Les approches utilisées sont basées sur des modèles statistiques a priori des deux sources (musique et voix), notamment sur des Modèles de Mélange de Gaussiennes (MMG). Une méthode d’adaptation des modèles aux caractéristiques des sources mélangées est proposée, et une étude comparative des différents modèles et estimateurs est effectuée. Les résultats montrent que l’adaptation du modèle de musique sur les parties non-vocales des chansons permet d’obtenir de bonnes performances dans un cadre réaliste