Chaotic dynamics in a storage-ring Free Electron Laser

The temporal dynamics of a storage-ring Free Electron Laser is here investigated with particular attention to the case in which an external modulation is applied to the laser-electron beam detuning. The system is shown to produce bifurcations, multi-furcations as well as chaotic regimes. The peculiarities of this phenomenon with respect to the analogous behavior displayed by conventional laser sources are pointed out. Theoretical results, obtained by means of a phenomenological model reproducing the evolution of the main statistical parameters of the system, are shown to be in a good agreement with experiments carried out on the Super-ACO Free Electron Laser.Comment: submitted to Europ Phys. Journ.

ARC-EN-CIEL beam dynamics

MOPC023International audienceARC-EN-CIEL project is based on a CW 1.3 GHz superconducting (SC) linac accelerator delivering high charge, subpicosecond and low emittance electron bunches at high repetition rate [1]. According to the electron energy, it provides tunable light sources of high brightness in the VUV to soft X-ray wavelength domain. The project will evolve into three phases: first and second phases are based on high brightness single pass SC linac configuration with a low average current (few µA), while third phase integrates recirculation loops to increase the average current (up to 100 mA)

Deep Spatio-Temporal Random Fields for Efficient Video Segmentation.

In this work we introduce a time- and memory-efficient method for structured prediction that couples neuron decisions across both space at time. We show that we are able to perform exact and efficient inference on a densely-connected spatio-temporal graph by capitalizing on recent advances on deep Gaussian Conditional Random Fields (GCRFs). Our method, called VideoGCRF is (a) efficient, (b) has a unique global minimum, and (c) can be trained end-to-end alongside contemporary deep networks for video understanding. We experiment with multiple connectivity patterns in the temporal domain, and present empirical improvements over strong baselines on the tasks of both semantic and instance segmentation of videos. Our implementation is based on the Caffe2 framework and will be available at https://github.com/siddharthachandra/gcrf-v3.0

Cellular Skeletons: A New Approach to Topological Skeletons with Geometric Features

This paper introduces a new kind of skeleton for binary volumes called the cellular skeleton. This skeleton is not a subset of voxels of a volume nor a subcomplex of a cubical complex: it is a chain complex together with a reduction from the original complex. Starting from the binary volume we build a cubical complex which represents it regarding 6 or 26-connectivity. Then the complex is thinned using the proposed method based on elementary collapses, which preserves significant geometric features. The final step reduces the number of cells using Discrete Morse Theory. The resulting skeleton is a reduction which preserves the homology of the original complex and the geometrical information of the output of the previous step. The result of this method, besides its skeletonization content, can be used for computing the homology of the original complex, which usually provides well shaped homology generators

The ARC-EN-CIEL radiation sources

MOPC005International audienceThe ARC-EN-CIEL (Accelerator-Radiation for Enhanced Coherent Intense Extended Light) project proposes a panoply of light sources for the scientific community on a 1 GeV superconducting LINAC (phase 2) on which two ERL loops (1 and 2 GeV) are added in phase 3. LEL1 (200-1.5 nm), LEL2 (10-0.5 nm) and LEL4 (2-0.2 nm) are three kHz High Gain Harmonic Generation Free Electron Laser sources seeded with the High order Harmonics generated in Gas, with 100-30 FWHM pulses. A collaboration, which has been set-up with the SCSS Prototype Accelerator in Japan to test this key concept of ARC-EN-CIEL, has led to the experimental demonstration of the seeding with HHG and the observation up the 7th non linear harmonic with a seed at 160 nm. LEL3 (40-8 nm) installed on the 1 GeV loop is a MHz FEL oscillator providing higher average power and brilliance. In addition, in vacuum undulator spontaneous emission source extend the spectral range above 10 keV and intense THz radiation is generated by edge radiation of bending magnets. Optimisations and light sources characteristics are described

Chaos in free electron laser oscillators

The chaotic nature of a storage-ring Free Electron Laser (FEL) is investigated. The derivation of a low embedding dimension for the dynamics allows the low-dimensionality of this complex system to be observed, whereas its unpredictability is demonstrated, in some ranges of parameters, by a positive Lyapounov exponent. The route to chaos is then explored by tuning a single control parameter, and a period-doubling cascade is evidenced, as well as intermittence.Comment: Accepted in EPJ

On the equivalence between hierarchical segmentations and ultrametric watersheds

We study hierarchical segmentation in the framework of edge-weighted graphs. We define ultrametric watersheds as topological watersheds null on the minima. We prove that there exists a bijection between the set of ultrametric watersheds and the set of hierarchical segmentations. We end this paper by showing how to use the proposed framework in practice in the example of constrained connectivity; in particular it allows to compute such a hierarchy following a classical watershed-based morphological scheme, which provides an efficient algorithm to compute the whole hierarchy.Comment: 19 pages, double-colum

Fully Parallel Hyperparameter Search: Reshaped Space-Filling

Space-filling designs such as scrambled-Hammersley, Latin Hypercube Sampling and Jittered Sampling have been proposed for fully parallel hyperparameter search, and were shown to be more effective than random or grid search. In this paper, we show that these designs only improve over random search by a constant factor. In contrast, we introduce a new approach based on reshaping the search distribution, which leads to substantial gains over random search, both theoretically and empirically. We propose two flavors of reshaping. First, when the distribution of the optimum is some known $P_0$, we propose Recentering, which uses as search distribution a modified version of $P_0$ tightened closer to the center of the domain, in a dimension-dependent and budget-dependent manner. Second, we show that in a wide range of experiments with $P_0$ unknown, using a proposed Cauchy transformation, which simultaneously has a heavier tail (for unbounded hyperparameters) and is closer to the boundaries (for bounded hyperparameters), leads to improved performances. Besides artificial experiments and simple real world tests on clustering or Salmon mappings, we check our proposed methods on expensive artificial intelligence tasks such as attend/infer/repeat, video next frame segmentation forecasting and progressive generative adversarial networks

Scene Segmentation Driven by Deep Learning and Surface Fitting

This paper proposes a joint color and depth segmentation scheme exploiting together geometrical clues and a learning stage. The approach starts from an initial over-segmentation based on spectral clustering. The input data is also fed to a Convolutional Neural Network (CNN) thus producing a per-pixel descriptor vector for each scene sample. An iterative merging procedure is then used to recombine the segments into the regions corresponding to the various objects and surfaces. The proposed algorithm starts by considering all the adjacent segments and computing a similarity metric according to the CNN features. The couples of segments with higher similarity are considered for merging. Finally the algorithm uses a NURBS surface fitting scheme on the segments in order to understand if the selected couples correspond to a single surface. The comparison with state-of-the-art methods shows how the proposed method provides an accurate and reliable scene segmentation