Experiments in randomly agitated granular assemblies close to the jamming transition

We present here the preliminary results obtained for two experiments on randomly agitated granular assemblies using a novel way of shaking. First we discuss the transport properties of a 2D model system undergoing classical shaking that show the importance of large scale dynamics for this type of agitation and offer a local view of the microscopic motions of a grain. We then develop a new way of vibrating the system allowing for random accelerations smaller than gravity. Using this method we study the evolution of the free surface as well as results from a light scattering method for a 3D model system. The final aim of these experiments is to investigate the ideas of effective temperature on the one hand as a function of inherent states and on the other hand using fluctuation dissipation relations.Comment: Contribution to the volume "Unifying Concepts in Granular Media and Glasses", edt.s A. Coniglio, A. Fierro, H.J. Herrmann and M. Nicodem

Implicitizing rational curves by the method of moving quadrics

International audienceA new technique for finding implicit matrix-based representations of rational curves in arbitrary dimension is introduced. It relies on the use of moving quadrics following curve parameterizations, providing a high-order extension of the implicit matrix representations built from their linear counterparts, the moving planes. The matrices we obtain offer new, more compact, implicit representations of rational curves. Their entries are filled by linear and quadratic forms in the space variables and their ranks drop exactly on the curve. Typically, for a general rational curve of degree d we obtain a matrix whose size is half of the size of the corresponding matrix obtained with the moving planes method. We illustrate the advantages of these new matrices with some examples, including the computation of the singularities of a rational curve

Contrastive Multimodal Learning for Emergence of Graphical Sensory-Motor Communication

In this paper, we investigate whether artificial agents can develop a shared language in an ecological setting where communication relies on a sensory-motor channel. To this end, we introduce the Graphical Referential Game (GREG) where a speaker must produce a graphical utterance to name a visual referent object while a listener has to select the corresponding object among distractor referents, given the delivered message. The utterances are drawing images produced using dynamical motor primitives combined with a sketching library. To tackle GREG we present CURVES: a multimodal contrastive deep learning mechanism that represents the energy (alignment) between named referents and utterances generated through gradient ascent on the learned energy landscape. We demonstrate that CURVES not only succeeds at solving the GREG but also enables agents to self-organize a language that generalizes to feature compositions never seen during training. In addition to evaluating the communication performance of our approach, we also explore the structure of the emerging language. Specifically, we show that the resulting language forms a coherent lexicon shared between agents and that basic compositional rules on the graphical productions could not explain the compositional generalization

Drum extraction in single channel audio signals using multi-layer non negative matrix factor deconvolution

International audienceIn this paper, we propose a supervised multilayer factorization method designed for harmonic/percussive source separation and drum extraction. Our method decomposes the audio signals in sparse orthogonal components which capture the harmonic content, while the drum is represented by an extension of non negative matrix factorization which is able to exploit time-frequency dictionaries to take into account non stationary drum sounds. The drum dictionaries represent various real drum hits and the decomposition has more physical sense and allows for a better interpretation of the results. Experiments on real music data for a harmonic/percussive source separation task show that our method outperforms other state of the art algorithms. Finally, our method is very robust to non stationary harmonic sources that are usually poorly decomposed by existing methods

Heap Formation in Granular Media

Using molecular dynamics (MD) simulations, we find the formation of heaps in a system of granular particles contained in a box with oscillating bottom and fixed sidewalls. The simulation includes the effect of static friction, which is found to be crucial in maintaining a stable heap. We also find another mechanism for heap formation in systems under constant vertical shear. In both systems, heaps are formed due to a net downward shear by the sidewalls. We discuss the origin of net downward shear for the vibration induced heap.Comment: 11 pages, 4 figures available upon request, Plain TeX, HLRZ-101/9

Scaling Behavior of Granular Particles in a Vibrating Box

Using numerical and analytic methods, we study the behavior of granular particles contained in a vibrating box. We measure, by molecular dynamics (MD) simulation, several quantities which characterize the system. These quantities--the density and the granular temperature fields, and the vertical expansion--obey scaling in the variable $x = Af$. Here, $A$ and $f$ are the amplitude and the frequency of the vibration. The behavior of these quantities is qualitatively different for small and large values of $x$. We also study the system using Navier-Stokes type equations developed by Haff. We develop a boundary condition for moving boundaries, and solve for the density and the temperature fields of the steady state in the quasi-incompressible limit, where the average separation between the particles is much smaller than the average diameter of the particles. The fields obtained from Haff's equations show the same scaling as those from the simulations. The origin of the scaling can be easily understood. The behavior of the fields from the theory is consistent with the simulation data for small $x$, but they deviate significantly for large $x$. We argue that the deviation is due to the breakdown of the quasi-incompressibility condition for large $x$.Comment: LaTeX, 26 pages, 9 figures available upon reques

Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exiting

Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resource-constrained devices still proved challenging. Therefore, we present an early-exiting model based on nsNet2 that provides several levels of accuracy and resource savings by halting computations at different stages. Moreover, we adapt the original architecture by splitting the information flow to take into account the injected dynamism. We show the trade-offs between performance and computational complexity based on established metrics.Comment: Accepted at the MLSP 202

Time resolved particle dynamics in granular convection

We present an experimental study of the movement of individual particles in a layer of vertically shaken granular material. High-speed imaging allows us to investigate the motion of beads within one vibration period. This motion consists mainly of vertical jumps, and a global ordered drift. The analysis of the system movement as a whole reveals that the observed bifurcation in the flight time is not adequately described by the Inelastic Bouncing Ball Model. Near the bifurcation point, friction plays and important role, and the branches of the bifurcation do not diverge as the control parameter is increased. We quantify the friction of the beads against the walls, showing that this interaction is the underlying mechanism responsible for the dynamics of the flow observed near the lateral wall