Gerbert de Montreuil, La Continuation de Perceval. Quatrième continuation

En 1925, paraissait le premier volume de la Continuation de Perceval de Gerbert de Montreuil par Mary Williams, mais c’est Marguerite Oswald qui, en 1975, en acheva l’édition intégrale, à partir de deux manuscrits français de la Bnf. L’ouvrage parut en trois volumes et demeura la seule édition disponible durant de nombreuses décennies. Il lui manquait cependant une introduction scientifique, une cohérence affirmée dans la lecture des variantes, ainsi que dans l’établissement du texte et des notes. L’œuvre d’origine picarde, datée du premier tiers du XIIIe siècle, propose le récit de nouvelles pérégrinations de Perceval, qui a pour ambition de faire réparer une petite brisure dans la lame de l’épée magique que lui avait présentée le Roi Pêcheur au château du Graal. Il n’y parviendra que 17 000 vers plus tard, après maintes aventures « felenesses et dures », un mariage avec Blanchefleur, de chaleureuses retrouvailles avec Tristan, Gauvain, le vieux Roi Ermite qui veille sur le tombeau de sa mère et même avec Gornumant de Grohaut, son maître ès armes. Cette nouvelle édition critique de la Continuation de Perceval est précédée d’une riche introduction qui examine la tradition manuscrite et l’attribution - souvent débattue - de l’œuvre à Gerbert de Montreuil ; et qui étudie de manière approfondie la langue franco-picarde, celle du copiste et de l’auteur. Frédérique Le Nan y souligne également la grande qualité du récit, souvent méconnu ou mal jugé. L’ouvrage propose enfin un recueil des sources iconographiques du ms. fr. 12576, des notes et un important glossaire

Robust federated learning with noisy communication

Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs

A Cosmology-Independent Calibration of Gamma-Ray Burst Luminosity Relations and the Hubble Diagram

An important concern in the application of gamma-ray bursts (GRBs) to cosmology is that the calibration of GRB luminosity/energy relations depends on the cosmological model, due to the lack of a sufficient low-redshift GRB sample. In this paper, we present a new method to calibrate GRB relations in a cosmology-independent way. Since objects at the same redshift should have the same luminosity distance and since the distance moduli of Type Ia supernovae (SNe Ia) obtained directly from observations are completely cosmology independent, we obtain the distance modulus of a GRB at a given redshift by interpolating from the Hubble diagram of SNe Ia. Then we calibrate seven GRB relations without assuming a particular cosmological model and construct a GRB Hubble diagram to constrain cosmological parameters. From the 42 GRBs at $1.4<z\le6.6$, we obtain $\Omega_{\rm M}=0.25_{-0.05}^{+0.04}$, $\Omega_{\Lambda}=0.75_{-0.04}^{+0.05}$ for the flat $\Lambda$CDM model, and for the dark energy model with a constant equation of state $w_0=-1.05_{-0.40}^{+0.27}$, which is consistent with the concordance model in a 1-$\sigma$ confidence region.Comment: 7 pages, 3 figures, 1 table, now matches the editorially revised version; accepted for publication in ApJ (vol 685)

Relaying systems with reciprocity mismatch : impact analysis and calibration

Cooperative beamforming can provide significant performance improvement for relaying systems with the help of the channel state information (CSI). In time-division duplexing (TDD) mode, the estimated CSI will deteriorate due to the reciprocity mismatch. In this work, we examine the impact and the calibration of the reciprocity mismatch in relaying systems. To evaluate the impact of the reciprocity mismatch for all devices, the closed-form expression of the achievable rate is first derived. Then, we analyze the performance loss caused by the reciprocity mismatch at sources, relays, and destinations respectively to show that the mismatch at relays dominates the impact. To compensate the performance loss, a two-stage calibration scheme is proposed for relays. Specifically, relays perform the intra-calibration based on circuits independently. Further, the inter-calibration based on the discrete Fourier transform (DFT) codebook is operated to improve the calibration performance by cooperation transmission, which has never been considered in previous work. Finally, we derive the achievable rate after relays perform the proposed reciprocity calibration scheme and investigate the impact of estimation errors on the system performance. Simulation results are presented to verify the analytical results and to show the performance of the proposed calibration approach

NOMA-enhanced computation over multi-access channels

Massive numbers of nodes will be connected in future wireless networks. This brings great difficulty to collect a large amount of data. Instead of collecting the data individually, computation over multi-access channels (CoMAC) provides an intelligent solution by computing a desired function over the air based on the signal-superposition property of wireless channels. To improve the spectrum efficiency in conventional CoMAC, we propose the use of non-orthogonal multiple access (NOMA) for functions in CoMAC. The desired functions are decomposed into several sub-functions, and multiple sub-functions are selected to be superposed over each resource block (RB). The corresponding achievable rate is derived based on sub-function superposition, which prevents a vanishing computation rate for large numbers of nodes. We further study the limiting case when the number of nodes goes to infinity. An exact expression of the rate is derived that provides a lower bound on the computation rate. Compared with existing CoMAC, the NOMA-based CoMAC not only achieves a higher computation rate but also provides an improved non-vanishing rate. Furthermore, the diversity order of the computation rate is derived, which shows that the system performance is dominated by the node with the worst channel gain among these sub-functions in each RB