Access and metro network convergence for flexible end-to-end network design

This paper reports on the architectural, protocol, physical layer, and integrated testbed demonstrations carried out by the DISCUS FP7 consortium in the area of access - metro network convergence. Our architecture modeling results show the vast potential for cost and power savings that node consolidation can bring. The architecture, however, also recognizes the limits of long-reach transmission for low-latency 5G services and proposes ways to address such shortcomings in future projects. The testbed results, which have been conducted end-to-end, across access - metro and core, and have targeted all the layers of the network from the application down to the physical layer, show the practical feasibility of the concepts proposed in the project

Magnetic Alignment of Pulsed Solenoids Using the Pulsed Wire Method

A unique application of the pulsed-wire measurement method has been implemented for alignment of 2.5 T pulsed solenoid magnets. The magnetic axis measurement has been shown to have a resolution of better than 25 {micro}m. The accuracy of the technique allows for the identification of inherent field errors due to, for example, the winding layer transitions and the current leads. The alignment system is developed for the induction accelerator NDCX-II under construction at LBNL, an upgraded Neutralized Drift Compression experiment for research on warm dense matter and heavy ion fusion. Precise alignment is essential for NDCX-II, since the ion beam has a large energy spread associated with the rapid pulse compression such that misalignments lead to corkscrew deformation of the beam and reduced intensity at focus. The ability to align the magnetic axis of the pulsed solenoids to within 100 pm of the induction cell axis has been demonstrated

Attenuation of Counterregulatory Responses to Recurrent Hypoglycemia by Active Thalamic Inhibition: A Mechanism for Hypoglycemia-Associated Autonomic Failure

Hypoglycemia, the limiting factor in the glycemic management of diabetes, is the result of the interplay of therapeutic insulin excess and compromised glycemic defenses. The key feature of the latter is an attenuated sympathoadrenal response to hypoglycemia that typically follows an episode of recent antecedent iatrogenic hypoglycemia, a phenomenon termed hypoglycemia-associated autonomic failure (HAAF) in diabetes. We investigated the role of cerebral mechanisms in HAAF by measuring regional brain activation during recurrent hypoglycemia with attenuated counterregulatory responses and comparing it to initial hypoglycemia in healthy individuals

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

LIRA: Lifelong Image Restoration from Unknown Blended Distortions

Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task. To alleviate this problem, we raise the novel lifelong image restoration problem for blended distortions. We first design a base fork-join model in which multiple pre-trained expert models specializing in individual distortion removal task work cooperatively and adaptively to handle blended distortions. When the input is degraded by a new distortion, inspired by adult neurogenesis in human memory system, we develop a neural growing strategy where the previously trained model can incorporate a new expert branch and continually accumulate new knowledge without interfering with learned knowledge. Experimental results show that the proposed approach can not only achieve state-of-the-art performance on blended distortions removal tasks in both PSNR/SSIM metrics, but also maintain old expertise while learning new restoration tasks.Comment: ECCV2020 accepte

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum