Photonic Quantum Networks formed from NV(-) centers.

In this article we present a simple repeater scheme based on the negatively-charged nitrogen vacancy centre in diamond. Each repeater node is built from modules comprising an optical cavity containing a single NV(-), with one nuclear spin from (15)N as quantum memory. The module uses only deterministic processes and interactions to achieve high fidelity operations (>99%), and modules are connected by optical fiber. In the repeater node architecture, the processes between modules by photons can be in principle deterministic, however current limitations on optical components lead the processes to be probabilistic but heralded. Our resource-modest repeater architecture contains two modules at each node, and the repeater nodes are then connected by entangled photon pairs. We discuss the performance of such a quantum repeater network with modest resources and then incorporate more resource-intense strategies step by step. Our architecture should allow large-scale quantum information networks with existing or near future technology

Influence of the Water Content on the Diffusion Coefficients of Li⁺ and Water across Naphthalenic Based Copolyimide Cation-Exchange Membranes

The transport of lithium ions in cation-exchange membranes based on sulfonated copolyimide membranes is reported. Diffusion coefficients of lithium are estimated as a function of the water content in membranes by using pulsed field gradient (PFG) NMR and electrical conductivity techniques. It is found that the lithium transport slightly decreases with the diminution of water for membranes with water content lying in the range 14 < λ < 26.5, where λ is the number of molecules of water per fixed sulfonate group. For λ < 14, the value of the diffusion coefficient of lithium experiences a sharp decay with the reduction of water in the membranes. The dependence of the diffusion of lithium on the humidity of the membranes calculated from conductivity data using Nernst–Planck type equations follows a trend similar to that observed by NMR. The possible explanation of the fact that the Haven ratio is higher than the unit is discussed. The diffusion of water estimated by 1H PFG-NMR in membranes neutralized with lithium decreases as λ decreases, but the drop is sharper in the region where the decrease of the diffusion of protons of water also undergoes considerable reduction. The diffusion of lithium ions computed by full molecular dynamics is similar to that estimated by NMR. However, for membranes with medium and low concentration of water, steady state conditions are not reached in the computations and the diffusion coefficients obtained by MD simulation techniques are overestimated. The curves depicting the variation of the diffusion coefficient of water estimated by NMR and full dynamics follow parallel trends, though the values of the diffusion coefficient in the latter case are somewhat higher. The WAXS diffractograms of fully hydrated membranes exhibit the ionomer peak at q = 2.8 nm⁻1, the peak being shifted to higher q as the water content of the membranes decreases. The diffractograms present additional peaks at higher q, common to wet and dry membranes, but the peaks are better resolved in the wet membranes. The ionomer peak is not detected in the diffractograms of dry membranes.The authors acknowledge financial support provided by the DGICYT (Dirección General de Investigación Cientifíca y Tecnológica) through Grant MAT2011-29174-C02-02

NightOwls: a pedestrians at night dataset

We introduce a comprehensive public dataset, NightOwls, for pedestrian detection at night. In comparison to daytime conditions, pedestrian detection at night is more challenging due to variable and low illumination, reflections, blur, and changing contrast. NightOwls consists of 279k frames in 40 sequences recorded at night across 3 countries by an industry-standard camera, including different seasons and weather conditions. All the frames are fully annotated and contain additional object attributes such as occlusion, pose and difficulty, as well as tracking information to identify the same object across multiple frames. A large number of background frames for evaluating the robustness of detectors is included, a validation set for local hyper-parameter tuning, as well as a testing set for central evaluation on a submission server is provided. As a baseline for pedestrian detection at night time, we compare the performance of ACF, Checkerboards, Faster R-CNN, RPN+BF, and SDS-RCNN. In particular, we demonstrate that state-of-the-art pedestrian detectors do not perform well at night, even when specifically trained on night data, and we show there is a clear gap in accuracy between day and night detections. We believe that the availability of a comprehensive night dataset may further advance the research of pedestrian detection, as well as object detection and tracking at night in general.</p

{NightOwls}: {A} Pedestrians at Night Dataset

We introduce a comprehensive public dataset, NightOwls, for pedestrian detection at night. In comparison to daytime conditions, pedestrian detection at night is more challenging due to variable and low illumination, reflections, blur, and changing contrast. NightOwls consists of 279k frames in 40 sequences recorded at night across 3 countries by an industry-standard camera, including different seasons and weather conditions. All the frames are fully annotated and contain additional object attributes such as occlusion, pose and difficulty, as well as tracking information to identify the same object across multiple frames. A large number of background frames for evaluating the robustness of detectors is included, a validation set for local hyper-parameter tuning, as well as a testing set for central evaluation on a submission server is provided. As a baseline for pedestrian detection at night time, we compare the performance of ACF, Checkerboards, Faster R-CNN, RPN+BF, and SDS-RCNN. In particular, we demonstrate that state-of-the-art pedestrian detectors do not perform well at night, even when specifically trained on night data, and we show there is a clear gap in accuracy between day and night detections. We believe that the availability of a comprehensive night dataset may further advance the research of pedestrian detection, as well as object detection and tracking at night in general.</p