Towards supramolecular heterojunctions : self-assembled hydrogen-bonded architectures for organic photovoltaic devices

Ces travaux ont pour but la conception et la synthèse de composants moléculaires photo-et électro-actifs programmés l’auto-organiser en hétérojonctions supramoléculaires actives en conversion photovoltaïque. L’utilisation de fullerène (C60) et d'oligothiophène portant des motifs de reconnaissances moléculaires par liaisons hydrogène permet la conception d’architectures supramoléculaires en ruban, optimisées pour la séparation et la transport de charges efficaces. L’étude de monocouches auto-assemblées portant des groupes de reconnaissance moléculaires permet de structurer la couche active et augmente la réponse photovoltaïque des dispositifs. La fabrication de cellules solaires organiques à l’état solide avec ces matériaux auto-assemblées a également été étudiée.The aim of this research is to focus on the implementation of supramolecular self-assembly of photo-and electro-active components programmed to self-organize into molecular heterojunctions for efficient light-to-electrical energy conversion. The incorporation of fullerene and oligothiophene appended with complementary hydrogen-bonding molecular recognition motifs allows the design of supramolecular architectures engineered to achieve efficient charge separation and transport. In addition, the incorporation of self-assembled monolayers bearing hydrogen-bonding molecular recognition end-groups on electrode surface further enhances the photovoltaic response of the functional supramolecular devices. The fabrication of solid-state organic solar cells with the self-assembled photoactive materials also has been investigated

One Point is All You Need: Directional Attention Point for Feature Learning

We present a novel attention-based mechanism for learning enhanced point features for tasks such as point cloud classification and segmentation. Our key message is that if the right attention point is selected, then "one point is all you need" -- not a sequence as in a recurrent model and not a pre-selected set as in all prior works. Also, where the attention point is should be learned, from data and specific to the task at hand. Our mechanism is characterized by a new and simple convolution, which combines the feature at an input point with the feature at its associated attention point. We call such a point a directional attention point (DAP), since it is found by adding to the original point an offset vector that is learned by maximizing the task performance in training. We show that our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks such as ModelNet40, ShapeNetPart, and S3DIS demonstrate that our DAP-enabled networks consistently outperform the respective original networks, as well as all other competitive alternatives, including those employing pre-selected sets of attention points

Efficient RSU Selection Approaches for Load Balancing in Vehicular Ad Hoc Networks

Due to advances in wireless communication technologies, wireless transmissions gradually replace traditional wired data transmissions. In recent years, vehicles on the move can also enjoy the convenience of wireless communication technologies by assisting each other in message exchange and form an interconnecting network, namely Vehicular Ad Hoc Networks (VANETs). In a VANET, each vehicle is capable of communicating with nearby vehicles and accessing information provided by the network. There are two basic communication models in VANETs, V2V and V2I. Vehicles equipped with wireless transceiver can communicate with other vehicles (V2V) or roadside units (RSUs) (V2I). RSUs acting as gateways are entry points to the Internet for vehicles. Naturally, vehicles tend to choose nearby RSUs as serving gateways. However, due to uneven density distribution and high mobility nature of vehicles, load imbalance of RSUs can happen. In this paper, we study the RSU load-balancing problem and propose two solutions. In the first solution, the whole network is divided into sub-regions based on RSUs’ locations. A RSU provides Internet access for vehicles in its sub-region and the boundaries between sub-regions change dynamically to adopt to load migration. In the second solution, vehicles choose their serving RSUs distributedly by taking their future trajectories and RSUs’ loading information into considerations. From simulation results, the proposed methods can improve packet delivery ratio, packet delay, and load balance among RSUs

Anisotropy characteristics of exposed gravel beds revealed in high-point-density airborne laser scanning data

The aim of this study was to examine the relationship between the anisotropy direction of exposed gravel bed and flow direction. Previous studies have shown that the anisotropy direction of a gravel bed surface can be visually determined in the elliptical contours of 2-D variogram surface (2DVS). In this letter, airborne laser scanning (ALS) point clouds were acquired at a gravel bed, and the whole data set was divided into a series of 6 m × 6 m subsets. To estimate the direction of anisotropy, we proposed an ellipse-fitting-based automatic procedure with consideration given to the grain size characteristic d50 to estimate the primary axis of anisotropy [hereafter referred to as the primary continuity direction (PCD)] in the 2DVS. The ALS-derived PCDs were compared to the flow directions (for both high and low flow) derived from hydrodynamic model simulation. Comparison of ALS-derived PCDs and simulated flow directions suggested that ALS-derived PCDs could be used to infer flow direction at different flow rates. Furthermore, we found that the ALS-derived PCDs estimated from any elliptical contour of the 2DVS exhibited a similar orientation when the contours of the 2DVS reveal the clear anisotropic structure, demonstrating the robustness of the technique

Field-Free Switching in Symmetry Breaking Multilayers: The Critical Role of Interlayer Chiral Exchange

It is crucial to realize field-free, deterministic, current-induced switching in spin-orbit torque magnetic random-access memory (SOT-MRAM) with perpendicular magnetic anisotropy (PMA). A tentative solution has emerged recently, which employs the interlayer chiral exchange coupling or the interlayer Dzyaloshinskii-Moriya interaction (i-DMI) to achieve symmetry breaking. We hereby investigate the interlayer DMI in a Pt/Co multilayer system with orthogonally magnetized layers, using repeatedly stacked [Pt/Co]n structure with PMA, and a thick Co layer with in-plane magnetic anisotropy (IMA). We clarify the origin and the direction of such symmetry breaking with relation to the i-DMI effective field, and show a decreasing trend of the said effective field magnitude to the stacking number (n). By comparing the current-induced field-free switching behavior for both PMA and IMA layers, we confirm the dominating role of i-DMI in such field-free switching, excluding other possible mechanisms such as tilted-anisotropy and unconventional spin currents that may have arisen from the symmetry breaking

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance. Our code is available at https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201