Mode Coupling in Space-division Multiplexed Systems

Even though fiber-optic communication systems have been engineered to nearly approach the Shannon capacity limit, they still cannot meet the exponentially-growing bandwidth demand of the Internet. Space-division multiplexing (SDM) has attracted considerable attention in recent years due to its potential to address this capacity crunch. In SDM, the transmission channels support more than one spatial mode, each of which can provide the same capacity as a single-mode fiber. To make SDM practical, crosstalk among modes must be effectively managed. This dissertation presents three techniques for crosstalk management for SDM. In some cases such as intra-datacenter interconnects, even though mode crosstalk cannot be completely avoided, crosstalk among mode groups can be suppressed in properly-designed few-mode fibers to support mode group-multiplexed transmission. However, in most cases, mode coupling is unavoidable. In free-space optical (FSO) communication, mode coupling due to turbulence manifests as wavefront distortions. Since there is almost no modal dispersion in FSO, we demonstrate the use of few-mode pre-amplified receivers to mitigate the effect of turbulence without using adaptive optics. In fiber-optic communication, multi-mode fibers or long-haul few-mode fibers not only suffer from mode crosstalk but also large modal dispersion, which can only be compensated electronically using multiple-input-multiple-output (MIMO) digital signal processing (DSP). In this case, we take the counterintuitive approach of introducing strong mode coupling to reduce modal group delay and DSP complexity

Region-Aware Exposure Consistency Network for Mixed Exposure Correction

Exposure correction aims to enhance images suffering from improper exposure to achieve satisfactory visual effects. Despite recent progress, existing methods generally mitigate either overexposure or underexposure in input images, and they still struggle to handle images with mixed exposure, i.e., one image incorporates both overexposed and underexposed regions. The mixed exposure distribution is non-uniform and leads to varying representation, which makes it challenging to address in a unified process. In this paper, we introduce an effective Region-aware Exposure Correction Network (RECNet) that can handle mixed exposure by adaptively learning and bridging different regional exposure representations. Specifically, to address the challenge posed by mixed exposure disparities, we develop a region-aware de-exposure module that effectively translates regional features of mixed exposure scenarios into an exposure-invariant feature space. Simultaneously, as de-exposure operation inevitably reduces discriminative information, we introduce a mixed-scale restoration unit that integrates exposure-invariant features and unprocessed features to recover local information. To further achieve a uniform exposure distribution in the global image, we propose an exposure contrastive regularization strategy under the constraints of intra-regional exposure consistency and inter-regional exposure continuity. Extensive experiments are conducted on various datasets, and the experimental results demonstrate the superiority and generalization of our proposed method. The code is released at: https://github.com/kravrolens/RECNet.Comment: Accepted by AAAI 202

Multi-modality Empowered Network For Facial Action Unit Detection

This paper presents a new thermal empowered multi-task network (TEMT-Net) to improve facial action unit detection. Our primary goal is to leverage the situation that the training set has multi-modality data while the application scenario only has one modality. Thermal images are robust to illumination and face color. In the proposed multi-task framework, we utilize both modality data. Action unit detection and facial landmark detection are correlated tasks. To utilize the advantage and the correlation of different modalities and different tasks, we propose a novel thermal empowered multi-task deep neural network learning approach for action unit detection, facial landmark detection and thermal image reconstruction simultaneously. The thermal image generator and facial landmark detection provide regularization on the learned features with shared factors as the input color images. Extensive experiments are conducted on the BP4D and MMSE databases, with the comparison to the state-of-the-art methods. The experiments show that the multi-modality framework improves the AU detection significantly

ELUCID - Exploring the Local Universe with reConstructed Initial Density field III: Constrained Simulation in the SDSS Volume

A method we developed recently for the reconstruction of the initial density field in the nearby Universe is applied to the Sloan Digital Sky Survey Data Release 7. A high-resolution N-body constrained simulation (CS) of the reconstructed initial condition, with $3072^3$ particles evolved in a 500 Mpc/h box, is carried out and analyzed in terms of the statistical properties of the final density field and its relation with the distribution of SDSS galaxies. We find that the statistical properties of the cosmic web and the halo populations are accurately reproduced in the CS. The galaxy density field is strongly correlated with the CS density field, with a bias that depend on both galaxy luminosity and color. Our further investigations show that the CS provides robust quantities describing the environments within which the observed galaxies and galaxy systems reside. Cosmic variance is greatly reduced in the CS so that the statistical uncertainties can be controlled effectively even for samples of small volumes.Comment: submitted to ApJ, 19 pages, 22 figures. Please download the high-resolution version at http://staff.ustc.edu.cn/~whywang/paper