CNN Injected Transformer for Image Exposure Correction

Capturing images with incorrect exposure settings fails to deliver a satisfactory visual experience. Only when the exposure is properly set, can the color and details of the images be appropriately preserved. Previous exposure correction methods based on convolutions often produce exposure deviation in images as a consequence of the restricted receptive field of convolutional kernels. This issue arises because convolutions are not capable of capturing long-range dependencies in images accurately. To overcome this challenge, we can apply the Transformer to address the exposure correction problem, leveraging its capability in modeling long-range dependencies to capture global representation. However, solely relying on the window-based Transformer leads to visually disturbing blocking artifacts due to the application of self-attention in small patches. In this paper, we propose a CNN Injected Transformer (CIT) to harness the individual strengths of CNN and Transformer simultaneously. Specifically, we construct the CIT by utilizing a window-based Transformer to exploit the long-range interactions among different regions in the entire image. Within each CIT block, we incorporate a channel attention block (CAB) and a half-instance normalization block (HINB) to assist the window-based self-attention to acquire the global statistics and refine local features. In addition to the hybrid architecture design for exposure correction, we apply a set of carefully formulated loss functions to improve the spatial coherence and rectify potential color deviations. Extensive experiments demonstrate that our image exposure correction method outperforms state-of-the-art approaches in terms of both quantitative and qualitative metrics

Role of Kupffer cells in liver injury induced by CpG oligodeoxynucleotide and flucloxacillin in mice

CpG oligodeoxynucleotide (CpG-ODN) is a Toll-like receptor 9 (TLR9) agonist that can induce innate immune responses. In a previous study, flucloxacillin (FLUX; 100 mg/kg, gavage)-induced liver injury in mice was enhanced by co-administration of CpG-ODN (40 μg/mouse, intraperitoneally). In this study, the mechanism of CpG-ODN sensitization to FLUX-induced liver injury was further investigated in mice inhibited of Kupffer cells (KCs) function by gadolinium chloride (GdCl3; 10 mg/kg, intravenously). GdCl3-treated mice administrated with CpG-ODN and FLUX showed lower liver injury than wild-type (WT) mice treated with CpG-ODN and FLUX. Upregulation of Fas and FasL by CpG-ODN was also inhibited in GdCl3-treated mice and mitochondrial swelling in response to FLUX failed to occur regardless of pre-treatment with CpG-ODN. When FasL-mutant gld/gld mice were treated with CpG-ODN, mitochondrial swelling in response to FLUX was also inhibited. These results suggest that KCs play an essential role in liver injury induced by CpG-ODN and FLUX. CpG-ODN may activate KCs, resulting in induction of Fas/FasL-mediated apoptosis of hepatocytes. The Fas/FasL pathway may also be an upstream regulator of CpG-ODN- and FLUX-induced changes in mitochondrial permeability transition. These results enhance our understanding of the mechanism of the adjuvant effect of CpG-ODN in this mouse model of liver injury

Under-Display Camera Image Restoration with Scattering Effect

The under-display camera (UDC) provides consumers with a full-screen visual experience without any obstruction due to notches or punched holes. However, the semi-transparent nature of the display inevitably introduces the severe degradation into UDC images. In this work, we address the UDC image restoration problem with the specific consideration of the scattering effect caused by the display. We explicitly model the scattering effect by treating the display as a piece of homogeneous scattering medium. With the physical model of the scattering effect, we improve the image formation pipeline for the image synthesis to construct a realistic UDC dataset with ground truths. To suppress the scattering effect for the eventual UDC image recovery, a two-branch restoration network is designed. More specifically, the scattering branch leverages global modeling capabilities of the channel-wise self-attention to estimate parameters of the scattering effect from degraded images. While the image branch exploits the local representation advantage of CNN to recover clear scenes, implicitly guided by the scattering branch. Extensive experiments are conducted on both real-world and synthesized data, demonstrating the superiority of the proposed method over the state-of-the-art UDC restoration techniques. The source code and dataset are available at \url{https://github.com/NamecantbeNULL/SRUDC}.Comment: Accepted to ICCV202

Kerker-Type Positional Disorder Immune Metasurfaces

Metasurfaces that can work without the rigorous periodic arrangement of meta-atoms are highly desired by practical optical micro-nano devices. In this work, we proposed two kinds of Kerker-type metasurfaces possessing positional disorder immunity. The metasurfaces are composed of two different core-shell cylinders satisfying the first and second Kerker conditions, respectively. Even with large positional disorder perturbation of the meta-atoms, the metasurfaces can still maintain the same excellent performances as periodic ones, such as the total transmission and magnetic mirror responses. This disorder immunity is due to the unidirectional forward and backward scatterings of a single core-shell cylinder leading to very weak lateral couplings between neighboring cylinders thus rarely affecting the multiple scatterings in the forward or backward direction. In contrast, the dominant response of the disordered non-Kerker-type metasurface decreases significantly. Our findings provide a new idea for designing robust metasurfaces and extend the scope of metasurface applications in sensing and communication under complex practical circumstances.Comment: 18 pages, 9 figure

Development of Braking Force Distribution Strategy for Dual-Motor-Drive Electric Vehicle

In the development of the optimal braking force distribution strategy for a dual-motor-drive electric vehicle (DMDEV) with a series cooperative braking system, three key factors were taken into consideration, i.e. the regenerative force distribution coefficient between the front and the rear motor (β), the energy recovery coefficient at the wheels (α3), and the front-and-rear-axle braking force distribution coefficient (λ). First, the overall power loss model of the two surface-mounted permanent magnetic synchronous motors (SMPMSMs) was created based on the d-q axis equivalent circuit model. The optimal relationship of β and the overall efficiency of the dual-motor system were confirmed, where the latter was quite different from that obtained from the traditional look-up table method for the motors' efficiency. Then, four dimensionless evaluation coefficients were used to evaluate braking stability, regenerative energy transfer efficiency, and energy recovery at the wheels. Finally, based on several typical braking operations, the comprehensive effects of the four coefficients on braking stability and energy recovery were revealed. An optimal braking force distribution strategy balancing braking stability and energy recovery is suggested for a DMDEV with a series cooperative braking system

Diffraction of digital micromirror device gratings and its effect on properties of tunable fiber lasers

A digital micromirror device (DMD) is a kind of widely used spatial light modulator. We apply DMD as wavelength selector in tunable fiber lasers. Based on the two-dimensional diffraction theory, the diffraction of DMD and its effect on properties of fiber laser parameters are analyzed in detail. The theoretical results show that the diffraction efficiency is strongly dependent upon the angle of incident light and the pixel spacing of DMD. Compared with the other models of DMDs, the 0.55 in. DMD grating is an approximate blazed state in our configuration, which makes most of the diffracted radiation concentrated into one order. It is therefore a better choice to improve the stability and reliability of tunable fiber laser systems

LightGrad: Lightweight Diffusion Probabilistic Model for Text-to-Speech

Recent advances in neural text-to-speech (TTS) models bring thousands of TTS applications into daily life, where models are deployed in cloud to provide services for customs. Among these models are diffusion probabilistic models (DPMs), which can be stably trained and are more parameter-efficient compared with other generative models. As transmitting data between customs and the cloud introduces high latency and the risk of exposing private data, deploying TTS models on edge devices is preferred. When implementing DPMs onto edge devices, there are two practical problems. First, current DPMs are not lightweight enough for resource-constrained devices. Second, DPMs require many denoising steps in inference, which increases latency. In this work, we present LightGrad, a lightweight DPM for TTS. LightGrad is equipped with a lightweight U-Net diffusion decoder and a training-free fast sampling technique, reducing both model parameters and inference latency. Streaming inference is also implemented in LightGrad to reduce latency further. Compared with Grad-TTS, LightGrad achieves 62.2% reduction in paramters, 65.7% reduction in latency, while preserving comparable speech quality on both Chinese Mandarin and English in 4 denoising steps.Comment: Accepted by ICASSP 202

Development of Drive Control Strategy for Front-and-Rear-Motor-Drive Electric Vehicle (FRMDEV)

In order to achieve both high-efficiency drive and low-jerk mode switch in FRMDEVs, a drive control strategy is proposed, consisting of top-layer torque distribution aimed at optimal efficiency and low-layer coordination control improving mode-switch jerk. First, with the use of the off-line particle swarm optimization algorithm (PSOA), the optimal switching boundary between single-motor-drive mode (SMDM) and dual-motor drive mode (DMDM) was modelled and a real-time torque distribution model based on the radial basis function (RBF) was created to achieve the optimal torque distribution. Then, referring to the dynamic characteristics of mode switch tested on a dual-motor test bench, a torque coordination strategy by controlling the variation rate of the torque distribution coefficient during the mode-switch process was developed. Finally, based on a hardware-in-loop (HIL) test platform and an FRMDEV, the proposed drive control strategy was verified. The test results show that both drive economy and comfort were improved significantly by the use of the developed drive control strategy

ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs

In this paper, we present ZeroPrompt (Figure 1-(a)) and the corresponding Prompt-and-Refine strategy (Figure 3), two simple but effective \textbf{training-free} methods to decrease the Token Display Time (TDT) of streaming ASR models \textbf{without any accuracy loss}. The core idea of ZeroPrompt is to append zeroed content to each chunk during inference, which acts like a prompt to encourage the model to predict future tokens even before they were spoken. We argue that streaming acoustic encoders naturally have the modeling ability of Masked Language Models and our experiments demonstrate that ZeroPrompt is engineering cheap and can be applied to streaming acoustic encoders on any dataset without any accuracy loss. Specifically, compared with our baseline models, we achieve 350 $\sim$ 700ms reduction on First Token Display Time (TDT-F) and 100 $\sim$ 400ms reduction on Last Token Display Time (TDT-L), with theoretically and experimentally equal WER on both Aishell-1 and Librispeech datasets.Comment: accepted by interspeech 202