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

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

Parallel navigation for 3-D autonomous vehicles

summary:In this paper, parallel navigation is proposed to track the target in three-dimensional space. Firstly, the polar kinematics models for the vehicle and the target are established. Secondly, parallel navigation is derived by using polar kinematics models. Thirdly, cell decomposition method is applied to implement obstacle avoidance. Fourthly, a brief study is given on the influence of uncertainties. Finally, simulations are conducted by MATLAB. Simulation results demonstrate the effectiveness of the parallel navigation

miR-494 Sensitizes Gastric Cancer Cells to TRAIL Treatment Through Downregulation of Survivin

Background/Aims: TNF-related apoptosis-inducing ligand (TRAIL) is a novel and low-toxic anti-tumor drug used for various cancers. However, cancer cells usually develop mechanisms to acquire the resistance against TRAIL. Among these changes, dysregulation of microRNAs (miRNAs) usually occurs in cancer cells and is responsible for induction of drug resistance. Methods: Expression of miR-494 in gastric cancer tissues and cell lines was detected by quantitative reverse transcriptase real time PCR (qRT-PCR) analysis. Effect of miR-494 on regulating the TRAIL sensitivity to gastric cancer cell lines was evaluated by MTT assays. Bioinformatics and luciferase reporter assays were used to confirm the regulation of miR-494 on survivin. Mitochondrial apoptosis pathway in gastric cancer cells was tested by western blot and flow cytometry analysis. Results: Obvious downregulation of miR-494 was observed in gastric cancer cells. Furthermore, we found that expression profile of miR-494 was associated with TRAIL-sensitivity in gastric cancer. Enforced expression of miR-494 was found to sensitize the gastric cancer cells to TRAIL-induced cytotoxicity. Mechanically, Luciferase reporter assays proved that survivin was the target of miR-494 in gastric cancer cells. Enforced expression of miR-494 decreased the expression of survivin, and thus promoted the TRAIL-induced mitochondria collapse and apoptosis pathway. Conclusion: MiR-494/survivin axis represents a potential mechanism which is responsible for TRAIL resistance in gastric cancer cells. Increasing the miR-494 expression may serve as a novel therapeutic strategy to sensitize gastric cancer cells to TRAIL treatment

Perbandingan Perhitungan Trafik Jam Sibuk CDMA 2000 1x Pada BTS Inner City Dan BTS Outer City Dengan Mempergunakan Metode ADPH, TCBH, FDMH Dan FDMP

Cellular communication system is a wireless communication system where the subscriber can move within a wide network coverage. Code Division Multiple Access (CDMA) is a multiuser access technology that is each user uses a unique code contained in the access channel in the system. Calculation and determination of peak hours can be done by several methods such as: Average Daily Peak Hour (ADPH), Time Consistent Busy Hour (TCBH), Fixed Daily Measurement Hour (FDMH), Fixed Daily Measurement Period (FDMP). The effectiveness of the channel should be determined by occupancy both at inner city territory and outer city  territory location. Using design Erlang (Erl) for supply channel at Base Transceiver Station (BTS) that provided, BTS has a design Erlang of 369,83 Erl at inner city and it has a design Erlang of 241,8 Erl at outer city. Peak hour on the inner city occurred at 12:00 to 15:00, whereas the outer city of peak hour occurred at 18:00 to 21:00. Effectiveness value that determined by operator are : <20% = low occupancy (not effective), 21% to 69% = normal occupancy (effective), and > 70% = high occupancy (very effective). In this case occupancy values obtained in each method is between 21% to 69% which means effectiv

A high interferon gamma signature of CD8+ T cells predicts response to neoadjuvant immunotherapy plus chemotherapy in gastric cancer

BackgroundWhile the tumor microenvironment (TME) affects immune checkpoint blockade (ICB) efficacy, ICB also reshapes the characteristics of TME. Thus far, studies have focused on the TME evolution during neoadjuvant or adjuvant ICB therapy in gastric cancer (GC). However, the interaction between TME characteristics and neoadjuvant immunotherapy plus chemotherapy remains to be elucidated.MethodsWe performed single-cell RNA sequencing on ten GC specimens pre- and post-neoadjuvant camrelizumab plus mFOLFOX6 to determine the impact of the TME on the efficacy of the combination therapy and the remodeling of TME by the therapy.ResultsA high baseline interferon gamma (IFN-γ) signature in CD8+ T cells predicts better responses to the combination therapy. We also observed that the IFN-γ signature significantly decreased in multiple cell types, and the exhausted signature of CD8+ T cells was significantly suppressed during the neoadjuvant therapy.ConclusionsOur data reveal interactions between the TME and neoadjuvant immunotherapy plus chemotherapy in GC. Importantly, it also highlights the signature of CD8+ T cells in predicting response to the combination therapy in GC

Is It Possible to Reduce Agricultural Carbon Emissions through More Efficient Irrigation: Empirical Evidence from China

Although irrigation systems are critical to the long-term viability of agriculture, they also contribute a significant amount of carbon dioxide emissions. This creates a conflict between reducing greenhouse gas emissions and promoting agricultural growth. Researchers may be able to gain a better understanding of the subject by looking at the connection between irrigation water efficiency (IWE) and agricultural carbon emissions (ACE). With data from 30 Chinese provinces collected between 2002 and 2019, this study examines the dynamic effect of IWE on ACE. According to the results, IWE has the potential to significantly raise ACE. The positive effects of IWE become more pronounced as ACE increases, according to the heterogeneity analysis. ACE in northern China is also more vulnerable to IWE than other ACE regions. Irrigation scales appear to be a significant channel through which IWE positively affects ACE, according to an investigation of possible mechanisms. However, the increased IWE causes the planting structure adjustments, which aids in the reduction of ACE. The results of this study have significant ramifications for public policy

No-Wait Job Shop Scheduling Using a Population-Based Iterated Greedy Algorithm

When no-wait constraint holds in job shops, a job has to be processed with no waiting time from the first to the last operation, and the start time of a job is greatly restricted. Using key elements of the iterated greedy algorithm, this paper proposes a population-based iterated greedy (PBIG) algorithm for finding high-quality schedules in no-wait job shops. Firstly, the Nawaz–Enscore–Ham (NEH) heuristic used for flow shop is extended in no-wait job shops, and an initialization scheme based on the NEH heuristic is developed to generate start solutions with a certain quality and diversity. Secondly, the iterated greedy procedure is introduced based on the destruction and construction perturbator and the insert-based local search. Furthermore, a population-based co-evolutionary scheme is presented by imposing the iterated greedy procedure in parallel and hybridizing both the left timetabling and inverse left timetabling methods. Computational results based on well-known benchmark instances show that the proposed algorithm outperforms two existing metaheuristics by a significant margin