Binary sampling ghost imaging: add random noise to fight quantization caused image quality decline

When the sampling data of ghost imaging is recorded with less bits, i.e., experiencing quantization, decline of image quality is observed. The less bits used, the worse image one gets. Dithering, which adds suitable random noise to the raw data before quantization, is proved to be capable of compensating image quality decline effectively, even for the extreme binary sampling case. A brief explanation and parameter optimization of dithering are given.Comment: 8 pages, 7 figure

Negative exponential behavior of image mutual information for pseudo-thermal light ghost imaging: Observation, modeling, and verification

When use the image mutual information to assess the quality of reconstructed image in pseudo-thermal light ghost imaging, a negative exponential behavior with respect to the measurement number is observed. Based on information theory and a few simple and verifiable assumptions, semi-quantitative model of image mutual information under varying measurement numbers is established. It is the Gaussian characteristics of the bucket detector output probability distribution that leads to this negative exponential behavior. Designed experiments verify the model.Comment: 13 pages, 6 figure

SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentation

Recent real-time semantic segmentation methods usually adopt an additional semantic branch to pursue rich long-range context. However, the additional branch incurs undesirable computational overhead and slows inference speed. To eliminate this dilemma, we propose SCTNet, a single branch CNN with transformer semantic information for real-time segmentation. SCTNet enjoys the rich semantic representations of an inference-free semantic branch while retaining the high efficiency of lightweight single branch CNN. SCTNet utilizes a transformer as the training-only semantic branch considering its superb ability to extract long-range context. With the help of the proposed transformer-like CNN block CFBlock and the semantic information alignment module, SCTNet could capture the rich semantic information from the transformer branch in training. During the inference, only the single branch CNN needs to be deployed. We conduct extensive experiments on Cityscapes, ADE20K, and COCO-Stuff-10K, and the results show that our method achieves the new state-of-the-art performance. The code and model is available at https://github.com/xzz777/SCTNetComment: Accepted by AAAI 2024; typos corrected; code and models have been released at https://github.com/xzz777/SCTNe

In situ characterization of tensile behavior of laser rapid solidified Al–Si heterogeneous microstructures

Heterogeneous Al–Si microstructure comprising of sub-micron-scale Al dendrites and nanoscale Al–Si fibrous eutectic was fabricated by processing as-cast Al-20wt.%Si alloy using laser rapid solidification. In situ tension tests explored high tensile strength ( ∼ 600 MPa) and ductility ( ∼ 10%) and high strain hardening rate ( ∼ 7 GPa). Microstructural characterization revealed the plastic co[1]deformation mechanisms between soft Al dendrites and hard nanoscale Al–Si eutectic. The progression of plasticity in nanoscale Al–Si eutectic with increasing applied strain is accommodated by dislocation plasticity in the nano-Al channels and cracking Si nanofibers. The propagation of nano-cracks is suppressed by surrounding Al, retaining good ductility of the sample

Mechanism of METTL14-mediated ERα m6A regulation of endometrial cancer metastasis

Background and purpose: Aberrant N6-methyladenosine (m6A) modification caused by dysregulation of methyltransferase-like factor 14 (METTL14) plays an important role in the progression of various cancers, and it is unclear whether it is involved in the endometrial cancer (EC) progression. This study aimed to investigate the role of aberrant m6A modification caused by dysregulation of METTL14 in EC invasion and metastasis. Methods: Ninety-six EC patients who underwent curative surgery in Qinghai Provincial People’s Hospital from 2017 to 2021 were enrolled. RNA (70 pairs) or proteins (10 pairs) were isolated from frozen tissues for real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR) or immunoblot analysis to assess METTL14 expression in EC. The expression of METTL14 and its correlation with clinicopathological features of EC were assessed. The biological effects of METTL14 in EC were determined in vitro and in vivo. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) combined with RNA sequencing (RNA-seq), and following m6A dot blot, MeRIP-RTFQ-PCR, RIP-RTFQ-PCR or dual luciferase reporter assays were employed to screen and validate the candidate targets of METTL14. Results: The mRNA expression and protein levels of METTL14 were significantly downregulated in EC compared with matched adjacent tissues. Compared with the METTL14 high expression group, the METTL14 low expression group had a significant increase in International Federation of Gynecology and Obstetrics (FIGO) stage, infiltration depth, lymphovascular invasion, lymph node metastasis and the number of cases of tumor metastasis (P<0.05). Functionally, METTL14 inhibited the proliferation and invasive capacity of EC cells in vitro and in vivo. Mechanistically, METTL14-mediated demethylation of m6A resulted in post-transcriptional repression of estrogen receptor alpha (ERα). Furthermore, compared with METTL14, ERα induced oncogenic behavior of tumors. Conclusion: METTL14 attenuates ERα expression in EC cells in a m6A-dependent manner, thereby inhibiting tumor metastasis and invasion