MixNet: Towards Effective and Efficient UHD Low-Light Image Enhancement

With the continuous advancement of imaging devices, the prevalence of Ultra-High-Definition (UHD) images is rising. Although many image restoration methods have achieved promising results, they are not directly applicable to UHD images on devices with limited computational resources due to the inherently high computational complexity of UHD images. In this paper, we focus on the task of low-light image enhancement (LLIE) and propose a novel LLIE method called MixNet, which is designed explicitly for UHD images. To capture the long-range dependency of features without introducing excessive computational complexity, we present the Global Feature Modulation Layer (GFML). GFML associates features from different views by permuting the feature maps, enabling efficient modeling of long-range dependency. In addition, we also design the Local Feature Modulation Layer (LFML) and Feed-forward Layer (FFL) to capture local features and transform features into a compact representation. This way, our MixNet achieves effective LLIE with few model parameters and low computational complexity. We conducted extensive experiments on both synthetic and real-world datasets, and the comprehensive results demonstrate that our proposed method surpasses the performance of current state-of-the-art methods. The code will be available at \url{https://github.com/zzr-idam/MixNet}

A new method for promoting lily flowering

FT is thought to be the florigen in plants. In this research, a new method for promoting lily flowering was introduced. The function of FT gene cloned from Arabidopsis on promoting lily flowering was analyzed. pET-30a-FT vector was constructed to indicate the expression of FT:eGFP fuse protein in prokaryotic cells. FT:eGFP was also constructed into plant virus vector-pGR106. Agrobacterium GV3101 harboring pGR106-FT was injected into lily. The injected lily showed early flowering when compared with the control plants. The detection of eGFP and PCR analysis indicated that the virus harboring FT:eGFP was replicated and expressed in the host plants. The results showed that FT:eGFP fuse protein functioned in promoting lily flowering.Key words: FT, viral vector, lily

5 GHz TMRT observations of 71 pulsars

We present integrated pulse profiles at 5~GHz for 71 pulsars, including eight millisecond pulsars (MSPs), obtained using the Shanghai Tian Ma Radio Telescope (TMRT). Mean flux densities and pulse widths are measured. For 19 normal pulsars and one MSP, these are the first detections at 5~GHz and for a further 19, including five MPSs, the profiles have a better signal-to-noise ratio than previous observations. Mean flux density spectra between 400~MHz and 9~GHz are presented for 27 pulsars and correlations of power-law spectral index are found with characteristic age, radio pseudo-luminosity and spin-down luminosity. Mode changing was detected in five pulsars. The separation between the main pulse and interpulse is shown to be frequency independent for six pulsars but a frequency dependence of the relative intensity of the main pulse and interpulse is found. The frequency dependence of component separations is investigated for 20 pulsars and three groups are found: in seven cases the separation between the outmost leading and trailing components decreases with frequency, roughly in agreement with radius-to-frequency mapping; in eleven cases the separation is nearly constant; in the remain two cases the separation between the outmost components increases with frequency. We obtain the correlations of pulse widths with pulsar period and estimate the core widths of 23 multi-component profiles and conal widths of 17 multi-component profiles at 5.0~GHz using Gaussian fitting and discuss the width-period relationship at 5~GHz compared with the results at at 1.0~GHz and 8.6~GHz.Comment: 46 pages, 14 figures, 8 Tables, accepted by Ap

Short-term surgical and long-term survival outcomes after laparoscopic distal gastrectomy with D2 lymphadenectomy for gastric cancer

BACKGROUND: Laparoscopic distal gastrectomy (LDG) for gastric cancer has gradually gained popularity. However, the long-term oncological outcomes of LDG have rarely been reported. This study aimed to investigate the survival outcomes of LDG, and evaluate the early surgical outcomes of laparoscopy-assisted distal gastrectomy (LADG) and totally laparoscopic distal gastrectomy (TLDG). METHODS: Clinical outcomes of 240 consecutive patients with gastric cancer who underwent LDG at our institution between October 2004 and April 2013 were analyzed. Early surgical outcomes of LADG and TLDG were compared and operative experiences were evaluated. RESULTS: Of the 240 patients, 93 underwent LADG and 147 underwent TLDG. There were 109 T1, 36 T2, 31 T3, and 64 T4a lesions. The median follow-up period was 31.5 months (range: 4–106 months). Tumor recurrence was observed in 40 patients and peritoneal recurrence was observed most commonly. The 5-year disease-free survival (DFS) and overall survival (OS) rates according to tumor stage were 90.3% and 93.1% in stage I, 72.7% and 67.6% in stage II, and 34.8% and 41.5% in stage III, respectively. No significant differences in early surgical outcomes were noted such as operation time, blood loss and postoperative recovery between LADG and TLDG (P >0.05). CONCLUSIONS: LDG for gastric cancer had acceptable long-term oncologic outcomes. The early surgical outcomes of the two commonly used LDG methods were similar

Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCL$_{SED}$, and its variant - UCL-EC$_{SED}$, for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012\_100, Events2018\_100, and CrisisLexT\_7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.Comment: Accepted by TKDE 202

Spectral-spatial self-attention networks for hyperspectral image classification.

This study presents a spectral-spatial self-attention network (SSSAN) for classification of hyperspectral images (HSIs), which can adaptively integrate local features with long-range dependencies related to the pixel to be classified. Specifically, it has two subnetworks. The spatial subnetwork introduces the proposed spatial self-attention module to exploit rich patch-based contextual information related to the center pixel. The spectral subnetwork introduces the proposed spectral self-attention module to exploit the long-range spectral correlation over local spectral features. The extracted spectral and spatial features are then adaptively fused for HSI classification. Experiments conducted on four HSI datasets demonstrate that the proposed network outperforms several state-of-the-art methods