Modeling correlation noise statistics at decoder for multi-view distributed video coding

Recently, multi-view distributed video coding (MDVC) receives more and more attention, as its low-complexity encoder and high-complexity decoder coding paradigm suits for many applications such as sensor networks, in which several view sequences are required to be coded by a few power-constraint encoders. Modeling the correlation noises between original frame and side information frame is a hot research issue in distributed video coding (DVC), since it is a vital factor affecting the coding efficiency. This paper firstly proposes a novel method to model the correlation noises in MDVC. And an algorithm to online estimate the model at decoder using the knowledge of adjacent views is also presented. Experiment results show that the proposed correlation model can significantly improve coding efficiency. ?2009 IEEE.EI

High Genetic Diversity and Low Differentiation of Michelia coriacea (Magnoliaceae), a Critically Endangered Endemic in Southeast Yunnan, China

Michelia coriacea, a critically endangered tree, has a restricted and fragmented distribution in Southeast Yunnan Province, China. The genetic diversity, genetic structure and gene flow in the three extant populations of this species were detected by 10 inter-simple sequence repeat (ISSR) markers and 11 simple sequence repeat (SSR) markers. Examination of genetic diversity revealed that the species maintained a relatively high level of genetic diversity at the species level (percentage of polymorphic bands) PPB = 96.36% from ISSRs; PPL (percentage of polymorphic loci) = 95.56% from SSRs, despite several fragmental populations. Low levels of genetic differentiation among the populations of M. coriacea were detected by Nei’s Gst = 0.187 for ISSR and Wright’s Fst = 0.090 for SSR markers, which is further confirmed by Bayesian model-based STRUCTURE and PCoA analysis that could not reveal a clear separation between populations, although YKP was differentiated to other two populations by ISSR markers. Meanwhile, AMOVA analysis also indicated that 22.84% and 13.90% of genetic variation existed among populations for ISSRs and SSRs, respectively. The high level of genetic diversity, low genetic differentiation, and the population, structure imply that the fragmented habitat and the isolated population of M. coriacea may be due to recent over-exploitation. Conservation and management of M. coriacea should concentrate on maintaining the high level of genetic variability through both in and ex-situ conservation actions

VEGI174 protein and its functional domain peptides exert antitumour effects on renal cell carcinoma

Vascular endothelial growth inhibitor (VEGI) has been identified as an anti‑angiogenic cytokine. However, the effects of VEGI174 protein, and its functional domain peptides V7 and V8, on renal cell carcinoma (RCC) remain unknown. In the present study, the protein and peptides were biosynthesised as experimental agents. The A498 and 786‑O RCC cell lines, and an established mouse xenograft model, were separately treated with VEGI174, V7 or V8. Cellular functions, including proliferation, migration and invasion, were subsequently detected. Cell migration and invasion were monitored using the xCELLigence system. Furthermore, tumour growth and mouse behaviours, including mobility, appetite and body weight, were assessed. The results demonstrated that VEGI174, V7 and V8 inhibited the proliferation, migration and invasion of A498 and 786‑O cell lines when administered at concentrations of 1 and 100 pM, 10 nM and 1 µM. The inhibitory effects exhibited dose‑ and time‑dependent antitumour activity. Furthermore, VEGI174, V7 and V8 inhibited tumour growth in A498 and 786‑O xenograft mice. In the A498 xenografts, the tumour growth inhibition (TGI) rates in the VEGI174‑, V7‑ and V8‑treated groups were 71, 20 and 31%, respectively. In the 786‑O xenografts, the TGI rates in the VEGI174‑, V7‑ and V8‑treated groups were 34, 26 and 31%, respectively. There was no significant loss in body weight and no cases of mortality were observed for all treated mice. In conclusion, VEGI174, V7 and V8 exhibited potential antitumour effects and were well tolerated in vivo. V7 and V8, as functional domain peptides of the VEGI174 protein, may be studied for the future treatment of RCC

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics

Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the Rp2, Rc2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the Rp2, Rc2, and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family

IntroductionInsect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy.MethodsTo address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters.ResultsExperimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%.DiscussionOur model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO