RVM Classification of Hyperspectral Images Based on Wavelet Kernel Non-negative Matrix Fractorization

A novel kernel framework for hyperspectral image classification based on relevance vector machine (RVM) is presented in this paper. The new feature extraction algorithm based on Mexican hat wavelet kernel non-negative matrix factorization (WKNMF) for hyperspectral remote sensing images is proposed. By using the feature of multi-resolution analysis, the new method of nonlinear mapping capability based on kernel NMF can be improved. The new classification framework of hyperspectral image data combined with the novel WKNMF and RVM. The simulation experimental results on HYDICE and AVIRIS data sets are both show that the classification accuracy of proposed method compared with other experiment methods even can be improved over 10% in some cases and the classification precision of small sample data area can be improved effectively

High-fidelity Facial Avatar Reconstruction from Monocular Video with Generative Priors

High-fidelity facial avatar reconstruction from a monocular video is a significant research problem in computer graphics and computer vision. Recently, Neural Radiance Field (NeRF) has shown impressive novel view rendering results and has been considered for facial avatar reconstruction. However, the complex facial dynamics and missing 3D information in monocular videos raise significant challenges for faithful facial reconstruction. In this work, we propose a new method for NeRF-based facial avatar reconstruction that utilizes 3D-aware generative prior. Different from existing works that depend on a conditional deformation field for dynamic modeling, we propose to learn a personalized generative prior, which is formulated as a local and low dimensional subspace in the latent space of 3D-GAN. We propose an efficient method to construct the personalized generative prior based on a small set of facial images of a given individual. After learning, it allows for photo-realistic rendering with novel views and the face reenactment can be realized by performing navigation in the latent space. Our proposed method is applicable for different driven signals, including RGB images, 3DMM coefficients, and audios. Compared with existing works, we obtain superior novel view synthesis results and faithfully face reenactment performance.Comment: 8 pages, 7 figure

Lipopolysaccharide-induced immune stress negatively regulates broiler chicken growth via the COX-2-PGE2-EP4 signaling pathway

AimsImmune stress in broiler chickens is characterized by the development of persistent pro-inflammatory responses that contribute to degradation of production performance. However, the underlying mechanisms that cause growth inhibition of broilers with immune stress are not well defined.MethodsA total of 252 1-day-old Arbor Acres(AA) broilers were randomly allocated to three groups with six replicates per group and 14 broilers per replicate. The three groups comprised a saline control group, an Lipopolysaccharide (LPS) (immune stress) group, and an LPS and celecoxib group corresponding to an immune stress group treated with a selective COX-2 inhibitor. Birds in LPS group and saline group were intraperitoneally injected with the same amount of LPS or saline from 14d of age for 3 consecutive days. And birds in the LPS and celecoxib group were given a single intraperitoneal injection of celecoxib 15 min prior to LPS injection at 14 d of age.ResultsThe feed intake and body weight gain of broilers were suppressed in response to immune stress induced by LPS which is an intrinsic component of the outer membrane of Gram-negative bacteria. Cyclooxygenase-2 (COX-2), a key enzyme that mediates prostaglandin synthesis, was up-regulated through MAPK-NF-κB pathways in activated microglia cells in broilers exposed to LPS. Subsequently, the binding of prostaglandin E2 (PGE2) to the EP4 receptor maintained the activation of microglia and promoted the secretion of cytokines interleukin-1β and interleukin-8, and chemokines CX3CL1 and CCL4. In addition, the expression of appetite suppressor proopiomelanocortin protein was increased and the levels of growth hormone-releasing hormone were reduced in the hypothalamus. These effects resulted in decreased expression of insulin-like growth factor in the serum of stressed broilers. In contrast, inhibition of COX-2 normalized pro-inflammatory cytokine levels and promoted the expression of Neuropeptide Y and growth hormone-releasing hormone in the hypothalamus which improved the growth performance of stressed broilers. Transcriptomic analysis of the hypothalamus of stressed broilers showed that inhibition of COX-2 activity significantly down-regulated the expression of the TLR1B, IRF7, LY96, MAP3K8, CX3CL1, and CCL4 genes in the MAPK-NF-κB signaling pathway.ConclusionThis study provides new evidence that immune stress mediates growth suppression in broilers by activating the COX-2-PGE2-EP4 signaling axis. Moreover, growth inhibition is reversed by inhibiting the activity of COX-2 under stressed conditions. These observations suggest new approaches for promoting the health of broiler chickens reared in intensive conditions

One-Step Synthesis of Air-Stable Sulfur-Doped Molybdenum Phosphide Catalyst

Transition-metal phosphides prepared from temperature-programmed reduction (TPR) are generally known to be unstable and prone to surface structure decomposition upon exposure to air. In this work, air-stable molybdenum phosphide (MoP) was prepared using TPR modified by sulfur. This catalyst was exposed to ambient atmosphere for up to 150 days and still maintained its surface and bulk structures as the fresh sample derived from in situ reduction. The metal phosphosulfide phase generated during TPR not only contributes to the solid structural properties but also exhibits superior catalytic activity in various hydrofining reactions compared to traditional MoP catalysts. Additionally, this preparation strategy was used to synthesize other sulfur-doped metal phosphides, such as CoP and Cu3P. Both of these catalysts exhibited excellent air-stability.</p

NOFA: NeRF-based One-shot Facial Avatar Reconstruction

3D facial avatar reconstruction has been a significant research topic in computer graphics and computer vision, where photo-realistic rendering and flexible controls over poses and expressions are necessary for many related applications. Recently, its performance has been greatly improved with the development of neural radiance fields (NeRF). However, most existing NeRF-based facial avatars focus on subject-specific reconstruction and reenactment, requiring multi-shot images containing different views of the specific subject for training, and the learned model cannot generalize to new identities, limiting its further applications. In this work, we propose a one-shot 3D facial avatar reconstruction framework that only requires a single source image to reconstruct a high-fidelity 3D facial avatar. For the challenges of lacking generalization ability and missing multi-view information, we leverage the generative prior of 3D GAN and develop an efficient encoder-decoder network to reconstruct the canonical neural volume of the source image, and further propose a compensation network to complement facial details. To enable fine-grained control over facial dynamics, we propose a deformation field to warp the canonical volume into driven expressions. Through extensive experimental comparisons, we achieve superior synthesis results compared to several state-of-the-art methods

Highway Traffic Flow Nonlinear Character Analysis and Prediction

In order to meet the highway guidance demand, this work studies the short-term traffic flow prediction method of highway. The Yu-Wu highway which is the main road in Chongqing, China, traffic flow time series is taken as the study object. It uses phase space reconstruction theory and Lyapunov exponent to analyze the nonlinear character of traffic flow. A new Volterra prediction method based on model order reduction via quadratic-linear systems (QLMOR) is applied to predict the traffic flow. Compared with Taylor-expansion-based methods, these QLMOR-reduced Volterra models retain more information of the system and more accuracy. The simulation results using this new Volterra model to predict short time traffic flow reveal that the accuracy of chaotic traffic flow prediction is enough for highway guidance and could be a new reference for intelligent highway management

Effects of dietary chlorogenic acid on cecal microbiota and metabolites in broilers during lipopolysaccharide-induced immune stress

AimsThe aim of this study was to investigate the effects of chlorogenic acid (CGA) on the intestinal microorganisms and metabolites in broilers during lipopolysaccharide (LPS)-induced immune stress.MethodsA total of 312 one-day-old Arbor Acres (AA) broilers were randomly allocated to four groups with six replicates per group and 13 broilers per replicate: (1) MS group (injected with saline and fed the basal diet); (2) ML group (injected with 0.5 mg LPS/kg and fed the basal diet); (3) MA group (injected with 0.5 mg LPS/kg and fed the basal diet supplemented with 1,000 mg/kg CGA); and (4) MB group (injected with saline and fed the basal diet supplemented with 1,000 mg/kg CGA).ResultsThe results showed that the abundance of beneficial bacteria such as Bacteroidetes in the MB group was significantly higher than that in MS group, while the abundance of pathogenic bacteria such as Streptococcaceae was significantly decreased in the MB group. The addition of CGA significantly inhibited the increase of the abundance of harmful bacteria such as Streptococcaceae, Proteobacteria and Pseudomonas caused by LPS stress. The population of butyric acid-producing bacteria such as Lachnospiraceae and Coprococcus and beneficial bacteria such as Coriobacteriaceae in the MA group increased significantly. Non-targeted metabonomic analysis showed that LPS stress significantly upregulated the 12-keto-tetrahydroleukotriene B4, riboflavin and mannitol. Indole-3-acetate, xanthurenic acid, L-formylkynurenine, pyrrole-2-carboxylic acid and L-glutamic acid were significantly down-regulated, indicating that LPS activated inflammation and oxidation in broilers, resulting in intestinal barrier damage. The addition of CGA to the diet of LPS-stimulated broilers significantly decreased 12-keto-tetrahydro-leukotriene B4 and leukotriene F4 in arachidonic acid metabolism and riboflavin and mannitol in ABC transporters, and significantly increased N-acetyl-L-glutamate 5-semialdehyde in the biosynthesis of amino acids and arginine, The presence of pyrrole-2-carboxylic acid in D-amino acid metabolism and the cecal metabolites, indolelactic acid, xanthurenic acid and L-kynurenine, indicated that CGA could reduce the inflammatory response induced by immune stress, enhance intestinal barrier function, and boost antioxidant capacity.ConclusionWe conclude that CGA can have a beneficial effect on broilers by positively altering the balance of intestinal microorganisms and their metabolites to inhibit intestinal inflammation and barrier damage caused by immune stress

Fair Resource Allocation with QoS Guarantee in Secure Multiuser TDMA Networks

We investigate a secure multiuser time division multiple access (TDMA) system with statistical delay quality of service (QoS) guarantee in terms of secure effective capacity. An optimal resource allocation policy is proposed to minimize the β-fair cost function of the average user power under the individual QoS constraint, which also balances the energy efficiency and fairness among the users. First, convex optimization problems associated with the resource allocation policy are formulated. Then, a subgradient iteration algorithm based on the Lagrangian duality theory and the dual decomposition theory is employed to approach the global optimal solutions. Furthermore, considering the practical channel conditions, we develop a stochastic subgradient iteration algorithm which is capable of dynamically learning the intended wireless channels and acquiring the global optimal solution. It is shown that the proposed optimal resource allocation policy depends on the delay QoS requirement and the channel conditions. The optimal policy can save more power and achieve the balance of the energy efficiency and the fairness compared with the other resource allocation policies

Improving the Latent Space of Image Style Transfer

Existing neural style transfer researches have studied to match statistical information between the deep features of content and style images, which were extracted by a pre-trained VGG, and achieved significant improvement in synthesizing artistic images. However, in some cases, the feature statistics from the pre-trained encoder may not be consistent with the visual style we perceived. For example, the style distance between images of different styles is less than that of the same style. In such an inappropriate latent space, the objective function of the existing methods will be optimized in the wrong direction, resulting in bad stylization results. In addition, the lack of content details in the features extracted by the pre-trained encoder also leads to the content leak problem. In order to solve these issues in the latent space used by style transfer, we propose two contrastive training schemes to get a refined encoder that is more suitable for this task. The style contrastive loss pulls the stylized result closer to the same visual style image and pushes it away from the content image. The content contrastive loss enables the encoder to retain more available details. We can directly add our training scheme to some existing style transfer methods and significantly improve their results. Extensive experimental results demonstrate the effectiveness and superiority of our methods.Comment: 9 pages, 8 figure