Novel Image Mosaic Algorithm for Concrete Pavement Surface Image Reconstruction

AbstractIn this paper, a novel image mosaic method for concrete pavement surface image sequences reconstruction has been proposed. Harris corner points are extracted uniformly from the overlapped areas of concrete pavement surface images, which are considered feature points. The commonly used circular projection method is applied for coarse matching step and an improved point matching method is proposed for invariance of image rotation and distortion. The image fusion strategy of fading in and fading out is employed for the smooth and seamless of mosaic image. For the practical pavement surface images, which exists rotation and distortion, the corresponding experimental results show that the proposed image matching method has higher precision and stronger robustness

Efficient-Adam: Communication-Efficient Distributed Adam

Distributed adaptive stochastic gradient methods have been widely used for large-scale nonconvex optimization, such as training deep learning models. However, their communication complexity on finding $\varepsilon$-stationary points has rarely been analyzed in the nonconvex setting. In this work, we present a novel communication-efficient distributed Adam in the parameter-server model for stochastic nonconvex optimization, dubbed {\em Efficient-Adam}. Specifically, we incorporate a two-way quantization scheme into Efficient-Adam to reduce the communication cost between the workers and server. Simultaneously, we adopt a two-way error feedback strategy to reduce the biases caused by the two-way quantization on both the server and workers, respectively. In addition, we establish the iteration complexity for the proposed Efficient-Adam with a class of quantization operators, and further characterize its communication complexity between the server and workers when an $\varepsilon$-stationary point is achieved. Finally, we apply Efficient-Adam to solve a toy stochastic convex optimization problem and train deep learning models on real-world vision and language tasks. Extensive experiments together with a theoretical guarantee justify the merits of Efficient Adam.Comment: IEEE Transactions on Signal Processin

The swimming behavior of the aquatic larva of Neoneuromus ignobilis (Megaloptera: Corydalidae: Corydalinae).

In order to explore the pattern and significance of swimming, through photos and videos we observed and recorded the swimming behavior of the aquatic larvae of Megaloptera in detail for the first time using the endemic Chinese species Neoneuromus ignobilis Navas, 1932 as the test insect, which were collected from the Dadu River and reared in nature-simulated environments. Four swimming postures are recognized and described herein in detail, i. e., vertical, parallel, back and side swimming, and these postures were used by the larvae disproportionately, with a frequency of 89.08%, 5. 49%, 4. 40% and 0. 61% , respectively. The swimming larvae tend to pose their body into an S-shape, with various degree of sinuation. By changing the directions of the head and tail, they can easily rise up or sink and change swimming postures. The propulsion was generated by the wriggling of the body while the legs were mostly held close to the body. Larvae of different instars varied greatly in swimming ability, the 6th ins tar larvae being the best and most active swimmer compared to the 2nd and final instars. The larvae may also employ complex defense behaviors not often known from relatively ancient insect groups, like chemical defense as secretion from the end of abdomen

Optical Field Enhancement in Nanoscale Slot Waveguides of Hyperbolic Metamaterials

Nanoscale slot waveguides of hyperbolic metamaterials are proposed and demonstrated for achieving large optical field enhancement. The dependence of the enhanced electric field within the air slot on waveguide mode coupling and permittivity tensors of hyperbolic metamaterials is analyzed both numerically and analytically. Optical intensity in the metamaterial slot waveguide can be more than 25 times stronger than that in a conventional silicon slot waveguide, due to tight optical mode confinement enabled by the ultrahigh refractive indices supported in hyperbolic metamaterials. The electric field enhancement effects are also verified with the realistic metal-dielectric multilayer waveguide structure.Comment: 13 pages, 4 figure

Long-term fenofibrate treatment impaired glucose-stimulated insulin secretion and up-regulated pancreatic NF-kappa B and iNOS expression in monosodium glutamate-induced obese rats: Is that a latent disadvantage?

<p>Abstract</p> <p>Background</p> <p>Fenofibrate, a PPAR alpha agonist, has been widely used in clinics as lipid-regulating agent. PPAR alpha is known to be expressed in many organs including pancreatic beta cells and regulate genes involved in fatty acid metabolism. Some reports based on cell lines or animals have provided evidences that PPAR alpha agonists may affect (increased or suppressed) beta cell insulin secretion, and several studies are producing interesting but still debated results.</p> <p>Methods</p> <p>In this research, we investigated the long term effects of fenofibrate on beta cell function in a metabolic syndrome animal model, monosodium glutamate (MSG) induced obese rats. Obese MSG rats were administered by gavage with fenofibrate at a dose of 100 mg/kg for 12 weeks. Oral glucose tolerance and insulin tolerance tests were performed to evaluate glucose metabolism and insulin sensitivity. We have used the hyperglycemic clamp technique to evaluate the capacity of beta cell insulin secretion. This technique provides an unbiased approach to understand the beta cell function in vivo. The changes of gene and protein expression in the pancreas and islets were also analyzed by Real-Time-PCR, Western blot and immunostaining.</p> <p>Results</p> <p>Fenofibrate reduced the plasma lipid levels within a few days, and showed no beneficial effects on glucose homeostasis or insulin sensitivity in obese MSG rats. But the animals treated with fenofibrate exhibited significantly decreased fasting plasma insulin and impaired insulin secretory response to glucose stimulation. Further studies confirmed that fenofibrate increased MDA level and decreased total ATPase activity in pancreatic mitochondrion, accompanied by the upregulation of iNOS and NF-kappa B and TNF alpha expression in pancreatic islets of obese MSG rats.</p> <p>Conclusions</p> <p>Long-term fenofibrate treatment disrupted beta cell function, and impaired glucose-stimulated insulin secretion in obese MSG rats, perhaps to some extent associated with the activated inflammatory pathway and increased formation of oxidative products, especially the up-regulation of NF-kappa B and iNOS expression in islets.</p

SegViT: Semantic Segmentation with Plain Vision Transformers

We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit. Previous ViT-based segmentation networks usually learn a pixel-level representation from the output of the ViT. Differently, we make use of the fundamental component -- attention mechanism, to generate masks for semantic segmentation. Specifically, we propose the Attention-to-Mask (ATM) module, in which the similarity maps between a set of learnable class tokens and the spatial feature maps are transferred to the segmentation masks. Experiments show that our proposed SegVit using the ATM module outperforms its counterparts using the plain ViT backbone on the ADE20K dataset and achieves new state-of-the-art performance on COCO-Stuff-10K and PASCAL-Context datasets. Furthermore, to reduce the computational cost of the ViT backbone, we propose query-based down-sampling (QD) and query-based up-sampling (QU) to build a Shrunk structure. With the proposed Shrunk structure, the model can save up to $40\%$ computations while maintaining competitive performance.Comment: 9 Pages, NeurIPS 202