Oklahoma State University–Stillwater

About: Oklahoma State University–Stillwater is a education organization based out in Stillwater, Oklahoma, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 18267 authors who have published 36743 publications receiving 1107500 citations. The organization is also known as: Oklahoma State University & OKState.

Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces with a Recurrent Neural Network

Abstract: A recurrent neural network (RNN) called CrackNet‐R is proposed in the article for fully automated pixel‐level crack detection on three‐dimensional (3D) asphalt pavement surfaces. In the ar...

Global Regularity for the Two-Dimensional Anisotropic Boussinesq Equations with Vertical Dissipation

Abstract: This paper establishes the global in time existence of classical solutions to the two-dimensional anisotropic Boussinesq equations with vertical dissipation. When only vertical dissipation is present, there is no direct control on the horizontal derivatives and the global regularity problem is very challenging. To solve this problem, we bound the derivatives in terms of the \({L^\infty}\) -norm of the vertical velocity v and prove that \({\|v\|_{L^{r}}}\) with \({2\leqq r < \infty}\) does not grow faster than \({\sqrt{r \log r}}\) at any time as r increases. A delicate interpolation inequality connecting \({\|v\|_{L^\infty}}\) and \({\|v\|_{L^r}}\) then yields the desired global regularity.

Interaction of the tick immune system with transmitted pathogens

Abstract: Ticks are hematophagous arachnids transmitting a wide variety of pathogens including viruses, bacteria, and protozoans to their vertebrate hosts. The tick vector competence has to be intimately linked to the ability of transmitted pathogens to evade tick defense mechanisms encountered on their route through the tick body comprising midgut, hemolymph, salivary glands or ovaries. Tick innate immunity is, like in other invertebrates, based on an orchestrated action of humoral and cellular immune responses. The direct antimicrobial defense in ticks is accomplished by a variety of small molecules such as defensins, lysozymes or by tick-specific antimicrobial compounds such as microplusin/hebraein or 5.3-kDa family proteins. Phagocytosis of the invading microbes by tick hemocytes seems to be mediated by the primordial complement-like system composed of thioester-containing proteins, fibrinogen-related lectins and convertase-like factors. Moreover, an important role in survival of the ingested microbes seems to be played by host proteins and redox balance maintenance in the tick midgut. Here, we summarize recent knowledge about the major components of tick immune system and focus on their interaction with the relevant tick-transmitted pathogens, represented by spirochetes (Borrelia), rickettsiae (Anaplasma), and protozoans (Babesia). Availability of the tick genomic database and feasibility of functional genomics based on RNA interference greatly contribute to the understanding of molecular and cellular interplay at the tick-pathogen interface and may provide new targets for blocking the transmission of tick pathogens.

Completely Automated CNN Architecture Design Based on Blocks

Abstract: The performance of convolutional neural networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extensive expertise in both CNNs and the investigated problem domain is required, which is not necessarily available to every interested user. To address this problem, we propose to automatically evolve CNN architectures by using a genetic algorithm (GA) based on ResNet and DenseNet blocks. The proposed algorithm is completely automatic in designing CNN architectures. In particular, neither preprocessing before it starts nor postprocessing in terms of CNNs is needed. Furthermore, the proposed algorithm does not require users with domain knowledge on CNNs, the investigated problem, or even GAs. The proposed algorithm is evaluated on the CIFAR10 and CIFAR100 benchmark data sets against 18 state-of-the-art peer competitors. Experimental results show that the proposed algorithm outperforms the state-of-the-art CNNs hand-crafted and the CNNs designed by automatic peer competitors in terms of the classification performance and achieves a competitive classification accuracy against semiautomatic peer competitors. In addition, the proposed algorithm consumes much less computational resource than most peer competitors in finding the best CNN architectures.