Astronomy in the Cloud: Using MapReduce for Image Coaddition

In the coming decade, astronomical surveys of the sky will generate tens of terabytes of images and detect hundreds of millions of sources every night. The study of these sources will involve computation challenges such as anomaly detection and classification, and moving object tracking. Since such studies benefit from the highest quality data, methods such as image coaddition (stacking) will be a critical preprocessing step prior to scientific investigation. With a requirement that these images be analyzed on a nightly basis to identify moving sources or transient objects, these data streams present many computational challenges. Given the quantity of data involved, the computational load of these problems can only be addressed by distributing the workload over a large number of nodes. However, the high data throughput demanded by these applications may present scalability challenges for certain storage architectures. One scalable data-processing method that has emerged in recent years is MapReduce, and in this paper we focus on its popular open-source implementation called Hadoop. In the Hadoop framework, the data is partitioned among storage attached directly to worker nodes, and the processing workload is scheduled in parallel on the nodes that contain the required input data. A further motivation for using Hadoop is that it allows us to exploit cloud computing resources, e.g., Amazon's EC2. We report on our experience implementing a scalable image-processing pipeline for the SDSS imaging database using Hadoop. This multi-terabyte imaging dataset provides a good testbed for algorithm development since its scope and structure approximate future surveys. First, we describe MapReduce and how we adapted image coaddition to the MapReduce framework. Then we describe a number of optimizations to our basic approach and report experimental results comparing their performance.Comment: 31 pages, 11 figures, 2 table

The Design and Construction of Public Service Building in Developing Rural Regions During the Post COVID-19 Period: Cased on a Chinese Village Centre

The spread of COVID-19 has caused an increasing demand for public medical room. Cases of Chinese Huoshenshan Hospital and mobile cabin hospitals proved the effectiveness of constructing emergent medical buildings. However, these cases, usually with strict requirements on technology and infrastructure, are hard to implement in developing rural regions. Therefore, there is an urgent need for adapting industrial construction to the rural situation. This research introduced an adaptive approach for rural projects delivery during COVID-19. It is based on a longitudinal case study, recording and analysing the construction process of a village centre in Jiangsu, China, from 2019 to 2020. By comparing the construction process of actual operation and traditional method, the advantages in a shorter building period and lower labour density were verified. This research pointed out neglected risks in developing countries and provided a practical construction approach in these areas. It supported the prevention of COVID-19 global wide

Effects of different factors on the forward extraction of soy protein in reverse micelle systems

Reverse micelle extraction is a new technology for the extraction of protein. In this research, three kinds of reverse micelle systems, anionic surfactant sodium bis(2-ethylhexyl) sulfosuccinate (AOT) reverse micelle system, sodium dodecyl sulfate (SDS) reverse micelle system, and cationic surfactant cetyltrimethyl ammonium bromide (CTAB) reverse micelle system, were used to extract soy protein respectively. Effects of soy flour concentration, Wo ([H2O]/[AOT]), temperature, time, pH, ionic strength and ultrasonic power on forward extraction efficiency of soy protein were investigated. The effect of AOT reverse micelle diameter was studied as well. AOT reverse micelle system had higher extraction efficiency than SDS and CTAB systems. The main factors that affected the forward extraction were soy flour concentration, temperature and pH. The optimal conditions in AOT system were soy flour concentration being 0.007 g/ml, Wo 16, pH 6.5, temperature 34°C, time 20 min, KCl concentration of 0.1 mol/L and ultrasound power of 240 W. Under these conditions, the extraction efficiency of soy protein was 85.5%. The forward extraction efficiency of soy protein in AOT reverse micelle system increased with the increase of the reverse micelle diameter. Reverse micelle extraction is an effective way to extract soy protein.Key words: Soy protein, reverse micelle, forward extraction, sodium bis(2-ethylhexyl) sulfosuccinate (AOT), sodium dodecyl sulfate (SDS), cetyltrimethyl ammonium bromide (CTAB)

Enhanced glutathione peroxidases (GPx) activity in young barley seedlings enriched with selenium

To assess whether young barley seedlings could be used as a selenium-enriched food material, a field experiment was carried out. Barley seeds were soaked in aqueous solutions at 20°C for 24 h. The solutions contained selenium in the form of sodium selenite and were at 0, 10, 20 and 30 μg/ml, respectively. Then the seeds were sowed in a non-selenium-fertilized field. Seedlings were harvested 2, 3, 4, and 5 weeks after sowing. Results show that selenium treatment did not affect (P>0.05) the biomass yield, height and stem diameter of young barley seedlings but significantly increased (P<0.05) selenium content and enhanced chlorophyll accumulation and glutathione peroxidases (GPx) activity in young barley seedlings. This study indicates that the method of soaking seeds in selenium solutions was feasible in enhancing selenium content in young barley seedlings which could be used as a  seleniumenriched food material.Key words: Selenium, young barley seedlings, growth, chlorophyll, glutathione peroxidases

Deep Convolutional Generative Adversarial Networks Based Flame Detection in Video

Real-time flame detection is crucial in video based surveillance systems. We propose a vision-based method to detect flames using Deep Convolutional Generative Adversarial Neural Networks (DCGANs). Many existing supervised learning approaches using convolutional neural networks do not take temporal information into account and require substantial amount of labeled data. In order to have a robust representation of sequences with and without flame, we propose a two-stage training of a DCGAN exploiting spatio-temporal flame evolution. Our training framework includes the regular training of a DCGAN with real spatio-temporal images, namely, temporal slice images, and noise vectors, and training the discriminator separately using the temporal flame images without the generator. Experimental results show that the proposed method effectively detects flame in video with negligible false positive rates in real-time

Stochastic Budget Optimization in Internet Advertising

Internet advertising is a sophisticated game in which the many advertisers "play" to optimize their return on investment. There are many "targets" for the advertisements, and each "target" has a collection of games with a potentially different set of players involved. In this paper, we study the problem of how advertisers allocate their budget across these "targets". In particular, we focus on formulating their best response strategy as an optimization problem. Advertisers have a set of keywords ("targets") and some stochastic information about the future, namely a probability distribution over scenarios of cost vs click combinations. This summarizes the potential states of the world assuming that the strategies of other players are fixed. Then, the best response can be abstracted as stochastic budget optimization problems to figure out how to spread a given budget across these keywords to maximize the expected number of clicks. We present the first known non-trivial poly-logarithmic approximation for these problems as well as the first known hardness results of getting better than logarithmic approximation ratios in the various parameters involved. We also identify several special cases of these problems of practical interest, such as with fixed number of scenarios or with polynomial-sized parameters related to cost, which are solvable either in polynomial time or with improved approximation ratios. Stochastic budget optimization with scenarios has sophisticated technical structure. Our approximation and hardness results come from relating these problems to a special type of (0/1, bipartite) quadratic programs inherent in them. Our research answers some open problems raised by the authors in (Stochastic Models for Budget Optimization in Search-Based Advertising, Algorithmica, 58 (4), 1022-1044, 2010).Comment: FINAL versio

Topological mechanochemistry of graphene

In view of a formal topology, two common terms, namely, connectivity and adjacency, determine the quality of C-C bonds of sp2 nanocarbons. The feature is the most sensitive point of the inherent topology of the species so that such external action as mechanical deformation should obviously change it and result in particular topological effects. The current paper describes the effects caused by uniaxial tension of a graphene molecule in due course of a mechanochemical reaction. Basing on the molecular theory of graphene, the effects are attributed to both mechanical loading and chemical modification of edge atoms of the molecule. The mechanical behavior is shown to be not only highly anisotropic with respect to the direction of the load application, but greatly dependent on the chemical modification of the molecule edge atoms thus revealing topological character of the graphene deformation.Comment: 9 pages, 10 figures, 1 table. arXiv admin note: text overlap with arXiv:1301.094

Well-posedness for degenerate third order equations with delay and applications to inverse problems

[EN] In this paper, we study well-posedness for the following third-order in time equation with delay <disp-formula idoperators defined on a Banach space X with domains D(A) and D(B) such that t)is the state function taking values in X and u(t): (-, 0] X defined as u(t)() = u(t+) for < 0 belongs to an appropriate phase space where F and G are bounded linear operators. Using operator-valued Fourier multiplier techniques we provide optimal conditions for well-posedness of equation (0.1) in periodic Lebesgue-Bochner spaces Lp(T,X), periodic Besov spaces Bp,qs(T,X) and periodic Triebel-Lizorkin spaces Fp,qs(T,X). A novel application to an inverse problem is given.The first, second and third authors have been supported by MEC, grant MTM2016-75963-P. The second author has been supported by AICO/2016/30. The fourth author has been supported by MEC, grant MTM2015-65825-P.Conejero, JA.; Lizama, C.; Murillo-Arcila, M.; Seoane Sepúlveda, JB. (2019). 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Structure-Sensitive Mechanism of Nanographene Failure

The response of a nanographene sheet to external stresses is considered in terms of a mechanochemical reaction. The quantum chemical realization of the approach is based on a coordinate-of-reaction concept for the purpose of introducing a mechanochemical internal coordinate (MIC) that specifies a deformational mode. The related force of response is calculated as the energy gradient along the MIC, while the atomic configuration is optimized over all of the other coordinates under the MIC constant-pitch elongation. The approach is applied to the benzene molecule and (5, 5) nanographene. A drastic anisotropy in the microscopic behavior of both objects under elongation along a MIC has been observed when the MIC is oriented either along or normally to the C-C bonds chain. Both the anisotropy and high stiffness of the nanographene originate at the response of the benzenoid unit to stress.Comment: 19 pages, 7 figures 1 tabl