GHIGLS: HI mapping at intermediate Galactic latitude using the Green Bank Telescope

This paper introduces the data cubes from GHIGLS, deep Green Bank Telescope surveys of the 21-cm line emission of HI in 37 targeted fields at intermediate Galactic latitude. The GHIGLS fields together cover over 1000 square degrees at 9.55' spatial resolution. The HI spectra have an effective velocity resolution about 1.0 km/s and cover at least -450 < v < +250 km/s. GHIGLS highlights that even at intermediate Galactic latitude the interstellar medium is very complex. Spatial structure of the HI is quantified through power spectra of maps of the column density, NHI. For our featured representative field, centered on the North Ecliptic Pole, the scaling exponents in power-law representations of the power spectra of NHI maps for low, intermediate, and high velocity gas components (LVC, IVC, and HVC) are -2.86 +/- 0.04, -2.69 +/- 0.04, and -2.59 +/- 0.07, respectively. After Gaussian decomposition of the line profiles, NHI maps were also made corresponding to the narrow-line and broad-line components in the LVC range; for the narrow-line map the exponent is -1.9 +/- 0.1, reflecting more small scale structure in the cold neutral medium (CNM). There is evidence that filamentary structure in the HI CNM is oriented parallel to the Galactic magnetic field. The power spectrum analysis also offers insight into the various contributions to uncertainty in the data. The effect of 21-cm line opacity on the GHIGLS NHI maps is estimated.Comment: Accepted for publication in The Astrophysical Journal, 2015 July 16. 32 pages, 21 figures (Fig. 10 new). Minor revisions from review, particularly Section 8 and Appendix C; results unchanged. Additional surveys added and made available; new Appendix B. Added descriptions of available FITS files and links to four illustrative movies on enhanced GHIGLS archive (www.cita.utoronto.ca/GHIGLS/

Sêmen de caprino pode transmitir o vírus da CAE.

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Frações húmicas e propriedade químicas e físicas de latossolos.

O trabalho estabeleceu correlações entre as frações orgânicas e algumas propriedades físicas e químicas de horizontes A e B de persis Latossolos, oriundas de diferentes regiões do Brasi

Recuperação de pomares jovens de cajueiro anão precoce pela substituição de copa.

A substituicao de copaem plantas jovens de cajueiro anao-precoce e uma alternativa eficaz para dar iniformidade e aumentar a producao dos pomares formados com mudas propagadas por semente; (...) A substituicao de copa permite, a partir do quarto ano de frutificacao ...bitstream/CNPAT-2010/5354/1/Ct-023.pd

Efeito do número de brotações enxertadas na produção do cajueiro anão precoce com copa substituída (Anacardium occidentale L.).

Trabalho ... demostra a viabilidade da substituicao de copa, como alternativa de recuperar pomares de cajueiro constituidos de mudas de pe franco.bitstream/CNPAT-2010/5353/1/Ct-024.pd

Período apropriado para a substituição de copa do cajueiro anão precoce ou para a enxertia direta no campo.

O estudo tem o objetivo de apresentar uma alternativa para formacão de pomares de cajueiro anão precoce, em razão da grande dificuldade de disponibilidade de mudas enxertadas a época própria para plantio.bitstream/CNPAT-2010/11938/1/Pa-044.pd

Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery

Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic monitoring and disaster management. The huge variation in object scale, orientation, category, and complex backgrounds, as well as the different camera sensors pose great challenges for current algorithms. In this work, we propose a new method consisting of a novel joint image cascade and feature pyramid network with multi-size convolution kernels to extract multi-scale strong and weak semantic features. These features are fed into rotation-based region proposal and region of interest networks to produce object detections. Finally, rotational non-maximum suppression is applied to remove redundant detections. During training, we minimize joint horizontal and oriented bounding box loss functions, as well as a novel loss that enforces oriented boxes to be rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on oriented bounding box detection tasks on the challenging DOTA dataset, outperforming all published methods by a large margin (+6% and +12% absolute improvement, respectively). Furthermore, it generalizes to two other datasets, NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines even when trained on DOTA. Our method can be deployed in multi-class object detection applications, regardless of the image and object scales and orientations, making it a great choice for unconstrained aerial and satellite imagery.Comment: ACCV 201