Sequential optimization for efficient high-quality object proposal generation

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING ++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster

Microstructure and Mechanical Properties of TiC Nanoparticle-Reinforced Iron-Matrix Composites

Uniformly distributed TiC nanoparticle-reinforced iron-based composites were successfully fabricated by planetary milling in argon and subsequent hot pressing procedures. Nearly full density composite specimens could be obtained via 6-hour milling and hot pressing at 1100° C under 50 MPa. Spherical TiC particles and fine fibrous Fe₃C phases were observed to form the iron-matrix composites and subjected to comparative analysis. Microstructural analysis results show that theaverage diameter of TiC particles and the length of Fe₃C phases tend to decrease with an increase in a TiC volume content. The compression yield strength of hot-pressed composites increased in proportion to the TiC content, resulting in 1.3 GPa for 7.5% TiC. The relationship between the microstructural characteristics and the yield strength of TiC-reinforced composites was also investigated. Based on the Orowan strengthening mechanism, a higher strength is observed for a high TiC content, mainly due to reduced distance between reinforcing TiC nanoparticles.Композитные материалы на основе железа, армированные равномерно распространенными наночастицами TiC, получены с помощью планетарного фрезерования в аргоне и последующего горячего прессования. Путем измельчения в течение 6 часов и горячего прессования материала при температуре 1100° C и давлении 50 MПa оказалось возможным получить образцы композитных материалов с почти максимальной плотностью. Исследованы сферические частицы TiC и волокнистые мелкодисперсные Fe₃C фазы, которые образуют матрицу композитного материала на основе железа. Микроструктурный анализ показал, что усредненный диаметр частиц TiC и длина Fe₃C фаз уменьшаются с увеличением объемного содержания частиц TiC. Значение предела текучести при сжатии композитных материалов, полученных горячим прессованием, увеличивается пропорционально содержанию частиц TiC: 1,3 ГПа для 7,5% TiC. Исследована взаимосвязь между микроструктурными характеристиками и пределом текучести композитных материалов, армированных частицами TiC. На основе механизма упрочнения Орована можно предположить, что более высокое значение прочности имеет место при большем содержании частиц TiC, в основном вследствие сокращения расстояния между армирующими наночастицами TiC.Композитні матеріали на основі заліза, армовані рівномірно розповсюдженими нано-частинками ТіС, отримано за допомогою планетарного фрезерування в аргоні і подальшого гарячого пресування. Шляхом подрібнення протягом 6 годин і гарячого пресування матеріалу за температури 1100° C і тиску 50 МПа можна отримати зразки композитних матеріалів із майже максимальною щільністю. Досліджено сферичні частинки ТіС і волокнисті дрібнодисперсні Fe₃C фази, які сприяють виникненню матриці композитного матеріалу на основі заліза. Мікроструктурний аналіз показав, що усереднений діаметр частинок ТіС і довжина Fe₃C фаз зменшуються зі збільшенням об’ємного вмісту частинок ТіС. Значення границі текучості при стисканні композитних матеріалів, отриманих гарячим пресуванням, збільшується пропорційно вмісту ТіС частинок: 1,3 ГПа для 7,5% ТіС. Досліджено взаємозв’язок між мікроструктурними характеристиками і границею текучості композитних матеріалів, армованих частинками ТіС. На основі механізму зміцнення Орована можна припустити, що більш високі значення міцності відмічаються за більшого вмісту частинок ТіС, в основному внаслідок скорочення відстані між армуючими наночастинками ТіС

Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea

This study aims to improve the accuracy of bathymetry predicted by gravity-geologic method (GGM) using the optimal machine learning model selected from machine learning techniques. In this study, several machine learning techniques were utilized to determine the optimal model from the performance of depth and gravity anomalies. In addition, a tuning density contrast calculated from satellite altimetry-derived free-air gravity anomalies (FAGAs) was applied to estimate enhanced bathymetry. By comparison with shipborne depth, the accuracy of the bathymetry estimated by using satellite altimetry-derived FAGAs and machine learning was evaluated. The findings reveal that the bathymetry predicted by the optimal machine learning using the Gaussian process regression and the GGM with a tuning density contrast can enhance the accuracy of 82.64 m, showing an improvement of 67.40% in the RMSE at shipborne depth measurements. Although the tuning density is larger than 1.67 g/cm3, bathymetry using satellite altimetry-derived FAGAs and machine learning can be effectively improved with higher accuracy

Development of black ice prediction model using GIS-based multi-sensor model validation

Fog, freezing rain, and snow (melt) quickly condense on road surfaces, forming black ice that is difficult to identify and causes major accidents on highways. As a countermeasure to prevent icing car accidents, it is necessary to predict the amount and location of black ice. This study advanced previous models through machine learning and multi-sensor-verified results. Using spatial (hill shade, river system, bridge, and highway) and meteorological (air temperature, cloudiness, vapour pressure, wind speed, precipitation, snow cover, specific heat, latent heat, and solar radiation energy) data from the study area (Suncheon–Wanju Highway in Gurye-gun, Jeollanam-do, South Korea), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with a geographic information system in units of square metres. The intermediate factors calculated as input factors were road temperature and road moisture, modelled using a deep neural network (DNN) and numerical methods. Considering the results of the DNN, the root mean square error was improved by 148.6 % and reliability by 11.43 % compared to a previous study (linear regression). Based on the model results, multiple sensors were buried at four selected points in the study area. The model was compared with sensor data and verified with the upper-tailed test (with a significance level of 0.05) and fast Fourier transform (freezing does not occur when frequency = 0.00001 Hz). Results of the verified simulation can provide valuable data for government agencies like road traffic authorities to prevent traffic accidents caused by black ice

Modeling the Subsurface Structure of Sunspots

While sunspots are easily observed at the solar surface, determining their subsurface structure is not trivial. There are two main hypotheses for the subsurface structure of sunspots: the monolithic model and the cluster model. Local helioseismology is the only means by which we can investigate subphotospheric structure. However, as current linear inversion techniques do not yet allow helioseismology to probe the internal structure with sufficient confidence to distinguish between the monolith and cluster models, the development of physically realistic sunspot models are a priority for helioseismologists. This is because they are not only important indicators of the variety of physical effects that may influence helioseismic inferences in active regions, but they also enable detailed assessments of the validity of helioseismic interpretations through numerical forward modeling. In this paper, we provide a critical review of the existing sunspot models and an overview of numerical methods employed to model wave propagation through model sunspots. We then carry out an helioseismic analysis of the sunspot in Active Region 9787 and address the serious inconsistencies uncovered by \citeauthor{gizonetal2009}~(\citeyear{gizonetal2009,gizonetal2009a}). We find that this sunspot is most probably associated with a shallow, positive wave-speed perturbation (unlike the traditional two-layer model) and that travel-time measurements are consistent with a horizontal outflow in the surrounding moat.Comment: 73 pages, 19 figures, accepted by Solar Physic

Immune-Related Gene Expression in Two B-Complex Disparate Genetically Inbred Fayoumi Chicken Lines Following Eimeria maxima Infection

To investigate the influence of genetic differences in the MHC on susceptibility to avian coccidiosis, M5.1 and M15.2 B-haplotype-disparate Fayoumi chickens were orally infected with live Eimeria maxima oocysts, and BW gain, fecal oocyst production, and expression of 14 immune-related genes were determined as parameters of protective immunity. Weight loss was reduced and fecal parasite numbers were lower in birds of the M5.1 line compared with M15.2 line birds. Intestinal intraepithelial lymphocytes from M5.1 chickens expressed greater levels of transcripts encoding interferon-γ (IFN-γ), interleukin-1β (IL-1β), IL-6, IL-8, IL-12, IL-15, IL-17A, inducible nitric oxide synthase, and lipopolysaccharide-induced tumor necrosis factor-α factor and lower levels of mRNA for IFN-α, IL-10, IL-17D, NK-lysin, and tumor necrosis factor superfamily 15 compared with the M15.2 line. In the spleen, E. maxima infection was associated with greater expression levels of IFN-γ, IL-15, and IL-8 and lower levels of IL-6, IL-17D, and IL-12 in M5.1 vs. M15.2 birds. These results suggest that genetic determinants within the chicken MHC influence resistance to E. maxima infection by controlling the local and systemic expression of immune-related cytokine and chemokine genes

Complete measurement of three-body photodisintegration of 3He for photon energies between 0.35 and 1.55 GeV

The three-body photodisintegration of 3He has been measured with the CLAS detector at Jefferson Lab, using tagged photons of energies between 0.35 GeV and 1.55 GeV. The large acceptance of the spectrometer allowed us for the first time to cover a wide momentum and angular range for the two outgoing protons. Three kinematic regions dominated by either two- or three-body contributions have been distinguished and analyzed. The measured cross sections have been compared with results of a theoretical model, which, in certain kinematic ranges, have been found to be in reasonable agreement with the data.Comment: 22 pages, 25 eps figures, 2 tables, submitted to PRC. Modifications: removed 2 figures, improvements on others, a few minor modifications to the tex

eta-prime photoproduction on the proton for photon energies from 1.527 to 2.227 GeV

Differential cross sections for the reaction gamma p -> eta-prime p have been measured with the CLAS spectrometer and a tagged photon beam with energies from 1.527 to 2.227 GeV. The results reported here possess much greater accuracy than previous measurements. Analyses of these data indicate for the first time the coupling of the etaprime N channel to both the S_11(1535) and P_11(1710) resonances, known to couple strongly to the eta N channel in photoproduction on the proton, and the importance of j=3/2 resonances in the process.Comment: 6 pages, 3 figure

Measurement of the Deuteron Structure Function F2 in the Resonance Region and Evaluation of Its Moments

Inclusive electron scattering off the deuteron has been measured to extract the deuteron structure function F2 with the CEBAF Large Acceptance Spectrometer (CLAS) at the Thomas Jefferson National Accelerator Facility. The measurement covers the entire resonance region from the quasi-elastic peak up to the invariant mass of the final-state hadronic system W~2.7 GeV with four-momentum transfers Q2 from 0.4 to 6 (GeV/c)^2. These data are complementary to previous measurements of the proton structure function F2 and cover a similar two-dimensional region of Q2 and Bjorken variable x. Determination of the deuteron F2 over a large x interval including the quasi-elastic peak as a function of Q2, together with the other world data, permit a direct evaluation of the structure function moments for the first time. By fitting the Q2 evolution of these moments with an OPE-based twist expansion we have obtained a separation of the leading twist and higher twist terms. The observed Q2 behaviour of the higher twist contribution suggests a partial cancellation of different higher twists entering into the expansion with opposite signs. This cancellation, found also in the proton moments, is a manifestation of the "duality" phenomenon in the F2 structure function