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

Investigation of nonlocal data-driven methods for subgrid-scale stress modelling in large eddy simulation

A nonlocal subgrid-scale stress (SGS) model is developed based on the convolution neural network (CNN), a powerful supervised data-driven approach. The CNN is an ideal approach to naturally consider nonlocal spatial information in prediction due to its wide receptive field. The CNN-based models used here only take primitive flow variables as input, then the flow features are automatically extracted without any $priori$ guidance. The nonlocal models trained by direct numerical simulation (DNS) data of a turbulent channel flow at $Re_{\tau}=178$ are accessed in both the $priori$ and $posteriori$ test, providing physically reasonable flow statistics (like mean velocity and velocity fluctuations) closing to the DNS results even when extrapolating to a higher Reynolds number $Re_{\tau}=600$. In our model, the backscatter is also predicted well and the numerical simulation is stable. The nonlocal models outperform local data-driven models like artificial neural network and some SGS models, e.g. the Smagorinsky model in actual large eddy simulation (LES). The model is also robust since stable solutions can be obtained when examining the grid resolution from one-half to double of the spatial resolution used in training. We also investigate the influence of receptive fields and suggest using the two-point correlation analysis as a quantitative method to guide the design of nonlocal physical models. To facilitate the combination of machine learning (ML) algorithms to computational fluid dynamics (CFD), a novel heterogeneous ML-CFD framework is proposed. The present study provides the effective data-driven nonlocal methods for SGS modelling in the LES of complex anisotropic turbulent flows.Comment: 17 pages, 10 figure

Transfer to the Collinear Libration Point L3 in the Sun-Earth+Moon System

The collinear libration point L3 of the sun-earth+moon system is an ideal place for some space missions. Although there has been a great amount of work concerning the applications of the other two collinear libration points L1 and L2, little work has been done about the point L3. In this paper, the dynamics of the libration points was briefly introduced first. Then a way to transfer the spacecraft to the collinear libration point L3 via the invariant manifolds of the other two collinear libration points was proposed. Theoretical works under the model of circular restricted three-body problem were done. For the sun-earth+moon system, this model is a good approximation. The results obtained are useful when a transfer trajectory under the real solar system is designed

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 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% 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.Comment: Accepted by TPAM

Detection of gamma-ray emission from the Coma cluster with Fermi Large Area Telescope and tentative evidence for an extended spatial structure

Many galaxy clusters have giant halos of non-thermal radio emission, indicating the presence of relativistic electrons in the clusters. Relativistic protons may also be accelerated by merger and/or accretion shocks in galaxy clusters. These cosmic-ray (CR) electrons and/or protons are expected to produce gamma-rays through inverse-Compton scatterings or inelastic $pp$ collisions respectively. Despite of intense efforts in searching for high-energy gamma-ray emission from galaxy clusters, conclusive evidence is still missing so far. Here we report the discovery of $\ge 200$ MeV gamma-ray emission from the Coma cluster direction with an unbinned likelihood analysis of the 9 years of {\it Fermi}-LAT Pass 8 data. The gamma-ray emission shows a spatial morphology roughly coincident with the giant radio halo, with an apparent excess at the southwest of the cluster. Using the test statistic analysis, we further find tentative evidence that the gamma-ray emission at the Coma center is spatially extended. The extended component has an integral energy flux of $\sim 2\times 10^{-12}{\rm \ erg\ cm^{-2}\ s^{-1}}$ in the energy range of 0.2 - 300 GeV and the spectrum is soft with a photon index of $\simeq-2.7$. Interpreting the gamma-ray emission as arising from CR proton interaction, we find that the volume-averaged value of the CR to thermal pressure ratio in the Coma cluster is about $\sim 2\%$. Our results show that galaxy clusters are likely a new type of GeV gamma-ray sources, and they are probably also giant reservoirs of CR protons.Comment: 10 pages, 10 figures, Accepted by Physical Review D, more spatial models for the gamma-ray emission are used, systematic checks on the results are adde

CRC-based Reliable WiFi Backscatter Communiation for Supply Chain Management

Supply chain management is aimed to keep going long-term performance of the supply chain and minimize the costs. Backscatter technology provides a more efficient way of being able to identify items and real-time monitoring. Among the backscatter systems, the ambient backscatter communication (AmBC) system provides a prospect of ultra-low energy consumption and does not require controlled excitation devices. In this paper, we introduce CRCScatter, a CRC reverse algorithm-based AmBC system using a single access point (AP). A CRC reverse decoder is applied to reverse the ambient data from CRC32 sequence in the backscatter packet and realize single-AP decoding. Based on the nature of DBPSK modulation in WiFi signal, the CRCScatter system obtains the tag data by XOR and Differential decoder. Our simulation results verify the effectiveness of our proposed system in the low SNR regime. The average decoding time of CRCScatter system is independent of the length of tag data. Furthermore, our system can append redundant bits in the tag data to improve the decoding accuracy while not increasing the decoding time

Multiple Events of Allopolyploidy in the Evolution of the Racemose Lineages in Prunus (Rosaceae) Based on Integrated Evidence from Nuclear and Plastid Data.

Prunus is an economically important genus well-known for cherries, plums, almonds, and peaches. The genus can be divided into three major groups based on inflorescence structure and ploidy levels: (1) the diploid solitary-flower group (subg. Prunus, Amygdalus and Emplectocladus); (2) the diploid corymbose group (subg. Cerasus); and (3) the polyploid racemose group (subg. Padus, subg. Laurocerasus, and the Maddenia group). The plastid phylogeny suggests three major clades within Prunus: Prunus-Amygdalus-Emplectocladus, Cerasus, and Laurocerasus-Padus-Maddenia, while nuclear ITS trees resolve Laurocerasus-Padus-Maddenia as a paraphyletic group. In this study, we employed sequences of the nuclear loci At103, ITS and s6pdh to explore the origins and evolution of the racemose group. Two copies of the At103 gene were identified in Prunus. One copy is found in Prunus species with solitary and corymbose inflorescences as well as those with racemose inflorescences, while the second copy (II) is present only in taxa with racemose inflorescences. The copy I sequences suggest that all racemose species form a paraphyletic group composed of four clades, each of which is definable by morphology and geography. The tree from the combined At103 and ITS sequences and the tree based on the single gene s6pdh had similar general topologies to the tree based on the copy I sequences of At103, with the combined At103-ITS tree showing stronger support in most clades. The nuclear At103, ITS and s6pdh data in conjunction with the plastid data are consistent with the hypothesis that multiple independent allopolyploidy events contributed to the origins of the racemose group. A widespread species or lineage may have served as the maternal parent for multiple hybridizations involving several paternal lineages. This hypothesis of the complex evolutionary history of the racemose group in Prunus reflects a major step forward in our understanding of diversification of the genus and has important implications for the interpretation of its phylogeny, evolution, and classification

Movable Fiber-Integrated Hybrid Plasmonic Waveguide on Metal Film

A waveguide structure consisting of a tapered nanofiber on a metal film is proposed and analyzed to support highly localized hybrid plasmonic modes. The hybrid plasmonic mode can be efficiently excited through the in-line tapered fiber based on adiabatic conversion and collected by the same fiber, which is very convenient in the experiment. Due to the ultrasmall mode area of plasmonic mode, the local electromagnetic field is greatly enhanced in this movable waveguide, which is potential for enhanced coherence light emitter interactions, such as waveguide quantum electrodynamics, single emitter spectrum and nonlinear optics