Moduli Spaces of Sheaves on Hirzebruch Orbifolds

We provide a stacky fan description of the total space of certain split vector bundles, as well as their projectivization, over toric Deligne-Mumford stacks. We then specialize to the case of Hirzebruch orbifold $\mathcal{H}_{r}^{ab}$ obtained by projectivizing $\mathcal{O} \oplus \mathcal{O}(r)$ over the weighted projective line $\mathbb{P}(a,b)$. Next, we give a combinatorial description of toric sheaves on $\mathcal{H}_{r}^{ab}$ and investigate their basic properties. With fixed choice of polarization and a generating sheaf, we describe the fixed point locus of the moduli scheme of $\mu$-stable torsion free sheaves of rank $1$ and $2$ on $\mathcal{H}_{r}^{ab}$. Finally, we show that if $\mathcal{X}$ is the total space of the canonical bundle over a Hirzebruch orbifold, then we can obtain generating functions of Donaldson-Thomas invariants

SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints

Since the PointNet was proposed, deep learning on point cloud has been the concentration of intense 3D research. However, existing point-based methods usually are not adequate to extract the local features and the spatial pattern of a point cloud for further shape understanding. This paper presents an end-to-end framework, SK-Net, to jointly optimize the inference of spatial keypoint with the learning of feature representation of a point cloud for a specific point cloud task. One key process of SK-Net is the generation of spatial keypoints (Skeypoints). It is jointly conducted by two proposed regulating losses and a task objective function without knowledge of Skeypoint location annotations and proposals. Specifically, our Skeypoints are not sensitive to the location consistency but are acutely aware of shape. Another key process of SK-Net is the extraction of the local structure of Skeypoints (detail feature) and the local spatial pattern of normalized Skeypoints (pattern feature). This process generates a comprehensive representation, pattern-detail (PD) feature, which comprises the local detail information of a point cloud and reveals its spatial pattern through the part district reconstruction on normalized Skeypoints. Consequently, our network is prompted to effectively understand the correlation between different regions of a point cloud and integrate contextual information of the point cloud. In point cloud tasks, such as classification and segmentation, our proposed method performs better than or comparable with the state-of-the-art approaches. We also present an ablation study to demonstrate the advantages of SK-Net

Verification of {\Gamma}7_{7} symmetry assignment for the top valence band of ZnO by magneto-optical studies of the free A exciton state

The circularly-polarized and angular-resolved magneto-photoluminescence spectroscopy was carried out to study the free A exciton 1S state in wurtzite ZnO at 5 K.Comment: 4 figures, 16 pages. arXiv admin note: substantial text overlap with arXiv:0706.396

トウ チョウアンジョウ ト ヘイジョウキョウ ノ ヒカク ケンキュウ

講

Calidad de servicio en computación en la nube: técnicas de modelado y sus aplicaciones

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management