THE INTERACTIVE TRANSFORMATION OF URBAN AND RURAL SPACES: QUESTIONING AUTHENTICITY, (POST) PRODUCTIVISM, AND ECOLOGY BETWEEN ITALY AND CHINA

L'opera raccoglie e illustra lo stato delle ricerche in corso nel Dottorato di Ricerca in Architettura, Storia e Progetto e presenta i principali ambiti di studio del dottorato stess

Integration of orbital-dependent exchange-correlation potentials

In density-functional theory, one can approximate either the exchange-correlation energy functional or the corresponding Kohn--Sham effective potential, which is then converted into an energy functional by functional integration. A directly approximated potential may depend on the electron density explicitly or implicitly through Kohn--Sham orbitals. A potential that depends on the electron density explicitly can be converted into an energy functional by evaluating the Leeuwen--Baerends line integral along some path of electron densities. We extend this technique to orbital-dependent potentials by integrating them along the path of scaled orbitals. Using this method, we assign energy expressions to the Slater, Becke--Johnson and van Leeuwen--Baerends model Kohn--Sham potentials. We also investigate the conditions under which the zero-force test for functional derivatives holds in finite basis set. Specifically, we show that any functional derivative of an explicitly density-dependent functional satisfies the zero-force test in any finite basis set. Approximate exchange-correlation potentials constructed by the Ryabinkin--Kohut--Staroverov (RKS) method are found to pass the zero-force test only in the basis-set limit. Our results confirm that RKS potentials obtained from Hartree--Fock wave functions are practically indistinguishable from exact exchange potentials when a large basis set is employed

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available.Comment: Accepted by ICCV 201