The 1997-98 Korean financial crisis

노트 : presentation by Kim, Kihwan at The High-Level Seminar on Crisis Prevention in Emerging MarketsOrganized by The International Monetary Fund and The Government of singaporeSingaporeJuly 10-11, 200

Managing motion triggered executables in distributed mobile databases

Mobile devices have brought new applications into our daily life. However, ecient man- agement of these objects to support new applications is challenging due to the distributed nature and mobility of mobile objects. This dissertation describes a new type of mobile peer- to-peer (M-P2P) computing, namely geotasking, and presents ecient management of mobile objects to support geotasking. Geotasking mimics human interaction with the physical world. Humans generate information using sensing ability and store information to geographical lo- cations. Humans also retrieve this information from the physical locations. For instance, an installation of a new stop sign at some intersection in town is analogous to an insertion of a new data item into the database. Instead of processing regular data as in traditional data management systems, geotasking manages a collection of geotasks, each dened as a computer program bound to a geographical region. The hardware platform for geotasking consists of popular networked position-aware mobile devices such as cell phones, personal digital assis- tants, and laptops. We design and implement novel system software to facilitate programming and ecient management of geotasks. Such management includes inserts, deletes, updates, retrieval and execution of a geotask triggered by mobile object correlations, geotask mobil- ity, and geotask dependency. Geotasking enables useful applications ranging from warning of dangerous areas for military and search-and-rescue missions to monitoring the population in a certain area for trac management to informing tourists of exciting events in an area and other such applications. Geotasking provides a distributed and unied solution for supporting various types of applications

Geometry-Aware Learning of Maps for Camera Localization

Maps are a key component in image-based camera localization and visual SLAM systems: they are used to establish geometric constraints between images, correct drift in relative pose estimation, and relocalize cameras after lost tracking. The exact definitions of maps, however, are often application-specific and hand-crafted for different scenarios (e.g. 3D landmarks, lines, planes, bags of visual words). We propose to represent maps as a deep neural net called MapNet, which enables learning a data-driven map representation. Unlike prior work on learning maps, MapNet exploits cheap and ubiquitous sensory inputs like visual odometry and GPS in addition to images and fuses them together for camera localization. Geometric constraints expressed by these inputs, which have traditionally been used in bundle adjustment or pose-graph optimization, are formulated as loss terms in MapNet training and also used during inference. In addition to directly improving localization accuracy, this allows us to update the MapNet (i.e., maps) in a self-supervised manner using additional unlabeled video sequences from the scene. We also propose a novel parameterization for camera rotation which is better suited for deep-learning based camera pose regression. Experimental results on both the indoor 7-Scenes dataset and the outdoor Oxford RobotCar dataset show significant performance improvement over prior work. The MapNet project webpage is https://goo.gl/mRB3Au.Comment: CVPR 2018 camera ready paper + supplementary materia