Sparsistent filtering of comovement networks from high-dimensional data

Simulation of Smoke Emission from Fires in High-Rise Buildings Using the 3D Model Generated from 2-Dimensional Cadastral Data

Having a 3-Dimensional model of high-rise buildings can be used in disaster management such as fire cases to reduce casualties. The fundamental dilemma in 3D building modeling is the unavailability of suitable data sources. However, available cadastral 2D maps could be used as low-cost and attainable resources for 3D building modeling. Smoke will be a great threat to people's health during a f...

Comovement of Central European stock markets using wavelet coherence: Evidence from high-frequency data

Methods for regression analysis in high-dimensional data

By evolving science, knowledge and technology, new and precise methods for measuring, collecting and recording information have been innovated, which have resulted in the appearance and development of high-dimensional data. The high-dimensional data set, i.e., a data set in which the number of explanatory variables is much larger than the number of observations, cannot be easily analyzed by ...

Two Dimensional Filtering of Seismic Data

High resolution surveys with single shots and geophones record wide spectral bands of reflectionenergy but the signal quality is often unacceptable due to a high content of random and organized noise. Coherency filtering of such data may provide misleading sections reflecting the bias of the processor. We have found that two-dimensional filtering with short operators handles this problematic si...

Eecient Recovery of Low-dimensional Structure from High-dimensional Data

Many modeling tasks in computer vision. e.g. structure from motion, shape/reeectance from shading , lter synthesis have a low-dimensional intrinsic structure even though the dimension of the input data can be relatively large. We propose a simple but surprisingly eeective iterative randomized algorithm that drastically cuts down the time required for recovering the intrinsic structure. The comp...