A blocking and regularization approach to high dimensional realized covariance estimation

We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven grouping of assets of similar trading frequency ensures the reduction of data loss due to refresh time sampling. In an extensive simulation study mimicking the empirical features of the S&P 1500 universe we show that the ’RnB’ estimator yields efficiency gains and outperforms competing kernel estimators for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application of forecasting daily covariances of the S&P 500 index confirms the simulation results

DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model

The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed-up factors. Evaluation is done on two single-person and two multi-person pose estimation benchmarks. The proposed approach significantly outperforms best known multi-person pose estimation results while demonstrating competitive performance on the task of single person pose estimation. Models and code available at http://pose.mpi-inf.mpg.deComment: ECCV'16. High-res version at https://www.d2.mpi-inf.mpg.de/sites/default/files/insafutdinov16arxiv.pd

An investigation of pulsar searching techniques with the Fast Folding Algorithm

Here we present an in-depth study of the behaviour of the Fast Folding Algorithm, an alternative pulsar searching technique to the Fast Fourier Transform. Weaknesses in the Fast Fourier Transform, including a susceptibility to red noise, leave it insensitive to pulsars with long rotational periods (P > 1 s). This sensitivity gap has the potential to bias our understanding of the period distribution of the pulsar population. The Fast Folding Algorithm, a time-domain based pulsar searching technique, has the potential to overcome some of these biases. Modern distributed-computing frameworks now allow for the application of this algorithm to all-sky blind pulsar surveys for the first time. However, many aspects of the behaviour of this search technique remain poorly understood, including its responsiveness to variations in pulse shape and the presence of red noise. Using a custom CPU-based implementation of the Fast Folding Algorithm, ffancy, we have conducted an in-depth study into the behaviour of the Fast Folding Algorithm in both an ideal, white noise regime as well as a trial on observational data from the HTRU-S Low Latitude pulsar survey, including a comparison to the behaviour of the Fast Fourier Transform. We are able to both confirm and expand upon earlier studies that demonstrate the ability of the Fast Folding Algorithm to outperform the Fast Fourier Transform under ideal white noise conditions, and demonstrate a significant improvement in sensitivity to long-period pulsars in real observational data through the use of the Fast Folding Algorithm.Comment: 19 pages, 15 figures, 3 table

Plane rectification through robust vanishing point tracking using the expectation-maximization algorithm

This paper introduces a new strategy for plane rectification in sequences of images, based on the Expectation-Maximization (EM) algorithm. Our approach is able to compute simultaneously the parameters of the dominant vanishing point in the image plane and the most significant lines passing through it. It is based on a novel definition of the likelihood distribution of the gradient image considering both the position and the orientation of the gradient pixels. Besides, the mixture model in which the EM algorithm operates is extended, compared to other works, to consider an additional component to control the presence of outliers. Some synthetic data tests are described to show the robustness and efficiency of the proposed method. The plane rectification results show that the method is able to remove the perspective and affine distortion of real traffic sequences without the need to compute two vanishing point

Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images

In this thesis, we aim to solve the problem of automatic image rectification and repeated patterns detection on 2D urban images, using novel low-rank based techniques. Repeated patterns (such as windows, tiles, balconies and doors) are prominent and significant features in urban scenes. Detection of the periodic structures is useful in many applications such as photorealistic 3D reconstruction, 2D-to-3D alignment, facade parsing, city modeling, classification, navigation, visualization in 3D map environments, shape completion, cinematography and 3D games. However both of the image rectification and repeated patterns detection problems are challenging due to scene occlusions, varying illumination, pose variation and sensor noise. Therefore, detection of these repeated patterns becomes very important for city scene analysis. Given a 2D image of urban scene, we automatically rectify a facade image and extract facade textures first. Based on the rectified facade texture, we exploit novel algorithms that extract repeated patterns by using Kronecker product based modeling that is based on a solid theoretical foundation. We have tested our algorithms in a large set of images, which includes building facades from Paris, Hong Kong and New York