相关论文: Advances in the RXTE Proportional Counter Array Ca…

In this paper, we summarize the RXTE measurements of cyclotron resonance scattering features in 8 accreting pulsars. The wide bandpass and modest resolution of the RXTE instruments make them ideal for measuring these generally broad…

A first proposal of a sparse and cellwise robust PCA method is presented. Robustness to single outlying cells in the data matrix is achieved by substituting the squared loss function for the approximation error by a robust version. The…

Tensor, also known as multi-dimensional array, arises from many applications in signal processing, manufacturing processes, healthcare, among others. As one of the most popular methods in tensor literature, Robust tensor principal component…

Numerous applications in data mining and machine learning require recovering a matrix of minimal rank. Robust principal component analysis (RPCA) is a general framework for handling this kind of problems. Nuclear norm based convex surrogate…

We present timing and spectral analysis of RXTE-PCA observations of SMC X-1 between January 1996 and December 2003. From observations around 30 August 1996 with a time span of $\sim 6$ days, we obtain a precise timing solution for the…

We present updated hard X-ray polarization measurements of the Crab pulsar and nebula obtained with the balloon-borne polarimeter XL-Calibur in the ~19-64 keV energy range. During the flight, intermittent GPS-failure resulted in poorly…

Principal component analysis (PCA) is known to be sensitive to outliers, so that various robust PCA variants were proposed in the literature. A recent model, called REAPER, aims to find the principal components by solving a convex…

Principal component analysis (PCA) is one of the most powerful tools in machine learning. The simplest method for PCA, the power iteration, requires $\mathcal O(1/\Delta)$ full-data passes to recover the principal component of a matrix with…

Principal component analysis (PCA) is often used to reduce the dimension of data by selecting a few orthonormal vectors that explain most of the variance structure of the data. L1 PCA uses the L1 norm to measure error, whereas the…

RXTE has been operating for nearly 2 years and is planning the third. The spacecraft performance has been good and the three instruments are operating well. Observations have been made of the range of targets suitable for RXTE, including…

We study robust PCA for the fully observed setting, which is about separating a low rank matrix $\boldsymbol{L}$ and a sparse matrix $\boldsymbol{S}$ from their sum $\boldsymbol{D}=\boldsymbol{L}+\boldsymbol{S}$. In this paper, a new…

We have detected pulsed emission from the Vela pulsar at 2 - 30 keV during a 93 ks observation with the Rossi X-Ray Timing Explorer (RXTE). The RXTE pulse profile shows two peaks, that are roughly in phase with the EGRET peaks, but does not…

Polarimetric phased arrays (PPAs) enhance radar target detection and anti-jamming capabilities, but their conventional dual transmit/receive (T/R) channel architecture leads to high cost and system complexity. To address these limitations,…

Space-time adaptive processing (STAP) is a well-known technique in detecting slow-moving targets in the presence of a clutter-spreading environment. When considering the STAP system deployed with conformal radar array (CFA), the training…

Principal component analysis (PCA) is a widely employed statistical tool used primarily for dimensionality reduction. However, it is known to be adversely affected by the presence of outlying observations in the sample, which is quite…

It is well known that Principal Component Analysis (PCA) is strongly affected by outliers and a lot of effort has been put into robustification of PCA. In this paper we present a new algorithm for robust PCA minimizing the trimmed…

The CHIME Pathfinder is a new interferometric radio telescope that uses a hybrid FPGA/GPU FX correlator. The GPU-based X-engine of this correlator processes over 819 Gb/s of 4+4-bit complex astronomical data from N=256 inputs across a 400…

This article presents two area/latency optimized gate level asynchronous full adder designs which correspond to early output logic. The proposed full adders are constructed using the delay-insensitive dual-rail code and adhere to the…

Principal Component Analysis (PCA) is one of the most used tools for extracting low-dimensional representations of data, in particular for time series. Performances are known to strongly depend on the quality (amount of noise) and the…

We propose a new method for robust PCA -- the task of recovering a low-rank matrix from sparse corruptions that are of unknown value and support. Our method involves alternating between projecting appropriate residuals onto the set of…