Related papers: Advances in the RXTE Proportional Counter Array Ca…

Despite recent progress in video generation, inference speed remains a major bottleneck. A common acceleration strategy involves reusing model outputs via caching mechanisms at fixed intervals. However, we find that such fixed-frequency…

Principal Component Analysis (PCA) is a very successful dimensionality reduction technique, widely used in predictive modeling. A key factor in its widespread use in this domain is the fact that the projection of a dataset onto its first…

We present the calibration of the \textit{Nuclear Spectroscopic Telescope Array} (\nustar) X-ray satellite. We used the Crab as the primary effective area calibrator and constructed a piece-wise linear spline function to modify the…

Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA…

This article presents the design of a new asynchronous early output full adder which when cascaded leads to a relative-timed ripple carry adder (RCA). The relative-timed RCA requires imposing a very small relative-timing assumption to…

Sparse principal component analysis (PCA) is a popular dimensionality reduction technique for obtaining principal components which are linear combinations of a small subset of the original features. Existing approaches cannot supply…

In this paper, we consider the problem of Robust Matrix Completion (RMC) where the goal is to recover a low-rank matrix by observing a small number of its entries out of which a few can be arbitrarily corrupted. We propose a simple…

Robust Principal Component Analysis (RPCA) is a fundamental technique for decomposing data into low-rank and sparse components, which plays a critical role for applications such as image processing and anomaly detection. Traditional RPCA…

A new readout technique was developed with the primary aim of keeping the number of channels in the front-end electronics as low as possible when scaling up the sensitive area of a Resistive Plate Chamber (RPC). The readout method here…

Approximate ripple carry adders (RCAs) and carry lookahead adders (CLAs) are presented which are compared with accurate RCAs and CLAs for performing a 32-bit addition. The accurate and approximate RCAs and CLAs are implemented using a…

Accurate calibration of control parameters in quantum gates is crucial for high-fidelity operations, yet it represents a significant time and resource challenge, necessitating periods of downtime for quantum computers. Robust Phase…

We analyze Rossi X-ray Timing Explorer (RXTE) Proportional Counter Array (PCA) data of the transient low mass X-ray binary (LMXB) system 1A 1744-361. We explore the X-ray intensity and spectral evolution of the source, perform timing…

Principal component analysis (PCA) requires the computation of a low-rank approximation to a matrix containing the data being analyzed. In many applications of PCA, the best possible accuracy of any rank-deficient approximation is at most a…

PCA is widely used in health and care research to analyze complex HD datasets, such as patient health records, genetic data, and medical imaging. By reducing dimensionality, PCA helps identify key patterns and trends, which can aid in…

We report detailed timing and spectral analysis of RXTE-PCA data obtained from observations during the outburst of a transient X-ray pulsar 1A 1118--61 in January 2009. The pulse profile showed significant evolution during the outburst and…

Principal component analysis (PCA) is recognised as a quintessential data analysis technique when it comes to describing linear relationships between the features of a dataset. However, the well-known sensitivity of PCA to non-Gaussian…

We present timing analysis for a Rossi X-ray Timing Explorer observation of Cygnus X-1 in its hard/low state. This was the first RXTE observation of Cyg X-1 taken after it transited back to this state from its soft/high state. RXTE's large…

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for grounding large language models in external knowledge sources, improving the precision of agents responses. However, high-dimensional language model embeddings,…

Principal component analysis (PCA) is a widely used dimension reduction technique in machine learning and multivariate statistics. To improve the interpretability of PCA, various approaches to obtain sparse principal direction loadings have…

Recently Resistive-RAM (RRAM) crossbar has been used in the design of the accelerator of convolutional neural networks (CNNs) to solve the memory wall issue. However, the intensive multiply-accumulate computations (MACs) executed at the…