Results 41 to 50 of about 391,798 (266)

Sparse Cointegration

open access: yesSSRN Electronic Journal, 2014
Cointegration analysis is used to estimate the long-run equilibrium relations between several time series. The coefficients of these long-run equilibrium relations are the cointegrating vectors. In this paper, we provide a sparse estimator of the cointegrating vectors.
Wilms, Ines, Croux, Christophe
openaire   +2 more sources

Adaptive sparse tiling for sparse matrix multiplication [PDF]

open access: yesProceedings of the 24th Symposium on Principles and Practice of Parallel Programming, 2019
Tiling is a key technique for data locality optimization and is widely used in high-performance implementations of dense matrix-matrix multiplication for multicore/manycore CPUs and GPUs. However, the irregular and matrix-dependent data access pattern of sparse matrix multiplication makes it challenging to use tiling to enhance data reuse.
Changwan Hong   +4 more
openaire   +1 more source

A New Sparse Collaborative Low-Rank Prior Knowledge Representation for Thick Cloud Removal in Remote Sensing Images

open access: yesRemote Sensing
Efficiently removing clouds from remote sensing imagery presents a significant challenge, yet it is crucial for a variety of applications. This paper introduces a novel sparse function, named the tri-fiber-wise sparse function, meticulously engineered ...
Dong-Lin Sun, Teng-Yu Ji, Meng Ding
doaj   +1 more source

Image Extrapolation Using Sparse Methods

open access: yesCommunications, 2013
Image extrapolation is the specific application in image processing. You have to extrapolate the image for example when you want to process the given image piecewise.
Jan Spirik, Jan Zatyik
doaj   +1 more source

Low-Rank Matrix Recovery Approach for Clutter Rejection in Real-Time IR-UWB Radar-Based Moving Target Detection

open access: yesSensors, 2016
The detection of a moving target using an IR-UWB Radar involves the core task of separating the waves reflected by the static background and by the moving target.
Donatien Sabushimike   +5 more
doaj   +1 more source

Sparse Warcasting

open access: yesScottish Journal of Political Economy, 2023
ABSTRACT Forecasting economic activity during institutional collapse requires nowcasts derived exclusively from alternative data sources. Such sources are abundant yet theoretically unanchored and potentially weakly informative. This study examines whether sparse supervised dimension reduction extracts reliable signals in a context ...
openaire   +2 more sources

Natural Killer Cells in Paediatric Soft Tissue Sarcomas: A Systematic Review

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Paediatric soft tissue sarcomas (pSTS) are a rare and heterogeneous group of malignant tumours arising in tissues of mesenchymal origin. The role of natural killer (NK) cells in pSTS remains poorly understood, with evidence fragmented across small preclinical studies and early‐phase clinical trials.
Raya Dean   +7 more
wiley   +1 more source

Establishing an Apheresis Medicine Program in a Resource‐Constrained Setting: A 5‐Year Experience From Lagos, Nigeria

open access: yesTherapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background Establishing a comprehensive apheresis medicine program in a resource‐constrained setting presents significant structural, financial, and logistical challenges. Despite the growing clinical importance of apheresis services globally, published experience from sub‐Saharan Africa remains sparse.
Folasade Adelekan‐Popoola   +4 more
wiley   +1 more source

Forecasting the Dialysis Burden in Japan: Validation‐Based Projections of Prevalence and Incidence Through 2050

open access: yesTherapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post‐peak phase or whether recent declines partly reflect pandemic‐related disruptions remains uncertain.
Hatice Şahin   +2 more
wiley   +1 more source

Procedural Learning With Robust Visual Features via Low Rank Prior

open access: yesIEEE Access, 2019
In order to apply a convolutional neural network (CNN) to unseen datasets, a common way is to train a CNN using a pre-trained model on a big dataset by fine-tuning it instead of starting from scratch.
Haifeng Li   +5 more
doaj   +1 more source

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