1,242 research outputs found
CLT for linear spectral statistics of normalized sample covariance matrices with the dimension much larger than the sample size
Let
where is a matrix, consisting of independent and
identically distributed (i.i.d.) real random variables with mean zero
and variance one. When , under fourth moment conditions a central
limit theorem (CLT) for linear spectral statistics (LSS) of
defined by the eigenvalues is established. We also explore its applications in
testing whether a population covariance matrix is an identity matrix.Comment: Published at http://dx.doi.org/10.3150/14-BEJ599 in the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
TransNFV: Integrating Transactional Semantics for Efficient State Management in Virtual Network Functions
Managing shared mutable states in high concurrency state access operations is
a persistent challenge in Network Functions Virtualization (NFV). This is
particularly true when striving to meet chain output equivalence (COE)
requirements. This paper presents TransNFV, an innovative NFV framework that
incorporates transactional semantics to optimize NFV state management. The
TransNFV integrates VNF state access operations as transactions, resolves
transaction dependencies, schedules transactions dynamically, and executes
transactions efficiently. Initial findings suggest that TransNFV maintains
shared VNF state consistency, meets COE requirements, and skillfully handles
complex cross-flow states in dynamic network conditions. TransNFV thus provides
a promising solution to enhance state management and overall performance in
future NFV platforms
CNN Injected Transformer for Image Exposure Correction
Capturing images with incorrect exposure settings fails to deliver a
satisfactory visual experience. Only when the exposure is properly set, can the
color and details of the images be appropriately preserved. Previous exposure
correction methods based on convolutions often produce exposure deviation in
images as a consequence of the restricted receptive field of convolutional
kernels. This issue arises because convolutions are not capable of capturing
long-range dependencies in images accurately. To overcome this challenge, we
can apply the Transformer to address the exposure correction problem,
leveraging its capability in modeling long-range dependencies to capture global
representation. However, solely relying on the window-based Transformer leads
to visually disturbing blocking artifacts due to the application of
self-attention in small patches. In this paper, we propose a CNN Injected
Transformer (CIT) to harness the individual strengths of CNN and Transformer
simultaneously. Specifically, we construct the CIT by utilizing a window-based
Transformer to exploit the long-range interactions among different regions in
the entire image. Within each CIT block, we incorporate a channel attention
block (CAB) and a half-instance normalization block (HINB) to assist the
window-based self-attention to acquire the global statistics and refine local
features. In addition to the hybrid architecture design for exposure
correction, we apply a set of carefully formulated loss functions to improve
the spatial coherence and rectify potential color deviations. Extensive
experiments demonstrate that our image exposure correction method outperforms
state-of-the-art approaches in terms of both quantitative and qualitative
metrics
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