9,121 research outputs found
Solving a class of zero-sum stopping game with regime switching
This paper studies a class of zero-sum stopping game in a regime switching
model. A verification theorem as a sufficient criterion for Nash equilibriums
is established based on a set of variational inequalities (VIs). Under an
appropriate regularity condition for solutions to the VIs, a suitable system of
algebraic equations is derived via the so-called smooth-fit principle. Explicit
Nash equilibrium stopping rules of threshold-type for the two players and the
corresponding value function of the game in closed form are obtained. Numerical
experiments are reported to demonstrate the dependence of the threshold levels
on various model parameters. A reduction to the case with no regime switching
is also presented as a comparison
A note on inversion of Toeplitz matrices
AbstractIt is shown that the invertibility of a Toeplitz matrix can be determined through the solvability of two standard equations. The inverse matrix can be denoted as a sum of products of circulant matrices and upper triangular Toeplitz matrices. The stability of the inversion formula for a Toeplitz matrix is also considered
Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection
SAM is a segmentation model recently released by Meta AI Research and has
been gaining attention quickly due to its impressive performance in generic
object segmentation. However, its ability to generalize to specific scenes such
as camouflaged scenes is still unknown. Camouflaged object detection (COD)
involves identifying objects that are seamlessly integrated into their
surroundings and has numerous practical applications in fields such as
medicine, art, and agriculture. In this study, we try to ask if SAM can address
the COD task and evaluate the performance of SAM on the COD benchmark by
employing maximum segmentation evaluation and camouflage location evaluation.
We also compare SAM's performance with 22 state-of-the-art COD methods. Our
results indicate that while SAM shows promise in generic object segmentation,
its performance on the COD task is limited. This presents an opportunity for
further research to explore how to build a stronger SAM that may address the
COD task. The results of this paper are provided in
\url{https://github.com/luckybird1994/SAMCOD}
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