1,331 research outputs found
Exploring the Higgs Sector of a Most Natural NMSSM and its Prediction on Higgs Pair Production at the LHC
As a most natural realization of the Next-to Minimal Supersymmetry Standard
Model (NMSSM), {\lambda}-SUSY is parameterized by a large {\lambda} around one
and a low tan below 10. In this work, we first scan the parameter space
of {\lambda}-SUSY by considering various experimental constraints, including
the limitation from the Higgs data updated by the ATLAS and CMS collaborations
in the summer of 2014, then we study the properties of the Higgs bosons. We get
two characteristic features of {\lambda}-SUSY in experimentally allowed
parameter space. One is the triple self coupling of the SM-like Higgs boson may
get enhanced by a factor over 10 in comparison with its SM prediction. The
other is the pair production of the SM-like Higgs boson at the LHC may be two
orders larger than its SM prediction. All these features seems to be
unachievable in the Minimal Supersymmetric Standard Model and in the NMSSM with
a low {\lambda}. Moreover, we also find that naturalness plays an important
role in selecting the parameter space of {\lambda}-SUSY, and that the Higgs
obtained with the latest data is usually significantly smaller than
before due to the more consistency of the two collaboration measurements
Time-delayed impulsive control for discrete-time nonlinear systems with actuator saturation
This paper focuses on the problem of time-delayed impulsive control with actuator saturation for discrete-time dynamical systems. By establishing a delayed impulsive difference inequality, combining with convex analysis and inequality techniques, some sufficient conditions are obtained to ensure exponential stability for discrete-time dynamical systems via time-delayed impulsive controller with actuator saturation. The designed controller admits the existence of some transmission delays in impulsive feedback law, and the control input variables are required to stay within an availability zone. Several numerical simulations are also given to demonstrate the effectiveness of the proposed results. 
Properties of Heavy Higgs Bosons and Dark Matter under Current Experimental Limits in the NMSSM
Searches for new particles beyond the Standard Model (SM) are an important
task for the Large Hadron Collider (LHC). In this paper, we investigate the
properties of the heavy non-SM Higgs bosons in the -term extended
Next-to-Minimal Supersymmetric Standard Model (NMSSM). We scan the
parameter space of the NMSSM considering the basic constraints from Higgs
data, dark matter (DM) relic density, and LHC searches for sparticles. And we
also consider the constraints from the LZ2022 experiment and the muon anomaly
constraint at 2 level.
We find that the LZ2022 experiment has a strict constraint on the parameter
space of the NMSSM, and the limits from the DM-nucleon spin-independent
(SI) and spin-dependent (SD) cross-sections are complementary. Then we discuss
the exotic decay modes of heavy Higgs bosons decaying into SM-like Higgs boson.
We find that for doublet-dominated Higgs and , the main exotic decay
channels are , , and , and the branching ratio can reach to about
23, 10, 35 and 10 respectively. At the 13 TeV LHC, the
production cross-section of and
can reach to about pb and
pb, respectively
IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse
Despite its success, generative adversarial networks (GANs) still suffer from
mode collapse, i.e., the generator can only map latent variables to a partial
set of modes in the target distribution. In this paper, we analyze and seek to
regularize this issue with an independent and identically distributed (IID)
sampling perspective and emphasize that holding the IID property referring to
the target distribution for generation can naturally avoid mode collapse. This
is based on the basic IID assumption for real data in machine learning.
However, though the source samples {z} obey IID, the generations {G(z)} may not
necessarily be IID sampling from the target distribution. Based on this
observation, considering a necessary condition of IID generation that the
inverse samples from target data should also be IID in the source distribution,
we propose a new loss to encourage the closeness between inverse samples of
real data and the Gaussian source in latent space to regularize the generation
to be IID from the target distribution. Experiments on both synthetic and
real-world data show the effectiveness of our model.Comment: Accepted in IJCAI 202
NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images
Object counting is a hot topic in computer vision, which aims to estimate the
number of objects in a given image. However, most methods only count objects of
a single category for an image, which cannot be applied to scenes that need to
count objects with multiple categories simultaneously, especially in aerial
scenes. To this end, this paper introduces a Multi-category Object Counting
(MOC) task to estimate the numbers of different objects (cars, buildings,
ships, etc.) in an aerial image. Considering the absence of a dataset for this
task, a large-scale Dataset (NWPU-MOC) is collected, consisting of 3,416 scenes
with a resolution of 1024 1024 pixels, and well-annotated using 14
fine-grained object categories. Besides, each scene contains RGB and Near
Infrared (NIR) images, of which the NIR spectrum can provide richer
characterization information compared with only the RGB spectrum. Based on
NWPU-MOC, the paper presents a multi-spectrum, multi-category object counting
framework, which employs a dual-attention module to fuse the features of RGB
and NIR and subsequently regress multi-channel density maps corresponding to
each object category. In addition, to modeling the dependency between different
channels in the density map with each object category, a spatial contrast loss
is designed as a penalty for overlapping predictions at the same spatial
position. Experimental results demonstrate that the proposed method achieves
state-of-the-art performance compared with some mainstream counting algorithms.
The dataset, code and models are publicly available at
https://github.com/lyongo/NWPU-MOC
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