1,332 research outputs found
Direct CP violation in
We study the direct CP violation in the decay process in the
Standard Model. An interesting mechanism involving the charge symmetry
violating mixing between and is applied to enlarge the CP
asymmetry. With this mechanism, the maximum differential and localized
integrated CP asymmetries can reach and
, respectively, which still leave plenty room
for CP-violating New Physics to be discovered through this process
Possible open-charmed pentaquark molecule --- the bound state --- in the Bethe-Salpeter formalism
We study the -wave bound state in the Bethe-Salpeter formalism in
the ladder and instantaneous approximations. With the kernel generated by the
hadronic effective Lagrangian, two open-charmed bound states, which quantum
numbers are , and , ,
respectively, are predicted as new candidates of hadronic pentaquark molecules
in our formalism. If existing, they could contribute to the broad 3188 eV
structure near the five new narrow states observed recently by the
LHCb Collaboration.Comment: 8 pages, 4 figures, accepted by Eur. Phys. J.
First Principles Study of Adsorption of on Al Surface with Hybrid Functionals
Adsorption of molecule on Al surface has been a long standing puzzle
for the first principles calculation. We have studied the adsorption of
molecule on the Al(111) surface using hybrid functionals. In contrast to the
previous LDA/GGA, the present calculations with hybrid functionals successfully
predict that molecule can be absorbed on the Al(111) surface with a
barrier around 0.20.4 eV, which is in good agreement with
experiments. Our calculations predict that the LUMO of molecule is
higher than the Fermi level of the Al(111) surface, which is responsible for
the barrier of the adsorption.Comment: 14 pages, 5 figure
First-principles study on the effective masses of zinc-blend-derived Cu_2Zn-IV-VI_4 (IV = Sn, Ge, Si and VI = S, Se)
The electron and hole effective masses of kesterite (KS) and stannite (ST)
structured Cu_2Zn-IV-VI_4 (IV = Sn, Ge, Si and VI = S, Se) semiconductors are
systematically studied using first-principles calculations. We find that the
electron effective masses are almost isotropic, while strong anisotropy is
observed for the hole effective mass. The electron effective masses are
typically much smaller than the hole effective masses for all studied
compounds. The ordering of the topmost three valence bands and the
corresponding hole effective masses of the KS and ST structures are different
due to the different sign of the crystal-field splitting. The electron and hole
effective masses of Se-based compounds are significantly smaller compared to
the corresponding S-based compounds. They also decrease as the atomic number of
the group IV elements (Si, Ge, Sn) increases, but the decrease is less notable
than that caused by the substitution of S by Se.Comment: 14 pages, 6 figures, 2 table
Semi-supervised classification of polarimetric SAR images using Markov random field and two-level Wishart mixture model
In this work, we propose a semi-supervised method for classification of polarimetric synthetic aperture radar (PolSAR) images. In the proposed method, a 2-level mixture model is constructed by associating each component density with a unique Wishart mixture model (instead of a single Wishart distribution as that in the conventional Wishart mixture model). This modeling scheme facilitates the accurate description of data for the categories, each of which includes multiple subcategories. The learning algorithm for the proposed model is developed based on variational inference and all the update equations are obtained in closed form. In the learning algorithm, the spatial interdependencies are incorporated by imposing a Markov random field prior on the indicator variable to alleviate the speckle effect on the classification results. The experimental results demonstrate the improved performance of the proposed method compared with the unsupervised version and supervised version of the proposed model as well as an existing method for semi-supervised classification
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization
Efficiently deploying deep neural networks on low-resource edge devices is
challenging due to their ever-increasing resource requirements. To address this
issue, researchers have proposed multiplication-free neural networks, such as
Power-of-Two quantization, or also known as Shift networks, which aim to reduce
memory usage and simplify computation. However, existing low-bit Shift networks
are not as accurate as their full-precision counterparts, typically suffering
from limited weight range encoding schemes and quantization loss. In this
paper, we propose the DenseShift network, which significantly improves the
accuracy of Shift networks, achieving competitive performance to full-precision
networks for vision and speech applications. In addition, we introduce a method
to deploy an efficient DenseShift network using non-quantized floating-point
activations, while obtaining 1.6X speed-up over existing methods. To achieve
this, we demonstrate that zero-weight values in low-bit Shift networks do not
contribute to model capacity and negatively impact inference computation. To
address this issue, we propose a zero-free shifting mechanism that simplifies
inference and increases model capacity. We further propose a sign-scale
decomposition design to enhance training efficiency and a low-variance random
initialization strategy to improve the model's transfer learning performance.
Our extensive experiments on various computer vision and speech tasks
demonstrate that DenseShift outperforms existing low-bit multiplication-free
networks and achieves competitive performance compared to full-precision
networks. Furthermore, our proposed approach exhibits strong transfer learning
performance without a drop in accuracy. Our code was released on GitHub
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