Study of the gluonic quartic gauge couplings at muon colliders

The potential of the muon colliders open up new possibilities for the exploration of new physics beyond the Standard Model. It is worthwhile to investigate whether muon colliders are suitable for studying gluonic quartic gauge couplings~(gQGCs), which can be contributed by dimension-8 operators in the framework of the Standard Model effective field theory, and are intensively studied recently. In this paper, we study the sensitivity of the process $\mu^+\mu^-\to j j \nu\bar{\nu}$ to gQGCs. Our result indicate that the muon colliders with c.m. energies larger than $4\;{\rm TeV}$ can be more sensitive to gQGCs than the Large Hadron Collider.Comment: 11 pages, 5 figure

1S0 pairing correlation in symmetric nuclear matter with Debye screening effects

AbstractThe 1S0 pairing of symmetric nuclear matter is discussed in the framework of relativistic nuclear theory with Dyson–Schwinger equations (DSEs). The in-medium nucleon and meson propagators are treated in a more self-consistent way through meson polarizations. The screening effects on mesons due to in-medium nucleon excitation are found to reduce the 1S0 pairing gap and shift remarkably the gap peak to low density region

2-Ferrocenyl-6-methyl­pyridin-3-ol

In the title compound, [Fe(C5H5)(C11H10NO)], the dihedral angle between the pyridyl and substituted cyclo­penta­dienyl rings is 20.4 (3)°. The H atoms of the methyl group are disordered over two positions; their site-occupation factors were fixed at 0.5. The crystal structure is stabilized by well defined inter­molecular O—H⋯N and C—H⋯O hydrogen bonds, leading to the formation of a two-dimensional network parallel to (101)

Mutual Information Rate of Gaussian and Truncated Gaussian Inputs on Intensity-Driven Signal Transduction Channels

In this letter, we investigate the mutual information rate (MIR) achieved by an independent identically distributed (IID) Gaussian input on the intensity-driven signal transduction channel. Specifically, the asymptotic expression of the continuous-time MIR is given. Next, aiming at low computational complexity, we also deduce an approximately numerical solution for this MIR. Moreover, the corresponding lower and upper bounds that can be used to find the capacity-achieving input distribution parameters are derived in closed-form. Finally, simulation results show the accuracy of our analysis.Comment: Accepted for publication in IEEE Communications Letter

SMMix: Self-Motivated Image Mixing for Vision Transformers

CutMix is a vital augmentation strategy that determines the performance and generalization ability of vision transformers (ViTs). However, the inconsistency between the mixed images and the corresponding labels harms its efficacy. Existing CutMix variants tackle this problem by generating more consistent mixed images or more precise mixed labels, but inevitably introduce heavy training overhead or require extra information, undermining ease of use. To this end, we propose an efficient and effective Self-Motivated image Mixing method (SMMix), which motivates both image and label enhancement by the model under training itself. Specifically, we propose a max-min attention region mixing approach that enriches the attention-focused objects in the mixed images. Then, we introduce a fine-grained label assignment technique that co-trains the output tokens of mixed images with fine-grained supervision. Moreover, we devise a novel feature consistency constraint to align features from mixed and unmixed images. Due to the subtle designs of the self-motivated paradigm, our SMMix is significant in its smaller training overhead and better performance than other CutMix variants. In particular, SMMix improves the accuracy of DeiT-T/S, CaiT-XXS-24/36, and PVT-T/S/M/L by more than +1% on ImageNet-1k. The generalization capability of our method is also demonstrated on downstream tasks and out-of-distribution datasets. Code of this project is available at https://github.com/ChenMnZ/SMMix

Spatial Re-parameterization for N:M Sparsity

This paper presents a Spatial Re-parameterization (SpRe) method for the N:M sparsity in CNNs. SpRe is stemmed from an observation regarding the restricted variety in spatial sparsity present in N:M sparsity compared with unstructured sparsity. Particularly, N:M sparsity exhibits a fixed sparsity rate within the spatial domains due to its distinctive pattern that mandates N non-zero components among M successive weights in the input channel dimension of convolution filters. On the contrary, we observe that unstructured sparsity displays a substantial divergence in sparsity across the spatial domains, which we experimentally verified to be very crucial for its robust performance retention compared with N:M sparsity. Therefore, SpRe employs the spatial-sparsity distribution of unstructured sparsity to assign an extra branch in conjunction with the original N:M branch at training time, which allows the N:M sparse network to sustain a similar distribution of spatial sparsity with unstructured sparsity. During inference, the extra branch can be further re-parameterized into the main N:M branch, without exerting any distortion on the sparse pattern or additional computation costs. SpRe has achieved a commendable feat by matching the performance of N:M sparsity methods with state-of-the-art unstructured sparsity methods across various benchmarks. Code and models are anonymously available at \url{https://github.com/zyxxmu/SpRe}.Comment: 11 pages, 4 figure

Model-Driven Based Deep Unfolding Equalizer for Underwater Acoustic OFDM Communications

It is challenging to design an equalizer for the complex time-frequency doubly-selective channel. In this paper, we employ the deep unfolding approach to establish an equalizer for the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, namely UDNet. Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer. Moreover, we consider the QPSK equalization as a four-classification task and adopt minimum Kullback-Leibler (KL) to achieve a smaller symbol error rate (SER) with the one-hot coding instead of the MMSE criterion. In addition, we introduce a sliding structure based on the banded approximation of the channel matrix to reduce the network size and aid UDNet to perform well for different-length signals without changing the network structure. Furthermore, we apply the measured at-sea doubly-selective UWA channel and offshore background noise to evaluate the proposed equalizer. Experimental results show that the proposed UDNet performs better with low computational complexity. Concretely, the SER of UDNet is nearly an order of magnitude lower than that of MMSE