arXiv subjects

Shibo Shen

Publications and source records attributed to Shibo Shen.

3 recordsLinked to original sources

arXiv2026

Quenching of Nonrelativistic $p$-Wave Spin Splitting by Reduced $c\text{-}f$ Coupling in $\text{CeNiAsO}$

The application of spin-space group symmetries to noncollinear antiferromagnets has led to the prediction of odd-parity, nonrelativistic spin splittings, making the physical realization of a practical $p$-wave magnet a central pursuit in spintronics. The layered heavy-fermion oxypnictide $\text{CeNiAsO}$ has been widely regarded as a prototypical platform to verify this paradigm. Here, we investigate the electronic structure of single-crystal $\text{CeNiAsO}$ using high-resolution, ultra-low-temperature and resonant angle-resolved photoemission spectroscopy (ARPES), and $ab-initio$ calculations. Across the consecutive magnetic transitions into the ordered phases, our spectroscopic data reveal neither the expected band folding associated with a spin density wave nor any observable $p$-wave spin splitting, demonstrating that the conduction bands retain full degeneracy. By tracking the temperature dependence of the Ce 4$f$ spectral weight via resonant ARPES, we find negligible $c\text{-}f$ hybridization near the Fermi level within magnetically ordered states, confirming that the Ce 4$f$ electrons reside close to the localized limit. Our findings establish a clear many-body constraint on the projection of real-space magnetic symmetries onto momentum-space electronic bands, demonstrating that symmetry classifications constitute a necessary framework but are not a sufficient condition for nonrelativistic spin splittings in the presence of strong electronic correlations.

cond-mat.str-el↗

arXiv2026

Global magnetic phase diagram and multiple unconventional magnets in NiAs-type compounds

NiAs-type compounds such as CrSb and MnTe host $g$-wave altermagnet (AM) state. In order to search other possible unconventional magnets in this system, we present a global magnetic phase diagram based on a classical $J_1$-$J_2$-$J_3$ Heisenberg model and density functional theory (DFT) calculations. We find another $g$-wave AM state and two $f$-wave OPMs in the phase diagram. Intriguingly, we show that a mixed-parity of the $f$-wave OPM and $g$-wave AM state can naturally emerge in an umbrella-like noncollinear magnetic structure. Our DFT calculations show that CrSe and CrTe$_{1-x}$Se$_x$ are generally in such mixing state with dominated $f$-wave component. The interlayer next-nearest-neighbor coupling $J_3$ is shown to be crucial in determining the phase diagram and in inducing strong competition between conventional and unconventional magnets. Inspired by this, we demonstrate that AM or OPM could be realized by applying chemical doping or strain to conventional magnets. Our results provide a guidance for design of both even- and odd-parity as well as mixed-parity unconventional magnets in NiAs-type compounds.

cond-mat.mtrl-sci↗

arXiv2020

Learning to Prune in Training via Dynamic Channel Propagation

In this paper, we propose a novel network training mechanism called "dynamic channel propagation" to prune the neural networks during the training period. In particular, we pick up a specific group of channels in each convolutional layer to participate in the forward propagation in training time according to the significance level of channel, which is defined as channel utility. The utility values with respect to all selected channels are updated simultaneously with the error back-propagation process and will adaptively change. Furthermore, when the training ends, channels with high utility values are retained whereas those with low utility values are discarded. Hence, our proposed scheme trains and prunes neural networks simultaneously. We empirically evaluate our novel training scheme on various representative benchmark datasets and advanced convolutional neural network (CNN) architectures, including VGGNet and ResNet. The experiment results verify the superior performance and robust effectiveness of our approach.

cs.CV↗