Yanfei Wu | Searcharxiv

arXiv2026

Symmetry Origins of the Field-Free Superconducting Diode Effect in the Kagome Superconductor CsV$_3$Sb$_5$

Field-free superconducting diode effects require both inversion-symmetry breaking and an internal time-reversal-symmetry (TRS) breaking field, making them sensitive probes of hidden order in superconductors. In centrosymmetric kagome AV$_3$Sb$_5$, the inversion symmetry generally should generally preclude the observation of the superconducting diode effect. Furthermore, though TRS breaking has been reported in the superconducting regime of CsV$_3$Sb$_5$, whether it is generated by superconductivity or inherited from charge-density-wave (CDW) order remains unresolved. Here we show that pristine CsV$_3$Sb$_5$ devices exhibit no intrinsic field-free superconducting diode effect, whereas surface oxidation or asymmetric etching activates a large nonreciprocal supercurrent. Moreover, the response is stochastic, with sweep-dependent polarity and magnitude, indicating metastable TRS-breaking domain configurations. Small out-of-plane magnetic fields stabilize the superconducting diode response, consistent with field selection of such domains. Finally, when long-range CDW order is suppressed by Ti doping, the SDE disappears. Our results establish the symmetry requirements for the field-free SDE in CsV$_3$Sb$_5$, reveal its stochastic domain-controlled character, and link superconducting-state TRS breaking to CDW-related order.

cond-mat.supr-con↗

arXiv2024

Fire and Smoke Detection with Burning Intensity Representation

An effective Fire and Smoke Detection (FSD) and analysis system is of paramount importance due to the destructive potential of fire disasters. However, many existing FSD methods directly employ generic object detection techniques without considering the transparency of fire and smoke, which leads to imprecise localization and reduces detection performance. To address this issue, a new Attentive Fire and Smoke Detection Model (a-FSDM) is proposed. This model not only retains the robust feature extraction and fusion capabilities of conventional detection algorithms but also redesigns the detection head specifically for transparent targets in FSD, termed the Attentive Transparency Detection Head (ATDH). In addition, Burning Intensity (BI) is introduced as a pivotal feature for fire-related downstream risk assessments in traditional FSD methodologies. Extensive experiments on multiple FSD datasets showcase the effectiveness and versatility of the proposed FSD model. The project is available at \href{https://xiaoyihan6.github.io/FSD/}{https://xiaoyihan6.github.io/FSD/}.

cs.CV↗