Electron-doped phosphorene: A potential monolayer superconductor

We predict by first-principles calculations that the electron-doped phosphorene is a potential BCS-like superconductor. The stretching modes at the Brillouin-zone center are remarkably softened by the electron-doping, which results in the strong electron-phonon coupling. The superconductivity can be introduced by a doped electron density ($n_{2D}$) above $1.3 \times10^{14}$ cm$^{-2}$, and may exist over the liquid helium temperature when $n_{2D}>2.6 \times10^{14}$ cm$^{-2}$. The maximum critical temperature is predicted to be higher than 10 K. The superconductivity of phosphorene will significantly broaden the applications of this novel material

Video Anomaly Detection and Explanation via Large Language Models

Video Anomaly Detection (VAD) aims to localize abnormal events on the timeline of long-range surveillance videos. Anomaly-scoring-based methods have been prevailing for years but suffer from the high complexity of thresholding and low explanability of detection results. In this paper, we conduct pioneer research on equipping video-based large language models (VLLMs) in the framework of VAD, making the VAD model free from thresholds and able to explain the reasons for the detected anomalies. We introduce a novel network module Long-Term Context (LTC) to mitigate the incapability of VLLMs in long-range context modeling. We design a three-phase training method to improve the efficiency of fine-tuning VLLMs by substantially minimizing the requirements for VAD data and lowering the costs of annotating instruction-tuning data. Our trained model achieves the top performance on the anomaly videos of the UCF-Crime and TAD benchmarks, with the AUC improvements of +3.86\% and +4.96\%, respectively. More impressively, our approach can provide textual explanations for detected anomalies.Comment: 9 pages, 6 figure