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Topological Weyl semimetals (TWSs) with pairs of Weyl points and topologically protected Fermi arc states have broadened the classification of topological phases and provide superior platform for study of topological superconductivity. Here we report the nontrivial superconductivity and topological features of sulfur-doped -phase MoTe with enhanced T compared with type-II TWS MoTe It is found that -phase S-doped MoTe (MoTe S , ∼ 0.2) is a two-band -wave bulk superconductor (∼0.13 meV and 0.26 meV), where the superconducting behavior can be explained by the pairing model. Further, measurements of the quasi-particle interference (QPI) patterns and a comparison with band-structure calculations reveal the existence of Fermi arcs in MoTe S More interestingly, a relatively large superconducting gap (∼1.7 meV) is detected by scanning tunneling spectroscopy on the sample surface, showing a hint of topological nontrivial superconductivity based on the pairing of Fermi arc surface states. Our work demonstrates that the -phase MoTe S is not only a promising topological superconductor candidate but also a unique material for study of superconductivity.

Improving photothermal conversion efficiency (PCE) is critical to facilitate therapeutic performance during photothermal therapy (PTT). However, current strategies of prompting PCE always involve complex synthesis or modification of photothermal agents, thereby significantly inhibiting the practical applications and fundamental understanding of photothermal conversion. A facile strategy is herein present for boosting PCE by transforming photothermal agents from aggregated state to dispersed state. Compared to aggregated state, the developed photothermal agents with semiconducting nature can rotate freely in dispersed state, which allows for an efficient nonradiative dissipation through twisted intramolecular charge transfer (TICT) effect, consequentially offering excellent photothermal performance. Noteworthy, the state transformation can be achieved by virtue of releasing photothermal molecules from nanoparticles on the basis of a pH‐responsive polymer nanocarrier, and the PCE is elevated from 43% to 60% upon changing the pH values from 7.4 to 5.0. Moreover, the nanoparticle disassembly and state transformation behaviors can also smoothly proceed in lysosome of cancer cells, demonstrating a distinct photothermal therapeutic performance for cancer ablation. It is hoped that this strategy of transforming state to boost PCE would be a new platform for practical applications of PTT technique.

The better accuracy and efficiency trade-off has been a challenging problem in object detection. In this work, we are dedicated to studying key optimizations and neural network architecture choices for object detection to improve accuracy and efficiency. We investigate the applicability of the anchor-free strategy on lightweight object detection models. We enhance the backbone structure and design the lightweight structure of the neck, which improves the feature extraction ability of the network. We improve label assignment strategy and loss function to make training more stable and efficient. Through these optimizations, we create a new family of real-time object detectors, named PP-PicoDet, which achieves superior performance on object detection for mobile devices. Our models achieve better trade-offs between accuracy and latency compared to other popular models. PicoDet-S with only 0.99M parameters achieves 30.6% mAP, which is an absolute 4.8% improvement in mAP while reducing mobile CPU inference latency by 55% compared to YOLOX-Nano, and is an absolute 7.1% improvement in mAP compared to NanoDet. It reaches 123 FPS (150 FPS using Paddle Lite) on mobile ARM CPU when the input size is 320. PicoDet-L with only 3.3M parameters achieves 40.9% mAP, which is an absolute 3.7% improvement in mAP and 44% faster than YOLOv5s. As shown in Figure 1, our models far outperform the stateof-the-art results for lightweight object detection. Code and pre-trained models are available at PaddleDetection 1 .

Clinical records of traditional Chinese medicine (TCM) are documented by TCM doctors during their routine diagnostic work. These records contain abundant knowledge and reflect the clinical experience of TCM doctors. In recent years, with the modernization of TCM clinical practice, these clinical records have begun to be digitized. Data mining (DM) and machine learning (ML) methods provide an opportunity for researchers to discover TCM regularities buried in the large volume of clinical records. There has been some work on this problem. Existing methods have been validated on a limited amount of manually well-structured data. However, the contents of most fields in the clinical records are unstructured. As a result, the previous methods verified on the well-structured data will not work effectively on the free-text clinical records (FCRs), and the FCRs are, consequently, required to be structured in advance. Manually structuring the large volume of TCM FCRs is time-consuming and labor-intensive, but the development of automatic methods for the structuring task is at an early stage. Therefore, in this paper, symptom name recognition (SNR) in the chief complaints, which is one of the important tasks to structure the FCRs of TCM, is carefully studied. The SNR task is reasonably treated as a sequence labeling problem, and several fundamental and practical problems in the SNR task are studied, such as how to adapt a general sequence labeling strategy for the SNR task according to the domain-specific characteristics of the chief complaints and which sequence classifier is more appropriate to solve the SNR task. To answer these questions, a series of elaborate experiments were performed, and the results are explained in detail.