High-sensitivity Fiber Bragg grating temperature sensor at high temperature

A method of making full use of the durable strain which fiber Bragg grating (FBG) can undertake is presented, which hugely improves the sensitivities of FBG temperature sensors at high temperature. When a sensor is manufactured at room temperature, its FBG should be given a pre-relaxing length according to the temperature it is asked to measure; once the temperature rise to the asked one, its FBG starts to be stretched and it starts to work with high sensitivity. The relationship between the pre-relaxing length and the working temperature is analyzed. In experiments, when the pre-relaxing lengths are 0.2mm、0.5mm、0.6mm, the working temperatures rise 25℃、50℃、61℃, respectively, and the sensitivities are almost the same (675pm/℃). The facts that the experimental results agree well with the theoretical analyses verify this method’s validity

Valley vortex states and degeneracy lifting via photonic higher-band excitation

We demonstrate valley-dependent vortex generation in a photonic graphene. Without breaking the inversion symmetry, excitation of two equivalent valleys leads to formation of an optical vortex upon Bragg-reflection to the third valley, with its chirality determined by the valley degree of freedom. Vortex-antivortex pairs with valley-dependent topological charge flipping are also observed and corroborated by numerical simulations. Furthermore, we develop a three-band effective Hamiltonian model to describe the dynamics of the coupled valleys, and find that the commonly used two-band model is not sufficient to explain the observed vortex degeneracy lifting. Such valley-polarized vortex states arise from high-band excitation without inversion symmetry breaking or synthetic-field-induced gap opening. Our results from a photonic setting may provide insight for the study of valley contrasting and Berry-phase mediated topological phenomena in other systems

YOLO-FaceV2: A Scale and Occlusion Aware Face Detector

In recent years, face detection algorithms based on deep learning have made great progress. These algorithms can be generally divided into two categories, i.e. two-stage detector like Faster R-CNN and one-stage detector like YOLO. Because of the better balance between accuracy and speed, one-stage detectors have been widely used in many applications. In this paper, we propose a real-time face detector based on the one-stage detector YOLOv5, named YOLO-FaceV2. We design a Receptive Field Enhancement module called RFE to enhance receptive field of small face, and use NWD Loss to make up for the sensitivity of IoU to the location deviation of tiny objects. For face occlusion, we present an attention module named SEAM and introduce Repulsion Loss to solve it. Moreover, we use a weight function Slide to solve the imbalance between easy and hard samples and use the information of the effective receptive field to design the anchor. The experimental results on WiderFace dataset show that our face detector outperforms YOLO and its variants can be find in all easy, medium and hard subsets. Source code in https://github.com/Krasjet-Yu/YOLO-FaceV

Unconventional Flatband Line States in Photonic Lieb Lattices

Flatband systems typically host "compact localized states"(CLS) due to destructive interference and macroscopic degeneracy of Bloch wave functions associated with a dispersionless energy band. Using a photonic Lieb lattice(LL), we show that conventional localized flatband states are inherently incomplete, with the missing modes manifested as extended line states which form non-contractible loops winding around the entire lattice. Experimentally, we develop a continuous-wave laser writing technique to establish a finite-sized photonic LL with specially-tailored boundaries, thereby directly observe the unusually extended flatband line states.Such unconventional line states cannot be expressed as a linear combination of the previously observed CLS but rather arise from the nontrivial real-space topology.The robustness of the line states to imperfect excitation conditions is discussed, and their potential applications are illustrated

Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Quantization of transformer language models faces significant challenges due to the existence of detrimental outliers in activations. We observe that these outliers are asymmetric and concentrated in specific channels. To address this issue, we propose the Outlier Suppression+ framework. First, we introduce channel-wise shifting and scaling operations to eliminate asymmetric presentation and scale down problematic channels. We demonstrate that these operations can be seamlessly migrated into subsequent modules while maintaining equivalence. Second, we quantitatively analyze the optimal values for shifting and scaling, taking into account both the asymmetric property and quantization errors of weights in the next layer. Our lightweight framework can incur minimal performance degradation under static and standard post-training quantization settings. Comprehensive results across various tasks and models reveal that our approach achieves near-floating-point performance on both small models, such as BERT, and large language models (LLMs) including OPTs, BLOOM, and BLOOMZ at 8-bit and 6-bit settings. Furthermore, we establish a new state of the art for 4-bit BERT

Analysis and evaluation of rice grain quality in Indica rice (Oryza sativa L.)

Rice quality is a comprehensive quantitative trait greatly influenced by heredity and environment. Here, 11 rice quality traits and Rapid Visco-Analyser (RVA) profiles of 30 indica rice germplasms were detected and analyzed. In addition, we used grain size genes and starch synthesis gene Wx to detect the rice quality genotypes of rice. The results showed different degrees of correlation among rice quality traits. In addition, principal component analysis (PCA) divided rice quality traits into four principal components, and the cumulative contribution rate reached 82.478%. Cluster analysis divided 30 rice varieties into five categories. The first four types had better rice quality. Identification of rice quality genes indicated that most of the genotypes were GS3, GS9, GW5, GW8 and Wxb, and a few were GW7 and Wxa. Identifying rice quality characteristics and genotypes of rice varieties may lay a theoretical foundation for promoting the cultivation of new rice varieties, enabling breeders andresearchers to develop better rice varieties