Accurate modelling of the optics of high resolution liquid crystal devices including diffractive effects

An accurate method to model the optical behaviour of liquid crystal (LC) devices, particularly suited to devices where diffractive effects are present is described here. An accurate electromagnetic modelling programme that takes into account the full non-uniformity and anisotropy of the LC has been developed. This is combined with an existing in-house LC finite element modelling programme based on the Landau – De Gennes theory, that uses the order tensor representation of the LC orientation and allows an accurate descriptions of structures containing LC defects and small features. The electromagnetic model is based on the total field/scattered field (TF-SF) approach to electromagnetic scattering problems and is implemented using finite differences in the frequency domain (FDFD) in a form that can accommodate perfectly matched layers (PMLs) and periodic boundary conditions. The resultant matrix problem is solved efficiently using an especially adapted form of a sweeping preconditioner and the generalised minimum residual method (GMRes). This method has been implemented in 2D and is demonstrated here with the design and analysis of a reconfigurable blazed phase grating that utilises an LC defect to produce an abrupt fly-back, with the capability of short periods and high diffraction efficiency

A Study on SPOC: In the Case of English Vocabulary Teaching

Lexical ability of word formation, lexical collocation, grammatical pattern and contextual meaning are crucial to foreign language learners, especially to the English majors of local universities. This study first reviews the previous researches about knowledge of vocabulary and proposes the four elements in depth of English vocabulary knowledge. Second, it reviews some important vocabulary teaching theories and points out that the basis of vocabulary teaching based on SPOC is Connectivism. Third, it explains blended teaching modes based on SPOC. Fourth, it describes a vocabulary teaching experiment based on blended mode of SPOC conducted in English major class of a local university, including experimental process and experimental result analysis. Finally, it concludes that the SPOC-based vocabulary teaching which is very suitable for English majors of local universities is superior to traditional ones, with its significant promoting effect on the improvement of the depth of English majors’ vocabulary knowledge

Robust Fault Detection and Estimation in Nonlinear Systems with Unknown Constant Time-Delays

This paper studies the problem of fault detection and estimation in nonlinear time-delayed systems with unknown inputs, where the time-delays are supposed to be constant but unknown. A new fault detection filter, which can estimate online the time-delays, is first introduced. Then, a reference residual model is proposed to formulate the robust fault detection filter design problem as an H∞ model-matching problem. Furthermore, by a novel robust adaptive fault estimation algorithm, the classical assumption that the time derivative of the output error should be known is removed. In addition, applying a robust H∞ optimization control technique, sufficient conditions for the existence of the fault detection filter (FDF) are derived in terms of linear matrix inequality (LMI). Finally, simulation results are presented to illustrate the effectiveness of the proposed algorithm

Effects of Qi-Fang-Xi-Bi-Granules on Cartilage Morphology and C/ebp α

Objective. To investigate the effects of Qi-Fang-Xi-Bi-Granules (QFXBGs) on cartilage morphology and methylation of C/ebpα (CCAAT/enhancer binding proteinα) at the promoter region. Methods. Knee osteoarthritis (KOA) modeling was performed in rats in accordance with Hulth’s method, and control group received sham operation. Eight weeks after KOA modeling, the rats in the KOA modeling group were further divided into 6 groups. Each group was given the appropriate drug. After 8 weeks, half of the rats were used for Micro-CT scan, HE staining, ABH/OG staining, immunohistochemistry, and TUNNEL staining of the knee joint tissue, and the other half were used to examine C/ebpα promoter methylation. Results. The three dose groups of QFXBGs all showed lower degrees of surface fissures and flaking, thicker cartilage layer, and restored chondrocyte and subchondral bone morphology, compared with the KOA model group. C/ebpα-22 promoter methylation levels in the high- and low-dose groups were significantly higher than that in the KOA modeling group (p<0.05), while C/ebpα-2 promoter methylation level in the medium-dose group was significantly higher than that in the KOA modeling group (p<0.05). Conclusions. QFXBGs may alleviate articular cartilage degeneration through promoting C/ebpα-2 or C/ebpα-22 methylation at specific promoter sites

Multi-omics analysis reveals the mechanism underlying the edaphic adaptation in wild barley at Evolution Slope (Tabigha)

At the microsite “Evolution Slope”, Tabigha, Israel, wild barley (Hordeum spontaneum) populations adapted to dry Terra Rossa soil, and its derivative abutting wild barley population adapted to moist and fungi-rich Basalt soil. However, the mechanisms underlying the edaphic adaptation remain elusive. Accordingly, whole genome bisulfite sequencing, RNA-sequencing, and metabolome analysis are performed on ten wild barley accessions inhabiting Terra Rossa and Basalt soil. A total of 121 433 differentially methylated regions (DMRs) and 10 478 DMR-genes are identified between the two wild barley populations. DMR-genes in CG context (CG-DMR-genes) are enriched in the pathways related with the fundamental processes, and DMR-genes in CHH context (CHH-DMR-genes) are mainly associated with defense response. Transcriptome and metabolome analysis reveal that the primary and secondary metabolisms are more active in Terra Rossa and Basalt wild barley populations, respectively. Multi-omics analysis indicate that sugar metabolism facilitates the adaptation of wild barley to dry Terra Rossa soil, whereas the enhancement of phenylpropanoid/phenolamide biosynthesis is beneficial for wild barley to inhabit moist and fungi pathogen-rich Basalt soil. The current results make a deep insight into edaphic adaptation of wild barley and provide elite genetic and epigenetic resources for developing barley with high abiotic stress tolerance

CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models

Code generation models based on the pre-training and fine-tuning paradigm have been increasingly attempted by both academia and industry, resulting in well-known industrial models such as Codex, CodeGen, and PanGu-Coder. To evaluate the effectiveness of these models, multiple existing benchmarks are proposed, including only cases of generating a standalone function, i.e., a function that may invoke or access only built-in functions and standard libraries. However, non-standalone functions, which typically are not included in the existing benchmarks, constitute more than 70% of the functions in popular open-source projects, and evaluating models' effectiveness on standalone functions cannot reflect these models' effectiveness on pragmatic code generation scenarios. To help bridge the preceding gap, in this paper, we propose a benchmark named CoderEval, consisting of 230 Python and 230 Java code generation tasks carefully curated from popular real-world open-source projects and a self-contained execution platform to automatically assess the functional correctness of generated code. CoderEval supports code generation tasks from six levels of context dependency, where context refers to code elements such as types, APIs, variables, and consts defined outside the function under generation but within the dependent third-party libraries, current class, file, or project. CoderEval can be used to evaluate the effectiveness of models in generating code beyond only standalone functions. By evaluating three code generation models on CoderEval, we find that the effectiveness of these models in generating standalone functions is substantially higher than that in generating non-standalone functions. Our analysis highlights the current progress and pinpoints future directions to further improve a model's effectiveness by leveraging contextual information for pragmatic code generation

Fulminant psittacosis complicated with multiple organ dysfunction syndrome: a case report

The cases of fulminant psittacosis complicated with multiple organ dysfunction syndrome （MODS） have been rarely reported in China. In this article， clinical manifestations and treatment of a patient with fulminant psittacosis complicated with MODS were summarized and analyzed. The 80-year-old male patient developed respiratory failure in Emergency Department and received mechanical ventilation， which rapidly progressed into acute respiratory distress syndrome （ARDS） complicated with MODS. Metagenomic next-generation sequencing （mNGS） using bronchoalveolar lavage fluid （BALF） samples confirmed the pathogen of Chlamydia psittaci. Then， moxifloxacin， doxycycline and omadacycline were given， which yielded favorable efficacy and prognosis. The diagnosis and treatment of this patient suggests that fulminant psittacosis can be manifested with respiratory failure， severe pneumonia with rapid progression and MODS. Imaging manifestations consist of pneumonia， bronchial inflation sign and pleural effusion. mNGS can be performed to identify the rare pathogens during early stage and confirm the diagnosis. Tetracyclines， macrolides and quinolones can be delivered as antibacterial drugs

SPTS v2: Single-Point Scene Text Spotting

End-to-end scene text spotting has made significant progress due to its intrinsic synergy between text detection and recognition. Previous methods commonly regard manual annotations such as horizontal rectangles, rotated rectangles, quadrangles, and polygons as a prerequisite, which are much more expensive than using single-point. For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost single-point annotation by the proposed framework, termed SPTS v2. SPTS v2 reserves the advantage of the auto-regressive Transformer with an Instance Assignment Decoder (IAD) through sequentially predicting the center points of all text instances inside the same predicting sequence, while with a Parallel Recognition Decoder (PRD) for text recognition in parallel. These two decoders share the same parameters and are interactively connected with a simple but effective information transmission process to pass the gradient and information. Comprehensive experiments on various existing benchmark datasets demonstrate the SPTS v2 can outperform previous state-of-the-art single-point text spotters with fewer parameters while achieving 19$\times$ faster inference speed. Most importantly, within the scope of our SPTS v2, extensive experiments further reveal an important phenomenon that single-point serves as the optimal setting for the scene text spotting compared to non-point, rectangular bounding box, and polygonal bounding box. Such an attempt provides a significant opportunity for scene text spotting applications beyond the realms of existing paradigms. Code will be available at https://github.com/bytedance/SPTSv2.Comment: arXiv admin note: text overlap with arXiv:2112.0791

SPTS: Single-Point Text Spotting

Existing scene text spotting (i.e., end-to-end text detection and recognition) methods rely on costly bounding box annotations (e.g., text-line, word-level, or character-level bounding boxes). For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost annotation of a single-point for each instance. We propose an end-to-end scene text spotting method that tackles scene text spotting as a sequence prediction task. Given an image as input, we formulate the desired detection and recognition results as a sequence of discrete tokens and use an auto-regressive Transformer to predict the sequence. The proposed method is simple yet effective, which can achieve state-of-the-art results on widely used benchmarks. Most significantly, we show that the performance is not very sensitive to the positions of the point annotation, meaning that it can be much easier to be annotated or even be automatically generated than the bounding box that requires precise positions. We believe that such a pioneer attempt indicates a significant opportunity for scene text spotting applications of a much larger scale than previously possible. The code will be publicly available

Reconfigurable surfaces using fringing electric fields from nanostructured electrodes in nematic liquid crystals

Liquid crystals with a varying phase profile enable reconfigurable and intelligent devices to be designed, which are capable of manipulating incident electromagnetic fields in display, telecommunications as well as wearable applications. The active control of defects in these devices is becoming more important, especially since the electrodes used to manipulate them are shrinking to nanometer length scales. In this paper, a simple subwavelength, 1D, interdigitated metal electrode structure that can be reconfigured using nematic liquid crystals aligned in the homeotropic, planar, and hybrid methods are demonstrated. Accurate electro‐optic modeling of the directors and the defects are shown, which are induced by the fringing electric fields. Applied voltages result in liquid crystal reorientation near the bottom surface, such that defects are induced between the electrodes. The height of the electrodes does not affect the lateral position of these defects. Rather, this can be achieved by increasing the biasing voltage on the top electrode, which also leads to greater splay‐bend in the bulk of the material. These results therefore aim to generalize the control of defects in complex anisotropic nematic liquid crystals using simple interdigitated structures for a range of reconfigurable intelligent surface applications