Effects of orientational and positional randomness of particles on photonic band gap

A recent work [PRL, 126, 208002 (2021)] has explored how thermal noise-induced randomness in a self-assembled photonic crystal affects photonic band gaps (PBGs). For the system of a two-dimensional photonic crystal composed of a self-assembled array of rods with square cross sections, it was found that its PBGs can exist over an extensive range of packing densities. Counterintuitively, at intermediate packing densities, the transverse magnetic (TM) band gap of the self-assembled system can be larger than that of its corresponding perfect system (rods arranged in a perfect square lattice and having identical orientations). Due to shape anisotropicity, the randomness in the self-assembled system contains two kinds of randomness, i.e., positional and orientational randomness of the particles. In this work, we further investigate how PBGs are influenced solely by positional or orientational randomness. We find that compared to the perfect situation, the introduction of only orientational randomness decreases the transverse electric (TE) band gap while having no obvious effects on the transverse magnetic (TM) band gap. In contrast, the introduction of only positional randomness decreases the TE band gap significantly, while it can widen or narrow the TM band gap, depending on the parameter range. We also discuss the thermal (i.e., self-assembled) system where two kinds of randomness are present. Our study contributes to a better understanding of the role orientational randomness and positional randomness play on PBGs, and may benefit the PBG engineering of photonic crystals through self-assembly approaches

Face Alignment Using Boosting and Evolutionary Search

In this paper, we present a face alignment approach using granular features, boosting, and an evolutionary search algorithm. Active Appearance Models (AAM) integrate a shape-texture-combined morphable face model into an efficient fitting strategy, then Boosting Appearance Models (BAM) consider the face alignment problem as a process of maximizing the response from a boosting classifier. Enlightened by AAM and BAM, we present a framework which implements improved boosting classifiers based on more discriminative features and exhaustive search strategies. In this paper, we utilize granular features to replace the conventional rectangular Haar-like features, to improve discriminability, computational efficiency, and a larger search space. At the same time, we adopt the evolutionary search process to solve the deficiency of searching in the large feature space. Finally, we test our approach on a series of challenging data sets, to show the accuracy and efficiency on versatile face images

Spin-dependent gain and loss in photonic quantum spin Hall systems

Topological phases are greatly enriched by including non-Hermiticity. While most works focus on the topology of the eigenvalues and eigenstates, how topologically nontrivial non-Hermitian systems behave in dynamics has only drawn limited attention. Here, we consider a breathing honeycomb lattice known to emulate the quantum spin Hall effect and exhibits higher-order corner modes. We find that non-reciprocal intracell couplings introduce gain in one pseudo-spin subspace while loss with the same magnitude in the other. In addition, non-reciprocal intracell couplings can also suppress the spin mixture of the edge modes at the boundaries and delocalize the higher-order corner mode. Our findings deepen the understanding of non-Hermitian topological phases and bring in the spin degree of freedom in manipulating the dynamics in non-Hermitian systems.Comment: 17 pages, 5 figure

Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is firstly employed to divide the image into meaningful patches (or superpixels) at different levels. Two saliency cues, intensity and compactness, are then proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. The two saliency maps over different cues are fused using a pixel-wise multiplication operator. Leaking regions are finally detected by thresholding the saliency map followed by a graph-cut segmentation. The proposed method has been validated using the only two publicly available datasets: one for malarial retinopathy and the other for diabetic retinopathy. The experimental results show that it outperforms one of the latest competitors and performs as well as a human expert for leakage detection and outperforms several state-of-the-art methods for saliency detection

The suppression of Finite Size Effect within a Few Lattices

Boundary modes localized on the boundaries of a finite-size lattice experience a finite size effect (FSE) that could result in unwanted couplings, crosstalks and formation of gaps even in topological boundary modes. It is commonly believed that the FSE decays exponentially with the size of the system and thus requires many lattices before eventually becoming negligibly small. Here we identify a special type of FSE of some boundary modes that apparently vanishes at some particular wave vectors along the boundary. Meanwhile, the number of wave vectors where the FSE vanishes equals the number of lattices across the strip. We analytically prove this type of FSE in a simple model and prove this peculiar feature. We also provide a physical system consisting of a plasmonic sphere array where this FSE is present. Our work points to the possibility of almost arbitrarily tunning of the FSE, which facilitates unprecedented manipulation of the coupling strength between modes or channels such as the integration of multiple waveguides and photonic non-abelian braiding.Comment: 22 pages, 8 figure

Braiding topology of symmetry-protected degeneracy points in non-Hermitian systems

Degeneracy points in non-Hermitian systems are of great interest. While a framework exists for understanding their behavior in the absence of symmetry, it does not apply to symmetry-protected degeneracy points with reduced codimension. In this work, we investigate the braiding topology and non-abelian conservation rule of these symmetry-protected degenerate points. We find that, contrary to simple annihilation, pairwise created symmetry-protected degeneracy points merge into a higher order degeneracy point, which goes beyond the abelian picture. We verify these findings using a model Hamiltonian and full-wave simulations in an electric circuit system.Comment: 17 pages, 7 figure

Association Between Cerebral Hypoperfusion and Cognitive Impairment in Patients With Chronic Vertebra-Basilar Stenosis

Objective: This study aimed to investigate the association between cognitive impairment and cerebral haemodynamic changes in patients with chronic vertebra-basilar (VB) stenosis.Methods: Patients with severe posterior circulation VB stenosis and infarction or a history of infarction for more than 2 weeks from January 2014 to January 2015 were enrolled (n = 96). They were divided into three groups, namely, the computed tomography perfusion (CTP) normal group, the CTP compensated group, and the CTP decompensated group. Cognitive function was assessed using a validated Chinese version of the Mini-Mental State Examination (MMSE), the Frontal Assessment Battery (FAB), and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Regression models were used to identify independent risk factors for cognitive impairment.Results: The MMSE and FAB scores of patients in the CTP decompensated group were significantly lower than those of patients in the CTP normal and CTP compensated groups (all p < 0.05). The RBANS total and its domain scores, including immediate memory, visual acuity, and delayed memory, in the CTP compensated and CTP decompensated groups were significantly lower than those in the CTP normal group (all p < 0.05). Multiple regression analyses showed that CTP compensation, CTP decompensation, severe VB tandem stenosis, and multiple infarctions were independent risk factors for cognitive impairment.Conclusions: Low perfusion caused by severe VB stenosis can lead to extensive cognitive impairments in areas such as immediate memory, visual span, and delayed memory

On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data

The Multiple-Network Poroelastic Theory (MPET) is a numerical model to characterize the transport of multiple fluid networks in the brain, which overcomes the problem of conducting separate analyses on individual fluid compartments and losing the interactions between tissue and fluids, in addition to the interaction between the different fluids themselves. In this paper, the blood perfusion results from MPET modeling are partially validated using cerebral blood flow (CBF) data obtained from arterial spin labeling (ASL) magnetic resonance imaging (MRI), which uses arterial blood water as an endogenous tracer to measure CBF. Two subjects—one healthy control and one patient with unilateral middle cerebral artery (MCA) stenosis are included in the validation test. The comparison shows several similarities between CBF data from ASL and blood perfusion results from MPET modeling, such as higher blood perfusion in the gray matter than in the white matter, higher perfusion in the periventricular region for both the healthy control and the patient, and asymmetric distribution of blood perfusion for the patient. Although the partial validation is mainly conducted in a qualitative way, it is one important step toward the full validation of the MPET model, which has the potential to be used as a testing bed for hypotheses and new theories in neuroscience research

Increased Cycling Cell Numbers and Stem Cell Associated Proteins as Potential Biomarkers for High Grade Human Papillomavirus+ve Pre-Neoplastic Cervical Disease

High risk (oncogenic) human papillomavirus (HPV) infection causes cervical cancer. Infections are common but most clear naturally. Persistent infection can progress to cancer. Pre-neoplastic disease (cervical intraepithelial neoplasia/CIN) is classified by histology (CIN1-3) according to severity. Cervical abnormalities are screened for by cytology and/or detection of high risk HPV but both methods are imperfect for prediction of which women need treatment. There is a need to understand the host virus interactions that lead to different disease outcomes and to develop biomarker tests for accurate triage of infected women. As cancer is increasingly presumed to develop from proliferative, tumour initiating, cancer stem cells (CSCs), and as other oncogenic viruses induce stem cell associated gene expression, we evaluated whether presence of mRNA (detected by qRT-PCR) or proteins (detected by flow cytometry and antibody based proteomic microarray) from stem cell associated genes and/or increased cell proliferation (detected by flow cytometry) could be detected in well-characterised, routinely collected cervical samples from high risk HPV+ve women. Both cytology and histology results were available for most samples with moderate to high grade abnormality. We found that stem cell associated proteins including human chorionic gonadotropin, the oncogene TP63 and the transcription factor SOX2 were upregulated in samples from women with CIN3 and that the stem cell related, cell surface, protein podocalyxin was detectable on cells in samples from a subset of women with CIN3. SOX2, TP63 and human gonadotrophin mRNAs were upregulated in high grade disease. Immunohistochemistry showed that SOX2 and TP63 proteins clearly delineated tumour cells in invasive squamous cervical cancer. Samples from women with CIN3 showed increased proliferating cells. We believe that these markers may be of use to develop triage tests for women with high grade cervical abnormality to distinguish those who may progress to cancer from those who may be treated more conservatively

Mixed inference machine reading comprehension method based on symbolic logic

With the rapid development of machine learning, challenging question and answer datasets have also emerged, and the machine reading comprehension technology has emerged. Traditional machine reading comprehension methods mostly focus on the understanding word level semantics, with the weak ability to extract logical relationships from text, resulting in the lower ability of logical reasoning. In order to strengthen the ability of machine reading comprehension method to extract the logical relationship of text and the ability of logical reasoning, a neural symbol model based on logical reasoning was proposed, and the logical expressions captured by the neural symbol model were converted into text input and trained in a mixed reasoning reading comprehension model based on symbolic logic. The mixed reasoning reading comprehension model based on symbolic logic is different from the traditional machine reading comprehension model. It uses symbolic definition and logical capture to extract logical symbols and generate logical expressions. The research results show that the accuracy and F-measure values of the neural symbol model based on the logical reasoning are 70.08% and 70.05%, respectively, when the training set sample size is 4000. The accuracy of the mixed reasoning reading comprehension model based on symbolic logic in the logical reasoning data set of the standard postgraduate entrance examination is 88.31%, which is higher than the 58.74% of the language perception map network model. The accuracy rate in the four-choice and one-choice question-and-answer data set is 40.92%, which is 1.58% higher than that of the language awareness graph network model. In summary, the neural symbol model and hybrid inference reading comprehension model proposed in the study have superior performance, which can capture the logical relationship of text in data sets well, improve the model feature abstraction and reasoning ability, effectively shorten the training time and improve the model efficiency