LEACS: a learnable and efficient active contour model with space-frequency pooling for medical image segmentation.

Diseases can be diagnosed and monitored by extracting regions of interest (ROIs) from medical images. However, accurate and efficient delineation and segmentation of ROIs in medical images remain challenging due to unrefined boundaries, inhomogeneous intensity and limited image acquisition. To overcome these problems, we propose an end-to-end learnable and efficient active contour segmentation model, which integrates a global convex segmentation (GCS) module into a light-weighted encoder-decoder convolutional segmentation network with a multiscale attention module (ED-MSA). The GCS automatically obtains the initialization and corresponding parameters of the curve deformation according to the prediction map generated by the ED-MSA, while provides the refined object boundary prediction for ED-MSA optimization. To provide precise and reliable initial contour for the GCS, we design the space-frequency pooling operation layers in the encoder stage of ED-MSA, which can effectively reduce the number of iterations of the GCS. Beside, we construct ED-MSA using the depth-wise separable convolutional residual module to mitigate the overfitting of the model. The effectiveness of our method is validated on four challenging medical image datasets. Code is here:https://github.com/Yang-fashion/ED-MSA_GCS.

Multilevel resistive switching memory in lead-free double perovskite La[Formula: see text]NiFeO[Formula: see text] films.

Dense and flat La[Formula: see text]NiFeO[Formula: see text] (LNFO) films were fabricated on the indium tin oxide-coated glass (ITO/glass) substrate by sol-gel method. The bipolar resistive switching behavior (BRS) could be maintained in 100 cycles and remained after 30 days, indicating that the LNFO-based RS device owned good memory stability. Surprisingly, the multilevel RS characteristics were firstly observed in the Au/LNFO/ITO/glass device. The high resistance states (HRSs) and low resistance state (LRS) with the maximum ratio of [Formula: see text] 500 could be remained stably in 900 s and 130 cycles, demonstrating the fine retention and endurance ability of this LNFO-based RS device. The BRS behavior of Au/LNFO/ITO/glass devices primarily obeyed the SCLC mechanism controlled by oxygen vacancies (OVs) dispersed in the LNFO layer. Under the external electric field, injected electrons were captured or discharged by OVs during trapping or detrapping process in the LNFO layer. Thus, the resistive state switched between HRS and LRS reversibly. Moreover, the modulation of Schottky-like barrier formed at the Au/LNFO interface was contributed to the resistive states switchover. It was related to the change in OVs located at the dissipative region near the Au/LNFO interface. The multilevel RS ability of LNFO-based devices in this work provides an opportunity for researching deeply on the high density RS memory in lead-free double perovskite oxides-based devices.

Lopsided optical diffraction in a loop electromagnetically induced grating.

We propose a theoretical scheme in a cold rubidium-87 (87Rb) atomic ensemble with a non-Hermitian optical structure, in which a lopsided optical diffraction grating can be realized just with the combination of single spatially periodic modulation and loop-phase. Parity-time (PT) symmetric and parity-time antisymmetric (APT) modulation can be switched by adjusting different relative phases of the applied beams. Both PT symmetry and PT antisymmetry in our system are robust to the amplitudes of coupling fields, which allows optical response to be modulated precisely without symmetry breaking. Our scheme shows some nontrivial optical properties, such as lopsided diffraction, single-order diffraction, asymmetric Dammam-like diffraction, etc. Our work will benefit the development of versatile non-Hermitian/asymmetric optical devices.

Hot Deformation Behavior and Processing Map Considering Strengthening Effect for Al-10.0Zn-3.0Mg-2.8Cu Alloy.

In this paper, a hot processing map that takes into the strengthening effect into account is optimized for the Al-10.0Zn-3.0Mg-2.8Cu alloy, mainly considering the crushing and dissolving behavior of the insoluble phase. The hot deformation experiments were performed by compression testing with strain rates ranging from 0.001 to 1 s-1 and the temperature ranging from 380 to 460 °C. The hot processing map was established at the strain of 0.9. It exhibits that the appropriate hot processing region is located at the temperature from 431 to 456 °C and its strain rate is within 0.004-0.108 s-1. The recrystallization mechanisms and insoluble phase evolution were demonstrated using the real-time EBSD-EDS detection technology for this alloy. It is verified that the work hardening can also be consumed by the coarse insoluble phase refinement with the strain rate increasing from 0.001 to 0.1 s-1, besides the traditional recovery and recrystallization, but the effect of the insoluble phase crushing was weakened when strain rate increased over 0.1 s-1. Better refinement of the insoluble phase was around strain rate in 0.1 s-1, which exhibits adequate dissolving during the solid solution treatment, leading to excellent aging strengthen effects. Finally, the hot processing region was further optimized, so that the strain rate approaches 0.1 s-1 instead of 0.004-0.108 s-1. This will provide a theoretical support for the subsequent deformation of the Al-10.0Zn-3.0Mg-2.8Cu alloy and its' engineering application in aerospace, defense and military fields.

Retrieval and Ranking of Combining Ontology and Content Attributes for Scientific Document.

Traditional mathematical search models retrieve scientific documents only by mathematical expressions and their contexts and do not consider the ontological attributes of scientific documents, which result in gaps between the queries and the retrieval results. To solve this problem, a retrieval and ranking model is constructed that synthesizes the information of mathematical expressions with related texts, and the ontology attributes of scientific documents are extracted to further sort the retrieval results. First, the hesitant fuzzy set of mathematical expressions is constructed by using the characteristics of the hesitant fuzzy set to address the multi-attribute problem of mathematical expression matching; then, the similarity of the mathematical expression context sentence is calculated by using the BiLSTM two-way coding feature, and the retrieval result is obtained by synthesizing the similarity between the mathematical expression and the sentence; finally, considering the ontological attributes of scientific documents, the retrieval results are ranked to obtain the final search results. The MAP_10 value of the mathematical expression retrieval results on the Ntcir-Mathir-Wikipedia-Corpus dataset is 0.815, and the average value of the NDCG@10 of the scientific document ranking results is 0.9; these results prove the effectiveness of the scientific document retrieval and ranking method.

A retrieval and ranking method of mathematical documents based on CA-YOLOv5 and HFS.

In a retrieval system for mathematical documents based on mathematical expressions, the input and matching of mathematical expressions are key steps that affect the system's usability, accessibility and efficiency because of their special attributes. Therefore, this paper mainly focuses on improving the input efficiency and matching accuracy of mathematical expressions. This paper proposes a method for retrieval and ranking of mathematical documents based on CA-YOLOv5 and HFS (hesitation fuzzy set) by utilizing the advantages of CA (coordinate attention) model and YOLOv5 in target detection and the superiority of HFS in multiattribute decision-making. By embedding the CA model into the YOLOv5 network, the mathematical expressions in layout images are extracted and recognized to form mathematical query expressions. These expressions are then analyzed to obtain similarity evaluation features and matched with the candidate mathematical expressions indexed with the same features in a library of mathematical documents by employing the HFS as the similarity evaluation measure. Experiments were performed based on the TFD-ICDAR2019v2 dataset and the NTCIR dataset. The F1-score of the mathematical expression detection result was 76.54%, the MAP (mean average precision) of the mathematical documents retrieval result was 71.73%, and the average nDCG of mathematical documents ranking was 80.89%.

Multi-attribute scientific documents retrieval and ranking model based on GBDT and LR.

Scientific documents contain a large number of mathematical expressions and texts containing mathematical semantics. Simply using mathematical expressions or text to retrieve scientific documents can hardly meet retrieval needs. The real difficulty in retrieving scientific documents is to effectively integrate mathematical expressions and related textual features. Therefore, this study proposes a multi-attribute scientific documents retrieval and ranking model based on GBDT (gradient boosting decision tree) and LR (logistic regression) by integrating the expressions and text contained in scientific documents. First, the similarities of the five attributes are calculated, including mathematical expression symbols, mathematical expression sub-forms, mathematical expression context, scientific document keywords and the frequency of mathematical expressions. Next, the GBDT model is used to discretize and reorganize the five attributes. Finally, the reorganized features are input into the LR model, and the final retrieval and ranking results of scientific documents are obtained. The experiment in this study was carried out on the NTCIR dataset. The average value of the final MAP@20 of the scientific document recall was 81.92%. The average value of the scientific document ranking nDCG@20 was 86.05%.

A Pavement Crack Detection Method Based on Multiscale Attention and HFS.

To solve the problem of low detection accuracy due to the loss of detailed information when extracting pavement crack features in traditional U-shaped networks, a pavement crack detection method based on multiscale attention and hesitant fuzzy set (HFS) is proposed. First, the encoding-decoding structure is used to construct a pavement crack segmentation network, ResNeXt50 is used to extract features in the encoding stage, and a multiscale feature fusion module (MFF) is designed to obtain multiscale context information. Second, in the decoding stage, a high-efficiency dual attention module (EDA) is used to enhance the ability of capturing details of the cracks while suppressing background noise. Finally, the membership degree of the crack is calculated based on the advantages of the HFS in multiattribute decision-making to obtain the similarity of the crack, and the binary image after segmentation is judged by the hesitation fuzzy measure. The experiment was conducted on the public road crack dataset Crack500. In terms of segmentation performance, the evaluation indexes Intersection over Union (IoU), Precision, and Dice coefficients of the proposed network reached 55.56%, 74.26%, and 67.43%, respectively; in terms of classification performance, for transversal and longitudinal cracks, the classification accuracy was 84% ± 0.5%, while the block and the alligator were both 78% ± 0.5%. The experimental results prove that the crack details detected by the proposed method are more abundant, and the image detection effect of complex topological structures and small cracks are better.

Light-activated Multilevel Resistive Switching Storage in Pt/Cs2AgBiBr6/ITO/Glass Devices.

High-density Cs2AgBiBr6 films with uniform grains were prepared by a simple one-step and low-temperature sol-gel method on indium tin oxide (ITO) substrates. An explicit tristate bipolar resistance switching behavior was observed in the Pt/Cs2 AgBiBr6/ITO/glass devices under irradiation of 10 mW/cm2 (445 nm). This behavior was stable over 1200 s. The maximum ratio of the high and low resistance states was about 500. Based on the analysis of electric properties, valence variation and absorption spectra, the resistive switching characteristics were attributed to the trap-controlled space charge-limited current mechanism due to the bromine vacancies in the Cs2AgBiBr6 layer. On the other hand, it is suggested that the ordering of the Schottky-like barrier located at Pt/Cs2AgBiBr6 affects the three-state resistance switching behavior under light irradiation. The ability to adjust the photoelectrical properties of Cs2AgBiBr6-based resistive switching memory devices is a promising strategy to develop high-density memory.

An effective deep network for automatic segmentation of complex lung tumors in CT images.

PURPOSE: Accurate segmentation of complex tumors in lung computed tomography (CT) images is essential to improve the effectiveness and safety of lung cancer treatment. However, the characteristics of heterogeneity, blurred boundaries, and large-area adhesion to tissues with similar gray-scale features always make the segmentation of complex tumors difficult. METHODS: This study proposes an effective deep network for the automatic segmentation of complex lung tumors (CLT-Net). The network architecture uses an encoder-decoder model that combines long and short skip connections and a global attention unit to identify target regions using multiscale semantic information. A boundary-aware loss function integrating Tversky loss and boundary loss based on the level-set calculation is designed to improve the network's ability to perceive boundary positions of difficult-to-segment (DTS) tumors. We use a dynamic weighting strategy to balance the contributions of the two parts of the loss function. RESULTS: The proposed method was verified on a dataset consisting of 502 lung CT images containing DTS tumors. The experiments show that the Dice similarity coefficient and Hausdorff distance metric of the proposed method are improved by 13.2% and 8.5% on average, respectively, compared with state-of-the-art segmentation models. Furthermore, we selected three additional medical image datasets with different modalities to evaluate the proposed model. Compared with mainstream architectures, the Dice similarity coefficient is also improved to a certain extent, which demonstrates the effectiveness of our method for segmenting medical images. CONCLUSIONS: Quantitative and qualitative results show that our method outperforms current mainstream lung tumor segmentation networks in terms of Dice similarity coefficient and Hausdorff distance. Note that the proposed method is not limited to the segmentation of complex lung tumors but also performs in different modalities of medical image segmentation.

Entanglement optimization of filtered output fields in cavity optomechanics.

Output entanglement is a key element in quantum information processing. Here, we show how to obtain optimal entanglement between two filtered output fields in a three-mode optomechanical system. First, we obtain the key analytical expression of optimal time delay between the two filtered output fields, from which we can obtain the optimal coupling for output entanglement without time delay. In this case, our linearized analysis predicts that the entanglement saturates to an optimal value as the optomechanical coupling is increased. Furthermore, we obtain the optimal output entanglement with time delay. These results should be very helpful in conceiving new optomechanical schemes of quantum information processing with their efficiency depending critically on the degree of output entanglement.

All-optical transistor based on Rydberg atom-assisted optomechanical system.

We study the optical response of a double optomechanical cavity system assisted by two Rydberg atoms. The target atom is only coupled with one side cavity by a single cavity mode, and gate one is outside the cavities. It has been realized that a long-range manipulation of optical properties of a hybrid system, by controlling the Rydberg atom decoupled with the optomechanical cavity. Switching on the coupling between atoms and cavity mode, the original spatial inversion symmetry of the double cavity structure has been broken. Combining the controllable optical non-reciprocity with the coherent perfect absorption/transmission/synthesis effect (CPA/CPT/CPS reported by [ X.-B.Yan Opt. Express 22, 4886 (2014)], we put forward the theoretical schemes of an all-optical transistor which contains functions such as a controllable diode, rectifier, and amplifier by controlling a single gate photon.

Intrinsic link of asymmetric reflection and diffraction in non-Hermitian gratings.

Asymmetric reflection in Bragg gratings and asymmetric diffraction in diffraction gratings are both linked to parity-time (PT) symmetry in non-Hermitian optics, but their direct relation has not been examined. To fill this gap, we first consider a PT-symmetric sinusoidal grating to compare the contrast of forward and backward reflectivities and the ratio of ±1-order diffraction efficiencies. Analytical and numerical results show that they change with identical tendencies and peaks at same positions in a wide parameter space, indicating thus an intrinsic link in both PT symmetric and PT broken phases. The underlying physics is found to be that the unbalanced coupling strengths between forward and backward reflected waves are identical to those between 0-order and ±1-order diffracted waves. We then consider a non-Hermitian grating dynamically induced in cold atomic lattices to include higher-order diffractions and corresponding reflections.Full numerical calculations show that the aforementioned findings hold also true in this complicated but practical grating, even in more general non-Hermitian cases beyond the exact PT symmetry.

Cooperative nonlinear grating sensitive to light intensity and photon correlation.

Utilizing dipole blockade of Rydberg excitations, we study an ensemble of stationary atoms driven into the four-level N configuration for achieving a new kind of electromagnetically induced grating in the presence of a traveling-wave and a standing-wave classical control fields. This grating shows cooperative optical nonlinearities as manifested by the sensitivity of output diffraction patterns to input light intensities (photon correlations) of a quantum probe field, promising then an essential opportunity for distinguishing weaker and stronger (bunched and anti-bunched) light fields.

[3D Super-resolution Reconstruction and Visualization of Pulmonary Nodules from CT Image].

The aim of this study was to propose an algorithm for three-dimensional projection onto convex sets (3D POCS) to achieve super resolution reconstruction of 3D lung computer tomography (CT) images, and to introduce multi-resolution mixed display mode to make 3D visualization of pulmonary nodules. Firstly, we built the low resolution 3D images which have spatial displacement in sub pixel level between each other and generate the reference image. Then, we mapped the low resolution images into the high resolution reference image using 3D motion estimation and revised the reference image based on the consistency constraint convex sets to reconstruct the 3D high resolution images iteratively. Finally, we displayed the different resolution images simultaneously. We then estimated the performance of provided method on 5 image sets and compared them with those of 3 interpolation reconstruction methods. The experiments showed that the performance of 3D POCS algorithm was better than that of 3 interpolation reconstruction methods in two aspects, i.e., subjective and objective aspects, and mixed display mode is suitable to the 3D visualization of high resolution of pulmonary nodules.

Sparse group composition for robust left ventricular epicardium segmentation.

Left ventricular (LV) epicardium segmentation in cardiac magnetic resonance images (MRIs) is still a challenging task, where the a-priori knowledge like those that incorporate the heart shape model is usually used to derive reasonable segmentation results. In this paper, we propose a sparse group composition (SGC) approach to model multiple shapes simultaneously, which extends conventional sparsity-based single shape prior modeling to incorporate a-priori spatial constraint information among multiple shapes on-the-fly. Multiple interrelated shapes (shapes of epi- and endo-cardium of myocardium in the case of LV epicardium segmentation) are regarded as a group, and sparse linear composition of training groups is computed to approximate the input group. A framework of iterative procedure of refinement based on SGC and segmentation based on deformation model is utilized for LV epicardium segmentation, in which an improved shape-constraint gradient Chan-Vese model (GCV) acted as deformation model. Compared with the standard sparsity-based single shape prior modeling, the refinement procedure has strong robust for relative gross and not much sparse errors in the input shape and the initial epicardium location can be estimated without complicated landmark detection due to modeling spatial constraint information among multiple shapes effectively. Proposed method was validated on 45 cardiac cine-MR clinical datasets and the results were compared with expert contours. The average perpendicular distance (APD) error of contours is 1.50±0.29mm, and the dice metric (DM) is 0.96±0.01. Compared to the state-of-the-art methods, our proposed approach appealed competitive segmentation performance and improved robustness.

Pulmonary nodule detection in CT images based on shape constraint CV model.

PURPOSE: Accurate detection of pulmonary nodules remains a technical challenge in computer-aided diagnosis systems because some nodules may adhere to the blood vessels or the lung wall, which have low contrast compared to the surrounding tissues. In this paper, the analysis of typical shape features of candidate nodules based on a shape constraint Chan-Vese (CV) model combined with calculation of the number of blood branches adhered to nodule candidates is proposed to reduce false positive (FP) nodules from candidate nodules. METHODS: The proposed scheme consists of three major stages: (1) Segmentation of lung parenchyma from computed tomography images. (2) Extraction of candidate nodules. (3) Reduction of FP nodules. A gray level enhancement combined with a spherical shape enhancement filter is introduced to extract the candidate nodules and their sphere-like contour regions. FPs are removed by analysis of the typical shape features of nodule candidates based on the CV model using spherical constraint and by investigating the number of blood branches adhered to the candidate nodules. The constrained shapes of CV model are automatically achieved from the extracted candidate nodules. RESULTS: The detection performance was evaluated on 127 nodules of 103 cases including three types of challenging nodules, which are juxta-pleural nodules, juxta-vascular nodules, and ground glass opacity nodules. The free-receiver operating characteristic (FROC) curve shows that the proposed method is able to detect 88% of all the nodules in the data set with 4 FPs per case. CONCLUSIONS: Evaluation shows that the authors' method is feasible and effective for detection of three types of nodules in this study.

Coherent perfect absorption, transmission, and synthesis in a double-cavity optomechanical system.

We study a double-cavity optomechanical system in which a movable mirror with perfect reflection is inserted between two fixed mirrors with partial transmission. This optomechanical system is driven from both fixed end mirrors in a symmetric scheme by two strong coupling fields and two weak probe fields. We find that three interesting phenomena: coherent perfect absorption (CPA), coherent perfect transmission (CPT), and coherent perfect synthesis (CPS) can be attained within different parameter regimes. That is, we can make two input probe fields totally absorbed by the movable mirror without yielding any energy output from either end mirror (CPA); make an input probe field transmitted from one end mirror to the other end mirror without suffering any energy loss in the two cavities (CPT); make two input probe fields synthesized into one output probe field after undergoing either a perfect transmission or a perfect reflection (CPS). These interesting phenomena originate from the efficient hybrid coupling of optical and mechanical modes and may be all-optically controlled to realize novel photonic devices in quantum information networks.