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Using pure Ti and hexagonal-BN powder as raw materials, TiB2–TiN ceramic composites were synthesized by high-pressure method at 5.0 GPa and 1400–1800 °C. The phase composition, microstructure, density and hardness of TiB2–TiN ceramic composites were analyzed. The results shown that when the pressure was constant, the synthesis temperature had an important effect on the phase composition of the samples. When TiB2–TiN ceramic composites synthesized at 1800 °C for 30 min, the relative density reached 96.2% and the maximum Vickers hardness reached 17.9 GPa with the load of 4.9 N. The grain size of the TiB2–TiN ceramic composites changed with the change of synthesis temperature, which resulted in the change of density and hardness.

Saccadic eye movement as a voluntary activity has become the basis of a promising approach for the implementation of human–computer interaction. Seeking blind source separation (BSS) model that can exactly describe eye movement generation and improve the quality of saccadic electrooculography (EOG) signals, we performed a comparative study between an instantaneous and a convolutional mixing model for multichannel EOG signals. Experiments involving seven subjects were carried out in a laboratory environment. The independent component analysis (ICA) method was adopted for BSS. The statistical indicators of the residual statistical dependence and the total square cross correlation for the convolutional model were 0.04 (second order)/0.08 (fourth order) and 1.25, respectively, which were lower than those of the instantaneous model by 0.3 (second order)/0.17 (fourth order) and 0.32, respectively. In addition, the average classification accuracies of the convolutional model were 95.76% (within subject) and 94.08% (between subject) in the case of upward, downward, left, and right saccade tasks, which were higher than those of the instantaneous model by 1.69% and 5.53%, respectively. Experiments revealed that the quality of the saccadic signals separated using the convolutional model was higher than that achieved using the instantaneous model. The outcomes of this research can serve as a valuable reference for multichannel EOG analysis and application.

Most face relighting methods are able to handle diffuse shadows, but struggle to handle hard shadows, such as those cast by the nose. Methods that propose techniques for handling hard shadows often do not produce geometrically consistent shadows since they do not directly leverage the estimated face geometry while synthesizing them. We propose a novel differentiable algorithm for synthesizing hard shadows based on ray tracing, which we incorporate into training our face relighting model. Our proposed algorithm directly utilizes the estimated face geometry to synthesize geometrically consistent hard shadows. We demonstrate through quantitative and qualitative experiments on Multi-PIE and FFHQ that our method produces more geometrically consistent shadows than previous face relighting methods while also achieving state-of-the-art face relighting performance under directional lighting. In addition, we demonstrate that our differentiable hard shadow modeling improves the quality of the estimated face geometry over diffuse shading models., Comment: Accepted to CVPR 2022

Deformable image registration (DIR) can be used to track cardiac motion. Conventional DIR algorithms aim to establish a dense and non-linear correspondence between independent pairs of images. They are, nevertheless, computationally intensive and do not consider temporal dependencies to regulate the estimated motion in a cardiac cycle. In this paper, leveraging deep learning methods, we formulate a novel hierarchical probabilistic model, termed DragNet, for fast and reliable spatio-temporal registration in cine cardiac magnetic resonance (CMR) images and for generating synthetic heart motion sequences. DragNet is a variational inference framework, which takes an image from the sequence in combination with the hidden states of a recurrent neural network (RNN) as inputs to an inference network per time step. As part of this framework, we condition the prior probability of the latent variables on the hidden states of the RNN utilised to capture temporal dependencies. We further condition the posterior of the motion field on a latent variable from hierarchy and features from the moving image. Subsequently, the RNN updates the hidden state variables based on the feature maps of the fixed image and the latent variables. Different from traditional methods, DragNet performs registration on unseen sequences in a forward pass, which significantly expedites the registration process. Besides, DragNet enables generating a large number of realistic synthetic image sequences given only one frame, where the corresponding deformations are also retrieved. The probabilistic framework allows for computing spatio-temporal uncertainties in the estimated motion fields. Our results show that DragNet performance is comparable with state-of-the-art methods in terms of registration accuracy, with the advantage of offering analytical pixel-wise motion uncertainty estimation across a cardiac cycle and being a motion generator. We will make our code publicly available.

Valued for its toughness and durability, Kaihua paper has been famous in China since the Qing Dynasty and is in the focus of the study on paper life extension. Understanding of the complex degradation behavior of handmade paper under various aging conditions is an essential precondition for preparing contemporary long-life handmade paper. However, reactions sequence and quantitative analysis of microstructure of cellulose and its relation to macroscopic deterioration of handmade paper still are difficult to interpret. Herein, different types of Kaihua handmade papers, produced by various raw materials and crafts, were artificially aged to study the different evolution of their multi-scale structures in dry-heat (DH) and wet-heat (WH) conditions systematically. Two-dimensional correlation spectroscopy (2D-COS) distinguished possible carbonyl vibrations involving hydrolysis and oxidation of cellulose and gave sequential changes of various carbonyl groups, illustrating different evolution behaviors of molecular structure of papers in DH and WH aging process. The energy and distance of hydrogen bond, crystal size and microfiber accessibility were quantitatively calculated during the degradation of cellulose. The dual roles of water molecules, as plasticizers for fibers and as carriers of protons, continuously promoting the hydrolysis of cellulose at molecular and supramolecular scales were elucidated further. In addition, correlations between microstructural evolution and macroscopic deterioration of handmade papers involving reduced mechanical properties and yellowing were revealed.

Dense TiB2 TiC composites were synthesized by high pressure method at 5.5 GPa and 1400–1800 °C with pure Ti and B4C powders as raw materials. The phase composition, density, microstructure, hardness and thermal conductivity of TiB2 TiC composites were analyzed. The results indicated that, the formation of intermediate phases were strongly depended on the synthesis temperature. TiB2 TiC composites prepared under high pressure condition had a dense microstructure without obvious pores, which could achieve the bulk density and the relative density about 4.40–4.57 g/cm3 and 95.1%–98.7%. Vickers hardness values was up to 22.1–30.1 GPa with the load of 4.9 N and 20.3–28.9 GPa with the load of 9.8 N. Thermal conductivity of the TiB2 TiC composites was measured to be 51.2–74.5 W/m−1 K−1. The density and grain size of the TiB2 TiC composites changed with the increase of temperature, leading to the change of hardness and thermal conductivity.

Existing face relighting methods often struggle with two problems: maintaining the local facial details of the subject and accurately removing and synthesizing shadows in the relit image, especially hard shadows. We propose a novel deep face relighting method that addresses both problems. Our method learns to predict the ratio (quotient) image between a source image and the target image with the desired lighting, allowing us to relight the image while maintaining the local facial details. During training, our model also learns to accurately modify shadows by using estimated shadow masks to emphasize on the high-contrast shadow borders. Furthermore, we introduce a method to use the shadow mask to estimate the ambient light intensity in an image, and are thus able to leverage multiple datasets during training with different global lighting intensities. With quantitative and qualitative evaluations on the Multi-PIE and FFHQ datasets, we demonstrate that our proposed method faithfully maintains the local facial details of the subject and can accurately handle hard shadows while achieving state-of-the-art face relighting performance., Accepted to CVPR 2021

A simple and rapid colorimetric assay for determination of mercury(II) ion (Hg2+) in aqueous solutions was developed based on aggregation of gold/silver alloy nanocomposites (Au/Ag NCs). Au/Ag NCs were aggregated after the addition of Hg2+ and the positively charged amino acid, lysine. The different aggregation degrees of Au/Ag NCs is indicated by variations in the absorption spectra and accompanied by a color change from orange yellow to yellowish green. Under the optimal conditions, the shift of absorbance ratio (A650/A447) was proportional to Hg2+ concentrations in the range of 0.01–10.0 μM. The limit of detection is 4.8 nM. The proposed colorimetric assay was extremely specific for Hg2+ and other environmentally relevant metal ions did not interfere with the determination.

Recent progresses in visual tracking have greatly improved the tracking performance. However, challenges such as occlusion and view change remain obstacles in real world deployment. A natural solution to these challenges is to use multiple cameras with multiview inputs, though existing systems are mostly limited to specific targets ( e.g. human), static cameras, and/or require camera calibration. To break through these limitations, we propose a generic multiview tracking (GMT) framework that allows camera movement, while requiring neither specific object model nor camera calibration. A key innovation in our framework is a cross-camera trajectory prediction network (TPN), which implicitly and dynamically encodes camera geometric relations, and hence addresses missing target issues such as occlusion. Moreover, during tracking, we assemble information across different cameras to dynamically update a novel collaborative correlation filter (CCF), which is shared among cameras to achieve robustness against view change. The two components are integrated into a correlation filter tracking framework, where features are trained offline using existing single view tracking datasets. For evaluation, we first contribute a new generic multiview tracking dataset (GMTD) with careful annotations, and then run experiments on the GMTD and CAMPUS datasets. The proposed GMT algorithm shows clear advantages in terms of robustness over state-of-the-art ones.

We propose a novel real-time selfie video stabilization method. Our method is completely automatic and runs at 26 fps. We use a 1D linear convolutional network to directly infer the rigid moving least squares warping which implicitly balances between the global rigidity and local flexibility. Our network structure is specifically designed to stabilize the background and foreground at the same time, while providing optional control of stabilization focus (relative importance of foreground vs. background) to the users. To train our network, we collect a selfie video dataset with 1005 videos, which is significantly larger than previous selfie video datasets. We also propose a grid approximation to the rigid moving least squares that enables the real-time frame warping. Our method is fully automatic and produces visually and quantitatively better results than previous real-time general video stabilization methods. Compared to previous offline selfie video methods, our approach produces comparable quality with a speed improvement of orders of magnitude. Our code and selfie video dataset is available at https://github.com/jiy173/selfievideostabilization.

Feature selection is very important for pattern recognition to reduce the dimensions of data and to improve the efficiency of learning algorithms. Recent research on new approaches has focused mostly on improving accuracy and reducing computing time. This paper presents a flexible feature-selection method based on an optimized kernel mutual information (OKMI) approach. Mutual information (MI) has been applied successfully in decision trees to rank variables; its aim is to connect class labels with the distribution of experimental data. The use of MI removes irrelevant features and decreases redundant features. However, MI is usually less robust when the data distribution is not centralized. To overcome this problem, we propose to use the OKMI approach, which combines MI and a kernel function. This approach may be used for feature selection with nonlinear models by defining kernels for feature vectors and class-label vectors. By optimizing the objection equations, we develop a new feature-selection algorithm that combines both MI and kernel learning, we discuss the relationship among various kernel-selection methods. Experiments were conducted to compare the new technique applied to various data sets with other methods, and in each case the OKMI approach performs better than the other methods in terms of feature-classification accuracy and computing time. OKMI method solves the problem of computation complexity in the probability of distribution, and avoids this problem by finding the optimal features at very low computational cost. As a result, the OKMI method with the proposed algorithm is effective and robust over a wide range of real applications on expert systems.

Ning Chen,1 Xiao-yan Yang,1,2 Chang-e Guo,1 Xin-ning Bi,1 Jian-hua Chen,1 Hong-ying Chen,1 Hong-pin Li,1 Hong-ying Lin,1 Yu-jie Zhang1 1School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 2Nanjing Sanhome Pharmaceutical Co., Ltd., Nanjing, China Abstract: Epiberberine (EPI) is a novel and potentially effective therapeutic and preventive agent for diabetes and cardiovascular disease. To evaluate its potential value for drug development, a specific, sensitive and robust high-performance liquid chromatography-tandem mass spectrometry assay for the determination of EPI in rat biological samples was established. This assay was used to study the pharmacokinetics, bioavailability and excretion of EPI in rats after oral administration. In addition, a cocktail method was used to compare the inhibition characteristics of EPI on cytochrome P450 (CYP450) isoforms in human liver microsomes (HLMs) and rat liver microsomes (RLMs). The results demonstrated that EPI was rapidly absorbed and metabolized after oral administration (10, 54 or 81mg/kg) in rats, with Tmax of 0.37–0.42h and T1/2 of 0.49–2.73h. The Cmax and area under the curve values for EPI increased proportionally with the dose, and the oral absolute bioavailability was 14.46%. EPI was excreted mainly in bile and feces, and after its oral administration to rats, EPI was eliminated predominantly by the kidneys. A comparison of the current half-maximal inhibitory concentration and Ki values revealed that EPI demonstrated an obvious inhibitory effect on CYP2C9 and CYP2D6. Furthermore, its effect was stronger in HLM than in RLM, more likely to be a result of noncompetitive inhibition. Keywords: epiberberine, bioavailability, excretion kinetics, CYP inhibition type

In this paper, we present a novel framework to detect line segments in man-made environments. Specifically, we propose to describe junctions, line segments and relationships between them with a simple graph, which is more structured and informative than end-point representation used in existing line segment detection methods. In order to extract a line segment graph from an image, we further introduce the PPGNet, a convolutional neural network that directly infers a graph from an image. We evaluate our method on published benchmarks including York Urban and Wireframe datasets. The results demonstrate that our method achieves satisfactory performance and generalizes well on all the benchmarks. The source code of our work is available at \url{https://github.com/svip-lab/PPGNet}., To appear in CVPR 2019

Following recent technological advances there is a growing interest in building non-intrusive methods that help us communicate with computing devices. In this regard, accurate information from eye is a promising input medium between a user and computing devices. In this paper we propose a method that captures the degree of eye closeness. Although many methods exist for detection of eyelid openness, they are inherently unable to satisfactorily perform in real world applications. Detailed eye state estimation is more important, in extracting meaningful information, than estimating whether eyes are open or closed. However, learning reliable eye state estimator requires accurate annotations which is cost prohibitive. In this work, we leverage synthetic face images which can be generated via computer graphics rendering techniques and automatically annotated with different levels of eye openness. These synthesized training data images, however, have a domain shift from real-world data. To alleviate this issue, we propose a weakly-supervised method which utilizes the accurate annotation from the synthetic data set, to learn accurate degree of eye openness, and the weakly labeled (open or closed) real world eye data set to control the domain shift. We introduce a data set of 1.3M synthetic face images with detail eye openness and eye gaze information, and 21k real-world images with open/closed annotation. The dataset will be released online upon acceptance. Extensive experiments validate the effectiveness of the proposed approach.

Deploying a deep learning model on mobile/IoT devices is a challenging task. The difficulty lies in the trade-off between computation speed and accuracy. A complex deep learning model with high accuracy runs slowly on resource-limited devices, while a light-weight model that runs much faster loses accuracy. In this paper, we propose a novel decomposition method, namely DAC, that is capable of factorizing an ordinary convolutional layer into two layers with much fewer parameters. DAC computes the corresponding weights for the newly generated layers directly from the weights of the original convolutional layer. Thus, no training (or fine-tuning) or any data is needed. The experimental results show that DAC reduces a large number of floating-point operations (FLOPs) while maintaining high accuracy of a pre-trained model. If 2% accuracy drop is acceptable, DAC saves 53% FLOPs of VGG16 image classification model on ImageNet dataset, 29% FLOPS of SSD300 object detection model on PASCAL VOC2007 dataset, and 46% FLOPS of a multi-person pose estimation model on Microsoft COCO dataset. Compared to other existing decomposition methods, DAC achieves better performance., Accepted by IEEE Winter Conference on Applications of Computer Vision (WACV 2019)

The efficient targeting of drugs to tumor cell and subsequent rapid drug release remain primary challenges in the development of nanomedicines for cancer therapy. Here, we constructed a glucose transporter 1 (GLUT1)-targeting and tumor cell microenvironment-sensitive drug release Glucose–PEG–PAMAM-s-s–Camptothecin-Cy7 (GPCC) conjugate to tackle the dilemma. The conjugate was characterized by a small particle size, spherical shape, and glutathione (GSH)-sensitive drug release. In vitro tumor targeting was explored in monolayer (2D) and multilayer tumor spheroid (3D) HepG2 cancer cell models (GLUT1+). The cellular uptake of GPCC was higher than that in the control groups and that in normal L02 cells (GLUT1−), likely due to the conjugated glucose moiety. Moreover, the GPCC conjugate exhibited stronger cytotoxicity, higher S arrest and enhanced apoptosis and necrosis rate in HepG2 cells than control groups but not L02 cells. However, the cytotoxicity of GPCC was lower than that of free CPT, which could be explained by the slower release of CPT from the GPCC compared with free CPT. Additional in vivo tumor targeting experiments demonstrated the superior tumor-targeting ability of the GPCC conjugate, which significantly accumulated in tumor meanwhile minimize in normal tissues compared with control groups. The GPCC conjugate showed better pharmacokinetic properties, enabling a prolonged circulation time and increased camptothecin area under the curve (AUC). These features contributed to better therapeutic efficacy and lower toxicity in H22 hepatocarcinoma tumor-bearing mice. The GLUT1-targeting, GSH-sensitive GPCC conjugate provides an efficient, safe and economic approach for tumor cell targeted drug delivery.

With the outstanding performance in 2014 at the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14), an effective convolutional neural network (CNN) model named GoogLeNet has drawn the attention of the mainstream machine learning field. In this paper we plan to take an insight into the application of the GoogLeNet in the Handwritten Chinese Character Recognition (HCCR) on the database HCL2000 and CASIA-HWDB with several necessary adjustments and also state-of-the-art improvement methods for this end-to-end approach. Through the experiments we have found that the application of the GoogLeNet for the Handwritten Chinese Character Recognition (HCCR) results into significant high accuracy, to be specific more than 99% for the final version, which is encouraging for us to further research.

Background Cervical cancer (CC) is a leading cause of mortality in females, especially in developing countries. The two viral oncoproteins E6 and E7 mediate the oncogenic activities of high-risk human papillomavirus (hrHPV), and hrHPV, especially HPV16 or/and HPV18 (HPV16/18) play critical roles in CC through different pathways. STK31 gene of which the expression has been proven to be regulated by the methylation status of its promoter, is one of the novel cancer/testis (CT) genes and plays important roles in human cancers. Reasearches have indicated that viral infection is correlated to the methylation statuses of some genes. Herein, we detected methylation status of the STK31 gene in cervical tumors and explored its interaction with HPV16 or/and HPV18 (HPV16/18) infection. Methods Bisulfite genomic sequencing PCR (BGS) combined with TA clone, methylation-specific PCR (MSP) were used to analyze methylation statuses of the STK31 gene promoter/exon 1 region in HPV16/18-positive, HPV-negative CC cell lines; ectopically expressed HPV16 E6, -E7, and -E6/E7 CC cells; normal cervical tissues and cervical tumor tissues of different stages. The mRNA and protein expressions of STK31 were detected by RT-PCR and western blotting. Results The STK31 gene promoter/exon 1 was hypomethylated in the HPV16/18-positive cell lines HeLa, SiHa and CaSki, and the mRNA and protein expression were detected. In contrast, the STK31 gene exhibited hypermethylation and silenced expression in the HPV-negative CC cells C33A and HT-3. Compared with the primary HPV-negative CC cell lines, the STK31 methylation was downregulated, and STK31 expression was induced in the HPV16E7/E67 transfected cells. The methylation statuses and expressions of STK31 were verified in the cervical tumor samples at different stages. Additionally, chemotherapy treatment may influence STK31 expression by regulating its methylation status. Conclusions STK31 may be a novel cellular target gene for the HPV16 oncogeneE7. The HPV16 oncogene E7 may affect STK31 expression through a methylation-mediated mechanism. The aberrant methylation of the STK31 promoter/exon 1 region may be a precursor of human cervical carcinogenesis and a potential DNA aberrant methylation biomarker of conditions ranging from precancerous disease to invasive cancer. Electronic supplementary material The online version of this article (doi:10.1186/s12985-016-0515-5) contains supplementary material, which is available to authorized users.

We developed a simple and efficient method for synthesizing Te nanowires from CdTe nanoparticles with ethylenediaminetetraacetic acid disodium salt dehydrate (EDTA) as shape controller under hydrothermal condition. The system could both complete the transformation to Te and reduce the interference of CdTeS by adjusting the concentration of EDTA, which was proved by inductively coupled plasma mass spectrometry, X-ray diffraction patterns, and Raman spectra. It was found that the as-prepared Te nanowires display strong fluorescence emission in the blue-violet region. The nanowires exhibit a pretty good morphology with the average diameter of ca. 30 nm and a length up to micrometer scale. Moreover, a possible transformation mechanism of CdTe nanoparticles into Te nanowires is also discussed.

In this paper, we present a class of compactly supported refinable componentwise constant functions with 2 × 2 dilation matrix A, where A is diagonalizable. A sufficient condition for the compactly supported refinable functions being componentwise constant functions is derived. Furthermore, an iteration algorithm is developed to compute the constant on each component of the functions' support. Finally, two examples are given to illustrate how to use the method proposed to construct the refinable componentwise constant functions. In particular, the second example shows that the componentwise constant function solution of the refinement equation is not locally integrable in R 2 .

A resonance light scattering (RLS) spectrometry for determining DNA based on localized surface plasmon resonance (LSPR) of gold nanorods was developed. The fabrication and characterization of the biosensor utilized gold nanorods as the optical transducer was described. The DNA was determined based on the binding of DNA to gold nanorods by electrostatic adsorption. The RLS intensity of the gold nanorods was enhanced in the presence of DNA. The peaks of the enhanced RLS spectrum were at the wavelength 311 and 581 nm. The binding of DNA to the gold nanorods causes an increase of the RLS intensity that is responsive to the concentration of DNA. Under the optimal conditions, the enhanced RLS intensities are directly proportional to the DNA concentrations in the range of 0.05–0.5 μg/mL. The proposed method was successfully applied to the determination of DNA in the samples and the results were satisfactory.

In this paper, we propose a blind image watermarking algorithm based on the multiband wavelet transformation and the empirical mode decomposition. Unlike the watermark algorithms based on the traditional two-band wavelet transform, where the watermark bits are embedded directly on the wavelet coefficients, in the proposed scheme, we embed the watermark bits in the mean trend of some middle-frequency subimages in the wavelet domain. We further select appropriate dilation factor and filters in the multiband wavelet transform to achieve better performance in terms of perceptually invisibility and the robustness of the watermark. The experimental results show that the proposed blind watermarking scheme is robust against JPEG compression, Gaussian noise, salt and pepper noise, median filtering, and Con-vFilter attacks. The comparison analysis demonstrate that our scheme has better performance than the watermarking schemes reported recently.

This study aims to quantitatively analyze and compute the velocity constant of threecompartment modeling, which is based on 13 N-NH3 PET images of human brain tumors. We selected the parietal lobe, cerebellum, frontal lobe, and the average of these three reference regions to analyze the transfer constant ratio, K3/K4, in three-compartment modeling. The study was based on the results of three-compartment modeling. Data sampling was performed for a left frontal lobe tumor, and simultaneously, data sampling was performed for three reference regions (including the parietal lobe, cerebellum, and right frontal lobe). The dynamic frames were 4×10 s, 7× 20 s, 4×60 s, and 1×480 s. The parietal lobe, cerebellum, frontal lobe and the average of the three reference regions, as determined by the slopes of the fitted curves, are 1.6207, 1.5931, 1.5293, and 1.5803, respectively. The F-test values are 5552.4, 2943.6, 3756.8, and 5650.2, respectively; the average F-test value is the largest. Thus, we determined the K3/K4 as 0.5803; therefore, the transfer constant K4 is 1.7216 times that of K3 for the clinical use of 13 N-NH3 PET tracer for brain tumors. The 13 N-NH3 PET tracer is feasible for clinical use with brain tumors. The transfer constant, K4, is approximately two times of K3 in the threecompartment modeling of the parietal lobe, cerebellum, frontal lobe and the average of the three reference regions. This method is feasible and effective.

Bladder leiomyoma is a rare benign tumor and it could be easily misdiagnosed with many other pelvic diseases, especially obstetrical and gynecological diseases; abdominal, laparoscopic, and transurethral resection of bladder leiomyoma have been reported. Herein, we present a case of bladder leiomyoma misdiagnosed with a vaginal mass preoperatively; the mass was isolated, enucleated from the bladder neck, and removed transvaginally; to the best of our knowledge, this is the first case of intramural leiomyoma of bladder neck that has been enucleated transvaginally only without cystotomy.

Background Ultrasound is considered a reliable, widely available, non-invasive, and inexpensive imaging technique for assessing and detecting the development phases of cancer; both in vivo and ex vivo, and for understanding the effects on cell cycle and viability after ultrasound treatment. Methods Based on the topological continuity characteristics, and that adjacent points or areas represent similar features, we propose a topological penalized convex objective function of sparse coding, to recognize similar cell phases. Results This method introduces new features using a deep learning method of sparse coding with topological continuity characteristics. Large-scale comparison tests demonstrate that the RAW can outperform SIFT GIST and HoG as the input features with this method, achieving higher sensitivity, specificity, F1 score, and accuracy. Conclusions Experimental results show that the proposed topological sparse coding technique is valid and effective for extracting new features, and the proposed system was effective for cell recognition of microscopy images of theMDA-MB-231 cell line. This method allows features from sparse coding learning methods to have topological continuity characteristics, and the RAW features are more applicable for the deep learning of the topological sparse coding method than SIFT GIST and HoG.

Correction for ‘Synthesis and characterization of boron and nitrogen co-doped diamond crystals under high pressure and high temperature conditions’ by Meihua Hu et al., CrystEngComm, 2017, 19, 4571–4575.

In this paper, we consider the asymptotic regularity of Daubechies scaling functions and construct examples ofM-band scaling functions which are both orthonormal and cardinal forMϵ 3.