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As a typical big data representative, the information of global Cellular Signal Strength (CSS), defined as the signal power received by mobile phones, plays an important role in geographic flow analysis, because the density of such information can reflect the urbanization variables such as population, gross domestic product, built-up area, electric power consumption, and etc. Despite the importance, the real-time analysis of global CSS distribution remains a challenging problem due to the large data scale. In this paper, a Display-driven Computing (DisDC) technique is designed and applied to provide efficient large scale interactive CSS visualization, generating results by calculating the value of each pixel that directly for display that can dramatically improve system capacity in big data handling. Specifically, we present an efficient CSS measurement algorithm, which introduces spatial indexes and a corresponding query strategy; besides, an optimized parallel computing architecture is proposed to ensure the ability of real-time visualization. Experiments show that our approach obviously outperforms traditional methods and is capable of handling more than 40 million base stations in real-time. Moreover, an online demonstration is provided at https://github.com/MemoryMmy/CSSMap.

Knowledge graph is becoming an indispensable resource for numerous AI applications. However, the knowledge graph often suffers from its incompleteness. Building a complete, high-quality knowledge graph is time-consuming and requires significant human annotation efforts. In this paper, we study the Knowledge Graph Population task, which aims at extending the scale of structured knowledge, with a special focus on reducing data preparation and annotation efforts. Previous works mainly based on discriminative methods build classifiers and verify candidate triplets that are extracted from texts, which heavily rely on the quality of data collection and co-occurrance of entities in the text. We introduce a generative perspective to approach this task. A generative model KGGEN is proposed, which samples from the learned data distribution for each relation and can generate triplets regardless of entity pair co-occurrence in the corpus. To further improve the generation quality while alleviate human annotation efforts, adversarial learning is adopted to not only encourage generating high quality triplets, but also give model the ability to automatically assess the generation quality. Quantitative and qualitative experimental results conducted on two real-world generic knowledge graphs show that KGGEN generates novel and meaningful triplets and less human annotation comparing with the state-of-the-art approaches.

A novel approach to achieving ultrafast real-time user localization in optically controlled beam-steering wireless communications (OWC) is proposed and demonstrated in this letter. Ultrafast wavelength encoded user localization at 50 MHz rate is achieved based on photonic time stretch concept and a passive beam scanner using a 45° tilted fibre grating (TFG). Not only the angular separation but also the linear distance from the access point can be determined simultaneously. More importantly, challenging instantaneous optical wavelength identification within nanosecond range is successfully achieved through chirped microwave frequency encoding and reservoir computing (RC) based frequency identification. A proof-of-concept experiment was carried out to verify the proposed approach. The results show that RC is more efficient in frequency chirp identification than the conventional short-time Fourier transform approach.

Purpose This study aims to optimize the structure of gold-sputtered U-shaped plastic fiber sensors. Design/methodology/approach A group of U-shaped Au-sputtered plastic optical fiber sensing probes with polishing angles of 45°, 90° and 135° is prepared. Findings The experimental results show that the spectral response and sensitivity of the sensor at 45°polishing angle is twice that of the sensor at 90°. Research limitations/implications Due to the limitations of laboratory temperature and equipment, the overall effect has not reached the ideal, but the expected effect has been obvious. Experiments also optimize the sensor. Practical implications Optical fiber sensing has always been an indispensable part of various fields. Social implications Sensor optimization is of great help to the progress of technology and the development of science and technology. Originality/value The authors have no conflicts of interest to disclose.

Conventional absorption spectroscopy falls short in complexity and measurement speed. Here, a new type of compressive sensing absorption spectroscopy technique employing a photoelastic modulator (PEM) is presented to address these challenges. The PEM is used to modulate the input spectrum and create multiple harmonic orders as the measurement channels. A sensing matrix is formed by randomly selecting elements from the modulation matrix. The measurement results of different frequency channels are simultaneously captured via a single-pixel photodetector without changing the physical parameters (such as PEM configurations or modulation frequencies). In a measurement example, the first five orders of harmonics are selected, and the input optical spectrum is successfully measured with a compression ratio of 1/16. To further verify the utility of our approach, the infrared spectrum with CO2 absorption is successfully reconstructed with low error.

Introduction The aim of this study was to clarify the efficacy and safety of metabolic surgery in Chinese patients with type 2 diabetes mellitus (T2DM) and a body mass index (BMI) of 27.5–32.5 kg/m2. Methods A total of 99 patients with T2DM were enrolled in this retrospective cohort study. Of these patients, 53 had a BMI of 27.5–32.5 kg/m2 and had undergone metabolic surgery (n = 21) or were on conventional antidiabetic therapy (n = 32)]; 46 had a BMI ≥ 32.5 kg/m2 and all had undergone metabolic surgery. Primary endpoints included the triple endpoint [hemoglobin A1c

Purpose: To investigate the neuroprotective effect of Shenxiong Drop Pill (SXDP) pretreatment on rats with middle cerebral artery occlusion (MCAO) in rats, and the mechanism involved.Methods: Ninety-nine SD rats were randomly assigned to 4 groups: control group, MCAO group, shamoperated group and SXDP group. The MCAO model was established via thread occlusion. Rats in the SXDP group was administered SXDP 7 days before induction of MCAO. Neurological deficit score (NDS) was determined using Bederson's neurological behavioral scoring method, while cerebral infarction volume was measured using TTC staining. Integrated optical density (IOD) of Nissl Body was evaluated via Nissl staining. Brain water content was measured using dry-wet method. The expression level of AQP4 in brain tissues was determined using immunocytochemistry.Results: The SXDP treatment resulted in significant reduction in NDS, marked improvement in IOD of Nissl Body, and significant reductions in cerebral infarction volume, brain water content, and expression level of AQP4, relative to control (p< 0.05).Conclusion: These results suggest that SXDP pretreatment exerts neuroprotective effect against cerebral ischemia in rats by decreasing in cerebral edema through a mechanism involving downregulation of the expression of AQP4. Keywords: Middle cerebral artery occlusion, Cerebral ischemia, Aquaporins-4, Cerebral edema, Neuroprotection

Objective To explore whether the red blood cell count multiplied by hematocrit index (RBCHct) in blood routine parameters can indicate the risk of impaired fasting blood glucose (IFG), and whether it is related to insulin resistance and inflammation. Methods In this cross-sectional study, previous history of diabetes was excluded, and people with normal and impaired IFG were included. We use Spearman analysis to evaluate the correlation between RBCHct index and fasting plasma glucose, insulin resistance homeostasis model assessment (HOMA-IR), and hypersensitive C-reactive protein (hs-CRP). Binary logistic regression analysis was used to evaluate the RBCHct index for assessing the potential risk of IFG, and the receiver operating characteristic (ROC) curve was used to evaluate the RBCHct index for diagnosing insulin resistance and chronic low-grade inflammatory efficacy among those with IFG. Results Correlation analysis showed that the RBCHct index and fasting plasma glucose (r=0.088, P=0.003); HOMA-IR (r=0.199, P

The peripheral nervous system lacks lymphatic vessels and is protected by the blood–nerve barrier, which prevents lymphocytes and antibodies from entering the neural parenchyma. Peripheral nerve injury results in degeneration of the distal nerve and myelin degeneration causes macrophage aggregation, T lymphocyte infiltration, major histocompatibility complex class II antigen expression, and immunoglobulin G deposition in the nerve membrane, which together result in nerve edema and therefore affect nerve regeneration. In the present paper, we show myelin expression was absent from the sciatic nerve at 7 days after injury, and the expression levels of lymphatic vessel endothelial hyaluronan receptor 1 (LYVE‐1) and Prospero Homeobox 1 (Prox1) were significantly increased in the sciatic nerve at 7 days after injury. The lymphatic vessels were distributed around the myelin sheath and co‐localized with lymphatic endothelial cells. Prox1 induces the formation of new lymphatic vessels, which play important roles in the elimination of tissue edema as well as in morphological and functional restoration of the damaged nerve. This study provides evidence of the involvement of new lymphatic vessels in nerve repair after sciatic nerve injury., The peripheral nervous system lacks lymphatic vessels and is protected by the blood–nerve barrier. In the present study, increased expression of lymphatic vessel endothelial hyaluronan receptor 1 (LYVE‐1) and Prospero Homeobox 1 (Prox1) were observed in the sciatic nerve 7 days after injury, suggesting the presence of lymphatic vessels. Therefore, this study provides evidence of new lymphatic vessels involvement (mediated by Prox1) in nerve repair after sciatic nerve injury.

Purpose Diabetic kidney disease (DKD), which is related to inflammation and immune response, is the primary vascular complication of diabetes mellitus and also the leading etiology of end-stage renal disease. Urinary extracellular vesicles (UEVs) are an attractive source for biomarker detection as they involve molecular constituents derived from their parental sections of the nephron. In this study, we aimed to search for a potential biomarker in UEVs for the early diagnosis and prediction of DKD, especially before the emergence of microalbuminuria. Patients and methods UEVs were isolated from the urine of healthy subjects, pre-diabetic, and diabetic patients with varying degrees of kidney damage by ultracentrifugation, and the extracted UEVs were used to measure alpha1-antitrypsin (α1-AT) by Western blot. To explore the function of α1-AT in the inflammatory process leading to DKD, we silenced the expression of α1-AT in renal tubular epithelial cells using cell transfection techniques to assess the differential expression of the inflammatory factors such as MCP-1 and TNF-α using qRT-PCR. Results There was no expression of α1-AT in the UEVs of either healthy or pre-diabetic subjects. Its expression was significantly increased in the UEVs of diabetic patients with normoalbuminuria (prior to microalbuminuria), which was more sensitive and more stable than other renal indexes to predict DKD. Additionally, the expression of α1-AT in UEVs was gradually upregulated with the aggravation of DKD and the decline of renal function. In vitro, the mRNA expression of MCP-1 and TNF-α was significantly decreased when the generation of α1-AT in tubular epithelial cells was inhibited under high glucose stimulation. Conclusion Our results suggest that α1-AT derived from UEVs, especially in diabetic patients with normoalbuminuria, might serve as a potential noninvasive biomarker for diagnosis of DKD early in the development of the disease and may predict the future decline of renal function.

Objective To investigate which plasma lipid parameters are useful for detecting chronic kidney disease (CKD) in a Chinese population without known CKD or renal impairment. Methods This was a prospective study. In southern Chinese cities from 2012 to 2013, a total of 1037 subjects aged ≥ 18 years old received a survey. Logistic regression and multiple linear regression analyses were performed. The lipid parameters studied included total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), non-high-density lipoprotein cholesterol (nHDL-C), TG/HDL-C ratio, TC/HDL-C ratio and nHDL-C/HDL-C ratio. Results After adjusting for confounding factors, the fourth percentile of logTG/HDL-C was observed to be an independent risk factor for CKD (OR = 2.453, P < 0.001), and the highest quantile of the logTG/HDL-C ratio was associated with a higher prevalence of CKD (P < 0.05). This risk was reduced when the model was adjusted with Insulin resistance (IR) (OR = 2.034, P < 0.05). In the group of women, glucose metabolism disorders, high uric acid, and obesity, this risk was increased. Multiple regression models showed that log TG and nonHDL-C/HDL-C were negatively correlated with eGFR (P < 0.05), while log TG and TC were positively correlated with logACR (P < 0.05). The area under the curve (ROC) of lgTG/HDL was 0.623 (p < 0.001). Conclusion The serum logTG/HDL-C ratio is the only suitable predictor of CKD, and IR may be the mechanism. This risk needs to be controlled in a specific population. Log TG and nonHDL-C/HDL-C were negatively correlated with eGFR, while log TG and TC were positively correlated with logACR.

When responding to nuclear and radiological emergencies, rapid and on-site detections of possible internal radioactive contaminants are required for early dose estimation and medical triage. Nasal swab analysis is an effective method to provide valuable information for early and fast estimates of alpha radionuclide inhalation intakes and resultant doses. In this study, to improve the quality of nasal swab measurements, a specialised double-detector alpha counter was designed. Various parameters including swab materials, sample pre-preparation, angle-dependence and vacuum dependence were investigated to optimise the reliability and convenience of the nasal swab method. An improved procedure of direct nasal swab measurement was eventually established, which could be used to obtain early data for initial dose assessment during the first response of nuclear and radiological emergencies.

Purpose To investigate the correlation between visceral obesity and pathogenesis of chronic kidney disease (CKD) among non-diabetic individuals, and to evaluate the potential of visceral adiposity index (VAI) as a predictor of CKD. Patients and Methods From December 2017 to March 2018, 1877 non-diabetic participants (male n=699, female n=1208) in southern China were recruited for a cross-sectional survey. Males and females were divided into four groups according to gender-specific quartiles of VAI scores. A logistic regression model was established to analyze the correlation between visceral adiposity index and CKD. Results Visceral adiposity index was positively correlated with CKD and was negatively associated with estimated glomerular filtration rate (eGFR). Using group one as the control, odds ratios (ORs) were calculated to determine the risk of developing CKD as VAI increased (male: group four 2.73 [P

Guo-bao Hong,1,2 Pei-chun Gao,1,3 Yun-yin Chen,1 Yue Xia,1 Xiao-su Ke,1 Xiao-fei Shao,1 Chong-xiang Xiong,1 Hai-shan Chen,1 Hua Xiao,1 Jing Ning,1 He-qun Zou1 1Department of Nephrology, Institute of Nephrology and Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, People’s Republic of China; 2Department of Nephrology, The Affiliated Nanhai Hospital of Southern Medical University, Foshan 528200, People’s Republic of China; 3School of Public Health, Southern Medical University, Guangzhou, Guangdong 510630, People’s Republic of ChinaCorrespondence: He-qun ZouDepartment of Nephrology, Institute of Nephrology and Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, People’s Republic of ChinaTel +86-20-6278-4391Email hequnzou@hotmail.comPurpose: Metabolic syndrome (MetS), characterized by a constellation of insulin resistance, central obesity, hypertension, and hyperlipidemia, is a global health threat. High-sensitivity C-reactive protein (hs-CRP) has been shown to be associated with type 2 diabetes and cardiovascular disease; however, its association with incident MetS is less known. Therefore, the aim of this study was to examine the prospective association between hs-CRP and MetS among a Chinese population in a 5-year follow-up study.Patients and Methods: The levels of hs-CRP were measured using serum samples collected at baseline recruitment in 2012 from 886 participants without MetS. Follow-up interviews were conducted in 2018, and MetS was diagnosed by 2017 criteria from the Chinese Diabetes Society. Multivariate logistic regression models were used to assess the overall and sex-specific associations between hs-CRP and incident MetS. The odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were computed with adjustment for demographic, socioeconomic, clinical, and lifestyle factors.Results: After a mean follow-up duration of 5.40 ± 0.56 years, 116 (13.3%) participants developed MetS. In the total study population, increased hs-CRP levels were associated with a higher risk of MetS (OR comparing extreme quartiles of hs-CRP: 4.06 [95% CI: 1.91– 8.65]) in the fully-adjusted model. When stratified by sex, the positive association was only observed in women (OR: 4.82 [1.89– 12.3]) but not in men (OR: 3.15 [0.82– 12.1]; P-interaction = 0.039).Conclusion: In this study of a Chinese population, a positive association between hs-CRP and incident MetS was found only in women and not in men. Sex-specific prediction and intervention of MetS using hs-CRP as a target should be further evaluated.Keywords: inflammation, high-sensitivity C-reactive protein, metabolic syndrome, cohort study, follow up

Purpose We compared the efficacy and safety of beinaglutide, a glucagon-like peptide-1 (GLP-1) analogue with metformin in lowering the bodyweight of patients who were overweight/obese and non-diabetic. Patients and Methods Seventy-eight non-diabetic patients were randomly selected and beinaglutide or metformin was administered for 12 weeks. The primary endpoints were changes in body weight and the proportions of patients who lost≥5 and≥10% of their baseline body weights. Results A total of 64 patients completed the study; patients in the beinaglutide group exhibited more bodyweight loss than those in the metformin group [(9.5±0.8%; 9.1±0.9 kg) and (5.1±0.9%; 4.5±0.8 kg), respectively, corresponding to a difference of approximately 4.5 kg (95% confidence interval, 2.2–6.9 kg; P Conclusions Beinaglutide is more efficient than metformin at reducing weight and fat mass in patients who are overweight/obese and non-diabetic. Beinaglutide may be a useful therapeutic option for overweight/obesity control in the Chinese population.

Unmanned aerial vehicles (UAVs) have become an indispensable part of our intelligent society. Accurate UAV image geo-localization is fundamental in many applications such as search and rescue, precision agriculture, change detection. Previously, the UAV image geo-localization was usually calculated by photogrammetry method or by matching with the well-located reference images. The photogrammetry method relies heavily on the accuracy of the UAV positioning parameters, however, the construction and use of high-precision positioning equipment are expensive and difficult to be widely used. The method based on image matching requires good similarity between matching images, which is hard to be satisfied as the reference image and UAV image are heterologous. The existing methods are difficult to achieve accurate image geo-location on UAVs equipped with economical positioning devices. To address this issue, we propose a coarse-to-fine UAV image geo-localization pipeline. Firstly, we implement the coarse UAV image geo-localization by utilizing the UAV’s position parameters. Then, a deep learning algorithm multi-stage Lucas-Kanade (MS-LK) is proposed to refine the UAV image geo-localization results iteratively. MS-LK can make full use of the UAV images’ global texture. We validate the effectiveness of the proposed method on two real UAV data sets. The results show that the proposed UAV image geo-localization method can promote the results of the traditional photogrammetry-based approaches.

Robust zero-watermarking is an effective and distortion-free technique for copyright protection and has become a research hotspot in the field of digital watermarking. In the face of weak robustness and security of existing schemes, this paper presents a novel robust and secure color image zero-watermarking algorithm based on majority voting pattern and hyper-chaotic encryption. In the proposed algorithm, firstly an original color image is decomposed by one-level discrete wavelet transform (DWT) and the corresponding low-frequency components of three channels are partitioned into blocks. Then, to resist strong attacks, we construct a distinguishable robust binary feature matrix extracted from all blocks and color components by using a combination of Frobenius norm in the singular value decomposition (SVD) domain and majority voting pattern. To promote security, a binary copyright logo is confused and diffused by hyper-chaotic Lorenz system. Finally, it performs a bitwise exclusive-or operation on the binary feature matrix and the encrypted copyrighted logo to obtain a zero-watermark signal. Experimental results indicate that in addition to the high-level security, the proposed zero-watermarking algorithm also has a stronger robustness against common geometric and non-geometric attacks, including rotation, scaling, filtering, JPEG compression and noise addition, compared with some existing typical zero-watermarking and traditional watermarking methods.

Purpose Metabolic syndrome (MetS), which is a global public health problem, is a state of chronic low-grade inflammation. This study looked at the changes in hematological parameters and the predictive value of the lymphocyte to high-density lipoprotein cholesterol (HDL-C) ratio (LHR) as a new index in subjects with and without MetS in coastal cities in southern China. Patients and methods In this cross-sectional study, there were 852 participants (n = 598 with MetS and n = 254 without MetS). MetS was defined in accordance with the National Cholesterol Education Program, Adult Treatment Panel III (NCEP-ATP III) criteria. Results MetS was positively correlated with white blood cell count, total lymphocyte count, neutrophil count, red blood cell count, hematocrit, hemoglobin, and high-sensitivity C-reactive protein levels (p

In this paper, we introduce a refractive index (RI) sensor based on the surface plasmon resonance (SPR) effect. A commercial plastic optical fiber (POF) is fabricated with a D-shaped macrobend structure by heat setting and side polishing techniques to expose the core and enhance the RI sensitivity. Gold is sputtered onto the polished surface of the POF, generating a film with a thickness of approximately 10 nm. When the RI of the environment changes, the propagation attenuation in the macrobend section and the SPR spectrum change simultaneously. Therefore, the RI can be determined by the dual mechanism of bending loss and spectral shift. The experimental results show that as the RI of the immersion liquid varies from 1.33 to 1.40, the sensing probe exhibits a bending loss change and a spectral shift. This result indicates that the sensitivities of the probe in regard to bending loss and SPR are 43 dB/RIU and 1570 nm/RIU, respectively.

Online visualization and query of massive geo-spatial data are facing increasing challenges with the explosive growth of location-based spatial datasets. In the practical scenario, online visualization is carried out in a progressive way, namely, a sketchy view map is first presented, and more detailed view maps are produced gradually as the viewport scale goes deeper. One approach is to use the multi-scale spatial index technique. However, it loses the original data attribute and cannot provide spatial statistics information. The paper is to provide an improved index structure, the Geo-Gap tree, which aims to enhance online interactive access to large spatial datasets, as well as enable one to compute statistical attributes like aggregation at the coarse level. Therefore, the first focus of Geo-Gap tree is improving the efficiency of tree building. For this purpose, an adaptive geohash coding is introduced to reduce the computing of neighboring objects. And, this phase can be improved in parallel once objects are partitioned. Compare to Gap tree, the cost of building the Geo-Gap tree can be greatly reduced. The second contribution is to choose data at different level based on sampling so that a sample for each level can be served as a progressive query result. The third contribution is an estimation of progressive query results, which ensure that progressive query accuracy can be controlled within the range of theoretical analysis. With the query continuing to execute, the query results become more and more accurate. The method is now integrated successfully into a high-performance geographic information system called HiGIS.

Geographic meshing system is an essential technology in the digital earth framework and has essential applications in the integration and organization of heterogeneous spatial data, along with corresponding coding method. But current meshing and coding methods show unsatisfying locality and performance. To make an improvement, an adaptive Hilbert-Geohash meshing and coding method called AHG is proposed, which could represent both the location and the approximate size of the coded object directly by the meshing hierarchy and the corresponding coding length. This unique feature helps to accelerate the spatial range query and neighbor query. By simple string operations, many candidate objects that do not meet the query criteria can be quickly filtered out without precise spatial calculation. In addition, AHG code can also support spatial size query by finding objects whose size is within a certain interval quickly, without calculating the precise size of each object, which brings great convenience to spatial size statistics of a massive spatial dataset. Demonstrated by experiments over different types of the spatial dataset in a common PC environment, AHG shows favorable stability and scalability besides its capability in accelerating spatial query. The method is now applied successfully in several spatial query tools in a high-performance geographic information system called HiGIS.

We present DiSA, a Display-driven Spatial Analysis framework for interactive analysis of large-scale geographical vector data. DiSA calculates visualization of analysis results directly using a parallel per-pixel approach with efficient fine-grained spatial indexes. Compared with conventional object-based methods, DiSA can greatly reduce the computational complexity (from O(n) to O(log(n)) in some cases), making it less sensitive to data volumes. Experimental results verify that DiSA can provide analysis of billion-scale spatial objects in milliseconds. We demonstrate DiSA with various application scenarios including raw data exploration, spatial buffer and overlay analysis, and global cellular signal strength analysis. Users can explore 10 millions of spatial objects, adjust algorithm parameters, and always see the results in real-time on a personal computer.

A new low-cost and data-efficient method for high-throughput microfluidic single-cell screening is proposed and demonstrated. Only one low-speed single-pixel photodetector is needed. Morphology-based object classification is achieved using reservoir computing. Our experiment shows that the frames involving the target cell can be recognized distinctly.

Objective The aim of this study was to predict disease-free (DFS) and overall (OS) survival of cancer patients through expression of CUE domain containing 2 (CUEDC2) protein. Methods In this retrospective study, we investigated CUEDC2 expression in 75 serous ovarian cancer tissues and 34 tubal fimbria tissues by immunohistochemistry. Chemoresistance was analyzed using clinical follow-up data. Results CUEDC2 expression scores were 1.35 ± 0.60, 1.54 ± 0.57, 1.78 ± 0.71, and 2.13 ± 0.27 for International Federation of Gynecology and Obstetrics (FIGO) stages I, II, III, and IV tissues, respectively, indicating that CUEDC2 expression increased with stage and that scores differed between patients with early and advanced cancers. We found no differences in CUEDC2 expression for tissues with low, medium, and high differentiation. CUEDC2 expression was unrelated to patient age, pathological grade, or presence or absence of lymph node metastasis, but was related to tumor stage. For CUEDC2-positive patients, median DFS and OS survival were 32.6 and 54.3 months, respectively. For CUEDC2-negative patients, median DFS and OS were 51.9 and 63.5 months, respectively. Expression of CUEDC2 was correlated with DFS but not OS. Conclusion CUEDC2 is highly expressed in ovarian cancer tissues and is related to tumor stage and DFS.

Rapid visualization of large-scale spatial vector data is a long-standing challenge in Geographic Information Science. In existing methods, the computation overheads grow rapidly with data volumes, leading to the incapability of providing real-time visualization for large-scale spatial vector data, even with parallel acceleration technologies. To fill the gap, we present HiVision, a display-driven visualization model for large-scale spatial vector data. Different from traditional data-driven methods, the computing units in HiVision are pixels rather than spatial objects to achieve real-time performance, and efficient spatial-index-based strategies are introduced to estimate the topological relationships between pixels and spatial objects. HiVision can maintain exceedingly good performance regardless of the data volume due to the stable pixel number for display. In addition, an optimized parallel computing architecture is proposed in HiVision to ensure the ability of real-time visualization. Experiments show that our approach outperforms traditional methods in rendering speed and visual effects while dealing with large-scale spatial vector data, and can provide interactive visualization of datasets with billion-scale points/segments/edges in real-time with flexible rendering styles. The HiVision code is open-sourced at https://github.com/MemoryMmy/HiVision with an online demonstration., 19 pages 16 figures

In the big data era, real-time analysis of large-scale spatial data remains a challenging problem since the computational scales of conventional data-driven methods expand rapidly with data volumes. This paper presents display-driven computing (DisDC), a new computing model for interactive analysis of large-scale spatial vector data. The computing units in DisDC are screen pixels for display rather than spatial objects in traditional data-driven methods, and the core task of DisDC is to judge the spatial relationships between pixels and spatial objects. As the number of pixels for display is stable, DisDC is less sensitive to data volumes and can be used to provide real-time data analysis of large-scale spatial vector data. Experiments show that DisDC is capable of handling billion-scale spatial vector data, and an online demonstration is provided (https://github.com/MemoryMmy/DisDC).

Onboard observation task planning plays an essential role in satellite autonomy, which has attracted considerable attention from researchers in recent years. Most of the existing studies solve the satellite onboard observation task planning problem (SOOTP) by searching algorithms. However, the limited computing resources and the changes of onboard condition present a new challenge for these methods. In this paper, we develop a sequential decision-making model and propose a deep learning-based planning method to solve the SOOTP. Instead of generating a short-term or long-term plan in advance, the sequential decision-making model enables the satellite to decide the observation task to execute in real-time. In the deep learning-based planning method, a long short-term memory-based encoding network is designed to extract the features and a classification network is used to make such a decision. In the experiment, we compared our method with the gated recurrent unit network and other three searching algorithms based on five scenarios. The experimental results show that our method can solve problems with 90.3%–93.7% accuracy, 2.19%–3.95% profit gap, and 0.004–0.006 s response time per task, which confirms its feasibility.

Purpose This paper aims to propose a liquid level sensor with a multi-S-bend plastic optical fiber. Design/methodology/approach The principle of liquid sensing used is based on the leakage of higher modes out of the fiber and repeated regeneration in the following bend sections. Therefore, a propagation loss was introduced in every bend section of the fiber with the loss depending on the refractive index of the environment. Findings Therefore, a continue shift in the liquid level can be detected by observing changes in the propagation loss of the fiber. The sensor features compactness and a flexible resolution. Originality/value Compared with the exited ones, the sensor has capability of continue liquid measurement and a greater measurement range.

In this work, a magnetic sensing scheme based on whispering gallery mode (WGM) hollow microbubble resonator (HMR) filled with magnetic fluid (MF) is proposed. The HMR is made by the pressure-assisted arc discharge method on the microcapillary. WGM is excited by evanescent fields in HMR. The quality factor of resonator is 4.02 × 106. Then, the influence of the wall thickness of HMR and the concentration of MF on the sensor is studied both theoretically and experimentally. When MF is infiltrated in the HMR, the refractive index (RI) of the MF increases with the increase of the magnetic field intensity, resulting in the shift of the resonance wavelength. By reducing the wall thickness, the magnetic sensing sensitivity is increased from 2.6 pm/mT to 25.21 pm/mT within the magnetic field intensity range of 2 mT–20 mT. In addition, the increase in the concentration of MF and the clustering phenomenon caused by it have also been observed. This work provides a novel magnetic sensing scheme, and the proposed WGM resonator has great application prospects in optofluidic sensing.

Interstitial pneumonia (IP) is a lethal complication in lymphoma patients undergoing chemotherapy. A total of 2212 consecutive patients diagnosed with lymphoma between 2009 and 2014 were enrolled in the present study. IP was defined as diffuse pulmonary interstitial infiltrate found on computed tomography scans. IP was observed in 106 patients. Of these, 23 patients were excluded from the study. Finally, 83 patients with IP were included in this study. The incidence of IP was 3.9% (7/287) in Hodgkin lymphoma and 2.4% (76/1925) in non-Hodgkin lymphoma (P = 0.210). The median number of chemotherapy cycles before IP was 3. The median time from the cessation of chemotherapy to IP was 17 days. Eighty-two (98.8%) patients recovered after the treatment with glucocorticoids. Sixty-six (79.5%) patients had a delay in chemotherapy, and 14 (16.9%) patients had premature termination of chemotherapy. Sixty-nine patients were re-treated with chemotherapy after remission from IP, of which 22 (31.9%) experienced IP recurrence. The incidence of IP recurrence was significantly higher in patients re-treated with a similar regimen than in those re-treated with an alternative regimen (65.4 vs. 11.6%, P

A side-polished plastic optical fiber (POF) was proposed for liquid’s refractive index (RI) measurement. The POF was polished into an arc shape on the surface of the fiber for increasing its RI sensitivity. The experiment shows that the straight side-polished POFs are not sensitive enough as the RI probes. After the straight side-polished POFs were bent into the U-shaped probes, the sensitivities were markedly increased. By changing the structural parameters, the RI sensing performance of the U-shaped probe was optimized, a sensitivity of 864%/RIU with a resolution of 3.3 × 10−4/RIU in the RI range of 1.33–1.44 was obtained. In addition, the characteristic of the temperature dependence of the sensor was presented. The sensor is a low cost solution for RI sensing applications which has the features of simple structure, compact size, and intensity modulation at visible wavelength.

Road networks play a significant role in modern city management. It is necessary to continually extract current road structure, as it changes rapidly with the development of the city. Due to the success of semantic segmentation based on deep learning in the application of computer vision, extracting road networks from VHR (Very High Resolution) imagery becomes a method of updating geographic databases. The major shortcoming of deep learning methods for road networks extraction is that they need a massive amount of high quality pixel-wise training datasets, which is hard to obtain. Meanwhile, a large amount of different types of VGI (volunteer geographic information) data including road centerline has been accumulated in the past few decades. However, most road centerlines in VGI data lack precise width information and, therefore, cannot be directly applied to conventional supervised deep learning models. In this paper, we propose a novel weakly supervised method to extract road networks from VHR images using only the OSM (OpenStreetMap) road centerline as training data instead of high quality pixel-wise road width label. Large amounts of paired Google Earth images and OSM data are used to validate the approach. The results show that the proposed method can extract road networks from the VHR images both accurately and effectively without using pixel-wise road training data.

As an essential part of geographic information retrieval, gazetteer services are closely related to people's life quality. It is important to construct intelligent gazetteer service and improve its retrieval quality. Considering that the accuracy of document matching in large-scale data scenarios is relatively low and it is difficult to obtain the true intention of user queries, we propose a gazetteer retrieval method which takes into account the spatial relationship semantics. Firstly, the multivariable geo-location retrieval pattern of gazetteer service is defined as the basis for constructing the spatial relationship semantic model. Secondly, a measurement formula is given for calculating attribute similarity and spatial relationship similarity. In particular, we propose a similarity measurement approach that reconstructs geographical name entity so as to adapt the characteristics of non-point objects' spatial relationship. Finally, our proposal is evaluated on real-world datasets against traditional methods. The results reveal that the proposed method can not only enrich user's geo-location retrieval pattern, but also improve the effectiveness and quality of gazetteer services under the background of big data.

The relevance of geographic entities has always been the focus of research on geographic information retrieval, geographic knowledge graph and recommendation systems. Traditional research methods, which use spatial or semantic similarity to calculate the correlation between regions, have certain one-sidedness and limitations. The network topology can clearly represent the relationship between entities. However, semantic relationships are difficult to define, so there are few cases where network-related algorithms are used to solve the relevance of geographic entities. With the development of the Internet, web pages provide people with a huge amount of information, and geographical names as a key element are often ignored by researchers, and the rich semantic information contained in it needs further research. This study attempts to explore the geographic entity relevance of integrated semantics and spatial factors based on textual data from a network perspective. Based on the community mining algorithm, the experiment studies the aggregation characteristics of geographic entities and can find areas that are close to each other and tightly related, which is more satisfied with people's common sense.

In this paper, we propose a novel outlier detection method for robust manifold learning. First, we assume that the input high-dimensional data can be represented by an integration of local linear patches, and each patch consists of samples in the local neighborhood that maintains a linear relationship. Then, we use a hierarchical divisive clustering method to seek maximum linear patches (MLPs) and present a local density based scheme to detect outliers in each MLP. In order to evaluate the performance of outlier detection, we also propose an improved outlier detection evaluation method based on manifold distance, which is suitable for robust manifold learning. Last, we give several experiments to demonstrate the effectiveness of the proposed outlier detection method and evaluation method.

A fast-axis-adjustable photoelastic modulator (Faa-PEM) is developed and studied, and a spectropolarimetric imaging method using the Faa-PEM and an acousto-optic tunable filter is presented. The Faa-PEM modulation retardation amplitude and fast-axis direction of the Faa-PEM can be adjusted by adjusting the two drives with no mechanical rotation. The I, Q, and U of the Stokes parameters of the incident light can be obtained by adjusting the retardation amplitude and fast-axis direction of the Faa-PEM. This measurement method does not require high-frequency components, and thus a general area array detector can realize the measurement. Theoretical simulation analyses show that the relative errors ΔI/I, ΔQ/Q, ΔU/U, and ΔDoLP/DoLP are less than 5.6% and that the deviation error ΔAoLP is less than 0.2° (at I=1.00, Q=0.707, U=0.707, V=0.0, −0.1°≤Δθ0≤0.1°, and −0.05 rad≤Δδ0≤0.05 rad). A prototype was developed and tested. The test results show that the average absolute relative error of the degree of linear polarization is 0.36%, and the average absolute deviation error of the angle of linear polarization is 0.048°. The method has potential application value in spectropolarimetric imaging technology.

Buffer and overlay analysis are fundamental operations which are widely used in Geographic Information Systems (GIS) for resource allocation, land planning, and other relevant fields. Real-time buffer and overlay analysis for large-scale spatial data remains a challenging problem because the computational scales of conventional data-oriented methods expand rapidly with data volumes. In this paper, we present HiBO, a visualization-oriented buffer-overlay analysis model which is less sensitive to data volumes. In HiBO, the core task is to determine the value of pixels for display. Therefore, we introduce an efficient spatial-index-based buffer generation method and an effective set-transformation-based overlay optimization method. Moreover, we propose a fully optimized hybrid-parallel processing architecture to ensure the real-time capability of HiBO. Experiments on real-world datasets show that our approach is capable of handling ten-million-scale spatial data in real time. An online demonstration of HiBO is provided (http://www.higis.org.cn: 8080/hibo).

Accessibility is an important issue in transport geography, land planning and many other related fields. Accessibility problems become computationally demanding when involving high-resolution requirements. In this demonstration, we present an accessibility analysis toolbox with open source code1, named HiAccess, based on a high performance Geographic Information System, which promotes the performance of geocomputation by leveraging the power of high performance computing. HiAccess demonstrates the striking performance of measuring high-resolution (using 100m x 100m grids) accessibility of a city (in total over 250k grids, roads with 232k segments, 40 facilities) in 1 second without preprocessing, while ArcGIS takes nearly 1 hour to achieve a less satisfactory result.

Unmanned aerial vehicles (UAVs) have been increasingly used in earth observation, public safety, military and civilian applications due to its portability, high mobility and flexibility. In some GPS-denied environments, accurate drone position cannot be obtained due to occlusion, multi-path interference and other factors. While understanding and localization the content of the images is vital for earth observation, map revision, multi-source image fusion, disaster relief, smart city and other applications. The progress of computer vision and convolutional neural networks(CNNs) in image processing provide a promising solution to locate UAVs aerial image and mapping to the large-scale reference image. Firstly, key localization techniques based on image retrieval-----image description, image matching and position mapping are summarized considering the characteristics of UAVs aerial images. And then, image localization based on extracting deep semantic features and image localization based on classification method by subdividing areas are recommended. Throughout this paper, we will have an insight into the prospect of the UAVs image localization and the challenges to be faced.

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.

A plastic optical fiber (POF) probe with a macro-bending tapered structure was proposed and optimized for the refractive index (RI) sensing. The principle of operation is based on the macro-bending loss and evanescent wave, which are introduced by the macro-bending tapered structure on the fiber, and modulated by the environmental RI. A heating and drawing method and thermal setting process were used to fabricate a tapered POF and form a stable macro-bending structure for the probe. The probes with cladding and without cladding were fabricated. By changing the taper waist and curvature radius, the sensing performance of the probe was optimized. The highest sensitivity of the probe with cladding reached 937%/RIU in the RI range of 1.33–1.41. By removing the cladding, the RI sensing range was expanded to 1.33–1.45, and the sensitivity was about 800%/RIU, lower than the ones with cladding. In addition, the temperature dependence of the sensor was investigated. The POF probe is a low cost solution for RI sensing applications, which has the features of simple structure, compact, and intensity modulation at visible wavelength.

The temperature influence to a refractive index (RI) sensor based on a macro-bending tapered plastic optical fiber (POF) was investigated experimentally. The total temperature dependence loss (TDLtotal) and total temperature dependence RI deviation (TDRtotal) were measured at different temperature (10–60 °C) over an RI range of 1.33–1.41. The temperature dependence RI deviation of the sensor itself was obtained by subtracting the temperature dependence RI of measured liquid from TDRtotal. Therefore, the influence of temperature variation to the sensor was characterized and corrected.

A multi-point liquid level sensor was proposed in this letter, which is based on a plastic optical fiber with a race-track helical structure. The principle of liquid sensing is based on higher modes leak out and regenerate repeatedly in the bend and straight sections, respectively. Therefore, propagation loss was introduced in the bend sections of the fiber submerged under the liquid. The liquid level shift can be detected by observing alterations in the propagation loss changes. The sensor features compactness and independence from the refractive index of the liquid. The level measurement resolution is flexible and expected to reach the same order of magnitude as the diameter.

Distant1 supervised relation extraction is a commonly adopted technique to extract relations from unstructured text. In distant supervision, an existing database is heuristically aligned to text and the alignment results are processed as labeled data. Comparing to hand-labeled data, distant supervision data is available in large quantity, but contains much more errors. In the past few years, Convolutional Neural Networks (CNNs) are applied to distant relation extraction and achieved remarkable results. However, standard CNN architectures do not reflect the multi-granularity nature of linguistic data, and have difficulties capturing long-term dependencies. We propose an architecture named Multi-Path Convolutional Network to address this problem. Inspired by the Inception module used in image recognition systems, we apply multiple convolution layers with different kernel size in parallel and combine the output with another deeper CNN sub-network. In addition, we utilize two levels of attention to discern valuable information from irrelevant context: a word-level attention to capture important parts of a sentence, and a sentence-level attention to improve classification from multiple sentences. By experiments, we show that our approach achieves outstanding results on the NYT-Freebase dataset and outperforms several existing approaches.

A novel multiple microwave frequency measurement method is proposed. The operational principle is based on stimulated Brillouin scattering (SBS) in few-mode fiber (FMF). Due to the existence of SBS between the different modes of FMF, there are several different Brillouin frequency shifts in the fiber. Thus, a microwave frequency can be measured using an existing technique; even if the difference in the unknown frequencies is twice that of one of the Brillouin frequency shifts, the frequencies can be distinguished from the acquired signal powers of different ports of a mode-division multiplexer.