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In this paper, we study an energy-efficient computation offloading for vehicular edge computing systems, where multiple roadside units assist vehicular users to offload computation tasks to edge servers. Our goal is to minimize the users’ energy consumption by optimizing user association, data partition, transmit power, and computation resources, subject to the constraints of partial tasks offloading, user latency, maximum transmit power, outage performance, and computation capacity of edge servers. We utilize deep learning for user association to avoid combinatorial complexity, and develop an efficient optimization algorithm to optimize other variables. The resulting algorithm has scalable complexity with convergence guarantee, as confirmed by our theoretical analysis. Simulation results demonstrate that the introduced resource allocation algorithm can significantly reduce the total energy consumption of users.

Many applications such as smart metering and location based services pose strong privacy requirements but achieving privacy protection at the client side is a non-trial problem as payment for the services must be computed by the server at the end of each billing period. In this paper, we propose a privacy preservation and billing scheme termed PPDIR based on delayed information release. PPDIR relies on a novel group signature mechanism and the asymmetric Rabin cryptosystem to protect the privacy of the clients and their requests, to achieve accountability and non-repudiation, and to shift the computational complexity to the server side. It adopts a secret token for anonymity and the token is updated for each client at the beginning of each billing period and securely released only to the server at the end of the billing period. Such a strategy can prevent the server from linking a client’s requests made at different billing periods. It also prevents any adversary from linking any request to any client. Note that the server is able to figure out all requests made by a client within a billing period after receiving the delayed token, which is unavoidable for billing purpose. We prove the security properties of the group signature scheme, and analyze the security strength of PPDIR. Our study indicates that PPDIR can achieve privacy-preservation, confidentiality, non-repudiation, accountability, and other security objectives. We also evaluate the performance of our scheme in terms of communication and computational overheads.

In the area of large-scale graph data representation and semi-supervised learning, deep graph-based convolutional neural networks have been widely applied. However, typical graph convolutional network (GCN) aggregates information of neighbor nodes based on binary neighborhood similarity (adjacency matrix). It treats all neighbor nodes of one node equally, which does not suppress the influence of dissimilar neighbor nodes. In this paper, we investigate GCN based on similarity matrix instead of adjacency matrix of graph nodes. Gaussian heat kernel similarity in Euclidean space is first adopted, which is named EGCN. Then biologically inspired manifold similarity is trained in reproducing kernel Hilbert space (RKHS), based on which a manifold GCN (named MGCN) is proposed for graph data representation and semi-supervised learning with four different kernel types. The proposed method is evaluated with extensive experiments on four benchmark document citation network datasets. The objective function of manifold similarity learning converges very quickly on different datasets using various kernel functions. Compared with state-of-the-art methods, our method is very competitive in terms of graph node recognition accuracy. In particular, the recognition rates of MGCN (Gaussian kernel) and MGCN (Polynomial Kernel) outperform that of typical GCN about 3.8% on Cora dataset, 3.5% on Citeseer dataset, 1.3% on Pubmed dataset and 4% on Cora_ML dataset, respectively. Although the proposed MGCN is relatively simple and easy to implement, it can discover local manifold structure by manifold similarity learning and suppress the influence of dissimilar neighbor nodes, which shows the effectiveness of the proposed MGCN.

This aim of this study was to observe the effect of Yang Yan Qing E Wan (YYQEW) on senescent phenotypes and the expression of β-catenin and p16INK4a in the hydrogen peroxide (H2O2)-induced premature senescence of normal human skin fibroblasts (NHSFs). Primary normal human skin fibroblasts were randomly divided into a normal group, a blank group, a model group, and a YYQEW group. The cells of the model group and the YYQEW group were exposed to 150 μmol/L H2O2 for 2 h. The morphological changes of the cells were analyzed by microscopy and by kits used to estimate the activities of the senescence-associated β-galactosidase (SA-β-gal), reactive oxygen species (ROS), and superoxide dismutase (SOD). The outcomes revealed that dyeing rate proportion of SA-β-gal was 2.78% ± 0.22% in the normal group, 2.83% ± 0.29% in the blank group, 37.58% ± 2.56% in the model group, and 28.39% ± 0.93% in the YYQEW group. The number of SA-β-gal positive cells was thus significantly higher in the model group than in the normal or blank group. There were also fewer SA-β-gal positive cells in the YYQEW group compared with the model group. The expression of ROS and p16INK4a in the model group increased significantly compared with that in the normal or blank groups, while the expression of ROS and p16INK4a in the YYQEW group decreased significantly compared with that in the model group. The expression of SOD and β-catenin in the model group decreased significantly compared with that in the normal or blank group, and the expression of SOD and β-catenin in the YYQEW group increased significantly compared with that in the model group. Overall, it was found that YYQEW was able to delay the senescence of NHSFs induced by H2O2 treatment by alleviating oxidative stress and regulating a number of senescence-related molecules, such as β-catenin and p16INK4a.

With the proliferation of versatile devices and data-consuming services, the quest for spectrum efficiency has led to the merging of three disruptive technologies: millimeter-wave (mmWave) communications, massive MIMO (maMIMO), and non-orthogonal multiple access (NOMA). Emerging wireless networks move toward ultra-dense deployment of massive devices with diverse service demands, which call for efficient spectrum sharing even on mmWave bands. This article studies some key techniques that account for the unique angular selectivity of mmWave maMIMO channels and thus enable re-engineering the spectrum sharing paradigm of NOMA. An overview is provided on research challenges and opportunities related to spectrum and energy efficiency of spectrum-shared NOMA-mmWave systems with maMIMO, with focus on high-performance and low-complexity channel sensing, optimal sensing resource allocation, security and privacy provisioning, and learning- aided real-time system optimization.

Importance Cancer is the main cause of death by disease in children. Children experience the highest incidence of cancer in the first year of life. However, there is no comprehensive registration system for children with tumors in China. Objective To summarize the diagnosis and treatment of infant cancer and analyze the status of standardized diagnosis and management among several treatment centers in Beijing, China, thereby providing evidence to guide further clinical research. Methods From January 1, 2010 to December 31, 2019, patients with newly diagnosed infantile malignant solid tumors were admitted to six large tertiary pediatric solid tumor diagnosis and treatment centers in Beijing. The epidemiology, clinical features, and therapeutic effects of tumors in these patients were analyzed retrospectively. All patients were followed up until March 31, 2020. Results In total, 938 patients were enrolled in this study. There were 530 boys (56.5%) and 408 girls (43.5%); the median age was 6.0 months (range, 0–12.0 months). The three most common tumors were retinoblastoma in 366 patients (39.0%), neuroblastoma in 266 patients (28.4%), hepatoblastoma in 133 patients (14.2%), and central nervous system tumors in 52 patients (5.5%). The estimated 5‐year overall survival rate was 81.3% ± 1.8%, and the 5‐year event‐free survival rate was 71.8% ± 2.9%. The 5‐year overall survival rates of non‐rhabdomyosarcoma soft tissue sarcoma, neuroblastoma, and retinoblastoma were 100%, 88% ± 2.2%, and 86.9% ±2.1%, respectively. The 5‐year event‐free survival rates were 81.1% ± 2.7% for neuroblastoma, 81.6% ± 9.8% for non‐rhabdomyosarcoma soft tissue sarcoma, and 72.7% ± 14.1% for extracranial malignant germ cell tumors. Interpretation The three most common infantile malignant solid tumors were retinoblastoma, neuroblastoma, and hepatoblastoma. Multidisciplinary combined diagnosis and treatment is needed for infantile tumors., Children experience the highest incidence of cancer in the first year of life. We summarized 10‐years data of infantile malignant solid tumors from multiple centers in Beijing, China to provide evidence to support further clinical studies.

Standard plenoptic camera can be used to capture multi-dimensional radiation information of high temperature luminous flame to reconstruct the temperature distribution. In this study, a novel method for reconstructing three-dimensional temperature field is proposed. This method is based on the optical tomography combined with standard plenoptic camera. The flame projection information from different planes is contained in one radiation image. In this model, we introduced the effective concept of the nearest neighbor method in the frequency domain to strip the interference of redundant information in the projection and to realize three-dimensional deconvolution. The flame emission intensity received by the pixels on the charge-coupled device sensor can be obtained according to the optical tomographic model. The temperature distributions of the axisymmetric and non-axisymmetric flames can be reconstructed by solving the mathematical model with the nearest neighbor method. The numerical results show that three-dimensional temperature fields of high temperature luminous flames can be retrieved, proving the validity of the proposed method.

After half a century of development, auxiliary liver transplantation (ALT) technology gradually matured and major indications of ALT have been gradually expanded. This review summarized the history of ALT and introduced indications for ALT which including metabolic liver disease, fulminant hepatic failure, highly sensitized kidney transplantation, prevention of hepatic resection of small hepatic syndrome, etc.; at the same time, the hot issues related to ALT were discussed, including the regulation of hepatic portal blood flow of transplanted liver and residual liver, how to treat the graft liver and remaining liver on second stage. Additionally, the expansion of indications for ALT which included the implementation of ALT for patients with liver cancer and ALT for patients with liver cirrhosis was discussed. It was believed that ALT can greatly alleviate the contradiction of insufficient source of graft liver.

The thermal design of detectors can effectively reduce problems caused by high-power consumption, such as shortened detector life, accuracy reduction, and chip damage. However, the traditional numerical model for the thermal design of detector arrays is difficult to construct and solve accurately, requiring a complex model structure, massive grid division, and considerable computation. In this study, a novel thermal design method, called the unit–array equivalent method (UAEM), is proposed for the thermal design of detector arrays consisting of multiple complex repetitive detector units. The method can simplify the detector unit structure in an array by using an equivalent approach for ensuring model accuracy. The effectiveness of the method is verified through numerical experiments on heat conductivity, surface gas, and surface radiation. Meanwhile, this method is used to study the ALICE Inner Tracking System, and the equivalence and correlation of UAEM are examined. Compared with the model constructed by traditional methods, this method saves 95% of the calculation time. At the same time, the average error between the equivalent model and the experiment is within 0.35 °C. This method solves the contradiction between model accuracy and computation. Thus, an accurate and fast numerical approach for the thermal analysis of detector arrays is provided.

Social networks have gained tremendous popularity recently. Millions of people use social network apps to share precious moments with friends and family. Users are often asked to provide personal information such as name, gender, and address when using social networks. However, as the social network data are collected, analyzed, and re-published at a large scale, personal information might be misused by unauthorized third parties and even attackers. Therefore, extensive research has been carried out to protect the data from privacy violations in social networks. The most popular technique is graph perturbation, which modifies the local topological structure of a social network user (a vertex) via various randomization techniques before the social graph data is published. Nevertheless, graph anonymization may affect the usability of the data as random noises are introduced, decreasing user experience. Therefore, a trade-off between privacy protection and data usability must be sought. In this paper, we employ various graph and application utility metrics to investigate this trade-off. More specifically, we conduct an empirical study by implementing five state-of-the-art anonymization algorithms to analyze the graph and application utilities on a Facebook and a Twitter dataset. Our results indicate that most anonymization algorithms can partially or conditionally preserve the graph and application utilities and any single anonymization algorithm may not always perform well on different datasets. Finally, drawing on the reviewed graph anonymization techniques, we provide a brief overview on future research directions and challenges involved therein.

To observe the effect of collecting and retransfusing autologous platelet rich plasma (aPRP) on the amount of allogeneic blood usage in total arch replacement (Sun's surgery) and the outcomes 30 days after surgery.A prospective, randomized trial.A tertiary university hospital specialized in cardiovascular diseases.The study comprised 120 patients undergoing Sun's surgery for Stanford type A acute aortic dissection.aPRP was harvested before incision and was re-transfused after heparin neutralization for patients in the treatment group.There was no significant difference in preoperative demographic data between the 2 study groups. Intraoperative transfusions of erythrocyte (p = 0.009), plasma (p = 0.017), cryoprecipitate (p = 0.002), and platelets (p0.001) in the treatment group were reduced significantly. In addition, less blood loss was observed in the treatment group (p = 0.002). The durations of postoperative mechanical ventilation (p = 0.029) and hospitalization (p = 0.002) of the treatment group were significantly shorter. There were no statistically significant differences in the length of intensive care unit stay, the incidence of complications, and mortality 30 days after surgery.In total arch replacement (Sun's surgery), collecting and retransfusing aPRP reduced intraoperative transfusions of erythrocyte, plasma, and cryoprecipitate and decreased the duration of postoperative mechanical ventilation and hospitalization. This technique had no significant effect on the incidence of complications and mortality 30 days postoperatively.

Health-related Internet of Things (IoT) devices are becoming more popular in recent years. On the one hand, users can access information of their health conditions more conveniently; on the other hand, they are exposed to new security risks. In this paper, we presented, to the best of our knowledge, the first in-depth security analysis on home-use electroencephalography (EEG) IoT devices. Our key contributions are twofold. First, we reverse-engineered the home-use EEG system framework via which we identified the design and implementation flaws. By exploiting these flaws, we developed two sets of novel easy-to-exploit PoC attacks, which consist of four remote attacks and one proximate attack. In a remote attack, an attacker can steal a user’s brain wave data through a carefully crafted program while in the proximate attack, the attacker can steal a victim’s brain wave data over-the-air without accessing the victim’s device on any sense when he is close to the victim. As a result, all the 156 brain–computer interface (BCI) apps in the NeuroSky App store are vulnerable to the proximate attack. We also discovered that all the 31 free apps in the NeuroSky App store are vulnerable to at least one remote attack. Second, we proposed a novel deep learning model of a joint recurrent convolutional neural network (RCNN) to infer a user’s activities based on the reduced-featured EEG data stolen from the home-use EEG IoT devices, and our evaluation over the real-world EEG data indicates that the inference accuracy of the proposed RCNN is can reach 70.55%.

Recent trends show that the popularity of online social networks (OSNs) has been increasing rapidly. From daily communication sites to online communities, an average person’s daily life has become dependent on these online networks. Hence, it has become evident that protection should be provided to these networks from unwanted intruders. In this paper, we consider the data privacy on OSNs at the network level rather than the user level. This network-level privacy helps us to prevent information leakage to third-party users, such as advertisers. We propose a novel scheme that combines the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We use an enhanced equi-cardinal clustering (ECC) as a way to achieve $k$ -anonymity. We further improve $k$ -anonymity with $l$ -diversity. Our proposed enhanced ECC ensures that there are at least “ $k$ ” users in any given network as well as the attributes in each cluster has at least $l$ -distinct values. We further provide proofs on how the proposed ECC ensures $k$ -anonymity and the maximum information loss. We consider a weighted directed social network graph as an input to our method to consider the existing complexities in a social network. With the help of two real-world data sets, we evaluate this method in terms of privacy and efficiency.

Cooperative jamming is deemed as a promising physical layer-based approach to secure wireless transmissions in the presence of eavesdroppers. In this paper, we investigate cooperative jamming in a two-tier 5G heterogeneous network (HetNet), where the macrobase stations (MBSs) at the macrocell tier are equipped with large-scale antenna arrays to provide space diversity and the local base stations (LBSs) at the local cell tier adopt non-orthogonal multiple access (NOMA) to accommodate dense local users (LUs). In the presence of imperfect channel state information, we propose three robust secrecy transmission algorithms that can be applied to various scenarios with different security requirements. The first algorithm employs robust beamforming (RBA) that aims to optimize the secrecy rate of a marcouser (MU) in a macrocell. The second algorithm provides robust power allocation (RPA) that can optimize the secrecy rate of an LU in a local cell. The third algorithm tackles a robust joint optimization (RJO) problem across tiers that seek the maximum secrecy sum rate of a target MU and a target LU robustly. We employ convex optimization techniques to find feasible solutions to these highly non-convex problems. The numerical results demonstrate that the proposed algorithms are highly effective in improving the secrecy performance of a two-tier HetNet.

With the development of Internet of Things (IoT), heterogeneous sensory data appears everywhere in our lives. Unlike traditional sensory data, heterogeneous sensory data often involves variety modalities of data in one set, so that it is called as the multi-modal sensory data in this paper. The appearance of such data making it possible to monitor more complicated objects and improve monitoring accuracy. However, due to lack of integration model for multi-modal sensory data, most of the existing sensory data management algorithms only consider single modal sensory data, resulting in insufficient utilization of sensory data. Thus, we propose a model for integrating the heterogeneous sensory data generated in a IoT system based on Hidden Markov Process in the paper. The distributed algorithm for constructing such a model is then presented. The integration model can be applied to many applications, while we take the cooperative event detection as an example for illustration. The extensive theoretical analysis and experimental results show that all the proposed algorithms are efficient and effective .

In this paper, we propose a novel hybrid uplink-downlink non-orthogonal multiple access scheme (HUD-NOMA) for coordinated multi-point (CoMP) networks. By using NOMA technology and network coding for effective interference control, the proposed HUD-NOMA scheme improves the transmission rate of edge users. Although there is a slight decrease in signal reception reliability to the users with strong received signals, the sum rate of the whole system is increased. Since the intended interferences are effectively manageable by the legitimate users, the HUD-NOMA scheme is resistant to eavesdroppers. Then, by using a stochastic geometry approach, we further analyze the secrecy outage probability (SOP) of the CoMP network applying the proposed HUD-NOMA. Numerical results verify that our scheme outperforms the existing method in terms of higher transmission rates and lower SOP.

We propose a fully convolutional multi-person pose estimation framework using dynamic instance-aware convolutions, termed FCPose. Different from existing methods, which often require ROI (Region of Interest) operations and/or grouping post-processing, FCPose eliminates the ROIs and grouping post-processing with dynamic instance-aware keypoint estimation heads. The dynamic keypoint heads are conditioned on each instance (person), and can encode the instance concept in the dynamically-generated weights of their filters. Moreover, with the strong representation capacity of dynamic convolutions, the keypoint heads in FCPose are designed to be very compact, resulting in fast inference and making FCPose have almost constant inference time regardless of the number of persons in the image. For example, on the COCO dataset, a real-time version of FCPose using the DLA-34 backbone infers about 4.5x faster than Mask R-CNN (ResNet-101) (41.67 FPS vs. 9.26FPS) while achieving improved performance. FCPose also offers better speed/accuracy trade-off than other state-of-the-art methods. Our experiment results show that FCPose is a simple yet effective multi-person pose estimation framework. Code is available at: https://git.io/AdelaiDet, Comment: Accepted to Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR) 2021. Code is at https://git.io/AdelaiDet

The total organic carbon (TOC) content is a critical parameter for estimating shale oil resources. However, common TOC prediction methods rely on empirical formulas, and their applicability varies widely from region to region. In this study, a novel data-driven Bayesian optimization extreme gradient boosting (XGBoost) model was proposed to predict the TOC content using wireline log data. The lacustrine shale in the Damintun Sag, Bohai Bay Basin, China, was used as a case study. Firstly, correlation analysis was used to analyze the relationship between the well logs and the core-measured TOC data. Based on the degree of correlation, six logging curves reflecting TOC content were selected to construct training dataset for machine learning. Then, the performance of the XGBoost model was tested using K -fold cross-validation, and the hyperparameters of the model were determined using a Bayesian optimization method to improve the search efficiency and reduce the uncertainty caused by the rule of thumb. Next, through the analysis of prediction errors, the coefficient of determination ( R 2 ) of the TOC content predicted by the XGBoost model and the core-measured TOC content reached 0.9135. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 0.63, 0.77, and 12.55%, respectively. In addition, five commonly used methods, namely, Δ log R method, random forest, support vector machine, K -nearest neighbors, and multiple linear regression, were used to predict the TOC content to confirm that the XGBoost model has higher prediction accuracy and better robustness. Finally, the proposed approach was applied to predict the TOC curves of 20 exploration wells in the Damintun Sag. We obtained quantitative contour maps of the TOC content of this block for the first time. The results of this study facilitate the rapid detection of the sweet spots of the lacustrine shale oil.

We present a high-performance method that can achieve mask-level instance segmentation with only bounding-box annotations for training. While this setting has been studied in the literature, here we show significantly stronger performance with a simple design (e.g., dramatically improving previous best reported mask AP of 21.1% in Hsu et al. (2019) to 31.6% on the COCO dataset). Our core idea is to redesign the loss of learning masks in instance segmentation, with no modification to the segmentation network itself. The new loss functions can supervise the mask training without relying on mask annotations. This is made possible with two loss terms, namely, 1) a surrogate term that minimizes the discrepancy between the projections of the ground-truth box and the predicted mask; 2) a pairwise loss that can exploit the prior that proximal pixels with similar colors are very likely to have the same category label. Experiments demonstrate that the redesigned mask loss can yield surprisingly high-quality instance masks with only box annotations. For example, without using any mask annotations, with a ResNet-101 backbone and 3x training schedule, we achieve 33.2% mask AP on COCO test-dev split (vs. 39.1% of the fully supervised counterpart). Our excellent experiment results on COCO and Pascal VOC indicate that our method dramatically narrows the performance gap between weakly and fully supervised instance segmentation. Code is available at: https://git.io/AdelaiDet, Code is available at: https://git.io/AdelaiDet

BackgroundThe recent emergence and rapid global spread of coronavirus disease 2019 (COVID-19) is leading to public health crises worldwide. Alcohol consumption and cigarette smoking (CS) are two known risk factors in many diseases including respiratory infections.MethodsWe performed a multi-center study in the four largest hospitals designated for COVID-19 patients in Wuhan. There are totally 1547 patients diagnosed with COVID-19 enrolled in the study, alcohol consumption and CS history were evaluated among these patients. The epidemiology, laboratory findings and outcomes of patients contracted COVID-19 were further studied.ResultsOur findings indicated that COVID-19 patients with a history of CS tend to have more severe outcomes than non-smoking patients. However, alcohol consumption did not reveal significant effects on neither development of severe illness nor death rates in COVID-19 patients.ConclusionCS is a risk factor for developing severe illness and increasing mortality during the SARS-CoV-2 infection. We believe that our findings will provide a better understanding on the effects of alcohol intake and CS exposure in COVID-19 patients.

With the rise of the artificial intelligence wave, deep learning processors have received extensive attention from academia and industry due to their strong computing power, low power consumption, small area and other architectural advantages. In order to ensure algorithm compatibility, deep learning processors include many precision designs, its verification space is huge. For this reason, based on the traditional hierarchical verification, in the system-level precision verification, a dedicated data set is proposed. The experiment shows that compared with the traditional random data set, dedicated data set can greatly reduce the simulation time while ensuring the completeness of the verification.

BackgroundCavernous hemangioma, as a rare tumor, is difficult to differentiate from retroperitoneal lymphoma and paraganglioma. They are more difficult to excise completely through open surgery and traditional laparoscopic surgery. The study aimed to evaluate the role of DaVinci surgical system in laparoscopic resection of parapelvic cavernous hemangioma.Case presentationA 46-year-old female, who diagnosed as parapelvic cavernous hemangioma accompanying with thrombosis and calcification, was performed laparoscopic resection using DaVinci surgical system under general anesthesia. The patient well recovered without recurrence or spread of the lesion after operation for 3 months as well as hydronephrosis was significantly relieved.ConclusionLaparoscopic resection of parapelvic cavernous hemangioma under the help of DaVinci surgical system was feasible and safe.

Purpose The aim of this study was to compare the efficacy of liver transplantation (LT) and liver resection (LR) for hepatocellular carcinoma (HCC) patients with portal vein tumor thrombus (PVTT) and to investigate risk factors affecting prognosis. Materials and methods A total of 94 HCC patients with PVTT type I (segmental PVTT) and PVTT type II (lobar PVTT) were involved and divided into LR (n=47) and LT groups (n=47). Recurrence-free survival (RFS) and overall survival (OS) were compared before and after inverse probability of treatment weighting (IPTW). Prognostic factors for RFS and OS were explored. Results Two treatment groups were well-balanced using IPTW. In the entire cohort, LT provided a better prognosis than LR. Among patients with PVTT type I, RFS was better with LT (p=0.039); OS was not different significantly between LT and LR (p=0.093). In subgroup analysis of PVTT type I patients with α-fetoprotein (AFP) levels >200 ng/mL, LT elicited significantly longer median RFS (18.0 months vs. 2.1 months, p=0.022) and relatively longer median OS time (23.6 months vs. 9.8 months, p=0.065). Among patients with PVTT type II, no significant differences in RFS and OS were found between LT and LR (p=0.115 and 0.335, respectively). Multivariate analyses showed treatment allocation (LR), tumor size (>5 cm), AFP and aspartate aminotransferase (AST) levels to be risk factors of RFS and treatment allocation (LR), AFP and AST as risk factors for OS. Conclusion LT appeared to afford a better prognosis for HCC with PVTT type I than LR, especially in patients with AFP levels >200 ng/mL.

To date, instance segmentation is dominated by twostage methods, as pioneered by Mask R-CNN. In contrast, one-stage alternatives cannot compete with Mask R-CNN in mask AP, mainly due to the difficulty of compactly representing masks, making the design of one-stage methods very challenging. In this work, we propose a simple singleshot instance segmentation framework, termed mask encoding based instance segmentation (MEInst). Instead of predicting the two-dimensional mask directly, MEInst distills it into a compact and fixed-dimensional representation vector, which allows the instance segmentation task to be incorporated into one-stage bounding-box detectors and results in a simple yet efficient instance segmentation framework. The proposed one-stage MEInst achieves 36.4% in mask AP with single-model (ResNeXt-101-FPN backbone) and single-scale testing on the MS-COCO benchmark. We show that the much simpler and flexible one-stage instance segmentation method, can also achieve competitive performance. This framework can be easily adapted for other instance-level recognition tasks. Code is available at: https://git.io/AdelaiDet, Accepted to Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020

The photothermal characteristics of semi-transparent medium are the basis for describing the transmission process of photothermal radiation. Therefore, the related measurement researches have important application value for the non-destructive testing of high-tech module. In the present work, an infrared thermal wave radar imaging (TWRI) technique combined with sequential quadratic programming (SQP) algorithm was proposed for simultaneous reconstruction of the photothermal property distributions in 2D semi-transparent materials containing defects. The Fourier transform, Hilbert transform, and the Chirp lock-in correlation algorithm were applied and the results of the amplitude and phase information of the temperature signals were compared, so as to identify the position of internal defects, which reduced the number of parameters needed to reconstruct. Then, the SQP algorithm was introduced to simultaneously reconstruct the absorption coefficient and thermal conductivity coefficient distributions in the medium. The TWRI-SQP technique absorbs the advantages of fast identification of defects by TWRI technique and the accurate reconstruction of the photothermal properties of the semi-transparent materials by SQP algorithm. The number of internal defects, geometric dimensioning, shape, and photothermal properties can be reconstructed accurately. The results show that this method is practical and robust to detect the photothermal properties of 2D semi-transparent materials with defects.

Given that the consumption of pure varieties for Paridis Rhizome was increasing and causing a “ripple effect”, i.e. numerous other unofficial Paris species were harvested and traded as the succedaneum of Paridis. The purpose of this study was to establish an effective strategy to distinguish Paris species and trace geographical origins of pure varieties of Paridis samples. A total of 87 batch samples of wild Paris were analyzed by UPLC and FTIR. Among them, 79 batch samples of five species and 40 batch samples of Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz were used to establish pattern recognition models of PLS-DA, SVM and random forest simultaneously, basing on polyphyllin content, FTIR spectra, UPLC chromatograms, low- and mid-level data fusion strategy. Quantitative analysis results revealed that more obvious fluctuation of polyphyllin content was shown in different species sample than that of pure varieties of Paridis collected from various origins. Pattern recognition models showed that compared with polyphyllin content, FTIR spectra, UPLC chromatograms and low-level data fusion method, mid-level data fusion strategy showed a better accuracy (>92%) in PLS-DA model to classify Paridis samples according to botanical and geographical origins. Similar results were obtained in SVM and random forest models. Thus, the classification of these three pattern recognition models were verified each other and demonstrated that mid-level data fusion strategy combined with chemometrics could recognize different Paris species and trace the pure varieties of Paridis geographical origins, correctly.

Hyperoxia (>90% oxygen) is commonly implemented in mechanically ventilated patients. Reports suggest that hyperoxia is directly associated with in-hospital mortality in ventilated patients. Certain studies also show that mortality in women undergoing mechanical ventilation is significantly higher than that in men. Additionally, females are predisposed to certain cardiac electrophysiological risks, including QTc prolongation. In this study, we assessed the impact of hyperoxia in male and female mice (C57BL/6J) at age 8-10 weeks. On completion of either hyperoxia or normoxia exposures, physical, hemodynamic, biochemical, functional, electrophysiological, and molecular assessments were conducted. Hyperoxia-exposed mice lost a significant amount of body mass, compared with normoxia controls, in both sexes. However, while both genders developed brady-arrhythmia after hyperoxia exposure, female mice exhibited significantly reduced heart rates compared with males, with significantly elevated RR intervals. Additionally, 50% mortality was observed in females, whereas no mortality was reported in males. Furthermore, unlike in male mice, we observed no hypertrophy upon hyperoxia exposure in female mice. We reported that both hyperoxia-treated male and female mice exhibit significant hyperdynamic left ventricular ejection fraction, which is marked by % ejection fraction > 70 compared with the normoxia controls. We also noted significant reductions in stroke volume and cardiac output in both mice with hyperoxia. Surface ECG also demonstrated that hyperoxia exposure significantly augments RR, PR, QRS, QTc, and JT intervals in both sexes. Molecular analysis of left ventricular tissue demonstrated dysregulation of potassium ion channels in hyperoxia-treated males and females. In summary, we determined that sex differences are present with 72 hr hyperoxia exposure.

The knocking combustion of n-butanol (bio-butanol) combined with exhaust gas recirculation (EGR) in turbocharge direct injection spark ignition (TISI) engine with high compression ratio is studied. Mainly analyze the influence of EGR rate and composition on knock combustion and the indication effect of unconventional emission of free radicals on knock. The research results show that adding appropriate EGR can reduce the engine knock intensity, but the introduction of a large amount of EGR can cause the knock intensity to increase. When the EGR rate is 8%, the MAPO is less than 0.1 MPa, indicating that knocking combustion does not occur in the cylinder. When the EGR rate is increased to 15%, the MAPO is greater than 0.15 MPa, indicating that knocking combustion is intensified. Knock combustion is mainly concentrated on the intake side, which is related to the flame propagation speed. The addition of EGR can reduce the temperature in the cylinder, mainly because the specific heat capacity of H2O and CO2 in the EGR component is relatively large, which can significantly reduce the temperature of the mixture. The variation of CH2O, CH3CHO and C2H4 can reflect the knock intensity of the mixture in the cylinder. In conclusion, the introduction of EGR into the TISI engine can effectively reduce the knock intensity. The variation of mixture composition can better reflect the knock region and knock intensity.

Paris polyphylla var. yunnanensis, as perennial plants, its quality is closely related to growth period. Different harvest years determine the dry matter accumulation of its medicinal parts and the dynamic accumulation of active ingredients, as well as its economic value and medicinal value. Therefore, it is necessary to establish a systematic evaluation method for the identification and evaluation of P. polyphylla var. yunnanensis with different growth years. Deep learning has a powerful ability in recognition. This study extends it to the identification analysis of medicinal plants from the perspective of spectrum. For the first time, two-dimensional correlation spectroscopy (2DCOS) based on the attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR) combined with residual neural network (Resnet) was used to identify growth years. 525 samples were collected, 4725 2DCOS images were drawn, and the dry matter accumulation in rhizomes of different growth years and different sampling sites were briefly analyzed. The results show that the eight-year-old P. polyphylla var. yunnanensis in Dali has higher economic value and medicinal value. The synchronous 2DCOS models based on ATR-FTIR can realize the identification of growth years with accuracy of 100%. Synchronous 2DCOS are more suitable for the identification of medicinal plants with complex systems. 2DCOS images with different colors and second derivative processing cannot optimize the modeling results. In summary, the method we established is innovative and feasible. It not only solved the identification of growth years, expanded the application field of deep learning, but could also be extended to further research on other medicinal plants.

Boletus edulis (B. edulis) is a well-known edible mushroom species in the world due to its high nutritional values. However, its nutritional value varies greatly depending on geographical origins. This study aimed to discriminate the geographical regions of B. edulis by using a novel digital image method based on two dimensional correlation spectra (2DCOS) or integrative two dimensional correlation spectra (i2DCOS). In our research, 106 fruiting bodies of wild-grown B. edulis mushrooms were collected from 2011 to 2014 in 6 geographical regions. We intercepted 1750-400 cm−1 fingerprint regions from their mid-infrared (MIR) spectra, and converted them into 2DCOS or i2DCOS spectra with matlab2017b. Then, a residual convolutional neural network (ResNet) was established with 95 (90%) spectral images. In our model, the discrimination of geographical regions of the Boletus was using directly synchronous 2DCOS, asynchronous 2DCOS or i2DCOS spectral images instead of data matric from these spectra. In the synchronous 2DCOS spectra model, these 95 samples could be correctly classified as their respective regions with 100% accuracy in the train set and 100% accuracy in the test set, and all 11 (10%) samples of external validation set were discriminated correctly. The results indicated that the synchronous 2DCOS spectra model has good discrimination performance, and the new analytical method in this paper can be used for quality control of food, herb and agricultural products.

In this paper, we present a signcryption scheme called CP_ABSC based on Ciphertext-Policy Attribute Based Encryption (CP_ABE) [ 7 ] to secure the multicast communications in smart grids that require access control, data encryption, and authentication to ensure message integrity and confidentiality. CP_ABSC provides algorithms for key management, signcryption, and designcryption. It can be used to signcrypt a message based on the access rights specified by the message itself. A user can designcrypt a ciphertext if and only if it possesses the attributes required by the access structure of the data. Thus CP_ABSC effectively defines a multicast group based on the access rights of the data specified by the data itself, which differs significantly from the traditional Internet based multicast where the destination group is predetermined and must be known by the data source. CP_ABSC provides collusion attack resistance, message authentication, forgery prevention, and confidentiality. It can be easily applied in smart grids to secure the instructions/commands broadcast from a utility company to multiple smart meters (push-based multicast) and the data retrieved from a smart meter to multiple destinations (pull-based multicast). Compared to CP_ABE, CP_ABSC combines encryption with signature at a lower computational cost for signcryption and a slightly higher cost in designcryption for signature verification. We also consider the adoption of attribute-based signature (ABS), and conclude that CP_ABSC has a much lower computational cost than ABS.

Background Animal models of osteoarthritis (OA), including post-traumatic osteoarthritis and spontaneous osteoarthritis, have been established in many ways. In recent years, there have been many reports in various foreign academic journals, but animal models of post-traumatic osteoarthritis (distinct from spontaneous osteoarthritis) have rarely been established or summarized in these reports. Animal models of post-traumatic osteoarthritis show different characteristics depending on the animal species and modeling methods used, which is why we have written this article. Objective To summarize the research progress and research status of animal models of post-traumatic osteoarthritis. Methods A retrospective review of the animal model of post-traumatic osteoarthritis (OA) was conducted on the basis of reports retrieved from the PubMed database with the keywords for searching “animal model, post-traumatic osteoarthritis (PTOA)” from October 2006 to October 2016 and confided English language. A total of 80 academic articles on the study of animal models of traumatic osteoarthritis were retrieved, and 34 of them were included in this literature review after reading the free full-text of them. Results Different PTOA models based on different modeling methods and different animal species had their own characteristics. Different modeling methods should be selected according to different modeling animals. Conclusions Considering the project funds, experimental objectives and technical conditions, appropriate experimental animal and modeling method should be selected based on synthetic considerations to obtain an appropriate PTOA model and ideal experimental results.

Chronic low-back and lower extremity pain is mainly caused by lumbar disc herniation and radiculitis. Various surgery and nonsurgical modalities, including epidural injections, have been used to treat lumbar disc herniation or radiculitis. Therefore, we conducted this meta-analysis to assess the effects of the 2 interventions in managing various chronic low and lower extremity pain. A systematic literature search was conducted to identify randomized controlled trials, which compared the effect of local anesthetic with or without steroids. The outcomes included pain relief, functional improvement, opioid intake, and therapeutic procedural characteristics. Pooled estimates were calculated using a random-effects or fixed-effects model, depending on the heterogeneity between the included studies. Ten randomized controlled trials (involving 1111 patients) were included in this meta-analysis. The pooled results showed that 41.7% of patients who received local anesthetic with steroid (group 1) and 40.2% of patients who received local anesthetic alone (group 2) had significant improvement in pain relief, and the Numeric Rating Scale pain scales were significantly reduced by 4.09 scores [95% confidence interval (CI), -4.26 to -3.91] and 4.12 (95% CI, -4.35 to -3.89) scores, respectively. Similarly, 39.8% of patients in group 1 and 40.7% in group 2 achieved significantly improved functional status. The Oswestry Disability Indices in the 2 groups were reduced by 14.5 (95% CI, -15.24 to -13.75) and 12.37 (95% CI, -16.13 to -8.62), respectively. The average procedures per year in group 1 were 3.68 ± 1.17 and 3.68 ± 1.26 in group 2, with an average total relief per year of 31.67 ± 13.17 and 32.64 ± 13.92 weeks, respectively. The opioid intake decreased from baseline by 8.81 mg (95% CI, -12.24 to -5.38) and 16.92 mg (95% CI: -22.71 to -11.12) in the 2 groups, respectively. This meta-analysis confirms that epidural injections of local anesthetic with or without steroids have beneficial but similar effects in the treatment of patients with chronic low-back and lower extremity pain.

The localization factor is used to describe the band structures for P wave propagating normally in the nanoscaled nearly periodic layered phononic crystals. The localization factor is calculated by the transfer matrix method based on the nonlocal elastic continuum theory. Three kinds of nearly periodic arrangements are concerned, i.e., random disorder, quasi-periodicity and defects. The influences of randomly disordered degree of the sub-layer's thickness and mass density, the arrangement of quasi-periodicity and the location of defect on the band structures and cut-off frequency are analyzed in detail.

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.

3D point cloud semantic and instance segmentation are crucial and fundamental for 3D scene understanding. Due to the complex structure, point sets are distributed off-balance and diversely, appearing as both category and pattern imbalance. It has been proved that deep networks can easily forget the non-dominant cases during training, which influences the model generalization and leads to unsatisfactory performance. Although re-weighting on instances may reduce the influence, it is hard to find a balance between the dominant and the non-dominant cases. To tackle the above issue, we propose a memory-augmented network that learns and memorizes the representative prototypes that encode both geometry and semantic information. The prototypes are shared by diverse 3D points and recorded in a universal memory module. During training, the memory slots are dynamically associated with both dominant and non-dominant cases, alleviating the forgetting issue. In testing, the distorted observations and rare cases can thus be augmented by retrieving the stored prototypes, leading to better generalization. Experiments on the benchmarks, i.e., S3DIS and ScanNetV2, show the superiority of our method on both effectiveness and efficiency, which substantially improves the accuracy not only on the entire dataset but also on non-dominant classes and samples.

Recent progress has been made on developing a unified framework for joint text detection and recognition in natural images, but existing joint models were mostly built on two-stage framework by involving ROI pooling, which can degrade the performance on recognition task. In this work, we propose convolutional character networks, referred as CharNet, which is an one-stage model that can process two tasks simultaneously in one pass. CharNet directly outputs bounding boxes of words and characters, with corresponding character labels. We utilize character as basic element, allowing us to overcome the main difficulty of existing approaches that attempted to optimize text detection jointly with a RNN-based recognition branch. In addition, we develop an iterative character detection approach able to transform the ability of character detection learned from synthetic data to real-world images. These technical improvements result in a simple, compact, yet powerful one-stage model that works reliably on multi-orientation and curved text. We evaluate CharNet on three standard benchmarks, where it consistently outperforms the state-of-the-art approaches [25, 24] by a large margin, e.g., with improvements of 65.33%->71.08% (with generic lexicon) on ICDAR 2015, and 54.0%->69.23% on Total-Text, on end-to-end text recognition. Code is available at: https://github.com/MalongTech/research-charnet., To appear in ICCV 2019

This paper presents a hidden chamber detector (HCD) using radio frequency (RF) signals’ penetration and reflection characteristics. The sensor of the chamber detector is a linear frequency modulated continuous wave (LFMCW) time division multiple access (TDMA) multiple input multiple output (MIMO) synthetic aperture radar (SAR). The basic idea of the sensor system is to scan the target wall and form a 2-D image of the wall with a high depth resolution and a proper angular resolution (cross range resolution). When there is no hidden chamber behind the wall, the radar will receive reflected signals from the room wall. While if there is a hidden chamber behind the room wall, there will be reflected signals from the room wall and the hidden chamber walls. Thus, the hidden chamber can be detected using the reflected singles from the chamber walls. The gated LFMCW TDMA MIMO SAR is configured with multiple transmitting antennas and multiple receiving antennas. Each transmitting and receiving antenna pair constructs an equivalent virtual array element. All virtual array elements construct the virtual antenna array (or synthetic aperture). The virtual array oversees the angular resolution along the array direction (vertical direction), and the narrow antenna beam is in charge of the angular resolution in the cross-array direction (horizontal direction). The high depth resolution is obtained using the gated LFMCW TDMA radar. Simulations show that the hidden chamber detector can detect chambers larger than 20cm by 20cm by 20cm at a distance of 0.3m away from the room wall.

Recent semantic segmentation methods exploit encoder-decoder architectures to produce the desired pixel-wise segmentation prediction. The last layer of the decoders is typically a bilinear upsampling procedure to recover the final pixel-wise prediction. We empirically show that this oversimple and data-independent bilinear upsampling may lead to sub-optimal results. In this work, we propose a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs. The main advantage of the new upsampling layer lies in that with a relatively lower-resolution feature map such as $\frac{1}{16}$ or $\frac{1}{32}$ of the input size, we can achieve even better segmentation accuracy, significantly reducing computation complexity. This is made possible by 1) the new upsampling layer's much improved reconstruction capability; and more importantly 2) the DUpsampling based decoder's flexibility in leveraging almost arbitrary combinations of the CNN encoders' features. Experiments demonstrate that our proposed decoder outperforms the state-of-the-art decoder, with only $\sim$20\% of computation. Finally, without any post-processing, the framework equipped with our proposed decoder achieves new state-of-the-art performance on two datasets: 88.1\% mIOU on PASCAL VOC with 30\% computation of the previously best model; and 52.5\% mIOU on PASCAL Context., Accepted to Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2019. Content may change prior to final publication

Traditional Chinese medicine Panax notoginseng is a valuable geo-authentic herbal material. The difference of growth environment in different producing areas has significant influence on the quality of traditional Chinese medicine, and origin identification is an important part of the quality assessment of P. notoginseng. In this study, Fourier transform mid-infrared (FT-MIR) and near infrared (NIR) sensor technologies combined with single spectra analysis and multi-sensor information fusion strategy (low-, mid- and high-level) for the origin identification of 210 P. notoginseng samples from five cities in Yunnan Province, China. FT-MIR spectra were considered to play a greater role in data analysis than NIR spectra. Random forest (RF) was used to establish classification models. The result of the random forest Boruta (RF-Bo) model and the random forest variable selection (RF-Vs) model based on high-level multi-sensor information fusion strategy was satisfactory. In addition, the RF-Bo model based on high-level multi-sensor information fusion strategy was faster and simpler in data analysis and the accuracy was 95.6%.

Fresnel incoherent correlation holography (FINCH) is a unique three-dimensional (3D) imaging technique which has the advantages of scanning-free, high resolution, and easy matching with existing mature optical systems. In this article, an incoherent digital holographic spectral imaging method with high accuracy of spectral reconstruction based on liquid crystal tunable filter (LCTF) and FINCH is proposed. Using the programmable characteristics of spatial light modulator (SLM), a series of phase masks, none of whose focal lengths changes with wavelength, is designed and made. For each wavelength of LCTF output, SLM calls three phase masks with different phase constants at the corresponding wavelength, and CCD records three holograms. The spectral images obtained by this method have a constant magnification, which can achieve pixel-level image registration, restrain image registration errors, and improve spectral reconstruction accuracy. The results show that this method can not only obtain the 3D spatial information and spectral information of the object simultaneously, but also have high accuracy of spectral reconstruction and excellent color reproducibility.

// Pan Chen 1, 2, * , Xiaofang Guo 2, 3, * , Liming Zhang 2, 4 , Wenling Zhang 2 , Qingyu Zhou 3 , Zhi Tian 3 , Ying Zheng 2 , Qianjin Liao 1, 2 , Heran Wang 1, 2 , Guiyuan Li 1, 2 , Jin Huang 5 , Xiayu Li 6 1 Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, China 2 Key Laboratory of Carcinogenesis of Ministry of Health, Cancer Research Institute, Central South University, Xiangya Road, Changsha, Hunan 410078, China 3 Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL33612, USA 4 The Department of Laboratory Medicine, Huaihua Medical College, Huaihua, Hunan 418000, China 5 Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan 410078, China 6 Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, China * These authors have contributed equally to this work Correspondence to: Xiayu Li, email: lixiayu@163.com Jin Huang, email: jinhuang@csu.edu.cn Keywords: miRNA-200c, c-Myc, PTEN, nasopharyngeal carcinoma Received: May 12, 2016 Accepted: November 23, 2016 Published: December 23, 2016 ABSTRACT The c-Myc transcription factor regulates a complex transcriptional program that leads to cellular transformation by targeting a large number of protein-encoding genes and non-coding RNAs. In this study, we show that a microRNA, miR-200c, is a novel c-Myc target that promotes cellular transformation and metastasis in nasopharyngeal carcinoma. MiR-200c achieves this oncogenic effect, at least in part, by targeting and inhibiting the tumor suppressor gene PTEN (phosphatase and tensin homolog), which is a key inhibitor of the AKT kinase signaling that promotes tumorigenesis in nasopharyngeal carcinoma. Our study thus identifies cMyc-miR-200c-PTEN-AKT as a functional module that promotes cellular transformation in nasopharyngeal carcinoma.

As a result of the pressure from population explosion, agricultural land resources require further protecting and rationally utilizing. Intercropping technique has been widely applied for agricultural production to save cultivated area, improve crop quality, and promote agriculture economy. In this study, we employed high-performance liquid chromatography (HPLC) and ultraviolet-visible spectroscopy (UV-vis) combined with chemometrics for determination and qualitative evaluation of several kinds of intercropping system with Gentiana rigescens Franch. ex Hemsl. (GR), which is used as an hepatic protector in local communities in China. Results revealed that GR in a Camellia sinensis intercropping system contained most gentiopicroside, sweroside, and total active constituents (six chemical indicators), whose content reached 91.09 ± 3.54, 1.03 ± 0.06, and 104.05 ± 6.48 mg g(-1), respectively. The two applied quantitative and qualitative methods reciprocally verified that GR with 2 years of growth period performed better in terms of quality than 1 year, collectively.

Mobile opportunistic networks make use of node mobility to provide occasional contact opportunities for mobile devices to deliver data. In this paper, we investigate the inherent properties of data disseminations in mobile opportunistic networks by answering the question regarding how far and how fast a packet can be disseminated in mobile opportunistic networks . This problem has been overlooked by recent research, but it is very important because it reveals the potential for mobile opportunistic networks to support emerging applications such as mobile commerce and emergency services that may involve time and location sensitive information dissemination. Our investigations are taken from the perspectives of small and large scales. From the perspective of small scale, the probability distributions of the minimum time needed by the data to spread to a given region, namely, $\mathcal{T}$ , are deduced for both the one-copy case and the multiple-copy case. From the perspective of large scale, by using the large deviation theory, the probability distributions of $\mathcal{T}$ are deduced for both the one-copy case and the multiple-copy case when the destination region is far enough from the data origin.