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Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning based on deep neural networks (ensemble deep learning) has shown significant performances in improving the generalization of learning system. However, since modern deep neural networks usually have millions to billions of parameters, the time and space overheads for training multiple base deep learners and testing with the ensemble deep learner are far greater than that of traditional ensemble learning. Though several algorithms of fast ensemble deep learning have been proposed to promote the deployment of ensemble deep learning in some applications, further advances still need to be made for many applications in specific fields, where the developing time and computing resources are usually restricted or the data to be processed is of large dimensionality. An urgent problem needs to be solved is how to take the significant advantages of ensemble deep learning while reduce the required expenses so that many more applications in specific fields can benefit from it. For the alleviation of this problem, it is essential to know about how ensemble learning has developed under the era of deep learning. Thus, in this article, we present fundamental discussions focusing on data analyses of published works, methodologies, recent advances and unattainability of traditional ensemble learning and ensemble deep learning. We hope this article will be helpful to realize the intrinsic problems and technical challenges faced by future developments of ensemble learning under the era of deep learning., Comment: 47 pages, 8 figures, 15 tables

Cell localization is still facing two unresolved challenges: 1) the large variability in cell size and shape, coupled with the heterogeneous intensity distribution of lightly stained cells, presents the bottleneck limiting accurate cell localization; 2) existing cell location map is unreasonable, which loses cell scale information and significantly affects the accuracy of cell counting. To address the challenges from the cell shape and heterogeneous intensity distribution, we propose a novel gradient-aware and shape-adaptive Difference-Deformable Convolution (DDConv), which can focus on the gradient information of lightly stained cells for overcoming the challenge of heterogeneous intensity distribution and meanwhile adaptively adjust the shape of the convolutional kernel for overcoming the challenge of the large variability in cell shape. Moreover, to solve the challenge of unreasonable location map, we propose a new cell location map, called Pseudo-Scale Instance (PSI) map. Our PSI map enables adaptively computing the scale information and associating it with each cell’s annotation, which addresses the unreasonable challenges in existing cell location map and advanced makes the model sensitive to the size of cells. We analyze and evaluate DDConv and PSI on two challenging cell localization tasks. Compared with existing methods, our proposed method has significantly improved the localization performance, setting a new benchmark for cell localization tasks.

In the field of medical image analysis, especially in the study of pathological morphology images of tumor tissues, accurately locating cells in images is a crucial task. However, this task faces multiple challenges, such as differences in cell staining intensity and cell morphology, and variance in cell shape and scale caused by manual stretching. Currently, the solution is to use fixed-scale density maps to supervise the model’s training, which makes it difficult for the model to handle cells of different scales and cell localization scenarios under different magnifications. To address this problem, this paper proposes a new cell location map called Pseudo Scale Instance (PSI) map, which has instance-level scale information. Additionally, to address the problem of significant differences between complex cell shapes and masks in PSI maps, this paper introduces a gradient alignment module based on Difference Deformable Convolution (DDC). The module uses gradient information to calculate the offsets in the deformable convolution, making the model’s output closer to the ground truth. Experiments show that the PSI map proposed in this paper can effectively utilize scale information to improve the baseline performance of cell localization and counting. Moreover, the DDC module can effectively bridge the gap between cell images and ground truth, thereby alleviating the problem of diverse cell shapes. Compared with existing methods, the proposed method has significantly improved the localization performance on two public datasets, setting a new benchmark for cell localization tasks.

Cell localization is an important area of medical image analysis, which is dedicated to predicting the precise location of cells in an image. The existing localization paradigm is to predict the density map using a Convolutional Neural Networks (CNN) model based on vanilla convolution and then process the density map using a local maximum search strategy to obtain the cell location and number information. However, there are three main problems in this paradigm: 1) CNN models based on vanilla convolution have difficulty in handling large variations in cell color; 2) The density map is difficult to provide accurate cell location information and ideal gradient information, and the information loss is more obvious in dense regions; 3) The post-processing strategy of density maps is susceptible to background noise and mutual interference between negative and positive cells. To tackle the above issues, we have made a comprehensive update of the existing paradigm, which consists of three parts: 1) A multi-scale gradient aggregation module based on difference convolution to effectively mitigate the challenge of large variations in cell color; 2) A new exponential distance transform map that provides accurate cell location information along with ideal gradient information for model learning; 3) A post-processing strategy named cell center localization strategy based on location maps that can significantly improve the localization performance. Extensive experiments on multiple datasets show that our approach can substantially improve cell localization and counting performance, establishing a new baseline for the cell localization task, and thereby increasing the efficiency of computer-aided diagnosis.

Background The definitions of contrast-associated acute kidney injury (CA-AKI) are diverse and have different predictive effects for prognosis, which are adverse for clinical practice. Few articles have discussed the relationship between these definitions and long-term prognosis in patients with diabetes. Methods A total of 1154 diabetic patients who were undergoing coronary angiography (CAG) were included in this study. Two definitions of CA-AKI were used: CA-AKIA was defined as an increase ≥ 0.3 mg/dl or > 50% in serum creatinine (SCr) from baseline within 72 h after CAG, and CA-AKIB was defined as an increase ≥ 0.5 mg/dl or > 25% in SCr from baseline within 72 h after CAG. We used Cox regression to evaluate the association of these two CA-AKI definitions with long-term mortality and calculate the population attributable risks (PARs) of different definitions for long-term prognosis. Results During the median follow-up period of 7.4 (6.2–8.2) years, the overall long-term mortality was 18.84%, and the long-term mortality in patients with CA-AKI according to both CA-AKIA and CA-AKIB criteria were 36.73% and 28.86%, respectively. We found that CA-AKIA (HR: 2.349, 95% CI 1.570–3.517, p = 0.001) and CA-AKIB (HR: 1.608, 95% CI 1.106–2.339, p = 0.013) were associated with long-term mortality. The PARs were the highest for CA-AKIA (31.14%), followed by CA-AKIB (14.93%). Conclusions CA-AKI is a common complication in diabetic patients receiving CAG. The two CA-AKI definitions are significantly associated with a poor long-term prognosis, and CA-AKIA, with the highest PAR, needs more clinical attention.

Due to the difficulty to accurately define the morphologies of Helicobacter Pylori (H. pylori) and the complexity of dealing with the whole slide images (WSIs), no computer-aided solution has currently been presented for detecting H. pylori infection in WSIs. We present the first image semantic segmentation solution for the computer-aided detection of H. pylori in WSIs. The solution only requires polygon annotations as weak supervision, which roughly, instead of pixel-level accurately, label the H. pylori infected areas in WSIs. We propose a new weakly supervised multi-task learning framework (WSMLF) that aims to improve the segmentation performance by more effectively exploiting the weak supervision. To make more effective usage of the weak supervision, we extract multiple inaccurate targets representing different modes of the true target from the available weak annotations. For improvement of the segmentation performance, we design a weakly supervised multi-task learning algorithm that can automatically learn from the weighted summarization of the extracted multiple inaccurate targets. These two advances constitute the resulting technique WSMLF. Introducing the proposed WSMLF to several common deep image semantic segmentation approaches for the detection of H. pylori in WSIs, we observe that WSMLF can enable these approaches to achieve more reasonable segmentation results, which eventually improve the detection performance of H. pylori by at most 6%. WSMLF provides new thoughts for more effectively employing weak supervision to achieve more effective results for image semantic segmentation.

The phase relations in the Ce2Co17-Sm2Co17 system over the whole concentration range have been studied by means of Xray powder diffraction, differential thermal analysis and scanning electron microscopy equipped with energy dispersive Xray spectroscopy. The X-ray powder diffraction results reveal that all the alloys (Ce1-xSmx)2Co17 are similar to the end member of the investigated system, Sm2Co17. It is implied that continuous solid solutions are formed in this system. The lattice parameters and unit cell volumes of (Ce1-xSmx)2Co17 solid solutions increase linearly with x increasing from 0 to 1.0. The occurrence of the polymorphic transformation reaction α-(Ce, Sm)2Co17 = β-(Ce, Sm)2Co17 is confirmed in the Ce2Co17-Sm2Co17 system, but its transition temperature cannot be determined. The differential thermal analysis measurements show that both the decomposition temperature and the Curie temperature of the (Ce1-xSmx)2Co17 alloys increase gradually with increasing Sm content. Based on the X-ray powder diffraction results and differential thermal analysis data, the tentative vertical section of Ce2Co17-Sm2Co17 pseudobinary system has been constructed.

Multi-model ensemble is an important fundamental technique of practical value for many artificial intelligence applications. However, the usage for multi-model ensemble has been limited when it is combined with deep neural networks to construct ensemble of deep neural networks. Due to the big time and computing resources required to train and to integrate multiple deep neural networks for the achievement of multi-model ensemble, the engineering application field where developing time and computing resources are usually restricted, has not yet widespreadly benefited from ensemble of deep neural networks. To alleviate this situation, we present a new multi-model ensemble approach entitled feature transferring based multi-model ensemble (FTBME), for ensemble of deep neural networks. Primarily, we propose a feature transferring based multi-model training strategy to more affordably find multiple extra models based on a given previously optimized deep neural network model. Sequentially, to develop better ensemble solutions, we design a more effective random greedy based ensemble selection strategy to filter out models non-positive to ensemble generalization. Finally, inspired by the idea of averaging parameter points, we propose to fuse the obtained models in weight space which eventually reduces the expense of ensemble at the testing stage to a single deep neural network model while retaining the generalization. These three advances constitute the resulting technique FTBME. We conducted extensive experiments using deep neural networks, from light weight to complex, on ImageNet, CIFAR-10 and CIFAR-100. Results show that, given a deep neural network model which has been well-optimized and reaching its limit, FTBME can obtain better generalization with minor extra training expense while maintaining the expense to a single model at ensemble testing. This promising property of FTBME make us believe that it could be leveraged to broaden the usage for ensemble of deep neural networks, alleviating the situation that the engineering application field has not yet widespreadly benefited from ensemble of deep neural networks.

Non-high-density lipoprotein cholesterol (non-HDL-C) and low-density lipoprotein cholesterol (LDL-C) were established as the target for blood lipid management among patients with coronary artery disease (CAD). Previous study reported a negative relation between baseline LDL-C levels and long-term prognosis. However, the association between baseline non-HDL-C concentration and clinical outcomes is unknown.A total of 41,182 CAD patients admitted to Guangdong Provincial People's Hospital in China were included in this study from January 2007 to December 2018 and divided into two groups (non-HDL-C 2.2 mmol/L, n = 3236; non-HDL-C ≥ 2.2 mmol/L, n = 37,946). The Kaplan-Meier method, Cox regression analyses and restricted cubic splines were used to assess the association between non-HDL-C levels and long-term all-cause mortality.The overall mortality was 12.74% (n = 5247) over a median follow-up period of 5.20 years. Kaplan-Meier analysis showed that low non-HDL-C levels were paradoxically associated with a worse prognosis. After adjustment for baseline confounders (e.g., age, sex and comorbidities, etc.), multivariate Cox regression analysis revealed that low non-HDL-C levels (2.2 mmol/L) were not significantly associated with all-cause mortality (adjusted HR, 1.03; 95% CI, 0.93-1.14). After adjustment for nutritional status, the risk of all-cause mortality in patients with low non-HDL-C levels decreased (adjusted HR, 0.86; 95% CI, 0.78-0.95). In the final multivariate Cox model adjusting for full covariates, low non-HDL-C level was related to better prognosis (adjusted HR, 0.88; 95% CI, 0.80-0.98).This study found a paradoxical association between baseline non-HDL-C concentration and long-term all-cause mortality. Malnutrition mainly mediates to the non-HDL-C paradox. Elevated non-HDL-C concentration is still a risk factor of long-term all-cause mortality after considering nutritional status.

Background. Previous studies have shown that heart failure (HF) and chronic kidney disease (CKD) have common genetic mechanisms, overlapping pathophysiological pathways, and therapeutic drug—sodium-glucose cotransporter 2 (SGLT2) inhibitors. Methods. The genetic pleiotropy metaCCA method was applied on summary statistics data from two independent meta-analyses of GWAS comprising more than 1 million people to identify shared variants and pleiotropic effects between HF and CKD. Targets of SGLT2 inhibitors were predicted by SwissTargetPrediction and DrugBank databases. To refine all genes, we performed using versatile gene-based association study 2 (VEGAS2) and transcriptome-wide association studies (TWAS) for HF and CKD, respectively. Gene enrichment and KEGG pathway analyses were used to explore the potential functional significance of the identified genes and targets. Results. After metaCCA analysis, 4,624 SNPs and 1,745 genes were identified to be potentially pleiotropic in the univariate and multivariate SNP-multivariate phenotype analyses, respectively. 21 common genes were detected in both metaCCA and SGLT2 inhibitors’ target prediction. In addition, 169 putative pleiotropic genes were identified, which met the significance threshold both in metaCCA analysis and in the VEGAS2 or TWAS analysis for at least one disease. Conclusion. We identified novel variants associated with HF and CKD using effectively incorporating information from different GWAS datasets. Our analysis may provide new insights into HF and CKD therapeutic approaches based on the pleiotropic genes, common targets, and mechanisms by integrating the metaCCA method, TWAS and VEGAS2 analyses, and target prediction of SGLT2 inhibitors.

Background. Contrast-associated acute kidney injury (CA-AKI) is a major adverse effect of coronary angiography (CAG). Patients with chronic kidney disease (CKD) and coronary artery disease (CAD) are at high risk of CA-AKI. This study aimed to investigate the association between prognostic nutritional index (PNI) and CA-AKI in this high-risk population. Methods. This study enrolled a total of 4,391 patients. CA-AKI was defined as a serum creatinine increase ≥0.3 mg/dL or 50% from baseline within the first 48 hours following CAG. The PNI was calculated upon hospital admission: serum albumin (g/L) + 5 × total lymphocyte count (109/L). PNI was analysed from the high level to low level as a continuous variable and categorical variable which was divided into four groups by quartile. Restricted cubic splines and logistic regression were applied. Results. Overall, 13.09% (575/4391) of patients developed CA-AKI. PNI score was significantly lower in patients with CA-AKI than that in patients without CA-AKI ( P < 0.01 ). The relationship between PNI score and CA-AKI was linear. A logistic regression model revealed that decreased PNI score was associated with increased risk of CA-AKI [per 1-point decrement; adjusted OR = 1.08, 95% CI, 1.05–1.09; compared with Quartile 1 (PNI ≥ 46.30), Quartile 4 (PNI

Aluminum ion-exchanged Ca-montmorillonite with different concentrations of aluminum ion were prepared and applied in the conversion of carbohydrates to 5-hydroxymethylfurfural (HMF). It was found that two types of acid sites were present in the catalyst, Lewis acid sites for isomerization of glucose and Bronsted acid sites for dehydration of fructose. With Al-mont-10 as catalyst, the yield of HMF converted from glucose was 80.4% in a biphase system (THF: NaCl solution) at 180 °C for 2.5 h. Further, the catalyst also shows excellent catalytic performance in the conversion of starch and inulin, and the yield of HMF reached 60.1% and 81.6% under identical conditions, respectively.

Background Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Predictive biomarkers of treatment response are crucial for guiding treatment decisions. With the hypothesis that histological information on tumor biopsy images could predict NAC response in breast cancer, we proposed a novel deep learning (DL)-based biomarker that predicts pCR from images of hematoxylin and eosin (H&E)-stained tissue and evaluated its predictive performance. Methods In total, 540 breast cancer patients receiving standard NAC were enrolled. Based on H&E-stained images, DL methods were employed to automatically identify tumor epithelium and predict pCR by scoring the identified tumor epithelium to produce a histopathological biomarker, the pCR-score. The predictive performance of the pCR-score was assessed and compared with that of conventional biomarkers including stromal tumor-infiltrating lymphocytes (sTILs) and subtype. Results The pCR-score derived from H&E staining achieved an area under the curve (AUC) of 0.847 in predicting pCR directly, and achieved accuracy, F1 score, and AUC of 0.853, 0.503, and 0.822 processed by the logistic regression method, respectively, higher than either sTILs or subtype; a prediction model of pCR constructed by integrating sTILs, subtype and pCR-score yielded a mean AUC of 0.890, outperforming the baseline sTIL-subtype model by 0.051 (0.839, P = 0.001). Conclusion The DL-based pCR-score from histological images is predictive of pCR better than sTILs and subtype, and holds the great potentials for a more accurate stratification of patients for NAC.

Background Several studies have found that a low baseline low -density lipoprotein cholesterol (LDL-C) concentration was associated with poor prognosis in patients with acute coronary syndrome (ACS), which is called the “cholesterol paradox”. Low LDL-C concentration may reflect underlying malnutrition, which was strongly associated with increased mortality. The aim of this study was to investigate the cholesterol paradox in patients with CAD and the effects of malnutrition. Method A total of 41,229 CAD patients admitted to Guangdong Provincial People’s Hospital in China were included in this study from January 2007 to December 2018 and divided into two groups (LDL-C n = 4863; LDL-C ≥ 1.8 mmol/L, n = 36,366). The Kaplan-Meier method and Cox regression analyses were used to assess the association between LDL-C levels and long-term all-cause mortality and the effect of malnutrition. Result In this real-world cohort (mean age 62.9 years; 74.9% male), there were 5257 cases of all-cause death during a median follow-up of 5.20 years [interquartile range (IQR): 3.05–7.78 years]. Kaplan–Meier analysis showed that low LDL-C levels were associated with a worse prognosis. After adjusting for baseline confounders (e.g., age, sex and comorbidities, etc.), multivariate Cox regression analysis revealed that a low LDL-C level ( Conclusion This study demonstrated that the cholesterol paradox existed in CAD patients but disappeared after accounting for the effects of malnutrition.

The appearance of cloud computing has provided an efficient and economical way for users to receive computing resources with a pay-as-you-go model from cloud service provider. Cloud computing has attracted wide attention in both academia and industrial application. However, with the widely deploying of large-scale data center, high energy consumption becomes one of the key challenges and difficulties that restricting the development of cloud computing. Due to the limitations and dynamics of resources, how to map the cloud tasks to the resources aiming at decreasing the energy consumption has remained an essential issue. In this paper, we have made some improvements to the DeepJS and present DeepEnergyJS to minimize energy consumption for data center that receives enormous numbers of tasks arrive dynamically. We firstly introduce a power model to estimate the power of data center of the moment, then design a reward function that reflects the goal of optimizing power consumption and implement the simulation experiment on independent workloads to verity its availability of reducing the energy consumption. Finally, DeepJS considered only the independent workloads, we model inter-task dependencies in jobs and then conduct experiments in a similar way with independent workloads to prove that DeepEnergyJS can be extended for workloads that with dependencies which make it more functional. What’s more, our experiments are based on different configured servers while the severs used in DeepJS are all the same, which make our study closer to the real cloud computing environment. The experiments results show that DeepEnergyJS can significantly outperforms three baselines in both independent workloads and workloads with dependencies.

Objective: Previous studies have shown that the relationship between hypertension (HT) and contrast-associated acute kidney injury (CA-AKI) is not clear. We apply a systematic review and meta-analysis to assess the association between HT and CA-AKI. Methods: We searched for articles on the study of risk factors for CA-AKI in the Embase, Medline, and Cochrane Database of Systematic Reviews (by March 25, 2021). Two authors independently performed quality assessment and extracted data such as the studies’ clinical setting, the definition of CA-AKI, and the number of patients. The CA-AKI was defined as a serum creatinine (SCr) increase ≥25% or ≥0.5 mg/dL from baseline within 72 h. We used fixed or random models to pool adjusted OR (aOR) by STATA. Results: A total of 45 studies (2,830,338 patients) were identified, and the average incidence of CA-AKI was 6.48%. There was an increased risk of CA-AKI associated with HT (aOR: 1.378, 95% CI: 1.211–1.567, I2 = 67.9%). In CA-AKI with a SCr increase ≥50% or ≥0.3 mg/dL from baseline within 72 h, an increased risk of CA-AKI was associated with HT (aOR: 1.414, 95% CI: 1.152–1.736, I2 = 0%). In CA-AKI with a Scr increase ≥50% or ≥0.3 mg/dL from baseline within 7 days, HT increases the risk of CA-AKI (aOR: 1.317, 95% CI: 1.049–1.654, I2 = 51.5%). Conclusion: Our meta-analysis confirmed that HT is an independent risk factor for CA-AKI and can be used to identify risk stratification. Physicians should pay more attention toward prevention and treatment of patients with HT in clinical practice.

Background Contrast-associated acute kidney injury (CA-AKI) is a common complication after coronary angiography (CAG), which brings a poor prognosis. But up to now, there were fewer studies to discuss the incidence of CA-AKI comprehensively. We comprehensively explore the incidence of CA-AKI after coronary angiography. Methods We searched Medline, Embase, and Cochrane Database of Systematic Reviews (to 30th June 2019). We evaluated the world’s incidence of the CA-AKI, and associated mortality, and to described geographic variations according to countries, regions, and economies. CA-AKI was defined as an increase in serum creatinine ≥ 0.5 mg/dl or ≥ 25% within 72 h. Random effects model meta-analyses and meta-regressions was performed to derive the sources of heterogeneity. Results A total of 134 articles (1,211,106 participants) were included in our meta-analysis. Most studies originated from China, Japan, Turkey and United States, from upper middle income and high income countries. The pooled incidence of CA-AKI after coronary angiography was 12.8% (95% CI 11.7–13.9%), and the CA-AKI associated mortality was 20.2% (95% CI 10.7–29.7%). The incidence of CA-AKI and the CA-AKI associated mortality were not declined over time (Incidence rate change: 0.23% 95% CI − 0.050 to 0.510 p = 0.617; Mortality rate change: − 1.05% 95% CI − 3.070 to 0.970 p = 0.308, respectively). Conclusion CA-AKI was a universal complication in many regions, and the burden of CA-AKI remains severe. In clinical practice, physicians should pay more attention to the occurrence and active prevention and treatment of CA-AKI. Graphic abstract

BackgroundRisk stratification is recommended as the key step to prevent contrast-associated acute kidney injury (CA-AKI) among at-risk patients following coronary angiography (CAG) and/or percutaneous coronary intervention (PCI). Patients with hypoalbuminemia are prone to CA-AKI and do not have their own risk stratification tool. Therefore, this study developed and validated a new model for predicting CA-AKI among hypoalbuminemia patients CAG/PCI.Methods1272 patients with hypoalbuminemia receiving CAG/PCI were enrolled and randomly allocated (2:1 ratio) into the development cohort (n = 848) and the validation cohort (n = 424). CA-AKI was defined as an increase of ≥0.3 mg/dL or 50% in serum creatinine (SCr) compared to baseline in the 48 to 72 h after CAG/PCI. A prediction model was established with independent predictors according to stepwise logistic regression, showing as a nomogram. The discrimination of the new model was evaluated by the area under the curve (AUC) and was compared to the classic Mehran CA-AKI model. The Hosmer-Lemeshow test was conducted to assess the calibration of our model.ResultsOverall, 8.4% (71/848) patients of the development group and 11.2% (48/424) patients of the validation group experienced CA-AKI. A new nomogram included estimated glomerular filtration rate (eGFR), serum albumin (ALB), age and the use of intra-aortic balloon pump (IABP); showed better predictive ability than the Mehran score (C-index 0.756 vs. 0.693,p = 0.02); and had good calibration (Hosmer–Lemeshow testp = 0.187).ConclusionsWe developed a simple model for predicting CA-AKI among patients with hypoalbuminemia undergoing CAG/PCI, but our findings need validating externally.Trial registrationhttp://www.ClinicalTrials.govNCT01400295, retrospectively registered 21 July 2011.

Accurate estimation of food volume is critical in the medical field. However, estimating food volume is a challenging task due to the diverse nature of food, multi-scale and other characteristics. In this paper, we explore the relationship between the properties and volume of the object (food and reference) in the image. By combining Faster R-CNN, Grabcut, Median filtering, and CNN algorithm, we propose a framework for estimating food volume based on reference. The framework uses a front view which contains reference and food to estimate food volume and is applied to image datasets for 5 kinds of foods. The experimental results show the effective performance of this method for predicting volume, and the mean absolute error of each kind of food is less than 4.5%, which shows the model is robust to estimate volume for irregular food.

Additional file 1: Table S1. Baseline characteristics of non-PCI group and PCI group in total cohort. Table S2. Baseline characteristics of CA-AKI group and non CA-AKI group in developing cohort. Table S3. Multivariate Cox regression analysis of risk factors for long-term mortality. Table S4. Multivariate logistic regression of variates with statistically significant difference in univariate analysis.

In this paper, we novelly consider visual localization in active and passive two ways, with simple definition that active localization assists device to estimate location of its interest while passive localization aids device to estimate its own location in environment. Expecting to indicate some insights into visual localization, we specifically performed two explorations on active localization and more importantly explored to upgrade them from active to passive localization with extra geometry information available. In order to produce unconstrained and accurate 2D location estimation of interested object, we constructed an active localization system by fusing detection, tracking and recognition. Based on recognition, we proposed a collaborative strategy making mutual enhancement between detection and tracking possible to obtain better performance on 2D location estimation. Meanwhile, to actively estimate semantic location of interested visual region, we employed latest state-of-the-art light weight CNN models specifically designed for efficiency and trained two of them with large place dataset in perspective of scene recognition. What’s more, using depth information available from RGB-D camera, we improved the active system for 2D location of interested object to a passive system for relative 3D location of device to the interested object. Firstly estimated was the 3D location of the interested object in the coordinate system of device, then relative location of device to the interested object in world coordinate system was deduced with appropriate assumption. Evaluations both subjectively on a RGB-D sequence obtained in a lab environment and practically on a robotic platform in an office environment indicated that the improved system was suitable for autonomous following robot. As well, the active system for rough semantic location estimation of interested visual region was promoted to a passive system for fine location estimation of device, with available 3D map describing the visited environment. In perspective of place recognition, we first adopted one of the efficient CNN models previously trained for semantic location estimation as a base to generate CNN features for both retrieval of candidate loops in the map and geometrical consistency checking of retrieved loops, then true loops were used to deduce fine location of device itself in environment. Comparison with state-of-the-art results reflected that the promoted system was adequate for long-term robotic autonomy. Achieving favorable performances, the presented four explorations have implied adequacy for elaborating on some insights into visual localization.

Adaptively learning the difference between object and background, discriminative trackers are able to overcome the complex background problem in visual object tracking. However, they are not robust enough to handle the out-of-plane rotation of object, which reduces recall performance. Meanwhile, allowing individual parts certain criterion of freedom, part-based trackers can better handle the out-of-plane rotation problem. However, they are prone to be affected by complex background, leading to low precision performance. To simultaneously address both issues, we propose a collaborative strategy that makes mutual enhancement between a discriminative tracker and a part-based tracker possible to obtain better overall performance. On one hand, we use validated results from the part-based tracker to update the discriminative tracker for recall performance improvement. On the other hand, based on confident results from the discriminative tracker we adaptively update the part-based tracker for simultaneous precision performance improvement. Experiments on various challenge sequences show that our approach achieved the state-of-the-art performance, which demonstrated the effectiveness of mutual collaboration between the two trackers.

In order to correlate the physico-chemical properties of Ce x Zr 1-x O 2 catalysts with their catalytic activities for direct carboxylation of methanol to dimethyl carbonate (DMC), ceria-zirconia mixed oxides with the same nominal composition (Ce 0.5 Zr 0.5 O 2 ), which were prepared by different methods, were applied. The catalysts were characterized by means of XRD, N 2 adsorption, ICP-MS, NH 3 -TPD, CO 2 -TPD, Raman spectroscopy and XPS. Significant variations in their physico-chemical properties were observed by varying the preparation conditions. The results showed that the catalytic activity of the ceria-zirconia mixed oxides increased with improving the proportion of Ce 4+ , acidity and basicity of the catalyst surface. And the number of surface Ce 4+ content is the most important factor.

In view of the massive data transmission and storage of various types of ocean data, and the limitations of IPv4 transmission protocols on marine projects, we propose a digital ocean cloud platform architecture based on IPv6 intelligent gateway, which utilizes an unlimited number of address resource pools of IPv6. The characteristics of efficient and secure data dissemination combined with cloud computing service technology, based on distributed storage technology, provide a safe and reliable storage environment for marine sensor data, ocean images and text, and ocean data through cloud computing technology and ocean data can be aggregated, classified and processed and through cloud computing technology can deepen the value of ocean data.