Object detection has been one of the hottest issues in the field of remote sensing image analysis. In this letter, an efficient object detection framework is proposed, which combines the strength of ...the unsupervised feature learning of deep belief networks (DBNs) and visual saliency. In particular, we propose an efficient coarse object locating method based on a saliency mechanism. The method could avoid an exhaustive search across the image and generate a small number of bounding boxes, which can locate the object quickly and precisely. After that, the trained DBN is used for feature extraction and classification on subimages. The feature learning of the DBN is operated by pretraining each layer of restricted Boltzmann machines (RBMs) using the general layerwise training algorithm. An unsupervised blockwise pretraining strategy is introduced to train the first layer of RBMs, which combines the raw pixels with a saliency map as inputs. This makes an RBM generate local and edge filters. The precise edge position information and pixel value information are more efficient to build a good model of images. Comparative experiments are conducted on the data set acquired by QuickBird with a 60-cm resolution. The results demonstrate the accuracy and efficiency of our method.
In this paper we examine the long-run relationship between gold and oil spot and futures markets. We draw on the conceptual framework that when oil price rises, it creates inflationary pressures, ...which instigate investments in gold as a hedge against inflation. We test for the long-run relationship between gold and oil futures prices at different maturity and unravel evidence of cointegration. This implies that: (a) investors use the gold market as a hedge against inflation and (b) the oil market can be used to predict the gold market prices and vice versa, thus these two markets are jointly inefficient, at least for the sample period considered in this study.
Radar emitter classification (REC) is very important in both civil and military fields. It becomes more and more difficult to classify the intercepted radar signals with the increasing complexity of ...radar signals. An efficient classification method using weighted-xgboost (w-xgboost) model for the complex radar signals is proposed in this study. The xgboost method is widely used by data scientists and performs very well in many machine learning projects. The authors use a large data set which consists of different types of attributes (such as continuous data, categorical data, and discrete data) to train the model. A smooth weight function is introduced to solve the data deviation problem. Experiment results show that the authors’ w-xgboost method achieves a better performance than several existing machine learning algorithms on the test set.
In this letter, we present a new method to detect inshore ships using shape and context information. We first propose a new energy function based on an active contour model to segment water and land ...and minimize it with an iterative global optimization method. The proposed energy performs well on the different intensity distributions between water and land and produces a result that can be well used in shape and context analyses. In the segmented image, ships are detected with successive shape analysis, including shape analysis in the localization of ship head and region growing in computing the width and length of ship. Finally, to locate ships accurately and remove the false alarms, we unify them with a binary linear programming problem by utilizing the context information. Experiments on QuickBird images show the robustness and precision of our method.
In this letter, we propose a novel framework for large-satellite-image annotation using multifeature joint sparse coding (MFJSC) with spatial relation constraint. The MFJSC model imposes an l 1, 2 ...-mixed-norm regularization on encoded coefficients of features. The regularization will encourage the coefficients to share a common sparsity pattern, which will preserve the cross-feature information and eliminate the constraint that they must have identical coefficients. Spatial dependences between patches of large images are useful for the annotation task but are usually ignored or insufficiently exploited in other methods. In this letter, we design a spatial-relation-constrained classifier to utilize the output of MFJSC and the spatial dependences to annotate images more precisely. Experiments on a data set of 21 land-use classes and QuickBird images show the discriminative power of MFJSC and the effectiveness of our annotation framework.
We study the impact of capital market openness on high‐frequency market quality in China. The Shanghai–Hong Kong Stock Connect program (SHHKConnect) opens China's stock market to foreign investors ...and offers a natural experiment to investigate this question. Using a difference‐in‐differences approach, we find that market liberalization leads to lower quoted spread, lower effective spread, lower market depth, and higher short‐term volatility. Our findings imply that opening the markets to more sophisticated foreign investors is associated with higher competition and more cross‐market arbitrage activities, narrowing the spread and reducing liquidity providers’ profits, but increasing the price impact and short‐term volatility of connected stocks.
Display omitted
•In-situ 9.4 T MRI verified the detoxification role of ZnO/GO against Cd in liver.•ZnO/GO efficiently inhibited Cd-induced toxicity in various targeted cells.•Zn ions released from ...ZnO/GO competitively suppressed the uptake of Cd.•ZnO/GO promoted Cd excretion through up-regulating the expression of MRP1.
Environmental cadmium (Cd) pollution has been verified to associated with various hepatic diseases, as Cd has been classified as one of the TOP 20 Hazardous Substances and liver is the main target of Cd poisoning. However, to design efficient hepatic antidotes with excellent detoxification capacity and reveal their underlying mechanism(s) are still challenges in Cd detoxification. Herein, ZnO/GO nanocomposites with favorable biocompatibility was uncovered their advanced function against Cd-elicited liver damage at the in situ level in vivo by 9.4 T magnetic resonance imaging (MRI). To explore the cellular detoxification mechanism, ZnO/GO nanocomposites was found to effectively inhibit the cyto- and geno-toxicity of Cd with the maximum antagonistic efficiency to be approximately 90%. Mechanistically, ZnO/GO nanocomposites competitively inhibited the cellular Cd uptake through releasing Zn ions, and significantly promoted Cd excretion via targeting the efflux pump of multidrug resistance associated protein1 (MRP1), which was confirmed by mass spectra and immunohistochemical analysis in kidney, a main excretion organ of Cd. Our data provided a novel approach against Cd-elicited hepatotoxic responses by constructed ZnO/GO nanocomposites both in vitro and in vivo, which may have promising application in prevention and detoxification for Cd poisoning.
With the increasing use of mobile GPS (global positioning system) devices, a large volume of trajectory data on users can be produced. In most existing work, trajectories are usually divided into a ...set of stops and moves. In trajectories, stops represent the most important and meaningful part of the trajectory; there are many data mining methods to extract these locations. DBSCAN (density-based spatial clustering of applications with noise) is a classical density-based algorithm used to find the high-density areas in space, and different derivative methods of this algorithm have been proposed to find the stops in trajectories. However, most of these methods required a manually-set threshold, such as the speed threshold, for each feature variable. In our research, we first defined our new concept of move ability. Second, by introducing the theory of data fields and by taking our new concept of move ability into consideration, we constructed a new, comprehensive, hybrid feature–based, density measurement method which considers temporal and spatial properties. Finally, an improved DBSCAN algorithm was proposed using our new density measurement method. In the Experimental Section, the effectiveness and efficiency of our method is validated against real datasets. When comparing our algorithm with the classical density-based clustering algorithms, our experimental results show the efficiency of the proposed method.
Automatic inshore ship recognition, which includes target localization and type recognition, is an important and challenging task. However, existing ship recognition methods mainly focus on the ...classification of ship samples or clips. These methods rely deeply on the detection algorithm to complete localization and recognition in large scene images. In this letter, we present an integrated framework to automatically locate and recognize inshore ships in large scene satellite images. Different from traditional object recognition methods using two steps of detection-classification, the proposed framework could locate inshore ships and identify types without the detection step. Considering ship size is a useful feature, a novel multimodel method is proposed to utilize this feature. And an Euclidean-distance-based fusion strategy is used to combine candidates given by models. This fusion strategy could effectively separate side-by-side ships. To handle large scene images efficiently, scale-invariant feature transform registration is also integrated into the framework to utilize geographic information. All of these make the framework an end-to-end fashion which could automatically recognize inshore ships in large scene satellite images. Experiments on Quickbird images show that this framework could achieve the actual applied requirements.
Objective
: A protein-based leaking-proof theranostic nanoplatform for dual-modality imaging-guided tumor photodynamic therapy (PDT) has been designed.
Impact Statement
: A site-specific conjugation ...of chlorin e6 (Ce6) to ferrimagnetic ferritin (MFtn-Ce6) has been constructed to address the challenge of unexpected leakage that often occurs during small-molecule drug delivery.
Introduction
: PDT is one of the most promising approaches for tumor treatment, while a delivery system is typically required for hydrophobic photosensitizers. However, the nonspecific distribution and leakage of photosensitizers could lead to insufficient drug accumulation in tumor sites.
Methods
: An engineered ferritin was generated for site-specific conjugation of Ce6 to obtain a leaking-proof delivery system, and a ferrimagnetic core was biomineralized in the cavity of ferritin, resulting in a fluorescent ferrimagnetic ferritin nanoplatform (MFtn-Ce6). The distribution and tumor targeting of MFtn-Ce6 can be detected by magnetic resonance imaging (MRI) and fluorescence imaging (FLI).
Results
: MFtn-Ce6 showed effective dual-modality MRI and FLI. A prolonged in vivo circulation and increased tumor accumulation and retention of photosensitizer was observed. The time-dependent distribution of MFtn-Ce6 can be precisely tracked in real time to find the optimal time window for PDT treatment. The colocalization of ferritin and the iron oxide core confirms the high stability of the nanoplatform in vivo. The results showed that mice treated with MFtn-Ce6 exhibited marked tumor-suppressive activity after laser irradiation.
Conclusion
: The ferritin-based leaking-proof nanoplatform can be used for the efficient delivery of the photosensitizer to achieve an enhanced therapeutic effect. This method established a general approach for the dual-modality imaging-guided tumor delivery of PDT agents.
PDF
