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Semi-supervised object detection has recently achieved substantial progress. As a mainstream solution, the self-labeling-based methods train the detector on both labeled data and unlabeled data with pseudo labels predicted by the detector itself, but their performances are always limited. Through experimental analysis, we reveal the underlying reason is that the detector is misguided by the incorrect pseudo labels predicted by itself (dubbed self-errors). These self-errors can hurt performance even worse than random-errors, and can be neither discerned nor rectified during the self-labeling process. In this paper, we propose an effective detection framework named CrossRectify, to obtain accurate pseudo labels by simultaneously training two detectors with different initial parameters. Specifically, the proposed approach leverages the disagreements between detectors to discern the self-errors and refines the pseudo label quality by the proposed cross-rectifying mechanism. Extensive experiments show that CrossRectify achieves outperforming performances over various detector structures on 2D and 3D detection benchmarks.

With the development of the Internet, more and more creators publish articles on social media. How to automatically filter high quality content from a large number of multimedia articles is one of the core functions of information recommendation, search engine, and other systems. However, existing approaches typically suffer from two limitations: (1) They usually model content as word sequences, which ignores the semantics provided by non-consecutive phrases, long-distance word dependency, and visual information. (2) They rely on a large amount of manually annotated data to train a quality assessment model while users may only provide labels of interest in a single class for a small number of samples in reality. To address these limitations, we propose a Multimodal Graph Convolutional Networks (MGCN) to model the semantic representations in a unified framework for High Quality Content Recognition. Instead of viewing text content as word sequences, we convert them into graphs, which can model non-consecutive phrases and long-distance word dependency for better obtaining the composition of semantics. Besides, visual content is also modeled into the graphs to provide complementary semantics. A well-designed graph convolutional network is proposed to capture the semantic representations based on these graphs. Furthermore, we employ a non-negative risk estimator for high quality content recognition and the loss is back-propagated for model learning. Experiments on real datasets validate the effectiveness of our approach.

A critical bottleneck in pedestrian detection is the detection of small-scale pedestrians, which have low contrast and blurry shapes in images and videos. Considered that the body shape of a pedestrian is always rectangular (the height is greater than the width), we propose an asymmetric multi-stage network (AMS-Net) for small-scale pedestrian detection. The proposed method has two main advantages. (1) It considers the asymmetry of a pedestrian’s body shape in pedestrian detection. The rectangular anchors are used to generate various rectangular proposals that have a height greater than the width. In addition, asymmetric rectangular convolution kernels are adopted for capturing the compact features of the pedestrian body. (2) The proposed AMS-Net gradually rejects the non-pedestrian boxes according to coarse-to-fine features in a three-stage framework. The proposed AMS-Net significantly improves the performance of pedestrian detection on the Far subset of the Caltech testing set (the miss rate decreases from 60.79% to 51.36%). It also achieves competitive performance on the INRIA, ETH, KITTI and CityPersons benchmarks.

The effects of welding speed on the molten pool dynamics, the weld forming and the diffusible hydrogen content in deposited metal during underwater wet flux cored arc welding (FCAW) were studied. The molten pool was elongated with a higher solidification speed, as the welding speed was increased. The faster solidification improved the stability of the molten pool by reducing the bubbles and gas disturbance during FCAW, whereas it caused more hydrogen remaining in the deposited metal and increased the diffusible hydrogen content of the weld. According to the variation coefficient of weld reinforcement, the weld formation was improved at first and then deteriorated with the increasing welding speed. More bulges appeared in the weld at a low welding speed and pits were formed more easily with a high welding speed. The occurrence of the defects was closely related to the molten pool behaviors, which were determined by the pool angles during FACW.

We present a practical approach to improve the precision of face alignment for a single image. Recently, face alignment is deemed as a regression problem, and convolutional neural networks (CNNs) or recurrent neural networks (RNNs) are utilized to predict the coordinates of facial landmarks. However, most existing methods only adopt Euclidean loss as the optimization target for each landmark, and neglect the correlations between them, which we think may be inappropriate. To address this issue, in this paper, we introduce a novel Relative Coordinates Constraint (RCC) loss function for face alignment, which considers the relative coordinates between any pairs of landmarks as a new supervision signal. More importantly, we prove that the proposed RCC loss function is trainable and can be easily incorporated in existing CNNs optimization procedure. With the joint supervision of Euclidean loss and RCC loss, we train a robust and light CNNs framework for face alignment. Extensive experimental results on several datasets show that the precision of face alignment improved significantly by the proposed RCC loss and quantitative results are comparable to state-of-the-art methods (mean error 5.39 on 300-W and 6.99 on AFLW). In addition, the proposed framework is also an efficient solution (300 FPS on CPU). We share the implementation code of our proposed methods at https://github.com/nianfudong/RCC-loss .

Most of existing multimodal event classification methods fuse the traditional hand-crafted features with some manually defined weights, which may be not suitable to the event classification task with large amounts of photos. Besides, the feature extraction and event classification model are always performed separately, which cannot capture the most useful features to describe the semantic concepts of complex events. To deal with these issues, we propose a novel discriminative multimodal embedding (DME) model for event classification in user generated photos by jointly learning the representation together with the classifier in a unified framework. In the proposed DME model, we can effectively resolve the multimodal, intra-class variation and inter-class confusion challenges by using the contrastive constraints on the multimodal event data. Extensive experimental results on two collected datasets demonstrate the effectiveness of the proposed DME model for event classification.

In this study, a special phenomenon of gas evolution in the metal droplet and melt pool during underwater wet welding was investigated by in-situ imaging method in a simulated deep-water environment. In general, the dissolved hydrogen escaped from molten droplet and molten pool in the form of bubbles during molten metal solidification. As the increase of hydrostatic pressure, the gas cannot expand enough to burst the droplet and release gas, but instead of entering into molten pool again. The combinations of the internal pressure in the bubble and hydrogen-rich atmosphere induced by welding arc resulted in that the melt pool has been subjected to dual influences. The diffusible hydrogen content in the deposited metal significantly increased from 23.3 to 66.3 ml/100 g with increasing the water depth to 150 m, which was related to the high hydrogen partial pressure and the rapid solidification rate of molten metal.

Underwater wet welding (UWW) of 304 stainless steel was performed using a set of wires with various calcium fluoride contents. The appearances, compositions, microstructural characteristics and mechanical properties of welding joints were analyzed. As the proportion of CaF2 rose, the welding appearances had been improved owing to the increasing coverage of slag. The depth-width ratio descended from 33.5% to 15.7% with an increase of CaF2 from 0% to 65% due to the reduction of the molten pool heat. Microstructural observations showed that the fully austenitic weld metal was consisted of four regions and the proportion of each region varied with the content of CaF2. The t8/5 cooling times of the heat affected zone were prolonged from 4.4 s to 6.1 s as the CaF2 content increased from 0% to 65%, which contributed to the microstructure variation. Besides, the uppermost element in the welds was Ni (at least 65%) whose content gradually went up with more and more CaF2 in the wire. Under the same conditions, the concentration of the solutes in the Ni-based welds lowered, encouraging the dislocation to be smaller and more dispersive. It was 20% CaF2 that the wire contained when the weld reached the best mechanical property with 522 MPa tensile strength and 132.74 J/cm2 impact toughness.

An improved chemical etching method named as etch pit real-time observation (EPRTO) method was proposed for the measurement and evaluation of the defects in Cd 1−x Zn x Te materials. Unlike the traditional defect chemical etching method in which the etch pits (EPs) are measured after etching with a fixed time, by using EPRTO method, the EPs can be characterized while etching. A special etching apparatus is designed to observe and record the whole formation process of the surface EPs under the infrared transmission microscope (IRTM). The new method can accurately judge the characteristic of every etch pit (EP) formed on the surfaces of Cd 1−x Zn x Te materials. By using EPRTO method, the EPs corresponding to dislocations and bulk defects can be recognized. The defect EPs and defect residual EPs can be distinguished. The threading lengths of the dislocations (DTLs) in the materials can be measured. A set of more complete and accurate measurement parameters have been proposed to evaluate the defect properties of Cd 1−x Zn x Te materials, which can provide a more effective means for studying the correlations of material defect properties with crystal growth technology and device performance.

Tetragonal CuFeS2 anchored in ethylenediamine-modified reduced graphene oxide (CuFeS2/EN-rGO) was synthesized via a simple solvothermal method, and evaluated for the first time as an anode material for sodium ion batteries via constant current charge/discharge, rate charge/discharge, cyclic voltammetry, electrochemical impedance spectroscopy. Ex-situ XRD revealed sodium storage mechanism of CuFeS2, indicating that its capacity is mainly provided by the conversion reaction. Compared with CuFeS2, reversible capacity and cycle performance of CuFeS2/EN-rGO was significantly improved by high electrical conductivity of EN-rGO. At 1.0 A g-1, the discharge capacity of CuFeS2/EN-rGO maintained 247.8 mAh g-1 at 250th cycle, corresponding to 0.14% discharge capacity decay rate per cycle compared with its second discharge capacity. Even at 3.2 A g-1, it can reached a discharge capacity of 296.2 mAh g-1. The results show that CuFeS2/EN-rGO has certain development prospects as an anode material for sodium ion batteries.

The effect of metal transfer mode on spatter and arc stability during underwater flux-cored wire wet welding at different process parameters are investigated adopting the synchronous acquisition system of X-ray image and electric signal. Two spatter modes i.e. the local droplet repelled spatter and the droplet explosion spatter were observed for the first time. The generation of the local droplet repelled spatter is attributed to the excessive and unstable repulsive forces, while the droplet explosion spatter is caused by the unstable repulsive forces and gas dynamic force. Welding spatters and arc stability depend on the metal transfer mode. During wide-angle globular repelled transfer mode, the droplet repelled spatter mode is observed and the forming frequencies of the local droplet repelled spatter and droplet explosion spatter are higher than other transfer modes. The short-circuit explosions are observed in short-circuit explosive transfer mode, causing numerous short-circuit explosive spatters. With the increase of arc voltage, both the spatter loss coefficient and voltage variation coefficient decrease firstly to the minimum at the arc voltage of 32 V and then increases gradually, attributed to the type and proportion of metal transfer mode.

Recent works on video captioning mainly learn the map from low-level visual features to language description directly without explicitly representing the high-level semantic video concepts (e.g. objects, actions in the annotated sentences). To bridge the semantic gap, in this paper, addressing it, we propose a novel video attribute representation learning algorithm for video concept understanding and utilize the learned explicit video attribute representation to improve video captioning performance. To achieve it, firstly, inspired by the success of spectrogram in audio processing, a novel mid-level video representation named “video response map” (VRM) is proposed, by which the frame sequence could be represented by a single image representation. Therefore, the video attributes representation learning could be converted to a well-studied multi-label image classification problem. Then in the captions prediction step, the learned video attributes feature is integrated with the single frame feature to improve previous sequence-to-sequence language generation model by adjusting the LSTM (Long-Short Term Memory) input units. The proposed video captioning framework could both handle variable frame inputs and utilize high-level semantic video attribute features. Experimental results on video captioning tasks show that the proposed method, utilizing only RGB frames as input without extra video or text training data, could achieve competitive performance with state-of-the-art methods. Furthermore, the extensive experimental evaluations on the UCF-101 action classification benchmark well demonstrate the representation capability of the proposed VRM.

Aims Pharmacological treatment of prehypertension may ameliorate hypertension and improve vascular structure and function. This study investigated 1) whether early treatment with either losartan or amlodipine at the onset of prehypertension can prevent hypertension and 2) whether losartan and amlodipine equally improve vascular remodeling and function in a rat model of hypertension. Materials and methods Stroke-prone spontaneously hypertensive (SHRSP) rats were administered losartan, amlodipine or saline for 6 or 16 weeks at the onset of prehypertension. Wistar-Kyoto rats were used as a control. All groups were observed for 40 weeks. Systolic blood pressure was measured using the tail-cuff method. Vascular structure and function were determined by microscopy and vascular ring contractility assays, respectively. Angiotensin II (Ang II) and aldosterone (Aldo) were measured by radioimmunoassays. Angiotensin II type 1 receptor (AT1R) and angiotensin II type 2 receptor (AT2R) expression was measured by western blot. Key findings Losartan effectively reduced progression from prehypertension to hypertension as well as vascular remodeling and improved vascular contractility in SHRSP rats. Long-term losartan (16 weeks) had greater benefits than short-term (6 weeks) treatment. Losartan increased Ang II and decreased Aldo levels in the serum and vessel walls of resistance vessels in a time-dependent manner. Losartan significantly decreased AT1R and increased AT2R vascular expression. Amlodipine had no effect on vascular AT1R and AT2R expression. Significance Losartan administered at the onset of prehypertension is more effective than amlodipine in ameliorating hypertension and improving vascular remodeling and function, which is likely mediated by the renin–angiotensin–aldosterone system.

Domain adaptation aims to learn an adaptive classifier for target data using the labelled source data from a different distribution. Most proposed works construct cross-domain classifier by exploring one-sided property of the input data, i.e., either geometric or statistical property. Therefore they may ignore the complementarity between the two properties. Moreover, many previous methods implement knowledge transfer with two separated steps: divergence minimization and classifier construction, which degrades the adaptation robustness. In order to address such problems, we propose a u nified c ross-domain classification method via g eometric and s tatistical adaptations (UCGS). UCGS models the divergence minimization and classifier construction in a unified way based on structural risk minimization principle and coupled adaptations theory. Specifically, UCGS constructs an adaptive model by simultaneously minimizing the structural risk on labelled source data, using Maximum Mean Discrepancy (MMD) criterion to implement statistical adaptation, and flexibly employing the Nystrom method to explore the geometric connections between domains. A domain-invariant graph is successfully constructed to link the two domains geometrically. The standard supervised methods can be used to instantiate UCGS to handle inter-domain classification problems. Comprehensive experiments show the superiority of UCGS on several real-world datasets.

To determine the arc stability during underwater wet welding in boric acid solutions with various concentrations, a real-time electrical signal acquisition system is established. Four methods for determining the arc stability, i.e., dynamic characteristics, probability distribution of voltage and current, current and voltage cyclograms, and coefficients of variation of voltage and current, were adopted. There were two short-circuited modes, which referred to the long-periodic short-circuits model and the short-periodic short-circuits model. Metal transfer images of the two modes were obtained, and the proportion of the long-periodic model increased with the concentration of boric acid in aqueous solution. The arc stability during underwater wet welding deteriorated with increasing the boric acid concentration.

Objective: To investigate the level of 20-Hydroxyeicosatetraenoic acid (20-HETE) in model of pulmonary hypertension (PH) and its effect on the proliferation of pulmonary arterial smooth muscle cells (PASMCs). Methods Twenty male Sprague-Dawley rats were randomly divided into two groups, including control group and PH group. PH was induced by intra-peritoneal injection of 20 mg/kg monocrotaline (MCT) twice in a week in 10 rats, and control rats were given equal amount of saline. Mean pulmonary arterial pressure (mPAP), right ventricular hypertrophy index (RVHI) and pulmonary vascular remodeling index (WA%, WT%) were assessed at the week 4. The levels of 20-HETE were analysed by liquid chromatography tandem-mass spectrometry (LC-MS/MS). EdU test was used to determine the proliferation of PASMCs. Intracellular levels of reactive oxygen species (ROS) were detected using DCFH-DA dye. Results (1) Prominent right ventricular hypertrophy and pulmonary vascular remodeling were verified in PH rats; (2) 20-HETE levels in lung tissue and serum were significantly lifted in PH rats; (3) Increased 20-HETE levels in cell culture supernatants were significantly noted in hypoxia condition; (4) Proliferation of PASMCs was induced by 20-HETE and hypoxia, and was inhibited by HET0016; (5) Production of ROS was elevated by 20-HETE and hypoxia, and was reduced by HET0016; Conclusion Vascular remodeling was demonstrated in PH rats. 20-HETE levels were significantly increased in PH rats and under hypoxia condition. PASMCs proliferation and ROS production were elevated by 20-HETE and could be inhibited by HET0016, a specific inhibitor of 20-HETE. Taken together, changes in the level of 20-HETE may be related to the excessive proliferation of PASMCs in PH rats.

Glycerol was chosen to simulate the petroleum environment and the welding process and joint’s properties were investigated with a research method referred to underwater wet welding (UWW) in order to study the technology of oil pipeline emergency repair on the inner surface. The droplet transfer process, the molten pool fluctuation behavior and the mechanical properties of the welded joint were studied compared with UWW. Because of the higher viscosity, the bubbles’ volume was larger and rose more slowly in glycerol than that in water. Meanwhile, the droplet transfer process in glycerol was more stable and the welded joints were smoother and more glabrous than those underwater ones. The heat dissipation was slowed down by a low heat dissipation rate of glycerol and the direction was fixed by small scale convection, resulting in a constantly vertical growth of crystal grains in welding seam. Besides, the two welded joints had nearly the same microhardness value except that in the β area, where the fine grains improved the hardness of the welded joint in water. A higher tensile strength (580 MPa) and lower impact toughness (116 J/cm2) welded joint were obtained in glycerol due to the effect of the giant columnar crystal region.

Based on an X-ray imaging system, the characteristic of underwater wet welding droplets was revealed, which was deemed to be the essential factor contributing to the unstable underwater wet welding process. By observing the X-ray images, it is confirmed that the inflation-exhaustion process in the droplet occurred during the entire process from the droplet forming to entering into the molten pool. The void in the droplet leaded to lower gravity and greater repulsive force and deteriorated the welding process stability. On the one hand, the spatter-like droplet transfer mode was relatively easy to occur, increasing the frequency of the droplet repelled spatter. On the other hand, the arc easily broke and the arc stability was deteriorated. In addition, the droplet rupture process tended to promote the generation of gas escaping spatter. The process was effected by material gas solubility, the surface tension, the temperature according to the establishing force models.

In this paper, we formulate image annotation as a Multi-correlation Learning to Rank (MLRank) problem, i.e., ranking the relevance of tags to an image considering the visual similarity and the semantic relevance. Unlike typical learning to rank algorithms, which assume that the ranking objects are independent, we attempt to rank relational data by exploring the consistency between ''visual similarity'' and ''semantic relevance''. The consistency means that similar images are usually annotated with relevant tags to reflect similar semantic themes, and vice versa. We define the two cases as the image-bias consistency and the tag-bias consistency respectively, which are both formulated into the optimization problem for rank learning. To obtain an explicit solution of the ranking model, we relax the optimization problem in two manners by attaching the constraints corresponding to the image-bias and tag-bias consistency with different sequential orders respectively, which lead to a uniform ranking model. Experimental results show that the proposed MLRank method outperforms the state-of-the-arts on three benchmarks including Corel5K, IAPR TC12 and NUS-WIDE.

Automatically learning and grouping key motion patterns in a traffic scene captured by a static camera is a fundamental and challenging task for intelligent video surveillance. To learn motion patterns, trajectory obtained by object tracking is parameterized, and scene image is spatially and evenly divided into multiple regular cell blocks which potentially contain several primary motion patterns. Then, for each block, Gaussian Mixture Model (GMM) is adopted to learn its motion patterns based on the parameters of trajectories. Grouping motion pattern can be done by clustering blocks indirectly, and each cluster of blocks corresponds to a certain motion pattern. For one particular block, each of its motion pattern (Gaussian component) can be viewed as an instance, and all motion patterns (Gaussian components) constitute a bag which can correspond to multiple semantic clusters. Therefore, blocks can be grouped as a Multi-instance Multi-cluster Learning (MIMCL) problem, and a novel Maximum Margin Multi-instance Multi-cluster Learning (M^4L) algorithm is proposed. To avoid processing a difficult optimization problem, M^4L is further relaxed and solved by making use of a combination of the Cutting Plane method and Constrained Concave-Convex Procedure (CCCP). Extensive experiments are conducted on multiple real world video sequences containing various patterns and the results validate the effectiveness of our proposed approach.

International trade promotes economic development and leads to peace and stability in a country. Political instability affects trade through its direct effect on income and prices and indirectly through its influence on investment in physical capital. This study aims to examine the impact of foreign political instability on Chinese exports. A panel data set of 121 importing countries, covering time period from 1988 to 2011 is used to investigate the potential impact of foreign political instability on Chinese exports. The data was analyzed using the alternative dynamic panel and dynamic system generalized method of moments (SGMM). Three measures of political instability including political safety, revolutionary wars and adverse regime change have been used to analyze the impact of foreign political instability on Chinese exports. Results suggest that foreign political instability (adverse regime change) has negative and statistically significant impact on Chinese export. The results further show that income and real exchange rate have a positive and significant impact on Chinese exports.

To build an effective dimensionality reduction model usually requires sufficient data. Otherwise, traditional dimensionality reduction methods might be less effective. However, sufficient data cannot always be guaranteed in real applications. In this paper we focus on performing unsupervised dimensionality reduction on the high-dimensional and small-sized data, in which the dimensionality of target data is high and the number of target data is small. To handle the problem, we propose a novel Self-taught Dimensionality Reduction (STDR) approach, which is able to transfer external knowledge (or information) from freely available external (or auxiliary) data to the high-dimensional and small-sized target data. The proposed STDR consists of three steps: First, the bases are learnt from sufficient external data, which might come from the same ''type'' or ''modality'' of target data. The bases are the common part between external data and target data, i.e., the external knowledge (or information). Second, target data are reconstructed by the learnt bases by proposing a novel joint graph sparse coding model, which not only provides robust reconstruction ability but also preserves the local structures amongst target data in the original space. This process transfers the external knowledge (i.e., the learnt bases) to target data. Moreover, the proposed solver to the proposed model is theoretically guaranteed that the objective function of the proposed model converges to the global optimum. After this, target data are mapped into the learnt basis space, and are sparsely represented by the bases, i.e., represented by parts of the bases. Third, the sparse features (that is, the rows with zero (or small) values) of the new representations of target data are deleted for achieving the effectiveness and the efficiency. That is, this step performs feature selection on the new representations of target data. Finally, experimental results at various types of datasets show the proposed STDR outperforms the state-of-the-art algorithms in terms of k-means clustering performance.

In this paper, we propose a novel method named Mixed Kernel CCA (MKCCA) to achieve easy yet accurate implementation of dimensionality reduction. MKCCA consists of two major steps. First, the high dimensional data space is mapped into the reproducing kernel Hilbert space (RKHS) rather than the Hilbert space, with a mixture of kernels, i.e. a linear combination between a local kernel and a global kernel. Meanwhile, a uniform design for experiments with mixtures is also introduced for model selection. Second, in the new RKHS, Kernel CCA is further improved by performing Principal Component Analysis (PCA) followed by CCA for effective dimensionality reduction. We prove that MKCCA can actually be decomposed into two separate components, i.e. PCA and CCA, which can be used to better remove noises and tackle the issue of trivial learning existing in CCA or traditional Kernel CCA. After this, the proposed MKCCA can be implemented in multiple types of learning, such as multi-view learning, supervised learning, semi-supervised learning, and transfer learning, with the reduced data. We show its superiority over existing methods in different types of learning by extensive experimental results.

Human action recognition is a challenging task due to significant intra-class variations, occlusion, and background clutter. Most of the existing work use the action models based on statistic learning algorithms for classification. To achieve good performance on recognition, a large amount of the labeled samples are therefore required to train the sophisticated action models. However, collecting labeled samples is labor-intensive. To tackle this problem, we propose a boosted multi-class semi-supervised learning algorithm in which the co-EM algorithm is adopted to leverage the information from unlabeled data. Three key issues are addressed in this paper. Firstly, we formulate the action recognition in a multi-class semi-supervised learning problem to deal with the insufficient labeled data and high computational expense. Secondly, boosted co-EM is employed for the semi-supervised model construction. To overcome the high dimensional feature space, weighted multiple discriminant analysis (WMDA) is used to project the features into low dimensional subspaces in which the Gaussian mixture models (GMM) are trained and boosting scheme is used to integrate the subspace models. Thirdly, we present the upper bound of the training error in multi-class framework, which is able to guide the novel classifier construction. In theory, the proposed solution is proved to minimize this upper error bound. Experimental results have shown good performance on public datasets.

AdaBoost has been applied to object detection to construct the detectors with high performance of discrimination and generalization by single-feature learner. However, the poor discriminative power of extremely weak single-feature learners limits its application for general object detection. In this paper, we propose a novel comprehensive learner design mechanism toward effective object detection in terms of both discrimination and generalization abilities. Firstly, the part-sense multi-feature learners are designed to linearly combine the multiple local features to improve the descriptive and discriminative capacity of the learner. Secondly, we formulate the feature selection in part-sense multi-feature learner as a weighted LASSO regression. Using Least Angle Regression (LARS) method, our approach can choose features adaptively, efficiently and as few as possible to guarantee generalization performance. Finally, a robust L1-regularized gradient boosting is proposed to integrate our part-sense sparse features learner into an object classifier. Extensive experiments and comparisons on the face dataset and the human dataset show the proposed approach outperforms the traditional single-feature learner and other multi-feature learners in discriminative and generalization abilities.

In this paper, we propose a topographic subspace learning algorithm, named key-coding learning, which utilizes irrelevant unlabeled auxiliary data to facilitate image classification and retrieval tasks. It is worth noticing that we do not need to assume the auxiliary data follows the same class labels or generative distribution as the target training data. Firstly, the subspace model is learnt from enormous scale- and rotation-invariant SURF descriptors extracted from auxiliary and training images, which makes model insensitive to geometric and photometric image transformation. Then the bases of model are pooled by clustering to generate topographic basis banks. We provide insights to show that the topographic model is highly biologically plausible in simulating the complex cells in the visual cortex. Finally we generate the succinct sparse representations by mapping target data into this topographic model. Due to the capability of transferring knowledge, the proposed topographic subspace model can effectively address insufficient training data problem for image classification and is also helpful for generating discriminative features for image retrieval. Intensive experiments are conducted on three image datasets to evaluate the performance of our proposed model, the experimental results are encouraging and promising.

To investigate long-term influence of early treatment of spontaneously hypertensive rats (SHR) with losartan (Los) on blood pressure, left ventricular hypertrophy and expression of angiotensin type 1 receptor-associated protein (ATRAP) in myocardium, and to explore the role of DNA methylation in the

Fluorites are one of the main vein minerals in the Maoniuping REE deposit, Sichuan Province, China. This paper analyzes the Sr and Nd isotopic compositions of fluorites in the orefield. Their ( 87 Sr/ 86 Sr)o and ( 143 Nd/ 144 Nd) 0 (0.706031–0.706237 and 0.512370–0.512412, respectively) are similar to those of carbonatites (0.706074–0.706149 and 0.512383–0.512406, respectively) and syenites (0.705972–0.706302 and 0.512378–0.512405, respectively) in the orefield and all lie in the narrow range between EM I and EM II in the diagram of ( 87 Sr/ 86 Sr) i vs. ( 143 Nd/ 144 Nd) i . In combination with other geological and geochemical data, we suggested that the ore-forming fluids of the Maoniuping REE deposit mainly came from syenitecarbonatite magmas.

Pure Gd1−xNaxCu2O4 and Nd1−yNayCu2O4 crystals were synthesized by using the anodic electrocrystallization technique from molten KOH/NaOH/KNO3 at 280–300°C. The resulting crystals were chemically and physically characterized by using SEM, EDX, XRD, XPS and magnetic susceptibility measurements.

Large Ba1−xKxBiO3 (BKBO) single crystals were electrosyntheiszed in air by using KOH/KNO3 flux. The effect of KNO3 concentration (CKNO3) in the flux on the K content (x) and superconductivity of BKBO was also investigated. The results show that addition of KNO3 into KOH not only makes the electrolyte stable in air, but also promotes the growth of BKBO crystals. The results also show that the electrodeposited BKBO crystals have a superconducting transition at 31 K when CKNO3 (wt.%)≤17%. However, the superconductivity is suppressed when CKNO3>25%. EDX analyses reveal that CKNO3 affects the K content, for example, x=0.40 for CKNO3≤17%, however, x decreases with increasing CKNO3 when CKNO3≥25%.

La1-x(Sr,Na)(x)Cu2O4 crystals were prepared by the molten salt electrochemical method while La2-y(Sr,Na)(y)CuO4 superconductors were prepared by direct precipitation from the same molten salt. The resulting rare earth cuprates were characterized by using SEM, EDX, XRD, XPS and magnetic susceptibility measurements. (C) 1998 Elsevier Science Ltd. All rights reserved.