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We study the satellite formation reconfiguration within a fixed orbit transfer time and aim to minimize or balance its fuel consumption. In this study, the formation will end up with an in-plane formation regardless of its initial state. We take consideration of the constraints of relative motion equation, initial and terminal states, collision avoidance, and maximum thrust. We attach an unknown offset in the terminal state of the formation and adapt a convex-programming-based iterative approach to jointly optimize the fuel usage and the offset of the formations. The proposed algorithms clearly outperform benchmarks in all the numerically tested situations with respect to both minimization of the total fuel consumption and minimization of the maximum fuel usages across all the satellites of the formation. [ABSTRACT FROM AUTHOR]

Video super-resolution converts low-resolution videos to sharp high-resolution ones. In order to make better use of temporal information in video super-resolution, we design inverse recurrent net and hybrid local fusion. We concatenate the original low-resolution input sequence and its inverse sequence repeatedly. The new sequence is viewed as a combination of different stages, and is processed sequentially by using orent net. The outputs of the last two stages in opposite directions are fused to generate the final images. Our inverse recurrent net can extract more bidirectional temporal information in the input sequence, without adding parameter to the corresponding unidirectional recurrent net. We also propose a hybrid local fusion method which uses parallel fusion and cascade fusion for incorporating sliding-window-based methods into our inverse recurrent net. Extensive experimental results demonstrate the effectiveness of the proposed inverse recurrent net and hybrid local fusion, in terms of visual quality and quantitative evaluations. The code will be released at https://github.com/5ofwind. [ABSTRACT FROM AUTHOR]

Deep convolutional neural network has made great achievements in sea–land clutter classification for over-the-horizon radar (OTHR). The premise is that a large number of labeled training samples must be provided for a sea–land clutter classifier. In practical engineering applications, it is relatively easy to obtain label-free sea–land clutter samples. However, the labeling process is extremely cumbersome and requires expertise in the field of OTHR. To solve this problem, we propose an improved generative adversarial network, namely weighted loss semi-supervised generative adversarial network (WL-SSGAN). Specifically, we propose a joint feature matching loss by weighting the middle layer features of the discriminator of semi-supervised generative adversarial network (SSGAN). Furthermore, we propose the weighted loss of WL-SSGAN by linearly weighting the standard adversarial loss of SSGAN and the joint feature matching loss. The classification performance of WL-SSGAN is evaluated on sea–land clutter datasets. The experimental results show that WL-SSGAN can improve the performance of the fully supervised classifier with only a small number of labeled samples by utilizing a large number of unlabeled sea–land clutter samples. Further, the proposed weighted loss is superior to both the adversarial loss and the feature matching loss. Additionally, we compare WL-SSGAN with conventional semi-supervised classification methods and demonstrate that WL-SSGAN achieves the highest classification accuracy. [ABSTRACT FROM AUTHOR]

In this article, we consider the problem of optimally selecting a subset of transmitters from a transmitter set available to a multiple-input and multiple-output radar network. The aim is to minimize the location estimation error of underlying targets under a power constraint. We formulate it as a minimum-variance estimation problem and show that the underlying variance reduction function is submodular. From the properties of submodularity, we present a balanced selection policy which minimizes the worst-case error value using a minimax strategy. A greedy algorithm with guaranteed performance with respect to optimal solutions is given to efficiently implement the scheduling policy. The effectiveness and the efficiency of the proposed algorithm are demonstrated in simulated examples. [ABSTRACT FROM AUTHOR]

Deep learning occupies an undisputed dominance in crowd counting. This paper proposes a novel convolutional neural network architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposed SegCrowdNet still adaptively highlights the human head region and suppresses the non‐head region by segmentation. With the guidance of an attention mechanism, the proposed SegCrowdNet pays more attention to the human head region and automatically encodes the highly refined density map. The crowd count can be obtained by integrating the density map. To adapt the variation of crowd counts, SegCrowdNet intelligently classifies the crowd count of each image into several groups. In addition, the multi‐scale features are learned and extracted in the proposed SegCrowdNet to overcome the scale variations of the crowd. To verify the effectiveness of this proposed method, extensive experiments are conducted on four challenging datasets. The results demonstrate that the proposed SegCrowdNet achieves excellent performance compared with the state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Context: Diclofenac and celastrol are always used together for the treatment of rheumatoid arthritis; the herb-drug interaction potential between diclofenac and celastrol is still unknown. Objective: This study investigates the effects of diclofenac on the pharmacokinetics of celastrol in rats. Materials and methods: Twelve male Sprague-Dawley rats were divided into two groups and received celastrol (1 mg/kg) or both celastrol (1 mg/kg) and diclofenac (10 mg/kg) by oral gavage, and blood samples were collected via the oculi chorioideae vein and determined using the LC-MS method developed in this study. Additionally, the effects of diclofenac on the transport of celastrol were investigated using a Caco-2 cell transwell model. Results: Diclofenac could significantly (p < 0.05) decrease the Cmax (from 66.93 ± 10.28 to 41.25 ± 8.06 ng/mL) and AUC0-t (from 765.84 ± 163.61 to 451.33 ± 110.88 μg × h/L) of celastrol in rats. The efflux ratio of celastrol increased significantly (p < 0.05) from 3.12 to 4.55 with the treatment of diclofenac. Discussion and conclusion: These results indicated that diclofenac could decrease the system exposure of celastrol in rats when they are co-administered, and these effects might be exerted via decreasing its absorption in intestine. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel multi-task semi-supervised method. To sufficiently exploit massive unlabeled data, multi-task pseudo-labels and global to local self-correction strategy are proposed. Specifically, labeled images and massive amounts of unlabeled images with proposed multi-task pseudo-labels are leveraged for model optimization. The density level of the whole image is predicted in classification task. The density is estimated in density regression task. The crowd area is segmented out in segmentation task. To suppress incorrect predictions caused by the inevitable noises from some unlabeled data misleading the model, the counting relationship between classification task and density task is exploited to propose the global self-correction strategy, and the semantic consistency between density task and segmentation task is mined to propose the local self-correction strategy. The classification task and segmentation task contribute in generating the final highly refined density map from the density task. Extensive experiments on six benchmark datasets indicate the superiority of our method over the SOTA methods in semi-supervised paradigm. [ABSTRACT FROM AUTHOR]

Over the past two years deep convolutional neural networks have pushed the performance of computer vision systems to soaring heights on semantic segmentation. In this study, the authors present a novel semantic segmentation method of using a deep fully convolutional neural network to achieve image segmentation results with more precise boundary localisation. The above segmentation engine is trainable, and consists of an encoder network with widening residual skipped connections and a decoder network with a pixel‐wise classification layer. Here the encoder network with widening residual skipped connections allows the combination of shallow layer features and deep layer semantic features, and the decoder network with classification layer maps the low‐resolution encoder features to full resolution image with pixel‐wise classification. The experimental results on PASCAL VOC 2012 semantic segmentation dataset and Cityscapes dataset show that the proposed method is effective and competitive. [ABSTRACT FROM AUTHOR]

We study the generic problem of path optimization for a critical infrastructure link between two locations on the surface of the Earth in the vicinity of earthquake-prone areas. The problem has two (conflicting) objective functions, one for minimizing the construction cost of the link and the other for minimizing the number of potential repairs along the link in the wake of earthquakes. In our model, the Earth's surface is approximated by a triangulated manifold, and ground motion intensity data are used to provide a measure of repair rate. We approach the multiobjective variational problem by first converting it into a single objective variational problem using the weighted sum method. Then, we show that the problem can be further transformed into an Eikonal equation and solved by a computationally efficient algorithm based on the fast marching method. Extensive simulations are performed on real-world three-dimensional geographical data, from which we obtain Pareto optimal solutions that provide insight and guidance to design trade-offs between cost effectiveness and seismic resilience. [ABSTRACT FROM AUTHOR]

Whether it be a road, railway track, pipeline, or fiber-optic cable, achieving a low probability of “cable” damage is important for the proper operation of modern infrastructure. This paper considers the fundamental problem of how to connect two points with a cable that crosses an earthquake fault line. We first develop a model, under certain general assumptions, for the cable break probability. Then, we formulate a multiobjective optimization problem, with cable cost and probability of cable break being the two objectives. For two important and meaningful sets of cable shape alternatives, the Pareto front is determined for these two objectives. All the analytical results are verified by simulations. [ABSTRACT FROM AUTHOR]

Due to the limited capture range of common imaging sensors, a scene with high dynamic range usually cannot be well described by a single still image because some regions in it may be under-exposed or over-exposed. In this paper, a new multi-exposure fusion method based on dense scale invariant feature transform (SIFT) is presented. In our algorithm, the dense SIFT descriptor is first employed as the activity level measurement to extract local details from source images, and then adopted to remove ghosting artifacts when the captured scene is dynamic with moving objects. Furthermore, two popular weight distribution strategies for local contrast extraction, namely, “weighted-average” and “winner-take-all” are studied in this paper. The effects of these two strategies on the fusion results are compared and discussed. Experimental results demonstrate the effectiveness of the proposed method in terms of both visual quality and objective evaluation. [ABSTRACT FROM AUTHOR]

In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR‐based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub‐dictionaries are learned from numerous high‐quality image patches which have been pre‐classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub‐dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi‐focus and multi‐modal image sets demonstrate that the ASR‐based fusion method can outperform the conventional SR‐based method in terms of both visual quality and objective assessment. [ABSTRACT FROM AUTHOR]

Highlights: [•] FFSA system is designed from biomechanical information of human foot motion. [•] The sensing element conductance value is proportional to the force applied on it. [•] Foot–ground contact area formed by the edge sensing elements of ECA. [•] Feasibility and validity of FFSA system are experimentally tested in IPF system. [Copyright &y& Elsevier]

Background: The nucleoprotein (NP) of influenza A virus is a multifunctional protein that plays a critical role in the replication and transcription of the viral genome. Therefore, examining host factors that interact with NP may shed light on the mechanism of host restriction barriers and the tissue tropism of influenza A virus. Here, Cyclophilin E (CypE), a member of the peptidyl-propyl cis-trans isomerase (PPIase) family, was found to bind to NP and inhibit viral replication and transcription. Methodology/Principal Findings: In the present study, CypE was found to interact with NP but not with the other components of the viral ribonucleoprotein complex (vRNP): PB1, PB2, and PA. Mutagenesis data revealed that the CypE domain comprised of residues 137-186 is responsible for its binding to NP. Functional analysis results indicated that CypE is a negative regulator in the influenza virus life cycle. Furthermore, knock-down of CypE resulted in increased levels of three types of viral RNA, suggesting that CypE negatively affects viral replication and transcription. Moreover, up-regulation of CypE inhibited the activity of influenza viral polymerase. We determined that the molecular mechanism by which CypE negatively regulates influenza virus replication and transcription is by interfering with NP self-association and the NP-PB1 and NP-PB2 interactions. Conclusions/Significance: CypE is a host restriction factor that inhibits the functions of NP, as well as viral replication and transcription, by impairing the formation of the vRNP. The data presented here will help us to better understand the molecular mechanisms of host restriction barriers, host adaptation, and tissue tropism of influenza A virus. [ABSTRACT FROM AUTHOR]

The investigation of community structures in complex networks is an important issue in many domains and disciplines. In this paper, we propose a novel method to address the problem based on evaluation of the community structure. By testing the proposed algorithm on artificial and real-world networks, experimental results demonstrate that our approach is both accurate and fast. Our algorithm may shed light on uncovering the universal principles of network architectures and topologies. [ABSTRACT FROM AUTHOR]

• Utilizing spatial correlation of ionosphere is important in OTHR target tracking. • GMRF represents ionospheric parameters more accurately for OTHR target tracking. • Joint estimation and decision using both OTHR and ionosondes measurements helps. The ionosphere is the propagation medium for radio waves transmitted by an over-the-horizon radar (OTHR). Ionospheric parameters, typically, virtual ionospheric heights (VIHs), are required to perform coordinate registration for OTHR multitarget tracking and localization. The inaccuracy of ionospheric parameters has a significant deleterious effect on the target localization of OTHR. Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms. In this paper, we consider the variation of the ionosphere with location and the spatial correlation of the ionosphere. We use a Gaussian Markov random field (GMRF) to model the VIHs, providing a more accurate representation of the VIHs for OTHR target tracking. Based on expectation-conditional maximization and GMRF modeling of the VIHs, we propose a novel joint optimization solution, namely ECM-GMRF, to perform target state estimation, multipath data association and VIHs estimation simultaneously. In ECM-GMRF, the measurements from both ionosondes and OTHR are exploited to estimate the VIHs, leading to a better estimation of the VIHs which improves the accuracy of data association and target state estimation, and vice versa. The simulation indicates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• A new multiscale geometrical analysis tool called multiscale shearing non-local guided averaging filter is constructed to decompose the source images. • An anti-noise spatial frequency is adopted to measure the clarity of the source images. • The non-local guided averaging filter is used to refine the focus measure map of each approximate subband. • Multiscale convolutional sparse representation is introduced to merge the directional detail subbands. The multiscale transform based image fusion method cannot effectively preserve detail information and easily produce artifacts. Faced with these problems, we present a novel multi-focus fusion method based on multiscale shearing non-local guided averaging filter (MSNLGA). First, we construct a new multiscale geometrical analysis tool called MSNLGA, which combines the non-local guided averaging filter with the shearing filter bank. The MSNLGA can represent the intrinsic geometric structure of image sparsely because of its good property in multiscale, multi-direction and shift-invariance. Then, the MSNLGA is used to decompose source images to obtain approximate subbands and directional detail subbands. For the approximate subbands, we extract the anti-noise spatial frequency feature from the source images to guide its fusion. For the directional detail subbands, we introduce the convolutional sparse representation, which is a model that can achieve sparse representation of an entire subband, to represent each subband so as to obtain the activity level measurement to fuse directional detail subbands. Finally, the fused image can be obtained by the inverse MSNLGA of the fused subbands. The experimental results show that the proposed method can be competitive with or even outperform the state-of-the-art fusion methods in terms of both visual and quantitative evaluations. [ABSTRACT FROM AUTHOR]

The dense optical flow estimation under occlusion is a challenging task. Occlusion may result in ambiguity in optical flow estimation, while accurate occlusion detection can reduce the error. In this paper, we propose a robust optical flow estimation algorithm with reliable occlusion detection. Firstly, the occlusion areas in successive video frames are detected by integrating various information from multiple sources including feature matching, motion edges, warped images and occlusion consistency. Then optimization function with occlusion coefficient and selective region smoothing are used to obtain the optical flow estimation of the non-occlusion areas and occlusion areas respectively. Experimental results show that the algorithm proposed in this paper is an effective algorithm for dense optical flow estimation. [ABSTRACT FROM AUTHOR]

We consider multitarget detection and tracking problem for a class of multipath detection system where one target may generate multiple measurements via multiple propagation paths, and the association relationship among targets, measurements, and propagation paths is unknown. In order to effectively utilize multipath measurements from one target to improve detection and tracking performance, a tracker has to handle high-dimensional estimation of latent variables including target existence state, target kinematic state, and multipath data association. Based on variational Bayesian inference, we propose a novel joint detection and tracking algorithm that incorporates multipath data association, target detection, and target state estimation in a unified Bayesian framework. The posterior probabilities of these latent variables are derived in a closed-form iterative manner, which is effective for reducing the performance deterioration caused by the coupling between estimation errors and identification errors. Loopy belief propagation is exploited to approximately calculate the probability of multipath data association, saving the computational cost significantly. Simulation results of over-the-horizon radar multitarget tracking show that the proposed algorithm outperforms multihypothesis multipath track fusion and multidetection (hypothesis-oriented) multiple hypothesis tracker, especially under low signal-to-noise ratio circumstance. [ABSTRACT FROM AUTHOR]

Crowd counting is a challenging problem due to the scene complexity and scale variation. Although deep learning has achieved great improvement in crowd counting, scene complexity affects the judgement of these methods and they usually regard some objects as people mistakenly; causing potentially enormous errors in the crowd counting result. To address the problem, we propose a novel end-to-end model called Crowd Attention Convolutional Neural Network (CAT-CNN). Our CAT-CNN can adaptively assess the importance of a human head at each pixel location by automatically encoding a confidence map. With the guidance of the confidence map, the position of human head in estimated density map gets more attention to encode the final density map, which can avoid enormous misjudgements effectively. The crowd count can be obtained by integrating the final density map. To encode a highly refined density map, the total crowd count of each image is classified in a designed classification task and we first explicitly map the prior of the population-level category to feature maps. To verify the efficiency of our proposed method, extensive experiments are conducted on three highly challenging datasets. Results establish the superiority of our method over many state-of-the-art methods. [ABSTRACT FROM AUTHOR]

By integrating the information contained in multiple images of the same scene into one composite image, pixel-level image fusion is recognized as having high significance in a variety of fields including medical imaging, digital photography, remote sensing, video surveillance, etc. In recent years, deep learning (DL) has achieved great success in a number of computer vision and image processing problems. The application of DL techniques in the field of pixel-level image fusion has also emerged as an active topic in the last three years. This survey paper presents a systematic review of the DL-based pixel-level image fusion literature. Specifically, we first summarize the main difficulties that exist in conventional image fusion research and discuss the advantages that DL can offer to address each of these problems. Then, the recent achievements in DL-based image fusion are reviewed in detail. More than a dozen recently proposed image fusion methods based on DL techniques including convolutional neural networks (CNNs), convolutional sparse representation (CSR) and stacked autoencoders (SAEs) are introduced. At last, by summarizing the existing DL-based image fusion methods into several generic frameworks and presenting a potential DL-based framework for developing objective evaluation metrics, we put forward some prospects for the future study on this topic. The key issues and challenges that exist in each framework are discussed. [ABSTRACT FROM AUTHOR]

Maintenance for nuclear fusion vessel is crucial, yet it faces great difficulty due to the complex internal physical and geometric conditions. Since the limitation on inherent strength, load, size, etc, a manipulator robot can only complete very limited tasks. Robotic arm systems for remote operation such as JET and MPD can carry certain tools to complete a variety of operating tasks, but it is difficult to achieve the system which is very complex. Therefore, if the inherent idea of using a single robot to complete the specified functions can change, it is possible to make the problems simpler and easier to solve by adding auxiliary robots working together with the robotic arm systems to complete the assigned tasks. Under the above background, based on the deeply analyzing and refining the functional requirements of the vessel operation robot, proceeding from the perspective of ability to move and carry a certain operating device, this paper presents a wormlike creeping mobile robot walking on the V-shaped circular slot inside a nuclear fusion vessel such as EAST (Experimental Advanced Superconducting Tokamak). We have designed and implemented the principle prototype of the robot which has chain structure with n (n ≥2) creeping units. Each creeping unit is of three-part structure, which consists of fore segment, mid segment and back segment connected by bidirectional universal joint. The fore and back segments stretch the paws to contact the surface of V-shaped slot, while the mid segment realizes the overall movement of robot. In order to evaluate the design and implementation of the mobile robot platform, functional analysis is devoted to modules of robot. Especially for the requirements of walking stability on the V-shaped slot, creeping gait planning is analyzed to construct a multi-axis coordinating movement control scheme for creeping motion. Finally, by using the simulated EAST nuclear fusion vessel, the principle prototype is tested, and the preliminary evaluation of its functional index and related technical parameters is carried out. The experimental results show that the principle prototype of the robot can achieve stable walking on the V-shaped circular slot at the bottom of the nuclear fusion vessel, and it has a certain ability to carry equipments. [ABSTRACT FROM AUTHOR]

In image fusion literature, multi-scale transform (MST) and sparse representation (SR) are two most widely used signal/image representation theories. This paper presents a general image fusion framework by combining MST and SR to simultaneously overcome the inherent defects of both the MST- and SR-based fusion methods. In our fusion framework, the MST is firstly performed on each of the pre-registered source images to obtain their low-pass and high-pass coefficients. Then, the low-pass bands are merged with a SR-based fusion approach while the high-pass bands are fused using the absolute values of coefficients as activity level measurement. The fused image is finally obtained by performing the inverse MST on the merged coefficients. The advantages of the proposed fusion framework over individual MST- or SR-based method are first exhibited in detail from a theoretical point of view, and then experimentally verified with multi-focus, visible-infrared and medical image fusion. In particular, six popular multi-scale transforms, which are Laplacian pyramid (LP), ratio of low-pass pyramid (RP), discrete wavelet transform (DWT), dual-tree complex wavelet transform (DTCWT), curvelet transform (CVT) and nonsubsampled contourlet transform (NSCT), with different decomposition levels ranging from one to four are tested in our experiments. By comparing the fused results subjectively and objectively, we give the best-performed fusion method under the proposed framework for each category of image fusion. The effect of the sliding window’s step length is also investigated. Furthermore, experimental results demonstrate that the proposed fusion framework can obtain state-of-the-art performance, especially for the fusion of multimodal images. [ABSTRACT FROM AUTHOR]

Multi-focus image fusion technique is an important approach to obtain a composite image with all objects in focus. The key point of multi-focus image fusion is to develop an effective activity level measurement to evaluate the clarity of source images. This paper proposes a novel image fusion method for multi-focus images with dense scale invariant feature transform (SIFT). The main novelty of this work is that it shows the great potential of image local features such as the dense SIFT used for image fusion. Particularly, the local feature descriptor can not only be employed as the activity level measurement, but also be used to match the mis-registered pixels between multiple source images to improve the quality of the fused image. In our algorithm, via the sliding window technique, the dense SIFT descriptor is first used to measure the activity level of source image patches to obtain an initial decision map, and then the decision map is refined with feature matching and local focus measure comparison. Experimental results demonstrate that the proposed method can be competitive with or even outperform the state-of-the-art fusion methods in terms of both subjective visual perception and objective evaluation metrics. [ABSTRACT FROM AUTHOR]

The different reconstruction parameters of CT imaging lead to domain shifts, which limits the generalization of deep learning models and their applications in computer-aided diagnosis systems. In this paper, we investigate the multi-source domain generalization (DG) problem in the context of lung nodule detection from CT images. We first identify the reconstructed convolution kernel as the key parameter leading to domain shifts. Accordingly, we reorganize the public LUNA16 dataset into a domain generalization benchmark, i.e.,, LUNA-DG. Then, we propose a novel DG method by adversarial frequency alignment (AFA). Specifically, we devise an adaptive transition module (ATM) to learn a frequency attention map that can align different domain images in a common frequency domain. For this purpose, a fidelity discriminator and a multi-domain discriminator are used to train the ATM alternately and adversarially. In addition, to mitigate the issue of ineffective gradient back-propagation in naive multi-domain adversarial learning, we propose a novel random domain adversarial learning (RDAL) strategy that can back-propagate effective gradient signals and gradually reduce the gap between multiple domains. The ATM can be combined with nodule detection models through differentiable Fast Fourier Transform (FFT) and inverse FFT, allowing end-to-end training. Experimental results on both LUNA-DG and our in-house datasets validate the superiority of AFA over representative DG methods. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, several sufficient conditions are obtained to guarantee that the n-dimensional cellular neural network can have even (⩽2 n ) memory patterns. In addition, the estimations of attractive domain of such stable memory patterns are obtained. These conditions, which can be directly derived from the parameters of the neural networks, are easily verified. A new design procedure for cellular neural networks is developed based on stability theory (rather than the well-known perceptron training algorithm), and the convergence in the new design procedure is guaranteed by the obtained local stability theorems. Finally, the validity and performance of the obtained results are illustrated by two examples. [Copyright &y& Elsevier]

Abstract: In this Letter, we have shown that the n-dimensional cellular neural network and delay cellular neural network can have not more than memory patterns, can have memory patterns which are locally exponentially stable. And we have obtained the estimates of attractive domain of such locally exponentially stable memory patterns. In addition, we have derived the conditions that the equilibrium point is locally exponentially stable when the equilibrium point locate the designated position. Some sufficient conditions have been obtained to guarantee the global exponential stability for the cellular neural networks. Those conditions can be directly derived from the parameters of the neural networks, are very easy to verified. The results presented in this Letter are the improvement and extension of the existed ones. Finally, the validity and performance of the results are illustrated by two simulation results. [Copyright &y& Elsevier]

This paper presents new theoretical results on global exponential stability of cellular neural networks with time-varying delays. The stability conditions depend on external inputs, connection weights and delays of cellular neural networks. Using these results, global exponential stability of cellular neural networks can be derived, and the estimate for location of equilibrium point can also be obtained. Finally, the simulating results demonstrate the validity and feasibility of our proposed approach. [ABSTRACT FROM AUTHOR]

Chest X‐ray images taken under different conditions follow different distributions, preventing the models trained on a domain from generalising well on the other domain. In this paper, a generative domain adaptation (GDA) method is proposed to address this issue and facilitate the learning process for downstream analysis. GDA adapts different domains to a virtual common one where images are aligned at the appearance level. To this end, a domain shared generator is used to transform the input images and two competitive discriminators are used to adversarially supervise the transforming process. The domain discriminator drives the generator to narrow the domain gap while the fidelity discriminator forces the generator to keep the inherent information. Moreover, a specific classification or detection network is attached to the generator to supervise it in a task‐oriented manner. Experiment results on a large‐scale dataset containing 46k chest X‐ray images demonstrate that GDA outperforms representative domain adaptation methods by a large margin for both disease classification and lesion detection as well as provides useful transformed images to assist experts for diagnosis. [ABSTRACT FROM AUTHOR]

In problems of parameter estimation from sensor data, the Fisher information provides a measure of the performance of the sensor; effectively, in an infinitesimal sense, how much information about the parameters can be obtained from the measurements. From the geometric viewpoint, it is a Riemannian metric on the manifold of parameters of the observed system. In this paper, we consider the case of parameterized sensors and answer the question, "How best to reconfigure a sensor (vary the parameters of the sensor) to optimize the information collected?" A change in the sensor parameters results in a corresponding change to the metric. We show that the change in information due to reconfiguration exactly corresponds to the natural metric on the infinite-dimensional space of Riemannian metrics on the parameter manifold, restricted to finite-dimensional sub-manifold determined by the sensor parameters. The distance measure on this configuration manifold is shown to provide optimal, dynamic sensor reconfiguration based on an information criterion. Geodesics on the configuration manifold are shown to optimize the information gain but only if the change is made at a certain rate. An example of configuring two bearings-only sensors to optimally locate a target is developed in detail to illustrate the mathematical machinery, with Fast Marching methods employed to efficiently calculate the geodesics and illustrate the practicality of using this approach. [ABSTRACT FROM AUTHOR]

Isoflurane inhalation can cause nerve damage, and miR-191 is abnormally expressed in nerve crush injuries. This study aimed to explore the effect of miR-191 on isoflurane-induced cognition impairment and neurotoxicity in vivo and in vitro, as well as its potential mechanism. Sprague-Dawley male rats and primary hippocampal neurons were applied and exposed to 2% isoflurane. The level of miR-191 was regulated both in vitro and in vivo to investigate the role of miR-191 in isoflurane-induced neurotoxicity. Morris water maze assay was used to evaluate the neurological function of rats. The level of miR-191 was measured by qRT-PCR. CCK-8 assay and Flow cytometry were applied to detect the cell viability and apoptosis. Dual luciferase reporter gene detection was used for the target gene analysis. miR-191 was up-regulated in the hippocampal tissues of rats exposed to isoflurane. Downregulation of miR-191 ameliorated isoflurane-induced cognition impairment, including the increase of the neurological score and the escape latency, and the decrease of the time spent in the original quadrant for the rats exposed to isoflurane. Isoflurane exposure inhibited hippocampal neuron viability and promoted cell apoptosis, which was reversed by down-regulation of miR-191. BDNF is a target gene of miR-191. Isoflurane causes some neurotoxic effect which is mediated through miR-191 abnormal expression targeting BDNF. Downregulation of miR-191 has a protective role against isoflurane-induced neurotoxicity, regulates the vitality and slows down neuronal cell apoptosis. [ABSTRACT FROM AUTHOR]

We present a robust multi-focus image fusion (MFIF) method based on lazy random walks (LRW) and multiscale focus measures in this paper. Firstly, the multiscale sum of the weighted modified Laplacian (MSWML) is used to extract the focus information of source images at different scales. Then, we compute the scale-consistent focus maps by collaboratively employing the local focus information as well as the global focus information. After that, the scale-consistent focus maps are fed into LRW-based estimation framework to produce the fusion weight maps. Finally, a weighted-sum strategy is used to generate the fused image. Experimental results show that our method consistently produces high quality fused images with few artifacts and achieves better performance than some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A unified message passing approximates the intractable inference of target tracking. • Posterior PDFs of latent variables are updated iteratively in a closed-loop manner. • Joint estimation and decision improves target detection and tracking performance. This paper considers the problem of detecting and tracking multiple maneuvering targets, which suffers from the intractable inference of high-dimensional latent variables that include target kinematic state, target visibility state, motion mode-model association, and data association. A unified message passing algorithm that combines belief propagation (BP) and mean-field (MF) approximation is proposed for simplifying the intractable inference. By assuming conjugate-exponential priors for target kinematic state, target visibility state, and motion mode-model association, the MF approximation decouples the joint inference of target kinematic state, target visibility state, motion mode-model association into individual low-dimensional inference, yielding simple message passing update equations. The BP is exploited to approximate the probabilities of data association events since it is compatible with hard constraints. Finally, the approximate posterior probability distributions are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue between the estimations of target kinematic state and target visibility state and decisions on motion mode-model association and data association. The performance of the proposed algorithm is demonstrated by comparing with the well-known multiple maneuvering target tracking algorithms, including interacting multiple model joint probabilistic data association, interacting multiple model hypothesis-oriented multiple hypothesis tracker and multiple model generalized labeled multi-Bernoulli. [ABSTRACT FROM AUTHOR]

Atorvastatin combined with amlodipine (ALDP) can efficiently treat the hypertension with coronary heart disease. However, the drug-drug interaction between atorvastatin and ALDP are still unknown. This study investigates the effects of atorvastatin on the pharmacokinetics of ALDP in rats and clarifies its main mechanism. The pharmacokinetic profiles of oral administration of ALDP (1 mg/kg) in Sprague-Dawley rats, with or without pretreatment of atorvastatin (1.5 mg/kg/d for 7 d) were investigated. The effects of atorvastatin on the metabolism of ALDP were also investigated using rat liver microsomes. The results showed that atorvastatin could significantly increase the peak plasma concentration (from 18.28 ± 2.65 to 24.13 ± 1.96 ng/mL) and decrease the oral clearance (from 4.57 ± 1.15 to 1.79 ± 0.28 L/h/kg) of ALDP. In the rat liver microsome systems, the intrinsic clearance rate of ALDP was decreased by the pretreatment with atorvastatin (39.26 ± 2.1 vs. 33.24 ± 3.3 μL/min/mg protein). Those results indicated that atorvastatin could significantly affect the pharmacokinetic of ALDP, via inhibiting the metabolism of ALDP in rats. [ABSTRACT FROM AUTHOR]

The fusion of infrared and visible images of the same scene aims to generate a composite image which can provide a more comprehensive description of the scene. In this paper, we propose an infrared and visible image fusion method based on convolutional neural networks (CNNs). In particular, a siamese convolutional network is applied to obtain a weight map which integrates the pixel activity information from two source images. This CNN-based approach can deal with two vital issues in image fusion as a whole, namely, activity level measurement and weight assignment. Considering the different imaging modalities of infrared and visible images, the merging procedure is conducted in a multi-scale manner via image pyramids and a local similarity-based strategy is adopted to adaptively adjust the fusion mode for the decomposed coefficients. Experimental results demonstrate that the proposed method can achieve state-of-the-art results in terms of both visual quality and objective assessment. [ABSTRACT FROM AUTHOR]

As is well known, activity level measurement and fusion rule are two crucial factors in image fusion. For most existing fusion methods, either in spatial domain or in a transform domain like wavelet, the activity level measurement is essentially implemented by designing local filters to extract high-frequency details, and the calculated clarity information of different source images are then compared using some elaborately designed rules to obtain a clarity/focus map. Consequently, the focus map contains the integrated clarity information, which is of great significance to various image fusion issues, such as multi-focus image fusion, multi-modal image fusion, etc. However, in order to achieve a satisfactory fusion performance, these two tasks are usually difficult to finish. In this study, we address this problem with a deep learning approach, aiming to learn a direct mapping between source images and focus map. To this end, a deep convolutional neural network (CNN) trained by high-quality image patches and their blurred versions is adopted to encode the mapping. The main novelty of this idea is that the activity level measurement and fusion rule can be jointly generated through learning a CNN model, which overcomes the difficulty faced by the existing fusion methods. Based on the above idea, a new multi-focus image fusion method is primarily proposed in this paper. Experimental results demonstrate that the proposed method can obtain state-of-the-art fusion performance in terms of both visual quality and objective assessment. The computational speed of the proposed method using parallel computing is fast enough for practical usage. The potential of the learned CNN model for some other-type image fusion issues is also briefly exhibited in the experiments. [ABSTRACT FROM AUTHOR]

Aims: Oxidative stress plays an important role in the development and progression of diabetic nephropathy (DN). We aimed to investigate if resveratrol (RSV) could ameliorate hyperglycemia-induced oxidative stress in renal tubules via modulating the SIRT1/FOXO3a pathway.Methods: The effects of RSV on diabetes rats were assessed by periodic acid-Schiff, Masson staining, immunohistochemistry, and western blot analyses. Additionally, oxidative indicators (such as catalase, superoxide dismutase, reactive oxygen species, and malondialdehyde), the deacetylase activity of SIRT1 and protein expressions of SIRT1, FOXO3a, and acetylated-FOXO3a were measured. These indicators were similarly evaluated in an in vitro study. Furthermore, the silencing of SIRT1 was used to confirm its role in the resistance to oxidative stress and the relationship between SIRT1 and FOXO3a in vitro.Results: After 16weeks of RSV treatment, the renal function and glomerulosclerosis of rats with DN was dramatically ameliorated. RSV treatment increased SIRT1 deacetylase activity, subsequently decreasing the expression of acetylated-FOXO3a and inhibiting the oxidative stress caused by hyperglycemia both in vivo and in vitro. The silencing of SIRT1 in HK-2 cells aggravated the high glucose-induced oxidative stress and overexpression of acetylated-FOXO3a; RSV treatment failed to protect against these effects.Conclusions: RSV modulates the SIRT1/FOXO3a pathway by increasing SIRT1 deacetylase activity, subsequently ameliorating hyperglycemia-induced renal tubular oxidative stress damage. This mechanism provides the basis for a new approach to developing an effective DN treatment, which is of great clinical significance for reducing the morbidity and mortality associated with DN. [ABSTRACT FROM AUTHOR]

Many target tracking problems can actually be cast as joint tracking problems where the underlying target state may only be observed via the relationship with a latent variable. In the presence of uncertainties in both observations and latent variable, which encapsulates the target tracking into a variational problem, the expectation–maximization (EM) method provides an iterative procedure under Bayesian inference framework to estimate the state of target in the process which minimizes the latent variable uncertainty. In this paper, we treat the joint tracking problem using a united framework under the EM method and provide a comprehensive overview of various EM approaches in joint tracking context from their necessity, benefits, and challenging viewpoints. Some examples on the EM application idea are presented. In addition, future research directions and open issues for using EM method in the joint tracking are given. [ABSTRACT FROM AUTHOR]

Reconstructing a full-resolution stereoscopic video from an asymmetric stereoscopic video is a challenging task. The existing approaches require depth information, which imposes an additional challenge in data acquisition. In this paper, we propose a novel scheme that is capable of obtaining super-resolution and depth estimation simultaneously from an asymmetric stereoscopic video. The proposed scheme models the video super-resolution and stereo matching with a unified energy function. Then, we apply an alternating optimization method to minimize this energy function, which can be implemented with a two-step algorithm. In the first step we calculate the initial depth map by using a region-based cooperative optimization technique while considering the temporal consistency in video. In the second step we resolve the super-resolution problem under the guidance of the depth information. It is effective because each step benefits from the additional improvement over the previous step. We iteratively update the two steps until stable depth and super-resolution results are obtained. We have conducted a series of experiments on public stereoscopic video sequences to evaluate the performance of the proposed method. Both objective indexes and subjective visual comparisons verify that the proposed scheme can achieve satisfactory super-resolution results and high-quality depth map simultaneously. In particular, the subjective evaluation experiments on a 3-D monitor show that this scheme outperforms others and achieves the best visual sharpness. [ABSTRACT FROM AUTHOR]

With the rapid advances in 3D capture and display technology, tag ranking for stereo images will be a potential application on web image retrieval. Directly extending the existing approaches to stereo images is problematic as it may neglect the representative 3D elements. In this paper, a novel automatic tag saliency ranking algorithm for stereo images is presented. Specifically, a novel method of interacting with stereo images is proposed to segment the two images into regions simultaneously. Then tags annotated on the image-level are propagated to the region-level via an improved multi-instance learning algorithm. In the next, a new 3D saliency detection algorithm is proposed using occlusion cues along with the contrast in color and disparity. And finally, tags are re-ranked according to the saliency values of the corresponding regions. Moreover, to evaluate the performance of our approach, an annotated stereo image dataset is set up. The experimental results on this dataset demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Monocular depth estimation from a single still image is one of the most challenging fundamental problems in scenes understanding. As a pixel level regression problem, the inherent continuous large range of depth itself causes three main difficulties: (1) The unbalance between large value and small value during regressing; (2) Exploiting multi-scale contextual information; (3) Preserving spatial and semantic structures. To overcome these difficulties, this paper presents a novel spatially coherent sliced network for monocular depth estimation (SCS-Net). It first uses feature pyramids network to form the feature fusions of hierarchical feature maps. Then, the depth is sliced to supervise the estimation in different ranges generated from the feature fusions of multi-scale contexts. The holistic depth is invoked as the supervised signal of aggregated feature maps to ensure the learning of global structure of the scene. The self-spatial-attention mechanism further takes advantage of the semantics of objects and the spatial pixel relations to maintain the coherence of space and semantics in depth sliced estimation. Finally, the regulations of second-order depth information further make the estimated boundaries not too smooth. Numerous ablation experiments and comparisons on three popular indoor and outdoor benchmark datasets indicate the effectiveness and robustness of the proposed approach. [Display omitted] • Spatially coherent sliced network regresses sliced depth from feature fusions pyramidally. • Our network introduces the scale and shift regression to maintain spatial coherence. • Self-spatial-attention mechanism refine potentially compromised semantic coherence. • Experimental results show competitive results and promising performance in urban road scenes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel Stereo-Vision-Assisted (SVA) model for depth map super-resolution. Given a low-resolution depth map as input, we recover a high-resolution depth map using the registered high-resolution color stereo image pair. First, based on the mutual benefits between raw depth map and features of high resolution color image, we model the relationship with two constraint terms of local and non-local priors and sufficiently explore their complementary nature. Moreover, by considering reliable disparity pixels calculated from stereo matching algorithm, we formulate a stereo disparity regularization term to further reinforce the preservation of fine depth detail. Hence, our SVA objective function includes a non-local prior constraint, a local prior constraint and a stereo disparity prior constraint. In addition, we employ an efficient algorithm to optimize the objective function. Experimental results demonstrate that our approach can obtain superior performance in comparison with other methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a closed loop method to resolve the multi-view super-resolution problem. For the mixed-resolution multi-view case, where the input is one high-resolution view along with its neighboring low-resolution views, our method can give the super-resolution results and obtain a high-quality depth map simultaneously. The closed loop method consists of two parts: part I, stereo matching and depth maps fusion; and part II, super-resolution. Under the guidance of the estimated depth information, the super-resolution problem can be formulated as an optimization problem. It can be solved approximately by a three-step method, which involves disparity-based pixel mapping, nonlocal construction and final fusion. Based on the super-resolution results, we can update the disparity maps and fuse them into a more reliable depth map. We repeat the loop several times until obtaining stable super-resolution results and depth maps simultaneously. The experimental results on public dataset show that the proposed method can achieve high-quality performance at different scale factors. [ABSTRACT FROM AUTHOR]

Motivated by tracking a manoeuvring target in electronic counter environments, the authors present the problem of joint estimation and identification of a class of discrete‐time stochastic systems with unknown inputs in both the plant and sensors. Based on the expectation‐maximum criterion, the joint optimisation scheme of state estimation, parameter identification and iteration terminate decision were derived. A numerical example of tracking a manoeuvring target accompanied range gate pull‐off is utilised to verify the proposed scheme. [ABSTRACT FROM AUTHOR]

Abstract: Social groups on photo sharing Websites, such as Flickr, are self-organized communities to share photos and conversations with common interest and have gained massive popularity. Currently, users have to manually assign each photo to the appropriated group. Manual assignment requires users to be familiar with existing photos in each group. It is intractable and tedious, and thus prohibits users from exploiting the relevant groups. For solution to the problem, group recommendation has attracted increasing attention recently, which aims to suggest groups to user for a particular photo. Existing works pose group recommendation as an automatic group prediction problem with a purpose of predicting the groups of each photo automatically. Despite of dramatic progress in automatic group prediction, the prediction results are still not accurate enough. In this paper, we propose an interactive group recommendation framework with Human-in-the-Loop. Given a user''s photo collection, we employ the pre-built group classifiers to predict the group of each photo. These predictions are used as the initial group recommendations. We then select a small number of representative photos from the collection and ask user to assign the groups of them. Once obtaining user''s feedbacks on the representative photos, we infer the groups of remaining photos through group propagation over multiple sparse graphs among the photos. We conduct experiment on 30 Flickr groups with 239,700 photos. The experimental results demonstrate that the proposed framework is able to provide accurate group recommendations with quite a small amount of user efforts. [Copyright &y& Elsevier]

Abstract: Recent research has discovered that leveraging ontology is an effective way to facilitate semantic video concept detection. As an explicit knowledge representation, a formal ontology definition usually consists of a lexicon, properties, and relations. In this paper, we present a comprehensive representation scheme for video semantic ontology in which all the three components are well studied. Specifically, we leverage LSCOM to construct the concept lexicon, describe concept property as the weights of different modalities which are obtained manually or by data-driven approach, and model two types of concept relations (i.e., pairwise correlation and hierarchical relation). In contrast with most existing ontologies which are only focused on one or two components for domain-specific videos, the proposed ontology is more comprehensive and general. To validate the effectiveness of this ontology, we further apply it to video concept detection. The experiments on TRECVID 2005 corpus have demonstrated a superior performance compared to existing key approaches to video concept detection. [Copyright &y& Elsevier]

Abstract: Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multiple labels. This framework is characterized by simultaneously exploiting the inherent correlations among multiple labels and the label consistency over the graph. Based on the proposed framework, we further develop two novel graph-based algorithms. We apply the proposed methods to video concept detection over TRECVID 2006 corpus and report superior performance compared to the state-of-the-art graph-based approaches and the representative semi-supervised multi-label learning methods. [Copyright &y& Elsevier]

A recent comment (Chang, 2014) theoretically demonstrated that for the linear system with the correlated process and measurement noises, the GASF framework proposed in the paper Wang et al. (2012) was equivalent to the conventional de-coupling filtering framework. In this note, we would further show that such equivalence between the two frameworks can be justified in a more general way, even for the nonlinear system. [ABSTRACT FROM AUTHOR]