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Energy harvesting (EH) aided communications hold a great potential in the design of green communication systems for their high energy efficiency. However, the random power supply due to EH may cause an intolerable delay in data transmission. To overcome this, a Reliable Energy Source (RES) is desired to provide transmission power when the large delay is induced. In this paper, we study the delay-optimal scheduling policy for EH aided communications with the constraint of average power provided by RES. More specifically, the delay-minimal scheduling is obtained through the two-dimensional Markov chain modeling and linear programming (LP) formulation. To further reduce the computational complexity, we present a value iteration algorithm, based on which we not only reveal a threshold-based structure of the delay-optimal scheduling policy for EH-aided communications with large-capacity batteries, but also conceive a low complexity policy that is asymptotically optimal. For EH-aided communications with finite-capacity batteries, we present a unified framework based on large deviation theory. The non-asymptotic framework demonstrates that the delay-power tradeoff curve of the low complexity scheduling policy is capable of converging to that of the delay-optimal policy exponentially as the capacity of the battery increases.

Multistatic SAR is able to observe targets from different angles simultaneously, which enhances the information acquiring capability. However, multistatic SAR can still be affected by electromagnetic jamming, resulting in the misinterpretation of multistatic SAR images. This article proposes a method to locate multiple range-deception jammers and suppress jamming signals. First, the echo model of multistatic SAR under a multijammer environment is established. Second, the detection of interested targets in multistatic SAR images can be achieved through visual saliency detection methods based on spectral residual. Third, location distribution features of false targets in multistatic SAR images are analyzed, and the Euclidean distance criteria are used to effectively distinguish false targets. Accurate localization is then achieved by combing multistatic SAR configuration information. Finally, using a linear constrained minimum variance beamforming algorithm to suppress jamming signals, multistatic SAR images without jamming signals can be obtained. Simulation results validate the effectiveness of the proposed method in this article.

In this work, we focus on the optimization of charging scheduling policy for shared electric vehicles (EVs) integrated with wind power generation. This problem is of significant importance nowadays because of the large adoption of EVs in modern societies and the increasing penetration of renewables. A particular challenge of the problem is the large action space, the size of which may increase exponentially with respect to the number of EVs in the system. This makes the problem difficult to solve in practice. A lot of efforts have been made to overcome the above difficulty. The previous study proposed least-laxity-longer-processing-time-first (LLLP) principle, a rule-based algorithm to schedule EVs’ battery charging. The LLLP principle assigns higher priority to vehicles with less laxity and longer processing time. We extend the LLLP principle and further study the structural property of the charging scheduling problem. The main contributions in this work are as follows. First, we show that the LLLP applies to our problem and may be used to narrow down the action space while preserving the global optimality. Second, we provide a modified LLLP algorithm that may construct a policy in $O(NT)$ , where $N$ is the number of the EVs and $T$ is the number of time steps in the scheduling problem. Third, we use numerical experiments to show that the new algorithm performs better than other existing algorithms, including the least-laxity-shorter-processing-time-first (LLSP) principle, the earliest-deadline-first (EDF) principle, and the latest-deadline-first (LDF) principle. The new algorithm finds near-optimal policies (within 1% performance loss) and is at least 40 times faster than CPLEX. We hope that this work provides insight into the charging scheduling of shared EVs in general.

Low-latency and energy-efficient wireless communication holds the potential of enabling the industrial internet of things (IIoT), automatic driving, and telesurgery. Cross-layer scheduling, which is aware of both the channel and queue states, has attracted considerable attention recently because it is capable of reducing the average delay substantially while meeting a given average power constraint. As a result, there has been considerable work, in which joint channel and buffer aware scheduling is formulated as a constrained Markov decision process (CMDP). In general, the optimal solution to a CMDP problem is characterized by the stationary probability of actions, yielding probabilistic cross-layer scheduling with possibly high complexity in practice. In this paper, we are interested in the low-latency and energy-efficient stationary cross-layer scheduling policy for wireless channels with continuous channel states, e.g. Rayleigh fading. It is interestingly shown that a deterministic cross-layer scheduling policy can achieve the optimal tradeoff between the average delay and power. In other words, the signaling complexity of cross-layer scheduling can be significantly reduced without causing sub-optimality. Simulation results also demonstrate that our work provides a low-complexity solution for low-latency and energy-efficient wireless communications.

The prevalence of online user-generated content has attracted great interest in textual sentiment analysis, which provides a low-cost yet effective way to discern consumers and markets. A mainstream of sentiment analysis is to construct a classification model with Bag-of-Words (BoW) features, but the large vocabulary base and skewed distribution of term frequency consistently pose research challenges, which is made even worse by the limited valid sentiment labels. In light of this, in this paper, we propose a novel method called Structural Holes based Sentiment Classifier (SHSC) for BoW-based sentiment classification. The key to SHSC is to reinforce the classification contribution of semantically rich words with clear-cut sentiment polarity. To this end, a word co-occurrence network is carefully constructed to represent both high and low frequency words. The work to find classification-inefficient words is then transformed into the identification of so-called bridge nodes that occupy the positions of structural holes in the network. Two interesting measures, i.e., information advantage rank and control advantage weight, are then designed elaborately for this purpose, which are based on the proposed sentiment-label propagation and short-path computation algorithms, respectively. SHSC finally feeds this information as the key regularizers into a simple regression model to guide parametric learning. Extensive experiments on real-world text datasets demonstrate the advantage of our SHSC model over competitive benchmarks, particularly when sentiment labels are scarce. The effectiveness of uncovering structural holes for sentiment classification is also carefully verified with some robustness checks and demonstration cases.

The fuzzy cognitive map (FCM) is a powerful model for system state prediction and interpretable knowledge representation. Recent years have witnessed the tremendous efforts devoted to enhancing the basic FCM, such as introducing temporal factors, uncertainty or fuzzy rules to improve interpretation, and introducing fuzzy neural networks or wavelets to improve time series prediction. But how to achieve high-precision yet interpretable prediction in cross-domain real-life applications remains a great challenge. In this article, we propose a novel FCM extension called deep FCM (DFCM) for multivariate time series forecasting, in order to take both the advantage of FCM in interpretation and the advantage of deep neural networks in prediction. Specifically, to improve the predictive power, DFCM leverages a fully connected neural network to model connections (relationships) among concepts in a system, and a recurrent neural network to model unknown exogenous factors that have influences on system dynamics. Moreover, to foster model interpretability encumbered by the embedded deep structures, a partial derivative-based approach is proposed to measure the connection strengths between concepts in DFCM. An alternate function gradient descent algorithm is then proposed for parameter inference. The effectiveness of DFCM is validated over four publicly available datasets with the presence of seven baselines. DFCM indeed provides an important clue to building interpretable predictors for real-life applications.

Background This study explored the relevance between rs1695 and susceptibility to the lung cancer in the Chinese Han population. Stratification analysis was conducted on the basis of age, gender, smoking status, tumor-related family history, and pathological type to observe relations between rs1695 and susceptibility to lung cancer in the subgroups. Methods A case-control study was performed with 974 lung cancer patients who were pathologically diagnosed and 1005 healthy cases based on physical examination to analyze the association between rs1695 and the risk of lung cancer. Results The frequencies of the AA, GA, and GG genotypes of rs1695 were 68.4%, 28.7%, and 2.9% in cases and 64.8%, 30.8%, and 4.2% in controls, respectively. After adjustment for age, gender, smoking status, and family history, it appears that the rs1695 G allele decreases the risk of lung cancer (OR = 0.811, 95% CI 0.684–0.961, P = 0.016). Moreover, compared with the AA genotype, the GA + GG genotype decreased lung cancer susceptibility (OR = 0.808, 95% CI 0.663–0.985, P = 0.035) and the GG genotype (OR = 0.591, 95% CI 0.347–0.988, P = 0.048). In a stratified analysis, the risk of lung cancer in the G allele carriers decreased among the males, patients without a tumor-related family history, and patients with lung adenocarcinoma, especially in smokers. Conclusion The polymorphism of locus rs1695 is related to the risk of lung cancer and is expected to be a target for the prediction of lung cancer.

In synthetic aperture radar (SAR) imaging, moving target is generally mixed with stationary targets. Meanwhile, the image of a moving target is distorted and displaced due to the lack of its prior velocity information. Furthermore, imaging of a moving target for geosynchronous (GEO) spaceborne-airborne bistatic SAR (GEO SA-BiSAR) is a more challenging problem because the echo is sub-Nyquist sampled in azimuth. In this article, a simultaneous moving and stationary target imaging method for GEO SA-BiSAR is proposed. First, range models and the corresponding echo models of moving and stationary targets are established. The observation models for both moving and stationary targets with two receiving channels are derived based on the inverse of an efficient imaging algorithm. After that, the imaging problem of moving and stationary targets is modeled as a joint velocity estimation and sparse decomposition problem, which aims at optimizing the entropy of the moving target image and residual error of the formed images at the same time. Finally, a joint optimization method based on the particle swarm optimization (PSO) method and alternating direction method of multipliers (ADMM) is applied to achieve the imaging of moving and stationary targets and estimation of the moving target velocity. With two receiving channels, the accurate separation and focusing of stationary and moving targets as well as the precise estimation of moving target velocity can be achieved with sub-Nyquist sampling echo. Simulation results are presented to validate the effectiveness of the proposed method.

Objectives: In order to explore the factors that lead to the difference of outcome between strategic change and organizational performance.Methods: This paper takes the correlation coefficient between strategic change and organizational performance as the effect value, and conducts Meta integration analysis and Meta regression analysis on 23 important literatures involving 7225 enterprise samples from 2008 to 2018.Results: Firstly, the meta-integration method is used to estimate the overall results of existing empirical studies. The results show that strategic change is significantly positively correlated with organizational performance. Second, there are too many moderating factors that lead to different conclusions of research on strategic change-organizational performance. Therefore, Meta regression method is used to explore the impact of 10 moderating factors on the relationship between the two. First, positive strategic change has a better moderating effect; Second, compared with small and medium-sized enterprises, the performance of strategic change of large-scale enterprises is better; Third, the more recent the year of publication, the less supportive the relationship between strategic change and organizational performance; Fourth, there is no significant difference between data types and the relationship between strategic change and organizational performance, but data time span has a significant negative moderating effect.Conclusions: This study shows that the quantitative literature study of meta analysis not only helps to resolve the existing theoretical disputes, but also helps to explore new theoretical studies in the context of COVID-19 epidemic in the future, meanwhile, it also provides a novel framework for quantitative literature analysis.

Recent years have witnessed the emergence of worldwide megalopolises and the accompanying public safety events, making urban safety a top priority in modern urban management. Among various threats, dangerous goods such as gas and hazardous chemicals transported through cities have bred repeated tragedies and become the deadly “bomb” we sleep with every day. While tremendous research efforts have been devoted to dealing with dangerous goods transportation (DGT) issues, further study is still in great need to quantify this problem and explore its intrinsic dynamics from a big data perspective. In this article, we present a novel system called DGeye , to feature a fusion between DGT trajectory data and residential population data for dangers perception and prediction. Specifically, DGeye first develops a probabilistic graphical model-based approach to mine spatio-temporally adjacent risk patterns from population-aware risk trajectories. Then, DGeye builds the novel causality network among risk patterns for risk pain-point identification, risk source attribution, and online risky state prediction. Experiments on both Beijing and Tianjin cities demonstrate the effectiveness of DGeye in real-life DGT risk management. As a case in point, our report powered by DGeye successfully drove the government to lay down gas pipelines for the famous Guijie food street in Beijing.

Big data analytics (BDA) is regarded as an advanced tool for achieving sustainable development as part of the grand challenges (GCs). However, it is not clear how BDA can be used by data scientists to solve the GCs with multisource data in a cross-disciplinary approach. Based on a case study of city-based dangerous goods transportation (DGT), this paper explores how data scientists use BDA to triangulate data, methods, knowledge and solutions for solving GCs. The contribution of this study is threefold: (1) it contributes to research on GCs and discusses how BDA can be used in problem solving for multidomain GCs from a management perspective; (2) it enriches the theory of information triangulation and proposes several steps for information triangulation in BDA to solve GCs; and (3) it contributes some practical implications for the management of organizations when solving social problems and pursuing sustainable development.

Wildfires in the U.S. are growing in extent and severity, causing billions of dollars in damage each year. Wildfire policy has increased its focus on integrating fire suppression with risk mitigation through fuel reduction. In this context incentives to elicit management activities that mitigate fire risk are critical, because landowners may not carry out sufficient risk mitigation on their own. We examine the effectiveness and welfare implications of several incentive policies, including two novel approaches not studied in the context of wildfire management: liability rules and voluntary agreements. Our analysis uses a threshold model of public good provision, which allows for each landowner's mitigation choices to depend on the total amount of mitigation in the landscape. Our results suggest that the risk mitigation threshold is critical in determining the effectiveness and welfare effects of different incentive programs. When the threshold is high, only voluntary agreements and cost sharing can increase mitigation effort and welfare. Negligence standards can also be effective and welfare enhancing but only when the mitigation threshold is sufficiently low.

PurposeThe purpose of this paper evaluates the effects of the Great Western Development (GWD) policy on agricultural intensification, land use, agricultural production and rural poverty in western China.Design/methodology/approachThe authors collect county-level data on land use, input application, grain crop production, income, poverty and geophysical characteristics for 1996–2005 and use a quasi-natural experimental design of difference-in-differences (DD) in the empirical analysis.FindingsResults suggest that the GWD policy significantly increased the grain crop production in western China. This increase resulted from higher yield, with increased fertilizer use and agricultural electricity consumption per hectare, and more land allocated to grow grain crops. The policy also increased land-use concentration, reduced crop diversity and alleviated rural poverty in western China.Originality/valueThis paper makes three contributions. First, the authors add to the growing literature on the GWD policy by evaluating its effects on farm household decisions and exploring the mechanisms and broad socioeconomic impacts in western China. Second, the authors take advantage of a quasi-natural experimental design to improve the identification strategy where input use, land allocation, production and off-farm labor participation are all endogenous in a farm household. Third, the authors explore a long list of variables within one integrated dataset to present a comprehensive picture of the impact of the GWD policy.

We have developed a miniature two-photon microscope equipped with an axial scanning mechanism and a long-working-distance miniature objective to enable multi-plane imaging over a volume of 420 × 420 × 180 μm3 at a lateral resolution of ~1 μm. Together with the detachable design that permits long-term recurring imaging, our miniature two-photon microscope can help decipher neuronal mechanisms in freely behaving animals. A two-photon miniature microscope with enlarged field of view and axial scanning capabilities has been developed and applied in freely moving mice.

Traditional computation is driven by parallel accelerators or distributed computation nodes in order to improve computing performance, save energy, and decrease delays in accessing memory. Recently, artificial intelligent algorithms, frameworks, and computing models are growing to help with high computational performance.

Due to its ability of forming continuous images for a ground scene of interest, the video synthetic aperture radar (SAR) has been studied in recent years. However, as video SAR needs to reconstruct many frames, the data are of enormous amount and the imaging process is of large computational cost, which limits its applications. In this article, we exploit the redundancy property of multiframe video SAR data, which can be modeled as low-rank tensor, and formulate the video SAR imaging process as a low-rank tensor recovery problem, which is solved by an efficient alternating minimization method. We empirically compare the proposed method with several state-of-the-art video SAR imaging algorithms, including the fast back-projection (FBP) method and the compressed sensing (CS)-based method. Experiments on both simulated and real data show that the proposed low-rank tensor-based method requires significantly less amount of data samples while achieving similar or better imaging performance.

Objective: The expression and function of platinum transporters affect drug tissue concentration and therapeutic effects. We had previously characterized functional variant of platinum intake transporter SLC31A1 gene. We aimed to investigate the association of platinum efflux transporter gene ABCG2 polymorphism and combined ABCG2 and SLC31A1 polymorphisms with clinical outcomes of NSCLC patients receiving platinum-based chemotherapy. Methods: We genotyped thirteen tagging and functional SNPs of ABCG2 in 1004 patients, and assessed their association with response, toxicity and survival using unconditional logistic regression and Cox proportional hazards regression analyses respectively. Results: Nonsynonymous rs2231142 (odds ratio [OR] 2.07; 95 % confidence interval [CI] 1.26-3.63), rs1871744 (OR 0.60; 95 % CI 0.42-0.87) and their haplotype and diplotype were associated with objective response. Rs4148157 was associated with shorter overall survival (Log-rank P = 0.002; hazard ratio [HR] 1.22; 95 % CI 1.05-1.42). Furthermore, the combined SLC31A1 rs2233914 and ABCG2 rs1871744 genotype was significantly associated with poor response (OR 0.31; 95 % CI 0.17-0.56; P interaction = 0.003). And the combined genotypes of the functional rs10759637 of SLC31A1 and the nonsynonymous rs2231142 (Log-rank P = 5.20×10-5; HR 1.47; 95 % CI 1.19-1.81; P interaction = 0.007) or linked rs4148157 of ABCG2 were significantly associated with poor survival. Conclusion: This study reveals divergent association of ABCG2 polymorphism with response and survival of NSCLC patients receiving platinum-based chemotherapy, demonstrates the combined effects of functional variants of ABCG2 and SLC31A1 on clinical outcomes, and highlights pharmacogenetic relevance of platinum transporter genes interaction.

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naive Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.

In recent years, short text topic modeling has drawn considerable attentions from interdisciplinary researchers. Various customized topic models have been proposed to tackle the semantic sparseness nature of short texts. Most (if not all) of them follow the bag-of-words assumption, which, however, is not adequate since word order and phrases are often critical to capturing the meaning of texts. On the other hand, while some existing topic models are sensitive to word order, they do not perform well on short texts due to the severe data sparseness. To address these issues, we propose the Pseudo-document-based Topical N-Grams model (PTNG), which alleviates the data sparsity problem of short texts while is sensitive to word order. Extensive experiments on three real-world data sets with state-of-the-art baselines demonstrate the high quality of topics learned by PTNG according to UCI coherence scores and more discriminative semantic representation of short texts according to classification results.

With the 1 h-averaged data of atmospheric precipitable water vapor (PWV) for 2015–2018 retrieved from 18 ground-based Global Positioning System (GPS) observation stations near Poyang Lake (PL), China, the diurnal variations of the PWV during midsummer (July-August) are studied by the harmonic method. Results show that significant diurnal variations of PWV are found at the 18 GPS stations. The harmonics with 24 h cycle (diurnal cycle) over PL (i.e., Duchang and Poyang) and Nanchang city only have about 50% (or even smaller than 50%) of variance contribution with the amplitude of about 0.2 mm, while above 70% (or even 80%) of variance contribution occurs elsewhere around PL, with the amplitude of about 0.9 mm. The harmonics with diurnal cycles in most stations peak from afternoon to evening (i.e., 1200-2000 LST), but one exception is Duchang site, where the diurnal cycle peaks in the morning (i.e., 1000 LST). Moreover, the harmonics with 12 h cycle (semidiurnal cycle) have the relatively uniform amplitude of about 0.2 mm, but their variance contributions show uneven distribution, with the contributions of about or above 50% in PL and Nanchang city (the semidiurnal cycles peak about 0000 LST or 1200 LST) and below 30% (or even 10%) in other areas. The preliminary diagnosis analysis shows that the diurnal variation of the low-level (below 850 hPa) air temperature (increasing after the sunrise, decreasing after the sunset, and peaking around 1400-1800 LST) may be responsible for the diurnal cycle. Moreover, in PL (Duchang and Poyang) and Nanchang city, the effects (heating or cooling) of lake and urban, the diurnal variation of the 10 m wind over PL, and the acceleration of PL on overlying air also contributed to the diurnal variation of PWV.

It is always deemed crucial to identify the key factors that could have significant impact on the stock market trend. Recently, an interesting phenomenon has emerged that some of President Trump’s posts in Twitter can surge into a dominant role on the stock market for a certain time period, although studies along this line are still in their infancy. Therefore, in this article, we study whether and how this new-rising information can help boost the performance of stock market prediction. Specifically, we have found that the echoing reinforced effect of financial news with Trump’s market-related tweets can influence the market movement—that is, some of Trump’s tweets directly impact the stock market in a short time, and the impact can be further intensified when it echoes with other financial news reports. Along this line, we propose a deep information echoing model to predict the hourly stock market trend, such as the rise and fall of the Dow Jones Industrial Average. In particular, to model the discovered echoing reinforced impact, we design a novel information echoing module with a gating mechanism in a sequential deep learning framework to capture the fused knowledge from both Trump’s tweets and financial news. Extensive experiments have been conducted on the real-world U.S. stock market data to validate the effectiveness of our model and its interpretability in understanding the usability of Trump’s posts. Our proposed deep echoing model outperforms other baselines by achieving the best accuracy of 60.42% and obtains remarkable accumulated profits in a trading simulation, which confirms our assumption that Trump’s tweets contain indicative information for short-term market trends. Furthermore, we find that Trump’s tweets about trade and political events are more likely to be associated with short-term market movement, and it seems interesting that the impact would not degrade as time passes.

Lifelong topic model (LTM), an emerging paradigm for never-ending topic learning, aims to yield higher-quality topics as time passes through knowledge accumulated from the past yet learned for the future. In this paper, we propose a novel lifelong topic model based on non-negative matrix factorization (NMF), called Affinity Regularized NMF for LTM (NMF-LTM), which to our best knowledge is distinctive from the popular LDA-based LTMs. NMF-LTM achieves lifelong learning by introducing word-word graph Laplacian as semantic affinity regularization. Other priors such as sparsity, diversity, and between-class affinity are incorporated as well for better performance, and a theoretical guarantee is provided for the algorithmic convergence to a local minimum. Extensive experiments on various public corpora demonstrate the effectiveness of NMF-LTM, particularly its human-like behaviors in two carefully designed learning tasks and the ability in topic modeling of big data. A further exploration of semantic relatedness in knowledge graphs and a case study on a large-scale real-world corpus exhibit the strength of NMF-LTM in discovering high-quality topics in an efficient and robust way.

Employer Brand Evaluation (EBE) is to understand an employer’s unique characteristics to identify competitive edges. Traditional approaches rely heavily on employers’ financial information, including financial reports and filings submitted to the Securities and Exchange Commission (SEC), which may not be readily available for private companies. Fortunately, online recruitment services provide a variety of employers’ information from their employees’ online ratings and comments, which enables EBE from an employee’s perspective. To this end, in this article, we propose a method named Company Profiling–based Collaborative Topic Regression (CPCTR) to collaboratively model both textual (i.e., reviews) and numerical information (i.e., salaries and ratings) for learning latent structural patterns of employer brands. With identified patterns, we can effectively conduct both qualitative opinion analysis and quantitative salary benchmarking. Moreover, a Gaussian processes--based extension, GPCTR, is proposed to capture the complex correlation among heterogeneous information. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and generalizability of our methods in real-life applications. The results clearly show that our methods outperform state-of-the-art baselines and enable a comprehensive understanding of EBE.

Solar thermal and coal are two major energy sources in solar aided power generation (SAPG) systems. Reasonably distinguishing the solar shares of the total electricity output is a problem that must be solved. This study modeled the energy balance, exergy balance and solar exergy balance of the key subsystems in an SAPG system. Meanwhile, this study also designed a new solar share evaluation method by tracing solar exergy flows and allocating exergy destruction. How and how much solar exergy was transferred from the sun to electricity could be theoretically estimated by analyzing the distributions of solar share and solar exergy destruction. The proposed method considered energy level differences between solar energy and coal without assuming plant efficiency or solar shares in each energy stream to be constant. The proposed method could not only be used to calculate solar-generated electricity on the point of exergy transfer and destruction, but also be further used for excavating the potential of solar share expansion.

Vendor capabilities are an important research stream in the IT outsourcing literature. The extant studies have generated rich findings on how vendors develop capabilities, but these findings have mainly been based on the condition of vendors improving their existing business. How vendors develop capabilities to move up the value chain has received little prior research attention. A key challenge for vendors in moving up the value chain is the development of new capabilities while maintaining the existing ones. This challenge is consistent with achieving exploration and exploitation in organizational ambidexterity. Hence, we adopt organizational ambidexterity as our theoretical lens. By conducting a case study on four IT outsourcing vendors in China, we identify four classifications for achieving ambidexterity in capability development and the context for each classification. These classifications are based on combinations of different structural arrangements and processes for achieving ambidexterity. Our study contributes to the research on IT outsourcing vendor capabilities by improving our understanding of capability development in vendors moving up the value chain. It also contributes to the organizational ambidexterity literature by identifying the factors that influence the choices of different structural arrangements and processes.

Fraud detection is usually compared to finding a needle in a haystack and remains a challenging task because fraudulent acts are buried in massive amounts of normal behavior and true intentions may be disguised in a single snapshot. Indeed, fraudulent incidents usually take place in consecutive time steps to gain illegal benefits, which provides unique clues for probing fraudulent behavior by considering a complete behavioral sequence rather than detecting fraud from a snapshot of behavior. Additionally, fraudulent behavior may involve different parties, such that the interaction patterns between sources and targets can help distinguish fraudulent acts from normal behavior. Therefore, in this paper, we model the attributed behavioral sequences generated from consecutive behaviors in order to capture the sequential patterns, while those that deviate from the pattern can be detected as fraudulence. Considering the characteristics of the behavioral sequence, we propose a novel model, NHA-LSTM, by augmenting the traditional LSTM with a modified forget gate, where the interval time between consecutive time steps is considered. Furthermore, we design a self-historical attention mechanism to allow for long time dependencies, which can help identify repeated or cyclical appearances. In addition, we propose an enhanced network embedding method, FraudWalk, to construct embeddings for the nodes in the interaction network with regard to higher-order interactions and particular time constraints for revealing potential group fraudulence. The node embeddings, along with the feature vectors, are fed into the model to capture the interactions between sources and targets. To validate the effectiveness of sequential behavior embeddings, we experiment on a real-world telecommunication dataset with prediction and classification tasks based on the learned embeddings. The experimental results show that the learned embeddings can better identify fraudulent behavior. Finally, we visualize the weights of the attention mechanism to provide a rational interpretation of human behavioral patterns.

In recent years, the research of multistatic synthetic aperture radar (SAR) has become a hot spot because of its great potentials in military and civil applications. However, it is inevitably disturbed by electronic countermeasures. In the multistatic SAR image, the false target caused by transmitting deceptive jamming leads to misjudgment of the image information. In this article, a new antideceptive jamming method for multistatic SAR is proposed. This method can locate the deceptive jammer and effectively suppress it. First of all, the echo model of multistatic SAR in the jamming environment is set up. Based on this model, the relationship expression for the location of jammer is derived. Then, we use the maximally stable extremal region method and the Euclidean-distance-based method to detect and identify false target in the multistatic SAR image. By using the location information of false target, receivers, and transmitters, the expression of the jammer localization is solved to locate the jammer. Finally, the minimum variance distortionless response beamforming method and image mosaic method are used to effectively suppress the jamming. The simulation results show that the method is effective.

BackgroundThe 5-year survival rate of patients with lung cancer in China is less than 20% and predicting their prognosis is challenging. We investigated the association between a common non-synonymous single nucleotide polymorphism (SNP), rs7214723, in the Ca2+/calmodulin-dependent protein kinase kinase 1 (CAMKK1) gene and the prognosis of patients with lung cancer.MethodsGenomic DNA was extracted from the blood samples of 839 patients with lung cancer, recruited from Changhai Hospital (n = 536) and Taizhou Institute of Health Sciences (n = 352), and genotyped using the SNPscan technique. The association between patient prognosis and the genotypic data for CAMKK1 was analyzed using a multivariate Cox proportional hazards model adjusted for multiple potential confounders. The CRISPR/Cas9 gene-editing system was used to introduce point mutations in the CAMKK1 rs7214723 of A549 and NCI-H358 cells. Subsequently, Cell proliferation and migration ability were assessed with the Cell Counting Kit-8 and scratch assay. The Annexin V-FITC apoptosis detection kit was used to detect cell apoptosis.ResultsThe CAMKK1 rs7214723 recessive CC genotype conferred significantly better overall survival (CC vs. TT + TC: adjusted hazard ratio = 0.78, 95% confidence interval [CI], 0.61-1.00, P = 0.049) than the TT + TC genotypes. Stratified analysis showed that the CAMKK1 rs7214723 CC genotype and recessive CC genotype conferred a significantly decreased risk of death in patients who were male, had a smoking history, or had stage III + IV cancer, compared with the TT and TT + TC genotypes. Relative to the TT + TC genotypes, the rs7214723 recessive CC genotype was also associated with a decreased risk of death in patients aged < 60 years (CC vs. TT + TC: adjusted hazard ratio = 0.59, 95% CI, 0.37-0.93, P = 0.024) and patients with squamous cell carcinoma (CC vs. TT + TC: adjusted hazard ratio = 0.65, 95% CI, 0.44-0.98, P = 0.038). Remarkably, CRISPR/Cas9-guided single nucleotide editing demonstrated that CAMKK1 rs7214723 T > C mutation significantly inhibits cell proliferation and migration and promotes cell apoptosis.ConclusionsCAMKK1 SNP rs7214723 may be a significant prognostic factor for the risk of death among patients with lung cancer.

PurposeGlobal interest in adopting the International Financial Reporting Standards (IFRS) has risen rapidly; however, the Middle Eastern and North African (MENA) countries have reacted differently towards the international diffusion. The purpose of this study is to examine the impact of the IFRS adoption/rejection decision on the quality of MENA region firms' financial reporting.Design/methodology/approachThe quality of accounting is examined through five metrics models to measure earnings smoothing, managing earnings towards a target and timely loss recognition. The research sample consists of nine countries over a period of ten years (2006–2015), resulting in 3,040 firm-year observations in the main phase, and 2,580 firm-year observations in the additional analysis.FindingsThe findings reveal that the overall sample of IFRS adopters in the MENA region has benefited from the adoption of IFRS, as the results show that there is a reduction in earnings management for IFRS adopters in comparison to local standards adopters. The sub-sample analyses also reveal that firms that adopted IFRS, in both the rentier (oil-dependent states) and non-rentier states, have a higher financial reporting quality than non-IFRS adopters. However, the magnitude of the financial reporting quality was higher for IFRS adopters in rentier states.Research limitations/implicationsSimilarly to previous research in this field, this study adopts a strict sample selection approach. Such an approach may limit the sample size, although the researchers have taken every possible step to ensure the use of an adequate sample size. The researchers acknowledge the strict period of ten years, despite having stated its rationale and importance of a more extended period to the quest of the paper.Practical implicationsThis research provides valuable input by evaluating the current status of MENA region firms' financial reporting quality, based on their followed accounting regime. The implications of this paper result in better-informed decisions for investors as the information contents of the annual reports enhance comparisons that facilitate the further flow of investments. This research also provides significant insight into the International Accounting Standards Board (IASB). The findings of this study will assist the IASB in understanding the MENA region by measuring the consequences of the countries' decisions on the quality of firms' financial reporting.Originality/valueThe findings of this study contribute to the literature by revealing that countries with medium levels of governance quality have benefited the most from the IFRS adoption, while IFRS adopters in countries with stronger governance quality demonstrate lower financial reporting quality.

Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset.

Video synthetic aperture radar (SAR) have received more and more attention in recent years as it can provide continuous images of the observed area. However, the enormous data generated by the multi-frame images in video SAR brings big challenges to its transmission, storage and processing, especially for small unmanned aerial vehicle (UAV) platform. In this paper, we aim at proposing an efficient video formation method for SAR system with reduced data. The video formation problem is modelled as a joint low-rank and sparse tensors recovery problem. After that, this problem is solved by an efficient tensor recovery method based on alternating direction method of multiplier. Compared with frequency-domain or time-domain imaging methods, the amount of data samples used can be greatly reduced. Numerical simulations validate the effectiveness of the proposed method.

Synthetic Aperture Radar (SAR) image is highly useful in civilian and military fields, such as ship detection, maritime search and rescue. Considering the target detection from SAR image, we propose a SAR image reconstruction and target extraction method via low-rank and sparsity constrains from under-sampled data. Firstly, the low-rank and sparsity constrains are incorporated into the SAR image reconstruction model, and the objective function is established based on Robust Principal Component Analysis (RPCA) theory. Then, the Augment Lagrange Multiplier (ALM) algorithm is used to transform this objective function to a convex optimization problem. Lastly, SAR image reconstruction and target extraction are obtained by Alternating Direction Method of Multipliers (ADMM) algorithm. The simulations are conducted to verify the effectiveness of the proposed method.

The clutter background poses a challenge to the detection and recognition of targets from synthetic aperture radar (SAR) images, especially when the target is submerged by the clutter. In this paper, we propose a target-oriented SAR waveform optimization method to deal with this problem. The proposed method constructs an optimization criterion jointing the signal-to-clutter ratio (SCR) and the resolution of transmitted waveform. Based on the prior information of the frequency response of the interested target, the clutter suppression performance and the range resolution performance are jointly optimized. The simulation results show that the proposed method can effectively improve the SCR of SAR image in the condition of low SCR, while the resolution performance is guaranteed.

Traditional synthetic aperture radar (SAR) imaging methods focus on high-resolution imaging of the observation scene. In the application of specific target imaging and detection, such as threat target search, the priori knowledge of target can be used for better task performance. Therefore, it is highly desirable to improve the image quality of the target. In this paper, we propose a SAR formation method based on the sparse dictionary. Firstly, the sparse dictionary is learned through the SAR images of a specific target via K-SVD method. Then the SAR formation model is established as a sparse reconstruction problem by incorporating the sparse dictionary. Lastly, the problem is solved via Augment Lagrange Multiplier (ALM) method and Alternating Direction Method of Multipliers (ADMM) method. The proposed method is validated by the simulation, and the results show that the proposed method can effectively reconstruct the target.

Multistatic SAR can obtain information from different angles simultaneously, it has application potential in many fields. However, it still be affected by jammers. When deceptive jammers jamming the SAR system, the false target is generated in the SAR image. In this paper, when the false target is generated on the sea by jammer which located at the coast, the anti-jamming method is proposed. Firstly, echo model of multistatic SAR in deceptive jamming environment is established. Then, false targets are detected and recognized. Next, the jammer localization method is proposed according to relationship between the jammer and false targets. Finally, the jammer location is obtained and jamming signal suppression is achieved. Simulation results validate the effectiveness of the proposed method in this paper.

Geosynchronous (GEO) spaceborne–airborne bistatic synthetic aperture radar (GEO-BiSAR), where the high-altitude transmitter provides continuous illumination for the receiver, is capable of providing benefits to remote sensing applications. However, obtaining a focused image with high efficiency is a challenging work. The severe 2-D space-variant range cell migration and Doppler modulation introduced by the two moving platforms make the echo hard to be focused. Moreover, the azimuth ambiguity due to the low pulse repetition frequency adopted by the GEO illuminator seriously deteriorates the quality of the final image. In order to simultaneously suppress the azimuth ambiguity and obtain well-focused images, a weighted fast factorized backprojection (FFBP) method is proposed for multichannel GEO BiSAR in this letter. First, the Doppler ambiguity of GEO BiSAR is analyzed and the azimuth multichannel receiving technique is introduced. Then, the multichannel transfer function for GEO BiSAR is derived. Based on the multichannel transfer function, a weighted FFBP method is proposed to achieve accurate focusing and ambiguity suppression. Finally, the simulation results validate the effectiveness of the proposed method.

In the era of big data, it is extremely challenging to decide what information to receive and filter out in order to effectively acquire high-quality information, particularly in social media where large-scale User Generated Contents (UGC) is widely and quickly disseminated. Considering that each individual user in social network can take actions to drive the process of information diffusion, it is naturally appealing to aggregate spreading information effectively at the individual level by regarding each user as a social sensor. Along this line, in this paper, we propose a framework for effective information acquisition in social media. To be more specific, we introduce a novel measurement, the preference-based Detection Ability to evaluate the ability of social sensors to detect diffusing events, and the problem of effective information acquisition is then reduced to achieving social sensing maximization through discovering valid social sensors. In pursuit of social sensing maximization, we propose two algorithms to resolve the longstanding problems in traditional greedy methods from the perspectives of efficiency and performance. On the one hand, we propose an efficient algorithm termed LeCELF, which resolves the redundant re-evaluations in the traditional Cost-Effective Lazy Forward (CELF) algorithm. On the other hand, we observe the participation paradox phenomenon in the social sensing network, and proceed to propose a randomized selection-based algorithm called FRIENDOM to choose social sensors to improve the effectiveness of information acquisition. Experiments on a disease spreading network and real-world microblog datasets have validated that LeCELF greatly reduces the running time, whereas FRIENDOM achieves a better detection performance. The proposed framework and corresponding algorithms can be applicable in many other settings in resolving information overload problems.

Organizational improvisation has been increasingly observed in information systems development (ISD), which leads to both positive and negative results. Extant studies have examined how to conduct effective improvisation, but findings are mainly based on variance-based models; a process model is missing. Our study fills this gap by conducting an in-depth case study of the Tencent messaging system development. Our study identifies a four-phase process model featuring, a continuous iteration between improvisational search and build. Our findings make important contributions to organizational improvisation literature in ISD and general management and provide a step-by-step guide for ISD managers to conduct effective improvisation.

Nowadays, people are facing more and more threats from solar UV radiation due to global ozone layer depletion and the ozone hole. Therefore, it is highly desirable to develop a fast, simple, and sensitive sensor for solar UV radiation. In this work, recently emerging perovskite quantum dots (PQDs) with excellent optical properties are used for sensing solar UV radiation. PQDs with high quantum yields are protected in solid paraffin to obtain paraffin-PQD composite (P-PQD) film. The hydrophobic and compact paraffin not only can protect PQDs from degradation by water and air for long-term use but also is transparent to UV light and thus maintains the high quantum yield of PQDs. The P-PQD film shows high sensing sensitivity for solar UV radiation based on which a novel visual ratiometric luminescence sensing strategy for UV light has been developed. The visual ratiometric luminescence sensor for solar UV radiation adopts a paraffin-encapsulated red fluorescence PQD (P-r-PQD) film as the sensing unit for UV light and a green luminescence light-emitting diode as reference light. When exposed to different solar UV radiation, the sensing film shows wide color variations from green to red, which can easily observed by the naked eye or recorded by a camera. On the basis of this, a simple and visualized solar UV index-reporting system has been established.

Tangeretin is a polymethoxylated favone isolated from the peel of citrus fruits that possesses a broad spectrum of pharmacological functions. However, there has been limited information regarding tangeretin’s potential protective impact on the myocardium. As the myocardium is widely recognized as a potential target of oxidative stress induced by physical fatigue, this study aimed to assess tangeretin’s potential protective effects related to myocardial oxidative stress in fatigued mice. Using an intervention/control protocol on 5 groups of mice (N = 50; 1 control; 4 intervention; 4 week duration) with exhaustive swimming as the measured outcome, we found that tangeretin enhanced the activity of antioxidant enzymes, reduced the level of oxidative stress and injury, and protected myocardial morphology and ultrastructure. It was also found that tangeretin activated the Keap1/Nrf2 signaling axis to up-regulate the expression of phase II antioxidant and detoxifying enzymes. These findings suggest that tangeretin may impart beneficial effects by reducing physical fatigue-associated myocardial injury.

Bistatic range sum (BRS) equation and Doppler equation of bistatic synthetic aperture radar (BiSAR) can be used to achieve target localization in BiSAR images. Because of the double square roots ex...

Purpose Lung cancer is the leading cause of cancer death worldwide, and the predominant risk factor for its development is smoking. Thymidylate synthase (TYMS) is a key enzyme in DNA synthesis that catalyzes the conversion of deoxyuridine monophosphate to dTMP. Rs931794, a single nucleotide polymorphism located in the TYMS gene, was suggested to be associated with cancer risk. Methods To analyze the interaction between rs3819102 and environmental factors on the risk of lung cancer in a Chinese population, single nucleotide polymorphismscan was used to genotype this polymorphism in 974 lung cancer cases and 1005 control subjects. Results The frequencies of TT, CT, and CC genotypes of TYMS rs3819102 were 61.8%, 32.9%, and 5.3% in controls, and 53.8%, 38.4%, and 7.8% in cases, respectively. Compared with the TT genotype, the CT (odds ratio [OR], 1.380; 95% confidence interval [CI], 1.131-1.683), and CC (OR, 1.786; 95% CI, 1.213-2.644) genotypes were associated with an increased risk of lung cancer after adjustment for age, gender, smoking status, and family history. The C allele of rs3819102 is the risk allele for lung carcinogenesis in a dominant model (OR, 1.435; 95% CI, 1.188-1.735). In a stratified analysis, the risk effects of both the CT and CC genotypes of rs3819102 were more evident in subgroups of smokers and people without a family history of cancer. Conclusion The rs3819102 polymorphism in TYMS might increase susceptibility to environmental factors and contribute to the risk of lung cancer. The C allele is a risk allele in lung carcinogenesis.

Despite that the accuracy and efficiency of stereo matching technology have significantly improved in the past decades, the issue of edge-blurring remains a challenge to most of the existing approaches. In this paper, we propose a minimum spanning tree (MST) based stereo matching method by using the image edge and segmentation optimization to preserve the image boundary. We first exploit a fast disparity range estimation method by combining the Surf and Akaze feature points to improve the computational efficiency. Second, we utilize the image edges and brightness information to generate a self-adaptive weight function, which is able to significantly improve the accuracy of MST aggregating in the regions of complex texture and boundaries with similar color distribution. Third, we employ the image segmentation to extract the invalid regions of the estimated disparity map, and propose a post-processing scheme to refine the disparity result. Finally, we run our method on several Middlebury and KITTI datasets. The comparison results between our method and other state-of-the-art approaches demonstrate that the proposed method has high accuracy for disparity computation and is especially robust to the edge-blurring.

Recently, compressive sensing (CS) has been applied in synthetic aperture radar (SAR) imaging, which is increasingly in the focus of study and shows great potential. In CS-based SAR imaging, motion errors of the moving platform introduce inaccuracies in the observation model, which cause various degradations in the final images. To accomplish accurate motion compensation during CS-based SAR imaging, we propose a fast CS-based SAR imaging integrated with motion compensation method. First, CS-based imaging based on the utilization of inverse observation deduced from the inversion of conventional imaging procedures is applied, which is much more computational efficient than the exact observation model. Then, an improved inverse observation model integrated with motion compensation is derived. In the improved model, spatially variant azimuth phase errors are taken into consideration. Joint SAR imaging and motion compensation are formulated as a sparse recovery problem and solved in an iterative way, wherein each iteration both image formation and motion compensation are carried out. The processing of SAR data shows that the proposed method can obtain better focused images compared with the existing SAR imaging and motion compensation methods.

Maximal multi-photon entangled states, known as NOON states, play an essential role in quantum metrology. With the number of photons growing, NOON states are becoming increasingly powerful and advantageous for obtaining supersensitive and super-resolved measurements. In this paper, we propose a universal scheme for generating three- and four-photon path-entangled NOON states on a reconfigurable photonic chip via photons subtracted from pairs and detected by heralding counters. Our method is postselection free, enabling phase supersensitive measurements and sensing at the Heisenberg limit. Our NOON-state generator allows for integration of quantum light sources as well as practical and portable precision phase-related measurements.

BACKGROUND Oxygen extraction fraction (OEF) elevation predicts increased ischemic stroke incidence among patients with carotid steno-occlusive disease, and can be estimated from quantitative susceptibility mapping (QSM) MRI. PURPOSE To explore QSM oximetry during acetazolamide (ACZ) challenge, hypothesizing that detectable OEF alterations will reflect hemodynamic compromise in unilateral cerebrovascular disease (CVD) patients. STUDY TYPE Retrospective. SUBJECTS Fourteen unilateral CVD patients, and 24 healthy controls (HC). FIELD STRENGTH/SEQUENCE Multiecho gradient echo (GRE) and T1 -weighted images at 3T. ASSESSMENT We constructed QSM images and R2* maps from multiecho GRE images. QSM-OEF maps were generated from the susceptibility difference between venous blood and background brain tissue. Intrasubject diseased/contralateral hemisphere OEF ratios in the middle cerebral artery (MCA) territories were calculated. Intravascular susceptibility in the straight sinus (SS) and MCA was also measured. STATISTICAL TESTS The result significance was determined using t-tests and Pearson's correlation. RESULTS Mean and standard deviation for the patient diseased/contralateral OEF ratios were 1.15 ± 0.14 at baseline and 1.23 ± 0.17 post-ACZ. Disease group R2* ratios were 0.95 ± 0.05 at baseline and 1.03 ± 0.08 post-ACZ. Left/right OEF and R2* ratios for the HC group were 0.98 ± 0.06 and 0.99 ± 0.038, respectively. Susceptibility (ppb) in the SS and MCA in patients was 162.63 ± 35.4 and -22.33 ± 13.70, respectively, at baseline, 124.56 ± 37.43 and -19.27 ± 23.14 post-ACZ. The HC group SS and MCA susceptibility was 146.10 ± 24.79 and -19.59 ± 12.37, respectively. Patient group OEF ratios were greater than 1.0 before and after ACZ challenge (P < 0.01 and < 0.001, respectively, one-sample t-test), and were greater than HC ratios (P < 0.001 unpaired t-test). OEF and R2* ratios increased from baseline to post-ACZ (P = 0.024, 0.004, respectively, paired t-test). Detectable blood oxygenation change was confirmed by finding SS susceptibility decreased from baseline to post-ACZ (P < 0.001, paired t-test), while MCA susceptibility did not change significantly (P = 0.67, paired t-test). DATA CONCLUSION These results suggest QSM is sensitive to dynamic OEF modulation during hemodynamic augmentation. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:175-182.

Objectives Vascular permeability can reflect tumorigenesis and metastasis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess microvascular permeability by pharmacokinetic parameter estimation. Most estimation methods require manually selected arterial input function (AIF) or reference regions (RR). However, the result will be unstable due to the annotation, which relies on personal experience. In this study, we propose an automatic framework for evaluating vascular permeability of bone metastases from prostate cancer without selecting AIF. Materials and methods This retrospective study comprised of 15 prostate cancer patients with bone metastases. Based on clinical consensus for three typical DCE-MRI curve patterns, three characteristic curves as regularization constraints were introduced to the extended Tofts model (ETM) using clustering strategy, and the Clustering-based Blind identification of Multichannel (CBM) framework was then proposed for pharmacokinetic parameter estimation. With automatic segmentation of the whole bone area, we obtained the estimation of the pharmacokinetic parameters in the bone area and quantified for bone metastases. Two experienced radiologists compared the CBM estimations with the diagnostic results and we compared the estimations with those of the ETM in bone metastasis regions to evaluate the feasibility of the CBM framework. Results The higher signal regions of Ktransand Kepindicated the metastasis of prostate cancer, which is consistent with the cancer area marked by the radiologists. In addition, the Ktransand Kepin bone metastasis regions were significantly higher than in normal bone regions (P Conclusion The proposed CBM framework can provide a fully automatic and reliable quantitative estimation of vascular permeability for bone metastases in prostate cancer patients. Key words: DCE-MRI, Prostate bone metastases, Pharmacokinetic parameters, Clustering-based Blind identification of Multichannel framework (CBM framework).

BACKGROUND: This study aimed to investigate radiation-induced lymphopenia and its potential risk factors in patients with breast cancer receiving adjuvant radiotherapy. METHODS: Breast cancer patients received adjuvant radiotherapy (RT) at our hospital with peripheral lymphocyte counts (PLC) at pre-and immediately after RT (post-RT) were eligible. The primary endpoints were any grade of lymphopenia post-RT and nadir-PLC/pre-PLC

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.