A deep-neural-network-based hybrid method for semi-supervised classification of polarimetric SAR data

This paper proposes a deep-neural-network-based semi-supervised method for polarimetric synthetic aperture radar (PolSAR) data classification. The proposed method focuses on achieving a well-trained deep neural network (DNN) when the amount of the labeled samples is limited. In the proposed method, the probability vectors, where each entry indicates the probability of a sample associated with a category, are first evaluated for the unlabeled samples, leading to an augmented training set. With this augmented training set, the parameters in the DNN are learned by solving the optimization problem, where the log-likelihood cost function and the class probability vectors are used. To alleviate the “salt-and-pepper” appearance in the classification results of PolSAR images, the spatial interdependencies are incorporated by introducing a Markov random field (MRF) prior in the prediction step. The experimental results on two realistic PolSAR images demonstrate that the proposed method effectively incorporates the spatial interdependencies and achieves the good classification accuracy with a limited number of labeled samples

Hidden Trends in 90 Years of Harvard Business Review

In this paper, we demonstrate and discuss results of our mining the abstracts of the publications in Harvard Business Review between 1922 and 2012. Techniques for computing n-grams, collocations, basic sentiment analysis, and named-entity recognition were employed to uncover trends hidden in the abstracts. We present findings about international relationships, sentiment in HBR's abstracts, important international companies, influential technological inventions, renown researchers in management theories, US presidents via chronological analyses.Comment: 6 pages, 14 figures, Proceedings of 2012 International Conference on Technologies and Applications of Artificial Intelligenc

REACTIN: Regulatory Activity Inference of Transcription Factors Underlying Human Diseases with Application to Breast Cancer

Genetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. In many cancers, alteration of TFs results in aberrant activity of them without changing their gene expression level. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, however, it is still challenge to reveal the activity change of TFs. Here we propose a method, called REACTIN (REgulatory ACTivity INference), which integrates TF binding data with gene expression data to identify TFs with significantly differential activity between disease and normal samples. REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets. When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer. Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients

An Evaluation of Taiwanese B&B Service Quality Using the IPA Model

According to a December 2011 report released by Taiwan’s Tourism Bureau, there were 3,763 bed-and-breakfast guesthouses (B&B) in Taiwan, 3,367 of which were legal with a combined 13,389 rooms, increasing by 96 percent from December 2006. It seems that the B&B sector is quite a popular target for investors. As the word-of-mouth advertising has been considered one of the most influential marketing methods, those who invest in B&Bs must manage to utilize their limited resources to improve customer satisfaction in a fast-growing and competitive market. The best marketing approach in reaching out to B&B customers, as suggested by this study’s author, would be word-of-mouth adverting. A PZB framed questionnaire is used in this study to explore the expectations and satisfaction of B&B customers both before and after the accommodation period, with the Importance-Performance Analysis (IPA) model applied to analyze and measure the service quality. Findings from the questionnaire survey showed 3 out of the totally 23 service factors falling in the “concentrated concerned” quadrant (i.e., tidiness, architectural characteristics, and reasonable rates); 6 factors falling in the “continued maintenance” quadrant (i.e., adequate parking place, commitment to customers, handling of customers’ opinions, legality of B&B, grievance handling, and the local specialties-ordering service); 10 factors falling in the “low priority” quadrant (i.e., modern facilities, safety devices, availability of breakfast, security of online reservations); 4 factors falling in the “over-strived” quadrant (i.e., the availability of custom-made services, the ability to grasp customers’ needs, the availability of tour packages, and the availability of experiences regarding local industries)

Bis[μ-1,2-bis­(1H-imidazol-1-ylmeth­yl)benzene-κ2 N 3:N 3′]disilver(I) 3-carboxyl­ato-4-hydroxy­benzene­sulfonate methanol solvate trihydrate

In the title compound, [Ag2(C14H14N4)2](C7H4O6S)·CH3OH·3H2O, the complex dication has a binuclear structure in which each AgI ion is two-coordinated in a slightly distorted linear coordination geometry. The two AgI atoms are bridged by two 1,2-bis­[(1H-imidazol-1-yl)meth­yl]benzene (IBI) ligands, forming a 22-membered ring. In the dication, π–π inter­actions are observed between the imidazole rings with centroid–centroid distances of 3.472 (3) and 3.636 (3) Å. In the crystal, the uncoordinated water mol­ecules, anions and methanol solvent mol­ecules are linked into chains along the b axis by O—H⋯O hydrogen bonds. In addition, π–π inter­actions are observed between the benzene rings of the IBI ligands, with a centroid–centroid distance of 3.776 (2) Å. The sulfonate group is disordered over two orientations with occupancies of 0.676 (12) and 0.324 (12)

Right ventricular exclusion for hepatocellular carcinoma metastatic to the heart

We used for the first time a right ventricular exclusion procedure for the treatment of hepatocellular carcinoma metastatic to the right ventricle. Our case report shows that this surgical option can be effective as rescue therapy for right ventricular outflow tract obstruction secondary to myocardial metastasis in critically ill patients. Most notably, this technique can prevent inadvertent dislodgement of tumor cells

FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow

Multiple object tracking (MOT) has been successfully investigated in computer vision. However, MOT for the videos captured by unmanned aerial vehicles (UAV) is still challenging due to small object size, blurred object appearance, and very large and/or irregular motion in both ground objects and UAV platforms. In this paper, we propose FOLT to mitigate these problems and reach fast and accurate MOT in UAV view. Aiming at speed-accuracy trade-off, FOLT adopts a modern detector and light-weight optical flow extractor to extract object detection features and motion features at a minimum cost. Given the extracted flow, the flow-guided feature augmentation is designed to augment the object detection feature based on its optical flow, which improves the detection of small objects. Then the flow-guided motion prediction is also proposed to predict the object's position in the next frame, which improves the tracking performance of objects with very large displacements between adjacent frames. Finally, the tracker matches the detected objects and predicted objects using a spatially matching scheme to generate tracks for every object. Experiments on Visdrone and UAVDT datasets show that our proposed model can successfully track small objects with large and irregular motion and outperform existing state-of-the-art methods in UAV-MOT tasks.Comment: Accepted by ACM Multi-Media 202

Semi-supervised classification of polarimetric SAR images using Markov random field and two-level Wishart mixture model

In this work, we propose a semi-supervised method for classification of polarimetric synthetic aperture radar (PolSAR) images. In the proposed method, a 2-level mixture model is constructed by associating each component density with a unique Wishart mixture model (instead of a single Wishart distribution as that in the conventional Wishart mixture model). This modeling scheme facilitates the accurate description of data for the categories, each of which includes multiple subcategories. The learning algorithm for the proposed model is developed based on variational inference and all the update equations are obtained in closed form. In the learning algorithm, the spatial interdependencies are incorporated by imposing a Markov random field prior on the indicator variable to alleviate the speckle effect on the classification results. The experimental results demonstrate the improved performance of the proposed method compared with the unsupervised version and supervised version of the proposed model as well as an existing method for semi-supervised classification

Unsupervised classification of multilook polarimetric SAR data using spatially variant wishart mixture model with double constraints

This paper addresses the unsupervised classification problems for multilook Polarimetric synthetic aperture radar (PolSAR) images by proposing a patch-level spatially variant Wishart mixture model (SVWMM) with double constraints. We construct this model by jointly modeling the pixels in a patch (rather than an individual pixel) so as to effectively capture the local correlation in the PolSAR images. More importantly, a responsibility parameter is introduced to the proposed model, providing not only the possibility to represent the importance of different pixels within a patch but also the additional flexibility for incorporating the spatial information. As such, double constraints are further imposed by simultaneously utilizing the similarities of the neighboring pixels, respectively, defined on two different parameter spaces (i.e., the hyperparameter in the posterior distribution of mixing coefficients and the responsibility parameter). Furthermore, the variational inference algorithm is developed to achieve effective learning of the proposed SVWMM with the closed-form updates, facilitating the automatic determination of the cluster number. Experimental results on several PolSAR data sets from both airborne and spaceborne sensors demonstrate that the proposed method is effective and it enables better performances on unsupervised classification than the conventional methods