Active hashing and its application to image and text retrieval

In recent years, hashing-based methods for large-scale similarity search have sparked considerable research interests in the data mining and machine learning communities. While unsupervised hashing-based methods have achieved promising successes for metric similarity, they cannot handle semantic similarity which is usually given in the form of labeled point pairs. To overcome this limitation, some attempts have recently been made on semi-supervised hashing which aims at learning hash functions from both metric and semantic similarity simultaneously. Existing semi-supervised hashing methods can be regarded as passive hashing since they assume that the labeled pairs are provided in advance. In this paper, we propose a novel framework, called active hashing, which can actively select the most informative labeled pairs for hash function learning. Specifically, it identifies the most informative points to label and constructs labeled pairs accordingly. Under this framework, we use data uncertainty as a measure of informativeness and develop a batch mode algorithm to speed up active selection. We empirically compare our method with a state-of-the-art passive hashing method on two benchmark data sets, showing that the proposed method can reduce labeling cost as well as overcome the limitations of passive hashing.     

Ketamine effects on the urogenital system--changes in the urinary bladder and sperm motility

Different doses of ketamine (10 mg/kg, 20 mg/kg, 30 mg/kg, 40 mg/kg, 50 mg/kg, and 60 mg/kg) were injected i.p. (I.P.), respectively, to male ICR mice to determine the optimal dosage for chronic administration. At and above 40 mg/kg I.P. injection, mice had almost no hindlimb movement during swimming test. Subsequently, 30 mg/kg was used as the dose for the study in the toxicity of long-term ketamine administration on urinary bladder and sperm motility. The treatment group were subdivided into two (n = 10 each group); one received daily ketamine treatment i.p. for 3 months and another group for 6 months. Corresponding number of mice in control groups (n = 5 each group) received saline injection instead of ketamine. Terminal dUTP nick and labeling (TUNEL) study and Sirius red staining were carried out on the sectioned slides of the urinary bladders to study the degree of apoptosis in both epithelium and muscular layers of the urinary bladder and the relative thickness of the muscular layers in this organ was also computed. Apoptosis in the bladder epithelium was observed initially in the 3-month ketamine treated mice and the number of apoptotic cells was significantly different (P < 0.05) between the 3-month and 6-month ketamine treated mice and the control. The relative thickness of muscular layers in the bladder wall also decreased significantly (P < 0.05) when the 6-month treated mice and the control were compared. Sirius red staining revealed increase of collagen in the urinary bladder of the treated mice, most evidently 6 months after ketamine treatment. In addition, the sperm motility was studied and there was a statistically significant difference between the control and ketamine treated groups in the percentages of sperms which were motile (P < 0.05). This suggested that the chronic administration of ketamine affected the genital system as well.     

Robust path-based spectral clustering

Spectral clustering and path-based clustering are two recently developed clustering approaches that have delivered impressive results in a number of challenging clustering tasks. However, they are not robust enough against noise and outliers in the data. In this paper, based on M-estimation from robust statistics, we develop a robust path-based spectral clustering method by defining a robust path-based similarity measure for spectral clustering under both unsupervised and semi-supervised settings. Our proposed method is significantly more robust than spectral clustering and path-based clustering. We have performed experiments based on both synthetic and real-world data, comparing our method with some other methods. In particular, color images from the Berkeley segmentation data set and benchmark are used in the image segmentation experiments. Experimental results show that our method consistently outperforms other methods due to its higher robustness.     

A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.     

Approximations and reducts with covering generalized rough sets

The covering generalized rough sets are an improvement of traditional rough set model to deal with more complex practical problems which the traditional one cannot handle. It is well known that any generalization of traditional rough set theory should first have practical applied background and two important theoretical issues must be addressed. The first one is to present reasonable definitions of set approximations, and the second one is to develop reasonable algorithms for attributes reduct. The existing covering generalized rough sets, however, mainly pay attention to constructing approximation operators. The ideas of constructing lower approximations are similar but the ideas of constructing upper approximations are different and they all seem to be unreasonable. Furthermore, less effort has been put on the discussion of the applied background and the attributes reduct of covering generalized rough sets. In this paper we concentrate our discussion on the above two issues. We first discuss the applied background of covering generalized rough sets by proposing three kinds of datasets which the traditional rough sets cannot handle and improve the definition of upper approximation for covering generalized rough sets to make it more reasonable than the existing ones. Then we study the attributes reduct with covering generalized rough sets and present an algorithm by using discernibility matrix to compute all the attributes reducts with covering generalized rough sets. With these discussions we can set up a basic foundation of the covering generalized rough set theory and broaden its applications.     

Characterization of Nonbonded Poly(ethylene oxide) Coating for Capillary Electrophoresis via Continuous Monitoring of Electroosmotic Flow

We examined changes in a poly(ethylene oxide) (PEO) coating by continuously monitoring the electroosmotic flow (EOF) in a fused-silica capillary during electrophoresis. An imaging CCD camera was used to follow the motion of a fluorescent neutral marker zone along the length of the capillary. The PEO coating was shown to reduce the velocity of EOF by more than 1 order of magnitude compared to a bare capillary at pH 7.0. However, it did not reduce EOF efficiently at pH 8.2. The coating protocol was important, especially at an intermediate pH of 7.7. Capillary reconditioning with an acidified solution of PEO was necessary in order to create a stable and efficient coating. In all cases we observed a gradual increase of EOF during extended runs, suggesting that the coating is slowly being degraded. The increase of pH in the cathodic (detection-end) buffer reservoir beyond pH ～8.0, e.g., as a result of electrolysis, had a large impact on the stability of the coating. This phenomenon may be used for the efficient and fast regeneration of the column surface and provides a simpler and more reliable alternative to pressure flushing of the capillary.     

An economical piezoelectric phase modulator for fiber optic sensors

A homemade piezoelectric phase modulator for interfero-metric fiber optic sensors was fabricated using piezoelectric buzzers as strain elements. Six piezoelectric elements were embedded between the two halves of a bakelite cylinder split along its axis and secured tightly together again to form a cylinder. Single-mode optical fiber was then wound around the cylinder to complete the unit. Up to a frequency of 500 Hz, the phase shift produced by the modulator is linearly proportional to the amplitude of the applied voltage. The sensitivity of the phase modulator is about 3.6 rad/V and has a dynamic range of 1,000 rad, which is sufficient for most phase modulation purposes.     

Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning

The development of evolutionary algorithms for optimization has always been a stimulating and growing research area with an increasing demand in using them to solve complex industrial optimization problems. A novel immunity-based hybrid evolutionary algorithm known as Hybrid Artificial Immune Systems (HAIS) for solving both unconstrained and constrained multi-objective optimization problems is developed in this research. The algorithm adopts the clonal selection and immune suppression theories, with a sorting scheme featuring uniform crossover, multi-point mutation, non-dominance and crowding distance sorting to attain the Pareto optimal front in an efficient manner. The proposed algorithm was verified with nine benchmarking functions on its global optimal search ability as well as compared with four optimization algorithms to assess its diversity and spread. Sensitivity analysis was also carried out to investigate the selection of key parameters of the algorithm. It is found that the developed immunity-based hybrid evolutionary algorithm provides a useful means for solving optimization problems and has successfully applied to the problem of global repositioning of containers, which is one of a constrained multi-objective optimization problem. The developed HAIS will assist shipping liners on timely decision making and planning of container repositioning operations in global container transportation business in an optimized and cost effective manner.     

View-Aware Image Object Compositing and Synthesis from Multiple Sources

Image compositing is widely used to combine visual elements from separate source images into a single image. Although recent image compositing techniques are capable of achieving smooth blending of the visual elements from different sources, most of them implicitly assume the source images are taken in the same viewpoint. In this paper, we present an approach to compositing novel image objects from multiple source images which have different viewpoints. Our key idea is to construct 3D proxies for meaningful components of the source image objects, and use these 3D component proxies to warp and seamlessly merge components together in the same viewpoint. To realize this idea, we introduce a coordinate-frame based single-view camera calibration algorithm to handle general types of image objects, a structure-aware cuboid optimization algorithm to get the cuboid proxies for image object components with correct structure relationship, and finally a 3D-proxy transformation guided image warping algorithm to stitch object components. We further describe a novel application based on this compositing approach to automatically synthesize a large number of image objects from a set of exemplars. Experimental results show that our compositing approach can be applied to a variety of image objects, such as chairs, cups, lamps, and robots, and the synthesis application can create novel image objects with significant shape and style variations from a small set of exemplars.