Non-negative matrix factorization for semi-supervised data clustering

Traditional clustering algorithms are inapplicable to many real-world problems where limited knowledge from domain experts is available. Incorporating the domain knowledge can guide a clustering algorithm, consequently improving the quality of clustering. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for data clustering. In SS-NMF, users are able to provide supervision for clustering in terms of pairwise constraints on a few data objects specifying whether they “must” or “cannot” be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the data similarity matrix to infer the clusters. Theoretically, we show the correctness and convergence of SS-NMF. Moveover, we show that SS-NMF provides a general framework for semi-supervised clustering. Existing approaches can be considered as special cases of it. Through extensive experiments conducted on publicly available datasets, we demonstrate the superior performance of SS-NMF for clustering.

Generating useful images for medical applications from the Visible Korean Human

For the Visible Korean Human (VKH), a male cadaver was serially ground off to acquire the serially sectioned images (SSIs) of a whole human body. Thereafter, more than 700 structures in the SSIs were outlined to produce detailed segmented images; the SSIs and segmented images were volume- and surface-reconstructed to create three-dimensional models. For outlining and reconstruction, popular software (Photoshop, MRIcro, Maya, AutoCAD, 3ds max, and Rhino) was mainly used; the technique can be reproduced by other investigators for creating their own images. For refining the segmentation and volume reconstruction, the VOXEL-MAN system was used. The continuously upgraded technique was applied to a female cadaver's pelvis to produce the SSIs with 0.1mm sized intervals and 0.1mm x 0.1mm sized pixels. The VKH data, distributed worldwide, encouraged researchers to develop virtual dissection, virtual endoscopy, and virtual lumbar puncture contributing to medical education and clinical practice. In the future, a virtual image library including all the Visible Human Project data, Chinese Visible Human data, and VKH data will hopefully be established where users will be able to download one of the data sets for medical applications.     

Caustic spot light for rendering caustics

It is difficult to render caustic patterns at interactive frame rates. This paper introduces new rendering techniques that relax current constraints, allowing scenes with moving, non-rigid scene objects, rigid caustic objects, and rotating directional light sources to be rendered in real-time with GPU hardware acceleration. Because our algorithm estimates the intensity and the direction of caustic light, rendering of non-Lambertian surfaces is supported. Previous caustics algorithms have separated the problem into pre-rendering and rendering phases, storing intermediate results in data structures such as photon maps or radiance transfer functions. Our central idea is to use specially parameterized spot lights, called caustic spot lights (CSLs), as the intermediate representation of a two-phase algorithm. CSLs are flexible enough that a small number can approximate the light leaving a caustic object, yet simple enough that they can be efficiently evaluated by a pixel shader program during accelerated rendering.We extend our approach to support changing lighting direction by further dividing the pre-rendering phase into per-scene and per-frame components: the per-frame phase computes frame-specific CSLs by interpolating between CSLs that were pre-computed with differing light directions.     

An improved NEH-based heuristic for the permutation flowshop problem

NEH is an effective heuristic for solving the permutation flowshop problem with the objective of makespan. It includes two phases: generate an initial sequence and then construct a solution. The initial sequence is studied and a strategy is proposed to solve job insertion ties which may arise in the construct process. The initial sequence which is generated by combining the average processing time of jobs and their standard deviations shows better performance. The proposed strategy is based on the idea of balancing the utilization among all machines. Experiments show that using this strategy can improve the performance of NEH significantly. Based on the above ideas, a heuristic NEH-D (NEH based on Deviation) is proposed, whose time complexity is O(mn²), the same as that of NEH. Computational results on benchmarks show that the NEH-D is significantly better than the original NEH.     

Knowledge discovery in corporate events by neural network rule extraction

Over the last two decades, artificial neural networks (ANN) have been applied to solve a variety of problems such as pattern classification and function approximation. In many applications, it is desirable to extract knowledge from trained neural networks for the users to gain a better understanding of the network’s solution. In this paper, we use a neural network rule extraction method to extract knowledge from 2222 dividend initiation and resumption events. We find that the positive relation between the short-term price reaction and the ratio of annualized dividend amount to stock price is primarily limited to 96 small firms with high dividend ratios. The results suggest that the degree of short-term stock price underreaction to dividend events may not be as dramatic as previously believed. The results also show that the relations between the stock price response and firm size is different across different types of firms. Thus, drawing the conclusions from the whole dividend event data may leave some important information unexamined. This study shows that neural network rule extraction method can reveal more knowledge from the data.     

Collapse-free thermal bonding technique for large area microchambers in plastic lab-on-a-chip applications

Bonding is an essential step to form microchannels or microchambers in lab-on-a-chip applications. In this paper, we present a novel plastic thermal bonding technique to seal and form large area microchambers (planar characteristic width and length on the order of 1 mm and characteristic thickness on the order of 10–100 μm) without collapse by introducing a holed pressure equalizing plate (HPEP) that includes holes of the same size and shape as the microchambers. To demonstrate the proposed technique, two types of large area microchambers [(1) 20 × 10 mm and 40 μm thick and (2) 12 × 2.5 mm and 120 μm thick] with microchannels were designed and replicated on plastic substrates by means of hot embossing and injection molding processes with prepared two nickel mold inserts. The replicated large area microchambers as well as the microchannels in the plastic lab-on-a-chip were successfully sealed (i.e., no leakage) and formed without any collapse by the proposed thermal bonding technique with the help of the HPEP.     

Design and fabrication of an electrochemically actuated microvalve

A microfluidic valve based on electrochemical (ECM) actuation was designed, fabricated using UV-LIGA microfabrication technologies. The valve consists of an ECM actuator, polydimethylsiloxane (PDMS) membrane and a micro chamber. The flow channels and chamber are made of cured SU-8 polymer. The hydrogen gas bubbles were generated in the valve microchamber with Pt black electrodes (coated with platinum nanoparticles) and filled with 1 M of NaCl solution. The nano particles coated on the working electrode helps to boost the surface-to-volume ratio of the electrode for faster reversible electrolysis and faster valve operation. To test the functionality of the microvalve, a simple micropump based on ECM principle was also integrated in the system to deliver a microscopic volume of fluid through the valve. The experimental results have showed that an approximately 300 μm deflection of valve membrane was achieved by applying a bias voltage of ?1.5 V across the electrodes. The pressure in the valve chamber was estimated to be about 200 KPa. Experimental results proved that the valve can be easily operated by controlling the electrical signals supplied to the ECM actuators.     

一种新的基于遗传操作的改进型遗传算法

交叉与变异是遗传算法的重要操作,提出了一种新的基于遗传操作的改进型遗传算法.采用最优保留和改进的轮盘赌选择方法,通过基因交叉概率控制交叉,根据高斯分布改进了交叉算子和变异算子,保证了算法的全局搜索能力、局部搜索能力及收敛速度.通过标准函数的数值实验,验证了新算法的有效性.     

地空大容量传输自适应均衡技术

地空信道远距离、低仰角、高速信息传输时,信道引入的多径将不可避免,多径引起的符号间的干扰和衰落将变得十分严重,传统的自适应均衡技术多基于解调器正常工作,动态适应性较差,均衡效果有限。本文详细介绍了采用均衡器与解调器联合设计原理,并结合多谱勒校正技术,能够满足在地空低莱斯因子信道下,解调器一均衡器校正多谱勒频偏的同时均衡动态多径,达到平稳传输效果。