Lifting the Veil: Characteristics, Clinical Significance, and Application of ß-2-Microglobulin as Biomarkers and Its Detection with Biosensors.

Because ß-2-microglobulin (ß2M) is a surface protein that is present on most nucleated cells, it plays a key role in the human immune system and the kidney glomeruli to regulate homeostasis. The primary clinical significance of ß2M is in dialysis-related amyloidosis, a complication of end-stage renal disease caused by a gradual accumulation of ß2M in the blood. Therefore, the function of ß2M in kidney-related diseases has been extensively studied to evaluate its glomerular and tubular functions. Because increased ß2M shedding due to rapid cell turnover may indicate other underlying medical conditions, the possibility to use ß2M as a versatile biomarker rose in prominence across multiple disciplines for various applications. Therefore, this work has reviewed the recent use of ß2M to detect various diseases and its progress as a biomarker. While the use of state-of-the-art ß2M detection requires sophisticated tools, high maintenance, and labor cost, this work also has reported the use of biosensor to quantify ß2M over the past decade. It is hoped that a portable and highly efficient ß2M biosensor device will soon be incorporated in point-of-care testing to provide safe, rapid, and reliable test results.

Gallium Nitride (GaN) High-Electron-Mobility Transistors with Thick Copper Metallization Featuring a Power Density of 8.2 W/mm for Ka-Band Applications.

Copper-metallized gallium nitride (GaN) high-electron-mobility transistors (HEMTs) using a Ti/Pt/Ti diffusion barrier layer are fabricated and characterized for Ka-band applications. With a thick copper metallization layer of 6.8 µm adopted, the device exhibited a high output power density of 8.2 W/mm and a power-added efficiency (PAE) of 26% at 38 GHz. Such superior performance is mainly attributed to the substantial reduction of the source and drain resistance of the device. In addition to improvement in the Radio Frequency (RF) performance, the successful integration of the thick copper metallization in the device technology further reduces the manufacturing cost, making it extremely promising for future fifth-generation mobile communication system applications at millimeter-wave frequencies.

Transfer representation learning for medical image analysis.

There are two major challenges to overcome when developing a classifier to perform automatic disease diagnosis. First, the amount of labeled medical data is typically very limited, and a classifier cannot be effectively trained to attain high disease-detection accuracy. Second, medical domain knowledge is required to identify representative features in data for detecting a target disease. Most computer scientists and statisticians do not have such domain knowledge. In this work, we show that employing transfer learning can remedy both problems. We use Otitis Media (OM) to conduct our case study. Instead of using domain knowledge to extract features from labeled OM images, we construct features based on a dataset entirely OM-irrelevant. More specifically, we first learn a codebook in an unsupervised way from 15 million images collected from ImageNet. The codebook gives us what the encoders consider being the fundamental elements of those 15 million images. We then encode OM images using the codebook and obtain a weighting vector for each OM image. Using the resulting weighting vectors as the feature vectors of the OM images, we employ a traditional supervised learning algorithm to train an OM classifier. The achieved detection accuracy is 88.5% (89.63% in sensitivity and 86.9% in specificity), markedly higher than all previous attempts, which relied on domain experts to help extract features.

Parallel spectral clustering in distributed systems.

Spectral clustering algorithms have been shown to be more effective in finding clusters than some traditional algorithms, such as k-means. However, spectral clustering suffers from a scalability problem in both memory use and computational time when the size of a data set is large. To perform clustering on large data sets, we investigate two representative ways of approximating the dense similarity matrix. We compare one approach by sparsifying the matrix with another by the Nyström method. We then pick the strategy of sparsifying the matrix via retaining nearest neighbors and investigate its parallelization. We parallelize both memory use and computation on distributed computers. Through an empirical study on a document data set of 193,844 instances and a photo data set of 2,121,863, we show that our parallel algorithm can effectively handle large problems.

Enhanced perceptual distance functions and indexing for image replica recognition.

The proliferation of digital images and the widespread distribution of digital data that has been made possible by the Internet has increased problems associated with copyright infringement on digital images. Watermarking schemes have been proposed to safeguard copyrighted images, but watermarks are vulnerable to image processing and geometric distortions and may not be very effective. Thus, the content-based detection of pirated images has become an important application. In this paper, we discuss two important aspects of such a replica detection system: distance functions for similarity measurement and scalability. We extend our previous work on perceptual distance functions, which proposed the Dynamic Partial Function (DPF), and present enhanced techniques that overcome the limitations of DPF. These techniques include the Thresholding, Sampling, and Weighting schemes. Experimental evaluations show superior performance compared to DPF and other distance functions. We then address the issue of using these perceptual distance functions to efficiently detect replicas in large image data sets. The problem of indexing is made challenging by the high-dimensionality and the nonmetric nature of the distance functions. We propose using Locality Sensitive Hashing (LSH) to index images while using the above perceptual distance functions and demonstrate good performance through empirical studies on a very large database of diverse images.