Strictly positive-definite spike train kernels for point-process divergences

Exploratory tools that are sensitive to arbitrary statistical variations in spike train observations open up the possibility of novel neuroscientific discoveries. Developing such tools, however, is difficult due to the lack of Euclidean structure of the spike train space, and an experimenter usually prefers simpler tools that capture only limited statistical features of the spike train, such as mean spike count or mean firing rate. We explore strictly positive-definite kernels on the space of spike trains to offer both a structural representation of this space and a platform for developing statistical measures that explore features beyond count or rate. We apply these kernels to construct measures of divergence between two point processes and use them for hypothesis testing, that is, to observe if two sets of spike trains originate from the same underlying probability law. Although there exist positive-definite spike train kernels in the literature, we establish that these kernels are not strictly definite and thus do not induce measures of divergence. We discuss the properties of both of these existing nonstrict kernels and the novel strict kernels in terms of their computational complexity, choice of free parameters, and performance on both synthetic and real data through kernel principal component analysis and hypothesis testing.     

Improving Application Launch Performance on Solid State Drives

Application launch performance is of great importance to system platform developers and vendors as it greatly affects the degree of users’ satisfaction.The single most effective way to improve application launch performance is to replace a hard disk drive (HDD) with a solid state drive (SSD),which has recently become affordable and popular.A natural question is then whether or not to replace the traditional HDD-aware application launchers with a new SSD-aware optimizer.We address this question by analyzing the inefficiency of the HDD-aware application launchers on SSDs and then proposing a new SSD-aware application prefetching scheme,called the Fast Application STarter (FAST).The key idea of FAST is to overlap the computation (CPU) time with the SSD access (I/O) time during an application launch.FAST is composed of a set of user-level components and system debugging tools provided by Linux OS (operating system).Hence,FAST can be easily deployed in any recent Linux versions without kernel recompilation.We implement FAST on a desktop PC with an SSD running Linux 2.6.32 OS and evaluate it by launching a set of widely-used applications,demonstrating an average of 28% reduction of application launch time as compared to PC without a prefetcher.     

A literature review and classification of recommender systems research

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. Although academic research on recommender systems has increased significantly over the past 10 years, there are deficiencies in the comprehensive literature review and classification of that research. For that reason, we reviewed 210 articles on recommender systems from 46 journals published between 2001 and 2010, and then classified those by the year of publication, the journals in which they appeared, their application fields, and their data mining techniques. The 210 articles are categorized into eight application fields (books, documents, images, movie, music, shopping, TV programs, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). Our research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this paper helps anyone who is interested in recommender systems research with insight for future research direction.     

Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting

This study considers real estate appraisal forecasting problem. While there is a great deal of literature about use of artificial intelligence and multiple linear regression for the problem, there has been always controversy about which one performs better. Noting that this controversy is due to difficulty finding proper predictor variables in real estate appraisal, we propose a modified version of ridge regression, i.e., ridge regression coupled with genetic algorithm (GA-Ridge). In order to examine the performance of the proposed method, experimental study is done for Korean real estate market, which verifies that GA-Ridge is effective in forecasting real estate appraisal. This study addresses two critical issues regarding the use of ridge regression, i.e., when to use it and how to improve it.     

Removal efficiencies of endocrine disrupting chemicals by coagulation/flocculation, ozonation, powdered/granular activated carbon adsorption, and chlorination

Removal efficiencies of endocrine disrupting chemicals (EDCs), bisphenol A and nonylphenol, during various types of water treatment processes were evaluated extensively using laboratory- and pilot-scale experiments. The specific processes of interest were coagulation/flocculation sedimentation/filtration (conventional water treatment process), powdered activated carbon (PAC), granular activated carbon (GAC), ozonation and chlorination. Batch sorption tests, coagulation tests, and ozone oxidation tests were also performed at higher concentrations with 14 EDCs including bisphenol A. The conventional water treatment process had very low removal efficiencies (0 to 7%) for all the EDCs except DEHP, DBP and DEP that were removed by 53%, 49%, and 46%, respectively. Ozonation at 1 mgO₃/ L removed 60% of bisphenol A and 89% of nonylphenol, while chlorination at 1 mg/L removed 58% and 5%, respectively. When ozone and chlorine doses were 4 and 5 mg/L, respectively, both EDCs were not detected. PAC removal efficiencies ranged from 15% to 40% at 3 to 10 mg/L of PAC with a contact time of 15 minutes. In the high concentration batch sorption tests, EDC removal efficiencies by PAC were closely related to octanol-water partition coefficient (K_ow). GAC adsorption was very effective water treatment process. The type and service time of GAC did not affect EDC removal efficiencies. The combination of ozonation and GAC in series appears to remove EDCs effectively to safe levels while conventional water treatment could not.     

Microstructures and tensile behavior of carbon nanotube reinforced Cu matrix nanocomposites

Carbon nanotubes (CNTs) have been considered as an ideal reinforcement to improve the mechanical performance of monolithic materials. However, the CNT/metal nanocomposites have shown lower strength than expected. In this study, the CNT reinforced Cu matrix nanocomposites were fabricated by spark plasma sintering (SPS) of high energy ball-milled nano-sized Cu powders with multi-wall CNTs, and followed by cold rolling process. The microstructure of CNT/Cu nanocomposites consists of two regions including CNT/Cu composite region, where most CNTs are distributed, and CNT free Cu matrix region. The stress–strain curves of CNT/Cu nanocomposites show a two-step yielding behavior, which is caused from the microstructural characteristics consisting of two regions and the load transfer between these regions. The CNT/Cu nanocomposites show a tensile strength of 281 MPa, which is approximately 1.6 times higher than that of monolithic Cu. It is confirmed that the key issue to enhance the strength of CNT/metal nanocomposite is homogeneous distribution of CNTs.     

Sensitivity enhancement for H2 gas detection using a SnO2–Ag2O–PdOx nanocrystalline system

Thick film H₂ sensors were fabricated using SnO₂ loaded with Ag₂O and PdO_x. The composition that gave highest sensitivity for H₂ was in the wt.% ratio of SnO₂:Ag₂O:PdO_x as 93:5:2. The nano-crystalline powders of SnO₂–Ag₂O–PdO_x composites synthesized by sol–gel method were screen printed on alumina substrates. Fabricated sensors were tested against gases like H₂, CH₄, C₃H₈, C₂H₅OH and SO₂. The composite material was found sensitive against H₂ at the working temperature 125 °C, with minor interference of other gases. H₂ gas as low as 100 ppm can be detected by the present fabricated sensors. It was found that the sensors based on SnO₂–Ag₂O–PdO_x nanocrystalline system exhibited high performance, high selectivity and very short response time to H₂ at ppm level. These characteristics make the sensor to be a promising candidate for detecting low concentrations of H₂.     

Preparation of Pt supported on mesoporous carbons for the reduction of oxygen in polymer electrolyte membrane fuel cell (PEMFC)

Porous carbon materials (SM-C, HS-C and TM-C) were prepared using commercial colloidal silicas (SM-30, HS-40 and TM-50) and a resorcinol-formaldehyde resin as a removable template and a carbon precursor, respectively. All of the prepared carbons had high surface areas with narrow pore size distributions. In particular, the pore diameter of the carbons could be controlled over a range of mesopore size by the use of an appropriate silica employed as a template. Mesoporous carbon templated using TM-50 had the largest pore size, while that for SM-C, was the smallest. Pt nanoparticles were supported on these mesoporous carbons for use as a catalyst in a polymer electrolyte membrane fuel cell (PEMFC). The crystallite size of the Pt catalyst was found to be closely related to the properties of the corresponding carbon support. A carbon support with a large pore size and a high surface roughness was found to favor the dispersion of Pt crystallite. In a single cell test, the Pt catalysts supported on mesoporous carbons exhibited higher cell performance than that on activated carbon. In particular, the Pt/TM-C catalyst showed the best cell performance among the catalysts tested. In addition to the high surface area of the active metal, the large pore size of the Pt/TM-C appears to have positive effect on the distribution of ionomer, resulting in facile formation of a triple-phase boundary.     

The preparation and characterization of porous carbons for hydrogen storage

Porous carbon materials with a cylindrical pore structure were prepared using ordered mesoporous silica as a removable template. To investigate the effect of the structural and textural properties of the products on hydrogen adsorption capacity, different carbon precursors and synthetic methods were used in their preparation. All of the carbon materials prepared showed a well-defined pore structure with a high surface area irrespective of the carbon precursor used in the preparation. Hydrogen adsorption tests indicated that the capacity of the materials for hydrogen adsorption was highly dependent on total surface area and the pore structure. Based on the N₂ sorption results, the total surface area was directly correlated with the hydrogen adsorption capacity.     

A Study on the Pd/a-Si/Ni Seed Layer for Metal-Induced Lateral Crystallization and Poly-Si TFTs

The effect of Pd on the growth rate of metal-induced lateral crystallization (MILC) from Ni seed and the electrical properties of thin-film transistors (TFTs) fabricated on the films crystallized by MILC were investigated. When the Pd metal is placed on the amorphous-silicon/Ni-seed layer, the MILC growth rate is two to three times faster than that of conventional Ni-MILC, without any degradation of TFTs. These results were explained by a stress that is generated by the formation of Pd₂Si