Regularization and Kernelization of the Maximin Correlation Approach

Robust classification becomes challenging when each class consists of multiple subclasses. Examples include multi-font optical character recognition and automated protein function prediction. In correlation-based nearest-neighbor classification, the maximin correlation approach (MCA) provides the worst-case optimal solution by minimizing the maximum misclassification risk through an iterative procedure. Despite the optimality, the original MCA has drawbacks that have limited its wide applicability in practice. That is, the MCA tends to be sensitive to outliers, cannot effectively handle nonlinearities in datasets, and suffers from having high computational complexity. To address these limitations, we propose an improved solution, named regularized maximin correlation approach (R-MCA). We first reformulate MCA as a quadratically constrained linear programming (QCLP) problem, incorporate regularization by introducing slack variables in the primal problem of the QCLP, and derive the corresponding Lagrangian dual. The dual formulation enables us to apply the kernel trick to R-MCA so that it can better handle nonlinearities. Our experimental results demonstrate that the regularization and kernelization make the proposed R-MCA more robust and accurate for various classification tasks than the original MCA. Furthermore, when the data size or dimensionality grows, R-MCA runs substantially faster by solving either the primal or dual (whichever has a smaller variable dimension) of the QCLP.Comment: Submitted to IEEE Acces

Naphthalimide Trifluoroacetyl Acetonate: A Hydrazine-Selective Chemodosimetric Sensor

The trifluoroacetyl acetonate naphthalimide derivative 1 has been synthesized in good yield. In acetonitrile solution, compound 1 reacts selectively with hydrazine (NH2NH2) to give a five-membered ring. This leads to OFF-ON fluorescence with a maximum intensity at 501 nm as well as easily discernible color changes. Based on a readily discernible and reproducible 3.9% change in overall fluorescence intensity, the limit of detection for 1 is 3.2 ppb (0.1 mu M), which is below the accepted limit for hydrazine set by the U.S. Environmental Protection Agency (EPA). Compound 1 is selective for hydrazine over other amines, including NH4OH, NH2OH, ethylenediamine, methylamine, n-butylamine, piperazine, dimethylamine, triethylamine, pyridine, and is not perturbed by environmentally abundant metal ions. When supported on glass-backed silica gel TLC plates, compound 1 acts as a fluorimetric and colorimetric probe for hydrazine vapor at a partial pressure of 9.0 mm Hg, with selectivity over other potentially interfering volatile analytes, including ammonia, methylamine, n-butylamine, formaldehyde, acetaldehyde, H2O2, HCl, and CO2 being observed. Probe 1 can also be used for the detection of hydrazine in HeLa cells and does so without appreciable interference from other biologically abundant amines and metal ions.U.S. National Science Foundation CHE-1057904Robert A. Welch Foundation F-1018CRI project grant from National Research Foundation of Korea (NRF)Korea government (MSIP) 2009-0081566Chemistr

Precarious working youth and pension reform in the Republic of Korea and Italy

This paper focuses on two aspects of the welfare state: the old age pension system and the labor market, where the majority of youth are working in precarious jobs. We discuss the interplay between pension funds and the increase in young atypical workers by studying the case of Italy and the Republic of Korea, closely analyzing the projected benefit level of both standard and nonstandard workers among the youth population in Korea in order to assess where young workers will find themselves after retirement age and what Korea can learn from the case of Italy.This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2012S1A3A2033416)

Estimates of Discharge Coefficient in Levee Breach Under Two Different Approach Flow Types

The amount of released water (discharge) in a levee breach is a primary input variable to establish an emergency action plan for the area next to the levee. However, although several studies have been conducted, there is still no widely applicable discharge coefficient formula; this needs to be known to estimate discharge amount through an opening caused by a levee breach. Sometimes, the discharge coefficient developed for a sharp crested side weir is used to rate the discharge, but, in case of a levee breach, the resulting geometry and flow types are similar to that over a broad crested weir. Thus, in this study, two different openings—rectangular and trapezoidal shape—are constructed in the center of a levee at a height of 0.6m to replicate levee breach scenarios, and the effect of two different approach flow types—the river type approach and reservoir type approach—are explored to suggest a discharge coefficient formula applicable for discharge rating for a levee breach. The results show that the ratio of head above the bottom of an opening and the opening width is a key variable for calculating the discharge coefficient of a reservoir type, but the approach Froude number should also be considered for a river type approach. The measured data are used to improve rating equations and will be useful in the future to validate computational fluid dynamics simulations of wave propagation during levee failure into the inundation area

Special Report - Smart Projects in Smart Asia

What is Smart? What does Smart mean in todays context? Before technology, the term was used to refer to human excellence in our actions, mannerisms, facial expressions, languages and thoughts. More recently, its definition has been narrowed down to describe the capacity of artificial intelligence. Smart is the new thing. Products that are not Smart are now objects of the past and the future will be all about creating Smart-er products and services. The rapid development of personal or family friendly technology such as Smartphones, Smart tablets, Smart TV, etc. is analogous with the evolution from using a broom to clean the living room to handling an automatic vacuum cleaner. Smart scenarios and technological innovations of the future have already been extensively studied and explored. The idea that hundreds of remote sensors will receive information and analyze it with a microprocessor, and then carry out ones unconscious command, is one that we can largely relate with a desirable and Smart future. Yet are these complicated processes and automated actions really the Smart environment we are looking for? 스마트하다는 것 오늘날 스마트하다는 것은 무엇을 의미하는가? 행동, 정서, 표정, 언어, 생각 등 인간의 이성과 지성의 모든 부분에 걸쳐 우수함 지칭하던 이 형용사는 인간이 아닌 인공지능의 기능의 우수 성을 뜻하는 좁은 의미로 변화했다. 스마트가 관건이다. 요즘 시대에 스마트하지 않은 물건 이면 옛것이 되고, 스마트한 정도가 미래를 만들어내는 척도가 되었다. 스마트폰, 스마트타블 렛, 스마트TV 등 개인용, 가정용 전자제품을 위시한 생활전반에 소용되는 스마트함의 진화 는 마치 손으로 마루바닥을 훔치던 시절에서 산업사회를 걸쳐 전기기계를 가진 진공청소기의 진화에 걸맞다. 스마트한 환경에 대한 기술개발과 라이프시나리오는 이미 오래전부터 진행되 어 왔다. 수십개, 수백개의 센서가 정보를 받아들이고 마이크로 프로세서가 이를 분석하여 내 가 원하는 동작을 어떤 명령어 없이도 원격으로 제어하고 라이프스타일을 관리해 주는 똑똑 한 환경이 인류가 추구하는 미래라는 것은 우리 모두가 다 알고 있는 바이다. 그런데 우리가 원하는 스마트함은 이렇게 복잡한 프로세스가 진행되는 환경에서 모든 것이 자동으로 일어 나는 그런 환경이 맞는가

Lookalike Targeting on Others\u27 Journeys: Brand Versus Performance Marketing

Lookalike targeting is a widely used model-based ad targeting approach that uses a seed database of individuals to identify matching “lookalikes” for targeted customer acquisition. An advertiser has to make two key choices: (1) who to seed on and (2) seed-match rank range. First, we find that seeding on others’ journey stage can be effective in new customer acquisition; despite the cold start nature of customer acquisition using Lookalike audiences, third parties can indeed identify factors unobserved to the advertiser that move individuals along the journey and can be correlated with the lookalikes. Further, while journey-based seeding adds no incremental value for brand marketing (click-through), seeding on more downstream stages improves performance marketing (donation) outcomes. Second, we evaluate audience expansion strategies by lowering match ranks between the seed and lookalikes to increase acquisition reach. The drop in effectiveness with lower match rank range is much greater for performance marketing than for brand marketing. Performance marketers can alleviate the problem by making the ad targeting explicit, and thus increase perceived relevance; however, it has no incremental impact for higher match lookalikes. Increasing perceived targeting relevance makes acquisition cost comparable for both high and low match ranks

Lookalike Targeting on Others\u27 Journeys: Brand Versus Performance Marketing

Lookalike Targeting is a widely used model-based ad targeting approach that uses a seed database of individuals to identify matching “lookalikes” for targeted customer acquisition. An advertiser has to make two key choices: (1) who to seed on and (2) seed-match rank range. First, we assess if and how seeding by others’ journey stages impact clickthrough (upstream behavior desirable for brand marketing) and donation (downstream behavior desirable in performance marketing). Overall, we find that lookalike targeting using other’s journeys can be effective-third parties can indeed identify factors unobserved to the advertiser merely from others’ journey stage to improve targeting. Further, while it is sufficient to seed on upstream journey stages for brand marketing, seeding on more downstream stages improves performance marketing outcomes. Second, we assess the effectiveness of expanding the target audience with lower match ranks between seed and lookalikes. The drop in effectiveness with lower match rank range is much greater for performance marketing (donation) than for brand marketing (click-through). However, performance marketers can alleviate the reduction in ad effectiveness for low match ranks by making targeting more salient; but increasing salience has little impact for high match rank. Overall, by increasing salience, performance marketers can make acquisition cost comparable for high and low match ranks