Conditions for Generic Initial Ideals to be Almost Reverse Lexicographic

Let $I$ be a homogeneous Artinian ideal in a polynomial ring $R=k[x_1,...,x_n]$ over a field $k$ of characteristic 0. We study an equivalent condition for the generic initial ideal \gin(I) with respect to reverse lexicographic order to be almost reverse lexicographic. As a result, we show that Moreno-Socias conjecture implies Fr\"{o}berg conjecture. And for the case \Codim I \le 3, we show that $R/I$ has the strong Lefschetz property if and only if \gin(I) is almost reverse lexicographic. Finally for a monomial complete intersection Artinian ideal $I=(x_1^{d_1},...,x_n^{d_n})$, we prove that \gin(I) is almost reverse lexicographic if $d_i > \sum_{j=1}^{i-1} d_j - i + 1$ for each $i \ge 4$. Using this, we give a positive partial answer to Moreno-Socias conjecture, and to Fr\"{o}berg conjecture.Comment: 10 page

Self-Directed Learning in the Workplace: Implications for the Legislation of Trade Union Education in South Korea

The purpose of this study is to theorize self-directed learning (SDL) in the workplace from the perspectives of human resource development (HRD), adult education (AdEd), and lifelong learning in order to suggest the implications for the legislation of trade union education (TUE) in South Korea. Since legislation at the national level can promote workers‘ participation in TUE in the context of SDL for industrial democracy through humanization of education, the South Korean government should provide trade unions with appropriate legislative, financial, and administrative support. Keywords: self-directed learning, trade union education, adult education

Corporate Universities and Adult Education: Implications for Theory and Practice

The purpose of this paper is to explore characteristics of corporate universities (CUs) from the adult education (AdEd) perspective in order to identify implications for AdEd theory and practice. Through an integrative literature review of CUs, the differences among CUs, human resource development centers, and traditional universities are investigated. Considering the AdEd characteristics of CUs, such as individuals’ learning and qualifications/certifications of higher education, the partnership/collaboration model of CU is suggested in terms of workplace learning, which is the overlapping field of HRD and AdEd. Ultimately, to promote participatory AdEd in the workplace, nations should play crucial roles in providing administrative and financial support to CUs

Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting

This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses.Comment: 10 pages, 11 figures. To appear in ICCV 2017, Spotlight Presentatio

Discriminating Pathological and Non-pathological Internet Gamers Using Sparse Neuroanatomical Features

Internet gaming disorder (IGD) is often diagnosed on the basis of nine underlying criteria from the latest version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Here, we examined whether such symptom-based categorization could be translated into computation-based classification. Structural MRI (sMRI) and diffusion-weighted MRI (dMRI) data were acquired in 38 gamers diagnosed with IGD, 68 normal gamers diagnosed as not having IGD, and 37 healthy non-gamers. We generated 108 features of gray matter (GM) and white matter (WM) structure from the MRI data. When regularized logistic regression was applied to the 108 neuroanatomical features to select important ones for the distinction between the groups, the disordered and normal gamers were represented in terms of 43 and 21 features, respectively, in relation to the healthy non-gamers, whereas the disordered gamers were represented in terms of 11 features in relation to the normal gamers. In support vector machines (SVM) using the sparse neuroanatomical features as predictors, the disordered and normal gamers were discriminated successfully, with accuracy exceeding 98%, from the healthy non-gamers, but the classification between the disordered and normal gamers was relatively challenging. These findings suggest that pathological and non-pathological gamers as categorized with the criteria from the DSM-5 could be represented by sparse neuroanatomical features, especially in the context of discriminating those from non-gaming healthy individuals

New gene selection method for classification of cancer subtypes considering within-class variation

AbstractIn this work we propose a new method for finding gene subsets of microarray data that effectively discriminates subtypes of disease. We developed a new criterion for measuring the relevance of individual genes by using mean and standard deviation of distances from each sample to the class centroid in order to treat the well-known problem of gene selection, large within-class variation. Also this approach has the advantage that it is applicable not only to binary classification but also to multiple classification problems. We demonstrated the performance of the method by applying it to the publicly available microarray datasets, leukemia (two classes) and small round blue cell tumors (four classes). The proposed method provides a very small number of genes compared with the previous methods without loss of discriminating power and thus it can effectively facilitate further biological and clinical researches

Protective Effect of Proanthocyanidin against Diabetic Oxidative Stress

We investigated the antidiabetic potential of proanthocyanidin and its oligomeric form in STZ-induced diabetic model rats and db/db type 2 diabetic mice. Proanthocyanidin ameliorated the diabetic condition by significant decreases of serum glucose, glycosylated protein, and serum urea nitrogen as well as decreases of urinary protein and renal-AGE in STZ-induced diabetic rats and decrease of serum glucose as well as significant decrease of glycosylated protein in db/db type 2 diabetic mice. The suppression of ROS generation and elevation of the GSH/GSSG ratio were also observed in the groups administered proanthocyanidin. Moreover, proanthocyanidin, especially its oligomeric form, affected the inflammatory process with the regulation of related protein expression, iNOS, COX-2 and upstream regulators, NF-κB, and the IκB-α. In addition, it had a marked effect on hyperlipidemia through lowering significant levels of triglycerides, total cholesterol, and NEFA. Moreover, expressions in the liver of SREBP-1 and SREBP-2 were downregulated by the administration of proanthocyanidins. The protective effect against hyperglycemia and hyperlipidemia in type 1 and 2 diabetic models was significantly strong in the groups administered the oligomeric rather than polymeric form. This suggests that oligomers act as a regulator in inflammatory reactions caused by oxidative stress in diabetes