Youth Involvement and Voting Age: Evidence from South Korea

HonorsPolitical ScienceUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/169378/1/csungmin.pd

An Apparel Brands Channel Strategy: The Case of Oliver in Korea

The purpose of this case study was to describe the development of a channel strategy for an apparel brand, BoKids, designed to distribute its brand, Oliver, efficiently to customers. Bokids launched its childrens apparel brand, Oliver, in Korea by signing a brand license contract with Oliver of USA. When the brand was launched in 2005, Oliver was positioned as a brand with a reasonable price and a high quality product, which was sold primarily through department stores. In 2007, Oliver was suffering from sluggish sales volumes, and switched its main distribution channel from department stores to discount stores, which are the number 1 retail format in Korea. Oliver was compelled to adjust the price range of its main products to $20 30 in order to satisfy the needs of discount store customers. However, Oliver has considered Internet shopping as another channel for the Oliver brand, as Internet shopping is rapidly gaining popularity in Korea. This case can be used in conjunction with discussions on marketing topics, such as the design of marketing channels (Chapter 6, Designing the Marketing Channel, Marketing Channels: A Management View, 7th Edition by Bert Rosenbloom, South-Western College Pub, 2007) for senior level marketing seminars

China Market Entry Strategy Of Paris Baguette

This case study analyzes the global strategy of Paris Baguette, a leading bakery franchise in Korea. Because of stricter regulations in the local market, Paris Baguette has encouraged franchises to target overseas markets. The company made first inroads into the Chinese market in 2004 with a bakery cafe in Shanghai. The main point of Paris Baguette’s global strategy is summarized by high quality, style, diversification, and localization. Also, Paris Baguette directly operates its flagship store from headquarters, due to the poor legal environment in China. In this study, we analyze strategies of China market and suggest considerations for future business expansion

Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition

Skeleton-based action recognition has attracted considerable attention due to its compact skeletal structure of the human body. Many recent methods have achieved remarkable performance using graph convolutional networks (GCNs) and convolutional neural networks (CNNs), which extract spatial and temporal features, respectively. Although spatial and temporal dependencies in the human skeleton have been explored, spatio-temporal dependency is rarely considered. In this paper, we propose the Inter-Frame Curve Network (IFC-Net) to effectively leverage the spatio-temporal dependency of the human skeleton. Our proposed network consists of two novel elements: 1) The Inter-Frame Curve (IFC) module; and 2) Dilated Graph Convolution (D-GC). The IFC module increases the spatio-temporal receptive field by identifying meaningful node connections between every adjacent frame and generating spatio-temporal curves based on the identified node connections. The D-GC allows the network to have a large spatial receptive field, which specifically focuses on the spatial domain. The kernels of D-GC are computed from the given adjacency matrices of the graph and reflect large receptive field in a way similar to the dilated CNNs. Our IFC-Net combines these two modules and achieves state-of-the-art performance on three skeleton-based action recognition benchmarks: NTU-RGB+D 60, NTU-RGB+D 120, and Northwestern-UCLA.Comment: 12 pages, 5 figure

Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers

Since the recent advent of regulations for data protection (e.g., the General Data Protection Regulation), there has been increasing demand in deleting information learned from sensitive data in pre-trained models without retraining from scratch. The inherent vulnerability of neural networks towards adversarial attacks and unfairness also calls for a robust method to remove or correct information in an instance-wise fashion, while retaining the predictive performance across remaining data. To this end, we consider instance-wise unlearning, of which the goal is to delete information on a set of instances from a pre-trained model, by either misclassifying each instance away from its original prediction or relabeling the instance to a different label. We also propose two methods that reduce forgetting on the remaining data: 1) utilizing adversarial examples to overcome forgetting at the representation-level and 2) leveraging weight importance metrics to pinpoint network parameters guilty of propagating unwanted information. Both methods only require the pre-trained model and data instances to forget, allowing painless application to real-life settings where the entire training set is unavailable. Through extensive experimentation on various image classification benchmarks, we show that our approach effectively preserves knowledge of remaining data while unlearning given instances in both single-task and continual unlearning scenarios.Comment: AAAI 2024 camera ready versio

Correlation of hypoxia inducible transcription factor in breast cancer and SUVmax of F-18 FDG PET/CT

BACKGROUND: Tumor hypoxia induces the expression of several genes via the hypoxia-inducible transcription factor-1 alpha (HIF-1a). It is associated with the prognosis of several cancers. We studied the immunohistochemical expression of HIF-1a in patients with invasive ductal cancer (IDC) of the breast and the possible correlation with the maximum standardized uptake value of the primary tumor (pSUVmax) as well as other biological parameters. Prognostic significance of pSUVmax and expression of HIF-1a for the prediction of progression-free survival (PFS) was also assessed. MATERIAL AND METHODS: Two-hundred seven female patients with IDC who underwent pretreatment fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (F-18 FDG PET/CT) were enrolled. The pSUVmax was compared with clinicopathological parameters including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), axillary lymph node (LN) metastasis, stage and HIF-1a expression. The prognostic value of pSUVmax for PFS was assessed using the Kaplan-Meier method. RESULTS: pSUVmax was significantly higher in patients with HIF-1a expression ≥ 2 compared to patients with HIF-1a expression < 2 (5.2 ± 4.5 vs. 3.7 ± 3.1, p = 0.008). pSUVmax was also significantly higher in higher stage (p < 0.000001), ER-negative tumors (p < 0.0001), PR-negative tumors (p = 0.0011) and positive LN metastasis (p = 0.0013). pSUVmax was significantly higher in patients with progression compared to patients who were disease-free (6.8 ± 4.4 vs. 4.1 ± 3.7, p = 0.0005). A receiver-operating characteristic curve demonstrated a pSUVmax of 6.51 to be the optimal cutoff for predicting PFS (sensitivity: 53.6%, specificity: 86.0%). Patients with high pSUVmax (> 6.5) had significantly shorter PFS compared to patients with low pSUVmax (p < 0.0001). CONCLUSIONS: pSUVmax on pretreatment F-18 FDG PET/ CT reflect expression of HIF-1a and can be used as a good surrogate marker for the prediction of progression in patients with IDC. The amount of FDG uptake is determined by the presence of glucose metabolism and hypoxia in breast cancer cell