The Impact of Cultural Distance on the Performance of Foreign Subsidiaries: Evidence From the Korean Market

This study investigates whether the cultural distance between Korea and the home countries of foreign subsidiaries in Korea affects the subsidiaries’ financial performance. It contributes to the literature on international business in that it sheds light on cultural distance, a well-established but somewhat neglected concept in international business. Unlike most of the previous studies that have used cultural distance as a control or moderating variable, this study uses it as an independent variable in the context of globalization through foreign direct investments in Korea. Focusing on the possible positive side of broad cultural distance, we hypothesize that the performances of foreign subsidiaries are likely to be better when the cultural distance between their home countries and Korea increases. To test our hypothesis, we have conducted an empirical analysis, using data collected from 472 foreign subsidiaries doing business in Korea. The results support our argument that cultural distance has a positive impact on financial performance. This study finds that having cultural similarities with a foreign market does not guarantee success. Instead, it shows that firms can gain opportunities when incorporating in a foreign national market with broad cultural distance.&nbsp

Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel

Explaining generalizations and preventing over-confident predictions are central goals of studies on the loss landscape of neural networks. Flatness, defined as loss invariability on perturbations of a pre-trained solution, is widely accepted as a predictor of generalization in this context. However, the problem that flatness and generalization bounds can be changed arbitrarily according to the scale of a parameter was pointed out, and previous studies partially solved the problem with restrictions: Counter-intuitively, their generalization bounds were still variant for the function-preserving parameter scaling transformation or limited only to an impractical network structure. As a more fundamental solution, we propose new prior and posterior distributions invariant to scaling transformations by \textit{decomposing} the scale and connectivity of parameters, thereby allowing the resulting generalization bound to describe the generalizability of a broad class of networks with the more practical class of transformations such as weight decay with batch normalization. We also show that the above issue adversely affects the uncertainty calibration of Laplace approximation and propose a solution using our invariant posterior. We empirically demonstrate our posterior provides effective flatness and calibration measures with low complexity in such a practical parameter transformation case, supporting its practical effectiveness in line with our rationale

Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure

A Power-Efficient 3-D On-Chip Interconnect for Multi-Core Accelerators with Stacked L2 Cache

The use of multi-core clusters is a promising option for data-intensive embedded applications such as multi-modal sensor fusion, image understanding, mobile augmented reality. In this paper, we propose a power-efficient 3-D on-chip interconnect for multi-core clusters with stacked L2 cache memory. A new switch design makes a circuit-switched Mesh-of-Tree (MoT) interconnect reconfigurable to support power-gating of processing cores, memory blocks, and unnecessary interconnect resources (routing switch, arbitration switch, inverters placed along the on-chip wires). The proposed 3-D MoT improves the power efficiency up to 77% in terms of energy-delay product (EDP)

Intelligent smartphone-based multimode imaging otoscope for the mobile diagnosis of otitis media

Otitis media (OM) is one of the most common ear diseases in children and a common reason for outpatient visits to medical doctors in primary care practices. Adhesive OM (AdOM) is recognized as a sequela of OM with effusion (OME) and often requires surgical intervention. OME and AdOM exhibit similar symptoms, and it is difficult to distinguish between them using a conventional otoscope in a primary care unit. The accuracy of the diagnosis is highly dependent on the experience of the examiner. The development of an advanced otoscope with less variation in diagnostic accuracy by the examiner is crucial for a more accurate diagnosis. Thus, we developed an intelligent smartphone-based multimode imaging otoscope for better diagnosis of OM, even in mobile environments. The system offers spectral and autofluorescence imaging of the tympanic membrane using a smartphone attached to the developed multimode imaging module. Moreover, it is capable of intelligent analysis for distinguishing between normal, OME, and AdOM ears using a machine learning algorithm. Using the developed system, we examined the ears of 69 patients to assess their performance for distinguishing between normal, OME, and AdOM ears. In the classification of ear diseases, the multimode system based on machine learning analysis performed better in terms of accuracy and F1 scores than single RGB image analysis, RGB/fluorescence image analysis, and the analysis of spectral image cubes only, respectively. These results demonstrate that the intelligent multimode diagnostic capability of an otoscope would be beneficial for better diagnosis and management of OM. © 2021 OSA - The Optical Society. All rights reserved.1

Improved Stability Criteria for Delay Neural Networks: High-order Matrix-valued Polynomial Approach

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Membership-Function-Dependent Stability Conditions Using Fuzzy Lyapunov Functions

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최적 퍼지 이론을 이용한 회전형 도립진자의 제어 및 구현

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산세 공정의 산농도 제어를 위한 제한조건을 고려한 모델 예측 제어기 구현

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