North Korea's emulation Chinese politics : an examination of Mao Tse-Tung's ideological influence

North Korean leader, Kim Il-song has been known among western observers as a "faithful disciple of Stalin." Furthermore, Communist North Korea has been known to the western world as faithfully following the Soviet model. The central argument of my study is to refute this notion. One should note that there are similar revolutionary backgrounds between China and North Korea, similarities of social conditions, colonial or semi-colonial status, and a timing of revolution. The Chinese model would be a prototype for Asian communism. My study shows abundant evidences that North Korea should be included in the category of Asian communism rather than western communism, and Kim Il-song is a disciple of Mao Tse-tung rather than being Stalin's

Construction of the Radio Map with Defective GPS Position Information

The basic idea of RSS-based indoor positioning is to estimate the receiver location by matching the measured received signal strength indicator (RSSI) with preestablished RSSI collections with corresponding locations, known as the radio map. Hence, constructing an accurate radio map directly relates to accurate positioning performance in RSS-based indoor positioning. RSS-based indoor positioning can be easily conducted with a radio map that surveys every location, but a complete radio map cannot be constructed when the map area includes locations that are physically impossible to reach or denied access. In addition, measurement errors or device problems can occur during the survey, resulting in degradation of the radio map. We analyzed incidents that occurred in actual RSSI surveys that could disrupt the construction of the radio map and proposed methods to construct a more accurate radio map.Comment: 5 page

A Differentiable Framework for End-to-End Learning of Hybrid Structured Compression

Filter pruning and low-rank decomposition are two of the foundational techniques for structured compression. Although recent efforts have explored hybrid approaches aiming to integrate the advantages of both techniques, their performance gains have been modest at best. In this study, we develop a \textit{Differentiable Framework~(DF)} that can express filter selection, rank selection, and budget constraint into a single analytical formulation. Within the framework, we introduce DML-S for filter selection, integrating scheduling into existing mask learning techniques. Additionally, we present DTL-S for rank selection, utilizing a singular value thresholding operator. The framework with DML-S and DTL-S offers a hybrid structured compression methodology that facilitates end-to-end learning through gradient-base optimization. Experimental results demonstrate the efficacy of DF, surpassing state-of-the-art structured compression methods. Our work establishes a robust and versatile avenue for advancing structured compression techniques.Comment: 11 pages, 5 figures, 6 table

The boundary of Rauzy fractal and discrete tilings

The Rauzy fractal is a domain in the two-dimensional plane constructed by the Rauzy substitution, a substitution rule on three letters. The Rauzy fractal has a fractal-like boundary, and the currently known its constructions is not only for its boundary but also for the entire domain. In this paper, we show that all points in the Rauzy fractal have a layered structure. We propose two methods of constructing the Rauzy fractal using layered structures. We show how such layered structures can be used to construct the boundary of the Rauzy fractal with less computation than conventional methods. There is a self-replicating pattern in one of the layered structure in the Rauzy fractal. We introduce a notion of self-replicating word and visualize how some self-replicating words on three letters creates discrete tiling of the two dimensional plane

Ensuring the visibility and traceability of items through logistics chain of automotive industry based on AutoEPCNet Usage

Traceability in logistics is the capability of the participants to trace the products throughout the supply chain by means of either the product and/or container identifiers in a forward and/or backward direction. In today's competitive economic environment, traceability is a key concept related to all products and all types of supply chains. The goal of this paper is to describe development of application that enables to create and share information about the physical movement and status of products as they travel throughout the supply chain. The main purpose of this paper is to describe the development of RFID based track and trace system for ensuring the visibility and traceability of items in logistics chain especially in automotive industry. The proposed solution is based on EPCglobal Network Architecture

Role of appetitive phenotype trajectory groups on child body weight during a family-based treatment for children with overweight or obesity.

ObjectiveEmerging evidence suggests that individual appetitive traits may usefully explain patterns of weight loss in behavioral weight loss treatments for children. The objective of this study was to identify trajectories of child appetitive traits and the impact on child weight changes over time.MethodsSecondary data analyses of a randomized noninferiority trial conducted between 2011 and 2015 evaluated children's appetitive traits and weight loss. Children with overweight and obesity (mean age = 10.4; mean BMI z = 2.0; 67% girls; 32% Hispanic) and their parent (mean age = 42.9; mean BMI = 31.9; 87% women; 31% Hispanic) participated in weight loss programs and completed assessments at baseline, 3, 6,12, and 24 months. Repeated assessments of child appetitive traits, including satiety responsiveness, food responsiveness and emotional eating, were used to identify parsimonious grouping of change trajectories. Linear mixed-effects models were used to identify the impact of group trajectory on child BMIz change over time.ResultsOne hundred fifty children and their parent enrolled in the study. The three-group trajectory model was the most parsimonious and included a high satiety responsive group (HighSR; 47.4%), a high food responsive group (HighFR; 34.6%), and a high emotional eating group (HighEE; 18.0%). Children in all trajectories lost weight at approximately the same rate during treatment, however, only the HighSR group maintained their weight loss during follow-ups, while the HighFR and HighEE groups regained weight (adjusted p-value < 0.05).ConclusionsDistinct trajectories of child appetitive traits were associated with differential weight loss maintenance. Identified high-risk subgroups may suggest opportunities for targeted intervention and maintenance programs

Detection of Pedestrian Turning Motions to Enhance Indoor Map Matching Performance

A pedestrian navigation system (PNS) in indoor environments, where global navigation satellite system (GNSS) signal access is difficult, is necessary, particularly for search and rescue (SAR) operations in large buildings. This paper focuses on studying pedestrian walking behaviors to enhance the performance of indoor pedestrian dead reckoning (PDR) and map matching techniques. Specifically, our research aims to detect pedestrian turning motions using smartphone inertial measurement unit (IMU) information in a given PDR trajectory. To improve existing methods, including the threshold-based turn detection method, hidden Markov model (HMM)-based turn detection method, and pruned exact linear time (PELT) algorithm-based turn detection method, we propose enhanced algorithms that better detect pedestrian turning motions. During field tests, using the threshold-based method, we observed a missed detection rate of 20.35% and a false alarm rate of 7.65%. The PELT-based method achieved a significant improvement with a missed detection rate of 8.93% and a false alarm rate of 6.97%. However, the best results were obtained using the HMM-based method, which demonstrated a missed detection rate of 5.14% and a false alarm rate of 2.00%. In summary, our research contributes to the development of a more accurate and reliable pedestrian navigation system by leveraging smartphone IMU data and advanced algorithms for turn detection in indoor environments.Comment: Submitted to ICTC 202

Strategic Asset Allocation Of Credit Guarantors

How to manage the portfolio of credit guarantors is important in practice and public policy, but has not been investigated well in the prior literature. We empirically compare four different approaches in managing credit guarantor portfolios. The four approaches are equal weighted, minimum variance, mean variance optimization and equal risk contribution methods. In terms of risk return ratio, the mean variance optimization model performs best in out-of-sample test. This result contrasts with previous findings against mean variance optimization. Our results are robust. The results do not change as the characteristics of guarantee portfolio vary

Meta-Learning with a Geometry-Adaptive Preconditioner

Model-agnostic meta-learning (MAML) is one of the most successful meta-learning algorithms. It has a bi-level optimization structure where the outer-loop process learns a shared initialization and the inner-loop process optimizes task-specific weights. Although MAML relies on the standard gradient descent in the inner-loop, recent studies have shown that controlling the inner-loop's gradient descent with a meta-learned preconditioner can be beneficial. Existing preconditioners, however, cannot simultaneously adapt in a task-specific and path-dependent way. Additionally, they do not satisfy the Riemannian metric condition, which can enable the steepest descent learning with preconditioned gradient. In this study, we propose Geometry-Adaptive Preconditioned gradient descent (GAP) that can overcome the limitations in MAML; GAP can efficiently meta-learn a preconditioner that is dependent on task-specific parameters, and its preconditioner can be shown to be a Riemannian metric. Thanks to the two properties, the geometry-adaptive preconditioner is effective for improving the inner-loop optimization. Experiment results show that GAP outperforms the state-of-the-art MAML family and preconditioned gradient descent-MAML (PGD-MAML) family in a variety of few-shot learning tasks. Code is available at: https://github.com/Suhyun777/CVPR23-GAP.Comment: Accepted at CVPR 2023. Code is available at: https://github.com/Suhyun777/CVPR23-GAP; This is an extended version of our previous CVPR23 wor