A Novel Hybrid Dimensionality Reduction Method using Support Vector Machines and Independent Component Analysis

Due to the increasing demand for high dimensional data analysis from various applications such as electrocardiogram signal analysis and gene expression analysis for cancer detection, dimensionality reduction becomes a viable process to extracts essential information from data such that the high-dimensional data can be represented in a more condensed form with much lower dimensionality to both improve classification accuracy and reduce computational complexity. Conventional dimensionality reduction methods can be categorized into stand-alone and hybrid approaches. The stand-alone method utilizes a single criterion from either supervised or unsupervised perspective. On the other hand, the hybrid method integrates both criteria. Compared with a variety of stand-alone dimensionality reduction methods, the hybrid approach is promising as it takes advantage of both the supervised criterion for better classification accuracy and the unsupervised criterion for better data representation, simultaneously. However, several issues always exist that challenge the efficiency of the hybrid approach, including (1) the difficulty in finding a subspace that seamlessly integrates both criteria in a single hybrid framework, (2) the robustness of the performance regarding noisy data, and (3) nonlinear data representation capability. This dissertation presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk (supervised criterion) from Support Vector Machine (SVM) and data independence (unsupervised criterion) from Independent Component Analysis (ICA). The projection from SVM directly contributes to classification performance improvement in a supervised perspective whereas maximum independence among features by ICA construct projection indirectly achieving classification accuracy improvement due to better intrinsic data representation in an unsupervised perspective. For linear dimensionality reduction model, I introduce orthogonality to interrelate both projections from SVM and ICA while redundancy removal process eliminates a part of the projection vectors from SVM, leading to more effective dimensionality reduction. The orthogonality-based linear hybrid dimensionality reduction method is extended to uncorrelatedness-based algorithm with nonlinear data representation capability. In the proposed approach, SVM and ICA are integrated into a single framework by the uncorrelated subspace based on kernel implementation. Experimental results show that the proposed approaches give higher classification performance with better robustness in relatively lower dimensions than conventional methods for high-dimensional datasets

ARM MOTIONS FOR DIFFERENT TARGET POSITIONS DURING TAEKWONDO ROUNDHOUSE KICKS

The purpose of this study was to investigate arm motions for five different target positions during Taekwondo roundhouse kicks. Nine Taekwondo experts performed roundhouse kicks at a target. A 3D motion analysis was conducted. One-way repeated ANOVA was used to compare the arm motion among five conditions. This study reveals that a higher kick needs the increased vertical separation of the right and left arm (elbow and wrist) in release phase. For a longer kick at Body level, elbows should be more vertically apart and wrists should be more horizontally apart in the release phase. Both attackers and counter attackers in Taekwondo athletes can use the arm swing characteristics at different target heights and distances

MASKER: Masked Keyword Regularization for Reliable Text Classification

Pre-trained language models have achieved state-of-the-art accuracies on various text classification tasks, e.g., sentiment analysis, natural language inference, and semantic textual similarity. However, the reliability of the fine-tuned text classifiers is an often underlooked performance criterion. For instance, one may desire a model that can detect out-of-distribution (OOD) samples (drawn far from training distribution) or be robust against domain shifts. We claim that one central obstacle to the reliability is the over-reliance of the model on a limited number of keywords, instead of looking at the whole context. In particular, we find that (a) OOD samples often contain in-distribution keywords, while (b) cross-domain samples may not always contain keywords; over-relying on the keywords can be problematic for both cases. In light of this observation, we propose a simple yet effective fine-tuning method, coined masked keyword regularization (MASKER), that facilitates context-based prediction. MASKER regularizes the model to reconstruct the keywords from the rest of the words and make low-confidence predictions without enough context. When applied to various pre-trained language models (e.g., BERT, RoBERTa, and ALBERT), we demonstrate that MASKER improves OOD detection and cross-domain generalization without degrading classification accuracy. Code is available at https://github.com/alinlab/MASKER.Comment: AAAI 2021. First two authors contributed equall

A Suboptimal Approach to Antenna Design Problem With Kernel Regression

This paper proposes a novel iterative algorithm based on a Kernel regression as a suboptimal approach to reliable and efficient antenna optimization. In our approach, the complex and non-linear cost surface calculated from antenna characteristics is fitted into a simple linear model using Kernels, and an argument that minimizes this Kernel regression model is used as a new input to calculate its cost using numerical simulations. This process is repeated by updating coefficients of the Kernel regression model with new entries until meeting the stopping criteria. At every iteration, existing inputs are partitioned into a limited number of clusters to reduce the computational time and resources and to prevent unexpected over-weighted situations. The proposed approach is validated for the Rastrigins function as well as a real engineering problem using an antipodal Vivaldi antenna in comparison with a genetic algorithm. Furthermore, we explore the most appropriate Kernel that minimizes the least-square error when fitting the antenna cost surface. The results demonstrate that the proposed process is suitable to be used in antenna design problems as a reliable approach with a fast convergence time

Study on the Changes in Enzyme and Insulin-like Growth Factor-1 Concentrations in Blood Serum and Growth Characteristics of Velvet Antler during the Antler Growth Period in Sika Deer ()

This study was conducted to investigate changes in blood enzyme parameters and to evaluate the relationship between insulin-like growth factor-1 (IGF-1), antler growth and body weight during the antler growth of sika deer (Cervus nippon). Serum enzyme activity and IGF-1 concentrations were measured in blood samples collected from the jugular and femoral veins at regular intervals during the antler growth period. Blood samples were taken in the morning from fasted stags (n = 12) which were healthy and showed no clinical signs of disease. Alfalfa was available ad libitum and concentrates were given at 1% of body weight to all stags. The experimental diet was provided at 9 am with water available at all times. There were no significant differences in alkaline phosphatase, aspartate aminotransferase, and alanine aminotransferase during antler growth, but alkaline phosphatase concentrations increased with antler growth progression, and the highest alkaline phosphatase concentration was obtained 55 days after antler casting. Serum IGF-1 concentrations measured from blood samples taken from the jugular vein during antler growth, determined that levels of IGF-1 was associated with body weight and antler growth patterns. Serum IGF-1 concentrations were higher at the antler cutting date than other sampling dates. Antler length increased significantly during antler growth (p<0.001), and there was a similar trend to between right and left beams. Body weight increased with antler growth but was not significant. Consequently it appeared that serum alkaline phosphatase concentration was related to antler growth and both antler growth and body weight were associated positively with IGF-1 concentrations during antler growth

Influence of surgery involving tendons around the knee joint on ankle motion during gait in patients with cerebral palsy

Background Simultaneous motion of the knee and ankle joints is required for many activities including gait. We aimed to evaluate the influence of surgery involving tendons around the knee on ankle motion during gait in the sagittal plane in cerebral palsy patients. Methods We included data from 55 limbs in 34 patients with spastic cerebral palsy. Patients were followed up after undergoing only distal hamstring lengthening with or without additional rectus femoris transfer. The patients mean age at the time of knee surgery was 11.2 ± 4.7 years, and the mean follow-up duration was 2.2 ± 1.5 years (range, 0.9–6.0 years). Pre- and postoperative kinematic variables that were extracted from three-dimensional gait analyses were then compared to assess changes in ankle motion after knee surgery. Outcome measures included ankle dorsiflexion at initial contact, peak ankle dorsiflexion during stance, peak ankle dorsiflexion during swing, and dynamic range of motion of the ankle. Various sagittal plane knee kinematics were also measured and used to predict ankle kinematics. A linear mixed model was constructed to estimate changes in ankle motion after adjusting for multiple factors. Results Improvement in total range of motion of the knee resulted in improved motion of the ankle joint. We estimated that after knee surgery, ankle dorsiflexion at initial contact, peak ankle dorsiflexion during stance, peak ankle dorsiflexion during swing, and dynamic range of motion of the ankle decreased, respectively, by 0.4° (p = 0.016), 0.6° (p < 0.001), 0.2° (p = 0.038), and 0.5° (p = 0.006) per degree increase in total range of motion of the knee after either knee surgery. Furthermore, dynamic range of motion of the ankle increased by 0.4° per degree increase in postoperative peak knee flexion during swing. Conclusions Improvement in total knee range of motion was found to be correlated with improvement in ankle kinematics after surgery involving tendons around the knee. As motion of the knee and ankle joints is cross-linked, surgeons should be aware of potential changes in the ankle joint after knee surgery.This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2016R1C1B2008557), and was partly supported by the Technology Innovation Program funded By the Ministry of Trade, Industry and Energy (MOTIE) of Korea (10049785) and SNUBH research fund (grant no. 02-2012-018). No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article

STRUCTURAL BEHAVIOR OF H-SHAPED BRACE REINFORCED WITH NON-WELDED COLD-FORMED ELEMENT

The Thirteenth East Asia-Pacific Conference on Structural Engineering and Construction (EASEC-13), September 11-13, 2013, Sapporo, Japan