Teacher as Stranger: “Releasing” Imagination for Teaching Controversial Public Issues

This study utilized the term "teacher as stranger" from Maxine Greene's (1973) Teacher as Stranger to explore how teachers teach contemporary controversial public issues in Taiwan (e.g., national identity, sovereignty, and ethnic issues). Using a case study design, this study documents how six social studies teachers make curricular decisions about teaching controversial public issues and create possibility for their students to imaginatively engage controversial public issues. Findings illuminate that teachers challenge the stereotype of Asian teachers as always following centralized curriculum; these teachers instead collaborate authentic curricular resources and decenter the exam-centric and curriculum-centric classroom space. In sum, this study, refracted through the national context of Taiwan, helps us understand the possibility of Taiwanese teachers’ curricular-instructional decisions and increased autonomy and authority.

What Can We Learn from Taiwanese Teachers About Teaching Controversial Public Issues

This study explores how history teachers in Taiwan make curricular decisions while engaging controversial public issues. The main political controversies discussed in Taiwanese society center on the relationship between Taiwan and the People’s Republic of China. This study documents how four social studies teachers formulate their curricular decisions through the intersecting lenses of professional knowledge and personal beliefs. Findings illuminate the role of personal experience and belief in teacher’s curricular-instructional gatekeeping in socially divisive contexts. In sum, this study helps as understand the relationship between a teacher’s own imaginative worldview, sense of personal and professional identity, and their classroom teaching practices

Low-Resource Music Genre Classification with Advanced Neural Model Reprogramming

Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often required for fine-tuning pre-trained neural networks with target domain data. In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR). NMR aims at re-purposing a pre-trained model from a source domain to a target domain by modifying the input of a frozen pre-trained model. In addition to the known, input-independent, reprogramming method, we propose an advanced reprogramming paradigm: Input-dependent NMR, to increase adaptability to complex input data such as musical audio. Experimental results suggest that a neural model pre-trained on large-scale datasets can successfully perform music genre classification by using this reprogramming method. The two proposed Input-dependent NMR TL methods outperform fine-tuning-based TL methods on a small genre classification dataset.Comment: Submitted to ICASSP 2023. Some experimental results were reduced due to the space limit. The implementation will be available at https://github.com/biboamy/music-repr

Endurance Exercise Training Programs Intestinal Lipid Metabolism in a Rat Model of Obesity and Type 2 Diabetes

Endurance exercise has been shown to improve metabolic outcomes in obesity and type 2 diabetes; however, the physiological and molecular mechanisms for these benefits are not completely understood. Although endurance exercise has been shown to decrease lipogenesis, promote fatty acid oxidation (FAO), and increase mitochondrial biosynthesis in adipose tissue, muscle, and liver, its effects on intestinal lipid metabolism remain unknown. The absorptive cells of the small intestine, enterocytes, mediate the highly efficient absorption and processing of nutrients, including dietary fat for delivery throughout the body. We investigated how endurance exercise altered intestinal lipid metabolism in obesity and type 2 diabetes using Otsuka Long-Evans Tokushima Fatty (OLETF) rats. We assessed mRNA levels of genes associated with intestinal lipid metabolism in nonhyperphagic, sedentary Long-Evans Tokushima Otsuka (LETO) rats (L-Sed), hyperphagic, sedentary OLETF rats (O-Sed), and endurance exercised OLETF rats (O-EndEx). O-Sed rats developed hyperphagia-induced obesity (HIO) and type 2 diabetes compared with L-Sed rats. O-EndEx rats gained significantly less weight and fat pad mass, and had improved serum metabolic parameters without change in food consumption compared to O-Sed rats. Endurance exercise resulted in dramatic up-regulation of a number of genes in intestinal lipid metabolism and mitochondrial content compared with sedentary rats. Overall, this study provides evidence that endurance exercise programs intestinal lipid metabolism, likely contributing to its role in improving metabolic outcomes in obesity and type 2 diabetes

Treatment Learning Causal Transformer for Noisy Image Classification

Current top-notch deep learning (DL) based vision models are primarily based on exploring and exploiting the inherent correlations between training data samples and their associated labels. However, a known practical challenge is their degraded performance against "noisy" data, induced by different circumstances such as spurious correlations, irrelevant contexts, domain shift, and adversarial attacks. In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy by jointly estimating their treatment effects. Motivated from causal variational inference, we propose a transformer-based architecture, Treatment Learning Causal Transformer (TLT), that uses a latent generative model to estimate robust feature representations from current observational input for noise image classification. Depending on the estimated noise level (modeled as a binary treatment factor), TLT assigns the corresponding inference network trained by the designed causal loss for prediction. We also create new noisy image datasets incorporating a wide range of noise factors (e.g., object masking, style transfer, and adversarial perturbation) for performance benchmarking. The superior performance of TLT in noisy image classification is further validated by several refutation evaluation metrics. As a by-product, TLT also improves visual salience methods for perceiving noisy images.Comment: Accepted to IEEE WACV 2023. The first version was finished in May 201

Synthesis of SnO 2

Zinc oxides deposited on Tin dioxide nanowires have been successfully synthesized by atomic layer deposition (ALD). The diameter of SnO2-ZnO core-shell nanowires is 100 nm by ALD 200 cycles. The result of electricity measurements shows that the resistance of SnO2-ZnO core-shell nanowires (ALD: 200 cycles) is 925 Ω, which is much lower than pure SnO2 nanowires (3.6 × 106 Ω). The result of UV light test shows that the recovery time of SnO2-ZnO core-shell nanowires (ALD: 200 cycles) is 328 seconds, which is lower than pure SnO2 nanowires (938 seconds). These results demonstrated that the SnO2-ZnO core-shell nanowires have potential application as UV photodetectors with high photon-sensing properties

IMECE 2005-83000 AN EFFICIENT VOLUMETRIC-ERROR MEASUREMENT METHOD FOR FIVE-AXIS MACHINE TOOLS

ABSTRACT Accurate measurement of volumetric errors plays an important role for error compensation for multi-axis machines. The error measurements for volumetric errors of five-axis machines are usually very complex and costly than that for three-axis machines. In this study, a direct and simple measurement method using telescoping ball-bar system for volumetric errors for different types of five-axis machines was developed. The method using two-step measurement methodology and incorporating with derived error models, can quickly determine the five degrees-of-freedom (DOF) volumetric errors of five-axis machine tools. Comparing to most of the current used measurement methods, the proposed method provides the advantages of low cost, high efficiency, easy setup, and high accuracy