Building transnational labor markets: The case of Taiwan

Taiwan has embedded itself in the global markets and established strong economic relations with many countries, especially the neighbors in East Asia. However, working in a foreign country, as part of international economic exchange is still constrained by the regulations and/or socio-cultural barriers in respective countries. Nonetheless, it is argued that transnational labor markets, which are primarily constructed by private actors, are emerging in East Asia. Taking up Taiwan as a case, this study investigates how private actors - temporary help agencies - go ahead of the states and forge institutions that facilitate labor mobility across national borders in the absence of supra-national institutions, such as EU or ASEAN Economic Community, where freedom of movement has taken place or is expected to launch. Based on interviews with staffing agencies, union activists and government officials, it is found that staffing agencies serve as a transnational HR management function, as they develop international networks and provide their clients and workers with services such as visa application and employment arrangements that accommodate to business, employment and social welfare regulations in both sending and receiving countries. Moreover, staffing agencies translate and diffuse socio-cultural meanings between countries by engaging in socio-cultural training for workers as well as their clients to ensure the success of cross-border labor placements. This research contributes to the understanding of transnational labor mobility by studying the process of building transnational institutions and how these institutions make sense to the involved actors

Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201

Long-term RFID SLAM using Short-Range Sparse Tags

While on the path forward to the long-term or lifelong robotics, one of the most important capabilities is to have a reliable localization and mapping module. Data association and loop detection play critical roles in the localization and mapping problem. By utilizing the radio frequency identification (RFID) technology, these problems can be solved using the extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) with the tag information. But one of the critical barriers to the long-term SLAM is the overconfidence issue. In this paper, we focus on solving the overconfidence issue, which is introduced by the linearization errors. An Unit Circle Representation (UCR) is proposed to diminish the error in the prediction stage and a Correlation Coefficient Preserved Inflation (CCPI) is developed to recover the overconfidence issue in the update stage. Based on only odometry and sparse short-range RFID data, the proposed method is capable to compensate the linearization errors in both simulation and real experiments

Disordered Fe vacancies and superconductivity in potassium-intercalated iron selenide (K2-xFe4+ySe5)

The parent compound of an unconventional superconductor must contain unusual correlated electronic and magnetic properties of its own. In the high-Tc potassium intercalated FeSe, there has been significant debate regarding what the exact parent compound is. Our studies unambiguously show that the Fe-vacancy ordered K2Fe4Se5 is the magnetic, Mott insulating parent compound of the superconducting state. Non-superconducting K2Fe4Se5 becomes a superconductor after high temperature annealing, and the overall picture indicates that superconductivity in K2-xFe4+ySe5 originates from the Fe-vacancy order to disorder transition. Thus, the long pending question whether magnetic and superconducting state are competing or cooperating for cuprate superconductors may also apply to the Fe-chalcogenide superconductors. It is believed that the iron selenides and related compounds will provide essential information to understand the origin of superconductivity in the iron-based superconductors, and possibly to the superconducting cuprates

Comparison of Renal Function and Other Health Outcomes in Vegetarians versus Omnivores in Taiwan

Renal disease is one of the top 10 leading causes of death, and the incidence of end-stage renal disease in Taiwan is the highest in the world. Many dietitians consider the diet of plant origin consumed by vegans to be ‘lighter’ and ‘more healthful’ than the diet of both plant and animal origin consumed by omnivores. Dietary protein has significant effects on renal functions. The study explored the effects of both the diets on renal functions. The study subjects included 102 Buddhist nun vegetarians and an equal number of matched control group (omnivores). A cross-sectional study was performed to investigate the effects of the diet of plant origin and the diet of both plant and animal origin on renal functions. There was no difference in the renal functions between the two groups. However, systolic blood pressure, blood urea nitrogen, serum sodium, glucose, cholesterol levels, and urinary specific gravity were lower in the vegetarian group. Although these results were compatible with general concepts regarding diet of plant origin, after adjusting for age, the duration of intake of this diet had no effect on the renal functions. Based on the findings, it is concluded that the renal functions, in terms of the estimated glomerular filtration rate, were not different between the vegetarians and the omnivores

Variability of morphology in beat-to-beat photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment

We investigated the beat-to-beat fluctuation of the photoplethysmography (PPG) waveform. The motivation is that morphology variability extracted from the arterial blood pressure (ABP) has been found to correlate with baseline condition and short-term surgical outcome of the patients undergoing liver transplant surgery. Numerous interactions of physiological mechanisms regulating the cardiovascular system could underlie the variability of morphology. We used the unsupervised manifold learning algorithm, Dynamic Diffusion Map, to quantify the multivariate waveform morphological variation. Due to the physical principle of light absorption, PPG waveform signals are more susceptible to artifact and are nominally used only for visual inspection of data quality in clinical medical environment. But on the other hand, the noninvasive, easy-to-use nature of PPG grants a wider range of biomedical application, which inspired us to investigate the variability of morphology information from PPG waveform signal. We developed data analysis techniques to improve the performance and validated with the real-life clinical database

Age as a predisposing factor of respiratory alkalosis in accidental carbon monoxide poisoning

AbstractAimsThe purpose of this study was to determine the frequency of and identify the predisposing factors for respiratory alkalosis in patients with accidental carbon monoxide (CO) poisoning.MethodsPatients presenting to the emergency department with accidental CO poisoning were retrospectively identified and divided into Group A (no respiratory alkalosis) and Group B (respiratory alkalosis). Charts were reviewed for neurologic status, various demographic factors, and laboratory data.ResultsA total 96 patients, 37 (38.5%) men and 59 (61.5%) women, were identified. Of these, the 58 (60.4%) patients without respiratory alkalosis were placed in Group A and the 38 (39.6%) patients with respiratory alkalosis were placed in Group B. Independent multivariate predictors of CO poisoning presenting with respiratory alkalosis were age [odds ratio (OR), 1.04; 95% confidence interval (CI), 1.01–1.08] and respiratory rate (OR, 1.16; 95% CI, 1.01–1.33). The rates of respiratory alkalosis in patients younger than 15 years, 15–29 years, 30–44 years, 45–59 years, and older than 59 years were 17.4%, 32.4%, 51.9%, 75%, and 75%, respectively (p<0.01).ConclusionsRespiratory alkalosis in the patients with CO poisoning is not an uncommon finding, and as age increases, the percentage becomes higher. When emergency physicians are faced with patients presenting with respiratory alkalosis of undetermined cause, CO poisoning should be taken into consideration, especially in the elderly