Subversions of the Social Hierarchy: Social Closure as Adaptation Strategy by the Female Marriage Migrants of Taiwan

This study explores how female marriage migrants employ various forms of social closure to help them adapt to the receiving society. As for female migration itself, although it has begun to dominate the migration flow it has not yet been discussed and theorized as a unique phenomenon in immigration studies. This phenomenon must, however, be viewed within the context of international hypergamy, which has become an increasingly notable trend in many countries, especially those of East Asia. Female marriage migrants, coming toTaiwanchiefly from Southeast Asian countries and fromChina, often are depicted by the mainstream discourse as being inferior. This study has found that by creating, and in some cases transforming, social closure, these female marriage migrants are able to reshape their group identity, to reposition themselves within the stratification at least within the parameters of their own minds, and thereby to cope with the discriminatory environment and unfavorable social hierarchy of Taiwanese society

Learning Discriminative Shrinkage Deep Networks for Image Deconvolution

Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms and data terms of the latent clear images. However, explicitly designing these two terms is quite challenging and usually leads to complex optimization problems which are difficult to solve. In this paper, we propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms. In contrast to most existing methods that use deep convolutional neural networks (CNNs) or radial basis functions to simply learn the regularization term, we formulate both the data term and regularization term and split the deconvolution model into data-related and regularization-related sub-problems according to the alternating direction method of multipliers. We explore the properties of the Maxout function and develop a deep CNN model with a Maxout layer to learn discriminative shrinkage functions to directly approximate the solutions of these two sub-problems. Moreover, given the fast-Fourier-transform-based image restoration usually leads to ringing artifacts while conjugate-gradient-based approach is time-consuming, we develop the Conjugate Gradient Network to restore the latent clear images effectively and efficiently. Experimental results show that the proposed method performs favorably against the state-of-the-art ones in terms of efficiency and accuracy

Interpretations of Domain Adaptations via Layer Variational Analysis

Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utilizing layer variational analysis proves that the success of transfer learning can be guaranteed with corresponding data conditions. Moreover, our theoretical calculation yields intuitive interpretations towards the knowledge transfer process. Subsequently, an alternative method for network-based transfer learning is derived. The method shows an increase in efficiency and accuracy for domain adaptation. It is particularly advantageous when new domain data is sufficiently sparse during adaptation. Numerical experiments over diverse tasks validated our theory and verified that our analytic expression achieved better performance in domain adaptation than the gradient descent method.Comment: Published at ICLR 202

Back-end of line compatible transistors for hybrid CMOS applications

The low-temperature back-end of line (BEOL) compatible transparent amorphous oxide semiconductor (TAOS) TFTs and poly-Si TFTs are the suitable platforms for three-dimensional (3D) integration hybrid CMOS technologies. The n-channel amorphous indium tungsten oxide (a-IWO) ultra-thin-film transistors (UTFTs) have been successfully fabricated and demonstrated in the category of indium oxide based thin film transistors (TFTs). We have scaled down thickness of a-IWO channel to 4nm. The proposed a-IWO UTFTs with low operation voltages exhibit good electrical characteristics: near ideal subthreshold swing (S.S.) ~ 63mV/dec., high field-effect mobility (FE) ~ 25.3 cm2/V-s. In addition, we also have fabricated the novel less metal contamination Ni-induced lateral crystallization (LC-NILC) p-channel poly-Si TFTs. The matched electrical characteristics of n-channel and p-channel devices with low operation voltage and low IOFF are exhibiting the promising candidate for future hybrid CMOS applications

Intertwined Orders and Electronic Structure in Superconducting Vortex Halos

We present a comprehensive study of vortex structures in $d$-wave superconductors from large-scale renormalized mean-field theory of the square-lattice $t$-$t'$-$J$ model, which has been shown to provide a quantitative modeling for high-$T_c$ cuprate superconductors. With an efficient implementation of the kernel polynomial method for solving electronic structures, self-consistent calculations involving up to $10^5$ variational parameters are performed to investigate the vortex solutions on lattices of up to $10^4$ sites. By taking into account the strong correlation of the model, our calculations shed new lights on two puzzling results that have emerged from recent scanning tunneling microscopy (STM) experiments. The first concerns the issue of the zero-biased-conductance peak (ZBCP) at the vortex core for a uniform $d$-wave superconducting state. Despite its theoretical prediction, the ZBCP was not observed in most doping range of cuprates except in heavily over-doped samples at low magnetic field. The second issue is the nature of the checkerboard charge density waves (CDWs) with a period of about 8 unit cells in the vortex halo at optimal doping. Although it has been suggested that such bipartite structure arises from low-energy quasiparticle interference, another intriguing scenario posits that the checkerboard CDWs originate from an underlying bidirectional pair-density wave (PDW) ordering with the same period. We present a coherent interpretation of these experimental results based on systematic studies of the doping and magnetic field effects on vortex solutions with and without a checkerboard structure. The mechanism of the emergent intertwined orders within the vortex halo is also discussed.Comment: 19 pages, 7 figure