<ARTICLES>An Imperative Cultural Trend?: International Film Co-production Policy in Japan

In this article, I investigate the process of agenda building and implementation of the Japanese film policy which was concomitant with the Japanese cultural policy of ‘Cool Japan.’ This article is composed of three major analyses: 1) a review of the sociological literature and critiques on cultural industries and policy; 2) the discourse regarding the Cool Japan strategy and film policy in Japan; and 3) the incentive for international film co-production and the globalization of cultural policy in East Asia. First, this article explores the policymaking process associated with the Cool Japan strategy and Japanese film policy by analyzing the archival documents of the various governmental institutes in Japan. The Cool Japan policy has been built up as a national and cultural agenda by the government (an agenda-building effect) in the wake of globalization in East Asia. The promotion of the state of Japan is the main goal of this strategy, but the state does not always consider the current circumstances and problems of the Japanese film industry. The incentive for international film coproduction was created as the state was influenced by neighboring nations, even though the co-production strategy was initially exploited by a handful of directors who already had a track record of co-productions. I compare the cultural and film policies of South Korea and PRC with those of Japan by charting their interplay and examining their implications for global trends in the film industry. However, policy theory and discourse cannot be understood without also examining the industry situation itself. It is necessary to investigate the incentive provided for international film co-production in tandem with the Cool Japan strategy, in the name of which the government has committed several grave blunders. Thus, while the incentive for international film coproduction in Japan is policy-driven, at the same time it is also intertwined with media globalization in the competitive circumambience. Eventually, the question raised here must be: is it an imperative cultural trend or a commercial necessity for the Japanese film industry

GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps

Data augmentation is now an essential part of the image training process, as it effectively prevents overfitting and makes the model more robust against noisy datasets. Recent mixing augmentation strategies have advanced to generate the mixup mask that can enrich the saliency information, which is a supervisory signal. However, these methods incur a significant computational burden to optimize the mixup mask. From this motivation, we propose a novel saliency-aware mixup method, GuidedMixup, which aims to retain the salient regions in mixup images with low computational overhead. We develop an efficient pairing algorithm that pursues to minimize the conflict of salient regions of paired images and achieve rich saliency in mixup images. Moreover, GuidedMixup controls the mixup ratio for each pixel to better preserve the salient region by interpolating two paired images smoothly. The experiments on several datasets demonstrate that GuidedMixup provides a good trade-off between augmentation overhead and generalization performance on classification datasets. In addition, our method shows good performance in experiments with corrupted or reduced datasets.Comment: Published at AAAI2023 (Oral

Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models

Assessing the fidelity and diversity of the generative model is a difficult but important issue for technological advancement. So, recent papers have introduced k-Nearest Neighbor ($k$NN) based precision-recall metrics to break down the statistical distance into fidelity and diversity. While they provide an intuitive method, we thoroughly analyze these metrics and identify oversimplified assumptions and undesirable properties of kNN that result in unreliable evaluation, such as susceptibility to outliers and insensitivity to distributional changes. Thus, we propose novel metrics, P-precision and P-recall (PP\&PR), based on a probabilistic approach that address the problems. Through extensive investigations on toy experiments and state-of-the-art generative models, we show that our PP\&PR provide more reliable estimates for comparing fidelity and diversity than the existing metrics. The codes are available at \url{https://github.com/kdst-team/Probablistic_precision_recall}

NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency

We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data. In NaturalInversion, we propose: (1) a Feature Transfer Pyramid which uses enhanced image prior of the original data by combining the multi-scale feature maps extracted from the pre-trained classifier, (2) a one-to-one approach generative model where only one batch of images are synthesized by one generator to bring the non-linearity to optimization and to ease the overall optimizing process, (3) learnable Adaptive Channel Scaling parameters which are end-to-end trained to scale the output image channel to utilize the original image prior further. With our NaturalInversion, we synthesize images from classifiers trained on CIFAR-10/100 and show that our images are more consistent with original data distribution than prior works by visualization and additional analysis. Furthermore, our synthesized images outperform prior works on various applications such as knowledge distillation and pruning, demonstrating the effectiveness of our proposed method.Comment: Published at AAAI 202

Extreme coefficients in Geographically Weighted Regression and their effects on mapping

This study deals with the issue of extreme coefficients in geographically weighted regression (GWR) and their effects on mapping coefficients using three datasets with different spatial resolutions. We found that although GWR yields extreme coefficients regardless of the resolution of the dataset or types of kernel function, 1) the GWR tends to generate extreme coefficients for less spatially dense datasets, 2) coefficient maps based on polygon data representing aggregated areal units are more sensitive to extreme coefficients, and 3) coefficient maps using bandwidths generated by a fixed calibration procedure are more vulnerable to the extreme coefficients than adaptive calibration.extreme coefficient, fixed and adaptive calibrations, geographically weighted regression, Mapping, Research Methods/ Statistical Methods,

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

Inactivation of Medial Prefrontal Cortex Impairs Time Interval Discrimination in Rats

Several lines of evidence suggest the involvement of prefrontal cortex in time interval estimation. The underlying neural processes are poorly understood, however, in part because of the paucity of physiological studies. The goal of this study was to establish an interval timing task for physiological recordings in rats, and test the requirement of intact medial prefrontal cortex (mPFC) for performing the task. We established a temporal bisection procedure using six different time intervals ranging from 3018 to 4784 ms that needed to be discriminated as either long or short. Bilateral infusions of muscimol (GABAA receptor agonist) into the mPFC significantly impaired animal's performance in this task, even when the animals were required to discriminate between only the longest and shortest time intervals. These results show the requirement of intact mPFC in rats for time interval discrimination in the range of a few seconds

α-Actinin-4 promotes the progression of prostate cancer through the Akt/GSK-3β/β-catenin signaling pathway

The first-line treatment for prostate cancer (PCa) is androgen ablation therapy. However, prostate tumors generally recur and progress to androgen-independent PCa (AIPC) within 2-3 years. alpha-Actinin-4 (ACTN4) is an actin-binding protein that belongs to the spectrin gene superfamily and acts as an oncogene in various cancer types. Although ACTN4 is involved in tumorigenesis and the epithelial-mesenchymal transition of cervical cancer, the role of ACTN4 in PCa remains unknown. We found that the ACTN4 expression level increased during the transition from androgen-dependent PCa to AIPC. ACTN4 overexpression resulted in enhanced proliferation and motility of PCa cells. Increased beta-catenin due to ACTN4 promoted the transcription of genes involved in proliferation and metastasis such as CCND1 and ZEB1. ACTN4-overexpressing androgen-sensitive PCa cells were able to grow in charcoal-stripped media. In contrast, ACTN4 knockdown using si-ACTN4 and ACTN4 nanobody suppressed the proliferation, migration, and invasion of AIPC cells. Results of the xenograft experiment revealed that the mice injected with LNCaPACTN4 cells exhibited an increase in tumor mass compared with those injected with LNCaPMock cells. These results indicate that ACTN4 is involved in AIPC transition and promotes the progression of PCa