Differentiable Forward and Backward Fixed-Point Iteration Layers

Recently, several studies proposed methods to utilize some classes of optimization problems in designing deep neural networks to encode constraints that conventional layers cannot capture. However, these methods are still in their infancy and require special treatments, such as analyzing the KKT condition, for deriving the backpropagation formula. In this paper, we propose a new layer formulation called the fixed-point iteration (FPI) layer that facilitates the use of more complicated operations in deep networks. The backward FPI layer is also proposed for backpropagation, which is motivated by the recurrent back-propagation (RBP) algorithm. But in contrast to RBP, the backward FPI layer yields the gradient by a small network module without an explicit calculation of the Jacobian. In actual applications, both the forward and backward FPI layers can be treated as nodes in the computational graphs. All components in the proposed method are implemented at a high level of abstraction, which allows efficient higher-order differentiations on the nodes. In addition, we present two practical methods of the FPI layer, FPI_NN and FPI_GD, where the update operations of FPI are a small neural network module and a single gradient descent step based on a learnable cost function, respectively. FPI\_NN is intuitive, simple, and fast to train, while FPI_GD can be used for efficient training of energy networks that have been recently studied. While RBP and its related studies have not been applied to practical examples, our experiments show the FPI layer can be successfully applied to real-world problems such as image denoising, optical flow, and multi-label classification

Mediating the East Asian Era of the Olympic Games (2018–2022):Introduction to the Special Issue

In the span of four years from 2018 to 2022, three consecutive Olympic Games were held in East Asia – namely PyeongChang 2018 in South Korea, Tokyo 2020 in Japan and Beijing 2022 in China. Given the geographic concentration of global multi-sports mega-events in the Far East, this period has been referred to as the ‘East Asian era’ of the Olympic Games. This introduction to the special issue outlines two major themes of the changes during the East Asian era: (1) the shift of economic and geopolitical power from the West to the East; and (2) the changes and challenges offered by the Olympic Agenda 2020 reforms and theCOVID-19 pandemic within East Asia. After that, each contribution will be introduced and briefly described. Overall, by collecting contributions focusing on the 2018–2022 Olympic Games, this special issue critically analyses the state of play in the formations of dominant and emerging discourses during the East Asia era and offers its implications for a broader understanding of the continuity and changes to the economic, political, social, cultural and ecological dimensions of the Olympic Movement

Differentiable Forward and Backward Fixed-Point Iteration Layers

Recently, several studies have proposed methods to utilize some classes of optimization problems in designing deep neural networks to encode constraints that conventional layers cannot capture. However, these methods are still in their infancy and require special treatments, such as the analysis of the Karush-Kuhn-Tucker (KKT) condition, to derive the backpropagation formula. In this paper, we propose a new formulation called the fixed-point iteration (FPI) layer, which facilitates the use of more complicated operations in deep networks. The backward FPI layer, which is motivated by the recurrent backpropagation (RBP) algorithm, is also proposed. However, in contrast to RBP, the backward FPI layer yields the gradient using a small network module without explicitly calculating the Jacobian. In actual applications, both forward and backward FPI layers can be treated as nodes in the computational graphs. All the components of our method are implemented at a high level of abstraction, which allows efficient higher-order differentiations on the nodes. In addition, we present two practical methods, the neural net FPI (FPI_NN) layer and the gradient descent FPI (FPI_GD) layer, whereby the FPI update operations are a small neural network module and a single gradient descent step based on a learnable cost function, respectively. FPI_NN is intuitive and simple, while FPI_GD can be used to efficient train energy function networks that have been studied recently. While RBP and related studies have not been applied to practical examples, our experiments show that the FPI layer can be successfully applied to real-world problems such as image denoising, optical flow, and multi-label classification.Y

Introduction : colonial modernity and beyond in East Asian contexts

This special issue examines colonial modernity in both historic and contemporary East Asia in order to illuminate the region's modern lives and sensibilities. The colonial modernity thesis, first articulated by Tani E. Barlow in Formations of Colonial Modernity in East Asia (1997), has been influential in explaining the construction of East Asian modernity and its development during the colonial periods. The rapid industrialization, urbanization, and capitalist expansion that characterize East Asia today make it imperative to revisit the traces of colonial modernity that are still evident there. To counter the premise that Asian modernity is static and coherent, this special issue illustrates not only the continuity of colonial modernity but also its ruptures since the early 1900s. The editors will present approaches that move beyond the dichotomist perspective that interprets Asia and Asian modernity only through comparisons with the West. Our goal is to reiterate the validity and significance of the colonial modernity thesis, using varied current perspectives, in a way that reveals the multiplicity of modernities in present-day East Asia

Investigating 'Fear of Missing Out' (FOMO) as an extrinsic motive affecting sport event consumer's behavioral intention and FOMO-driven consumption's influence on intrinsic rewards, extrinsic rewards, and consumer satisfaction.

This study posits that Fear of Missing Out (FOMO) can function as an extrinsic motive stimulating sport event consumption by inducing consumers to overcome leisure constraints. Also, FOMO-driven consumption is proposed to affect consumption experience for being grounded on extrinsic than intrinsic rewards. In Study 1, the moderation of FOMO between intrapersonal and structural constraints and sport media viewing intention are tested. In Study 2, the relations among FOMO-driven consumption, intrinsic rewards (i.e., enjoyment), extrinsic rewards (i.e., social adherence), and consumer satisfaction are assessed. Study 1 results support the notion that FOMO can boost sport media viewing intention through two mechanisms: by directly stimulating intention and by lifting the negative effect of constraints on intention. In Study 2, FOMO-driven consumption shows a stronger link to extrinsic than intrinsic rewards, extrinsic reward is marginally but negatively associated with intrinsic reward, and intrinsic reward is a stronger predictor of satisfaction. Overall, FOMO is identified as a meaningful extrinsic motive for sport event consumption though its effects on consumer satisfaction are arguable. Implications for FOMO-driven marketing are discussed

A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications

This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. This leads to unnecessary energy loss due to redundant transmission. In the proposed model, IoT-cloud provides sensing services on demand based on interest and location of mobile users. By taking advantages of the cloud as a coordinator, sensing scheduling of sensors is controlled by the cloud, which knows when and where mobile users request for sensing services. Therefore, when there is no demand, sensors are put into an inactive mode to save energy. Through extensive analysis and experimental results, we show that the location-based model achieves a significant improvement in terms of network lifetime compared to the periodic model

A Qualitative Systemic Review of Public-private Partnership in Promoting Physical Activity

The purpose of the study is to conduct a comprehensive review of public–private partnership (PPP) literature that pertains to promoting physical activity. A qualitative systematic review guided data search and screening process, and the findings were synthesized and interpreted using a qualitative content analysis method. Literature was searched from 16 academic and 6 gray literature databases. A total of 1,117 articles were initially searched, full texts of 186 articles were assessed, and 13 articles that met the inclusion criteria were finally included. PPPs have been initiated in various contexts including implementing the pledge policy, program coordination, and infrastructure supports. Public-sector partners were identified in a range of vertical and horizontal levels. Private partners were mainly manufacturers and/or retailers related to physical activity, sport facility operators, professional sport teams, or companies for providing infrastructures for active transportation. Public and private organizations have performed various roles of funding the initiatives, developing and implementing diverse resources, and taking actions to deliver benefits to the communities. Several challenges were reported when developing, implementing, and evaluating the partnership initiatives. The outcomes of the current review can be utilized to anticipate pragmatic issues when public and private partners jointly participate in physical activity promotion