N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution

While some studies have proven that Swin Transformer (SwinT) with window self-attention (WSA) is suitable for single image super-resolution (SR), SwinT ignores the broad regions for reconstructing high-resolution images due to window and shift size. In addition, many deep learning SR methods suffer from intensive computations. To address these problems, we introduce the N-Gram context to the image domain for the first time in history. We define N-Gram as neighboring local windows in SwinT, which differs from text analysis that views N-Gram as consecutive characters or words. N-Grams interact with each other by sliding-WSA, expanding the regions seen to restore degraded pixels. Using the N-Gram context, we propose NGswin, an efficient SR network with SCDP bottleneck taking all outputs of the hierarchical encoder. Experimental results show that NGswin achieves competitive performance while keeping an efficient structure, compared with previous leading methods. Moreover, we also improve other SwinT-based SR methods with the N-Gram context, thereby building an enhanced model: SwinIR-NG. Our improved SwinIR-NG outperforms the current best lightweight SR approaches and establishes state-of-the-art results. Codes will be available soon.Comment: 8 pages (main content) + 14 pages (supplementary content

A Fast and Scalable Re-routing Algorithm based on Shortest Path and Genetic Algorithms J. Lee, J. Yang Jungkyu Lee

This paper presents a fast and scalable re-routing algorithm that adapts to dynamically changing networks. The proposed algorithm, DGA, integrates Dijkstra’s shortest path algorithm with the genetic algorithm. Dijkstra’s algorithm is used to define the predecessor array that facilitates the initialization process of the genetic algorithm. Then the genetic algorithm keeps finding the best routes with appropriate genetic operators under dynamic traffic situations. Experimental results demonstrate that DGA produces routes with less traveling time and computational overhead than pure genetic algorithm-based approaches as well as Dijkstra’s algorithm in largescale routing problems

Design and Implementation of Application-Level Multicasting Services over ATM Networks

Abstract. The ACS (Adaptive Communication System) is a multithreaded message-passing system that provides application programmers with multithreading and flexible communication services. This paper outlines the general software architecture of ACS and describes how the ACS architecture is applied to implement its flexible application-level group communication services. We provide the performance results of ACS multicasting services and compare them with those of p4, PVM, and MPI

Fair Inputs and Fair Outputs: The Incompatibility of Fairness in Privacy and Accuracy

Fairness concerns about algorithmic decision-making systems have been mainly focused on the outputs (e.g., the accuracy of a classifier across individuals or groups). However, one may additionally be concerned with fairness in the inputs. In this paper, we propose and formulate two properties regarding the inputs of (features used by) a classifier. In particular, we claim that fair privacy (whether individuals are all asked to reveal the same information) and need-to-know (whether users are only asked for the minimal information required for the task at hand) are desirable properties of a decision system. We explore the interaction between these properties and fairness in the outputs (fair prediction accuracy). We show that for an optimal classifier these three properties are in general incompatible, and we explain what common properties of data make them incompatible. Finally we provide an algorithm to verify if the trade-off between the three properties exists in a given dataset, and use the algorithm to show that this trade-off is common in real data