261 research outputs found
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
Data-Driven Theory Refinement Algorithms for Bioinformatics
Bioinformatics and related applications call for efficient algorithms for knowledge intensive learning and data driven knowledge refinement. Knowledge based artificial neural networks offer an attractive approach to extending or modifying incomplete knowledge bases or domain theories. We present results of experiments with several such algorithms for data driven knowledge discovery and theory refinement in some simple bioinformatics applications. Results of experiments on the ribosome binding site and promoter site identification problems indicate that the performance of KBDistAl and Tiling Pyramid algorithms compares quite favorably with those of substantially more computationally demanding techniques
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
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