487 research outputs found
Direct Realization of Digital Differentiators in Discrete Domain for Active Damping of LCL-Type Grid-Connected Inverter
A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading.
Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in this paper. In contrast to conventional psychology-based eye movement models, ours is based on a recurrent neural network (RNN) to generate a gaze point prediction sequence, by using the combination of convolutional neural networks (CNN), bidirectional long short-term memory networks (LSTM), and conditional random fields (CRF). The model uses the eye movement data of a reader reading some texts as training data to predict the eye movements of the same reader reading a previously unseen text. A theoretical analysis of the model is presented to show its excellent convergence performance. Experimental results are then presented to demonstrate that the proposed model can achieve similar prediction accuracy while requiring fewer features than current machine learning models
GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing
Most of the existing clustering methods are based on a single granularity of
information, such as the distance and density of each data. This most
fine-grained based approach is usually inefficient and susceptible to noise.
Therefore, we propose a clustering algorithm that combines multi-granularity
Granular-Ball and minimum spanning tree (MST). We construct coarsegrained
granular-balls, and then use granular-balls and MST to implement the clustering
method based on "large-scale priority", which can greatly avoid the influence
of outliers and accelerate the construction process of MST. Experimental
results on several data sets demonstrate the power of the algorithm. All codes
have been released at https://github.com/xjnine/GBMST
All-to-key Attention for Arbitrary Style Transfer
Attention-based arbitrary style transfer studies have shown promising
performance in synthesizing vivid local style details. They typically use the
all-to-all attention mechanism -- each position of content features is fully
matched to all positions of style features. However, all-to-all attention tends
to generate distorted style patterns and has quadratic complexity, limiting the
effectiveness and efficiency of arbitrary style transfer. In this paper, we
propose a novel all-to-key attention mechanism -- each position of content
features is matched to stable key positions of style features -- that is more
in line with the characteristics of style transfer. Specifically, it integrates
two newly proposed attention forms: distributed and progressive attention.
Distributed attention assigns attention to key style representations that
depict the style distribution of local regions; Progressive attention pays
attention from coarse-grained regions to fine-grained key positions. The
resultant module, dubbed StyA2K, shows extraordinary performance in preserving
the semantic structure and rendering consistent style patterns. Qualitative and
quantitative comparisons with state-of-the-art methods demonstrate the superior
performance of our approach
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