Cosmological Simulation for Fuzzy Dark Matter Model

Fuzzy Dark Matter (FDM), motivated by string theory, has recently become a hot candidate for dark matter. The rest mass of FDM is believed to be $\sim 10^{-22}$eV and the corresponding de-Broglie wave length is $\sim 1$kpc. Therefore, the quantum effect of FDM plays an important role in structure formation. In order to study the cosmological structure formation in FDM model, several simulation techniques have been introduced. We review the current status and challenges in the cosmological simulation for the FDM model in this paper.Comment: 10 pages, 2 tables, published on Front. Astron. Space Sci. under the topic: Dark Matter - Where is it? What is it

Study of saliency in objective video quality assessment

Reliably predicting video quality as perceived by humans remains challenging and is of high practical relevance. A significant research trend is to investigate visual saliency and its implications for video quality assessment. Fundamental problems regarding how to acquire reliable eye-tracking data for the purpose of video quality research and how saliency should be incorporated in objective video quality metrics (VQMs) are largely unsolved. In this paper, we propose a refined methodology for reliably collecting eye-tracking data, which essentially eliminates bias induced by each subject having to view multiple variations of the same scene in a conventional experiment. We performed a large-scale eye-tracking experiment that involved 160 human observers and 160 video stimuli distorted with different distortion types at various degradation levels. The measured saliency was integrated into several best known VQMs in the literature. With the assurance of the reliability of the saliency data, we thoroughly assessed the capabilities of saliency in improving the performance of VQMs, and devised a novel approach for optimal use of saliency in VQMs. We also evaluated to what extent the state-of-the-art computational saliency models can improve VQMs in comparison to the improvement achieved by using “ground truth” eye-tracking data. The eye-tracking database is made publicly available to the research community

Towards a reliable collection of eye-tracking data for image quality research: challenges, solutions and applications

Image quality assessment potentially benefits from the addition of visual attention. However, incorporating aspects of visual attention in image quality models by means of a perceptually optimized strategy is largely unexplored. Fundamental challenges, such as how visual attention is affected by the concurrence of visual signals and their distortions; whether visual attention affected by distortion or that driven by the original scene only should be included in an image quality model; and how to select visual attention models for the image quality application context, remain. To shed light on the above unsolved issues, designing and performing eye-tracking experiments are essential. Collecting eye-tracking data for the purpose of image quality study is so far confronted with a bias due to the involvement of stimulus repetition. In this paper, we propose a new experimental methodology to eliminate such inherent bias. This allows obtaining reliable eye-tracking data with a large degree of stimulus variability. In fact, we first conducted 5760 eye movement trials that included 160 human observers freely viewing 288 images of varying quality. We then made use of the resulting eye-tracking data to provide insights into the optimal use of visual attention in image quality research. The new eye-tracking data are made publicly available to the research community

Adaptive Speech Quality Aware Complex Neural Network for Acoustic Echo Cancellation with Supervised Contrastive Learning

Acoustic echo cancellation (AEC) is designed to remove echoes, reverberation, and unwanted added sounds from the microphone signal while maintaining the quality of the near-end speaker's speech. This paper proposes adaptive speech quality complex neural networks to focus on specific tasks for real-time acoustic echo cancellation. In specific, we propose a complex modularize neural network with different stages to focus on feature extraction, acoustic separation, and mask optimization receptively. Furthermore, we adopt the contrastive learning framework and novel speech quality aware loss functions to further improve the performance. The model is trained with 72 hours for pre-training and then 72 hours for fine-tuning. The proposed model outperforms the state-of-the-art performance.Comment: Submitted to International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2023. Under revie

A saliency dispersion measure for improving saliency-based image quality metrics

Objective image quality metrics (IQMs) potentially benefit from the addition of visual saliency. However, challenges to optimising the performance of saliency-based IQMs remain. A previous eye-tracking study has shown that gaze is concentrated in fewer places in images with highly salient features than in images lacking salient features. From this, it can be inferred that the former are more likely to benefit from adding a saliency term to an IQM. To understand whether these ideas still hold when using computational saliency instead of eyetracking data, we first conducted a statistical evaluation using 15 state of the art saliency models and 10 well-known IQMs. We then used the results to devise an algorithm which adaptively incorporates saliency in IQMs for natural scenes, based on saliency dispersion. Experimental results demonstrate this can give significant improvement

A saliency dispersion measure for improving saliency-based image quality metrics

Objective image quality metrics (IQMs) potentially benefit from the addition of visual saliency. However, challenges to optimising the performance of saliency-based IQMs remain. A previous eye-tracking study has shown that gaze is concentrated in fewer places in images with highly salient features than in images lacking salient features. From this, it can be inferred that the former are more likely to benefit from adding a saliency term to an IQM. To understand whether these ideas still hold when using computational saliency instead of eyetracking data, we first conducted a statistical evaluation using 15 state of the art saliency models and 10 well-known IQMs. We then used the results to devise an algorithm which adaptively incorporates saliency in IQMs for natural scenes, based on saliency dispersion. Experimental results demonstrate this can give significant improvement

State of the art: Eye-tracking studies in medical imaging

Eye-tracking – the process of measuring where people look in a visual field – has been widely used to study how humans process visual information. In medical imaging, eye-tracking has become a popular technique in many applications to reveal how visual search and recognition tasks are performed, providing information that can improve human performance. In this paper, we present a comprehensive review of eye-tracking studies conducted with medical images and videos for diverse research purposes, including identification of degree of expertise, development of training, and understanding and modelling of visual search patterns. In addition, we present our recent eye-tracking study that involves a large number of screening mammograms viewed by experienced breast radiologists. Based on the eye-tracking data, we evaluate the plausibility of predicting visual attention by computational models