Labelled data are limited and self-supervised learning is one of the most important approaches for reducing labelling requirements. While it has been extensively explored in the image domain, it has so far not receive...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Labelled data are limited and self-supervised learning is one of the most important approaches for reducing labelling requirements. While it has been extensively explored in the image domain, it has so far not received the same amount of attention in the acoustic domain. Yet, reducing labelling is a key requirement for many acoustic applications. Specifically in bioacoustic, there are rarely sufficient labels for fully supervised learning available. This has led to the widespread use of acoustic recognisers that have been pre-trained on unrelated data for bioacoustic tasks. We posit that training on the actual task data and combining self-supervised pre-training with few-shot classification is a superior approach that has the ability to deliver high accuracy even when only a few labels are available. To this end, we introduce and evaluate a new architecture that combines CNN-based preprocessing with feature extraction based on state space models (SSMs). This combination is motivated by the fact that CNN-based networks alone struggle to capture temporal information effectively, which is crucial for classifying acoustic signals. SSMs, specifically S4 and Mamba, on the other hand, have been shown to have an excellent ability to capture long-range dependencies in sequence data. We pre-train this architecture using contrastive learning on the actual task data and subsequent fine-tuning with an extremely small amount of labelled data. We evaluate the performance of this proposed architecture for (n-shot, n-way) classification on standard benchmarks as well as real-world data. Our evaluation shows that it outperforms state-of-the-art architectures on the few-shot classification problem.
Simultaneous functional PET/MR (sf-PET/MR) presents a cutting-edge multimodal neuroimaging technique. It provides an unprecedented opportunity for concurrently monitoring and integrating multifaceted brain networks bu...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Simultaneous functional PET/MR (sf-PET/MR) presents a cutting-edge multimodal neuroimaging technique. It provides an unprecedented opportunity for concurrently monitoring and integrating multifaceted brain networks built by spatiotemporally covaried metabolic activity, neural activity, and cerebral blood flow (perfusion). Albeit high scientific/clinical values, short in hardware accessibility of PET/MR hinders its applications, let alone modern AI-based PET/MR fusion models. Our objective is to develop a clinically feasible AI-based disease diagnosis model trained on comprehensive sf-PET/MR data with the power of, during inferencing, allowing single modality input (e.g., PET only) as well as enforcing multimodal-based accuracy. To this end, we propose Mx-ARM, a multimodal Mixture-of-experts Alignment and Reconstruction Model. It is modality detachable and exchangeable, allocating different multi-layer perceptrons dynamically ("mixture of experts") through learnable weights to learn respective representations from different modalities. Such design will not sacrifice model performance in uni-modal situation. To fully exploit the inherent complex and nonlinear relation among modalities while producing fine-grained representations for uni-modal inference, a modal alignment module is utilized to line up a dominant modality (e.g., PET) with representations of auxiliary modalities (MR). We further adopt multimodal reconstruction to promote the quality of learned features. Experiments on precious multimodal sf-PET/MR data for Mild Cognitive Impairment diagnosis showcase the efficacy of Mx-ARM toward clinically feasible precision medicine.
wavelets were developed independently by mathematicians, quantum physicists, electrical engineers, and geologists, but collaborations among these fields during the last decade have led to new and varied applications. ...
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wavelets were developed independently by mathematicians, quantum physicists, electrical engineers, and geologists, but collaborations among these fields during the last decade have led to new and varied applications. What are wavelets and why might they be useful to you?
We propose a construction of directional - or Gabor - continuous wavelets on the sphere. We provide a criterion to measure their angular selectivity. We finally discuss implementation issues and potential applications...
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ISBN:
(纸本)0819450804
We propose a construction of directional - or Gabor - continuous wavelets on the sphere. We provide a criterion to measure their angular selectivity. We finally discuss implementation issues and potential applications. The code for the spherical wavelet transform is avalaible in the YAWTB Matlab toolbox, http://***.
Wavelet transform is a powerful and useful mathematical tool for signalprocessing. In this paper detailed description of procedures for numerical integration and derivation in Haar domain has been done. These procedu...
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ISBN:
(纸本)0819450804
Wavelet transform is a powerful and useful mathematical tool for signalprocessing. In this paper detailed description of procedures for numerical integration and derivation in Haar domain has been done. These procedures are necessary both in control systems analysis, and, especially, for control design and development. A detailed comparison between classical methods of evaluation and the Haar way is presented and critically discussed.
Conventional techniques for signal analysis and processing in the time-frequency domain are not well adapted to digital processing of music signals. This restricts the features and quality of applications. We present ...
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ISBN:
(纸本)0819450804
Conventional techniques for signal analysis and processing in the time-frequency domain are not well adapted to digital processing of music signals. This restricts the features and quality of applications. We present the current status of a research initiative on this problem. A novel family of wavelet-like bases allows a tiling of the time-frequency plane that is better adapted to digital music signals. This will allow performance enhancements in all kinds of digital audio applications.
We present here an explicit time-domain representation of any compactly supported dyadic scaling function as a sum of harmonic splines. The leading term in the decomposition corresponds to the fractional splines, a re...
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ISBN:
(纸本)0819450804
We present here an explicit time-domain representation of any compactly supported dyadic scaling function as a sum of harmonic splines. The leading term in the decomposition corresponds to the fractional splines, a recent, continuous-order generalization of the polynomial splines.
The problem of image denoising has received more attention than the problem of image sharpening. In the paper, we propose that wavelet-based algorithms for image denoising can be used to perform image sharpening. Cons...
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ISBN:
(纸本)0819450804
The problem of image denoising has received more attention than the problem of image sharpening. In the paper, we propose that wavelet-based algorithms for image denoising can be used to perform image sharpening. Consequently, a variety of new image sharpening techniques becomes available. We examine the sharpening of natural images using an algorithm for image denoising with oriented complex 2D wavelets.
Density conditions have turned out to be a powerful tool for deriving necessary conditions for weighted wavelet systems to possess an upper or lower frame bound. In this paper we study different definitions of density...
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ISBN:
(纸本)0819450804
Density conditions have turned out to be a powerful tool for deriving necessary conditions for weighted wavelet systems to possess an upper or lower frame bound. In this paper we study different definitions of density and compare them with respect to their appropriateness and practicality.
This paper explores the application of wavelets to a variety of real life problems and more specifically to imageprocessing problems. A general review of the construction and analysis of wavelet analysis will be pres...
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ISBN:
(纸本)081942840X
This paper explores the application of wavelets to a variety of real life problems and more specifically to imageprocessing problems. A general review of the construction and analysis of wavelet analysis will be presented. The issues like multiresolution analysis in the context of sensor integration and pattern recognition and other salient features of the images using wavelets will be discussed in detail.
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