Dictionary learning

In photoacoustic tomography (PAT), imaging speed is an essential metric that is restricted by the pulse laser repetition rate and the number of channels on the data acquisition card (DAQ). Reconstructing the initial sound pressure distribution with fewer elements can significantly reduce hardware costs and back-end acquisition pressure. However, undersampling will result in artefacts in the photoacoustic image, degrading its quality. Dictionary learning (DL) has been utilised for various image reconstruction techniques, but they disregard the uniformity of pixels in overlapping blocks...

Optical coherence tomography (OCT) is a non-invasive and effective tool for the imaging of retinal tissue. However, the heavy speckle noise, resulting from multiple scattering of the light waves, obscures important morphological structures and impairs the clinical diagnosis of ocular diseases. In this paper, we propose a novel and powerful model known as tensor ring decomposition-guided dictionary learning (TRGDL) for OCT image denoising, which can simultaneously utilize two useful complementary priors, i.e...

The Koopman operator has received attention for providing a potentially global linearization representation of the nonlinear dynamical system. To estimate or control the original system, the invertibility problem is introduced into the data-driven modeling, i.e., the observables are required to be reconstructed the original system's states. Existing methods cannot solve this problem perfectly. Only linear or nonlinear but lossy reconstruction can be achieved. This paper proposed a novel data-driven modeling approach, denoted as the Extended Dynamic Mode Decomposition with Invertible Dictionary Learning (EDMD-IDL) to address this issue, which can be interpreted as a further extension of the classical Extended Dynamic Mode Decomposition (EDMD)...

INTRODUCTION: Instagram is a popular social networking platform for sharing photos with a large proportion of youth and young adult users. We aim to identify key features in anti-vaping Instagram image posts associated with high social media user engagement by artificial intelligence. AIMS AND METHODS: We collected 8972 anti-vaping Instagram image posts and hand-coded 2200 Instagram images to identify nine image features such as warning signs and person-shown vaping...

Synapses preferentially respond to particular temporal patterns of activity with a large degree of heterogeneity that is informally or tacitly separated into classes. Yet, the precise number and properties of such classes are unclear. Do they exist on a continuum and, if so, when is it appropriate to divide that continuum into functional regions? In a large dataset of glutamatergic cortical connections, we perform model-based characterization to infer the number and characteristics of functionally distinct subtypes of synaptic dynamics...

We present UniSpec, an attention-driven deep neural network designed to predict comprehensive collision-induced fragmentation spectra, thereby improving peptide identification in shotgun proteomics. Utilizing a training data set of 1.8 million unique high-quality tandem mass spectra (MS2) from 0.8 million unique peptide ions, UniSpec learned with a peptide fragmentation dictionary encompassing 7919 fragment peaks. Among these, 5712 are neutral loss peaks, with 2310 corresponding to modification-specific neutral losses...

Traditional image acquisition for cryo focused ion-beam scanning electron microscopy (FIB-SEM) tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the Focused ion-beam scanning electron microscopy FIB-SEM without an appreciable loss in image quality...

This paper shows a comprehensive review on various maximum power point tracking (MPPT) techniques of the solar photovoltaic (PV) cell. It is well understood that power from a solar PV array is sometimes not sufficient, so it is required to extract the maximum power to meet the load demand. In this regard, different techniques were used for comparative analysis like perturb and observe (P & O), fuzzy logic control (FLC), incremental conductance (IC), ripple correction control (RCC), artificial neural network (ANN), particle swarm optimization (PSO), lyapunov control scheme (LCS), and fisher discrimination dictionary learning (FDDL)...

As a vital mechanical sub-component, the health monitoring of rolling bearings is important. Vibration signal analysis is a commonly used approach for fault diagnosis of bearings. Nevertheless, the collected vibration signals cannot avoid interference from noises which has a negative influence on fault diagnosis. Thus, denoising needs to be utilized as an essential step of vibration signal processing. Traditional denoising methods need expert knowledge to select hyperparameters. And data-driven methods based on deep learning lack interpretability and a clear justification for the design of architecture in a "black-box" deep neural network...

The rise of urbanization coupled with pollution has highlighted the importance of outdoor self-cleaning coatings. These revolutionary coatings contribute to the longevity of various surfaces and reduce maintenance costs for a wide range of applications. Despite ongoing research to develop efficient and durable self-cleaning coatings, adopting systematic research methodologies could accelerate these advancements. In this work, we use Natural Language Processing (NLP) strategies to generate open- and traceable-sourced datasets about self-cleaning coating materials from 39,011 multi-disciplinary papers...

In this paper, we address the related tasks of medication extraction, event classification, and context classification from clinical text. The data for the tasks were obtained from the National Natural Language Processing (NLP) Clinical Challenges (n2c2) Track 1. We developed a named entity recognition (NER) model based on BioClinicalBERT and applied a dictionary-based fuzzy matching mechanism to identify the medication mentions in clinical notes. We developed a unified model architecture for event classification and context classification...

Several studies have shown that about 80% of the medical information in an electronic health record is only available through unstructured data. Resources such as medical terminologies in languages other than English are limited and restrain the NLP tools. We propose here to leverage English based resources in other languages using a combination of translation, word alignment, entity extraction and term normalization (TAXN). We implement this extraction pipeline in an open-source library called "medkit". We demonstrate the interest of this approach through a specific use-case: enriching a phenotypic dictionary for post-acute sequelae in COVID-19 (PASC)...

Loneliness is a global public health issue, but the dynamics of loneliness are not understood. Through a global loneliness map, we plan to understand the dynamics of loneliness better by analyzing social media data on loneliness through social intelligence analysis. In this paper, we present the first proof of concept of the global loneliness map. Data on loneliness using keywords associated with loneliness was collected from the USA and analyzed to find meaningful associations of themes with loneliness. The NLP tool used for sentiment analysis of the tweets is a valence aware dictionary for sentiment reasoning (VADER)...

Clinical narratives recording behaviours and emotions of patients are available from EHRs in a forensic psychiatric centre located in Tasmania. This rich data has not been used in risk prediction. Prior work demonstrates natural language processing can be used to identify patient symptoms in these free-text records and can then be used to predict risk. Four dictionaries containing descriptive words of harm were created using the Diagnostic and Statistical Manual of Mental Disorders, the Unified Medical Language System repository, English negative and positive sentiment words, and high-frequency words from the Corpus of Contemporary American English...

The study presents an innovative diagnostic framework that synergises Convolutional Neural Networks (CNNs) with a Multi-feature Kernel Supervised within-class-similar Discriminative Dictionary Learning (MKSCDDL). This integrative methodology is designed to facilitate the precise classification of individuals into categories of Alzheimer's Disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) statuses while also discerning the nuanced phases within the MCI spectrum. Our approach is distinguished by its robustness and interpretability, offering clinicians an exceptionally transparent tool for diagnosis and therapeutic strategy formulation...

BACKGROUND: Physicians are hesitant to forgo the opportunity of entering unstructured clinical notes for structured data entry in electronic health records. Does free text increase informational value in comparison with structured data? OBJECTIVE: This study aims to compare information from unstructured text-based chief complaints harvested and processed by a natural language processing (NLP) algorithm with clinician-entered structured diagnoses in terms of their potential utility for automated improvement of patient workflows...

The multi-view data clustering has attracted much interest from researchers, and the large-scale multi-view clustering has many important applications and significant research value. In this article, we fully make use of the consensus and complementary information, and exploit a bipartite graph to depict the duality relationship between original points and anchor points. To be specific, representative anchor points are selected for each view to construct corresponding anchor representation matrices, and all views' anchor points are utilized to construct a common representation matrix...

Purpose Numerous techniques based on deep learning have been utilized in sparse view computed tomography (CT) imaging. Nevertheless, the majority of techniques are instinctively constructed utilizing state-of-the-art opaque convolutional neural networks (CNNs) and lack interpretability. Moreover, CNNs tend to focus on local receptive fields and neglect nonlocal self-similarity prior information. Obtaining diagnostically valuable images from sparsely sampled projections is a challenging and ill-posed task. Method To address this issue, we propose a unique and understandable model named DCDL-GS for sparse view CT imaging...

In histopathology, the tissue slides are usually stained by common H&E stain or special stains (MAS, PAS, and PASM, etc.) to clearly show specific tissue structures. The rapid development of deep learning provides a good solution to generate virtual staining images to significantly reduce the time and labor costs associated with histochemical staining. However, most existing methods need to train a special model for every two stains, which consumes a lot of computing resources with the increasing of staining types...

BACKGROUND: Vaccine Adverse Events ReportingSystem (VAERS) is a promising resource of tracking adverse events following immunization. Medical Dictionary for Regulatory Activities (MedDRA) terminology used for coding adverse events in VAERS reports has several limitations. We focus on developing an automated system for semantic extraction of adverse events following vaccination and their temporal relationships for a better understanding of VAERS data and its integration into other applications...