Artificial intelligence facial recognition of obstructive sleep apnea: a Bayesian meta-analysis.

Purpose: Conventional obstructive sleep apnea (OSA) diagnosis via polysomnography can be costly and inaccessible. Recent advances in artificial intelligence (AI) have enabled the use of craniofacial photographs to diagnose OSA. This meta-analysis aims to clarify the diagnostic accuracy of this innovative approach.

Auditable and Verifiable Federated Learning Based on Blockchain-Enabled Decentralization.

Auditability and verifiability are critical elements in establishing trustworthiness in federated learning (FL). These principles promote transparency, accountability, and independent validation of FL processes. Incorporating auditability and verifiability is imperative for building trust and ensuring the robustness of FL methodologies.

DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation.

Objective: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to generate conduction currents in the head and disrupt brain functions. To rapidly evaluate the tDCS-induced current density in near real-time, this paper proposes a deep learning-based emulator, named DeeptDCS.

Methods: The emulator leverages Attention U-net taking the volume conductor models (VCMs) of head tissues as inputs and outputting the three-dimensional current density distribution across the entire head.

Efficient joint model learning, segmentation and model updating for visual tracking.

The Tracking-by-segmentation framework is widely used in visual tracking to handle severe appearance change such as deformation and occlusion. Tracking-by-segmentation methods first segment the target object from the background, then use the segmentation result to estimate the target state. In existing methods, target segmentation is formulated as a superpixel labeling problem constrained by a target likelihood constraint, a spatial smoothness constraint and a temporal consistency constraint.

The Chinese consensus for surgical treatment of traumatic rib fractures 2021 (C-STTRF 2021).

Rib fracture is the most common injury in chest trauma. Most of patients with rib fractures were treated conservatively, but up to 50% of patients, especially those with combined injury such as flail chest, presented chronic pain or chest wall deformities, and more than 30% had long-term disabilities, unable to retain a full-time job. In the past two decades, surgery for rib fractures has achieving good outcomes.

Label propagation via local geometry preserving for deep semi-supervised image recognition.

In this paper, we propose a novel transductive pseudo-labeling based method for deep semi-supervised image recognition. Inspired from the superiority of pseudo labels inferred by label propagation compared with those inferred from network, we argue that information flow from labeled data to unlabeled data should be kept noiseless and with minimum loss. Previous research works use scarce labeled data for feature learning and solely consider the relationship between two feature vectors to construct the similarity graph in feature space, which causes two problems that ultimately lead to noisy and incomplete information flow from labeled data to unlabeled data.

End-to-end novel visual categories learning via auxiliary self-supervision.

Semi-supervised learning has largely alleviated the strong demand for large amount of annotations in deep learning. However, most of the methods have adopted a common assumption that there is always labeled data from the same class of unlabeled data, which is impractical and restricted for real-world applications. In this research work, our focus is on semi-supervised learning when the categories of unlabeled data and labeled data are disjoint from each other.

R-ELMNet: Regularized extreme learning machine network.

Principal component analysis network (PCANet), as an unsupervised shallow network, demonstrates noticeable effectiveness on datasets of various volumes. It carries a two-layer convolution with PCA as filter learning method, followed by a block-wise histogram post-processing stage. Following the structure of PCANet, extreme learning machine auto-encoder (ELM-AE) variants are employed to replace the PCA's role, which come from extreme learning machine network (ELMNet) and hierarchical ELMNet.

ELM embedded discriminative dictionary learning for image classification.

Dictionary learning is a widely adopted approach for image classification. Existing methods focus either on finding a dictionary that produces discriminative sparse representation, or on enforcing priors that best describe the dataset distribution. In many cases, the dataset size is often small with large intra-class variability and nondiscriminative feature space.

Quantitative Analysis of Gas Phase IR Spectra Based on Extreme Learning Machine Regression Model.

Advanced chemometric analysis is required for rapid and reliable determination of physical and/or chemical components in complex gas mixtures. Based on infrared (IR) spectroscopic/sensing techniques, we propose an advanced regression model based on the extreme learning machine (ELM) algorithm for quantitative chemometric analysis. The proposed model makes two contributions to the field of advanced chemometrics.

Simultaneously learning affinity matrix and data representations for machine fault diagnosis.

Recently, preserving geometry information of data while learning representations have attracted increasing attention in intelligent machine fault diagnosis. Existing geometry preserving methods require to predefine the similarities between data points in the original data space. The predefined affinity matrix, which is also known as the similarity matrix, is then used to preserve geometry information during the process of representations learning.

Slice-Based Online Convolutional Dictionary Learning.

Convolutional dictionary learning (CDL) aims to learn a structured and shift-invariant dictionary to decompose signals into sparse representations. While yielding superior results compared to traditional sparse coding methods on various signal and image processing tasks, most CDL methods have difficulties handling large data, because they have to process all images in the dataset in a single pass. Therefore, recent research has focused on online CDL (OCDL) which updates the dictionary with sequentially incoming signals.

Analysis of early treatment of multiple injuries combined with severe pelvic fracture.

Purpose: To summarize and analyze the early treatment of multiple injuries combined with severe pelvic fractures, especially focus on the hemostasis methods for severe pelvic fractures, so as to improve the successful rate of rescue for the fatal hemorrhagic shock caused by pelvic fractures.

Methods: A retrospective analysis was conducted in 68 cases of multiple trauma combined with severe pelvic fractures in recent 10 years (from Jan. 2006 to Dec.

Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation.

In many practical transfer learning scenarios, the feature distribution is different across the source and target domains (i.e., nonindependent identical distribution).

Blind Noisy Image Quality Assessment Using Sub-Band Kurtosis.

Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a purpose, it is annoying. There exist significant statistical differences between distortion-free natural images and noisy images that become evident upon comparing the empirical probability distribution histograms of their discrete wavelet transform (DWT) coefficients.

Taste Recognition in E-Tongue Using Local Discriminant Preservation Projection.

Electronic tongue (E-Tongue), as a novel taste analysis tool, shows a promising perspective for taste recognition. In this paper, we constructed a voltammetric E-Tongue system and measured 13 different kinds of liquid samples, such as tea, wine, beverage, functional materials, etc. Owing to the noise of system and a variety of environmental conditions, the acquired E-Tongue data shows inseparable patterns.

Large-Scale Automated Sleep Staging.

Study Objectives: Automated sleep staging has been previously limited by a combination of clinical and physiological heterogeneity. Both factors are in principle addressable with large data sets that enable robust calibration. However, the impact of sample size remains uncertain.

Learning Polychronous Neuronal Groups Using Joint Weight-Delay Spike-Timing-Dependent Plasticity.

Polychronous neuronal group (PNG), a type of cell assembly, is one of the putative mechanisms for neural information representation. According to the reader-centric definition, some readout neurons can become selective to the information represented by polychronous neuronal groups under ongoing activity. Here, in computational models, we show that the frequently activated polychronous neuronal groups can be learned by readout neurons with joint weight-delay spike-timing-dependent plasticity.

Dimension Reduction With Extreme Learning Machine.

Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.

A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics.

Big dimensional data is a growing trend that is emerging in many real world contexts, extending from web mining, gene expression analysis, protein-protein interaction to high-frequency financial data. Nowadays, there is a growing consensus that the increasing dimensionality poses impeding effects on the performances of classifiers, which is termed as the "peaking phenomenon" in the field of machine intelligence. To address the issue, dimensionality reduction is commonly employed as a preprocessing step on the Big dimensional data before building the classifiers.

NMF-Based Image Quality Assessment Using Extreme Learning Machine.

Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.