In this paper, we propose a Dual-Constraint Diffusion Model (DCDM) to contrast aging appearance for facial age estimation, addressing the key issue of noisy labels. Existing methods for face age estimation are plagued by class imbalance and noisy supervision signals, which disrupt the ordinal relationships between age categories and hinder effective feature decoupling in existing models. To overcome these challenges, the proposed DCDM develops a label-independent Paired Comparison, ensuring accurate sample labeling and maintaining continuity in age estimation. Moreover, we incorporate a Dual-Constraint Diffusion Model to effectively separate and recombine age-related and unrelated features, thus facilitating the generation of high-fidelity and continuous age-progressed facial representations. Lastly, we optimize our model parameters by exploiting the age difference information via an active learning framework. Comparative evaluations on several in-the-wild datasets demonstrate that our DCDM significantly achieves superior results compared to existing state-of-the-art methods in facial age estimation.

MOTIVATION: Single-cell Hi-C (scHi-C) technologies have significantly advanced our understanding of the 3D genome organization. However, scHi-C data are often sparse and noisy, leading to substantial computational challenges in downstream analyses. RESULTS: In this study, we introduce SHICEDO, a novel deep-learning model specifically designed to enhance scHi-C contact matrices by imputing missing or sparsely captured chromatin contacts through a generative adversarial framework. SHICEDO leverages the unique structural characteristics of scHi-C matrices to derive customized features that enable effective data enhancement. Additionally, the model incorporates a channel-wise attention mechanism to mitigate the over-smoothing issue commonly associated with scHi-C enhancement methods. Through simulations and real-data applications, we demonstrate that SHICEDO outperforms the state-of-the-art methods, achieving superior quantitative and qualitative results. Moreover, SHICEDO enhances key structural features in scHi-C data, thus enabling more precise delineation of chromatin structures such as A/B compartments, TAD-like domains, and chromatin loops. AVAILABILITY AND IMPLEMENTATION: SHICEDO is publicly available at https://github.com/wmalab/SHICEDO.

This study investigates a fuzzy consensus problem for nonlinear multi-agent systems (MASs) with parameter uncertainties via dynamic event-triggered mechanism. The interval type-2 (IT2) fuzzy systems are used to model MASs, and a dynamic event-triggered fuzzy controller is presented. A Lyapunov-Krasovskii functional is constructed in which the sampling property is included, based on which the sufficient condition is determined for dynamic event-triggered fuzzy controller, while all agents are guaranteed to be consensus exponentially. Finally, the MASs of tunnel diode circuit and one-link manipulator are used to verify the efficiency of the presented control method, respectively.

SUMMARY: HiCube is a lightweight web application for interactive visualization and exploration of diverse types of genomics data at multiscale resolutions. Especially, HiCube displays synchronized views of Hi-C contact maps and 3D genome structures with user-friendly annotation and configuration tools, thereby facilitating the study of 3D genome organization and function. AVAILABILITY AND IMPLEMENTATION: HiCube is implemented in Javascript and can be installed via NPM. The source code is freely available at GitHub (https://github.com/wmalab/HiCube).

MOTIVATION: The high-throughput chromosome conformation capture (Hi-C) technique has enabled genome-wide mapping of chromatin interactions. However, high-resolution Hi-C data requires costly, deep sequencing; therefore, it has only been achieved for a limited number of cell types. Machine learning models based on neural networks have been developed as a remedy to this problem. RESULTS: In this work, we propose a novel method, EnHiC, for predicting high-resolution Hi-C matrices from low-resolution input data based on a generative adversarial network (GAN) framework. Inspired by non-negative matrix factorization, our model fully exploits the unique properties of Hi-C matrices and extracts rank-1 features from multi-scale low-resolution matrices to enhance the resolution. Using three human Hi-C datasets, we demonstrated that EnHiC accurately and reliably enhanced the resolution of Hi-C matrices and outperformed other GAN-based models. Moreover, EnHiC-predicted high-resolution matrices facilitated the accurate detection of topologically associated domains and fine-scale chromatin interactions. AVAILABILITY AND IMPLEMENTATION: EnHiC is publicly available at https://github.com/wmalab/EnHiC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

With the rapid adoption of smartphones and tablets, more and more remote medical diagnostic applications have mushroomed. Tongue Diagnosis (TD) is a kind of noninvasive diagnostic technique, which offers significant information for health conditions. However, it is rather tough to extract the tongue from a high-quality image, in which there is a definite large area of the tongue, to say nothing of extracting the tongue from a digital image captured by photographers who often lack the necessary skills using different mobile front facing cameras. Fundamentally, automatic tongue image segmentation is difficult due to two special factors: the particularity of the tongue and the diversity of the image. Our paper first addresses these problems by proposing a new end-to-end iterative network for tongue image matting, which directly learns the alpha matte from the input image by correcting misunderstanding in intermediate steps. Neither user interaction nor initialization is required. In addition, we create a large-scale tongue image matting dataset including 7,0680 training images. Compared with other high-performance algorithms, our algorithm achieves the true sense of the pixel-wise automatic tongue segmentation.

The health management service mode in traditional medicine for the public has attracted more and more attentions in recent years. With the development of large number of health related apps and devices, large quantities of valuable health data can be transmitted and collected through electronic devices such as smart phones. Accordingly, the quantization and digitization of the information in the four diagnostic methods in traditional Chinese medicine have played a more important role at present. In which the inspection diagnosis technology in traditional Chinese medicine can conduct effective diagnosis on health conditions by combining with modern technologies at the premise of reserving the features of traditional medicine. Therefore, the quantization of inspection diagnosis information is especially important. This article mainly introduces the method of tongue diagnosis information quantization in quantization of inspection diagnosis information.

Objective: To design the lip-color features and classification model and provide an automatic, quantitative method based on the lip detection in facial image. Methods: In this paper, We adopted the lip segmentation algorithm based on the three-dimensional mixture Skin Gaussian Model and color classification in SVM to solve this problem. Specifically, we used the GMM based iterations to confirm skin-color pixels that belong to the skin area. In the process of optimization, we extracted two-dimensional GMM with five parameters as the characteristic to obtain the main color and secondary color to extract and classify the features of the 877 lip images. Consequence: The accuracy and stability of classification method was successfully proved. Conclusion: Firstly, We proposed a Skin Gaussian mixture Model that can be used for the robust and accurate segmentation of facial skin in a probabilistic manner. Secondly, we extracted five parameters from the two-dimensional GMM of the a, b component in the laboratory space as the characteristic of each picture. Finally, we classified the lips through the SVM classifier. As for the lips with mixed color, the method we proposed can have a good performance in identifying the secondary colors, thereby improving the identification precision of the image segmentation and color classification of the lip.

Summary Brain data processing has been embracing the big data era driven by the rapid advances of neuroscience as well as the experimental techniques for recording neuronal activities. Processing of massive brain data has become a constant in neuroscience research and practice, which is vital in revealing the hidden information to better understand the brain functions and malfunctions. Brain data are routinely non‐linear and non‐stationary in nature, and existing algorithms and approaches to neural data processing are generally complicated in order to characterize the non‐linearity and non‐stationarity. Brain big data processing has pressing needs for appropriate computing technologies to address three grand challenges: (1) efficiency, (2) scalability and (3) reliability. Recent advances of computing technologies are making non‐linear methods viable in sophisticated applications of massive brain data processing. General‐purpose Computing on the Graphics Processing Unit (GPGPU) technology fosters an ideal environment for this purpose, which benefits from the tremendous computing power of modern graphics processing units in massively parallel architecture that is frequently an order of magnitude larger than the modern multi‐core CPUs. This article first recaps significant speed‐ups of existing algorithms aided by GPGPU in neuroimaging and processing electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) and etc. The article then demonstrates a series of successful approaches to processing EEG data in various dimensions and scales in a massively parallel manner: (1)decomposition:a massively parallel Ensemble Local Mean Decomposition (ELMD) algorithm aided by GPGPU can decompose EEG series, which forms the basis of further time‐frequency transformation, in real‐time without sacrificing the precision of processing; (2)synchronization measurement:a parallelized Nonlinear Interdependence (NLI) method for global synchronization measurement of multivariate EEG with speed‐up of more than 1000 times, and it was successful in localization of epileptic focus; and (3)dimensionality reduction:a large‐scale Parallel Factor Analysis which excels in run‐time performance and scales far better by hundreds of times than conventional approach does, and it supports fast factorization of EEG with more than 1000 channels. Through these practices, the massively parallel computing technology manifests great potentials in addressing the grand challenges of brain big data processing. Copyright © 2016 John Wiley & Sons, Ltd.

It has long been an important issue in various disciplines to examine massive multidimensional data superimposed by a high level of noises and interferences by extracting the embedded multi-way factors. With the quick increases of data scales and dimensions in the big data era, research challenges arise in order to (1) reflect the dynamics of large tensors while introducing no significant distortions in the factorization procedure and (2) handle influences of the noises in sophisticated applications. A hierarchical parallel processing framework over a GPU cluster, namely H-PARAFAC, has been developed to enable scalable factorization of large tensors upon a “divide-and-conquer” theory for Parallel Factor Analysis (PARAFAC). The H-PARAFAC framework incorporates a coarse-grained model for coordinating the processing of sub-tensors and a fine-grained parallel model for computing each sub-tensor and fusing sub-factors. Experimental results indicate that (1) the proposed method breaks the limitation on the scale of multidimensional data to be factorized and dramatically outperforms the traditional counterparts in terms of both scalability and efficiency, e.g., the runtime increases in the order of n2 when the data volume increases in the order of n3, (2) H-PARAFAC has potentials in refraining the influences of significant noises, and (3) H-PARAFAC is far superior to the conventional window-based counterparts in preserving the features of multiple modes of large tensors.

Line detection plays a vital role in visual analysis tasks like Traditional Chinese Medicine (TCM) image analytics. However, most of the current methods ignore line thickness and perform poorly for the lines with different widths. This paper proposes a novel line detection method by using the water flow method. Unlike most edge-based and region-based line detectors, the water flow method is applied to obtaining the whole line response map by simply imitating the movement of water in the image smoothed by guided filter, which is viewed as a geomorphological map. In addition, this paper also proposes an adaptive parameter selection method so that the line detection can be more robust and accurate. Experimental results demonstrate the effectiveness of the proposed method on tongue crack images in comparison to the existing line extraction methods.

It has long been an important issue in various disciplines to examine massive multi-dimensional data by extracting the embedded multi-way factors. With the quick increases in both scales and dimensions of data under analysis, research challenges arise in order to reflect the dynamics of large-scale tensors while introducing no significant distortions in the factorization procedure in sophisticated applications. A massively parallel computing framework, namely H-PARAFAC, has been developed to enable Parallel Factor Analysis (PARAFAC) of massive tensors upon a "divide-and-conquer" theory (a modified alternating least squares approach). The hierarchical framework incorporates a coarse-grained model for coordinating the processing of sub tensors and a fine-grained parallel model for computing each sub tensor and fusing sub-factors. Experiments have been performed on a GPU cluster, and the results indicate that (1) the proposed method breaks the limitation on the size of data to be factorized, and (2) it dramatically outperforms the traditional counterparts in terms of both scalability and efficiency, e.g., The runtime increases linearly with the data volume increases in the order of n3.