Most facial landmark detection methods predict landmarks by mapping the input appearance features to heatmaps and have achieved promising results. However, when face image is suffering from large poses, heavy occlusions complicated illuminations, they cannot learn discriminative feature representations effective shape constraints, nor can accurately value of each element in heatmap, limiting their accuracy. To address this problem, we propose a novel Reference Heatmap Transformer (RHT)...

Herein, a dual self-protected DNAzyme-based 3D DNA walker (dSPD walker), composed of activated walking particles (ac-dSPWPs) and track (TPs), was constructed for ultrasensitive ultrahigh-speed fluorescence detection imaging microRNAs (miRNAs) in living cells. Impressively, compared with the defect that "one" target miRNA only initiates arm conventional single DNAzyme walker, dSPD benefits from secondary amplification spatial confinement effect could guide to generate "n" targets, thereby...

Neighborhoods form a set-level approximation of data distribution for learning tasks. Due to the advantages generalization and nonparametric property, neighborhood models have been widely used classification. However, existing neighborhood-based classification methods rigidly assign certain class label each instance lack strategies handle uncertain instances. The far-fetched instances may suffer serious risks. To tackle this problem, in article, we propose novel shadowed set construct...

Heatmap regression (HR) has become one of the mainstream approaches for face alignment and obtained promising results under constrained environments. However, when a image suffers from large pose variations, heavy occlusions complicated illuminations, performances HR methods degrade greatly due to low resolutions generated landmark heatmaps exclusion important high-order information that can be used learn more discriminative features. To address problem faces with extremely poses occlusions,...

As one of the most prevalent branches transfer learning, domain adaptation is dedicated to generalizing knowledge a source target perform machine learning tasks. In adaptation, key strategy overcome shift between different domains and learn shared features with invariance. However, existing methods focus on extracting common domains, do not consider problem class center in caused by this process. Specifically, when we align distributions, often ignore inherent feature attributes data, or...

Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance. However, most of the existing regression-based methods neglect to explore high-order feature correlations, which is very important learn more representative features enhance shape constraints. Moreover, no explicit global constraints have added final predicted landmarks, leads a reduction accuracy. To address these issues, this article, we propose multiorder multiconstraint...

Low-Light Image Enhancement (LLIE) presents challenges due to texture information loss and uneven illumination, which can distort feature distribution reduce the quality of enhanced images. However, current deep learning methods for LLIE only use supervised from clear images extract low-light image features, while disregarding negative in (i.e., low illumination noise). To address these challenges, we propose a novel method, LACR-VAE, by leveraging considering illumination. In particular,...

Herein, an innovative fluorescent sensor was courageously empoldered for precise and ultrasensitive detection imaging of target miRNA-21 through the agency a dextrous target-motivated polymerization/nicking DNA nanomachineries based on hyperbranched rolling circle amplification (HB-RCA)-assisted multiposition strand displacement reaction (SDR) signal approach. Impressively, ingenious technique not only realized recycling via but also involved HB-RCA induced by released transformation as...

Herein, a fluorescence light-up 3D DNA walker (FLDW) was powered and accelerated by endogenous adenosine-5'-triphosphate (ATP) molecules to construct biosensor for sensitive rapid label-free detection imaging of microRNA-221 (miRNA-221) in malignant tumor cells. Impressively, ATP as the driving force accelerator FLDW could significantly accelerate operation rate FLDW, reduce likelihood errors signaling, improve sensitivity imaging. When initiated output H1-op transformed target miRNA-221,...

Outlier detection refers to the identification of anomalous samples that deviate significantly from distribution normal data and has been extensively studied used in a variety practical tasks. However, most unsupervised outlier methods are carefully designed detect specified outliers, while real-world may be entangled with different types outliers. In this study, we propose fuzzy rough sets-based multi-scale method identify various Specifically, novel integrates relative granule density is...

Subspace clustering has attracted significant interest for its capacity to partition high-dimensional data into multiple subspaces. The current approaches subspace predominantly revolve around two key aspects: 1) the construction of an effective similarity matrix and 2) pursuit sparsity within projection matrix. However, assessing whether dimensionality projected is true challenging. Therefore, performance may decrease when dealing with scenarios such as overlap, insufficient dimensions,...

Neighborhood rough sets are an effective model for handling numerical and categorical data entangled with vagueness, imprecision, or uncertainty. However, existing neighborhood set models their feature selection methods treat each sample equally, whereas different types of samples inherently play roles in constructing granules evaluating the goodness features. In this study, weight information is first introduced into sets, a novel weighted consequently constructed. Then, considering lack...