The recent introduction of thermally activated delayed fluorescence (TADF) emitters is regarded as an important breakthrough for the development high efficiency organic light-emitting devices (OLEDs). planar D and A groups are generally used to construct TADF their rigid structure large steric hindrance. In this work, it shown that many frequently nonaromatic (noncontinuous conjugation or without satisfying Hückel's rule) segments, such 9,9-dimethyl-9,10-dihydroacridine, actually...

Contrastive learning has recently attracted plenty of attention in deep graph clustering due to its promising performance. However, complicated data augmentations and time-consuming convolutional operations undermine the efficiency these methods. To solve this problem, we propose a simple contrastive (SCGC) algorithm improve existing methods from perspectives network architecture, augmentation, objective function. As our includes two main parts, that is, preprocessing backbone. A low-pass...

Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that existing mining methods two problems as follows. 1) In hardness measurement, important structural information overlooked similarity calculation, degrading representativeness of selected negative samples. 2)...

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction. It been proven significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc. According types, KGR models can be roughly divided into three categories, i.e., static models, temporal multi-modal models. Early works this domain mainly focus KGR, recent...

Knowledge graph embedding (KGE) aims at learning powerful representations to benefit various artificial intelligence applications. Meanwhile, contrastive has been widely leveraged in as an effective mechanism enhance the discriminative capacity of learned representations. However, complex structures KG make it hard construct appropriate pairs. Only a few attempts have integrated strategies with KGE. But, most them rely on language models ( <italic...

Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the timeline, has been widely studied alleviate incompleteness issues in TKG, which is composed of a series KG snapshots at different timestamps. Two types information, i.e., intra-snapshot structural information and inter-snapshot interactions, mainly contribute learned representations for reasoning previous models. However, these models fail leverage (1) semantic correlations between relationships former...

Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast static graphs, temporal graphs are typically organized as node interaction sequences over continuous time rather than an adjacency matrix. Most methods model current interactions by incorporating historical neighborhood. However, such only consider first-order information while disregarding crucial high-order...

Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe following drawback, which limits performance further improvement. The existing models mainly focus on consistency same samples different views while ignoring circumstance similar but cross-view scenarios. To solve this problem, propose a novel Dual calibration network for Multi-View Clustering (DealMVC). Specifically,...

Abstract In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and Assay Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting fact that scRNA mode holds significantly richer information compared to scATAC. This disregard hinders model benefits from insights derived multiple modalities, compromising overall performance. To this end, we...

GraIL and its variants have shown their promising capacities for inductive relation reasoning on knowledge graphs. However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed Besides, enclosing subgraph extraction most GraIL-based restricts model extracting enough discriminative information reasoning. Consequently, expressive ability of these is limited. To address problems, we propose a novel framework,...

This review summarizes and discusses the recent advances future prospects of carbon dots as nanotheranostic agents for anticancer applications.

In this work, we propose a novel concept to develop two fluorophores 2-(10H-phenothiazin-10-yl)thianthrene 5,5,10,10-tetraoxide (PTZ-TTR) and 2-(4-(10H-phenothiazin-10-yl)phenyl)thianthrene (PTZ-Ph-TTR) showing dual conformations for highly efficient single-emitter white organic light-emitting diodes (WOLEDs). Both molecules exist in stable conformations. Their nearly orthogonal forms own lower energy levels show thermally activated delayed fluorescence (TADF) characteristics, whereas their...

Abstract Clustering methods have been widely used in single-cell RNA-seq data for investigating tumor heterogeneity. Since traditional clustering fail to capture the high-dimension methods, deep drawn increasing attention these years due their promising strengths on task. However, existing consider either attribute information of each cell or structure between different cells. In other words, they cannot sufficiently make use all this simultaneously. To end, we propose a novel fusion model,...

Contrastive graph node clustering via learnable data augmentation is a hot research spot in the field of unsupervised learning. The existing methods learn sampling distribution pre-defined to generate data-driven augmentations automatically. Although promising performance has been achieved, we observe that these strategies still rely on augmentations, semantics augmented can easily drift. reliability view for contrastive learning not be guaranteed, thus limiting model performance. To address...

Single-cell multi-view clustering is essential for analyzing the different cell subtypes of same from views. Some attempts have been made, but most these models still struggle to handle single-cell sequencing data, primarily due their non-specific design cellular data. We observe that such data distinctively exhibits: (1) a profusion high-order topological correlations, (2) disparate distribution information across views, and (3) inherent fuzzy characteristics, indicating cell's potential...

Thermally activated delayed fluorescence (TADF) emitters have attracted much interest for their great applications in organic light-emitting diodes (OLEDs), but the TADF OLEDs are limited by large efficiency roll-offs. In this study, we report two coumarin-based emitters, 3-methyl-6-(10H-phenoxazin-10-yl)-1H-isochromen-1-one (PHzMCO) and 9-(10H-phenoxazin-10-yl)-6H-benzo[c]chromen-6-one (PHzBCO), with relatively high photoluminescence quantum yields (PLQYs) extremely small singlet-triplet...

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction. It been proven significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc. According types, KGR models can be roughly divided into three categories, i.e., static models, temporal multi-modal models. Early works this domain mainly focus KGR, recent...

Deep graph clustering has recently received significant attention due to its ability enhance the representation learning capabilities of models in unsupervised scenarios. Nevertheless, deep for temporal graphs, which could capture crucial dynamic interaction information, not been fully explored. It means that many clustering-oriented real-world scenarios, graphs can only be processed as static graphs. This causes loss information but also triggers huge computational consumption. To solve...

Multi-view clustering (MVC), which effectively fuses information from multiple views for better performance, has received increasing attention. Most existing MVC methods assume that multi-view data are fully paired, means the mappings of all corresponding samples between predefined or given in advance. However, correspondence is often incomplete real-world applications due to corruption sensor differences, referred as data-unpaired problem (DUP) literature. Although several attempts have...

Nanozyme-based metabolic regulation triggered by tumor-specific endogenous stimuli has emerged as a promising therapeutic strategy for tumors. The current efficacy, however, is constrained the limited concentration of substrates and plasticity Consequently, implementation efficient in tumor therapy urgently needed. Herein, versatile nanozyme-based nicotinamide adenine dinucleotide (NADH) circulating oxidation nanoreactor reported. First, synthesized cobalt-doped hollow carbon spheres...

Few-shot relation reasoning on knowledge graphs (FS-KGR) is an important and practical problem that aims to infer long-tail relations has drawn increasing attention these years. Among all the proposed methods, self-supervised learning (SSL) which effectively extract hidden essential inductive patterns relying only support sets, have achieved promising performance. However, existing SSL methods simply cut down connections between high-frequency relations, ignores fact, i.e., two kinds of...