Fluorescence-tunable hydrogels especially emitting white-light were achieved by swelling hydrogels in solutions containing two kinds of dyes. The fluorescence of the dyes was enhanced by the ...orthogonal supramolecular complexation with different binding sites in the hydrogels.
•The first work on dynamic graph convolutional network for multi-video summarization.•Two strategies are proposed to solve the class imbalance problem of the task.•A diversity regularization is ...designed to encourage a diverse summarization.•The proposed model can generate diverse summaries and achieve state-of-the-art performances.
Multi-video summarization is an effective tool for users to browse multiple videos. In this paper, multi-video summarization is formulated as a graph analysis problem and a dynamic graph convolutional network is proposed to measure the importance and relevance of each video shot in its own video as well as in the whole video collection. Two strategies are proposed to solve the inherent class imbalance problem of video summarization task. Moreover, we propose a diversity regularization to encourage the model to generate a diverse summary. Extensive experiments are conducted, and the comparisons are carried out with the state-of-the-art video summarization methods, the traditional and novel graph models. Our method achieves state-of-the-art performances on two standard video summarization datasets. The results demonstrate the effectiveness of our proposed model in generating a representative summary for multiple videos with good diversity.
Selective recognition of neutral hydrophilic molecules in water is a challenge for supramolecular chemistry but commonplace in nature. By mimicking the binding pocket of natural receptors, ...endo-functionalized molecular tubes are proposed to meet this challenge. We found that two molecular tubes with inwardly directed hydrogen-bond donors recognize highly hydrophilic solvent molecules in water with high selectivity. In the complexes, hydrogen bonding occurs in the deep and hydrophobic cavity. The cooperative action between hydrogen bonding and hydrophobic effects accounts for the high affinity and selectivity. The molecular receptor is fluorescent and can detect concentrations of 1,4-dioxanea known carcinogen and persistent environmental contaminantin water at a limit of 119 ppb. The method simplifies the analytic procedure for this highly hydrophilic molecule.
Cathode design is indispensable for building Li‐O2 batteries with long cycle life. A composite of carbon‐wrapped Mo2C nanoparticles and carbon nanotubes is prepared on Ni foam by direct hydrolysis ...and carbonization of a gel composed of ammonium heptamolybdate tetrahydrate and hydroquinone resin. The Mo2C nanoparticles with well‐controlled particle size act as a highly active oxygen reduction reactions/oxygen evolution reactions (ORR/OER) catalyst. The carbon coating can prevent the aggregation of the Mo2C nanoparticles. The even distribution of Mo2C nanoparticles results in the homogenous formation of discharge products. The skeleton of porous carbon with carbon nanotubes protrudes from the composite, resulting in extra voids when applied as a cathode for Li‐O2 batteries. The batteries deliver a high discharge capacity of ≈10 400 mAh g−1 and a low average charge voltage of ≈4.0 V at 200 mA g−1. With a cutoff capacity of 1000 mAh g−1, the Li‐O2 batteries exhibit excellent charge–discharge cycling stability for over 300 cycles. The average potential polarization of discharge/charge gaps is only ≈0.9 V, demonstrating the high ORR and OER activities of these Mo2C nanoparticles. The excellent cycling stability and low potential polarization provide new insights into the design of highly reversible and efficient cathode materials for Li‐O2 batteries.
A composite of carbon‐wrapped Mo2C nanoparticles and carbon nanotubes is prepared on Ni foam via a simple carbonization method. The even distribution of Mo2C nanoparticles with well‐controlled size shows enhanced oxygen reduction reaction/oxygen evolution reaction activities. The binder‐free cathode for Li‐O2 batteries results in high rate capability and outstanding long term cycle ability.
Mechanically interlocked and entangled molecular architectures represent one of the elaborate topological superstructures engineered at a molecular resolution. Here we report a methodology for ...fabricating mechanically selflocked molecules (MSMs) through highly efficient one-step amidation of a pseudorotaxane derived from dual functionalized pillar5arene (P5A) threaded by α,ω-diaminoalkane (DA-n; n=3-12). The monomeric and dimeric pseudo1catenanes thus obtained, which are inherently chiral due to the topology of P5A used, were isolated and fully characterized by NMR and circular dichroism spectroscopy, X-ray crystallography and DFT calculations. Of particular interest, the dimeric pseudo1catenane, named 'gemini-catenane', contained stereoisomeric meso-erythro and dl-threo isomers, in which two P5A moieties are threaded by two DA-n chains in topologically different patterns. This access to chiral pseudo1catenanes and gemini-catenanes will greatly promote the practical use of such sophisticated chiral architectures in supramolecular and materials science and technology.
Electroencephalography (EEG)-based affective computing has a scarcity problem. As a result, it is difficult to build effective, highly accurate and stable models using machine learning algorithms, ...especially deep learning models. Data augmentation has recently shown performance improvements in deep learning models with increased accuracy, stability and reduced overfitting. In this paper, we propose a novel data augmentation framework, named the generative adversarial network-based self-supervised data augmentation (GANSER). As the first to combine adversarial training with self-supervised learning for EEG-based emotion recognition, the proposed framework generates high-quality and high-diversity simulated EEG samples. In particular, we utilize adversarial training to learn an EEG generator and force the generated EEG signals to approximate the distribution of real samples, ensuring the quality of the augmented samples. A transformation operation is employed to mask parts of the EEG signals and force the generator to synthesize potential EEG signals based on the unmasked parts to produce a wide variety of samples. A masking possibility during transformation is introduced as prior knowledge to generalize the classifier for the augmented sample space. Finally, numerous experiments demonstrate that our proposed method can improve emotion recognition with an increase in performance and achieve state-of-the-art results.
Image steganalysis is to discriminate innocent images (cover images) and those suspected images (stego images) with hidden messages. The task is challenging since modifications to cover images due to ...message hiding are extremely small. To handle this difficulty, modern approaches proposed using convolutional neural network (CNN) models to detect steganography with paired learning, i.e., cover images and their stegos are both in training set. In this paper, we explore an important technique in CNN models, the batch normalization (BN), for the task of image steganalysis in the paired learning framework. Our theoretical analysis shows that a CNN model with multiple batch normalization layers is difficult to be generalized to new data in the test set when it is well trained with paired learning. To address this problem, we propose a novel normalization technique called shared normalization (SN) in this paper. Unlike the BN layer utilizing the mini-batch mean and standard deviation to normalize each input batch, SN shares consistent statistics for training samples. Based on the proposed SN layer, we further propose a novel neural network model for image steganalysis. Extensive experiments demonstrate that the proposed network with SN layers is stable and can detect the state-of-the-art steganography with better performances than previous methods.
With deep learning (DL) development, EEG-based emotion recognition has attracted increasing attention. Diverse DL algorithms emerge and intelligently decode human emotion from EEG signals. However, ...the lack of a toolbox encapsulating these techniques hampers further the design, development, testing, implementation, and management of intelligent systems. To tackle this bottleneck, we propose a Python toolbox, TorchEEGEMO, which divides the workflow into five modules: datasets, transforms, model_selection, models, and trainers. Each module includes plug-and-play functions to construct and manage a stage in the workflow. Recognizing the frequent access to time windows of interest, we introduce a window-centric parallel input/output system, bolstering the efficiency of DL systems. We finally conduct extensive experiments to provide the benchmark results of supported modules. Our extensive experimental results demonstrate the versatility and applicability of TorchEEGEMO across various scenarios.
•The first deep learning toolbox towards EEG-based emotion recognition.•A workflow that divides the recognition system into five plug-and-play modules.•Built-in functions cover datasets, transformations, models, algorithms, and more.•A novel window-centric EEG I/O is to enhance system effectiveness.•Experiments demonstrate benchmark performance across various scenarios.
Gastric cancer is the fourth most common cancer in the world and the second leading cause of cancerrelated death.More than 80%of diagnoses occur at the middle to late stage of the ...disease,highlighting an urgent need for novel biomarkers detectable at earlier stages.Recently,aberrantly expressed microRNAs(miRNAs)have received a great deal of attention as potential sensitive and accurate biomarkers for cancer diagnosis and prognosis.This review summarizes the current knowledge about potential miRNA biomarkers for gastric cancer that have been reported in the publicly available literature between 2008 and 2013.Available evidence indicates that aberrantly expressed miRNAs in gastric cancer correlate with tumorigenesis,tumor proliferation,distant metastasis and invasion.Furthermore,tissue and cancer types can be classified using miRNA expression profiles and next-generation sequencing.As miRNAs in plasma/serum are well protected from RNases,they remain stable under harsh conditions.Thus,potential functions of these circulating miRNAs can be deduced and may implicate their diagnostic value in cancer detection.Circulating miRNAs,as well as tissue miRNAs,may allow for the detection of gastric cancer at an early stage,prediction of prognosis,and monitoring of recurrence and/or lymph node metastasis.Taken together,the data suggest that the participation of miRNAs in biomarker development will enhance the sensitivity and specificity of diagnostic and prognostic tests for gastric cancer.
Video moment localization, as an important branch of video content analysis, has attracted extensive attention in recent years. However, it is still in its infancy due to the following challenges: ...cross-modal semantic alignment and localization efficiency. To address these impediments, we present a cross-modal semantic alignment network. To be specific, we first design a video encoder to generate moment candidates, learn their representations, as well as model their semantic relevance. Meanwhile, we design a query encoder for diverse query intention understanding. Thereafter, we introduce a multi-granularity interaction module to deeply explore the semantic correlation between multi-modalities. Thereby, we can effectively complete target moment localization via sufficient cross-modal semantic understanding. Moreover, we introduce a semantic pruning strategy to reduce cross-modal retrieval overhead, improving localization efficiency. Experimental results on two benchmark datasets have justified the superiority of our model over several state-of-the-art competitors.
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