Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images

This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 $\times$ 400 high resolution RGB-D images. It first down-sample the images to get features and then up-sample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for image-wise and 91.02% for object-wise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.Comment: Submitted to ROBIO 201

Osthole induces G2/M arrest and apoptosis in lung cancer A549 cells by modulating PI3K/Akt pathway

<p>Abstract</p> <p>Background</p> <p>To explore the effects of Osthole on the proliferation, cell cycle and apoptosis of human lung cancer A549 cells.</p> <p>Methods</p> <p>Human lung cancer A549 cells were treated with Osthole at different concentrations. Cell proliferation was measured using the MTT assay. Cell cycle was evaluated using DNA flow cytometry analysis. Induction of apoptosis was determined by flow cytometry and fluorescent microscopy. The expressions of Cyclin B1, p-Cdc2, Bcl-2, Bax, t-Akt and p-Akt were evaluated by Western blotting.</p> <p>Results</p> <p>Osthole inhibited the growth of human lung cancer A549 cells by inducing G2/M arrest and apoptosis. Western blotting demonstrated that Osthole down-regulated the expressions of Cyclin B1, p-Cdc2 and Bcl-2 and up-regulated the expressions of Bax in A549 cells. Inhibition of PI3K/Akt signaling pathway was also observed after treating A549 cells with Osthole.</p> <p>Conclusions</p> <p>Our findings suggest that Osthole may have a therapeutic application in the treatment of human lung cancer.</p

Similarity-Aware Multimodal Prompt Learning for Fake News Detection

The standard paradigm for fake news detection mainly utilizes text information to model the truthfulness of news. However, the discourse of online fake news is typically subtle and it requires expert knowledge to use textual information to debunk fake news. Recently, studies focusing on multimodal fake news detection have outperformed text-only methods. Recent approaches utilizing the pre-trained model to extract unimodal features, or fine-tuning the pre-trained model directly, have become a new paradigm for detecting fake news. Again, this paradigm either requires a large number of training instances, or updates the entire set of pre-trained model parameters, making real-world fake news detection impractical. Furthermore, traditional multimodal methods fuse the cross-modal features directly without considering that the uncorrelated semantic representation might inject noise into the multimodal features. This paper proposes a Similarity-Aware Multimodal Prompt Learning (SAMPLE) framework. First, we incorporate prompt learning into multimodal fake news detection. Prompt learning, which only tunes prompts with a frozen language model, can reduce memory usage significantly and achieve comparable performances, compared with fine-tuning. We analyse three prompt templates with a soft verbalizer to detect fake news. In addition, we introduce the similarity-aware fusing method to adaptively fuse the intensity of multimodal representation and mitigate the noise injection via uncorrelated cross-modal features. For evaluation, SAMPLE surpasses the F1 and the accuracies of previous works on two benchmark multimodal datasets, demonstrating the effectiveness of the proposed method in detecting fake news. In addition, SAMPLE also is superior to other approaches regardless of few-shot and data-rich settings

Efficient solar-driven nitrogen fixation over carbon-tungstic-acid hybrids

Ammonia synthesis under mild conditions is of supreme interest. Photocatalytic nitrogen fixation with water at room temperature and atmospheric pressure is an intriguing strategy. However, the efficiency of this method has been far from satisfied for industrialization, mainly due to the sluggish cleavage of the N≡N bond. Herein, we report a carbon–tungstic‐acid (WO3⋅H2O) hybrid for the co‐optimization of N2 activation as well as subsequent photoinduced protonation. Efficient ammonia evolution reached 205 μmol g−1 h−1 over this hybrid under simulated sunlight. Nitrogen temperature‐programmed desorption revealed the decisive role of carbon in N2 adsorption. Photoactive WO3⋅H2O guaranteed the supply of electrons and protons for subsequent protonation. The universality of carbon modification for enhancing the N2 reduction was further verified over various photocatalysts, shedding light on future materials design for ideal solar energy utilization

Monitoring the Invasion of Spartina alterniflora

Spartina alterniflora was introduced to Beihai, Guangxi (China), for ecological engineering purposes in 1979. However, the exceptional adaptability and reproductive ability of this species have led to its extensive dispersal into other habitats, where it has had a negative impact on native species and threatens the local mangrove and mudflat ecosystems. To obtain the distribution and spread of Spartina alterniflora, we collected HJ-1 CCD imagery from 2009 and 2011 and very high resolution (VHR) imagery from the unmanned aerial vehicle (UAV). The invasion area of Spartina alterniflora was 357.2 ha in 2011, which increased by 19.07% compared with the area in 2009. A field survey was conducted for verification and the total accuracy was 94.0%. The results of this paper show that VHR imagery can provide details on distribution, progress, and early detection of Spartina alterniflora invasion. OBIA, object based image analysis for remote sensing (RS) detection method, can enable control measures to be more effective, accurate, and less expensive than a field survey of the invasive population

UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training

Magnetic resonance imaging~(MRI) have played a crucial role in brain disease diagnosis, with which a range of computer-aided artificial intelligence methods have been proposed. However, the early explorations usually focus on the limited types of brain diseases in one study and train the model on the data in a small scale, yielding the bottleneck of generalization. Towards a more effective and scalable paradigm, we propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics. Different from previous pre-training techniques for the unitary vision or textual feature, or with the brute-force alignment between vision and language information, we leverage the unique characteristic of report information in different granularity to build a hierarchical alignment mechanism, which strengthens the efficiency in feature learning. Our UniBrain is validated on three real world datasets with severe class imbalance and the public BraTS2019 dataset. It not only consistently outperforms all state-of-the-art diagnostic methods by a large margin and provides a superior grounding performance but also shows comparable performance compared to expert radiologists on certain disease types

Bi_2WO_6 quantum dot-intercalated ultrathin montmorillonite nanostructure and its enhanced photocatalytic performance

The kinetic competition between electron-hole recombination and water oxidation is a key limitation for the development of efficient solar water splitting materials. In this study, we present a solution for solving this challenge by constructing a quantum dot-intercalated nanostructure. For the first time, we show the interlayer charge of the intercalated nanostructure can significantly inhibit the electron-hole recombination in photocatalysis. For Bi_2WO_6 quantum dots (QDs) intercalated in a montmorillonite (MMT) nanostructure as an example, the average lifetime of the photogenerated charge carriers was increased from 3.06 μs to 18.8 μs by constructing the intercalated nanostructure. The increased lifetime markedly improved the photocatalytic performance of Bi_2WO_6 both in solar water oxidation and environmental purification. This work not only provides a method to produce QD-intercalated ultrathin nanostructures but also a general route to design efficient semiconductor-based photoconversion materials for solar fuel generation and environmental purification