Search

Differentiable architecture search (DARTS) provides a fast solution to Neural Architecture Search (NAS) by using gradient descent over a continuous search space. Despite its high search speed, DARTS suffers from several problems such as ambiguous topology selection, operation co-adaptation, incomplete NAS pipeline, and large memory consumption. To address these problems, we first introduce topology parameters into the search space to explicitly model the network topology, which ensures the searched network architecture is well-defined. Next, we propose two sampling strategies to sample independent child networks for training and evaluation, which solve the co-adaptation problem while making the NAS pipeline complete. Finally, we use hard pruning to avoid invalid computations, which greatly reduces memory consumption. The proposed Automatic Topology Learning for Differentiable Architecture Search (ATL-DAS) algorithm performs favorably against the state-of-the-art approaches on CIFAR10 and CIFAR100 with error rates of 2.49% and 16.8%, respectively. Moreover, the architectures searched perform well across various visual tasks, including ImageNet classification, COCO object detection, and Composition-1K image matting, highlighting the potential of ATL-DAS for real-world applications. • Topology parameters enable explicit search for optimized network typologies. • Sampling strategies avoid operation co-adaptation and make the NAS pipeline complete. • Hard pruning reduces memory consumption. • Experiments on CIFAR10, CIFAR100, and various visual tasks validate the effectiveness of ATL-DAS. [ABSTRACT FROM AUTHOR]

Building extraction, which is a fundamental task in the community of remote sensing image analysis, has been extensively applied in various applications related to smart cities. Due to the complicated background information in urban areas, how to extract building footprints from high-resolution aerial images is challenging. The recent achievements of deep learning have shed light on building extraction and other remote sensing domain tasks. However, the heavy consumption of computational resources and the design of the neural architectures became the biggest bottleneck of utilizing deep learning techniques to improve the performance. In this work, we developed a Neural Architecture Search (NAS) algorithm, dubbed BuildingNAS, for building extraction from high-resolution aerial images. In particular, we built an efficient candidate operation set upon Separable Factorized Residual Blocks as our cell-level search space. Different from previous NAS in semantic segmentation tasks, we employed the hierarchical search space and proposed the Single-Path Sampling strategy to eliminate excessive GPU memory comsumption in searching process. In addition, we proposed an entropy regularized objective for the optimization of architecture parameters. As the result, the larger batch size can be adopted in the whole pipeline to accelerate the searching process, and make the resulted architecture more stable and accurate. We evaluated our proposed algorithm in WHUBuilding Dataset, the derived network achieved mIoU of 86.95% with only 2.05G FLOPs and 3.10 M parameters. The comparison results demonstrate that the network discovered by our algorithm can achieve great efficiency-accuracy trade-off. • The attempt of applying neural architecture search algorithm in segmentation of aerial images. • More lightweight hierarchical search space for neural architecture search of segmentation task. • Entropy regularized objective makes the searching process more effectively. • Better trades off the accuracy and efficiency of neural networks by using neural architecture search. • Achieved mIoU of 86.85% with only 2.05G Flops and 3.10 M parameters on WHUBuilding Dataset. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) causes great suffering to patients because of its heterogeneity, poor prognosis, and chemotherapy resistance. Ferroptosis is characterized by iron-dependent oxidative damage by accumulating intracellular lipid peroxides to lethal levels, and plays a vital role in the treatment of TNBC based on its intrinsic characteristics. To identify the relationship between chemotherapy resistance and ferroptosis in TNBC, we analyzed the single cell RNA-sequencing public dataset of GSE205551. It was found that the expression of Gpx4 in DOX-resistant TNBC cells was significantly higher than that in DOX-sensitive TNBC cells. Based on this finding, we hypothesize that inducing ferroptosis by inhibiting the expression of Gpx4 can reduce the resistance of TNBC to DOX and enhance the therapeutic effect of chemotherapy on TNBC. Herein, dihydroartemisinin (DHA)-loaded polyglutamic acid-stabilized Fe 3 O 4 magnetic nanoparticles (Fe 3 O 4 -PGA-DHA) was combined with DOX-loaded polyaspartic acid-stabilized Fe 3 O 4 magnetic nanoparticles (Fe 3 O 4 -PASP-DOX) for ferroptosis-enhanced chemotherapy of TNBC. Compared with Fe 3 O 4 -PASP-DOX, Fe 3 O 4 -PGA-DHA + Fe 3 O 4 -PASP-DOX demonstrated significantly stronger cytotoxicity against different TNBC cell lines and achieved significantly more intracellular accumulation of reactive oxygen species and lipid peroxides. Furthermore, transcriptomic analyses demonstrated that Fe 3 O 4 -PASP-DOX-induced apoptosis could be enhanced by Fe 3 O 4 -PGA-DHA-induced ferroptosis and Fe 3 O 4 -PGA-DHA + Fe 3 O 4 -PASP-DOX might trigger ferroptosis in MDA-MB-231 cells by inhibiting the PI3K/AKT/mTOR/GPX4 pathway. Fe 3 O 4 -PGA-DHA + Fe 3 O 4 -PASP-DOX showed superior anti-tumor efficacy on MDA-MB-231 tumor-bearing mice, providing great potential for improving the therapeutic effect of TNBC. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • 3,3-Disubstituted isoindolin-1-ones were prepared by radical addition and cyclization in moderate to good yields. • Toluene derivatives were used as radical precursors and two new C–C bonds were formed in one step. • The reactions were carried out under environmentally friendly conditions with excellent atom economy. Abstract Copper(I) catalyzed radical benzylation and cyclization reaction of tertiary enamides was investigated and 3,3-disubstituted N-Alkyl isoindolin-1-ones were obtained in moderate to good yields. In this reaction, two new C–C bonds were formed in one step with high atom economy. Possible reaction pathway for the formation of the products was also discussed in this paper. [ABSTRACT FROM AUTHOR]

The segmentation of high-resolution (HR) remote sensing images is very important in modern society, especially in the fields of industry, agriculture and urban modelling. Through the neural network, the machine can effectively and accurately extract the surface feature information. However, using the traditional deep learning methods requires plentiful efforts in order to find a robust architecture. In this paper, we introduce a neural network architecture search (NAS) method, called NAS-HRIS, which can automatically search neural network architecture on the dataset. The proposed method embeds a directed acyclic graph (DAG) into the search space and designs the differentiable searching process, which enables it to learn an end-to-end searching rule by using gradient descent optimization. It uses the Gumbel-Max trick to provide an efficient way when drawing samples from a non-continuous probability distribution, and it improves the efficiency of searching and reduces the memory consumption. Compared with other NAS, NAS-HRIS consumes less GPU memory without reducing the accuracy, which corresponds to a large amount of HR remote sensing imagery data. We have carried out experiments on the WHUBuilding dataset and achieved 90.44% MIoU. In order to fully demonstrate the feasibility of the method, we made a new urban Beijing Building dataset, and conducted experiments on satellite images and non-single source images, achieving better results than SegNet, U-Net and Deeplab v3+ models, while the computational complexity of our network architecture is much smaller. [ABSTRACT FROM AUTHOR]

Copper(I)‐catalyzed radical 1,2‐benzylarylation of N‐phenethylenamides was developed and 1,1‐disubstituted N‐acyl tetrahydroisoquinolines were obtained in moderate to good yields. In this process, two new C–C bonds were formed in one‐step fashion with high atom economy. A possible reaction pathway for the formation of the products is also discussed. [ABSTRACT FROM AUTHOR]

A diagram is presented regarding the oxidative coupling reaction of N-acyl glycine esters and malonates by using iron as catalyst.

Deforestation in the Amazon rainforest results in reduced biodiversity, habitat loss, climate change, and other destructive impacts. Hence obtaining location information on human activities is essential for scientists and governments working to protect the Amazon rainforest. We propose a novel remote sensing image classification framework that provides us with the key data needed to more effectively manage deforestation and its consequences. We introduce the attention module to separate the features which are extracted from CNN(Convolutional Neural Network) by channel, then further send the separated features to the LSTM(Long-Short Term Memory) network to predict labels sequentially. Moreover, we propose a loss function by calculating the co-occurrence matrix of all labels in the dataset and assigning different weights to each label. Experimental results on the satellite image dataset of the Amazon rainforest show that our model obtains a better F 2 score compared to other methods, which indicates that our model is effective in utilizing label dependencies to improve the performance of multi-label image classification. [ABSTRACT FROM AUTHOR]

Abstract: The effect of a metal layer over an elastic substrate on surface acoustic wave propagating in the structure can be evaluated precisely for semi-infinite solids and infinite plates, but there is no accurate analytical solution if the finite size of the plate has to be considered. By expanding displacements with eigensolutions of surface acoustic waves in a semi-inifite solid, a set of two-dimensional equations similar to the Mindlin plate theory are obtained. Then for a thin electrode layer, the effect is considered through the approximation of displacements in the metal layer with the ones in the substrate, and an integration over the thickness incorporated the properties of the metal layer into equations through the modification of material properties with the decaying indices of surface acoustic waves and the thickness of the metal layer. Using AT-cut quartz crystal as the substrate, we present the effect of silver electrode layers of finite thickness on the phase velocity of propagating surface acoustic waves. [Copyright &y& Elsevier]