MSADCN:Multi-Scale Attentional Densely Connected Network for Automated Bone Age Assessment

Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.

Analysis of One-Dimensional Compression under a Wide Range of Stress with Densely Arrayed BPM

In this paper,the densely arrayed bonded particle model is proposed for simulation of granular materials with discrete element method(DEM)considering particle crushing.This model can solve the problem of pore calculation after the grains are crushed,and reduce the producing time of specimen.In this work,several one-dimensional compressing simulations are carried out to investigate the effect of particle crushing on mechanical properties of granular materials under a wide range of stress.The results show that the crushing process of granular materials can be divided into four different stages according to er-logσy curves.At the end of the second stage,there exists a yield point,after which the physical and mechanical properties of specimens will change significantly.Under extremely high stress,particle crushing will wipe some initial information of specimens,and specimens with different initial gradings and void ratios present some similar characteristics.Particle crushing has great influence on grading,lateral pressure coefficient and compressibility of granular materials,and introduce extra irreversible volume deformation,which is necessary to be considered in modelling of granular materials in wide stress range.

Densely packed ultrafine SnO_(2) nanoparticles grown on carbon cloth for selective CO_(2) reduction to formate

Electrochemical reduction of CO_(2) to fuels and chemicals is a viable strategy for CO_(2) utilization and renewable energy storage.Developing free-standing electrodes from robust and scalable electrocatalysts becomes highly desirable.Here,dense SnO_(2) nanoparticles are uniformly grown on three-dimensional(3D)fiber network of carbon cloth(CC)by a facile dip-coating and calcination method.Importantly,Zn modification strategy is employed to restrain the growth of long-range order of SnO_(2) lattices and to produce rich grain boundaries.The hybrid architecture can act as a flexible electrode for CO_(2)-to-formate conversion,which delivers a high partial current of 18.8 m A cm-2 with a formate selectivity of 80%at a moderate cathodic potential of-0.947 V vs.RHE.The electrode exhibits remarkable stability over a 16 h continuous operation.The superior performance is attributed to the synergistic effect of ultrafine SnO_(2) nanoparticles with abundant active sites and 3D fiber network of the electrode for efficient mass transport and electron transfer.The sizeable electrodes hold promise for industrial applications.

ZTE Chairman Bullish on Telecom Business Opportunities in Developing and Densely Populated Nations

ZTE Corporation (ZTE), a leading global provider of telecommunications equipment and network solutions,

EXPONENTIALLY BOUNDED C-SEMIGROUP AND INTEGRATED SEMIGROUP WITH NONDENSELY DEFINED GENERATORS I: APPROXIMATION:

In this paper, by Laplace transform version of the Trotter-Kato approximation theorem and the integrated C-semigroup introduced by Myadera, the authors obtained some Trotter-Kato approximation theorems on exponentially bounded C-semigroups, where the range of C (and so the domain of the generator) may not be dense. The authors deduced the corresponding results on exponentially bounded integrated semigroups with nondensely generators. The results of this paper extended and perfected the results given by Lizama, Park and Zheng.

Mixed Attention Densely Residual Network for Single Image Super-Resolution

Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance further.The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information.Specifically,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network.This structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature learning.The qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.

Rotating Massive Strangeon Stars and X-Ray Plateau of Short GRBs

Strangeon stars,which are proposed to describe the nature of pulsar-like compact stars,have passed various observational tests.The maximum mass of a non-rotating strangeon star could be high,which implies that the remnants of binary strangeon star mergers could even be long-lived massive strangeon stars.We study rigidly rotating strangeon stars in the slowly rotating approximation,using the Lennard-Jones model for the equation of state.Rotation can significantly increase the maximum mass of strangeon stars with unchanged baryon numbers,enlarging the mass-range of long-lived strangeon stars.During spin-down after merger,the decrease of radius of the remnant will lead to the release of gravitational energy.Taking into account the efficiency of converting the gravitational energy luminosity to the observed X-ray luminosity,we find that the gravitational energy could provide an alternative energy source for the plateau emission of X-ray afterglow.The fitting results of X-ray plateau emission of some short gamma-ray bursts suggest that the magnetic dipole field strength of the remnants can be much smaller than that of expected when the plateau emission is powered only by spin-down luminosity of magnetars.

量子Loop代数U_(q)(L(sl_(2)))的单权模

用构造的方法解决量子Loop代数U_(q)(L(sl_(2)))具有一个一维权空间的单权模的结构问题,得到了任意一个具有一维权空间的单权模必同构于U_(q)(L(sl_(2)))的四类单权模之一.此外,还构造了一类权空间维数为2的既非最高权也非最低权的量子Loop代数U_(q)(L(sl_(2)))的单权模.

Attention-Based Residual Dense Shrinkage Network for ECG Denoising

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac diagnosis.However,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological analysis.Therefore,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic research.Aiming at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this paper.Firstly,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise suppression.Finally,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG signal.Experiments on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

Twisted Yangian Y(sp_(2))的单权模

讨论了Twisted Yangian Y(sp_(N))的权模问题,构造了Y(sp_(2))的一类无限维单权模,分类了具有一个一维权空间的Y(sp_(2))单权模.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

Adaptive Densely Residual Network for Image Super-Resolution

Many networks are designed to stack a large number of residual blocks,deepen the network and improve network performance through short residual connec-tion,long residual connection,and dense connection.However,without consider-ing different contributions of different depth features to the network,these de-signs have the problem of evaluating the importance of different depth features.To solve this problem,this paper proposes an adaptive densely residual net-work(ADRNet)for the single image super resolution.ADRN realizes the evalua-tion of distributions of different depth features and learns more representative features.An adaptive densely residual block(ADRB)was designed,combining 3 residual blocks(RB)and dense connection was added.It learned the attention score of each dense connection through adaptive dense connections,and the at-tention score reflected the importance of the features of each RB.To further en-hance the performance of ADRB,a multi-direction attention block(MDAB)was introduced to obtain multidirectional context information.Through comparative experiments,it is proved that theproposed ADRNet is superior to the existing methods.Through ablation experiments,it is proved that evaluating features of different depths helps to improve network performance.

3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks

To overcome the computational burden of processing three-dimensional(3 D)medical scans and the lack of spatial information in two-dimensional(2 D)medical scans,a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2 D convolutional neural networks(2 D-CNNs).In order to combine the lowlevel features and high-level features,we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process.Further,in order to resolve the problems of the blurred boundary of the glioma edema area,we superimposed and fused the T2-weighted fluid-attenuated inversion recovery(FLAIR)modal image and the T2-weighted(T2)modal image to enhance the edema section.For the loss function of network training,we improved the cross-entropy loss function to effectively avoid network over-fitting.On the Multimodal Brain Tumor Image Segmentation Challenge(BraTS)datasets,our method achieves dice similarity coefficient values of 0.84,0.82,and 0.83 on the BraTS2018 training;0.82,0.85,and 0.83 on the BraTS2018 validation;and 0.81,0.78,and 0.83 on the BraTS2013 testing in terms of whole tumors,tumor cores,and enhancing cores,respectively.Experimental results showed that the proposed method achieved promising accuracy and fast processing,demonstrating good potential for clinical medicine.

Two-dimensional densely ordered packings of non-convex bending and assembled rods

The investigation of the problem of particle packing has provided basic insights into the structure,symmetry,and physical properties of condensed matter.Dense packings of non-spherical particles have many applications,both in research and industry.We report the two-dimensional dense packing patterns of bending and assembled rods,which are non-convexly deformed from simple objects and modeled as entangled particles.Monte Carlo simulations and further analytical constructions are carried out to explore possible densely packed structures.Two typical densely packed structures of C-bending rods are found,and their packing densities are identified as being functions of the aspect ratio and central angle.Six shapes of assembled rods,representing the combined deformations of rods,are employed in simulations with the packing structures classified into three types.The dense packing density of each packing pattern is derived as a function of different shape parameters.In contrast with the case of disordered packings,both the shape and order are verified to affect the packing density.

Attention-based neural network for end-to-end music separation

The end-to-end separation algorithm with superior performance in the field of speech separation has not been effectively used in music separation.Moreover,since music signals are often dual channel data with a high sampling rate,how to model longsequence data and make rational use of the relevant information between channels is also an urgent problem to be solved.In order to solve the above problems,the performance of the end-to-end music separation algorithm is enhanced by improving the network structure.Our main contributions include the following:(1)A more reasonable densely connected U-Net is designed to capture the long-term characteristics of music,such as main melody,tone and so on.(2)On this basis,the multi-head attention and dualpath transformer are introduced in the separation module.Channel attention units are applied recursively on the feature map of each layer of the network,enabling the network to perform long-sequence separation.Experimental results show that after the introduction of the channel attention,the performance of the proposed algorithm has a stable improvement compared with the baseline system.On the MUSDB18 dataset,the average score of the separated audio exceeds that of the current best-performing music separation algorithm based on the time-frequency domain(T-F domain).

Shear wave splitting analysis of local earthquakes from dense arrays in Shimian,Sichuan

The Shimian area of Sichuan sits at the junction of the Bayan Har block.Sichuan-Yunnan rhombic block,and Yangtze block,where several faults intersect.This region features intense tectonic activity and frequent earthquakes.In this study,we used local seismic waveform data recorded using dense arrays deployed in the Shimian area to obtain the shear wave splitting parameters at 55 seismic stations and thereby determine the crustal anisotropic characteristics of the region.We then analyzed the crustal stress pattern and tectonic setting and explored their relationship in the study area.Although some stations returned a polarization direction of NNW-SSE.a dominant polarization direction of NW-SE was obtained for the fast shear wave at most seismic stations in the study area.The polarization directions of the fast shear wave were highly consistent throughout the study-area.This orientation was in accordance with the direction of the regional principal compressive stress and parallel to the trend of the Xianshuihe and Daliangshan faults.The distribution of crustal anisotropy in this area was affected by the regional tectonic stress field and the fault structures.The mean delay time between fast and slow shear waves was 3.83 ms/km.slightly greater than the values obtained in other regions of Sichuan.This indicates that the crustal media in our study area had a high anisotropic strength and also reveals the influence of tectonic complexity resulting from the intersection of multiple faults on the strength of seismic anisotropy.

Comparative analysis on gas–solid drag models in MFIX-DEM simulations of bubbling fluidized bed

In this study,the open-source software MFIX-DEM simulations of a bubbling fluidized bed(BFB)are applied to assess nine drag models according to experimental and direct numerical simulation(DNS)results.The influence of superficial gas velocity on gas–solid flow is also examined.The results show that according to the distribution of time-averaged particle axial velocity in y direction,except for Wen–Yu and Tenneti–Garg–Subramaniam(TGS),other drag models are consistent with the experimental and DNS results.For the TGS drag model,the layer-by-layer movement of particles is observed,which indicates the particle velocity is not correctly predicted.The time domain and frequency domain analysis results of pressure drop of each drag model are similar.It is recommended to use the drag model derived from DNS or fine grid computational fluid dynamics–discrete element method(CFD-DEM)data first for CFD-DEM simulations.For the investigated BFB,the superficial gas velocity less than 0.9 m·s^(-1) should be adopted to obtain normal hydrodynamics.

The failure propagation of weakly stable sediment:A reason for the formation of high-velocity turbidity currents in submarine canyons

The long-distance movement of turbidity currents in submarine canyons can transport large amounts of sediment to deep-sea plains.Previous studies show obvious differences in the turbidity current velocities derived from the multiple cables damage events ranging from 5.9 to 28.0 m/s and those of field observations between 0.15 and 7.2 m/s.Therefore,questions remain regarding whether a turbid fluid in an undersea environment can flow through a submarine canyon for a long distance at a high speed.A new model based on weakly stable sediment is proposed(proposed failure propagation model for weakly stable sediments,WS S-PFP model for short)to explain the high-speed and long-range motion of turbidity currents in submarine canyons through the combination of laboratory tests and numerical analogs.The model is based on two mechanisms:1)the original turbidity current triggers the destabilization of the weakly stable sediment bed and promotes the destabilization and transport of the soft sediment in the downstream direction and 2)the excitation wave that forms when the original turbidity current moves into the canyon leads to the destabilization and transport of the weakly stable sediment in the downstream direction.The proposed model will provide dynamic process interpretation for the study of deep-sea deposition,pollutant transport,and optical cable damage.

Discovery of pockmarks in the Zengmu Basin,southern South China Sea and the implication

The Zengmu Basin located in the shallow water area of the southern South China Sea,is rich in oil and gas resources,within which faults and mud-diapir are developed,but it is unknown whether oil and gas migrate to the seafloor surface.The newly collected multibeam data across the Zengmu Basin reveal a large number of depressions,with depths of 2-4 m,widths of several tens of meters,large distribution range of 1.8-8 km along survey line,up to~50 km,and their backscatter intensity(-26 dB)is much greater than that of the surrounding area(-38 dB).Combined with the developed mud-diapir and fracture structures,and abundant oil and gas resources within this basin,these depressions are presumed to be pockmarks.Furthermore,more than 110 mono-sized small circular pockmarks,with a depth of less than 1 m and a width of 5 m,are observed in an area of less than 0.03 km2,which are not obliterated by sediment infilling with high sedimentation rate,implying an existence of unit-pockmarks that are or recently were active.In addition,seismic profiles across the Zengmu Basin show characterization of upward migration of hydrocarbons,expressed as mud-diapir structures,bright spots in the shallow formation with characteristics of“low frequency increase and high frequency attenuation”.The subbottom profiles show the mud-diapir structures,as well as the gas-bearing blank zones beneath the seafloor.These features suggest large gas leaking and occurrence of large amounts of carbonate nodules on the seafloor.This indicates the complex and variable substrate type in the Zengmu Basin,while the area was once thought to be mainly silty sand and find sand.This is the first report on the discovery of pockmarks in the Zengmu Basin;it will provide basic information for submarine stability and marine engineering in China’s maritime boundaries.