SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

With the improvement of the national economic level,the number of vehicles is still increasing year by year.According to the statistics of National Bureau of Statics,the number is approximately up to 327 million in China by the end of 2018,which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is growing.Illegal parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision,which may miss detection and cost much manpower.Due to the rapidly developing deep learning sweeping the world in recent years,object detection methods relying on background segmentation cannot meet the requirements of complex and various scenes on speed and precision.Thus,an improved Single Shot MultiBox Detector(SSD)based on deep learning is proposed in our study,we introduce attention mechanism by spatial transformer module which gives neural networks the ability to actively spatially transform feature maps and add contextual information transmission in specified layer.Finally,we found out the best connection layer in the detection model by repeated experiments especially for small objects and increased the precision by 1.5%than the baseline SSD without extra training cost.Meanwhile,we designed an illegal parking vehicle detection method by the improved SSD,reaching a high precision up to 97.3%and achieving a speed of 40FPS,superior to most of vehicle detection methods,will make contributions to relieving the negative impact of illegal parking.

Dynamical analysis,circuit realization,and application in pseudorandom number generators of a fractional-order laser chaotic system

A new five-dimensional fractional-order laser chaotic system(FOLCS)is constructed by incorporating complex variables and fractional calculus into a Lorentz-Haken-type laser system.Dynamical behavior of the system,circuit realization and application in pseudorandom number generators are studied.Many types of multi-stable states are discovered in the system.Interestingly,there are two types of state transition phenomena in the system,one is the chaotic state degenerates to a periodical state,and the other is the intermittent chaotic oscillation.In addition,the complexity of the system when two parameters change simultaneously is measured by the spectral entropy algorithm.Moreover,a digital circuit is design and the chaotic oscillation behaviors of the system are verified on this circuit.Finally,a pseudo-random sequence generator is designed using the FOLCS,and the statistical characteristics of the generated pseudo-random sequence are tested with the NIST-800-22.This study enriches the research on the dynamics and applications of FOLCS.

A Blockchain-Based Framework for Secure Storage and Sharing of Resumes

In response to problems in the centralized storage of personal resumes on third-party recruitment platforms,such as inadequate privacy protection,inability of individuals to accurately authorize downloads,and inability to determine who downloaded the resume and when,this study proposes a blockchain-based framework for secure storage and sharing of resumes.Users can employ an authorized access mechanism to protect their privacy rights.The proposed framework uses smart contracts,interplanetary file system,symmetric encryption,and digital signatures to protect,verify,and share resumes.Encryption keys are split and stored in multiple depositories through secret-sharing technology to improve the security of these keys.Corresponding key escrow incentives are implemented using smart contracts to automatically verify the correctness of keys and encourage the active participation of honest key escrow parties.This framework combines blockchain and searchable symmetric encryption technology to realize multikeyword search using inverted indexing and Bloom filters and verify search results on the chain.Escrow search service fees are charged through contracts.Only after the search results are verified can the search service provider obtain the search fee,thus ensuring fair and efficient search for encrypted resumes.The framework is decentralized,secure,and tamper-evident,and achieves controlled sharing while protecting personal privacy and information security.

Urdnet:A Cryo-EM Particle Automatic Picking Method

Cryo-Electron Microscopy(Cryo-EM)images are characterized by the low signal-to-noise ratio,low contrast,serious background noise,more impurities,less data,difficult data labeling,simpler image semantics,and relatively fixed structure,while U-Net obtains low resolution when downsampling rate information to complete object category recognition,obtains highresolution information during upsampling to complete precise segmentation and positioning,fills in the underlying information through skip connection to improve the accuracy of image segmentation,and has advantages in biological image processing like Cryo-EM image.This article proposes A U-Net based residual intensive neural network(Urdnet),which combines point-level and pixel-level tags,used to accurately and automatically locate particles from cryo-electron microscopy images,and solve the bottleneck that cryo-EM Single-particle biologicalmacromolecule reconstruction requires tens of thousands of automatically picked particles.The 80S ribosome,HCN1 channel and TcdA1 toxin subunits,and other public protein datasets have been trained and tested on Urdnet.The experimental results show that Urdnet could reach the same excellent particle picking performances as the mainstream methods of RELION,DeepPicker,and acquire the 3Dstructure of picked particleswith higher resolution.

A Noise Extraction Method for Cryo-EM Single-Particle Denoising

Cryo-Electron Microscopy(cryo-EM)has become a powerful method to study the structure and function of biological macromolecules.However,in clustering tasks based on the projection angle of particles in cryoEM,the noise considerably affects the clustering results.Existing denoising algorithms are ineffective due to the extremely low signal-to-noise ratio(SNR)of cryo-EM images and the complexity of noise types.The noise of a single particle greatly influences the orientation estimation of the subsequent clustering task,and the result of the clustering task directly affects the accuracy of the 3D reconstruction.In this paper,we propose a construction method of cryo-EM denoising dataset that uses U-Net to extract noise blocks from cryoEM images,superimpose the noise block with the projected pure particles to construct our simulated dataset.Then we adopt a supervised generative adversarial network(GAN)with perceptual loss to train on our simulated dataset and denoise the real cryo-EM single particle.The method can solve the problem of poor denoising performance caused by assuming that the noise of the Gaussian distribution does not conform to the noise distribution of cryo-EM,and it can retain the useful information of particles to a great extent.We compared traditional image filtering methods and the classic deep learning denoising algorithm DnCNN on the simulated and real datasets.Experiment results show that the method based on deep learning has more advantages than traditional image denoising methods.It is worth mentioning that our method achieves a competitive peak signal to noise ratio(PSNR)and structural similarity(SSIM).Moreover,visualization results,indicate that our method can retain the structure information and orientation information of particles to a greater extent compared with other state-of-the-art image denoising methods.It means that our denoising task can provide considerable help for subsequent cryo-EM clustering tasks.

CenterPicker:An Automated Cryo-EM Single-Particle Picking Method Based on Center Point Detection

Cryo-electron microscopy(cryo-EM)has become one of the mainstream techniques for determining the structures of proteins andmacromolecular complexes,with prospects for development and significance.Researchers must select hundreds of thousands of particles from micrographs to acquire the database for single-particle cryo-EM reconstruction.However,existing particle picking methods cannot ensure that the particles are in the center of the bounding box because the signal-to-noise ratio(SNR)of micrographs is extremely low,thereby directly affecting the efficiency and accuracy of 3D reconstruction.We propose an automated particle-picking method(CenterPicker)based on particle center point detection to automatically select a large number of high-quality particles from low signal-to-noise,low-contrast refrigerated microscopy images.The method uses a fully convolutional neural network to generate a keypoint heatmap.The heatmap value represents the probability that a micrograph pixel belongs to a particle center area.CenterPicker can process images of any size and can directly predict the center point and size of the particle.The network implements multiscale feature fusion and introduces an attention mechanism to improve the feature fusion part to obtain more accurate selection results.We have conducted a detailed evaluation of CenterPicker on a range of datasets,and results indicate that it excels in single-particle picking tasks.

Pt/Mo chalcogenide composite deriving from Pt-Mo_(6)S_8 by high temperature shock for enhanced HER performance

Highly efficient catalysts for electrolysis of water are crucial to the development of hydrogen energy which is helpful to carbon neutralization.Recently,high temperature shock(HTS),with advantage of rapid speed,universality and scalable production,has been a promising method in synthesis of nanomaterials.In this paper,HST was used to treat low Pt loading Mo_(6)S_(8)for enhanced water splitting performance.Impressively,the optimized MoS_(2)/MoO_(2)/Mo_(6)S_(8)nano-composite with low Pt mass loading(~4%)displays well hydrogen evolution reaction(HER)electrochemical performance.The overpotential is 124 mV to reach 10 mA/cm^(2)and the corresponding Tafel slope is 88 mV/dec in acidic electrolyte.Its mass activity is 6.2 mA/μg_(Pt)at-124 mV vs.RHE,which is almost 2 times relative to 20%Pt/C.Moreover,it presents distinguished stability even after 2000 cycles.This work will broaden the way of catalysts preparation and the application of hydrogen evolution.

Prediction of cognitive scores by joint use of movie-watching fMRI connectivity and eye tracking via Attention-CensNet

Background:Brain functional connectivity under the naturalistic paradigm has been shown to be better at predicting individual behaviors than other brain states,such as rest and doing tasks.Nevertheless,the state-of-the-art methods have found it difcult to achieve desirable results from movie watching paradigm functional magnetic resonance imaging(mfMRI)-induced brain functional connectivity,especially when there are fewer datasets.Incorporating other physical measurements into the prediction method may enhance accuracy.Eye tracking,becoming popular due to its portability and lower expense,can provide abundant behavioral features related to the output of human's cognition,and thus might supplement the mfMRI in observing participants'subconscious behaviors.However,there are very few studies on how to effectively integrate the multimodal information to strengthen the performance by a unified framework.objective:A fusion approach with mfMRI and eye tracking,based on convolution with edge-node switching in graph neural networks(CensNet),is proposed in this article.Methods:In this graph model,participants are designated as nodes,mfMRI derived functional connectivity as node features,and different eye-tracking features are used to compute similarity between participants to construct heterogeneous graph edges.By taking multiple graphs as different channels,we introduce squeeze-and-excitation attention module to CensNet(A-CensNet)to integrate graph embeddings from multiple channels into one.Results:The proposed model outperforms those using a single modality and single channel,and state-of-the-art methods.Conclusions:The results indicate that brain functional activities and eye behaviors might complement each other in interpreting trait-like phenotypes.

A Survey of MRI-Based Brain Tumor Segmentation Methods

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core,and edema from normal brain tissues of White Matter（WM）, Gray Matter（GM）, and Cerebrospinal Fluid（CSF）. MRIbased brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging（MRI） images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods. Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given. Then, the preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced. Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for MRI-based brain tumor segmentation methods.

Corrigendum to fundamental functional differences between gyri and sulci: Implications for brain function, cognition and behavior

The original version of this paper contained an error in figure 5.This has been corrected online.

Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

Folding of the cerebral cortex is a prominent characteristic of mammalian brains.Alterations or deficits in cortical folding are strongly correlated with abnormal brain function,cognition,and behavior.Therefore,a precise mapping between the anatomy and function of the brain is critical to our understanding of the mechanisms of brain structural architecture in both health and diseases.Gyri and sulci,the standard nomenclature for cortical anatomy,serve as building blocks to make up complex folding patterns,providing a window to decipher cortical anatomy and its relation with brain functions.Huge efforts have been devoted to this research topic from a variety of disciplines including genetics,cell biology,anatomy,neuroimaging,and neurology,as well as involving computational approaches based on machine learning and artificial intelligence algorithms.However,despite increasing progress,our understanding of the functional anatomy of gyro-sulcal patterns is still in its infancy.In this review,we present the current state of this field and provide our perspectives of the methodologies and conclusions concerning functional differentiation between gyri and sulci,as well as the supporting information from genetic,cell biology,and brain structure research.In particular,we will further present a proposed framework for attempting to interpret the dynamic mechanisms of the functional interplay between gyri and sulci.Hopefully,this review will provide a comprehensive summary of anatomo-functional relationships in the cortical gyro-sulcal system together with a consideration of how these contribute to brain function,cognition,and behavior,as well as to mental disorders.