Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience...Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.展开更多
The surge of large-scale models in recent years has led to breakthroughs in numerous fields,but it has also introduced higher computational costs and more complex network architectures.These increasingly large and int...The surge of large-scale models in recent years has led to breakthroughs in numerous fields,but it has also introduced higher computational costs and more complex network architectures.These increasingly large and intricate networks pose challenges for deployment and execution while also exacerbating the issue of network over-parameterization.To address this issue,various network compression techniques have been developed,such as network pruning.A typical pruning algorithm follows a three-step pipeline involving training,pruning,and retraining.Existing methods often directly set the pruned filters to zero during retraining,significantly reducing the parameter space.However,this direct pruning strategy frequently results in irreversible information loss.In the early stages of training,a network still contains much uncertainty,and evaluating filter importance may not be sufficiently rigorous.To manage the pruning process effectively,this paper proposes a flexible neural network pruning algorithm based on the logistic growth differential equation,considering the characteristics of network training.Unlike other pruning algorithms that directly reduce filter weights,this algorithm introduces a three-stage adaptive weight decay strategy inspired by the logistic growth differential equation.It employs a gentle decay rate in the initial training stage,a rapid decay rate during the intermediate stage,and a slower decay rate in the network convergence stage.Additionally,the decay rate is adjusted adaptively based on the filter weights at each stage.By controlling the adaptive decay rate at each stage,the pruning of neural network filters can be effectively managed.In experiments conducted on the CIFAR-10 and ILSVRC-2012 datasets,the pruning of neural networks significantly reduces the floating-point operations while maintaining the same pruning rate.Specifically,when implementing a 30%pruning rate on the ResNet-110 network,the pruned neural network not only decreases floating-point operations by 40.8%but also enhances the classification accuracy by 0.49%compared to the original network.展开更多
The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classificati...The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classification.However,BERT’s size and computational demands limit its practicality,especially in resource-constrained settings.This research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization techniques.Despite Bengali being the sixth most spoken language globally,NLP research in this area is limited.Our approach addresses this gap by creating an efficient BERT-based model for Bengali text.We have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory size.Our best results demonstrate significant improvements in both speed and efficiency.For instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 MB.These results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.展开更多
Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a sign...Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.展开更多
The dynamic routing mechanism in evolvable networks enables adaptive reconfiguration of topol-ogical structures and transmission pathways based on real-time task requirements and data character-istics.However,the heig...The dynamic routing mechanism in evolvable networks enables adaptive reconfiguration of topol-ogical structures and transmission pathways based on real-time task requirements and data character-istics.However,the heightened architectural complexity and expanded parameter dimensionality in evolvable networks present significant implementation challenges when deployed in resource-con-strained environments.Due to the critical paths ignored,traditional pruning strategies cannot get a desired trade-off between accuracy and efficiency.For this reason,a critical path retention pruning(CPRP)method is proposed.By deeply traversing the computational graph,the dependency rela-tionship among nodes is derived.Then the nodes are grouped and sorted according to their contribu-tion value.The redundant operations are removed as much as possible while ensuring that the criti-cal path is not affected.As a result,computational efficiency is improved while a higher accuracy is maintained.On the CIFAR benchmark,the experimental results demonstrate that CPRP-induced pruning incurs accuracy degradation below 4.00%,while outperforming traditional feature-agnostic grouping methods by an average 8.98%accuracy improvement.Simultaneously,the pruned model attains a 2.41 times inference acceleration while achieving 48.92%parameter compression and 53.40%floating-point operations(FLOPs)reduction.展开更多
End-to-end object detection Transformer(DETR)successfully established the paradigm of the Transformer architecture in the field of object detection.Its end-to-end detection process and the idea of set prediction have ...End-to-end object detection Transformer(DETR)successfully established the paradigm of the Transformer architecture in the field of object detection.Its end-to-end detection process and the idea of set prediction have become one of the hottest network architectures in recent years.There has been an abundance of work improving upon DETR.However,DETR and its variants require a substantial amount of memory resources and computational costs,and the vast number of parameters in these networks is unfavorable for model deployment.To address this issue,a greedy pruning(GP)algorithm is proposed,applied to a variant denoising-DETR(DN-DETR),which can eliminate redundant parameters in the Transformer architecture of DN-DETR.Considering the different roles of the multi-head attention(MHA)module and the feed-forward network(FFN)module in the Transformer architecture,a modular greedy pruning(MGP)algorithm is proposed.This algorithm separates the two modules and applies their respective optimal strategies and parameters.The effectiveness of the proposed algorithm is validated on the COCO 2017 dataset.The model obtained through the MGP algorithm reduces the parameters by 49%and the number of floating point operations(FLOPs)by 44%compared to the Transformer architecture of DN-DETR.At the same time,the mean average precision(mAP)of the model increases from 44.1%to 45.3%.展开更多
3D sparse convolution has emerged as a pivotal technique for efficient voxel-based perception in autonomous systems,enabling selective feature extraction from non-empty voxels while suppressing computational waste.Des...3D sparse convolution has emerged as a pivotal technique for efficient voxel-based perception in autonomous systems,enabling selective feature extraction from non-empty voxels while suppressing computational waste.Despite its theoretical efficiency advantages,practical implementations face under-explored limitations:the fixed geometric patterns of conventional sparse convolutional kernels inevitably process non-contributory positions during sliding-window operations,particularly in regions with uneven point cloud density.To address this,we propose Hierarchical Shape Pruning for 3D Sparse Convolution(HSP-S),which dynamically eliminates redundant kernel stripes through layer-adaptive thresholding.Unlike static soft pruning methods,HSP-S maintains trainable sparsity patterns by progressively adjusting pruning thresholds during optimization,enlarging original parameter search space while removing redundant operations.Extensive experiments validate effectiveness of HSP-S acrossmajor autonomous driving benchmarks.On KITTI’s 3D object detection task,our method reduces 93.47%redundant kernel computations whilemaintaining comparable accuracy(1.56%mAP drop).Remarkably,on themore complexNuScenes benchmark,HSP-S achieves simultaneous computation reduction(21.94%sparsity)and accuracy gains(1.02%mAP(mean Average Precision)and 0.47%NDS(nuScenes detection score)improvement),demonstrating its scalability to diverse perception scenarios.This work establishes the first learnable shape pruning framework that simultaneously enhances computational efficiency and preserves detection accuracy in 3D perception systems.展开更多
Microglia are the main non-neuronal cells in the central nervous system that have important roles in brain development and functional connectivity of neural circuits.In brain physiology,highly dynamic microglial proce...Microglia are the main non-neuronal cells in the central nervous system that have important roles in brain development and functional connectivity of neural circuits.In brain physiology,highly dynamic microglial processes are facilitated to sense the surrounding environment and stimuli.Once the brain switches its functional states,microglia are recruited to specific sites to exert their immune functions,including the release of cytokines and phagocytosis of cellular debris.The crosstalk of microglia between neurons,neural stem cells,endothelial cells,oligodendrocytes,and astrocytes contributes to their functions in synapse pruning,neurogenesis,vascularization,myelination,and blood-brain barrier permeability.In this review,we highlight the neuron-derived“find-me,”“eat-me,”and“don't eat-me”molecular signals that drive microglia in response to changes in neuronal activity for synapse refinement during brain development.This review reveals the molecular mechanism of neuron-microglia interaction in synaptic pruning and presents novel ideas for the synaptic pruning of microglia in disease,thereby providing important clues for discovery of target drugs and development of nervous system disease treatment methods targeting synaptic dysfunction.展开更多
In this study,vine pruning wastes(VPW)were used as raw material to develop an alternative activated carbon(VPW-AC)for adsorbing and concentrating rare earth elements cerium(Ce(Ⅲ))and lanthanum(La(Ⅲ))from synthetic a...In this study,vine pruning wastes(VPW)were used as raw material to develop an alternative activated carbon(VPW-AC)for adsorbing and concentrating rare earth elements cerium(Ce(Ⅲ))and lanthanum(La(Ⅲ))from synthetic and real leachate solutions.The Ce and La adsorption studies evaluated the effects of VPW-AC dosage,pH,contact time,rare earth initial concentration,and temperature.The VPW-AC adsorbent was subjected to many physicochemical characterization methods to correlate and understand its adsorptive performance.The characterization data indicate a carbonaceous adsorbent with a specific surface area of 467 m^(2)/g.Zeta potential indicates a material with a negatively charged surface at a pH higher than 3.1,which is extremely beneficial to cations removal.For both rare earths elements(REEs),the adsorption capacity increases with the increase of the pH,reaching its maximum at pH 4-6.The kinetic data are well fitted by Avrami-fractional o rder,while the Liu model agreeably fits equilibrium data.The maximum adsorption capacities for Ce(Ⅲ)and La(Ⅲ)are 48.45 and 53.65 mg/g at 298 K,respectively.The thermodynamic studies suggest that the adsorption process is favorable,spontaneous,and exothermic for both REEs.Pore filling,surface complexation,and ion exchange are the dominant mechanisms.Finally,the VPW-AC was subjected to the recovery of REEs from real phosphogypsum leachate,and it is proved that it can be successfully used to recover REEs in a real process.展开更多
Clinical researches including the Mayo Anesthesia Safety in Kids (MASK) study have found that children undergoing multiple anesthesia may have a higher risk of fne motor control difculties. However, the underlying mec...Clinical researches including the Mayo Anesthesia Safety in Kids (MASK) study have found that children undergoing multiple anesthesia may have a higher risk of fne motor control difculties. However, the underlying mechanisms remain elusive. Here, we report that erythropoietin receptor (EPOR), a microglial receptor associated with phagocytic activity, was signifcantly downregulated in the medial prefrontal cortex of young mice after multiple sevofurane anesthesia exposure. Importantly, we found that the inhibited erythropoietin (EPO)/EPOR signaling axis led to microglial polarization, excessive excitatory synaptic pruning, and abnormal fne motor control skills in mice with multiple anesthesia exposure, and those above-mentioned situations were fully reversed by supplementing EPO-derived peptide ARA290 by intraperitoneal injection. Together, the microglial EPOR was identifed as a key mediator regulating early synaptic development in this study, which impacted sevoflurane-induced fine motor dysfunction. Moreover, ARA290 might serve as a new treatment against neurotoxicity induced by general anesthesia in clinical practice by targeting the EPO/EPOR signaling pathway.展开更多
The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Rec...The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Recognition(HAR)efforts.However,the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained systems.This paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain,which reduces the model’s depth and accelerates activity inference.Unlike traditional pruning methods that focus on the spatial domain and the importance of filters,this method converts sensor data,such as HAR data,to the frequency domain for analysis.It emphasizes the low-frequency components by calculating their energy spectral density values.Subsequently,filters that meet the predefined thresholds are retained,and redundant filters are removed,leading to a significant reduction in model size without compromising performance or incurring additional computational costs.Notably,the proposed algorithm’s effectiveness is empirically validated on a standard five-layer CNNs backbone architecture.The computational feasibility and data sensitivity of the proposed scheme are thoroughly examined.Impressively,the classification accuracy on three benchmark HAR datasets UCI-HAR,WISDM,and PAMAP2 reaches 96.20%,98.40%,and 92.38%,respectively.Concurrently,our strategy achieves a reduction in Floating Point Operations(FLOPs)by 90.73%,93.70%,and 90.74%,respectively,along with a corresponding decrease in memory consumption by 90.53%,93.43%,and 90.05%.展开更多
Prunusmumehas high ornamental value,and its maintenance and management should be more meticulous,with pruning being an important task.Pruning can make P.mume more robust,reduce the occurrence of diseases and pests,mai...Prunusmumehas high ornamental value,and its maintenance and management should be more meticulous,with pruning being an important task.Pruning can make P.mume more robust,reduce the occurrence of diseases and pests,maintain a good shape,and promote more flowering,further improving its ornamental value.The difficulty of pruning lies in flexibly adopting suitable pruning methods according to the time of the tree,which requires understanding the impact of pruning operations on the growth and flowering of P.mume,as well as some techniques in pruning operations.This paper introduces the botanical characteristics of P.mume,common pruning methods and achievable effects of P.mume,and suitable time for using various methods,and analyzes the possible consequences and reasons of some incorrect operations.Moreover,corresponding correct practices are provided,which can provide reference for standardized pruning of P.mume,thereby reducing or avoiding losses caused by improper operation.展开更多
Almond pruning biomass is an important agricultural residue that has been scarcely studied for the co-production of sugars and solid biofuels.In this work,the production of monosaccharides from almond prunings was opt...Almond pruning biomass is an important agricultural residue that has been scarcely studied for the co-production of sugars and solid biofuels.In this work,the production of monosaccharides from almond prunings was optimised by a two-step process scheme:pretreatment with dilute sulphuric acid(0.025 M,at 185.9-214.1℃for 0.8-9.2 min)followed by enzyme saccharification of the pretreated cellulose.The application of a response surface methodology enabled the mathematical modelling of the process,establishing pretreatment conditions to maximise both the amount of sugar in the acid prehydrolysate(23.4 kg/100 kg raw material,at 195.7℃for 3.5 min)and the enzymatic digestibility of the pretreated cellulose(45.4%,at 210.0℃for 8.0 min).The highest overall sugar yield(36.8 kg/100 kg raw material,equivalent to 64.3%of all sugars in the feedstock)was obtained with a pretreatment carried out at 197.0℃for 4.0 min.Under these conditions,moreover,the final solids showed better properties for thermochemical utilisation(22.0 MJ/kg heating value,0.87%ash content,and 72.1 mg/g moisture adsorption capacity)compared to those of the original prunings.展开更多
A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can ...A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.展开更多
Pepper (Capsicum annuum L.) is an important agricultural crop because of the nutritional value of the fruit and its economic importance.Various techniques have been practiced to enhance pepper's productivity and n...Pepper (Capsicum annuum L.) is an important agricultural crop because of the nutritional value of the fruit and its economic importance.Various techniques have been practiced to enhance pepper's productivity and nutritional value.Therefore,this study was conducted to determine the impact of different training methods and biostimulant applications on sweet pepper plants'growth,yield,and chemical composition under greenhouse conditions.For the training method,unpruned plants were compared with one stem and two stem plants.Unpruned plants had the fruit number of 33.98,fruit weight of 2.18 kg·plant^(-1),and total marketable yield of 1 090.0 kg·hm^(-2).One stem plant gave the best average fruit weight of 86.63 g,vitamin C content of 13.66 mg·kg^(-1)FW,and TSS content of 7.21%.However,two stem plants had the highest fruit setting of 62.41%,carotenoid content of 0.14 mg·kg^(-1)FW,and fruit chlorophyll content of 3.57 mg·kg^(-1)FW.For biostimulant applications,control plants were compared with the Disper Root (DR) and Disper Vital (DV).DR application significantly increased total sugar,carotenoid,fruit chlorophyll,and TSS contents compared to the control and DV applications.While,applying DV increased fruit setting,plant fruit number,weight,and total marketable yield.In addition,integrating one stem plant with the DR application improved fiber,vitamin C,and TSS contents significantly.Two stem plants,and the DV application improved fruit setting and carotenoid content.Thus,one and two stem training methods integrated with the DR and DV biostimulant applications could be considered for developing agricultural practices to obtain commercial yield and improve the nutrition values of sweet peppers,as unpruned plants without biostimulant applications have a negative impact.展开更多
Microglia are the macrophages that populate the brain parenchyma.Research in the past decades has identified them as both essential guardians of the brain and significant contributors to various neurological diseases....Microglia are the macrophages that populate the brain parenchyma.Research in the past decades has identified them as both essential guardians of the brain and significant contributors to various neurological diseases.A highly versatile cell type,microglia have been shown to fulfill a multitude of critical roles in the central nervous system,including facilitating neurogenesis and myelination,pruning synapses,removing debris and waste,modulating neuronal activity,supporting the blood-brain barrier,repairing tissue damage,and surveilling against microbial invasions under physiological conditions(Prinz et al.,2021;Paolicelli et al.,2022).展开更多
Colorectal cancer is the most common cancer with a second mortality rate.Polyp lesion is a precursor symptom of colorectal cancer.Detection and removal of polyps can effectively reduce the mortality of patients in the...Colorectal cancer is the most common cancer with a second mortality rate.Polyp lesion is a precursor symptom of colorectal cancer.Detection and removal of polyps can effectively reduce the mortality of patients in the early period.However,mass images will be generated during an endoscopy,which will greatly increase the workload of doctors,and long-term mechanical screening of endoscopy images will also lead to a high misdiagnosis rate.Aiming at the problem that computer-aided diagnosis models deeply depend on the computational power in the polyp detection task,we propose a lightweight model,coordinate attention-YOLOv5-Lite-Prune,based on the YOLOv5 algorithm,which is different from state-of-the-art methods proposed by the existing research that applied object detection models or their variants directly to prediction task without any lightweight processing,such as faster region-based convolutional neural networks,YOLOv3,YOLOv4,and single shot multibox detector.The innovations of our model are as follows:First,the lightweight EfficientNetLite network is introduced as the new feature extraction network.Second,the depthwise separable convolution and its improved modules with different attention mechanisms are used to replace the standard convolution in the detection head structure.Then,theα-intersection over union loss function is applied to improve the precision and convergence speed of the model.Finally,the model size is compressed with a pruning algorithm.Our model effectively reduces parameter amount and computational complexity without significant accuracy loss.Therefore,the model can be successfully deployed on the embedded deep learning platform,and detect polyps with a speed above 30 frames per second,which means the model gets rid of the limitation that deep learning models must rely on high-performance servers.展开更多
Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalizat...Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.展开更多
In response to challenges posed by complex backgrounds,diverse target angles,and numerous small targets in remote sensing images,alongside the issue of high resource consumption hindering model deployment,we propose a...In response to challenges posed by complex backgrounds,diverse target angles,and numerous small targets in remote sensing images,alongside the issue of high resource consumption hindering model deployment,we propose an enhanced,lightweight you only look once version 8 small(YOLOv8s)detection algorithm.Regarding network improvements,we first replace tradi-tional horizontal boxes with rotated boxes for target detection,effectively addressing difficulties in feature extraction caused by varying target angles.Second,we design a module integrating convolu-tional neural networks(CNN)and Transformer components to replace specific C2f modules in the backbone network,thereby expanding the model’s receptive field and enhancing feature extraction in complex backgrounds.Finally,we introduce a feature calibration structure to mitigate potential feature mismatches during feature fusion.For model compression,we employ a lightweight channel pruning technique based on localized mean average precision(LMAP)to eliminate redundancies in the enhanced model.Although this approach results in some loss of detection accuracy,it effec-tively reduces the number of parameters,computational load,and model size.Additionally,we employ channel-level knowledge distillation to recover accuracy in the pruned model,further enhancing detection performance.Experimental results indicate that the enhanced algorithm achieves a 6.1%increase in mAP50 compared to YOLOv8s,while simultaneously reducing parame-ters,computational load,and model size by 57.7%,28.8%,and 52.3%,respectively.展开更多
Deep neural network(DNN)models have achieved remarkable performance across diverse tasks,leading to widespread commercial adoption.However,training high-accuracy models demands extensive data,substantial computational...Deep neural network(DNN)models have achieved remarkable performance across diverse tasks,leading to widespread commercial adoption.However,training high-accuracy models demands extensive data,substantial computational resources,and significant time investment,making them valuable assets vulnerable to unauthorized exploitation.To address this issue,this paper proposes an intellectual property(IP)protection framework for DNN models based on feature layer selection and hyper-chaotic mapping.Firstly,a sensitivity-based importance evaluation algorithm is used to identify the key feature layers for encryption,effectively protecting the core components of the model.Next,the L1 regularization criterion is applied to further select high-weight features that significantly impact the model’s performance,ensuring that the encryption process minimizes performance loss.Finally,a dual-layer encryption mechanism is designed,introducing perturbations into the weight values and utilizing hyperchaotic mapping to disrupt channel information,further enhancing the model’s security.Experimental results demonstrate that encrypting only a small subset of parameters effectively reduces model accuracy to random-guessing levels while ensuring full recoverability.The scheme exhibits strong robustness against model pruning and fine-tuning attacks and maintains consistent performance across multiple datasets,providing an efficient and practical solution for authorization-based DNN IP protection.展开更多
基金supported by the National Natural Science Foundation of China,No.31760290,82160688the Key Development Areas Project of Ganzhou Science and Technology,No.2022B-SF9554(all to XL)。
文摘Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.
基金supported by the National Natural Science Foundation of China under Grant No.62172132.
文摘The surge of large-scale models in recent years has led to breakthroughs in numerous fields,but it has also introduced higher computational costs and more complex network architectures.These increasingly large and intricate networks pose challenges for deployment and execution while also exacerbating the issue of network over-parameterization.To address this issue,various network compression techniques have been developed,such as network pruning.A typical pruning algorithm follows a three-step pipeline involving training,pruning,and retraining.Existing methods often directly set the pruned filters to zero during retraining,significantly reducing the parameter space.However,this direct pruning strategy frequently results in irreversible information loss.In the early stages of training,a network still contains much uncertainty,and evaluating filter importance may not be sufficiently rigorous.To manage the pruning process effectively,this paper proposes a flexible neural network pruning algorithm based on the logistic growth differential equation,considering the characteristics of network training.Unlike other pruning algorithms that directly reduce filter weights,this algorithm introduces a three-stage adaptive weight decay strategy inspired by the logistic growth differential equation.It employs a gentle decay rate in the initial training stage,a rapid decay rate during the intermediate stage,and a slower decay rate in the network convergence stage.Additionally,the decay rate is adjusted adaptively based on the filter weights at each stage.By controlling the adaptive decay rate at each stage,the pruning of neural network filters can be effectively managed.In experiments conducted on the CIFAR-10 and ILSVRC-2012 datasets,the pruning of neural networks significantly reduces the floating-point operations while maintaining the same pruning rate.Specifically,when implementing a 30%pruning rate on the ResNet-110 network,the pruned neural network not only decreases floating-point operations by 40.8%but also enhances the classification accuracy by 0.49%compared to the original network.
文摘The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classification.However,BERT’s size and computational demands limit its practicality,especially in resource-constrained settings.This research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization techniques.Despite Bengali being the sixth most spoken language globally,NLP research in this area is limited.Our approach addresses this gap by creating an efficient BERT-based model for Bengali text.We have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory size.Our best results demonstrate significant improvements in both speed and efficiency.For instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 MB.These results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.
文摘Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.
基金Supported by the National Key Research and Development Program of China(No.2022ZD0119003)and the National Natural Science Founda-tion of China(No.61834005).
文摘The dynamic routing mechanism in evolvable networks enables adaptive reconfiguration of topol-ogical structures and transmission pathways based on real-time task requirements and data character-istics.However,the heightened architectural complexity and expanded parameter dimensionality in evolvable networks present significant implementation challenges when deployed in resource-con-strained environments.Due to the critical paths ignored,traditional pruning strategies cannot get a desired trade-off between accuracy and efficiency.For this reason,a critical path retention pruning(CPRP)method is proposed.By deeply traversing the computational graph,the dependency rela-tionship among nodes is derived.Then the nodes are grouped and sorted according to their contribu-tion value.The redundant operations are removed as much as possible while ensuring that the criti-cal path is not affected.As a result,computational efficiency is improved while a higher accuracy is maintained.On the CIFAR benchmark,the experimental results demonstrate that CPRP-induced pruning incurs accuracy degradation below 4.00%,while outperforming traditional feature-agnostic grouping methods by an average 8.98%accuracy improvement.Simultaneously,the pruned model attains a 2.41 times inference acceleration while achieving 48.92%parameter compression and 53.40%floating-point operations(FLOPs)reduction.
基金Shanghai Municipal Commission of Economy and Information Technology,China(No.202301054)。
文摘End-to-end object detection Transformer(DETR)successfully established the paradigm of the Transformer architecture in the field of object detection.Its end-to-end detection process and the idea of set prediction have become one of the hottest network architectures in recent years.There has been an abundance of work improving upon DETR.However,DETR and its variants require a substantial amount of memory resources and computational costs,and the vast number of parameters in these networks is unfavorable for model deployment.To address this issue,a greedy pruning(GP)algorithm is proposed,applied to a variant denoising-DETR(DN-DETR),which can eliminate redundant parameters in the Transformer architecture of DN-DETR.Considering the different roles of the multi-head attention(MHA)module and the feed-forward network(FFN)module in the Transformer architecture,a modular greedy pruning(MGP)algorithm is proposed.This algorithm separates the two modules and applies their respective optimal strategies and parameters.The effectiveness of the proposed algorithm is validated on the COCO 2017 dataset.The model obtained through the MGP algorithm reduces the parameters by 49%and the number of floating point operations(FLOPs)by 44%compared to the Transformer architecture of DN-DETR.At the same time,the mean average precision(mAP)of the model increases from 44.1%to 45.3%.
文摘3D sparse convolution has emerged as a pivotal technique for efficient voxel-based perception in autonomous systems,enabling selective feature extraction from non-empty voxels while suppressing computational waste.Despite its theoretical efficiency advantages,practical implementations face under-explored limitations:the fixed geometric patterns of conventional sparse convolutional kernels inevitably process non-contributory positions during sliding-window operations,particularly in regions with uneven point cloud density.To address this,we propose Hierarchical Shape Pruning for 3D Sparse Convolution(HSP-S),which dynamically eliminates redundant kernel stripes through layer-adaptive thresholding.Unlike static soft pruning methods,HSP-S maintains trainable sparsity patterns by progressively adjusting pruning thresholds during optimization,enlarging original parameter search space while removing redundant operations.Extensive experiments validate effectiveness of HSP-S acrossmajor autonomous driving benchmarks.On KITTI’s 3D object detection task,our method reduces 93.47%redundant kernel computations whilemaintaining comparable accuracy(1.56%mAP drop).Remarkably,on themore complexNuScenes benchmark,HSP-S achieves simultaneous computation reduction(21.94%sparsity)and accuracy gains(1.02%mAP(mean Average Precision)and 0.47%NDS(nuScenes detection score)improvement),demonstrating its scalability to diverse perception scenarios.This work establishes the first learnable shape pruning framework that simultaneously enhances computational efficiency and preserves detection accuracy in 3D perception systems.
基金supported by the National Natural Science Foundation of ChinaNo.32200778(to QC)+5 种基金the Natural Science Foundation of Jiangsu ProvinceNo.BK20220494(to QC)Suzhou Medical and Health Technology Innovation ProjectNo.SKY2022107(to QC)a grant from the Clinical Research Center of Neurological Disease in The Second Affiliated Hospital of Soochow UniversityNos.ND2022A04(to QC)and ND2023B06(to JS)。
文摘Microglia are the main non-neuronal cells in the central nervous system that have important roles in brain development and functional connectivity of neural circuits.In brain physiology,highly dynamic microglial processes are facilitated to sense the surrounding environment and stimuli.Once the brain switches its functional states,microglia are recruited to specific sites to exert their immune functions,including the release of cytokines and phagocytosis of cellular debris.The crosstalk of microglia between neurons,neural stem cells,endothelial cells,oligodendrocytes,and astrocytes contributes to their functions in synapse pruning,neurogenesis,vascularization,myelination,and blood-brain barrier permeability.In this review,we highlight the neuron-derived“find-me,”“eat-me,”and“don't eat-me”molecular signals that drive microglia in response to changes in neuronal activity for synapse refinement during brain development.This review reveals the molecular mechanism of neuron-microglia interaction in synaptic pruning and presents novel ideas for the synaptic pruning of microglia in disease,thereby providing important clues for discovery of target drugs and development of nervous system disease treatment methods targeting synaptic dysfunction.
基金Project supported by the Brazilian National Council for Scientific and Technological Development/CNPq(405982/2022—4,303992/2021-2)。
文摘In this study,vine pruning wastes(VPW)were used as raw material to develop an alternative activated carbon(VPW-AC)for adsorbing and concentrating rare earth elements cerium(Ce(Ⅲ))and lanthanum(La(Ⅲ))from synthetic and real leachate solutions.The Ce and La adsorption studies evaluated the effects of VPW-AC dosage,pH,contact time,rare earth initial concentration,and temperature.The VPW-AC adsorbent was subjected to many physicochemical characterization methods to correlate and understand its adsorptive performance.The characterization data indicate a carbonaceous adsorbent with a specific surface area of 467 m^(2)/g.Zeta potential indicates a material with a negatively charged surface at a pH higher than 3.1,which is extremely beneficial to cations removal.For both rare earths elements(REEs),the adsorption capacity increases with the increase of the pH,reaching its maximum at pH 4-6.The kinetic data are well fitted by Avrami-fractional o rder,while the Liu model agreeably fits equilibrium data.The maximum adsorption capacities for Ce(Ⅲ)and La(Ⅲ)are 48.45 and 53.65 mg/g at 298 K,respectively.The thermodynamic studies suggest that the adsorption process is favorable,spontaneous,and exothermic for both REEs.Pore filling,surface complexation,and ion exchange are the dominant mechanisms.Finally,the VPW-AC was subjected to the recovery of REEs from real phosphogypsum leachate,and it is proved that it can be successfully used to recover REEs in a real process.
基金supported by grants from the National Natural Science Foundation of China(82101534,82171170,and 82201414)the Natural Science Foundation of Shaanxi Provence(2024JCYBMS659)the Science and Technology Plan in Xi’an of China(23YXYJ0134)。
文摘Clinical researches including the Mayo Anesthesia Safety in Kids (MASK) study have found that children undergoing multiple anesthesia may have a higher risk of fne motor control difculties. However, the underlying mechanisms remain elusive. Here, we report that erythropoietin receptor (EPOR), a microglial receptor associated with phagocytic activity, was signifcantly downregulated in the medial prefrontal cortex of young mice after multiple sevofurane anesthesia exposure. Importantly, we found that the inhibited erythropoietin (EPO)/EPOR signaling axis led to microglial polarization, excessive excitatory synaptic pruning, and abnormal fne motor control skills in mice with multiple anesthesia exposure, and those above-mentioned situations were fully reversed by supplementing EPO-derived peptide ARA290 by intraperitoneal injection. Together, the microglial EPOR was identifed as a key mediator regulating early synaptic development in this study, which impacted sevoflurane-induced fine motor dysfunction. Moreover, ARA290 might serve as a new treatment against neurotoxicity induced by general anesthesia in clinical practice by targeting the EPO/EPOR signaling pathway.
基金supported by National Natural Science Foundation of China(Nos.61902158 and 62202210).
文摘The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Recognition(HAR)efforts.However,the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained systems.This paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain,which reduces the model’s depth and accelerates activity inference.Unlike traditional pruning methods that focus on the spatial domain and the importance of filters,this method converts sensor data,such as HAR data,to the frequency domain for analysis.It emphasizes the low-frequency components by calculating their energy spectral density values.Subsequently,filters that meet the predefined thresholds are retained,and redundant filters are removed,leading to a significant reduction in model size without compromising performance or incurring additional computational costs.Notably,the proposed algorithm’s effectiveness is empirically validated on a standard five-layer CNNs backbone architecture.The computational feasibility and data sensitivity of the proposed scheme are thoroughly examined.Impressively,the classification accuracy on three benchmark HAR datasets UCI-HAR,WISDM,and PAMAP2 reaches 96.20%,98.40%,and 92.38%,respectively.Concurrently,our strategy achieves a reduction in Floating Point Operations(FLOPs)by 90.73%,93.70%,and 90.74%,respectively,along with a corresponding decrease in memory consumption by 90.53%,93.43%,and 90.05%.
文摘Prunusmumehas high ornamental value,and its maintenance and management should be more meticulous,with pruning being an important task.Pruning can make P.mume more robust,reduce the occurrence of diseases and pests,maintain a good shape,and promote more flowering,further improving its ornamental value.The difficulty of pruning lies in flexibly adopting suitable pruning methods according to the time of the tree,which requires understanding the impact of pruning operations on the growth and flowering of P.mume,as well as some techniques in pruning operations.This paper introduces the botanical characteristics of P.mume,common pruning methods and achievable effects of P.mume,and suitable time for using various methods,and analyzes the possible consequences and reasons of some incorrect operations.Moreover,corresponding correct practices are provided,which can provide reference for standardized pruning of P.mume,thereby reducing or avoiding losses caused by improper operation.
基金supported by the Operative Program FEDER Andalucía 2014-2020(Junta de Andalucía-MINECO-FEDER)by the grant funded 2021/00591/001using the support to the research Action 1 of University of Jaén.
文摘Almond pruning biomass is an important agricultural residue that has been scarcely studied for the co-production of sugars and solid biofuels.In this work,the production of monosaccharides from almond prunings was optimised by a two-step process scheme:pretreatment with dilute sulphuric acid(0.025 M,at 185.9-214.1℃for 0.8-9.2 min)followed by enzyme saccharification of the pretreated cellulose.The application of a response surface methodology enabled the mathematical modelling of the process,establishing pretreatment conditions to maximise both the amount of sugar in the acid prehydrolysate(23.4 kg/100 kg raw material,at 195.7℃for 3.5 min)and the enzymatic digestibility of the pretreated cellulose(45.4%,at 210.0℃for 8.0 min).The highest overall sugar yield(36.8 kg/100 kg raw material,equivalent to 64.3%of all sugars in the feedstock)was obtained with a pretreatment carried out at 197.0℃for 4.0 min.Under these conditions,moreover,the final solids showed better properties for thermochemical utilisation(22.0 MJ/kg heating value,0.87%ash content,and 72.1 mg/g moisture adsorption capacity)compared to those of the original prunings.
文摘A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.
文摘Pepper (Capsicum annuum L.) is an important agricultural crop because of the nutritional value of the fruit and its economic importance.Various techniques have been practiced to enhance pepper's productivity and nutritional value.Therefore,this study was conducted to determine the impact of different training methods and biostimulant applications on sweet pepper plants'growth,yield,and chemical composition under greenhouse conditions.For the training method,unpruned plants were compared with one stem and two stem plants.Unpruned plants had the fruit number of 33.98,fruit weight of 2.18 kg·plant^(-1),and total marketable yield of 1 090.0 kg·hm^(-2).One stem plant gave the best average fruit weight of 86.63 g,vitamin C content of 13.66 mg·kg^(-1)FW,and TSS content of 7.21%.However,two stem plants had the highest fruit setting of 62.41%,carotenoid content of 0.14 mg·kg^(-1)FW,and fruit chlorophyll content of 3.57 mg·kg^(-1)FW.For biostimulant applications,control plants were compared with the Disper Root (DR) and Disper Vital (DV).DR application significantly increased total sugar,carotenoid,fruit chlorophyll,and TSS contents compared to the control and DV applications.While,applying DV increased fruit setting,plant fruit number,weight,and total marketable yield.In addition,integrating one stem plant with the DR application improved fiber,vitamin C,and TSS contents significantly.Two stem plants,and the DV application improved fruit setting and carotenoid content.Thus,one and two stem training methods integrated with the DR and DV biostimulant applications could be considered for developing agricultural practices to obtain commercial yield and improve the nutrition values of sweet peppers,as unpruned plants without biostimulant applications have a negative impact.
基金funded by NIH grants HL154720-03S1, AG057587, AG074283, DK122708-03S1, BrightFocus ADR A20183775Brown Foundation 2020 Healthy Aging Initiative (to WC)
文摘Microglia are the macrophages that populate the brain parenchyma.Research in the past decades has identified them as both essential guardians of the brain and significant contributors to various neurological diseases.A highly versatile cell type,microglia have been shown to fulfill a multitude of critical roles in the central nervous system,including facilitating neurogenesis and myelination,pruning synapses,removing debris and waste,modulating neuronal activity,supporting the blood-brain barrier,repairing tissue damage,and surveilling against microbial invasions under physiological conditions(Prinz et al.,2021;Paolicelli et al.,2022).
基金the National Natural Science Foundation of China(Nos.81971767,62103263 and 62103267)the Shanghai Science and Technology Commission(Nos.19142203800,19441913800 and 19441910600)。
文摘Colorectal cancer is the most common cancer with a second mortality rate.Polyp lesion is a precursor symptom of colorectal cancer.Detection and removal of polyps can effectively reduce the mortality of patients in the early period.However,mass images will be generated during an endoscopy,which will greatly increase the workload of doctors,and long-term mechanical screening of endoscopy images will also lead to a high misdiagnosis rate.Aiming at the problem that computer-aided diagnosis models deeply depend on the computational power in the polyp detection task,we propose a lightweight model,coordinate attention-YOLOv5-Lite-Prune,based on the YOLOv5 algorithm,which is different from state-of-the-art methods proposed by the existing research that applied object detection models or their variants directly to prediction task without any lightweight processing,such as faster region-based convolutional neural networks,YOLOv3,YOLOv4,and single shot multibox detector.The innovations of our model are as follows:First,the lightweight EfficientNetLite network is introduced as the new feature extraction network.Second,the depthwise separable convolution and its improved modules with different attention mechanisms are used to replace the standard convolution in the detection head structure.Then,theα-intersection over union loss function is applied to improve the precision and convergence speed of the model.Finally,the model size is compressed with a pruning algorithm.Our model effectively reduces parameter amount and computational complexity without significant accuracy loss.Therefore,the model can be successfully deployed on the embedded deep learning platform,and detect polyps with a speed above 30 frames per second,which means the model gets rid of the limitation that deep learning models must rely on high-performance servers.
基金supported in part by the Jiangsu Province Construction System Science and Technology Project(No.2024ZD056)the Research Development Fund of Xi’an Jiaotong-Liverpool University(No.RDF-24-01-097).
文摘Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.
基金supported in part by the National Natural Foundation of China(Nos.52472334,U2368204)。
文摘In response to challenges posed by complex backgrounds,diverse target angles,and numerous small targets in remote sensing images,alongside the issue of high resource consumption hindering model deployment,we propose an enhanced,lightweight you only look once version 8 small(YOLOv8s)detection algorithm.Regarding network improvements,we first replace tradi-tional horizontal boxes with rotated boxes for target detection,effectively addressing difficulties in feature extraction caused by varying target angles.Second,we design a module integrating convolu-tional neural networks(CNN)and Transformer components to replace specific C2f modules in the backbone network,thereby expanding the model’s receptive field and enhancing feature extraction in complex backgrounds.Finally,we introduce a feature calibration structure to mitigate potential feature mismatches during feature fusion.For model compression,we employ a lightweight channel pruning technique based on localized mean average precision(LMAP)to eliminate redundancies in the enhanced model.Although this approach results in some loss of detection accuracy,it effec-tively reduces the number of parameters,computational load,and model size.Additionally,we employ channel-level knowledge distillation to recover accuracy in the pruned model,further enhancing detection performance.Experimental results indicate that the enhanced algorithm achieves a 6.1%increase in mAP50 compared to YOLOv8s,while simultaneously reducing parame-ters,computational load,and model size by 57.7%,28.8%,and 52.3%,respectively.
基金supported in part by the National Natural Science Foundation of China under Grant No.62172280in part by the Key Scientific Research Projects of Colleges and Universities in Henan Province,China under Grant No.23A520006in part by Henan Provincial Science and Technology Research Project under Grant No.222102210199.
文摘Deep neural network(DNN)models have achieved remarkable performance across diverse tasks,leading to widespread commercial adoption.However,training high-accuracy models demands extensive data,substantial computational resources,and significant time investment,making them valuable assets vulnerable to unauthorized exploitation.To address this issue,this paper proposes an intellectual property(IP)protection framework for DNN models based on feature layer selection and hyper-chaotic mapping.Firstly,a sensitivity-based importance evaluation algorithm is used to identify the key feature layers for encryption,effectively protecting the core components of the model.Next,the L1 regularization criterion is applied to further select high-weight features that significantly impact the model’s performance,ensuring that the encryption process minimizes performance loss.Finally,a dual-layer encryption mechanism is designed,introducing perturbations into the weight values and utilizing hyperchaotic mapping to disrupt channel information,further enhancing the model’s security.Experimental results demonstrate that encrypting only a small subset of parameters effectively reduces model accuracy to random-guessing levels while ensuring full recoverability.The scheme exhibits strong robustness against model pruning and fine-tuning attacks and maintains consistent performance across multiple datasets,providing an efficient and practical solution for authorization-based DNN IP protection.
