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
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
The oil palm leaf miner, Coelaenomenodera lameensis, is currently the most destructive pest of oil palm in Ghana and other African oil palm growing countries, causing significant losses in fresh fruit bunch yield. Pro...The oil palm leaf miner, Coelaenomenodera lameensis, is currently the most destructive pest of oil palm in Ghana and other African oil palm growing countries, causing significant losses in fresh fruit bunch yield. Progressive pruning is an oil palm pruning method in which pruning is done at the same time as fresh fruit bunch harvesting. This study evaluated the impact of progressive pruning on leaf miner population in oil palm and how these two factors (leaf miner and progressive pruning) affect the yield of oil palm at the Benso Oil Palm Plantation Public listed company (BOPP. Plc). Five distinct blocks in the plantation were selected for observations on fronds at various ranks (33, 25, or 17) based on the degree of defoliation by counting the number of pests on leaflets at different phases of insect development. Fronds from selected plots were sampled in a Completely Randomized Design (CRD). The size of plots used for the study ranged between 19 to 45 hectares. A minimum of 78 fronds were evenly cut from each block for pest count depending on the block size. Secondary data on annual yields of fresh fruit bunches before and after the introduction of progressive pruning were also obtained from BOPP. Plc records from 2011-2020. The results from the analyzed data on leaf miner index before and after the introduction of progressive pruning showed that progressive pruning has, to a high extent (64% to 36%), reduced leaf miner populations in the plantation. Paired t-test on fresh fruit bunch yield has also revealed a significant (p < 0.001) increase in annual fresh fruit bunch yield due to progressive pruning. A regression analysis, however, revealed a lower rate of yield loss (3.05 to 2.70 tonnes) to leaf miner infestation after the introduction of progressive pruning. The study recommends progressive pruning as a key cultural practice for improving crop yields in leaf miner prone plantations.展开更多
Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intellig...Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.展开更多
Secondary lignocellulosic biomass has proved to be useful as an energy source through its oxidation by means of combustion processes.In accordance with the above,in this paper,we wanted to study the ash from urban pru...Secondary lignocellulosic biomass has proved to be useful as an energy source through its oxidation by means of combustion processes.In accordance with the above,in this paper,we wanted to study the ash from urban pruning residues that are generated in cities in the Neotropics.Species such as Licania tomentosa,Azadirachta indica,Ficus benjamina,Terminalia catappa,Leucaena leucocephala,Prosopis juliflora and Pithecellobium dulce were selected because they have been previously studied and showed potential for thermal energy generation.These materials were calcined in an oxidizing atmosphere and characterized by X-ray diffraction and fluorescence,scanning electron microscopy with microchemistry,BET surface area,thermal gravimetric analysis,and differential scanning calorimetry.The pH and apparent density were also established.The results show high basicity materials(average pH 10),a behavior associated with the presence of chemical elements such as calcium,potassium,magnesium,chlorine,phosphorus,and sulfur.Structurally,these materials have a very significant amorphous fraction(between 49%and 74.5%),the dominant crystalline phases are calcite,arcanite,sylvite,and hydroxyapatite.These ashes have low surface area and do not exceed 13 m^(2)/g.Two characteristic morphological aspects were observed in these ashes:a morphology of rounded grains where silicon content is highlighted,and lamellar morphologies where the presence of chlorine is highlighted.Thermally,these ashes show four significant mass loss events(400℃,430℃,680℃,and 920℃),causing mass losses that vary between 25%and 40%.Through this study,it was possible to establish that,from a chemical point of view,these ashes are less dangerous in comparison with those of a mineral coal that was used as a reference.However,they require additional treatments for their disposal due to their high basicity.Because of their composition,these ashes have the potential to be used in the ceramic and cement industries,and in the manufacture of fertilizers.展开更多
Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these...Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs.展开更多
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.
文摘The oil palm leaf miner, Coelaenomenodera lameensis, is currently the most destructive pest of oil palm in Ghana and other African oil palm growing countries, causing significant losses in fresh fruit bunch yield. Progressive pruning is an oil palm pruning method in which pruning is done at the same time as fresh fruit bunch harvesting. This study evaluated the impact of progressive pruning on leaf miner population in oil palm and how these two factors (leaf miner and progressive pruning) affect the yield of oil palm at the Benso Oil Palm Plantation Public listed company (BOPP. Plc). Five distinct blocks in the plantation were selected for observations on fronds at various ranks (33, 25, or 17) based on the degree of defoliation by counting the number of pests on leaflets at different phases of insect development. Fronds from selected plots were sampled in a Completely Randomized Design (CRD). The size of plots used for the study ranged between 19 to 45 hectares. A minimum of 78 fronds were evenly cut from each block for pest count depending on the block size. Secondary data on annual yields of fresh fruit bunches before and after the introduction of progressive pruning were also obtained from BOPP. Plc records from 2011-2020. The results from the analyzed data on leaf miner index before and after the introduction of progressive pruning showed that progressive pruning has, to a high extent (64% to 36%), reduced leaf miner populations in the plantation. Paired t-test on fresh fruit bunch yield has also revealed a significant (p < 0.001) increase in annual fresh fruit bunch yield due to progressive pruning. A regression analysis, however, revealed a lower rate of yield loss (3.05 to 2.70 tonnes) to leaf miner infestation after the introduction of progressive pruning. The study recommends progressive pruning as a key cultural practice for improving crop yields in leaf miner prone plantations.
基金supported by Key-Area Research and Development Program of Guangdong Province(2021B0101420002)the Major Key Project of PCL(PCL2021A09)+3 种基金National Natural Science Foundation of China(62072187)Guangdong Major Project of Basic and Applied Basic Research(2019B030302002)Guangdong Marine Economic Development Special Fund Project(GDNRC[2022]17)Guangzhou Development Zone Science and Technology(2021GH10,2020GH10).
文摘Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.
基金Ministry of Science,Technology and Innovation of Colombia through the“Fondo Francisco Joséde Caldas”National Financing Fund for Science,Technology and Innovation for the financing provided for the development of the project (Project 120885272102,Call 852 of 2019).
文摘Secondary lignocellulosic biomass has proved to be useful as an energy source through its oxidation by means of combustion processes.In accordance with the above,in this paper,we wanted to study the ash from urban pruning residues that are generated in cities in the Neotropics.Species such as Licania tomentosa,Azadirachta indica,Ficus benjamina,Terminalia catappa,Leucaena leucocephala,Prosopis juliflora and Pithecellobium dulce were selected because they have been previously studied and showed potential for thermal energy generation.These materials were calcined in an oxidizing atmosphere and characterized by X-ray diffraction and fluorescence,scanning electron microscopy with microchemistry,BET surface area,thermal gravimetric analysis,and differential scanning calorimetry.The pH and apparent density were also established.The results show high basicity materials(average pH 10),a behavior associated with the presence of chemical elements such as calcium,potassium,magnesium,chlorine,phosphorus,and sulfur.Structurally,these materials have a very significant amorphous fraction(between 49%and 74.5%),the dominant crystalline phases are calcite,arcanite,sylvite,and hydroxyapatite.These ashes have low surface area and do not exceed 13 m^(2)/g.Two characteristic morphological aspects were observed in these ashes:a morphology of rounded grains where silicon content is highlighted,and lamellar morphologies where the presence of chlorine is highlighted.Thermally,these ashes show four significant mass loss events(400℃,430℃,680℃,and 920℃),causing mass losses that vary between 25%and 40%.Through this study,it was possible to establish that,from a chemical point of view,these ashes are less dangerous in comparison with those of a mineral coal that was used as a reference.However,they require additional treatments for their disposal due to their high basicity.Because of their composition,these ashes have the potential to be used in the ceramic and cement industries,and in the manufacture of fertilizers.
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2022-0-00377,Development of Intelligent Analysis and Classification Based Contents Class Categorization Technique to Prevent Imprudent Harmful Media Distribution).
文摘Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs.
