Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in hi...Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.展开更多
Background:Post-mortem and magnetic resonance imaging(MRI)studies of the central sulcus,as an indicator of motor cortex,have shown that in the general population there is greater representation of the dominant compare...Background:Post-mortem and magnetic resonance imaging(MRI)studies of the central sulcus,as an indicator of motor cortex,have shown that in the general population there is greater representation of the dominant compared to the non-dominant hand.Studies of musicians,who are highly skilled in performing complex finger movements,have suggested this dominance is affected by musical training,but methods and findings have been mixed.Objective:In the present study,an automated image analysis pipeline using a 3D mesh approach was applied to measure central sulcus(CS)asymmetry on MR images obtained for a cohort of right-handed pianists and matched controls.Methods:The depth,length,and surface area(SA)of the CS and thickness of the cortical mantle adjacent to the CS were measured in each cerebral hemisphere by applying the BrainVISA Morphologist 2012 software pipeline to 3D T1-weighted MR images of the brain obtained for 15 right-handed pianists and 14 controls,matched with respect to age,sex,and handedness.Asymmetry indices(AIs)were calculated for each parameter and multivariate analysis of covariance(MANCOVA),and post hoc tests were performed to compare differences between the pianist and control groups.Results:A one-way MANCOVA across the four AIs,controlling for age and sex,revealed a significant main effect of group(P=0.04),and post hoc analysis revealed that while SA was significantly greater in the left than the right cerebral hemisphere in controls(P<0.001),there was no significant difference between left and right SA in the pianists(P=0.634).Independent samples t-tests revealed that the SA of right CS was significantly larger in pianists compared to controls(P=0.015),with no between-group differences in left CS.Conclusions:Application of an image analysis pipeline to 3D MR images has provided robust evidence of sig-nificantly increased representation of the non-dominant hand in the brain of pianists compared to age-,sex-,and handedness-matched controls.This finding supports prior research showing structural differences in the central sulcus in musicians and is interpreted to reflect the long-term motor training and high skill level of right-handed pianists in using their left hand.展开更多
Background:A long-haul flight across more than five time zones may produce a circadian rhythm sleep disorder known as jet lag.Little is known about the effect of jet lag on white matter(WM)functional connectivity(FC)....Background:A long-haul flight across more than five time zones may produce a circadian rhythm sleep disorder known as jet lag.Little is known about the effect of jet lag on white matter(WM)functional connectivity(FC).Objective:The present study is to investigate changes in WM FC in subjects due to recovery from jet lag after flying across six time zones.Methods:Here,resting-state functional magnetic resonance imaging was performed in 23 participants within 24 hours of flying and again 50 days later.Gray matter(GM)and WM networks were identified by k-means clustering.WM FC and functional covariance connectivity(FCC)were analyzed.Next,a sliding window method was used to establish dynamic WM FC.WM static and dynamic FC and FCC were compared between when participants had initially completed their journey and 50 days later.Emotion was assessed using the Positive and Negative Affect Schedule and the State Anxiety Inventory.Results:All participants were confirmed to have jet lag symptoms by the Columbian Jet Lag Scale.The static FC strengthes of cingulate network(WM7)-sensorimotor network and ventral frontal network-visual network were lower after the long-haul flight compared with recovery.Corresponding results were obtained for the dynamic FC analysis.The analysis of FCC revealed weakened connections between the WM7 and several other brain networks,especially the precentral/postcentral network.Moreover,a negative correlation was found between emotion scores and the FC between the WM7 and sensorimotor related regions.Conclusions:The results of this study provide further evidence for the existence of WM networks and show that jet lag is associated with alterations in static and dynamic WM FC and FCC,especially in sensorimotor networks.Jet lag is a complex problem that not only is related to sleep rhythm but also influences emotion.展开更多
基金supported by the ‘‘Detection of very low-flux background neutrons in China Jinping Underground Laboratory’’ project of the National Natural Science Foundation of China(No.11275134)
文摘Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.
基金Research assistance from Dr.Emma Moore and funding from the European Commission under the Marie Curie Intra-European Fellowship Programme(EC FP7-2010-PEOPLE-IEF 276529)the Marie Curie Action Networks for Initial Training(EC PITN-GA-2009-238157-EBRAMUS)the Society for Education,Music and Psychology Research(SEMPRE)and by the Reid School of Music,Edinburgh College of Art,University of Edinburgh is gratefully acknowledged.
文摘Background:Post-mortem and magnetic resonance imaging(MRI)studies of the central sulcus,as an indicator of motor cortex,have shown that in the general population there is greater representation of the dominant compared to the non-dominant hand.Studies of musicians,who are highly skilled in performing complex finger movements,have suggested this dominance is affected by musical training,but methods and findings have been mixed.Objective:In the present study,an automated image analysis pipeline using a 3D mesh approach was applied to measure central sulcus(CS)asymmetry on MR images obtained for a cohort of right-handed pianists and matched controls.Methods:The depth,length,and surface area(SA)of the CS and thickness of the cortical mantle adjacent to the CS were measured in each cerebral hemisphere by applying the BrainVISA Morphologist 2012 software pipeline to 3D T1-weighted MR images of the brain obtained for 15 right-handed pianists and 14 controls,matched with respect to age,sex,and handedness.Asymmetry indices(AIs)were calculated for each parameter and multivariate analysis of covariance(MANCOVA),and post hoc tests were performed to compare differences between the pianist and control groups.Results:A one-way MANCOVA across the four AIs,controlling for age and sex,revealed a significant main effect of group(P=0.04),and post hoc analysis revealed that while SA was significantly greater in the left than the right cerebral hemisphere in controls(P<0.001),there was no significant difference between left and right SA in the pianists(P=0.634).Independent samples t-tests revealed that the SA of right CS was significantly larger in pianists compared to controls(P=0.015),with no between-group differences in left CS.Conclusions:Application of an image analysis pipeline to 3D MR images has provided robust evidence of sig-nificantly increased representation of the non-dominant hand in the brain of pianists compared to age-,sex-,and handedness-matched controls.This finding supports prior research showing structural differences in the central sulcus in musicians and is interpreted to reflect the long-term motor training and high skill level of right-handed pianists in using their left hand.
基金supported by the National Natural Science Foundation(Grant Nos.81771812,81971595,81621003,81820108018,and 81901828)the Innovation Spark Project of Sichuan University(No.2019SCUH0003).
文摘Background:A long-haul flight across more than five time zones may produce a circadian rhythm sleep disorder known as jet lag.Little is known about the effect of jet lag on white matter(WM)functional connectivity(FC).Objective:The present study is to investigate changes in WM FC in subjects due to recovery from jet lag after flying across six time zones.Methods:Here,resting-state functional magnetic resonance imaging was performed in 23 participants within 24 hours of flying and again 50 days later.Gray matter(GM)and WM networks were identified by k-means clustering.WM FC and functional covariance connectivity(FCC)were analyzed.Next,a sliding window method was used to establish dynamic WM FC.WM static and dynamic FC and FCC were compared between when participants had initially completed their journey and 50 days later.Emotion was assessed using the Positive and Negative Affect Schedule and the State Anxiety Inventory.Results:All participants were confirmed to have jet lag symptoms by the Columbian Jet Lag Scale.The static FC strengthes of cingulate network(WM7)-sensorimotor network and ventral frontal network-visual network were lower after the long-haul flight compared with recovery.Corresponding results were obtained for the dynamic FC analysis.The analysis of FCC revealed weakened connections between the WM7 and several other brain networks,especially the precentral/postcentral network.Moreover,a negative correlation was found between emotion scores and the FC between the WM7 and sensorimotor related regions.Conclusions:The results of this study provide further evidence for the existence of WM networks and show that jet lag is associated with alterations in static and dynamic WM FC and FCC,especially in sensorimotor networks.Jet lag is a complex problem that not only is related to sleep rhythm but also influences emotion.
