Temporal link prediction has attracted increasingattention in various fields of complex networkanalysis, which has important value in the theory andapplication. However, many existing similarity-basedtemporal link pre...Temporal link prediction has attracted increasingattention in various fields of complex networkanalysis, which has important value in the theory andapplication. However, many existing similarity-basedtemporal link prediction methods, only analyze the influenceof the edge or the point, ignoring the influenceof the structures in the network. In this paper, boththe spatial-domain model and the time-domain modelare taken into consideration, and a novel temporal linkprediction method based on the evolution of motif features(TLP-EMF) is proposed. Firstly, a new generalizedsemi-triangle motif is proposed. And the multilevelcontribution of motif point (MP) and motif edge(ME) are described, which is based on the relationshipbetween the full-triangle and the semi-triangle. Secondly,the motif point density (MPD) index and themotif edge density (MED) index are also proposed ina similar way. Thirdly, a novel motif character fusionindex (MCF) and a novel motif character density index(MCD) are proposed for the spatial-informationprocessing. Furthermore, a novel forecasting model ofthe adaptive exponential weighted moving (AEWM)method is proposed for the time-domain evolution. Ituses the one-order exponential function to fit the effectof time evolution and uses the global attenuationparameter to adaptively quantify the changes in exponentialparameters. Experiments on three real social network data sets show that the proposed method caneffectively improve the accuracy of temporal link prediction.展开更多
In this study,we examined plant health prediction through the Normalized Difference Vegetation Index(NDVI)calculated from satellite image derived reflectance values in the near-infrared and red spectra.The problem is ...In this study,we examined plant health prediction through the Normalized Difference Vegetation Index(NDVI)calculated from satellite image derived reflectance values in the near-infrared and red spectra.The problem is formulated as a temporal data prediction problem.Using MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V061 dataset,we designed and implemented Reservoir Computing(RC)models and transformer-based models including pretrained language model,and compared the prediction performance of these models to traditional machine learning and deep learning methods such as Nonlinear Regression,Decision Tree,Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM)network,and DLinear.It is observed that the DLinear/LSTM model showed exceptional predictive accuracy,while the pretrained RC model significantly enhanced traditional RC model forecasts.Additionally,Frozen Pretrained Transformer(FPT),a pretrained language model,showed superior performance in predicting specific NDVI values(most often peak or lowest NDVI),suggesting its effectiveness in precise temporal predictions.Furthermore,transformer-based models,specifically PatchTST and FPT,demonstrated substantial mean squared error reductions,particularly in limited data scenarios(1%,5%,15%and 50%sample sizes),indicating their robustness in precise NDVI temporal predictions when data is limited.The findings in this study demonstrated the effectiveness of emerging machine learning techniques such as reservoir computing and pretrained language model for remote sensing and their contributions in precision agriculture.展开更多
This study examines whether a group of captive false killer whales(P seudorca crassidens) showed variations in the vocal rate around feeding times. The high level of motivation to express appetitive behaviors in capti...This study examines whether a group of captive false killer whales(P seudorca crassidens) showed variations in the vocal rate around feeding times. The high level of motivation to express appetitive behaviors in captive animals may lead them to respond with changes of the behavioral activities during the time prior to food deliveries which are referred to as food anticipatory activity. False killer whales at Qingdao Polar Ocean World(Qingdao, China) showed signifi cant variations of the rates of both the total sounds and sound classes(whistles, clicks, and burst pulses) around feedings. Precisely, from the Transition interval that recorded the lowest vocalization rate(3.40 s/m/d), the whales increased their acoustic emissions upon trainers' arrival(13.08 s/m/d). The high rate was maintained or intensifi ed throughout the food delivery(25.12 s/m/d), and then reduced immediately after the animals were fed(9.91 s/m/d). These changes in the false killer whales sound production rates around feeding times supports the hypothesis of the presence of a food anticipatory vocal activity. Although sound rates may not give detailed information regarding referential aspects of the animal communication it might still shed light about the arousal levels of the individuals during different social or environmental conditions. Further experiments should be performed to assess if variations of the time of feeding routines may affect the vocal activity of cetaceans in captivity as well as their welfare.展开更多
文摘Temporal link prediction has attracted increasingattention in various fields of complex networkanalysis, which has important value in the theory andapplication. However, many existing similarity-basedtemporal link prediction methods, only analyze the influenceof the edge or the point, ignoring the influenceof the structures in the network. In this paper, boththe spatial-domain model and the time-domain modelare taken into consideration, and a novel temporal linkprediction method based on the evolution of motif features(TLP-EMF) is proposed. Firstly, a new generalizedsemi-triangle motif is proposed. And the multilevelcontribution of motif point (MP) and motif edge(ME) are described, which is based on the relationshipbetween the full-triangle and the semi-triangle. Secondly,the motif point density (MPD) index and themotif edge density (MED) index are also proposed ina similar way. Thirdly, a novel motif character fusionindex (MCF) and a novel motif character density index(MCD) are proposed for the spatial-informationprocessing. Furthermore, a novel forecasting model ofthe adaptive exponential weighted moving (AEWM)method is proposed for the time-domain evolution. Ituses the one-order exponential function to fit the effectof time evolution and uses the global attenuationparameter to adaptively quantify the changes in exponentialparameters. Experiments on three real social network data sets show that the proposed method caneffectively improve the accuracy of temporal link prediction.
基金supported by USDA/NIFA Award 2022-38821-37338(Project No.TEXXQRD9908).
文摘In this study,we examined plant health prediction through the Normalized Difference Vegetation Index(NDVI)calculated from satellite image derived reflectance values in the near-infrared and red spectra.The problem is formulated as a temporal data prediction problem.Using MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V061 dataset,we designed and implemented Reservoir Computing(RC)models and transformer-based models including pretrained language model,and compared the prediction performance of these models to traditional machine learning and deep learning methods such as Nonlinear Regression,Decision Tree,Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM)network,and DLinear.It is observed that the DLinear/LSTM model showed exceptional predictive accuracy,while the pretrained RC model significantly enhanced traditional RC model forecasts.Additionally,Frozen Pretrained Transformer(FPT),a pretrained language model,showed superior performance in predicting specific NDVI values(most often peak or lowest NDVI),suggesting its effectiveness in precise temporal predictions.Furthermore,transformer-based models,specifically PatchTST and FPT,demonstrated substantial mean squared error reductions,particularly in limited data scenarios(1%,5%,15%and 50%sample sizes),indicating their robustness in precise NDVI temporal predictions when data is limited.The findings in this study demonstrated the effectiveness of emerging machine learning techniques such as reservoir computing and pretrained language model for remote sensing and their contributions in precision agriculture.
基金Supported by grants from the Institute of Hydrobiology,Chinese Academy of Sciences
文摘This study examines whether a group of captive false killer whales(P seudorca crassidens) showed variations in the vocal rate around feeding times. The high level of motivation to express appetitive behaviors in captive animals may lead them to respond with changes of the behavioral activities during the time prior to food deliveries which are referred to as food anticipatory activity. False killer whales at Qingdao Polar Ocean World(Qingdao, China) showed signifi cant variations of the rates of both the total sounds and sound classes(whistles, clicks, and burst pulses) around feedings. Precisely, from the Transition interval that recorded the lowest vocalization rate(3.40 s/m/d), the whales increased their acoustic emissions upon trainers' arrival(13.08 s/m/d). The high rate was maintained or intensifi ed throughout the food delivery(25.12 s/m/d), and then reduced immediately after the animals were fed(9.91 s/m/d). These changes in the false killer whales sound production rates around feeding times supports the hypothesis of the presence of a food anticipatory vocal activity. Although sound rates may not give detailed information regarding referential aspects of the animal communication it might still shed light about the arousal levels of the individuals during different social or environmental conditions. Further experiments should be performed to assess if variations of the time of feeding routines may affect the vocal activity of cetaceans in captivity as well as their welfare.
