Background Continuous emotion recognition as a function of time assigns emotional values to every frame in a sequence.Incorporating long-term temporal context information is essential for continuous emotion recognitio...Background Continuous emotion recognition as a function of time assigns emotional values to every frame in a sequence.Incorporating long-term temporal context information is essential for continuous emotion recognition tasks.Methods For this purpose,we employ a window of feature frames in place of a single frame as inputs to strengthen the temporal modeling at the feature level.The ideas of frame skipping and temporal pooling are utilized to alleviate the resulting redundancy.At the model level,we leverage the skip recurrent neural network to model the long-term temporal variability by skipping trivial information for continuous emotion recognition.Results The experimental results using the AVEC 2017 database demonstrate that our proposed methods are beneficial to a performance improvement.Further,the skip long short-term memory(LSTM)model can focus on the critical emotional state when training the models,thereby achieving a better performance than the LSTM model and other methods.展开更多
Predicting human motion based on historical motion sequences is a fundamental problem in computer vision,which is at the core of many applications.Existing approaches primarily focus on encoding spatial dependencies a...Predicting human motion based on historical motion sequences is a fundamental problem in computer vision,which is at the core of many applications.Existing approaches primarily focus on encoding spatial dependencies among human joints while ignoring the temporal cues and the complex relationships across non-consecutive frames.These limitations hinder the model’s ability to generate accurate predictions over longer time horizons and in scenarios with complex motion patterns.To address the above problems,we proposed a novel multi-level spatial and temporal learning model,which consists of a Cross Spatial Dependencies Encoding Module(CSM)and a Dynamic Temporal Connection Encoding Module(DTM).Specifically,the CSM is designed to capture complementary local and global spatial dependent information at both the joint level and the joint pair level.We further present DTM to encode diverse temporal evolution contexts and compress motion features to a deep level,enabling the model to capture both short-term and long-term dependencies efficiently.Extensive experiments conducted on the Human 3.6M and CMU Mocap datasets demonstrate that our model achieves state-of-the-art performance in both short-term and long-term predictions,outperforming existing methods by up to 20.3% in accuracy.Furthermore,ablation studies confirm the significant contributions of the CSM and DTM in enhancing prediction accuracy.展开更多
Interval timing is involved in a variety of cognitive behaviors such as associative learning and decision-making.While it has been shown that time estimation is adaptive to the temporal context,it remains unclear how ...Interval timing is involved in a variety of cognitive behaviors such as associative learning and decision-making.While it has been shown that time estimation is adaptive to the temporal context,it remains unclear how interval timing behavior is influenced by recent trial history.Here we found that,in mice trained to perform a licking-based interval timing task,a decrease of inter-reinforcement interval in the previous trial rapidly shifted the time of anticipatory licking earlier.Optogenetic inactivation of the anterior lateral motor cortex(ALM),but not the medial prefrontal cortex,for a short time before reward delivery caused a decrease in the peak time of anticipatory licking in the next trial.Electrophysiological recordings from the ALM showed that the response profiles preceded by short and long inter-reinforcement intervals exhibited task-engagement-dependent temporal scaling.Thus,interval timing is adaptive to recent experience of the temporal interval,and ALM activity during time estimation reflects recent experience of interval.展开更多
Background:Cellular automata(CA)-based models have been extensively used in urban sprawl modeling.Presently,most studies focused on the improvement of spatial representation in the modeling,with limited efforts for co...Background:Cellular automata(CA)-based models have been extensively used in urban sprawl modeling.Presently,most studies focused on the improvement of spatial representation in the modeling,with limited efforts for considering the temporal context of urban sprawl.In this paper,we developed a Logistic-Trend-CA model by proposing a trend-adjusted neighborhood as a weighting factor using the information of historical urban sprawl and integrating this factor in the commonly used Logistic-CA model.We applied the developed model in the Beijing-Tianjin-Hebei region of China and analyzed the model performance to the start year,the suitability surface,and the neighborhood size.Results:Our results indicate the proposed Logistic-Trend-CA model outperforms the traditional Logistic-CA model significantly,resulting in about 18%and 14%improvements in modeling urban sprawl at medium(1 km)and fine(30 m)resolutions,respectively.The proposed Logistic-Trend-CA model is more suitable for urban sprawl modeling over a long temporal interval than the traditional Logistic-CA model.In addition,this new model is not sensitive to the suitability surface calibrated from different periods and spaces,and its performance decreases with the increase of the neighborhood size.Conclusion:The proposed model shows potential for modeling future urban sprawl spanning a long period at regional and global scales.展开更多
基金the National Key Research&Development Plan of China(2017YFB1002804)the National Natural Science Foundation of China(NSFC)(61831022,61771472,61773379,61901473).
文摘Background Continuous emotion recognition as a function of time assigns emotional values to every frame in a sequence.Incorporating long-term temporal context information is essential for continuous emotion recognition tasks.Methods For this purpose,we employ a window of feature frames in place of a single frame as inputs to strengthen the temporal modeling at the feature level.The ideas of frame skipping and temporal pooling are utilized to alleviate the resulting redundancy.At the model level,we leverage the skip recurrent neural network to model the long-term temporal variability by skipping trivial information for continuous emotion recognition.Results The experimental results using the AVEC 2017 database demonstrate that our proposed methods are beneficial to a performance improvement.Further,the skip long short-term memory(LSTM)model can focus on the critical emotional state when training the models,thereby achieving a better performance than the LSTM model and other methods.
基金supported by the Urgent Need for Overseas Talent Project of Jiangxi Province(Grant No.20223BCJ25040)the Thousand Talents Plan of Jiangxi Province(Grant No.jxsg2023101085)+3 种基金the National Natural Science Foundation of China(Grant No.62106093)the Natural Science Foundation of Jiangxi(Grant Nos.20224BAB212011,20232BAB212008,20242BAB25078,and 20232BAB202051)The Youth Talent Cultivation Innovation Fund Project of Nanchang University(Grant No.XX202506030015)funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R759),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Predicting human motion based on historical motion sequences is a fundamental problem in computer vision,which is at the core of many applications.Existing approaches primarily focus on encoding spatial dependencies among human joints while ignoring the temporal cues and the complex relationships across non-consecutive frames.These limitations hinder the model’s ability to generate accurate predictions over longer time horizons and in scenarios with complex motion patterns.To address the above problems,we proposed a novel multi-level spatial and temporal learning model,which consists of a Cross Spatial Dependencies Encoding Module(CSM)and a Dynamic Temporal Connection Encoding Module(DTM).Specifically,the CSM is designed to capture complementary local and global spatial dependent information at both the joint level and the joint pair level.We further present DTM to encode diverse temporal evolution contexts and compress motion features to a deep level,enabling the model to capture both short-term and long-term dependencies efficiently.Extensive experiments conducted on the Human 3.6M and CMU Mocap datasets demonstrate that our model achieves state-of-the-art performance in both short-term and long-term predictions,outperforming existing methods by up to 20.3% in accuracy.Furthermore,ablation studies confirm the significant contributions of the CSM and DTM in enhancing prediction accuracy.
基金supported by the National Science and Technology Innovation 2030 Major Program of China(2021ZD0203700/2021ZD0203703)the National Natural Science Foundation of China(31771151 and 32171030)+2 种基金Lingang Lab(LG202104-01-03)a Shanghai Municipal Science and Technology Major Project(2018SHZDZX05)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB32010200)。
文摘Interval timing is involved in a variety of cognitive behaviors such as associative learning and decision-making.While it has been shown that time estimation is adaptive to the temporal context,it remains unclear how interval timing behavior is influenced by recent trial history.Here we found that,in mice trained to perform a licking-based interval timing task,a decrease of inter-reinforcement interval in the previous trial rapidly shifted the time of anticipatory licking earlier.Optogenetic inactivation of the anterior lateral motor cortex(ALM),but not the medial prefrontal cortex,for a short time before reward delivery caused a decrease in the peak time of anticipatory licking in the next trial.Electrophysiological recordings from the ALM showed that the response profiles preceded by short and long inter-reinforcement intervals exhibited task-engagement-dependent temporal scaling.Thus,interval timing is adaptive to recent experience of the temporal interval,and ALM activity during time estimation reflects recent experience of interval.
基金This study was supported by the National Science Foundation(CBET-1803920).
文摘Background:Cellular automata(CA)-based models have been extensively used in urban sprawl modeling.Presently,most studies focused on the improvement of spatial representation in the modeling,with limited efforts for considering the temporal context of urban sprawl.In this paper,we developed a Logistic-Trend-CA model by proposing a trend-adjusted neighborhood as a weighting factor using the information of historical urban sprawl and integrating this factor in the commonly used Logistic-CA model.We applied the developed model in the Beijing-Tianjin-Hebei region of China and analyzed the model performance to the start year,the suitability surface,and the neighborhood size.Results:Our results indicate the proposed Logistic-Trend-CA model outperforms the traditional Logistic-CA model significantly,resulting in about 18%and 14%improvements in modeling urban sprawl at medium(1 km)and fine(30 m)resolutions,respectively.The proposed Logistic-Trend-CA model is more suitable for urban sprawl modeling over a long temporal interval than the traditional Logistic-CA model.In addition,this new model is not sensitive to the suitability surface calibrated from different periods and spaces,and its performance decreases with the increase of the neighborhood size.Conclusion:The proposed model shows potential for modeling future urban sprawl spanning a long period at regional and global scales.
