Learning long-term temporal contexts using skip RNN for continuous emotion recognition

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.

Influence of Recent Trial History on Interval Timing

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.

An improved urban cellular automata model by using the trend-adjusted neighborhood

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.