Mother’s Way

Mother’s way is my 2D graduate thesis film. The production phase of this film was between September 2018 to August 2019. This is a film about the sincere feelings between mother and son. The protagonist of this film is an old man who has been estranged from his family for decades. He decides to visit his mother after getting the news that she is seriously ill. During his journey, his misunderstandings between them are eradicated through his mother\u27s diary. Mother’s way is mainly made using the 2D animation software TVPaint. At different stages of production, Adobe Photoshop, Adobe After Effects, Adobe Premiere and Pro Tools were also used. The final output format is 1080P HD with a high-quality stereophonic track. In this thesis paper, I will describe more details behind the scenes in the chronological order of the whole production phase

Research on Spillover Effect of Paid Search Advertising Channels

With the diversification of paid search advertising channels, e-commerce enterprises are paying more and more attention on how to evaluate the effectiveness of different paid search advertising channels correctly and accurately to choose the optimal advertising channel or channels. We develop a multivariate time series model to investigate the spillover effect of paid search advertising channels based on the ad click-through rate and conversion rate, and calibrate the model using an e-commerce site\u27s web log data. We determine the long-term equilibrium relationship between each channel\u27s advertisement clicks through the co-integration test and evaluate the effect of short-term fluctuations in the interaction between each channel advertisement clicks through the vector error correction model. Based on the empirical results, this paper puts forward suggestions on the advertising strategy of this e-commerce website

Predictive Coding Based Multiscale Network with Encoder-Decoder LSTM for Video Prediction

We present a multi-scale predictive coding model for future video frames prediction. Drawing inspiration on the ``Predictive Coding" theories in cognitive science, it is updated by a combination of bottom-up and top-down information flows, which can enhance the interaction between different network levels. However, traditional predictive coding models only predict what is happening hierarchically rather than predicting the future. To address the problem, our model employs a multi-scale approach (Coarse to Fine), where the higher level neurons generate coarser predictions (lower resolution), while the lower level generate finer predictions (higher resolution). In terms of network architecture, we directly incorporate the encoder-decoder network within the LSTM module and share the final encoded high-level semantic information across different network levels. This enables comprehensive interaction between the current input and the historical states of LSTM compared with the traditional Encoder-LSTM-Decoder architecture, thus learning more believable temporal and spatial dependencies. Furthermore, to tackle the instability in adversarial training and mitigate the accumulation of prediction errors in long-term prediction, we propose several improvements to the training strategy. Our approach achieves good performance on datasets such as KTH, Moving MNIST and Caltech Pedestrian. Code is available at https://github.com/Ling-CF/MSPN

Projected Spatiotemporal Dynamics of Drought under Global Warming in Central Asia

Drought, one of the most common natural disasters that have the greatest impact on human social life, has been extremely challenging to accurately assess and predict. With global warming, it has become more important to make accurate drought predictions and assessments. In this study, based on climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we used the Palmer Drought Severity Index (PDSI) to analyze and project drought characteristics and their trends under two global warming scenarios—1.5 °C and 2.0 °C—in Central Asia. The results showed a marked decline in the PDSI in Central Asia under the influence of global warming, indicating that the drought situation in Central Asia would further worsen under both warming scenarios. Under the 1.5 °C warming scenario, the PDSI in Central Asia decreased first and then increased, and the change time was around 2080, while the PDSI values showed a continuous decline after 2025 in the 2.0 °C warming scenario. Under the two warming scenarios, the spatial characteristics of dry and wet areas in Central Asia are projected to change significantly in the future. In the 1.5 °C warming scenario, the frequency of drought and the proportion of arid areas in Central Asia were significantly higher than those under the 2.0 °C warming scenario. Using the Thornthwaite (TH) formula to calculate the PDSI produced an overestimation of drought, and the Penman–Monteith (PM) formula is therefore recommended to calculate the index

Interdecadal changes on the seasonal prediction of the western North Pacific summer climate around the late 1970s and early 1990s

Identifying predictability and the corresponding sources for the western North Pacific (WNP) summer climate in the case of non-stationary teleconnections during recent decades benefits for further improvements of long-range prediction on the WNP and East Asian summers. In the past few decades, pronounced increases on the summer sea surface temperature (SST) and associated interannual variability are observed over the tropical Indian Ocean and eastern Pacific around the late 1970s and over the Maritime Continent and western–central Pacific around the early 1990s. These increases are associated with significant enhancements of the interannual variability for the lower-tropospheric wind over the WNP. In this study, we further assess interdecadal changes on the seasonal prediction of the WNP summer anomalies, using May-start retrospective forecasts from the ENSEMBLES multi-model project in the period 1960–2005. It is found that prediction of the WNP summer anomalies exhibits an interdecadal shift with higher prediction skills since the late 1970s, particularly after the early 1990s. Improvements of the prediction skills for SSTs after the late 1970s are mainly found around tropical Indian Ocean and the WNP. The better prediction of the WNP after the late 1970s may arise mainly from the improvement of the SST prediction around the tropical eastern Indian Ocean. The close teleconnections between the tropical eastern Indian Ocean and WNP summer variability work both in the model predictions and observations. After the early 1990s, on the other hand, the improvements are detected mainly around the South China Sea and Philippines for the lower-tropospheric zonal wind and precipitation anomalies, associating with a better description of the SST anomalies around the Maritime Continent. A dipole SST pattern over the Maritime Continent and the central equatorial Pacific Ocean is closely related to the WNP summer anomalies after the early 1990s. This teleconnection mode is quite predictable, which is realistically reproduced by the models, presenting more predictable signals to the WNP summer climate after the early 1990s

Interpretable Image Recognition with Hierarchical Prototypes

Vision models are interpretable when they classify objects on the basis of features that a person can directly understand. Recently, methods relying on visual feature prototypes have been developed for this purpose. However, in contrast to how humans categorize objects, these approaches have not yet made use of any taxonomical organization of class labels. With such an approach, for instance, we may see why a chimpanzee is classified as a chimpanzee, but not why it was considered to be a primate or even an animal. In this work we introduce a model that uses hierarchically organized prototypes to classify objects at every level in a predefined taxonomy. Hence, we may find distinct explanations for the prediction an image receives at each level of the taxonomy. The hierarchical prototypes enable the model to perform another important task: interpretably classifying images from previously unseen classes at the level of the taxonomy to which they correctly relate, e.g. classifying a hand gun as a weapon, when the only weapons in the training data are rifles. With a subset of ImageNet, we test our model against its counterpart black-box model on two tasks: 1) classification of data from familiar classes, and 2) classification of data from previously unseen classes at the appropriate level in the taxonomy. We find that our model performs approximately as well as its counterpart black-box model while allowing for each classification to be interpreted.Comment: Published as a full paper at HCOMP 201

Changes in the relationship between ENSO and the East Asian winter monsoon under global warming

Changes in the relationship between El Niño-Southern Oscillation (ENSO) and the East Asian winter monsoon (EAWM) at various global warming levels during the 21st century are examined using the Max Planck Institute Grand Ensemble Representative Concentration Pathway 8.5 experiments. The externally forced component of this relationship (i.e. forced by greenhouse gases and anthropogenic aerosols emissions) strengthens from present-day to +1.5 °C, and then weakens until +3 °C. These changes are characterized by variations in strength and location of the core of El Niño-related warming and associated deep convection anomalies over the equatorial Pacific leading to circulation anomalies across the Asian-Pacific region. Under global warming, the ENSO–EAWM relationship is strongly related to the background mean state of both the EAWM and ENSO, through changes in the EAWM strength and the shift of the ENSO pattern. Anthropogenic aerosols play a key role in influencing the ENSO–EAWM relationship under moderate warming (up to 1.5 °C)