@article{wu2021bark,title={Deep BarkID: A Portable Tree Bark Identification System by Knowledge Distillation},author={Wu, Fanyou and Gazo, Rado and Benes, Bedrich and Haviarova, Eva},journal={European Journal of Forest Research},year={2021},doi={10.1007/s10342-021-01407-7}}
Species identification is one of the key steps in the management and conservation planning of many forest ecosystems. We introduce Deep BarkID, a portable tree identification system that detects tree species from bark images. Existing bark identification systems rely heavily on massive computing power access, which may be scarce in many locations. Our approach is deployed as a smartphone application that does not require any connection to a database. Its intended use is in a forest, where internet connection is often unavailable. The tree bark identification is expressed as a bark image classification task, and it is implemented as a convolutional neural network (CNN). This research focuses on developing light-weight CNN models through knowledge distillation. Overall, we achieved 96.12% accuracy for tree species classification tasks for ten common tree species in Indiana, USA. We also captured and prepared thousands of bark images—a dataset that we call Indiana Bark Dataset—and we make it available at https://github.com/wufanyou/DBID.
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Wood Identification Based on Longitudinal Section Images by Using Deep Learning
Fanyou Wu,
Rado Gazo,
Eva Haviarova,
Bedrich Benes
@article{wu2021wood,title={Wood Identification Based on Longitudinal Section Images by Using Deep Learning},author={Wu, Fanyou and Gazo, Rado and Haviarova, Eva and Benes, Bedrich},journal={Wood Science and Technology},year={2021},doi={10.1007/s00226-021-01261-1},pages={553-563},volume={55},number={2}}
Automatic species identification has the potential to improve the efficacy and automation of wood processing systems significantly. Recent advances in deep learning allowed for the automation of many previously difficult tasks, and in this paper, we investigate the feasibility of using Deep Convolutional Neural Networks (CNNs) for hardwood lumber identification. In particular, we tested two highly effective CNNs (ResNet-50 and DenseNet-121) as well as lightweight MobileNet-V2. Overall, we achieved 98.2% accuracy for 11 common hardwood species classification tasks.
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Behavior2vector: Embedding Users' Personalized Travel Behavior to Vector
Yang Liu,
Fanyou Wu,
Cheng Lyu,
Xin Liu,
Zhiyuan Liu
IEEE Transactions on Intelligent Transportation Systems
2021
@article{liu2021behavior2vector,author={Liu, Yang and Wu, Fanyou and Lyu, Cheng and Liu, Xin and Liu, Zhiyuan},journal={IEEE Transactions on Intelligent Transportation Systems},title={Behavior2vector: Embedding Users' Personalized Travel Behavior to Vector},year={2021},doi={10.1109/TITS.2021.3078229}}
We investigate how to effectively and efficiently embed users' personalized travel behaviors to vectors in this paper. Based on an example scenario of travel mode choice in intelligent transportation system, three data structures representing users' travel behaviors are defined, namely heterogeneous graph of users' travel behaviors, user travel behavior k-partite graph, and personalized user travel behavior sentence set. This paper systematically analyzes the principle of existing methods and provides intuitions for the problem of learning travel behavior representation in intelligent transportation system. Then we propose the Behavior2vector, which is an improved method tailored for embedding users' personalized travel behaviors to vectors. In our experiments, we design a travel mode choice model based on machine learning, which uses both hand-crafted basic features and embedded vector features. We further quantify the impact of various factors on travel mode choice and use travel big data to test the hypothesis of traffic assignment models, e.g., travelers always choose the path with the shortest path. In addition, we also compared with the existing graph embedding methods and essentially discussed their advantages and disadvantages.
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A hybrid method with rules and optimization to solve the last-mile delivery problems
Fanyou Wu,
Yang Liu
In Technical Proceedings of the Amazon Last Mile Routing Research Challenge
2021
@inproceedings{wu2021lastmile,title={A hybrid method with rules and optimization to solve the last-mile delivery problems},author={Wu, Fanyou and Liu, Yang},booktitle={Technical Proceedings of the Amazon Last Mile Routing Research Challenge},year={2021},editor={Winkenbach, Matthias and Parks, Steven and Noszek, Joseph}}
Using historical data to help route planning is significant since the real world is complicated, and the quality of a route is not only defined by its theoretical cost. This report proposed a two-step method that involved learning history and performing classic solutions to vehicle routing problems (VRP). Specifically, the task of the Last Mile Routing Research Challenge is decomposed into two steps: 1) predicting zone level sequence and 2) perform VRP within a single zone. Our method reaches 0.042 locally based on a train test split. The code can be found https://github.com/wufanyou/TLab-Last-Mile.
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Learning to Reposition on an Online Taxi-hailing Platform
Yang Liu,
Fanyou Wu,
Cheng Lyu,
Zhiyuan Liu,
Jiepin Ye
Under review ( Transportation Research: Part C )
2021
@article{liu2021learning,title={Learning to Reposition on an Online Taxi-hailing Platform},author={Liu, Yang and Wu, Fanyou and Lyu, Cheng and Liu, Zhiyuan and Ye, Jiepin},journal={Under review ( Transportation Research: Part C )},year={2021}}
The vehicle dispatching system is one of the most critical problems in online taxi-hailing platforms, which requires adapting the operation and management strategy to the dynamics of demand and supply. In this paper, we propose a single-agent deep reinforcement learning approach for the vehicle repositioning problem by reallocating vacant vehicles to regions with a large demand gap in advance. The simulator and the vehicle repositioning algorithm are designed based on industrial-scale real-world data and the workflow of online taxi-hailing platforms, ensuring the practical value of our approach. Besides, the vehicle repositioning problem is translated in analogy with the load balancing problem in computers. Inspired by the recommendation system, the high concurrency of repositioning requests is addressed by sorting the actions as a recommendation list, whereby matching action with requests. Experiments demonstrate that the proposed approach is superior to the existing ones. It is also worth noting that the proposed approach won first place in the vehicle repositioning task of KDD Cup 2020.
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Traffic4cast at NeurIPS 2020 - yet more on the unreasonable effectiveness of gridded geo-spatial processes
Michael Kopp,
David Kreil,
Moritz Neun,
David Jonietz,
Henry Martin,
Pedro Herruzo,
Aleksandra Gruca,
Ali Soleymani,
Fanyou Wu,
Yang Liu,
Jingwei Xu,
Jianjin Zhang,
Jay Santokhi,
Alabi Bojesomo,
Hasan Al Marzouqi,
Panos Liatsis,
Pak Hay Kwok,
Qi Qi,
Sepp Hochreiter
In Proceedings of the NeurIPS 2020 Competition and Demonstration Track
2021
@inproceedings{Kopp2021,title={Traffic4cast at NeurIPS 2020 - yet more on the unreasonable effectiveness of gridded geo-spatial processes},author={Kopp, Michael and Kreil, David and Neun, Moritz and Jonietz, David and Martin, Henry and Herruzo, Pedro and Gruca, Aleksandra and Soleymani, Ali and Wu, Fanyou and Liu, Yang and Xu, Jingwei and Zhang, Jianjin and Santokhi, Jay and Bojesomo, Alabi and Marzouqi, Hasan Al and Liatsis, Panos and Kwok, Pak Hay and Qi, Qi and Hochreiter, Sepp},booktitle={Proceedings of the NeurIPS 2020 Competition and Demonstration Track},pages={325--343},year={2021},editor={Escalante, Hugo Jair and Hofmann, Katja},volume={133},series={Proceedings of Machine Learning Research},month={06--12 Dec},publisher={PMLR}}
The IARAI Traffic4cast competition at NeurIPS 2019 showed that neural networks can successfully predict future traffic conditions 15 minutes into the future on simply aggregated GPS probe data in time and space bins, thus interpreting the challenge of forecasting traffic conditions as a movie completion task. U-nets proved to be the winning architecture then, demonstrating an ability to extract relevant features in the complex, real-world, geo-spatial process that is traffic derived from a large data set. The IARAI Traffic4cast challenge at NeurIPS 2020 build on the insights of the previous year and sought to both challenge some assumptions inherent in our 2019 competition design and explore how far this neural network technique can be pushed. We found that the prediction horizon can be extended successfully to 60 minutes into the future, that there is further evidence that traffic depends more on recent dynamics than on the additional static or dynamic location specific data provided and that a reasonable starting point when exploring a general aggregated geo-spatial process in time and space is a U-net architecture.
2020
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TLab: Traffic Map Movie Forecasting Based on HR-NET
Fanyou Wu,
Yang Liu,
Zhiyuan Liu,
Xiaobo Qu,
Rado Gazo,
Eva Haviarova
@article{wu2020tlab,title={TLab: Traffic Map Movie Forecasting Based on HR-NET},author={Wu, Fanyou and Liu, Yang and Liu, Zhiyuan and Qu, Xiaobo and Gazo, Rado and Haviarova, Eva},journal={arXiv preprint arXiv:2011.07728},year={2020},arxiv={2011.07728}}
The problem of the effective prediction for large-scale spatio-temporal traffic data has long haunted researchers in the field of intelligent transportation. Limited by the quantity of data, citywide traffic state prediction was seldom achieved. Hence the complex urban transportation system of an entire city cannot be truly understood. Thanks to the efforts of organizations like IARAI, the massive open data provided by them has made the research possible. In our 2020 Competition solution, we further design multiple variants based on HR-NET and UNet. Through feature engineering, the hand-crafted features are input into the model in a form of channels. It is worth noting that, to learn the inherent attributes of geographical locations, we proposed a novel method called geo-embedding, which contributes to significant improvement in the accuracy of the model. In addition, we explored the influence of the selection of activation functions and optimizers, as well as tricks during model training on the model performance. In terms of prediction accuracy, our solution has won 2nd place in NeurIPS 2020, Traffic4cast Challenge.
2019
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Efficient Project Gradient Descent for Ensemble Adversarial Attack
Fanyou Wu,
Rado Gazo,
Eva Haviarova,
Bedrich Benes
@article{wu2019efficient,title={Efficient Project Gradient Descent for Ensemble Adversarial Attack},author={Wu, Fanyou and Gazo, Rado and Haviarova, Eva and Benes, Bedrich},journal={arXiv preprint arXiv:1906.03333},arxiv={1906.03333},year={2019}}
Recent advances show that deep neural networks are not robust to deliberately crafted adversarial examples which many are generated by adding human imperceptible perturbation to clear input. Consider l2 norms attacks, Project Gradient Descent (PGD) and the Carlini and Wagner (C&W) attacks are the two main methods, where PGD control max perturbation for adversarial examples while C&W approach treats perturbation as a regularization term optimized it with loss function together. If we carefully set parameters for any individual input, both methods become similar. In general, PGD attacks perform faster but obtains larger perturbation to find adversarial examples than the C&W when fixing the parameters for all inputs. In this report, we propose an efficient modified PGD method for attacking ensemble models by automatically changing ensemble weights and step size per iteration per input. This method generates smaller perturbation adversarial examples than PGD method while remains efficient as compared to C&W method. Our method won the first place in IJCAI19 Targeted Adversarial Attack competition.
@article{wu2017phase,author={Wu, Fanyou and Kärenlampi, Petri P},title={Phase transition in a growing network},journal={Journal of Complex Networks},volume={6},number={5},pages={788-799},year={2017},month=dec,issn={2051-1329},doi={10.1093/comnet/cnx058},month_numeric={12}}
We present a probabilistic model for network growth with preferential attachment and self-attractivity. Instead of connecting to a predetermined number of existing nodes, any new node makes a finite number of attempts to connect to previous ones, any trial having a finite probability of success. We find a percolation phase transition which significantly differs from that of related models. Node degree distribution appears scale-free for large degrees, the exponent depending on system connectivity. Cluster size distribution becomes scale-free at the phase transition. Cluster diameter in percolating clusters increases logarithmically with cluster size, but becomes a power-law function at the phase transition. Counted number of boxes becomes exponentially reduced with box size, indicating non-fractal cluster geometry in the connected phase. The geometry however becomes fractal at the phase transition, with power-law exponent 2.