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Submitted
Saeed, Ahmed, Harras, Khaled, Zegura, Ellen, Ammar, Mostafa.  Submitted.  Local and Low-cost Whitespace Detection. Proc. IEEE International Conference on Distributed Computing Systems}, issue date = {June 20017.
2017
Yang, Shan, Liang, Junbang, Lin, Ming C..  2017.  Learning-Based Cloth Material Recovery From Video. The IEEE International Conference on Computer Vision (ICCV).
J. Zhao, C. K. Chang, L. Itti.  2017.  Learning to Recognize Objects by Retaining other Factors of Variation. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA. :1-9.

Most ConvNets formulate object recognition from natural images as a single task classification problem, and attempt to learn features useful for object categories, but invariant to other factors of variation such as pose and illumination. They do not explicitly learn these other factors; instead, they usually discard them by pooling and normalization. Here, we take the opposite approach: we train ConvNets for object recognition by retaining other factors (pose in our case) and learning them jointly with object category. We design a new multi-task leaning (MTL) ConvNet, named disentangling CNN (disCNN), which explicitly enforces the disentangled representations of object identity and pose, and is trained to predict object categories and pose transformations. disCNN achieves significantly better object recognition accuracies than the baseline CNN trained solely to predict object categories on the iLab-20M dataset, a large-scale turntable dataset with detailed pose and lighting information. We further show that the pretrained features on iLab-20M generalize to both Washington RGB-D and ImageNet datasets, and the pretrained disCNN features are significantly better than the pretrained baseline CNN features for fine-tuning on ImageNet.

Z. Kassas, J. Morales, K. Shamaei, J. Khalife.  2017.  LTE steers UAV. GPS World Magazine. 28:18–25.
Jacek Cyranka, Md. Ariful Islam, Greg Byrne, Paul L. Jones, Scott A. Smolka, Radu Grosu.  2017.  Lagrangian Reachabililty. Computer Aided Verification - 29th International Conference, {CAV} 2017 Proceedings, Part {I}. :379–400.
Jacek Cyranka, Md. Ariful Islam, Greg Byrne, Paul Jones, Scott A. Smolka, Radu Grosu.  2017.  Lagrangian Reachability. International Conference on Computer Aided Verification (CAV 2017). :379–400.
Abhinav Ganesan, Sidharth Jaggi, Venkatesh Saligrama.  2017.  Learning Immune-Defectives Graph Through Group Tests. {IEEE} Trans. Information Theory. 63:3010–3028.
Namaki, M, Chowdhury, F, Islam, M, Doppa, J, Wu, Y.  2017.  Learning to Speed Up Query Planning in Graph Databases. International Conference on Automated Planning and Scheduling.
Giraldo, Jairo, Cardenas, Alvaro, Kantarcioglu, Murat.  2017.  Leveraging Unique CPS Properties to Design Better Privacy-Enhancing Algorithms. Proceedings of the Hot Topics in Science of Security: Symposium and Bootcamp. :1–12.
A. Pourhabib, R. Tuo, S. He, Y. Ding,, J.Z. Huang.  2017.  Local calibration of computer experiments. Journal of the American Statistical Association. revised and re-submitted
Shakiba Yaghoubi, Georgios Fainekos.  2017.  Local Descent for Temporal Logic Falsification of Cyber-Physical Systems. Seventh Workshop on Design, Modeling and Evaluation of Cyber Physical Systems.
Melissaris, Themis, Shaw, Kelly, Martonosi, Margaret.  2017.  Locomotive: Optimizing mobile web traffic using selective compression. A World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2017 IEEE 18th International Symposium on. :1–4.
Marcell Vazquez{-}Chanlatte, Jyotirmoy V. Deshmukh, Xiaoqing Jin, Sanjit A. Seshia.  2017.  Logical Clustering and Learning for Time-Series Data. 29th International Conference on Computer Aided Verification (CAV). :305–325.
Taheri, Ehsan, Kolmanovsky, Ilya, Girard, Anouck.  2017.  Low-thrust trajectory optimization for multi-asteroid mission: an indirect approach. Proceedings of 27th AAS/AIAA Space Flight Mechanics Meeting. :687–700.