This year's Complex Adaptive Systems 2016 Conference will be held November 2-4, 2016 in Los Angeles, CA with the theme "Engineering Cyber Physical Systems: Applying Theory to Practice."
The use of robots in society could be expanded by using reinforcement learning (RL) to allow robots to learn and adapt to new situations on-line. RL is a paradigm for learning sequential decision making tasks, usually formulated as a Markov Decision Process (MDP). For an RL algorithm to be practical for robotic control tasks, it must learn in very few samples, while continually taking actions in real-time. In addition, the algorithm must learn efficiently in the face of noise, sensor/actuator delays, and continuous state features.
Securing critical networked cyber-physical systems (NCPSs) such as the power grid or transportation systems has emerged as a major national and global priority. The networked nature of such systems renders them vulnerable to a range of attacks both in cyber and physical domains as corroborated by recent threats such as the Stuxnet virus.
Submitted by Anonymous on Mon, 02/15/2016 - 12:20pm
The 4th IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)
Non-Volatile memory (NVM) technologies have demonstrated great potentials on improving many aspects of present and future memory hierarchy, offering high integration density, larger capacity, zero standby power and good resilience to soft errors. The recent research progress of various NVMs, e.g., NAND flash, PCM, STT-RAM, RRAM, FeRAM, etc., have drawn tremendous attentions from both academy and industry.
co-located with the 13th QEST International Conference on Quantitative Evaluation of SysTems (QEST) and the 14th International Conference on Formal Modeling and Analysis of Timed Systems (FORMATS).
