Exploring the Design Space of Immersive Urban Analytics

Recent years have witnessed the rapid development and wide adoption of immersive head-mounted devices, such as HTC VIVE, Oculus Rift, and Microsoft HoloLens. These immersive devices have the potential to significantly extend the methodology of urban visual analytics by providing critical 3D context information and creating a sense of presence. In this paper, we propose an theoretical model to characterize the visualizations in immersive urban analytics. Further more, based on our comprehensive and concise model, we contribute a typology of combination methods of 2D and 3D visualizations that distinguish between linked views, embedded views, and mixed views. We also propose a supporting guideline to assist users in selecting a proper view under certain circumstances by considering visual geometry and spatial distribution of the 2D and 3D visualizations. Finally, based on existing works, possible future research opportunities are explored and discussed.Comment: 23 pages,11 figure

Deep Predictive Policy Training using Reinforcement Learning

Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small sub-network with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.Comment: This work is submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems 2017 (IROS2017

Preliminary Studies on the fluctuation of the biomass of sizefractionated zooplankton in sea grass bed of Pulau Tinggi, Malaysia

Zooplanktons biomass was extensively studied in the sea grass bed of Pulau Tinggi, Malaysia for six months. In 2015, sampling months were April, June, October, whereas in 2016, April, June, August were the sampling months. A cone shaped plankton net was used with 0.30 m mouth, 1.00 m length and 100 μm mesh size. The fractionation of zooplankton size was carried out in to >2000 μm (large), 501-2000 μm (medium) and <500 μm (small). Zooplankton was classified as copepods, larvaceans, chaetognaths, cnidarians, ctenophores, decapods and polychaetes. Copepods were categorized as Calanoida, Poecilostomatoida, Cyclopoida and Harpacticoida but identified as a total of 54 species, 26 genera and 19 families. We conclude that among the biomass of 3 size fractions; medium (36%) was dominant followed by large and small (32% each) throughout the study period

Nonperturbative Quantum Gravity

Asymptotic safety describes a scenario in which general relativity can be quantized as a conventional field theory, despite being nonrenormalizable when expanding it around a fixed background geometry. It is formulated in the framework of the Wilsonian renormalization group and relies crucially on the existence of an ultraviolet fixed point, for which evidence has been found using renormalization group equations in the continuum. "Causal Dynamical Triangulations" (CDT) is a concrete research program to obtain a nonperturbative quantum field theory of gravity via a lattice regularization, and represented as a sum over spacetime histories. In the Wilsonian spirit one can use this formulation to try to locate fixed points of the lattice theory and thereby provide independent, nonperturbative evidence for the existence of a UV fixed point. We describe the formalism of CDT, its phase diagram, possible fixed points and the "quantum geometries" which emerge in the different phases. We also argue that the formalism may be able to describe a more general class of Ho\v{r}ava-Lifshitz gravitational models.Comment: Review, 146 pages, many figure