Intrinsically Motivated Learning of Visual Motion Perception and Smooth Pursuit

We extend the framework of efficient coding, which has been used to model the development of sensory processing in isolation, to model the development of the perception/action cycle. Our extension combines sparse coding and reinforcement learning so that sensory processing and behavior co-develop to optimize a shared intrinsic motivational signal: the fidelity of the neural encoding of the sensory input under resource constraints. Applying this framework to a model system consisting of an active eye behaving in a time varying environment, we find that this generic principle leads to the simultaneous development of both smooth pursuit behavior and model neurons whose properties are similar to those of primary visual cortical neurons selective for different directions of visual motion. We suggest that this general principle may form the basis for a unified and integrated explanation of many perception/action loops.Comment: 6 pages, 5 figure

Search for the signal of monotop production at the early LHC

We investigate the potential of the early LHC to discover the signal of monotops, which can be decay products of some resonances in models such as R-parity violating SUSY or SU(5), etc. We show how to constrain the parameter space of the models by the present data of $Z$ boson hadronic decay branching ratio, $K^0-\bar{K^0}$ mixing and dijet productions at the LHC. Then, we study the various cuts imposed on the events, reconstructed from the hadronic final states, to suppress backgrounds and increase the significance in detail. And we find that in the hadronic mode the information from the missing transverse energy and reconstructed resonance mass distributions can be used to specify the masses of the resonance and the missing particle. Finally, we study the sensitivities to the parameters at the LHC with $\sqrt{s}$=7 TeV and an integrated luminosity of $1 {\rm fb}^{-1}$ in detail. Our results show that the early LHC may detect this signal at 5$\sigma$ level for some regions of the parameter space allowed by the current data.Comment: 25 pages, 18 figures, 3 tables, version published in Phys.Rev.

Diphoton plus ZZ production at the ILC at O(α4){\cal O}(\alpha^4)

Precision measurement for the production of a $Z$-boson in association with two photons is important for investigating the Higgs boson and exploring new physics at the International Linear Collider. It could be used to study the $ZZ\gamma\gamma$ anomalous quartic gauge coupling. In this work we report on our calculation of the full ${\cal O} (\alpha^4)$ contributions to the $e^+e^- \to Z \gamma\gamma$ process in the standard model, and we analyze the electroweak (EW) quantum effects on the total cross section. We investigate the dependence of the $Z\gamma\gamma$ production rate on the event selection scheme and provide distributions for some important kinematic observables. We find that the next-to-leading order (NLO) EW corrections can enhance the total cross section quantitatively from $2.32\%$ to $9.61\%$ when the colliding energy goes up from $250 GeV$ to $1 TeV$, and the NLO EW corrections show obviously a non trivial phase space dependence. We conclude that in studying the signal process $e^+e^- \to ZH \to Z \gamma\gamma$, the background process $e^+e^- \to Z \gamma\gamma$ can be suppressed significantly if we take appropriate kinematic cuts on the final products.Comment: 18 pages, 10 figure

Accessibility-Based Clustering for Efficient Learning of Locomotion Skills

For model-free deep reinforcement learning of quadruped locomotion, the initialization of robot configurations is crucial for data efficiency and robustness. This work focuses on algorithmic improvements of data efficiency and robustness simultaneously through automatic discovery of initial states, which is achieved by our proposed K-Access algorithm based on accessibility metrics. Specifically, we formulated accessibility metrics to measure the difficulty of transitions between two arbitrary states, and proposed a novel K-Access algorithm for state-space clustering that automatically discovers the centroids of the static-pose clusters based on the accessibility metrics. By using the discovered centroidal static poses as the initial states, we can improve data efficiency by reducing redundant explorations, and enhance the robustness by more effective explorations from the centroids to sampled poses. Focusing on fall recovery as a very hard set of locomotion skills, we validated our method extensively using an 8-DoF quadrupedal robot Bittle. Compared to the baselines, the learning curve of our method converges much faster, requiring only 60% of training episodes. With our method, the robot can successfully recover to standing poses within 3 seconds in 99.4% of the test cases. Moreover, the method can generalize to other difficult skills successfully, such as backflipping.</p