Early Turn-taking Prediction with Spiking Neural Networks for Human Robot Collaboration

Turn-taking is essential to the structure of human teamwork. Humans are typically aware of team members' intention to keep or relinquish their turn before a turn switch, where the responsibility of working on a shared task is shifted. Future co-robots are also expected to provide such competence. To that end, this paper proposes the Cognitive Turn-taking Model (CTTM), which leverages cognitive models (i.e., Spiking Neural Network) to achieve early turn-taking prediction. The CTTM framework can process multimodal human communication cues (both implicit and explicit) and predict human turn-taking intentions in an early stage. The proposed framework is tested on a simulated surgical procedure, where a robotic scrub nurse predicts the surgeon's turn-taking intention. It was found that the proposed CTTM framework outperforms the state-of-the-art turn-taking prediction algorithms by a large margin. It also outperforms humans when presented with partial observations of communication cues (i.e., less than 40% of full actions). This early prediction capability enables robots to initiate turn-taking actions at an early stage, which facilitates collaboration and increases overall efficiency.Comment: Submitted to IEEE International Conference on Robotics and Automation (ICRA) 201

Regulating capital flows in emerging markets: The IMF and the global financial crisis

In the wake of the financial crisis the International Monetary Fund (IMF) began to publicly express support for what have traditionally been referred to as ‘capital controls’. This paper empirically examines the extent to which the change in IMF discourse on these matters has resulted in significant changes in actual IMF policy advice. By creating and analyzing a database of IMF Article IV reports, we examine whether the financial crisis had an independent impact on IMF support for capital controls. We find that the IMF’s level of support for capital controls has increased as a result of the crisis and as the vulnerabilities associated with capital flows accentuate

Observation of Quantized Hall Effect and Shubnikov-de Hass Oscillations in Highly Doped Bi2Se3: Evidence for Layered Transport of Bulk Carriers

Bi2Se3 is an important semiconductor thermoelectric material and a prototype topological insulator. Here we report observation of Shubnikov-de Hass (SdH) oscillations accompanied by quantized Hall resistances (Rxy) in highly-doped n-type Bi2Se3 with bulk carrier concentrations of few 10^19 cm^-3. Measurements under tilted magnetic fields show that the magnetotransport is 2D-like, where only the c-axis component of the magnetic field controls the Landau level formation. The quantized step size in 1/Rxy is found to scale with the sample thickness, and average ~e2/h per quintuple layer (QL). We show that the observed magnetotransport features do not come from the sample surface, but arise from the bulk of the sample acting as many parallel 2D electron systems to give a multilayered quantum Hall effect. Besides revealing a new electronic property of Bi2Se3, our finding also has important implications for electronic transport studies of topological insulator materials.Comment: accepted by Physical Review Letters (2012

Optical pumping of quantum dot nuclear spins

An all-optical scheme to polarize nuclear spins in a single quantum dot is analyzed. The hyperfine interaction with randomly oriented nuclear spins presents a fundamental limit for electron spin coherence in a quantum dot; by cooling the nuclear spins, this decoherence mechanism could be suppressed. The proposed scheme is inspired by laser cooling methods of atomic physics and implements a "controlled Overhauser effect" in a zero-dimensional structure