A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning

For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility. Optimization-based approaches, such as Model Predictive Control (MPC), can provide such optimal policies, but their computational complexity is generally unacceptable for real-time implementation. To address this problem, we propose a fast integrated planning and control framework that combines learning- and optimization-based approaches in a two-layer hierarchical structure. The first layer, defined as the "policy layer", is established by a neural network which learns the long-term optimal driving policy generated by MPC. The second layer, called the "execution layer", is a short-term optimization-based controller that tracks the reference trajecotries given by the "policy layer" with guaranteed short-term safety and feasibility. Moreover, with efficient and highly-representative features, a small-size neural network is sufficient in the "policy layer" to handle many complicated driving scenarios. This renders online imitation learning with Dataset Aggregation (DAgger) so that the performance of the "policy layer" can be improved rapidly and continuously online. Several exampled driving scenarios are demonstrated to verify the effectiveness and efficiency of the proposed framework

OVSNet : Towards One-Pass Real-Time Video Object Segmentation

Video object segmentation aims at accurately segmenting the target object regions across consecutive frames. It is technically challenging for coping with complicated factors (e.g., shape deformations, occlusion and out of the lens). Recent approaches have largely solved them by using backforth re-identification and bi-directional mask propagation. However, their methods are extremely slow and only support offline inference, which in principle cannot be applied in real time. Motivated by this observation, we propose a efficient detection-based paradigm for video object segmentation. We propose an unified One-Pass Video Segmentation framework (OVS-Net) for modeling spatial-temporal representation in a unified pipeline, which seamlessly integrates object detection, object segmentation, and object re-identification. The proposed framework lends itself to one-pass inference that effectively and efficiently performs video object segmentation. Moreover, we propose a maskguided attention module for modeling the multi-scale object boundary and multi-level feature fusion. Experiments on the challenging DAVIS 2017 demonstrate the effectiveness of the proposed framework with comparable performance to the state-of-the-art, and the great efficiency about 11.5 FPS towards pioneering real-time work to our knowledge, more than 5 times faster than other state-of-the-art methods.Comment: 10 pages, 6 figure

Bond relaxation, electronic and magnetic behavior of 2D metals structures Y on Li(110) surface

We investigated the bond, electronic and magnetic behavior of adsorption Yttrium atoms on Lithium (110) surface using a combination of Bond-order-length-strength(BOLS) correlation and density-functional theory(DFT). We found that adsorption Y atoms on Li(110) surfaces form two-dimensional (2D) geometric structures of hexagon, nonagon, solid hexagonal, quadrangle and triangle. The consistent with the magnetic moment are 6.66{\mu}B, 5.54{\mu}B, 0.28{\mu}B, 1.04{\mu}B, 2.81{\mu}B, respectively. In addition, this work could pave the way for design new 2D metals electronic and magnetic properties

Optically-Nonactive Assorted Helices Array with Interchangeable Magnetic/Electric Resonance

We report here the designing of optically-nonactive metamaterial by assembling metallic helices with different chirality. With linearly polarized incident light, pure electric or magnetic resonance can be selectively realized, which leads to negative permittivity or negative permeability accordingly. Further, we show that pure electric or magnetic resonance can be interchanged at the same frequency band by merely changing the polarization of incident light for 90 degrees. This design demonstrates a unique approach to construct metamaterial.Comment: 15 pages, 4 figure

Buildings as systems.

Massachusetts Institute of Technology. Dept. of Architecture. Thesis. 1966. M.Arch.Accompanying drawings held by MIT Museum.M.Arch

Synthesis of CdTe quantum dot-conjugated CC49 and their application for in vitro imaging of gastric adenocarcinoma cells

The purpose of this experiment was to investigate the visible imaging of gastric adenocarcinoma cells in vitro by targeting tumor-associated glycoprotein 72 (TAG-72) with near-infrared quantum dots (QDs). QDs with an emission wavelength of about 550 to 780 nm were conjugated to CC49 monoclonal antibodies against TAG-72, resulting in a probe named as CC49-QDs. A gastric adenocarcinoma cell line (MGC80-3) expressing high levels of TAG-72 was cultured for fluorescence imaging, and a gastric epithelial cell line (GES-1) was used for the negative control group. Transmission electron microscopy indicated that the average diameter of CC49-QDs was 0.2 nm higher compared with that of the primary QDs. Also, fluorescence spectrum analysis indicated that the CC49-QDs did not have different optical properties compared to the primary QDs. Immunohistochemical examination and in vitro fluorescence imaging of the tumors showed that the CC49-QDs probe could bind TAG-72 expressed on MGC80-3 cells