Engineered spin phase diagram of two interacting electrons in semiconductor nanowire quantum dots

Spin properties of two interacting electrons in a quantum dot (QD) embedded in a nanowire with controlled aspect ratio and longitudinal magnetic fields are investigated by using a configuration interaction (CI) method and exact diagonalization (ED) techniques. The developed CI theory based on a three-dimensional (3D) parabolic model provides explicit formulations of the Coulomb matrix elements and allows for straightforward and efficient numerical implementation. Our studies reveal fruitful features of spin singlet-triplet transitions of two electrons confined in a nanowire quantum dot (NWQD), as a consequence of the competing effects of geometry-controlled kinetic energy quantization, the various Coulomb interactions, and spin Zeeman energies. The developed theory is further employed to study the spin phase diagram of two quantum-confined electrons in the regime of "cross over" dimensionality, from quasi-two-dimensional (disk-like) QDs to finite one-dimensional (rod-like) QDs.Comment: 9 pages, 6 figure

No More Discrimination: Cross City Adaptation of Road Scene Segmenters

Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases. Instead of collecting a large number of annotated images of each city of interest to train or refine the segmenter, we propose an unsupervised learning approach to adapt road scene segmenters across different cities. By utilizing Google Street View and its time-machine feature, we can collect unannotated images for each road scene at different times, so that the associated static-object priors can be extracted accordingly. By advancing a joint global and class-specific domain adversarial learning framework, adaptation of pre-trained segmenters to that city can be achieved without the need of any user annotation or interaction. We show that our method improves the performance of semantic segmentation in multiple cities across continents, while it performs favorably against state-of-the-art approaches requiring annotated training data.Comment: 13 pages, 10 figure

Scanning Near-shore Intertidal Terrain Using Ground LiDAR

Intertidal zone refers to the area under and above the water during high and low tides. Traditionally, this zone is not within the scope of land management authorities. Moreover, in accordance with principals set out by existing plans, intertidal zones are excluded from management zones. Boundaries should therefore be set at the land and sea border. Traditionally, methods in determining this have included the traditional theodolite (total station) method, mapping and aerial photography (photogrammetry). However, existing operational restrictions lower efficiency, in addition to increasing time and operational costs. Therefore this paper explores the practicality of a user- friendly, ground-based high resolution laser scanning technology. This method offers easy operation and high-density characteristics with an instrument platform that can be installed on elevated rooftops. High accuracy and resolution is achieved using a stop-and-go method producing Digital Terrain Model (DTM) data. The range of the completed data is 61km in length, 2.5km in width, and -0.5m depth, with a sampling error of approximately ±2cm. Through the implementation discussed in this research, accurate information about the changes of topography in intertidal areas can be obtained

When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective

Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social network, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing. A prototype of social network inspired mobile multimedia and sensing application is presented for illustrative purpose. Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.Comment: To appear in Oct. IEEE Communications Magazine, feature topic on "Social Networks Meet Next Generation Mobile Multimedia Internet