arXiv subjects

Anna Jordan

Publications and source records attributed to Anna Jordan.

5 recordsLinked to original sources

arXiv2022

Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes

The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins. We thus reinterpreted the challenge of forecasting traffic conditions as a movie completion task. U-Nets proved to be the winning architecture, demonstrating an ability to extract relevant features in this complex real-world geo-spatial process. Building on the previous competitions, Traffic4cast 2021 now focuses on the question of model robustness and generalizability across time and space. Moving from one city to an entirely different city, or moving from pre-COVID times to times after COVID hit the world thus introduces a clear domain shift. We thus, for the first time, release data featuring such domain shifts. The competition now covers ten cities over 2 years, providing data compiled from over 10^12 GPS probe data. Winning solutions captured traffic dynamics sufficiently well to even cope with these complex domain shifts. Surprisingly, this seemed to require only the previous 1h traffic dynamic history and static road graph as input.

cs.LG↗

arXiv2021

Dual Encoding U-Net for Spatio-Temporal Domain Shift Frame Prediction

The landscape of city-wide mobility behaviour has altered significantly over the past 18 months. The ability to make accurate and reliable predictions on such behaviour has likewise changed drastically with COVID-19 measures impacting how populations across the world interact with the different facets of mobility. This raises the question: "How does one use an abundance of pre-covid mobility data to make predictions on future behaviour in a present/post-covid environment?" This paper seeks to address this question by introducing an approach for traffic frame prediction using a lightweight Dual-Encoding U-Net built using only 12 Convolutional layers that incorporates a novel approach to skip-connections between Convolutional LSTM layers. This approach combined with an intuitive handling of training data can model both a temporal and spatio-temporal domain shift (gitlab.com/alchera/alchera-traffic4cast-2021).

cs.LG↗

arXiv2020

Temporal Autoencoder with U-Net Style Skip-Connections for Frame Prediction

Finding sustainable and novel solutions to predict city-wide mobility behaviour is an ever-growing problem given increased urban complexity and growing populations. This paper seeks to address this by describing a traffic frame prediction approach that uses Convolutional LSTMs to create a Temporal Autoencoder with U-Net style skip-connections that marry together recurrent and traditional computer vision techniques to capture spatio-temporal dependencies at different scales without losing topological details of a given city. Utilisation of Cyclical Learning Rates is also presented, improving training efficiency by achieving lower loss scores in fewer epochs than standard approaches.

cs.LG↗

arXiv2018

Low Magnetic Field Regime of a Gate-Defined Constriction in High-Mobility Graphene

We report on the evolution of the coherent electronic transport through a gate-defined constriction in a high-mobility graphene device from ballistic transport to quantum Hall regime upon increasing the magnetic field. At low field, the conductance exhibits Fabry-P\'erot resonances resulting from the npn cavities formed beneath the top-gated regions. Above a critical field $B^*$ corresponding to the cyclotron radius equal to the npn cavity length, Fabry-P\'erot resonances vanish and snake trajectories are guided through the constriction with a characteristic set of conductance oscillations. Increasing further the magnetic field allows us to probe the Landau level spectrum in the constriction, with distortions due to the combination of confinement and de-confinement of Landau levels in a saddle potential. These observations are confirmed by numerical calculations.

cond-mat.mes-hall↗

arXiv2016

Tunable transmission of quantum Hall edge channels with full degeneracy lifting in split-gated graphene devices

Charge carriers in the quantum Hall regime propagate via one-dimensional conducting channels that form along the edges of a two-dimensional electron gas. Controlling their transmission through a gate-tunable constriction, also called quantum point contact (QPC), is fundamental for many coherent transport experiments. However, in graphene, tailoring a QPC with electrostatic gates remains challenging due to the formation of p-n junctions below gate electrodes along which electron and hole edge channels co-propagate and mix, short-circuiting the constriction. Here we show that this electron-hole mixing is drastically reduced in high mobility boron-nitride/graphene/boron-nitride van-der-Waals heterostructures thanks to the full degeneracy lifting of the Landau levels, enabling QPC operation with full channel pinch-off. We demonstrate gate-tunable selective transmission of quantum Hall edge channels through the QPC, both in the integer and the fractional quantum Hall regimes. This gate-control of edge channel propagation in graphene van-der-Waals heterostructures opens the door to quantum Hall interferometry and electron quantum optics experiments in the integer and fractional quantum Hall regimes of graphene.

cond-mat.mes-hall↗