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Adapting Learned Image Codecs to Screen Content via Adjustable Transformations

Dogaroglu, H. Burak, Koyuncu, A. Burakhan, Boev, Atanas, Alshina, Elena, Steinbach, Eckehard

arXiv.org Artificial Intelligence

As learned image codecs (LICs) become more prevalent, their low coding efficiency for out-of-distribution data becomes a bottleneck for some applications. To improve the performance of LICs for screen content (SC) images without breaking backwards compatibility, we propose to introduce parameterized and invertible linear transformations into the coding pipeline without changing the underlying baseline codec's operation flow. We design two neural networks to act as prefilters and postfilters in our setup to increase the coding efficiency and help with the recovery from coding artifacts. Our end-to-end trained solution achieves up to 10% bitrate savings on SC compression compared to the baseline LICs while introducing only 1% extra parameters.


On The Impact of Replacing Private Cars with Autonomous Shuttles: An Agent-Based Approach

Bogdoll, Daniel, Karsch, Louis, Amritzer, Jennifer, Zöllner, J. Marius

arXiv.org Artificial Intelligence

The European Green Deal aims to achieve climate neutrality by 2050, which demands improved emissions efficiency from the transportation industry. This study uses an agent-based simulation to analyze the sustainability impacts of shared autonomous shuttles. We forecast travel demands for 2050 and simulate regulatory interventions in the form of replacing private cars with a fleet of shared autonomous shuttles in specific areas. We derive driving-related emissions, energy consumption, and non-driving-related emissions to calculate life-cycle emissions. We observe reduced life-cycle emissions from 0.4% to 9.6% and reduced energy consumption from 1.5% to 12.2%.


On the Geometric Ergodicity of Hamiltonian Monte Carlo

Livingstone, Samuel, Betancourt, Michael, Byrne, Simon, Girolami, Mark

arXiv.org Machine Learning

We establish general conditions under which Markov chains produced by the Hamiltonian Monte Carlo method will and will not be geometrically ergodic. We consider implementations with both position-independent and position-dependent integration times. In the former case we find that the conditions for geometric ergodicity are essentially a non-vanishing gradient of the log-density which asymptotically points towards the centre of the space and does not grow faster than linearly. In an idealised scenario in which the integration time is allowed to change in different regions of the space, we show that geometric ergodicity can be recovered for a much broader class of tail behaviours, leading to some guidelines for the choice of this free parameter in practice.