13 citations · 21 across the 6 of their papers we have counts for
8 papers
When local gains fail to transfer: Frozen Earth-observation embeddings across wildfires
Philipp Stark, Alexandros Sopasakis, Ola Hall
Frozen Earth-observation embeddings are judged almost entirely by spatially blocked cross-validation inside one study region. We show that this number does not predict accuracy in…
Using Text-Based Life Trajectories from Swedish Register Data to Predict Residential Mobility with Pretrained Transformers
Philipp Stark, Alexandros Sopasakis, Ola Hall +1
We transform large-scale Swedish register data into textual life trajectories to address two long-standing challenges in data analysis: high cardinality of categorical variables an…
Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems
Dino Pjanić, Alexandros Sopasakis, Andres Reial +1
The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success…
Improved Anomaly Detection through Conditional Latent Space VAE Ensembles
Oskar Åström, Alexandros Sopasakis
We propose a novel Conditional Latent space Variational Autoencoder (CL-VAE) to perform improved pre-processing for anomaly detection on data with known inlier classes and unknown…
Enhancing Carbon Emission Reduction Strategies using OCO and ICOS data
Oskar Åström, Carina Geldhauser, Markus Grillitsch +2
We propose a methodology to enhance local CO2 monitoring by integrating satellite data from the Orbiting Carbon Observatories (OCO-2 and OCO-3) with ground level observations from…
Learning-Based UE Classification in Millimeter-Wave Cellular Systems With Mobility
Dino Pjanić, Alexandros Sopasakis, Harsh Tataria +2
Millimeter-wave cellular communication requires beamforming procedures that enable alignment of the transmitter and receiver beams as the user equipment (UE) moves. For efficient b…
Latent space conditioning for improved classification and anomaly detection
Erik Norlander, Alexandros Sopasakis
We propose a new type of variational autoencoder to perform improved pre-processing for clustering and anomaly detection on data with a given label. Anomalies however are not known…
Error analysis of coarse-grained kinetic Monte Carlo method
Markos A Katsoulakis, Petr Plechac, Alexandros Sopasakis
In this paper we investigate the approximation properties of the coarse-graining procedure applied to kinetic Monte Carlo simulations of lattice stochastic dynamics. We provide bot…