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Anmerkung zu ArbG Düsseldorf v. 5.3.2020 – 9 Ca 6557/18 – nicht rechtskräftig
Das ArbG Düsseldorf hat einem ehemaligen Arbeitnehmer einen immateriellen Schadensersatz von 5 000 Euro wegen einer verspäteten und teilweise unrichtigen datenschutzrechtlichen Auskunft seitens seines vormaligen Arbeitgebers zugesprochen. Der Beitrag setzt sich mit dieser Entscheidung grundsätzlich auseinander.
With economic weight shifting toward net zero, now is the time for ECAs, Exim-Banks, and PRIs to lead. Despite previous success, aligning global economic governance to climate goals requires additional activities across export finance and investment insurance institutions. The new research project initiated by Oxford University, ClimateWorks Foundation, and Mission 2020 including other practitioners and academics from institutions such as Atradius DSB, Columbia University, EDC, FMO and Offenburg University focuses on reshaping future trade and investment governance in light of climate action. The idea of a ‘Berne Union Net Zero Club’ is an important item in a potential package of reforms. This can include realigning mandates and corporate strategies, principles of intervention, as well as ECA, Exim-Bank and PRI operating models in order to accelerate net zero transformation. Full transparency regarding Berne Union members’ activities would be an excellent starting point. We invite all interested parties in the sector to come together to chart our own path to net zero
VR als Chance für Museen
(2020)
Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present an unsupervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without superivison. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an autoencoder to generate suitable latent representation. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features could be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.
Unternehmerische Entscheidungen sind im Regelfall riskant. Um das Ausmaß des Risikos deutlich zu machen, hat sich in der Praxis die Anfertigung von Szenarioanalysen durchgesetzt. Damit jedoch werden vorliegende Risiken systematisch unterschätzt. Bei wichtigen Entscheidungen sollte besser eine Sensitivitätsanalyse oder eine Simulation durchgeführt werden.
We introduce an open source python framework named PHS-Parallel Hyperparameter Search to enable hyperparameter optimization on numerous compute instances of any arbitrary python function. This is achieved with minimal modifications inside the target function. Possible applications appear in expensive to evaluate numerical computations which strongly depend on hyperparameters such as machine learning. Bayesian optimization is chosen as a sample efficient method to propose the next query set of parameters.