Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC

Relationformer: A Unified Framework for Image-to-Graph Generation

A comprehensive representation of an image requires understanding objects and their mutual relationship, especially in image-to-graph generation, e.g., road network extraction, blood-vessel network extraction, or scene graph generation. Traditionally, image-to-graph generation is addressed with a two-stage approach consisting of object detection followed by a separate relation prediction, which prevents simultaneous object-relation interaction. This work proposes a unified one-stage transformer-based framework, namely Relationformer, that jointly predicts objects and their relations. We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly. In addition to existing [obj]-tokens, we propose a novel learnable token, namely [rln]-token. Together with [obj]-tokens, [rln]-token exploits local and global semantic reasoning in an image through a series of mutual associations. In combination with the pair-wise [obj]-token, the [rln]-token contributes to a computationally efficient relation prediction. We achieve state-of-the-art performance on multiple, diverse and multi-domain datasets that demonstrate our approach's effectiveness and generalizability

Surrogate Models and Mixtures of Experts in Aerodynamic Performance Prediction for Mission Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140436/1/6.2014-2301.pd

Enhancement of Rydberg-mediated single-photon nonlinearities by electrically tuned Förster resonances

We demonstrate experimentally that Stark-tuned Förster resonances can be used to substantially increase the interaction between individual photons mediated by Rydberg interaction inside an optical medium. This technique is employed to boost the gain of a Rydberg-mediated single-photon transistor and to enhance the non-destructive detection of single Rydberg atoms. Furthermore, our all-optical detection scheme enables high-resolution spectroscopy of two-state Förster resonances, revealing the fine structure splitting of high-n Rydberg states and the non-degeneracy of Rydberg Zeeman substates in finite fields. We show that the ∣50S1/2,48S1/2⟩↔∣49P1/2,48P1/2⟩ pair state resonance in 87Rb enables simultaneously a transistor gain G>100 and all-optical detection fidelity of single Rydberg atoms F>0.8. We demonstrate for the first time the coherent operation of the Rydberg transistor with G>2 by reading out the gate photon after scattering source photons. Comparison of the observed readout efficiency to a theoretical model for the projection of the stored spin wave yields excellent agreement and thus successfully identifies the main decoherence mechanism of the Rydberg transistor

Plasma–liquid interactions: a review and roadmap

Plasma–liquid interactions represent a growing interdisciplinary area of research involving plasma science, fluid dynamics, heat and mass transfer, photolysis, multiphase chemistry and aerosol science. This review provides an assessment of the state-of-the-art of this multidisciplinary area and identifies the key research challenges. The developments in diagnostics, modeling and further extensions of cross section and reaction rate databases that are necessary to address these challenges are discussed. The review focusses on non-equilibrium plasmas