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A Modifiable Architectural Design for Commercial Greenhouses Energy Economic Dispatch Testbed

Autor: Clausen, Christian Skafte Beck, Jørgensen, Bo Nørregaard, Ma, Zheng Grace

Publikováno v: In: Energy Informatics. EI.A 2023. Lecture Notes in Computer Science, vol 14467

Facing economic challenges due to the diverse objectives of businesses, and consumers, commercial greenhouses strive to minimize energy costs while addressing CO2 emissions. This scenario is intensified by rising energy costs and the global imperativ

Externí odkaz: http://arxiv.org/abs/2401.03888

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Quantum Two-Way Communication Protocol Beyond Superdense Coding

Autor: Valentini, Lorenzo, Jensen, Kristian Skafte, Christensen, René Bødker, Chiani, Marco, Popovski, Petar

We introduce a generalization of one-way superdense coding to two-way communication protocols for transmitting classical bits by using entangled quantum pairs. The proposed protocol caters for provision of entangled pairs, which is assumed to be give

Externí odkaz: http://arxiv.org/abs/2309.02837

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Atomic Anatomy of Low-Inertia Power Systems

Autor: Sahoo, Subham, Malkhandi, Arpan, Jensen, Kristian Skafte

In this article, we determine a fundamental anatomical modeling parallelism between low-inertia power systems and Bohr's atomic model. The proposed atomic architecture will serve as a microscopic building block, where we validate the structural analo

Externí odkaz: http://arxiv.org/abs/2305.12574

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EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text

Autor: Ahmed, Rafsan, Berntsson, Petter, Skafte, Alexander, Rashed, Salma Kazemi, Klang, Marcus, Barvesten, Adam, Olde, Ola, Lindholm, William, Arrizabalaga, Antton Lamarca, Nugues, Pierre, Aits, Sonja

Background Medical research generates millions of publications and it is a great challenge for researchers to utilize this information in full since its scale and complexity greatly surpasses human reading capabilities. Automated text mining can help

Externí odkaz: http://arxiv.org/abs/2304.07805

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Akademický článek

Tumor-agnostic cancer therapy using antibodies targeting oncofetal chondroitin sulfate

Publikováno v: Nature Communications, Vol 15, Iss 1, Pp 1-20 (2024)

Abstract Molecular similarities between embryonic and malignant cells can be exploited to target tumors through specific signatures absent in healthy adult tissues. One such embryonic signature tumors express is oncofetal chondroitin sulfate (ofCS),

Externí odkaz: https://doaj.org/article/b0285b53e90a45a8a481845883fadcba

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Laplacian Autoencoders for Learning Stochastic Representations

Autor: Miani, Marco, Warburg, Frederik, Moreno-Muñoz, Pablo, Detlefsen, Nicke Skafte, Hauberg, Søren

Established methods for unsupervised representation learning such as variational autoencoders produce none or poorly calibrated uncertainty estimates making it difficult to evaluate if learned representations are stable and reliable. In this work, we

Externí odkaz: http://arxiv.org/abs/2206.15078

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Is an encoder within reach?

Autor: Hauschultz, Helene, Moreno-Muños, Rasmus Berg Palm. Pablo, Detlefsen, Nicki Skafte, Plessis, Andrew Allan du, Hauberg, Søren

The encoder network of an autoencoder is an approximation of the nearest point projection onto the manifold spanned by the decoder. A concern with this approximation is that, while the output of the encoder is always unique, the projection can possib

Externí odkaz: http://arxiv.org/abs/2206.01552

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