Zobrazeno 1 - 10
of 17 434
pro vyhledávání: '"Behrendt, A."'
The advent of wurtzite ferroelectrics is enabling a new generation of ferroelectric devices for computer memory that has the potential to bypass the von Neumann bottleneck, due to their robust polarization and silicon compatibility. However, the micr
Externí odkaz:
http://arxiv.org/abs/2410.18816
Online spaces allow people to discuss important issues and make joint decisions, regardless of their location or time zone. However, without proper support and thoughtful design, these discussions often lack structure and politeness during the exchan
Externí odkaz:
http://arxiv.org/abs/2409.07780
Autor:
Behrendt, Finn, Bhattacharya, Debayan, Mieling, Robin, Maack, Lennart, Krüger, Julia, Opfer, Roland, Schlaefer, Alexander
Unsupervised Anomaly Detection (UAD) methods rely on healthy data distributions to identify anomalies as outliers. In brain MRI, a common approach is reconstruction-based UAD, where generative models reconstruct healthy brain MRIs, and anomalies are
Externí odkaz:
http://arxiv.org/abs/2407.12474
Stance detection holds great potential for enhancing the quality of online political discussions, as it has shown to be useful for summarizing discussions, detecting misinformation, and evaluating opinion distributions. Usually, transformer-based mod
Externí odkaz:
http://arxiv.org/abs/2406.12480
Recent hydrodynamical simulations of the late stages of supernova remnant (SNR) evolution have revealed that as they merge with the ambient medium, SNRs implode, leading to the formation of dense clouds in their center. While being highly chemically
Externí odkaz:
http://arxiv.org/abs/2406.04792
Autor:
Bhattacharya, Debayan, Behrendt, Finn, Becker, Benjamin Tobias, Maack, Lennart, Beyersdorff, Dirk, Petersen, Elina, Petersen, Marvin, Cheng, Bastian, Eggert, Dennis, Betz, Christian, Hoffmann, Anna Sophie, Schlaefer, Alexander
Purpose: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised learning methods require large labelled dataset exhibiting diverse an
Externí odkaz:
http://arxiv.org/abs/2404.18599
Stance detection is an important task for many applications that analyse or support online political discussions. Common approaches include fine-tuning transformer based models. However, these models require a large amount of labelled data, which mig
Externí odkaz:
http://arxiv.org/abs/2404.08078
Autor:
Behrendt, Maike, Wagner, Stefan Sylvius, Ziegele, Marc, Wilms, Lena, Stoll, Anke, Heinbach, Dominique, Harmeling, Stefan
Measuring the quality of contributions in political online discussions is crucial in deliberation research and computer science. Research has identified various indicators to assess online discussion quality, and with deep learning advancements, auto
Externí odkaz:
http://arxiv.org/abs/2404.02761
Autor:
Behrendt, Finn, Bhattacharya, Debayan, Maack, Lennart, Krüger, Julia, Opfer, Roland, Mieling, Robin, Schlaefer, Alexander
Supervised deep learning techniques show promise in medical image analysis. However, they require comprehensive annotated data sets, which poses challenges, particularly for rare diseases. Consequently, unsupervised anomaly detection (UAD) emerges as
Externí odkaz:
http://arxiv.org/abs/2403.14262
Autor:
Bhattacharya, Debayan, Reuter, Konrad, Behrendt, Finn, Maack, Lennart, Grube, Sarah, Schlaefer, Alexander
Commonly employed in polyp segmentation, single image UNet architectures lack the temporal insight clinicians gain from video data in diagnosing polyps. To mirror clinical practices more faithfully, our proposed solution, PolypNextLSTM, leverages vid
Externí odkaz:
http://arxiv.org/abs/2402.11585