Publications associées à Target audience

Re-evaluating the mythical divide between traditional and novel cardiovascular risk factors in rheumatoid arthritis

Cardiovascular (CV) risk factors for rheumatoid arthritis (RA) are conventionally classified as 'traditional' and 'novel'. We argue that this classification is obsolete and potentially counterproductive. Further, we discuss problems with the common practic ...

Bmj Publishing Group2024

Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the synthetic spindle datasets of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the synthetic generated training dataset of simulated muscle spindles during arm passive movements gene ...

Zenodo2024

When Your AI Becomes a Target: AI Security Incidents and Best Practices

Alexandre Massoud Alahi, Kathrin Grosse

In contrast to vast academic efforts to study AI security, few real-world reports of AI security incidents exist. Released incidents prevent a thorough investigation of the attackers' motives, as crucial information about the company and AI application is ...

AAAI Press2024

Diffusion in Style

Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Martin Nicolas Everaert

We present Diffusion in Style, a simple method to adapt Stable Diffusion to any desired style, using only a small set of target images. It is based on the key observation that the style of the images generated by Stable Diffusion is tied to the initial lat ...

2023

Affordance segmentation of hand-occluded containers from exocentric images

Andrea Cavallaro

Visual affordance segmentation identifies the surfaces of an object an agent can interact with. Common challenges for the identification of affordances are the variety of the geometry and physical properties of these surfaces as well as occlusions. In this ...

Ieee Computer Soc2023

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

Jean-Philippe Thiran, Roser Vinals Terres

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been most ...

2023

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Jiancheng Yang, Li Zhang, Zhigang Li, Bo Du

Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesi ...

Springer International Publishing Ag2023

Model-based impurity emission front control using deuterium fueling and nitrogen seeding in TCV

Basil Duval, Christian Gabriel Theiler, Cristian Galperti, Artur Perek

This paper presents the first result using nitrogen-seeded exhaust feedback control of the NII impurity emission front in TCV. The NII emission front position is consistently located below its commonly used CIII counterpart, indicating the NII emission fro ...

IOP Publishing Ltd2023

Building structures made of reused cut reinforced concrete slabs and walls: A case study

Corentin Jean Dominique Fivet, Maléna Bastien Masse, Nicole Widmer

Reuse of reinforced concrete (RC) components reduces construction’s environmental impacts and waste. Rather than crushing the concrete of an obsolete structure, its components are saw cut and later reassembled in a new structure. The theoretical feasibilit ...

CRC Press2023

Automated mixing of maximally localized Wannier functions into target manifolds

Nicola Marzari, Giovanni Pizzi, Junfeng Qiao

Maximally localized Wannier functions (MLWFs) are widely used in electronic-structure calculations. We have recently developed automated approaches to generate MLWFs that represent natural tight-binding sets of atomic-like orbitals; these describe accurate ...

Nature Portfolio2023