Results 71 to 80 of about 116,908 (254)

A State‐Adaptive Koopman Control Framework for Real‐Time Deformable Tool Manipulation in Robotic Environmental Swabbing

Advanced Robotics Research, EarlyView.
This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi, Amirreza Davar, Dongyi Wang   +2 more
wiley   +1 more source

Inverting the Generator of a Generative Adversarial Network [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2019
Under review at IEEE ...
Antonia Creswell, Anil Anthony Bharath
openaire   +7 more sources

Early Radiation Therapy Response Assessment Using Multi‐Scale Photoacoustic Imaging

Advanced Science, EarlyView.
Tomographic and mesoscopic photoacoustics capture intratumoural features of radioresistance and response. ABSTRACT There is a critical unmet clinical need to identify biomarkers that predict and detect radiation therapy (RT) response in cancer. Using the unique capabilities of multi‐scale photoacoustic imaging (PAI) to depict tumor oxygenation and ...
Thierry L. Lefebvre, Mariam‐Eleni Oraiopoulou, Ellie V. Bunce, Thomas R. Else, Lorna C. Wright, Monika A. Golinska, Lina Hacker, Cara Brodie, Steven Kupczak, Yi Cheng, Lisa Young, Paul W. Sweeney, Sarah E. Bohndiek   +12 more
wiley   +1 more source

Evaluating the Utilities of Foundation Models in Single‐Cell Data Analysis

Advanced Science, EarlyView.
This study delivers the first systematic, task‐level evaluation of single‐cell foundation models across eight core analytical tasks. By benchmarking 10 leading models with the scEval framework, it reveals where foundation models truly add value, where task‐specific methods still dominate, and provides concrete, reproducible guidelines to steer the next
Tianyu Liu, Kexing Li, Yuge Wang, Hongyu Li, Hongyu Zhao   +4 more
wiley   +1 more source

DOOM Level Generation Using Generative Adversarial Networks [PDF]

2018 IEEE Games, Entertainment, Media Conference (GEM), 2018
We applied Generative Adversarial Networks (GANs) to learn a model of DOOM levels from human-designed content. Initially, we analysed the levels and extracted several topological features. Then, for each level, we extracted a set of images identifying the occupied area, the height map, the walls, and the position of game objects.
GIACOMELLO, EDOARDO, Lanzi, PL, Loiacono, D   +2 more
openaire   +3 more sources

High‐Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million‐Patient Diabetes Cohort

Advanced Science, EarlyView.
This study generates high‐fidelity synthetic longitudinal records for a million‐patient diabetes cohort, successfully replicating clinical predictive performance. However, deeper analysis reveals algorithmic biases and trajectory inconsistencies that escape standard quality metrics. These findings challenge current validation norms, demonstrating why a
Francisco Ortuño, Víctor M. de la Oliva Roque, Javier‐Ignacio Ramirez‐Lopez, David P. Kreil, Joaquín Dopazo, Carlos Loucera   +5 more
wiley   +1 more source

Optoelectronic generative adversarial networks

Communications Physics
Recent breakthroughs in artificial intelligence generative content technology are driving transformational change. Diffractive optical networks offer a promising solution for high-speed, low-power generative models.
Jumin Qiu, Ganqing Lu, Tingting Liu, Dejian Zhang, Shuyuan Xiao, Tianbao Yu   +5 more
doaj   +1 more source

Adversarial Example Detection and Classification With Asymmetrical Adversarial Training

, 2020
The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains.
Kolouri, Soheil, Rohde, Gustavo K., Yin, Xuwang   +2 more
core  

Flow-based network traffic generation using Generative Adversarial Networks [PDF]

Computers & Security, 2019
Flow-based data sets are necessary for evaluating network-based intrusion detection systems (NIDS). In this work, we propose a novel methodology for generating realistic flow-based network traffic. Our approach is based on Generative Adversarial Networks (GANs) which achieve good results for image generation.
Ring, Markus, Schlör, Daniel, Landes, Dieter, Hotho, Andreas   +3 more
openaire   +2 more sources

Solid Harmonic Wavelet Bispectrum for Image Analysis

Advanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown, Mathilda Avirett‐Mackenzie, Carolin Villforth, Georgios Exarchakis   +3 more
wiley   +1 more source