1 citations · 1 across the 9 of their papers we have counts for
10 papers
Flux Vacua, Asymptotic Periods, and Relatively Anosov Representations
Paul Balavoine, Severin Lüst
We develop a topological method to detect classical flux vacua of M-theory on Calabi-Yau fourfolds with a single complex structure modulus using only asymptotic period data and mon…
Backdoors in Learning-Based Industrial Robotic Arm Manipulation: An Empirical Security Study
Zijian Zhang, Zhen Zeng, Zhongshu Gu +1
Learning-based models (e.g., visuomotor and Vision-Language-Action (VLA)) are increasingly explored for industrial robotic manipulation, where model predictions are directly transl…
Dffusion: Capturing the Beam-Beam Physics of Collisions with Diffusion Models
Antonio Chahine, Mariarosaria D'Alfonso, Jan Eysermans +10
Beam-induced backgrounds at high-luminosity colliders, such as the FCC-ee, are dominated by incoherent pair creation (IPC), and require computationally expensive simulatio…
QUIVER: Quantum-Informed Views for Enhanced Representations in Large ML Models
Aritra Bal, Michael Binder, Markus Klute +2
Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example. We introduce QUIVER (QUantum-Informed Views for En…
Particle-Lund Multimodality in Jet Taggers
Loukas Gouskos, Benedikt Maier
The Lund plane offers a physics-motivated, hierarchical representation of QCD radiation within jets, while transformer-based taggers have reached state-of-the-art performance by le…
From Information Geometry to Jet Substructure: A Triality of Cumulant Tensors, Energy Correlators, and Hypergraphs
Aritra Bal, Markus Klute, Benedikt Maier +1
Pairwise Fisher graphs capture local covariance information, but they cannot distinguish an irreducible multi-observable radiation pattern from a collection of ordinary pairwise co…
Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors
Max Marriott-Clarke, Lazar Novakovic, Elizabeth Ratzer +3
We propose a novel clustering approach for point-cloud segmentation based on supervised contrastive metric learning (CML). Rather than predicting cluster assignments or object-cent…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
QINNs: Quantum-Informed Neural Networks
Aritra Bal, Markus Klute, Benedikt Maier +3
Classical deep neural networks can learn rich multi-particle correlations in collider data, but their inductive biases are rarely anchored in physics structure. We propose quantum-…
Continuous-variable photonic quantum extreme learning machines for fast collider-data selection
Benedikt Maier, Michael Spannowsky, Simon Williams
We study continuous-variable photonic quantum extreme learning machines as fast, low-overhead front-ends for collider data processing. Data is encoded in photonic modes through qua…