The Lamarr Scientific Forum is rounding off the first day with a closing and all information needed on dinner plans.
We begin program day number 2 with a short look back at the previous day and ahead at today's program.
Chirality information (i.e., information that allows distinguishing left from right) is ubiquitous for various data modes in computer vision, including images, videos, point clouds, and meshes. Contrary to symmetry, for which there has been a lot of research in the image domain, chirality information in shape analysis (point clouds and meshes) has remained underdeveloped. Although many shape...
This work explores the applicability of synthetic data for training deep learning models aimed at real-time classification of astronomical radio signals. Building on previous research where lightweight convolutional neural networks (CNNs) using DM-time representations showed promising performance in detecting transient signals, we now turn to the question of whether synthetic datasets can...
Tractography enables the reconstruction of white matter pathways from diffusion MRI and is a key tool for studying brain connectivity in both research and clinical contexts. Within the overall tractography pipeline, the parcellation step assigns individual streamlines to specific anatomical bundles, or discards them as false positive detections. We introduce PETParc (Parallel Efficient...
The post-surgical gauze retention can lead to serious complications and necessitate additional surgery for its removal. Due to data scarcity, the research on gauze segmentation on real-world surgical data remains underexplored. This work presents first investigation of gauze segmentation on real-surgical data. We use prevalently used segmentation architectures, including CNN-based,...
Large Language Models (LLMs) remain vulnerable to adversarial jailbreaks, yet existing attacks rely on handcrafted priors or require white-box access for gradient propagation. We show that token-level iterative optimization can succeed without gradients and introduce RAILS (RAndom Iterative Local Search), a simple yet effective method using only model logits with a query budget comparable to...
Social sciences define values as preferred behaviors or outcomes that motivate an individual's actions or judgments.
While LLMs often reflect biases from their training data, it remains unclear what values underlie their generation processes, and whether such internal value systems can be measured or modified.
In this paper, we investigate whether fine-tuning can steer a model’s internal...
Forecasting high-energy flares in blazars—active galactic nuclei with relativistic plasma jets oriented toward Earth—over extended temporal horizons presents a significant challenge due to the complex variability inherent in their light curves. In this study, we investigate the long-term predictability of flare activity using over 15 years of photon flux observations from the Fermi-LAT...
Traditional interpretability techniques such as rule-based models and feature attribution methods, each offer complementary strengths, however are often applied in isolation. Rule-based approaches are intuitive and logically structured, making them easy to understand, but they often struggle to scale effectively. On the other hand, feature attribution techniques like SHAP are well-suited to...
Understanding causal relationships in oncology is essential for improving treatment strategies and generating testable medical hypotheses. We present CaDSIm (Causal Discovery with Simultaneous Imputation), a new method for learning causal structures and associated Structural Equation Models from real world pan-cancer data, which is typically high dimensional, noisy, and incomplete.
Our...
