Diffusion-based image generation models can enhance image quality when conditioned on ground truth labels. Here, we conduct a comprehensive experimental study on image-level conditioning for diffusion models using cluster assignments. We investigate how individual clustering determinants, such as the number of clusters and the clustering method, impact image synthesis across three different datasets. Given the optimal number of clusters with respect to image synthesis, we show that cluster-conditioning can achieve state-of-the-art performance, with an FID of 1.67 for CIFAR10 and 2.17 for CIFAR100, along with a strong increase in training sample efficiency. We further propose a novel empirical method to estimate an upper bound for the optimal number of clusters. Unlike existing approaches, we find no significant association between clustering performance and the corresponding cluster-conditional FID scores. Code is available at https://github.com/HHU—MMBS/cedm—official—wavc2025

LEDs on a strip, when turned on and off in a specific order, result in the perception of apparent motion (i.e. beta movement). In the automotive domain such chase lights have been used to alter drivers' perception of driving speed by manipulating the pixel speed of LEDs. We argue that the perceived velocity of beta movement in the peripheral view is not only based on the actual pixel speed but can be influenced by other factors such as frequency, width and brightness of lit LED segments. We conducted a velocity matching experiment (N=25) by systematically varying these three properties, in order to determine their influence on a participant's perceived velocity in a vehicle mock-up. Results show that a higher frequency and stronger brightness increased perceived velocity, whereas segment width had no influence. We discuss how findings may be applied when designing systems that use beta movement to influence the perception of ambient light velocity.

Abstract The GEOsciences Network (GEON, www.geongrid.org ) is a large‐scale collaborative cyberinfrastructure project involving information technology and geoscience researchers from multiple institutions. The GEON infrastructure provides portal, middleware, and data resources to facilitate scientific discovery for domain scientists using applications, tools, and services. It consists of both a service‐oriented Web/Grid framework and application toolkits, using the Web service and portlet programming model to represent applications. Based on those grid environments, we have developed the SYNSEIS (SYNthetic SEISmogram) tool within the GEON infrastructure to support personalized experiments in seismology. In this paper, we present an overview of SYNSEIS from a user point of view, and demonstrate how one can use a simple management scheme to perform a parameter sweep and distribute the work in computational resources, using a scientific application that was not specifically designed to perform parameter sweeps. The performance advantages to be gained by using this scheme with scientific codes for dealing with a large number of jobs on computational grids are very substantial. In particular, we identify the earthquake simulations in the SYNSEIS tool as an example application that can benefit from running jobs on computational resources and subsequently promote the sharing of computational resources among partner sites involved in the GEON project. Finally, we also discuss the parallel scaling behavior of our primary earthquake simulation application. Copyright © 2010 John Wiley & Sons, Ltd.