图像生成

Generating large images via deep learning requires patching input data to accommodate hardware memory limitations, then assembling output patches, a process that can introduce stitching artifacts when neighboring patches do not align at borders. While these artifacts are known to affect segmentation tasks, their impact on generative models for style-transfer remains poorly understood. We investigated three stitching approaches and two patch dimensionalities (2D vs 3D) using cycleGAN models trained on cryo-electron microscopy datasets. We evaluated both perceptual quality and performance on downstream mitochondria segmentation. Our key findings reveal that: (1) FID scores fail to detect subtle stitching artifacts that significantly impact downstream segmentation performance, (2) 3D models with artifact-free stitching marginally outperform 2D models on downstream tasks, though the improvement barely justifies the computational cost, and (3) 2D models train more stably due to larger batch sizes. Additionally, we demonstrate that ensembling predictions from three orthogonal directions can improve low-quality volumes but provides no benefit for high-quality outputs. These results demonstrate that maximizing generative model performance on large scientific datasets requires careful consideration and mitigation of stitching artifacts, and that perceptual metrics alone are insufficient for evaluating domain adaptation quality in biomedical imaging.

Machine learning is often touted to improve the efficiency of ICT, but that small gain is overwhelmed by the enormous carbon, water, and land footprints of data centers and ML-ready devices. We survey the electricity consumption of ML applications in training and inference, focusing on electricity-intensive image generation. Our team of a computer engineer, a media scholar, and an artist explore solutions including inexact computing; tiny language models; low-precision hardware architectures; hardware with limited capacity; and anticipating and mitigating energy demands at the design phase. We will sketch our work in progress of an ethical and aesthetically sophisticated tiny image generator using non-scraped data. Looking to the economic context, we will propose a true-cost accounting for the environmental impact of machine learning and suggest that the criterion of efficiency is driven by the shareholder-capitalist framing of ICT.

Measurements of cosmic-ray proton and helium spectra below the knee show deviations from simple power laws, including multi-TeV structures. A possible explanation is that one or a few nearby sources contribute an additional component to the local spectrum. However, previous study shows that a dominant local contribution is statistically unlikely under a homogeneous diffusion model. In this work, we investigate how this probability changes if cosmic rays experience inefficient transport near their sources, motivated by observations of extended gamma-ray emission around Galactic accelerators. We derive a series Green's function that enables fast calculation of the particle distribution in this scenario, making Monte Carlo calculations for Galactic source populations feasible. The inner slow-diffusion region delays escape and redistributes the arriving particles in time and energy. In Monte Carlo realizations, the probability that the strongest local source becomes comparable to the background at $10\,\rm{TeV}$ increases from about $0.4\%$ in homogeneous diffusion to $1.7$--$2.2\%$ in the two-zone models. Thus inhibited near-source transport weakens, but does not remove, the statistical difficulty. We then examine cataloged nearby candidate supernova remnants and show that a $10\,\rm{TeV}$ feature can be reproduced only with additional assumptions, especially a harder local injection spectrum and a favorable diffusion coefficient. The predicted contribution of a given source changes strongly among different particle transport model. Therefore, the local source interpretations are plausible but highly model dependent, and require independent constraints on source injection history, particle transport mechanisms, and local interstellar turbulence.