SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions

The remarkable capabilities of pretrained image diffusion models have been utilized not only for generating fixed-size images but also for creating panoramas. However, naive stitching of multiple images often results in visible seams. Recent techniques have attempted to address this issue by performing joint diffusions in multiple windows and averaging latent features in overlapping regions. However, these approaches, which focus on seamless montage generation, often yield incoherent outputs by blending different scenes within a single image. To overcome this limitation, we propose SyncDiffusion, a plug-and-play module that synchronizes multiple diffusions through gradient descent from a perceptual similarity loss. Specifically, we compute the gradient of the perceptual loss using the predicted denoised images at each denoising step, providing meaningful guidance for achieving coherent montages. Our experimental results demonstrate that our method produces significantly more coherent outputs compared to previous methods (66.35% vs. 33.65% in our user study) while still maintaining fidelity (as assessed by GIQA) and compatibility with the input prompt (as measured by CLIP score).Comment: Project page: https://syncdiffusion.github.i

Textually Relevant Spatial Skylines

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Horseshoe-based Bayesian nonparametric estimation of effective population size trajectories

Phylodynamics is an area of population genetics that uses genetic sequence data to estimate past population dynamics. Modern state-of-the-art Bayesian nonparametric methods for recovering population size trajectories of unknown form use either change-point models or Gaussian process priors. Change-point models suffer from computational issues when the number of change-points is unknown and needs to be estimated. Gaussian process-based methods lack local adaptivity and cannot accurately recover trajectories that exhibit features such as abrupt changes in trend or varying levels of smoothness. We propose a novel, locally-adaptive approach to Bayesian nonparametric phylodynamic inference that has the flexibility to accommodate a large class of functional behaviors. Local adaptivity results from modeling the log-transformed effective population size a priori as a horseshoe Markov random field, a recently proposed statistical model that blends together the best properties of the change-point and Gaussian process modeling paradigms. We use simulated data to assess model performance, and find that our proposed method results in reduced bias and increased precision when compared to contemporary methods. We also use our models to reconstruct past changes in genetic diversity of human hepatitis C virus in Egypt and to estimate population size changes of ancient and modern steppe bison. These analyses show that our new method captures features of the population size trajectories that were missed by the state-of-the-art methods.Comment: 36 pages, including supplementary informatio

Nonreceding hare lines: genetic continuity since the Late Pleistocene in European mountain hares (Lepus timidus)

Throughout time, climate changes have caused substantial rearrangements of habitats which have alternately promoted and disfavoured different types of taxa. At first glance, the mountain hare (Lepus timidus) shows the typical hallmarks of a cold-adapted species that has retreated to refugia since the onset of the current Holocene interglacial. In contrary to expectations, however, the species has a high contemporary genetic diversity with no clear differentiation between geographically isolated populations. In order to clarify the phylogeographic history of European mountain hares, we here analysed ancient DNA from the glacial populations that inhabited the previous midlatitude European tundra region. Our results reveal that the Ice Age hares had similar levels of genetic variation and lack of geographic structure as observed today, and the ancient samples were intermingled with modern individuals throughout the reconstructed evolutionary tree. This suggest a temporal genetic continuity in Europe, where the mountain hares were able to keep pace with the rapid changes at the last glacial/interglacial transition, and successfully track their shifting habitat to northern and alpine regions. Further, the temporal demographic analyses showed that the species’ population size in Europe appear to have been tightly linked with palaeoclimatic fluctuations, with increases and declines occurring during periods of global cooling and warming, respectively. Taken together, our results suggest that neither habitat shifts nor demographic fluctuations have had any substantial impact on the genetic diversity of European mountain hares. This remarkable resilience, which contrasts to a majority of previously investigated cold-adapted species, is likely due to its generalist nature which makes it less vulnerable to environmental changes

Mapping landscape attractiveness: A GIS-based landscape appreciation model for the Dutch countryside

Offering people scenic beauty is one of the most frequently mentioned landscape services. In the Netherlands it also has become an explicit policy goal: “we want a beautiful country to live and work in”. However, instruments to help policy makers and spatial planners to implement this relatively new goal are largely lacking. Where do people like the landscape in their living environment and where do they not? And which physical characteristics influence this appreciation and to what extent? To provide such information in a cost-efficient way, a model was developed to map, monitor, and simulate precisely this: the GIS-based Landscape Appreciation Model (GLAM). The model predicts the attractiveness of the landscape based solely on nationally available GIS-data on its physical aspects for each 250 x 250 metre cell. The model was calibrated using attractiveness ratings from a national survey among residents. The final model was evaluated using data from another Dutch survey of landscape appreciation among residents living in the vicinity of 52 areas that landscape experts considered being of high quality. In this article, we describe the theoretical background to GLAM, the attributes in the current version of the model, the final steps in calibrating the model, as well as its validation. We conclude with a discussion on the usefulness of GLAM for spatial policy

Hierarchical Entity Resolution using an Oracle

In many applications, entity references (i.e., records) and entities need to be organized to capture diverse relationships like type-subtype, is-A (mapping entities to types), and duplicate (mapping records to entities) relationships. However, automatic identification of such relationships is often inaccurate due to noise and heterogeneous representation of records across sources. Similarly, manual maintenance of these relationships is infeasible and does not scale to large datasets. In this work, we circumvent these challenges by considering weak supervision in the form of an oracle to formulate a novel hierarchical ER task. In this setting, records are clustered in a tree-like structure containing records at leaf-level and capturing record-entity (duplicate), entity-type (is-A) and subtype-supertype relationships. For effective use of supervision, we leverage triplet comparison oracle queries that take three records as input and output the most similar pair(s). We develop HierER, a querying strategy that uses record pair similarities to minimize the number of oracle queries while maximizing the identified hierarchical structure. We show theoretically and empirically that HierER is effective under different similarity noise models and demonstrate empirically that HierER can scale up to million-size datasets