Latest AI and machine learning research in dsm-5 for healthcare professionals.
In this paper, we study the diffusability (learnability) of variational autoencoders (VAE) in latent diffusion. First, we show that pixel-space diffusion trained with an MSE objective is inherently biased toward learning low and mid spatial frequencies, and that the power-law power spectral density (PSD) of natural images makes this bias perceptually beneficial. Motivated by this result, we propos...
Recent advances in image-based satellite 3D reconstruction have progressed along two complementary directions. On one hand, multi-date approaches using NeRF or Gaussian-splatting jointly model appearance and geometry across many acquisitions, achieving accurate reconstructions on opportunistic imagery with numerous observations. On the other hand, classical stereoscopic reconstruction pipelines de...
Multimodal remote sensing technology significantly enhances the understanding of surface semantics by integrating heterogeneous data such as optical i...
Document reconstruction constitutes a significant facet of document analysis and recognition, a field that has been progressively accruing interest ...
Building damage identification shortly after a disaster is crucial for guiding emergency response and recovery efforts. Although optical satellite i...
Accurate, detailed, and high-frequent bathymetry is crucial for shallow seabed areas facing intense climatological and anthropogenic pressures. Curr...
We introduce the S-EO dataset: a large-scale, high-resolution dataset, designed to advance geometry-aware shadow detection. Collected from diverse p...
The increasing population, thus financial interests, in coastal areas have increased the need to monitor coastal elevation and shoreline change. Tho...
Highly accurate geometric precision and dense image features characterize True Digital Orthophoto Maps (TDOMs), which are in great demand for applic...
3D reconstruction garners increasing attention alongside the advancement of high-level image applications, where dense stereo matching (DSM) serves ...
Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging...
Despite the remarkable success of diffusion models (DMs) in data generation, they exhibit specific failure cases with unsatisfactory outputs. We foc...
We derive asymptotically precise expressions for test and train errors of denoising score matching (DSM) in generative diffusion models. The score f...
Digital Surface Models (DSMs) are essential for accurately representing Earth's topography in geospatial analyses. DSMs capture detailed elevations ...
Modern GPUs, with their specialized hardware like tensor cores, are essential for demanding AI and deep learning applications. This study presents a...
Children with attention-deficit/hyperactivity disorder (ADHD) often face barriers to participating in organized sports, particularly when physical edu...
Millimeter-wave radar plays a vital role in 3D object detection for autonomous driving due to its all-weather and all-lighting-condition capabilitie...
MOTIVATION: Protein secondary structure prediction (PSSP) is one of the fundamental and challenging problems in the field of computational biology. Ac...
OBJECTIVE: Although hyperprolactinemia carries a long-term risk of morbidity, the threshold of dopamine D2/3 receptor (D2/3R) occupancy for hyperprola...
Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and patholo...