37 research outputs found
GFM: Building Geospatial Foundation Models via Continual Pretraining
Geospatial technologies are becoming increasingly essential in our world for
a wide range of applications, including agriculture, urban planning, and
disaster response. To help improve the applicability and performance of deep
learning models on these geospatial tasks, various works have begun
investigating foundation models for this domain. Researchers have explored two
prominent approaches for introducing such models in geospatial applications,
but both have drawbacks in terms of limited performance benefit or prohibitive
training cost. Therefore, in this work, we propose a novel paradigm for
building highly effective geospatial foundation models with minimal resource
cost and carbon impact. We first construct a compact yet diverse dataset from
multiple sources to promote feature diversity, which we term GeoPile. Then, we
investigate the potential of continual pretraining from large-scale
ImageNet-22k models and propose a multi-objective continual pretraining
paradigm, which leverages the strong representations of ImageNet while
simultaneously providing the freedom to learn valuable in-domain features. Our
approach outperforms previous state-of-the-art geospatial pretraining methods
in an extensive evaluation on seven downstream datasets covering various tasks
such as change detection, classification, multi-label classification, semantic
segmentation, and super-resolution
GroundLink: A Dataset Unifying Human Body Movement and Ground Reaction Dynamics
The physical plausibility of human motions is vital to various applications
in fields including but not limited to graphics, animation, robotics, vision,
biomechanics, and sports science. While fully simulating human motions with
physics is an extreme challenge, we hypothesize that we can treat this
complexity as a black box in a data-driven manner if we focus on the ground
contact, and have sufficient observations of physics and human activities in
the real world. To prove our hypothesis, we present GroundLink, a unified
dataset comprised of captured ground reaction force (GRF) and center of
pressure (CoP) synchronized to standard kinematic motion captures. GRF and CoP
of GroundLink are not simulated but captured at high temporal resolution using
force platforms embedded in the ground for uncompromising measurement accuracy.
This dataset contains 368 processed motion trials (~1.59M recorded frames) with
19 different movements including locomotion and weight-shifting actions such as
tennis swings to signify the importance of capturing physics paired with
kinematics. GroundLinkNet, our benchmark neural network model trained with
GroundLink, supports our hypothesis by predicting GRFs and CoPs accurately and
plausibly on unseen motions from various sources. The dataset, code, and
benchmark models are made public for further research on various downstream
tasks leveraging the rich physics information at
https://csr.bu.edu/groundlink/
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Earth system forecasting has traditionally relied on complex physical models
that are computationally expensive and require significant domain expertise. In
the past decade, the unprecedented increase in spatiotemporal Earth observation
data has enabled data-driven forecasting models using deep learning techniques.
These models have shown promise for diverse Earth system forecasting tasks but
either struggle with handling uncertainty or neglect domain-specific prior
knowledge, resulting in averaging possible futures to blurred forecasts or
generating physically implausible predictions. To address these limitations, we
propose a two-stage pipeline for probabilistic spatiotemporal forecasting: 1)
We develop PreDiff, a conditional latent diffusion model capable of
probabilistic forecasts. 2) We incorporate an explicit knowledge control
mechanism to align forecasts with domain-specific physical constraints. This is
achieved by estimating the deviation from imposed constraints at each denoising
step and adjusting the transition distribution accordingly. We conduct
empirical studies on two datasets: N-body MNIST, a synthetic dataset with
chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset.
Specifically, we impose the law of conservation of energy in N-body MNIST and
anticipated precipitation intensity in SEVIR. Experiments demonstrate the
effectiveness of PreDiff in handling uncertainty, incorporating domain-specific
prior knowledge, and generating forecasts that exhibit high operational
utility.Comment: Technical repor
A genome-wide association study identifies FSHR rs2300441 associated with follicle-stimulating hormone levels
Follicle-stimulating hormone (FSH) and luteinizing hormone (LH) play critical roles in female reproduction, while the underlying genetic basis is poorly understood. Genome-wide association studies (GWASs) of FSH and LH levels were conducted in 2590 Chinese females including 1882 polycystic ovary syndrome (PCOS) cases and 708 controls. GWAS for FSH level identified multiple variants at FSHR showing genome-wide significance with the top variant (rs2300441) located in the intron of FSHR. The A allele of rs2300441 led to a reduced level of FSH in the PCOS group (β = −.43, P = 6.70 × 10−14) as well as in the control group (β = −.35, P = 6.52 × 10−4). In the combined sample, this association was enhanced after adjusting for the PCOS status (before: β = −.38, P = 1.77 × 10−13; after: β = −.42, P = 3.33 × 10−16), suggesting the genetic effect is independent of the PCOS status. The rs2300441 explained sevenfold higher proportion of the FSH variance than the total variance explained by the two previously reported FSHR missense variants (rs2300441 R2 = 1.40% vs rs6166 R2 = 0.17%, rs6165 R2 = 0.03%). GWAS for LH did not identify any genome-wide significant associations. In conclusion, we identified genome-wide significant association between variants in FSHR and circulating FSH first, with the top associated variant rs2300441 might be a primary contributor at the population level
Constitutive analysis for hot deformation behaviour of novel bimetal consisting of pearlitic steel and low carbon steel
To understand the high temperature flow behaviour of a novel pearlitic steel (PS) and low carbon steel (LCS) bimetal, hot compression tests in a wide range of temperature and strain rate were conducted on a Gleeble 3500 thermo mechanical simulator, and the constitutive model was developed based on the experimental data. The measured true stress-strain curves exhibited three types of variation patterns, which are (i) a plateau type, (ii) single peak type and (iii) multi peaks type. These patterns well displayed the effects of the deformation temperature, strain rate and plastic strain on the flow behaviour of the bimetal. By incorporating the Zener-Hollomon parameter and material parameter functions of α(ε), n(ε), Q(ε) and A(ε) into Arrhenius-type constitutive equation, the flow stress values predicted by the proposed model show a good agreement with experimental results by the evidence of reproducing true stress-strain curves accurately, high value of correlation coefficient (R=0.9873) and low value of average absolute relative error (AARE=4.81%). The proposed constitutive equation can be used to realise numerical simulation and determine processing parameters during hot-working of the PS/LCS bimetal
Coupled modelling of fluid flow, heat transfer and interfacial evolution behaviors in bimetal casting process
Additional file 1 of Ectopic intrapulmonary thyroid masquerading as metastatic carcinoma of the lung: a rare case scenario
Supplementary Material