94 research outputs found
Human Body Digital Twin: A Master Plan
The human body DT has the potential to revolutionize healthcare and wellness,
but its responsible and effective implementation requires consideration of
various factors. This article presents a comprehensive overview of the current
status and future prospects of the human body DT and proposes a five-level
roadmap for its development. The roadmap covers the development of various
components, such as wearable devices, data collection, data analysis, and
decision-making systems. The article also highlights the necessary support,
security, cost, and ethical considerations that must be addressed in order to
ensure responsible and effective implementation of the human body DT. The
proposed roadmap provides a framework for guiding future development and offers
a unique perspective on the future of the human body DT, facilitating new
interdisciplinary research and innovative solutions in this rapidly evolving
field.Comment: 3 figure
VCL Challenges 2023 at ICCV 2023 Technical Report: Bi-level Adaptation Method for Test-time Adaptive Object Detection
This report outlines our team's participation in VCL Challenges B Continual
Test_time Adaptation, focusing on the technical details of our approach. Our
primary focus is Testtime Adaptation using bi_level adaptations, encompassing
image_level and detector_level adaptations. At the image level, we employ
adjustable parameterbased image filters, while at the detector level, we
leverage adjustable parameterbased mean teacher modules. Ultimately, through
the utilization of these bi_level adaptations, we have achieved a remarkable
38.3% mAP on the target domain of the test set within VCL Challenges B. It is
worth noting that the minimal drop in mAP, is mearly 4.2%, and the overall
performance is 32.5% mAP
Diffeomorphic Image Registration with Neural Velocity Field
Diffeomorphic image registration, offering smooth transformation and topology
preservation, is required in many medical image analysis tasks.Traditional
methods impose certain modeling constraints on the space of admissible
transformations and use optimization to find the optimal transformation between
two images. Specifying the right space of admissible transformations is
challenging: the registration quality can be poor if the space is too
restrictive, while the optimization can be hard to solve if the space is too
general. Recent learning-based methods, utilizing deep neural networks to learn
the transformation directly, achieve fast inference, but face challenges in
accuracy due to the difficulties in capturing the small local deformations and
generalization ability. Here we propose a new optimization-based method named
DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which
utilizes deep neural network to model the space of admissible transformations.
A multilayer perceptron (MLP) with sinusoidal activation function is used to
represent the continuous velocity field and assigns a velocity vector to every
point in space, providing the flexibility of modeling complex deformations as
well as the convenience of optimization. Moreover, we propose a cascaded image
registration framework (Cas-DNVF) by combining the benefits of both
optimization and learning based methods, where a fully convolutional neural
network (FCN) is trained to predict the initial deformation, followed by DNVF
for further refinement. Experiments on two large-scale 3D MR brain scan
datasets demonstrate that our proposed methods significantly outperform the
state-of-the-art registration methods.Comment: WACV 202
Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction
We present Hybrid-CSR, a geometric deep-learning model that combines explicit
and implicit shape representations for cortical surface reconstruction.
Specifically, Hybrid-CSR begins with explicit deformations of template meshes
to obtain coarsely reconstructed cortical surfaces, based on which the oriented
point clouds are estimated for the subsequent differentiable poisson surface
reconstruction. By doing so, our method unifies explicit (oriented point
clouds) and implicit (indicator function) cortical surface reconstruction.
Compared to explicit representation-based methods, our hybrid approach is more
friendly to capture detailed structures, and when compared with implicit
representation-based methods, our method can be topology aware because of
end-to-end training with a mesh-based deformation module. In order to address
topology defects, we propose a new topology correction pipeline that relies on
optimization-based diffeomorphic surface registration. Experimental results on
three brain datasets show that our approach surpasses existing implicit and
explicit cortical surface reconstruction methods in numeric metrics in terms of
accuracy, regularity, and consistency
Variation of soil bacterial communities along a chronosequence of Eucalyptus plantation
Eucalyptus is harvested for wood and fiber production in many tropical and sub-tropical habitats globally. Plantation has been controversial because of its influence on the surrounding environment, however, the influence of massive Eucalyptus planting on soil microbial communities is unclear. Here we applied high-throughput sequencing of the 16S rRNA gene to assess the microbial community composition and diversity of planting chronosequences, involving two, five and ten years of Eucalyptus plantation, comparing to that of secondary-forest in South China. We found that significant changes in the composition of soil bacteria occurred when the forests were converted from secondary-forest to Eucalyptus. The bacterial community structure was clearly distinct from control and five year samples after Eucalyptus was grown for 2 and 10 years, highlighting the influence of this plantation on local soil microbial communities. These groupings indicated a cycle of impact (2 and 10 year plantations) and low impact (5-year plantations) in this chronosequence of Eucalyptus plantation. Community patterns were underpinned by shifts in soil properties such as pH and phosphorus concentration. Concurrently, key soil taxonomic groups such as Actinobacteria showed abundance shifts, increasing in impacted plantations and decreasing in low impacted samples. Shifts in taxonomy were reflected in a shift in metabolic potential, including pathways for nutrient cycles such as carbon fixation, which changed in abundance over time following Eucalyptus plantation. Combined these results confirm that Eucalyptus plantation can change the community structure and diversity of soil microorganisms with strong implications for land-management and maintaining the health of these ecosystems
Intelligent machines work in unstructured environments by differential neuromorphic computing
Efficient operation of intelligent machines in the real world requires
methods that allow them to understand and predict the uncertainties presented
by the unstructured environments with good accuracy, scalability and
generalization, similar to humans. Current methods rely on pretrained networks
instead of continuously learning from the dynamic signal properties of working
environments and suffer inherent limitations, such as data-hungry procedures,
and limited generalization capabilities. Herein, we present a memristor-based
differential neuromorphic computing, perceptual signal processing and learning
method for intelligent machines. The main features of environmental information
such as amplification (>720%) and adaptation (<50%) of mechanical stimuli
encoded in memristors, are extracted to obtain human-like processing in
unstructured environments. The developed method takes advantage of the
intrinsic multi-state property of memristors and exhibits good scalability and
generalization, as confirmed by validation in two different application
scenarios: object grasping and autonomous driving. In the former, a robot hand
experimentally realizes safe and stable grasping through fast learning (in ~1
ms) the unknown object features (e.g., sharp corner and smooth surface) with a
single memristor. In the latter, the decision-making information of 10
unstructured environments in autonomous driving (e.g., overtaking cars,
pedestrians) is accurately (94%) extracted with a 40*25 memristor array. By
mimicking the intrinsic nature of human low-level perception mechanisms, the
electronic memristive neuromorphic circuit-based method, presented here shows
the potential for adapting to diverse sensing technologies and helping
intelligent machines generate smart high-level decisions in the real world.Comment: 16 pages, 5 figure
The role of tidal interactions in the formation of slowly rotating early-type stars in young star clusters
The split main sequences found in the colour-magnitude diagrams of star
clusters younger than ~600 Myr are suggested to be caused by the dichotomy of
stellar rotation rates of upper main-sequence stars. Tidal interactions have
been suggested as a possible explanation of the dichotomy of the stellar
rotation rates. This hypothesis proposes that the slow rotation rates of stars
along the split main sequences are caused by tidal interactions in binaries. To
test this scenario, we measured the variations in the radial velocities of
slowly rotating stars along the split main sequence of the young Galactic
cluster NGC 2422 (~90 Myr) using spectra obtained at multiple epochs with the
Canada-France-Hawai'i Telescope. Our results show that most slowly rotating
stars are not radial-velocity variables. Using the theory of dynamical tides,
we find that the binary separations necessary to fully or partially synchronise
our spectroscopic targets, on time-scales shorter than the cluster age, predict
much larger radial velocity variations across multiple-epoch observations, or a
much larger radial velocity dispersion at a single epoch, than the observed
values. This indicates that tidal interactions are not the dominant mechanism
to form slowly rotating stars along the split main sequences. As the
observations of the rotation velocity distribution among B- and A-type stars in
binaries of larger separations hint at a much stronger effect of braking with
age, we discuss the consequences of relaxing the constraints of the dynamical
tides theory.Comment: 14 pages, 10 figures, 2 tables, accepted for publication in MNRA
Microbial Communities in Long-Term, Water-Flooded Petroleum Reservoirs with Different in situ Temperatures in the Huabei Oilfield, China
The distribution of microbial communities in the Menggulin (MGL) and Ba19 blocks in the Huabei Oilfield, China, were studied based on 16S rRNA gene analysis. The dominant microbes showed obvious block-specific characteristics, and the two blocks had substantially different bacterial and archaeal communities. In the moderate-temperature MGL block, the bacteria were mainly Epsilonproteobacteria and Alphaproteobacteria, and the archaea were methanogens belonging to Methanolinea, Methanothermobacter, Methanosaeta, and Methanocella. However, in the high-temperature Ba19 block, the predominant bacteria were Gammaproteobacteria, and the predominant archaea were Methanothermobacter and Methanosaeta. In spite of shared taxa in the blocks, differences among wells in the same block were obvious, especially for bacterial communities in the MGL block. Compared to the bacterial communities, the archaeal communities were much more conserved within blocks and were not affected by the variation in the bacterial communities
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