307 research outputs found
Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction
This study introduces a novel approach for image reconstruction based on a
diffusion model conditioned on the native data domain. Our method is applied to
multi-coil MRI and quantitative MRI reconstruction, leveraging the
domain-conditioned diffusion model within the frequency and parameter domains.
The prior MRI physics are used as embeddings in the diffusion model, enforcing
data consistency to guide the training and sampling process, characterizing MRI
k-space encoding in MRI reconstruction, and leveraging MR signal modeling for
qMRI reconstruction. Furthermore, a gradient descent optimization is
incorporated into the diffusion steps, enhancing feature learning and improving
denoising. The proposed method demonstrates a significant promise, particularly
for reconstructing images at high acceleration factors. Notably, it maintains
great reconstruction accuracy and efficiency for static and quantitative MRI
reconstruction across diverse anatomical structures. Beyond its immediate
applications, this method provides potential generalization capability, making
it adaptable to inverse problems across various domains
Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement
This paper proposes a novel self-supervised learning method, RELAX-MORE, for
quantitative MRI (qMRI) reconstruction. The proposed method uses an
optimization algorithm to unroll a model-based qMRI reconstruction into a deep
learning framework, enabling the generation of highly accurate and robust MR
parameter maps at imaging acceleration. Unlike conventional deep learning
methods requiring a large amount of training data, RELAX-MORE is a
subject-specific method that can be trained on single-subject data through
self-supervised learning, making it accessible and practically applicable to
many qMRI studies. Using the quantitative mapping as an example at
different brain, knee and phantom experiments, the proposed method demonstrates
excellent performance in reconstructing MR parameters, correcting imaging
artifacts, removing noises, and recovering image features at imperfect imaging
conditions. Compared with other state-of-the-art conventional and deep learning
methods, RELAX-MORE significantly improves efficiency, accuracy, robustness,
and generalizability for rapid MR parameter mapping. This work demonstrates the
feasibility of a new self-supervised learning method for rapid MR parameter
mapping, with great potential to enhance the clinical translation of qMRI
Dynamic RACH Partition for Massive Access of Differentiated M2M Services
In machine-to-machine (M2M) networks, a key challenge is to overcome the overload problem caused by random access requests from massive machine-type communication (MTC) devices. When differentiated services coexist, such as delay-sensitive and delay-tolerant services, the problem becomes more complicated and challenging. This is because delay-sensitive services often use more aggressive policies, and thus, delay-tolerant services get much fewer chances to access the network. To conquer the problem, we propose an efficient mechanism for massive access control over differentiated M2M services, including delay-sensitive and delay-tolerant services. Specifically, based on the traffic loads of the two types of services, the proposed scheme dynamically partitions and allocates the random access channel (RACH) resource to each type of services. The RACH partition strategy is thoroughly optimized to increase the access performances of M2M networks. Analyses and
simulation demonstrate the effectiveness of our design. The proposed scheme can outperform the baseline access class barring (ACB) scheme, which ignores service types in access control, in terms of access success probability and the average access delay
Improving lignocellulose thermal stability by chemical modification with boric acid for incorporating into polyamide
The preparation of bio-composites based on engineering plastic is always restricted by the low thermal stability of lignocellulose. In this study, the thermal stability of lignocellulose was improved by boric acid modification. Then, the borated lignocellulose was characterized to analyze the mechanism of involved in the improvement of thermal stability. Furthermore, the untreated and borated lignocellulose was combined with polyamide 6 to produce bio-composites. The effects of lignocellulose content and boric acid modification on the color, thermal stability and mechanical properties of the resulting composites were compared and analyzed. Boric acid protected lignocellulose from thermal degradation, increasing the lightness of the resulting composites. However, boric acid appeared to have a negative effect on the mechanical strength of the resulting composites. In summary, this study demonstrated that bio-composites based on engineering plastic could be prepared by improving the thermal stability of lignocellulose using a boric acid treatment
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting
With the acceleration of urbanization, traffic forecasting has become an
essential role in smart city construction. In the context of spatio-temporal
prediction, the key lies in how to model the dependencies of sensors. However,
existing works basically only consider the micro relationships between sensors,
where the sensors are treated equally, and their macroscopic dependencies are
neglected. In this paper, we argue to rethink the sensor's dependency modeling
from two hierarchies: regional and global perspectives. Particularly, we merge
original sensors with high intra-region correlation as a region node to
preserve the inter-region dependency. Then, we generate representative and
common spatio-temporal patterns as global nodes to reflect a global dependency
between sensors and provide auxiliary information for spatio-temporal
dependency learning. In pursuit of the generality and reality of node
representations, we incorporate a Meta GCN to calibrate the regional and global
nodes in the physical data space. Furthermore, we devise the cross-hierarchy
graph convolution to propagate information from different hierarchies. In a
nutshell, we propose a Hierarchical Information Enhanced Spatio-Temporal
prediction method, HIEST, to create and utilize the regional dependency and
common spatio-temporal patterns. Extensive experiments have verified the
leading performance of our HIEST against state-of-the-art baselines. We
publicize the code to ease reproducibility.Comment: 9 pages, accepted by CIKM'2
MLPST: MLP is All You Need for Spatio-Temporal Prediction
Traffic prediction is a typical spatio-temporal data mining task and has
great significance to the public transportation system. Considering the demand
for its grand application, we recognize key factors for an ideal
spatio-temporal prediction method: efficient, lightweight, and effective.
However, the current deep model-based spatio-temporal prediction solutions
generally own intricate architectures with cumbersome optimization, which can
hardly meet these expectations. To accomplish the above goals, we propose an
intuitive and novel framework, MLPST, a pure multi-layer perceptron
architecture for traffic prediction. Specifically, we first capture spatial
relationships from both local and global receptive fields. Then, temporal
dependencies in different intervals are comprehensively considered. Through
compact and swift MLP processing, MLPST can well capture the spatial and
temporal dependencies while requiring only linear computational complexity, as
well as model parameters that are more than an order of magnitude lower than
baselines. Extensive experiments validated the superior effectiveness and
efficiency of MLPST against advanced baselines, and among models with optimal
accuracy, MLPST achieves the best time and space efficiency
Acute rejection is associated with antibodies to non-Gal antigens in baboons using Gal-knockout pig kidneys
We transplanted kidneys from α1,3-galactosyltransferase knockout (GalT-KO) pigs into six baboons using two different immunosuppressive regimens, but most of the baboons died from severe acute humoral xenograft rejection. Circulating induced antibodies to non-Gal antigens were markedly elevated at rejection, which mediated strong complement-dependent cytotoxicity against GalT-KO porcine target cells. These data suggest that antibodies to non-Gal antigens will present an additional barrier to transplantation of organs from GalT-KO pigs to humans. © 2005 Nature Publishing Group
Conversion of lignocellulose into biochar and furfural through boron complexation and esterification reactions
The aim of this work was to study the conversion of lignocellulose into biochar and furfural through boron complexation and esterification reaction. Boric acid was used to modify lignocellulose to obtain a high biochar yield boron-lignocellulosic material through complexation and esterification reactions. Furthermore, clean furfural was obtained as the gas products of boron-lignocellulosic materials pyrolysis. The structures of the boron-lignocellulosic materials were characterized, and their compound principle was revealed. Boric acid treatments increased the initial thermal degradation temperature of lignocellulose and promoted the formation of biochar and furfural. The biochar yield rate increased by 135.7% from 18.6 to 42.9% at 600 ℃ after 5% boric acid solution treatment. Compared with pure lignocellulose, cleaner and higher quantities of furfural were obtained from boron-lignocellulose pyrolysis. Finally, the possible chemical decomposition pathways of boron-lignocellulosic materials were identified. This study provides a new perspective on the thermochemical conversion of lignocellulose to furfural and biochar
- …