86 research outputs found
Route Restoration Method for Sparse Taxi GPS trajectory based on Bayesian Network
In order to improve the availability of taxi GPS big data, we restore the chosen route for the sparse taxi GPS trajectory in this work. A trajectory restoration method based on Bayesian network is proposed. Compared with the traditional research solely based on time-spatial variables, this method additionally considers the characteristics of empty/heavy taxi status, weather conditions, drivers, vehicle running and other factors to carry out route restoration. A field case of grid network in Ningbo is taken to verify the applicability of the method, using the taxi GPS trajectory data from Ningbo Taxi Information Management Platform. The case results show that the accuracy of Bayesian network method based on multiple factors reaches 91.4%. Its performance is superior to the Multivariate logistic regression model. In addition, the proposed method is especially suitable for scenarios with a high missing rate of track data, such as a scene with timespan of about 5 min between neighbour trajectories
Learning Sparse Neural Networks with Identity Layers
The sparsity of Deep Neural Networks is well investigated to maximize the
performance and reduce the size of overparameterized networks as possible.
Existing methods focus on pruning parameters in the training process by using
thresholds and metrics. Meanwhile, feature similarity between different layers
has not been discussed sufficiently before, which could be rigorously proved to
be highly correlated to the network sparsity in this paper. Inspired by
interlayer feature similarity in overparameterized models, we investigate the
intrinsic link between network sparsity and interlayer feature similarity.
Specifically, we prove that reducing interlayer feature similarity based on
Centered Kernel Alignment (CKA) improves the sparsity of the network by using
information bottleneck theory. Applying such theory, we propose a plug-and-play
CKA-based Sparsity Regularization for sparse network training, dubbed CKA-SR,
which utilizes CKA to reduce feature similarity between layers and increase
network sparsity. In other words, layers of our sparse network tend to have
their own identity compared to each other. Experimentally, we plug the proposed
CKA-SR into the training process of sparse network training methods and find
that CKA-SR consistently improves the performance of several State-Of-The-Art
sparse training methods, especially at extremely high sparsity. Code is
included in the supplementary materials
Multi-environment lifelong deep reinforcement learning for medical imaging
Deep reinforcement learning(DRL) is increasingly being explored in medical
imaging. However, the environments for medical imaging tasks are constantly
evolving in terms of imaging orientations, imaging sequences, and pathologies.
To that end, we developed a Lifelong DRL framework, SERIL to continually learn
new tasks in changing imaging environments without catastrophic forgetting.
SERIL was developed using selective experience replay based lifelong learning
technique for the localization of five anatomical landmarks in brain MRI on a
sequence of twenty-four different imaging environments. The performance of
SERIL, when compared to two baseline setups: MERT(multi-environment-best-case)
and SERT(single-environment-worst-case) demonstrated excellent performance with
an average distance of pixels from the desired landmark across
all 120 tasks, compared to for MERT and for
SERT(), demonstrating the excellent potential for continuously learning
multiple tasks across dynamically changing imaging environments
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments
While Deep Reinforcement Learning has been widely researched in medical
imaging, the training and deployment of these models usually require powerful
GPUs. Since imaging environments evolve rapidly and can be generated by edge
devices, the algorithm is required to continually learn and adapt to changing
environments, and adjust to low-compute devices. To this end, we developed
three image coreset algorithms to compress and denoise medical images for
selective experience replayed-based lifelong reinforcement learning. We
implemented neighborhood averaging coreset, neighborhood sensitivity-based
sampling coreset, and maximum entropy coreset on full-body DIXON water and
DIXON fat MRI images. All three coresets produced 27x compression with
excellent performance in localizing five anatomical landmarks: left knee, right
trochanter, left kidney, spleen, and lung across both imaging environments.
Maximum entropy coreset obtained the best performance of
average distance error, compared to the conventional lifelong learning
framework's
TaNAC2, a NAC-type wheat transcription factor conferring enhanced multiple abiotic stress tolerances in Arabidopsis
Environmental stresses such as drought, salinity, and cold are major factors that significantly limit agricultural productivity. NAC transcription factors play essential roles in response to various abiotic stresses. However, the paucity of wheat NAC members functionally characterized to date does not match the importance of this plant as a world staple crop. Here, the function of TaNAC2 was characterized in Arabidopsis thaliana. A fragment of TaNAC2 was obtained from suppression subtractive cDNA libraries of wheat treated with polyethylene glycol, and its full-length cDNA was obtained by searching a full-length wheat cDNA library. Gene expression profiles indicated that TaNAC2 was involved in response to drought, salt, cold, and abscisic acid treatment. To test its function, transgenic Arabidopsis lines overexpressing TaNAC2–GFP controlled by the cauliflower mosaic virus 35S promoter were generated. Overexpression of TaNAC2 resulted in enhanced tolerances to drought, salt, and freezing stresses in Arabidopsis, which were simultaneously demonstrated by enhanced expression of abiotic stress-response genes and several physiological indices. Therefore, TaNAC2 has potential for utilization in transgenic breeding to improve abiotic stress tolerances in crops
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.
Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.This work is part of the ‘‘SpatioTemporal Omics Consortium’’ (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org. We would
like to thank the MOTIC China Group, Rongqin Ke (Huaqiao University, Xiamen,
China), Jiazuan Ni (Shenzhen University, Shenzhen, China), Wei Huang (Center
for Excellence in Brain Science and Intelligence Technology, Chinese Academy
of Sciences, Shanghai, China), and Jonathan S. Weissman (Whitehead Institute,
Boston, USA) for their help. This work was supported by the grant of Top Ten
Foundamental Research Institutes of Shenzhen, the Shenzhen Key Laboratory
of Single-Cell Omics (ZDSYS20190902093613831), and the Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011); Longqi Liu
was supported by the National Natural Science Foundation of China
(31900466) and Miguel A. Esteban’s laboratory at the Guangzhou Institutes of
Biomedicine and Health by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), National Natural Science Foundation of China (92068106), and the Guangdong Basic and Applied Basic Research
Foundation (2021B1515120075).S
Cell transcriptomic atlas of the non-human primate Macaca fascicularis.
Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding
Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland
Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for
technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene
expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi
from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing
reagents. This work was supported by the Shenzhen Basic Research Project for Excellent
Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics
(ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In
addition, L.L. was supported by the National Natural Science Foundation of China (31900466),
Y. Hou was supported by the Natural Science Foundation of Guangdong Province
(2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award
(419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences
(XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science
joint research project (GJHZ2093), the National Natural Science Foundation of China
(92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation
(2021B1515120075). M.L. was supported by the National Key Research and Development
Program of China (2021YFC2600200).S
Single-cell chromatin accessibility profiling of cell-state-specific gene regulatory programs during mouse organogenesis
In mammals, early organogenesis begins soon after gastrulation, accompanied by specification of various type of progenitor/precusor cells. In order to reveal dynamic chromatin landscape of precursor cells and decipher the underlying molecular mechanism driving early mouse organogenesis, we performed single-cell ATAC-seq of E8.5-E10.5 mouse embryos. We profiled a total of 101,599 single cells and identified 41 specific cell types at these stages. Besides, by performing integrated analysis of scATAC-seq and public scRNA-seq data, we identified the critical cis-regulatory elements and key transcription factors which drving development of spinal cord and somitogenesis. Furthermore, we intersected accessible peaks with human diseases/traits-related loci and found potential clinical associated single nucleotide variants (SNPs). Overall, our work provides a fundamental source for understanding cell fate determination and revealing the underlying mechanism during postimplantation embryonic development, and expand our knowledge of pathology for human developmental malformations
First M87 Event Horizon Telescope Results. VI. The Shadow and Mass of the Central Black Hole
We present measurements of the properties of the central radio source in M87 using Event Horizon Telescope data obtained during the 2017 campaign. We develop and fit geometric crescent models (asymmetric rings with interior brightness depressions) using two independent sampling algorithms that consider distinct representations of the visibility data. We show that the crescent family of models is statistically preferred over other comparably complex geometric models that we explore. We calibrate the geometric model parameters using general relativistic magnetohydrodynamic (GRMHD) models of the emission region and estimate physical properties of the source. We further fit images generated from GRMHD models directly to the data. We compare the derived emission region and black hole parameters from these analyses with those recovered from reconstructed images. There is a remarkable consistency among all methods and data sets. We find that >50% of the total flux at arcsecond scales comes from near the horizon, and that the emission is dramatically suppressed interior to this region by a factor >10, providing direct evidence of the predicted shadow of a black hole. Across all methods, we measure a crescent diameter of 42 +/- 3 mu as and constrain its fractional width to be <0.5. Associating the crescent feature with the emission surrounding the black hole shadow, we infer an angular gravitational radius of GM/Dc(2) = 3.8 +/- 0.4 mu as. Folding in a distance measurement of 16.8(-0.7)(+0.8) gives a black hole mass of M = 6.5. 0.2 vertical bar(stat) +/- 0.7 vertical bar(sys) x 10(9) M-circle dot. This measurement from lensed emission near the event horizon is consistent with the presence of a central Kerr black hole, as predicted by the general theory of relativity
First M87 Event Horizon Telescope Results. VI. The Shadow and Mass of the Central Black Hole
We present measurements of the properties of the central radio source in M87 using Event Horizon Telescope data obtained during the 2017 campaign. We develop and fit geometric crescent models (asymmetric rings with interior brightness depressions) using two independent sampling algorithms that consider distinct representations of the visibility data. We show that the crescent family of models is statistically preferred over other comparably complex geometric models that we explore. We calibrate the geometric model parameters using general relativistic magnetohydrodynamic (GRMHD) models of the emission region and estimate physical properties of the source. We further fit images generated from GRMHD models directly to the data. We compare the derived emission region and black hole parameters from these analyses with those recovered from reconstructed images. There is a remarkable consistency among all methods and data sets. We find that >50% of the total flux at arcsecond scales comes from near the horizon, and that the emission is dramatically suppressed interior to this region by a factor >10, providing direct evidence of the predicted shadow of a black hole. Across all methods, we measure a crescent diameter of 42 +/- 3 mu as and constrain its fractional width to b
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