90 research outputs found
FGO-ILNS: Tightly Coupled Multi-Sensor Integrated Navigation System Based on Factor Graph Optimization for Autonomous Underwater Vehicle
Multi-sensor fusion is an effective way to enhance the positioning
performance of autonomous underwater vehicles (AUVs). However, underwater
multi-sensor fusion faces challenges such as heterogeneous frequency and
dynamic availability of sensors. Traditional filter-based algorithms suffer
from low accuracy and robustness when sensors become unavailable. The factor
graph optimization (FGO) can enable multi-sensor plug-and-play despite data
frequency. Therefore, we present an FGO-based strapdown inertial navigation
system (SINS) and long baseline location (LBL) system tightly coupled
navigation system (FGO-ILNS). Sensors such as Doppler velocity log (DVL),
magnetic compass pilot (MCP), pressure sensor (PS), and global navigation
satellite system (GNSS) can be tightly coupled with FGO-ILNS to satisfy
different navigation scenarios. In this system, we propose a floating LBL slant
range difference factor model tightly coupled with IMU preintegration factor to
achieve unification of global position above and below water. Furthermore, to
address the issue of sensor measurements not being synchronized with the LBL
during fusion, we employ forward-backward IMU preintegration to construct
sensor factors such as GNSS and DVL. Moreover, we utilize the marginalization
method to reduce the computational load of factor graph optimization.
Simulation and public KAIST dataset experiments have verified that, compared to
filter-based algorithms like the extended Kalman filter and federal Kalman
filter, as well as the state-of-the-art optimization-based algorithm ORB-SLAM3,
our proposed FGO-ILNS leads in accuracy and robustness
Fast Learning Radiance Fields by Shooting Much Fewer Rays
Learning radiance fields has shown remarkable results for novel view
synthesis. The learning procedure usually costs lots of time, which motivates
the latest methods to speed up the learning procedure by learning without
neural networks or using more efficient data structures. However, these
specially designed approaches do not work for most of radiance fields based
methods. To resolve this issue, we introduce a general strategy to speed up the
learning procedure for almost all radiance fields based methods. Our key idea
is to reduce the redundancy by shooting much fewer rays in the multi-view
volume rendering procedure which is the base for almost all radiance fields
based methods. We find that shooting rays at pixels with dramatic color change
not only significantly reduces the training burden but also barely affects the
accuracy of the learned radiance fields. In addition, we also adaptively
subdivide each view into a quadtree according to the average rendering error in
each node in the tree, which makes us dynamically shoot more rays in more
complex regions with larger rendering error. We evaluate our method with
different radiance fields based methods under the widely used benchmarks.
Experimental results show that our method achieves comparable accuracy to the
state-of-the-art with much faster training.Comment: Accepted by lEEE Transactions on lmage Processing 2023. Project Page:
https://zparquet.github.io/Fast-Learning . Code:
https://github.com/zParquet/Fast-Learnin
Privacy-preserving design of graph neural networks with applications to vertical federated learning
The paradigm of vertical federated learning (VFL), where institutions
collaboratively train machine learning models via combining each other's local
feature or label information, has achieved great success in applications to
financial risk management (FRM). The surging developments of graph
representation learning (GRL) have opened up new opportunities for FRM
applications under FL via efficiently utilizing the graph-structured data
generated from underlying transaction networks. Meanwhile, transaction
information is often considered highly sensitive. To prevent data leakage
during training, it is critical to develop FL protocols with formal privacy
guarantees. In this paper, we present an end-to-end GRL framework in the VFL
setting called VESPER, which is built upon a general privatization scheme
termed perturbed message passing (PMP) that allows the privatization of many
popular graph neural architectures.Based on PMP, we discuss the strengths and
weaknesses of specific design choices of concrete graph neural architectures
and provide solutions and improvements for both dense and sparse graphs.
Extensive empirical evaluations over both public datasets and an industry
dataset demonstrate that VESPER is capable of training high-performance GNN
models over both sparse and dense graphs under reasonable privacy budgets
Enhancement on migration and biodegradation of Diaphorobacter sp. LW2 mediated by Pythium ultimum in soil with different particle sizes
IntroductionThe composition and structure of natural soil are very complex, leading to the difficult contact between hydrophobic organic compounds and degrading-bacteria in contaminated soil, making pollutants hard to be removed from the soil. Several researches have reported the bacterial migration in unsaturated soil mediated by fungal hyphae, but bacterial movement in soil of different particle sizes or in heterogeneous soil was unclear. The remediation of contaminated soil enhanced by hyphae still needs further research.MethodsIn this case, the migration and biodegradation of Diaphorobacter sp. LW2 in soil was investigated in presence of Pythium ultimum.ResultsHyphae could promote the growth and migration of LW2 in culture medium. It was also confirmed that LW2 was able to migrate in the growth direction and against the growth direction along hyphae. Mediated by hyphae, motile strain LW2 translocated over 3 cm in soil with different particle size (CS1, 1.0–2.0 mm; CS2, 0.5–1.0mm; MS, 0.25–0.5 mm and FS, <0.25 mm), and it need shorter time in bigger particle soils. In inhomogeneous soil, hyphae participated in the distribution of introduced bacteria, and the total number of bacteria increased. Pythium ultimum enhanced the migration and survival of LW2 in soil, improving the bioremediation of polluted soil.DiscussionThe results of this study indicate that the mobilization of degrading bacteria mediated by Pythium ultimum in soil has great potential for application in bioremediation of contaminated soil
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Over the past few years, graph neural networks (GNNs) have become powerful
and practical tools for learning on (static) graph-structure data. However,
many real-world applications, such as social networks and e-commerce, involve
temporal graphs where nodes and edges are dynamically evolving. Temporal graph
neural networks (TGNNs) have progressively emerged as an extension of GNNs to
address time-evolving graphs and have gradually become a trending research
topic in both academics and industry. Advancing research and application in
such an emerging field necessitates the development of new tools to compose
TGNN models and unify their different schemes for dealing with temporal graphs.
In this work, we introduce LasTGL, an industrial framework that integrates
unified and extensible implementations of common temporal graph learning
algorithms for various advanced tasks. The purpose of LasTGL is to provide the
essential building blocks for solving temporal graph learning tasks, focusing
on the guiding principles of user-friendliness and quick prototyping on which
PyTorch is based. In particular, LasTGL provides comprehensive temporal graph
datasets, TGNN models and utilities along with well-documented tutorials,
making it suitable for both absolute beginners and expert deep learning
practitioners alike.Comment: Preprint; Work in progres
Reconstructing Yeasts Phylogenies and Ancestors from Whole Genome Data
Phylogenetic studies aim to discover evolutionary relationships and histories. These studies are based on similarities of morphological characters and molecular sequences. Currently, widely accepted phylogenetic approaches are based on multiple sequence alignments, which analyze shared gene datasets and concatenate/coalesce these results to a final phylogeny with maximum support. However, these approaches still have limitations, and often have conflicting results with each other. Reconstructing ancestral genomes helps us understand mechanisms and corresponding consequences of evolution. Most existing genome level phylogeny and ancestor reconstruction methods can only process simplified real genome datasets or simulated datasets with identical genome content, unique genome markers, and limited types of evolutionary events. Here, we provide an alternative way to resolve phylogenetic problems based on analyses of real genome data. We use phylogenetic signals from all types of genome level evolutionary events, and overcome the conflicting issues existing in traditional phylogenetic approaches. Further, we build an automated computational pipeline to reconstruct phylogenies and ancestral genomes for two high-resolution real yeast genome datasets. Comparison results with recent studies and publications show that we reconstruct very accurate and robust phylogenies and ancestors. Finally, we identify and analyze the conserved syntenic blocks among reconstructed ancestral genomes and present yeast species
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
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