78 research outputs found
Shape Modeling with Spline Partitions
Shape modelling (with methods that output shapes) is a new and important task
in Bayesian nonparametrics and bioinformatics. In this work, we focus on
Bayesian nonparametric methods for capturing shapes by partitioning a space
using curves. In related work, the classical Mondrian process is used to
partition spaces recursively with axis-aligned cuts, and is widely applied in
multi-dimensional and relational data. The Mondrian process outputs
hyper-rectangles. Recently, the random tessellation process was introduced as a
generalization of the Mondrian process, partitioning a domain with non-axis
aligned cuts in an arbitrary dimensional space, and outputting polytopes.
Motivated by these processes, in this work, we propose a novel parallelized
Bayesian nonparametric approach to partition a domain with curves, enabling
complex data-shapes to be acquired. We apply our method to HIV-1-infected human
macrophage image dataset, and also simulated datasets sets to illustrate our
approach. We compare to support vector machines, random forests and
state-of-the-art computer vision methods such as simple linear iterative
clustering super pixel image segmentation. We develop an R package that is
available at
\url{https://github.com/ShufeiGe/Shape-Modeling-with-Spline-Partitions}
Random Tessellation Forests
Space partitioning methods such as random forests and the Mondrian process
are powerful machine learning methods for multi-dimensional and relational
data, and are based on recursively cutting a domain. The flexibility of these
methods is often limited by the requirement that the cuts be axis aligned. The
Ostomachion process and the self-consistent binary space partitioning-tree
process were recently introduced as generalizations of the Mondrian process for
space partitioning with non-axis aligned cuts in the two dimensional plane.
Motivated by the need for a multi-dimensional partitioning tree with non-axis
aligned cuts, we propose the Random Tessellation Process (RTP), a framework
that includes the Mondrian process and the binary space partitioning-tree
process as special cases. We derive a sequential Monte Carlo algorithm for
inference, and provide random forest methods. Our process is self-consistent
and can relax axis-aligned constraints, allowing complex inter-dimensional
dependence to be captured. We present a simulation study, and analyse gene
expression data of brain tissue, showing improved accuracies over other
methods.Comment: 11 pages, 4 figure
Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors
Visual localization is an attractive problem that estimates the camera
localization from database images based on the query image. It is a crucial
task for various applications, such as autonomous vehicles, assistive
navigation and augmented reality. The challenging issues of the task lie in
various appearance variations between query and database images, including
illumination variations, dynamic object variations and viewpoint variations. In
order to tackle those challenges, Panoramic Annular Localizer into which
panoramic annular lens and robust deep image descriptors are incorporated is
proposed in this paper. The panoramic annular images captured by the single
camera are processed and fed into the NetVLAD network to form the active deep
descriptor, and sequential matching is utilized to generate the localization
result. The experiments carried on the public datasets and in the field
illustrate the validation of the proposed system.Comment: Accepted by ITSC 201
Genome-Wide Association with Uncertainty in the Genetic Similarity Matrix
Genome-wide association studies (GWASs) are often confounded by population stratification and structure. Linear mixed models (LMMs) are a powerful class of methods for uncovering genetic effects, while controlling for such confounding. LMMs include random effects for a genetic similarity matrix, and they assume that a true genetic similarity matrix is known. However, uncertainty about the phylogenetic structure of a study population may degrade the quality of LMM results. This may happen in bacterial studies in which the number of samples or loci is small, or in studies with low-quality genotyping. In this study, we develop methods for linear mixed models in which the genetic similarity matrix is unknown and is derived from Markov chain Monte Carlo estimates of the phylogeny. We apply our model to a GWAS of multidrug resistance in tuberculosis, and illustrate our methods on simulated data
Improving Model Robustness with Latent Distribution Locally and Globally
In this work, we consider model robustness of deep neural networks against
adversarial attacks from a global manifold perspective. Leveraging both the
local and global latent information, we propose a novel adversarial training
method through robust optimization, and a tractable way to generate Latent
Manifold Adversarial Examples (LMAEs) via an adversarial game between a
discriminator and a classifier. The proposed adversarial training with latent
distribution (ATLD) method defends against adversarial attacks by crafting
LMAEs with the latent manifold in an unsupervised manner. ATLD preserves the
local and global information of latent manifold and promises improved
robustness against adversarial attacks. To verify the effectiveness of our
proposed method, we conduct extensive experiments over different datasets
(e.g., CIFAR-10, CIFAR-100, SVHN) with different adversarial attacks (e.g.,
PGD, CW), and show that our method substantially outperforms the
state-of-the-art (e.g., Feature Scattering) in adversarial robustness by a
large accuracy margin. The source codes are available at
https://github.com/LitterQ/ATLD-pytorch
Mutual-cognition for proactive human-robot collaboration: A mixed reality-enabled visual reasoning-based method
Human-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents’ operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent’s actions and environmental changes to make task planning strategies satisfying human–robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries, the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation
Development of a real-time loop-mediated isothermal amplification method for monitoring Pseudomonas lurida in raw milk throughout the year of pasture
IntroductionThe psychrophilic bacterium Pseudomonas lurida (P. lurida) and its thermostable alkaline proteases can seriously damage raw milk quality.MethodsIn this study, specific primers were designed for P. lurida’s gyrB and aprX genes, and a real-time loop-mediated isothermal amplification (RealAmp) rapid detection method was developed for the early monitoring of P. lurida and its proteases in raw milk. A phylogenetic tree of the gyrB and aprX genes of P. lurida was constructed to analyze the homology of the design sequence of the RealAmp primer. The DNA of 2 strains of P. lurida and 44 strains of non-P. lurida were detected via RealAmp to analyze the specificity of the primer.ResultsIt was found that aprX-positive proteases were produced by P. lurida-positive strains only when Pseudomonas fluorescens was negative. The dissociation temperatures of gyrB and aprX in the RealAmp-amplified products were approximately 85.0°C and 90.0°C, respectively. Moreover, DNA was detected through a 10-fold dilution of P. lurida in a pure bacterial solution and artificially contaminated skimmed milk. The limit of detection of P. lurida DNA copy number in the pure bacterial solution was 8.6 copies/μL and that in the 10% skimmed milk was 5.5 copies/μL. Further, 144 raw milk samples throughout the year from three farms in Hebei province were analyzed using RealAmp. The highest detection rate of P. lurida was 56% in the first and third quarters, and that of proteases was 36% in the second quarter. The detection rates of P. lurida and its proteases were the highest in samples collected from pasture 2 (52 and 46%, respectively), and the ability of P. lurida to produce proteases reached 88%.DiscussionIn conclusion, RealAmp established an early and rapid method for the detection of P. lurida and its proteases in raw milk samples, allowing the identification and control of contamination sources in a timely manner to ensure the quality of milk and dairy products
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