241 research outputs found
Novel Computational Methods for State Space Filtering
The state-space formulation for time-dependent models has been long used invarious applications in science and engineering. While the classical Kalman filter(KF) provides optimal posterior estimation under linear Gaussian models, filteringin nonlinear and non-Gaussian environments remains challenging.Based on the Monte Carlo approximation, the classical particle filter (PF) can providemore precise estimation under nonlinear non-Gaussian models. However, it suffers fromparticle degeneracy. Drawing from optimal transport theory, the stochastic map filter(SMF) accommodates a solution to this problem, but its performance is influenced bythe limited flexibility of nonlinear map parameterisation. To account for these issues,a hybrid particle-stochastic map filter (PSMF) is first proposed in this thesis, wherethe two parts of the split likelihood are assimilated by the PF and SMF, respectively.Systematic resampling and smoothing are employed to alleviate the particle degeneracycaused by the PF. Furthermore, two PSMF variants based on the linear and nonlinearmaps (PSMF-L and PSMF-NL) are proposed, and their filtering performance is comparedwith various benchmark filters under different nonlinear non-Gaussian models.Although achieving accurate filtering results, the particle-based filters require expensive computations because of the large number of samples involved. Instead, robustKalman filters (RKFs) provide efficient solutions for the linear models with heavy-tailednoise, by adopting the recursive estimation framework of the KF. To exploit the stochasticcharacteristics of the noise, the use of heavy-tailed distributions which can fit variouspractical noises constitutes a viable solution. Hence, this thesis also introduces a novelRKF framework, RKF-SGαS, where the signal noise is assumed to be Gaussian and theheavy-tailed measurement noise is modelled by the sub-Gaussian α-stable (SGαS) distribution. The corresponding joint posterior distribution of the state vector and auxiliaryrandom variables is estimated by the variational Bayesian (VB) approach. Four differentminimum mean square error (MMSE) estimators of the scale function are presented.Besides, the RKF-SGαS is compared with the state-of-the-art RKFs under three kinds ofheavy-tailed measurement noises, and the simulation results demonstrate its estimationaccuracy and efficiency.One notable limitation of the proposed RKF-SGαS is its reliance on precise modelparameters, and substantial model errors can potentially impede its filtering performance. Therefore, this thesis also introduces a data-driven RKF method, referred to asRKFnet, which combines the conventional RKF framework with a deep learning technique. An unsupervised scheduled sampling technique (USS) is proposed to improve theistability of the training process. Furthermore, the advantages of the proposed RKFnetare quantified with respect to various traditional RKFs
Magnons in Ferromagnetic Metallic Manganites
Ferromagnetic (FM) manganites, a group of likely half-metallic oxides, are of
special interest not only because they are a testing ground of the classical
doubleexchange interaction mechanism for the colossal magnetoresistance, but
also because they exhibit an extraordinary arena of emergent phenomena. These
emergent phenomena are related to the complexity associated with strong
interplay between charge, spin, orbital, and lattice. In this review, we focus
on the use of inelastic neutron scattering to study the spin dynamics, mainly
the magnon excitations in this class of FM metallic materials. In particular,
we discussed the unusual magnon softening and damping near the Brillouin zone
boundary in relatively narrow band compounds with strong Jahn-Teller lattice
distortion and charge/orbital correlations. The anomalous behaviors of magnons
in these compounds indicate the likelihood of cooperative excitations involving
spin, lattice, as well as orbital degrees of freedom.Comment: published in J. Phys.: Cond. Matt. 20 figure
How can sustainable public transport be improved? A traffic sign recognition approach using convolutional neural network
Sustainable public transport is an important factor to boost urban economic development, and it is also an important part of building a low-carbon environmental society. The application of driverless technology in public transport injects new impetus into its sustainable development. Road traffic sign recognition is the key technology of driverless public transport. It is particularly important to adopt innovative algorithms to optimize the accuracy of traffic sign recognition and build sustainable public transport. Therefore, this paper proposes a convolutional neural network (CNN) based on k-means to optimize the accuracy of traffic sign recognition, and it proposes a sparse maximum CNN to identify difficult traffic signs through hierarchical classification. In the rough classification stage, k-means CNN is used to extract features, and improved support vector machine (SVM) is used for classification. Then, in the fine classification stage, sparse maximum CNN is used for classification. The research results show that the algorithm improves the accuracy of traffic sign recognition more comprehensively and effectively, and it can be effectively applied in unmanned driving technology, which will also bring new breakthroughs for the sustainable development of public transport
A Risk Model Developed Based on Homologous Recombination Deficiency Predicts Overall Survival in Patients With Lower Grade Glioma
The role of homologous recombination deficiency (HRD) in lower grade glioma (LGG) has not
been elucidated, and accurate prognostic prediction is also important for the treatment and
management of LGG. The aim of this study was to construct an HRD-based risk model and to
explore the immunological and molecular characteristics of this risk model. The HRD score
threshold = 10 was determined from 506 LGG samples in The Cancer Genome Atlas cohort
using the best cut-off value, and patients with highHRDscores had worse overall survival. A total
of 251 HRD-related genes were identified by analyzing differentially expressed genes, 182 of
which were associated with survival. A risk score model based on HRD-related genes was
constructed using univariate Cox regression, least absolute shrinkage and selection operator
regression, and stepwise regression, and patients were divided into high- and low-risk groups
using the median risk score. High-risk patients had significantly worse overall survival than lowrisk
patients. The risk model had excellent predictive performance for overall survival in LGG and
was found to be an independent risk factor. The prognostic value of the riskmodel was validated
using an independent cohort. In addition, the risk score was associated with tumor mutation
burden and immune cell infiltration in LGG. High-risk patients had higher HRD scores and “hot”
tumor immune microenvironment, which could benefit from poly-ADP-ribose polymerase
inhibitors and immune checkpoint inhibitors. Overall, this big data study determined the
threshold of HRD score in LGG, identified HRD-related genes, developed a risk model
based on HRD-related genes, and determined the molecular and immunological
characteristics of the risk model. This provides potential new targets for future targeted
therapies and facilitates the development of individualized immunotherapy to improve prognosis
Evolution of spin-wave excitations in ferromagnetic metallic manganites
Neutron scattering results are presented for spin-wave excitations of three
ferromagnetic metallic MnO manganites (where and
are rare- and alkaline-earth ions), which when combined with
previous work elucidate systematics of the interactions as a function of
carrier concentration , on-site disorder, and strength of the lattice
distortion. The long wavelength spin dynamics show only a very weak dependence
across the series. The ratio of fourth to first neighbor exchange ()
that controls the zone boundary magnon softening changes systematically with
, but does not depend on the other parameters. None of the prevailing models
can account for these behaviors.Comment: Submitted to Phys. Rev. Let
Effect of nematic order on the low-energy spin fluctuations in detwinned BaFeNiAs
The origin of nematic order remains one of the major debates in iron-based
superconductors. In theories based on spin nematicity, one major prediction is
that the spin-spin correlation length at (0,) should decrease with
decreasing temperature below the structural transition temperature . Here
we report inelastic neutron scattering studies on the low-energy spin
fluctuations in BaFeNiAs under uniaxial pressure. Both
intensity and spin-spin correlation start to show anisotropic behavior at high
temperature, while the reduction of the spin-spin correlation length at
(0,) happens just below , suggesting strong effect of nematic order
on low-energy spin fluctuations. Our results favor the idea that treats the
spin degree of freedom as the driving force of the electronic nematic order.Comment: 5 pages, 4 figure
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