1,504 research outputs found
Riemannian Holonomy Groups of Statistical Manifolds
Normal distribution manifolds play essential roles in the theory of
information geometry, so do holonomy groups in classification of Riemannian
manifolds. After some necessary preliminaries on information geometry and
holonomy groups, it is presented that the corresponding Riemannian holonomy
group of the -dimensional normal distribution is
, for all . As a
generalization on exponential family, a list of holonomy groups follows.Comment: 11 page
Functional MRI-specific alterations in frontoparietal network in mild cognitive impairment: an ALE meta-analysis
BackgroundMild cognitive impairment (MCI) depicts a transitory phase between healthy elderly and the onset of Alzheimer's disease (AD) with worsening cognitive impairment. Some functional MRI (fMRI) research indicated that the frontoparietal network (FPN) could be an essential part of the pathophysiological mechanism of MCI. However, damaged FPN regions were not consistently reported, especially their interactions with other brain networks. We assessed the fMRI-specific anomalies of the FPN in MCI by analyzing brain regions with functional alterations.MethodsPubMed, Embase, and Web of Science were searched to screen neuroimaging studies exploring brain function alterations in the FPN in MCI using fMRI-related indexes, including the amplitude of low-frequency fluctuation, regional homogeneity, and functional connectivity. We integrated distinctive coordinates by activating likelihood estimation, visualizing abnormal functional regions, and concluding functional alterations of the FPN.ResultsWe selected 29 studies and found specific changes in some brain regions of the FPN. These included the bilateral dorsolateral prefrontal cortex, insula, precuneus cortex, anterior cingulate cortex, inferior parietal lobule, middle temporal gyrus, superior frontal gyrus, and parahippocampal gyrus. Any abnormal alterations in these regions depicted interactions between the FPN and other networks.ConclusionThe study demonstrates specific fMRI neuroimaging alterations in brain regions of the FPN in MCI patients. This could provide a new perspective on identifying early-stage patients with targeted treatment programs.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023432042, identifier: CRD42023432042
Enhancing the EAST-ADL error model with HiP-HOPS semantics
EAST-ADL is a domain-specific modelling language for the engineering of automotive embedded systems. The language has abstractions that enable engineers to capture a variety of information about design in the course of the lifecycle — from requirements to detailed design of hardware and software architectures. The specification of the EAST-ADL language includes an error model extension which documents language structures that allow potential failures of design elements to be specified locally. The effects of these failures are then later assessed in the context of the architecture design. To provide this type of useful assessment, a language and a specification are not enough; a compiler-like tool that can read and operate on a system specification together with its error model is needed. In this paper we integrate the error model of EAST-ADL with the precise semantics of HiP-HOPS — a state-of-the-art tool that enables dependability analysis and optimization of design models. We present the integration concept between EAST-ADL structure and HiP-HOPS error propagation logic and its transformation into the HiP-HOPS model. Source and destination models are represented using the corresponding XML formats. The connection of these two models at tool level enables practical EAST-ADL designs of embedded automotive systems to be analysed in terms of dependability, i.e. safety, reliability and availability. In addition, the information encoded in the error model can be re-used across different contexts of application with the associated benefits for cost reduction, simplification, and rationalisation of dependability assessments in complex engineering designs
Systematic study of proton radioactivity of spherical proton emitters within various versions of proximity potential formalisms
In this work we present a systematic study of the proton radioactivity
half-lives of spherical proton emitters within the Coulomb and proximity
potential model. We investigate 28 different versions of the proximity
potential formalisms developed for the description of proton radioactivity,
decay and heavy particle radioactivity. It is found that 21
of them are not suitable to deal with the proton radioactivity, because the
classical turning points cannot be obtained due to the fact
that the depth of the total interaction potential between the emitted proton
and the daughter nucleus is above the proton radioactivity energy. Among the
other 7 versions of the proximity potential formalisms, it is Guo2013 which
gives the lowest rms deviation in the description of the experimental
half-lives of the known spherical proton emitters. We use this proximity
potential formalism to predict the proton radioactivity half-lives of 13
spherical proton emitters, whose proton radioactivity is energetically allowed
or observed but not yet quantified, within a factor of 3.71.Comment: 10 pages, 5 figures. This paper has been accepted by The European
Physical Journal A (in press 2019
Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems
Surrogate-assisted evolutionary algorithms have been widely developed to
solve complex and computationally expensive multi-objective optimization
problems in recent years. However, when dealing with high-dimensional
optimization problems, the performance of these surrogate-assisted
multi-objective evolutionary algorithms deteriorate drastically. In this work,
a novel Classifier-assisted rank-based learning and Local Model based
multi-objective Evolutionary Algorithm (CLMEA) is proposed for high-dimensional
expensive multi-objective optimization problems. The proposed algorithm
consists of three parts: classifier-assisted rank-based learning,
hypervolume-based non-dominated search, and local search in the relatively
sparse objective space. Specifically, a probabilistic neural network is built
as classifier to divide the offspring into a number of ranks. The offspring in
different ranks uses rank-based learning strategy to generate more promising
and informative candidates for real function evaluations. Then, radial basis
function networks are built as surrogates to approximate the objective
functions. After searching non-dominated solutions assisted by the surrogate
model, the candidates with higher hypervolume improvement are selected for real
evaluations. Subsequently, in order to maintain the diversity of solutions, the
most uncertain sample point from the non-dominated solutions measured by the
crowding distance is selected as the guided parent to further infill in the
uncertain region of the front. The experimental results of benchmark problems
and a real-world application on geothermal reservoir heat extraction
optimization demonstrate that the proposed algorithm shows superior performance
compared with the state-of-the-art surrogate-assisted multi-objective
evolutionary algorithms. The source code for this work is available at
https://github.com/JellyChen7/CLMEA
Deep Heterogeneous Autoencoders for Collaborative Filtering
This paper leverages heterogeneous auxiliary information to address the data
sparsity problem of recommender systems. We propose a model that learns a
shared feature space from heterogeneous data, such as item descriptions,
product tags and online purchase history, to obtain better predictions. Our
model consists of autoencoders, not only for numerical and categorical data,
but also for sequential data, which enables capturing user tastes, item
characteristics and the recent dynamics of user preference. We learn the
autoencoder architecture for each data source independently in order to better
model their statistical properties. Our evaluation on two MovieLens datasets
and an e-commerce dataset shows that mean average precision and recall improve
over state-of-the-art methods.Comment: Proceedings of the IEEE International Conference on Data Mining, pp.
1164-1169, Singapore, 201
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