Search results for "clusterin"

Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance…

2019

Background. Stability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Although the stability of the ICA temporal courses differs from that of spatial components, temporal stability has not been considered during dimensionality decisions. New method. The current study aims to (1) develop an algorithm to incorporate temporal course stability into dimensionality selection and (2) test the impact of temporal course on the stability of the ICA decomposition of fMRI data via tensor clustering. Resting state fMRI data were analyzed with two popu…

The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks: A multimodal magnetoenc…

2022

Funding Information: This work was financially supported by the Academy of Finland (Finnish Center of Excellence in Computational Inference Research COIN and grants #292334, #294238 to SK; #255349, #315553 to RS; #257576 to JK; #286405 funding for TM), the Sigrid Jusélius Foundation (grant to RS), the Finnish Cultural Foundation (grant to ML), the Swedish Cultural Foundation in Finland (grant to ML), the Maud Kuistila Memorial Foundation (grant to ML), and Aalto Brain Center. Publisher Copyright: Copyright © 2022 Mononen, Kujala, Liljeström, Leppäaho, Kaski and Salmelin. Different neuroimaging methods can yield different views of task-dependent neural engagement. Studies examining the relat…

Dynamic Functional Connectivity in the Musical Brain

2019

Musical training causes structural and functional changes in the brain due to its sensory-motor demands. This leads to differences in how musicians perceive and process music as compared to non-musicians, thereby providing insights into brain adaptations and plasticity. Correlational studies and network analysis investigations have indicated the presence of large-scale brain networks involved in the processing of music and have highlighted differences between musicians and non-musicians. However, studies on functional connectivity in the brain during music listening tasks have thus far focused solely on static network analysis. Dynamic Functional Connectivity (DFC) studies have lately been …

Elemental and microbiota content in indoor and outdoor air using recuperation unit filters

2021

Detection and quantification of engineered nanomaterials in environmental systems require precise knowledge of the elemental composition, association, and ratios in homologous natural nanomaterials (NNMs). Here, we characterized soil NNMs at the single particle level using single particle-inductively coupled plasma-time of flight-mass spectrometer (SP-ICP-TOF-MS) in order to identify the elemental purity, composition, associations, and ratios within NNMs. Elements naturally present as a major constituent in NNMs such as Ti, and Fe occurred predominantly as pure/single metals, whereas elements naturally present at trace levels in NNMs occurred predominantly as impure/multi-metal NNMs such as…

Detector-based visual analysis of time-series data

2015

Information Extraction from Binary Skill Assessment Data with Machine Learning

2021

Strength training exercises are essential for rehabilitation, improving our health as well as in sports. For optimal and safe training, educators and trainers in the industry should comprehend exercise form or technique. Currently, there is a lack of tools measuring in-depth skills of strength training experts. In this study, we investigate how data mining methods can be used to identify novel and useful skill patterns from a binary multiple choice questionnaire test designed to measure the knowledge level of strength training experts. A skill test assessing exercise technique expertise and comprehension was answered by 507 fitness professionals with varying backgrounds. A triangulated appr…

Understanding the Study Experiences of Students in Low Agency Profile: Towards a Smart Education Approach

2020

In this paper, we use student agency analytics to examine how university students who assessed to have low agency resources describe their study experiences. Students ( n=292 ) completed the Agency of University Students (AUS) questionnaire. Furthermore, they reported what kinds of restrictions they experienced during the university course they attended. Four different agency profiles were identified using robust clustering. We then conducted a thematic analysis of the open-ended answers of students who assessed to have low agency resources. Issues relating to competence beliefs, self-efficacy, student-teacher relations, time as a resource, student well-being, and course contents seemed to …

Intrusion detection applications using knowledge discovery and data mining

2014

Exploring a large dataset : typical behavior of UHF signal propagation

2020

Radioverkon suunnittelua ja käyttöä varten täytyy radio aaltojen eteneminen ymmärtää hyvin. Tässä tutkimuksessa tutustutaan laajaan mittausaineistoon hetkellisiä tehoja maanlaajuisesta UHF verkosta. Spektrianalyysillä todettiin mitatussa tehossa olevan jaksollista vaihtelua taajuuksilla kerran ja kahdesti päivässä. Myös nopeampaa vaihtelua välillä 0:1 mHz ja 1:4 mHz todettiin 34% yhteyksistä. Hierarkisella ryhmittelyllä etsittiin tyypilliset mittausten arvojakaumat. Saaduissa arvojakaumien ryhmissä oli eri levyisiä vasemmalle tai oikealle vinoja tai symmetrisiä jakaumia. The design and operation of radio networks requires good understanding of radio propagation. This study explores a datase…

Improving Scalable K-Means++

2021

Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces produced by the random projection method for the initialization. The proposed methods are scalable and can be run in parallel, which make them suitable for initializing large-scale problems. In the experiments, comparison of the proposed methods to the K-means++ and K-means‖ methods is conducted using an extensive set of reference and synthetic large-scale datasets. Concerning the latter, a novel high-dimensional clustering data generation …