6,804 research outputs found
Disease Knowledge Transfer across Neurodegenerative Diseases
We introduce Disease Knowledge Transfer (DKT), a novel technique for
transferring biomarker information between related neurodegenerative diseases.
DKT infers robust multimodal biomarker trajectories in rare neurodegenerative
diseases even when only limited, unimodal data is available, by transferring
information from larger multimodal datasets from common neurodegenerative
diseases. DKT is a joint-disease generative model of biomarker progressions,
which exploits biomarker relationships that are shared across diseases. Our
proposed method allows, for the first time, the estimation of plausible,
multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare
neurodegenerative disease where only unimodal MRI data is available. For this
we train DKT on a combined dataset containing subjects with two distinct
diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD)
dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior
Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for
which only a limited number of Magnetic Resonance Imaging (MRI) scans are
available. Although validation is challenging due to lack of data in PCA, we
validate DKT on synthetic data and two patient datasets (TADPOLE and PCA
cohorts), showing it can estimate the ground truth parameters in the simulation
and predict unseen biomarkers on the two patient datasets. While we
demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other
forms of related neurodegenerative diseases. Source code for DKT is available
online: https://github.com/mrazvan22/dkt.Comment: accepted at MICCAI 2019, 13 pages, 5 figures, 2 table
Automatic estimation of harmonic tension by distributed representation of chords
The buildup and release of a sense of tension is one of the most essential
aspects of the process of listening to music. A veridical computational model
of perceived musical tension would be an important ingredient for many music
informatics applications. The present paper presents a new approach to
modelling harmonic tension based on a distributed representation of chords. The
starting hypothesis is that harmonic tension as perceived by human listeners is
related, among other things, to the expectedness of harmonic units (chords) in
their local harmonic context. We train a word2vec-type neural network to learn
a vector space that captures contextual similarity and expectedness, and define
a quantitative measure of harmonic tension on top of this. To assess the
veridicality of the model, we compare its outputs on a number of well-defined
chord classes and cadential contexts to results from pertinent empirical
studies in music psychology. Statistical analysis shows that the model's
predictions conform very well with empirical evidence obtained from human
listeners.Comment: 12 pages, 4 figures. To appear in Proceedings of the 13th
International Symposium on Computer Music Multidisciplinary Research (CMMR),
Porto, Portuga
Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models
In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli
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Improving music genre classification using automatically induced harmony rules
We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 Ă— 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates
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Improving music genre classification using automatically induced harmony rules
We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 Ă— 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates
Measuring measuring: Toward a theory of proficiency with the Constructing Measures framework
This paper is relevant to measurement educators who are interested in the variability of understanding and use of the four building blocks in the Constructing Measures framework (Wilson, 2005). It proposes a uni-dimensional structure for understanding Wilson’s framework, and explores the evidence for and against this conceptualization. Constructed and fixed choice response items are utilized to collect responses from 72 participants who range in experience and expertise with constructing measures. The data was scored by two raters and was analyzed with the Rasch partial credit model using ConQuest (1998). Guided by the 1999 Testing Standards, analyses of validity and reliability evidence provide support for the construct theory and limited uses of the instrument pending item design modifications
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