19,297 research outputs found
Higher-order Motif-based Time Series Classification for Forced Oscillation Source Location in Power Grids
Time series motifs are used for discovering higher-order structures of time
series data. Based on time series motifs, the motif embedding correlation field
(MECF) is proposed to characterize higher-order temporal structures of
dynamical system time series. A MECF-based unsupervised learning approach is
applied in locating the source of the forced oscillation (FO), a periodic
disturbance that detrimentally impacts power grids. Locating the FO source is
imperative for system stability. Compared with the Fourier analysis, the
MECF-based unsupervised learning is applicable under various FO situations,
including the single FO, FO with resonance, and multiple sources FOs. The
MECF-based unsupervised learning is a data-driven approach without any prior
knowledge requirement of system models or typologies. Tests on the UK
high-voltage transmission grid illustrate the effectiveness of MECF-based
unsupervised learning. In addition, the impacts of coupling strength and
measurement noise on locating the FO source by the MECF-based unsupervised
learning are investigated
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The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation
With recent breakthroughs in artificial neural networks, deep generative
models have become one of the leading techniques for computational creativity.
Despite very promising progress on image and short sequence generation,
symbolic music generation remains a challenging problem since the structure of
compositions are usually complicated. In this study, we attempt to solve the
melody generation problem constrained by the given chord progression. This
music meta-creation problem can also be incorporated into a plan recognition
system with user inputs and predictive structural outputs. In particular, we
explore the effect of explicit architectural encoding of musical structure via
comparing two sequential generative models: LSTM (a type of RNN) and WaveNet
(dilated temporal-CNN). As far as we know, this is the first study of applying
WaveNet to symbolic music generation, as well as the first systematic
comparison between temporal-CNN and RNN for music generation. We conduct a
survey for evaluation in our generations and implemented Variable Markov Oracle
in music pattern discovery. Experimental results show that to encode structure
more explicitly using a stack of dilated convolution layers improved the
performance significantly, and a global encoding of underlying chord
progression into the generation procedure gains even more.Comment: 8 pages, 13 figure
Review of Immunoinformatic approaches to in-silico B-cell epitope prediction
In this paper, the current state of in-silico, B-cell epitope prediction is discussed. Recommendations for improving some of the approaches encountered are outlined, along with the presentation of an entirely novel technique, which uses molecular mechanics for epitope classification, evaluation and prediction
Conflict and Computation on Wikipedia: a Finite-State Machine Analysis of Editor Interactions
What is the boundary between a vigorous argument and a breakdown of
relations? What drives a group of individuals across it? Taking Wikipedia as a
test case, we use a hidden Markov model to approximate the computational
structure and social grammar of more than a decade of cooperation and conflict
among its editors. Across a wide range of pages, we discover a bursty war/peace
structure where the systems can become trapped, sometimes for months, in a
computational subspace associated with significantly higher levels of
conflict-tracking "revert" actions. Distinct patterns of behavior characterize
the lower-conflict subspace, including tit-for-tat reversion. While a fraction
of the transitions between these subspaces are associated with top-down actions
taken by administrators, the effects are weak. Surprisingly, we find no
statistical signal that transitions are associated with the appearance of
particularly anti-social users, and only weak association with significant news
events outside the system. These findings are consistent with transitions being
driven by decentralized processes with no clear locus of control. Models of
belief revision in the presence of a common resource for information-sharing
predict the existence of two distinct phases: a disordered high-conflict phase,
and a frozen phase with spontaneously-broken symmetry. The bistability we
observe empirically may be a consequence of editor turn-over, which drives the
system to a critical point between them.Comment: 23 pages, 3 figures. Matches published version. Code for HMM fitting
available at http://bit.ly/sfihmm ; time series and derived finite state
machines at bit.ly/wiki_hm
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