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Parallels in the sequential organization of birdsong and human speech.
Human speech possesses a rich hierarchical structure that allows for meaning to be altered by words spaced far apart in time. Conversely, the sequential structure of nonhuman communication is thought to follow non-hierarchical Markovian dynamics operating over only short distances. Here, we show that human speech and birdsong share a similar sequential structure indicative of both hierarchical and Markovian organization. We analyze the sequential dynamics of song from multiple songbird species and speech from multiple languages by modeling the information content of signals as a function of the sequential distance between vocal elements. Across short sequence-distances, an exponential decay dominates the information in speech and birdsong, consistent with underlying Markovian processes. At longer sequence-distances, the decay in information follows a power law, consistent with underlying hierarchical processes. Thus, the sequential organization of acoustic elements in two learned vocal communication signals (speech and birdsong) shows functionally equivalent dynamics, governed by similar processes
Tangled String for Multi-Scale Explanation of Contextual Shifts in Stock Market
The original research question here is given by marketers in general, i.e.,
how to explain the changes in the desired timescale of the market. Tangled
String, a sequence visualization tool based on the metaphor where contexts in a
sequence are compared to tangled pills in a string, is here extended and
diverted to detecting stocks that trigger changes in the market and to
explaining the scenario of contextual shifts in the market. Here, the
sequential data on the stocks of top 10 weekly increase rates in the First
Section of the Tokyo Stock Exchange for 12 years are visualized by Tangled
String. The changing in the prices of stocks is a mixture of various timescales
and can be explained in the time-scale set as desired by using TS. Also, it is
found that the change points found by TS coincided by high precision with the
real changes in each stock price. As TS has been created from the data-driven
innovation platform called Innovators Marketplace on Data Jackets and is
extended to satisfy data users, this paper is as evidence of the contribution
of the market of data to data-driven innovations.Comment: 16 pages and 7 figures. The author started to write this paper as an
extension of the paper [20] in the reference list, but the content came to be
changed substantially, not by only minor extension but to a new pape
Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting
Spatio-temporal sequence forecasting is one of the fundamental tasks in
spatio-temporal data mining. It facilitates many real world applications such
as precipitation nowcasting, citywide crowd flow prediction and air pollution
forecasting. Recently, a few Seq2Seq based approaches have been proposed, but
one of the drawbacks of Seq2Seq models is that, small errors can accumulate
quickly along the generated sequence at the inference stage due to the
different distributions of training and inference phase. That is because
Seq2Seq models minimise single step errors only during training, however the
entire sequence has to be generated during the inference phase which generates
a discrepancy between training and inference. In this work, we propose a novel
curriculum learning based strategy named Temporal Progressive Growing Sampling
to effectively bridge the gap between training and inference for
spatio-temporal sequence forecasting, by transforming the training process from
a fully-supervised manner which utilises all available previous ground-truth
values to a less-supervised manner which replaces some of the ground-truth
context with generated predictions. To do that we sample the target sequence
from midway outputs from intermediate models trained with bigger timescales
through a carefully designed decaying strategy. Experimental results
demonstrate that our proposed method better models long term dependencies and
outperforms baseline approaches on two competitive datasets.Comment: ECAI 2020 Accepted, preprin
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
Prediction of popularity has profound impact for social media, since it
offers opportunities to reveal individual preference and public attention from
evolutionary social systems. Previous research, although achieves promising
results, neglects one distinctive characteristic of social data, i.e.,
sequentiality. For example, the popularity of online content is generated over
time with sequential post streams of social media. To investigate the
sequential prediction of popularity, we propose a novel prediction framework
called Deep Temporal Context Networks (DTCN) by incorporating both temporal
context and temporal attention into account. Our DTCN contains three main
components, from embedding, learning to predicting. With a joint embedding
network, we obtain a unified deep representation of multi-modal user-post data
in a common embedding space. Then, based on the embedded data sequence over
time, temporal context learning attempts to recurrently learn two adaptive
temporal contexts for sequential popularity. Finally, a novel temporal
attention is designed to predict new popularity (the popularity of a new
user-post pair) with temporal coherence across multiple time-scales.
Experiments on our released image dataset with about 600K Flickr photos
demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms,
with an average of 21.51% relative performance improvement in the popularity
prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1
A phenomenological cluster-based model of Ca2+ waves and oscillations for Inositol 1,4,5-trisphosphate receptor (IP3R) channels
Clusters of IP3 receptor channels in the membranes of the endoplasmic
reticulum (ER) of many non-excitable cells release calcium ions in a
cooperative manner giving rise to dynamical patterns such as Ca2+ puffs, waves,
and oscillations that occur on multiple spatial and temporal scales. We
introduce a minimal yet descriptive reaction-diffusion model of IP3 receptors
for a saturating concentration of IP3 using a principled reduction of a
detailed Markov chain description of individual channels. A dynamical systems
analysis reveals the possibility of excitable, bistable and oscillatory
dynamics of this model that correspond to three types of observed patterns of
calcium release -- puffs, waves, and oscillations respectively. We explain the
emergence of these patterns via a bifurcation analysis of a coupled two-cluster
model, compute the phase diagram and quantify the speed of the waves and period
of oscillations in terms of system parameters. We connect the termination of
large-scale Ca2+ release events to IP3 unbinding or stochasticity.Comment: 18 pages, 10 figure
A Center-Median Filtering Method for Detection of Temporal Variation in Coronal Images
Events in the solar corona are often widely separated in their timescales,
which can allow them to be identified when they would otherwise be confused
with emission from other sources in the corona. Methods for cleanly separating
such events based on their timescales are thus desirable for research in the
field. This paper develops a technique for identifying time-varying signals in
solar coronal image sequences which is based on a per-pixel running median
filter and an understanding of photon-counting statistics. Example applications
to 'EIT Waves' and small-scale dynamics are shown, both using data from the 193
Angstrom channel on AIA. The technique is found to discriminate EIT Waves more
cleanly than the running and base difference techniques most commonly used. It
is also demonstrated that there is more signal in the data than is commonly
appreciated, finding that the waves can be traced to the edge of the AIA field
of view when the data are rebinned to increase the signal-to-noise ratio.Comment: 15 pages, 7 Figures, Accepted to Journal of Space Weather and Space
Climate; version 2 has slight text changes and updated movie URL
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