10,424 research outputs found
Co-Morbidity Exploration on Wearables Activity Data Using Unsupervised Pre-training and Multi-Task Learning
Physical activity and sleep play a major role in the prevention and
management of many chronic conditions. It is not a trivial task to understand
their impact on chronic conditions. Currently, data from electronic health
records (EHRs), sleep lab studies, and activity/sleep logs are used. The rapid
increase in the popularity of wearable health devices provides a significant
new data source, making it possible to track the user's lifestyle real-time
through web interfaces, both to consumer as well as their healthcare provider,
potentially. However, at present there is a gap between lifestyle data (e.g.,
sleep, physical activity) and clinical outcomes normally captured in EHRs. This
is a critical barrier for the use of this new source of signal for healthcare
decision making. Applying deep learning to wearables data provides a new
opportunity to overcome this barrier.
To address the problem of the unavailability of clinical data from a major
fraction of subjects and unrepresentative subject populations, we propose a
novel unsupervised (task-agnostic) time-series representation learning
technique called act2vec. act2vec learns useful features by taking into account
the co-occurrence of activity levels along with periodicity of human activity
patterns. The learned representations are then exploited to boost the
performance of disorder-specific supervised learning models. Furthermore, since
many disorders are often related to each other, a phenomenon referred to as
co-morbidity, we use a multi-task learning framework for exploiting the shared
structure of disorder inducing life-style choices partially captured in the
wearables data. Empirical evaluation using actigraphy data from 4,124 subjects
shows that our proposed method performs and generalizes substantially better
than the conventional time-series symbolic representational methods and
task-specific deep learning models
Multimodal Clustering for Community Detection
Multimodal clustering is an unsupervised technique for mining interesting
patterns in -adic binary relations or -mode networks. Among different
types of such generalized patterns one can find biclusters and formal concepts
(maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode
case, closed -sets for -mode case, etc. Object-attribute biclustering
(OA-biclustering) for mining large binary datatables (formal contexts or 2-mode
networks) arose by the end of the last decade due to intractability of
computation problems related to formal concepts; this type of patterns was
proposed as a meaningful and scalable approximation of formal concepts. In this
paper, our aim is to present recent advance in OA-biclustering and its
extensions to mining multi-mode communities in SNA setting. We also discuss
connection between clustering coefficients known in SNA community for 1-mode
and 2-mode networks and OA-bicluster density, the main quality measure of an
OA-bicluster. Our experiments with 2-, 3-, and 4-mode large real-world networks
show that this type of patterns is suitable for community detection in
multi-mode cases within reasonable time even though the number of corresponding
-cliques is still unknown due to computation difficulties. An interpretation
of OA-biclusters for 1-mode networks is provided as well
Hyperbox based machine learning algorithms: A comprehensive survey
With the rapid development of digital information, the data volume generated
by humans and machines is growing exponentially. Along with this trend, machine
learning algorithms have been formed and evolved continuously to discover new
information and knowledge from different data sources. Learning algorithms
using hyperboxes as fundamental representational and building blocks are a
branch of machine learning methods. These algorithms have enormous potential
for high scalability and online adaptation of predictors built using hyperbox
data representations to the dynamically changing environments and streaming
data. This paper aims to give a comprehensive survey of literature on
hyperbox-based machine learning models. In general, according to the
architecture and characteristic features of the resulting models, the existing
hyperbox-based learning algorithms may be grouped into three major categories:
fuzzy min-max neural networks, hyperbox-based hybrid models, and other
algorithms based on hyperbox representations. Within each of these groups, this
paper shows a brief description of the structure of models, associated learning
algorithms, and an analysis of their advantages and drawbacks. Main
applications of these hyperbox-based models to the real-world problems are also
described in this paper. Finally, we discuss some open problems and identify
potential future research directions in this field.Comment: 7 figure
Learning Hidden Structures with Relational Models by Adequately Involving Rich Information in A Network
Effectively modelling hidden structures in a network is very practical but
theoretically challenging. Existing relational models only involve very limited
information, namely the binary directional link data, embedded in a network to
learn hidden networking structures. There is other rich and meaningful
information (e.g., various attributes of entities and more granular information
than binary elements such as "like" or "dislike") missed, which play a critical
role in forming and understanding relations in a network. In this work, we
propose an informative relational model (InfRM) framework to adequately involve
rich information and its granularity in a network, including metadata
information about each entity and various forms of link data. Firstly, an
effective metadata information incorporation method is employed on the prior
information from relational models MMSB and LFRM. This is to encourage the
entities with similar metadata information to have similar hidden structures.
Secondly, we propose various solutions to cater for alternative forms of link
data. Substantial efforts have been made towards modelling appropriateness and
efficiency, for example, using conjugate priors. We evaluate our framework and
its inference algorithms in different datasets, which shows the generality and
effectiveness of our models in capturing implicit structures in networks
Modelling Interaction of Sentence Pair with coupled-LSTMs
Recently, there is rising interest in modelling the interactions of two
sentences with deep neural networks. However, most of the existing methods
encode two sequences with separate encoders, in which a sentence is encoded
with little or no information from the other sentence. In this paper, we
propose a deep architecture to model the strong interaction of sentence pair
with two coupled-LSTMs. Specifically, we introduce two coupled ways to model
the interdependences of two LSTMs, coupling the local contextualized
interactions of two sentences. We then aggregate these interactions and use a
dynamic pooling to select the most informative features. Experiments on two
very large datasets demonstrate the efficacy of our proposed architecture and
its superiority to state-of-the-art methods.Comment: Submitted to IJCAI 201
Gaussian processes with built-in dimensionality reduction: Applications in high-dimensional uncertainty propagation
The prohibitive cost of performing Uncertainty Quantification (UQ) tasks with
a very large number of input parameters can be addressed, if the response
exhibits some special structure that can be discovered and exploited. Several
physical responses exhibit a special structure known as an active subspace
(AS), a linear manifold of the stochastic space characterized by maximal
response variation. The idea is that one should first identify this low
dimensional manifold, project the high-dimensional input onto it, and then link
the projection to the output. In this work, we develop a probabilistic version
of AS which is gradient-free and robust to observational noise. Our approach
relies on a novel Gaussian process regression with built-in dimensionality
reduction with the AS represented as an orthogonal projection matrix that
serves as yet another covariance function hyper-parameter to be estimated from
the data. To train the model, we design a two-step maximum likelihood
optimization procedure that ensures the orthogonality of the projection matrix
by exploiting recent results on the Stiefel manifold. The additional benefit of
our probabilistic formulation is that it allows us to select the dimensionality
of the AS via the Bayesian information criterion. We validate our approach by
showing that it can discover the right AS in synthetic examples without
gradient information using both noiseless and noisy observations. We
demonstrate that our method is able to discover the same AS as the classical
approach in a challenging one-hundred-dimensional problem involving an elliptic
stochastic partial differential equation with random conductivity. Finally, we
use our approach to study the effect of geometric and material uncertainties in
the propagation of solitary waves in a one-dimensional granular system.Comment: 37 pages, 20 figure
Data-driven Co-clustering Model of Internet Usage in Large Mobile Societies
Design and simulation of future mobile networks will center around human
interests and behavior. We propose a design paradigm for mobile networks driven
by realistic models of users' on-line behavior, based on mining of billions of
wireless-LAN records. We introduce a systematic method for large-scale
multi-dimensional coclustering of web activity for thousands of mobile users at
79 locations. We find surprisingly that users can be consistently modeled using
ten clusters with disjoint profiles. Access patterns from multiple locations
show differential user behavior. This is the first study to obtain such
detailed results for mobile Internet usage.Comment: 10 pages, 10 figure
Topic segmentation via community detection in complex networks
Many real systems have been modelled in terms of network concepts, and
written texts are a particular example of information networks. In recent
years, the use of network methods to analyze language has allowed the discovery
of several interesting findings, including the proposition of novel models to
explain the emergence of fundamental universal patterns. While syntactical
networks, one of the most prevalent networked models of written texts, display
both scale-free and small-world properties, such representation fails in
capturing other textual features, such as the organization in topics or
subjects. In this context, we propose a novel network representation whose main
purpose is to capture the semantical relationships of words in a simple way. To
do so, we link all words co-occurring in the same semantic context, which is
defined in a threefold way. We show that the proposed representations favours
the emergence of communities of semantically related words, and this feature
may be used to identify relevant topics. The proposed methodology to detect
topics was applied to segment selected Wikipedia articles. We have found that,
in general, our methods outperform traditional bag-of-words representations,
which suggests that a high-level textual representation may be useful to study
semantical features of texts
ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations
Understanding clothes from a single image has strong commercial and cultural
impacts on modern societies. However, this task remains a challenging computer
vision problem due to wide variations in the appearance, style, brand and
layering of clothing items. We present a new database called ModaNet, a
large-scale collection of images based on Paperdoll dataset. Our dataset
provides 55,176 street images, fully annotated with polygons on top of the 1
million weakly annotated street images in Paperdoll. ModaNet aims to provide a
technical benchmark to fairly evaluate the progress of applying the latest
computer vision techniques that rely on large data for fashion understanding.
The rich annotation of the dataset allows to measure the performance of
state-of-the-art algorithms for object detection, semantic segmentation and
polygon prediction on street fashion images in detail. The polygon-based
annotation dataset has been released https://github.com/eBay/modanet, we also
host the leaderboard at EvalAI:
https://evalai.cloudcv.org/featured-challenges/136/overview.Comment: Accepted as a full paper for an oral presentation at ACM Multimedia
2018, Seoul, South Korea. ModaNet is only for non-commercial researc
Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection
In an effort to assist factcheckers in the process of factchecking, we tackle
the claim detection task, one of the necessary stages prior to determining the
veracity of a claim. It consists of identifying the set of sentences, out of a
long text, deemed capable of being factchecked. This paper is a collaborative
work between Full Fact, an independent factchecking charity, and academic
partners. Leveraging the expertise of professional factcheckers, we develop an
annotation schema and a benchmark for automated claim detection that is more
consistent across time, topics and annotators than previous approaches. Our
annotation schema has been used to crowdsource the annotation of a dataset with
sentences from UK political TV shows. We introduce an approach based on
universal sentence representations to perform the classification, achieving an
F1 score of 0.83, with over 5% relative improvement over the state-of-the-art
methods ClaimBuster and ClaimRank. The system was deployed in production and
received positive user feedback.Comment: Accepted for ACM Digital Threats: Research and Practice (DTRAP
- …