15,231 research outputs found
Modeling Individual Cyclic Variation in Human Behavior
Cycles are fundamental to human health and behavior. However, modeling cycles
in time series data is challenging because in most cases the cycles are not
labeled or directly observed and need to be inferred from multidimensional
measurements taken over time. Here, we present CyHMMs, a cyclic hidden Markov
model method for detecting and modeling cycles in a collection of
multidimensional heterogeneous time series data. In contrast to previous cycle
modeling methods, CyHMMs deal with a number of challenges encountered in
modeling real-world cycles: they can model multivariate data with discrete and
continuous dimensions; they explicitly model and are robust to missing data;
and they can share information across individuals to model variation both
within and between individual time series. Experiments on synthetic and
real-world health-tracking data demonstrate that CyHMMs infer cycle lengths
more accurately than existing methods, with 58% lower error on simulated data
and 63% lower error on real-world data compared to the best-performing
baseline. CyHMMs can also perform functions which baselines cannot: they can
model the progression of individual features/symptoms over the course of the
cycle, identify the most variable features, and cluster individual time series
into groups with distinct characteristics. Applying CyHMMs to two real-world
health-tracking datasets -- of menstrual cycle symptoms and physical activity
tracking data -- yields important insights including which symptoms to expect
at each point during the cycle. We also find that people fall into several
groups with distinct cycle patterns, and that these groups differ along
dimensions not provided to the model. For example, by modeling missing data in
the menstrual cycles dataset, we are able to discover a medically relevant
group of birth control users even though information on birth control is not
given to the model.Comment: Accepted at WWW 201
Unsupervised Generative Adversarial Cross-modal Hashing
Cross-modal hashing aims to map heterogeneous multimedia data into a common
Hamming space, which can realize fast and flexible retrieval across different
modalities. Unsupervised cross-modal hashing is more flexible and applicable
than supervised methods, since no intensive labeling work is involved. However,
existing unsupervised methods learn hashing functions by preserving inter and
intra correlations, while ignoring the underlying manifold structure across
different modalities, which is extremely helpful to capture meaningful nearest
neighbors of different modalities for cross-modal retrieval. To address the
above problem, in this paper we propose an Unsupervised Generative Adversarial
Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for
unsupervised representation learning to exploit the underlying manifold
structure of cross-modal data. The main contributions can be summarized as
follows: (1) We propose a generative adversarial network to model cross-modal
hashing in an unsupervised fashion. In the proposed UGACH, given a data of one
modality, the generative model tries to fit the distribution over the manifold
structure, and select informative data of another modality to challenge the
discriminative model. The discriminative model learns to distinguish the
generated data and the true positive data sampled from correlation graph to
achieve better retrieval accuracy. These two models are trained in an
adversarial way to improve each other and promote hashing function learning.
(2) We propose a correlation graph based approach to capture the underlying
manifold structure across different modalities, so that data of different
modalities but within the same manifold can have smaller Hamming distance and
promote retrieval accuracy. Extensive experiments compared with 6
state-of-the-art methods verify the effectiveness of our proposed approach.Comment: 8 pages, accepted by 32th AAAI Conference on Artificial Intelligence
(AAAI), 201
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series
This paper proposes a novel fault diagnosis approach based on generative
adversarial networks (GAN) for imbalanced industrial time series where normal
samples are much larger than failure cases. We combine a well-designed feature
extractor with GAN to help train the whole network. Aimed at obtaining data
distribution and hidden pattern in both original distinguishing features and
latent space, the encoder-decoder-encoder three-sub-network is employed in GAN,
based on Deep Convolution Generative Adversarial Networks (DCGAN) but without
Tanh activation layer and only trained on normal samples. In order to verify
the validity and feasibility of our approach, we test it on rolling bearing
data from Case Western Reserve University and further verify it on data
collected from our laboratory. The results show that our proposed approach can
achieve excellent performance in detecting faulty by outputting much larger
evaluation scores
Manipulating Attributes of Natural Scenes via Hallucination
In this study, we explore building a two-stage framework for enabling users
to directly manipulate high-level attributes of a natural scene. The key to our
approach is a deep generative network which can hallucinate images of a scene
as if they were taken at a different season (e.g. during winter), weather
condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the
scene is hallucinated with the given attributes, the corresponding look is then
transferred to the input image while preserving the semantic details intact,
giving a photo-realistic manipulation result. As the proposed framework
hallucinates what the scene will look like, it does not require any reference
style image as commonly utilized in most of the appearance or style transfer
approaches. Moreover, it allows to simultaneously manipulate a given scene
according to a diverse set of transient attributes within a single model,
eliminating the need of training multiple networks per each translation task.
Our comprehensive set of qualitative and quantitative results demonstrate the
effectiveness of our approach against the competing methods.Comment: Accepted for publication in ACM Transactions on Graphic
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