22,760 research outputs found
Empirically measuring soft knowledge in visualization
In this paper, we present an empirical study designed to evaluate the hypothesis that humans’ soft knowledge can enhance
the cost-benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of
three classes of soft knowledge: (i) knowledge about application contexts, (ii) knowledge about the patterns to be observed (i.e.,
in relation to visualization task), and (iii) knowledge about statistical measures. We mapped these classes into three control
variables, and used real-world time series data to construct stimuli. The results of the study confirmed the positive contribution
of each class of knowledge towards the reduction of the potential distortion, while the knowledge about the patterns prevents
distortion more effectively than the other two classes
Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data
It is an enduring question how to combine revealed preference (RP) and stated
preference (SP) data to analyze travel behavior. This study presents a
framework of multitask learning deep neural networks (MTLDNNs) for this
question, and demonstrates that MTLDNNs are more generic than the traditional
nested logit (NL) method, due to its capacity of automatic feature learning and
soft constraints. About 1,500 MTLDNN models are designed and applied to the
survey data that was collected in Singapore and focused on the RP of four
current travel modes and the SP with autonomous vehicles (AV) as the one new
travel mode in addition to those in RP. We found that MTLDNNs consistently
outperform six benchmark models and particularly the classical NL models by
about 5% prediction accuracy in both RP and SP datasets. This performance
improvement can be mainly attributed to the soft constraints specific to
MTLDNNs, including its innovative architectural design and regularization
methods, but not much to the generic capacity of automatic feature learning
endowed by a standard feedforward DNN architecture. Besides prediction, MTLDNNs
are also interpretable. The empirical results show that AV is mainly the
substitute of driving and AV alternative-specific variables are more important
than the socio-economic variables in determining AV adoption. Overall, this
study introduces a new MTLDNN framework to combine RP and SP, and demonstrates
its theoretical flexibility and empirical power for prediction and
interpretation. Future studies can design new MTLDNN architectures to reflect
the speciality of RP and SP and extend this work to other behavioral analysis
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Radiologists in their daily work routinely find and annotate significant
abnormalities on a large number of radiology images. Such abnormalities, or
lesions, have collected over years and stored in hospitals' picture archiving
and communication systems. However, they are basically unsorted and lack
semantic annotations like type and location. In this paper, we aim to organize
and explore them by learning a deep feature representation for each lesion. A
large-scale and comprehensive dataset, DeepLesion, is introduced for this task.
DeepLesion contains bounding boxes and size measurements of over 32K lesions.
To model their similarity relationship, we leverage multiple supervision
information including types, self-supervised location coordinates and sizes.
They require little manual annotation effort but describe useful attributes of
the lesions. Then, a triplet network is utilized to learn lesion embeddings
with a sequential sampling strategy to depict their hierarchical similarity
structure. Experiments show promising qualitative and quantitative results on
lesion retrieval, clustering, and classification. The learned embeddings can be
further employed to build a lesion graph for various clinically useful
applications. We propose algorithms for intra-patient lesion matching and
missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde
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