374,437 research outputs found
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
On the power divergence in quasi gluon distribution function
Recent perturbative calculation of quasi gluon distribution function at
one-loop level shows the existence of extra linear ultraviolet divergences in
the cut-off scheme. We employ the auxiliary field approach, and study the
renormalization of gluon operators. The non-local gluon operator can mix with
new operators under renormalization, and the linear divergences in quasi
distribution function can be into the newly introduced operators. After
including the mixing, we find the improved quasi gluon distribution functions
contain only logarithmic divergences, and thus can be used to extract the gluon
distribution in large momentum effective theory.Comment: 18 pages, 10 figures. Published version in JHE
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