6,038 research outputs found
Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data
This paper presents a new method for shadow removal using unpaired data,
enabling us to avoid tedious annotations and obtain more diverse training
samples. However, directly employing adversarial learning and cycle-consistency
constraints is insufficient to learn the underlying relationship between the
shadow and shadow-free domains, since the mapping between shadow and
shadow-free images is not simply one-to-one. To address the problem, we
formulate Mask-ShadowGAN, a new deep framework that automatically learns to
produce a shadow mask from the input shadow image and then takes the mask to
guide the shadow generation via re-formulated cycle-consistency constraints.
Particularly, the framework simultaneously learns to produce shadow masks and
learns to remove shadows, to maximize the overall performance. Also, we
prepared an unpaired dataset for shadow removal and demonstrated the
effectiveness of Mask-ShadowGAN on various experiments, even it was trained on
unpaired data.Comment: Accepted to ICCV 201
Role Playing Learning for Socially Concomitant Mobile Robot Navigation
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile
robot to navigate socially with its human companion in populated environments.
Neural networks (NN) are constructed to parameterize a stochastic policy that
directly maps sensory data collected by the robot to its velocity outputs,
while respecting a set of social norms. An efficient simulative learning
environment is built with maps and pedestrians trajectories collected from a
number of real-world crowd data sets. In each learning iteration, a robot
equipped with the NN policy is created virtually in the learning environment to
play itself as a companied pedestrian and navigate towards a goal in a socially
concomitant manner. Thus, we call this process Role Playing Learning, which is
formulated under a reinforcement learning (RL) framework. The NN policy is
optimized end-to-end using Trust Region Policy Optimization (TRPO), with
consideration of the imperfectness of robot's sensor measurements. Simulative
and experimental results are provided to demonstrate the efficacy and
superiority of our method
Phonon and Raman scattering of two-dimensional transition metal dichalcogenides from monolayer, multilayer to bulk material
Two-dimensional (2D) transition metal dichalcogenide (TMD) nanosheets exhibit
remarkable electronic and optical properties. The 2D features, sizable
bandgaps, and recent advances in the synthesis, characterization, and device
fabrication of the representative MoS, WS, WSe, and MoSe TMDs
make TMDs very attractive in nanoelectronics and optoelectronics. Similar to
graphite and graphene, the atoms within each layer in 2D TMDs are joined
together by covalent bonds, while van der Waals interactions keep the layers
together. This makes the physical and chemical properties of 2D TMDs layer
dependent. In this review, we discuss the basic lattice vibrations of
monolayer, multilayer, and bulk TMDs, including high-frequency optical phonons,
interlayer shear and layer breathing phonons, the Raman selection rule,
layer-number evolution of phonons, multiple phonon replica, and phonons at the
edge of the Brillouin zone. The extensive capabilities of Raman spectroscopy in
investigating the properties of TMDs are discussed, such as interlayer
coupling, spin--orbit splitting, and external perturbations. The interlayer
vibrational modes are used in rapid and substrate-free characterization of the
layer number of multilayer TMDs and in probing interface coupling in TMD
heterostructures. The success of Raman spectroscopy in investigating TMD
nanosheets paves the way for experiments on other 2D crystals and related van
der Waals heterostructures.Comment: 30 pages, 23 figure
Forecasting construction demand : a vector error correction model with dummy variables
Modelling the level of demand for construction is vital in policy formulation and implementation as the construction industry plays an important role in a country’s economic development process. In construction economics, research efforts on construction demand modelling and forecasting are various, but few researchers have considered the impact of global economy events in construction demand modelling. An advanced multivariate modelling technique, namely the vector error correction (VEC) model with dummy variables, was adopted to predict demand in the Australian construction market. The results of prediction accuracy tests suggest that the general VEC model and the VEC model with dummy variables are both acceptable for forecasting construction economic indicators. However, the VEC model that considers external impacts achieves higher prediction accuracy than the general VEC model. The model estimates indicate that the growth in population, changes in national income, fluctuations in interest rates and changes in householder expenditure all play significant roles when explaining variations in construction demand. The VEC model with disturbances developed can serve as an experimentation using an advanced econometrical method which can be used to analyse the effect of specific events or factors on the construction market growth
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