Domain Adaptive Imitation Learning with Visual Observation

In this paper, we consider domain-adaptive imitation learning with visual observation, where an agent in a target domain learns to perform a task by observing expert demonstrations in a source domain. Domain adaptive imitation learning arises in practical scenarios where a robot, receiving visual sensory data, needs to mimic movements by visually observing other robots from different angles or observing robots of different shapes. To overcome the domain shift in cross-domain imitation learning with visual observation, we propose a novel framework for extracting domain-independent behavioral features from input observations that can be used to train the learner, based on dual feature extraction and image reconstruction. Empirical results demonstrate that our approach outperforms previous algorithms for imitation learning from visual observation with domain shift.Comment: Accepted to NeurIPS 202

Online Class-Incremental Continual Learning with Adversarial Shapley Value

As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption. While memory replay techniques have shown exceptional promise for this task of continual learning, the best method for selecting which buffered images to replay is still an open question. In this paper, we specifically focus on the online class-incremental setting where a model needs to learn new classes continually from an online data stream. To this end, we contribute a novel Adversarial Shapley value scoring method that scores memory data samples according to their ability to preserve latent decision boundaries for previously observed classes (to maintain learning stability and avoid forgetting) while interfering with latent decision boundaries of current classes being learned (to encourage plasticity and optimal learning of new class boundaries). Overall, we observe that our proposed ASER method provides competitive or improved performance compared to state-of-the-art replay-based continual learning methods on a variety of datasets.Comment: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21

Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation

As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have gained great popularity. However, the attribution methods based on these techniques provide lower resolution and blurry explanation maps that limit their explanation power. To circumvent this issue, visualization based on various layers is sought. In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation. We also propose a layer selection strategy that applies to the whole family of CNN-based models, based on which our extraction framework is applied to visualize the last layers of each convolutional block of the model. Moreover, we perform an empirical analysis of the efficacy of derived lower-level information to enhance the represented attributions. Comprehensive experiments conducted on shallow and deep models trained on natural and industrial datasets, using both ground-truth and model-truth based evaluation metrics validate our proposed algorithm by meeting or outperforming the state-of-the-art methods in terms of explanation ability and visual quality, demonstrating that our method shows stability regardless of the size of objects or instances to be explained.Comment: 9 pages, 9 figures, Accepted at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21

Regulating the surface of anion-doped TiO2 nanorods by hydrogen annealing for superior photoelectrochemical water oxidation

Dedications to achieve the highly efficient metal oxide semiconductor for the photoelectrochemical water splitting system have been persisted to utilize the TiO2 as the promising photoanode material. Herein, we report notable progress for nanostructured TiO2 photoanodes using facile sequential one-pot hydrothermal synthesis and annealing in hydrogen. A photocurrent density of 3.04 mA·cm−2 at 1.23 V vs. reversible hydrogen electrode was achieved in TiO2 nanorod arrays annealed in hydrogen ambient, which is approximately 4.25 times higher than that of pristine TiO2 annealed in ambient air. 79.2% of incident photon-to-current efficiency at 380 nm wavelength demonstrates the prominence of the material at the near-UV spectral range region and 100 h chronoamperometric test exhibits the stability of the photoanode. Detailed studies regarding crystallinity, bandgap, and elemental analysis provide the importance of the optimized annealing condition for the TiO2-based photoanodes. Water contact angle measurement displays the effect of hydrogen annealing on the hydrophilicity of the material. This study clearly demonstrates the marked improvement using the optimized hydrogen annealing, providing the promising methodologies for eco-friendly mass production of water splitting photoelectrodes.The authors gratefully acknowledge the fnancial support from the Creative, Material Discovery Program (2016M3D1A1027666, 2017M3D1A1040834, 2018M3D1A1058793) through the National Research Foundation of Korea funded by Ministry of Science and ICT, the Basic Research Laboratory Program through an NRF grant funded by the Korean Ministry of Science, ICT and Future Planning (2021R1A4A302787811), the KRISS (Korea Research Institute of Standards and Science) MPI Lab. Program and the National Research Foundation of Korea (NRF) grant funded by the Korea Government MSIT (2021R1C1C2006142), and Nuclear Energy R&D Program(2020M2D8A206983012). The Inter-University Semiconductor Research Center and Institute of Engineering Research at Seoul National University provided research facilities for this work

Crystal Facet Engineering of TiO2 Nanostructures for Enhancing Photoelectrochemical Water Splitting with BiVO4 Nanodots

Highlights Two types of BiVO4/TiO2 heterostructure photoanodes comprising TiO2 nanorods (NRs) and TiO2 nanoflowers (NFs) with different (001) and (110) crystal facets, respectively, were designed. The higher photoactivity of BiVO4/TiO2 NFs than BiVO4/TiO2 NRs was attributed to the improvement of charge separation by the TiO2 NFs. The formation of type II band alignment between BiVO4 nanodots and TiO2 NFs expedited electron transport and reduced charge recombination.Abstract Although bismuth vanadate (BiVO4) has been promising as photoanode material for photoelectrochemical water splitting, its charge recombination issue by short charge diffusion length has led to various studies about heterostructure photoanodes. As a hole blocking layer of BiVO4, titanium dioxide (TiO2) has been considered unsuitable because of its relatively positive valence band edge and low electrical conductivity. Herein, a crystal facet engineering of TiO2 nanostructures is proposed to control band structures for the hole blocking layer of BiVO4 nanodots. We design two types of TiO2 nanostructures, which are nanorods (NRs) and nanoflowers (NFs) with different (001) and (110) crystal facets, respectively, and fabricate BiVO4/TiO2 heterostructure photoanodes. The BiVO4/TiO2 NFs showed 4.8 times higher photocurrent density than the BiVO4/TiO2 NRs. Transient decay time analysis and time-resolved photoluminescence reveal the enhancement is attributed to the reduced charge recombination, which is originated from the formation of type II band alignment between BiVO4 nanodots and TiO2 NFs. This work provides not only new insights into the interplay between crystal facets and band structures but also important steps for the design of highly efficient photoelectrodes

Stimuli-Responsive Hydrogel Microlenses

This dissertation is aimed towards using stimuli-responsive pNIPAm-co-AAc microgels synthesized via free-radical precipitation polymerization to prepare stimuli-responsive hydrogel microlenses. Chapter 1 gives a detailed background of hydrogels, and their applications using responsive hydrogels. Chapter 2 describes the use of colloidal hydrogel microparticles as microlens elements and the fabrication method to form the hydrogel microlens arrays via Coulombic interactions. Chapter 3 shows the demonstration of tunable microlenses prepared by the method used in Chapter 2. In this chapter the microlenses are subjected to various pH and temperature in aqueous solutions. Chapter 4 describes that the microlens arrays constructed on Au nanoparticle-functionalized glass substrates by self-assembly display dramatic changes in lensing power in response to an impingent frequency-doubled Nd:YAG laser. The microlens photoswitching is highly reversible, with sub-millisecond lens switching times. Chapter 5 describes the development of bioresponsive hydrogel microlenses as a new protein detection technology. The microlens method is shown to be very specific for the target protein, with no detectable interference from nonspecific protein binding. Chapter 6 describes the use of bioresponsive hydrogel microlenses as a label-free biosensing scaffolding. These microstructures simultaneously act as the biosensors scaffolding/immobilization architecture, transducer, amplifier, and also allow for broad tunability of the analyte concentration to which the microlens is sensitive.Ph.D.Committee Chair: Lyon, L. Andrew; Committee Member: Dickson, Robert; Committee Member: Janata, Jiri; Committee Member: Srinivasarao, Mohan; Committee Member: Weck, Marcu

Optimal model-building strategy for rapid prototype manufacturing of sculpture surface.

Optimal model-building strategy for rapid prototype manufacturing of sculpture surface