Ion intercalation in layered MoO3 and WO3 nanostructure

Ph.DDOCTOR OF PHILOSOPH

Understanding the effects of doctors’ online profile pictures on patients’ decision-making

The effect of doctors’ profile pictures in online healthcare platforms has been investigated in prior research. However, little attention has focused on professional signals conveyed by doctors’ online profile pictures. To address the above-mentioned gap, this study examines the roles of doctors’ profile pictures in patients’ online decision-making based on cue utilization theory and impression management theory. Our research finds that a picture indicating professional information (e.g., professional attire and professional background) matters in attracting patients’ interest and their decision-making. However, the impacts of professional information change according to patients’ illness severity. Specifically, patients with low-severity illnesses care more about professional attire in the stage of glancing physicians, while patients with high-severity illnesses care more about professional attire in the stage of decision making. These findings contribute to the domain knowledge of online service design and delivery, especially in the arena of online health services

SwinVRNN: A Data-Driven Ensemble Forecasting Model via Learned Distribution Perturbation

Data-driven approaches for medium-range weather forecasting are recently shown extraordinarily promising for ensemble forecasting for their fast inference speed compared to traditional numerical weather prediction (NWP) models, but their forecast accuracy can hardly match the state-of-the-art operational ECMWF Integrated Forecasting System (IFS) model. Previous data-driven attempts achieve ensemble forecast using some simple perturbation methods, like initial condition perturbation and Monte Carlo dropout. However, they mostly suffer unsatisfactory ensemble performance, which is arguably attributed to the sub-optimal ways of applying perturbation. We propose a Swin Transformer-based Variational Recurrent Neural Network (SwinVRNN), which is a stochastic weather forecasting model combining a SwinRNN predictor with a perturbation module. SwinRNN is designed as a Swin Transformer-based recurrent neural network, which predicts future states deterministically. Furthermore, to model the stochasticity in prediction, we design a perturbation module following the Variational Auto-Encoder paradigm to learn multivariate Gaussian distributions of a time-variant stochastic latent variable from data. Ensemble forecasting can be easily achieved by perturbing the model features leveraging noise sampled from the learned distribution. We also compare four categories of perturbation methods for ensemble forecasting, i.e. fixed distribution perturbation, learned distribution perturbation, MC dropout, and multi model ensemble. Comparisons on WeatherBench dataset show the learned distribution perturbation method using our SwinVRNN model achieves superior forecast accuracy and reasonable ensemble spread due to joint optimization of the two targets. More notably, SwinVRNN surpasses operational IFS on surface variables of 2-m temperature and 6-hourly total precipitation at all lead times up to five days

PolyBuilding: Polygon Transformer for End-to-End Building Extraction

We present PolyBuilding, a fully end-to-end polygon Transformer for building extraction. PolyBuilding direct predicts vector representation of buildings from remote sensing images. It builds upon an encoder-decoder transformer architecture and simultaneously outputs building bounding boxes and polygons. Given a set of polygon queries, the model learns the relations among them and encodes context information from the image to predict the final set of building polygons with fixed vertex numbers. Corner classification is performed to distinguish the building corners from the sampled points, which can be used to remove redundant vertices along the building walls during inference. A 1-d non-maximum suppression (NMS) is further applied to reduce vertex redundancy near the building corners. With the refinement operations, polygons with regular shapes and low complexity can be effectively obtained. Comprehensive experiments are conducted on the CrowdAI dataset. Quantitative and qualitative results show that our approach outperforms prior polygonal building extraction methods by a large margin. It also achieves a new state-of-the-art in terms of pixel-level coverage, instance-level precision and recall, and geometry-level properties (including contour regularity and polygon complexity)

Learning Motor Skills of Reactive Reaching and Grasping of Objects

Reactive grasping of objects is an essential capability of autonomous robot manipulation, which is yet challenging to learn such sensorimotor control to coordinate coherent hand-finger motions and be robust against disturbances and failures. This work proposed a deep reinforcement learning based scheme to train feedback control policies which can coordinate reaching and grasping actions in presence of uncertainties. We formulated geometric metrics and task-orientated quantities to design the reward, which enabled efficient exploration of grasping policies. Further, to improve the success rate, we deployed key initial states of difficult hand-finger poses to train policies to overcome potential failures due to challenging configurations. The extensive simulation validations and benchmarks demonstrated that the learned policy was robust to grasp both static and moving objects. Moreover, the policy generated successful failure recoveries within a short time in difficult configurations and was robust with synthetic noises in the state feedback which were unseen during training

Exploring AADL verification tool through model transformation

International audienceArchitecture Analysis and Design Language (AADL) is often used to model safety-critical real-time systems. Model transformation is widely used to extract a formal specification so that AADL models can be verified and analyzed by existing tools. Timed Abstract State Machine (TASM) is a formalism not only able to specify behavior and communication but also timing and resource aspects of the system. To verify functional and nonfunctional properties of AADL models, this paper presents a methodology for translating AADL to TASM. Our main contribution is to formally define the translation rules from an adequate subset of AADL (including thread component, port communication, behavior annex and mode change) into TASM. Based on these rules, a tool called AADL2TASM is implemented using Atlas Transformation Language (ATL). Finally, a case study from an actual data processing unit of a satellite is provided to validate the transformation and illustrate the practicality of the approach