Studies on Personalized HCI

Ph.DDOCTOR OF PHILOSOPH

The strategies preventing particle transportation into the inlets of nuclear power plants: Mechanisms of physical oceanography

The formation of aquatic organism aggregations near the inlets of nuclear power plants (NPPs) has become an important global concern, as the aggregated organisms can block the cooling systems of NPPs, and, therefore, threaten their operational safety. In this study we focus on the trajectory of aquatic organisms, that is., how these organisms can be transported to the inlets of NPPs by physical ocean processes related to currents and waves. The Changjiang NPP, located on the west side of Hainan Island in China, is occasionally subject to serious gulfweed blocking events in spring. To study the physical mechanism, with the use of a three-dimensional numerical current–wave-coupled model, the current and wave conditions near the NPP were simulated. Based on the model, several particle-tracking simulations were run to evaluate the extent of the blocking that occurred in the inlet of the NPP’s cooling system with different forcings introduced. The results showed that the windage effect and the surface Stokes drift induced by waves were the main causes of blocking events in the Changjiang NPP, with the former transporting surface particles from upstream and the latter transporting surrounding particles onshore, into the NPP’s inlet. Further simulations revealed that bending of the inlet and changing the offshore mouth to downstream mouth could limit the blocking greatly, as particles were seldom transported into the mouth by cross-shore transport processes such as the Stokes drift. We suggest that such findings may provide a valuable reference for the development of strategies to prevent aquatic organism aggregation events in other NPPs

Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds

Policy-based algorithms equipped with deep neural networks have achieved great success in solving high-dimensional policy optimization problems in reinforcement learning. However, current analyses cannot explain why they are resistant to the curse of dimensionality. In this work, we study the sample complexity of the neural policy mirror descent (NPMD) algorithm with convolutional neural networks (CNN) as function approximators. Motivated by the empirical observation that many high-dimensional environments have state spaces possessing low-dimensional structures, such as those taking images as states, we consider the state space to be a $d$-dimensional manifold embedded in the $D$-dimensional Euclidean space with intrinsic dimension $d\ll D$. We show that in each iteration of NPMD, both the value function and the policy can be well approximated by CNNs. The approximation errors are controlled by the size of the networks, and the smoothness of the previous networks can be inherited. As a result, by properly choosing the network size and hyperparameters, NPMD can find an $\epsilon$-optimal policy with $\widetilde{O}(\epsilon^{-\frac{d}{\alpha}-2})$ samples in expectation, where $\alpha\in(0,1]$ indicates the smoothness of environment. Compared to previous work, our result exhibits that NPMD can leverage the low-dimensional structure of state space to escape from the curse of dimensionality, providing an explanation for the efficacy of deep policy-based algorithms

Fostering User Engagement: Rhetorical Devices for Applause Generation Learnt from TED Talks

One problem that every presenter faces when delivering a public discourse is how to hold the listeners' attentions or to keep them involved. Therefore, many studies in conversation analysis work on this issue and suggest qualitatively con-structions that can effectively lead to audience's applause. To investigate these proposals quantitatively, in this study we an-alyze the transcripts of 2,135 TED Talks, with a particular fo-cus on the rhetorical devices that are used by the presenters for applause elicitation. Through conducting regression anal-ysis, we identify and interpret 24 rhetorical devices as triggers of audience applauding. We further build models that can rec-ognize applause-evoking sentences and conclude this work with potential implications

Embodied Executable Policy Learning with Language-based Scene Summarization

Large Language models (LLMs) have shown remarkable success in assisting robot learning tasks, i.e., complex household planning. However, the performance of pretrained LLMs heavily relies on domain-specific templated text data, which may be infeasible in real-world robot learning tasks with image-based observations. Moreover, existing LLMs with text inputs lack the capability to evolve with non-expert interactions with environments. In this work, we introduce a novel learning paradigm that generates robots' executable actions in the form of text, derived solely from visual observations, using language-based summarization of these observations as the connecting bridge between both domains. Our proposed paradigm stands apart from previous works, which utilized either language instructions or a combination of language and visual data as inputs. Moreover, our method does not require oracle text summarization of the scene, eliminating the need for human involvement in the learning loop, which makes it more practical for real-world robot learning tasks. Our proposed paradigm consists of two modules: the SUM module, which interprets the environment using visual observations and produces a text summary of the scene, and the APM module, which generates executable action policies based on the natural language descriptions provided by the SUM module. We demonstrate that our proposed method can employ two fine-tuning strategies, including imitation learning and reinforcement learning approaches, to adapt to the target test tasks effectively. We conduct extensive experiments involving various SUM/APM model selections, environments, and tasks across 7 house layouts in the VirtualHome environment. Our experimental results demonstrate that our method surpasses existing baselines, confirming the effectiveness of this novel learning paradigm.Comment: 15 pages. arXiv admin note: text overlap with arXiv:2107.06912 by other author