Inclusive multi-sensory landscape: directing visually impaired people in a perception world

This thesis explored the use of inclusive landscape design to provide visually impaired people and normal people with enhanced multi-sensory experiences, and for recognizing space, navigating move through spaces. Inclusive design is human design, inviting people in and giving the communicative power to space through stimulating one’s intuition and senses by repetition, sequencing, or patterning in design that signals time, space, and movement through the layouts of walking trajectories between important nodes or places of refuge. Through the visually impaired issue studies, solutions, and methods exploration, I developed principles as a solver, applied them on one site to transform space for testing my theory. This theory aimed to enhance public awareness of visually impaired people, pay attention to their outdoor experiences and provide everyone enhanced space experiences and motivate multisensory to emphasize the critical nodes, connect the fragmented spaces, direct people walking through intersections safely, and indicatively

Efficient preparation of the AKLT State with Measurement-based Imaginary Time Evolution

Quantum state preparation plays a crucial role in several areas of quantum information science, in applications such as quantum simulation, quantum metrology and quantum computing. However, typically state preparation requires resources that scale exponentially with the problem size, due to their probabilistic nature or otherwise, making studying such models challenging. In this article, we propose a method to prepare the ground state of the Affleck-Lieb-Kennedy-Tasaki (AKLT) model deterministically using an measurement-based imaginary time evolution (MITE) approach. By taking advantage of the special properties of the AKLT state, we show that it can be prepared efficiently using the MITE approach. Estimates based on the convergence of a sequence of local projections, as well as direct evolution of the MITE algorithm suggest a constant scaling with respect to the number of AKLT sites, which is an exponential improvement over the naive estimate for convergence. We show that the procedure is compatible with qubit-based simulators, and show that using a variational quantum algorithm for circuit recompilation, the measurement operator required for MITE can be well approximated by a circuit with a much shallower circuit depth compared with the one obtained using the default Qiskit method.Comment: 11 pages, 7 figure

Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling

Learning to denoise has emerged as a prominent paradigm to design state-of-the-art deep generative models for natural images. How to use it to model the distributions of both continuous real-valued data and categorical data has been well studied in recently proposed diffusion models. However, it is found in this paper to have limited ability in modeling some other types of data, such as count and non-negative continuous data, that are often highly sparse, skewed, heavy-tailed, and/or overdispersed. To this end, we propose learning to jump as a general recipe for generative modeling of various types of data. Using a forward count thinning process to construct learning objectives to train a deep neural network, it employs a reverse count thickening process to iteratively refine its generation through that network. We demonstrate when learning to jump is expected to perform comparably to learning to denoise, and when it is expected to perform better. For example, learning to jump is recommended when the training data is non-negative and exhibits strong sparsity, skewness, heavy-tailedness, and/or heterogeneity.Comment: ICML 202