856 research outputs found
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
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