1,357 research outputs found
Fluctuating Nature of Light-Enhanced -Wave Superconductivity: A Time-Dependent Variational Non-Gaussian Exact Diagonalization Study
Engineering quantum phases using light is a novel route to designing
functional materials, where light-induced superconductivity is a successful
example. Although this phenomenon has been realized experimentally, especially
for the high- cuprates, the underlying mechanism remains mysterious. Using
the recently developed variational non-Gaussian exact diagonalization method,
we investigate a particular type of photoenhanced superconductivity by
suppressing a competing charge order in a strongly correlated electron-electron
and electron-phonon system. We find that the -wave superconductivity pairing
correlation can be enhanced by a pulsed laser, consistent with recent
experiments based on gap characterizations. However, we also find that the
pairing correlation length is heavily suppressed by the pump pulse, indicating
that light-enhanced superconductivity may be of fluctuating nature. Our
findings also imply a general behavior of nonequilibrium states with competing
orders, beyond the description of a mean-field framework.Comment: 17 pages, 8 figure
LLM-Powered Conversational Voice Assistants: Interaction Patterns, Opportunities, Challenges, and Design Guidelines
Conventional Voice Assistants (VAs) rely on traditional language models to
discern user intent and respond to their queries, leading to interactions that
often lack a broader contextual understanding, an area in which Large Language
Models (LLMs) excel. However, current LLMs are largely designed for text-based
interactions, thus making it unclear how user interactions will evolve if their
modality is changed to voice. In this work, we investigate whether LLMs can
enrich VA interactions via an exploratory study with participants (N=20) using
a ChatGPT-powered VA for three scenarios (medical self-diagnosis, creative
planning, and debate) with varied constraints, stakes, and objectivity. We
observe that LLM-powered VA elicits richer interaction patterns that vary
across tasks, showing its versatility. Notably, LLMs absorb the majority of VA
intent recognition failures. We additionally discuss the potential of
harnessing LLMs for more resilient and fluid user-VA interactions and provide
design guidelines for tailoring LLMs for voice assistance
Designing Network Design Strategies Through Gradient Path Analysis
Designing a high-efficiency and high-quality expressive network architecture
has always been the most important research topic in the field of deep
learning. Most of today's network design strategies focus on how to integrate
features extracted from different layers, and how to design computing units to
effectively extract these features, thereby enhancing the expressiveness of the
network. This paper proposes a new network design strategy, i.e., to design the
network architecture based on gradient path analysis. On the whole, most of
today's mainstream network design strategies are based on feed forward path,
that is, the network architecture is designed based on the data path. In this
paper, we hope to enhance the expressive ability of the trained model by
improving the network learning ability. Due to the mechanism driving the
network parameter learning is the backward propagation algorithm, we design
network design strategies based on back propagation path. We propose the
gradient path design strategies for the layer-level, the stage-level, and the
network-level, and the design strategies are proved to be superior and feasible
from theoretical analysis and experiments.Comment: 12 pages, 9 figure
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