71 research outputs found
How does destination crisis event type impact tourist emotion and forgiveness? The moderating role of destination crisis history
Drawing on attribution theory and situational crisis communication theory, this study investigates how destination crisis events impact tourist sympathy, anger, and intentions of forgiveness in four experiments. It also examines the moderating effects of destination crisis history (none vs. similar vs. dissimilar). The results of Study 1 and Study 2 revealed that external crisis events cause more sympathy and tourist forgiveness than internal ones, but less anger. They also indicated that sympathy and anger play a mediating role in destination crisis events and tourist forgiveness. The results of Study 3 and Study 4 revealed that destination crisis history predicts the impact of crisis events on tourist emotion and forgiveness. In particular, when there is no destination crisis history or similar crisis history, an external crisis event will garner more sympathy and forgiveness than an internal crisis event. These findings provide theoretical and practical implications for destination crisis management
EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization
Mixed-Precision Quantization~(MQ) can achieve a competitive
accuracy-complexity trade-off for models. Conventional training-based search
methods require time-consuming candidate training to search optimized per-layer
bit-width configurations in MQ. Recently, some training-free approaches have
presented various MQ proxies and significantly improve search efficiency.
However, the correlation between these proxies and quantization accuracy is
poorly understood. To address the gap, we first build the MQ-Bench-101, which
involves different bit configurations and quantization results. Then, we
observe that the existing training-free proxies perform weak correlations on
the MQ-Bench-101. To efficiently seek superior proxies, we develop an automatic
search of proxies framework for MQ via evolving algorithms. In particular, we
devise an elaborate search space involving the existing proxies and perform an
evolution search to discover the best correlated MQ proxy. We proposed a
diversity-prompting selection strategy and compatibility screening protocol to
avoid premature convergence and improve search efficiency. In this way, our
Evolving proxies for Mixed-precision Quantization~(EMQ) framework allows the
auto-generation of proxies without heavy tuning and expert knowledge. Extensive
experiments on ImageNet with various ResNet and MobileNet families demonstrate
that our EMQ obtains superior performance than state-of-the-art mixed-precision
methods at a significantly reduced cost. The code will be released.Comment: Accepted by ICCV202
ParZC: Parametric Zero-Cost Proxies for Efficient NAS
Recent advancements in Zero-shot Neural Architecture Search (NAS) highlight
the efficacy of zero-cost proxies in various NAS benchmarks. Several studies
propose the automated design of zero-cost proxies to achieve SOTA performance
but require tedious searching progress. Furthermore, we identify a critical
issue with current zero-cost proxies: they aggregate node-wise zero-cost
statistics without considering the fact that not all nodes in a neural network
equally impact performance estimation. Our observations reveal that node-wise
zero-cost statistics significantly vary in their contributions to performance,
with each node exhibiting a degree of uncertainty. Based on this insight, we
introduce a novel method called Parametric Zero-Cost Proxies (ParZC) framework
to enhance the adaptability of zero-cost proxies through parameterization. To
address the node indiscrimination, we propose a Mixer Architecture with
Bayesian Network (MABN) to explore the node-wise zero-cost statistics and
estimate node-specific uncertainty. Moreover, we propose DiffKendall as a loss
function to directly optimize Kendall's Tau coefficient in a differentiable
manner so that our ParZC can better handle the discrepancies in ranking
architectures. Comprehensive experiments on NAS-Bench-101, 201, and NDS
demonstrate the superiority of our proposed ParZC compared to existing
zero-shot NAS methods. Additionally, we demonstrate the versatility and
adaptability of ParZC by transferring it to the Vision Transformer search
space
Tunable photochemical deposition of silver nanostructures on layered ferroelectric CuInPS6
2D layered ferroelectric materials such as CuInPS6 (CIPS) are promising
candidates for novel and high-performance photocatalysts, owning to their
ultrathin layer thickness, strong interlayer coupling, and intrinsic
spontaneous polarization, while how to control the photocatalytic activity in
layered CIPS remains unexplored. In this work, we report for the first time the
photocatalytic activity of ferroelectric CIPS for the chemical deposition of
silver nanostructures (AgNSs). The results show that the shape and spatial
distribution of AgNSs on CIPS are tunable by controlling layer thickness,
environmental temperature, and light wavelength. The ferroelectric polarization
in CIPS plays a critical role in tunable AgNS photodeposition, as evidenced by
layer thickness and temperature dependence experiments. We further reveal that
AgNS photodeposition process starts from the active site creation, selective
nanoparticle nucleation/aggregation, to the continuous film formation.
Moreover, AgNS/CIPS heterostructures prepared by photodeposition exhibit
excellent resistance switching behavior and good surface enhancement Raman
Scattering activity. Our findings provide new insight into the photocatalytic
activity of layered ferroelectrics and offer a new material platform for
advanced functional device applications in smart memristors and enhanced
chemical sensors.Comment: 18 pages, 5 figure
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