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

New Damage Identification Method for Operational Metro Tunnel Based on Perturbation Theory and Fuzzy Logic

The structural health of operational metro tunnels is closely related to public safety. Prior research has focused on the locations of structural damage, but few researchers have examined both the location of damage and identifying the degree of damage, especially in metro shield tunnels. This paper proposes a new method for identifying structural damage that entails locating and detecting the degradation of tunnel performance, with a special focus on characterizing the degree of damage. First, the dynamic behaviors (modal frequencies and shapes) of different damage levels are obtained from an analytical model of the original tunnel structure. Second, a modal strain energy damage indicator (MSEDI) is introduced to locate the damage, regardless of size. Once the location of the damage is identified using MSEDI, a fuzzy logic-based damage identification (FLBDI) method is used to determine the actual extent of the damage. Finally, a simplified model of the tunnel is created using the Euler-Bernoulli beam theory and Winkler’s foundation, to further test the procedure under an incomplete modal information condition and with differing noise levels. The results reveal that the fuzzy logic- based system can identify the degree of damage and structural degradation with very high accuracy, in which the location of damage and the prediction of performance degradation is satisfactorily confirmed.</p

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

Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models

Despite the remarkable capabilities, Large Language Models (LLMs) face deployment challenges due to their extensive size. Pruning methods drop a subset of weights to accelerate, but many of them require retraining, which is prohibitively expensive and computationally demanding. Recently, post-training pruning approaches introduced novel metrics, enabling the pruning of LLMs without retraining. However, these metrics require the involvement of human experts and tedious trial and error. To efficiently identify superior pruning metrics, we develop an automatic framework for searching symbolic pruning metrics using genetic programming. In particular, we devise an elaborate search space encompassing the existing pruning metrics to discover the potential symbolic pruning metric. We propose an opposing operation simplification strategy to increase the diversity of the population. In this way, Pruner-Zero allows auto-generation of symbolic pruning metrics. Based on the searched results, we explore the correlation between pruning metrics and performance after pruning and summarize some principles. Extensive experiments on LLaMA and LLaMA-2 on language modeling and zero-shot tasks demonstrate that our Pruner-Zero obtains superior performance than SOTA post-training pruning methods. Code at: \url{https://github.com/pprp/Pruner-Zero}.Comment: Accepted by ICML2024, 29 pages, 4 figure

Tunable photochemical deposition of silver nanostructures on layered ferroelectric CuInP2_2S6

2D layered ferroelectric materials such as CuInP$_2$S6 (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

Arctic Amplification of marine heatwaves under global warming

Marine heatwaves (MHWs) and total heat exposures (THEs), extreme warming events occurring across the global oceans, seriously threaten marine ecosystems and coastal communities as the climate warms. However, future changes in MHWs and THEs in the Arctic Ocean, where unique marine ecosystems are present, are still unclear. Here, based on the latest CMIP6 climate simulations, we find that both MHWs and THEs in the Arctic Ocean are anticipated to intensify in a warming climate, mainly due to Arctic sea ice decline and long-term warming trend, respectively. Particularly striking is the projected rise in MHW mean intensity during the 21st century in the Arctic Ocean, surpassing the global average by more than sevenfold under the CMIP6 SSP585 scenario. This phenomenon, coined the ‘Arctic MHW Amplification’, underscores an impending and disproportionately elevated threat to the Arctic marine life, necessitating targeted conservation and adaptive strategies