Shoreline Change on the East Coast: Exploring the Role of Shoreline Curvature

The low sloping sandy shoreline of the East Coast is one of the most dynamic and complicated systems influenced by a series of factors. Shoreline curvature has been mentioned in several pieces of literature as one of these factors, as it influences the shaping processes of the shoreline through affecting the alongshore sediment transport. However, only a few quantitative research or evidence has been provided to show the curvature influence on shoreline change rate. Using the coastline contour data of the east coast, the curvature has been calculated and smoothed on different scales (1-km, 3-km and 5-km) in this project. The results of correlation analysis of selected shoreline segments in Florida and North Carolina indicate the existence of a significant correlation between curvature and shoreline change rate. The greatest coefficient was observed on the 3-km scale of selected shoreline segments, which is similar to previous foundings. The results also show that the strength of correlation varies from one location to another

Risk Identification of Sudden Water Pollution on Fuzzy Fault Tree in Beibu-Gulf Economic Zone

AbstractSudden water pollution incident has the characteristics of instantaneity and uncertainty. Based on the characteristics, fuzzy fault tree analysis method was used to identify the potential risks of water pollution in Beibu-Gulf economic zone, and it also combined with the collected data and analysis results. The research results showed that the abnormal discharge of sewage was the main risk factor of the economic zone; the probability value of water pollution potential risk in this study area ranged from 4.6 percent to17.7 percent,which considered the random uncertainty and fuzzy uncertainty of the causes. This research could be considered as an instruction for future risk management, and it will play a great role in the healthy development of ecological environment

How Online Extended Reality (XR) Promotes Consumer Offline Engagement

Using extended-reality (XR) simulation to replicate physical surroundings has become increasingly prevalent in engaging online consumers with offline businesses. However, the efficacy of this XR technology remains ambiguous. To justify the huge investments in XR-related technologies, we investigate the impacts of extended surroundings on consumers’ offline engagement with associated businesses. Specifically, we utilize a natural experimental design on a leading housing platform that applies XR simulation to present the surrounding environment of housing estates. By combining propensity score matching and difference-in-differences, our findings indicate that extended surroundings increase consumer offline engagement outcomes, particularly word-of-mouth volume, and valence. Furthermore, we examine the heterogeneous effects moderated by three business characteristics. To our knowledge, this is the first to examine the impacts of XR simulation of extended surroundings. Therefore, this research offers significant implications for the literature and practice related to XR and omnichannel marketing

HiHPQ: Hierarchical Hyperbolic Product Quantization for Unsupervised Image Retrieval

Existing unsupervised deep product quantization methods primarily aim for the increased similarity between different views of the identical image, whereas the delicate multi-level semantic similarities preserved between images are overlooked. Moreover, these methods predominantly focus on the Euclidean space for computational convenience, compromising their ability to map the multi-level semantic relationships between images effectively. To mitigate these shortcomings, we propose a novel unsupervised product quantization method dubbed \textbf{Hi}erarchical \textbf{H}yperbolic \textbf{P}roduct \textbf{Q}uantization (HiHPQ), which learns quantized representations by incorporating hierarchical semantic similarity within hyperbolic geometry. Specifically, we propose a hyperbolic product quantizer, where the hyperbolic codebook attention mechanism and the quantized contrastive learning on the hyperbolic product manifold are introduced to expedite quantization. Furthermore, we propose a hierarchical semantics learning module, designed to enhance the distinction between similar and non-matching images for a query by utilizing the extracted hierarchical semantics as an additional training supervision. Experiments on benchmarks show that our proposed method outperforms state-of-the-art baselines.Comment: Accepted by AAAI 202

S2SNet: A Pretrained Neural Network for Superconductivity Discovery

Superconductivity allows electrical current to flow without any energy loss, and thus making solids superconducting is a grand goal of physics, material science, and electrical engineering. More than 16 Nobel Laureates have been awarded for their contribution to superconductivity research. Superconductors are valuable for sustainable development goals (SDGs), such as climate change mitigation, affordable and clean energy, industry, innovation and infrastructure, and so on. However, a unified physics theory explaining all superconductivity mechanism is still unknown. It is believed that superconductivity is microscopically due to not only molecular compositions but also the geometric crystal structure. Hence a new dataset, S2S, containing both crystal structures and superconducting critical temperature, is built upon SuperCon and Material Project. Based on this new dataset, we propose a novel model, S2SNet, which utilizes the attention mechanism for superconductivity prediction. To overcome the shortage of data, S2SNet is pre-trained on the whole Material Project dataset with Masked-Language Modeling (MLM). S2SNet makes a new state-of-the-art, with out-of-sample accuracy of 92% and Area Under Curve (AUC) of 0.92. To the best of our knowledge, S2SNet is the first work to predict superconductivity with only information of crystal structures. This work is beneficial to superconductivity discovery and further SDGs. Code and datasets are available in https://github.com/zjuKeLiu/S2SNetComment: Accepted to IJCAI 202

FPGA-based Degradation Evaluation for Traction Power Module with Deep Recurrent Autoencoder

The timely and quantitative evaluation of the degradation is crucial for traction inverter systems in railway applications. The implementation in the industry is impeded by two major challenges including the varying operational profiles and the scalability for system-level applications. This paper proposes a deep recurrent autoencoder-based degradation evaluation method, to assess the degradation level of the traction power module online. The recurrent structure is embedded for processing multivariate time series condition monitoring data stream, in order to exploit the inherent time dependence to improve the accuracy and robustness. The autoencoder-based framework enables the scalability of the proposed method to system-level applications and can be applied under varying operating conditions. The method is experimentally demonstrated on an FPGA-based hardware platform.</p

HICF: Hyperbolic Informative Collaborative Filtering

Considering the prevalence of the power-law distribution in user-item networks, hyperbolic space has attracted considerable attention and achieved impressive performance in the recommender system recently. The advantage of hyperbolic recommendation lies in that its exponentially increasing capacity is well-suited to describe the power-law distributed user-item network whereas the Euclidean equivalent is deficient. Nonetheless, it remains unclear which kinds of items can be effectively recommended by the hyperbolic model and which cannot. To address the above concerns, we take the most basic recommendation technique, collaborative filtering, as a medium, to investigate the behaviors of hyperbolic and Euclidean recommendation models. The results reveal that (1) tail items get more emphasis in hyperbolic space than that in Euclidean space, but there is still ample room for improvement; (2) head items receive modest attention in hyperbolic space, which could be considerably improved; (3) and nonetheless, the hyperbolic models show more competitive performance than Euclidean models. Driven by the above observations, we design a novel learning method, named hyperbolic informative collaborative filtering (HICF), aiming to compensate for the recommendation effectiveness of the head item while at the same time improving the performance of the tail item. The main idea is to adapt the hyperbolic margin ranking learning, making its pull and push procedure geometric-aware, and providing informative guidance for the learning of both head and tail items. Extensive experiments back up the analytic findings and also show the effectiveness of the proposed method. The work is valuable for personalized recommendations since it reveals that the hyperbolic space facilitates modeling the tail item, which often represents user-customized preferences or new products.Comment: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22