Damage Detection Using High Order Longitudinal Guided Waves in the Anchorage Zone of Stayed-Cable

High order longitudinal guided waves (HOLGW) are studied for the damage detection in the anchorage zone of stayed cable through the theoretical analysis, numerical simulation and experimental validation. First, based on the theory of elastic wave propagation in cylinder, the dispersion curves of longitudinal modes were obtained and calculated analytically and the high-frequency such as 5MHz corresponding to the higher order longitudinal guided wave modes are identified for the damage detection. Then, the ultrasonic guided waves propagating in a steel wire with or without defects were simulated by using the finite element method and the effects of defect depth and length on the reflection coefficient are studied. Finally, the free wires and a tested cable were studied experimentally. The results show that the finite element method is able to model the high-order guided wave propagation in the steel wire. The agreement between the experiment and theory has demonstrated that the HOLGW is a potential candidate for the damage detection in anchorage zones of stayed-cables

How to Retrain Recommender System? A Sequential Meta-Learning Method

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community. Our first belief is that retraining the model on historical data is unnecessary, since the model has been trained on it before. Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference. To address this dilemma, we propose a new training method, aiming to abandon the historical data during retraining through learning to transfer the past training experience. Specifically, we design a neural network-based transfer component, which transforms the old model to a new model that is tailored for future recommendations. To learn the transfer component well, we optimize the "future performance" -- i.e., the recommendation accuracy evaluated in the next time period. Our Sequential Meta-Learning(SML) method offers a general training paradigm that is applicable to any differentiable model. We demonstrate SML on matrix factorization and conduct experiments on two real-world datasets. Empirical results show that SML not only achieves significant speed-up, but also outperforms the full model retraining in recommendation accuracy, validating the effectiveness of our proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202

Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization

Weakly-supervised temporal action localization aims to localize and recognize actions in untrimmed videos with only video-level category labels during training. Without instance-level annotations, most existing methods follow the Segment-based Multiple Instance Learning (S-MIL) framework, where the predictions of segments are supervised by the labels of videos. However, the objective for acquiring segment-level scores during training is not consistent with the target for acquiring proposal-level scores during testing, leading to suboptimal results. To deal with this problem, we propose a novel Proposal-based Multiple Instance Learning (P-MIL) framework that directly classifies the candidate proposals in both the training and testing stages, which includes three key designs: 1) a surrounding contrastive feature extraction module to suppress the discriminative short proposals by considering the surrounding contrastive information, 2) a proposal completeness evaluation module to inhibit the low-quality proposals with the guidance of the completeness pseudo labels, and 3) an instance-level rank consistency loss to achieve robust detection by leveraging the complementarity of RGB and FLOW modalities. Extensive experimental results on two challenging benchmarks including THUMOS14 and ActivityNet demonstrate the superior performance of our method.Comment: Accepted by CVPR 2023. Code is available at https://github.com/RenHuan1999/CVPR2023_P-MI

Elastic Valley Spin Controlled Chiral Coupling in Topological Valley Phononic Crystals

Distinct from the phononic valley pseudo-spin, the real physical spin of elastic waves adds a novel tool-kit capable of envisaging the valley-spin physics of topological valley phononic crystals from a local viewpoint. Here, we report the observation of local elastic valley spin as well as the hidden elastic spin-valley locking mechanism overlooked before. We demonstrate that the selective one-way routing of valley phonon states along the topological interface can be reversed by imposing the elastic spin meta-source at different interface locations with opposite valley-spin correspondence. We unveil the physical mechanism of selective directionality as the elastic spin controlled chiral coupling of valley phonon states, through both analytical theory and experimental measurement of the opposite local elastic spin density at different interface locations for different transport directions. The elastic spin of valley topological edge phonons can be extended to other topological states and offers new tool to explore topological metamaterials.Comment: 5 pages, 3 figures, of main text + supplementary 10 figures. To be published in Phys. Rev. Let

Rescue Tail Queries: Learning to Image Search Re-rank via Click-wise Multimodal Fusion

ABSTRACT Image search engines have achieved good performance for head (popular) queries by leveraging text information and user click data. However, there still remain a large number of tail (rare) queries with relatively unsatisfying search results, which are often overlooked in existing research. Image search for these tail queries therefore provides a grand challenge for research communities. Most existing re-ranking approaches, though effective for head queries, cannot be extended to tail. The assumption of these approaches that the re-ranked list should not go far away from the initial ranked list is not applicable to the tail queries. The challenge, thus, relies on how to leverage the possibly unsatisfying initial ranked results and the very limited click data to solve the search intent gap of tail queries. To deal with this challenge, we propose to mine relevant information from the very few click data by leveraging click-wise-based image pairs and query-dependent multimodal fusion. Specifically, we hypothesize that images with more clicks are more relevant to the given query than the ones with no or relatively less clicks and the effects of different visual modalities to re-rank images are query-dependent. We therefore propose a novel query-dependent learning to re-rank approach for tail queries, called "click-wise multimodal fusion." The approach can not only effectively expand training data by learning relevant information from the constructed click-wise-based image pairs, but also fully explore the effects of multiple visual modalities by adaptively predicting the query-dependent fusion weights. The experiments conducted on a real-world dataset with 100 tail queries show that our proposed approach can significantly improve initial search results by 10.88% and 9.12% in terms of NDCG@5 and NDCG@10, respectively, and outperform several existing re-ranking approaches. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Retrieval models General Terms Algorithms, Experimentation, Performance Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. . Existing commercial search engines achieve very limited image search performance for tail queries

Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation

Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as the final-stage filter to rank items for a user. The key to addressing the CTR task is learning feature interactions that are useful for prediction, which is typically achieved by fitting historical click data with the Empirical Risk Minimization (ERM) paradigm. Representative methods include Factorization Machines and Deep Interest Network, which have achieved wide success in industrial applications. However, such a manner inevitably learns unstable feature interactions, i.e., the ones that exhibit strong correlations in historical data but generalize poorly for future serving. In this work, we reformulate the CTR task -- instead of pursuing ERM on historical data, we split the historical data chronologically into several periods (a.k.a, environments), aiming to learn feature interactions that are stable across periods. Such feature interactions are supposed to generalize better to predict future behavior data. Nevertheless, a technical challenge is that existing invariant learning solutions like Invariant Risk Minimization are not applicable, since the click data entangles both environment-invariant and environment-specific correlations. To address this dilemma, we propose Disentangled Invariant Learning (DIL) which disentangles feature embeddings to capture the two types of correlations separately. To improve the modeling efficiency, we further design LightDIL which performs the disentanglement at the higher level of the feature field. Extensive experiments demonstrate the effectiveness of DIL in learning stable feature interactions for CTR. We release the code at https://github.com/zyang1580/DIL.Comment: 11 pages, 6 Postscript figures, to be published in SIGIR202

AI-Oriented Two-Phase Multi-Factor Authentication in SAGINs: Prospects and Challenges

Space-air-ground integrated networks (SAGINs), which have emerged as an expansion of terrestrial networks, provide flexible access, ubiquitous coverage, high-capacity backhaul, and emergency/disaster recovery for mobile users (MUs). While the massive benefits brought by SAGIN may improve the quality of service, unauthorized access to SAGIN entities is potentially dangerous. At present, conventional crypto-based authentication is facing challenges, such as the inability to provide continuous and transparent protection for MUs. In this article, we propose an AI-oriented two-phase multi-factor authentication scheme (ATMAS) by introducing intelligence to authentication. The satellite and network control center collaborate on continuous authentication, while unique spatial-temporal features, including service features and geographic features, are utilized to enhance the system security. Our further security analysis and performance evaluations show that ATMAS has proper security characteristics which can meet various security requirements. Moreover, we shed light on lightweight and efficient authentication mechanism design through a proper combination of spatial-temporal factors.Comment: Accepted by IEEE Consumer Electronics Magazin

miR-16-2* Interferes with WNT5A to Regulate Osteogenesis of Mesenchymal Stem Cells

Background/Aims: Osteoporosis is a bone metabolic disease characterized by a systemic impairment of bone mass, which results in increased propensity of fragility fractures. A reduction in the differentiation of MSCs into osteoblasts contributes to the impaired bone formation observed in osteoporosis. Mesenchymal stem cells (MSCs) are induced to differentiate into preosteoblasts, which are regulated by the signaling cascades initiated by the various signals, including miRNAs. miR-16-2* is a newly discovered miRNA that participates in diagnosis and prognosis of hepatocellular carcinoma, cervical cancer and chronic lymphocytic leukemia. However, the effect of miR-16-2* on the regulation of osteoblast differentiation and the mechanism responsible are still unclear. Here we discuss the contribution of miR-16-2* to osteoporosis, osteoblast differentiation and mineralization. Methods: The expression pattern of miR-16-2* during osteogenesis or in osteoporosis bone samples was validated by quantitative real-time PCR (qRT-PCR). The human bone marrow mesenchymal stem cells (hBMSCs) were induced to differentiate into osteoblasts by osteogenic induced medium containing dexamethasone, ascorbate-2-phosphat, beta-glycerophosphate and vitamin-D3. The target genes of miR-16-2* were predicted by TargetScan and PicTar. The mRNA and protein levels of osteogenic key markers were detected using qRT-PCR or western blot respectively. The WNT signal activity was analyzed by TOP/FOP reporter assay. Results: The expression of miR-16-2* in patient bone tissue with osteoporosis was negatively correlated with bone formation related genes. During osteoblast differentiation process, the expression of miR-16-2* was significantly decreased. Upregulation of miR-16-2* in hBMSCs impaired the osteogenic differentiation while the downregulation of miR-16-2* increased this process. Upregulation the expression of miR-16-2* could also block the WNT signal pathway by directly target WNT5A. Furthermore, knockdown of miR-16-2* could promote the activation of RUNX2, possibly by lifting the inhibitory effect of miR-16-2* on WNT pathway. Conclusion: Taken together, we report a novel biological role of miR-16-2* in osteogenesis through regulating WNT5A response for the first time. Our data support the potential utilization of miRNA-based therapies in regenerative medicine