This paper addresses the problem of optimizing the ultrasonic shock wave signal gain based on the Time - Reversal Mirror (TRM) technology. A method system combining channel modeling, Virtual Time - Reversal Mirror (VTRM) simulation, and adaptive frequency - domain modulation is proposed. The research estimates the transfer function of the dispersive waveguide, analyzes the signal transmission characteristics in complex media through VTRM simulation, and further designs two frequency - domain modulation methods: frequency - domain windowing and frequency - domain iterative weighting. These methods optimize the spectral characteristics of the TRM excitation signal, thereby enhancing the energy focusing effect of the shock wave. The simulation results show that the optimized TRM method can effectively improve the shock wave gain and has a stronger signal enhancement ability compared with the traditional method. This optimization method can effectively overcome the limitation of the focusing performance of the traditional TRM technology in complex media, providing theoretical support for the design and optimization of ultrasonic shock wave signal generation devices.

The expressiveness of historical reviews in capturing user preferences has garnered significant attention in recommender systems. However, this technology still has certain limitations. Firstly, irrelevant reviews can introduce noise that may adversely affect the performance of the model. Secondly, existing approaches often assume a flat structure for review features, thus failing to capture the intricate and hierarchical nature of user–item interactions. Thirdly, it is challenging for review-based recommendation models to effectively assess the usefulness of reviews due to sparse supervision signals. To address these challenges, we propose a novel Hierarchical Gating and Discriminative model for rating prediction. Specifically, we introduce a local gating module that utilizes personalized end-to-end differential thresholds to select reviews in a relatively “hard” manner, thereby minimizing the impact of noisy reviews while facilitating model training. Additionally, we incorporate a global gating module to assess the overall usefulness of review signals by estimating the uncertainties inherent in historical reviews. Moreover, we propose a hierarchical discriminative network to develop self-supervision signals at both global and local levels to guide the learning of the hierarchical gating network. Extensive experiments on public datasets have demonstrated the effectiveness of the proposed model, and further investigations provide deep insight into its superiority.

Semantic segmentation of point cloud data, an efficient 3-D scattered point representation, is a fundamental task for various applications, such as autonomous driving and 3-D telepresence. In recent years, deep learning techniques have achieved significant progress in semantic segmentation, especially in the 2-D image setting. However, due to the irregularity of point clouds, most of them cannot be applied to this special data representation directly. While recent works are able to handle the irregularity problem and maintain the permutation invariance, most of them fail to capture the valuable high-dimensional local feature in fine granularity. Inspired by fuzzy mathematical methods and the analysis on the drawbacks of current state-of-the-art works, in this article, we propose a novel deep neural model, Fuzzy3DSeg, that is able to directly feed in the point clouds while maintaining invariant to the permutation of the data feeding order. We deeply integrate the learning of the fuzzy neighborhood feature of each point into our network architecture, so as to perform operations on high-dimensional features. We demonstrate the effectiveness of this network architecture level integration, compared with methods of the fuzzy data preprocessing cascading neural network. Comprehensive experiments on two challenging datasets demonstrate that the proposed Fuzzy3DSeg significantly outperforms the state-of-the-art methods.

In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with collaborative filtering, and interacts group information with users/items for bridging similar users/items. Therefore, a user's preference over an unobserved item, in DBRec, can be bridged by the users within the same group who have rated the item, or the user-rated items that share the same group with the unobserved item. In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations. We jointly integrate collaborative filtering, latent group discovering and hierarchical modelling into a unified framework, so that all the model parameters can be learned toward the optimization of the objective function. We validate the effectiveness of the proposed model with two real datasets, and demonstrate its advantage over the state-of-the-art recommendation models with extensive experiments.

Unlike traditional video recommendations, micro-video inherits the characteristics of social platforms, such as social relation. A large amount of micro-videos showing explosive growth is badly affecting the user’s choice. In this paper, we propose a multi-source multi-net micro-video recommendation model that recommends micro-videos fitting users’ best interests. Different from existing works, as micro-video inherits the characteristics of social platforms, we simultaneously incorporate multi-source content data of items and multi-networks of users to learn user and item representations for recommendation. This information can be complementary to each other in a way that multi-modality data can bridge the semantic gap among items, while multi-type user networks, such as following and reposting, are able to propagate the preferences among users. Furthermore, to discover the hidden categories of micro-videos that properly match users’ interests, we interactively learn the user–item representations and perform the hidden item category clustering. The resulted categorical representations are interacted with user representations to model user preferences at different levels of hierarchies. Finally, multi-source content item data, multi-type user networks and hidden item categories are jointly modelled in a unified recommender, and the parameters of the model are collaboratively learned to boost the recommendation performance. Experiments on a real dataset demonstrate the effectiveness of the proposed model and its advantage over the state-of-the-art baselines.

In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA). Unlike previous works, we propose a hybrid attention mechanism to model question-answer pairs. Specifically, for each word, we calculate the intra-sentence attention indicating its local importance and the inter-sentence attention implying its importance to the counterpart sentence. The inter-sentence attention is based on the interactions between question-answer pairs, and the combination of these two attention mechanisms enables us to align the most informative parts in question-answer pairs for sentence matching. Additionally, we exploit user information for answer selection due to the fact that users are more likely to provide correct answers in their areas of expertise. We model users from their written answers to alleviate data sparsity problem, and then learn user representations according to the informative parts in sentences that are useful for question-answer matching task. This mean of modelling users can bridge the semantic gap between different users, as similar users may have the same way of wording their answers. The representations of users, questions and answers are learnt in an end-to-end neural network in a mean that best explains the interrelation between question-answer pairs. We validate the proposed model on a public dataset, and demonstrate its advantages over the baselines with thorough experiments.

Numerous methods have been proposed to address different aspects of human activity recognition. However, most of the previous approaches are static in terms of the data sources used for the recognition task. As sensors can be added or can fail and be replaced by different types of sensors, creating an activity recognition model that is able to leverage dynamically available sensors becomes important. In this paper, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. Specifically, we propose sensor and activity context models to address the problem of sensor heterogeneity, so that sensor readings can be pre-processed and populated into the recognition system properly. Based on those context models, we propose the learning-to-rank method for activity learning and its adaptation. To model the temporal characteristics of the human behaviours, we add temporal regularization into the learning and prediction phases. We use comprehensive datasets to demonstrate effectiveness of the proposed method, and show its advantage over the conventional machine learning algorithms in terms of recognition accuracy. Our method outperforms hybrid models that combine typical machine learning methods with graphical models (i.e. HMM, CRF) for temporal smoothing.

Applications involve dynamic linked library or legacy code which is difficult to optimize for static compiler, however, can be applied with optimization techniques by run-time system who focuses on binary code instead of source code. In this paper, we present a run-time system which observes the behaviors of the applications and performs optimizations base on the information it collects, and it works on the multi-core platform to boost performance. First, we establish a framework of traditional dynamic optimization system, base on which we propose the ways to reduce the overhead of the system, including different ways of trace construction and basic block linking. Second, we take advantage of hardware resources that multi-core platform provides and exploit parallelism in our system. Finally, we evaluate the performance of our system and have two major results. First, basic block linking can greatly improve the performance of the system, because without it, the system spends much of its time on interpreting and context switching. Second, the benchmarks that running on the system have a speedup of 1.05 on average, depending on the program's behavior.