On the Characteristics of Ground Motion and the Improvement of the Input Mode of Complex Layered Sites

It is a hot research topic to perform the dynamic interaction analysis between the engineering structure and the soil by using the time-domain method. This paper studies the seismic behaviour of the layered sites and the seismic response of the structures using the viscous-spring artificial boundary theory. The artificial boundary model of viscous-spring is initially based on homogeneous foundation. For the layered site (Foundation), the traditional homogeneous model or equivalent load input mode is not suitable, which may bring great error. By introducing the changes of coefficients and phases of reflection and transmission of seismic waves at the interface between layers, an improved method of equivalent load input mode of traditional viscous-spring artificial boundary model is proposed. This new wave model can simulate the propagation law of seismic wave in layered site more accurately, which is available for the seismic performance of engineering structure under the condition of large and complex layered site. At last, the simplified homogeneous model, the equivalent load input method and the improved layered model input method are used to study the seismic response of the engineering example. It is shown that the results calculated by the three methods are different, which shows that the homogeneous foundation model and the conventional equivalent load input method of seismic wave cannot simulate the seismic force accurately, whereas the improved wave input model can better reflect the characteristic of traveling wave in layered sites

The Simple Criteria of SLOCC Equivalence Classes

We put forward an alternative approach to the SLOCC classification of entanglement states of three-qubit and four-qubit systems. By directly solving matrix equations, we obtain the relations satisfied by the amplitudes of states. The relations are readily tested since in them only addition, subtraction and multiplication occur.Comment: The original version was submitted to PRA in Feb. 2005, the paper No. is AA10020. 14 pages for the present version. No figure

Rank-Aware Negative Training for Semi-Supervised Text Classification

Semi-supervised text classification-based paradigms (SSTC) typically employ the spirit of self-training. The key idea is to train a deep classifier on limited labeled texts and then iteratively predict the unlabeled texts as their pseudo-labels for further training. However, the performance is largely affected by the accuracy of pseudo-labels, which may not be significant in real-world scenarios. This paper presents a Rank-aware Negative Training (RNT) framework to address SSTC in learning with noisy label manner. To alleviate the noisy information, we adapt a reasoning with uncertainty-based approach to rank the unlabeled texts based on the evidential support received from the labeled texts. Moreover, we propose the use of negative training to train RNT based on the concept that ``the input instance does not belong to the complementary label''. A complementary label is randomly selected from all labels except the label on-target. Intuitively, the probability of a true label serving as a complementary label is low and thus provides less noisy information during the training, resulting in better performance on the test data. Finally, we evaluate the proposed solution on various text classification benchmark datasets. Our extensive experiments show that it consistently overcomes the state-of-the-art alternatives in most scenarios and achieves competitive performance in the others. The code of RNT is publicly available at:https://github.com/amurtadha/RNT.Comment: TACL 202

Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving

This article studies the problem of image segmentation-based semantic communication in autonomous driving. In real traffic scenes, detecting the key objects (e.g., vehicles, pedestrians and obstacles) is more crucial than that of other objects to guarantee driving safety. Therefore, we propose a vehicular image segmentation-oriented semantic communication system, termed VIS-SemCom, where image segmentation features of important objects are transmitted to reduce transmission redundancy. First, to accurately extract image semantics, we develop a semantic codec based on Swin Transformer architecture, which expands the perceptual field thus improving the segmentation accuracy. Next, we propose a multi-scale semantic extraction scheme via assigning the number of Swin Transformer blocks for diverse resolution features, thus highlighting the important objects' accuracy. Furthermore, the importance-aware loss is invoked to emphasize the important objects, and an online hard sample mining (OHEM) strategy is proposed to handle small sample issues in the dataset. Experimental results demonstrate that the proposed VIS-SemCom can achieve a coding gain of nearly 6 dB with a 60% mean intersection over union (mIoU), reduce the transmitted data amount by up to 70% with a 60% mIoU, and improve the segmentation intersection over union (IoU) of important objects by 4%, compared to traditional transmission scheme.Comment: 10 pages, 8 figure

Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction

Automatic bill payment is an important part of business operations in fintech companies. The practice of deduction was mainly based on the total amount or heuristic search by dividing the bill into smaller parts to deduct as much as possible. This article proposes an end-to-end approach of automatically learning the optimal deduction paths (deduction amount in order), which reduces the cost of manual path design and maximizes the amount of successful deduction. Specifically, in view of the large search space of the paths and the extreme sparsity of historical successful deduction records, we propose a deep hierarchical reinforcement learning approach which abstracts the action into a two-level hierarchical space: an upper agent that determines the number of steps of deductions each day and a lower agent that decides the amount of deduction at each step. In such a way, the action space is structured via prior knowledge and the exploration space is reduced. Moreover, the inherited information incompleteness of the business makes the environment just partially observable. To be precise, the deducted amounts indicate merely the lower bounds of the available account balance. To this end, we formulate the problem as a partially observable Markov decision problem (POMDP) and employ an environment correction algorithm based on the characteristics of the business. In the world's largest electronic payment business, we have verified the effectiveness of this scheme offline and deployed it online to serve millions of users

Traffic-aware multiple mix zone placement for protecting location privacy

Abstract—Privacy protection is of critical concern to Location-Based Service (LBS) users in mobile networks. Long-term pseudonyms, although appear to be anonymous, in fact em-power third-party service providers to continuously track users’ movements. Researchers have proposed the mix zone model to allow pseudonym changes in protected areas. In this paper, we investigate a new form of privacy attack to the LBS system that an adversary reveals a user’s true identity and complete moving tra-jectory with the aid of side information. We propose a new metric to quantify the system’s resilience to such attacks, and suggest using multiple mix zones to tackle this problem. A mathematical model is presented that treats the deployment of multiple mix zones as a cost constrained optimization problem. Furthermore, the influence of traffic density is also taken into account to enhance the protection effectiveness. The placement optimization problem is NP-hard. We therefore design two heuristic algorithms as practical and effective means to strategically select mix zone locations, and consequently reduce the privacy risks of mobile users trajectories. The effectiveness of our proposed solutions is demonstrated through extensive simulations on real-world mobile user data traces. I