Analysis and design of a magnetically levitated planar motor with novel multilayer windings

This paper proposes a novel permanent magnet planar motor with moving multilayer orthogonal overlapping windings. This novel motor topology can achieve a five-degrees-of-freedom drive using two sets of x-direction windings and two sets of y-direction windings in a coreless configuration. The orthogonal multilayer construction guarantees a high utilization of the magnetic field and realizes decoupling between the x-direction thrust and the y-direction thrust. The topology and operating principle of the planar motor are introduced in this paper. The analytical modeling of the motor is established based on the equivalent current method, and the expressions of forces are derived. The force characteristics of the two-layer and three-layer winding topologies are compared, and the design guidelines of a planar motor are proposed. The analytical and 3-D finite-element model results are validated with the experimental results of a tested prototype

Modelling the dependability in Network Function Virtualisation

Network Function Virtualization has been brought up to allow the TSPs to have more possibilities and flexibilities to provision services with better load optimizing, energy utilizing and dynamic scaling. Network functions will be decoupled from the underlying dedicated hardware into software instances that run on commercial off-the-shelf servers. However, the development is still at an early stage and the dependability concerns raise by the virtualization of the network functions are touched on only briefly. Particularly, the evaluation of the NFV-based services dependability has never been conducted. Towards this goal, this thesis aims to address the dependability concern of NFV and uses a two-level availability approach to construct a quantitative evaluation about how to assess the availability of an NFV-based service and how NFV elements in the network shall be deployed to provide a more dependable network service. A two-level availability model has been developed based on the various NFV-based network. The first level is focusing on the topology of the network and the connectivity requirements for provisioning an NFV-based service. In the second level, the Stochastic Activity Network (SAN) model of different network elements such as VNF, NFV-MANO and datacenter have been developed to evaluate the availability. Eventually, these two types of models have been merged together to illustrate the overall availability/unavailability of the NFV-based services in different use cases. In the end, analysis and evaluation have been conducted based on the obtained results from the two-level availability model. There are seven different scenarios have been simulated with regard to the deployment of NFV across the network. And the outcome on how the variations of the NFV elements deployment influence the dependability of the NFV-based services will be presented along with some suggestions about the NFV deployment in provisioning an end-to-end service

Strategy to select most efficient RCT samples based on observational data

Randomized experiments can provide unbiased estimates of sample average treatment effects. However, estimates of population treatment effects can be biased when the experimental sample and the target population differ. In this case, the population average treatment effect can be identified by combining experimental and observational data. A good experiment design trumps all the analyses that come after. While most of the existing literature centers around improving analyses after RCTs, we instead focus on the design stage, fundamentally improving the efficiency of the combined causal estimator through the selection of experimental samples. We explore how the covariate distribution of RCT samples influences the estimation efficiency and derive the optimal covariate allocation that leads to the lowest variance. Our results show that the optimal allocation does not necessarily follow the exact distribution of the target cohort, but adjusted for the conditional variability of potential outcomes. We formulate a metric to compare and choose from candidate RCT sample compositions. We also develop variations of our main results to cater for practical scenarios with various cost constraints and precision requirements. The ultimate goal of this paper is to provide practitioners with a clear and actionable strategy to select RCT samples that will lead to efficient causal inference

Graph Unlearning with Efficient Partial Retraining

Graph Neural Networks (GNNs) have achieved remarkable success in various real-world applications. However, GNNs may be trained on undesirable graph data, which can degrade their performance and reliability. To enable trained GNNs to efficiently unlearn unwanted data, a desirable solution is retraining-based graph unlearning, which partitions the training graph into subgraphs and trains sub-models on them, allowing fast unlearning through partial retraining. However, the graph partition process causes information loss in the training graph, resulting in the low model utility of sub-GNN models. In this paper, we propose GraphRevoker, a novel graph unlearning framework that better maintains the model utility of unlearnable GNNs. Specifically, we preserve the graph property with graph property-aware sharding and effectively aggregate the sub-GNN models for prediction with graph contrastive sub-model aggregation. We conduct extensive experiments to demonstrate the superiority of our proposed approach.Comment: 8 pages, 3 figures, accepted by The Web Conference 2024 (PhD Symposium Track

When and why does economic inequality predict prosocial behaviour? Examining the role of interpersonal trust among different targets

Previous research suggests that economic inequality has caused a wide range of negative societal impacts. However, little is known about how economic inequality influences prosocial behaviour as a socioecological environment determinant. In five studies (N = 62,342), we examined whether economic inequality reduces prosocial behaviour by decreasing interpersonal trust and the moderation role of interpersonal targets. Studies 1, 2a, and 2b showed that interpersonal trust mediated the negative relationship between perceived economic inequality and prosocial behaviour. In Study 3, we used data from the World Values Survey to explore the relation between inequality and trust and found that it was moderated by the closeness of trust targets. In Study 4, we demonstrated that economic inequality only reduced trust and prosocial behaviour towards strangers, but not among friends and family. Taken together, the current research shed light on how economic inequality undermines trust and negatively impacts prosocial behaviour among different targets

Fast Graph Condensation with Structure-based Neural Tangent Kernel

The rapid development of Internet technology has given rise to a vast amount of graph-structured data. Graph Neural Networks (GNNs), as an effective method for various graph mining tasks, incurs substantial computational resource costs when dealing with large-scale graph data. A data-centric manner solution is proposed to condense the large graph dataset into a smaller one without sacrificing the predictive performance of GNNs. However, existing efforts condense graph-structured data through a computational intensive bi-level optimization architecture also suffer from massive computation costs. In this paper, we propose reforming the graph condensation problem as a Kernel Ridge Regression (KRR) task instead of iteratively training GNNs in the inner loop of bi-level optimization. More specifically, We propose a novel dataset condensation framework (GC-SNTK) for graph-structured data, where a Structure-based Neural Tangent Kernel (SNTK) is developed to capture the topology of graph and serves as the kernel function in KRR paradigm. Comprehensive experiments demonstrate the effectiveness of our proposed model in accelerating graph condensation while maintaining high prediction performance. The source code is available on https://github.com/WANGLin0126/GCSNTK.Comment: 10 pages, 6 figures, 5 table

Interlayer magnetic interactions and ferroelectricity in π\pi/3-twisted CrX2_2 (X = Se, Te) bilayers

Recently, two-dimensional (2D) bilayer magnetic systems have been widely studied. Their interlayer magnetic interactions play a vital role in the magnetic properties. In this paper, we theoretically studied the interlayer magnetic interactions, magnetic states and ferroelectricity of $\pi$/3-twisted CrX$_2$ (X = Se, Te) bilayers ($\pi$/3-CrX$_2$). Our study reveals that the lateral shift could switch the magnetic state of the $\pi$/3-CrSe$_2$ between interlayer ferromagnetic and antiferromagnetic, while just tuning the strength of the interlayer antiferromagnetic interactions in $\pi$/3-CrTe$_2$. Furthermore, the lateral shift can alter the off-plane electric polarization in both $\pi$/3-CrSe$_2$ and $\pi$/3-CrTe$_2$. These results show that stacking is an effective way to tune both the magnetic and ferroelectric properties of 1T-CrX$_2$ bilayers, making the 1T-CrX$_2$ bilayers hold promise for 2D spintronic devices

The Improvement of Reliability of High-k/Metal Gate pMOSFET Device with Various PMA Conditions

The oxygen and nitrogen were shown to diffuse through the TiN layer in the high-k/metal gate devices during PMA. Both the oxygen and nitrogen annealing will reduce the gate leakage current without increasing oxide thickness. The threshold voltages of the devices changed with various PMA conditions. The reliability of the devices, especially for the oxygen annealed devices, was improved after PMA treatments