Optimal VM placement in data centres with architectural and resource constraints

Recent advance in virtualisation technology enables service provisioning in a flexible way by consolidating several virtual machines (VMs) into a single physical machine (PM). The inter-VM communications are inevitable when a group of VMs in a data centre provide services in a collaborative manner. With the increasing demands of such intra-data-centre traffics, it becomes essential to study the VM-to-PM placement such that the aggregated communication cost within a data centre is minimised. Such optimisation problem is proved NP-hard and formulated as an integer programming with quadratic constraints in this paper. Different from existing work, our formulation takes into consideration of data-centre architecture, inter-VM traffic pattern, and resource capacity of PMs. Furthermore, a heuristic algorithm is proposed and its high efficiency is extensively validated

OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning

Graph domain adaptation models are widely adopted in cross-network learning tasks, with the aim of transferring labeling or structural knowledge. Currently, there mainly exist two limitations in evaluating graph domain adaptation models. On one side, they are primarily tested for the specific cross-network node classification task, leaving tasks at edge-level and graph-level largely under-explored. Moreover, they are primarily tested in limited scenarios, such as social networks or citation networks, lacking validation of model's capability in richer scenarios. As comprehensively assessing models could enhance model practicality in real-world applications, we propose a benchmark, known as OpenGDA. It provides abundant pre-processed and unified datasets for different types of tasks (node, edge, graph). They originate from diverse scenarios, covering web information systems, urban systems and natural systems. Furthermore, it integrates state-of-the-art models with standardized and end-to-end pipelines. Overall, OpenGDA provides a user-friendly, scalable and reproducible benchmark for evaluating graph domain adaptation models. The benchmark experiments highlight the challenges of applying GDA models to real-world applications with consistent good performance, and potentially provide insights to future research. As an emerging project, OpenGDA will be regularly updated with new datasets and models. It could be accessed from https://github.com/Skyorca/OpenGDA.Comment: Under Revie

Transduction of Adeno-Associated Virus Vectors Targeting Hair Cells and Supporting Cells in the Neonatal Mouse Cochlea

Adeno-associated virus (AAV) is the preferred vector for gene therapy of hereditary deafness, and different viral serotypes, promoters and transduction pathways can influence the targeting of AAV to different types of cells and the expression levels of numerous exogenous genes. To determine the transduction and expression patterns of AAV with different serotypes or promoters in hair cells and supporting cells in the neonatal mouse cochlea, we examined the expression of enhanced green fluorescent protein (eGFP) for five different types of AAV vectors [serotypes 2, 9, and Anc80L65 with promoter cytomegalovirus (CMV)-beta-Globin and serotypes 2 and 9 with promoter chicken beta-actin (CBA)] in in vitro cochlear explant cultures and we tested the transduction of AAV2/2-CBA, AAV2/9-CBA, and AAV2/Anc80L65-CMV by in vivo microinjection into the scala media of the cochlea. We found that each AAV vector had its own transduction and expression characteristics in hair cells and supporting cells in different regions of the cochlea. There was a tonotopic gradient for the in vitro transduction of AAV2/2-CBA, AAV2/9-CBA, AAV2/2-CMV, and AAV2/9-CMV in outer hair cells (OHCs), with more OHCs expressing eGFP at the base of the cochlea than at the apex. AAV2/2-CBA in vitro and AAV2/Anc80L65-CMV in vivo induced more supporting cells expressing eGFP at the apex than in the base. We found that AAV vectors with different promoters had different expression efficacies in hair cells and supporting cells of the auditory epithelium. The CMV-beta-Globin promoter could drive the expression of the delivered construct more efficiently in hair cells, while the CBA promoter was more efficient in supporting cells. The in vitro and in vivo experiments both demonstrated that AAV2/Anc80L65-CMV was a very promising vector for gene therapy of deafness because of its high transduction rates in hair cells. These results might be useful for selecting the appropriate vectors for gene delivery into different types of inner ear cells and thus improving the effectiveness of gene therapy

FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks

Community Search (CS), a crucial task in network science, has attracted considerable interest owing to its prowess in unveiling personalized communities, thereby finding applications across diverse domains. Existing research primarily focuses on traditional homogeneous networks, which cannot be directly applied to heterogeneous information networks (HINs). However, existing research also has some limitations. For instance, either they solely focus on single-type or multi-type community search, which severely lacking flexibility, or they require users to specify meta-paths or predefined community structures, which poses significant challenges for users who are unfamiliar with community search and HINs. In this paper, we propose an innovative method, FCS-HGNN, that can flexibly identify either single-type or multi-type communities in HINs based on user preferences. We propose the heterogeneous information transformer to handle node heterogeneity, and the edge-semantic attention mechanism to address edge heterogeneity. This not only considers the varying contributions of edges when identifying different communities, but also expertly circumvents the challenges presented by meta-paths, thereby elegantly unifying the single-type and multi-type community search problems. Moreover, to enhance the applicability on large-scale graphs, we propose the neighbor sampling and depth-based heuristic search strategies, resulting in LS-FCS-HGNN. This algorithm significantly improves training and query efficiency while maintaining outstanding community effectiveness. We conducted extensive experiments on five real-world large-scale HINs, and the results demonstrated that the effectiveness and efficiency of our proposed method, which significantly outperforms state-of-the-art methods.Comment: 13 page

A New Switched State Jump Observer for Traffic Density Estimation in Expressways Based on Hybrid-Dynamic-Traffic-Network-Model

When faced with problems such as traffic state estimation, state prediction, and congestion identification for the expressway network, a novel switched observer design strategy with jump states is required to reconstruct the traffic scene more realistically. In this study, the expressway network is firstly modeled as the special discrete switched system, which is called the piecewise affine system model, a partition of state subspace is introduced, and the convex polytopes are utilized to describe the combination modes of cells. Secondly, based on the hybrid dynamic traffic network model, the corresponding switched observer (including state jumps) is designed. Furthermore, by applying multiple Lyapunov functions and S-procedure theory, the observer design problem can be converted into the existence issue of the solutions to the linear matrix inequality. As a result, a set of gain matrices can be obtained. The estimated states start to jump when the mode changes occur, and the updated value of the estimated state mainly depends on the estimated and the measured values at the previous time. Lastly, the designed state jump observer is applied to the Beijing Jingkai expressway, and the superiority and the feasibility are demonstrated in the application results

Superconductivity in the cobalt-doped V3Si A15 intermetallic compound

The A15 structure of superconductors is a prototypical type-II superconductor that has generated considerable interest since the early history of superconducting materials. This paper discusses the superconducting properties of previously unreported V3-xCoxSi alloys. It is found that the lattice parameter decreases with increasing cobalt-doped content and leads to an increased residual resistivity ratio (RRR) value of the V3-xCoxSi system. Meanwhile, the superconducting transition temperature (Tc) cobalt-doped content. Furthermore, the fitted data show that the increase of cobalt-doped content also reduces the lower/upper critical fields of the V3-xCoxSi system. Type-II superconductivity is demonstrated on all V3-xCoxSi samples. With higher Co-doped content, V3-xCoxSi alloys may have superconducting and structural phase transitions at low-temperature regions. As the electron/atom (e/a) ratio increases, the Tc variation trend of V3Si is as pronounced as in crystalline alloys and monotonically follows the trend observed for amorphous superconductors.Comment: 20 pages, 7 figure