Middleware services for distributed virtual environments

PhD ThesisDistributed Virtual Environments (DVEs) are virtual environments which allow dispersed users to interact with each other and the virtual world through the underlying network. Scalability is a major challenge in building a successful DVE, which is directly affected by the volume of message exchange. Different techniques have been deployed to reduce the volume of message exchange in order to support large numbers of simultaneous participants in a DVE. Interest management is a popular technique for filtering unnecessary message exchange between users. The rationale behind interest management is to resolve the "interests" of users and decide whether messages should be exchanged between them. There are three basic interest management approaches: region-based, aura-based and hybrid approaches. However, if the time taken for an interest management approach to determine interests is greater than the duration of the interaction, it is not possible to guarantee interactions will occur correctly or at all. This is termed the Missed Interaction Problem, which all existing interest management approaches are susceptible to. This thesis provides a new aura-based interest management approach, termed Predictive Interest management (PIM), to alleviate the missed interaction problem. PIM uses an enlarged aura to detect potential aura-intersections and iii initiate message exchange. It utilises variable message exchange frequencies, proportional to the intersection degree of the objects' expanded auras, to restrict bandwidth usage. This thesis provides an experimental system, the PIM system, which couples predictive interest management with the de-centralised server communication model. It utilises the Common Object Request Broker Architecture (CORBA) middleware standard to provide an interoperable middleware for DVEs. Experimental results are provided to demonstrate that PIM provides a scalable interest management approach which alleviates the missed interaction problem

Middleware services for distributed virtual environments

PhD ThesisDistributed Virtual Environments (DVEs) are virtual environments which allow dispersed users to interact with each other and the virtual world through the underlying network. Scalability is a major challenge in building a successful DVE, which is directly affected by the volume of message exchange. Different techniques have been deployed to reduce the volume of message exchange in order to support large numbers of simultaneous participants in a DVE. Interest management is a popular technique for filtering unnecessary message exchange between users. The rationale behind interest management is to resolve the "interests" of users and decide whether messages should be exchanged between them. There are three basic interest management approaches: region-based, aura-based and hybrid approaches. However, if the time taken for an interest management approach to determine interests is greater than the duration of the interaction, it is not possible to guarantee interactions will occur correctly or at all. This is termed the Missed Interaction Problem, which all existing interest management approaches are susceptible to. This thesis provides a new aura-based interest management approach, termed Predictive Interest management (PIM), to alleviate the missed interaction problem. PIM uses an enlarged aura to detect potential aura-intersections and iii initiate message exchange. It utilises variable message exchange frequencies, proportional to the intersection degree of the objects' expanded auras, to restrict bandwidth usage. This thesis provides an experimental system, the PIM system, which couples predictive interest management with the de-centralised server communication model. It utilises the Common Object Request Broker Architecture (CORBA) middleware standard to provide an interoperable middleware for DVEs. Experimental results are provided to demonstrate that PIM provides a scalable interest management approach which alleviates the missed interaction problem

Load balancing for massively multiplayer online games

Supporting thousands, possibly hundreds of thousands, of players is a requirement that must be satisfied when delivering server based online gaming as a commercial concern. Such a requirement may be satisfied by utilising the cumulative processing resources afforded by a cluster of servers. Clustering of servers allow great flexibility, as the game provider may add servers to satisfy an increase in processing demands, more players, or remove servers for routine maintenance or upgrading. If care is not taken, the way processing demands are distributed across a cluster of servers may hinder such flexibility and also hinder player interaction within a game. In this paper we present an approach to load balancing that is simple and effective, yet maintains the flexibility of a cluster while promoting player interaction

CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation

To improve instance-level detection/segmentation performance, existing self-supervised and semi-supervised methods extract either task-unrelated or task-specific training signals from unlabeled data. We show that these two approaches, at the two extreme ends of the task-specificity spectrum, are suboptimal for the task performance. Utilizing too little task-specific training signals causes underfitting to the ground-truth labels of downstream tasks, while the opposite causes overfitting to the ground-truth labels. To this end, we propose a novel Class-Agnostic Semi-Supervised Learning (CA-SSL) framework to achieve a more favorable task-specificity balance in extracting training signals from unlabeled data. CA-SSL has three training stages that act on either ground-truth labels (labeled data) or pseudo labels (unlabeled data). This decoupling strategy avoids the complicated scheme in traditional SSL methods that balances the contributions from both data types. Especially, we introduce a warmup training stage to achieve a more optimal balance in task specificity by ignoring class information in the pseudo labels, while preserving localization training signals. As a result, our warmup model can better avoid underfitting/overfitting when fine-tuned on the ground-truth labels in detection and segmentation tasks. Using 3.6M unlabeled data, we achieve a significant performance gain of 4.7% over ImageNet-pretrained baseline on FCOS object detection. In addition, our warmup model demonstrates excellent transferability to other detection and segmentation frameworks.Comment: Appeared in ECCV202

miRNA-135a promotes breast cancer cell migration and invasion by targeting HOXA10

<p>Abstract</p> <p>Background</p> <p>miRNAs are a group of small RNA molecules regulating target genes by inducing mRNA degradation or translational repression. Aberrant expression of miRNAs correlates with various cancers. Although miR-135a has been implicated in several other cancers, its role in breast cancer is unknown. <it>HOXA10 </it>however, is associated with multiple cancer types and was recently shown to induce p53 expression in breast cancer cells and reduce their invasive ability. Because <it>HOXA10 </it>is a confirmed miR-135a target in more than one tissue, we examined miR-135a levels in relation to breast cancer phenotypes to determine if miR-135a plays role in this cancer type.</p> <p>Methods</p> <p>Expression levels of miR-135a in tissues and cells were determined by poly (A)-RT PCR. The effect of miR-135a on proliferation was evaluated by CCK8 assay, cell migration and invasion were evaluated by transwell migration and invasion assays, and target protein expression was determined by western blotting. GFP and luciferase reporter plasmids were constructed to confirm the action of miR-135a on downstream target genes including <it>HOXA10</it>. Results are reported as means ± S.D. and differences were tested for significance using 2-sided Student"s t-test.</p> <p>Results</p> <p>Here we report that miR-135a was highly expressed in metastatic breast tumors. We found that the expression of miR-135a was required for the migration and invasion of breast cancer cells, but not their proliferation. <it>HOXA10</it>, which encodes a transcription factor required for embryonic development and is a metastasis suppressor in breast cancer, was shown to be a direct target of miR-135a in breast cancer cells. Our analysis showed that miR-135a suppressed the expression of <it>HOXA10 </it>both at the mRNA and protein level, and its ability to promote cellular migration and invasion was partially reversed by overexpression of <it>HOXA10</it>.</p> <p>Conclusions</p> <p>In summary, our results indicate that miR-135a is an onco-miRNA that can promote breast cancer cell migration and invasion. <it>HOXA10 </it>is a target gene for miR-135a in breast cancer cells and overexpression of <it>HOXA10 </it>can partially reverse the miR-135a invasive phenotype.</p

Predictive Interest Management: An Approach to Managing Message Dissemination for Distributed Virtual Environments

Interest management aims to overcome limited network resources to provide a distributed virtual environment (DVE) that is scalable in terms of number of users and virtual world complexity (number of objects). Interest management limits interactions between objects in a virtual world by only allowing objects to communicate their actions to other objects that fall within their influence. An important aspect of any interest management scheme is the ability to identify when objects should be interacting and enable such interaction via message passing. Existing approaches to interest management are not suited to objects that may travel at greatly varying speeds and may only interact briefly. In such a scenario, the time taken by existing interest management schemes to resolve which objects influence each other may be too large to enable the desired interaction to occur. In this paper we present an approach to interest management based on the predicted movement of objects. Our approach determines the frequency of message exchange between objects on the likelihood that such objects will influence each other in the near future. Via this mechanism we aim to ensure a scalable DVE that may satisfy message exchange requirements of briefly interacting objects irrelevant of the speed such objects may traverse a virtual world

Determining Collisions between Moving Spheres for Distributed Virtual Environments

We present an approach to collision detection that is appropriate for satisfying the requirements of interest management schemes used in distributed virtual environments. Such environments are characterized by their distributed deployment over a number of nodes connected via a computer network. The aim of an interest management scheme is to identify when objects that populate a simulation supported by a distributed virtual environment (objects could be hosted on different nodes) should be interacting via message exchange while preventing objects that should not be interacting from exchanging messages. The approach to collision detection presented in this paper produces accurate results when determining object interactions. Furthermore, we present variations on our approach that exploit any coherence that may exist in a simulation to provide a solution that may scale for large numbers of objects. 1

Expanding Spheres: A Collision Detection Algorithm for Interest Management in Networked Games

Abstract. We present a collision detection algorithm (Expanding Spheres) for interest management in networked games. The aim of all interest management schemes is to identify when objects that inhabit a virtual world should be interacting and to enable such interaction via message passing while preventing objects that should not be interacting from exchanging messages. Preventing unnecessary message exchange provides a more scalable solution for networked games. A collision detection algorithm is required by interest management schemes as object interaction is commonly determined by object location in the virtual world: the closer objects are to each other the more likely they are to interact. The collision detection algorithm presented in this paper is designed specifically for interest management schemes and produces accurate results when determining object interactions. We present performance figures that indicate that our collision detection algorithm is scalable. 1