Deep learning for domain-specific action recognition in tennis

Recent progress in sports analytics has been driven by the availability of spatio-temporal and high level data. Video-based action recognition in sports can significantly contribute to these advances. Good progress has been made in the field of action recognition but its application to sports mainly focuses in detecting which sport is being played. In order for action recognition to be useful in sports analytics a finer-grained action classification is needed. For this reason we focus on the fine-grained action recognition in tennis and explore the capabilities of deep neural networks for this task. In our model, videos are represented as sequences of features, extracted using the well-known Inception neural network, trained on an independent dataset. Then a 3-layered LSTM network is trained for the classification. Our main contribution is the proposed neural network architecture that achieves competitive results in the challenging THETIS dataset, comprising videos of tennis actions

Modelling and Validation of Response Times in Zoned RAID

We present and validate an enhanced analytical queueing network model of zoned RAID. The model focuses on RAID levels 01 and 5, and yields the distribution of I/O request response time. Whereas our previous work could only support arrival streams of I/O requests of the same type, the model presented here supports heterogeneous streams with a mixture of read and write requests. This improved realism is made possible through multiclass extensions to our existing model. When combined with priority queueing, this development also enables more accurate modelling of the way subtasks of RAID 5 write requests are scheduled. In all cases we derive analytical results for calculating not only the mean but also higher moments and the full distribution of I/O request response time. We validate our mode

Cluster Grid based Response-time analysis module for the PIPE Tool.

Generalized Stochastic Petri Nets (GSPNs) are a widely used high-level formalism used for modelling discrete-event systems. The Platform Independent Petri net Editor (PIPE) is an open source software project that allows creation, analysis and simulation of Petri Nets. This tool paper presents a PIPE module for response-time analysis of a Petri net’s underlying Continuous Time Markov Chain (CTMC). Jobs are submitted via a web interface, from within PIPE or from a browser. The parallel computations are run using Grid Engine on a cluster hosted at Imperial College London. 1

Hypergraph-based parallel computation of passage time densities in large semi-Markov models

AbstractPassage time densities and quantiles are important performance and quality of service metrics, but their numerical derivation is, in general, computationally expensive. We present an iterative algorithm for the calculation of passage time densities in semi-Markov models, along with a theoretical analysis and empirical measurement of its convergence behaviour. In order to implement the algorithm efficiently in parallel, we use hypergraph partitioning to minimise communication between processors and to balance workloads. This enables the analysis of models with very large state spaces which could not be held within the memory of a single machine. We produce passage time densities and quantiles for very large semi-Markov models with over 15 million states and validate the results against simulation

SIMULATION AND MODELLING OF RAID 0 SYSTEM PERFORMANCE

RAID systems are fundamental components of modern storage infrastructures. It is therefore important to model their performance effectively. This paper describes a simulation model which predicts the cumulative distribution function of I/O request response time in a RAID 0 system consisting of homogeneous zoned disk drives. The model is constructed in a bottom-up manner, starting by abstracting a single disk drive as an M/G/1 queue. This is then extended to model a RAID 0 system using a split-merge queueing network. Simulation results of I/O request response time for RAID 0 systems with various numbers of disks are computed and compared against device measurements

Performance queries on Semi-Markov Stochastic Petri nets with an extended continuous Stochastic logic

Semi-Markov Stochastic Petri Nets (SM-SPNs) are a highlevel formalism for defining semi-Markov processes. We present an extended Continuous Stochastic Logic (eCSL) which provides an expressive way to articulate performance queries at the SM-SPN model level. eCSL supports queries involving steady-state, transient and passage time measures. We demonstrate this by formulating and answering eCSL queries on an SM-SPN model of a distributed voting system with up to ¢¤£¦ ¥ states.