Likelihood-based inference for correlated diffusions

We address the problem of likelihood based inference for correlated diffusion processes using Markov chain Monte Carlo (MCMC) techniques. Such a task presents two interesting problems. First, the construction of the MCMC scheme should ensure that the correlation coefficients are updated subject to the positive definite constraints of the diffusion matrix. Second, a diffusion may only be observed at a finite set of points and the marginal likelihood for the parameters based on these observations is generally not available. We overcome the first issue by using the Cholesky factorisation on the diffusion matrix. To deal with the likelihood unavailability, we generalise the data augmentation framework of Roberts and Stramer (2001 Biometrika 88(3):603-621) to d-dimensional correlated diffusions including multivariate stochastic volatility models. Our methodology is illustrated through simulation based experiments and with daily EUR /USD, GBP/USD rates together with their implied volatilities

Inference for stochastic volatility model using time change transformations

We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrisation defined through transformations that operate on the time scale of the diffusion. A novel MCMC scheme which overcomes the inherent difficulties of time change transformations is also presented. The algorithm is fast to implement and applies to models with stochastic volatility. The methodology is tested through simulation based experiments and illustrated on data consisting of US treasury bill rates.Imputation, Markov chain Monte Carlo, Stochastic volatility

Likelihood-based inference for correlated diffusions

We address the problem of likelihood based inference for correlated diffusion processes using Markov chain Monte Carlo (MCMC) techniques. Such a task presents two interesting problems. First, the construction of the MCMC scheme should ensure that the correlation coefficients are updated subject to the positive definite constraints of the diffusion matrix. Second, a diffusion may only be observed at a finite set of points and the marginal likelihood for the parameters based on these observations is generally not available. We overcome the first issue by using the Cholesky factorisation on the diffusion matrix. To deal with the likelihood unavailability, we generalise the data augmentation framework of Roberts and Stramer (2001 Biometrika 88(3):603-621) to d-dimensional correlated diffusions including multivariate stochastic volatility models. Our methodology is illustrated through simulation based experiments and with daily EUR /USD, GBP/USD rates together with their implied volatilities.Markov chain Monte Carlo, Multivariate stochastic volatility, Multivariate CIR model, Cholesky Factorisation

Bus allocation to short-turning and interlining lines.

We propose injecting flexibility into public transport service planning by introducing a demand driven method for generating and assigning buses to short-turning and interlining services. This study formulates, solves and applies the problem of assigning vehicles to the lines of a bus network subject to the dual objective of (a) improving the passenger waiting times at stops and (b) reducing the operational costs. At first, the vehicle allocation problem is expanded with the explicit consideration of interlining and short-turning lines that provide greater operational flexibility. The paper introduces a rule-based approach for generating interlining and short-turning lines that are considered as "virtual lines" because some of them might remain inactive if their operation does not improve the vehicle allocation solution. The bus allocation problem to existing and virtual lines is modeled as a combinatorial, multi-objective optimization problem and is solved with a Genetic Algorithm (GA) meta-heuristic that can return improved solutions by avoiding the exhaustive exploration of a combinatorial solution space. The vehicle allocation to existing and virtual lines is applied to the bus network of The Hague with the use of Automated Fare Collection (AFC) data from 24 weekdays and General Transit Feed Specification (GTFS) data. Sensitivity analysis results demonstrate a significant reduction potential in passenger waiting time and operational costs without adding a large number of short-turning and interlining line options that could impede the practicality of the bus services

Inference for stochastic volatility models using time change transformations

We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrisation defined through transformations that operate on the time scale of the diffusion. A novel MCMC scheme which overcomes the inherent difficulties of time change transformations is also presented. The algorithm is fast to implement and applies to models with stochastic volatility. The methodology is tested through simulation based experiments and illustrated on data consisting of US treasury bill rates

Suspended liquid particle disturbance on laser-induced blast wave and low density distribution

The impurity effect of suspended liquid particles on the laser-induced gas breakdown was experimentally investigated in quiescent gas. The focus of this study is the investigation of the influence of the impurities on the shock wave structure as well as the low density distribution. A 532 nm Nd:YAG laser beam with an 188 mJ/pulse was focused on the chamber filled with suspended liquid particles 0.9 ± 0.63 μm in diameter. Several shock waves are generated by multiple gas breakdowns along the beam path in the breakdown with particles. Four types of shock wave structures can be observed: (1) the dual blast waves with a similar shock radius, (2) the dual blast waves with a large shock radius at the lower breakdown, (3) the dual blast waves with a large shock radius at the upper breakdown, and (4) the triple blast waves. The independent blast waves interact with each other and enhance the shock strength behind the shock front in the lateral direction. The triple blast waves lead to the strongest shock wave in all cases. The shock wave front that propagates toward the opposite laser focal spot impinges on one another, and thereafter a transmitted shock wave (TSW) appears. The TSW interacts with the low density core called a kernel; the kernel then longitudinally expands quickly due to a Richtmyer-Meshkov-like instability. The laser-particle interaction causes an increase in the kernel volume which is approximately five times as large as that in the gas breakdown without particles. In addition, the laser-particle interaction can improve the laser energy efficiency

Forensic Analysis of the Nintendo 3DS NAND

Games consoles present a particular challenge to the forensics investigator due to the nature of the hardware and the inaccessibility of the file system. Many protection measures are put in place to make it deliberately difficult to access raw data in order to protect intellectual property, enhance digital rights management of software and, ultimately, to protect against piracy. History has shown that many such protections on game consoles are circumvented with exploits leading to jailbreaking/rooting and allowing unauthorized software to be launched on the games system. This paper details methods that enable the investigator to extract system activity, deleted images, Internet history items, relevant friends list information, the console\u27s serial number and plaintext WiFi access point passwords. This is all possible with the use of publicly available, open-source security circumvention techniques that perform a non-invasive physical dump of the internal NAND storage of the Nintendo 3DS handheld device. It will also be shown that forensic integrity is maintained and a detailed analysis is possible without altering original evidence

New records and observations of macroalgae and associated pathogens from the Falkland Islands, Patagonia and Tierra del Fuego

Subantarctic and Antarctic regions remain little explored with regards to their seaweed diversity. This study is based upon collections in the early 1970s and 2007–2013. It is supported by sequencing COI (mitochondrial cytochrome oxidase I) and reports new records for four species of brown algae Hincksia granulosa, Hincksia sandriana, Myriotrichia clavaeformis, Syringoderma australe), four red algae (Erythrotrichia carnea, Paraglossum salicifolium, Phycodrys antarctica, Plumariopsis eatonii), one green alga (Chaetomorpha aerea) and of the oomycete Anisolpidium ectocarpii. A further four brown algae are reported at genus level and discussed (Cladostephus sp., Colpomenia sp., Dictyota sp., Punctaria sp.). Observations of the biology of three brown algal taxa (Cladothele decaisnei, Geminocarpus geminatus, Halopteris obovata) from the region are also reported here