Semi-parametric forecasting of spikes in electricity prices

The occurrence of extreme movements in the spot price of electricity represents a significant source of risk to retailers. A range of approaches have been considered with respect to modelling electricity prices; these models, however, have relied on time-series approaches, which typically use restrictive decay schemes placing greater weight on more recent observations. This study develops an alternative, semi-parametric method for forecasting, which uses state-dependent weights derived from a kernel function. The forecasts that are obtained using this method are accurate and therefore potentially useful to electricity retailers in terms of risk management.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

A comparative study of the semi-elastic and fully-elastic MapReduce models

MapReduce is a computation model for processing large data sets in parallel on large clusters of machines, in a reliable, fault-tolerant manner. A MapReduce computation is broken down into a number of map tasks and reduce tasks, which are performed by so called mappers and reducers, respectively. The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation in cloud computing. From the computational point of view, the mappers/reducers placement problem is a generation of the classical bin packing problem, which is NP-complete. Thus, in this paper we propose a new heuristic algorithm for the mappers/reducers placement problem in cloud computing and evaluate it by comparing with other several heuristics on solution quality and computation time by solving a set of test problems with various characteristics. The computational results show that our heuristic algorithm is much more efficient than the other heuristics and it can obtain a better solution in a reasonable time. Furthermore, we verify the effectiveness of our heuristic algorithm by comparing the mapper/reducer placement for a benchmark problem generated by our heuristic algorithm with a conventional mapper/reducer placement which puts a fixed number of mapper/reducer on each machine. The comparison results show that the computation using our mapper/reducer placement is much cheaper than the computation using the conventional placement while still satisfying the computation deadline.

Discussion of : constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

This is a discussion of the journal article: "Construcing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation". The article and discussion have appeared in the Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation.

Visible and near infrared spectroscopy of hayabusa reentry using semi-autonomous tracking

Aground-based tracking camera and coaligned slitless spectrograph were used to measure the spectral signature of visible radiation emitted from the Hayabusa capsule as it entered into the Earth’s atmosphere in June 2010. Good quality spectra were obtained, which showed the presence of radiation from the heat shield of the vehicle and the shock-heated air in front of the vehicle. An analysis of the blackbody nature of the radiation concluded that the peak average temperature of the surface was about (3100± 100)K. Line spectra from oxygen and nitrogen atoms were used to infer a peak average shock-heated gas temperature of around((7000±400))K.

Demonstration of nonlinear absorption in Au semi-continuous film by electrical measurement

We have demonstrated the nonlinear absorption at 532 nm wavelength in an Au semi-continuous film (SF) resulting from smearing of the Fermi distribution and diffusion of conduction electrons into the substrate. The Au SF was irradiated by a pulsed laser with 8 ns pulse width at 532 nm in near resonance with the interband transition of the Au. We determined the temperature increase in the SF for different intensities by electrical measurement. We calculated the temperature increase by using a 1D heat transport equation; comparing the results of the calculation with measured values for the temperature increase, revealed the nonlinear absorption in the Au SF. We employed this deviation from linear behaviour to determine the nonlinear absorption coefficient.

Reversible and semi-blind relational database watermarking

Database watermarking has received significant research attention in the current decade. Although, almost all watermarking models have been either irreversible (the original relation cannot be restored from the watermarked relation) and/or non-blind (requiring original relation to detect the watermark in watermarked relation). This model has several disadvantages over reversible and blind watermarking (requiring only watermarked relation and secret key from which the watermark is detected and original relation is restored) including inability to identify rightful owner in case of successful secondary watermarking, inability to revert the relation to original data set (required in high precision industries) and requirement to store unmarked relation at a secure secondary storage. To overcome these problems, we propose a watermarking scheme that is reversible as well as blind. We utilize difference expansion on integers to achieve reversibility. The major advantages provided by our scheme are reversibility to high quality original data set, rightful owner identification, resistance against secondary watermarking attacks, and no need to store original database at a secure secondary storage.

Biolistic transformation of elite indica rice (Oryza sativa L.) cultivars through semi-solid and liquid medium selection systems

Following microprojectile mediated delivery of a plasmid construct (pAHC-25) encoding bar (bialophos resistance) gene into five-day-old scutellar calli derived from mature embryos, the effectiveness of selection procedure for bar-gene expressing tissue was compared for two indica rice cultivars (IR-64 and Karnal Local). While IR-64 transformants could be selected through the generally used semi-solid selection medium, the same procedure was not effective in the basmati cultivar Karnal Local. In the latter case, while lower concentrations (2–4 mg 1−1) of the selective agent phosphinothricin (PPT) yielded only escapes, higher concentrations (6–8 mg l−1) inhibited proliferation of transformed as well as untransformed sectors. For Karnal Local, a liquid medium based selection system was successfully utilized for recovering transformed sectors and, eventually, regenerants. The study demonstrates the generation of transformants of two elite indica cultivars using the environment-independent system of mature embryos from seeds.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover

Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making

Flammability and thermal properties of novel semi aromatic polyamide/organoclay nanocomposite

The development of semi aromatic polyamide/organoclays nanocomposites (PANC) is reported in this communication. New polyamide (PA) was successfully synthesized through direct polycondensation reaction between bio-based diacid and aromatic diamine. PA exhibited strong UV vis absorption band at 412 nm. Its photoluminescence spectrum showed maximum band at 511 nm in the green region. The surface modification of montmorillonite was carried out through ion-exchange reaction using 1,4-bis[4-aminophenoxy]butane (APB) as a modifier. Then PANCs containing 3 and 6 wt.% of the modified montmorillonite (MMT-APB) were prepared. Flammability and thermal properties of PA and the nanocomposites were studied by microscale combustion calorimeter (MCC), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). TGA results in both air and nitrogen atmospheres indicated improving in thermal properties of PANCs compared to the neat PA. According to MCC analysis, a 31.6% reduction in pHRR value has been achieved by introducing 6 wt.% of the organoclay in PA matrix.

A semi-alternating direction method for a 2-D fractional FitzHugh–Nagumo monodomain model on an approximate irregular domain

A FitzHugh-Nagumo monodomain model has been used to describe the propagation of the electrical potential in heterogeneous cardiac tissue. In this paper, we consider a two-dimensional fractional FitzHugh-Nagumo monodomain model on an irregular domain. The model consists of a coupled Riesz space fractional nonlinear reaction-diffusion model and an ordinary differential equation, describing the ionic fluxes as a function of the membrane potential. Secondly, we use a decoupling technique and focus on solving the Riesz space fractional nonlinear reaction-diffusion model. A novel spatially second-order accurate semi-implicit alternating direction method (SIADM) for this model on an approximate irregular domain is proposed. Thirdly, stability and convergence of the SIADM are proved. Finally, some numerical examples are given to support our theoretical analysis and these numerical techniques are employed to simulate a two-dimensional fractional Fitzhugh-Nagumo model on both an approximate circular and an approximate irregular domain.

A fast semi-implicit difference method for a nonlinear two-sided space-fractional diffusion equation with variable diffusivity coefficients

In this paper, we derive a new nonlinear two-sided space-fractional diffusion equation with variable coefficients from the fractional Fick’s law. A semi-implicit difference method (SIDM) for this equation is proposed. The stability and convergence of the SIDM are discussed. For the implementation, we develop a fast accurate iterative method for the SIDM by decomposing the dense coefficient matrix into a combination of Toeplitz-like matrices. This fast iterative method significantly reduces the storage requirement of O(n2)O(n2) and computational cost of O(n3)O(n3) down to n and O(nlogn)O(nlogn), where n is the number of grid points. The method retains the same accuracy as the underlying SIDM solved with Gaussian elimination. Finally, some numerical results are shown to verify the accuracy and efficiency of the new method.

The synthesis, characterization, X-ray structure and magnetism of dinuclear-based bis[μ-(1,1,3,3-tetracyano-2-ethoxypropenido-κ2N,N′) (1,1,3,3-tetracyano-2-ethoxypropenido-κN)(2,2′-bipyridine)copper(II)] organized in alternating chains via semi-coordinating Cu–N distances

The monoanionic ligand 1,1,3,3 tetracyano-2 ethoxypropenide (tcnoet) is reported with its Cu(II)–bpy complex of formula [Cu2(µ-tcnoet)2(tcnoet)2(bpy)2]. The structure has been determined using X-ray diffraction and features an alternating chain with bridging tcnoet ligands. One ligand acts as a bidentate, dinucleating ligand with one short Cu–N and one medium Cu–N bond, whereas the other tcnoet is largely monodentate, albeit with a very weak interdimer Cu–N bond. Despite the arrangement in dinuclear units, further arranged into linear chains through the non-bridging tcnoet ligand, the compound shows no significant magnetic exchange, as deduced from magnetic susceptibility down to 4 K. Ligand-field, IR and EPR spectra in the solid state and in frozen solution are reported and are consistent with the overall structure.