Visualisation of heterogeneous data with the generalised generative topographic mapping

Heterogeneous and incomplete datasets are common in many real-world visualisation applications. The probabilistic nature of the Generative Topographic Mapping (GTM), which was originally developed for complete continuous data, can be extended to model heterogeneous (i.e. containing both continuous and discrete values) and missing data. This paper describes and assesses the resulting model on both synthetic and real-world heterogeneous data with missing values.

Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.

Evolutionary market agents and heterogeneous service providers:achieving desired resource allocations

In future massively distributed service-based computational systems, resources will span many locations, organisations and platforms. In such systems, the ability to allocate resources in a desired configuration, in a scalable and robust manner, will be essential.We build upon a previous evolutionary market-based approach to achieving resource allocation in decentralised systems, by considering heterogeneous providers. In such scenarios, providers may be said to value their resources differently. We demonstrate how, given such valuations, the outcome allocation may be predicted. Furthermore, we describe how the approach may be used to achieve a stable, uneven load-balance of our choosing. We analyse the system's expected behaviour, and validate our predictions in simulation. Our approach is fully decentralised; no part of the system is weaker than any other. No cooperation between nodes is assumed; only self-interest is relied upon. A particular desired allocation is achieved transparently to users, as no modification to the buyers is required.

Application of the Heterogeneous System Prediction Method to Pattern Recognition Problem

* This work was financially supported by RFBR-04-01-00858.

Extreme Situations Prediction by MultidimenSional Heterogeneous Time Series Using Logical Decision Functions

* The work is supported by RFBR, grant 04-01-00858-a

Multidimensional Heterogeneous Variable Prediction Based on Experts’ Statements

* The work was supported by the RFBR under Grant N07-01-00331a.

An adaptive IEEE 802.15.4 MAC layer transmission scheme for applications with heterogeneous traffic

IEEE 802.15.4 networks has the features of low data rate and low power consumption. It is a strong candidate technique for wireless sensor networks and can find many applications to smart grid. However, due to the low network and energy capacities it is critical to maximize the bandwidth and energy efficiencies of 802.15.4 networks. In this paper we propose an adaptive data transmission scheme with CSMA/CA access control, for applications which may have heavy traffic loads such as smart grids. The adaptive access control is simple to implement. Its compatibility with legacy 802.15.4 devices can be maintained. Simulation results demonstrate the effectiveness of the proposed scheme with largely improved bandwidth and power efficiency. © 2013 International Information Institute.

Solid base catalysed 5-HMF oxidation to 2,5-FDCA over Au/hydrotalcites:fact or fiction?

Nanoparticulate gold has emerged as a promising catalyst for diverse mild and efficient selective aerobic oxidations. However, the mechanism of such atom-economical transformations, and synergy with functional supports, remains poorly understood. Alkali-free Mg-Al hydrotalcites are excellent solid base catalysts for the aerobic oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furan dicarboxylic acid (FDCA), but only in concert with high concentrations of metallic gold nanoparticles. In the absence of soluble base, competitive adsorption between strongly-bound HMF and reactively-formed oxidation intermediates site-blocks gold. Aqueous NaOH dramatically promotes solution phase HMF activation, liberating free gold sites able to activate the alcohol function within the metastable 5-hydroxymethyl-2-furancarboxylic acid (HMFCA) reactive intermediate. Synergistic effects between moderate strength base sites within alkali-free hydrotalcites and high gold surface concentrations can afford highly selective and entirely heterogeneous catalysts for aqueous phase aldehyde and alcohol cascade oxidations pertinent to biomass transformation.

Temperature management for heterogeneous multi-core FPGAs using adaptive evolutionary multi-objective approaches

Heterogeneous multi-core FPGAs contain different types of cores, which can improve efficiency when used with an effective online task scheduler. However, it is not easy to find the right cores for tasks when there are multiple objectives or dozens of cores. Inappropriate scheduling may cause hot spots which decrease the reliability of the chip. Given that, our research builds a simulating platform to evaluate all kinds of scheduling algorithms on a variety of architectures. On this platform, we provide an online scheduler which uses multi-objective evolutionary algorithm (EA). Comparing the EA and current algorithms such as Predictive Dynamic Thermal Management (PDTM) and Adaptive Temperature Threshold Dynamic Thermal Management (ATDTM), we find some drawbacks in previous work. First, current algorithms are overly dependent on manually set constant parameters. Second, those algorithms neglect optimization for heterogeneous architectures. Third, they use single-objective methods, or use linear weighting method to convert a multi-objective optimization into a single-objective optimization. Unlike other algorithms, the EA is adaptive and does not require resetting parameters when workloads switch from one to another. EAs also improve performance when used on heterogeneous architecture. A efficient Pareto front can be obtained with EAs for the purpose of multiple objectives.

Novel Ni-Mg-Al-Ca catalyst for enhanced hydrogen production for the pyrolysis-gasification of a biomass/plastic mixture

A Ni-Mg-Al-Ca catalyst was prepared by a co-precipitation method for hydrogen production from polymeric materials. The prepared catalyst was designed for both the steam cracking of hydrocarbons and for the in situ absorption of CO2 via enhancement of the water-gas shift reaction. The influence of Ca content in the catalyst and catalyst calcination temperature in relation to the pyrolysis-gasification of a wood sawdust/polypropylene mixture was investigated. The highest hydrogen yield of 39.6molH2/g Ni with H2/CO ratio of 1.90 was obtained in the presence of the Ca containing catalyst of molar ratio Ni:Mg:Al:Ca=1:1:1:4, calcined at 500°C. In addition, thermogravimetric and morphology analyses of the reacted catalysts revealed that Ca introduction into the Ni-Mg-Al catalyst prevented the deposition of filamentous carbon on the catalyst surface. Furthermore, all metals were well dispersed in the catalyst after the pyrolysis-gasification process with 20-30nm of NiO sized particles observed after the gasification without significant aggregation.

Optimal Control of Heterogeneous Systems

Цветомир Цачев - В настоящия доклад се прави преглед на някои резултати от областта на оптималното управление на непрекъснатите хетерогенни системи, публикувани в периодичната научна литература в последните години. Една динамична система се нарича хетерогенна, ако всеки от нейните елементи има собствена динамиката. Тук разглеждаме оптимално управление на системи, чиято хетерогенност се описва с едномерен или двумерен параметър – на всяка стойност на параметъра отговаря съответен елемент на системата. Хетерогенните динамични системи се използват за моделиране на процеси в икономиката, епидемиологията, биологията, опазване на обществената сигурност (ограничаване на използването на наркотици) и др. Тук разглеждаме модел на оптимално инвестиране в образование на макроикономическо ниво [11], на ограничаване на последствията от разпространението на СПИН [9], на пазар на права за въглеродни емисии [3, 4] и на оптимален макроикономически растеж при повишаване на нивото на върховите технологии [1]. Ключови думи: оптимално управление, непрекъснати хетерогенни динамични системи, приложения в икономиката и епидемиолегията

Visualisation of heterogeneous data with simultaneous feature saliency using Generalised Generative Topographic Mapping

Most machine-learning algorithms are designed for datasets with features of a single type whereas very little attention has been given to datasets with mixed-type features. We recently proposed a model to handle mixed types with a probabilistic latent variable formalism. This proposed model describes the data by type-specific distributions that are conditionally independent given the latent space and is called generalised generative topographic mapping (GGTM). It has often been observed that visualisations of high-dimensional datasets can be poor in the presence of noisy features. In this paper we therefore propose to extend the GGTM to estimate feature saliency values (GGTMFS) as an integrated part of the parameter learning process with an expectation-maximisation (EM) algorithm. The efficacy of the proposed GGTMFS model is demonstrated both for synthetic and real datasets.

Automated Assessment Through Integration of Heterogeneous Systems with a Workflow Engine

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

Quality assessment of video transmission in heterogeneous networks as applied to real-time telecare

This work looks into video quality assessment applied to the field of telecare and proposes an alternative metric to the more traditionally used PSNR based on the requirements of such an application. We show that the Pause Intensity metric introduced in [1] is also relevant and applicable to heterogeneous networks with a wireless last hop connected to a wired TCP backbone. We demonstrate through our emulation testbed that the impairments experienced in such a network architecture are dominated by continuity based impairments rather than artifacts, such as motion drift or blockiness. We also look into the implication of using Pause Intensity as a metric in terms of the overall video latency, which is potentially problematic should the video be sent and acted upon in real-time. We conclude that Pause Intensity may be used alongside the video characteristics which have been suggested as a measure of the overall video quality. © 2012 IEEE.

An investigation of support effects on the Pt-catalysed selective transformation of α,β-unsaturated substrates

Pt catalyst series were prepared on mesoporous SBA-15, SBA-16, KIT-6, true liquidcrystal-templated meso-macroporous SBA-15 and a commercial, low surface area silicasupport. Support structure can be easily fabricated using surfactant templating as a mode ofstringent control on porosity, surface area and internal structure. The impact of varying Pt-support physicochemical properties was systematically studied for the selective transformation of allylic substrates under chemoselective oxidation and hydrogenation regimes, a class of reactions highly applicable to industry. Pt-based heterogeneous catalysts are well-known for their utilisation in the hydrogenation of α,β-unsaturated aldehydes,although the mode of action and lack of systematic studies in the literature fuels continuing debate into the role of Pt nanoparticles and support choice for this area. This project attempts to shed some light on several frequently asked questions in this field. Successful support synthesis and stability after Pt impregnation is confirmed through HRTEM, XRD and N2 porosimetry. Decreasing metal loading promoted dispersion values,regardless of support choice, with surface PtO2 content also showing visible enhancement.Increasing support surface area and mesoporosity exhibited the following trend on Pt dispersion augmentation; low surface area commercial silica < true liquid crystal-templated SBA-15 < SBA-15 < SBA-16 ~ KIT-6. For the selective oxidation of cinnamyl alcohol,increasing PtO2 surface population confers substantial rate enhancements, with turnover frequencies evidencing PtO2 to be the active species .In the Pt-catalysed hydrogenation of cinnamaldehyde, strong support insensitivity was observed towards catalytic activity; as turnover frequencies normalised to Pt metal reveal constant values. However, structure sensitivity to the desired unsaturated alcohol arose,evidencing the requirement of flat, extended Pt (111) facets for C=O hydrogenation. Pt/SBA-15 proved the most selective, reflecting suppressed cinnamyl alcohol hydrogenation, with DRIFTS and in-situ ATR-IR evidencing the key role of support polarity in re-orientation of cinnamaldehyde to favour di-σCO adsorption and C=O versus C=C hydrogenation. High pressures increased activity, whilst a dramatic shift in selectivity from dominant C=C (1 bar)to C=O hydrogenation (10 bar) was also observed, attributed to surface crowding and suppression of di-σCC and η4 di-σCO+πC=C cinnamaldehyde binding modes.