Generation of cluster states in optomechanical quantum systems

We consider an optomechanical quantum system composed of a single cavity mode interacting with N mechanical resonators. We propose a scheme for generating continuous-variable graph states of arbitrary size and shape, including the so-called cluster states for universal quantum computation. The main feature of this scheme is that, differently from previous approaches, the graph states are hosted in the mechanical degrees of freedom rather than in the radiative ones. Specifically, via a 2N-tone drive, we engineer a linear Hamiltonian which is instrumental to dissipatively drive the system to the desired target state. The robustness of this scheme is assessed against finite interaction times and mechanical noise, confirming it as a valuable approach towards quantum state engineering for continuous-variable computation in a solid-state platform.

Anytime Algorithms for Solving Possibilistic MDPs and Hybrid MDPs

The ability of an agent to make quick, rational decisions in an uncertain environment is paramount for its applicability in realistic settings. Markov Decision Processes (MDP) provide such a framework, but can only model uncertainty that can be expressed as probabilities. Possibilistic counterparts of MDPs allow to model imprecise beliefs, yet they cannot accurately represent probabilistic sources of uncertainty and they lack the efficient online solvers found in the probabilistic MDP community. In this paper we advance the state of the art in three important ways. Firstly, we propose the first online planner for possibilistic MDP by adapting the Monte-Carlo Tree Search (MCTS) algorithm. A key component is the development of efficient search structures to sample possibility distributions based on the DPY transformation as introduced by Dubois, Prade, and Yager. Secondly, we introduce a hybrid MDP model that allows us to express both possibilistic and probabilistic uncertainty, where the hybrid model is a proper extension of both probabilistic and possibilistic MDPs. Thirdly, we demonstrate that MCTS algorithms can readily be applied to solve such hybrid models.

YSOVAR: Mid-infrared Variability of Young Stellar Objects and Their Disks in the Cluster IRAS 20050+2720

We present a time-variability study of young stellar objects (YSOs) in the cluster IRAS 20050+2720, performed at 3.6 and 4.5 μm with the Spitzer Space Telescope; this study is part of the Young Stellar Object VARiability (YSOVAR) project. We have collected light curves for 181 cluster members over 60 days. We find a high variability fraction among embedded cluster members of ca. 70%, whereas young stars without a detectable disk display variability less often (in ca. 50% of the cases) and with lower amplitudes. We detect periodic variability for 33 sources with periods primarily in the range of 2–6 days. Practically all embedded periodic sources display additional variability on top of their periodicity. Furthermore, we analyze the slopes of the tracks that our sources span in the color–magnitude diagram (CMD). We find that sources with long variability time scales tend to display CMD slopes that are at least partially influenced by accretion processes, while sources with short variability timescales tend to display extinction-dominated slopes. We find a tentative trend of X-ray detected cluster members to vary on longer timescales than the X-ray undetected members.

Interpretable and Reconfigurable Clustering of Document Datasets by Deriving Word-based Rules

Clusters of text documents output by clustering algorithms are often hard to interpret. We describe motivating real-world scenarios that necessitate reconfigurability and high interpretability of clusters and outline the problem of generating clusterings with interpretable and reconfigurable cluster models. We develop two clustering algorithms toward the outlined goal of building interpretable and reconfigurable cluster models. They generate clusters with associated rules that are composed of conditions on word occurrences or nonoccurrences. The proposed approaches vary in the complexity of the format of the rules; RGC employs disjunctions and conjunctions in rule generation whereas RGC-D rules are simple disjunctions of conditions signifying presence of various words. In both the cases, each cluster is comprised of precisely the set of documents that satisfy the corresponding rule. Rules of the latter kind are easy to interpret, whereas the former leads to more accurate clustering. We show that our approaches outperform the unsupervised decision tree approach for rule-generating clustering and also an approach we provide for generating interpretable models for general clusterings, both by significant margins. We empirically show that the purity and f-measure losses to achieve interpretability can be as little as 3 and 5%, respectively using the algorithms presented herein.

Accelerating Large Scale Centroid-based Clustering with Locality Sensitive Hashing

Most traditional data mining algorithms struggle to cope with the sheer scale of data efficiently. In this paper, we propose a general framework to accelerate existing clustering algorithms to cluster large-scale datasets which contain large numbers of attributes, items, and clusters. Our framework makes use of locality sensitive hashing (LSH) to significantly reduce the cluster search space. We also theoretically prove that our framework has a guaranteed error bound in terms of the clustering quality. This framework can be applied to a set of centroid-based clustering algorithms that assign an object to the most similar cluster, and we adopt the popular K-Modes categorical clustering algorithm to present how the framework can be applied. We validated our framework with five synthetic datasets and a real world Yahoo! Answers dataset. The experimental results demonstrate that our framework is able to speed up the existing clustering algorithm between factors of 2 and 6, while maintaining comparable cluster purity.

Effect of providing free glasses on children's educational outcomes in China: cluster randomized controlled trial

Objective: To assess the effect of provision of free glasses on academic performance in rural Chinese children with myopia. Design: Cluster randomized, investigator masked, controlled trial.Setting 252 primary schools in two prefectures in western China, 2012-13. Participants: 3177 of 19 934 children in fourth and fifth grades (mean age 10.5 years) with visual acuity <6/12 in either eye without glasses correctable to >6/12 with glasses. 3052 (96.0%) completed the study.Interventions Children were randomized by school (84 schools per arm) to one of three interventions at the beginning of the school year: prescription for glasses only (control group), vouchers for free glasses at a local facility, or free glasses provided in class. Main outcome measures: Spectacle wear at endline examination and end of year score on a specially designed mathematics test, adjusted for baseline score and expressed in standard deviations. Results: Among 3177 eligible children, 1036 (32.6%) were randomized to control, 988 (31.1%) to vouchers, and 1153 (36.3%) to free glasses in class. All eligible children would benefit from glasses, but only 15% wore them at baseline. At closeout glasses wear was 41% (observed) and 68% (self reported) in the free glasses group, and 26% (observed) and 37% (self reported) in the controls. Effect on test score was 0.11 SD (95% confidence interval 0.01 to 0.21) when the free glasses group was compared with the control group. The adjusted effect of providing free glasses (0.10, 0.002 to 0.19) was greater than parental education (0.03, −0.04 to 0.09) or family wealth (0.01, −0.06 to 0.08). This difference between groups was significant, but was smaller than the prespecified 0.20 SD difference that the study was powered to detect. Conclusions: The provision of free glasses to Chinese children with myopia improves children’s performance on mathematics testing to a statistically significant degree, despite imperfect compliance, although the observed difference between groups was smaller than the study was originally designed to detect. Myopia is common and rarely corrected in this setting. Trial Registration: Current Controlled Trials ISRCTN03252665.

Novel Application of Genetic Sequencing algorithms to optimisation of hardware resource sharing for DSP

Field programmable gate array (FPGA) technology is a powerful platform for implementing computationally complex, digital signal processing (DSP) systems. Applications that are multi-modal, however, are designed for worse case conditions. In this paper, genetic sequencing techniques are applied to give a more sophisticated decomposition of the algorithmic variations, thus allowing an unified hardware architecture which gives a 10-25% area saving and 15% power saving for a digital radar receiver.

Public health risk communication by text message in response to a cluster of invasive meningococcal infection in a primary school

Public health risk communication during emergencies should be rapid and accurate in order to allow the audience to take steps to prevent adverse outcomes. Delays to official communications may cause unnecessary anxiety due to uncertainty or inaccurate information circulating within the at-risk group. Modern electronic communications present opportunities for rapid, targeted public health risk communication. We present a case report of a cluster of invasive meningococcal disease in a primary school in which we used the school's mass short message service (SMS) text message system to inform parents and guardians of pupils about the incident, to tell them that chemoprophylaxis would be offered to all pupils and staff, and to advise them when to attend the school to obtain further information and antibiotics. Following notification to public health on a Saturday, an incident team met on Sunday, sent the SMS messages that afternoon, and administered chemoprophyaxis to 93% of 404 pupils on Monday. The use of mass SMS messages enabled rapid communication from an official source and greatly aided the public health response to the cluster.

Sequence and expression of complement factor H gene cluster variants and their roles in age-related macular degeneration risk

Purpose: To investigate how potentially functional genetic variants are coinherited on each of four common complement factor H (CFH) and CFH-related gene haplotypes and to measure expression of these genes in eye and liver tissues.

Methods: We sequenced the CFH region in four individuals (one homozygote for each of four common CFH region haplotypes) to identify all genetic variants. We studied associations between the haplotypes and AMD phenotypes in 2157 cases and 1150 controls. We examined RNA-seq profiles in macular and peripheral retina and retinal pigment epithelium/choroid/sclera (RCS) from eight eye donors and three liver samples.

Results: The haplotypic coinheritance of potentially functional variants (including missense variants, novel splice sites, and the CFHR3–CFHR1 deletion) was described for the four common haplotypes. Expression of the short and long CFH transcripts differed markedly between the retina and liver. We found no expression of any of the five CFH-related genes in the retina or RCS, in contrast to the liver, which is the main source of the circulating proteins.

Conclusions: We identified all genetic variants on common CFH region haplotypes and described their coinheritance. Understanding their functional effects will be key to developing and stratifying AMD therapies. The small scale of our expression study prevented us from investigating the relationships between CFH region haplotypes and their expression, and it will take time and collaboration to develop epidemiologic-scale studies. However, the striking difference between systemic and ocular expression of complement regulators shown in this study suggests important implications for the development of intraocular and systemic treatments.

Synthesis of FPGA-based accelerators implementing recursive algorithms

O desenvolvimento de sistemas computacionais é um processo complexo, com múltiplas etapas, que requer uma análise profunda do problema, levando em consideração as limitações e os requisitos aplicáveis. Tal tarefa envolve a exploração de técnicas alternativas e de algoritmos computacionais para optimizar o sistema e satisfazer os requisitos estabelecidos. Neste contexto, uma das mais importantes etapas é a análise e implementação de algoritmos computacionais. Enormes avanços tecnológicos no âmbito das FPGAs (Field-Programmable Gate Arrays) tornaram possível o desenvolvimento de sistemas de engenharia extremamente complexos. Contudo, o número de transístores disponíveis por chip está a crescer mais rapidamente do que a capacidade que temos para desenvolver sistemas que tirem proveito desse crescimento. Esta limitação já bem conhecida, antes de se revelar com FPGAs, já se verificava com ASICs (Application-Specific Integrated Circuits) e tem vindo a aumentar continuamente. O desenvolvimento de sistemas com base em FPGAs de alta capacidade envolve uma grande variedade de ferramentas, incluindo métodos para a implementação eficiente de algoritmos computacionais. Esta tese pretende proporcionar uma contribuição nesta área, tirando partido da reutilização, do aumento do nível de abstracção e de especificações algorítmicas mais automatizadas e claras. Mais especificamente, é apresentado um estudo que foi levado a cabo no sentido de obter critérios relativos à implementação em hardware de algoritmos recursivos versus iterativos. Depois de serem apresentadas algumas das estratégias para implementar recursividade em hardware mais significativas, descreve-se, em pormenor, um conjunto de algoritmos para resolver problemas de pesquisa combinatória (considerados enquanto exemplos de aplicação). Versões recursivas e iterativas destes algoritmos foram implementados e testados em FPGA. Com base nos resultados obtidos, é feita uma cuidada análise comparativa. Novas ferramentas e técnicas de investigação que foram desenvolvidas no âmbito desta tese são também discutidas e demonstradas.

Optimisation of viscoelastic treatments using genetic algorithms

Viscoelastic treatments are one of the most efficient treatments, as far as passive damping is concerned, particularly in the case of thin and light structures. In this type of treatment, part of the strain energy generated in the viscoelastic material is dissipated to the surroundings, in the form of heat. A layer of viscoelastic material is applied to a structure in an unconstrained or constrained configuration, the latter proving to be the most efficient arrangement. This is due to the fact that the relative movement of both the host and constraining layers cause the viscoelastic material to be subjected to a relatively high strain energy. There are studies, however, that claim that the partial application of the viscoelastic material is just as efficient, in terms of economic costs or any other form of treatment application costs. The application of patches of material in specific and selected areas of the structure, thus minimising the extension of damping material, results in an equally efficient treatment. Since the damping mechanism of a viscoelastic material is based on the dissipation of part of the strain energy, the efficiency of the partial treatment can be correlated to the modal strain energy of the structure. Even though the results obtained with this approach in various studies are considered very satisfactory, an optimisation procedure is deemed necessary. In order to obtain optimum solutions, however, time consuming numerical simulations are required. The optimisation process to use the minimum amount of viscoelastic material is based on an evolutionary geometry re-design and calculation of the modal damping, making this procedure computationally costly. To avert this disadvantage, this study uses adaptive layerwise finite elements and applies Genetic Algorithms in the optimisation process.

The Well London program - a cluster randomized trial of community engagement for improving health behaviors and mental wellbeing: baseline survey results

Background The Well London programme used community engagement, complemented by changes to the physical and social neighbourhood environment, to improve physical activity levels, healthy eating and mental wellbeing in the most deprived communities in London. The effectiveness of Well London is being evaluated in a pair-matched cluster randomised trial (CRT). The baseline survey data are reported here. Methods The CRT involved 20 matched pairs of intervention and control communities (defined as UK census lower super output areas; ranked in the 11% most deprived LSOAs in London by Index of Multiple Deprivation) across 20 London boroughs. The primary trial outcomes, sociodemographic information and environmental neighbourhood characteristics were assessed in three quantitative components within the Well London CRT at baseline: a cross-sectional, interviewer-administered adult household survey; a self-completed, school-based adolescent questionnaire; a fieldworker completed neighbourhood environmental audit. Baseline data collection occurred in 2008. Physical activity, healthy eating and mental wellbeing were assessed using standardised, validated questionnaire tools. Multiple imputation was used to account for missing data in the outcomes and other variables in the adult and adolescent surveys. Results There were 4107 adults and 1214 adolescent respondents in the baseline surveys. The intervention and control areas were broadly comparable with respect to the primary outcomes and key sociodemographic characteristics. The environmental characteristics of the intervention and control neighbourhoods were broadly similar. There was greater between cluster variation in the primary outcomes in the adult population compared to the adolescent population. Levels of healthy eating, smoking and self-reported anxiety/depression were similar in the Well London population and the national Health Survey for England. Levels of physical activity were higher in the Well London population but this is likely to be due to the different measurement tools used in the two surveys. Conclusions Randomisation of social interventions such as Well London is acceptable and feasible and in this study the intervention and control arms are well balanced with respect to the primary outcomes and key sociodemographic characteristics. The matched design has improved the statistical efficiency of the study amongst adults but less so amongst adolescents. Follow-up data collection will be completed 2012.

Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds

Semi-autonomous avatars should be both realistic and believable. The goal is to learn from and reproduce the behaviours of the user-controlled input to enable semi-autonomous avatars to plausibly interact with their human-controlled counterparts. A powerful tool for embedding autonomous behaviour is learning by imitation. Hence, in this paper an ensemble of fuzzy inference systems cluster the user input data to identify natural groupings within the data to describe the users movement and actions in a more abstract way. Multiple clustering algorithms are investigated along with a neuro-fuzzy classifier; and an ensemble of fuzzy systems are evaluated.

Completely supervised training algorithms for B-spline neural networks and neuro-fuzzy systems

Complete supervised training algorithms for B-spline neural networks and fuzzy rule-based systems are discussed. By interducing the relationship between B-spline neural networks and certain types of fuzzy models, training algorithms developed initially for neural networks can be adapted by fuzzy systems.