Debranding in Fantasy Realms: Perceived Marketing Opportunities within the Virtual World

This paper discusses the application of the concept of debranding within immersive virtual environments. In particular the issue of the media richness and vividness of experience is considered in these experience realms that may not be conducive to traditional branding invasive strategies. Brand equity is generally seen to be the desired outcome of branding strategies and the authors suggest that unless the virtual domains are considered as sacred spaces then brand equity may be compromised. The application of the above concepts is applied to the differing social spaces that operate within the different experience realms. The ideas of resonance, presence and interactivity are considered here. They lead to the development of a constructed positioning by the participants. Through the process of debranding, marketers may be able to enter these sacred spaces without negative impact to the brand. Perception of these virtual spaces was found to be partially congruent with this approach to branding. It thus presents a number of challenges for the owners of such virtual spaces and also virtual worlds in increasing the commercial utilization of investment in these environments

ZZ into 4ℓ expected sensitivity with the first ATLAS data

The qq->ZZ->4l process is of great interest because it has not been studied in the c.m. energies that the LHC will reach, it is the irreducible background to Higgs->ZZ->4l searches and it can be used to probe physics beyond the SM through the measurement of Triple Gauge Coupling parameters. In this talk, the analysis method for the measurement of the cross-section and neutral TGCs with simulated ATLAS data, and the expectations for early data taking will be presented

Distributed and parallel sparse convex optimization for radio interferometry with PURIFY

Next generation radio interferometric telescopes are entering an era of big data with extremely large data sets. While these telescopes can observe the sky in higher sensitivity and resolution than before, computational challenges in image reconstruction need to be overcome to realize the potential of forthcoming telescopes. New methods in sparse image reconstruction and convex optimization techniques (cf. compressive sensing) have shown to produce higher fidelity reconstructions of simulations and real observations than traditional methods. This article presents distributed and parallel algorithms and implementations to perform sparse image reconstruction, with significant practical considerations that are important for implementing these algorithms for Big Data. We benchmark the algorithms presented, showing that they are considerably faster than their serial equivalents. We then pre-sample gridding kernels to scale the distributed algorithms to larger data sizes, showing application times for 1 Gb to 2.4 Tb data sets over 25 to 100 nodes for up to 50 billion visibilities, and find that the run-times for the distributed algorithms range from 100 milliseconds to 3 minutes per iteration. This work presents an important step in working towards computationally scalable and efficient algorithms and implementations that are needed to image observations of both extended and compact sources from next generation radio interferometers such as the SKA. The algorithms are implemented in the latest versions of the SOPT (https://github.com/astro-informatics/sopt) and PURIFY (https://github.com/astro-informatics/purify) software packages {(Versions 3.1.0)}, which have been released alongside of this article.Comment: 25 pages, 5 figure

Exact distributed kinetic Monte Carlo simulations for on-lattice chemical kinetics: lessons learnt from medium- and large-scale benchmarks

Kinetic Monte-Carlo (KMC) simulations have been instrumental in multiscale catalysis studies, enabling the elucidation of the complex dynamics of heterogeneous catalysts and the prediction of macroscopic performance metrics, such as activity and selectivity. However, the accessible length- and time-scales have been a limiting factor in such simulations. For instance, handling lattices containing millions of sites with “traditional” sequential KMC implementations is prohibitive owing to large memory requirements and long simulation times. We have recently established an approach for exact, distributed, lattice-based simulations of catalytic kinetics which couples the Time-Warp algorithm with the Graph-Theoretical KMC framework, enabling the handling of complex adsorbate lateral interactions and reaction events within large lattices. In this work, we develop a lattice-based variant of the Brusselator system, a prototype chemical oscillator pioneered by Prigogine and Lefever in the late 60’s, to benchmark and demonstrate our approach. This system can form spiral wave patterns, which would be computationally intractable with sequential KMC, while our distributed KMC approach can simulate such patterns 16 and 36 times faster with 625 and 1600 processors, respectively. The medium- and large-scale benchmarks thus conducted, demonstrate the robustness of the approach, and reveal computational bottlenecks that could be targeted in further development efforts

Large-scale benchmarks of the Time-Warp/Graph-Theoretical Kinetic Monte Carlo approach for distributed on-lattice simulations of catalytic kinetics

We extend the work of Ravipati et al.[Comput. Phys. Commun., 2022, 270, 108148] in benchmarking the performance of large-scale, distributed, on-lattice kinetic Monte Carlo (KMC) simulations. Our software package, Zacros, employs a graph-theoretical approach to KMC, coupled with the Time-Warp algorithm for parallel discrete event simulations. The lattice is divided into equal subdomains, each assigned to a single processor; the cornerstone of the Time-Warp algorithm is the state queue, to which snapshots of the KMC (lattice) state are saved regularly, enabling historical KMC information to be corrected when conflicts occur at the subdomain boundaries. Focusing on three model systems, we highlight the key Time-Warp parameters that can be tuned to optimise KMC performance. The frequency of state saving, controlled by the state saving interval, δsnap, is shown to have the largest effect on performance, which favours balancing the overhead of re-simulating KMC history with that of writing state snapshots to memory. Also important is the global virtual time (GVT) computation interval, ΔτGVT, which has little direct effect on the progress of the simulation but controls how often the state queue memory can be freed up. We find that a vector data structure is, in general, more favourable than a linked list for storing the state queue, due to the reduced time required for allocating and de-allocating memory. These findings will guide users in maximising the efficiency of Zacros or other distributed KMC software, which is a vital step towards realising accurate, meso-scale simulations of heterogeneous catalysis

Κβαντομηχανική προσέγγιση βαθμωτών πεδίων σε καμπυλωμένο χωροχρόνο

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Φυσική και Τεχνολογικές Εφαρμογές

The Edinburgh cognitive and behavioral amyotrophic lateral sclerosis screen (ECAS):Sensitivity in differentiating between ALS and Alzheimer's disease in a Greek population

International audienceObjectives: (1) Adapt the ECAS into Greek, validate it in ALS patients and compare with the ALS-CBS. (2) Determine the sensitivity and specificity of ECAS in the differentiation between AD and non-demented ALS patients as compared with the ACE-III and mini-ACE. Methods: ALS patients (n = 28) were recruited and AD patients (n = 26) were matched in age, sex, and education with ALS patients (n = 24). The normative data were derived from a random sample of controls (n = 52). Bayes correlation analysis was conducted to examine convergent validity. Bayes t-test was performed to assess between groups’ differences. Receiver operating characteristics (ROC) curve analyses and area under the curve (AUC) were implemented to appraise the sensitivity and specificity in the differentiation between the AD and non-demented ALS patients. Results: The ECAS and its sub-scores in addition to the behavior interview demonstrated robust correlations with the ALS-CBS. Impairment in language and verbal fluency were the most prominent deficits in the ALS patients. The most frequently reported change was apathy. The ROC analysis demonstrated that the ECAS-ALS nonspecific score (comprising memory and visuospatial domains) is the most sensitive and specific in differentiating the AD from ALS patients. The other measures expressed high sensitivity, yet a poor specificity. Conclusions: The ECAS is a multi-purpose screening tool. The ECAS-ALS specific appraises the whole spectrum of the highly prevalent cognitive impairments in ALS. The ECAS-ALS nonspecific (memory and visuospatial) is a sensitive score to detect AD related deficits and is able to differentiate the AD from the non-demented ALS patients better than the ACE-III and mini-ACE

Coupling the time-warp algorithm with the graph-theoretical kinetic Monte Carlo framework for distributed simulations of heterogeneous catalysts

Despite the successful and ever widening adoption of kinetic Monte Carlo (KMC) simulations in the area of surface science and heterogeneous catalysis, the accessible length scales are still limited by the inherently sequential nature of the KMC framework. Simulating long-range surface phenomena, such as catalytic reconstruction and pattern formation, requires consideration of large surfaces/lattices, at the μm scale and beyond. However, handling such lattices with the sequential KMC framework is extremely challenging due to the heavy memory footprint and computational demand. The Time-Warp algorithm proposed by Jefferson [ACM. Trans. Program. Lang. Syst., 1985. 7: 404-425] offers a way to enable distributed parallelization of discrete event simulations. Thus, to enable high-fidelity simulations of challenging systems in heterogeneous catalysis, we have coupled the Time-Warp algorithm with the Graph-Theoretical KMC framework [J. Chem. Phys., 134(21): 214115; J. Chem. Phys., 139(22): 224706] and implemented the approach in the general-purpose KMC code Zacros. We have further developed a “parallel-emulation” serial algorithm, which produces identical results to those obtained from the distributed runs (with the Time-Warp algorithm) thereby validating the correctness of our implementation. These advancements make Zacros the first-of-its-kind general-purpose KMC code with distributed computing capabilities, thereby opening up opportunities for detailed meso-scale studies of heterogeneous catalysts and closer-than-ever comparisons of theory with experiments

A comparison of the Greek ACE-III, M-ACE, ACE-R, MMSE and ECAS in the assessment and identification of Alzheimer’s Disease

International audienceIntroduction: The present study attempted to adapt into Greek and validate the Addenbrooke's Cognitive Examination-III (ACE-III) and Mini-ACE (M-ACE) against their predecessors Addenbrooke's Cognitive Examination-Revised (ACE-R) and Mini-Mental State Examination (MMSE) in an Alzheimer's disease (AD) population. Notably, the present study also aimed to appraise the utility of each screen by conducting a comparison of the psychometric properties of ACE-III, MACE , ACE-R, MMSE, and Edinburgh Cognitive and Behavioural ALS Screen (ECAS) in detecting AD. Methods: Forty AD patients were recruited and matched with 38 controls. Bayes correlation analysis was conducted to examine convergent validity. Receiver operating characteristics curve analysis was implemented to appraise the sensitivity and specificity of the tests. Results: The ACE-III, MACE and ECAS and its sub-scores robustly correlated with ACE-R and MMSE. The ACE-III and the ECAS-ALS Non-Specific score were the most sensitive and specific tools in detecting AD, closely followed by ECAS Total score and MACE. Solely ECAS Total score correlated with the duration of disease. Discussion: ACE-III and MACE are validated and showed very good psychometric properties in detecting AD and may be considered in hectic clinical settings. ECAS total score and ECAS-ALS Non-Specific showed comparable psychometric properties and may be considered in poly-pathological clinics for the detection and monitoring of AD in patients with motor impairments common to neurodegenerative diseases