Benchmark su Dataset con valori incerti con Orion DBMS

Studio critico e bibliografico sullo stato dell'arte nel campo dei Database con valori incerti e sperimentazione e benchmark con il DBMS Orion utilizzando dataset realistici creati artificialmente.

Analisi delle prestazioni della piattaforma semantica smart-m3 mediante il benchmark lubm

Il software Smart-M3, ereditato dal progetto europeo SOFIA, conclusosi nel 2011, permette di creare una piattaforma d'interoperabilità indipendente dal tipo di dispositivi e dal loro dominio di utilizzo e che miri a fornire un Web Semantico di informazioni condivisibili fra entità software e dispositivi, creando ambienti intelligenti e collegamenti tra il mondo reale e virtuale. Questo è un campo in continua ascesa grazie al progressivo e regolare sviluppo sia della tecnologia, nell'ambito della miniaturizzazione dei dispositivi, che delle potenzialità dei sistemi embedded. Questi sistemi permettono, tramite l'uso sempre maggiore di sensori e attuatori, l'elaborazione delle informazioni provenienti dall’esterno. È evidente, come un software di tale portata, possa avere una molteplicità di applicazioni, alcune delle quali, nell’ambito della Biomedica, può esprimersi nella telemedicina e nei sistemi e-Heath. Per e-Health si intende infatti l’utilizzo di strumenti basati sulle tecnologie dell'informazione e della comunicazione, per sostenere e promuovere la prevenzione, la diagnosi, il trattamento e il monitoraggio delle malattie e la gestione della salute e dello stile di vita. Obiettivo di questa tesi è fornire un set di dati che mirino ad ottimizzare e perfezionare i criteri nella scelta applicativa di tali strutture. Misureremo prestazioni e capacità di svolgere più o meno velocemente, precisamente ed accuratamente, un particolare compito per cui tale software è stato progettato. Ciò si costruisce sull’esecuzione di un benchmark su diverse implementazioni di Smart-M3 ed in particolare sul componente centrale denominato SIB (Semantic Information Broker).

Benchmark Structures and Binding Energies of Small Water Clusters with Anharmonicity Corrections

For (H2O)n where n = 1–10, we used a scheme combining molecular dynamics sampling with high level ab initio calculations to locate the global and many low lying local minima for each cluster. For each isomer, we extrapolated the RI-MP2 energies to their complete basis set limit, included a CCSD(T) correction using a smaller basis set and added finite temperature corrections within the rigid-rotor-harmonic-oscillator (RRHO) model using scaled and unscaled harmonic vibrational frequencies. The vibrational scaling factors were determined specifically for water clusters by comparing harmonic frequencies with VPT2 fundamental frequencies. We find the CCSD(T) correction to the RI-MP2 binding energy to be small (<1%) but still important in determining accurate conformational energies. Anharmonic corrections are found to be non-negligble; they do not alter the energetic ordering of isomers, but they do lower the free energies of formation of the water clusters by as much as 4 kcal/mol at 298.15 K.

Benchmark Structures and Binding Energies of Small Water Clusters with Anharmonicity Corrections

For (H2O)n where n = 1–10, we used a scheme combining molecular dynamics sampling with high level ab initio calculations to locate the global and many low lying local minima for each cluster. For each isomer, we extrapolated the RI-MP2 energies to their complete basis set limit, included a CCSD(T) correction using a smaller basis set and added finite temperature corrections within the rigid-rotor-harmonic-oscillator (RRHO) model using scaled and unscaled harmonic vibrational frequencies. The vibrational scaling factors were determined specifically for water clusters by comparing harmonic frequencies with VPT2 fundamental frequencies. We find the CCSD(T) correction to the RI-MP2 binding energy to be small (

A benchmark of kriging-based infill criteria for noisy optimization

Responses of many real-world problems can only be evaluated perturbed by noise. In order to make an efficient optimization of these problems possible, intelligent optimization strategies successfully coping with noisy evaluations are required. In this article, a comprehensive review of existing kriging-based methods for the optimization of noisy functions is provided. In summary, ten methods for choosing the sequential samples are described using a unified formalism. They are compared on analytical benchmark problems, whereby the usual assumption of homoscedastic Gaussian noise made in the underlying models is meet. Different problem configurations (noise level, maximum number of observations, initial number of observations) and setups (covariance functions, budget, initial sample size) are considered. It is found that the choices of the initial sample size and the covariance function are not critical. The choice of the method, however, can result in significant differences in the performance. In particular, the three most intuitive criteria are found as poor alternatives. Although no criterion is found consistently more efficient than the others, two specialized methods appear more robust on average.

Hard thermal loop benchmark for the extraction of the nonperturbative QQ[over ¯] potential

The extraction of the finite temperature heavy quark potential from lattice QCD relies on a spectral analysis of the Wilson loop. General arguments tell us that the lowest lying spectral peak encodes, through its position and shape, the real and imaginary parts of this complex potential. Here we benchmark this extraction strategy using leading order hard-thermal loop (HTL) calculations. In other words, we analytically calculate the Wilson loop and determine the corresponding spectrum. By fitting its lowest lying peak we obtain the real and imaginary parts and confirm that the knowledge of the lowest peak alone is sufficient for obtaining the potential. Access to the full spectrum allows an investigation of spectral features that do not contribute to the potential but can pose a challenge to numerical attempts of an analytic continuation from imaginary time data. Differences in these contributions between the Wilson loop and gauge fixed Wilson line correlators are discussed. To better understand the difficulties in a numerical extraction we deploy the maximum entropy method with extended search space to HTL correlators in Euclidean time and observe how well the known spectral function and values for the real and imaginary parts are reproduced. Possible venues for improvement of the extraction strategy are discussed.