845 resultados para Sequential search
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The Standard Model of particle physics was developed to describe the fundamental particles, which form matter, and their interactions via the strong, electromagnetic and weak force. Although most measurements are described with high accuracy, some observations indicate that the Standard Model is incomplete. Numerous extensions were developed to solve these limitations. Several of these extensions predict heavy resonances, so-called Z' bosons, that can decay into an electron positron pair. The particle accelerator Large Hadron Collider (LHC) at CERN in Switzerland was built to collide protons at unprecedented center-of-mass energies, namely 7 TeV in 2011. With the data set recorded in 2011 by the ATLAS detector, a large multi-purpose detector located at the LHC, the electron positron pair mass spectrum was measured up to high masses in the TeV range. The properties of electrons and the probability that other particles are mis-identified as electrons were studied in detail. Using the obtained information, a sophisticated Standard Model expectation was derived with data-driven methods and Monte Carlo simulations. In the comparison of the measurement with the expectation, no significant deviations from the Standard Model expectations were observed. Therefore exclusion limits for several Standard Model extensions were calculated. For example, Sequential Standard Model (SSM) Z' bosons with masses below 2.10 TeV were excluded with 95% Confidence Level (C.L.).
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In this paper we propose two cooperation schemes to compose new parallel variants of the Variable Neighborhood Search (VNS). On the one hand, a coarse-grained cooperation scheme is introduced which is well suited for being enhanced with a solution warehouse to store and manage the so far best found solutions and a self-adapting mechanism for the most important search parameters. This makes an a priori parameter tuning obsolete. On the other hand, a fine-grained scheme was designed to reproduce the successful properties of the sequential VNS. In combination with the use of parallel exploration threads all of the best solutions and 11 out of 20 new best solutions for the Multi Depot Vehicle Routing Problem with Time Windows were found.
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This Letter presents a search for high-mass resonances decaying into tau(+)tau(-) final states using proton-proton collisions at root s = 7 TeV produced by the Large Hadron Collider. The data were recorded with the ATLAS detector and correspond to an integrated luminosity of 4.6 fb(-1). No statistically significant excess above the Standard Model expectation is observed; 95% credibility upper limits are set on the cross section times branching fraction of Z' resonances decaying into tau(+)tau(-) pairs as a function of the resonance mass. As a result, Z' bosons of the Sequential Standard Model with masses less than 1.40 TeV are excluded at 95% credibility.
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This paper presents a search for new particles in events with one lepton (electron or muon) and missing transverse momentum using 20.3 fb−1 of proton-proton collision data at TeX = 8 TeV recorded by the ATLAS experiment at the Large Hadron Collider. No significant excess beyond Standard Model expectations is observed. A W ′ with Sequential Standard Model couplings is excluded at the 95% confidence level for masses up to 3.24 TeV. Excited chiral bosons (W *) with equivalent coupling strengths are excluded for masses up to 3.21 TeV. In the framework of an effective field theory limits are also set on the dark matter-nucleon scattering cross-section as well as the mass scale M * of the unknown mediating interaction for dark matter pair production in association with a leptonically decaying W.
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The aim of this work is to evaluate the influence of S. pombe and T. delbrueckii species on the sensory quality of red wine when used in sequential and mixed fermentations with S. cerevisiae.
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In multimedia retrieval, a query is typically interactively refined towards the ‘optimal’ answers by exploiting user feedback. However, in existing work, in each iteration, the refined query is re-evaluated. This is not only inefficient but fails to exploit the answers that may be common between iterations. In this paper, we introduce a new approach called SaveRF (Save random accesses in Relevance Feedback) for iterative relevance feedback search. SaveRF predicts the potential candidates for the next iteration and maintains this small set for efficient sequential scan. By doing so, repeated candidate accesses can be saved, hence reducing the number of random accesses. In addition, efficient scan on the overlap before the search starts also tightens the search space with smaller pruning radius. We implemented SaveRF and our experimental study on real life data sets show that it can reduce the I/O cost significantly.
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Sequential pattern mining is an important subject in data mining with broad applications in many different areas. However, previous sequential mining algorithms mostly aimed to calculate the number of occurrences (the support) without regard to the degree of importance of different data items. In this paper, we propose to explore the search space of subsequences with normalized weights. We are not only interested in the number of occurrences of the sequences (supports of sequences), but also concerned about importance of sequences (weights). When generating subsequence candidates we use both the support and the weight of the candidates while maintaining the downward closure property of these patterns which allows to accelerate the process of candidate generation.
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This thesis investigates how web search evaluation can be improved using historical interaction data. Modern search engines combine offline and online evaluation approaches in a sequence of steps that a tested change needs to pass through to be accepted as an improvement and subsequently deployed. We refer to such a sequence of steps as an evaluation pipeline. In this thesis, we consider the evaluation pipeline to contain three sequential steps: an offline evaluation step, an online evaluation scheduling step, and an online evaluation step. In this thesis we show that historical user interaction data can aid in improving the accuracy or efficiency of each of the steps of the web search evaluation pipeline. As a result of these improvements, the overall efficiency of the entire evaluation pipeline is increased. Firstly, we investigate how user interaction data can be used to build accurate offline evaluation methods for query auto-completion mechanisms. We propose a family of offline evaluation metrics for query auto-completion that represents the effort the user has to spend in order to submit their query. The parameters of our proposed metrics are trained against a set of user interactions recorded in the search engine’s query logs. From our experimental study, we observe that our proposed metrics are significantly more correlated with an online user satisfaction indicator than the metrics proposed in the existing literature. Hence, fewer changes will pass the offline evaluation step to be rejected after the online evaluation step. As a result, this would allow us to achieve a higher efficiency of the entire evaluation pipeline. Secondly, we state the problem of the optimised scheduling of online experiments. We tackle this problem by considering a greedy scheduler that prioritises the evaluation queue according to the predicted likelihood of success of a particular experiment. This predictor is trained on a set of online experiments, and uses a diverse set of features to represent an online experiment. Our study demonstrates that a higher number of successful experiments per unit of time can be achieved by deploying such a scheduler on the second step of the evaluation pipeline. Consequently, we argue that the efficiency of the evaluation pipeline can be increased. Next, to improve the efficiency of the online evaluation step, we propose the Generalised Team Draft interleaving framework. Generalised Team Draft considers both the interleaving policy (how often a particular combination of results is shown) and click scoring (how important each click is) as parameters in a data-driven optimisation of the interleaving sensitivity. Further, Generalised Team Draft is applicable beyond domains with a list-based representation of results, i.e. in domains with a grid-based representation, such as image search. Our study using datasets of interleaving experiments performed both in document and image search domains demonstrates that Generalised Team Draft achieves the highest sensitivity. A higher sensitivity indicates that the interleaving experiments can be deployed for a shorter period of time or use a smaller sample of users. Importantly, Generalised Team Draft optimises the interleaving parameters w.r.t. historical interaction data recorded in the interleaving experiments. Finally, we propose to apply the sequential testing methods to reduce the mean deployment time for the interleaving experiments. We adapt two sequential tests for the interleaving experimentation. We demonstrate that one can achieve a significant decrease in experiment duration by using such sequential testing methods. The highest efficiency is achieved by the sequential tests that adjust their stopping thresholds using historical interaction data recorded in diagnostic experiments. Our further experimental study demonstrates that cumulative gains in the online experimentation efficiency can be achieved by combining the interleaving sensitivity optimisation approaches, including Generalised Team Draft, and the sequential testing approaches. Overall, the central contributions of this thesis are the proposed approaches to improve the accuracy or efficiency of the steps of the evaluation pipeline: the offline evaluation frameworks for the query auto-completion, an approach for the optimised scheduling of online experiments, a general framework for the efficient online interleaving evaluation, and a sequential testing approach for the online search evaluation. The experiments in this thesis are based on massive real-life datasets obtained from Yandex, a leading commercial search engine. These experiments demonstrate the potential of the proposed approaches to improve the efficiency of the evaluation pipeline.
Resumo:
We report on the shape resonance spectra of phenol-water clusters, as obtained from elastic electron scattering calculations. Our results, along with virtual orbital analysis, indicate that the well-known indirect mechanism for hydrogen elimination in the gas phase is significantly impacted on by microsolvation, due to the competition between vibronic couplings on the solute and solvent molecules. This fact suggests how relevant the solvation effects could be for the electron-driven damage of biomolecules and the biomass delignification [E. M. de Oliveira et al., Phys. Rev. A 86, 020701(R) (2012)]. We also discuss microsolvation signatures in the differential cross sections that could help to identify the solvated complexes and access the composition of gaseous admixtures of these species.
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Universidade Estadual de Campinas . Faculdade de Educação Física
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The n→π* absorption transition of formaldehyde in water is analyzed using combined and sequential classical Monte Carlo (MC) simulations and quantum mechanics (QM) calculations. MC simulations generate the liquid solute-solvent structures for subsequent QM calculations. Using time-dependent density functional theory in a localized set of gaussian basis functions (TD-DFT/6-311++G(d,p)) calculations are made on statistically relevant configurations to obtain the average solvatochromic shift. All results presented here use the electrostatic embedding of the solvent. The statistically converged average result obtained of 2300 cm-1 is compared to previous theoretical results available. Analysis is made of the effective dipole moment of the hydrogen-bonded shell and how it could be held responsible for the polarization of the solvent molecules in the outer solvation shells.
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The development of new anti-cancer drugs of algal origin represents one of the least explored frontiers in medicinal chemistry. In this regard, the diversity of micro- and macroalgae found in Brazilian coastal waters can be viewed as a largely untapped natural resource. In this report, we describe a comparative study on the cytotoxic properties of extracts obtained from the Laurencia complex: Laurencia aldingensis, L. catarinensis, L. dendroidea, L. intricata, L. translucida, L. sp, and Palisada flagellifera. All of these species were collected in the coastal waters of the State of Espírito Santo, Brazil. Four out of the twelve samples initially investigated were found to show significant levels of toxicity towards a model tumor cell line (human uterine sarcoma, MES-SA). The highest levels of cytotoxicity were typically associated with non-polar (hexane) algal extracts, while the lowest levels of cytotoxicity were found with the corresponding polar (methanol) extracts. In this report, we also describe a biological model currently in development that will not only facilitate the search for new anti-cancer drug candidates of algal origin, but also permit the identification of compounds capable of inducing the destruction of multi-drug resistant tumors with greater efficiency than the pharmaceuticals currently in clinical use.
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This paper describes a sequential injection chromatography procedure for determination of picloram in waters exploring the low backpressure of a 2.5 cm long monolithic C18 column. Separation of the analyte from the matrix was achieved in less than 60 s using a mobile phase composed by 20:80 (v v-1) acetonitrile:5.0 mmol L-1 H3PO4 and flow rate of 30 μL s-1. Detection was made at 223 nm with a 40 mm optical path length cell. The limits of detection and quantification were 33 and 137 μg L-1, respectively. The proposed method is sensitive enough to monitor the maximum concentration level for picloram in drinking water (500 μg L-1). The sampling frequency is 60 analyses per hour, consuming only 300 μL of acetonitrile per analysis. The proposed methodology was applied to spiked river water samples and no statistically significant differences were observed in comparison to a conventional HPLC-UV method.
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This work describes a photo-reactor to perform in line degradation of organic compounds by photo-Fenton reaction using Sequential Injection Analysis (SIA) system. A copper phthalocyanine-3,4',4²,4²¢-tetrasulfonic acid tetrasodium salt dye solution was used as a model compound for the phthalocyanine family, whose pigments have a large use in automotive coatings industry. Based on preliminary tests, 97% of color removal was obtained from a solution containing 20 µmol L-1 of this dye.
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Searches for field horizontal-branch (FHB) stars in the halo of the Galaxy in the past have been carried out by several techniques, such as objective-prism surveys and visual or infrared photometric surveys. By choosing adequate color criteria, it is possible to improve the efficiency of identifying bona fide FHB stars among the other objects that exhibit similar characteristics, such as main-sequence A-stars, blue stragglers, subdwarfs, etc. In this work, we report the results of a spectroscopic survey carried out near the south Galactic pole intended to validate FHB stars originally selected from the HK objective-prism survey of Beers and colleagues, based on near-infrared color indices. A comparison between the stellar spectra obtained in this survey with theoretical stellar atmosphere models allows us to determine T(eff), log g, and [Fe/H] for 13 stars in the sample. Stellar temperatures were calculated from measured (B-V)(o), when this measurement was available (16 stars). The color index criteria adopted in this work are shown to correctly classify 30% of the sample as FHB, 25% as non-FHB (main-sequence stars and subdwarfes), whereas 40% could not be distinguished between FHB and main-sequence stars. We compare the efficacy of different color criteria in the literature intended to select FHB stars, and discuss the use of the Mg II 4481 line to estimate the metallicity.