| 1. |
- Bolognesi, P., et al.
(författare)
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A study of the dynamical energy flow in uracil
- 2015
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Ingår i: Journal of Physics, Conference Series. - : IOP Publishing. - 1742-6588 .- 1742-6596. ; 635
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Tidskriftsartikel (refereegranskat)abstract
- The time resolved photoionization of C 1s in uracil following excitation of the neutral molecule by 260 nm pulses has been studied at LCLS.
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| 2. |
- Capannini, Gabriele, et al.
(författare)
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Quality versus efficiency in document scoring with learning-to-rank models
- 2016
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Ingår i: Information Processing & Management. - : Elsevier BV. - 0306-4573 .- 1873-5371. ; 52:6, s. 1161-1177
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Tidskriftsartikel (refereegranskat)abstract
- Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of documents and a user query, these functions are able to precisely predict a score for each of the documents, in turn exploited to effectively rank them. Although the scoring efficiency of LtR models is critical in several applications – e.g., it directly impacts on response time and throughput of Web query processing – it has received relatively little attention so far. The goal of this work is to experimentally investigate the scoring efficiency of LtR models along with their ranking quality. Specifically, we show that machine-learned ranking models exhibit a quality versus efficiency trade-off. For example, each family of LtR algorithms has tuning parameters that can influence both effectiveness and efficiency, where higher ranking quality is generally obtained with more complex and expensive models. Moreover, LtR algorithms that learn complex models, such as those based on forests of regression trees, are generally more expensive and more effective than other algorithms that induce simpler models like linear combination of features. We extensively analyze the quality versus efficiency trade-off of a wide spectrum of state-of-the-art LtR, and we propose a sound methodology to devise the most effective ranker given a time budget. To guarantee reproducibility, we used publicly available datasets and we contribute an open source C++ framework providing optimized, multi-threaded implementations of the most effective tree-based learners: Gradient Boosted Regression Trees (GBRT), Lambda-Mart (Λ-MART), and the first public-domain implementation of Oblivious Lambda-Mart (Ωλ-MART), an algorithm that induces forests of oblivious regression trees. We investigate how the different training parameters impact on the quality versus efficiency trade-off, and provide a thorough comparison of several algorithms in the quality-cost space. The experiments conducted show that there is not an overall best algorithm, but the optimal choice depends on the time budget.
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| 3. |
- Capannini, Gabriele, et al.
(författare)
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QuickRank : A C++ suite of learning to rank algorithms
- 2015
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Ingår i: CEUR Workshop Proceedings.
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Konferensbidrag (refereegranskat)abstract
- Ranking is a central task of many Information Retrieval (IR) problems, particularly challenging in the case of large-scale Web collections where it involves effectiveness requirements and effciency constraints that are not common to other ranking-based applications. This paper describes QuickRank, a C++ suite of effcient and effective Learning to Rank (LtR) algorithms that allows high-quality ranking functions to be devised from possibly huge training datasets. QuickRank is a project with a double goal: i) answering industrial need of Tiscali S.p.A. for a exible and scalable LtR solution for learning ranking models from huge training datasets; ii) providing the IR research community with a exible, extensible and effcient LtR framework to design LtR solutions and fairly compare the performance of different algorithms and ranking models. This paper presents our choices in designing QuickRank and report some preliminary use experiences.
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| 4. |
- Ueda, Kiyoshi, et al.
(författare)
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Roadmap on photonic, electronic and atomic collision physics : I. Light-matter interaction
- 2019
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Ingår i: Journal of Physics B. - : IOP PUBLISHING LTD. - 0953-4075 .- 1361-6455. ; 52:17
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Tidskriftsartikel (refereegranskat)abstract
- We publish three Roadmaps on photonic, electronic and atomic collision physics in order to celebrate the 60th anniversary of the ICPEAC conference. In Roadmap I, we focus on the light-matter interaction. In this area, studies of ultrafast electronic and molecular dynamics have been rapidly growing, with the advent of new light sources such as attosecond lasers and x-ray free electron lasers. In parallel, experiments with established synchrotron radiation sources and femtosecond lasers using cutting-edge detection schemes are revealing new scientific insights that have never been exploited. Relevant theories are also being rapidly developed. Target samples for photon-impact experiments are expanding from atoms and small molecules to complex systems such as biomolecules, fullerene, clusters and solids. This Roadmap aims to look back along the road, explaining the development of these fields, and look forward, collecting contributions from twenty leading groups from the field.
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