Retrieving Comparative Arguments using Ensemble Methods and Neural Information Retrieval

In this paper, we present a submission to the Touche lab's Task 2 on Argument Retrieval for Comparative Questions. Our team Katana supplies several approaches based on decision tree ensembles algorithms to rank comparative documents in accordance with their relevance and argumentative support. We use PyTerrier library to apply ensembles models to a ranking problem, considering statistical text features and features based on comparative structures. We also employ large contextualized language modelling techniques, such as BERT, to solve the proposed ranking task. To merge this technique with ranking modelling, we leverage neural ranking library OpenNIR. Our systems substantially outperforming the proposed baseline and scored first in relevance and second in quality according to the official metrics of the competition (for measure NDCG@5 score). Presented models could help to improve the performance of processing comparative queries in information retrieval and dialogue systems

LHCb upgrade software and computing : technical design report

This document reports the Research and Development activities that are carried out in the software and computing domains in view of the upgrade of the LHCb experiment. The implementation of a full software trigger implies major changes in the core software framework, in the event data model, and in the reconstruction algorithms. The increase of the data volumes for both real and simulated datasets requires a corresponding scaling of the distributed computing infrastructure. An implementation plan in both domains is presented, together with a risk assessment analysis

Physics case for an LHCb Upgrade II - Opportunities in flavour physics, and beyond, in the HL-LHC era

The LHCb Upgrade II will fully exploit the flavour-physics opportunities of the HL-LHC, and study additional physics topics that take advantage of the forward acceptance of the LHCb spectrometer. The LHCb Upgrade I will begin operation in 2020. Consolidation will occur, and modest enhancements of the Upgrade I detector will be installed, in Long Shutdown 3 of the LHC (2025) and these are discussed here. The main Upgrade II detector will be installed in long shutdown 4 of the LHC (2030) and will build on the strengths of the current LHCb experiment and the Upgrade I. It will operate at a luminosity up to 2×1034 cm−2s−1, ten times that of the Upgrade I detector. New detector components will improve the intrinsic performance of the experiment in certain key areas. An Expression Of Interest proposing Upgrade II was submitted in February 2017. The physics case for the Upgrade II is presented here in more depth. CP-violating phases will be measured with precisions unattainable at any other envisaged facility. The experiment will probe b → sl+l−and b → dl+l− transitions in both muon and electron decays in modes not accessible at Upgrade I. Minimal flavour violation will be tested with a precision measurement of the ratio of B(B0 → μ+μ−)/B(Bs → μ+μ−). Probing charm CP violation at the 10−5 level may result in its long sought discovery. Major advances in hadron spectroscopy will be possible, which will be powerful probes of low energy QCD. Upgrade II potentially will have the highest sensitivity of all the LHC experiments on the Higgs to charm-quark couplings. Generically, the new physics mass scale probed, for fixed couplings, will almost double compared with the pre-HL-LHC era; this extended reach for flavour physics is similar to that which would be achieved by the HE-LHC proposal for the energy frontier

Particle-identification techniques and performance at LHCb in Run 2

One of the most challenging data analysis tasks of modern High Energy Physics experiments is the identification of particles. In this proceedings we review the new approaches used for particle identification at the LHCb experiment. Machine-Learning based techniques are used to identify the species of charged and neutral particles using several observables obtained by the LHCb sub-detectors. We show the performances of various solutions based on Neural Network and Boosted Decision Tree models

MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering

Knowledge Graphs (KGs) are symbolically structured storages of facts. The KG embedding contains concise data used in NLP tasks requiring implicit information about the real world. Furthermore, the size of KGs that may be useful in actual NLP assignments is enormous, and creating embedding over it has memory cost issues. We represent KG as a 3rd-order binary tensor and move beyond the standard CP decomposition by using a data-specific generalized version of it. The generalization of the standard CP-ALS algorithm allows obtaining optimization gradients without a backpropagation mechanism. It reduces the memory needed in training while providing computational benefits. We propose a MEKER, a memory-efficient KG embedding model, which yields SOTA-comparable performance on link prediction tasks and KG-based Question Answering

Observation of a new Ξb−\Xi_b^- resonance

International audienceFrom samples of pp collision data collected by the LHCb experiment at s=7, 8 and 13 TeV, corresponding to integrated luminosities of 1.0, 2.0 and 1.5  fb-1, respectively, a peak in both the Λb0K- and Ξb0π- invariant mass spectra is observed. In the quark model, radially and orbitally excited Ξb- resonances with quark content bds are expected. Referring to this peak as Ξb(6227)-, the mass and natural width are measured to be mΞb(6227)-=6226.9±2.0±0.3±0.2  MeV/c2 and ΓΞb(6227)-=18.1±5.4±1.8  MeV/c2, where the first uncertainty is statistical, the second is systematic, and the third, on mΞb(6227)-, is due to the knowledge of the Λb0 baryon mass. Relative production rates of the Ξb(6227)-→Λb0K- and Ξb(6227)-→Ξb0π- decays are also reported

Observation of Bs0→D‾∗0ϕB_s^0 \to \overline{D}^{*0} \phi and search for B0→D‾0ϕB^0 \to \overline{D}^0 \phi decays

International audienceThe first observation of the Bs0→D¯*0ϕ decay is reported, with a significance of more than seven standard deviations, from an analysis of pp collision data corresponding to an integrated luminosity of 3  fb-1, collected with the LHCb detector at center-of-mass energies of 7 and 8 TeV. The branching fraction is measured relative to that of the topologically similar decay B0→D¯0π+π- and is found to be B(Bs0→D¯*0ϕ)=(3.7±0.5±0.3±0.2)×10-5, where the first uncertainty is statistical, the second systematic, and the third from the branching fraction of the B0→D¯0π+π- decay. The fraction of longitudinal polarization in this decay is measured to be fL=(73±15±4)%. The most precise determination of the branching fraction for the Bs0→D¯0ϕ decay is also obtained, B(Bs0→D¯0ϕ)=(3.0±0.3±0.2±0.2)×10-5. An upper limit, B(B0→D¯0ϕ)<2.0 (2.3)×10-6 at 90% (95%) confidence level is set. A constraint on the ω-ϕ mixing angle δ is set at |δ|<5.2° (5.5°) at 90% (95%) confidence level