Some aspects of radiative corrections and non-decoupling effects of heavy Higgs bosons in two Higgs Doublet Model

The possibility of having relatively large non-decoupling effects of the heavy Higgs particles within the two-Higgs doublet extension of the electroweak standard model is briefly discussed and demonstrated on an example of the one-loop amplitude of the process e^+e^- -> W^+W^-Comment: 6 pages, 3 figures; to appear in the proceedings of the AM2003 conferenc

Punção aspirativa por agulha fina em tumores cervicais

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Low-Energy Thermal Leptogenesis in an Extended NMSSM Model

Thermal leptogenesis in the canonical seesaw model in supersymmetry suffers from the incompatibility of a generic lower bound on the mass scale of the lightest right-handed neutrino and the upper bound on the reheating temperature of the Universe after inflation. This is resolved by adding an extra singlet superfield, with a discrete $Z_2$ symmetry, to the NMSSM (Next to Minimal Supersymmetric Standard Model). This generic mechanism is applicable to any supersymmetric model for lowering the scale of leptogenesis.Comment: 16 pages, revtex, 9 eps figure

The Little Review on Leptogenesis

This is a brief review on the scenario of baryogenesis through leptogenesis. Leptogenesis is an appealing scenario that may relate the observed baryon asymmetry in the Universe to the low-energy neutrino data. In this review talk, particular emphasis is put on recent developments on the field, such as the flavourdynamics of leptogenesis and resonant leptogenesis near the electroweak phase transition. It is illustrated how these recent developments enable the modelling of phenomenologically predictive scenarios that can directly be tested at the LHC and indirectly in low-energy experiments of lepton-number and lepton-flavour violation.Comment: 15 pages, based on a plenary presentation given at the DISCRETE'08 Symposium, 11-16 December 2008, Valencia, Spai

A common framework for learning causality

[EN] Causality is a fundamental part of reasoning to model the physics of an application domain, to understand the behaviour of an agent or to identify the relationship between two entities. Causality occurs when an action is taken and may also occur when two happenings come undeniably together. The study of causal inference aims at uncovering causal dependencies among observed data and to come up with automated methods to find such dependencies. While there exist a broad range of principles and approaches involved in causal inference, in this position paper we argue that it is possible to unify different causality views under a common framework of symbolic learning.This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. Diego Aineto is partially supported by the FPU16/03184 and Sergio Jimenez by the RYC15/18009, both programs funded by the Spanish government.Onaindia De La Rivaherrera, E.; Aineto, D.; Jiménez-Celorrio, S. (2018). A common framework for learning causality. Progress in Artificial Intelligence. 7(4):351-357. https://doi.org/10.1007/s13748-018-0151-yS35135774Aineto, D., Jiménez, S., Onaindia, E.: Learning STRIPS action models with classical planning. In: International Conference on Automated Planning and Scheduling, ICAPS-18 (2018)Amir, E., Chang, A.: Learning partially observable deterministic action models. J. Artif. Intell. Res. 33, 349–402 (2008)Asai, M., Fukunaga, A.: Classical planning in deep latent space: bridging the subsymbolic–symbolic boundary. In: National Conference on Artificial Intelligence, AAAI-18 (2018)Cresswell, S.N., McCluskey, T.L., West, M.M.: Acquiring planning domain models using LOCM. Knowl. Eng. Rev. 28(02), 195–213 (2013)Ebert-Uphoff, I.: Two applications of causal discovery in climate science. In: Workshop Case Studies of Causal Discovery with Model Search (2013)Ebert-Uphoff, I., Deng, Y.: Causal discovery from spatio-temporal data with applications to climate science. In: 13th International Conference on Machine Learning and Applications, ICMLA 2014, Detroit, MI, USA, 3–6 December 2014, pp. 606–613 (2014)Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N., Turner, H.: Nonmonotonic causal theories. Artif. Intell. 153(1–2), 49–104 (2004)Halpern, J.Y., Pearl, J.: Causes and explanations: a structural-model approach. Part I: Causes. Br. J. Philos. Sci. 56(4), 843–887 (2005)Heckerman, D., Meek, C., Cooper, G.: A Bayesian approach to causal discovery. In: Jain, L.C., Holmes, D.E. (eds.) Innovations in Machine Learning. Theory and Applications, Studies in Fuzziness and Soft Computing, chapter 1, pp. 1–28. Springer, Berlin (2006)Li, J., Le, T.D., Liu, L., Liu, J., Jin, Z., Sun, B.-Y., Ma, S.: From observational studies to causal rule mining. ACM TIST 7(2), 14:1–14:27 (2016)Malinsky, D., Danks, D.: Causal discovery algorithms: a practical guide. Philos. Compass 13, e12470 (2018)McCain, N., Turner, H.: Causal theories of action and change. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Innovative Applications of Artificial Intelligence Conference, AAAI 97, IAAI 97, 27–31 July 1997, Providence, Rhode Island, pp. 460–465 (1997)McCarthy, J.: Epistemological problems of artificial intelligence. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, MA, USA, 22–25 August 1977, pp. 1038–1044 (1977)McCarthy, J., Hayes, P.: Some philosophical problems from the standpoint of artificial intelligence. Mach. Intell. 4, 463–502 (1969)Pearl, J.: Reasoning with cause and effect. AI Mag. 23(1), 95–112 (2002)Pearl, J.: Causality: Models, Reasoning and Inference, 2nd edn. Cambridge University Press, Cambridge (2009)Spirtes, C.G.P., Scheines, R.: Causation, Prediction and Search, 2nd edn. The MIT Press, Cambridge (2001)Spirtes, P., Zhang, K.: Causal discovery and inference: concepts and recent methodological advances. Appl. Inform. 3, 3 (2016)Thielscher, M.: Ramification and causality. Artif. Intell. 89(1–2), 317–364 (1997)Triantafillou, S., Tsamardinos, I.: Constraint-based causal discovery from multiple interventions over overlapping variable sets. J. Mach. Learn. Res. 16, 2147–2205 (2015)Yang, Q., Kangheng, W., Jiang, Y.: Learning action models from plan examples using weighted MAX-SAT. Artif. Intell. 171(2–3), 107–143 (2007)Zhuo, H.H., Kambhampati, S: Action-model acquisition from noisy plan traces. In: International Joint Conference on Artificial Intelligence, IJCAI-13, pp. 2444–2450. AAAI Press (2013

Proposal for generalised Supersymmetry Les Houches Accord for see-saw models and PDG numbering scheme

The SUSY Les Houches Accord (SLHA) 2 extended the first SLHA to include various generalisations of the Minimal Supersymmetric Standard Model (MSSM) as well as its simplest next-to-minimal version. Here, we propose further extensions to it, to include the most general and well-established see-saw descriptions (types I/II/III, inverse, and linear) in both an effective and a simple gauged extension of the MSSM framework. In addition, we generalise the PDG numbering scheme to reflect the properties of the particles.Comment: 44 pages. Changed titl

Non-standard interactions versus non-unitary lepton flavor mixing at a neutrino factory

The impact of heavy mediators on neutrino oscillations is typically described by non-standard four-fermion interactions (NSIs) or non-unitarity (NU). We focus on leptonic dimension-six effective operators which do not produce charged lepton flavor violation. These operators lead to particular correlations among neutrino production, propagation, and detection non-standard effects. We point out that these NSIs and NU phenomenologically lead, in fact, to very similar effects for a neutrino factory, for completely different fundamental reasons. We discuss how the parameters and probabilities are related in this case, and compare the sensitivities. We demonstrate that the NSIs and NU can, in principle, be distinguished for large enough effects at the example of non-standard effects in the $\mu$-$\tau$-sector, which basically corresponds to differentiating between scalars and fermions as heavy mediators as leading order effect. However, we find that a near detector at superbeams could provide very synergistic information, since the correlation between source and matter NSIs is broken for hadronic neutrino production, while NU is a fundamental effect present at any experiment.Comment: 32 pages, 5 figures. Final version published in JHEP. v3: Typo in Eq. (27) correcte

Constraining Non-Standard Interactions of the Neutrino with Borexino

We use the Borexino 153.6 ton.year data to place constraints on non-standard neutrino-electron interactions, taking into account the uncertainty in the 7Be solar neutrino flux, and backgrounds due to 85Kr and 210Bi beta-decay. We find that the bounds are comparable to existing bounds from all other experiments. Further improvement can be expected in Phase II of Borexino due to the reduction in the 85Kr background.Comment: 21 pages, 16 pdf figures, 2 tables. Analysis updated including the uncertainty in sin^2\theta_{23}. Accepted in JHE

Distribution of Cortical Endoplasmic Reticulum Determines Positioning of Endocytic Events in Yeast Plasma Membrane

In many eukaryotes, a significant part of the plasma membrane is closely associated with the dynamic meshwork of cortical endoplasmic reticulum (cortical ER). We mapped temporal variations in the local coverage of the yeast plasma membrane with cortical ER pattern and identified micron-sized plasma membrane domains clearly different in cortical ER persistence. We show that clathrin-mediated endocytosis is initiated outside the cortical ER-covered plasma membrane zones. These cortical ER-covered zones are highly dynamic but do not overlap with the immobile and also endocytosis-inactive membrane compartment of Can1 (MCC) and the subjacent eisosomes. The eisosomal component Pil1 is shown to regulate the distribution of cortical ER and thus the accessibility of the plasma membrane for endocytosis