PYTHaim Group Main Publications & Presentations

Recent Articles

N. J. Costiris, E. Mavrommatis, K. A. Gernoth, and J. W. Clark, "Decoding β-decay systematics: A global statistical model for β^- half-lives", Phys. Rev. C 80, 044332 (2009) [21 pages].
Link Abstract References BibTex Citation

Full text doi:10.1103/PhysRevC.80.044332 Decoding β-decay systematics: A global statistical model for β- half-lives N. J. Costiris and E. Mavrommatis Physics Department, Division of Nuclear Physics & Particle Physics, University of Athens, GR-15771 Athens, Greece K. A. Gernoth Institut für Theoretische Physik, Johannes-Kepler-Universität, A-4040 Linz, Austria and School of Physics & Astronomy, Schuster Building, The University of Manchester, Manchester, M13 9PL, United Kingdom J. W. Clark McDonnell Center for the Space Sciences & Department of Physics, Washington University, St. Louis, Missouri 63130, USA Complexo Interdisciplinar, Centro de Mathemática e Aplicações Fundamentals, University of Lisbon, 1649-003 Lisbon, Portugal and Departamento de Física, Instituto Superior Técnico, Technical University of Lisbon, 1096 Lisbon, Portugal Received 4 May 2008; revised 19 January 2009; published 28 October 2009 Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. Continuing previous studies in which global statistical modeling is pursued within the general framework of machine learning theory, we implement advances in training algorithms designed to improve generalization, in application to the problem of reproducing and predicting the half-lives of nuclear ground states that decay 100% by the β- mode. More specifically, fully connected, multilayer feed-forward artificial neural network models are developed using the Levenberg-Marquardt optimization algorithm together with Bayesian regularization and cross-validation. The predictive performance of models emerging from extensive computer experiments is compared with that of traditional microscopic and phenomenological models as well as with the performance of other learning systems, including earlier neural network models as well as the support vector machines recently applied to the same problem. In discussing the results, emphasis is placed on predictions for nuclei that are far from the stability line, and especially those involved in r-process nucleosynthesis. It is found that the new statistical models can match or even surpass the predictive performance of conventional models for β-decay systematics and accordingly should provide a valuable additional tool for exploring the expanding nuclear landscape. © 2009 The American Physical Society URL: http://link.aps.org/doi/10.1103/PhysRevC.80.044332 DOI: 10.1103/PhysRevC.80.044332 PACS: 23.40.-s, 21.10.Tg, 26.30.-k, 07.05.Mh The Frontiers of Nuclear Science, A Long-Range Plan for the Next Decade, DOE/NSF Nuclear Science Advisory Committee, December 2007; NuPECC Long Range Plan 2004, Perspectives for Nuclear Physics Research in Europe in the Coming Decade and Beyond, edited by M. Harakeh et al., NuPECC Report, NuPECC, April 2004. B. Jonson and K. Riisager, Nucl. Phys. A693, 77 (2001). F. Kappeler, F. K. Thielemann, and M. Wiescher, Annu. Rev. Nucl. Part. Sci. 48, 175 (1998). M. Arnould, S. Goriely, and K. Takahashi, Phys. Rep. 450, 97 (2007). K.-L. Kratz, K. Farouqi, and B. Pfeiffer, Prog. Part. Nucl. Phys. 59, 147 (2007). I. N. Borzov, Nucl. Phys. A777, 645 (2006). K. Takahashi, M. Yamada, and T. Kondoh, At. Data Nucl. Data Tables 12, 101 (1973). H. Nakata, T. Tachibana, and M. Yamada, Nucl. Phys. A625, 521 (1997). E. Caurier, K. Langanke, G. M. Pinedo, and F. Nowaski, Nucl. Phys. A653, 439 (1999). H. Grawe, K. Langanke, and G. M. Pinedo, Rep. Progr. Phys. 70, 1525 (2007). H. V. Klapdor, Prog. Part. Nucl. Phys. 10, 131 (1983); 17, 419 (1986); 32, 261 (1994). H. V. Klapdor, J. Metzinger, and T. Oda, At. Data Nucl. Data Tables 31, 81 (1984). A. Staudt, E. Bender, K. Muto, and H. V. Klapdor, At. Data Nucl. Data Tables 44, 79 (1990). M. Hirsch, A. Staudt, K. Muto, and H. V. Klapdor, At. Data Nucl. Data Tables 53, 165 (1993). H. Homma, E. Bender, M. Hirsch, K. Muto, H. V. Klapdor-Kleingrothaus, and T. Oda, Phys. Rev. C 54, 2972 (1996). J. U. Nabi and H. V. Klapdor, At. Data Nucl. Data Tables 71, 149 (1999); 88, 237 (2004). P. Moller, J. R. Nix, W. D. Myers, and W. J. Swiatecki, At. Data Nucl. Data Tables. 59, 185 (1995). P. Moller and J. Randrup, Nucl. Phys. A514, 1 (1990). P. Moller, J. R. Nix, and K.-L. Kratz, At. Data Nucl. Data Tables 66, 131 (1997). P. Moller, B. Pfeiffer, and K.-L. Kratz, Phys. Rev. C 67, 055802 (2003), http://t16web.lanl.gov/Moller/publications/rspeed2002.html. J. Engel, M. Bender, J. Dobaczewski, W. Nazarewicz, and R. Surman, Phys. Rev. C 60, 014302 (1999). I. N. Borzov and S. Goriely, Phys. Rev. C 62, 035501 (2000). I. N. Borzov, Phys. Rev. C 67, 025802 (2003). T. Niksic, T. Marketin, D. Vretenar, N. Paar, and P. Ring, Phys. Rev. C 71, 014308 (2005). T. Marketin, D. Vretenar, and P. Ring, Phys. Rev. C 75, 024304 (2007). C. M. Bishop, Neural Networks for Pattern Recognition (Clarendon, Oxford, 1995). S. Haykin, Neural Networks: A Comprehensive Foundation (McMillan, New York, 1994). V. Vapnik, The Nature of Statistical Learning Theory (Springer, New York, 1995). N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge University Press, Cambridge, UK, 2002). J. W. Clark, in Scientific Applications of Neural Nets, edited by J. W. Clark, T. Lindenau, and M. L. Ristig (Springer-Verlag, Berlin, 1999), p. 1. K. A. Gernoth, in Scientific Applications of Neural Nets, edited by J. W. Clark, T. Lindenau, and M. L. Ristig (Springer-Verlag, Berlin, 1999), p. 139. E. T. Jaynes, Probability Theory: The Logic of Science (Cambridge University Press, Cambridge, UK, 2003). E. Mavrommatis, A. Dakos, K. A. Gernoth, and J. W. Clark, in Condensed Matter Theories, edited by J. da Providencia and F. B. Malik (Nova Science, New York, 1998), Vol. 13, pp. 423–438. J. W. Clark, E. Mavrommatis, S. Athanassopoulos, A. Dakos, and K. A. Gernoth, in Proc. of the Conf. on Fission Dynamics of Atomic Clusters and Nuclei, edited by D. M. Brink, F. F. Karpechire, F. B. Malik, and J. da Providencia (World Scientific, Singapore, 2001), pp. 76–85, arXiv:nucl-th/0109081. S. Athanassopoulos, E. Mavrommatis, K. A. Gernoth, and J. W. Clark, Nucl. Phys. A743, 222 (2004). S. Athanassopoulos, E. Mavrommatis, K. A. Gernoth, and J. W. Clark, in Advances in Nuclear Physics, Nuclear Astrophysics, Heavy Ions and Related Areas, edited by G. A. Lalazissis and C. C. Moustakidis (HNPS, Thessaloniki, 2005), p. 65 (to be published), arXiv:nucl-th/0511088. H. Li, J. W. Clark, E. Mavrommatis, S. Athanassopoulos, and K. A. Gernoth, in Condensed Matter Theories, edited by J. W. Clark, R. M. Panoff, and H. Li (Nova Science, New York, 2006), Vol. 20, pp. 505–519, arXiv:nucl-th/0506080. J. W. Clark and H. Li, in Recent Progress in Many-Body Theories, edited by S. Hernandez and H. Cataldo (World Scientific, Singapore, 2006), Vol. 8, pp. 47–61. H. Li, Ph.D. thesis, Washington University, 2006. M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design (PWS, Boston, 1996). M. T. Hagan and M. B. Menhaj, IEEE T. Neural Networ. 5, 989 (1994). D. J. C. MacKay, Neural Comput. 4, 415 (1992). F. D. Foresee and M. T. Hagan, in Proceedings of the 1997 IEEE International Joint Conference on Neural Networks (IEEE Press, Piscataway, New Jersey, 1997), Vol. 2, pp. 1930–1935. G. Audi, O. Bersillon, J. Blachot, and A. H. Wapstra, Nucl. Phys. A729, 3 (2003). P. T. Hosmer, H. Schatz, A. Aprahamian, O. Arndt, R. R. C. Clement, A. Estrade, K.-L. Kratz, S. N. Liddick, P. F. Mantica, W. F. Mueller et al., Phys. Rev. Lett. 94, 112501 (2005). D. Nguyen and B. Widrow, in Proceedings of the 1990 IEEE International Joint Conference on Neural Networks (Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1990), Vol. 3, pp. 21–26. N. Costiris, Diploma thesis, University of Athens, Greece, 2006. A. J. Smola and B. Schoelkopf, NeuroCOLT2 Tech. Rep. Series NC2-TR-1998-030, Royal Holloway College, London, 1998. N. Costiris, E. Mavrommatis, K. A. Gernoth, and J. W. Clark, in Advanced in Nuclear Physics, edited by A. Pakou et al. (HNPS, Ioannina, 2008), p. 243, arXiv:0809.0383. NUDAT (2.4) (all errata have been corrected from the original publications), http://www.nndc.bnl.gov. B. Pfeiffer, K.-L. Kratz, and P. Moller, Internal Report, Institut Fur Kernchemie, Mainz, 2003. G. Audi and A. H. Wapstra, Nucl. Phys. A595, 409 (1995). T. Kurtukian-Nieto, J. Benlliure, L. Audouin, F. Becker, B. Blank, I. N. Borzov, E. Casarejos, M. Fernandez-Ordonez, J. Giovinazzo, D. Henzlova et al., Nucl. Phys. A827, 587c (2009).   @Article{PhysRevC.80.044332,       title = {Decoding $\beta{}$-decay systematics: A global statistical model for $\beta{}^{-}$ half-lives},       author = {N. J. Costiris and E. Mavrommatis and K. A. Gernoth and J. W. Clark},       journal = {Phys. Rev.},       volume = {C80},       number = {4},       pages = {044332},       numpages = {21},       year = {2009},       doi = {10.1103/PhysRevC.80.044332},       publisher = {APS}   }


N. J. Costiris, E. Mavrommatis, K. A. Gernoth, J. W. Clark and H. Li, "Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines", in Proc. of the 17^th Symposium of HNPS, Advanced in Nuclear Physics, ed. by A. Pakou et al. (Art of Text & Graphic Arts Co, Thessaloniki, 2008) pp. 243-250.
Link Abstract References BibTex Citation

Full text: arXiv:0809.0383v1 [nucl-th] Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines N. J. Costiris, E. Mavrommatis, K. A. Gernoth, J. W. Clark, H. Li (Submitted on 2 Sep 2008) In this work, the beta-decay halflives problem is dealt as a nonlinear optimization problem, which is resolved in the statistical framework of Machine Learning (LM). Continuing past similar approaches, we have constructed sophisticated Artificial Neural Networks (ANNs) and Support Vector Regression Machines (SVMs) for each class with even-odd character in Z and N to global model the systematics of nuclei that decay 100% by the beta-minus-mode in their ground states. The arising large-scale lifetime calculations generated by both types of machines are discussed and compared with each other, with the available experimental data, with previous results obtained with neural networks, as well as with estimates coming from traditional global nuclear models. Particular attention is paid on the estimates for exotic and halo nuclei and we focus to those nuclides that are involved in the r-process nucleosynthesis. It is found that statistical models based on LM can at least match or even surpass the predictive performance of the best conventional models of beta-decay systematics and can complement the latter. Comments: 8 pages, 1 fiqure, Proceedings of the 17th HNPS Symposium Subjects: Nuclear Theory (nucl-th) Cite as: arXiv:0809.0383v1 [nucl-th] [1] NUPECC Long Range Plan 2004: ÒPerspectives for Nuclear Physics Research in Europe in the Coming Decade and Beyond Ó (April 2004); Opportunities in Nuclear Science: ÒThe Frontiers of Nuclear Science: A Long Range PlanÓ (DOE/NSF, 2007). [2] (a) K. Takahashi, M. Yamada and T. Kondoh, At. Data Nucl. Data Tables 12 (1973) 101; (b) H. Nakata, T. Tachibana and M. Yamada, Nucl. Phys. A625 (1997) 521. [Article] [3] For the latest models of the Klapdor group see: H. Homma, M. Bender, M. Hirsch, K. Muto, H. V. Klapdor and T. Oda, Phys. Rev. C54:6 (1996) 2972; J. U. Nabi and H. V. Klapdor, At. Data Nucl. Data Tables 71 (1999) 149; ibid 88 (2004) 237. [4] P. Möller, J. R. Nix and K.-L. Kratz, At. Data Nucl. Data Tables 66 (1997) 131 and references therein. [5] For the latest calculations see: H. Grawe, K. Langanke and G. Martinez - Pinedo, Rep. Progr. Phys. 70 (2007) 1525. [Article] [6] P. Möller, B. Pfeiffer and K.-L. Kratz, Phys. Rev. C67:5 (2003) 055802. [7] See for ex.: J. Engel et al., Phys. Rev. C60 (1999) 014302; I. N. Borzov, Nucl. Phys. A777 (2006) 645. [Article] [8] T. Niksic, T. Marketin, D. Vretenar, N. Paar and P. Ring, Phys. Rev. C71:1 (2005) 014308; T. Marketin, D. Vretenar and P.Ring, Phys. Rev. C75 (2007) 024304. [Article] [9] J. W. Clark, in Scientific Applications of Neural Nets, J. W. Clark, T. Lindenau and M. L. Ristig (eds) (Springer-Verlag, Berlin, 1999) p1; K. A. Gernoth, ibid, p. 139. [10] N. J. Costiris, E. Mavrommatis, K. A. Gernoth and J. W. Clark, in Proceedings of the 16th Hellenic Symposium of the Hellenic Nuclear Physics Society, (Athens, 2006) 210 (nucl-th/0701096). [11] J. W. Clark and H. Li, Int.J.Mod.Phys. B20 (2006) 5015 (nucl-th/0603037). [Article] [12] E. Mavrommatis, A. Dakos, K. A.Gernoth and J. W. Clark, in Condensed Matter Theories, Vol. 13, edited by J. da Providencia and F. B. Malik (Nova Sciences Publishers, Commack, NY, 1998) 423. [13] S. Haykin, Neural Networks: A Comprehensive Foundation (McMillan, N.Y.) 1993. [14] V. Vapnik, The Nature of Statistical Learning Theory (Springer, N.Y.) 1995. [15] N. Costiris, Diploma Thesis (University of Athens, Greece) 2006. [16] H. Li, Ph-D Thesis (Washington University, USA) 2008. [17] G. Audi, O. Bersillon, J. Blachot and A. H. Wapstra, Nucl. Phys. A729 (2003) 3. [Article] [18] B. Pfeiffer, K. L. Kratz and P. Möller, (Institut Für Kernchemie, Internal Report) 2003.   @INPROCEEDINGS{PYTHaim08,       title = {Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines},       author = {N. J. Costiris and E. Mavrommatis and K. A. Gernoth and J. W. Clark and H. Li},       booktitle = {Proc. of the 17$^{th}$ Symposium of HNPS, Advanced in Nuclear Physics},       editor = {A. Pakou and N. G. Nicolis and G. Pantis and T. S. Kosmas and K. G. Ioannides and X. Aslanoglou},       publisher = {Art of Text & Graphic Arts Co},       adress = {Thessaloniki},       pages = {243-250},       year = {2008},       eprint = {arXiv:0809.0383},   }


N. J. Costiris, E. Mavrommatis, K. A. Gernoth and J. W. Clark, "A Global Model of $\beta^-$-Decay Half-Lives Using Neural Networks", in Proc. of the 16^th Symposium of HNPS, Advanced in Nuclear Physics, ed. by E. Mavrommatis et al. (Art of Text & Graphic Arts Co, Thessaloniki, 2006) pp. 210-217.
Link Abstract References BibTex Citation

Full text: arXiv:nucl-th/0701096 A Global Model of $\beta^-$-Decay Half-Lives Using Neural Networks N. J. Costiris, E. Mavrommatis, K. A. Gernoth, and J. W. Clark, (Submitted on 31 Jan 2007) Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more recently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic theories. We present a global model of $\beta^-$-decay halflives of the class of nuclei that decay 100% by $\beta^-$ mode in their ground states. A fully-connected multilayered feed forward network has been trained using the Levenberg-Marquardt algorithm, Bayesian regularization, and cross-validation. The halflife estimates generated by the model are discussed and compared with the available experimental data, with previous results obtained with neural networks, and with estimates coming from traditional global nuclear models. Predictions of the new neural-network model are given for nuclei far from stability, with particular attention to those involved in r-process nucleosynthesis. This study demonstrates that in the framework of the $\beta^-$-decay problem considered here, global models based on ANNs can at least match the predictive performance of the best conventional global models rooted in nuclear theory. Accordingly, such statistical models can provide a valuable tool for further mapping of the nuclidic chart. Comments: 8 pages, 2 fiqures, Proceedings of the 16th Symposium of the Hellenic Nuclear Physics Society Subjects: Nuclear Theory (nucl-th); Astrophysics (astro-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Data Analysis, Statistics and Probability (physics.data-an) Cite as: arXiv:nucl-th/0701096v1 [1] Opportunities in Nuclear Science: extquotedblleft A Long Range Plan in the Next Decade extquotedblright (DOE/NSF, 2002); extquotedblleft Long Range Plan 2004 extquotedblright (NUPECC, 2004). [2] H. Nakata, T. Tachibana, M. Yamada, Nucl. Phys. A625, 521 (1997). [Article] [3] H. Homma et al., Phys. Rev. C 54, 2972 (1996). [Article] [4] P. Moller, B. Pfeiffer, K.-L. Kratz, Phys. Rev. C 67, 055802 (2003). [Article] [5] I. N. Borzov, S. Goriely, Phys. Rev. C 62 (2000) 035501. [Article] [6] T. Niksivc et al., Phys. Rev. C 71 (2005) 014308. [Article] [7] J. W. Clark, in Scientific Applications of Neural Nets, J. W. Clark, T. Lindenau, M. L. Ristig, eds. (Springer, Berlin, 1999), p. 1; K. A. Gernoth, ibid., p. 139. [8] H. Li, J. W. Clark, E. Mavrommatis, S. Athanassopoulos, K. A. Gernoth, in Condensed Matter Theories, Vol. 20, J. W. Clark, R. M. Panoff, H. Li, eds. (Nova Science Publishers, N.Y. 2006) p. 505, nucl-th/0506080; J. W. Clark, H. Li, in Recent Progress in Many-Body Theories, Vol. 8, S. Hernandez and H. Cataldo, eds. (World Scientific, Singapore, 2006), nucl-th/0603037. [9] N. Costiris, E. Mavrommatis, K. A. Gernoth, J. W. Clark, to be published. [10] E. Mavrommatis, A. Dakos, K. A. Gernoth, J. W. Clark, Condensed Matter Theories, Vol. 13, J. da Providencia, F. B. Malik, eds. (Nova Sciences Publishers, Commack, NY, 1998), p. 423. [11] J. W. Clark, E. Mavrommatis, S. Athanassopoulos, A. Dakos, K. A. Gernoth", Fission Dynamics of Atomic Clusters and Nuclei, D. M. Brink, F. F. Karpechine, F. B. Malik, and J. da Providencia, eds. (World Scientific, Singapore), pp. 76-85. [nucl-th/0109081] [12] H. Demuth, M. Beale, Guide Version 4 (The Mathworks Inc., 2000). [13] G. Audi, O. Bersillon, J. Blachot, A. H. Wapstra, Nucl. Phys. A729, 3 (2003). [Article] [14] P. Moller, J. R. Nix, K.-L. Kratz, At. Data Nucl. Data Tables 66, 131 (1997). [Article] [15] B. Pfeiffer, K.-L. Kratz, P. Moller, Institut für Kernchemie Internal Report (2003). [16] K. Takahashi, M. Yamada, T. Kondoh, At. Data Nucl. Data Tables 12, 101 (1973). [Article] [17] P. Moller, J. R. Nix, W. D. Myers, W. J. Swiatecki, At. Data Nucl. Data Tables 59, 185 (1995). [Article] [18] G. Audi, A. H. Wapstra, Nucl. Phys. A595, 409 (1995). [Article] [19] P. T. Hosmer et al., Phys. Rev. Lett. 94, 112501 (2005) [Article]   @INPROCEEDINGS{PYTHaim06,       title = {A Global Model of $\beta^-$-Decay Half-Lives Using Neural Networks},       author = {N. J. Costiris and E. Mavrommatis and K. A. Gernoth and J. W. Clark},       booktitle = {Proc. of the 16$^{th}$ Symposium of HNPS, Advanced in Nuclear Physics},       editor = {E. Mavrommatis and A. Karabarbounis and N.~F. Sparveris and E. Stiliaris and A. Tsapalis},       publisher = {Art of Text & Graphic Arts Co},       adress = {Thessaloniki},       pages = {210-217},       year = {2006},       eprint = {aarXiv:nucl-th/0701096},   }

Selected Articles

• J. W. Clark, and H. Li, "Application Of Support Vector Machines To Global Prediction Of Nuclear Properties", 13^th International Conference on Recent Progress in Many-Body Theories, (ed/s.) S. Hernandez, and H. Cataldo (World Scientfic, Singapore, 2006), vol. 8, (nucl-th/0603037); ibid Int. J. Mod. Phys. B20 (2006) 5015-5029.


• H. Li, J. W. Clark, E. Mavrommatis, S. Athanassopoulos, and K. A. Gernoth, "Modeling Nuclear Properties with Support Vector Machines", in Condensed Matter Theories, (ed/s.) J. W. Clark, R. M. Panoff, and H. Li (Nova Science Publishers, N.Y., 2006), vol. 20, p. 505, (nucl-th/0506080).


• S. Athanassopoulos, E. Mavrommatis, K. A. Gernoth, and J. W. Clark, "Nuclear mass systematics by complementing the Finite Range Droplet Model with neural networks", in Advances in Nuclear Physics, Nuclear Astrophysics, Heavy Ions and Related Areas, (ed/s.) G. A. Lalazissis, and C. C. Moustakidis (HNPS, Thessaloniki, 2005), p. 65, (nucl-th/0511088).


• S. Athanassopoulos, E. Mavrommatis, K. A. Gernoth, and J. W. Clark, "Nuclear mass systematics using neural networks", Nucl. Phys. A 743 (2004) 222-235.
  Full text doi:10.1016/j.nuclphysa.2004.08.006


• J. W. Clark, E. Mavrommatis, S. Athanassopoulos, A. Dakos, and K. A. Gernoth, "Statistical Modeling of Nuclear Systematics", in Proc. of the Conf. on Fission Dynamics of Atomic Clusters and Nuclei, (ed/s.) D. M. Brink, F. F. Karpechire, F. B. Malik, and J. da Providencia (World Scientific, Singapore, 2001), pp.76-85, (nucl-th/0109081).


• J. W. Clark, in Scientfic Applications of Neural Nets, edited by J. W. Clark, T. Lindenau, and M. L. Ristig (Springer-Verlag, Berlin, 1999), p. 1.


• Klaus A. Gernoth, "Nuclear Physics with Neural Networks", (ed/s.) J. W. Clark, T. Lindenau, and M. L. Ristig, Scientific Applications of Neural Nets, LNP 522 (Springer, Berlin, 1999) 139-169.
  Full text doi:10.1007/BFb0104279


• E. Mavrommatis, A. Dakos, K. A. Gernoth, and J. W. Clark, in Condensed Matter Theories, (ed/s.) J. da Providencia, and F. B. Malik, Commack (Nova Science Publishers, N.Y., 1998), vol. 13, p. 423.

In Prress Articles

Submitted Articles

Posters

• Half-Lives for R-Process Nucleosynthesis Using the ANN Statistical Global Model - 11^th Symposium on Nuclei in the Cosmos (Heidelberg, Germany) 19-23 July 2010. Abstract | Poster (pdf) | Poster (jpg) | Poster (A0 .pptx) | on-line view


• Statistical global modeling of β^- decay halflives systematics using Multilayer Feedforward Neural Networks and Support Vector Machines - 17^th Symbosium of HNPS (Ioannina, Greece) 29-30 May 2008
  Abstract (pdf) | Poster (A3 pdf) | Poster (A3 ps) | Poster (90x120 pptx) | on-line view
  


• International Nuclear Physics Conference 2007, Tokyo, Japan, June 3-8, 2007
  [upper part | bottom part]

Presentations

• 19^th Symbosium of HNPS ( Thessaloniki, Greece) 28 - 29 May 2010
  Abstract | Presentation (pdf) | Presentation (.pptx) | on-line view
• 16^th Symbosium of HNPS (Athens, Greece) 26 - 27 May 2006