Links to Useful and Interesting Research Papers

Papers of Interest

  • Baydin, A.G.; Heinrich, L.; Bhimji, W.; Gram-Hansen, B.; Louppe, G.; Shao, L.; Prabhat; Cranmer, K.; Wood, F. 2018. Efficient probabilistic inference in the quest for physics beyond the standard model. arXiv:1807.07706 [cs.LG]
  • Cousins, R.D. 2020. What is the likelihood function, and how is it used in particle physics?
    arXiv:2010.00356 [physics.data-an]
  • Ho, C.-L.; Ide, Y.; Konno, N.; Segawa, E.; Takumi, K. 2017. A spectral analysis of discrete-time quantum walks with related to birth and death chains arXiv:1706.01005 [quant-ph]
  • LaMont, C.H.; Wiggins, P.A. 2017. A correspondence between thermodynamics and inference arXiv:1706.01428 [math.ST]
  • Finn, C.; Lizier, J.T. 2018. Pointwise partial information decomposition using the specificity and ambiguity lattices Entropy 2018, 20, 297;
    doi:10.3390/e20040297
  • Galindo, S.; Cervantes-Cota, J.L. 2018. Clifford's attempt to test his gravitation hypothesis. arXiv:1807.09230 [physics.hist-ph]
  • Kewming, K.J.; Shrapnel, S.; White, A.G.; Romero, J. 2019. The advantages of ignorance. [[ https://arxiv.org/abs/1903.09487 | arXiv:1903.09487 [quant-ph] ]
  • Małkiewicz, P.; Miroszewski, A. 2017. Internal clock formulation of quantum mechanics. arXiv:1706.00743 [gr-qc]
  • Melnikov, A.A.; Nautrup, H.P.; Krenn, M.; Dunjko, V.; Tiersch, M.; Zeilinger, A.; Briegel, H.J. 2017. Active learning machine learns to create new quantum experiments. arXiv:1706.00868 [quant-ph]
  • Okawa, H.; Fujisawa, K.; Yamamoto, Y.; Hirai, R.; Yasutake, N.; Nagakura, H.; Yamada, S. 2018. The W4 method: a new multi-dimensional root-finding scheme for nonlinear systems of equations arXiv:1809.04495 [cs.NA]
  • Proietti, M.; Pickston, A.; Graffitti, F.; Barrow, P.; Kundys, D.; Branciard, C.; Ringbauer, M.; Fedrizzi, A. 2019. Experimental rejection of observer-independence in the quantum world. arXiv:1902.05080 [quant-ph]
  • Schindler, J. 2020. Basics of observational entropy
    arXiv:2010.00142 [quant-ph]
  • Smolyaninov, I.I. 2019. Giant Unruh effect in hyperbolic metamaterial waveguides, arXiv:1811.08555 [physics.optics]

Metamaterial Physics

Experimental Design and Active Learning:

  • Bell, A.J. 2003. The co-information lattice. In Proc. 4th International Symposium on Independent Component Analysis and Blind Source Separation (ICA2003?), pp. 921-926. http://www.menem.com/~ilya/digital_library/dependence/bell-02.pdf
  • Cox R.T. 1979. Of inference and inquiry. In: The Maximum Entropy Formalism (eds. R.D. Levine & M. Tribus), MIT Press, Cambridge, pp. 119-167.
  • Díaz-Pachón, D.A.; Sáenz, J.P.; Rao, J.S.; Dazard J.-E. 2020. Mode hunting through active information.
    arXiv:2011.05794 [physics.data-an]
  • Fedorov V.V. 1972. Theory of Optimal Experiments. New York:Academic.
  • Fry R. L. 2002. The engineering of cybernetic systems. In: R.L. Fry (ed.) Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Baltimore MD, USA, AIP Conf. Proc. 617, Melville NY:AIP, pp. 497–528.
  • Hincks, I.; Alexander, T.; Kononenko, M.; Soloway, B.; Cory, D.G. 2018. Hamiltonian Learning with Online Bayesian Experiment Design in Practice, arXiv:1806.02427 [quant-ph]
  • Lindley D.V. 1956. On the measure of information provided by an experiment. Ann. Math. Statist. 27, 986–1005.
  • Loredo T.J. 2003. Bayesian adaptive exploration. In: G. J. Erickson, Y. Zhai (eds.) Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Jackson Hole WY, USA, AIP Conf. Proc. 707, Melville NY:AIP, pp. 330–346.
  • MacKay? D.J.C. 1992. Information-based objective functions for active data selection. Neural Computation, 4(4), 589–603. http://www.mitpressjournals.org/doi/pdf/10.1162/neco.1992.4.4.590
  • Olsson L., Nehaniv C.L., Polani D. 2005. Sensor adaptation and development in robots by entropy maximization of sensory data, In Proceedings of the 6th IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA 2005).
  • Polani D., Kim J.T., and Martinetz T. 2001. An Information-Theoretic Approach for the Quantification of Relevance. In: J. Kelemen and P. Sosik (eds.), Advances in Artificial Life (Proc. 6th European Conference on Artificial Life, Prague, Sept 10-14), LNCS. Springer 2001.
  • Sebastiani P. and Wynn H.P. 1997 Bayesian experimental design and Shannon information. In 1997 Proceedings of the Section on Bayesian Statistical Science, 176-181. American Statistical Association, 1997. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.6037&rep=rep1&type=pdf
  • Sebastiani P. and Wynn H.P. 2000. Maximum entropy sampling and optimal Bayesian experimental design. J. Roy. Stat. Soc. B, 62:145-157, 2000.
  • Shannon C.E., Weaver W. 1949. The Mathematical Theory of Information, University of Illinois Press, Urbana IL.
  • Thrun, S., Burgard, W., Fox, D. Probabilistic Robotics. 2005. MIT Press.
  • Wiener N. 1948. Cybernetics or Control and Communication in the Animal and the Machine, Cambridge:MIT Press.

Nested Sampling

Search

  • Vuculescu, O.; Pedersen, M.K.; Bergenholtz, C.; Sherson, J.F. 2019. Search in a fitness landscape: How to assess the difficulty of a search problem
    arXiv:1912.07954 [physics.data-an]

Quantum Mechanics and Probability Theory:

Dark Matter:

Gravitational Waves:

  • Wang, Y.; Pardo, K.; Chang, T.-C.; Doré, O. 2020.
    Gravitational Wave Detection with Photometric Surveys
    arXiv:2010.02218 [gr-qc]

Technosignatures:

Quantum Computing:

Other Foundations:

Astrobiology:

Life on Earth

SETI:

Fireball Detection:

  • Devillepoix, H.A.R.; Bland, P.A; Sansom, E.K.; Towner, M.C.; Cupák, M.; Howie, R.M.; Hartig, B.A.D.; Jansen-Sturgeon, T.; Cox, M.A. 2018. Observation of metre-scale impactors by the Desert Fireball Network
    arXiv:1808.09195 [astro-ph.EP]

Panspermia:

  • Grishin, E.; Perets, H.B.; Avni Y. 2018. Planet seeding and lithopanspermia through gas-assisted capture of interstellar objects.
    arXiv:1804.09716 [astro-ph.EP]

Planetary Habitability:

Astrobiology: Mars