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The main question is: why and how can we ever predict based on a finite sample? The question is not answered by statistical learning theory. Here, I suggest that prediction requires belief in "predictability" of the underlying dependence,…

Machine Learning · Computer Science 2022-01-28 Marina Sapir

A range of experimental results point to the existence of a massive neutrino. The recent high precision measurements of the cosmic microwave background and the large scale surveys of galaxies can be used to place an upper bound on this…

Astrophysics · Physics 2009-06-12 C. Zunckel , P. G Ferreira

We give a simple argument to show that no purely metric-based, relativistic formulation of Milgrom's Modified Newtonian Dynamics (MOND) whose energy functional is stable (in the sense of being quadratic in perturbations) can be consistent…

Astrophysics · Physics 2014-10-13 M. E. Soussa , R. P. Woodard

Newtonian limit of Extended Theories of Gravity (in particular, higher--order and scalar--tensor theories) is theoretically discussed taking into account recent observational and experimental results.

General Relativity and Quantum Cosmology · Physics 2007-05-23 Salvatore Capozziello

Like any field of empirical science, AI may be approached axiomatically. We formulate requirements for a general-purpose, human-level AI system in terms of postulates. We review the methodology of deep learning, examining the explicit and…

Artificial Intelligence · Computer Science 2018-06-26 Eray Özkural

We investigate the possibility of discriminating between Modified Newtonian Dynamics (MOND) and Newtonian gravity with dark matter, by studying the vertical dynamics of disk galaxies. We consider models with the same circular velocity in…

Astrophysics · Physics 2009-06-23 Carlo Nipoti , Pasquale Londrillo , HongSheng Zhao , Luca Ciotti

In Logik der Forschung and later works, Karl Popper proposed a set of methodological rules for scientists. Among these were requirements that theories evolve in the direction of increasing content, and that new theories should only be…

History and Philosophy of Physics · Physics 2022-08-30 David Merritt

MOND predicts that the asymptotic gravitational potential of an isolated, bounded (baryonic) mass, M, is phi(r)=(MGa0)^{1/2}ln(r); a0 is the MOND constant. Relativistic MOND theories predict that the lensing effects of M are dictated by…

Cosmology and Nongalactic Astrophysics · Physics 2013-07-26 Mordehai Milgrom

We discuss the criteria that must be satisfied by a well-posed variational principle. We clarify the role of Gibbons-Hawking-York type boundary terms in the actions of higher derivative models of gravity, such as F(R) gravity, and argue…

General Relativity and Quantum Cosmology · Physics 2010-04-28 Ethan Dyer , Kurt Hinterbichler

We present detailed semi-analytical models for the formation of disk galaxies both in a Universe dominated by dark matter (DM), and in one for which the force law is given by modified Newtonian dynamics (MOND). We tune the models to fit the…

Astrophysics · Physics 2009-10-31 Frank C. van den Bosch , Julianne J. Dalcanton

MOND dynamics consists of a modification of the acceleration with respect to the one provided by Newtonian mechanics. In this paper we investigate whether it can be derived from a velocity-dependent deformation of the coordinates of the…

General Relativity and Quantum Cosmology · Physics 2017-02-01 Ennio Gozzi

We use observations related to the variation of fundamental constants, in order to impose constraints on the viable and most used $f(T)$ gravity models. In particular, for the fine-structure constant we use direct measurements obtained by…

General Relativity and Quantum Cosmology · Physics 2017-04-18 Rafael C. Nunes , Alexander Bonilla , Supriya Pan , Emmanuel N. Saridakis

Modified Newtonian dynamics (MOND) is an empirically motivated modification of Newtonian gravity or inertia suggested by Milgrom as an alternative to cosmic dark matter. The basic idea is that at accelerations below a0 ~ 10^{-8} cm/s^2 ~…

Astrophysics · Physics 2009-11-07 Robert H. Sanders , Stacy S. McGaugh

Deep learning models have been the subject of study from various perspectives, for example, their training process, interpretation, generalization error, robustness to adversarial attacks, etc. A trained model is defined by its decision…

Machine Learning · Computer Science 2019-08-09 Roozbeh Yousefzadeh , Dianne P O'Leary

For $1\le p \le \infty$, the Fr\'echet $p$-mean of a probability measure on a metric space is an important notion of central tendency that generalizes the usual notions in the real line of mean ($p=2$) and median ($p=1$). In this work we…

Probability · Mathematics 2025-07-03 Steven N. Evans , Adam Q. Jaffe

A wealth of astronomical data indicate the presence of mass discrepancies in the Universe. The motions observed in a variety of classes of extragalactic systems exceed what can be explained by the mass visible in stars and gas. Either (i)…

Cosmology and Nongalactic Astrophysics · Physics 2014-05-28 Benoit Famaey , Stacy McGaugh

Dark matter is the generally accepted paradigm in astrophysics and cosmology as a solution to the higher rate of rotation in galaxies, among many other reasons. But since there are still some problems encountered by the standard dark matter…

Astrophysics of Galaxies · Physics 2021-02-24 Hanna A. Sabat , Raed Z. Bani-Abdoh , Marwan S. Mousa

Modified Newtonian Dynamics (MoND) is an empirically modification of Newtonian gravity at largest scales in order to explain rotation curves of galaxies, as an alternative to nonbaryonic dark matter. But MoND theories can hardly connect…

Cosmology and Nongalactic Astrophysics · Physics 2010-07-21 Nelson Falcon

The modified Newtonian dynamics (MOND) paradigm is discussed in the context of asymptotic safe gravity. We estimate quantum correction to the logarithmic potential which is well known to account for the constancy of the circular velocity…

General Relativity and Quantum Cosmology · Physics 2022-08-23 Bijan Bagchi , Sauvik Sen

Existing work on understanding deep learning often employs measures that compress all data-dependent information into a few numbers. In this work, we adopt a perspective based on the role of individual examples. We introduce a measure of…

Machine Learning · Computer Science 2021-06-21 Robert J. N. Baldock , Hartmut Maennel , Behnam Neyshabur
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