Emptying a Paradox of Ground

Sometimes a fact can play a role in a grounding explanation, but the particular content of that fact make no difference to the explanation—any fact would do in its place. I call these facts vacuous grounds. I show that applying the distinction between-vacuous grounds allows us to give a principled solution to Kit Fine and Stephen Kramer’s paradox of (reflexive) ground. This paradox shows that on minimal assumptions about grounding and minimal assumptions about logic, we can show that grounding is reflexive, contra the intuitive character of grounds. I argue that we should never have accepted that grounding is irreflexive in the first place; the intuitions that support the irreflexive intuition plausibly only require that grounding be non-vacuously irreflexive. Fine and Kramer’s paradox relies, essentially, on a case of vacuous grounding and is thus no problem for this account

A New method for Analysis of Biomolecules Using the BSM-SG Atomic Models

Biomolecules and particularly proteins and DNA exhibit some mysterious features that cannot find satisfactory explanation by quantum mechanical modes of atoms. One of them, known as a Levinthal’s paradox, is the ability to preserve their complex three-dimensional structure in appropriate environments. Another one is that they possess some unknown energy mechanism. The Basic Structures of Matter Supergravitation Unified Theory (BSM-SG) allows uncovering the real physical structures of the elementary particles and their spatial arrangement in atomic nuclei. The resulting physical models of the atoms are characterized by the same interaction energies as the quantum mechanical models, while the structure of the elementary particles influence their spatial arrangement in the nuclei. The resulting atomic models with fully identifiable parameters and angular positions of the quantum orbits permit studying the physical conditions behind the structural and bonding restrictions of the atoms connected in molecules. A new method for a theoretical analysis of biomolecules is proposed. The analysis of a DNA molecule leads to formulation of hypotheses about the energy storage mechanism in DNA and its role in the cell cycle synchronization. This permits shedding a light on the DNA feature known as a C-value paradox. The analysis of a tRNA molecule leads to formulation of a hypothesis about a binary decoding mechanism behind the 20 flavors of the complex aminoacyle-tRNA synthetases - tRNA, known as a paradox

Religion and Political Form: Carl Schmitt’s Genealogy of Politics as Critique of Habermas’s Post-secular Discourse

Jürgen Habermas's post-secular account is rapidly attracting attention in many fields as a theoretical framework through which to reconsider the role of religion in contemporary societies. This work seeks to go beyond Habermas's conceptualisation by placing the post-secular discourse within a broader genealogy of the relationships between space, religion, and politics. Drawing on the work of Carl Schmitt, the aim of this article is to contrast the artificial separation between private and public, religious and secular, state and church, and the logic of inclusion/exclusion on which modernity was established. Revisiting this genealogy is also crucial to illustrating, in light of Schmitt's political theory, the problems underlying Habermas's proposal, emphasising its hidden homogenising and universalist logic in an attempt to offer an alternative reflection on the contribution of religious and cultural pluralism within Western democracies

A Theory Explains Deep Learning

This is our journal for developing Deduction Theory and studying Deep Learning and Artificial intelligence. Deduction Theory is a Theory of Deducing World’s Relativity by Information Coupling and Asymmetry. We focus on information processing, see intelligence as an information structure that relatively close object-oriented, probability-oriented, unsupervised learning, relativity information processing and massive automated information processing. We see deep learning and machine learning as an attempt to make all types of information processing relatively close to probability information processing. We will discuss about how to understand Deep Learning and Artificial intelligence and why Deep Learning is shown better performance than the other methods by metaphysical logic