We address the two issues raised by Bayle, Vallisneri, Babak, and Petiteau (in their gr-qc document arXiv:2106.03976) about our matrix formulation of Time-Delay Interferometry (TDI) (arXiv:2105.02054) \cite{TDJ21}. In so doing we explain and quantify our concerns about the results derived by Vallisneri, Bayle, Babak and Petiteau \cite{Vallisneri2020} by applying their data processing technique (named TDI-$\infty$) to the two heterodyne measurements made by a two-arm space-based GW interferometer. First we show that the solutions identified by the TDI-$\infty$ algorithm derived by Vallisneri, Bayle, Babak and Petiteau \cite{Vallisneri2020} {\underbar {do}} depend on the boundary-conditions selected for the two-way Doppler data. We prove this by adopting the (non-physical) boundary conditions used by Vallisneri {\it et al.} and deriving the corresponding analytic expression for a laser-noise-canceling combination. We show it to be characterized by a number of Doppler measurement terms that grows with the observation time and works for any time-dependent time delays. We then prove that, for a constant-arm-length interferometer whose two-way light times are equal to twice and three-times the sampling time, the solutions identified by TDI-$\infty$ are linear combinations of the TDI variable $X$. In the second part of this document we address the concern expressed by Bayle {\it et al.} regarding our matrix formulation of TDI when the two-way light-times are constant but not equal to integer multiples of the sampling time. We mathematically prove the homomorphism between the delay operators and their matrix representation \cite{TDJ21} holds in general. By sequentially applying two order-$m$ Fractional-Delay (FD) Lagrange filters of delays $l_1$, $l_2$ we find its result to be equal to applying an order-$m$ FD Lagrange filter of delay $l_1 + l_2$.

The mathematical concept of q-deformations, in particular the one of qnumbers, is used to study the genetic code(s). After considering two kinds of q-numbers, for comparison, a phenomenological classification scheme of the genetic code together with its numerous minor variants is, first, established. Next, numbers describing the presence of additional amino acids, such as Selenocysteine or/and Pyrrolysine, are also produced. Finally, a minimal number of amino acids, which could fit the small number of them which are thought to have been involved, at the origin of life on Earth, is found. All together, these results constitute our final semi-phenomenological model.

In the framework of the crystal basis model of the genetic code, where each codon is assigned to an irreducible representation of $U_{q \to 0}(sl(2) \oplus sl(2))$, single base mutation matrices are introduced. The strength of the mutation is assumed to depend on the "distance" between the codons. Preliminary general predictions of the model are compared with experimental data, with a satisfactory agreement.

Cellular automata (CA) have been utilized for decades as discrete models of many physical, mathematical, chemical, biological, and computing systems. The most widely known form of CA, the elementary cellular automaton (ECA), has been studied in particular due to its simple form and versatility. However, these dynamic computation systems possess evolutionary rules dependent on a neighborhood of adjacent cells, which limits their sampling radius and the environments that they can be used in. The purpose of this study was to explore the complex nature of one-dimensional CA in configurations other than that of the standard ECA. Namely, "long-distance cellular automata" (LDCA), a construct that had been described in the past, but never studied. I experimented with a class of LDCA that used spaced sample cells unlike ECA, and were described by the notation LDCA-x-y-n, where x and y represented the amount of spacing between the cell and its left and right neighbors, and n denoted the length of the initial tape for tapes of finite size. Some basic characteristics of ECA are explored in this paper, such as seemingly universal behavior, the prevalence of complexity with varying neighborhoods, and qualitative behavior as a function of x and y spacing. Focusing mainly on purely Class 4 behavior in LDCA-1-2, I found that Rule 73 could potentially be Turing universal through the emulation of a cyclic tag system, and revealed a connection between the mathematics of binary trees and Eulerian numbers that might provide insight into unsolved problems in both fields.

This paper presents an extension and an elaboration of the theory of differential similarity, which was originally proposed in arXiv:1401.2411 [cs.LG]. The goal is to develop an algorithm for clustering and coding that combines a geometric model with a probabilistic model in a principled way. For simplicity, the geometric model in the earlier paper was restricted to the three-dimensional case. The present paper removes this restriction, and considers the full $n$-dimensional case. Although the mathematical model is the same, the strategies for computing solutions in the $n$-dimensional case are different, and one of the main purposes of this paper is to develop and analyze these strategies. Another main purpose is to devise techniques for estimating the parameters of the model from sample data, again in $n$ dimensions. We evaluate the solution strategies and the estimation techniques by applying them to two familiar real-world examples: the classical MNIST dataset and the CIFAR-10 dataset.

In the quiet backwaters of cs.CV, cs.LG and stat.ML, a cornucopia of new learning systems is emerging from a primordial soup of mathematics-learning systems with no need for external supervision. To date, little thought has been given to how these self-supervised learners have sprung into being or the principles that govern their continuing diversification. After a period of deliberate study and dispassionate judgement during which each author set their Zoom virtual background to a separate Galapagos island, we now entertain no doubt that each of these learning machines are lineal descendants of some older and generally extinct species. We make five contributions: (1) We gather and catalogue row-major arrays of machine learning specimens, each exhibiting heritable discriminative features; (2) We document a mutation mechanism by which almost imperceptible changes are introduced to the genotype of new systems, but their phenotype (birdsong in the form of tweets and vestigial plumage such as press releases) communicates dramatic changes; (3) We propose a unifying theory of self-supervised machine evolution and compare to other unifying theories on standard unifying theory benchmarks, where we establish a new (and unifying) state of the art; (4) We discuss the importance of digital biodiversity, in light of the endearingly optimistic Paris Agreement.

The problem of the determinism of Quantum Mechanics has been a main one during the 20th century. At the same time, in the context of Logic and Set Theory, the importance of ancient paradoxes as well as the appearance of many new ones, has shed light on and deeply influenced the foundations of Mathematics and somehow of Physics. But, strangely, concerning Physics, a paradox which we call the Memory Paradox has remained yet undiscovered, despite its simplicity and remarkable consequences, mostly in Physics and surprisingly in classical Physics that appear to be non deterministic, contrary to the general belief since Newton, Laplace, etc.. The non determinism of Quantum Physics follows without any supplementary hypothesis. This paper extends a previous one (arXiv: 1203.2945v1 [physics.gen-ph] 13 Mar 2012).

The purpose of this paper is both to provide mathematical reinforcements to the paper [Mecozzi and Bellini : arXiv:1110.1253 [hep-ph]] by taking decoherence into consideration and to present some important problems related. We claim that neutrinos have superluminality as a latent possibility.

We present a new explanation for a quantum eraser. Mathematical description of the traditional explanation needs quantum-superposition states. However, the phenomenon can be explained without quantum-superposition states by introducing unobservable potentials which can be identified as an indefinite metric vector. In addition, a delayed choice experiment can also be explained by the interference between the photons and unobservable potentials, which seems like an unreal long-range correlation beyond the causality.

We apply techniques in natural language processing, computational linguistics, and machine-learning to investigate papers in hep-th and four related sections of the arXiv: hep-ph, hep-lat, gr-qc, and math-ph. All of the titles of papers in each of these sections, from the inception of the arXiv until the end of 2017, are extracted and treated as a corpus which we use to train the neural network Word2Vec. A comparative study of common n-grams, linear syntactical identities, word cloud and word similarities is carried out. We find notable scientific and sociological differences between the fields. In conjunction with support vector machines, we also show that the syntactic structure of the titles in different sub-fields of high energy and mathematical physics are sufficiently different that a neural network can perform a binary classification of formal versus phenomenological sections with 87.1% accuracy, and can perform a finer five-fold classification across all sections with 65.1% accuracy.