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We introduce new methods for phylogenetic tree quartet construction by using machine learning to optimize the power of phylogenetic invariants. Phylogenetic invariants are polynomials in the joint probabilities which vanish under a model of…

Populations and Evolution · Quantitative Biology 2007-05-23 Nicholas Eriksson , Yuan Yao

There is good support for the embedding of the Standard Model fermions in the chiral 16 representation of SO(10). Such an embedding is provided by the realistic free fermionic heterotic-string models. In this talk we demonstrate the…

High Energy Physics - Theory · Physics 2017-08-23 A. E. Faraggi , R. Garavuso , J. M. Isidro

In recent years, control methods based on different optimization techniques have shed light on the possibilities of processing information in many quantum systems. When exploring the transmission of quantum states, faster transmission times…

Quantum Physics · Physics 2026-01-13 Sofía Perón Santana , Ariel Fiuri , Martín Domínguez , Omar Osenda

Generating stable molecular conformations typically forces a tradeoff between the physical realism of energy-based relaxation and the sampling efficiency of data-driven generative models. While machine learning force fields (MLFFs) can…

In this paper, we employ genetic algorithms to explore the landscape of type IIB flux vacua. We show that genetic algorithms can efficiently scan the landscape for viable solutions satisfying various criteria. More specifically, we consider…

High Energy Physics - Theory · Physics 2020-01-08 Alex Cole , Andreas Schachner , Gary Shiu

We discuss the use of gauge fields to stabilize complex structure moduli in Calabi-Yau three-fold compactifications of heterotic string and M-theory. The requirement that the gauge fields in such models preserve supersymmetry leads to a…

High Energy Physics - Theory · Physics 2013-04-10 Lara B. Anderson , James Gray , Andre Lukas , Burt Ovrut

We construct a supersymmetric standard model in the context of the Z_{12-I} orbifold compactification of the E_8 x E_8' heterotic string theory. The gauge group is SU(3)_c x SU(2)_L x U(1)_Y x U(1)^4 x [SO(10) x U(1)^3]' with sin^2\theta_W…

High Energy Physics - Theory · Physics 2007-11-01 Jihn E. Kim , Ji-Hun Kim , Bumseok Kyae

The evidence for neutrino masses in atmospheric and solar neutrino experiments provides further support for the embedding of the Standard Model fermions in the chiral 16 SO(10) representation. Such an embedding is afforded by the realistic…

High Energy Physics - Theory · Physics 2011-07-19 Alon E. Faraggi , Richard Garavuso , Jose M. Isidro

This article is devoted to an overview of superstring perturbation theory from the point of view of super Riemann surfaces. We aim to elucidate some of the subtleties of superstring perturbation that caused difficulty in the early…

High Energy Physics - Theory · Physics 2016-07-26 Edward Witten

We search for the three-generation standard-like and/or Pati-Salam models from the $SO(32)$ heterotic string theory on smooth, quotient complete intersection Calabi-Yau threefolds with multiple line bundles, each with structure group…

High Energy Physics - Theory · Physics 2018-05-10 Hajime Otsuka

In this paper we investigate a neural network model in which weights between computational nodes are modified according to a local learning rule. To determine whether local learning rules are sufficient for learning, we encode the network…

Neural and Evolutionary Computing · Computer Science 2025-12-08 Jonathan Baxter

We explore the phenomenology of supersymmetric SO(10) grand unified theories with gauge mediated supersymmetry breaking. We show that if SO(10) breaking proceeds through intermediate left-right symmetric gauge groups which are broken at the…

High Energy Physics - Phenomenology · Physics 2009-10-31 M. Frank , H. Hamidian , K. Puolamaki

Techniques are presented for computing the cohomology of stable, holomorphic vector bundles over elliptically fibered Calabi-Yau threefolds. These cohomology groups explicitly determine the spectrum of the low energy, four-dimensional…

High Energy Physics - Theory · Physics 2008-11-26 Ron Donagi , Yang-Hui He , Burt A. Ovrut , Rene Reinbacher

In this talk I summarize published work on a systematic operator analysis for fermion masses in a class of effective supersymmetric SO(10) GUTs\cite{adhrs}~\footnote{This work is in collaboration with G. Anderson, S. Dimopoulos, L.J. Hall,…

High Energy Physics - Phenomenology · Physics 2016-09-01 Stuart Raby

Recently it was proposed that the ten dimensional tachyonic superstring vacua may serve as good starting points for the construction of viable phenomenological models. Such phenomenologically viable models enlarge the space of possible…

High Energy Physics - Theory · Physics 2020-04-29 Alon E. Faraggi , Viktor G. Matyas , Benjamin Percival

This paper presents a challenge to the community: Generative adversarial networks (GANs) can perfectly align independent English word embeddings induced using the same algorithm, based on distributional information alone; but fails to do…

Computation and Language · Computer Science 2018-09-05 Mareike Hartmann , Yova Kementchedjhieva , Anders Søgaard

Supersymmetric (SUSY) grand unified theories (GUTs) appear to be best motivated for understanding strong, weak and electromagnetic interactions of nature. We briefly review emergence of new formulas for running fermion masses valid in…

High Energy Physics - Phenomenology · Physics 2020-10-20 Mina Ketan Parida , Riyanka Samantaray

Supersymmetric heterotic string models, built from a stable holomorphic vector bundle $V$ on a Calabi-Yau threefold $X$, usually come with many vector bundle moduli whose stabilisation is a difficult and complex task. It is therefore of…

High Energy Physics - Theory · Physics 2015-06-03 Gottfried Curio

We examine several issues pertaining to statistical predictivity of the string theory landscape for weak scale supersymmetry (SUSY). We work within a predictive landscape wherein super-renormalizable terms scan while renormalizable terms do…

High Energy Physics - Phenomenology · Physics 2020-10-21 Howard Baer , Vernon Barger , Shadman Salam , Dibyashree Sengupta

Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and mode collapse still persist. In this paper,…

Machine Learning · Computer Science 2019-03-13 Thang Doan , Joao Monteiro , Isabela Albuquerque , Bogdan Mazoure , Audrey Durand , Joelle Pineau , R Devon Hjelm
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