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The theory of topological modular forms (TMF) predicts that elliptic genera of physical theories satisfy a certain divisibility property, determined by the theory's gravitational anomaly. In this note we verify this prediction in Duncan's…

High Energy Physics - Theory · Physics 2023-03-28 Jan Albert , Justin Kaidi , Ying-Hsuan Lin

In this short review we outline some recent developments in understanding string orbifolds. In particular, we outline the recent observation that string orbifolds do not precisely describe string propagation on quotient spaces, but rather…

High Energy Physics - Theory · Physics 2007-05-23 Eric Sharpe

Topological data analysis can extract effective information from higher-dimensional data. Its mathematical basis is persistent homology. The persistent homology can calculate topological features at different spatiotemporal scales of the…

Algebraic Topology · Mathematics 2023-09-29 Dinghua Shi , Zhifeng Chen , Chuang Ma , Guanrong Chen

Stick-breaking (SB) processes are often adopted in Bayesian mixture models for generating mixing weights. When covariates influence the sizes of clusters, SB mixtures are particularly convenient as they can leverage their connection to…

Methodology · Statistics 2024-08-16 Akira Horiguchi , Cliburn Chan , Li Ma

We study open and unoriented strings in a Topological Membrane (TM) theory through orbifolds of the bulk 3D space. This is achieved by gauging discrete symmetries of the theory. Open and unoriented strings can be obtained from all possible…

High Energy Physics - Theory · Physics 2014-11-18 P. Castelo Ferreira , Ian I. Kogan

Dealing with datasets of very high dimension is a major challenge in machine learning. In this paper, we consider the problem of feature selection in applications where the memory is not large enough to contain all features. In this…

Machine Learning · Statistics 2017-09-07 Antonio Sutera , Célia Châtel , Gilles Louppe , Louis Wehenkel , Pierre Geurts

Non-Abelian toroidal heterotic orbifolds have received comparatively little attention, mainly because of the significant computational challenges they pose, even at the level of computing their matter spectrum. Similarly, the SO(32)…

High Energy Physics - Theory · Physics 2025-06-11 Miguel Hernandez-Segura , Saul Ramos-Sanchez

When using machine learning for imbalanced binary classification problems, it is common to subsample the majority class to create a (more) balanced training dataset. This biases the model's predictions because the model learns from data…

Machine Learning · Computer Science 2025-11-03 Nathan Phelps , Daniel J. Lizotte , Douglas G. Woolford

In this work, we discovered a fundamental connection between selection for protein stability and emergence of preferred structures of proteins. Using standard exact 3-dimensional lattice model we evolve sequences starting from random ones…

Genomics · Quantitative Biology 2007-05-23 Konstantin B. Zeldovich , Igor N. Berezovsky , Eugene I. Sha

We review recent results on string coupling selection rules for heterotic orbifolds, derived using conformal field theory. Such rules are the first step towards understanding the viability of the recently obtained compactifications with…

High Energy Physics - Theory · Physics 2015-06-18 Susha L. Parameswaran , Ivonne Zavala

We investigate the existence of string vacua with Massive Spectrum Degeneracy Symmetry (MSDS) in Heterotic and Type II orbifold constructions. We present a classification of all possible Z_2^N-orbifolds with MSDS symmetry that can be…

High Energy Physics - Theory · Physics 2009-07-22 Ioannis Florakis , Costas Kounnas

We study topological string theory on elliptically fibered Calabi-Yau threefolds using mirror symmetry. We compute higher genus topological string amplitudes and express these in terms of polynomials of functions constructed from the…

High Energy Physics - Theory · Physics 2013-06-24 Murad Alim , Emanuel Scheidegger

The recent discovery of the Standard Model boson (SMS) and direct searches place new constraints and a new perspective on New Physics models. I mostly review supersymmetric model building, with special emphasizes on predictions of flavor…

High Energy Physics - Phenomenology · Physics 2013-06-13 Emilian Dudas

In this note we observe that, contrary to the usual lore, string orbifolds do not describe strings on quotient spaces, but rather seem to describe strings on objects called quotient stacks, a result that follows from simply unraveling…

High Energy Physics - Theory · Physics 2009-11-07 Eric Sharpe

Real networks exhibit nontrivial topological features such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are…

Social and Information Networks · Computer Science 2014-02-04 Sadegh Motallebi , Sadegh Aliakbary , Jafar Habibi

We construct bosonic and fermionic matrix-vector models which describe orbifolded string worldsheets at a limit in which the dimension of the vector space and the matrix order are taken to infinity. We evaluate tree-level one-loop or…

High Energy Physics - Theory · Physics 2009-11-10 C. -W. H. Lee

A review of orbifold geometry is given, followed by a review of the construction of four-dimensional heterotic string models by compactification on a six-dimensional Z_3 orbifold. Particular attention is given to the details of the…

High Energy Physics - Phenomenology · Physics 2007-05-23 Joel Giedt

We propose examples, which involve orbifolds by elements of the U-duality group, with M-theory moduli fixed at the eleven-dimensional Planck scale. We begin by reviewing asymmetric orbifold constructions in perturbative string theory, which…

High Energy Physics - Theory · Physics 2007-05-23 Michael Dine , Eva Silverstein

We propose a novel linearization of a constituent tree, together with a new locally normalized model. For each split point in a sentence, our model computes the normalizer on all spans ending with that split point, and then predicts a tree…

Computation and Language · Computer Science 2020-05-04 Yang Wei , Yuanbin Wu , Man Lan

In ensemble methods, the outputs of a collection of diverse classifiers are combined in the expectation that the global prediction be more accurate than the individual ones. Heterogeneous ensembles consist of predictors of different types,…

Machine Learning · Computer Science 2019-06-11 Maryam Sabzevari , Gonzalo Martínez-Muñoz , Alberto Suárez