We consider how microlocal methods developed for tomographic problems can be used to detect singularities of the Lorentzian metric of the Universe using measurements of the Cosmic Microwave Background radiation. The physical model we study is mathematically rigorous but highly idealized.
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48 results for “universal background model”
New method finds ideal circle patterns on spheres.
problem Finding ideal circle patterns on spheres with prescribed curvatures.
method Combinatorial Calabi flow in spherical geometry.
result Existence and convergence of the flow for ideal circle patterns.
Teaches advanced differential topology to master students with minimal background.
problem Teaching complex differential topology concepts to students with limited mathematical background.
method Cut-and-paste procedures, transversality, cobordism rings.
result Topics can be treated with 'bare hands' using simple mathematical tools.
UAED discovers adaptive environments for robust learning.
problem Avoiding spurious correlations in data.
method Unified framework that learns a distribution over data transformations.
result Improves worst-case accuracy on standard benchmarks.
New approach makes adversarial examples less suspicious without changing perceptual salience.
problem Robustness of deep neural networks to unsuspicious adversarial examples.
method Splitting images into foreground and background, allowing larger perturbations in background while maintaining low cognitive salience.
result Dual-perturbation attacks are effective against classifiers robust to conventional attacks and adversarial training yields more robust classifiers.
Improved knowledge graph embedding using taxonomic information.
problem Learning about domains in knowledge graphs using embedding models.
method Minimal modifications to existing knowledge graph completion methods to incorporate taxonomic information.
result Our model is fully expressive, respecting subclass and subproperty information.
Explains biquandle brackets and quivers for a topology talk.
problem None explicitly stated; focuses on background information.
method Review of biquandle concepts and related structures.
result Clarifies understanding of biquandle bracket quivers.
Matrix H-theory models stock market fluctuations using hierarchical multivariate distributions.
problem Understanding collective behavior in stock market fluctuations.
method Matrix H-theory framework for multivariate stochastic processes with hierarchical structure.
result Matrix H-theory effectively describes stock market fluctuations using Meijer G-functions.
Researchers compare two methods for handlebody constructions, finding they are related with a 'background charge'.
problem Comparing two methods for handlebody constructions in finite ribbon categories.
method Admissible skein module construction vs. ansular functor construction.
result An isomorphism between the two constructions is proven, with a 'background charge' that becomes trivial in the unimodular case.
We discuss the structure of "exceptional generalised geometry" (EGG), an extension of Hitchin's generalised geometry that provides a unified geometrical description of backgrounds in eleven-dimensional supergravity. On a d-dimensional background, as first described by Hull, the action of the generalised geometrical O(d…
In the first of these two lectures, I use a comparison to symplectic Khovanov homology to motivate the idea that the Jones polynomial and Khovanov homology of knots can be defined by counting the solutions of certain elliptic partial differential equations in 4 or 5 dimensions. The second lecture is devoted to a descri…
Geometry of geodesicsmath.DG
Lecture notes on geodesics in differential geometry.
problem Understanding geodesics in differential geometry.
method Expository lecture notes with exercises.
result Explains the geometry of geodesics.
Defines natural tensors for submanifolds of pseudo-Riemannian manifolds.
problem Characterizing tensors for submanifolds of pseudo-Riemannian manifolds.
method Constructs geodesic normal coordinates and expresses metric coefficients as polynomials in curvature and second fundamental form derivatives.
result Natural tensors are linear combinations of contractions of curvature and second fundamental form derivatives.
Measurements of cosmic microwave background (CMB) anisotropy are ideal experiments for discovering the non-trivial global topology of the universe. To evaluate the CMB anisotropy in multiply-connected compact cosmological models, one needs to compute the eigenmodes of the Laplace-Beltrami operator. Using the direct bou…
CosmoVAE uses deep learning to fill in missing parts of the cosmic microwave background map.
problem Missing observations in the CMB map due to thermal dust noise.
method Variational autoencoder (VAE) trained with a new loss function.
result CosmoVAE achieves state-of-the-art performance in CMB map inpainting.
Deep neural network improves speaker verification with short utterances.
problem Challenges in speaker recognition with short utterances.
method Proposes deep neural network mapping methods to improve i-vector performance.
result Deep neural network mapping significantly improves speaker verification performance with short utterances.
GDiff tackles blind denoising with Gibbs sampling and Monte Carlo inference.
problem Blind denoising of signals with unknown noise parameters.
method Gibbs Diffusion (GDiff) method that alternates sampling steps from a conditional diffusion model and a Monte Carlo sampler.
result GDiff achieves blind denoising of natural images and cosmic microwave background data.
The paper characterizes the geometry and topology of spin random fields.
problem Understanding the expected geometry and topology of spin random fields.
method Investigating the asymptotic behavior of geometric and topological functionals for spin random fields under scaling assumptions.
result Explicit results for monochromatic fields, showing non-universal asymptotic behavior and new generalized models.
SRTC model for background/foreground separation with missing pixels.
problem Background/foreground separation with missing pixels in videos.
method Smooth robust tensor completion (SRTC) model with tensor proximal alternating minimization (tenPAM).
result Global convergence guarantee for the proposed algorithm.
New energy definition for expanding de Sitter spacetime with umbilic boundaries.
problem Defining energy for spacetimes with expanding de Sitter background and umbilic boundaries.
method Adapting Liu-Yau energy to a quasi-local setting in expanding de Sitter spacetime.
result Positivity of the defined energy for certain values of the cosmological constant.
We classify and construct all the smooth Kaluza-Klein reductions to ten dimensions of the M2- and M5-brane configurations which preserve some of the supersymmetry. In this way we obtain a wealth of new supersymmetric IIA backgrounds describing composite configurations of D-branes, NS-branes and flux/nullbranes; bound s…
New theory maps spin structures on surfaces, generating key elements.
problem Mapping spin structures on surfaces with finite type.
method Introducing P(SL(2,Z)) and its action on Tess^+.
result Three combinatorial elements generate P(SL(2,Z)).
Curved Wiener Space Analysismath.PR
These notes represent a much expanded and updated version of the \textquotedblleft mini course\textquotedblright that the author gave at the ETH (Zürich) and the University of Zürich in February of 1995. The purpose of these notes is to first provide some basic background to Riemannian geometry and stochastic calculus …
Towards robust audio spoofing detection: a detailed comparison of traditional and learned featurescs.SD
This research aims to develop robust audio spoofing detection methods that work across various spoofing techniques.
problem Detecting audio spoofing attacks in speaker verification systems.
method Examined traditional and machine learned audio features for robust spoofing detection.
result Fused models based on both known and machine learned features achieve comparable performance with an EER of 12.
Starting from the recent classification of quotients of Freund--Rubin backgrounds in string theory of the type AdS_{p+1} x S^q by one-parameter subgroups of isometries, we investigate the physical interpretation of the associated quotients by discrete cyclic subgroups. We establish which quotients have well-behaved cau…
Investment models account for both probabilistic and possibilistic risks.
problem Investment and background risks are modeled probabilistically and possibilistically.
method Developed three investment models combining probabilistic and possibilistic risks.
result Approximate calculation formula for optimal investment solutions proved.
A Hawkes process model with a time-varying background rate is developed for analyzing the high-frequency financial data. In our model, the logarithm of the background rate is modeled by a linear model with a relatively large number of variable-width basis functions, and the parameters are estimated by a Bayesian method…
Automated decision making is used routinely throughout our everyday life. Recommender systems decide which jobs, movies, or other user profiles might be interesting to us. Spell checkers help us to make good use of language. Fraud detection systems decide if a credit card transactions should be verified more closely. M…
Background doesn't affect personality predictions in deep networks.
problem Understanding how background images influence personality attribution in deep learning models.
method Explicitly studied the effect of background images on personality prediction in deep residual networks, controlling for confounds.
result Adding background information to input decreases model performance for personality trait prediction.
The paper improves itemset quality assessment by incorporating background knowledge.
problem Assessing the quality of discovered itemsets is challenging due to many patterns being explainable by background knowledge.
method The authors introduce a maximum entropy approach to efficiently infuse additional background knowledge such as row margins, lazarus counts, and bounds of ones.
result More sophisticated models that incorporate background knowledge fit the data better and improve frequency prediction of itemsets.
Contact structures on 3-manifolds are analyzed by decomposing the manifold along convex surfaces. Background results of Giroux, Eliashberg, Colin, and Honda are discussed with an emphasis on examples. Convex decompositions are then used to give a new proof of the Gabai-Eliashberg-Thurston Theorem on the existence of un…
We construct a global geometric model for complex analytic equivariant elliptic cohomology for all compact Lie groups. Cocycles are specified by functions on the space of fields of the two-dimensional sigma model with background gauge fields and supersymmetry. We also consider a theory of free fe…
Generative Adversarial Networks generate PXD background noise efficiently.
problem Efficiently generate statistically independent PXD background noise samples.
method Conditional Generative Adversarial Networks (GANs) with contrastive learning.
result On-demand PXD background generator reduces storage requirements.
HI-SIGMA improves sensitivity in high-dimensional statistical inference with data-driven background models.
problem Performing high-dimensional statistical inference with complex backgrounds in high-energy physics.
method HI-SIGMA uses generative ML models to learn signal and background distributions, incorporating systematic uncertainties.
result HI-SIGMA provides improved sensitivity compared to classifier-based methods.
M. Kontsevich proposed a topological construction for an invariant Z of rational homology 3-spheres using configuration space integrals. G. Kuperberg and D. Thurston proved that Z is a universal real finite type invariant for integral homology spheres in the sense of Ohtsuki, Habiro and Goussarov. We review the Kontsev…
The paper analyzes the score field of diffusion models using Burgers dynamics.
problem Understanding the evolution of score fields in diffusion models.
method Analyzes the score field through Burgers-type evolution law for diffusion models.
result Identifies a universal \( anh\) interfacial term in the score field.
TPM improves medical image segmentation by separating foreground and background.
problem Few-shot medical image segmentation challenges due to background variability.
method Tied Prototype Model (TPM) focusing on foreground, adapting thresholds, and using class priors.
result TPM leads to improved segmentation accuracy compared to ADNet.
We consider bosonic supersymmetric backgrounds of ten-dimensional conformal supergravity. Up to local conformal isometry, we classify the maximally supersymmetric backgrounds, determine their conformal symmetry superalgebras and show how they arise as near-horizon geometries of certain half-BPS backgrounds or as a plan…
The paper examines how background risk affects portfolio selection and optimal reinsurance design.
problem Maximizing the probability of reaching a financial goal in the presence of background risk.
method Quantile formulation method to derive optimal solutions explicitly.
result The presence of background risk does not change the solution shape but alters the parameter values.
Derives path-integrals for superstrings on curved backgrounds using string geometry theory.
problem Calculating path-integrals for superstrings on curved backgrounds.
method Derives path-integrals from string geometry theory by considering fluctuations around string backgrounds.
result Derives path-integrals for perturbative superstrings on all string backgrounds.
The paper presents instructive interdisciplinary applications of constrained mechanics calculus in economics on a level appropriate for the undergraduate physics education. The aim of the paper is: 1. to meet the demand for illustrative examples suitable for presenting the background of the highly expanding research fi…
The -nearest neighbour (-NN) classifier is one of the oldest and most important supervised learning algorithms for classifying datasets. Traditionally the Euclidean norm is used as the distance for the -NN classifier. In this thesis we investigate the use of alternative distances for the -NN classifier. We …
Derives path integrals for perturbative strings on various backgrounds.
problem Calculating path integrals for strings on curved backgrounds.
method Derives path integrals from string geometry theory by considering fluctuations around string backgrounds.
result Derives path integrals of all order perturbative strings on various backgrounds.
The study explores highly supersymmetric backgrounds in 11D supergravity.
problem Understanding and constructing highly supersymmetric backgrounds in 11D supergravity.
method Definition of abstract symbols and a strong version of the Reconstruction Theorem, proposing a strategy to construct backgrounds, and providing an example with detailed computation.
result Bijective correspondence between highly supersymmetric backgrounds and abstract symbols, and a classical supersymmetry gap result.
In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation o…
Study uses GMM-UBM and i-vectors to assess Parkinson's patients via speech, handwriting, and gait.
problem Assessing neurological state of Parkinson's disease patients using speech, handwriting, and gait signals.
method GMM-UBM and i-vectors applied to speech, handwriting, and gait signals.
result Different feature sets from each signal are crucial for assessing Parkinson's patients.
We describe the construction of a Lie superalgebra associated to an arbitrary supersymmetric M-theory background, and discuss some examples. We prove that for backgrounds with more than 24 supercharges, the bosonic subalgebra acts locally transitively. In particular, we prove that backgrounds with more than 24 supersym…
New framework uses background knowledge to speed up causal discovery.
problem Scalable causal discovery for large datasets.
method Utilizes background knowledge during causal discovery process.
result Background knowledge reduces computational requirements and improves structure quality.