In this paper, we initiate the study of a parametrised version of Rieffel's strict deformation quantization. We apply it to give a classification of noncommutative principal torus bundles, in terms of parametrised strict deformation quantization of ordinary principal torus bundles. The paper also contains a putative de…
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Develops parametrised Poincaré duality for equivariant fixed points.
problem Understanding equivariant fixed points in non-presentable settings.
method Introduces parametrised Poincaré duality in parametrised higher category theory, proving basechange results.
result Generalises Cnossen's twisted ambidexterity to non-presentable settings and applies to isotropy separation methods.
In this paper, we use the parametrised strict deformation quantization of C*-bundles obtained in a previous paper, and give more examples and applications of this theory. In particular, it is used here to classify H_3-twisted noncommutative torus bundles over a locally compact space. This is extended to the case of gen…
Extended mean-field games with multi-dimensional singular controls and non-linear jump impactmath.OC
New framework for analyzing games with multi-dimensional singular controls and non-linear jumps.
problem Analyzing games with multi-dimensional singular controls and non-linear jump impacts.
method Probabilistic framework with novel class of MFGs (MFGs of parametrisations).
result Existence of equilibria and equivalence with MFGs of singular controls.
Combining data assimilation and machine learning to infer unresolved scale parametrisationphysics.comp-ph
Combines ML and DA to infer unresolved scale parametrisation from noisy data.
problem Training ML-based parametrisations from realistic, noisy and sparse observations.
method Two-step process: DA for state estimation, ML for model error prediction.
result Hybrid model produces better forecasts and attractor representation.
A new approach to unsupervised learning using recognition-parametrised models.
problem Discovering meaningful latent structure in observational data.
method Recognition-Parametrised Model (RPM) combining parametric and non-parametric components.
result Effective learning of latent structure without explicit generative models.
New method for handling multi-dimensional singular controls with jump costs in mean-field problems.
problem Handling jump costs in multi-dimensional singular controls.
method Introducing two-layer parametrisations to interpolate jumps on both distributional and pathwise levels.
result Derivation of a DPP and characterisation of the value function as a minimal super-solution to a quasi-variational inequality.
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisationstat.ML
The paper shows how label noise in training can lead to solutions that solve a Lasso program.
problem Understanding the implicit bias of training algorithms in overparametrised models.
method Analyzing the continuous time version of the training dynamics of a quadratically parametrised model.
result The stochastic flow implicitly solves a Lasso program, providing convergence guarantees and support recovery conditions.
We show that, for certain families of diffeomorphisms of high-dimensional spheres, the commutator of the Dehn twist along the zero-section of with the family of pullbacks gives a noncontractible family of compactly-supported symplectomorphisms. In particular, we find example…
Ultraknots and limit knotsmath.GT
The abstract proves every knot type can be parametrized by smooth functions and studies limit knot types.
problem Understanding all knot types and their parametrizations.
method Proving every knot type can be represented by smooth functions and studying limit knot types.
result Every knot type can be parametrized by smooth functions and limit knot types exist.
We characterise completely when limit sets, as parametrised by Cannon-Thurston maps, move discontinuously for a sequence of algebraically convergent quasi-Fuchsian groups.
In this paper we are dealing with mean curvature flow with surgeries of two-convex hypersurfaces. The main focus is to expand on the discussion in Section of Mean Curvature Flow with Surgeries of Two-Convex Hypersurfaces by Huisken and Sinestrari. Firstly we wish to establish how the neck detection lemma allows us …
We introduce a new parameter to measure the inhomogeneity of training datasets.
problem The need for non-stationary models in supervised learning.
method We introduce a new parameter, the inhomogeneity parameter, to measure the inhomogeneity of training datasets.
result A training set with a non-zero inhomogeneity parameter requires a non-stationary model for accurate predictions.
We present a procedure which allows one to integrate explicitly the class of checkerboard IC-nets which has recently been introduced as a generalisation of incircular (IC) nets. The latter class of privileged congruences of lines in the plane is known to admit a great variety of geometric properties which are also pres…
Researchers extend parametrization of Margulis spacetimes using strip deformations.
problem Parametrize Margulis spacetimes with decorated horoballs.
method Use strip deformations to parametrize complete finite-area hyperbolic surfaces with spikes decorated with horoballs.
result Generalized parametrization of Margulis spacetimes with photons.
The study characterizes quasiperiodic surfaces in pseudo-hyperbolic spaces with curvature conditions.
problem Characterizing quasiperiodic surfaces in pseudo-hyperbolic spaces.
method Curvature conditions, Gromov hyperbolicity, conformal hyperbolicity.
result Limit curves of quasiperiodic surfaces in the Einstein Universe have canonical quasisymmetric parametrizations.
Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study. Such different models often provide qualitatively concordant predictions over specific parametrisations, but it is generally unclear whether model predictions are quantitatively in agreement, …
Proposes a new Gaussian factor for probabilistic inference with degenerate settings.
problem Handling linear dependencies among random variables in Gaussian networks.
method Introduces a parametrised factor that relaxes the positive-definite constraint of the covariance matrix.
result Accurately accommodates degeneracies in probabilistic inference without significant computational overhead.
Study on deformations of Einstein and nearly G2 structures in 3-Sasaki manifolds.
problem Deformation theory of Einstein and nearly G2 structures in 3-Sasaki manifolds.
method Systematic study of deformation theory, focusing on infinitesimal deformations and their parametrization via eigenfunctions of the basic Laplacian.
result Infinitesimal Einstein deformations of coincide with infinitesimal deformations of .
In this article we introduce a definition for the moduli space of equivariant minimal immersions of the Poincaré disc into a non-compact symmetric space, where the equivariance is with respect to representations of the fundamental group of a compact Riemann surface of genus at least two. We then study this moduli space…
For a given bundle over a manifold, configuration-section spaces on parametrise finite subsets equipped with a section of defined on , with prescribed "charge" in a neighbourhood of the points . These spaces may be interpreted physically as spaces of fiel…
Study computability of real numbers from group properties.
problem Computability of real numbers from group properties.
method Analyzing -Betti numbers and -torsion of groups.
result Real numbers as -Betti numbers or -torsion are computable.
By studying the Higgs bundle equations with the gauge group replaced by the group of symplectic diffeomorphisms of the 2-sphere we encounter the notion of a folded hyperkaehler 4-manifold and conjecture the existence of a family of such metrics parametrised by an infinite-dimensional analogue of Teichmueller space.
We give sufficient conditions for a parametrised family of probability measures on a Riemannian manifold with boundary to be represented by random maps of class . The conditions allow for the probability densities to approach zero towards the boundary of the manifold. We also formulate two obstructions to regular …
New unknots with geometric constraints exist, proving a long-standing conjecture.
problem Existence of distinct isotopy classes of physical unknots with geometric constraints.
method Parametrised thickness and geometric thresholds to fragment isotopy classes.
result Existence of gordian unknots with prescribed geometric constraints.
Convolutional Neural Processes improve data efficiency in neural processes.
problem Improving data efficiency in neural processes for small datasets.
method Convolutional Neural Processes (ConvNPs) improve data efficiency by leveraging translation equivariance and convolutional neural networks.
result ConvNPs enhance the performance of neural processes in small-data problems.
We re-examine classical mechanics with both commuting and anticommuting degrees of freedom. We do this by defining the phase dynamics of a general Lagrangian system as an implicit differential equation in the spirit of Tulczyjew. Rather than parametrising our basic degrees of freedom by a specified Grassmann algebra, w…
Universal geometry organizes heterotic moduli spaces.
problem Understanding the structure of heterotic compactifications.
method Fibering compactification data over moduli space and analyzing universal curvatures.
result Deformations are components of universal curvatures, incorporating corrections.
Paper analyzes convergence of dynamic policy gradient for MDPs, improving performance in finite-time problems.
problem Optimal policies in finite-time MDPs are not stationary and require epoch-specific training.
method Introduces dynamic policy gradient combining dynamic programming and policy gradient, analyzes convergence for softmax parametrisation.
result Dynamic policy gradient training exploits finite-time structure, leading to better convergence bounds.
New approach predicts under latent shifts using high-dimensional images.
problem Prediction under latent subgroup shifts with high-dimensional observations.
method Recognition-parametrised model (RPM) for identifying causal latent structure.
result Successfully adapts predictions for high-dimensional image data.
New CMC surfaces with dihedral symmetry constructed from Darboux transforms.
problem Constructing closed CMC surfaces with specific symmetries.
method Using Darboux transforms on multiple covers to create new CMC surfaces.
result Explicit parametrisations of new closed CMC surfaces with dihedral symmetry.
New discrete models for constant mean curvature surfaces and tori.
problem Creating discrete models for constant mean curvature surfaces and tori.
method Integrable theory of discrete polarised curves and Darboux transforms.
result Closed-form discrete parametrisations of discrete isothermic cylinders, discrete constant mean curvature cylinders, and discrete isothermic tori.
Rolling two hyperboloid surfaces is described using a Monge normal form.
problem Describing the rolling motion of hyperboloid surfaces.
method Parametrization of using unimodular fractional linear transformations.
result Found a Monge normal form for the rolling of hyperboloid surfaces.
The effect of data encoding on the expressive power of variational quantum machine learning modelsquant-ph
Quantum models can approximate any function if data encoding allows for a rich enough frequency spectrum.
problem Theoretical properties of quantum machine learning models, particularly their expressive power.
method Investigated how data encoding affects the expressive power of parametrized quantum circuits.
result Quantum models can access increasingly rich frequency spectra by repeating data encoding gates, potentially making them universal function approximators.
In this article we investigate the dynamics of special solutions to the surface diffusion flow of idealised ribbons. This equation reduces to studying the curve diffusion flow for the profile curve of the ribbon. We provide: (1) a complete classification of stationary solutions; (2) qualitative results on shrinkers, tr…
We study the degree of polynomial representations of knots. We give the lexicographic degree of all two-bridge knots with 11 or fewer crossings. First, we estimate the total degree of a lexicographic parametrisation of such a knot. This allows us to transform this problem into a study of real algebraic trigonal plane c…
Conservation laws vanishing along characteristic directions of a given system of PDEs are known as characteristic conservation laws, or characteristic integrals. In 2D, they play an important role in the theory of Darboux-integrable equations. In this paper we discuss characteristic integrals in 3D and demonstrate that…
In this note, we extend the theory of Chern-Cheeger-Simons to construct canonical invariants for a one-parameter family of flat connections on a smooth manifold. These invariants lie in degrees -cohomology with $\C/\Z$-cohomology, for . Furthermore, they are shown to be rigid in a variation of paths (p…
Families of smooth closed oriented 4-manifolds with a complex spin structure are studied by means of a family version of the Bauer--Furuta invariants in the context of parametrised stable homotopy theory, leading to a definition of characteristic cohomotopy classes on Thom spectra associated to the classifying spaces o…
New method for foliated bundles using holonomy groupoids.
problem Constructing holonomy groupoids for foliated bundles.
method Partial connection on diffeological principal bundle of germs.
result New hierarchy of holonomy groupoids for foliated bundles.
Deep model predicts shapes of curves with multiple covariates.
problem Predicting shapes of planar curves with various covariates.
method Deep learning model using complex-valued functions, conditional covariance smoother with modality-specific encoders.
result Model accurately predicts shapes of curves with multimodal covariates.
We classify the dispersive Poisson brackets with one dependent variable and two independent variables, with leading order of hydrodynamic type, up to Miura transformations. We show that, in contrast to the case of a single independent variable for which a well known triviality result exists, the Miura equivalence class…
Study strip deformations of hyperbolic polygons with decorated vertices.
problem Understanding deformations of hyperbolic polygons with decorated vertices.
method Analyzing strip deformations of ideal hyperbolic polygons with horoballs.
result Arc complexes parameterize uniformly lengthening deformations.
We obtain the Weierstrass-Enneper representation for maximal graphs(whose Gauss map is one-one) in Lorentz-Minkowski space. For this we use the method of Barbishov and Chernikov, which they have used to find the solutions of Born-Infeld equation in hodographic coordinates. We could use their method in our case, because…
New method linearizes Darboux transformations of discrete curves.
problem Linearizing Darboux transformations of discrete curves.
method Expressing Darboux transformations as parallel sections of discrete connections in quaternionic formalism.
result Closed-form discrete parametrisations of all Darboux transforms and bicycle correspondences.
In this paper we investigate the moduli space of parabolic Higgs bundles over a punctured Riemann surface with varying weights at the punctures. We show that the harmonic metric depends analytically on the weights and the stable Higgs bundle. This gives a Higgs bundle generalisation of a theorem of McOwen on the existe…
Novel parametrized graph shift operators improve graph neural network performance.
problem Improving graph neural network performance on various datasets.
method Proposed a novel parametrized graph shift operator (PGSO) that optimizes parameters during training.
result PGSO improves accuracy in node and graph classification tasks on real-world datasets.
We show how a suitably twisted Spin-cobordism spectrum connects to the question of existence of metrics of positive scalar curvature on closed, smooth manifolds by building on fundamental work of Gromov, Lawson, Rosenberg, Stolz and others. We then investigate this parametrised spectrum, compute its -cohomology …