The volume density of a hyperbolic link is defined as the ratio of hyperbolic volume to crossing number. We study its properties and a closely-related invariant called the determinant density. It is known that the sets of volume densities and determinant densities of links are dense in the interval [0,v_{oct}]. We cons…
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48 results for “determinant density”
Study shows link determinant densities converge to Mahler measure.
problem Determinant densities of infinite links and their convergence.
method Folner convergence of finite links to infinite biperiodic alternating link L, Mahler measure of characteristic polynomial.
result Determinant densities of finite links converge to Mahler measure of L.
The of a hyperbolic link is defined to be the ratio of the hyperbolic volume of to the crossing number of . We show that there are sequences of non-alternating links with volume density approaching , where is the volume of the ideal hyperbolic octahedron. We show that the…
Graph Laplace operators uniquely identify metrics and densities on manifolds.
problem Identifying Riemannian metrics and sampling densities from graph Laplace operators.
method Analyzing intrinsic and extrinsic graph Laplace operators on compact Riemannian manifolds.
result Graph Laplace operators uniquely determine metrics and densities under certain conditions.
Bayesian method improves nanowire sensor parameter estimation.
problem Improving nanowire sensor parameter estimation.
method Bayesian inversion using PDE model and adaptive Metropolis algorithm.
result Simultaneous determination of nanowire sensor and analyte molecule properties.
Symmetry of neural network densities can be determined from correlation functions.
problem Determining symmetries of neural network densities without knowing the density itself.
method Symmetry-via-duality approach using invariance properties of correlation functions.
result Symmetries of neural network densities can be determined via dual computations of correlation functions.
Unique floating and buoyancy surfaces identify convex polytopes.
problem Identifying convex polytopes from their flotation and buoyancy surfaces.
method Proving uniqueness of surfaces for polytopes with uniform or prescribed density.
result Floating and buoyancy surfaces uniquely determine convex polytopes.
Study shows volume density in central harmonic spaces can vary arbitrarily.
problem Volume density in central harmonic spaces can vary arbitrarily.
method Analyzes asymptotics of volume density function in central harmonic manifolds.
result Volume density in central harmonic spaces can be specified arbitrarily and does not determine geometry.
Density spectra for knotsmath.GT
We recently discovered a relationship between the volume density spectrum and the determinant density spectrum for infinite sequences of hyperbolic knots. Here, we extend this study to new quantum density spectra associated to quantum invariants, such as Jones polynomials, Kashaev invariants and knot homology. We also …
DPSM clusters nodes in data and graph spaces via density propagation and subcluster merging.
problem Automatic clustering of nodes in data and graph spaces.
method Density-based node clustering with propagation process and spectral clustering on subclusters.
result DPSM effectively clusters nodes in both data and graph spaces.
Injective flows for star-like manifolds improve variational inference efficiency.
problem Efficiently modeling densities on star-like manifolds with exact Jacobian computation.
method Proposed injective flows for star-like manifolds with exact Jacobian computation.
result Exact Jacobian computation for star-like manifolds reduces computational cost to NFs.
Consider two manifolds~ and and a first-order Lagrangian for mappings , i.e., is an expression involving and its first derivatives whose value is an -form (or more generally, an -density) on~. One is usually interested in describing the extrema of the functional $\Cal L(u) =…
Determine lens spaces as closures of homology cobordisms over planar surfaces.
problem Identify conditions for lens spaces to be closures of homology cobordisms over planar surfaces.
method Use Chebotarev density theorem in the proof.
result Every lens space is represented as a closure of homology cobordism over a planar surface with three boundary components.
Eigenvalue density of empirical covariance matrix for correlated samplescond-mat.stat-mech
We describe a method to determine the eigenvalue density of empirical covariance matrix in the presence of correlations between samples. This is a straightforward generalization of the method developed earlier by the authors for uncorrelated samples. The method allows for exact determination of the experimental spectru…
REGS samples from unnormalized distributions using gradient flow and neural networks.
problem Sampling from unnormalized distributions with high accuracy and efficiency.
method REGS is a particle method that iteratively transforms samples from a reference distribution to match an unnormalized target distribution using Wasserstein gradient flow and neural networks.
result REGS outperforms state-of-the-art methods in sampling from challenging multimodal distributions and real datasets.
For a symplectic manifold with quantizing line bundle, a choice of almost complex structure determines a Laplacian acting on tensor powers of the bundle. For high tensor powers Guillemin-Uribe showed that there is a well-defined cluster of low-lying eigenvalues, whose distribution is described by a spectral density fun…
Neural Spline Flowsstat.ML
Neural spline flows enhance flow models with rational-quadratic splines.
problem Improving flexibility and density estimation in flow models.
method Proposes a new differentiable module based on monotonic rational-quadratic splines.
result Demonstrates improved performance in density estimation, variational inference, and generative modeling of images.
A^2-Net learns to estimate molecular structures from Cryo-EM data.
problem Estimating molecular structures from Cryo-EM density volumes.
method Learning-based approach using 3D detection and pose estimation.
result Achieves 91% coverage on new dataset and is hundreds of times faster.
D-NND clusters by learning density layers, avoiding over- and under-smoothing.
problem Challenges in density-based clustering, especially bandwidth determination.
method Hierarchical density learning through Deep Nearest Neighbor Descent.
result Avoids over- and under-smoothing, discovers underlying cluster structure reliably.
Paper introduces VDE, a variance-reduced determinant estimator.
problem Estimating determinants with low variance and efficiency.
method Combines variational inference and spherical normalizing flows.
result VDE achieves zero variance in ideal cases, requiring only one sample.
Barrieu, Rouault, and Yor [J. Appl. Probab. 41 (2004)] determined asymptotics for the logarithm of the distribution function of the Hartman-Watson distribution. We determine the asymptotics of the density. This refinement can be applied to the pricing of Asian options in the Black-Scholes model.
Study exact minimax rates for density estimation over convex classes, extending previous work.
problem Deriving minimax rates for density estimation over convex density classes.
method Building on Le Cam's work, determine exact minimax rates using local metric entropy.
result Exact minimax rates derived for any convex density class, including nonparametric and parametric cases.
Develops method to compute Chern-Simons potentials from higher-dimensional Pontryagin densities.
problem Computing Chern-Simons potentials from higher-dimensional Pontryagin densities.
method Systematic approach using a generic affine connection with non-vanishing torsion and non-metricity.
result Algorithm and code for determining Chern-Simons potential from Pontryagin density in arbitrary even dimensions.
Paper uses RNNs to design LDPC codes for binary erasure channels.
problem Designing capacity-approaching LDPC codes for binary erasure channels.
method Model Density Evolution using RNNs to determine LDPC code coefficients and structure.
result NDE improves LDPC design performance and complexity compared to differential evolution.
We develop a new approach to the conformal geometry of embedded hypersurfaces by treating them as conformal infinities of conformally compact manifolds. This involves the Loewner--Nirenberg-type problem of finding on the interior a metric that is both conformally compact and of constant scalar curvature. Our first resu…
Here we present an application of two maxentropic procedures to determine the probability density distribution of compound sums of random variables, using only a finite number of empirically determined fractional moments. The two methods are the Standard method of Maximum Entropy (SME), and the method of Maximum Entrop…
Study minimax rates for nonparametric density estimation with adversarial losses.
problem Estimating densities under various adversarial loss functions.
method General framework for analyzing minimax rates with different loss functions.
result Determines the minimax rate based on loss choice and density smoothness.
We prove two-sided inequalities for the -norm of a pushforward or pullback (with respect to an orientation-preserving diffeomorphism) on oriented volume and Riemannian manifolds. For a function or density on a volume manifold, these bounds depend only on the Jacobian determinant, which arises through the change of…
Improved GANs estimate convergence rate for density estimation.
problem Improving the accuracy of density estimation with GANs.
method Proved an oracle inequality for JS divergence between GAN estimate and true density.
result JS-divergence rate of convergence is .
We construct geodesics in the Wasserstein space of probability measure along which all the measures have an upper bound on their density that is determined by the densities of the endpoints of the geodesic. Using these geodesics we show that a local Poincaré inequality and the measure contraction property follow from t…
Study finds the number of modes in Gaussian kernel density estimators scales with sqrt(β log β).
problem Determining the number of clusters in Transformers.
method Used Kac-Rice formula and Edgeworth expansion to prove scaling.
result The expected number of modes scales as Θ(√(β log β)).
Researchers explore gauge freedom in entropies of -Gaussian measures.
problem Exploring the gauge freedom of entropies in -Gaussian measures.
method Introducing a refined -logarithmic function to demonstrate gauge freedom.
result Different escort expectations can lead to the same entropy but different relative entropies.
Estimating Dynamic Conditional Spread Densities to Optimise Daily Storage Trading of Electricitystat.AP
The paper optimizes daily storage trading of electricity using dynamic spread densities.
problem Optimizing daily storage trading of electricity based on price spreads.
method Formulated dynamic density functions based on skewed-t representations to model hourly electricity price spreads. Selected the best specification for each spread using the Pinball Loss function and calculated risk associated with spread arbitrages.
result Optimal daily operation of a battery storage facility determined from spread densities.
Paper proposes a new method for density estimation using tree tensor-network states.
problem Density estimation for complex graphical models with loops.
method Determines tree topology with Chow-Liu algorithm and uses sketching techniques to define tensor-network components.
result Sample complexity guarantees and empirical validation provided.
The Fisher-Rao metric on smooth densities is studied on compact manifolds.
problem Characterizing the Fisher-Rao metric on smooth densities.
method Analyzing geodesics, curvature, and completeness of the Fisher-Rao metric.
result Geodesics and curvature of the Fisher-Rao metric are determined.
We examine the vertical component of surface area in the warped product of a Euclidean interval and a fiber manifold with product density. We determine general conditions under which vertical fibers minimize vertical surface area among regions bounding the same volume and use these results to conclude that in many such…
We investigate the position of the Buchen-Kelly density in a family of entropy maximising densities which all match European call option prices for a given maturity observed in the market. Using the Legendre transform which links the entropy function and the cumulant generating function, we show that it is both the uni…
Coarse density of subsets of math.GT
Coarse density of subspaces in moduli space of Riemann surfaces.
problem Characterizing subspaces of moduli space that are coarsely dense.
method Using Teichmüller metric and projections of orbit closures in abelian differentials.
result Projections of certain strata in abelian differentials are coarsely dense in moduli space.
New method estimates densities using Sobolev regularization, outperforming existing algorithms.
problem Non-parametric density estimation with clear inductive bias.
method Regularizes Sobolev norm of density, approximates kernel via sampling, uses natural gradients for optimization.
result Method ranks second best on ADBench anomaly detection benchmark.
After having investigated the regular prisms and prism tilings in the $\SLR$ space in the previous work \cite{Sz13-1} of the second author, we consider the problem of geodesic ball packings related to those tilings and their symmetry groups . $\SLR$ is one of the eight Thurston geometries that can be de…
Logistic Gaussian process (LGP) priors provide a flexible alternative for modelling unknown densities. The smoothness properties of the density estimates can be controlled through the prior covariance structure of the LGP, but the challenge is the analytically intractable inference. In this paper, we present approximat…
FFJORD models generate complex distributions efficiently with unbiased density estimation.
problem Efficiently generating complex distributions with unbiased density estimation.
method FFJORD uses continuous-time invertible neural networks with Hutchinson's trace estimator for unbiased log-density estimation.
result FFJORD achieves state-of-the-art performance in high-dimensional density estimation, image generation, and variational inference.
A novel method estimates density using mixtures of histograms.
problem Sparse data sets make histogram estimation ineffective.
method Bayesian approach using collapsed Gibbs sampling.
result The method effectively estimates density functions in sparse data.
Optimizes basis for density-based atomic representations to enhance compactness and accuracy.
problem Improving the efficiency and accuracy of machine learning models for atomic properties.
method An unsupervised approach to determine the optimal basis set for atom density representations using splines.
result Optimal basis sets that encode structural information more compactly and accurately.
NMC improves MCMC convergence by analyzing gradients to determine optimal proposal densities.
problem Improving MCMC convergence in structured relational models.
method Newtonian Monte Carlo (NMC) uses first and second order gradients to determine a suitable proposal density.
result NMC outperforms existing methods in various domains, including non-conjugate models.
The paper studies geometric properties of hydrodynamical density manifolds.
problem Understanding the geometry of hydrodynamical density manifolds.
method Formulating connections, gradients, Hessians, parallel transports, and curvatures on these manifolds.
result Closed-form formulas for sectional curvatures in one-dimensional density manifolds.
B-NAF is a more compact flow for density estimation and inference.
problem Efficiently modeling complex density functions with fewer parameters.
method Directly models a bijection using a single feed-forward network with block matrices.
result B-NAF uses orders of magnitude fewer parameters while being competitive.
A Fourier transform approach optimizes clustering algorithms.
problem Optimizing clustering algorithms for accuracy and reliability.
method Fourier transform and Gaussian filtering to smooth density functions, detecting peaks as cluster centroids.
result Remarkable accuracy in finding cluster centroids, overcoming initialization problems.