New homology theories for orbifolds and weighted polyhedra.
problem Homology of orbifolds and weighted polyhedra.
method Introducing AW-homology and DW-homology from special triangulations.
result Invariant under orbifold isomorphisms and generalized Poincaré duality.
Paper computes motion groups of links using TQFTs, proving a conjecture.
problem Computing representations of motion groups of links in S3. method Dimension reduction of Dijkgraaf-Witten (DW) TQFTs.
result Proves a conjecture for motion groups of closed manifolds and torus links.
This paper approximates SU(2) Chern-Simons theory using finite group gauge theories.
problem Approximating SU(2) Chern-Simons theory with finite group gauge theories.
method Comparing Witten-Reshetikhin-Turaev and Dijkgraaf-Witten invariants on closed 3-manifolds.
result The asymptotics of the DW theory recovers the leading asymptotics of the CS theory at large level.
Canonical structure of the space-time symmetric analogue of the Hamiltonian formalism in field theory based on the De Donder-Weyl (DW) theory is studied. In n space-time dimensions the set of n polymomenta is associated to the space-time derivatives of field variables. The polysymplectic (n+1)-form generalizes th…
We show that any compact symplectic manifold (W,ω) with boundary embeds as a domain into a closed symplectic manifold, provided that there exists a contact plane ξon dW which is weakly compatible with omega, i.e. the restriction ω|ξdoes not vanish and the contact orientation of dW and its orientation as the boundary of…
New invariant for links in 3-sphere computed and computed using diagrams.
problem Computing invariants of links in 3-sphere.
method Defining and computing the parabolic Dijkgraaf-Witten invariant.
result Computed invariants of several links and partial information using diagrams.
DW-KNN improves KNN by integrating distance and neighbor reliability for better prediction accuracy.
problem Standard KNN assumes all neighbors are equally reliable, leading to unreliable predictions in heterogeneous feature spaces.
method DW-KNN integrates exponential distance with neighbor validity, providing instance-level interpretability and reducing hyperparameter sensitivity.
result DW-KNN achieves 0.8988 average accuracy, ranks 2nd among six methods, and has the lowest cross-validation variance.
We introduce a generalization of the Dijkgraaf-Witten invariants for cusped or compact oriented 3-manifolds. We show that the generalized DW invariants distinguish some pairs of cusped hyperbolic 3-manifolds with the same hyperbolic volumes and with the same Turaev-Viro invariants. We also present an example of a pair …
A CNN-based method improves DTI of the human heart, compensating for motion.
problem Signal loss due to heart motion in DTI.
method Invertible Wavelet Scattering using CNN.
result Effective motion compensation and improved fiber structures.
THEOREM. For every prime p and each n=2,3,...∞, there is an action of G=∏i=1∞(Z/pZ) on a two-dimensional compact metric space X with n-dimensional orbit space. This theorem was proved in [DW: A.N. Dranishnikov and J.E. West, Compact group actions that raise dimension to infinity, Topol…
Method learns SDEs from one trajectory using GP priors and randomized cross-validation.
problem Learning SDEs from a single trajectory.
method Combining CGC and data-adapted kernels learned via randomized cross-validation.
result Efficacy, robustness, and scope of the method demonstrated in numerical experiments.
One of the key challenges in predictive maintenance is to predict the impending downtime of an equipment with a reasonable prediction horizon so that countermeasures can be put in place. Classically, this problem has been posed in two different ways which are typically solved independently: (1) Remaining useful life (R…
Total variation regularization and total variation flows (TVF) have been widely applied for image enhancement and denoising. To include a generic preservation of crossing curvilinear structures in TVF we lift images to the homogeneous space M=Rd⋊Sd−1 of positions and orientations as a Lie group…
Study spherical doubly warped spacetimes for stellar collapse and cosmology.
problem Analyzing spherically symmetric spacetimes for stellar collapse and cosmology.
method Obtained results for Weyl and Ricci tensors on general doubly warped spacetimes.
result Friedmann equations deviate from standard FRW cosmology due to electric tensor terms.
Using the large deviation principle (LDP) for a re-scaled fractional Brownian motion BtH where the rate function is defined via the reproducing kernel Hilbert space, we compute small-time asymptotics for a correlated fractional stochastic volatility model of the form $dS_t=S_tσ(Y_t) (\barρ dW_t +ρdB_t), \,dY_t=dB^H…
New conditions ensure Dantzig-Wolfe relaxation matches rank-constrained optimization problems.
problem Rank-constrained optimization problems with linear matrix inequalities.
method Investigates Dantzig-Wolfe relaxation and develops conditions for exactness.
result Conditions for extreme point, convex hull, and objective exactness.
New energy functional and fields for Yang-Mills theory, proving monotonicity and vanishing theorems.
problem Developing new mathematical tools for Yang-Mills theory.
method Introducing normalized exponential Yang-Mills energy functional, deriving monotonicity formula and vanishing theorem.
result Monotonicity and vanishing theorems for exponential Yang-Mills fields.
An investor faced with a contingent claim may eliminate risk by perfect hedging, but as it is often quite expensive, he seeks partial hedging (quantile hedging or efficient hedging) that requires less capital and reduces the risk. Efficient hedging for European call option was considered in the standard Black-Scholes m…
We pose an optimal control problem arising in a perhaps new model for retirement investing. Given a control function f and our current net worth as X(t) for any t, we invest an amount f(X(t)) in the market. We need a fortune of M "superdollars" to retire and want to retire as early as possible. We model our c…
Survey reviews Hamilton-Jacobi theory in various geometric settings, focusing on Jacobi and Leibniz identities.
problem Analyzing Hamilton-Jacobi theory across different geometric backgrounds.
method Geometric review of Hamilton-Jacobi theory, focusing on Jacobi and Leibniz identities.
result Novel Hamilton-Jacobi equation for conformal Hamiltonian vector fields.
New method solves stochastic control problems with delays using deep learning.
problem Stochastic control problems with delayed control in drift and diffusion.
method Characterization via Riccati PDEs and deep learning scheme.
result Illustrates effect of delay on Markowitz portfolio allocation problem.
We study the Hamiltonian vector field v=(−∂f/∂w,∂f/∂z) on C2, where f=f(z,w) is a polynomial in two complex variables, which is non-degenerate with respect to its Newton's polygon. We introduce coordinates in four-dimensional neighbourhoods of the "points at infinity", in …
BSDEs help in financial pricing and utility maximization.
problem Financial pricing and utility maximization in complex market models.
method Introduces and applies BSDEs to financial problems.
result Utilizes BSDEs for simple utility maximization solutions.
The stochastic exponential Zt=exp{Mt−M0−(1/2)<M,M>t} of a continuous local martingale M is itself a continuous local martingale. We give a necessary and sufficient condition for the process Z to be a true martingale in the case where Mt=∫0tb(Yu)dWu and Y is a one-dimensional diffusion drive…
Node embeddings have become an ubiquitous technique for representing graph data in a low dimensional space. Graph autoencoders, as one of the widely adapted deep models, have been proposed to learn graph embeddings in an unsupervised way by minimizing the reconstruction error for the graph data. However, its reconstruc…
BNEM improves Boltzmann sampler efficiency.
problem Generating IID samples from Boltzmann distributions efficiently.
method Bootstrapped Noised Energy Matching (NEM) combined with diffusion-based learning and bootstrapping.
result BNEM achieves state-of-the-art performance with improved robustness.
Calabi-Yau theorem extended to Vaisman manifolds.
problem Uniqueness of Vaisman metrics and their characterization.
method Analyzing the Lee form and Lee class properties.
result Vaisman metrics uniquely determined by volume and Lee class.
Novel RKHS approach solves complex financial model equations.
problem Calibrating singular local stochastic volatility models.
method Reproducing Kernel Hilbert Space (RKHS) regularization.
result Regularized model is well-posed and replicates option prices.
Bayesian optimization (BO) is a powerful paradigm for derivative-free global optimization of a black-box objective function (BOF) that is expensive to evaluate. However, the overhead of BO can still be prohibitive for problems with highly expensive function evaluations. In this paper, we investigate how to reduce the r…
iEFM trains CNF models from unnormalized densities efficiently.
problem Training generators from energy functions or unnormalized densities.
method Iterated energy-based flow matching (iEFM) with simulation-free objective.
result iEFM outperforms existing methods in probabilistic modeling.
On any manifold, any non-degenerate symmetric 2-form (metric) and any skew-symmetric (differential) form W can be reduced to a canonical form at any point, but not in any neighborhood: the respective obstructions being the Riemannian tensor and dW. The obstructions to flatness (to reducibility to a canonical form) are …
VT-DIS improves sampling from Boltzmann distributions with minimal overhead.
problem Bias in Monte Carlo estimates from score-based diffusion models.
method Variance-Tuned Diffusion Importance Sampling (VT-DIS) adapts noise covariance to correct bias.
result VT-DIS achieves effective sample sizes of 80%, 35%, and 3.5% on benchmarks, using less computational budget.
We consider the stochastic volatility model dSt=σtStdWt,dσt=ωσtdZt, with (Wt,Zt) uncorrelated standard Brownian motions. This is a special case of the Hull-White and the β=1 (log-normal) SABR model, which are widely used in financial practice. We study the properties of this model, discretized in …
An interacting Black-Scholes model for option pricing, where the usual constant interest rate r is replaced by a stochastic time dependent rate r(t) of the form r(t)=r+f(t) dW/dt, accounting for market imperfections and prices non-alignment, was developed in [1]. The white noise amplitude f(t), called arbitrage bubble,…
The paper establishes conditions for harmonic forms on noncompact manifolds.
problem Conditions for harmonic forms on noncompact manifolds.
method Introducing Condition W and proving conditions for harmonic forms.
result Conditions for harmonic forms on noncompact manifolds are established.
Neural networks estimate SDEs with jump noise using a Tamed-Milstein scheme.
problem Estimating drift and diffusion functions in SDEs with jump noise.
method Tamed-Milstein scheme with neural networks as non-parametric approximators.
result Flexible estimation of complex nonlinear dynamics in systems with state-dependent noise.
This paper presents an algorithm for pricing perpetual American put options with asset-dependent discounting.
problem Pricing perpetual American put options with asset-dependent discounting.
method The approach involves a value function described by a stochastic process with negative exponential jumps and a discount function that depends on the asset price.
result Under certain conditions, the value function can be convex and represented in a closed form.
The paper constructs contact-hyperbolic manifolds with large automorphism groups.
problem Finding contact-hyperbolic manifolds with large automorphism groups.
method Holomorphic contact structures, pseudometrics, and symplectic quotients.
result Explicit examples of contact-hyperbolic contact manifolds are constructed.
Unified framework for optimizing portfolios with distributions over weights, returns, and parameters.
problem Traditional portfolio optimization treats expected returns, covariances, and allocations as fixed. Modern practice replaces at least one with a distribution.
method Unified framework using Gamma_theta(dw,dr) coupling to organize Bayesian, robust, chance-constrained, stochastic-allocation, and distributional reinforcement-learning methods.
result Synthetic and structural contributions, including a portfolio specialization of Wasserstein-CVaR duality and a static no-randomization theorem.
In this paper we bring to bear some new tools from statistical learning on the analysis of roll call data. We present a new data-driven model for roll call voting that is geometric in nature. We construct the model by adapting the "Partition Decoupling Method," an unsupervised learning technique originally developed fo…
The paper analyzes perpetual American options with asset-dependent discounting.
problem Optimal stopping problem for perpetual American options with varying discount rates.
method Analyzes the convexity of the value function, determines stopping regions, and proves HJB equation.
result Identifies the form of the value function and proves put-call symmetry.
The paper connects function theory, dynamics, and ergodic theory via Thurston's theory.
problem Function theory on Teichmüller space and dynamics of mapping class groups.
method Utilizes Thurston's theory and Sullivan's theory on discrete subgroups of hyperbolic space.
result Establishes connections between function theory, dynamics, and ergodic theory.
Survey of Floer theories and their connections.
problem None explicitly stated; focuses on surveying theories.
method None explicitly stated; focuses on surveying theories.
result None explicitly stated; focuses on surveying theories.
Lectures on topological field theories and differential cohomology.
problem Exploring topological field theories and their connections to differential cohomology.
method Introduction to topological field theory and generalized Abelian gauge theories.
result Explains the relationship between topological field theories and differential cohomology.
The paper defines strong emergence in field theories and proves it exists between certain theories.
problem Defining and proving the existence of strong emergence phenomena between field theories.
method Formal definition and sufficient conditions for emergence, proving existence in Euclidean background.
result Strong emergence exists between certain parameterized Lagrangian field theories.
This is the first paper in a series introducing a generalized Fredholm theory in a new class of smooth spaces called polyfolds. The theory will be illustrated in upcoming papers by applications to Floer Theory, Gromov-Witten Theory and Symplectic Field Theory.
Unified Higgs bundle vacua from M-theory on Spin(7) spaces.
problem Unifying Higgs bundle vacua from different string compactifications.
method Developed formalism for M-theory on local Spin(7) spaces and constructed explicit solutions.
result Unified 3D effective field theory from 4D M- and F-theory vacua.
Researchers find new G2-conifolds in M-theory with potential field theory duals.
problem Exploring the field theory interpretation of M-theory G2-conifolds. method Constructing G2-holonomy orbifolds from circle bundles over Calabi-Yau cones. result Many UV perturbative gauge theories have an infrared dual described by smooth G2-holonomy backgrounds in M-theory.