Study on trunk knot invariant and its relation to satellite and companion knots.
problem Understanding the trunk invariant of satellite knots and their companions.
method Analyzing the trunk invariant of satellite knots and their companions, deriving inequalities.
result Established inequalities relating the trunk of a satellite knot to its companion's trunk.
The paper explores knots' height, trunk, and representativity, finding gaps and bounds.
problem Investigating the properties of knots and their invariants.
method Analyzing conjectures, defining new invariants, and comparing knot positions.
result Found gaps between height and minimal height, and bounds on representativity.
We introduce two numerical invariants, the waist and the trunk of knots. The waist of a closed incompressible surface in the complement of a knot is defined as the minimal intersection number of all compressing disks for the surface in the 3-sphere and the knot. Then the waist of a knot is defined as the maximal waist …
Backpropagation-free trunk training improves model performance on various benchmarks.
problem Memory inefficiency and noisy gradient estimates in deep network training.
method Split Forward Gradient (Split-FG) method that splits network into trunk and head, estimating only trunk gradient.
result Split-FG achieves better performance than pure forward-gradient training and backpropagation on various benchmarks.
Satellite knots have a higher trunk number than their base knots.
problem Understanding the relationship between satellite knots and their base knots.
method Using the Thurston norm and properties of satellite patterns.
result The trunk number of satellite knots is strictly greater than the product of the Thurston norm and the trunk number of their base knots.
We construct a new invariant-the trunkenness-for volume-perserving vector fields on S^3 up to volume-preserving diffeomorphism. We prove that the trunkenness is independent from the helicity and that it is the limit of a knot invariant (called the trunk) computed on long pieces of orbits.
The trunk of a knot in S3, defined by Makoto Ozawa, is a measure of geometric complexity similar to the bridge number or width of a knot. We prove that for any two knots K1 and K2, we have tr(K1#K2)=max{tr(K1),tr(K2)}, confirming a conjecture of Ozawa. Another conjecture of Ozawa asserts that any…
Flattenings of knotted surfaces help define new invariants.
problem Understanding and quantifying knotted surfaces in 4-sphere.
method Using hyperbolic decompositions and projections onto 2-sphere.
result Introduced layering, trunk, and partition number invariants.
Machine learning assesses balance outside clinics, improving therapy efficiency.
problem Lack of feedback from PTs in home balance training.
method Trunk sway data analysis with multi-class SVM.
result ML model achieved 82% accuracy in assessing balance.
New invariants defined for volume-preserving flows on 3-manifolds.
problem Defining invariants for volume-preserving flows.
method Extending wrapping number and trunk to define invariants of links and flows.
result Wrappingness and trunkenness are not functions of helicity.
Defines combinatorial minimal surfaces in pseudomanifolds.
problem Finding minimal surfaces in complex geometric structures.
method Adapting thin position technique from pseudomanifolds.
result Combinatorial minimal surfaces always exist under mild conditions.
The main result of this paper is a new classification theorem for links (smooth embeddings in codimension 2). The classifying space is the rack space (defined in [Trunks and classifying spaces, Applied Categorical Structures, 3 (1995) 321--356]) and the classifying bundle is the first James bundle (defined in "James bu…
Random sampling improves DeepONet training efficiency without sacrificing accuracy.
problem Training DeepONet models with high computational and memory costs.
method Random sampling of inputs in the trunk network of DeepONet.
result Significant reduction in training time with comparable accuracy.
Innovative rack theory applied to Legendrian links.
problem Classifying and distinguishing Legendrian links.
method Purely rack-theoretic approach, Legendrian Reidemeister moves, cusps, homogeneous representations, modules.
result Invariant distinguishes infinitely many Legendrian unknots and trefoils.
A new training method improves stability and generalization of DeepONets.
problem Training deep operator networks (DeepONets) is challenging due to nonconvex and nonlinear nature.
method Two-step training method: first train trunk network, then branch network. Introduced Gram-Schmidt orthonormalization.
result Generalization error estimate and numerical examples demonstrating effectiveness.
Improved DeepONet variants using Transformer cross-conditioning enhance PDE solution efficiency.
problem Solving partial differential equations efficiently and accurately.
method Transformer-inspired DeepONet variants with bidirectional cross-conditioning.
result Improved efficiency and accuracy compared to modified DeepONet, with variant effectiveness tied to PDE characteristics.
The paper analyzes geometric densities and compression radii for knot types.
problem Optimizing geometric quantities associated with knot types.
method Develops a factorization framework for scale-covariant size functionals.
result Different minimizing sequences for density, compression, packing, and ropelength problems.
DeepONet learns operators for PDEs with varying parameters and initial conditions.
problem Learning operators for partial differential equations with different parameters or initial conditions.
method DeepONet uses a Branch net and Trunk net to minimize error between evaluated and expected outputs, incorporating a scalar auxiliary variable approach for energy dissipation.
result DeepONet can accurately approximate operators for PDEs with varying parameters or initial conditions.
Proposes a new method to learn operators for stochastic problems using DeepONet with autoencoder.
problem Efficiently solve forward and inverse stochastic problems with limited data.
method MultiAuto-DeepONet, a multi-resolution autoencoder DeepONet model.
result The model effectively handles high-dimensional stochastic inputs and reduces the number of trainable parameters.
AMORE uses neural operators to efficiently predict multiple thermochemical states in stiff chemical kinetics.
problem Efficiently integrating stiff chemical kinetics systems to reduce computational cost.
method Developed AMORE, a framework of adaptive multi-output operator network with two adaptive loss functions.
result Demonstrated improved accuracy and efficiency in predicting thermochemical states from initial conditions.
Deep learning framework predicts surface texture parameters and their uncertainties.
problem Predicting surface texture parameters and their uncertainties from multi-instrument datasets.
method Reproducible deep learning framework using multi-instrument dataset, quantile and heteroscedastic heads for uncertainty modeling, and post-hoc conformal calibration.
result High fidelity predictions (R2: Ra 0.9824, Rz 0.9847, RONt 0.9918) and well-modelled uncertainty targets (Ra_uncert 0.9899, Rz_uncert 0.9955).
Study approximates operators on labelled conditional distributions for non-exchangeable systems.
problem Approximating operators on constrained probability measures for non-exchangeable systems.
method Combines cylindrical approximations and DeepONet-type neural architecture for finite-dimensional representations.
result Establishes a universal approximation theorem for continuous operators on Mλ. DeepONets improve surrogate modeling for engineering systems.
problem Accurately modeling complex PDEs for engineering systems.
method DeepONets specialize in approximating mathematical operators for PDEs.
result DeepONets achieve high prediction accuracy and zero-shot capability.
DeepONets enhance spatial-temporal surrogates for structural dynamics.
problem Creating full spatial-temporal surrogates for dynamical systems under uncertainty.
method Proposed Full-Field Extended DeepONet (FExD) to learn full solution operator across multiple degrees of freedom.
result FExD achieves superior accuracy and computational efficiency compared to other models.
Enhanced DeepONet framework with uncertainty quantification for complex operators.
problem Learning complex operators with uncertainty quantification.
method Generalised variational inference (GVI) using Rényi's α-divergence.
result Superior predictive accuracy and uncertainty quantification.
Deep RNN detects FoG episodes in Parkinson's disease with high accuracy.
problem Detecting freezing episodes in Parkinson's disease patients.
method Deep Recurrent Neural Network (RNN) with Long Short-Term Memory cells on 3D-accelerometer measurements.
result Frequency domain features from trunk sensor achieve an AUC score of 93% in subject-independent method.
Study examines how body segments respond to random vibrations.
problem Understanding human body responses to random vibrations.
method 35 participants were tested with random noise signals. Multiple linear regression models were created to determine influential predictors of peak translational gains.
result Multiple predictors, including motion direction and body segment, significantly influence peak translational gains.
DeepONet learns nonlinear operators from data to identify differential equations.
problem Learning nonlinear operators from data to identify differential equations.
method DeepONet architecture with branch and trunk nets.
result DeepONet significantly reduces generalization error compared to fully-connected networks.
Abstract invariant cannot be expressed using various slice-torus invariants.
problem Cannot express Iida-Taniguchi's slice-torus invariant using other known invariants.
method Analysis of various known invariants and their properties.
result Iida-Taniguchi's slice-torus invariant cannot be realized as a linear combination of other invariants.
New equivalence found between knot invariants.
problem Understanding relationships between knot invariants.
method Comparing tree reductions of Kontsevich invariant with Orr invariants.
result Orr invariant of degree k is equivalent to tree reduction of Kontsevich invariant of degree <2k.
The θ invariant encompasses the Rozansky-Overbay invariant.
problem None explicitly stated in the abstract.
method Generalization of the Rozansky-Overbay invariant using the θ invariant. result The θ invariant recovers the Rozansky-Overbay invariant. Non-invariant complex structures on Lie groups are not biholomorphic to invariant ones.
problem Understanding non-invariant deformations of complex structures on Lie groups.
method Computed cohomologies to show non-biholomorphicity.
result Non-invariant complex structures are not biholomorphic to invariant ones.
Study of Bauer-Furuta invariants under Lie group actions and Galois coverings.
problem Investigating invariants of 4-manifolds under group actions and Galois coverings.
method Functorial approach to equivariant invariants and study in Galois covering situations.
result Ordinary invariants of quotients are determined by equivariant invariants of the covering manifold.
The Kuperberg invariant is shown to be gauge invariant for certain framed 3-manifolds.
problem Exploring gauge invariance of the Kuperberg invariant for specific 3-manifolds.
method Using hyperbolic 3-manifolds and finite-dimensional Hopf algebras.
result First examples of gauge invariants of general finite-dimensional Hopf algebras via topological methods.
Paper introduces new invariant for pairs of immersions.
problem Understanding behavior of immersions through tangencies and triple points.
method Introduces J2+-invariant for oriented pairs of immersions, invariant under inverse tangencies and triple points. result Invariant changes under direct tangencies but remains invariant under orientation change and inverse tangencies.
Constructs universal link invariants from intersections in configuration spaces.
problem Globalise topologically all coloured Jones polynomials and ADO polynomials.
method Defines new link invariants from graded intersections in configuration spaces.
result Recover all coloured Jones polynomials and ADO polynomials for links.
New polynomial invariant distinguishes singular links.
problem Distinguishing singular links using existing invariants.
method Generalized quandle polynomial to singquandles and constructed a singular link invariant.
result New polynomial invariant distinguishes singular links with same counting invariant.
We show that the perturbative g invariant of rational homology 3-spheres can be recovered from the LMO invariant for any simple Lie algebra g, i.e, the LMO invariant is universal among the perturbative invariants. This universality was conjectured in [25]. Since the perturbative invariants dominate …
Grid homology confirms the Upsilon invariant in knot theory.
problem Verifying the equivalence of Upsilon invariants in knot theory.
method Reconstructed Upsilon invariant using grid homology and proved equivalence.
result Upsilon invariants in knot Floer and grid homology are equivalent.
New invariant CWR for alternating links is stronger than existing invariants.
problem Developing a stronger invariant for alternating links.
method Introducing CWR invariant as an array of two-variable polynomials. result The CWR invariant is stronger than classical invariants like HOMFLYPT and Kauffman polynomials. Defines knot concordance invariant using instanton homology and Donaldson invariants.
problem Knot concordance and its classification.
method Defines an invariant φ for knots in the 3-sphere using Donaldson invariants and Floer's instanton homology. result The invariant φ coincides with a special case of an invariant defined by Froyshov. Combines combinatorial method to extend Milnor invariants to welded links.
problem Extending Milnor invariants to welded links.
method Combinatorial approach.
result Invariance of extended Milnor invariants for welded links.
Paper introduces a new invariant for virtual knotoids and proves it's a Vassiliev invariant of order one.
problem Tackles the problem of understanding invariants for virtual knotoids.
method Uses a 0-smoothing invariant constructed from local modifications at classical crossings.
result Demonstrates that the 0-smoothing invariant provides less information than the gluing invariant.
New family of knots with epsilon invariant nonzero despite Upsilon and phi being zero.
problem Comparing smooth concordance invariants.
method Building an infinite family of knots.
result Found knots with epsilon invariant nonzero but Upsilon and phi zero.
We construct two knot invariants. The first knot invariant is a sum constructed using linking numbers. The second is an invariant of flat knots and is a formal sum of flat knots obtained by smoothing pairs of crossings. This invariant can be used in conjunction with other flat invariants, forming a family of invariants…
Formula connects surface and curve invariants via slice transitions.
problem Computing surface invariants from curve invariants.
method Introducing differential measures for local changes across singular slice transitions.
result Explicit formula for surface invariant change during quadruple-point events.
In this article we introduce a family of transverse invariants arising from the deformations of Khovanov homology. This family includes the invariants introduced by Plamenevskaya and by Lipshitz, Ng, and Sarkar. Then, we investigate the invariants arising from Bar-Natan's deformation. These invariants, called β-invar…
New concordance invariants phi and phi_j are defined and studied.
problem Understanding the relationships between different concordance invariants.
method Defined and analyzed new invariants phi and phi_j, and provided recursive formulas.
result Found infinitely many knots with specific combinations of zero and nonzero phi invariant.