arXiv research

New method converts video of dye plumes into PDEs for better understanding.

Study uses AUVs and RL to map river plumes over multiple days.

Dye and Kauffman defined surface bracket polynomials for virtual links by use of surface states, and found a relationship between the surface states and the minimal genus of a surface in which a virtual link diagram is realized. They and Miyazawa independently defined a multivariable polynomial invariant of virtual lin…

MethaneMapper detects methane emissions with high accuracy and reduced model size.

We briefly review recent progress in techniques for modeling and analyzing hyperspectral images and movies, in particular for detecting plumes of both known and unknown chemicals. For detecting chemicals of known spectrum, we extend the technique of using a single subspace for modeling the background to a "mixture of s…

In this paper, we propose a novel mixture of expert architecture for learning polyhedral classifiers. We learn the parameters of the classifierusing an expectation maximization algorithm. Wederive the generalization bounds of the proposedapproach. Through an extensive simulation study, we show that the proposed method …

This paper is an introduction to virtual knot theory and an exposition of new ideas and constructions, including the parity bracket polynomial, the arrow polynomial, the parity arrow polynomial and categorifications of the arrow polynomial. The paper is relatively self-contained and it describes virtual knot theory bot…

The paper confirms a conjecture and extends arrow polynomial to twisted links.

In an article of 1967 W. Edge gave a description of some beautiful geometric properties of the Kummer surface complete intersection of three quadrics in $\mathbb P^5$. Working on it, R. Dye proved that all its osculating spaces have dimension less than the expected 5. Here we discuss these results, also at the light of…

Deep learning speeds up real-time emission monitoring.

Engine predicts real-time air quality with high resolution.

xVAE models extreme turbulence events in turbulent flows.

The flat plumbing basket presentation of a link is introduced by Furihata, Hirasawa and Kobayashi. In this paper, we show that the pass-equivalence and an equivalence introduced by using the flat plumbing basket presentation are the same relation. Furthermore, we obtain an evaluation of the minimal number of bands used…

We produce chain-level generators of the virtual Lee complex $Kh ' (V )$ and use them to convert the computable bounds on the Rasmussen invariant of classical knots due to Kawamura and Lobb into bounds on the virtual Rasmussen invariant as defined by Dye, Kaestner, and Kauffman. We also exhibit a class of diagrams fo…

This paper defines a theory of cobordism for virtual knots and studies this theory for standard and rotational virtual knots and links. Non-trivial examples of virtual slice knots are given. Determinations of the four-ball genus of positive virtual knots are given using the results of a companion paper by the author an…

We introduce an additional structure on ribbon graphs, arrow structure. We extend the Bollobás-Riordan polynomial to ribbon graph with this structure. The extended polynomial satisfies the contraction-deletion relations and naturally behaves with respect to the partial duality of ribbon graphs. We construct an arrow ri…

Research shows higher damages may encourage more disclosure in corporate disputes.

Bayesian inference calibrates Hall thruster model uncertainty at varying pressures.

This paper studies cobordism and concordance for virtual knots. We define the affine index polynomial, prove that it is a concordance invariant for knots and links (explaining when it is defined for links), show that it is also invariant under certain forms of labeled cobordism and study a number of examples in relatio…

Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not str…

The distribution of the returns for a stock are not well described by a normal probability density function (pdf). Student's t-distributions, which have fat tails, are known to fit the distributions of the returns. We present pricing of European call or put options using a log Student's t-distribution, which we call a …

A virtual knot is an equivalence class of embeddings of $S^1$ into thickened (closed oriented) surfaces, up to self-diffeomorphism of the surface and certain handle stabilisations. The slice genus of a virtual knot is defined diagrammatically, in direct analogy to that of a classical knot. However, it may be defined,…

Paper introduces a new skein relation for multivariable polynomials of virtual links.

Reduced-order model improves LES for atmospheric pollutant dispersion.

The paper studies knots in projective space using virtual link theory.

Deep learning calibrates CO2 storage formations from seismic and well data.

Zh-construction links virtual links to classical ones, simplifying knot invariants.

The paper develops sampling methods for ocean phenomena based on temperature and salinity measurements.

Unified framework for hybrid learning and optimization via active inference.

We present hidden fluid mechanics (HFM), a physics informed deep learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navier-Stokes equations. In particular, we seek to leverage the underlying conservation laws (i.e., for mass, momentum, and energy) to infer hid…

Engine forecasts NO2, O3, PM2.5, PM10 with high accuracy.

We introduce Tristram-Levine signatures of virtual knots and use them to investigate virtual knot concordance. The signatures are defined first for almost classical knots, which are virtual knots admitting homologically trivial representations. The signatures and $ω$-signatures are shown to give bounds on the topologic…

FNO model predicts GCS pressure fields with 81% less data, even with limited high-fidelity data.

Study on 2-valued dynamics on complex plane, showing some dynamics can't be group actions.

The paper studies dynamic star-shaped risk measures and their representation.

Study circles to understand dynamics and rigidity in homogeneous spaces.

A Riemmanian foliated dynamical system of 3-dimension $(\mathrm{RFDS}^{3})$ is a closed Riemannian 3-manifold with additional structures: foliation, dynamical system. In the context of arithmetic topology, it is a geometric/analytic analogue of an arithmetic scheme with a conjectural dynamical system suggested by C. De…

Paper connects dynamics of mechanical systems to Reeb dynamics.

Two heuristics solve dynamic multiple travelling salesmen problems.

In this paper we present a theoretical framework for studying coherent acceptability indices in a dynamic setup. We study dynamic coherent acceptability indices and dynamic coherent risk measures, and we establish a duality between them. We derive a representation theorem for dynamic coherent risk measures in terms of …

DOODL learns shared spectral dynamics across related dynamical systems.

We propose a new class of mappings, called Dynamic Limit Growth Indices, that are designed to measure the long-run performance of a financial portfolio in discrete time setup. We study various important properties for this new class of measures, and in particular, we provide necessary and sufficient condition for a Dyn…

In this paper we present a theoretical framework for determining dynamic ask and bid prices of derivatives using the theory of dynamic coherent acceptability indices in discrete time. We prove a version of the First Fundamental Theorem of Asset Pricing using the dynamic coherent risk measures. We introduce the dynamic …

Dynamical-VAE learns causal dynamics from POMDPs using future information.

Most real world phenomena such as sunlight distribution under a forest canopy, minerals concentration, stock valuation, exhibit nonstationary dynamics i.e. phenomenon variation changes depending on the locality. Nonstationary dynamics pose both theoretical and practical challenges to statistical machine learning algori…

Unified analysis of DLNs using DMFT reveals dynamics of loss convergence and generalization trade-offs.

dLDS models neural dynamics as sparse combinations of simpler components.

Reinforcement learning would enjoy better success on real-world problems if domain knowledge could be imparted to the algorithm by the modelers. Most problems have both hidden state and unknown dynamics. Partially observable Markov decision processes (POMDPs) allow for the modeling of both. Unfortunately, they do not p…