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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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79157236314 · Jun 202019922001200920172026
48 results for ghost points

Ghost points affect stability in finite difference schemes for diffusion equations.

problem Impact of ghost points on stability of finite difference schemes.
method Exploration of explicit Euler finite difference scheme with ghost points on diffusion equation.
result Stability of the scheme is affected by ghost points.

Ghost mechanism explains abrupt learning in RNNs, revealing constraints on optimization landscapes.

problem Understanding abrupt learning in recurrent neural networks (RNNs) trained on working memory tasks.
method Introducing the ghost mechanism, a process driven by saddle-node bifurcations, to analyze and model abrupt learning.
result A critical learning rate scales as an inverse power law with the timescale of computation, leading to vanishing and oscillatory gradients.

Computational ghost imaging is an imaging technique in which an object is imaged from light collected using a single-pixel detector with no spatial resolution. Recently, ghost cytometry has been proposed for a high-speed cell-classification method that involves ghost imaging and machine learning in flow cytometry. Ghos…

2019-03-14abs ↗pdf ↗

In this paper we define and study the "ghost loop orbifold" of an orbifold XX consisting of those loops that remain constant in the coarse moduli space of XX. We construct a configuration space model for the ghost loop orbifold using an idea of G. Segal. From this we exhibit the relation between the Hochschild and cy…

2002-10-15abs ↗pdf ↗

We consider the convex-concave saddle point problem minxmaxyf(x)+yAxg(y)\min_{x}\max_{y} f(x)+y^\top A x-g(y) where ff is smooth and convex and gg is smooth and strongly convex. We prove that if the coupling matrix AA has full column rank, the vanilla primal-dual gradient method can achieve linear convergence even if ff is not stron…

2018-02-05abs ↗pdf ↗

A new metric GNQ audits LLMs for privacy risks during training.

problem Auditing LLMs for privacy risks during training is computationally hard.
method Gradient Uniqueness (GNQ) metric derived from gradient descent, BS-Ghost GNQ for efficiency.
result GNQ successfully predicts sequence extractability and reveals risk heterogeneity.

The study counts periodic orbits on smooth manifolds, adding ghost orbits for completeness.

problem Counting periodic orbits of vector fields on smooth closed manifolds.
method Enlarging the space of orbits to include ghost orbits, defining weight functions, and showing constancy under deformation.
result The weight function remains constant as the vector field moves and ΓΓ deforms.

A BV algebra is a formal framework within which the BV quantization algorithm is implemented. In addition to the gauge symmetry, encoded in the BV master equation, the master action often exhibits further global symmetries, which may be in turn gauged. We show how to carry this out in a BV algebraic set up. Depending o…

2010-01-01abs ↗pdf ↗

Nyquist ghost artifacts in EPI are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the non-linear and time-varying local magnetic field changes. Recently, it was shown that …

2018-06-01abs ↗pdf ↗

We show that the (4,5)-torus knot T4,5T_{4,5} admits exactly one ghost character. We then show that this ghost character provides the following two important results. (1) It is known that for any knot KK every (meridionally) trace-free $\SL_2(\C)$-representation of the knot group G(K)G(K) yields an $\SL_2(\C)$-representat…

2017-08-02abs ↗pdf ↗

Study reveals hidden null components in overparametrized neural networks.

problem Hidden null components in overparametrized neural networks.
method Structure theorem of null space for neural networks using ridgelet transforms.
result Null components can be uniquely written as linear combinations of ridgelet transforms.

We show that the (4,5)(4,5)- and (5,6)(5,6)-torus knots admit ghost characters. Consequently, these knots provide counterexamples to Ng's conjecture, which proposes an isomorphism between the complexification of degree 00 abelian knot contact homology and the coordinate ring of the character variety of the 22-fold branched…

2017-08-02abs ↗pdf ↗

We use conformal, but ghostful, Weyl gravity to study its ghost-free, second derivative, partially massless (PM) spin 2 component in presence of Einstein gravity with positive cosmological constant. Specifically, we consider both gravitational- and self- interactions of PM via the fully non-linear factorization of conf…

2012-08-07abs ↗pdf ↗

We propose a model in which a spliced vector bundle (with an arbitrary number of gauge structures in the splice) possesses a geometry which do not split. The model employs connection 1-forms with values in a space-product of Lie algebras, and therefore interlaces the various gauge structures in a non-trivial manner. Sp…

1997-04-16abs ↗pdf ↗

Improved pricing of vanilla options using modified Adams method and sinh-acceleration.

problem Calibration of rough Heston model leads to incorrect implied volatility surfaces.
method Modified Adams method and sinh-acceleration for Fourier inversion.
result Corrected implied volatility surface is significantly flatter and fits data poorly.

Starting from a Lie algebroid A{\cal A} over a space V we lift its action to the canonical transformations on the principle affine bundle R{\cal R} over the cotangent bundle TVT^*V. Such lifts are classified by the first cohomology H1(A)H^1({\cal A}). The resulting object is the Hamiltonian algebroid AH{\cal A}^H over $…

2000-10-06abs ↗pdf ↗

Methodology to measure lag relevance in time series models.

problem Measuring lag relevance in machine learning models for univariate time series.
method Ghost variables, Shapley values, additive importance measures, auto-relevance and partial auto-relevance functions, one-step forecast.
result Calculated relevance measures successfully demonstrate expected lag structure in almost all cases.

This is a simple mathematical introduction into Feynman diagram technique, which is a standard physical tool to write perturbative expansions of path integrals near a critical point of the action. I start from a rigorous treatment of a finite dimensional case (which actually belongs more to multivariable calculus than …

2004-06-12abs ↗pdf ↗

We propose a procedure for assigning a relevance measure to each explanatory variable in a complex predictive model. We assume that we have a training set to fit the model and a test set to check the out of sample performance. First, the individual relevance of each variable is computed by comparing the predictions in …

2019-12-13abs ↗pdf ↗

We study the structure underlying Ng's conjecture, which relates the degree 00 abelian knot contact homology of a knot KK to the coordinate ring of the SL2(C)SL_2(\mathbf{C})-character variety X(Σ2K)X(Σ_2 K) of the 22-fold branched cover of the 33-sphere branched along KK. Our approach is based on the study of (meridional…

2017-08-02abs ↗pdf ↗

Quantization of a Lagrangian field system essentially depends on its degeneracy and implies its BRST extension defined by sets of non-trivial Noether and higher-stage Noether identities. However, one meets a problem how to select trivial and non-trivial higher-stage Noether identities. We show that, under certain condi…

2007-02-28abs ↗pdf ↗

The article applies Occam's Razor to non-parametric model building, minimizing the number of bits for data encoding.

problem Overlooking the role of model parameters in data encoding leads to inefficient probability density estimators.
method Extends bit counting to model parameters, providing a true measure of complexity for parametric models.
result Minimizing total bit requirement leads to smoother, more efficient probability density estimates and fewer relevant parameters.

Study on RL on volatility surfaces, proving no free lunch for law-seeking methods.

problem Aligning RL agents with no-arbitrage laws in volatile markets.
method Built a law manifold, defined penalties, and used a Goodhart decomposition.
result No free lunch theorem: Law-seeking RL cannot outperform baselines.

The paper speeds up and improves pricing and calibration for the rough Heston model.

problem Improving the accuracy and speed of pricing vanilla options under the rough Heston model.
method Combining modified Adams method with SINH-acceleration method for Fourier inversion.
result The model implied vol surface is much flatter and fits market data poorly, indicating ghost calibration.

A formulation for a non-trivial composition of two classical gauge structures is given: Two parent gauge structures of a common base space are synthesized so as to obtain a daughter structure which is fundamental by itself. The model is based on a pair of related connections that take their values in the product space …

1996-01-09abs ↗pdf ↗

Consider a physical system for which a mathematically rigorous geometric quantization procedure exists. Now subject the system to a finite set of irreducible first class (bosonic) constraints. It is shown that there is a mathematically rigorous BRST quantization of the constrained system whose cohomology at ghost numbe…

2006-04-12abs ↗pdf ↗

Oeljeklaus-Toma (OT) manifolds are certain compact complex manifolds built from number fields. Conversely, we show that the fundamental group often pins down the number field uniquely. We relate the first homology to some interesting ideal. OT manifolds are never Kähler, but carry an LCK metric (locally conformally Käh…

2015-03-07abs ↗pdf ↗

Gravitational interactions of higher spin fields are generically plagued by inconsistencies. We present a simple framework that couples higher spins to a broad class of gravitational backgrounds (including Ricci flat and Einstein) consistently at the classical level. The model is the simplest example of a Yang--Mills d…

2006-06-16abs ↗pdf ↗

Develops a new framework for analyzing sequential decision-making problems using information theory.

problem Lack of information-theoretic generalization bounds for sequential decision-making problems.
method Introduces a sequential supersample framework that separates learner filtration from proof-side enlargement, controlling the generalization gap by sequential CMI.
result Establishes a sequential CMI that controls the generalization gap in sequential decision-making problems.

A key component of most neural network architectures is the use of normalization layers, such as Batch Normalization. Despite its common use and large utility in optimizing deep architectures, it has been challenging both to generically improve upon Batch Normalization and to understand the circumstances that lend them…

2019-06-09abs ↗pdf ↗

We study a class of supersymmetric spinning particle models derived from the radial quantization of stationary, spherically symmetric black holes of four dimensional N= 2 supergravities. By virtue of the c-map, these spinning particles move in quaternionic Kaehler manifolds. Their spinning degrees of freedom describe m…

2010-03-11abs ↗pdf ↗

Study quantum aspects of 1-form symmetries using BV-BRST cohomology.

problem Quantum aspects of gauging continuous 1-form global symmetries.
method BV-BRST quantization of a U(1) 2-form gauge field, Lie 2-algebroid construction, Čech-de Rham bicomplex.
result Anomaly descent for U(1) 1-form symmetries is naturally set up in the Čech-de Rham bicomplex.

Study quantum aspects of 1-form symmetries using BV-BRST cohomology and gerbes.

problem Quantum aspects of gauging continuous 1-form global symmetries.
method BV-BRST quantization of a U(1) 2-form gauge field, Lie 2-algebroid construction, Čech-de Rham bicomplex.
result Anomaly descent for U(1) 1-form symmetries is naturally set up in the Čech-de Rham bicomplex.

We discuss a class of (local and non-local) theories of gravity that share same properties: i) they admit the Einstein spacetime with arbitrary cosmological constant as a solution; ii) the on-shell action of such a theory vanishes and iii) any (cosmological or black hole) horizon in the Einstein spacetime with a positi…

2012-03-13abs ↗pdf ↗

The proper action functional of (4k+3)-dimensional U(1)-Chern-Simons theory including the instanton sectors has a well known description: it is given on the moduli space of fields by the fiber integration of the cup product square of classes in degree-(2k+2) differential cohomology. We first refine this statement from …

2012-07-23abs ↗pdf ↗