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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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5111621 · Jul 202019922001200920172026
48 results for electron delocalization

A new model explains protein interactions via electron delocalization.

problem Understanding how protein interactions affect each other.
method Quantized discrete differential geometry of n-simplices.
result Allosteric regulation follows from the model of interactions.

A multi-scale model predicts atomic-scale properties using both local and long-range information.

problem Inability of machine-learning schemes to capture long-range physical effects.
method Combines local and non-local information in a multipole expansion framework.
result Demonstrates the ability to model electrostatics, polarization, and dispersion.

We define extensions of the L2L^2-analytic invariants of closed manifolds, called delocalized L2L^2-invariants. These delocalized invariants are constructed in terms of a nontrivial conjugacy class of the fundamental group. We show that in many cases, they are topological in nature. We show that the marked length spect…

1996-12-02abs ↗pdf ↗

In this paper we state and prove Morse type inequalities for Morse functions as well as for closed differential 1-forms. These inequalities involve delocalized Betti numbers. As an immediate consequence, we prove the vanishing of delocalized Betti numbers of manifolds fibering over the circle.

2008-07-31abs ↗pdf ↗

Study delocalized eta invariants for signature operators on proper manifolds.

problem Define and analyze delocalized eta invariants for signature operators on proper manifolds.
method Develop detailed heat-kernel analysis and apply to proper manifolds with boundary.
result Prove index formulas relating delocalized eta invariants to Atiyah-Patodi-Singer indices.

The paper defines higher invariants for groups of polynomial growth and proves their convergence.

problem Defining and proving convergence of higher invariants for groups of polynomial growth.
method Using delocalized cyclic cocycles and a determinant map construction.
result A well-defined pairing between delocalized cyclic cocyles and K-theory classes of C*-algebraic secondary higher invariants.

The unadjusted Langevin algorithm converges faster for some variables in high dimensions.

problem Sampling probability distributions in high-dimensional settings.
method Analysis of the unadjusted Langevin algorithm for strongly log-concave distributions.
result The delocalization of bias effect allows for faster convergence for a small number of variables.

New method controls bias in unadjusted Hamiltonian Monte Carlo and underdamped Langevin.

problem Bias in unadjusted Hamiltonian Monte Carlo and underdamped Langevin samplers.
method Delocalization of bias technique applied to these samplers.
result Control W2W_2 bias with O(K)O(\sqrt{K}) integration steps for high-dimensional distributions.

The paper proves universality in optimization problems with i.i.d. random vectors.

problem Optimization problems with i.i.d. random vectors and their projections.
method Proves universality of empirical risk minimization under specific conditions.
result The minimum value of the optimization problem is universal and depends only on the mean and covariance of the random vectors.

Non-Abelian actions are resolved using equivariant K-theory and delocalized cohomology.

problem Resolving non-Abelian actions on manifolds.
method Using equivariant K-theory and delocalized cohomology, the structure of the quotient space is described.
result A new model for non-Abelian equivariant K-theory and cohomology is developed.

Improved sampling from complex distributions with reduced bias.

problem Reducing bias in high-dimensional sampling algorithms.
method Hierarchical entropy analysis to weaken assumptions and expand scope.
result Bias reduction in low-dimensional marginals scales with lower dimension, not full dimension.

For any closed complex manifold XX, we calculate the Poincaré and Hodge polynomials of the delocalized equivariant cohomology H(Xn,Sn)H^*(X^n, S_n) with a grading specified by physicists. As a consequence, we recover a special case of a formula for the elliptic genera of symmetric products in Dijkgraaf-Moore-Verlinde-Verlin…

1999-10-05abs ↗pdf ↗

Let G be a finitely generated discrete group. In this paper we establish vanishing results for rho-invariants associated to (i) the spin-Dirac operator of a spin manifold with positive scalar curvature (ii) the signature operator of the disjoint union of a pair of homotopy equivalent oriented manifolds with fundamental…

2004-07-22abs ↗pdf ↗

New method detects global factors near BBP phase transition in high-dimensional data.

problem Detecting the number of global factors in noisy high-dimensional correlation matrices.
method Iterative Global Factor (IGF) algorithm combining adaptive edge recalibration and PR delocalization filter.
result IGF algorithm successfully detects global factors near BBP transition, improving over eigenvalue-only methods.

A refined form of the `Folk Theorem' that a smooth action by a compact Lie group can be (canonically) resolved, by iterated blow up, to have unique isotropy type was established by the authors in the context of manifolds with corners; the canonical construction induces fibrations on the boundary faces of the resolution…

2010-12-28abs ↗pdf ↗

We give a description of the delocalized twisted cohomology of an orbifold and the Chern character of a twisted vector bundle in terms of supersymmetric Euclidean field theories. This includes the construction of a twist functor for 111|1-dimensional EFTs from the data of a gerbe with connection.

2018-01-09abs ↗pdf ↗

Study on electronic banking satisfaction in Nigeria.

problem Limited research on factors enhancing end users' satisfaction in electronic banking.
method Empirical analysis of factors influencing electronic banking user satisfaction.
result Factors influencing electronic banking user satisfaction and their relationship with satisfaction.

Method learns molecular Hamiltonian for accurate electron dynamics predictions.

problem Predict electron dynamics in molecules using learned Hamiltonians.
method Combines linear statistical model with quantum Liouville equation time discretization.
result Predicted electron dynamics closely matches ground truth, even beyond training data.

DenSNet learns electron densities for molecular dynamics, enabling accurate spectroscopic predictions.

problem Lack of accurate electronic observables in MLIPs for molecular dynamics.
method DenSNet uses SE(3)-equivariant neural networks to predict electron densities and total energy.
result DenSNet predicts infrared spectra with excellent agreement to experimental data.

Let G be a discrete group, and let M be a closed spin manifold of dimension m>3 with pi_1(M)=G. We assume that M admits a Riemannian metric of positive scalar curvature. We discuss how to use the L2-rho invariant and the delocalized eta invariant associated to the Dirac operator on M in order to get information about t…

2006-04-13abs ↗pdf ↗

Agent-based model simulates speculative electronic market with price bubbles.

problem Understanding speculative behavior and price bubbles in electronic markets.
method Agent-based model with two types of traders: mean reverting and speculative.
result Speculative traders lead to increased volatility and price deviations from fundamental value.

Machine learning predicts electronic density of states for condensed matter.

problem Predicting the electronic density of states (DOS) in complex condensed matter systems.
method Developed a machine learning framework to predict DOS from density functional theory data, considering geometric configurations of atoms.
result Demonstrated the model's effectiveness in predicting DOS and its components for various silicon configurations.

[New and updated results were published in Nature Chemistry, doi:10.1038/s41557-020-0544-y.] The electronic Schrödinger equation describes fundamental properties of molecules and materials, but can only be solved analytically for the hydrogen atom. The numerically exact full configuration-interaction method is exponent…

2019-09-16abs ↗pdf ↗

Equivariant graph neural networks predict electron density for molecules, liquids, and solids.

problem Predicting electron density for molecules, liquids, and solids using machine learning.
method Equivariant graph neural networks for predicting electron density at query points.
result The model predicts electron density with accuracy beyond state of the art and significantly faster than traditional DFT methods.

This paper presents the results of an automated volatile organic compound (VOC) classification process implemented by embedding a machine learning algorithm into an Arduino Uno board. An electronic nose prototype is constructed to detect VOCs from three different fruits. The electronic nose is constructed using an arra…

2019-05-15abs ↗pdf ↗

A new method predicts electron density accurately from atom-centered models.

problem Predicting electron density accurately from atom-centered models.
method Gradient-based approach to minimize loss function in an optimized sparse feature space.
result Extremely accurate predictions of electron density and total energies.

We consider the Dirac equation in flat Minkowski 3-space and rewrite it as the Maxwell equation in Minkowski 4-space with torsion. The torsion tensor is defined as the dual of the electromagnetic vector potential. Our model clearly distinguishes the electron and the positron without resorting to "negative frequencies":…

2000-06-17abs ↗pdf ↗

Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using `arrow-pushing' diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction …

2018-05-23abs ↗pdf ↗

Develops methods to learn correlation potentials for time-dependent Kohn-Sham systems.

problem Learning the correlation potential for time-dependent Kohn-Sham systems.
method Optimizing a least-squares objective subject to the TDKS equation using adjoints.
result Learned correlation potential models match ground truth electron densities and can have memory.

Data-driven approach discovers molecular photoswitches with separated electronic absorption bands.

problem Engineering photoswitchable molecules with specific electronic absorption bands remains challenging.
method Data-driven discovery pipeline using Gaussian processes for multitask learning.
result Multioutput Gaussian process (MOGP) trained on four photoswitch transition wavelengths outperforms single-task models and TD-DFT.

Let ΓΓ be a discrete group. Assuming rational injectivity of the Baum-Connes assembly map, we provide new lower bounds on the rank of the positive scalar curvature bordism group and the relative group in Stolz' positive scalar curvature sequence for BΓ\mathrm{B} Γ. The lower bounds are formulated in terms of the part …

2017-09-21abs ↗pdf ↗

Symmetry-electronic fingerprints reveal competing magnetic phases in two-dimensional materials.

problem Predicting magnetic ground states, moments, and anisotropy in two-dimensional magnets.
method Introduce the symmetry-electronic fingerprint (SEF), a physically interpretable representation that encodes crystallographic symmetry operations, Wyckoff-site geometry, and site-resolved electronic structure.
result SEF-trained models accurately classify magnetic ordering and regress moments alongside anisotropy energies.

AHEAD improves financial market efficiency through ad-hoc auctions.

problem Improving financial market efficiency and reducing transaction costs.
method Introducing a new matching design (AHEAD) for electronic markets where participants can trade at a fixed price and trigger auctions when unsatisfied.
result A Nash equilibrium is achieved in the market, and ad-hoc auctions are more relevant and efficient than periodic auctions and continuous limit order books.

The smooth action of a compact Lie group on a compact manifold can be resolved to an iterated space, as made explicit by Pierre Albin and the second author. On the resolution the lifted action has fixed isotropy type, in an iterated sense, with connecting fibrations and this structure descends to a resolution of the qu…

2018-07-22abs ↗pdf ↗

We show that the emergence of systemic risk in complex systems can be understood from the evolution of functional networks representing interactions inferred from fluctuation correlations between macroscopic observables. Specifically, we analyze the long-term collective dynamics of the New York Stock Exchange between 1…

2018-07-09abs ↗pdf ↗

Let ΓΓ be a f.g. discrete group and let M~\tilde M be a Galois ΓΓ-covering of a smooth closed manifold MM. Let SΓ(M~)S_*^Γ(\tilde{M}) be the analytic structure group, appearing in the Higson-Roe analytic surgery sequence SΓ(M~)K(M)K(CrΓ)\to S_*^Γ(\tilde M)\to K_*(M)\to K_*(C_r^*Γ)\to. We prove that for an arbitrary discrete group ΓΓ

2019-05-28abs ↗pdf ↗

XLabel tool reduces medical experts' workload by 40% and explains its decisions.

problem Efficiently labeling large electronic health records.
method Visual-interactive tool using Explainable Boosting Machine (EBM) for classification and explanation.
result EBM achieves high accuracy and explainability, even with mislabeled data.