Results 101 to 110 of about 32,857 (304)

Heat Kernel Embeddings, Differential Geometry and Graph Structure

open access: yesAxioms, 2015
In this paper, we investigate the heat kernel embedding as a route to graph representation. The heat kernel of the graph encapsulates information concerning the distribution of path lengths and, hence, node affinities on the graph; and is found by ...
Hewayda ElGhawalby, Edwin R. Hancock
doaj   +1 more source

"On Approximation of the Solutions to Partial Differential Equations in Finance" [PDF]

open access: yes
This paper proposes a general approximation method for the solutions to second-order parabolic partial differential equations (PDEs) widely used in finance through an extension of Léandre's approach(Léandre (2006,2008)) and the Bismut identiy(e.g ...
Akihiko Takahashi, Toshihiro Yamada
core  

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

open access: yesAdvanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju   +5 more
wiley   +1 more source

Heat kernel asymptotics on sub-Riemannian manifolds with symmetries and applications to the bi-Heisenberg group

open access: yes, 2016
By adapting a technique of Molchanov, we obtain the heat kernel asymptotics at the sub-Riemannian cut locus, when the cut points are reached by an r-dimensional parametric family of optimal geodesics.
Neel, Robert W.   +2 more
core   +1 more source

The heat kernel in Riemann normal coordinates and multiloop Feynman graphs in curved spacetime

open access: yesJournal of High Energy Physics
We present a formalism for computing arbitrary scalar multi-loop Feynman graphs in curved spacetime using the heat kernel approach. To this end, we compute the off-diagonal components of the heat kernel in Riemann normal coordinates up to second order in
Igor Carneiro, Gero von Gersdorff
doaj   +1 more source

Convergence of Approximate Solutions for Transport-diffusion Equation in the Half-space with Neumann Condition

open access: yesИзвестия Иркутского государственного университета: Серия "Математика"
In this paper, we examine the question about the approximation of the solution to a transport-diffusion equation in a half-space with the homogenous Neumann condition.
R. Gherdaoui   +2 more
doaj   +1 more source

Feature extraction via heat flow analogy

open access: yes, 2009
Feature extraction is an important field of image processing and computer vision. Features can be classified as low-level and high-level. Low-level features do not give shape information of the objects, where the popular low-level feature ...
Direkoğlu, Cem
core  

SERS Facemask for Rapid and Portable Sensing Mycobacterium Tuberculosis Antigens for TB Screening

open access: yesAdvanced Science, EarlyView.
Our study introduced an Au─Ag embedded covalent organic framework (U@COF) ‐mediated facemask for sensing TB antigen ESAT‐6/CFP‐10 complex in clinical droplet samples toward TB screening. Practical analysis of clinical samples demonstrated the availability of our facemask, which is capable of identifying the TB subjects (N = 17) from healthy candidates (
Lingzhi Chen   +20 more
wiley   +1 more source

Multi‐Omics Insights Into the Mechanisms of Early Muscle Fiber Difference and Transformation Between Lean‐Type and Chinese Indigenous Pigs

open access: yesAdvanced Science, EarlyView.
Multi‐omics analyses uncover breed‐specific cis‐regulatory landscapes and higher‐order chromatin architectural differences that underlie early postnatal muscle fiber divergence in pigs. A super‐enhancer upstream of PPP3CB recruits MEF2C to activate PPP3CB transcription, while the PPP3CB–MEF2C positive feedback loop promotes oxidative muscle fiber ...
Shuailong Zheng   +8 more
wiley   +1 more source

Heat and Matérn Kernels on Matchings

open access: yesCoRR
Applying kernel methods to matchings is challenging due to their discrete, non-Euclidean nature. In this paper, we develop a principled framework for constructing geometric kernels that respect the natural geometry of the space of matchings. To this end, we first provide a complete characterization of stationary kernels, i.e.
Dmitry Eremeev   +2 more
openaire   +2 more sources

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