Zobrazeno 1 - 10
of 2 542
pro vyhledávání: '"Härkönen, P."'
We propose a statistical approach for estimating the mean line width in spectra comprising Lorentzian, Gaussian, or Voigt line shapes. Our approach uses Gaussian processes in two stages to jointly model a spectrum and its Fourier transform. We genera
Externí odkaz:
http://arxiv.org/abs/2404.06338
Autor:
Härkönen, Ville J.
We develop a non-relativistic quantum field theory of electrons and nuclei based on the Coulomb Hamiltonian. We derive the exact equations of motion and write these equations in the form of Hedin's equations for all species of identical particles inv
Externí odkaz:
http://arxiv.org/abs/2403.16103
In this paper, we make the explicit connection between image segmentation methods and end-to-end diarization methods. From these insights, we propose a novel, fully end-to-end diarization model, EEND-M2F, based on the Mask2Former architecture. Speake
Externí odkaz:
http://arxiv.org/abs/2401.12600
Generalizing the concept of the Macaulay inverse system, we introduce a way to describe localizations of an ideal in a polynomial ring. This leads to an approach to the differential primary decomposition as a description of the affine scheme defined
Externí odkaz:
http://arxiv.org/abs/2401.00597
Autor:
Härkönen, Ville J.
We compute the ab-initio electron density beyond the Born-Oppenheimer approximation in crystalline LiH and LiD with density functional methods. We report significant beyond Born-Oppenheimer corrections to electron density in the vicinity of nuclei eq
Externí odkaz:
http://arxiv.org/abs/2312.07411
End-to-end neural diarization with encoder-decoder based attractors (EEND-EDA) is a method to perform diarization in a single neural network. EDA handles the diarization of a flexible number of speakers by using an LSTM-based encoder-decoder that gen
Externí odkaz:
http://arxiv.org/abs/2312.06253
Autor:
Härkönen, Ville J.
Hydrogen has been the subject of intense research following the discovery of high-temperature superconductivity in hydrides, and as a result of continuous efforts to produce solid hydrogen. The Born-Oppenheimer approximation is the central piece of t
Externí odkaz:
http://arxiv.org/abs/2311.06114
We propose an approach utilizing gamma-distributed random variables, coupled with log-Gaussian modeling, to generate synthetic datasets suitable for training neural networks. This addresses the challenge of limited real observations in various applic
Externí odkaz:
http://arxiv.org/abs/2310.08055
Autor:
Elina Laukka, Sanna Lakoma, Marja Harjumaa, Suvi Hiltunen, Henna Härkönen, Miia Jansson, Riikka-Leena Leskelä, Susanna Martikainen, Paula Pennanen, Anastasiya Verho, Paulus Torkki
Publikováno v:
BMC Health Services Research, Vol 24, Iss 1, Pp 1-12 (2024)
Abstract Background While digital health and social services offer promising solutions, they often overlook the perspectives and needs of older adults. This study aims to comprehensively investigate the preferences of older adults regarding the use a
Externí odkaz:
https://doaj.org/article/8cafede271264470b2801ea103398b6a
Autor:
Simula, Kristoffer, Härkönen, Jan, Zhelezova, Iuliia, Drummond, Neil, Tuomisto, Filip, Makkonen, Ilja
Publikováno v:
Phys. Rev. B 108, 045201(2022)
Positron annihilation in solid state matter can be utilized to detect and identify open-volume defects. The momentum distribution of the annihilation radiation is an important observable in positron-based measurements, and can reveal information on t
Externí odkaz:
http://arxiv.org/abs/2305.08602