Zobrazeno 1 - 10
of 1 457
pro vyhledávání: '"Hølmer, P."'
A long-standing challenge in developing machine learning approaches has been the lack of high-quality labeled data. Recently, models trained with purely synthetic data, here termed synthetic clones, generated using large-scale pre-trained diffusion m
Externí odkaz:
http://arxiv.org/abs/2405.20469
Accurate predictions of when a component will fail are crucial when planning maintenance, and by modeling the distribution of these failure times, survival models have shown to be particularly useful in this context. The presented methodology is base
Externí odkaz:
http://arxiv.org/abs/2403.18739
In this paper, a family of neural network-based survival models is presented. The models are specified based on piecewise definitions of the hazard function and the density function on a partitioning of the time; both constant and linear piecewise de
Externí odkaz:
http://arxiv.org/abs/2403.18664
In the present work we revisit the problem of the quantum droplet in atomic Bose-Einstein condensates with an eye towards describing its ground state in the large density, so-called Thomas-Fermi limit. We consider the problem as being separable into
Externí odkaz:
http://arxiv.org/abs/2401.00213
Autor:
Chen, Xuwen, Holmer, Justin
We consider the quantum many-body dynamics at the weak-coupling scaling. We derive rigorously the quantum Boltzmann equation, which contains the classical hard sphere model and, effectively, the inverse power law model, from the many-body dynamics as
Externí odkaz:
http://arxiv.org/abs/2312.08239
Autor:
Igamberdiev, Timour, Vu, Doan Nam Long, Künnecke, Felix, Yu, Zhuo, Holmer, Jannik, Habernal, Ivan
Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private
Externí odkaz:
http://arxiv.org/abs/2311.14465
Predictive maintenance is an effective tool for reducing maintenance costs. Its effectiveness relies heavily on the ability to predict the future state of health of the system, and for this survival models have shown to be very useful. Due to the com
Externí odkaz:
http://arxiv.org/abs/2302.00629
Autor:
Aksoy, Ahmet Kerem, Dushev, Pavel, Zacharatou, Eleni Tzirita, Hemsen, Holmer, Charfuelan, Marcela, Quiané-Ruiz, Jorge-Arnulfo, Demir, Begüm, Markl, Volker
The growing operational capability of global Earth Observation (EO) creates new opportunities for data-driven approaches to understand and protect our planet. However, the current use of EO archives is very restricted due to the huge archive sizes an
Externí odkaz:
http://arxiv.org/abs/2208.10830
Autor:
Chen, Xuwen, Holmer, Justin
Publikováno v:
Annals of PDE 10 (2024), no. 2, Paper No. 14, 1-44
We consider the 3D Boltzmann equation with the constant collision kernel. We investigate the well/ill-posedness problem using the methods from nonlinear dispersive PDEs. We construct a family of special solutions, which are neither near equilibrium n
Externí odkaz:
http://arxiv.org/abs/2206.11931
The Benjamin Ono equation with a slowly varying potential is $$ \text{(pBO)} \qquad u_t + (Hu_x-Vu + \tfrac12 u^2)_x=0 $$ with $V(x)=W(hx)$, $0< h \ll 1$, and $W\in C_c^\infty(\mathbb{R})$, and $H$ denotes the Hilbert transform. The soliton profile i
Externí odkaz:
http://arxiv.org/abs/2106.02971