Zobrazeno 1 - 10
of 1 361
pro vyhledávání: '"P. Kuusela"'
Autor:
Gao, Riqiang, Ghesu, Florin C., Arberet, Simon, Basiri, Shahab, Kuusela, Esa, Kraus, Martin, Comaniciu, Dorin, Kamen, Ali
In contemporary radiotherapy planning (RTP), a key module leaf sequencing is predominantly addressed by optimization-based approaches. In this paper, we propose a novel deep reinforcement learning (DRL) model termed as Reinforced Leaf Sequencer (RLS)
Externí odkaz:
http://arxiv.org/abs/2406.01853
The deformation approach of arXiv:2104.07816 for computing zeta functions of one-parameter Calabi-Yau threefolds is generalised to cover also multiparameter manifolds. Consideration of the multiparameter case requires the development of an improved f
Externí odkaz:
http://arxiv.org/abs/2405.08067
Autor:
Jockers, Hans, Kotlewski, Sören, Kuusela, Pyry, McLeod, Andrew J., Pögel, Sebastian, Sarve, Maik, Wang, Xing, Weinzierl, Stefan
It has long been known that the maximal cut of the equal-mass four-loop banana integral is a period of a family of Calabi-Yau threefolds that depends on the kinematic variable $z=m^2/p^2$. We show that it can also be interpreted as a period of a fami
Externí odkaz:
http://arxiv.org/abs/2404.05785
Autor:
Kuusela, Pyry, McGovern, Joseph
We study the problem of computing Gopakumar-Vafa invariants for multiparameter families of symmetric Calabi-Yau threefolds admitting flops to diffeomorphic manifolds. There are infinite Coxeter groups, generated by permutations and flops, that act as
Externí odkaz:
http://arxiv.org/abs/2312.06753
In this work, we study the local zeta functions of Calabi-Yau fourfolds. This is done by developing arithmetic deformation techniques to compute the factor of the zeta function that is attributed to the horizontal four-form cohomology. This, in turn,
Externí odkaz:
http://arxiv.org/abs/2312.07611
We present an optimization-based framework to construct confidence intervals for functionals in constrained inverse problems, ensuring valid one-at-a-time frequentist coverage guarantees. Our approach builds upon the now-called strict bounds interval
Externí odkaz:
http://arxiv.org/abs/2310.02461
Through the Bayesian lens of data assimilation, uncertainty on model parameters is traditionally quantified through the posterior covariance matrix. However, in modern settings involving high-dimensional and computationally expensive forward models,
Externí odkaz:
http://arxiv.org/abs/2310.01397
Publikováno v:
Atmospheric Chemistry and Physics, Vol 24, Pp 9419-9433 (2024)
Through the Bayesian lens of four-dimensional variational (4D-Var) data assimilation, uncertainty in model parameters is traditionally quantified through the posterior covariance matrix. However, in modern settings involving high-dimensional and comp
Externí odkaz:
https://doaj.org/article/680d80a7c35e4a78a282ae5379d73ce3
Publikováno v:
Advances in Statistical Climatology, Meteorology and Oceanography, Vol 10, Pp 69-93 (2024)
Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intens
Externí odkaz:
https://doaj.org/article/cff6f586829849a3815e12938bbd425f
Autor:
Weitz, Philippe, Valkonen, Masi, Solorzano, Leslie, Carr, Circe, Kartasalo, Kimmo, Boissin, Constance, Koivukoski, Sonja, Kuusela, Aino, Rasic, Dusan, Feng, Yanbo, Pouplier, Sandra Sinius, Sharma, Abhinav, Eriksson, Kajsa Ledesma, Robertson, Stephanie, Marzahl, Christian, Gatenbee, Chandler D., Anderson, Alexander R. A., Wodzinski, Marek, Jurgas, Artur, Marini, Niccolò, Atzori, Manfredo, Müller, Henning, Budelmann, Daniel, Weiss, Nick, Heldmann, Stefan, Lotz, Johannes, Wolterink, Jelmer M., De Santi, Bruno, Patil, Abhijeet, Sethi, Amit, Kondo, Satoshi, Kasai, Satoshi, Hirasawa, Kousuke, Farrokh, Mahtab, Kumar, Neeraj, Greiner, Russell, Latonen, Leena, Laenkholm, Anne-Vibeke, Hartman, Johan, Ruusuvuori, Pekka, Rantalainen, Mattias
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, th
Externí odkaz:
http://arxiv.org/abs/2305.18033