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September 9, 2026
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You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
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On multistochastic Monge-Kantorovich problem, bitwise operations, and fractals

2018.
Gladkov N., Kolesnikov A., Zimin A.
The multistochastic (n,k)-Monge--Kantorovich problem on a product space ∏ni=1Xi is an extension of the classical Monge--Kantorovich problem. This problem is considered on the space of measures with fixed projections onto Xi1×…×Xik for all k-tuples {i1,…,ik}⊂{1,…,n} for a given 1≤k<n. In our paper we study well-posedness of the primal and the corresponding dual problem. Our central result describes a solution π to the following important model case: n=3,k=2,Xi=[0,1], the cost function c(x,y,z)=xyz, and the corresponding two--dimensional projections are Lebesgue measures on [0,1]2. We prove, in particular, that the mapping (x,y)→x⊕y, where ⊕ is the bitwise addition (xor- or Nim-addition) on [0,1]≅Z∞2, is the corresponding optimal transportation. In particular, the support of π is the Sierpiński tetrahedron. In addition, we describe a solution to the corresponding dual problem.
Research target: Mathematics
Language: English
Text on another site
Keywords: fractalsфракталыMonge-Kantorovich problemзадача Монжа-Канторовича
Publication based on the results of:
Стохастические алгоритмы и статистический анализ многомерных данных (2018)
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