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LA-based MarqVision, which uses AI to monitor marketplaces for counterfeits, raised a $48M Series B led by Peak XV Partners, bringing its total funding to ~$90M (Kate Park/TechCrunch)
https://techcrunch.com/2025/09/15/harvard-law-to-ai-marqvisi…

Harvard Law to AI: MarqVision lands $48M to combat brand abuse | TechCrunch
This LA-based startup raised $48M to tackle brand abuse

Markov dualities via the spectral decompositions of the two Markov generators in their bi-orthogonal basis of right and left eigenvectors
Cecile Monthus
https://arxiv.org/abs/2507.11041

Markov dualities via the spectral decompositions of the two Markov generators in their bi-orthogonal basis of right and left eigenvectors
The notion of Markov duality between two Markov processes that can live in two different configurations spaces $(x,{\tilde x})$ is revisited via the spectral decompositions of the two Markov generators in their bi-orthogonal basis of right and left eigenvectors. In this formulation, the two generators should have the same eigenvalues $(-E)$ that may be complex, while the duality function $Ω(x,{\tilde x})$ can be considered as a mapping between the right and the left eigenvectors of the two mod…

A Markovian Framing of WaveFunctionCollapse for Procedurally Generating Aesthetically Complex Environments
Franklin Yiu, Mohan Lu, Nina Li, Kevin Joseph, Tianxu Zhang, Julian Togelius, Timothy Merino, Sam Earle
https://arxiv.org/abs/2509.09919

A Markovian Framing of WaveFunctionCollapse for Procedurally Generating Aesthetically Complex Environments
Procedural content generation often requires satisfying both designer-specified objectives and adjacency constraints implicitly imposed by the underlying tile set. To address the challenges of jointly optimizing both constraints and objectives, we reformulate WaveFunctionCollapse (WFC) as a Markov Decision Process (MDP), enabling external optimization algorithms to focus exclusively on objective maximization while leveraging WFC's propagation mechanism to enforce constraint satisfaction. We emp…

Turkisasetus sai murskaavan vastaanoton https://animaliamedia.fi/turkisasetus-sai-murskaavan-vastaanoton/
Parlamentissa vaadittiin turkistarhauksen kieltämistä

Turkisasetus sai murskaavan vastaanoton   | Animalia-media
Suurin osa lausujista yhtyi Animalian näkemykseen ja kritisoi turkisasetuksen sisältöä. Moni vaati turkistarhauksen kieltämistä Suomessa. 

Stabilizing boundary time crystals through Non-markovian dynamics
Bandita Das, Rahul Ghosh, Victor Mukherjee
https://arxiv.org/abs/2508.09688 https://arxiv…

Stabilizing boundary time crystals through Non-markovian dynamics
We study Boundary time crystals (BTCs) in the presence of non-Markovian dynamics. In contrast to BTCs observed in earlier works in the Markovian regime, we show that non-Markovian dynamics can be highly beneficial for stabilizing BTCs over a wide range of parameter values, even in the presence of intermediate rates of dissipation. We analyze the effect of non-Markovian dynamics on BTCs using quantum Fisher information, order parameter, a measure of non-Markovianity, and a dynamical phase diagra…

Analysis and Design of Spare Strategy for Large-Scale Satellite Constellation Using Markov Chain
Seungyeop Han, Zachary Grieser, Shoji Yoshikawa, Takumi Noro, Takumi Suda, Koki Ho
https://arxiv.org/abs/2509.09957

Analysis and Design of Spare Strategy for Large-Scale Satellite Constellation Using Markov Chain
This paper presents a method for analyzing and designing an optimal spare-management policy in large-scale satellite constellations using a Markov chain model. To capture the stochastic nature of satellite failures and launch vehicle lead times, we adopt Markov chains to model both failure and replenishment processes. We reinvestigate an indirect spare strategy, modeled as a multi-echelon periodic-review reorder-point/order-quantity policy, in which spares are first delivered to parking orbits …

Finding the Smallest Possible Exact Aggregation of a Markov Chain
Patrick Sonnentag
https://arxiv.org/abs/2507.11157 https://arxiv.or…

Finding the Smallest Possible Exact Aggregation of a Markov Chain
Markov chains are an important tool for modelling and evaluating systems in computer science, economics, biology and numerous other fields. Thus, approximating Markov chains is a useful tool for decreasing the computational effort needed for analysis by reducing the size of the state space. So far, most approximative algorithms focused on finding approximations, which are, again, Markov chains. We remove this restriction and present an algorithm using the Arnoldi iteration. Further, we show tha…

Resetting Induces Memory Loss in Non-Markovian Processes
Debasish Saha, Rati Sharma
https://arxiv.org/abs/2509.11608 https://arxiv.org/pdf/2509.11608

Resetting Induces Memory Loss in Non-Markovian Processes
Stochastic resetting is a powerful strategy known to accelerate the first-passage time statistics of stochastic processes. While its effects on Markovian systems are well understood, a general framework for non-Markovian dynamics is still lacking, mostly due to its mathematical complexity. Here, we present an analytical and numerical framework to study non-Markovian processes under resetting, focusing on the first-passage properties of escape kinetics from metastable states. We show that resett…

Markov approximation for controlled Hawkes Jump-Diffusions with general kernels
Mahmoud Khabou, Mehdi Talbi
https://arxiv.org/abs/2507.11294 https://

Markov approximation for controlled Hawkes Jump-Diffusions with general kernels
We present a Markov approximation for jump-diffusions whose jump part consists in a Hawkes process with intensity driven by a general (possibly non-monotone) kernel. Under minimal integrability conditions, the kernel can be approximated by a linear combination of exponential functions. This implies that Hawkes jump-diffusions can be approximated with Markov jump-diffusions. We illustrate the usefulness of this approximation by applying it to a class of stochastic control problems.