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🇺🇦 #NowPlaying on KEXP's #VarietyMix
Mercyful Fate:
🎵 Evil
#MercyfulFate
https://mercyfulfate.bandcamp.com/album/evil
https://open.spotify.com/track/5pH1BecbNktYDbYw0XCEWn

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

Non-Markovian amplitude damping in a central spin model with random couplings
Mehboob Rashid, Rayees A Mala, Saima Bashir, Muzaffar Qadir Lone
https://arxiv.org/abs/2509.12670 h…

Non-Markovian amplitude damping in a central spin model with random couplings
Non-Markovian dynamics is central to quantum information processing, as memory effects strongly influence coherence preservation, metrology, and communication. In this work, we investigate the role of stochastic system--bath couplings in shaping non-Markovian behavior of open quantum systems, using the central spin model within a time-convolutionless master equation framework. We show that the character of the reduced dynamics depends jointly on the intrinsic memory of the environment and on th…

Least squares estimation of the transition density in bifurcating Markov models
S. Val\`ere Bitseki Penda
https://arxiv.org/abs/2509.12906 https://arxiv.or…

Least squares estimation of the transition density in bifurcating Markov models
In this article, we propose a least squares method for the estimation of the transition density in bifurcating Markov models. Unlike the kernel estimation, this method do not use the quotient which can be a source of errors. In order to study the rate of convergence for least squares estimators, we develop exponential inequalities for empirical process of bifurcating Markov chain under bracketing assumption. Unlike the classical processes, we observe that for bifurcating Markov chains, the comp…

Learning non-Markovian Dynamical Systems with Signature-based Encoders
Eliott Pradeleix, R\'emy Hosseinkhan-Boucher, Alena Shilova, Onofrio Semeraro, Lionel Mathelin
https://arxiv.org/abs/2509.12022

Learning non-Markovian Dynamical Systems with Signature-based Encoders
Neural ordinary differential equations offer an effective framework for modeling dynamical systems by learning a continuous-time vector field. However, they rely on the Markovian assumption - that future states depend only on the current state - which is often untrue in real-world scenarios where the dynamics may depend on the history of past states. This limitation becomes especially evident in settings involving the continuous control of complex systems with delays and memory effects. To capt…

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…

Investigation: healthcare exchanges in Nevada, Maine, Massachusetts, and Rhode Island share sensitive data with Google, LinkedIn, and Snapchat via web trackers (The Markup)
https://themarkup.org/pixel-hunt/2025/06/17/w…

We caught 4 more states sharing personal health data with Big Tech – The Markup
Healthcare exchanges in Nevada, Maine, Massachusetts and Rhode Island shared users’ sensitive health data with companies like Google and LinkedIn

Multi-Component VAE with Gaussian Markov Random Field
Fouad Oubari, Mohamed El-Baha, Raphael Meunier, Rodrigue D\'ecatoire, Mathilde Mougeot
https://arxiv.org/abs/2507.12165

Multi-Component VAE with Gaussian Markov Random Field
Multi-component datasets with intricate dependencies, like industrial assemblies or multi-modal imaging, challenge current generative modeling techniques. Existing Multi-component Variational AutoEncoders typically rely on simplified aggregation strategies, neglecting critical nuances and consequently compromising structural coherence across generated components. To explicitly address this gap, we introduce the Gaussian Markov Random Field Multi-Component Variational AutoEncoder , a novel gener…

Ancilla-train quantum algorithm for simulating non-Markovian open quantum systems
Hans Michael Christensen, Johannes Agerskov, Frederik Nathan
https://arxiv.org/abs/2509.12717 h…

Ancilla-train quantum algorithm for simulating non-Markovian open quantum systems
We present a quantum algorithm for simulating open quantum systems coupled to Gaussian environments valid for any configuration and coupling strength. The algorithm is for instance applicable to problems with strongly coupled, or non-Markovian, environments; problems with multiple environments out of mutual equilibrium; and problems with time-dependent Hamiltonians. We show that the algorithm can reproduce the true dynamics of such problems at arbitrary accuracy and, for a broad range of proble…