Tootfinder

Streaming Sequence-to-Sequence Learning with Delayed Streams Modeling
Neil Zeghidour, Eugene Kharitonov, Manu Orsini, V\'aclav Volhejn, Gabriel de Marmiesse, Edouard Grave, Patrick P\'erez, Laurent Mazar\'e, Alexandre D\'efossez
https://arxiv.org/abs/2509.08753

Streaming Sequence-to-Sequence Learning with Delayed Streams Modeling
We introduce Delayed Streams Modeling (DSM), a flexible formulation for streaming, multimodal sequence-to-sequence learning. Sequence-to-sequence generation is often cast in an offline manner, where the model consumes the complete input sequence before generating the first output timestep. Alternatively, streaming sequence-to-sequence rely on learning a policy for choosing when to advance on the input stream, or write to the output stream. DSM instead models already time-aligned streams with a …

Trump says he will fire labor statistics chief after weak jobs report (Neil Irwin/Axios)
https://www.axios.com/2025/08/01/trump-jobs-labor-statistics
http://www.memeorandum.com/250801/p123#a250801p123

Markov Decision Processing Networks
Sanidhay Bhambay, Thirupathaiah Vasantam, Neil Walton
https://arxiv.org/abs/2509.24541 https://arxiv.org/pdf/2509.24541…

Markov Decision Processing Networks
We introduce Markov Decision Processing Networks (MDPNs) as a multiclass queueing network model where service is a controlled, finite-state Markov process. The model exhibits a decision-dependent service process where actions taken influence future service availability. Viewed as a two-sided queueing model, this captures settings such as assemble-to-order systems, ride-hailing platforms, cross-skilled call centers, and quantum switches. We first characterize the capacity region of MDPNs. Unli…

Measuring the splashback feature: Dependence on halo properties and history
Qiaorong S. Yu (Oxford Math, Oxford Physics), Stephanie O'Neil (UPenn, Princeton, MIT), Xuejian Shen (MIT), Mark Vogelsberger (MIT), Sownak Bose (Durham), Boryana Hadzhyska (Lawrence Berkeley Lab, UC Berkeley, Cambridge Astronomy, Kavli Institute Cambridge), Lars Hernquist (CfA Harvard), Rahul Kannan (York), Monica Wu (NYU Math), Ziang Wu (NYU Math, NYU Engineering)

Measuring the splashback feature: Dependence on halo properties and history
In this study, we define the novel splashback depth $\mathcal{D}$ and width $\mathcal{W}$ to examine how the splashback features of dark matter haloes are affected by the physical properties of haloes themselves. We use the largest simulation run in the hydrodynamic MillenniumTNG project. By stacking haloes in bins of halo mass, redshift, mass-dependent properties such as peak height and concentration, and halo formation history, we measure the shape of the logarithmic slope of the density prof…

Contributions to the Theory of Clifford-Cyclotomic Circuits
Linh Dinh (Dalhousie University), Neil J. Ross (Dalhousie University)
https://arxiv.org/abs/2508.14674 https://

Contributions to the Theory of Clifford-Cyclotomic Circuits
Let $n$ be a positive integer divisible by 8. The Clifford-cyclotomic gate set $\mathcal{G}_n$ consists of the Clifford gates, together with a $z$-rotation of order $n$. It is easy to show that, if a circuit over $\mathcal{G}_n$ represents a unitary matrix $U$, then the entries of $U$ must lie in $\mathcal{R}_n$, the smallest subring of $\mathbb{C}$ containing $1/2$ and $\mathrm{exp}(2πi/n)$. The converse implication, that every unitary $U$ with entries in $\mathcal{R}_n$ can be represented by…

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