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Como vai a minha saúde mental? Saindo para o trabalho agora, esqueci de colocar os sapatos e desci boa parte da escadaria do prédio usando chinelos. Só percebi quando jš estava no andar de baixo. 🤡

Formation Channels of Gravitationally Resolvable Double White Dwarf Binaries Inside Globular Clusters
Lucas Hellstr\"om, Miros{\l}aw Giersz, Abbas Askar, Arkadiusz Hypki, Yuetong Zhao, Youjun Lu, Siqi Zhang, Ver\'onica V\'azquez-Aceves, Grzegorz Wiktorowicz
https://arxiv.org/abs/2506.13122

Formation Channels of Gravitationally Resolvable Double White Dwarf Binaries Inside Globular Clusters
Current gravitational wave detectors are sensitive to coalescing black holes and neutron stars. However, double white dwarfs (DWDs) have long been recognized as promising sources of gravitational waves, and upcoming detectors like LISA will be able to observe these systems in abundance. DWDs are expected to be the dominant gravitational wave (GW) sources in parts of the LISA frequency range, making it crucial to understand their formation for future detections. The Milky Way contains many white…

Configuration spaces and peak representations
Marcelo Aguiar, Sarah Brauner, Victor Reiner
https://arxiv.org/abs/2508.09898 https://arxiv.org/pdf/2508.0989…

Configuration spaces and peak representations
Within the group algebras of the symmetric and hyperoctahedral groups, one has their descent algebras and families of Eulerian idempotents. These idempotents are known to generate group representations with topological interpretations, as the cohomology of configuration spaces of types A and B. We provide an analogous cohomological interpretation for the representations generated by idempotents in the peak algebra, called the peak representations. We describe the peak representations as sums of…

Mallows Model with Learned Distance Metrics: Sampling and Maximum Likelihood Estimation
Yeganeh Alimohammadi, Kiana Asgari
https://arxiv.org/abs/2507.08108

Mallows Model with Learned Distance Metrics: Sampling and Maximum Likelihood Estimation
\textit{Mallows model} is a widely-used probabilistic framework for learning from ranking data, with applications ranging from recommendation systems and voting to aligning language models with human preferences~\cite{chen2024mallows, kleinberg2021algorithmic, rafailov2024direct}. Under this model, observed rankings are noisy perturbations of a central ranking $σ$, with likelihood decaying exponentially in distance from $σ$, i.e, $P (π) \propto \exp\big(-β\cdot d(π, σ)\big),$ where $β> 0…

Deep Learning-based mmWave MIMO Channel Estimation using sub-6 GHz Channel Information: CNN and UNet Approaches
Faruk Pasic, Lukas Eller, Stefan Schwarz, Markus Rupp, Christoph F. Mecklenbr\"auker
https://arxiv.org/abs/2506.11714

Deep Learning-based mmWave MIMO Channel Estimation using sub-6 GHz Channel Information: CNN and UNet Approaches
Future wireless multiple-input multiple-output (MIMO) systems will integrate both sub-6 GHz and millimeter wave (mmWave) frequency bands to meet the growing demands for high data rates. MIMO link establishment typically requires accurate channel estimation, which is particularly challenging at mmWave frequencies due to the low signal-to-noise ratio (SNR). In this paper, we propose two novel deep learning-based methods for estimating mmWave MIMO channels by leveraging out-of-band information fro…

Pilot-to-Data Power Ratio in RIS-Assisted Multiantenna Communication
Masoud Sadeghian, Angel Lozano, Gabor Fodor
https://arxiv.org/abs/2507.21785 https://a…

Pilot-to-Data Power Ratio in RIS-Assisted Multiantenna Communication
The optimization of the \gls{pdpr} is a recourse that helps wireless systems to acquire channel state information while minimizing the pilot overhead. While the optimization of the \gls{pdpr} in cellular networks has been studied extensively, the effect of the \gls{pdpr} in \gls{ris}-assisted networks has hardly been examined. This paper tackles this optimization when the communication is assisted by a RIS whose phase shifts are adjusted on the basis of the statistics of the channels. For a set…