Tootfinder

Der Euro muss rollen.
Steigende #Temperaturen im #Mittelmeerraum machen den kühleren Norden für den #Sommerurlaub attraktiver.
Die

Urlaub im Kühlen: Kann der Norden von "Coolcation" profitieren?
Angesichts der Hitze im Mittelmeerraum ziehen manche Sommerurlaube in kühleren Regionen in Betracht. Darin sehen Touristiker für Norddeutschland eine Chance - und werben bereits mit "Coolcation".

one of my hottest takes is that shared laundry machines in an apartment building is better than in-unit. it's a good example of how individual solutions can be worse than collective ones.
with shared machines you can use two machines at once at a less-busy time. a single in-unit machine would take twice as long. in-unit means the noise the machine generates is inside your apartment, so you can't have guests over or take a meeting while it's running

Yesterday at the @… conference, the Brücke-Museum Berlin presented its digitization project on Karl Schmidt-Rottluff’s collection of objects from colonial contexts. All objects have been published on #wikiCommons, allowing everyone to contribute and r…

Diffractive vector meson photo-production in Oxygen--Oxygen and Neon--Neon ultra-peripheral collisions at the LHC
J. Cepila, J. G. Contreras, M. Matas, A. Ridzikova
https://arxiv.org/abs/2509.11359

Diffractive vector meson photo-production in Oxygen--Oxygen and Neon--Neon ultra-peripheral collisions at the LHC
The energy-dependent hotspot model is used to predict cross sections for vector-meson diffractive photo-nuclear production off oxygen ($γ$O) and neon ($γ$Ne) that can be extracted from ultra-peripheral O--O and Ne--Ne collisions, recently recorded at the LHC. In both cases, two models are used to describe the nuclear shapes. Woods-Saxon prescriptions for O and Ne as well as an alpha-cluster description of O and a bowling-pin-like shape for Ne, according to the PGCM formalism. Predictions are …

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations
JingChun Wang, Meenu Upadhyay, Eric D. Boittier, Kham Lek Chaton, Valerii Andreichev, Mike Devereux, Shimoni Patel, Sena Aydin, Kai T\"opfer, Markus Meuwly
https://arxiv.org/abs/2509.11639

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations
Energy functions for pure and heterogenous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine learned potential energy surfaces (ML-PESs) a new era has started. Statistical models allow the representation of reference data from electronic structure calculations for chemical systems of almost arbitrary complexity at unprecedented detail and accuracy. Here, kernel- and neural network-based approaches for intramolecular degrees of freed…

Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training
Xin Fang, Guirui Zhong, Qing Wang, Fan Chu, Lei Wang, Mengui Qian, Mingqi Cai, Jiangzhao Wu, Jianqing Gao, Jun Du
https://arxiv.org/abs/2509.12845

Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training
Anomalous Sound Detection (ASD) is often formulated as a machine attribute classification task, a strategy necessitated by the common scenario where only normal data is available for training. However, the exhaustive collection of machine attribute labels is laborious and impractical. To address the challenge of missing attribute labels, this paper proposes an agglomerative hierarchical clustering method for the assignment of pseudo-attribute labels using representations derived from a domain-a…

Mapping the Perseus Galaxy Cluster with XRISM: Gas Kinematic Features and their Implications for Turbulence
Congyao Zhang, Irina Zhuravleva, Annie Heinrich, Elena Bellomi, Nhut Truong, John ZuHone, Eugene Churazov, Megan E. Eckart, Yutaka Fujita, Julie Hlavacek-Larrondo, Yuto Ichinohe, Maxim Markevitch, Kyoko Matsushita, Fran\c{c}ois Mernier, Eric D. Miller, Koji Mori, Hiroshi Nakajima, Anna Ogorzalek, Frederick S. Porter, Ay\c{s}eg\"ul T\"umer, Shutaro Ueda, Norbert Werner

Mapping the Perseus Galaxy Cluster with XRISM: Gas Kinematic Features and their Implications for Turbulence
In this paper, we present extended gas kinematic maps of the Perseus cluster by combining five new XRISM/Resolve pointings observed in 2025 with four Performance Verification datasets from 2024, totaling 745 ks net exposure. To date, Perseus remains the only cluster that has been extensively mapped out to ~0.7$r_{2500}$ by XRISM/Resolve, while simultaneously offering sufficient spatial resolution to resolve gaseous substructures driven by mergers and AGN feedback. Our observations cover multipl…

Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning
Amandip Sangha
https://arxiv.org/abs/2509.09991 https://

Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning
This paper presents a machine learning-based approach to estimate the energy consumption of virtual servers without access to physical power measurement interfaces. Using resource utilization metrics collected from guest virtual machines, we train a Gradient Boosting Regressor to predict energy consumption measured via RAPL on the host. We demonstrate, for the first time, guest-only resource-based energy estimation without privileged host access with experiments across diverse workloads, achiev…

Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
Alice E. A. Allen, Rui Li, Sakib Matin, Xing Zhang, Benjamin Nebgen, Nicholas Lubbers, Justin S. Smith, Richard Messerly, Sergei Tretiak, Garnet Kin-Lic Chan, Kipton Barros
https://arxiv.org/abs/2509.10872

Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly describe bond breaking and making energetics. Commonly used approaches such as Density Functional Theory (DFT) frequently fail for this task due to deficiencies that are well recognized. However, for high-fidelity approaches, creating large datasets of energies and forces for reactive processes to train machine learning …