Tootfinder

Alles so schön (un)bunt hier: Welche Theming-Möglichkeiten macOS 26 bringt
Wer die Optik von macOS verändern wollte, musste bislang zu speziellen Tools greifen. Mit Tahoe liefert die Apple nun teilweise selbst.

Enhancing eLoran Timing Accuracy via Machine Learning with Meteorological and Terrain Data
Taewon Kang, Seunghyeon Park, Pyo-Woong Son, Jiwon Seo
https://arxiv.org/abs/2506.15235 …

Enhancing eLoran Timing Accuracy via Machine Learning with Meteorological and Terrain Data
The vulnerabilities of global navigation satellite systems (GNSS) to signal interference have increased the demand for complementary positioning, navigation, and timing (PNT) systems. To address this, South Korea has decided to deploy an enhanced long-range navigation (eLoran) system as a complementary PNT solution. Similar to GNSS, eLoran provides highly accurate timing information, which is essential for applications such as telecommunications, financial systems, and power distribution. Howev…

Don't Make It Up: Preserving Ignorance Awareness in LLM Fine-Tuning
William F. Shen, Xinchi Qiu, Nicola Cancedda, Nicholas D. Lane
https://arxiv.org/abs/2506.14387

Don't Make It Up: Preserving Ignorance Awareness in LLM Fine-Tuning
Existing work on mitigating catastrophic forgetting in large language model (LLM) fine-tuning has primarily focused on preserving specific data or tasks, while critically overlooking the degradation of essential capabilities instilled through safety alignment, particularly the model's ability to faithfully express ignorance. In this work, we show that this capability is significantly degraded during conventional fine-tuning, leading to undesired behaviors such as hallucinations. To address this…

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Jisu Kim, Jiho Lee, Sangmin Oh, Yutack Park, Seungwoo Hwang, Seungwu Han, Sungwoo Kang, Youngho Kang
https://arxiv.org/abs/2506.15223

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio methods. Fine-tuning pretrained MLIPs offers a practical approach to improving accuracy for materials and properties where predictive performance is insufficient. However, this approach often induces catastrophic forgetting, undermining the generalizability that is a key advantage of pretrained MLI…

Una capturita de pantalla, por favor, para los pobres que no tenemos suscripción al mercurioso. 🙏🏻
Re: https://twitter.com/DAurenque/status/1935340959119614152

Skakande rapport från arabisk-israeliska staden Tamra.
Här bor en del av de två miljoner palestinier som har israeliskt medborgarskap. Men alla är inte lika inför de iranska bomberna.
Här blir ingen erbjuden evakuering. Ingen erbjuds ersättning.
https://www.d…

Sorg och vrede i arabisk-israeliska staden Tamra
Kriget mellan Iran och Israel slår även mot de två miljoner palestinier som har israeliskt medborgarskap.

Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
Yuto Harada, Yusuke Yamauchi, Yusuke Oda, Yohei Oseki, Yusuke Miyao, Yu Takagi
https://arxiv.org/abs/2506.14681

Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
Supervised fine-tuning (SFT) is a critical step in aligning large language models (LLMs) with human instructions and values, yet many aspects of SFT remain poorly understood. We trained a wide range of base models on a variety of datasets including code generation, mathematical reasoning, and general-domain tasks, resulting in 1,000+ SFT models under controlled conditions. We then identified the dataset properties that matter most and examined the layer-wise modifications introduced by SFT. Our…

Tuning spin-density separation via finite-range interactions: Dimensionality-driven signatures in dynamic structure factors
Xiaoran Ye, Yi Zhang, Ziheng Zhou, Zhaoxin Liang
https://arxiv.org/abs/2506.15092

Tuning spin-density separation via finite-range interactions: Dimensionality-driven signatures in dynamic structure factors
Spin-density separation, marked by distinct propagation velocities of spin and density excitations, epitomizes strong correlations, historically confined to one-dimensional (1D) systems. Recent ultracold quantum gas experiments, however, demonstrate its emergence in higher dimensions through precise tuning of intra- to interspecies interaction ratios, inspiring exploration of how dimensionality and interatomic interactions govern quantum correlations. We investigate this in two-component bosoni…

Einstein causality of quantum measurements in the Tomonaga-Schwinger picture
Samuel Fedida
https://arxiv.org/abs/2506.14693 https://a…

Einstein causality of quantum measurements in the Tomonaga-Schwinger picture
When quantum measurements are conducted over spacelike-separated regions of spacetime, a natural and commonly assumed physical postulate, called Einstein causality, asserts that they should commute. In this paper, we provide a generalisation to Lüders' rule à la Aharonov-Albert in those globally hyperbolic spacetimes which allow unitarily equivalent Hilbert spaces to be defined along Cauchy hypersurfaces, thus relying on the existence of an interaction picture à la Tomonaga-Schwinger. We sho…

Extending Spike-Timing Dependent Plasticity to Learning Synaptic Delays
Marissa Dominijanni, Alexander Ororbia, Kenneth W. Regan
https://arxiv.org/abs/2506.14984

Extending Spike-Timing Dependent Plasticity to Learning Synaptic Delays
Synaptic delays play a crucial role in biological neuronal networks, where their modulation has been observed in mammalian learning processes. In the realm of neuromorphic computing, although spiking neural networks (SNNs) aim to emulate biology more closely than traditional artificial neural networks do, synaptic delays are rarely incorporated into their simulation. We introduce a novel learning rule for simultaneously learning synaptic connection strengths and delays, by extending spike-timin…