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'Forever chemicals' detected in 65% of sampled private wells in Pennsylvania https://phys.org/news/2025-06-chemicals-sampled-private-wells-pennsylvania.html
PFAS Found in 65% of Private Wells Sampled in Pennsylvania

'Forever chemicals' detected in 65% of sampled private wells in Pennsylvania
In Pennsylvania, 3.5 million people are served by private well systems, according to Penn State Extension. To better understand potential contamination of the groundwater feeding these systems, a team of researchers from Penn State conducted a novel three-year citizen science study of per- and poly-fluoroalkyl substances (PFAS)—often referred to as forever chemicals—in 167 private wells across Pennsylvania.

🇺🇦 #NowPlaying on KEXP's #MorningShow
Skinflint:
🎵 Mbube the Great
#Skinflint

The “Appeal to Heaven” flag,
a popular symbol for Christian nationalists that was waved by January 6 rioters,
was raised over the Small Business Administration headquarters last week.
https://www.wired.com/story/far-right-appeal-to…

Far-Right ‘Appeal to Heaven’ Flag Flown Above Government Agency in DC
The “Appeal to Heaven” flag, a popular symbol for Christian nationalists that was waved by January 6 rioters, was raised over the Small Business Administration headquarters last week.

Excitation Amplitude Sampling for Low Variance Electronic Structure on Quantum Computers
Connor Lenihan, Oliver J. Backhouse, Tom W. A. Montgomery, Phalgun Lolur, M. J. Bhaseen, George H. Booth
https://arxiv.org/abs/2506.15438

Excitation Amplitude Sampling for Low Variance Electronic Structure on Quantum Computers
We combine classical heuristics with partial shadow tomography to enable efficient protocols for extracting information from correlated ab initio electronic systems encoded on quantum devices. By proposing the use of a correlation energy functional and sampling of a polynomial set of excitation amplitudes of the quantum state, we can demonstrate an almost two order of magnitude reduction in required number of shots for a given statistical error in the energy estimate, as well as observing a lin…

Jeg er på dagens andre sommeravslutning. Men jeg synes det er litt tidlig å avslutte sommeren! #norsktut

Enhancing Symbolic Machine Learning by Subsymbolic Representations
Stephen Roth, Lennart Baur, Derian Boer, Stefan Kramer
https://arxiv.org/abs/2506.14569 …

Enhancing Symbolic Machine Learning by Subsymbolic Representations
The goal of neuro-symbolic AI is to integrate symbolic and subsymbolic AI approaches, to overcome the limitations of either. Prominent systems include Logic Tensor Networks (LTN) or DeepProbLog, which offer neural predicates and end-to-end learning. The versatility of systems like LTNs and DeepProbLog, however, makes them less efficient in simpler settings, for instance, for discriminative machine learning, in particular in domains with many constants. Therefore, we follow a different approach:…

Versatile Symbolic Music-for-Music Modeling via Function Alignment
Junyan Jiang, Daniel Chin, Liwei Lin, Xuanjie Liu, Gus Xia
https://arxiv.org/abs/2506.15548

Versatile Symbolic Music-for-Music Modeling via Function Alignment
Many music AI models learn a map between music content and human-defined labels. However, many annotations, such as chords, can be naturally expressed within the music modality itself, e.g., as sequences of symbolic notes. This observation enables both understanding tasks (e.g., chord recognition) and conditional generation tasks (e.g., chord-conditioned melody generation) to be unified under a music-for-music sequence modeling paradigm. In this work, we propose parameter-efficient solutions fo…

Adaptive Data Augmentation for Thompson Sampling
Wonyoung Kim
https://arxiv.org/abs/2506.14479 https://arxiv.org/pdf/2506.14479

Adaptive Data Augmentation for Thompson Sampling
In linear contextual bandits, the objective is to select actions that maximize cumulative rewards, modeled as a linear function with unknown parameters. Although Thompson Sampling performs well empirically, it does not achieve optimal regret bounds. This paper proposes a nearly minimax optimal Thompson Sampling for linear contextual bandits by developing a novel estimator with the adaptive augmentation and coupling of the hypothetical samples that are designed for efficient parameter learning. …

#ARKSurvivalAscended #ARK #Linux Wildcard released Ragnarok last night, and just got my server install

GitHub - cdp1337/ARKSurvivalAscended-Linux: Simple install script for installing Windows version of Ark Survival Ascended on a Debian server. Registers with systemd for integration and creates convenience links in /home/steam/ArkSurvivalAscended.
Simple install script for installing Windows version of Ark Survival Ascended on a Debian server. Registers with systemd for integration and creates convenience links in /home/steam/ArkSurvivalAsc...

An Exact and Efficient Sampler for Dynamic Discrete Distributions
Lilith Orion Hafner, Adriano Meligrana
https://arxiv.org/abs/2506.14062 https://

An Exact and Efficient Sampler for Dynamic Discrete Distributions
Sampling from a dynamic discrete distribution involves sampling from a dynamic set of weighted elements, where elements can be added or removed at any stage of the sampling process. Although efficient for static sets, the Alias method becomes impractical in dynamic settings due to the need to reconstruct the sampler after each update, which incurs a computational cost proportional to the size of the distribution, making it unsuitable for applications requiring frequent insertions, deletions, or…