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Related Articles from SNS
Nuclear Charge Radii of Sr Isotopes: Reevaluation based on Transition Frequency Measurements in the $5s-5p-4d$ manifold in Sr$^+$
arXiv:2605.04281v2 Announce Type: replace Abstract: High-precision quasi-simultaneous collinear/anticollinear laser spectroscopy was performed to measure the $5s$ $^2S_{1/2}\rightarrow 5p$ $^2P_{1/2}$ (D1), the $5s$ $^2S_{1/2}\rightarrow 5p$ $^2P_{3/2}$ (D2), and the three $4d\rightarrow 5p$ transitions in naturally abundant Sr$^+$ isotopes. For absolute transition frequencies, an accuracy of up to 600 kHz was achieved, while common-mode rejection allowed us to extract isotope shifts with...
Tuning long-range interactions in Sr Rydberg atoms: the effect of series perturbations
arXiv:1505.07152v3 Announce Type: replace Abstract: We investigate the effect of series perturbation on the second-order dipole-dipole interactions between strontium atoms in the $5sns({^1}S_0)$ and $5snp({^1}P_1)$ Rydberg states as a means of engineering long-range interactions between atoms. The series perturbation in these atoms enables modifying the strength and the sign of the interaction by varying the principal quantum number $n$ of the Rydberg electron. We utilize experimentally...
Decomposable Neuro Symbolic Regression
Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data. However, most SR methods prioritize minimizing prediction error over identifying the governing equations, often producing overly complex or inaccurate expressions. To address this, we present a decomposable SR method that generates interpretable multivariate expressions leveraging transformer models, genetic...
Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
Announce Type: replace Abstract: Symbolic regression (SR) aims to discover interpretable analytical expressions that accurately describe observed data. Amortized SR promises to be much more efficient than the predominant genetic programming SR methods, but currently struggles to scale to realistic scientific complexity. We find that a key obstacle is the lack of a fast reduction of equivalent expressions to a concise normalized form.
Repeat offender on probation allegedly kills father who tracked his stolen truck using GPS
A father in Houston, Texas, was killed during a carjacking Saturday afternoon, and a repeat offender on probation has been charged with his murder. The Harris County Sheriff's Office said in a news release that the owner of the stolen car, identified as 56-year-old Louis Erebia, tracked down the vehicle and confronted the person accused of stealing it, London Hogan Sr. Hogan Sr. allegedly shot and killed Erebia, who was pronounced dead after being taken to a local hospital. Hogan Sr. is also...
Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration
arXiv:2605.00310v2 Announce Type: replace Abstract: Super-resolution (SR) techniques have made major advances in reconstructing high-resolution images from low-resolution inputs. The increased resolution provides visual enhancement and utility for monitoring tasks. In particular, SR has been increasingly developed for satellite-based Earth observation, with applications in urban planning, agriculture, ecology, and disaster response.
Rank-Factorized Implicit Neural Bias: Scaling Super-Resolution Transformer with FlashAttention
Announce Type: replace Abstract: Recent Super-Resolution~(SR) methods mainly adopt Transformers for their strong long-range modeling capability and exceptional representational capacity. However, most SR Transformers rely heavily on relative positional bias~(RPB), which prevents them from leveraging hardware-efficient attention kernels such as FlashAttention.
The Terminal Representation in Reinforcement Learning
arXiv:2605.31289v1 Announce Type: new Abstract: Representation learning is a powerful tool for spatio-temporal abstraction within reinforcement learning (RL). Two well established approaches are through the successor representation (SR) and the default representation (DR). The SR encodes states by the future trajectories they induce, capturing information flow decoupled from reward.
Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing
arXiv:2601.04051v3 Announce Type: replace Abstract: Symbolic regression aims to find symbolic expressions that describe datasets. Due to its inherent interpretability, symbolic regression (SR) is a powerful paradigm for scientific discovery. Recent advances have expanded SR to describe related phenomena using a single expression with varying sets of parameters, thereby introducing a single categorical variable.