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Poster No 857: Plastic changes in the occipital regions during pitch perception associate with blindness onset age; "Specifically, the primary visual cortex remains engaged in pitch processing tasks following late blindness." https://doi.org/10.5281/zenodo.15641972

This https://arxiv.org/abs/2406.00827 has been replaced.
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Comparing Experimental and Nonexperimental Methods: What Lessons Have We Learned Four Decades After LaLonde (1986)?
In 1986, Robert LaLonde published an article comparing nonexperimental estimates to experimental benchmarks (LaLonde 1986). He concluded that the nonexperimental methods at the time could not systematically replicate experimental benchmarks, casting doubt on their credibility. Following LaLonde's critical assessment, there have been significant methodological advances and practical changes, including (i) an emphasis on the unconfoundedness assumption separated from functional form consideration…

The DECam MAGIC Survey: Spectroscopic Follow-up of the Most Metal-Poor Stars in the Distant Milky Way Halo
Vinicius M. Placco, Guilherme Limberg, Anirudh Chiti, Deepthi S. Prabhu, Alexander P. Ji, Fabr\'icia O. Barbosa, William Cerny, Andrew B. Pace, Guy S. Stringfellow, David J. Sand, Clara E. Mart\'inez-V\'azquez, Alexander H. Riley, Silvia Rossi, Noelia E. D. No\"el, A. Katherina Vivas, Gustavo E. Medina, Alex Drlica-Wagner, Joanna D. Sakowska, Bur\c{c}in Mutlu-Pakd…

The DECam MAGIC Survey: Spectroscopic Follow-up of the Most Metal-Poor Stars in the Distant Milky Way Halo
In this work, we present high-resolution spectroscopic observations for six metal-poor stars with [Fe/H]<-3 (including one with [Fe/H]<-4), selected using narrow-band Ca II HK photometry from the DECam MAGIC Survey. The spectroscopic data confirms the accuracy of the photometric metallicities and allows for the determination of chemical abundances for 16 elements, from carbon to barium. The program stars have chemical abundances consistent with this metallicity range. A kinematic/dynamical anal…

A Review and Comparison of Different Sensitivity Analysis Techniques in Practice
Devin Francom, Abigael Nachtsheim
https://arxiv.org/abs/2506.11471 https:/…

A Review and Comparison of Different Sensitivity Analysis Techniques in Practice
There exist many methods for sensitivity analysis readily available to the practitioner. While each seeks to help the modeler answer the same general question -- How do sources of uncertainty or changes in the model inputs relate to uncertainty in the output? -- different methods are associated with different assumptions, constraints, and required resources, leading to conclusions that may vary in interpretability and level of detail. Thus, it is crucial that the practitioner selects the desire…

FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
Black Forest Labs, Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, Kyle Lacey, Yam Levi, Cheng Li, Dominik Lorenz, Jonas M\"uller, Dustin Podell, Robin Rombach, Harry Saini, Axel Sauer, Luke Smith

FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
We present evaluation results for FLUX.1 Kontext, a generative flow matching model that unifies image generation and editing. The model generates novel output views by incorporating semantic context from text and image inputs. Using a simple sequence concatenation approach, FLUX.1 Kontext handles both local editing and generative in-context tasks within a single unified architecture. Compared to current editing models that exhibit degradation in character consistency and stability across multip…

Advantages of Feedback in Distributed Data-Gathering for Accurate and Power-Efficient State-Estimation
Hyeongmin Choe, Soojean Han
https://arxiv.org/abs/2507.11924

Advantages of Feedback in Distributed Data-Gathering for Accurate and Power-Efficient State-Estimation
In distributed target-tracking sensor networks, efficient data gathering methods are necessary to save communication resources and assure information accuracy. This paper proposes a Feedback (FB) distributed data-gathering method which lets the central unit feed information back to the mobile sensors; each sensor then uses it to cancel redundant transmissions and reduce communication congestion. We rigorously compare its performance, in terms of mean-squared error (MSE) and cost of power per se…

Geometric Convergence to an Extreme Limit Space with nonnegative scalar curvature
Christina Sormani, Wenchuan Tian, Wai-Ho Yeung
https://arxiv.org/abs/2506.12491

Geometric Convergence to an Extreme Limit Space with nonnegative scalar curvature
In 2014, Gromov conjectured that sequences of manifolds with nonnegative scalar curvature should have subsequences which converge in some geometric sense to limit spaces with some notion of generalized nonnegative scalar curvature. In recent joint work with Changliang Wang, the authors found a sequence of warped product Riemannian metrics on $\Sph^2\times \Sph^1$ with nonnegative scalar curvature whose metric tensors converge in the $W^{1,p}$ sense for $p<2$ to an extreme warped product limit s…

Quantile Reward Policy Optimization: Alignment with Pointwise Regression and Exact Partition Functions
Simon Matrenok, Skander Moalla, Caglar Gulcehre
https://arxiv.org/abs/2507.08068 https://arxiv.org/pdf/2507.08068 https://arxiv.org/html/2507.08068
arXiv:2507.08068v1 Announce Type: new
Abstract: Aligning large language models with pointwise absolute rewards has so far required online, on-policy algorithms such as PPO and GRPO. In contrast, simpler methods that can leverage offline or off-policy data, such as DPO and REBEL, are limited to learning from preference pairs or relative signals. To bridge this gap, we introduce \emph{Quantile Reward Policy Optimization} (QRPO), which learns from pointwise absolute rewards while preserving the simplicity and offline applicability of DPO-like methods. QRPO uses quantile rewards to enable regression to the closed-form solution of the KL-regularized RL objective. This reward yields an analytically tractable partition function, removing the need for relative signals to cancel this term. Moreover, QRPO scales with increased compute to estimate quantile rewards, opening a new dimension for pre-computation scaling. Empirically, QRPO consistently achieves top performance on chat and coding evaluations -- reward model scores, AlpacaEval 2, and LeetCode -- compared to DPO, REBEL, and SimPO across diverse datasets and 8B-scale models. Finally, we find that training with robust rewards instead of converting them to preferences induces less length bias.
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Optical and electrical probing of plasmonic metal-molecule interactions
Andrei Stefancu, Wenxuan Tang, Ming Fu, Jordan Edwards, Naomi J. Halas, Ross C. Schofield, Toby Severs Millard, Peter Nordlander, Johannes Lischner, Pilar Carro, Rupert Oulton, Emiliano Cortes
https://arxiv.org/abs/2507.12128…

Optical and electrical probing of plasmonic metal-molecule interactions
Plasmonic nanostructures enable efficient light-to-energy conversion by concentrating optical energy into nanoscale volumes. A key mechanism in this process is chemical interface damping (CID), where surface plasmons are damped by adsorbed molecules, enabling the transfer of charge to adsorbed molecules. In this study, we investigate the relationship between CID and adsorbate-induced changes in DC electrical resistivity for four molecular adsorbates-adenine, 4-aminothiophenol (ATP), biphenyl th…

Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints
Ashfaq Iftakher, Rahul Golder, M. M. Faruque Hasan
https://arxiv.org/abs/2507.08124 https://arxiv.org/pdf/2507.08124 https://arxiv.org/html/2507.08124
arXiv:2507.08124v1 Announce Type: new
Abstract: Traditional physics-informed neural networks (PINNs) do not guarantee strict constraint satisfaction. This is problematic in engineering systems where minor violations of governing laws can significantly degrade the reliability and consistency of model predictions. In this work, we develop KKT-Hardnet, a PINN architecture that enforces both linear and nonlinear equality and inequality constraints up to machine precision. It leverages a projection onto the feasible region through solving Karush-Kuhn-Tucker (KKT) conditions of a distance minimization problem. Furthermore, we reformulate the nonlinear KKT conditions using log-exponential transformation to construct a general sparse system with only linear and exponential terms, thereby making the projection differentiable. We apply KKT-Hardnet on both test problems and a real-world chemical process simulation. Compared to multilayer perceptrons and PINNs, KKT-Hardnet achieves higher accuracy and strict constraint satisfaction. This approach allows the integration of domain knowledge into machine learning towards reliable hybrid modeling of complex systems.
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