Tootfinder

Efficient Autoregressive Inference for Transformer Probabilistic Models
Conor Hassan, Nasrulloh Loka, Cen-You Li, Daolang Huang, Paul E. Chang, Yang Yang, Francesco Silvestrin, Samuel Kaski, Luigi Acerbi
https://arxiv.org/abs/2510.09477

Efficient Autoregressive Inference for Transformer Probabilistic Models
Transformer-based models for amortized probabilistic inference, such as neural processes, prior-fitted networks, and tabular foundation models, excel at single-pass marginal prediction. However, many real-world applications, from signal interpolation to multi-column tabular predictions, require coherent joint distributions that capture dependencies between predictions. While purely autoregressive architectures efficiently generate such distributions, they sacrifice the flexible set-conditioning…

Engineering High-Order Harmonic Generation through Gas Confinement at Sub-Millimeter Lengths
Agata Azzolin, Gaia Giovannetti, Oliviero Cannelli, Sabine Rockenstein, Guangyu Fan, Md S. Ahsan, Lorenzo Colaizzi, Erik P. M{\aa}nsson, Noah Tettenborn, Linda Oberti, Davide Faccial\`a, Fabio Frassetto, Anna Gabriella Ciriolo, Dario W. Lodi, Alia Ashraf, Cristian Manzoni, Rebeca Mart\'inez V\'azquez, Michele Devetta, Roberto Osellame, Luca Poletto, Salvatore Stagira, Caterina Vozzi, Te…

Engineering High-Order Harmonic Generation through Gas Confinement at Sub-Millimeter Lengths
Attosecond light sources based on high-order harmonic generation (HHG) constitute to date the only table-top solution for producing coherent broadband radiation covering the spectral range from the extreme ultraviolet to the soft X-rays. The so-called emission cutoff can be extended towards higher photon energies by increasing the driving wavelength at the expense of conversion efficiency. An alternative route is to overdrive the process by using higher laser intensities, with the challenging r…

Toward Green Code: Prompting Small Language Models for Energy-Efficient Code Generation
Humza Ashraf, Syed Muhammad Danish, Zeeshan Sattar
https://arxiv.org/abs/2509.09947 https…

Toward Green Code: Prompting Small Language Models for Energy-Efficient Code Generation
There is a growing concern about the environmental impact of large language models (LLMs) in software development, particularly due to their high energy use and carbon footprint. Small Language Models (SLMs) offer a more sustainable alternative, requiring fewer computational resources while remaining effective for fundamental programming tasks. In this study, we investigate whether prompt engineering can improve the energy efficiency of SLMs in code generation. We evaluate four open-source SLMs…

NeuCODEX: Edge-Cloud Co-Inference with Spike-Driven Compression and Dynamic Early-Exit
Maurf Hassan, Steven Davy, Muhammad Zawish, Owais Bin Zuber, Nouman Ashraf
https://arxiv.org/abs/2509.19156

Replaced article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Rec...
Ashirbad Mishra, Jinyu Zhao, Soumik Dey, Hansi Wu, Binbin Li, Kamesh Madduri

Search for spontaneous fission of plutonium and americium nuclides using gamma spectrometry
I. S. Klyuyev, D. V. Kasperovych, V. V. Kobychev
https://arxiv.org/abs/2509.26191 htt…

Search for spontaneous fission of plutonium and americium nuclides using gamma spectrometry
Spontaneous fission in americium and plutonium isotopes was investigated using high-resolution gamma spectrometry. This process was investigated through analysis of a certified PuO2 sample spectrum obtained from the IAEA database of reference spectra. Measurements were carried out with a HPGe detector, and detection efficiency calibration was performed by Monte Carlo simulations using GEANT4. There are no peaks observed that can be ascribed to beta decay of expected spontaneous-fission daughter…