Building a Neural Network in Rust: A Step-by-Step Guide
When most developers think neural networks, they reach for Python. TensorFlow, PyTorch, Keras — the ecosystem is rich and the barrier to… Continue reading on Rustaceans »
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DF-3DRME: A Data-Friendly Learning Framework for 3D Radio Map Estimation based on Super-Resolution Technique
arXiv:2604.00676v1 Announce Type: new Abstract: High-Resolution three-dimensional (3D) radio maps (RMs) provide rich information about the radio landscape that is essential to a myriad of wireless applications in the future wireless networks. Although deep learning (DL) methods have shown their effectiveness in RM construction, existing approaches require massive high-resolution 3D RM samples in the training dataset, the acquisition of which is labor-intensive and time-consuming in practice. In this paper, our goal is to devise a data-friendly high-resolution 3D RM construction solution via training over a hybrid dataset, wherein the RMs associated with a small fraction of environment maps (EMs) are of high-resolution, while those corresponding to the majority of EMs are of low-resolution.
Global asteroseismology of 19,000 red giants in the TESS Continuous Viewing Zones
arXiv:2604.00498v1 Announce Type: cross Abstract: TESS (Transiting Exoplanet Survey Satellite) has produced long-term photometry for millions of stars across the sky. In this work, we present an asteroseismic catalogue of 19,151 red giants in the TESS Continuous Viewing Zones using sectors 1--87 (Years 1--7). We visually assessed the power spectra for oscillations, and then applied the computationally efficient nuSYD method to confirm reliability. We identified an increase of 80% in the number of previously known oscillating red giants at a TESS magnitude $>$ 8. We determined the frequency of maximum power ($\rm \nu_{max}$) and the large frequency separation ($\rm \Delta \nu$) using the pySYD pipeline, achieving typical precisions of 1.5% and 1%, respectively. We classified the stars into
Phase space integrity in neural network models of Hamiltonian dynamics: A Lagrangian descriptor approach
arXiv:2604.00473v1 Announce Type: new Abstract: We propose Lagrangian Descriptors (LDs) as a diagnostic framework for evaluating neural network models of Hamiltonian systems beyond conventional trajectory-based metrics. Standard error measures quantify short-term predictive accuracy but provide little insight into global geometric structures such as orbits and separatrices. Existing evaluation tools in dissipative systems are inadequate for Hamiltonian dynamics due to fundamental differences in the systems. By constructing probability density functions weighted by LD values, we embed geometric information into a statistical framework suitable for information-theoretic comparison. We benchmark physically constrained architectures (SympNet, H\'enonNet, Generalized Hamiltonian Neural Networks
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TRIMS: Trajectory-Ranked Instruction Masked Supervision for Diffusion Language Models
arXiv:2604.00666v1 Announce Type: new Abstract: Diffusion language models (DLMs) offer a promising path toward low-latency generation through parallel decoding, but their practical efficiency depends heavily on the decoding trajectory. In practice, this advantage often fails to fully materialize because standard training does not provide explicit supervision over token reveal order, creating a train-inference mismatch that leads to suboptimal decoding behavior. We propose Trajectory-Ranked Instruction Masked Supervision (TRIMS), a simple trajectory-guided supervised fine-tuning framework that injects trajectory supervision into standard Masked Diffusion Language Model (MDLM) training with minimal overhead. Instead of relying on costly DLM-based distillation, TRIMS uses lightweight signals
English to Central Kurdish Speech Translation: Corpus Creation, Evaluation, and Orthographic Standardization
arXiv:2604.00613v1 Announce Type: new Abstract: We present KUTED, a speech-to-text translation (S2TT) dataset for Central Kurdish, derived from TED and TEDx talks. The corpus comprises 91,000 sentence pairs, including 170 hours of English audio, 1.65 million English tokens, and 1.40 million Central Kurdish tokens. We evaluate KUTED on the S2TT task and find that orthographic variation significantly degrades Kurdish translation performance, producing nonstandard outputs. To address this, we propose a systematic text standardization approach that yields substantial performance gains and more consistent translations. On a test set separated from TED talks, a fine-tuned Seamless model achieves 15.18 BLEU, and we improve Seamless baseline by 3.0 BLEU on the FLEURS benchmark. We also train a Tra
Speech LLMs are Contextual Reasoning Transcribers
arXiv:2604.00610v1 Announce Type: new Abstract: Despite extensions to speech inputs, effectively leveraging the rich knowledge and contextual understanding of large language models (LLMs) in automatic speech recognition (ASR) remains non-trivial, as the task primarily involves direct speech-to-text mapping. To address this, this paper proposes chain-of-thought ASR (CoT-ASR), which constructs a reasoning chain that enables LLMs to first analyze the input speech and generate contextual analysis, thereby fully exploiting their generative capabilities. With this contextual reasoning, CoT-ASR then performs more informed speech recognition and completes both reasoning and transcription in a single pass. Moreover, CoT-ASR naturally supports user-guided transcription: while designed to self-genera
More Human, More Efficient: Aligning Annotations with Quantized SLMs
arXiv:2604.00586v1 Announce Type: new Abstract: As Large Language Model (LLM) capabilities advance, the demand for high-quality annotation of exponentially increasing text corpora has outpaced human capacity, leading to the widespread adoption of LLMs in automatic evaluation and annotation. However, proprietary LLMs often exhibit systematic biases that diverge from human expert consensus, lacks reproducibility, and raises data privacy concerns. Our work examines the viability of finetuning a quantized Small Language Model of 1.7B parameter size on limited human-annotated data to serve as a highly aligned, deterministic evaluator and annotator. By implementing a custom, multi-dimensional rubric framework and simple augmentation and regularization techniques, the proposed approach achieves h
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