Tootfinder

"We think of generating source code from a prompt as an AI-powered feature of modern IDEs, but the general problem has a rich history in research efforts and domain-specific programming systems." Join William Benton at this year's Berlin Buzzwords to hear him talk about the history of program synthesis, its relationship to the history of AI, and the lessons we can learn from it today.
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Multi-partonic interactions, iterated discontinuities and the virtuality expansion in deep inelastic scattering
Zeno Capatti, Lucien Huber, Michael Ruf
https://arxiv.org/abs/2506.16489

Multi-partonic interactions, iterated discontinuities and the virtuality expansion in deep inelastic scattering
We introduce a perturbative model that accounts for the contribution of multi-partonic interactions to collider observables. A key feature of this multi-parton model is that cross sections are organised in terms of building blocks that are separately infrared-finite in virtue of the KLN theorem. We find compact expressions for these building blocks in terms of double discontinuities of Feynman-type integrals obtained by endowing a vacuum graph with additional topological information. The framew…

How the spin-phase variability of cyclotron lines shapes the pulsed fraction spectra: insights from 4U 1538-52
Dimitrios K. Maniadakis, Ekaterina Sokolova-Lapa, Antonino D'A\`i, Elena Ambrosi, Carlo Ferrigno, Giancarlo Cusumano, Alessio Anitra, Luciano Burderi, Melania Del Santo, Tiziana Di Salvo, Felix F\"urst, Rosario Iaria, Valentina La Parola, Christian Malacaria, Peter Kretschmar, Fabio Pintore, Ciro Pinto, Guillermo Andres Rodriguez Castillo

How the spin-phase variability of cyclotron lines shapes the pulsed fraction spectra: insights from 4U 1538-52
We study the energy-dependent pulse profile of 4U 1538-52 and its phase-dependent spectral variability, with emphasis on the behavior around the cyclotron resonant scattering feature at around 21 keV. We analyze all available NuSTAR observations of 4U 1538-52. We decompose energy-resolved pulse profiles into Fourier harmonics to study their energy dependence. Specifically, we compute pulsed fraction spectra, cross-correlation and lag spectra, identifying discontinuities and linking them to feat…

SplashNet: Split-and-Share Encoders for Accurate and Efficient Typing with Surface Electromyography
Nima Hadidi, Jason Chan, Ebrahim Feghhi, Jonathan Kao
https://arxiv.org/abs/2506.12356

SplashNet: Split-and-Share Encoders for Accurate and Efficient Typing with Surface Electromyography
Surface electromyography (sEMG) at the wrists could enable natural, keyboard-free text entry, yet the state-of-the-art emg2qwerty baseline still misrecognizes $51.8\%$ of characters in the zero-shot setting on unseen users and $7.0\%$ after user-specific fine-tuning. We trace many of these errors to mismatched cross-user signal statistics, fragile reliance on high-order feature dependencies, and the absence of architectural inductive biases aligned with the bilateral nature of typing. To addres…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new/
[2/3]:
IPA-CHILDES & G2P : Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language Mode...

Self-Supervised Spatial Correspondence Across Modalities
Ayush Shrivastava, Andrew Owens
https://arxiv.org/abs/2506.03148 https://arx…

Self-Supervised Spatial Correspondence Across Modalities
We present a method for finding cross-modal space-time correspondences. Given two images from different visual modalities, such as an RGB image and a depth map, our model identifies which pairs of pixels correspond to the same physical points in the scene. To solve this problem, we extend the contrastive random walk framework to simultaneously learn cycle-consistent feature representations for both cross-modal and intra-modal matching. The resulting model is simple and has no explicit photo-con…

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis
Anna Seo Gyeong Choi, Alexander Richardson, Ryan Partlan, Sunny Tang, Sunghye Cho
https://arxiv.org/abs/2506.01129

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis
This study compares three acoustic feature extraction toolkits (OpenSMILE, Praat, and Librosa) applied to clinical speech data from individuals with schizophrenia spectrum disorders (SSD) and healthy controls (HC). By standardizing extraction parameters across the toolkits, we analyzed speech samples from 77 SSD and 87 HC participants and found significant toolkit-dependent variations. While F0 percentiles showed high cross-toolkit correlation (r=0.962 to 0.999), measures like F0 standard devia…

DeepPlantCRE: A Transformer-CNN Hybrid Framework for Plant Gene Expression Modeling and Cross-Species Generalization
Yingjun Wu, Jingyun Huang, Liang Ming, Pengcheng Deng, Maojun Wang, Zeyu Zhang
https://arxiv.org/abs/2505.09883

DeepPlantCRE: A Transformer-CNN Hybrid Framework for Plant Gene Expression Modeling and Cross-Species Generalization
The investigation of plant transcriptional regulation constitutes a fundamental basis for crop breeding, where cis-regulatory elements (CREs), as the key factor determining gene expression, have become the focus of crop genetic improvement research. Deep learning techniques, leveraging their exceptional capacity for high-dimensional feature extraction and nonlinear regulatory relationship modeling, have been extensively employed in this field. However, current methodologies present notable limi…

Attention Is Not Always the Answer: Optimizing Voice Activity Detection with Simple Feature Fusion
Kumud Tripathi, Chowdam Venkata Kumar, Pankaj Wasnik
https://arxiv.org/abs/2506.01365

Attention Is Not Always the Answer: Optimizing Voice Activity Detection with Simple Feature Fusion
Voice Activity Detection (VAD) plays a key role in speech processing, often utilizing hand-crafted or neural features. This study examines the effectiveness of Mel-Frequency Cepstral Coefficients (MFCCs) and pre-trained model (PTM) features, including wav2vec 2.0, HuBERT, WavLM, UniSpeech, MMS, and Whisper. We propose FusionVAD, a unified framework that combines both feature types using three fusion strategies: concatenation, addition, and cross-attention (CA). Experimental results reveal that …

Training-Free Voice Conversion with Factorized Optimal Transport
Alexander Lobashev, Assel Yermekova, Maria Larchenko
https://arxiv.org/abs/2506.09709 http…

Training-Free Voice Conversion with Factorized Optimal Transport
This paper introduces Factorized MKL-VC, a training-free modification for kNN-VC pipeline. In contrast with original pipeline, our algorithm performs high quality any-to-any cross-lingual voice conversion with only 5 second of reference audio. MKL-VC replaces kNN regression with a factorized optimal transport map in WavLM embedding subspaces, derived from Monge-Kantorovich Linear solution. Factorization addresses non-uniform variance across dimensions, ensuring effective feature transformation.…