Tootfinder

A2TTS: TTS for Low Resource Indian Languages
Ayush Singh Bhadoriya, Abhishek Nikunj Shinde, Isha Pandey, Ganesh Ramakrishnan
https://arxiv.org/abs/2507.15272

A2TTS: TTS for Low Resource Indian Languages
We present a speaker conditioned text-to-speech (TTS) system aimed at addressing challenges in generating speech for unseen speakers and supporting diverse Indian languages. Our method leverages a diffusion-based TTS architecture, where a speaker encoder extracts embeddings from short reference audio samples to condition the DDPM decoder for multispeaker generation. To further enhance prosody and naturalness, we employ a cross-attention based duration prediction mechanism that utilizes referenc…

Breaking the Bulkhead: Demystifying Cross-Namespace Reference Vulnerabilities in Kubernetes Operators
Andong Chen, Zhaoxuan Jin, Ziyi Guo, Yan Chen
https://arxiv.org/abs/2507.03387

Breaking the Bulkhead: Demystifying Cross-Namespace Reference Vulnerabilities in Kubernetes Operators
Kubernetes Operators, automated tools designed to manage application lifecycles within Kubernetes clusters, extend the functionalities of Kubernetes, and reduce the operational burden on human engineers. While Operators significantly simplify DevOps workflows, they introduce new security risks. In particular, Kubernetes enforces namespace isolation to separate workloads and limit user access, ensuring that users can only interact with resources within their authorized namespaces. However, Kuber…

Active View Selector: Fast and Accurate Active View Selection with Cross Reference Image Quality Assessment
Zirui Wang, Yash Bhalgat, Ruining Li, Victor Adrian Prisacariu
https://arxiv.org/abs/2506.19844

Active View Selector: Fast and Accurate Active View Selection with Cross Reference Image Quality Assessment
We tackle active view selection in novel view synthesis and 3D reconstruction. Existing methods like FisheRF and ActiveNeRF select the next best view by minimizing uncertainty or maximizing information gain in 3D, but they require specialized designs for different 3D representations and involve complex modelling in 3D space. Instead, we reframe this as a 2D image quality assessment (IQA) task, selecting views where current renderings have the lowest quality. Since ground-truth images for candid…

This https://arxiv.org/abs/2410.00527 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Wanna hear your voice? A sample is all we need!
Research on audio clue-based target speaker extraction (TSE) has focused on modeling mixtures and reference speech, achieving strong results in English due to abundant datasets. However, cross-lingual properties remain underexplored, as low-resource languages face challenges from limited annotated data and linguistic resources. To bridge this gap, we propose WHYV (Wanna Hear Your Voice), a cross-lingual TSE framework enabling zero-shot adaptation without fine-tuning. WHYV employs a frequency-mo…

Replication of Reference-Dependent Preferences and the Risk-Return Trade-Off in the Chinese Market
Penggan Xu
https://arxiv.org/abs/2505.20608 https://

Replication of Reference-Dependent Preferences and the Risk-Return Trade-Off in the Chinese Market
This study replicates the findings of Wang et al. (2017) on reference-dependent preferences and their impact on the risk-return trade-off in the Chinese stock market, a unique context characterized by high retail investor participation, speculative trading behavior, and regulatory complexities. Capital Gains Overhang (CGO), a proxy for unrealized gains or losses, is employed to explore how behavioral biases shape cross-sectional stock returns in an emerging market setting. Utilizing data from 1…

Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study
Leon Mayer, Tim R\"adsch, Dominik Michael, Lucas Luttner, Amine Yamlahi, Evangelia Christodoulou, Patrick Godau, Marcel Knopp, Annika Reinke, Fiona Kolbinger, Lena Maier-Hein
https://arxiv.org/abs/2506.06232

Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study
While traditional computer vision models have historically struggled to generalize to endoscopic domains, the emergence of foundation models has shown promising cross-domain performance. In this work, we present the first large-scale study assessing the capabilities of Vision Language Models (VLMs) for endoscopic tasks with a specific focus on laparoscopic surgery. Using a diverse set of state-of-the-art models, multiple surgical datasets, and extensive human reference annotations, we address t…

MULTI-SCOUT: Multistatic Integrated Sensing and Communications in 5G and Beyond for Moving Target Detection, Positioning, and Tracking
Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sastry Kompella, Gustave Anderson, Jonathan Ashdown
https://arxiv.org/abs/2507.02613

MULTI-SCOUT: Multistatic Integrated Sensing and Communications in 5G and Beyond for Moving Target Detection, Positioning, and Tracking
This paper presents a complete signal-processing chain for multistatic integrated sensing and communications (ISAC) using 5G Positioning Reference Signal (PRS). We consider a distributed architecture in which one gNB transmits a periodic OFDM-PRS waveform while multiple spatially separated receivers exploit the same signal for target detection, parameter estimation and tracking. A coherent cross-ambiguity function (CAF) is evaluated to form a range-Doppler map from which the bistatic delay and …

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.…

Strong Correlations, Green's Function Zeros and Topological Transitions in Orbital-Symmetry-Controlled Chemical Reactions
Ziren Xie, Amir Mirzanejad, Lukas Muechler
https://arxiv.org/abs/2506.18984

Strong Correlations, Green's Function Zeros and Topological Transitions in Orbital-Symmetry-Controlled Chemical Reactions
We integrate concepts from topological band theory and strong-correlation physics with the principle of orbital symmetry conservation, which characterizes reactions as symmetry allowed or forbidden based on whether molecular orbitals cross along the reaction coordinate. Using a $4π$ electrocyclization as an example, we show how Green's functions generalize the concepts of symmetry allowed and symmetry forbidden reactions even in the presence of strong multi-reference correlations. We demonstra…