Tootfinder

Heute Abend spielen die Rancors (Muc) Umluft 180° (Muc/LE) Kultur Rotze (Bitterfeld) im Abstand

Geht hin, Rancors sind super. Die anderen kenne ich nicht so wirklich.
https://rancors.bandcamp.com/

Rancors
RANCORS wurde 1998 als Street-/Deutschpunk Band gegründet und ist bis heute ihrer "Do it yourself" - Mentalität treu geblieben. Auch durch verschiedene Umbesetzungen in den letzten Jahren, blieb die Band offen für diverse musikalische Stilelemente, aber dennoch immer den Wurzeln des HC-Punk treu. Nathalie-Voc Mike-Bass/Voc Andy-Git Flo-Git/Voc. Wiggal-Drums/Voc.

$(\ell,\delta)$-Diversity: Linkage-Robustness via a Composition Theorem
V. Arvind Rameshwar, Anshoo Tandon
https://arxiv.org/abs/2506.18405 https://…

$(\ell,δ)$-Diversity: Linkage-Robustness via a Composition Theorem
In this paper, we consider the problem of degradation of anonymity upon linkages of anonymized datasets. We work in the setting where an adversary links together $t\geq 2$ anonymized datasets in which a user of interest participates, based on the user's known quasi-identifiers, which motivates the use of $\ell$-diversity as the notion of dataset anonymity. We first argue that in the worst case, such linkage attacks can reveal the exact sensitive attribute of the user, even when each dataset res…

A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners
Anna Raymaker, Akshaya Kumar, Miuyin Yong Wong, Ryan Pickren, Animesh Chhotaray, Frank Li, Saman Zonouz, Raheem Beyah
https://arxiv.org/abs/2506.15842

A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners
Maritime systems, including ships and ports, are critical components of global infrastructure, essential for transporting over 80% of the world's goods and supporting internet connectivity. However, these systems face growing cybersecurity threats, as shown by recent attacks disrupting Maersk, one of the world's largest shipping companies, causing widespread impacts on international trade. The unique challenges of the maritime environment--such as diverse operational conditions, extensive physi…

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…

Malliavin Calculus and Stochastic Differential Equations
Shizan Fang, Rongrong Tian
https://arxiv.org/abs/2507.13747 https://arxiv.or…

Malliavin Calculus and Stochastic Differential Equations
This paper is devoted to a study on SDEs with a bounded Borel drift b. We first remark that the original integration by parts formula due to P. Malliavin can be used to deal with derivatives with respect to space variables, then we obtain a link between the product of heat kernels and iterated divergences in Malliavin calculus. An explicit estimate for the derivative of solutions to SDE is obtained in terms of the L-infinity norm of b; as a result, we prove that the SDE defines a continuous flo…

Technical report: Impact of Duration Prediction on Speaker-specific TTS for Indian Languages
Isha Pandey, Pranav Gaikwad, Amruta Parulekar, Ganesh Ramakrishnan
https://arxiv.org/abs/2507.16875

Technical report: Impact of Duration Prediction on Speaker-specific TTS for Indian Languages
High-quality speech generation for low-resource languages, such as many Indian languages, remains a significant challenge due to limited data and diverse linguistic structures. Duration prediction is a critical component in many speech generation pipelines, playing a key role in modeling prosody and speech rhythm. While some recent generative approaches choose to omit explicit duration modeling, often at the cost of longer training times. We retain and explore this module to better understand i…

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models
Chen Feng, Yicheng Lin, Shaojie Zhuo, Chenzheng Su, Ramchalam Kinattinkara Ramakrishnan, Zhaocong Yuan, Xiaopeng Zhang
https://arxiv.org/abs/2507.07877

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models
Recent advances in Automatic Speech Recognition (ASR) have demonstrated remarkable accuracy and robustness in diverse audio applications, such as live transcription and voice command processing. However, deploying these models on resource constrained edge devices (e.g., IoT device, wearables) still presents substantial challenges due to strict limits on memory, compute and power. Quantization, particularly Post-Training Quantization (PTQ), offers an effective way to reduce model size and infere…