Tootfinder

FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation
Yuxuan Jiang, Zehua Chen, Zeqian Ju, Chang Li, Weibei Dou, Jun Zhu
https://arxiv.org/abs/2507.08557

FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation
Text-to-audio (T2A) generation has achieved promising results with the recent advances in generative models. However, because of the limited quality and quantity of temporally-aligned audio-text pairs, existing T2A methods struggle to handle the complex text prompts that contain precise timing control, e.g., "owl hooted at 2.4s-5.2s". Recent works have explored data augmentation techniques or introduced timing conditions as model inputs to enable timing-conditioned 10-second T2A generation, whi…

Pushchino multibeam pulsar search. VII. The results of the timing of 12 slow pulsars
S. A. Andrianov, V. A. Potapov, S. A. Tyul'bashev
https://arxiv.org/abs/2508.08711 https…

Pushchino multibeam pulsar search. VII. The results of the timing of 12 slow pulsars
We have performed timing of a number of known slow pulsars with poorly known coordinates and parameters of their intrinsic rotation. We used data from the archive of round-the-clock monitoring observations on the third (stationary) beam pattern of the Large Phased Array radio telescope (LPA LPI) at a frequency of 111 MHz, which has an unsatisfactory connection of the local quartz time standards to the reference scale (UTC). To compensate for the resulting errors, we applied an algorithm previou…

Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference
Kexin Chu, Zecheng Lin, Dawei Xiang, Zixu Shen, Jianchang Su, Cheng Chu, Yiwei Yang, Wenhui Zhang, Wenfei Wu, Wei Zhang
https://arxiv.org/abs/2508.08438

Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference
Global KV-cache sharing has emerged as a key optimization for accelerating large language model (LLM) inference. However, it exposes a new class of timing side-channel attacks, enabling adversaries to infer sensitive user inputs via shared cache entries. Existing defenses, such as per-user isolation, eliminate leakage but degrade performance by up to 38.9% in time-to-first-token (TTFT), making them impractical for high-throughput deployment. To address this gap, we introduce SafeKV (Secure and …

Assessing the Feasibility of Lightweight Whisper Models for Low-Resource Urdu Transcription
Abdul Rehman Antall, Naveed Akhtar
https://arxiv.org/abs/2508.09865 https://

Assessing the Feasibility of Lightweight Whisper Models for Low-Resource Urdu Transcription
This study evaluates the feasibility of lightweight Whisper models (Tiny, Base, Small) for Urdu speech recognition in low-resource settings. Despite Urdu being the 10th most spoken language globally with over 230 million speakers, its representation in automatic speech recognition (ASR) systems remains limited due to dialectal diversity, code-switching, and sparse training data. We benchmark these models on a curated Urdu dataset using word error rate (WER), without fine-tuning. Results show Wh…

[OT] Revealed: Apple is teaching its AI to adapt to the Trump era https://www.politico.eu/article/apple-teaching-artificial-intelligence-adapt-to-trump-era/ "updated guidelines on how the AI talks about diversity, equity and inclusion poli…

Apple is teaching its artificial intelligence to adapt to the Trump era
Apple policy documents enacted by a subcontractor show how the company shifted its approach to fine-tuning its AI in March, two months after the U.S. president was inaugurated.

The Milky Way could have many more satellite galaxies than scientists have previously been able to predict or observe,
according to new research.
Cosmologists at Durham University used a new technique combining the highest-resolution supercomputer simulations that exist,
alongside novel mathematical modeling, to predict the existence of missing "orphan" galaxies.

The Milky Way could be teeming with more satellite galaxies than previously thought
The Milky Way could have many more satellite galaxies than scientists have previously been able to predict or observe, according to new research. Cosmologists at Durham University used a new technique combining the highest-resolution supercomputer simulations that exist, alongside novel mathematical modeling, to predict the existence of missing "orphan" galaxies.

Beyond Predictions: A Study of AI Strength and Weakness Transparency Communication on Human-AI Collaboration
Tina Behzad, Nikolos Gurney, Ning Wang, David V. Pynadath
https://arxiv.org/abs/2508.09033

Beyond Predictions: A Study of AI Strength and Weakness Transparency Communication on Human-AI Collaboration
The promise of human-AI teaming lies in humans and AI working together to achieve performance levels neither could accomplish alone. Effective communication between AI and humans is crucial for teamwork, enabling users to efficiently benefit from AI assistance. This paper investigates how AI communication impacts human-AI team performance. We examine AI explanations that convey an awareness of its strengths and limitations. To achieve this, we train a decision tree on the model's mistakes, allo…

Control of nonreciprocal charge transport in topological insulator/superconductor heterostructures with Fermi level tuning and superconducting-layer thickness
Soma Nagahama, Yuki Sato, Minoru Kawamura, Ilya Belopolski, Ryutaro Yoshimi, Atsushi Tsukazaki, Naoya Kanazawa, Kei S Takahashi, Masashi Kawasaki, Yoshinori Tokura
https://

Control of nonreciprocal charge transport in topological insulator/superconductor heterostructures with Fermi level tuning and superconducting-layer thickness
Nonreciprocal charge transport (NCT) is defined as a phenomenon where electrical resistance depends on the current direction. It has been drawing much attention because it sensitively reflects the symmetry breaking of material systems. A topological insulator (TI)/superconductor (SC) heterostructure where the topological surface state (TSS) of the TI layer is proximitized with the SC layer is one such system that presents a sizable NCT due to a large spin-orbit coupling and superconductivity. H…

KG-Attention: Knowledge Graph-Guided Attention at Test-Time via Bidirectional Information Aggregation
Songlin Zhai, Guilin Qi, Yuan Meng
https://arxiv.org/abs/2507.08704

KG-Attention: Knowledge Graph-Guided Attention at Test-Time via Bidirectional Information Aggregation
Knowledge graphs (KGs) play a critical role in enhancing large language models (LLMs) by introducing structured and grounded knowledge into the learning process. However, most existing KG-enhanced approaches rely on parameter-intensive fine-tuning, which risks catastrophic forgetting and degrades the pretrained model's generalization. Moreover, they exhibit limited adaptability to real-time knowledge updates due to their static integration frameworks. To address these issues, we introduce the f…

ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition
Qingliang Meng, Hao Wu, Wei Liang, Wei Xu, Qing Zhao
https://arxiv.org/abs/2507.08477

ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition
The deep integration of large language models and automatic speech recognition systems has become a promising research direction with high practical value. To address the overfitting issue commonly observed in Low-Rank Adaptation (LoRA) during the supervised fine-tuning (SFT) stage, this work proposes an innovative training paradigm Iterative LoRA Training (ILT) in combination with an Iterative Pseudo Labeling strategy, effectively enhancing the theoretical upper bound of model performance. Bas…