Tootfinder

Dark energy constraints in light of theoretical priors
Neel Shah, Kazuya Koyama, Johannes Noller
https://arxiv.org/abs/2507.19450 https://arxiv.org/pdf/250…

Dark energy constraints in light of theoretical priors
In order to derive model-independent observational bounds on dark energy/modified gravity theories, a typical approach is to constrain parametrised models intended to capture the space of dark energy theories. Here we investigate in detail the effect that the nature of these parametrisations can have, finding significant effects on the resulting cosmological dark energy constraints. In order to observationally distinguish well-motivated and physical parametrisations from unphysical ones, it is …

Binaural Localization Model for Speech in Noise
Vikas Tokala, Eric Grinstein, Rory Brooks, Mike Brookes, Simon Doclo, Jesper Jensen, Patrick A. Naylor
https://arxiv.org/abs/2507.20027

Binaural Localization Model for Speech in Noise
Binaural acoustic source localization is important to human listeners for spatial awareness, communication and safety. In this paper, an end-to-end binaural localization model for speech in noise is presented. A lightweight convolutional recurrent network that localizes sound in the frontal azimuthal plane for noisy reverberant binaural signals is introduced. The model incorporates additive internal ear noise to represent the frequency-dependent hearing threshold of a typical listener. The loca…

SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models
Dipayan Saha, Shams Tarek, Hasan Al Shaikh, Khan Thamid Hasan, Pavan Sai Nalluri, Md. Ajoad Hasan, Nashmin Alam, Jingbo Zhou, Sujan Kumar Saha, Mark Tehranipoor, Farimah Farahmandi
https://arxiv.org/abs/2506.20415…

SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models
Ensuring the security of complex system-on-chips (SoCs) designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, comprehensiveness, and adaptability. The advent of large language models (LLMs), with their remarkable capabilities in natural language understanding, code generation, and advanced reasoning, presents a new paradigm for tackling these issues. Moving beyond monolithic models, an agentic ap…

Binaural Speech Enhancement Using Complex Convolutional Recurrent Networks
Vikas Tokala, Eric Grinstein, Mike Brookes, Simon Doclo, Jesper Jensen, Patrick A. Naylor
https://arxiv.org/abs/2507.20023

Binaural Speech Enhancement Using Complex Convolutional Recurrent Networks
From hearing aids to augmented and virtual reality devices, binaural speech enhancement algorithms have been established as state-of-the-art techniques to improve speech intelligibility and listening comfort. In this paper, we present an end-to-end binaural speech enhancement method using a complex recurrent convolutional network with an encoder-decoder architecture and a complex LSTM recurrent block placed between the encoder and decoder. A loss function that focuses on the preservation of spa…

Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models
Daniel Koh, Yannic Noller, Corina S. Pasareanu, Adrians Skapars, Youcheng Sun
https://arxiv.org/abs/2506.08171

Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models
Large language models (LLMs) have been successfully applied to a variety of coding tasks, including code generation, completion, and repair. However, more complex symbolic reasoning tasks remain largely unexplored by LLMs. This paper investigates the capacity of LLMs to reason about worst-case executions in programs through symbolic constraints analysis, aiming to connect LLMs and symbolic reasoning approaches. Specifically, we define and address the problem of worst-case symbolic constraints a…

Second-Order Conductivity Probes a Cascade of Singularities in a Moir\'e Superlattice
Tanweer Ahmed, Bao Q. Tu, Kenji Watanabe, Takashi Taniguchi, Marco Gobbi, F\`elix Casanova, Luis E. Hueso
https://arxiv.org/abs/2507.05969

Second-Order Conductivity Probes a Cascade of Singularities in a Moiré Superlattice
Systems lacking inversion symmetry inherently demonstrate a nonlinear electrical response (NLER) to an applied electric bias, emerging through extrinsic mechanisms. This response is highly sensitive to the electronic band structure, which can be engineered with remarkable precision in moiré superlattices formed from atomically thin quantum materials. Moiré superlattices host complex Fermi surface reconstructions near van Hove singularities (vHSs) in the electronic density of states. However, …

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Scotts Maphuma, Felo Le Tee & Thabza Tee feat. DJ Maphorisa & Djy Biza:
🎵 Yebo Lapho (Gogo) [feat. DJ Maphorisa & Djy Biza]
#ScottsMaphuma #FeloLeTee #ThabzaTee #DJMaphorisa
https://newmoneygang.bandcamp.com/track/yebo-lapho-gogo
https://open.spotify.com/track/0NLRYW9HyqHcbyzfAwskKN

Empirical Evaluation of Generalizable Automated Program Repair with Large Language Models
Viola Campos, Ridwan Shariffdeen, Adrian Ulges, Yannic Noller
https://arxiv.org/abs/2506.03283

Empirical Evaluation of Generalizable Automated Program Repair with Large Language Models
Automated Program Repair (APR) proposes bug fixes to aid developers in maintaining software. The state of the art in this domain focuses on using LLMs, leveraging their strong capabilities to comprehend specifications in natural language and to generate program code. Recent works have shown that LLMs can be used to generate repairs. However, despite the APR community's research achievements and several industry deployments in the last decade, APR still lacks the capabilities to generalize broad…

Man wünscht sich ja fast die Antideutsch-Debatten der Nuller Jahre zurück. (Und die waren schon die Hölle.)

Detecting Lifshitz Transitions Using Nonlinear Conductivity in Bilayer Graphene
Tanweer Ahmed, Harsh Varshney, Bao Q. Tu, Kenji Watanabe, Takashi Taniguchi, Marco Gobbi, F\`elix Casanova, Amit Agarwal, Luis E. Hueso
https://arxiv.org/abs/2507.05871

Detecting Lifshitz Transitions Using Nonlinear Conductivity in Bilayer Graphene
The second-order nonlinear electrical response (NLER) is an intrinsic property of inversion symmetry-broken systems which can provide deep insights into the electronic band structures of atomically thin quantum materials. However, the impact of Fermi surface reconstructions, also known as Lifshitz transitions, on the NLER has remained elusive. We investigated NLER in bilayer graphene (BLG), where the low-energy bands undergo Lifshitz transitions. Here, NLER undergoes a sign change near the Lifs…