Tootfinder

Dear Israelis, do not fucking contact Gazans on here asking them to help you “understand about people living in Gaza”. And don’t fucking use my name when doing that (“saw that you had a call with someone else I follow on Mastodon (aral)”).
The fucking privilege, man.
Your country is committing genocide. Do you understand that? Do you understand that Palestinians in Gaza are trying to fucking survive right now as your country starves them and shoots them and bombs them and they do…

HyperCLOVA X THINK Technical Report
NAVER Cloud HyperCLOVA X Team
https://arxiv.org/abs/2506.22403 https://arxiv.org/pdf/2506.22403…

HyperCLOVA X THINK Technical Report
We introduce HyperCLOVA X THINK, the first reasoning-focused large language model in the HyperCLOVA X family, pre-trained on roughly $6$ trillion high-quality Korean, and English tokens, augmented with targeted synthetic Korean data. It was implemented as a compute-memory-balanced Peri-LN Transformer scaled with $μ$P, pre-trained through a three-stage curriculum that expands the context window to $128$K tokens, and post-trained via supervised fine-tuning with Reinforcement Learning from Verifi…

Cross-Border Legal Adaptation of Autonomous Vehicle Design based on Logic and Non-monotonic Reasoning
Zhe Yu, Yiwei Lu, Burkhard Schafer, Zhe Lin
https://arxiv.org/abs/2507.22432

Cross-Border Legal Adaptation of Autonomous Vehicle Design based on Logic and Non-monotonic Reasoning
This paper focuses on the legal compliance challenges of autonomous vehicles in a transnational context. We choose the perspective of designers and try to provide supporting legal reasoning in the design process. Based on argumentation theory, we introduce a logic to represent the basic properties of argument-based practical (normative) reasoning, combined with partial order sets of natural numbers to express priority. Finally, through case analysis of legal texts, we show how the reasoning sys…

SleepWalk: Exploiting Context Switching and Residual Power for Physical Side-Channel Attacks
Sahan Sanjaya, Aruna Jayasena, Prabhat Mishra
https://arxiv.org/abs/2507.22306 https…

SleepWalk: Exploiting Context Switching and Residual Power for Physical Side-Channel Attacks
Context switching is utilized by operating systems to change the execution context between application programs. It involves saving and restoring the states of multiple registers and performing a pipeline flush to remove any pre-fetched instructions, leading to a higher instantaneous power consumption compared to typical program execution. In this paper, we introduce a physical power side-channel leakage source that exploits the power spike observed during a context switch, triggered by the inb…

Stochastic forest transition model dynamics and parameter estimation via deep learning
Satoshi Kumabe, Tianyu Song, Ton Viet Ta
https://arxiv.org/abs/2507.21486 https://

Stochastic forest transition model dynamics and parameter estimation via deep learning
Forest transitions, characterized by dynamic shifts between forest, agricultural, and abandoned lands, are complex phenomena. This study developed a stochastic differential equation model to capture the intricate dynamics of these transitions. We established the existence of global positive solutions for the model and conducted numerical analyses to assess the impact of model parameters on deforestation incentives. To address the challenge of parameter estimation, we proposed a novel deep learn…

The NIKA2 cosmological legacy survey at 2 mm: catalogs, colors, redshift distributions, and implications for deep surveys
M. B\'ethermin, G. Lagache, C. Carvajal-Bohorquez, R. Adam, P. Ade, H. Ajeddig, S. Amarantidis, P. Andr\'e, H. Aussel, A. Beelen, A. Beno\^it, S. Berta, L. J. Bing, A. Bongiovanni, J. Bounmy, O. Bourrion, M. Calvo, A. Catalano, D. Ch\'erouvrier, M. De Petris, F. -X. D\'esert, S. Doyle, E. F. C. Driessen, G. Ejlali, A. Ferragamo, A. Gomez, J. Goupy, C…

The NIKA2 cosmological legacy survey at 2 mm: catalogs, colors, redshift distributions, and implications for deep surveys
Millimeter galaxy surveys are particularly effective in detecting dusty star-forming galaxies at high redshift. While such observations are typically conducted at ~1mm, studies suggest that 2mm may be better suited for selecting sources at even higher redshifts. We use the unprecedented 2mm data from the N2CLS, together with the SIDES simulation, to study and interpret the statistical properties of 2mm-selected galaxies. We use the N2CLS robust sample at 2mm, which contains 25 sources in the de…

Constraints on multi-fluid cosmology in modified Gauss-Bonnet gravity models with different observational data sets
Praveen Kumar Dhankar, Albert Munyeshyaka, Aritra Sanyal, Safiqul Islam, Farook Rahaman
https://arxiv.org/abs/2507.22191

Constraints on multi-fluid cosmology in modified Gauss-Bonnet gravity models with different observational data sets
In the present work, we incorporate redshift-space distortion measurement to investigate the growth of large scale structure within the framework of multi-fluid cosmology in the context of modified Gauss-Bonnet gravity. Using three different modified Gauss-Bonnet gravity models, we compare the predictions of modified Gauss-Bonnet gravity expansion history-through the Friedmann equation with Hubble and BAO data sets and constrain models parameters. Within the context of multi-fluid cosmology in …

Evaluating Integrative Strategies for Incorporating Phenotypic Features in Spatial Transcriptomics
Levin M Moser, Ahmad Kamal Hamid, Esteban Miglietta, Nodar Gogoberidze, Beth A Cimini
https://arxiv.org/abs/2507.22212

Evaluating Integrative Strategies for Incorporating Phenotypic Features in Spatial Transcriptomics
Spatial transcriptomics (ST) technologies not only offer an unprecedented opportunity to interrogate intact biological samples in a spatially informed manner, but also set the stage for integration with other imaging-based modalities. However, how to best exploit spatial context and integrate ST with imaging-based modalities remains an open question. To address this, particularly under real-world experimental constraints such as limited dataset size, class imbalance, and bounding-box-based segm…

Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Jingwei Zuo, Maksim Velikanov, Ilyas Chahed, Younes Belkada, Dhia Eddine Rhayem, Guillaume Kunsch, Hakim Hacid, Hamza Yous, Brahim Farhat, Ibrahim Khadraoui, Mugariya Farooq, Giulia Campesan, Ruxandra Cojocaru, Yasser Djilali, Shi Hu, Iheb Chaabane, Puneesh Khanna, Mohamed El Amine Seddik, Ngoc Dung Huynh, Phuc Le Khac, Leen AlQadi, Billel Mokeddem, Mohamed Chami, Abdalgader Abubaker, Mikhail Lubin…

Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
In this report, we introduce Falcon-H1, a new series of large language models (LLMs) featuring hybrid architecture designs optimized for both high performance and efficiency across diverse use cases. Unlike earlier Falcon models built solely on Transformer or Mamba architectures, Falcon-H1 adopts a parallel hybrid approach that combines Transformer-based attention with State Space Models (SSMs), known for superior long-context memory and computational efficiency. We systematically revisited mod…