Tootfinder

Design high-confidence computers using trusted instructional set architecture and emulators
Shuangbao Paul Wang
https://arxiv.org/abs/2506.18780 https://…

Design high-confidence computers using trusted instructional set architecture and emulators
High-confidence computing relies on trusted instructional set architecture, sealed kernels, and secure operating systems. Cloud computing depends on trusted systems for virtualization tasks. Branch predictions and pipelines are essential in improving performance of a CPU/GPU. But Spectre and Meltdown make modern processors vulnerable to be exploited. Disabling the prediction and pipeline is definitely not a good solution. On the other hand, current software patches can only address non-essentia…

FramePrompt: In-context Controllable Animation with Zero Structural Changes
Guian Fang, Yuchao Gu, Mike Zheng Shou
https://arxiv.org/abs/2506.17301 https:/…

FramePrompt: In-context Controllable Animation with Zero Structural Changes
Generating controllable character animation from a reference image and motion guidance remains a challenging task due to the inherent difficulty of injecting appearance and motion cues into video diffusion models. Prior works often rely on complex architectures, explicit guider modules, or multi-stage processing pipelines, which increase structural overhead and hinder deployment. Inspired by the strong visual context modeling capacity of pre-trained video diffusion transformers, we propose Fram…

Ever wondered how Kubernetes runs on a tractor? At Berlin Buzzwords 2025, join Wieneke Keller & Sebastian Lenartowicz from Aurea Imaging to explore their innovative AgTech journey! Discover how they built an edge device with Python microservices and K3s for precision farming, tackling unique rural challenges. 
Learn more:

Precision farming powered by K3s and TensorRT
Aurea Imaging is an AgTech scaleup focusing on precision farming in orchards. By retrofitting our TreeScout sensor package to their tractors, farmers are able to collect data about their orchard down to the tree level as they perform other tasks. Using onboard stereo cameras, the tractor’s high-precision GPS, a machine-vision pipeline running in real time on the device, and cloud-based analytics, this data is turned into maps used by other agricultural machines that enable the grower to utili…

Accelerating Cloud-Based Transcriptomics: Performance Analysis and Optimization of the STAR Aligner Workflow
Piotr Kica, Sabina Licho{\l}ai, Micha{\l} Orzechowski, Maciej Malawski
https://arxiv.org/abs/2506.12611

Accelerating Cloud-Based Transcriptomics: Performance Analysis and Optimization of the STAR Aligner Workflow
In this work, we explore the Transcriptomics Atlas pipeline adapted for cost-efficient and high-throughput computing in the cloud. We propose a scalable, cloud-native architecture designed for running a resource-intensive aligner -- STAR -- and processing tens or hundreds of terabytes of RNA-sequencing data. We implement multiple optimization techniques that give significant execution time and cost reduction. The impact of particular optimizations is measured in medium-scale experiments followe…

FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
Zhaoyang Wang, Jie Li, Wen Lu, Lihuo He, Maoguo Gong, Xinbo Gao
https://arxiv.org/abs/2506.11545

FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information. As video frame rates continue to increase, the diminishing inter-frame differences further expose the limitations of traditional frame-to-frame information exploitation methods, which are inadequate for addressing current video super-resolution (VSR) demands. To overcome these challenges, we pro…

A pipeline for Megahertz X-ray Photon Correlation Spectroscopy on soft matter samples at the MID instrument of European XFEL
Aliaksandr Leonau, Felix Brausse, James Wrigley, Mads B. Jakobsen, Amir Tosson, Michelle Dargasz, Nimmi Das Anthuparambil, Felix Lehmk\"uhler, Anita Girelli, Maddalena Bin, Fivos Perakis, Sebastian Retzbach, Fajun Zhang, Frank Schreiber, Matheus Teodoro, Cammille Carinan, Robert Rosca, Fabio Dall Antonia, Wonhyuk Jo, Ulrike Boesenberg, Angel Rodriguez-Fernan…

A pipeline for Megahertz X-ray Photon Correlation Spectroscopy on soft matter samples at the MID instrument of European XFEL
In this paper we present the experimental protocol and data processing framework for Megahertz X-ray Photon Correlation Spectroscopy (MHz-XPCS) experiments on soft matter samples, implemented at the Materials Imaging and Dynamics (MID) instrument of the European X-ray Free-Electron Laser (EuXFEL). Due to the introduction of a standard configuration and the implementation of a highly automated data processing pipeline, MHz-XPCS measurements can now be conducted and analyzed with minimal user int…

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines
Athena Vakali, Ilias Dimitriadis
https://arxiv.org/abs/2506.09107 https:…

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines
AI models have become active decision makers, often acting without human supervision. The rapid advancement of AI technology has already caused harmful incidents that have hurt individuals and societies and AI unfairness in heavily criticized. It is urgent to disrupt AI pipelines which largely neglect human principles and focus on computational biases exploration at the data (pre), model(in), and deployment (post) processing stages. We claim that by exploiting the advances of agents technology,…

Towards Explainable Sequential Learning
Giacomo Bergami, Emma Packer, Kirsty Scott, Silvia Del Din
https://arxiv.org/abs/2505.23624 https://

Towards Explainable Sequential Learning
This paper offers a hybrid explainable temporal data processing pipeline, DataFul Explainable MultivariatE coRrelatIonal Temporal Artificial inTElligence (EMeriTAte+DF), bridging numerical-driven temporal data classification with an event-based one through verified artificial intelligence principles, enabling human-explainable results. This was possible through a preliminary a posteriori explainable phase describing the numerical input data in terms of concurrent constituents with numerical pay…

This https://arxiv.org/abs/2506.02197 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
This paper reviews the NTIRE 2025 RAW Image Restoration and Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Restoration and Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. The goal of this challenge is two fold, (i) restore RAW images with blur and noise degradations, (ii) upscale RAW Bayer images by 2x, considering unknown noise and blur. In the…

NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
Marcos V. Conde, Radu Timofte, Zihao Lu, Xiangyu Kongand Xiaoxia Xingand Fan Wangand Suejin Hanand MinKyu Parkand Tianyu Zhangand Xin Luoand Yeda Chenand Dong Liuand Li Pangand Yuhang Yangand Hongzhong Wangand Xiangyong Caoand Ruixuan Jiangand Senyan Xuand Siyuan Jiangand Xueyang Fuand Zheng-Jun Zhaand Tianyu Haoand Yuhong Heand Ruoqi Liand Yueqi Yangand Xiang Yuand Guanlan Hongand Minmin Yiand Yuanjia Chenand Liwen Zha…

NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
This paper reviews the NTIRE 2025 RAW Image Restoration and Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Restoration and Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. The goal of this challenge is two fold, (i) restore RAW images with blur and noise degradations, (ii) upscale RAW Bayer images by 2x, considering unknown noise and blur. In the…