Tootfinder

«This can be immensely empowering, and can improve public trust: it’s hard (and unwise) to give yourself over to a technology that won’t tell you how it works, particularly when its predefined settings allow only for meagre approaches to “user privacy”. As I ask my students, if you could develop an AI at your own home, and programme it to reflect your values and prioritise your safety, wouldn’t you trust it more? Well, the idea isn’t so outlandish – it only feels impossible because big tech fir…

Embedded DevOps: A Survey on the Application of DevOps Practices in Embedded Software and Firmware Development
Parthiv Katapara, Anand Sharma
https://arxiv.org/abs/2507.00421

Embedded DevOps: A Survey on the Application of DevOps Practices in Embedded Software and Firmware Development
The adoption of DevOps practices in embedded systems and firmware development is emerging as a response to the growing complexity of modern hardware--software co-designed products. Unlike cloud-native applications, embedded systems introduce challenges such as hardware dependency, real-time constraints, and safety-critical requirements. This literature review synthesizes findings from 20 academic and industrial sources to examine how DevOps principles--particularly continuous integration, conti…

Optimising cryptocurrency portfolios through stable clustering of price correlation networks
Ruixue Jing, Ryota Kobayashi, Luis Enrique Correa Rocha
https://arxiv.org/abs/2505.24831

Optimising cryptocurrency portfolios through stable clustering of price correlation networks
The emerging cryptocurrency market presents unique challenges for investment due to its unregulated nature and inherent volatility. However, collective price movements can be explored to maximise profits with minimal risk using investment portfolios. In this paper, we develop a technical framework that utilises historical data on daily closing prices and integrates network analysis, price forecasting, and portfolio theory to identify cryptocurrencies for building profitable portfolios under unc…

End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning
Christian Bongiorno, Efstratios Manolakis, Rosario Nunzio Mantegna
https://arxiv.org/abs/2507.01918

End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning
We develop a rotation-invariant neural network that provides the global minimum-variance portfolio by jointly learning how to lag-transform historical returns and how to regularise both the eigenvalues and the marginal volatilities of large equity covariance matrices. This explicit mathematical mapping offers clear interpretability of each module's role, so the model cannot be regarded as a pure black-box. The architecture mirrors the analytical form of the global minimum-variance solution yet …

Applying Vision Transformers on Spectral Analysis of Astronomical Objects
Luis Felipe Strano Moraes, Ignacio Becker, Pavlos Protopapas, Guillermo Cabrera-Vives
https://arxiv.org/abs/2506.00294

Applying Vision Transformers on Spectral Analysis of Astronomical Objects
We apply pre-trained Vision Transformers (ViTs), originally developed for image recognition, to the analysis of astronomical spectral data. By converting traditional one-dimensional spectra into two-dimensional image representations, we enable ViTs to capture both local and global spectral features through spatial self-attention. We fine-tune a ViT pretrained on ImageNet using millions of spectra from the SDSS and LAMOST surveys, represented as spectral plots. Our model is evaluated on key task…

Comparing the Performance of MC/DC's on-GPU Event-based Processing Methods in Multigroup and Continuous-energy Problems
Braxton Cuneo, Joanna Piper Morgan, Ilham Variansyah, Kyle E. Niemeyer
https://arxiv.org/abs/2506.00263

Comparing the Performance of MC/DC's on-GPU Event-based Processing Methods in Multigroup and Continuous-energy Problems
Monte Carlo / Dynamic Code (MC/DC) is a portable Monte Carlo neutron transport package for rapid numerical methods exploration in heterogeneous and HPC contexts, developed under the auspices of the Center for Exascale Monte Carlo Neutron Transport (CEMeNT). To support execution on GPUs, MC/DC delegates resource and execution management to Harmonize (another CEMeNT software project). In this paper, we describe and compare the performance of the two methods that Harmonize currently provides: a st…

TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
Qi Li, Shaheer U. Saeed, Yuliang Huang, Mingyuan Luo, Zhongnuo Yan, Jiongquan Chen, Xin Yang, Dong Ni, Nektarios Winter, Phuc Nguyen, Lucas Steinberger, Caelan Haney, Yuan Zhao, Mingjie Jiang, Bowen Ren, SiYeoul Lee, Seonho Kim, MinKyung Seo, MinWoo Kim, Yimeng Dou, Zhiwei Zhang, Yin Li, Tomy Varghese, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, Yipeng Hu

TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
Trackerless freehand ultrasound reconstruction aims to reconstruct 3D volumes from sequences of 2D ultrasound images without relying on external tracking systems, offering a low-cost, portable, and widely deployable alternative for volumetric imaging. However, it presents significant challenges, including accurate inter-frame motion estimation, minimisation of drift accumulation over long sequences, and generalisability across scanning protocols. The TUS-REC2024 Challenge was established to ben…

Global Descent Method for Non-convex Multi-objective Optimization Problems
Bikram Adhikary, Md Abu Talhamainuddin Ansary, Savin Treanta
https://arxiv.org/abs/2507.22390 https://…

Global Descent Method for Non-convex Multi-objective Optimization Problems
In this paper, we develop a global descent method for non-convex multi-objective optimization problems. The proposed approach builds upon foundational concepts from single-objective global descent techniques while removing the need for predefined scalars or ordering information of objective functions. Initially, the proposed method identifies a local weak efficient solution using any suitable descent algorithm, then applies an auxiliary function termed the multi-objective global descent functio…

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback
Qiushi Sun, Jinyang Gong, Lei Li, Qipeng Guo, Fei Yuan
https://arxiv.org/abs/2507.22080

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback
Acquiring high-quality instruction-code pairs is essential for training Large Language Models (LLMs) for code generation. Manually curated data is expensive and inherently limited in scale, motivating the development of code-centric synthesis methods. Yet, current approaches either focus on augmenting existing code or rely on predefined heuristics, both lacking rigorous data validation, which results in synthetic data that is ungrounded, repetitive, or overly simplistic. Inspired by collaborati…

This https://arxiv.org/abs/2409.04668 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Performance Portable Monte Carlo Neutron Transport in MCDC via Numba
Finding a software engineering approach that allows for portability, rapid development, and open collaboration for high-performance computing on GPUs and CPUs is a challenge. We implement a portability scheme using the Numba compiler for Python in Monte Carlo / Dynamic Code (MC/DC), a new neutron transport application for rapidly developing Monte Carlo. Using this scheme, we have built MC/DC as an application that can run as a pure Python, compiled CPU, or compiled GPU solver. In GPU mode, we u…