Tootfinder

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

FLIP: Flowability-Informed Powder Weighing
Nikola Radulov (Department of Computer Science University of Liverpool UK), Alex Wright (Department of Computer Science University of Liverpool UK), Thomas little (Department of Computer Science University of Liverpool UK), Andrew I. Cooper (Department of Chemistry university of Liverpool UK), Gabriella Pizzuto (Department of Computer Science University of Liverpool UK, Department of Chemistry university of Liverpool UK)

FLIP: Flowability-Informed Powder Weighing
Autonomous manipulation of powders remains a significant challenge for robotic automation in scientific laboratories. The inherent variability and complex physical interactions of powders in flow, coupled with variability in laboratory conditions necessitates adaptive automation. This work introduces FLIP, a flowability-informed powder weighing framework designed to enhance robotic policy learning for granular material handling. Our key contribution lies in using material flowability, quantifie…

This https://arxiv.org/abs/2505.24603 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique for multiple problems in data science and machine learning, with applications spanning computationally efficient optimization, coded computing, and federated learning. This operation also provides differential privacy guarantees due to its inherent randomness. In this work, we revisit this operation through the lens of Renyi Differential Privacy (RDP), providing a refined pri…

This https://arxiv.org/abs/2312.16139 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Abnormal component analysis
At the crossway of machine learning and data analysis, anomaly detection aims at identifying observations that exhibit abnormal behaviour. Be it measurement errors, disease development, severe weather, production quality default(s) (items) or failed equipment, financial frauds or crisis events, their on-time identification and isolation constitute an important task in almost any area of industry and science. While a substantial body of literature is devoted to detection of anomalies, little att…

ExDiff: A Framework for Simulating Diffusion Processes on Complex Networks with Explainable AI Integration
Annamaria Defilippo, Ugo Lomoio, Barbara Puccio, Pierangelo Veltri, Pietro Hiram Guzzi
https://arxiv.org/abs/2506.04271

ExDiff: A Framework for Simulating Diffusion Processes on Complex Networks with Explainable AI Integration
Understanding and controlling diffusion processes in complex networks is critical across domains ranging from epidemiology to information science. Here, we present ExDiff, an interactive and modular computational framework that integrates network simulation, graph neural networks (GNNs), and explainable artificial intelligence (XAI) to model and interpret diffusion dynamics. ExDiff combines classical compartmental models with deep learning techniques to capture both the structural and temporal …

This https://arxiv.org/abs/2505.23703 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability
Enhancing the mathematical reasoning capabilities of LLMs has garnered significant attention in both the mathematical and computer science communities. Recent works have made substantial progress in both Natural Language (NL) reasoning and Formal Language (FL) reasoning by leveraging the potential of pure Reinforcement Learning (RL) methods on base models. However, RL approaches struggle to impart new capabilities not presented in the base model, highlighting the need to integrate more knowledg…

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

Benchmarking Large Language Models for Polymer Property Predictions
Sonakshi Gupta, Akhlak Mahmood, Shivank Shukla, Rampi Ramprasad
https://arxiv.org/abs/2506.02129

Benchmarking Large Language Models for Polymer Property Predictions
Machine learning has revolutionized polymer science by enabling rapid property prediction and generative design. Large language models (LLMs) offer further opportunities in polymer informatics by simplifying workflows that traditionally rely on large labeled datasets, handcrafted representations, and complex feature engineering. LLMs leverage natural language inputs through transfer learning, eliminating the need for explicit fingerprinting and streamlining training. In this study, we finetune …

Machine Learning-Guided Discovery of Temperature-Induced Solid-Solid Phase Transitions in Inorganic Materials
Cibr\'an L\'opez, Joshua Ojih, Ming Hu, Josep Lluis Tamarit, Edgardo Saucedo, Claudio Cazorla
https://arxiv.org/abs/2506.01449

Machine Learning-Guided Discovery of Temperature-Induced Solid-Solid Phase Transitions in Inorganic Materials
Predicting solid-solid phase transitions remains a long-standing challenge in materials science. Solid-solid transformations underpin a wide range of functional properties critical to energy conversion, information storage, and thermal management technologies. However, their prediction is computationally intensive due to the need to account for finite-temperature effects. Here, we present an uncertainty-aware machine-learning-guided framework for high-throughput prediction of temperature-induce…

Motivando el uso y aprendizaje de Bash a trav\'es de concursos de programaci\'on
Luis Costero, Jorge Villarrubia, Francisco D. Igual
https://arxiv.org/abs/2506.00105

Motivando el uso y aprendizaje de Bash a través de concursos de programación
Command line learning and Bash usage are fundamental skills in systems administration, software development, and data science environments. However, their teaching has been neglected in many curricula, despite its relevance in the professional field. To address this gap, we developed an interactive competition that encourages students to improve their Bash skills through practical and competitive challenges. This gamified approach seeks to motivate autonomous learning and reinforce command line…

NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials
Chengbing Chen, Yutong Li, Rui Zhao, Zhoulin Liu, Zheyong Fan, Gang Tang, Zhiyong Wang
https://arxiv.org/abs/2506.01868

NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials
As a machine-learned potential, the neuroevolution potential (NEP) method features exceptional computational efficiency and has been successfully applied in materials science. Constructing high-quality training datasets is crucial for developing accurate NEP models. However, the preparation and screening of NEP training datasets remain a bottleneck for broader applications due to their time-consuming, labor-intensive, and resource-intensive nature. In this work, we have developed NepTrain and N…

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

This https://arxiv.org/abs/2307.08780 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Discounted-Sum Automata with Multiple Discount Factors
Discounting the influence of future events is a key paradigm in economics and it is widely used in computer-science models, such as games, Markov decision processes (MDPs), reinforcement learning, and automata. While a single game or MDP may allow for several different discount factors, nondeterministic discounted-sum automata (NDAs) were only studied with respect to a single discount factor. It is known that every class of NDAs with an integer as the discount factor has good computational prop…

This https://arxiv.org/abs/2406.04231 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Quantifying Misalignment Between Agents: Towards a Sociotechnical Understanding of Alignment
Existing work on the alignment problem has focused mainly on (1) qualitative descriptions of the alignment problem; (2) attempting to align AI actions with human interests by focusing on value specification and learning; and/or (3) focusing on a single agent or on humanity as a monolith. Recent sociotechnical approaches highlight the need to understand complex misalignment among multiple human and AI agents. We address this gap by adapting a computational social science model of human contentio…

This https://arxiv.org/abs/2505.08740 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
Parametric differential equations of the form du/dt = f(u, x, t, p) are fundamental in science and engineering. While deep learning frameworks such as the Fourier Neural Operator (FNO) can efficiently approximate solutions, they struggle with inverse problems, sensitivity estimation (du/dp), and concept drift. We address these limitations by introducing a sensitivity-based regularization strategy, called Sensitivity-Constrained Fourier Neural Operators (SC-FNO). SC-FNO achieves high accuracy in…

Symbolic Foundation Regressor on Complex Networks
Weiting Liu, Jiaxu Cui, Jiao Hu, En Wang, Bo Yang
https://arxiv.org/abs/2505.21879 https://

Symbolic Foundation Regressor on Complex Networks
In science, we are interested not only in forecasting but also in understanding how predictions are made, specifically what the interpretable underlying model looks like. Data-driven machine learning technology can significantly streamline the complex and time-consuming traditional manual process of discovering scientific laws, helping us gain insights into fundamental issues in modern science. In this work, we introduce a pre-trained symbolic foundation regressor that can effectively compress …

This https://arxiv.org/abs/2211.00694 has been replaced.
link: https://scholar.google.com/scholar?q=a

What we talk about when we talk about physics problem solving
I am a second-year cognitive science major, and as a student who has completed my physical science distributive requirements, I will likely never again come across Gauss's law. So why do I feel that the time and effort I devoted to solving Gauss's law problems was worth it? Partly, I inherently enjoy the learning process and the new perspective on the physical world I have acquired by understanding electromagnetism. But I was also inspired by the ways in which physics problems train the mind in…

This https://arxiv.org/abs/2410.12128 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Multimodal Fusion with Relational Learning for Molecular Property Prediction
Graph based molecular representation learning is essential for accurately predicting molecular properties in drug discovery and materials science; however, it faces significant challenges due to the intricate relationships among molecules and the limited chemical knowledge utilized during training. While contrastive learning is often employed to handle molecular relationships, its reliance on binary metrics is insufficient for capturing the complexity of these interactions. Multimodal fusion ha…

What might the future of research and education networking look like?
“Foresight 2030: Navigating Change” report explores 9 key challenges, 3 possible futures, and one key takeaway: change is coming, and collaboration is our strength.
📖 Read the article by Charles Hutchings, Raimundas Tuminauskas & Beatrix Weber −

Foresight 2030 – Navigating Change | GÉANT CONNECT Online
“Foresight 2030 is a great example of what NRENs do best: collaborate for the benefit of the unique and crucial digital infrastructure which supports research and education and the benefit they bring to science and learning, communities and economies internationally. The report demonstrates the reach and significance of the technical expertise NRENs convene within and

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

Introducing Quantum Computing to High-School Curricula: A Global Perspective
Mar\'ia Gragera Garc\'es, Luis G\'omez Orzechowski, Juan Francisco Rodr\'iguez Hern\'andez
https://arxiv.org/abs/2505.14809

Introducing Quantum Computing to High-School Curricula: A Global Perspective
Quantum computing is an emerging field with growing implications across science and industry, making early educational exposure increasingly important. This paper examines how quantum computing concepts can be introduced into high-school STEM curricula within existing structures to enhance foundational learning in mathematics, computer science, and physics. We outline a modular integration strategy introducing key quantum ideas into standard courses, leveraging open-source educational resources…