Tootfinder

Data-Driven Multiscale Topology Optimization of Soft Functionally Graded Materials with Large Deformations
Shiguang Deng, Horacio D. Espinosa, Wei Chen
https://arxiv.org/abs/2506.23422

Data-Driven Multiscale Topology Optimization of Soft Functionally Graded Materials with Large Deformations
Functionally Graded Materials (FGMs) made of soft constituents have emerged as promising material-structure systems in potential applications across many engineering disciplines, such as soft robots, actuators, energy harvesting, and tissue engineering. Designing such systems remains challenging due to their multiscale architectures, multiple material phases, and inherent material and geometric nonlinearities. The focus of this paper is to propose a general topology optimization framework that …

Origami of Multi-Layered Spaced Sheets
Guowei Wayne Tu, Evgueni T. Filipov
https://arxiv.org/abs/2507.00341 https://arxiv.org/pdf/250…

Origami of Multi-Layered Spaced Sheets
Two-dimensional (2D) origami tessellations such as the Miura-ori are often generalized to build three-dimensional (3D) architected materials with sandwich or cellular structures. However, such 3D blocks are densely packed with continuity of the internal material, while for many engineering structures with multi-physical functionality, it is necessary to have thin sheets that are separately spaced and sparsely connected. This work presents a framework for the design and analysis of multi-layered…

This https://arxiv.org/abs/2408.02664 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Noninvertible Gauge Symmetry in (2+1)d Topological Orders: A String-Net Model Realization
We develop a systematic framework for understanding symmetries in topological phases in 2+1 dimensions using the string-net model, encompassing both gauge symmetries that preserve anyon species and global symmetries permuting anyon species, including both invertible symmetries describable by groups and noninvertible symmetries described by categories. As an archetypal example, we reveal the first noninvertible categorical gauge symmetry of topological orders in 2+1 dimensions: the Fibonacci gau…

Engineering Magnetization with Photons: Nanoscale Advances in the Inverse Faraday Effect for Metallic and Plasmonic Systems
Chantal Hareau, Xingyu Yang, Maria Sanz, Matthew Sheldon, Mathieu Mivelle
https://arxiv.org/abs/2506.23515

Engineering Magnetization with Photons: Nanoscale Advances in the Inverse Faraday Effect for Metallic and Plasmonic Systems
The inverse Faraday effect, the ability of light to act as a source of magnetism, is a cornerstone of modern ultrafast optics. Harnessing this effect at the nanoscale promises to transform data storage and spintronics, yet its predictive understanding remains elusive. This review synthesizes recent progress in engineering the IFE within plasmonic architectures. We bridge the theoretical foundations, from classical drift current models to quantum descriptions, with the latest experimental milest…

Spin-State Engineering of Single Titanium Adsorbates on Ultrathin Magnesium Oxide
Soo-hyon Phark, Hong Thi Bui, We-hyo Seo, Yaowu Liu, Valeria Sheina, Curie Lee, Christoph Wolf, Andreas J. Heinrich, Roberto Robles, Nicolas Lorente
https://arxiv.org/abs/2507.23299

Spin-State Engineering of Single Titanium Adsorbates on Ultrathin Magnesium Oxide
Single atomic adsorbates on ultrathin insulating films provide a promising route toward bottom-up quantum architectures based on atomically identical yet individually addressable spin qubits on solid surfaces. A key challenge in engineering quantum-coherent spin nanostructures lies in understanding and controlling the spin state of individual adsorbates. In this work, we investigate single titanium (Ti) atoms adsorbed on MgO/Ag(100) surfaces using a combined scanning tunneling microscopy and el…

How (Not) To Write a Software Engineering Abstract
Lutz Prechelt, Lloyd Montgomery, Julian Frattini, Franz Zieris
https://arxiv.org/abs/2506.21634 https://…

How (Not) To Write a Software Engineering Abstract
Background: Abstracts are a particularly valuable element in a software engineering research article. However, not all abstracts are as informative as they could be. Objective: Characterize the structure of abstracts in high-quality software engineering venues. Observe and quantify deficiencies. Suggest guidelines for writing informative abstracts. Methods: Use qualitative open coding to derive concepts that explain relevant properties of abstracts. Identify the archetypical structure of abstra…

Isogeometric contact analysis in subsea umbilical and power cables
Tianjiao Dai (School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Hubei Key Laboratory of Naval Architecture and Ocean Engineering Hydrodynamics, HUST, Wuhan, China), Shuo Yang (Zoomlion Heavy Industry Science and Technology Co., Ltd., China), Xing Jin (China Offshore Engineering and Technology Co., Ltd., China), Svein S{\ae}vik (Department of Marine Technology, Norwegian U…

Isogeometric contact analysis in subsea umbilical and power cables
Subsea umbilical and power cables contain a large number of contact interfaces between different geometries and materials. These complex interactions rise significant challenges for accurately considering contact surface properties by using traditional analytical solutions or finite element methods. These properties have been identified as the most sensitive parameters when performing the numerical simulation for stress analysis. Therefore, it is essential to apply a novel approach for contact …

HyperFusion: Hierarchical Multimodal Ensemble Learning for Social Media Popularity Prediction
Liliang Ye (Huazhong University of Science and Technology, Wuhan, China), Yunyao Zhang (Huazhong University of Science and Technology, Wuhan, China), Yafeng Wu (Huazhong University of Science and Technology, Wuhan, China), Yi-Ping Phoebe Chen (La Trobe University, Melbourne, Australia), Junqing Yu (Huazhong University of Science and Technology, Wuhan, China), Wei Yang (Huazhong University of S…

HyperFusion: Hierarchical Multimodal Ensemble Learning for Social Media Popularity Prediction
Social media popularity prediction plays a crucial role in content optimization, marketing strategies, and user engagement enhancement across digital platforms. However, predicting post popularity remains challenging due to the complex interplay between visual, textual, temporal, and user behavioral factors. This paper presents HyperFusion, a hierarchical multimodal ensemble learning framework for social media popularity prediction. Our approach employs a three-tier fusion architecture that pro…

UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses
Weronika {\L}ajewska, Ivica Kostric, Gabriel Iturra-Bocaz, Mariam Arustashvili, Krisztian Balog
https://arxiv.org/abs/2506.22210

UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses
Retrieval-augmented generation (RAG) faces challenges related to factual correctness, source attribution, and response completeness. The LiveRAG Challenge hosted at SIGIR'25 aims to advance RAG research using a fixed corpus and a shared, open-source LLM. We propose a modular pipeline that operates on information nuggets-minimal, atomic units of relevant information extracted from retrieved documents. This multistage pipeline encompasses query rewriting, passage retrieval and reranking, nugget d…

A Segmentation Framework for Accurate Diagnosis of Amyloid Positivity without Structural Images
Penghan Zhu, Shurui Mei, Shushan Chen, Xiaobo Chu, Shanbo He, Ziyi Liu
https://arxiv.org/abs/2507.22336

A Segmentation Framework for Accurate Diagnosis of Amyloid Positivity without Structural Images
This study proposes a deep learning-based framework for automated segmentation of brain regions and classification of amyloid positivity using positron emission tomography (PET) images alone, without the need for structural MRI or CT. A 3D U-Net architecture with four layers of depth was trained and validated on a dataset of 200 F18-florbetapir amyloid-PET scans, with an 130/20/50 train/validation/test split. Segmentation performance was evaluated using Dice similarity coefficients across 30 br…