Tootfinder

Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa RYu
https://arxiv.org/abs/2506.00056

Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Artificial intelligence (AI) is reshaping inverse design across manufacturing domain, enabling high-performance discovery in materials, products, and processes. However, purely data-driven approaches often struggle in realistic settings characterized by sparse data, high-dimensional design spaces, and nontrivial physical constraints. This perspective argues for a new generation of design systems that transcend black-box modeling by integrating domain knowledge, physics-informed learning, and in…

Haptic Rapidly-Exploring Random Trees: A Sampling-based Planner for Quasi-static Manipulation Tasks
Lin Yang, Huu-Thiet Nguyen, Donghan Yu, Chen Lv, Domenico Campolo
https://arxiv.org/abs/2506.00351

Haptic Rapidly-Exploring Random Trees: A Sampling-based Planner for Quasi-static Manipulation Tasks
In this work, we explore how conventional motion planning algorithms can be reapplied to contact-rich manipulation tasks. Rather than focusing solely on efficiency, we investigate how manipulation aspects can be recast in terms of conventional motion-planning algorithms. Conventional motion planners, such as Rapidly-Exploring Random Trees (RRT), typically compute collision-free paths in configuration space. However, in manipulation tasks, intentional contact is often necessary. For example, whe…

Novel Benchmark for NER in the Wastewater and Stormwater Domain
Franco Alberto Cardillo, Franca Debole, Francesca Frontini, Mitra Aelami, Nan\'ee Chahinian, Serge Conrad
https://arxiv.org/abs/2506.01938

Novel Benchmark for NER in the Wastewater and Stormwater Domain
Effective wastewater and stormwater management is essential for urban sustainability and environmental protection. Extracting structured knowledge from reports and regulations is challenging due to domainspecific terminology and multilingual contexts. This work focuses on domain-specific Named Entity Recognition (NER) as a first step towards effective relation and information extraction to support decision making. A multilingual benchmark is crucial for evaluating these methods. This study deve…

Sensemaking Through Making: Developing Clinical Domain Knowledge by Crafting Synthetic Datasets and Prototyping System Architectures
Mihnea Stefan Calota, Wessel Nieuwenhuys, Janet Yi-Ching Huang, Lin-Lin Chen, Mathias Funk
https://arxiv.org/abs/2507.00821

Sensemaking Through Making: Developing Clinical Domain Knowledge by Crafting Synthetic Datasets and Prototyping System Architectures
Designers have ample opportunities to impact the healthcare domain. However, hospitals are often closed ecosystems that pose challenges in engaging clinical stakeholders, developing domain knowledge, and accessing relevant systems and data. In this paper, we introduce a making-oriented approach to help designers understand the intricacies of their target healthcare context. Using Remote Patient Monitoring (RPM) as a case study, we explore how manually crafting synthetic datasets based on real-w…

Kerr microscopy study of magnetic domains and their dynamics in bulk Ni-Mn-Ga austenite
A. Perevertov, I. Soldatov, R. Schaefer, R. H. Colman, O. Heczko
https://arxiv.org/abs/2506.23617

Kerr microscopy study of magnetic domains and their dynamics in bulk Ni-Mn-Ga austenite
The observation of magnetic domains on austenite Ni-Mn-Ga bulk samples has been a big challenge for many years. Using advanced Kerr microscopy, with automatic compensation of the sample motion and monochromatic LED light we were able to observe magnetic domains and follow their evolution with magnetic field on the _100_ faces of an austenite bulk single crystal. After mechanical polishing variable fine stress-induced domains patterns were observed at different locations. The surface coercivity …

Today I had to cancle a BBQ on Donauinsel, because the city issued a ban of open fire due to dryness and danger of fires spreading. Like many climate change effects, this happens earlier every year :-(

Overdetermined elliptic problems on model Riemannian manifolds
Jo\~ao Marcos do \'O, Jaqueline de Lima, M\'arcio Santos
https://arxiv.org/abs/2506.00697

Overdetermined elliptic problems on model Riemannian manifolds
We establish a rigidity theorem for annular sector-like domains in the setting of overdetermined elliptic problems on model Riemannian manifolds. Specifically, if such a domain admits a solution to the inhomogeneous Helmholtz equation satisfying both constant Dirichlet and constant Neumann boundary conditions, then the domain must be a spherical sector, and the solution must be radially symmetric. This result underscores the strong geometric constraints imposed by overdetermined boundary condit…

A scalar-mean curvature comparison theorem for manifolds with iterated conical singularities
Milan Jovanovic, Jinmin Wang
https://arxiv.org/abs/2506.24059 …

A scalar-mean curvature comparison theorem for manifolds with iterated conical singularities
We use the Dirac operator method to prove a scalar-mean curvature comparison theorem for spin manifolds which carry iterated conical singularities. Our approach is to study the index theory of a twisted Dirac operator on such singular manifolds. A dichotomy argument is used to prove the comparison theorem without knowing precisely the index of the twisted Dirac operator. This framework also enables us to prove a rigidity theorem of Euclidean domains and a spin positive mass theorem for asymptot…

Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation
Xihang Yu, Rajat Talak, Jingnan Shi, Ulrich Viereck, Igor Gilitschenski, Luca Carlone
https://arxiv.org/abs/2507.00984

Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation
Modern warehouse automation systems rely on fleets of intelligent robots that generate vast amounts of data -- most of which remains unannotated. This paper develops a self-supervised domain adaptation pipeline that leverages real-world, unlabeled data to improve perception models without requiring manual annotations. Our work focuses specifically on estimating the pose and shape of boxes and presents a correct-and-certify pipeline for self-supervised box pose and shape estimation. We extensive…

DAPFAM: A Domain-Aware Patent Retrieval Dataset Aggregated at the Family Level
Iliass Ayaou (ICube), Denis Cavallucci (ICube), Hicham Chibane (ICube)
https://arxiv.org/abs/2506.22141

DAPFAM: A Domain-Aware Patent Retrieval Dataset Aggregated at the Family Level
In the landscape of publicly available patent retrieval datasets, the need for explicit indomain and out-of-domain labeling, multi-jurisdiction coverage, balanced query domain representation and manageable sizes that support sub document level experiments on moderate computational resources is often overlooked. To address these gaps, we propose DAPFAM, a new open access domain-aware patent retrieval dataset constructed at the simple-family level. The dataset contains 1,247 domain balanced full …