Tootfinder

Early Detection of Branched Broomrape (Phelipanche ramosa) Infestation in Tomato Crops Using Leaf Spectral Analysis and Machine Learning
Mohammadreza Narimani, Alireza Pourreza, Ali Moghimi, Parastoo Farajpoor, Hamid Jafarbiglu, Mohsen B. Mesgaran
https://arxiv.org/abs/2509.12074

Early Detection of Branched Broomrape (Phelipanche ramosa) Infestation in Tomato Crops Using Leaf Spectral Analysis and Machine Learning
Branched broomrape (Phelipanche ramosa) is a chlorophyll-deficient parasitic weed that threatens tomato production by extracting nutrients from the host. We investigate early detection using leaf-level spectral reflectance (400-2500 nm) and ensemble machine learning. In a field experiment in Woodland, California, we tracked 300 tomato plants across growth stages defined by growing degree days (GDD). Leaf reflectance was acquired with a portable spectrometer and preprocessed (band denoising, 1 n…

From Legacy Fortran to Portable Kokkos:An Autonomous Agentic AI Workflow
Sparsh Gupta, Kamalavasan Kamalakkannan, Maxim Moraru, Galen Shipman, Patrick Diehl
https://arxiv.org/abs/2509.12443

From Legacy Fortran to Portable Kokkos:An Autonomous Agentic AI Workflow
Scientific applications continue to rely on legacy Fortran codebases originally developed for homogeneous, CPU-based systems. As High-Performance Computing (HPC) shifts toward heterogeneous GPU-accelerated architectures, many accelerators lack native Fortran bindings, creating an urgent need to modernize legacy codes for portability. Frameworks like Kokkos provide performance portability and a single-source C++ abstraction, but manual Fortran-to-Kokkos porting demands significant expertise and …

Multiple Giant Impacts and Chemical Equilibria: An Integrated Approach to Rocky Planet Formation
Haruya Maeda, Takanori Sasaki
https://arxiv.org/abs/2509.12713 https://

Multiple Giant Impacts and Chemical Equilibria: An Integrated Approach to Rocky Planet Formation
During the formation of rocky planets, the surface environments of growing protoplanets were dramatically different from those of present-day planets. The release of gravitational energy during accretion would have maintained a molten surface layer, forming a magma ocean. Simultaneously, sufficiently massive protoplanets could acquire hydrogen-rich proto-atmospheres by capturing gas from the protoplanetary disk. Chemical equilibration among the atmosphere, magma ocean, and iron core plays a key…

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
Hanwen Kang, Tenglong Lu, Zhanbin Qi, Jiandong Guo, Sheng Meng, Miao Liu
https://arxiv.org/abs/2508.10505

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine learning force fields (MLFFs) with 3D potential energy surface sampling and interpolation. Our method suppresses periodic self interactions via supercell expansion, builds a continuous PES from MLFF energies on a spatial grid, and extracts minimum energy pathways without predefined NEB images. Across twelve benchmark electrode and electrolyte materials including LiCoO2, Li…

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LAPIS: A Performance Portable, High Productivity Compiler Framework
Brian Kelley, Sivasankaran Rajamanickam
https://arxiv.org/abs/2509.25605 https://arxiv.…

LAPIS: A Performance Portable, High Productivity Compiler Framework
Portability, performance, and productivity are three critical dimensions for evaluating a programming model or compiler infrastructure. Several modern programming models for computational science focus on performance and portability. On the other end, several machine learning focused programming models focus on portability and productivity. A clear solution that is strong in all three dimensions has yet to emerge. A second related problem arises when use cases from computational science converg…

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Haoyu Dong, Pengkun Zhang, Mingzhe Lu, Yanzhen Shen, Guolin Ke
https://arxiv.org/abs/2509.06806

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Large language models (LLMs) possess broad world knowledge and strong general-purpose reasoning ability, yet they struggle to learn from many in-context examples on standard machine learning (ML) tasks, that is, to leverage many-shot demonstrations purely via in-context learning (ICL) without gradient descent. We introduce MachineLearningLM, a portable continued-pretraining framework that equips a general-purpose LLM with robust in-context ML capability while preserving its general knowledge an…

Author Once, Publish Everywhere: Portable Metadata Authoring with the CEDAR Embeddable Editor
Martin J. O'Connor, Marcos Martinez-Romero, Attila L. Egyedi, Mete U. Akdogan, Michael V. Dorf, Mark A. Musen
https://arxiv.org/abs/2508.00859

Author Once, Publish Everywhere: Portable Metadata Authoring with the CEDAR Embeddable Editor
High-quality, "rich" metadata are essential for making research data findable, interoperable, and reusable. The Center for Expanded Data Annotation and Retrieval (CEDAR) has long addressed this need by providing tools to design machine-actionable metadata templates that encode community standards in a computable form. To make these capabilities more accessible within real-world research workflows, we have developed the CEDAR Embeddable Editor (CEE)-a lightweight, interoperable Web Component tha…

Fairness for niche users and providers: algorithmic choice and profile portability
Elizabeth McKinnie, Anas Buhayh, Clement Canel, Robin Burke
https://arxiv.org/abs/2509.22660 h…

Fairness for niche users and providers: algorithmic choice and profile portability
Ensuring fair outcomes for multiple stakeholders in recommender systems has been studied mostly in terms of algorithmic interventions: building new models with better fairness properties, or using reranking to improve outcomes from an existing algorithm. What has rarely been studied is structural changes in the recommendation ecosystem itself. Our work explores the fairness impact of algorithmic pluralism, the idea that the recommendation algorithm is decoupled from the platform through which u…

Plug-and-Play Latent Diffusion for Electromagnetic Inverse Scattering with Application to Brain Imaging
Rui Guo, Yi Zhang, Yhonatan Kvich, Tianyao Huang, Maokun Li, Yonina C. Eldar
https://arxiv.org/abs/2509.04860

Plug-and-Play Latent Diffusion for Electromagnetic Inverse Scattering with Application to Brain Imaging
Electromagnetic (EM) imaging is an important tool for non-invasive sensing with low-cost and portable devices. One emerging application is EM stroke imaging, which enables early diagnosis and continuous monitoring of brain strokes. Quantitative imaging is achieved by solving an inverse scattering problem (ISP) that reconstructs permittivity and conductivity maps from measurements. In general, the reconstruction accuracy is limited by its inherent nonlinearity and ill-posedness. Existing methods…