Tootfinder

Geological resources may be able to provide billions—or even trillions—of metric tons of clean hydrogen,
according to estimates from US government researchers.
By tapping into existing hydrogen accumulations and stimulating underground rocks to make more of the electron-rich gas,
a new crop of energy entrepreneurs think they can put low-carbon hydrogen on the market for as little as $1 per kilogram.
The concept is proven at just one site today -- But if it works as w…

Trillions of tons of hydrogen may be waiting under our feet
A new energy industry is emerging around hydrogen made by rocks instead of renewables

FedProtoKD: Dual Knowledge Distillation with Adaptive Class-wise Prototype Margin for Heterogeneous Federated Learning
Md Anwar Hossen, Fatema Siddika, Wensheng Zhang, Anuj Sharma, Ali Jannesari
https://arxiv.org/abs/2508.19009

FedProtoKD: Dual Knowledge Distillation with Adaptive Class-wise Prototype Margin for Heterogeneous Federated Learning
Heterogeneous Federated Learning (HFL) has gained attention for its ability to accommodate diverse models and heterogeneous data across clients. Prototype-based HFL methods emerge as a promising solution to address statistical heterogeneity and privacy challenges, paving the way for new advancements in HFL research. This method focuses on sharing only class-representative prototypes among heterogeneous clients. However, these prototypes are often aggregated on the server using weighted averagin…

Atomic-Scale Heterogeneity of Hydrogen in Metal Hydrides Revealed by Electron Ptychography
Pengcheng Li, Chenglin Pua, Zehao Dong, Zhengxiong Su, Tao Liu, Chao Cai, Huahai Shen, Lin Gu, Zhen Chen
https://arxiv.org/abs/2507.18906

Atomic-Scale Heterogeneity of Hydrogen in Metal Hydrides Revealed by Electron Ptychography
Hydrogen plays critical roles in materials science, particularly for advancing technologies in hydrogen storage and phase manipulation, while also posing challenges like hydrogen embrittlement. Understanding its behavior, vital for improving material properties, requires precise determination of atomic-scale distribution-a persistent challenge due to hydrogen's weak electron scattering and high mobility, as well as the limitations of conventional transmission electron microscopy. We demonstrate…

PPipe: Efficient Video Analytics Serving on Heterogeneous GPU Clusters via Pool-Based Pipeline Parallelism
Z. Jonny Kong, Qiang Xu, Y. Charlie Hu
https://arxiv.org/abs/2507.18748

PPipe: Efficient Video Analytics Serving on Heterogeneous GPU Clusters via Pool-Based Pipeline Parallelism
With the rapid innovation of GPUs, heterogeneous GPU clusters in both public clouds and on-premise data centers have become increasingly commonplace. In this paper, we demonstrate how pipeline parallelism, a technique wellstudied for throughput-oriented deep learning model training, can be used effectively for serving latency-bound model inference, e.g., in video analytics systems, on heterogeneous GPU clusters. Our work exploits the synergy between diversity in model layers and diversity in GP…

Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous Graphs
Ziyu Zheng, Yaming Yang, Ziyu Guan, Wei Zhao, Weigang Lu
https://arxiv.org/abs/2506.20980

Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous Graphs
Real-world networks usually have a property of node heterophily, that is, the connected nodes usually have different features or different labels. This heterophily issue has been extensively studied in homogeneous graphs but remains under-explored in heterogeneous graphs, where there are multiple types of nodes and edges. Capturing node heterophily in heterogeneous graphs is very challenging since both node/edge heterogeneity and node heterophily should be carefully taken into consideration. Ex…

July 25, 2025 -- "We're going to end up shooting some of them." (Heather Cox Richardson/Letters from an American)
https://heathercoxrichardson.substack.com/p/july-25-2025
http://www.memeorandum.com/250726/p17#a250726p17

Federated Learning with Heterogeneous and Private Label Sets
Adam Breitholtz, Edvin Listo Zec, Fredrik D. Johansson
https://arxiv.org/abs/2508.18774 https://

Federated Learning with Heterogeneous and Private Label Sets
Although common in real-world applications, heterogeneous client label sets are rarely investigated in federated learning (FL). Furthermore, in the cases they are, clients are assumed to be willing to share their entire label sets with other clients. Federated learning with private label sets, shared only with the central server, adds further constraints on learning algorithms and is, in general, a more difficult problem to solve. In this work, we study the effects of label set heterogeneity on…

Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance
Hyunsung Cho, Minseok Moon, Jaehoon Kim, Eunkyung Koh, Hyeon-Deuk Kim, Rokyeon Kim, Gyehyun Park, Seungwu Han, Youngho Kang
https://arxiv.org/abs/2508.19674…

Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance
Understanding hydrogen diffusion is critical for improving the reliability and performance of oxide thin-film transistors (TFTs), where hydrogen plays a key role in carrier modulation and bias instability. In this work, we investigate hydrogen diffusion in amorphous IGZO ($a$-IGZO) and $c$-axis aligned crystalline IGZO (CAAC-IGZO) using machine learning interatomic potential molecular dynamics (MLIP-MD) simulations. We construct accurate phase-specific MLIPs by fine-tuning SevenNet-0, a univers…

August 26, 2025 -- Today, for the second time in as many days, President Donald J. Trump suggested ... (Heather Cox Richardson/Letters from an American)
https://heathercoxrichardson.substack.com/p/august-26-2025
http://www.memeorandum.com/250827/p54#a250827p54

Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
Hongbo Liu, Siyi Li, Zheng Yu
https://arxiv.org/abs/2508.18766 https://

Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
Drug-drug interactions (DDIs) are a major concern in clinical practice, as they can lead to reduced therapeutic efficacy or severe adverse effects. Traditional computational approaches often struggle to capture the complex relationships among drugs, targets, and biological entities. In this work, we propose HGNN-DDI, a heterogeneous graph neural network model designed to predict potential DDIs by integrating multiple drug-related data sources. HGNN-DDI leverages graph representation learning to…