Tootfinder

We're live in 60 minutes. And yes, we still have room for you.
https://www.linkedin.com/posts/gfriend_next-wednesday-tactics-s…

An incomplete list of people excluded from 'international' conferences that are in-person only and aren't streamed or recorded
- People with caring responsibilities
- People with disabilities
- People with health vulnerabilities
- People who don't fly in recognition of climate emergencies
- People who work for orgs with ethical no-fly policies
- People who have their travel disrupted due to climate chaos
- People who only engage with fully o…

High-Resolution Directional Depth Electrodes: Open-Source FEM Lead-Field Modeling, Characterization, and Validation
Takfarinas Medani, Jace Willis, Chris Wright, Yash Vakilna, Ryan Shores, Raymundo Cassani, Anand Joshi, Richard Leahy, John Seymour, John Mosher
https://arxiv.org/abs/2508.13212

High-Resolution Directional Depth Electrodes: Open-Source FEM Lead-Field Modeling, Characterization, and Validation
Depth electrodes used in stereoelectroencephalography (sEEG) and deep-brain stimulation (DBS) are essential tools for neural recording and stimulation. Traditional designs have limited spatial resolution, typically 8 to 16 cylindrical contacts (0.8 to 1.0 mm diameter) along a 5 to 10 cm shaft, restricting recordings from small or localized populations. Recent high-density, directional electrodes (HDsEEG) enable finer localization of local field potentials (LFPs) and spike timing. Yet, character…

Yes, of course the Texas flash floods disaster was fueled by man-made climate change. Duh.
And I'll predict the outcome of the attribution study too: "We conclude that it is extremely likely that the Texas flash floods were made significantly worse by climate change."
#extremeweather

Looking forward to speaking at Defuse – Design for Use in Dublin on November 10th about breaking the rules and doing things differently (and yes, the Small Web).
Mark it on your calendars and come along if you’re free.
https://defuse.ixd.ie

Label Inference Attacks against Federated Unlearning
Wei Wang, Xiangyun Tang, Yajie Wang, Yijing Lin, Tao Zhang, Meng Shen, Dusit Niyato, Liehuang Zhu
https://arxiv.org/abs/2508.06789

Label Inference Attacks against Federated Unlearning
Federated Unlearning (FU) has emerged as a promising solution to respond to the right to be forgotten of clients, by allowing clients to erase their data from global models without compromising model performance. Unfortunately, researchers find that the parameter variations of models induced by FU expose clients' data information, enabling attackers to infer the label of unlearning data, while label inference attacks against FU remain unexplored. In this paper, we introduce and analyze a new pr…

Near-Infrared Spectroscopy and Detection of Carbon Monoxide in the Type II Supernova SN 2023ixf
Seong Hyun Park, Jeonghee Rho, Sung-Chul Yoon, Jeniveve Pearson, Manisha Shrestha, Samaporn Tinyanont, T. R. Geballe, Ryan J. Foley, Aravind P. Ravi, Jennifer Andrews, David J. Sand, K. Azalee Bostroem, Chris Ashall, Peter Hoeflich, Stefano Valenti, Yize Dong, Nicolas Meza Retamal, Emily Hoang, Darshana Mehta, D. Andrew Howell, Joseph R. Farah, Giacomo Terreran, Estefania Padilla Gonzalez, M…

Near-Infrared Spectroscopy and Detection of Carbon Monoxide in the Type II Supernova SN 2023ixf
Core-collapse supernovae (CCSNe) may contribute a significant amount of dust in the early universe. Freshly formed coolant molecules (e.g., CO) and warm dust can be found in CCSNe as early as ~100 d after the explosion, allowing the study of their evolution with time series observations. In the Type II SN 2023ixf, we aim to investigate the temporal evolution of the temperature, velocity, and mass of CO and compare them with other CCSNe, exploring their implications for the dust formation in CCS…

"#Permaculture: how do we live in place, sustainably, in time?"
Yes, actually, that IS my project: what I'm doing with my life, right now. Building community with the people around me, to build resilience in the face of #ClimateEmergency
Planet: Critical: Resilien…

Impacts of Atmospheric Carbon Species and Stellar Type on Climates of Terrestrial Planets
Jared Landry, Hiroyuki Kurokawa, Tetsuo Taki, Yuka Fujii, Kosuke Aoki, Hidenori Genda
https://arxiv.org/abs/2508.05975

Impacts of Atmospheric Carbon Species and Stellar Type on Climates of Terrestrial Planets
The climates of terrestrial planets are largely determined by the composition of their atmospheres and spectral types of their host stars. Previous studies suggest a wide range of carbon species abundances (CO\textsubscript{2}, CO, and CH\textsubscript{4}) can result from variations in reducing fluxes and stellar spectral types which influence photochemistry. However, a systematic investigation of how varying carbon species, particularly CO, affect planetary climates across wide parameter space…

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
Jiaojiao Zhang, Yuqi Xu, Kun Yuan
https://arxiv.org/abs/2509.05213 https://arx…

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
This work addresses the key challenges of applying federated learning to large-scale deep neural networks, particularly the issue of client drift due to data heterogeneity across clients and the high costs of communication, computation, and memory. We propose FedSub, an efficient subspace algorithm for federated learning on heterogeneous data. Specifically, FedSub utilizes subspace projection to guarantee local updates of each client within low-dimensional subspaces, thereby reducing communicat…