Tootfinder

A live blog of Amazon's fall 2025 hardware event in New York City, where new Kindles, Echos, possibly a TV, and Alexa updates are expected (The Verge)
https://www.theverge.com/tech/786685/amazon-event-live-blog-were-here-for-new-echos-…

Amazon event live blog: we’re here for new Echos, Kindles, and more
Alexa Plus is already rolling out. Now it’s time for Amazon to put some great hardware behind it.

do you teach public school in nyc? very excited to be on a panel at this day-long event for educators about using the grateful dead as a primary source, next friday, 11/7 at brooklyn bowl. (think it's only open to nyc teachers.) https://nycdoesocialstudies.eventsmart

🧠 People with more life experience see and digest everyday 'events' more clearly, research finds
#brain

People with more life experience see and digest everyday 'events' more clearly, research finds
Research by Royal Holloway has found people with a varied social life mentally put their daily experiences into small "events," compared to more isolated people who see their world as one-long stretch.

PSTTS: A Plug-and-Play Token Selector for Efficient Event-based Spatio-temporal Representation Learning
Xiangmo Zhao, Nan Yang, Yang Wang, Zhanwen Liu
https://arxiv.org/abs/2509.22481

PSTTS: A Plug-and-Play Token Selector for Efficient Event-based Spatio-temporal Representation Learning
Mainstream event-based spatio-temporal representation learning methods typically process event streams by converting them into sequences of event frames, achieving remarkable performance. However, they neglect the high spatial sparsity and inter-frame motion redundancy inherent in event frame sequences, leading to significant computational overhead. Existing token sparsification methods for RGB videos rely on unreliable intermediate token representations and neglect the influence of event noise…

Me: signing up for con.racket-lang.org next week.
EventBrite: "Racket sounds just like, uh, Raclette. Wouldn't you like to attend the Swiss Society's Raclette event?"
(To be fair, if I lived in Boston I might well go to a raclette event. Better than fondue!)

Teachers’ Windrush Workshop. Free online event Sunday, 7 September 2025, 7 pm to 9 pm BST. The event isn't exclusive to teachers, but is also open to anyone who would like to learn how to share the truth about Windrush with others. #Windrush

Machine learning approaches to seismic event classification in the Ostrava region
Marek Pecha, Michael Skotnica, Jana Ru\v{s}ajov\'a, Bohdan Rieznikov, V\'it Wandrol, Mark\'eta R\"osnerov\'a, Jarom\'ir Knejzl\'ik
https://arxiv.org/abs/2509.22574

Machine learning approaches to seismic event classification in the Ostrava region
The northeastern region of the Czech Republic is among the most seismically active areas in the country. The most frequent seismic events are mining-induced since there used to be strong mining activity in the past. However, natural tectonic events may also occur. In addition, seismic stations often record explosions in quarries in the region. Despite the cessation of mining activities, mine-induced seismic events still occur. Therefore, a rapid differentiation between tectonic and anthropogeni…

So Eurostile wie schick!
https://c3d2.social/@sandro/115465728537845599

Important reminder! If you're no longer able to attend today's event, please cancel your attendance on our meetup page. This helps us plan for food and drinks. Thanks!
https://www.meetup.com/phpug-rhein-neckar/events/305478322/

PHPUGMRN 05/25, Thu, Oct 30, 2025, 7:00 PM | Meetup
**Agenda:** 19:00 CET - Doors open 19:30 CET - Intro 19:45 CET - The one hour expression language, Dan Leech 20:30 CET - Break 20:45 CET - Simplify Your Dev Workflow with D

Fast Feature Field ($\text{F}^3$): A Predictive Representation of Events
Richeek Das, Kostas Daniilidis, Pratik Chaudhari
https://arxiv.org/abs/2509.25146 https://

Fast Feature Field ($\text{F}^3$): A Predictive Representation of Events
This paper develops a mathematical argument and algorithms for building representations of data from event-based cameras, that we call Fast Feature Field ($\text{F}^3$). We learn this representation by predicting future events from past events and show that it preserves scene structure and motion information. $\text{F}^3$ exploits the sparsity of event data and is robust to noise and variations in event rates. It can be computed efficiently using ideas from multi-resolution hash encoding and de…