Phil BrowneAnyone interested in ocean skin temperatures derived from microwave imagers? #NewPaper
This is the last in the series where we look at the uncoupled system - it's all or nothing from here on!
Penn Linguistics#NewPaper by Jonathan Lee:
Lok, C., Lee, J. H. N., Matthews, S., & Yip, V. Responses to Cantonese A-not-A questions by Cantonese-English bilingual children. International Journal of Bilingualism. (Online first) https://doi.org/10.1177/13670069251405684
Lok, C., Lee, J. H. N., Matthews, S., & Yip, V. Responses to Cantonese A-not-A questions by Cantonese-English bilingual children. International Journal of Bilingualism. (Online first) https://doi.org/10.1177/13670069251405684
Nexus Research GroupNew publication in #EcologicalModelling! Benedikt Hartweg & his colleagues simulated tropical forests in #Brazil under disturbance scenarios and fitted allometries at different spatial scales. Super helpful insights for the new #BIOMASS mission by @esa
🔗 Learn more on our blog: https://www.geo.lmu.de/geographie/en/latest-news/news-overview/news/new-publication-in-ecological-modelling.html
#science #research #newpaper #ecologicalmodelling #forest #geography #remotesensing
abcxyz‼️Try the new @gnutiez dashboard!
http://visual.gnutiez.de/dashboard
#medien #medienkritik #zeitung #headline #journalismus #ddj #datajournalism #datenjournalismus #newpaper
Reddit Tech VN BotNghiên cứu mới chỉ ra tự chủ AI thực sự không phải là mô hình lớn hơn, mà dựa trên 4 trụ cột nhận thức: Nhận thức, Lý luận, Trí nhớ và Hành động. Một khung làm việc thú vị cho các tác nhân AI tự động.
#AI #TựChủAI #NhậnThức #NghiênCứuMới #AutonomousAgents #Cognition #NewPaper
https://www.reddit.com/r/LocalLLaMA/comments/1pfk8aw/new_paper_true_ai_autonomy_isnt_about_bigger/
I would love if anyone would look at my independent research dossier, available free on Substack. I would be thrilled to receive any constructive critique or feedback that anyone has: https://open.substack.com/pub/josephcornett/p/humanai-co-authorship-as-prompt-logic?r=48ca26&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false
K Belisario (she/her)Excited to share new #research I co-authored with amazing experts in the field of addiction and recovery!
In a sample of nearly 500 adults in Canada and the US with alcohol use disorder (AUD) who underwent a recovery attempt, substantial improvements in psychosocial wellbeing were reported for those who met the NIAAA definition of remission (low-risk drinking + no AUD symptoms present other than cravings).
With most focus on abstinent-based recovery and associated outcomes, these findings offer support for the use of an alternative definition of recovery!
Laurent PerrinetGlad to share the publication of our #newpaper :
A Predictive Approach to Enhance Time-Series Forecasting
By Skye Gunasekaran, Assel Kembay, Hugo Ladret, Rui-Jie Zhu, myself, Omid Kavehei and Jason Eshraghian
The lead author, Jason Eshragian, speaks most clearly about it:
For the amount of compute they burn, transformers are pretty bad at time-series data analysis. Which is pretty unsurprising if your objective is to predict the next token, one step at a time.
Brains, on the other hand, are predictive machines. Think of your daily commute to work. On Day 1, your brain was probably in overdrive to make sure you're not late, taking in all of your environment. On Day 1000, you're on full autopilot, barely burning mental energy unless something unexpected - like a major accident - forces you to adjust.
That's predictive coding in action: the brain continuously compares its expectations (no traffic) to reality (flipped car damn), then updates only when surprised.
Skye Gunasekaran has spent the past couple of years integrating this principle into Future-Guided Learning, where a "future" model guides a "past" forecasting model, dynamically minimizing surprise when reality deviates from predictions.
In our preprint, we show how drawing upon neuroscience-inspired ideas actually helps in time-series forecasting with deep learning. Efficiency isn't the only win from the brain; it's also pretty damn good at organizing long-range time-series information.
https://www.linkedin.com/feed/update/urn:li:activity:7378797683425296385/
https://www.nature.com/articles/s41467-025-63786-4
https://laurentperrinet.github.io/publication/gunasekaran-25/
Dimitris KontopoulosWe have a #NewPaper in Evolution (@journal_evo), led by Xueling Yi, on the #evolution of dietary preferences across (but not only) Phyllostomid #bats. 🦇
https://academic.oup.com/evolut/advance-article/doi/10.1093/evolut/qpaf154/8222499
Ingolf KühnI am happy to share our new paper, introducing our glacier forefield study platform in the Dachstein and Berchtesgaden Mountains
https://we.copernicus.org/articles/25/157/2025/
#newpaper #glacierforefields #alps
And by coincidence just in time for the #imc2025 #xp
Introducing a glacier forefield monitoring site network to understand succession in the Northern Limestone Alps
Abstract. Since the end of the Little Ice Age (ca. 1855), glaciers retreated in the Alps, leaving new ground for genuine primary succession. The patterns and processes of glacier forefield succession have been studied globally for decades. Surprisingly, no such analysis exists from the Northern Limestone Alps. We therefore initiated a monitoring scheme with permanent plots to study plant succession and vegetation assembly at four forefields, namely the Hallstätter Glacier, Großer Gosau Glacier (both at Dachstein massif, Austria), Watzmann Glacier, and Blaueis (both at Berchtesgaden National Park, Germany), which is abbreviated as the BDGF (Berchtesgaden-Dachstein Glacier Forefield) platform. The aim of the long-term research envisaged and performed in this platform is to get a better understanding of the vegetation succession and community assembly in the glacier forefield development of the Northern Limestone Alps, using a multidisciplinary approach. Here, we introduce the basic characteristics of the BDGF platform; i.e. we describe the monitoring network, the observational design, and the methodological approaches. We present the baseline vegetation characteristics, and we outline the studies already initiated or to be performed in the near future. The methodology encompasses a chronosequence approach, where plots, using a frequency grid frame of 1 m × 1 m, are placed in specific successional stages (related to age classes since deglaciation). We show that, as expected, species richness and cover increase with age. Unexpectedly, though, these processes seem to be much slower than what has been observed in the Central Alps on siliceous substrates. We suggest that this could be due to the geological substrate, i.e. its chemistry as well as its karstic conditions, but also due to the morphology of the terrain, which hardly enables species colonization from above (i.e. following gravity) but mainly from below.