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NGO links major chocolate brands to Liberia deforestation #Liberia

NGO links major chocolate brands to Liberia deforestation
Some of the world's best-known chocolate, from Mars to KitKat, is likely linked to deforestation in Liberia's rainforest despite many brands' claims of sustainability, according to research published by NGO Global Witness Tuesday.

A cacao rush drives ‘alarming’ deforestation in Liberia #Liberia

A cacao rush drives ‘alarming’ deforestation in Liberia
A rush to plant cacao in southeastern Liberia is destroying large tracts of rainforest. According to satellite data as well as interviews with local environmental advocates and government officials, the establishment of new plantations in regions that border Côte d’Ivoire is causing extensive deforestation and spurring conflicts over land. While some of the plantations are […]

🇺🇦 #NowPlaying on BBCRadio3's #ThroughTheNight
Richard Wagner, Zagreb Philharmonic Orchestra & Lovro von Matačić:
🎵 Siegfrieds Rheinfahrt [Siegfried's Rhine Journey] - from 'Götterdämmerung'
#RichardWagner
https://open.spotify.com/track/72wO2aJoBGoT4n82PxDuIW
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From Actions to Kinesics: Extracting Human Psychological States through Bodily Movements
Cheyu Lin, Katherine A. Flanigan
https://arxiv.org/abs/2510.04844 https://

From Actions to Kinesics: Extracting Human Psychological States through Bodily Movements
Understanding the dynamic relationship between humans and the built environment is a key challenge in disciplines ranging from environmental psychology to reinforcement learning (RL). A central obstacle in modeling these interactions is the inability to capture human psychological states in a way that is both generalizable and privacy preserving. Traditional methods rely on theoretical models or questionnaires, which are limited in scope, static, and labor intensive. We present a kinesics recog…

Implicit Updates for Average-Reward Temporal Difference Learning
Hwanwoo Kim, Dongkyu Derek Cho, Eric Laber
https://arxiv.org/abs/2510.06149 https://arxiv.…

Implicit Updates for Average-Reward Temporal Difference Learning
Temporal difference (TD) learning is a cornerstone of reinforcement learning. In the average-reward setting, standard TD($λ$) is highly sensitive to the choice of step-size and thus requires careful tuning to maintain numerical stability. We introduce average-reward implicit TD($λ$), which employs an implicit fixed point update to provide data-adaptive stabilization while preserving the per iteration computational complexity of standard average-reward TD($λ$). In contrast to prior finite-tim…