After defying multiple mass extinctions on Earth, the hardy plant passes an intergalactic test. Breakthroughs, discoveries, and DIY tips sent every weekday. While it may appear humble, Earth's moss is built darn tough. It thrives in extreme environments -from the bitter cold, low-oxygen air of the Himalayas, down to the parched sands of Death Valley. Some species even make their home among the lava fields of active volcanoes .

Recent literature [1] suggests that embedding a graph on an unit sphere leads to better generalization for graph transduction. However, the choice of optimal embedding and an efficient algorithm to compute the same remains open. In this paper, we show that orthonormal representations, a class of unit-sphere graph em-beddings are P AC learnable. Existing P AC-based analysis do not apply as the VC dimension of the function class is infinite. We propose an alternative P AC-based bound, which do not depend on the VC dimension of the underlying function class, but is related to the famous Lov asz ϑ function. The main contribution of the paper is SPORE, a SPectral regularized ORthonormal Embedding for graph transduction, derived from the P AC bound. SPORE is posed as a non-smooth convex function over an elliptope.

In Artificial Life (ALife) research, replicating Open-Ended Evolution (OEE)-the continuous emergence of novelty observed in biological life-has usually been pursued within isolated, closed system simulations, such as Tierra and Avida, which have typically plateaued after an initial burst of novelty, failing to achieve sustained OEE. Scholars suggest that OEE requires an open-environment system that continually exchanges information or energy with its environment. A recent technological innovation in Decentralized Physical Infrastructure Network (DePIN), which provides permissionless computational substrates, enables the deployment of Large Language Model-based AI agents on blockchains integrated with Trusted Execution Environments (TEEs). This enables on-chain agents to operate autonomously "in the wild," achieving self-sovereignty without human oversight. These agents can control their own social media accounts and cryptocurrency wallets, allowing them to interact directly with blockchain-based financial networks and broader human social media. Building on this new paradigm of on-chain agents, Spore.fun is a recent real-world AI evolution experiment that enables autonomous breeding and evolution of new on-chain agents. This paper presents a detailed case study of Spore.fun, examining agent behaviors and their evolutionary trajectories through digital ethology. We aim to spark discussion about whether open-environment ALife systems "in the wild," based on permissionless computational substrates and driven by economic incentives to interact with their environment, could finally achieve the long-sought goal of OEE.

Recent literature~\cite{ando} suggests that embedding a graph on an unit sphere leads to better generalization for graph transduction. However, the choice of optimal embedding and an efficient algorithm to compute the same remains open. In this paper, we show that orthonormal representations, a class of unit-sphere graph embeddings are PAC learnable. Existing PAC-based analysis do not apply as the VC dimension of the function class is infinite. We propose an alternative PAC-based bound, which do not depend on the VC dimension of the underlying function class, but is related to the famous Lov\'{a}sz~$\vartheta$ function.

With the second season of The Last of Us returning to our screens, it might be comforting to think that the show is purely fictional. But believe it or not, the show's haunting zombies aren't that far from reality. Real-life'zombie-making' fungi burrow into their host's flesh and manipulate their minds to turn them into hyperactive super spreaders. As it stands, these mind-warping parasites only affect certain insects. However, the stages of infection are eerily similar to those seen in the hit HBO show.

Recent literature [1] suggests that embedding a graph on an unit sphere leads to better generalization for graph transduction. However, the choice of optimal embedding and an efficient algorithm to compute the same remains open. In this paper, we show that orthonormal representations, a class of unit-sphere graph embeddings are PAC learnable. Existing PAC-based analysis do not apply as the VC dimension of the function class is infinite. We propose an alternative PAC-based bound, which do not depend on the VC dimension of the underlying function class, but is related to the famous Lovász ϑ function. The main contribution of the paper is SPORE, a SPectral regularized ORthonormal Embedding for graph transduction, derived from the PAC bound. SPORE is posed as a non-smooth convex function over an elliptope.

HBO's smash-hit adaptation The Last of Us is the latest in a string of horror stories featuring fungi as the source of fear. The zombie-like outbreak that takes place in the show, which is based on the dystopian video game series of the same name, stems from a mutated version of a parasitic mushroom which fictionally evolves to attack humans instead of insects. In Mexican Gothic, by Silvia Moreno-Garcia, the narrator knows something isn't right with a family and their mansion, and soon discovers an intergenerational secret intertwined with a mycelium network. In last year's What Moves the Dead, by T. Kingfisher, it's a mycologist who discovers the root of the town's sudden mysterious illnesses. Science-fiction's fungal fascination goes back much farther.

Scientists in Denmark have uncovered two new species of deadly fungi that devour from the inside, bursting from the abdomen of their still-living prey. The parasites--Strongwellsea acerosa and Strongwellsea tigrinae--infect adult flies, which continue to buzz around for days with massive holes in their bodies. As they do, the fungi rain spores from these holes down onto other unsuspecting flies. Thousands of torpedo-shaped spores can shoot out like a rocket from a single fly. The corpse of a fly with two large holes in its abdomen, caused by the fungus Strongwellsea tigrinae. Researchers from the Natural History Museum of Denmark and the University of Copenhagen's Department of Plant and Environmental Sciences have reported on the two new fungi.

Mass surveillance of the population by state agencies and corporate parties is now a well-known fact. Journalists and whistle-blowers still lack means to circumvent global spying for the sake of their investigations. With Spores, we propose a way for journalists and their sources to plan a posteriori file exchanges when they physically meet. We leverage on the multiplication of personal devices per capita to provide a lightweight, robust and fully anonymous decentralised file transfer protocol between users. Spores hinges on our novel concept of e-squads: one's personal devices, rendered intelligent by gossip communication protocols, can provide private and dependable services to their user. People's e-squads are federated into a novel onion routing network, able to withstand the inherent unreliability of personal appliances while providing reliable routing. Spores' performances are competitive, and its privacy properties of the communication outperform state of the art onion routing strategies.

The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in isolation on pattern classification tasks. This contrasts with the biological brain which purpose is to control a body in closed-loop. This paper contributes to bringing the fields of computational neuroscience and robotics closer together by integrating open-source software components from these two fields. The resulting framework allows to evaluate the validity of biologically-plausibe plasticity models in closed-loop robotics environments. We demonstrate this framework to evaluate Synaptic Plasticity with Online REinforcement learning (SPORE), a reward-learning rule based on synaptic sampling, on two visuomotor tasks: reaching and lane following. We show that SPORE is capable of learning to perform policies within the course of simulated hours for both tasks. Provisional parameter explorations indicate that the learning rate and the temperature driving the stochastic processes that govern synaptic learning dynamics need to be regulated for performance improvements to be retained. We conclude by discussing the recent deep reinforcement learning techniques which would be beneficial to increase the functionality of SPORE on visuomotor tasks.