The underlyingmetricis,however,non-parametric.Wedevelopamaximumlikelihood algorithm to infer the distribution parameters that relies on a combination of gradient descent and Monte Carlo integration. We further extend the LAND to mixture models, andprovidethecorresponding EMalgorithm.

To do so, we extend to neural activity the maximum occupancy principle (MOP) developed for behavior, and refer to this new neural principle asNeuroMOP.NeuroMOP posits thatthegoal ofthenervoussystem istomaximize future action-state entropy, a reward-free, intrinsic motivation that entails creating allpossible activity patterns while avoiding terminal ordangerous ones.

The Association for the Advancement of Artificial Intelligence's 2025 Spring Symposium Series was held in Burmingame, California, March 31-April 2, 2025. There were eight symposia in the spring program: AI for Engineering and Scientific Discoveries, AI for Health Symposium: Leveraging Artificial Intelligence to Revolutionize Healthcare, Current and Future Varieties of Human-AI Collaboration, GenAI@Edge: Empowering Generative AI at the Edge, Human-Compatible AI for Well-being: Harnessing Potential of GenAI for AI-Powered Science, Machine Learning and Knowledge Engineering for Trustworthy Multimodal and Generative AI, Symposium on Child-AI Interaction in the Era of Foundation Models, Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation. This report contains summaries of the workshops, which were submitted by some, but not all, of the workshop chairs. This symposium aims to advance and diversify the application of AI in emerging engineering and scientific discovery domains. Inspired by progress in large language models, generative AI, and AI-assisted scientific computing, we seek to foster new collaborations between industry and academia to tackle challenging problems in materials, manufacturing, and life sciences. We also plan to explore new directions in human-machine interaction for accelerating knowledge discovery and address related ethical considerations. Through invited speakers, panel discussions, and contributions from researchers with cross-disciplinary expertise, we hoped to cultivate partnerships that drive transformative advances in both AI and scientific research. No formal report was filed by the organizers for this symposium.

Thinking about science and technology in terms of return on investment misses the point. Here's what kids really need to know. That's what led me to become a professor. As a high school student, one of my major life goals was to figure out how to build an actual light sword. Doing so is all but impossible, so it didn't really matter if I went into engineering or science, but I pursued STEM just the same.

Nobel Prize 2025: What they are, when will the awards be announced? The Nobel Prize 2025 officially kicks off with the first award, for physiology or medicine, to be announced on Monday, setting the stage for a week of global anticipation. The full schedule, spanning from October 6 to 13, maps out a rapid succession of announcements: medicine, followed by physics, chemistry, literature, peace, and finally culminating with the economics prize next Monday. Here are the complete details of the schedule - and what to expect from this year's Nobel Prizes. What is the Nobel Prize?

Preference-based data often appear complex and noisy but may conceal underlying homogeneous structures. This paper introduces a novel framework of ranking structure recognition for preference-based data. We first develop an approach to identify dynamic ranking groups by incorporating temporal penalties into a spectral estimation for the celebrated Bradley-Terry model. To detect structural changes, we introduce an innovative objective function and present a practicable algorithm based on dynamic programming. Theoretically, we establish the consistency of ranking group recognition by exploiting properties of a random `design matrix' induced by a reversible Markov chain. We also tailor a group inverse technique to quantify the uncertainty in item ability estimates. Additionally, we prove the consistency of structure change recognition, ensuring the robustness of the proposed framework. Experiments on both synthetic and real-world datasets demonstrate the practical utility and interpretability of our approach.

The science is still emerging, but I couldn't deny how relaxed I felt. A mallet hovers over a bronze singing bowl, an instrument long used for meditation and relaxation practices. Breakthroughs, discoveries, and DIY tips sent every weekday. I drove up to my first sound bath experience not knowing quite what to expect. The meditative practice, which utilizes chimes, gongs, and an arsenal of "vibrational musical instruments," has existed in various forms for millennia but has seen a post-pandemic resurgence, particularly among the burgeoning, influencer-boosted online wellness community.

How to buy what people leave at airport security. Welcome to very weird corner of the internet. Transportation agent Johnny Smith holds up two swords surrendered by passengers traveling through Los Angeles International Airport. Breakthroughs, discoveries, and DIY tips sent every weekday. A decade ago, a woman famously downed an entire bottle of Rémy Martin XO Excellence rather than surrender it to airport security.

RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event took place from 15-21 July in Salvador, Brazil. The Logistics League forms part of the Industrial League and is an application-driven league inspired by the industrial scenario of a smart factory. Ahead of the Brazil meeting, we spoke with three key members of the league to find out more. Alexander Ferrein is a RoboCup Trustee overseeing the Industrial League, and Till Hofmann and Wataru Uemura are Logistics League Executive Committee members.

We propose a randomized neural network approach called RaNNDy for learning transfer operators and their spectral decompositions from data. The weights of the hidden layers of the neural network are randomly selected and only the output layer is trained. The main advantage is that without a noticeable reduction in accuracy, this approach significantly reduces the training time and resources while avoiding common problems associated with deep learning such as sensitivity to hyperparameters and slow convergence. Additionally, the proposed framework allows us to compute a closed-form solution for the output layer which directly represents the eigenfunctions of the operator. Moreover, it is possible to estimate uncertainties associated with the computed spectral properties via ensemble learning. We present results for different dynamical operators, including Koopman and Perron-Frobenius operators, which have important applications in analyzing the behavior of complex dynamical systems, and the Schrödinger operator. The numerical examples, which highlight the strengths but also weaknesses of the proposed framework, include several stochastic dynamical systems, protein folding processes, and the quantum harmonic oscillator.