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Déda et al. use neural networks for flow analysis & control in nonlinear fluid systems, showcasing accuracy & stabilization in complex dynamics. From the Lorenz system to confined cylinder flows, neural networks shine as effective models & controllers.

Physical Review Fluids

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The theory of higher order topological dynamics, which combines multilevel interactions between discrete topology and nonlinear dynamics, has the potential to enhance our understanding of complex systems such as the functions of the nervous system, the development of next-generation machine learning and the creation of advanced nodal processing algorithms. An important and unexpected collective behavior of signal processing in multilevel nodal networks has been observed to lead to a synchronization and diffusion of the irrotational and the solenoidal components of the systems revealing a deep relation of these mechanisms with the complexity of discrete topology. The perspective of the preliminary study linked here offers insights into how topology morphs dynamics, how dynamics stem from topology and how topology evolves dynamically. 🔗

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#SfN23 Come to WCC 147A at 2pm tomorrow to hear my talk on the neural basis of cuttlefish camouflage✨ Even better, come to the whole session (1-3pm) for neuroethology of sensorimotor systems in spiders, hydra, bats, octopus & more! 🕷️🐙🦇🐒 @SFNtweets

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Tessa Montague

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It's been incredible to see neural networks working so well on our humanoid robots Humanoids are crazy complex - an individual motor can rotate 360 degrees and you have 40+ joints. If you do the math, that means more possible robot states than atoms in the universe Figure has our own AI model called Helix that we've designed in-house. A single Helix neural network now outputs both manipulation and navigation, end-to-end from language and pixel input Every leap in machine learning has come from massive, diverse datasets. At Figure, we’re currently building the largest pretraining dataset for humanoids in history - excited to see what this unlocks

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$🚨🇷🇺🇨🇳 Russia and China develop breakthrough AI-powered earthquake forecasting method The new AI model predicts earthquakes by tracking changes in the Earth’s crust with superior accuracy—at a fraction of traditional computing costs. 🔸 Geoacoustic emissions are used — sounds produced as tectonic stress builds up underground — to pinpoint possible earthquake precursors 🔸The new system combines Physics-Informed Neural Networks (PINNs) with Kolmogorov-Arnold Networks (KANs) to process seismic data 🔸 The team proposes using human-made noise as seismic sonar to reveal underground details that natural signals miss 🔸 The approach slashes computing costs while delivering sharper 2D and 3D geological maps Beyond earthquake forecasting, the same technology could also aid mineral exploration and infrastructure planning—by revealing subsurface structures with unprecedented clarity$

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Sputnik

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Columbo couldnt say no to Kos' big ol puppy eyes! Just a short I got the idea to do that features my chosen voice actors for em! Tom Kenny as Kosmo & Peter Falk as Peter Fox/Columbo! 🔊is from SpongeBob "Squid Noir" & The In-Laws(1979)! ...Surprised I didnt use Columbo audio? xD

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Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction Note: Check below for full video. Abstract (cited): "In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion, and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. Our technique is particularly effective for high-quality scene reconstruction from large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. We introduce a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortions, and demonstrates state-of-the-art performance on both synthetic and real-world datasets."

MrNeRF

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TheNewPhysics

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Biomni Lab lets biologists collaborate with AI agents to finish complex tasks end-to-end. Here are 15 popular use cases, each link is a full replay so you can watch the agent work through every step: 1. Spatial transcriptomics analysis: map gene expression across tissue architecture from spatial transcriptomics data, with spatial clustering and neighborhood analysis. 2. Binder design: design de novo protein binders against a target structure using computational protein design tools. 3. Biomarker panel design: identify and optimize a multi-marker diagnostic or prognostic panel from omics data. 4. Clinical trial landscaping: search and summarize the trial landscape for a disease area, mapping phase, endpoints, and sponsor activity. 5. Survival analysis: pull clinical and expression data, fit Cox models, generate Kaplan-Meier curves, and identify prognostic markers. 6. scRNA-seq processing and annotation: from raw counts to UMAP clustering, marker gene detection, and automated cell type labeling. 7. Cell-cell communication: infer ligand-receptor interactions between cell types from single-cell data and map intercellular signaling networks. 8. Primer design for novel Cas13: analyze a putative Cas13 protein from a metagenomic screen—verify the ORF, identify HEPN domains, and design cloning primers with restriction sites and a FLAG 9. Proteomics differential expression: normalize mass spec data, run statistical tests, and visualize differentially abundant proteins. 10. Gene regulatory network inference: reconstruct transcription factor-target gene networks from expression data and identify key regulators. 11. Gene co-expression network analysis: build weighted co-expression networks, identify gene modules, and correlate them with phenotypic traits. 12. Microbiome analysis: process 16S/metagenomic sequencing data to profile microbial communities, diversity, and differential abundance. 13. Polygenic risk scores: compute and evaluate PRS from GWAS summary statistics against a target cohort. 14. Variant annotation: annotate genetic variants with functional predictions, allele frequencies, and clinical significance. 15. Fine-mapping: narrow GWAS loci to credible causal variants using statistical fine-mapping methods. Each of these would normally take days to weeks of scripting, debugging, and iteration. In Biomni Lab, the agent handles the full execution while you steer the science. Learn more:

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Do you ever wonder what happens inside the mind of an elite athlete? I absolutely love this perspective from gold medal freeskier Eileen Gu on the power of our mindset and belief system: "You can control how you think. And therefore, you can control who you are." Athletes don’t just magically create routines and habits. They’re supported by coaches who use the principles of behavioral science to set them up for success. That means using techniques to reduce decision fatigue and lower stress. Through neuroplasticity, the small choices we repeat every day can strengthen our neural circuits and rewire our brains to make certain behaviors more automatic, which is exactly what Gu has trained her mind to do. As Gu said, “With neuroplasticity on my side, I can become exactly who I want to be.” What was your favorite moment of the Winter 2026 Olympics? Let me know in the comments!

Arianna Huffington

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I believe the "Pit" described in the book of Revelation is a Galactic Federation prison deep under Antarctica, where "Satan" and "Lucifer" are already confined. From Pleiadian contact we know that the primary leader of the dark forces on Earth is a reptilian with four arms named Pidkozox. He's from the planet Uranus (Nebulac) in our solar system, but in a different dimension. Pidkozox is the father of two Sirian sons, Ashtar and Oppisheklio. Elder Ashtar is the leader of Sirius, a close friend to Elder Ikai, and a respected Galactic Federation commander. Oppisheklio is Earth's greatest deceiver, who played Good Cop to his father's Bad Cop. They were known by many names, including Enlil and Enki, Zeus and Apollo, Baal and Yahweh. They can't harm us directly and rely on human free will to carry out their plans. In blood rituals, they give commands to elites, who then control networks of puppets worldwide. We're in a system with oversight by Galactic Federation which allows dark forces to deceive us without harming us directly, and allows humans to harm each other with free will. Until the Shift, we're dealing with dark plans carried out by humans, as well as spiritual warfare, but we don't have to worry about physical encounters with negative extraterrestrials or defeating the dark forces.

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I believe the "Pit" described in the book of Revelation is a Galactic Federation prison deep under Antarctica, where "Satan" and "Lucifer" are already confined. From Pleiadian contact we know that the primary leader of the dark forces on Earth is a reptilian with four arms named Pidkozox. He's from the planet Uranus (Nebulac) in our solar system, but in a different dimension. Pidkozox is the father of two Sirian sons, Ashtar and Oppisheklio. Elder Ashtar is the leader of Sirius, a close friend to Elder Ikai, and a respected Galactic Federation commander. Oppisheklio is Earth's greatest deceiver, who played Good Cop to his father's Bad Cop. They were known by many names, including Enlil and Enki, Zeus and Apollo, Baal and Yahweh. They can't harm us directly and rely on human free will to carry out their plans. In blood rituals, they give commands to elites, who then control networks of puppets worldwide. We're in a system with oversight by Galactic Federation which allows dark forces to deceive us without harming us directly, and allows humans to harm each other with free will. Until the Shift, we're dealing with dark plans carried out by humans, as well as spiritual warfare, but we don't have to worry about physical encounters with negative extraterrestrials or defeating the dark forces.

Kab

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Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Animesh Garg

52,300 Aufrufe • vor 2 Jahren

DISASTER IN THE CHERNIHIV REGION: Due to continuous drone attacks, the entire power system has been destroyed; the blackout is now worse than in 2022. The situation in the region is critical: due to constant attacks by russian "Shahed" drones, tens of thousands of people are left without electricity, water, and communications. Children do not attend kindergartens and schools, as educational institutions are closed due to lack of electricity. People are sitting in the cold, and critical infrastructure is collapsing. Today, Novhorod-Siverskyi suffered about 20 drone strikes. Drones have been flying over Chernihiv and surrounding settlements for days; power engineers are working in critical conditions, and emergency crews cannot restore power supply due to continuous strikes. Despite four years of continuous drone attacks, the authorities have failed to establish effective protection; interceptor drones or air defense systems do not provide reliable control, and emergency services cannot work effectively. Local authorities and the media have barely covered the disaster. Law enforcement officers and volunteers are forced to patrol the territories and ensure the work of “Points of Invincibility,” while traffic controllers are on duty at

DISASTER IN THE CHERNIHIV REGION: Due to continuous drone attacks, the entire power system has been destroyed; the blackout is now worse than in 2022. The situation in the region is critical: due to constant attacks by russian "Shahed" drones, tens of thousands of people are left without electricity, water, and communications. Children do not attend kindergartens and schools, as educational institutions are closed due to lack of electricity. People are sitting in the cold, and critical infrastructure is collapsing. Today, Novhorod-Siverskyi suffered about 20 drone strikes. Drones have been flying over Chernihiv and surrounding settlements for days; power engineers are working in critical conditions, and emergency crews cannot restore power supply due to continuous strikes. Despite four years of continuous drone attacks, the authorities have failed to establish effective protection; interceptor drones or air defense systems do not provide reliable control, and emergency services cannot work effectively. Local authorities and the media have barely covered the disaster. Law enforcement officers and volunteers are forced to patrol the territories and ensure the work of “Points of Invincibility,” while traffic controllers are on duty at

EMPR.media

19,638 Aufrufe • vor 8 Monaten

OpenAI has introduced the ChatGPT Agent, which handles complex multi-step tasks from research to automation. Genspark goes further in some areas: In addition to user-friendly office tools (Slides, Docs, Sheets, AI Secretary, AI Drive), Genspark scores with dynamic tool orchestration and an intelligent feedback loop - a clear added value, especially for individuals and small teams. ChatGPT Agent Offers browser and API access, terminal control and deep search capabilities. Strengths include high security mechanisms, comprehensive user control and integration with productivity tools such as Gmail and Calendar. Ideal for end users and teams who need maximum control and data protection. Genspark Super Agent Enables no-code workflows, creates high-quality visual content (slides, videos) and automates entire workflows. With tool calling, the agent automatically selects the best solution from over 80 integrated tools - e.g. for CRM queries, task management or API access. The feedback loop allows the agent to monitor the use of a tool during execution and dynamically switch to another tool or adapt the workflow if necessary. Thanks to this multi-model architecture, Genspark often works more precisely and efficiently in benchmarks than comparable systems.

OpenAI has introduced the ChatGPT Agent, which handles complex multi-step tasks from research to automation. Genspark goes further in some areas: In addition to user-friendly office tools (Slides, Docs, Sheets, AI Secretary, AI Drive), Genspark scores with dynamic tool orchestration and an intelligent feedback loop - a clear added value, especially for individuals and small teams. ChatGPT Agent Offers browser and API access, terminal control and deep search capabilities. Strengths include high security mechanisms, comprehensive user control and integration with productivity tools such as Gmail and Calendar. Ideal for end users and teams who need maximum control and data protection. Genspark Super Agent Enables no-code workflows, creates high-quality visual content (slides, videos) and automates entire workflows. With tool calling, the agent automatically selects the best solution from over 80 integrated tools - e.g. for CRM queries, task management or API access. The feedback loop allows the agent to monitor the use of a tool during execution and dynamically switch to another tool or adapt the workflow if necessary. Thanks to this multi-model architecture, Genspark often works more precisely and efficiently in benchmarks than comparable systems.

Chubby♨️

176,267 Aufrufe • vor 11 Monaten