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This study proposes PDFuse, a robust, general training-free image fusion framework built on pre-trained latent diffusion models with projection-manifold regularization. By redefining fusion as a diffusion inference process constrained by multiple source images, PDFuse can adapt to varied image modalities and produce high-fidelity outputs utilizing the diffusion prior. To ensure both source consistency and full utilization of generative priors, we develop novel projection-manifold regularization, which consists of two core mechanisms. On the one hand, the Multisource Information Consistency Projection (MICP) establishes a projection system between diffusion latent representations and source images, solved efficiently via conjugate gradients to inject multi-source information into the inference. On the other hand, the Latent Manifold-preservation Guidance (LMG) aligns the latent distribution of diffusion variables with that of the sources, guiding generation to respect the model's manifold prior.

Turns out, the first animals to walk on land weren't amphibians. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. An illustration shows prehistoric baby crocodile-like animal known as an embolomere swimming with their mother in the background. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

"You would not believe the texts I got from these tech guys," NYT reporters Maggie Haberman and Jonathan Swan quote Donald Trump as telling associates in an upcoming book. Meta CEO Mark Zuckerberg and Amazon founder Jeff Bezos sought to ingratiate themselves with President Donald Trump after he won the 2024 election, and in return he mocked their efforts behind their backs, according to a new book by The New York Times reporters Maggie Haberman and Jonathan Swan. Zuckerberg once texted Trump a photo of a letter written by one of his grade-school-age children, who wrote that they "looked forward to the golden age of America," a slogan Trump had repeated at rallies during the presidential campaign. And over dinner at Trump's Mar-a-Lago club, Bezos denigrated The Washington Post to Trump and essentially described the newspaper as one of his worst financial investments, months before he unsuccessfully sought a business favor from the president. These episodes are detailed in the book, a copy of which WIRED obtained ahead of its release on June 23.

Embattled Gavin Newsom's stunning confession to Justin Trudeau caught on camera at World Cup when he thought no one was watching Tragic final moments of Hollywood legend's daughter and her husband revealed before being mysteriously found dead in their running SUV Boy, three, is thrown into crocodile enclosure at zoo: Man, 30, 'not known to him' arrested on suspicion of attempted murder Haunting final video of beloved Bay Area coffee shop owner, 52, who vanished without a trace: Investigator reveals'unnerving' new clues found inside her home Watch horrifying drone video that follows woman's plunge to death after bungee team threw her from bridge without rope Malia and Sasha Obama exude confidence as they attend grand opening of dad Barack's library Mystery surrounds JD Vance's dash to Switzerland as world holds breath for Iranians to confirm peace deal All my friends are suddenly getting divorced. Mid-life wives share taboo sex confessions about why they really leave... including common position that made one hate her husband: JANA HOCKING Taylor Swift's bottomless thirst for attention, her greed and sheer tackiness are now truly unbearable... this latest stunt has shown her true colors: MAUREEN CALLAHAN Male Israeli hostage sexually assaulted by Hamas captor describes multiple attacks he suffered - blindfolded and stripped naked at knifepoint... and'brutal' 20-minute ordeal World Cup's'sexiest fan' sends fans wild with barely-there outfit to watch her country's first game in Dallas Troubling new psychological side effect of Ozempic revealed... as users battle'ghost fat' after losing weight Infection found in wildlife evolved to spread between humans, experts fear... after two clusters are identified Sensational REAL reason Jelly Roll is divorcing Bunnie XO: Insiders reveal'preacher's wife' bombshell that's the talk of Nashville... truth about legendary rocker cuckolding rumor... and G-string mishap Fears of the'Big One' - an earthquake so big it devastates all of California - have risen to new heights after a disturbing discovery under America's most dangerous fault line. Researchers from the US and Switzerland revealed that the San Andreas Fault has reached its highest levels of stress in 1,000 years - adding that it has been more than 160 years since the giant crack in the Earth's crust had a major release of energy . The San Andreas is an 800-mile-long fault line which runs under most of California, passing by Los Angeles in the south and San Francisco in the north and connecting to several other major faults, most notably the San Jacinto Fault near Los Angeles. This connection is where researcher Liliane Burkhard from the University of Hawaiʻi at Mānoa said seismic stress has become so great at the southern end of the San Andreas that a rupture could travel along both fault lines - resulting in a mega quake .

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Recent advances in Multimodal Large Language Models (MLLMs) have significantly improved 2D visual understanding, prompting interest in their application to complex 3D reasoning tasks. However, it remains unclear whether these models can effectively capture the detailed spatial information required for robust real-world performance, especially cross-view consistency, a key requirement for accurate 3D reasoning. Considering this issue, we introduce Viewpoint Learning, a task designed to evaluate and improve the spatial reasoning capabilities of MLLMs. We present the Viewpoint-100K dataset, consisting of 100K object-centric image pairs with diverse viewpoints and corresponding question-answer pairs. Our approach employs a two-stage fine-tuning strategy: first, foundational knowledge is injected to the baseline MLLM via Supervised Fine-Tuning (SFT) on Viewpoint-100K, resulting in significant improvements across multiple tasks; second, generalization is enhanced through Reinforcement Learning using the Group Relative Policy Optimization (GRPO) algorithm on a broader set of questions. Additionally, we introduce a hybrid cold-start initialization method designed to simultaneously learn viewpoint representations and maintain coherent reasoning thinking. Experimental results show that our approach significantly activates the spatial reasoning ability of MLLM, improving performance on both in-domain and out-of-domain reasoning tasks. Our findings highlight the value of developing foundational spatial skills in MLLMs, supporting future progress in robotics, autonomous systems, and 3D scene understanding.

Open-world reinforcement learning challenges agents to develop intelligent behavior in vast exploration spaces. Recent approaches like LS-Imagine have advanced the field by extending imagination horizons through jumpy state transitions, yet remain limited by fixed exploration mechanisms and static jump thresholds that cannot adapt across changing task phases, resulting in inefficient exploration and lower completion rates. Humans demonstrate remarkable capabilities in openworld decision-making through a chain-like process of task decomposition, selective memory utilization, and adaptive uncertainty regulation. Inspired by human decision-making processes, we present Cognitive Predictive Processing (CPP), a novel framework that integrates three neurologically-inspired systems: a phaseadaptive cognitive controller that dynamically decomposes tasks into exploration, approach, and completion phases with adaptive parameters; a dual-memory integration system implementing dual-modal memory that balances immediate context with selective long-term storage; and an uncertainty-modulated prediction regulator that continuously updates environmental predictions to modulate exploration behavior. Comprehensive experiments in MineDojo demonstrate that these humaninspired decision-making strategies enhance performance over recent techniques, with success rates improving by an average of 4.6% across resource collection tasks while reducing task completion steps by an average of 7.1%. Our approach bridges cognitive neuroscience and reinforcement learning, excelling in complex scenarios that require sustained exploration and strategic adaptation while demonstrating how neural-inspired models can solve key challenges in open-world AI systems.

We introduce MusclePose as an end-to-end learnable physics-infused 3D human pose estimator that incorporates muscle-dynamics modeling to infer human dynamics from monocular video. Current physics pose estimators aim to predict physically plausible poses by enforcing the underlying dynamics equations that govern motion. Since this is an underconstrained problem without force-annotated data, methods often estimate kinetics with external physics optimizers that may not be compatible with existing learning frameworks, or are too slow for real-time inference. While more recent methods use a regression-based approach to overcome these issues, the estimated kinetics can be seen as auxiliary predictions, and may not be physically plausible. To this end, we build on existing regressionbased approaches, and aim to improve the biofidelity of kinetic inference with a multihypothesis approach -- by inferring joint torques via Lagrange's equations and via muscle dynamics modeling with muscle torque generators. Furthermore, MusclePose predicts detailed human anthropometrics based on values from biomechanics studies, in contrast to existing physics pose estimators that construct their human models with shape primitives. We show that MusclePose is competitive with existing 3D pose estimators in positional accuracy, while also able to infer plausible human kinetics and muscle signals consistent with values from biomechanics studies, without requiring an external physics engine.

The growing adoption of machine learning models for biological sequences has intensified the need for interpretable predictions, with Shapley values emerging as a theoretically grounded standard for model explanation. While effective for local explanations of individual input sequences, scaling Shapley-based interpretability to extract global biological insights requires evaluating thousands of sequences--incurring exponential computational cost per query. We introduce SHAP zero, a novel algorithm that amortizes the cost of Shapley value computation across large-scale biological datasets. After a one-time model sketching step, SHAP zero enables near-zero marginal cost for future queries by uncovering an underexplored connection between Shapley values, high-order feature interactions, and the sparse Fourier transform of the model. Applied to models of guide RNA efficacy, DNA repair outcomes, and protein fitness, SHAP zero explains predictions orders of magnitude faster than existing methods, recovering rich combinatorial interactions previously inaccessible at scale. This work opens the door to principled, efficient, and scalable interpretability for black-box sequence models in biology.