Multi-view 3D reconstruction remains a core challenge in computer vision. Recent methods, such as DUSt3R and its successors, directly regress pointmaps from image pairs without relying on known scene geometry or camera parameters. However, the performance of these models is constrained by the diversity and scale of available training data. In this work, we introduce Puzzles, a data augmentation strategy that synthesizes an unbounded volume of high-quality, posed video-depth data from just a single image or video clip. By simulating diverse camera trajectories and realistic scene geometry through targeted image transformations, Puzzles enhances data variety. Extensive experiments show that integrating Puzzles into existing video-based 3D reconstruction pipelines consistently boosts performance, all without modifying the underlying network architecture. Notably, models trained on only 10%of the original data, augmented with Puzzles, still achieve accuracy comparable to those trained on the full dataset.[Project

Diffusion models, though originally designed for generative tasks, have demonstrated impressive self-supervised representation learning capabilities. A particularly intriguing phenomenon in these models is the emergence of unimodal representation dynamics, where the quality of learned features peaks at an intermediate noise level. In this work, we conduct a comprehensive theoretical and empirical investigation of this phenomenon. Leveraging the inherent low-dimensionality structure of image data, we theoretically demonstrate that the unimodal dynamic emerges when the diffusion model successfully captures the underlying data distribution. The unimodality arises from an interplay between denoising strength and class confidence across noise scales. Empirically, we further show that, in classification tasks, the presence of unimodal dynamics reliably reflects the diffusion model's generalization: it emerges when the model generate novel images and gradually transitions to a monotonically decreasing curve as the model begins to memorize the training data.

Moscow oil refinery attack brings Russia's war with Ukraine closer to home There are moments when life in Moscow feels completely normal. In the south-east of the city an oil refinery had been hit during a Ukrainian drone attack - even from a distance the sight was surreal. Thick smoke billowing from the direction of the facility had turned the sky dark. Like a giant black shroud, it hung over the Moscow skyline. As extraordinary and eye-catching this was, so was the reaction of people near the refinery.

Serial dependence reflects how recent sensory history shapes current perception, producing two opposing biases: repulsion, where perception is repelled from recent stimuli, and attraction, where perception is drawn toward them. Repulsion typically occurs at the sensory perception stage, while attraction arises at the post-perception stage. To uncover the neural basis of these effects, we developed a two-layer continuous attractor neural network model incorporating synaptic short-term plasticity (STP). The lower layer, dominated by synaptic depression, models sensory processing and drives repulsion due to sustained neurotransmitter depletion. The higher layer, dominated by synaptic facilitation, models post-perception processing and drives attraction by sustained high neurotransmitter release probability. Our model successfully explains the serial dependence phenomena observed in the visual orientation judgment experiments, highlighting STP as the critical mechanism, with its time constants defining the temporal windows of repulsion and attraction. Furthermore, the model provides a neural foundation for the Bayesian interpretation of serial dependence. This study advances our understanding of how the neural system leverages STP to balance sensitivity in sensory perception with stability in post-perceptual cognition.

Plus: Gay bars in San Francisco using face scanners, France quits Palantir, Apple plans to change its private email, and more. Meta is testing face-recognition software built by the United States military and regional police department supplier Rank One, WIRED found in an investigation this week. Meta has been exploring the possibility of adding face recognition tech into its smart glasses, and WIRED previously reported that the app for the glasses contained code --now deleted--that would have enabled the company to activate face-recognition features on the devices. Anthropic is still negotiating with the Trump administration, after apparent White House concerns about the safety of new public model Claude Fable 5 resulted in Anthropic pulling the product off the market entirely. But security experts point out that AI models with advanced capabilities for discovering and exploiting software vulnerabilities--in other words, creating potentially dangerous hacking tools-- will be ubiquitous soon around the world .

We utilise a sampler originating from nonequilibrium statistical mechanics, termed here Jarzynski-adjusted Langevin algorithm (JALA), to build statistical estimation methods in latent variable models. We achieve this by leveraging Jarzynski's equality and developing algorithms based on a weighted version of the unadjusted Langevin algorithm (ULA) with recursively updated weights. Adapting this for latent variable models, we develop a sequential Monte Carlo (SMC) method that provides the maximum marginal likelihood estimate of the parameters, termed JALA-EM. Under suitable regularity assumptions on the marginal likelihood, we provide a nonasymptotic analysis of the JALA-EM scheme implemented with stochastic gradient descent and show that it provably converges to the maximum marginal likelihood estimate. We demonstrate the performance of JALA-EM on a variety of latent variable models and show that it performs comparably to existing methods in terms of accuracy and computational efficiency. Importantly, the ability to recursively estimate marginal likelihoods--an uncommon feature among scalable methods--makes our approach particularly suited for model selection, which we validate through dedicated experiments.

Granta said it would no longer be involved in'external publishing partnerships' in which it had no editorial control. Granta said it would no longer be involved in'external publishing partnerships' in which it had no editorial control. Literary magazine will no longer engage in'external publishing partnerships' after Commonwealth prize furore The prominent literary magazine Granta will no longer publish the winning entries of the annual Commonwealth short story prize after one of this year's winners drew widespread accusations of AI use. The magazine said it would no longer be involved in "external publishing partnerships" in which it had no editorial control. In a statement to the Guardian, Granta said: "The 2026 selection of the regional winners of the Commonwealth prize caused a great deal of controversy, based on the speculation that one or more of the stories may have been at least partially AI-generated, accusations that were strongly rejected by the authors. "For the sake of our own editorial integrity, the Granta Trust board has now taken the decision that we will no longer engage in external publishing partnerships.

Researchers have demonstrated they can make coffee comparable to conventional espresso using ultrasonic waves. Because the process doesn't need hot water, it consumes 75 percent less energy. What do you need to make a good espresso? Ground coffee, of course; a machine capable of generating pressure; and hot water, preferably heated to between 195 and 205 degrees Fahrenheit. But could one perhaps do without that last element?

We consider the problem of recovering a mental target (e.g., an image of a face) that a participant has in mind from paired EEG (i.e., brain responses) and image (i.e., perceived faces) data collected during interactive sessions without access to labeled information. The problem has been previously explored with labeled data but not via self-calibration, where labeled data is unavailable. Here, we present the first framework and an algorithm, CURSOR, that learns to recover unknown mental targets without access to labeled data or pre-trained decoders. Our experiments on naturalistic images of faces demonstrate that CURSOR can (1) predict image similarity scores that correlate with human perceptual judgments without any label information, (2) use these scores to rank stimuli against an unknown mental target, and (3) generate new stimuli indistinguishable from the unknown mental target (validated via a user study, N = 53). We release the brain response data set (N = 29), associated face images used as stimuli data, and a codebase to initiate further research on this novel task.

A huge fire at a luxury beach resort in the Dominican Republic killed one woman and forced nearly 1,700 guests to be evacuated on Friday. In a statement to local media, the DAEH emergency services said that a 46-year-old Italian tourist died, three people were taken to medical facilities and six others were treated on site. Drone footage shows how widespread the fire was, with buildings spanning the Viva Wyndham Dominicus Beach in Bayahibe on fire and thick black smoke billowing into the air. What sparked the early-morning blaze is not yet known, but an initial investigation found the flames spread quickly due to wind conditions and the flammable nature of the thatched roofs on some buildings. The country's Emergency Operations Center (COE) said the fire had been brought under control and guests had been moved to other hotels. It added that tourist activities in the town and surrounding area have been unaffected and can continue as normal.