Few-shot font generation (FFG) aims to learn the target style from a limited number of reference glyphs and generate the remaining glyphs in the target font. Previous works focus on disentangling the content and style features of glyphs, combining the content features of the source glyph with the style features of the reference glyph to generate new glyphs. However, the disentanglement is challenging due to the complexity of glyphs, often resulting in glyphs that are influenced by the style of the source glyph and prone to artifacts. We propose IF-Font, a novel paradigm which incorporates Ideographic Description Sequence (IDS) instead of the source glyph to control the semantics of generated glyphs. To achieve this, we quantize the reference glyphs into tokens, and model the token distribution of target glyphs using corresponding IDS and reference tokens. The proposed method excels in synthesizing glyphs with neat and correct strokes, and enables the creation of new glyphs based on provided IDS. Extensive experiments demonstrate that our method greatly outperforms state-of-the-art methods in both one-shot and few-shot settings, particularly when the target styles differ significantly from the training font styles.

In this paper, we propose to create animatable avatars for interacting hands with 3D Gaussian Splatting (GS) and single-image inputs. Existing GS-based methods designed for single subjects often yield unsatisfactory results due to limited input views, various hand poses, and occlusions. To address these challenges, we introduce a novel two-stage interaction-aware GS framework that exploits cross-subject hand priors and refines 3D Gaussians in interacting areas. Particularly, to handle hand variations, we disentangle the 3D presentation of hands into optimization-based identity maps and learning-based latent geometric features and neural texture maps. Learning-based features are captured by trained networks to provide reliable priors for poses, shapes, and textures, while optimization-based identity maps enable efficient one-shot fitting of out-of-distribution hands. Furthermore, we devise an interaction-aware attention module and a self-adaptive Gaussian refinement module. These modules enhance image rendering quality in areas with intra-and inter-hand interactions, overcoming the limitations of existing GS-based methods. Our proposed method is validated via extensive experiments on the large-scale InterHand2.6M dataset, and it significantly improves the state-of-the-art performance in image quality. Code and models will be released upon acceptance.

Transformers have shown impressive capabilities across various tasks, but their performance on compositional problems remains a topic of debate. In this work, we investigate the mechanisms of how transformers behave on unseen compositional tasks. We discover that the parameter initialization scale plays a critical role in determining whether the model learns inferential (reasoning-based) solutions, which capture the underlying compositional primitives, or symmetric (memory-based) solutions, which simply memorize mappings without understanding the compositional structure. By analyzing the information flow and vector representations within the model, we reveal the distinct mechanisms underlying these solution types. We further find that inferential (reasoning-based) solutions exhibit low complexity bias, which we hypothesize is a key factor enabling them to learn individual mappings for single anchors.

'Making people feel powerful' All Will Rise. 'Making people feel powerful' All Will Rise. Why an up-and-coming indie developer is returning Microsoft's money Don't get Pushing Buttons delivered to your inbox? V ideo games are in a funding crisis. Investor money flowed freely during the pandemic gaming boom, but now the well has run dry.

The tool is now available in every place where the company offers its AI Mode chatbot. After debuting in the US, Search Live is now available globally. After rolling out Search Live to all US Google app users last September, Google is now bringing the feature to every place where it offers its AI Mode chatbot. Search Live, if you need a reminder, allows you to point your phone's camera at an object or scene and ask questions about what you see in front of you. Google debuted the tool at I/O 2025 before it began rolling it out to users.

The math checks out, so hang on to those jeans. The trend's reliability may be waning as styles continue to diversify, however. Breakthroughs, discoveries, and DIY tips sent six days a week. Clothing trends come and go, but in some cases, they don't stay away for too long. For decades, both the fashion industry and its devotees have referenced the so-called "20-year-rule," which suggests society is liable to see certain styles return at semiregular intervals.

A trial involving thousands of flights between the US and Europe has found that planes produce fewer contrails if they follow flight paths recommended by an artificial intelligence to reduce their global warming impact. The streaks of condensation triggered by soot particles produced by aircraft engines are thought to cause more warming than the carbon dioxide that planes emit. Research has also shown that some ice-rich regions of the upper atmosphere are more likely to form contrails when a plane passes through them, and that AI can predict where these regions will be using detailed weather forecasts. We're finally solving the puzzle of how clouds will affect our climate There have been small-scale trials showing that planes rerouted through these regions will produce fewer contrails, but the practice has yet to be applied to commercial flights at scale. Now, Dinesh Sanekommu at Google and his colleagues have used an AI contrail-forecasting tool to give routing advice in a randomised control trial of more than 2400 real American Airlines flights.

A trial involving thousands of flights between the US and Europe has found that planes produce fewer contrails if they follow flight paths recommended by an artificial intelligence to reduce their global warming impact. The streaks of condensation triggered by soot particles produced by aircraft engines are thought to cause more warming than the carbon dioxide that planes emit. Research has also shown that some ice-rich regions of the upper atmosphere are more likely to form contrails when a plane passes through them, and that AI can predict where these regions will be using detailed weather forecasts. We're finally solving the puzzle of how clouds will affect our climate There have been small-scale trials showing that planes rerouted through these regions will produce fewer contrails, but the practice has yet to be applied to commercial flights at scale. Now, Dinesh Sanekommu at Google and his colleagues have used an AI contrail-forecasting tool to give routing advice in a randomised control trial of more than 2400 real American Airlines flights.

A trial involving thousands of flights between the US and Europe has found that planes produce fewer contrails if they follow flight paths recommended by an artificial intelligence to reduce their global warming impact. The streaks of condensation triggered by soot particles produced by aircraft engines are thought to cause more warming than the carbon dioxide that planes emit. Research has also shown that some ice-rich regions of the upper atmosphere are more likely to form contrails when a plane passes through them, and that AI can predict where these regions will be using detailed weather forecasts. We're finally solving the puzzle of how clouds will affect our climate There have been small-scale trials showing that planes bypassing these regions will produce fewer contrails, but the practice has yet to be applied to commercial flights at scale. Now, Dinesh Sanekommu at Google and his colleagues have used an AI contrail-forecasting tool to give routing advice in a randomised control trial of more than 2400 real American Airlines flights.

It has repeatedly been observed that loss minimization by stochastic gradient descent (SGD) leads to heavy-tailed distributions of neural network parameters. Here, we analyze a continuous diffusion approximation of SGD, called homogenized stochastic gradient descent (hSGD), and show in a regularized linear regression framework that it leads to an asymptotically heavy-tailed parameter distribution, even though local gradient noise is Gaussian. We give explicit upper and lower bounds on the tail-index of the resulting parameter distribution and validate these bounds in numerical experiments. Moreover, the explicit form of these bounds enables us to quantify the interplay between optimization hyperparameters and the tail-index. Doing so, we contribute to the ongoing discussion on links between heavy tails and the generalization performance of neural networks as well as the ability of SGD to avoid suboptimal local minima.