Tootfinder

Mapping the Turn: An Eulerian Binormal-Axis Diagnostic for Recirculating 3D Flows
John Marshall Cooper, Wen Wu
https://arxiv.org/abs/2605.18439 https://arxiv.org/pdf/2605.18439 https://arxiv.org/html/2605.18439
arXiv:2605.18439v1 Announce Type: new
Abstract: Three-dimensional (3D) recirculating flows are often interpreted qualitatively from selected streamline visualizations. In separated flows, such recirculating motion is central to the drag modulation, but the local orientation of recirculation remains difficult to quantify in a field-based form. This work introduces an Eulerian binormal-axis diagnostic that locally evaluates the orientation of streamline turning at each point in the velocity field, yielding a spatially resolved field of the recirculating direction. Motivated by the Frenet-Serret binormal direction of a curved streamline, the diagnostic uses the velocity vector and its convective acceleration to extract the local streamline-turning axis without requiring explicit streamline integration. The resulting direction is encoded with barycentric RGB weights to visualize streamwise, spanwise, and wall-normal turning axis contributions. The diagnostic is first applied to Hill's spherical vortex, which provides a controlled analytic example of 3D recirculating motion for interpreting the binormal-axis direction and the associated barycentric RGB encoding. It is then applied to the mean field of a pressure-gradient-induced 3D separation bubble. The resulting visualizations show that the diagnostic reveals orientation changes that are not apparent from streamline visualization. The proposed diagnostic therefore converts qualitative streamline impressions into a spatially resolved measure of local streamline-turning orientation, providing a quantitative complement to conventional 3D flow visualization.
toXiv_bot_toot

Sources: six months after acquiring Rivos, Meta is struggling to integrate the chip startup and halted development of a chip for training its largest AI models (The Information)
https://www.theinformation.com/articles/meta-bought-rivos-accelerate-ai-…

Meta Bought Rivos to Accelerate Its AI Chip Push. It Isn’t Working.
Meta Platforms bought semiconductor startup Rivos last year to accelerate development of in-house chips and reduce its reliance on Nvidia as it pours cash into data centers for its AI ambitions. Now six months since the acquisition closed, Meta is struggling to make it work, beset by problems ...

ANNS-AMP: Accelerating Approximate Nearest Neighbor Search via Adaptive Mixed-Precision Computing
Mingkai Chen, Cheng Liu, Shengwen Liang, Lei Zhang, Xiaowei Li, Huawei Li
https://arxiv.org/abs/2606.07156 https://arxiv.org/pdf/2606.07156 https://arxiv.org/html/2606.07156
arXiv:2606.07156v1 Announce Type: new
Abstract: Approximate nearest neighbor search(ANNS) is a critical kernel in modern applications such as LLM and recommendation systems.However,its efficiency is fundamentally limited by the need to compute distances between a query and a massive number of high-dimensional vectors,most of which are non-neighbors.Existing approaches reduce redundancy via index optimization or early termination,but remain constrained by fixed-precision computation,leading to unnecessary arithmetic and memory bandwidth overhead.This paper presents ANNS-AMP,an adaptive mixed-precision framework and accelerator that adapts the precision of distance computation to the characteristics of queries and data distribution.The key insight is that different regions of the vector space require different levels of precision to preserve top-k accuracy.ANNS-AMP leverages the clustered structure of PQ-based indices and introduces a lightweight predictor to determine cluster-level precision at runtime based on features such as scale,radius,and query distance.To efficiently realize variable-precision execution,we design a bit-serial accelerator with a bit-interleaved data layout,enabling throughput to scale with reduced precision while mitigating memory bandwidth bottlenecks and load imbalance through a greedy scheduling strategy.Moreover,the runtime predictor can also reuse the bit-serial computing array for efficient runtime prediction and can be fitted to the ANNS pipeline without performance penalty.According to our experiments on representative datasets,ANNS-AMP achieves 163.76x,10.57x,and 2.06x performance speedups on average,and reduces average energy consumption by 1100.00x,39.41x,and 6.66x compared to CPU,GPU,and customized ANNS accelerator baselines,respectively,while maintaining accuracy loss below 2.7%.These results demonstrate that adaptive mixed-precision computing is a promising direction for efficient large-scale ANNS.
toXiv_bot_toot

Despite its rise as an economic superpower,
China remains reliant on a global financial system anchored by the dollar.
Turning the renminbi into a globally accepted currency would let Beijing conduct more trade on its own terms and blunt a longstanding source of American leverage.
That push has gained momentum from the wars in Ukraine and Iran,
as sanctions drive American adversaries toward the renminbi to bypass the Western financial system.
In effect, China’…

War and Sanctions Accelerate China’s Currency Push
China’s bid to build a renminbi-based financial system beyond the U.S. dollar’s reach is gaining traction as a way to sidestep sanctions.

Cities and companies are making climate-friendly eating the new normal. NYC is leading by serving more plant-based meals in schools, while the EU's deforestation law ensures high-emission foods reflect their true environmental cost.
The secret? Training the next generation of chefs and making sustainable choices delicious and accessible.

How countries, cities and companies accelerate the transformation to plant-rich food
Learn what New York City, Oatly, Luxembourg, and the Electrolux Food Foundation are teaching us about scaling the food transition to plant rich diets.