Tencent Announces Global Rollout of Scenario-Based AI Capabilities to Accelerate Industrial Efficiency - Tencent 腾讯
Tencent Announces Global Rollout of Scenario-Based AI Capabilities to Accelerate Industrial Efficiency Tencent 腾讯
Could not retrieve the full article text.
Read on Google News - Tencent AI →Google News - Tencent AI
https://news.google.com/rss/articles/CBMiYEFVX3lxTE9ibnNHUzNUQXVFQ0w0RC00WUc4SVVFcWowbEZhb2hCVGgzNm02ekZibE4yeWhxSWxTWmN0SlR4dWU1NnlYcm9qVHFpVDVQbVJ5b0d4TmJDYnFBaUhEUVY5Uw?oc=5Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
announceglobal
Leapmotor targets global growth with Europe R&D hub, eyes Canada assembly
Leapmotor, one of China’s strongest-performing electric vehicle (EV) makers this year, is accelerating its global push with a new innovation centre in Europe and plans for potential local assembly in Canada, as it looks to entrench production of its low-cost smart cars overseas. The Hangzhou-based carmaker, backed by Fiat owner Stellantis, has also raised its overseas sales target by 50 per cent, betting that higher fuel costs and easing trade barriers will bolster demand for Chinese-made...
Pushing to Host the 'International Organization Campus Alliance': Korea Leaps Forward as a 'Rule-Maker' in the Global AI Hegemony - 데일리연합
Pushing to Host the 'International Organization Campus Alliance': Korea Leaps Forward as a 'Rule-Maker' in the Global AI Hegemony 데일리연합
Probabilistic AVL Trees (p-AVL): Relaxing Deterministic Balancing
arXiv:2604.02223v1 Announce Type: new Abstract: This paper studies the empirical behaviour of the p-AVL tree, a probabilistic variant of the AVL tree in which each imbalance is repaired with probability $p$. This gives an exact continuous interpolation from $p = 0$, which recovers the BST endpoint, to $p = 1$, which recovers the standard AVL tree. Across random-order insertion experiments, we track rotations per node, total imbalance events, average depth, average height, and a global imbalance statistic $\sigma$. The main empirical result is that even small nonzero p already causes a strong structural change. The goal here is empirical rather than fully theoretical: to document the behaviour of the p-AVL family clearly and identify the main patterns.
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Releases
trunk/834da621b18df19b513ee787c6926d43f928adfc: add API to check if a tensor is symm-mem-tensor (#178947)
In Helion autotuner, we need clone a input symm memory tensor properly if the kernel inplace update it. That requires us to know if a tensor is a symm memory tensor. Right now I call rendezvous for the tensor. If no exception is thrown, then it's a symm memory tensor. But it's not ideal there will be a lot of warnings complaining calling rendezvous on non-symm memory tensor I'll need to pass in the process group name to this API. But fundamentally check if a tensor is a symmetric memory tensor does not require the process group name. Pull Request resolved: #178947 Approved by: https://github.com/ngimel , https://github.com/fegin
DGAI: Decoupled On-Disk Graph-Based ANN Index for Efficient Updates and Queries
arXiv:2510.25401v3 Announce Type: replace Abstract: On-disk graph-based indexes are favored for billion-scale Approximate Nearest Neighbor Search (ANNS) due to their high performance and cost-efficiency. However, existing systems typically rely on a coupled storage architecture that co-locates vectors and graph topology, which introduces substantial redundant I/O during index updates, thereby degrading usability in dynamic workloads. In this paper, we propose a decoupled storage architecture that physically separates heavy vectors from the lightweight graph topology. This design substantially improves update performance by reducing redundant I/O during updates. However, it introduces I/O amplification during ANNS, leading to degraded query efficiency.To improve query performance within the
A Simple Average-case Analysis of Recursive Randomized Greedy MIS
arXiv:2604.01462v1 Announce Type: new Abstract: We revisit the complexity analysis of the recursive version of the randomized greedy algorithm for computing a maximal independent set (MIS), originally analyzed by Yoshida, Yamamoto, and Ito (2009). They showed that, on average per vertex, the expected number of recursive calls made by this algorithm is upper bounded by the average degree of the input graph. While their analysis is clever and intricate, we provide a significantly simpler alternative that achieves the same guarantee. Our analysis is inspired by the recent work of Dalirrooyfard, Makarychev, and Mitrovi\'c (2024), who developed a potential-function-based argument to analyze a new algorithm for correlation clustering. We adapt this approach to the MIS setting, yielding a more di
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!