Temporal link prediction, aiming at predicting future interactions among entities based on historical interactions, is crucial for a series of real-world applications. Although previous methods have demonstrated the importance of relative encodings for effective temporal link prediction, computational efficiency remains a major concern in constructing these encodings. Moreover, existing relative encodings are usually constructed based on structural connectivity, where temporal information is seldom considered. To address the aforementioned issues, we first analyze existing relative encodings and unify them as a function of temporal walk matrices. This unification establishes a connection between relative encodings and temporal walk matrices, providing a more principled way for analyzing and designing relative encodings. Based on this analysis, we propose a new temporal graph neural network called TPNet, which introduces a temporal walk matrix that incorporates the time decay effect to simultaneously consider both temporal and structural information. Moreover, TPNet designs a random feature propagation mechanism with theoretical guarantees to implicitly maintain the temporal walk matrices, which improves the computation and storage efficiency. Experimental results on 13 benchmark datasets verify the effectiveness and efficiency of TPNet, where TPNet outperforms other baselines on most datasets and achieves a maximum speedup of 33.3 compared to the SOTA baseline. Our code can be found at https://github.com/lxd99/TPNet.

BIT Special Zone Beijing Institute Of Technology Beijing, China, 100081 {haizhaoyang, liyuan.pan, In this supplementary material, we provide the proofs of convergence analysis in Section 1, 1-vs-1 transformation employed in the classification and semantic segmentation tasks in Section 2, the coordinate-wise and the preprocessing method of the LSTM teacher in Section 3, the loss functions of YOLO-v3 in Section 4, more experiments of image classification in Section 5, and the inferences of semantic segmentation in Section 6. A differentiable function e() is L-smooth with gradient Lipschitz constant C (uniformly Lipschitz continuous), if e(x) e(y) C x y, x, y. If e(x) ϵ, then x is an ϵ-first-order stationary point. For a differentiable function e(), if x is a SS1, and there exists ϵ > 0 so that for any y in the ϵ-neighborhood of x, we have e(x) e(y), then x is a local minimum. A saddle point x is an SS1 that is not a local minimum.

National security and military analyst Dr. Rebecca Grant joins'Fox & Friends First' to discuss President Donald Trump's historic business-focused trip to the Middle East and why a Trump-Putin meeting could be essential for peace in Ukraine. President Donald Trump secured 2 trillion worth of deals with Saudi Arabia, Qatar and the UAE during his trip to the Middle East last week in what some have argued is a move to counter China's influence in the region. While China has increasingly bolstered its commercial ties with top Middle Eastern nations who have remained steadfast in their refusal to pick sides amid growing geopolitical tension between Washington and Beijing, Trump may have taken steps to give the U.S. an edge over its chief competitor. But concern has mounted after Trump reversed a Biden-era policy – which banned the sale of AI-capable chips to the UAE and Saudi Arabia – that highly coveted U.S. technologies could potentially fall into the hands of Chinese companies, and in extension, the Chinese Communist Party (CCP). U.S. President Donald Trump walks with Saudi Crown Prince Mohammed Bin Salman during a welcoming ceremony in Riyadh, Saudi Arabia, May 13, 2025.

President Donald Trump signed a new executive order on Wednesday aimed at fostering AI education in K-12 schools and preparing students for an increasingly AI-centric workforce. The new directive's overarching goal is to "ensure the United States remains a global leader in this technological revolution," according to the press release. The policy seeks to "promote AI literacy and proficiency of K-12 students" while also training educators so they can integrate AI education into their curriculums. The move could be a response to recent developments in China. In March, the Beijing Municipal Education Commission announced that it would be making AI lessons mandatory for primary and secondary school students.

Over the weekend, humans running as fast as they could were chased by robots through the streets of Beijing, China. To be more specific, it was a half-marathon race, and the robots lagged far behind the humans. On Saturday, China held what it's calling the world's first humanoid half-marathon. Over 20 two-legged humanoid robots competed alongside real human runners, according to state-run news outlet Beijing Daily, via CNN World. The teams were from Chinese universities and companies publicizing their humanoid robotics advancements, which China's Ministry of Industry and Information Technology has dubbed a critical area for competing with the U.S. As CNN reports, local governments in major cities like Beijing, Shanghai, and Shenzhen have invested an estimated 10 billion in developing humanoid robotics to compete with humanoids from U.S. rivals like Boston Dynamics, Figure AI, and Elon Musk's Tesla.

Beijing held what's being called the world's first half-marathon for robots, allowing bipedal bots to compete alongside human runners, and as one might expect, ridiculousness ensued. The robots, which had human operators running with them, for the most part struggled to make it through the course at all, let alone complete the full 13 miles within the four-hour cutoff time. "One fell at the starting line," Bloomberg reports. "Another's head fell off and rolled on the ground. And one collapsed and broke into pieces."

On Saturday, about 12,000 human athletes ran in a half marathon race in Beijing, but most of the attention was on a group of other, unconventional participants: 21 humanoid robots. The event's organizers, which included several branches of Beijing's municipal government, claim it's the first time humans and bipedal robots have run in the same race, though they jogged on separate tracks. Six of the robots successfully finished the course, but they were unable to keep up with the speed of the humans. The fastest robot, Tiangong Ultra, developed by Chinese robotics company UBTech in collaboration with the Beijing Humanoid Robot Innovation Center, finished the race in two hours and 40 minutes after assistants changed its batteries three times and it fell down once. The slowest time allowed for human runners in the race was 3 hours and 10 minutes, and Tiangong Ultra was the only robot that barely qualified for a human participation award.

Humans took the lead over robots and kept it easily as they raced to victory at Beijing's Yizhuang half-marathon. Thousands of runners joined 21 humanoid robot rivals in a world-first 21km (13 miles, 352 yards) challenge in the Chinese capital on Saturday. But not every bot was up to the task. One collapsed moments after the starting gun and lay motionless for minutes before regaining its feet. Another slammed into a barrier after only a few strides, taking its handler down with it.

Robots ran alongside humans at the Yizhuang half-marathon in Beijing on Saturday. Twenty-one humanoid robots, designed by Chinese manufacturers, raced alongside thousands of runners over a 21km (13-mile) course that included slopes, turns and uneven surfaces. Some robots completed the race, while others struggled from the beginning. One robot fell at the starting line and lay flat for several minutes before getting up and taking off. While robots have made appearances at marathons in China in the past, this is the first time they have raced against humans over the course of a half-marathon.

Step by mechanical step, dozens of humanoid robots took to the streets of Beijing early Saturday, joining thousands of their flesh-and-blood counterparts in a world-first half-marathon showcasing China's drive to lead the global race in cutting-edge technology. The 21-kilometer event held in the Chinese capital's E-Town -- a state-backed hub for high-tech manufacturing -- is billed as a groundbreaking effort to test the limits of bipedal robots in real-world conditions. At the crack of the starter's gun, and as the Chinese pop song "I Believe" blared out from loudspeakers on repeat, the robots queued up one by one and took their first tentative steps.