Florida gets a lot of attention for its sinkholes, especially when they swallow cars and entire houses. But its sinkhole risk has nothing on Alaska's. Much of Alaska's soil is permafrost - ground that remains below 32 degrees Fahrenheit (0 degrees Celsius) for at least two consecutive years. It is often rich with ice, but when that ice melts, the ground can collapse. The consequences are the same as in Florida: substantial property damage as the land that buildings, roads and pipes were built on sinks .

Meta has introduced its first real-time audio model, Muse Voice Transcribe. The model can handle dictation and transcription for more than 20 speakers and can seamlessly handle multiple languages at once, Meta says. Meta CEO Mark Zuckerberg, who recently returned to X after three years of not posting on the platform, shared an example of the model's ability to handle multiple speakers and languages at once. In the video, the transcription is able to automatically distinguish between multiple speakers and switch between languages. It's even able to pick up on "code-switching" and transcribe sentences that use words from multiple languages.

CogTwin is a hybrid cognitive architecture framework designed to bring autonomous reasoning and real-time adaptation to digital twin systems. Presented at IJCAI 2025, this work aims to advance the state of digital twin technology by addressing key gaps in autonomy, cognition, and real-time decision-making. Digital twin technology has transformed how complex systems are managed, from smart cities to industrial processes. However, most current digital twins remain fundamentally reactive: they rely on pre-programmed rules and static data-driven models, and therefore struggle when confronted with unforeseen events or evolving conditions. Real-time learning, reasoning, and adaptation - hallmarks of human cognition - are largely absent.

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Apple regains top spot as world's most valuable company Apple has surpassed chipmaker Nvidia as the world's most valuable company as artificial intelligence-driven market pressures weigh on investors. Apple is now worth $4.88 trillion compared with Nvidia's $4.86 trillion, following a 3.5 percent decline in Nvidia's market value. The milestone marks the first time the Cupertino, California-based iPhone maker has held the top spot in more than a year. Last month, Apple unveiled a revamped version of its assistant, Siri AI, which enables the personal assistant to better understand the personal context of users' questions, access real-time information from the Web, and perform more complex tasks on behalf of users. "Market sentiment has shifted from rewarding model makers, then to semis, and now on to those companies that can turn compute into experiences and outcomes the customer will pay for, thus driving corporate earnings," Michael Monaghan, founder of Founder ETFs, told Al Jazeera. "Apple investors first questioned Apple's lower AI spend, but now have treated Apple's lower AI capital expenditure as an advantage, with the bull case being that Apple benefits from consumer AI without spending at cloud-infrastructure scale."

PCWorld reports that Windows PCs need updated 2023 Secure Boot certificates as older 2011 certificates expire in 2026, leaving systems vulnerable to malware. Hardware vendors, not Microsoft, control these critical security updates through UEFI/BIOS firmware, meaning unsupported older PCs may require hardware upgrades. Users can check their protection status in Windows Security app for a green Secure Boot checkmark and update firmware accordingly. You've probably seen countless warnings lately about Windows and expiring Secure Boot certificates . Why? Some PCs haven't gotten the updates yet--and won't unless you take action.

Qatar has become the place where FIFA experiments with the next generation of football technology. The results are already visible across this year's World Cup. To casual soccer viewers, the game may look like it always has--same green field, 22 players, a referee, and the familiar rhythm of play unfolding over 90 minutes. The changes are only visible if you look beneath the familiar surface. What appears to be a traditional match is now supported by layers of tracking systems, automated analysis, and real-time data that run quietly in the background.

Previous algorithms with provable guarantees either (1) achieve the same O(k)approximation ratio but require at least O(n2k) runtime, or (2) provide a better O(logk) approximation ratio at the cost of an impractical O(n2k) worst-case runtime for general d and k. Our algorithm extends to the Massively Parallel Computation (MPC) model, where it gives an MPC algorithm requiring eO(log1+ε n) rounds and total space O(n1+γ(d+k)). Empirically, we also demonstrate that our algorithmic ideas can be adapted to existing heuristic methods, leading to significant speed-ups while preserving comparable performance. On the hardness side, we study the related single-point k-anonymity problem, where the goal is to select k 1 additional records to make a given record indistinguishable. Assuming the dense vs random conjecture in complexity theory, we show that for n = kc, no algorithm can achieve a k1 O(1/c) approximation in poly(n) time, providing evidence for the inherent hardness of the k-anonymity problem.

In typical graph neural networks (GNNs), feature representation learning naturally evolves through iteratively updating node features and exchanging information based on graph topology. In this context, we conceptualize that the learning process in GNNs is a mean-field game (MFG), where each graph node is an agent, interacting with its topologically connected neighbors. However, current GNNs often employ the identical MFG strategy across different graph datasets, regardless of whether the graph exhibits homophilic or heterophilic characteristics. To address this challenge, we propose to formulate the learning mechanism into a variational framework of the MFG inverse problem, introducing an in-context selective message passing paradigm for each agent, which promotes the best overall outcome for the graph. Specifically, we seek for the application-adaptive transportation function (controlling information exchange throughout the graph) and reaction function (controlling feature representation learning on each agent), on the fly, which allows us to uncover the most suitable selective mechanism of message passing by solving an MFG variational problem through the lens of Hamiltonian flows. Taken together, our variational framework unifies existing GNN models into various mean-field games with distinct equilibrium states, each characterized by the learned in-context message passing operators. Furthermore, we present an agnostic end-to-end deep model, coined Game-of-GNN, to jointly identify the message passing mechanism and fine-tune the GNN hyper-parameters on top of the elucidated message passing operators. Game-of-GNN has achieved SOTA performance on diverse graph data, including popular benchmark datasets and human connectomes. More importantly, the mathematical insight of MFG framework provides a new window to understand the foundational principles of graph learning as an interactive dynamical system, which allows us to reshape the idea of designing next-generation GNN models.

Achieving seamless viewing of high-fidelity volumetric video, comparable to 2D video experiences, remains an open challenge. Existing volumetric video compression methods either lack the flexibility to adjust quality and bitrate within a single model for efficient streaming across diverse networks and devices, or struggle with real-time decoding and rendering on lightweight mobile platforms. To address these challenges, we introduce 4DGCPro, a novel hierarchical 4DGaussian compression framework that facilitates real-time mobile decoding and high-quality rendering via progressive volumetric video streaming in a single bitstream. Specifically, we propose a perceptually-weighted and compression-friendly hierarchical 4D Gaussian representation with motion-aware adaptive grouping to reduce temporal redundancy, preserve coherence, and enable scalable multi-level detail streaming. Furthermore, we present an end-to-end entropy-optimized training scheme, which incorporates layer-wise rate-distortion (RD) supervision and attribute-specific entropy modeling for efficient bitstream generation. Extensive experiments show that 4DGCPro enables flexible quality and multiple bitrate within a single model, achieving real-time decoding and rendering on mobile devices while outperforming existing methods in RD performance across multiple datasets. The corresponding author is Qiang Hu(qiang.hu@sjtu.edu.cn)