Colorectal cancer (CRC) grading is a critical prognostic factor but remains hampered by inter-observer variability and the privacy constraints of multi-institutional data sharing. While deep learning offers a path to automation, centralized training models conflict with data governance regulations and neglect the diagnostic importance of multi-scale analysis. In this work, we propose a scalable, privacy-preserving federated learning (FL) framework for CRC histopathological grading that integrates multi-scale feature learning within a distributed training paradigm. Our approach employs a dual-stream ResNetRS50 backbone to concurrently capture fine-grained nuclear detail and broader tissue-level context. This architecture is integrated into a robust FL system stabilized using FedProx to mitigate client drift across heterogeneous data distributions from multiple hospitals. Extensive evaluation on the CRC-HGD dataset demonstrates that our framework achieves an overall accuracy of 83.5%, outperforming a comparable centralized model (81.6%). Crucially, the system excels in identifying the most aggressive Grade III tumors with a high recall of 87.5%, a key clinical priority to prevent dangerous false negatives. Performance further improves with higher magnification, reaching 88.0% accuracy at 40x. These results validate that our federated multi-scale approach not only preserves patient privacy but also enhances model performance and generalization. The proposed modular pipeline, with built-in preprocessing, checkpointing, and error handling, establishes a foundational step toward deployable, privacy-aware clinical AI for digital pathology.

We study how Bayesian optimization (BO) can be accelerated on a target task with historical knowledge transferred from related source tasks. Existing works on BO with knowledge transfer either do not have theoretical guarantees or achieve the same regret as BO in the non-transfer setting, $\tilde{\mathcal{O}}(\sqrt{T γ_f})$, where $T$ is the number of evaluations of the target function and $γ_f$ denotes its information gain. In this paper, we propose the DeltaBO algorithm, in which a novel uncertainty-quantification approach is built on the difference function $δ$ between the source and target functions, which are allowed to belong to different reproducing kernel Hilbert spaces (RKHSs). Under mild assumptions, we prove that the regret of DeltaBO is of order $\tilde{\mathcal{O}}(\sqrt{T (T/N + γ_δ)})$, where $N$ denotes the number of evaluations from source tasks and typically $N \gg T$. In many applications, source and target tasks are similar, which implies that $γ_δ$ can be much smaller than $γ_f$. Empirical studies on both real-world hyperparameter tuning tasks and synthetic functions show that DeltaBO outperforms other baseline methods and support our theoretical claims.

Artificial intelligence (AI) systems hold great promise for advancing various scientific disciplines, and are increasingly used in real-world applications. Despite their remarkable progress, further capabilities are expected in order to achieve more general types of intelligence. A critical distinction in this context is between factual knowledge, which can be evaluated against true or false answers (e.g., "what is the capital of England?"), and probabilistic knowledge, reflecting probabilistic properties of the real world (e.g., "what is the sex of a computer science graduate in the US?"). In this paper, our goal is to build a benchmark for understanding the capabilities of LLMs in terms of knowledge of probability distributions describing the real world. Given that LLMs are trained on vast amounts of text, it may be plausible that they internalize aspects of these distributions. Indeed, LLMs are touted as powerful universal approximators of real-world distributions. At the same time, classical results in statistics, known as curse of dimensionality, highlight fundamental challenges in learning distributions in high dimensions, challenging the notion of universal distributional learning. In this work, we develop the first benchmark to directly test this hypothesis, evaluating whether LLMs have access to empirical distributions describing real-world populations across domains such as economics, health, education, and social behavior. Our results demonstrate that LLMs perform poorly overall, and do not seem to internalize real-world statistics naturally. When interpreted in the context of Pearl's Causal Hierarchy (PCH), our benchmark demonstrates that language models do not contain knowledge on observational distributions (Layer 1 of PCH), and thus the Causal Hierarchy Theorem implies that interventional (Layer 2) and counterfactual (Layer 3) knowledge of these models is also limited.

Many rare diseases offer limited established treatment options, leading patients to switch therapies when new medications emerge. To analyze the impact of such treatment switches within the low sample size limitations of rare disease trials, it is important to use all available data sources. This, however, is complicated when usage of measurement instruments change during the observation period, for example when instruments are adapted to specific age ranges. The resulting disjoint longitudinal data trajectories, complicate the application of traditional modeling approaches like mixed-effects regression. We tackle this by mapping observations of each instrument to a aligned low-dimensional temporal trajectory, enabling longitudinal modeling across instruments. Specifically, we employ a set of variational autoencoder architectures to embed item values into a shared latent space for each time point. Temporal disease dynamics and treatment switch effects are then captured through a mixed-effects regression model applied to latent representations. To enable statistical inference, we present a novel statistical testing approach that accounts for the joint parameter estimation of mixed-effects regression and variational autoencoders. The methodology is applied to quantify the impact of treatment switches for patients with spinal muscular atrophy. Here, our approach aligns motor performance items from different measurement instruments for mixed-effects regression and maps estimated effects back to the observed item level to quantify the treatment switch effect. Our approach allows for model selection as well as for assessing effects of treatment switching. The results highlight the potential of modeling in joint latent representations for addressing small data challenges.

The US government is aiming to ease the pressure on air traffic controllers suffering shutdown-related woes by curtailing flights. But airlines have experience with this kind of sudden disruption. Newark Liberty International Airport is one of the high-traffic airports that could see flight cuts starting Friday. The US Federal Aviation Administration plans to cut 10 percent of flights in 40 high-traffic airports on Friday morning if Congress fails to reopen the federal government by then, Transportation secretary Sean Duffy and FAA chief Bryan Bedford said Wednesday. The announcement came days after the US agency said it faced widespread shortages of air traffic controllers in half of the country's 30 busiest airports and hours-long security lines caused by absences of Transportation Security Administration agents.

Jensen Huang, the head of the world's most valuable company Nvidia, says King Charles III personally handed him a copy of a speech he delivered in 2023 that included a warning about the dangers of artificial intelligence. He said, there's something I want to talk to you about. And he handed me a letter, Huang told the BBC, speaking after receiving the 2025 Queen Elizabeth Prize for Engineering in a ceremony at St James's Palace. The letter was a copy of the speech delivered by the King in 2023 at the world's first AI Summit, held at Bletchley Park . In it the monarch said that the risks of AI needed to be tackled with a sense of urgency, unity and collective strength.

Virginia Tech researchers found that resistance training outperforms running for blood sugar control and diabetes prevention in a new mouse study.

Quantinuum has unveiled a third-generation quantum computer that could be easier to scale up than rival approaches. The USand UK-based company Quantinuum today unveiled Helios, its third-generation quantum computer, which includes expanded computing power and error correction capability. Like all other existing quantum computers, Helios is not powerful enough to execute the industry's dream money-making algorithms, such as those that would be useful for materials discovery or financial modeling. But Quantinuum's machines, which use individual ions as qubits, could be easier to scale up than quantum computers that use superconducting circuits as qubits, such as Google's and IBM's. "Helios is an important proof point in our road map about how we'll scale to larger physical systems," says Jennifer Strabley, vice president at Quantinuum, which formed in 2021 from the merger of Honeywell Quantum Solutions and Cambridge Quantum. Honeywell remains Quantinuum's majority owner.

In'watershed moment', Tesla board to vote on Musk's $1 trillion package Tesla's board is set to vote on CEO Elon Musk's $1 trillion pay package as major proxy adviser firms urge shareholders to reject the deal. The vote is scheduled for Thursday and will determine whether Musk secures what is the largest compensation package in corporate history. These firms often influence large passive funds that hold significant stakes in the electric carmaker. Tesla has faced mounting challenges this year, with global sales declining and investor confidence wavering. In July, Tesla reported a 13.5 percent decline in sales in the United States. They jumped 7.4 percent in the third quarter ending in September compared with the same period the year before, as US consumers scrambled to take advantage of a $7,500 EV tax credit that was set to expire that month.

The author of "The Eleventh Hour" looks back on a few works--by Mikhail Bulgakov, Franz Kafka, Voltaire, and E. M. Forster--that have helped him craft his own. Salman Rushdie prefers not to immerse himself in other people's writing when he is working on his own. "When I'm writing fiction, I tend not to read fiction. I actually don't want other people's voices to sneak into my head," Rushdie said recently. That's not to say that other writers' books aren't an important part of his process--posing questions, providing instruction, and offering models of characters.