In this paper, we address this gap by dividing a typical patient's clinical journey

Latent embeddings offer an effective way of defining a content gap.

Specially commissioned drones will be used to fly blood donations as part of a new trial. Currently, blood donations are processed in south Wales then transported by road, a journey that can take hours. The ultimate ambition of the Dragon's Heart project is to fly life-saving blood samples to the scenes of accidents using drones weighing about 55lb (25kg) and 5.5ft wide (1.7m). The pilot, which is due to start in early 2026, was described as significant and exciting by the Welsh Blood Service. A hatch in the top means the blood sits in the body of the drone, helping to control the temperature of the blood and minimise vibrations.

Large language models (LLMs) have demonstrated remarkable capabilities in language understanding and generation, leading to their widespread adoption across various fields. Among these, the medical field is particularly well-suited for LLM applications, as many medical tasks can be enhanced by LLMs. Despite the existence of benchmarks for evaluating LLMs in medical question-answering and exams, there remains a notable gap in assessing LLMs' performance in supporting patients throughout their entire hospital visit journey in real-world clinical practice. In this paper, we address this gap by dividing a typical patient's clinical journey into four stages: planning, access, delivery and ongoing care. For each stage, we introduce multiple tasks and corresponding datasets, resulting in a comprehensive benchmark comprising 12 datasets, of which five are newly introduced, and seven are constructed from existing datasets. This proposed benchmark facilitates a thorough evaluation of LLMs' effectiveness across the entire patient journey, providing insights into their practical application in clinical settings. Additionally, we evaluate three categories of LLMs against this benchmark: 1) proprietary LLM services such as GPT-4; 2) public LLMs like QWen; and 3) specialized medical LLMs, like HuatuoGPT2. Through this extensive evaluation, we aim to provide a better understanding of LLMs' performance in the medical domain, ultimately contributing to their more effective deployment in healthcare settings.

Biological agents are known to learn many different tasks over the course of their lives, and to be able to revisit previous tasks and behaviors with little to no loss in performance. In contrast, artificial agents are prone to'catastrophic forgetting' whereby performance on previous tasks deteriorates rapidly as new ones are acquired. This shortcoming has recently been addressed using methods that encourage parameters to stay close to those used for previous tasks. This can be done by (i) using specific parameter regularizers that map out suitable destinations in parameter space, or (ii) guiding the optimization journey by projecting gradients into subspaces that do not interfere with previous tasks.

Attribution modelling lies at the heart of marketing effectiveness, yet most existing approaches depend on user-level path data, which are increasingly inaccessible due to privacy regulations and platform restrictions. This paper introduces a Causal-Driven Attribution (CDA) framework that infers channel influence using only aggregated impression-level data, avoiding any reliance on user identifiers or click-path tracking. CDA integrates temporal causal discovery (using PCMCI) with causal effect estimation via a Structural Causal Model to recover directional channel relationships and quantify their contributions to conversions. Using large-scale synthetic data designed to replicate real marketing dynamics, we show that CDA achieves an average relative RMSE of 9.50% when given the true causal graph, and 24.23% when using the predicted graph, demonstrating strong accuracy under correct structure and meaningful signal recovery even under structural uncertainty. CDA captures cross-channel interdependencies while providing interpretable, privacy-preserving attribution insights, offering a scalable and future-proof alternative to traditional path-based models.

"Everyone thinks they're on this big now," Debbie said, refilling her glass. "I've had it with the journey. I've had it with you people." "I don't think I'm on a journey," Burt said. Life's too short to find out who we really are." It was the first time the six of them had got together for dinner in more than a year (since Maria's diagnosis), and after such a long time (and in celebration of Maria's remission) they'd expected to have more interesting things to tell one another, deeper things, but they were entering dessert territory now, a cake was on the table, and only superficial topics had been broached: Ervin's promotion, Jane and Burt's move to the suburbs, Katherine's recent purchase of a metabolism-tracking device--a pen-shaped item and the cause of Debbie's rant. "How much can you know about yourself, exactly?" she said. "The therapy, the vision quests, the birth charts--do we really need the data on metabolic flexibility, too?" Jane, in Katherine's defense, said that, the more you knew about yourself, the more useful you could be to society. Knowing whether Kat is in fat-or carb-burning mode doesn't help anyone." As a result of Katherine declining cake five minutes earlier, no one had touched it. No one, Debbie included, really wanted to. They'd all overeaten already, drunk too much, made private plans to atone for it the next day. The cake presented a challenge, it sat there taunting them, and Debbie knew this, that you couldn't serve cake to a group of fortysomethings without causing ripples, but what else could she have done? She got it, no one wanted to put on weight, but this was a gorgeous princess cake, just gorgeous, she'd had to drive all the way to Andersonville to get it from that Swedish bakery everyone talked about. Staring at it now, though, she wondered if the cake didn't look a little bit like a tit, the smooth half sphere, the small pink marzipan flower nippling the top of it--and, oh, God, did think it looked like a tit?

Heterogeneous Treatment Effect (HTE) and Conditional Average Treatment Effect (CATE) models relax the assumption that treatment effects are the same for every user. We present a large scale industrial framework for estimating HTE using experimental data from hundreds of millions of Snapchat users. By combining results across many experiments, the framework uncovers latent user characteristics that were previously unmeasurable and produces stable treatment effect estimates at scale. We describe the core components that enabled this system, including experiment selection, base learner design, and incremental training. We also highlight two applications: user influenceability to ads and user sensitivity to ads. An online A/B test using influenceability scores for targeting showed an improvement on key business metrics that is more than six times larger than what is typically considered significant.

Based on the reconstruction of the users' travel time distribution, the model is

At one point, Luis assumes that he and Esteban have been abandoned, only to realize, with a start, that their newfound friends are actually circling back to help. In such moments, we grasp the source of the story's mysterious power: a tough-minded understanding that kindness is rare yet persistent, and quite possibly an affront to the laws of nature. "Sirāt" is a chain of defiantly compassionate acts--noble human improbabilities that take on, in retrospect, an air of fatalistic inevitability. Laxe, a restless wanderer himself, knows Morocco well. He shot his first feature, "You All Are Captains" (2011), in Tangier, where he'd spent several years working at a shelter for disadvantaged children. Several of these children appeared in the movie--a formally playful collision of fiction and documentary in which Laxe, also making an appearance, slyly interrogated his European outsider-artist role. Next came "Mimosas" (2016), an elusive, arrestingly gorgeous drama about a caravan bearing a dying sheikh across Morocco's Atlas Mountains to his homeland. The film had the beauty of a travelogue and the opacity of a parable. Its most dynamic character was a fiery Muslim preacher who warned his fellow-travellers not to stray, geographically or morally.