The actor, well known for his roles in the Die Hard franchise, was diagnosed with frontotemporal dementia - a condition his family publicly disclosed in 2023. Emma Heming Willis spoke with CBS Mornings host Gayle King about her husband and her new book, which she hopes will help fellow caregivers. 'We love you, you will always be with us', says father of Minneapolis shooting victim Fletcher Merkel, 8, was one of two children killed in Wednesday's shooting at Annunciation Catholic School in Minneapolis. The Garnet wildfire in Fresno County has scorched nearly 14,000 acres (5,665 hectares) and remains uncontained. The BBC's Tom Bateman spoke with Patrick Scallen who lives near the Annunciation Church and ran towards the sound of gunfire.

From'nepo kids' to PM resignation: How the Nepal protests unfolded Nepal has been shaken by deadly protests that have led to the resignation of the country's Prime Minister KP Sharma Oli. The BBC's Charlotte Scarr is on the streets of Kathmandu, where she saw torched government buildings and military presence. The Himalayan nation has been experiencing its worst unrest in decades, after a campaign highlighting the lavish lifestyles of politicians' children and allegations of corruption took off on social media. Thirty people have been killed in the protests and more than 1,000 injured since the unrest began. The military parade was attended by world leaders including Vladimir Putin and Kim Jong Un and showcased China's new weapons.

Larry Ellison, the chair and chief technology officer of Oracle, is a supporter of Donald Trump and has regularly appeared at the White House. Larry Ellison, the chair and chief technology officer of Oracle, is a supporter of Donald Trump and has regularly appeared at the White House. Oracle co-founder's shares rose by 40% in early trading, valuing his fortune at $393bn, just ahead of Musk's $384bn US tech billionaire Larry Ellison is neck-and-neck with Elon Musk in the contest to be the world's richest person after briefly overtaking the Tesla chief executive on Wednesday Ellison's wealth surged after Oracle, the business software company in which he owns a stake of 41%, reported better than expected financial results. Oracle shares rose by more than 40% in early trading, at one point valuing the business software company at approximately $960bn (£707bn) and Ellison's stake at $393bn, just ahead of Musk's fortune of $384bn, according to Bloomberg's billionaires index. However, Ellison's lead was short-lived as the stock closed at $328, a rise of 36% valuing Ellison's shareholding at $378bn and putting Musk back ahead.

The disease has been plaguing the endangered marsupials since the 1990s. Breakthroughs, discoveries, and DIY tips sent every weekday. The first vaccine to protect endangered koalas from chlamydia has officially been approved in Australia. The vaccine was developed by the University of the Sunshine Coast's (UniSC) veterinary medicine division in eastern Australia and is another step towards ensuring the survival of the marsupial. The team spent over a decade developing the vaccine to protect the animals from the disease, which can cause urinary tract infections, blindness, infertility, and in some cases, death.

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A new report warns that the number of US investors in powerful commercial spyware rose sharply in 2024 and names new countries linked to the dangerous technology. The United States has emerged as the largest investor in commercial spyware --a global industry that has enabled the covert surveillance of journalists, human rights defenders, politicians, diplomats, and others, posing grave threats to human rights and national security . In 2024, 20 new US-based spyware investors were identified, bringing the total number of American backers of this technology to 31. This growth has largely outpaced other major investing countries such as Israel, Italy, and the United Kingdom, according to a new report published today by the Atlantic Council. The study surveyed 561 entities across 46 countries between 1992 and 2024, identifying 34 new investors.

Australia to spend $1.1bn on underwater'Ghost Shark' attack drones Australia will spend 1.7 billion Australian dollars ($1.1bn) on a fleet of extra-large underwater "Ghost Shark" attack drones, in a move that officials said would supplement the country's plans to acquire sophisticated nuclear-powered submarines. Australian Minister for Defence Richard Marles said on Wednesday that the Ghost Shark autonomous underwater vehicles will complement Australia's naval surface fleet and submarines to provide "a more capable and more lethal navy". "We have consistently articulated that Australia faces the most complex, in some ways, the most threatening, strategic landscape that we have had since the end of the second world war," Marles said. The government said it signed the $1.1bn, five-year contract with Anduril Australia to build, maintain and develop the uncrewed undersea vehicles in Australia. "This is the highest tech capability in the world," Marles said, adding that the drones would have a "very long range" as well as stealth capabilities.

More than 1,000 people across the UK with suspected dementia are to be offered a blood test for Alzheimer's disease which it is hoped could revolutionise diagnosis of the disease. The blood test can detect biomarkers for rogue proteins which accumulate in the brains of patients with the condition and will be used in addition to pen and paper cognitive tests, which often misdiagnose it in its early stages. Scientists leading the trial at University College London believe the blood test will improve the accuracy of diagnosis from 70% to more than 90% and want to see how that helps patients and clinicians. Patients will be recruited at 20 memory clinics as part of the study, which aims to see how well the test works within the NHS. Alzheimer's disease is the most common form of dementia and is associated with the build-up in the brain of two rogue proteins - amyloid and tau - which can accumulate for up to 20 years before symptoms emerge.

Nested logit (NL) has been commonly used for discrete choice analysis, including a wide range of applications such as travel mode choice, automobile ownership, or location decisions. However, the classical NL models are restricted by their limited representation capability and handcrafted utility specification. While researchers introduced deep neural networks (DNNs) to tackle such challenges, the existing DNNs cannot explicitly capture inter-alternative correlations in the discrete choice context. To address the challenges, this study proposes a novel concept - alternative graph - to represent the relationships among travel mode alternatives. Using a nested alternative graph, this study further designs a nested-utility graph neural network (NestGNN) as a generalization of the classical NL model in the neural network family. Theoretically, NestGNNs generalize the classical NL models and existing DNNs in terms of model representation, while retaining the crucial two-layer substitution patterns of the NL models: proportional substitution within a nest but non-proportional substitution beyond a nest. Empirically, we find that the NestGNNs significantly outperform the benchmark models, particularly the corresponding NL models by 9.2\%. As shown by elasticity tables and substitution visualization, NestGNNs retain the two-layer substitution patterns as the NL model, and yet presents more flexibility in its model design space. Overall, our study demonstrates the power of NestGNN in prediction, interpretation, and its flexibility of generalizing the classical NL model for analyzing travel mode choice.

We introduce a new convolutional AutoEncoder architecture for user modelling and recommendation tasks with several improvements over the state of the art. Firstly, our model has the flexibility to learn a set of associations and combinations between different interaction types in a way that carries over to each user and item. Secondly, our model is able to learn jointly from both the explicit ratings and the implicit information in the sampling pattern (which we refer to as `implicit feedback'). It can also make separate predictions for the probability of consuming content and the likelihood of granting it a high rating if observed. This not only allows the model to make predictions for both the implicit and explicit feedback, but also increases the informativeness of the predictions: in particular, our model can identify items which users would not have been likely to consume naturally, but would be likely to enjoy if exposed to them. Finally, we provide several generalization bounds for our model, which to the best of our knowledge, are among the first generalization bounds for auto-encoders in a Recommender Systems setting; we also show that optimizing our loss function guarantees the recovery of the exact sampling distribution over interactions up to a small error in total variation. In experiments on several real-life datasets, we achieve state-of-the-art performance on both the implicit and explicit feedback prediction tasks despite relying on a single model for both, and benefiting from additional interpretability in the form of individual predictions for the probabilities of each possible rating.