Forecasting the change in the distribution of viral variants is crucial for therapeutic design and disease surveillance. This task poses significant modeling challenges due to the sharp differences in virus distributions across sub-populations (e.g., countries) and their dynamic interactions. Existing machine learning approaches that model the variant distribution as a whole are incapable of making location-specific predictions and ignore transmissions that shape the viral landscape. In this paper, we propose a sub-population specific protein evolution model, which predicts the time-resolved distributions of viral proteins in different locations. The algorithm explicitly models the transmission rates between sub-populations and learns their interdependence from data. The change in protein distributions across all sub-populations is defined through a linear ordinary differential equation (ODE) parametrized by transmission rates. Solving this ODE yields the likelihood of a given protein occurring in particular sub-populations. Multi-year evaluation on both SARS-CoV-2 and influenza A/H3N2 demonstrates that our model outperforms baselines in accurately predicting distributions of viral proteins across continents and countries. We also find that the transmission rates learned from data are consistent with the transmission pathways discovered by retrospective phylogenetic analysis.

A murder investigation has been opened after a 16-year-old boy was found fatally injured in north London. Deonte Mowatt-Slater died after his motorbike hit a lamppost following a suspected stabbing. Met Police officers were called in the early hours of Tuesday to reports of a crash on Beachcroft Way in Islington. Medics at the scene said he was found to have a suspected knife injury, and despite attempts to save his life, he died. There have been no arrests and inquiries are ongoing.

Man guilty of army veteran hammer attack murder Cumbria PoliceJack Crawley attempted to burn Paul Taylor's body, before burying him in woodland A man who attacked an army veteran he had met for sex and bludgeoned him with a hammer has been found guilty of murder. Paul Taylor, 57, from Annan, Dumfriesshire, went missing last October, with his remains found in a shallow grave in woodland near Carlisle, Cumbria, in May. Jack Crawley, 20, of Carlisle, was found guilty of attacking him and trying to burn his body following a trial at the city's crown court. He will be sentenced on Wednesday. Crawley was also found guilty of the attempted murder of a man in York, who he met on the gay dating app Grindr and also attacked with a hammer, while he was on bail for killing Mr Taylor.

Representation learning of Text-Attributed Graphs (TAGs) has garnered significant attention due to its applications in various domains, including recommendation systems and social networks. Despite advancements in TAG learning methodologies, challenges remain in explainability due to the black-box nature of existing TAG representation learning models. This paper presents TAGExplainer, the first method designed to generate natural language explanations for TAG learning. TAGExplainer employs a generative language model that maps input-output pairs to explanations reflecting the model's decision-making process. To address the lack of annotated ground truth explanations in real-world scenarios, we propose first generating pseudo-labels that capture the model's decisions from saliency-based explanations, then the pseudo-label generator is iteratively trained based on three training objectives focusing on faithfulness and brevity via Expert Iteration, to improve the quality of generated pseudo-labels. The high-quality pseudo-labels are finally utilized to train an end-to-end explanation generator model. Extensive experiments are conducted to demonstrate the effectiveness of TAGExplainer in producing faithful and concise natural language explanations.

A "cocaine addicted" police officer who was found to be stealing drugs from an evidence store after he accidentally dropped a bag of white powder at his daughter's school has been jailed. Andrew Talbot, at the time a Greater Manchester Police detective, had taken just under 4kg (9lb) of cocaine worth almost 400,000 from police property rooms between 2018 and 2020. He also used the force's computer systems to find a drug dealer to help him sell the drugs on the streets of Manchester. The 54-year-old was found guilty of supplying the drug and misconduct in public office and sentenced to 19 years in jail at Liverpool Crown Court.GMPThe detective stole drugs from Greater Manchester's Police evidence rooms Sentencing him on Friday, Judge Neil Flewitt KC said Talbot had deceived colleagues to put a "significant" quantity of cocaine back into circulation as a result of his "addiction and greed". The investigation into Talbot by GMP's anti-corruption unit began in February 2020 after he dropped a small bag of cocaine outside his daughter's primary school.

We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user's image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models. All videos from this paper are available at https://go.fb.me/MovieGenResearchVideos.

Recent research on query generation has focused on using Large Language Models (LLMs), which despite bringing state-of-the-art performance, also introduce issues with hallucinations in the generated queries. In this work, we introduce relevance hallucination and factuality hallucination as a new typology for hallucination problems brought by query generation based on LLMs. We propose an effective way to separate content from form in LLM-generated queries, which preserves the factual knowledge extracted and integrated from the inputs and compiles the syntactic structure, including function words, using the powerful linguistic capabilities of the LLM. Specifically, we introduce a model-agnostic and training-free method that turns the Large Language Model into a Pointer-Generator (LargePiG), where the pointer attention distribution leverages the LLM's inherent attention weights, and the copy probability is derived from the difference between the vocabulary distribution of the model's high layers and the last layer. To validate the effectiveness of LargePiG, we constructed two datasets for assessing the hallucination problems in query generation, covering both document and video scenarios. Empirical studies on various LLMs demonstrated the superiority of LargePiG on both datasets. Additional experiments also verified that LargePiG could reduce hallucination in large vision language models and improve the accuracy of document-based question-answering and factuality evaluation tasks.

Pokémon maker confirms it was victim of hack The Pokémon CompanyPokémon is one of the world's best-known entertainment brands Pokémon maker Game Freak has confirmed it was the victim of a data leak after information appeared online over the weekend. The company, which has developed the Nintendo-exclusive video game series since 1996, said its servers were hacked in August this year. A statement said 2,606 items containing the names and email addresses of current, former and contract employees were accessed. The company did not comment on other information shared online claiming to show details of unreleased and upcoming projects. Game Freak said it would individually contact those affected where possible, and strengthen security measures to prevent similar hacks in future.

More than 200 people have taken part in the World Conker Championships, with many competing in fancy dress. The competition took place earlier at the Shuckburgh Arms in Southwick, Northamptonshire. The event saw participants go head-to-head using conkers threaded on to string to try and smash their opponent's nut. Since its inception in 1965, the event has raised more than 400,000 for charities that support the visually impaired.PA MediaHundreds of spectators attended the event which was first held in 1965 One man wore a green inflatable Yoda headpiece, while another wore a conker-themed hat. All participants were required to follow a stringent set of rules to ensure the event was as fair as possible, which included the conkers and laces being provided by organisers.

Complex systems can undergo critical transitions, where slowly changing environmental conditions trigger a sudden shift to a new, potentially catastrophic state. Early warning signals for these events are crucial for decision-making in fields such as ecology, biology and climate science. Generic early warning signals motivated by dynamical systems theory have had mixed success on real noisy data. More recent studies found that deep learning classifiers trained on synthetic data could improve performance. However, neither of these methods take advantage of historical, system-specific data. Here, we introduce an approach that trains machine learning classifiers directly on surrogate data of past transitions, namely surrogate data-based machine learning (SDML). The approach provides early warning signals in empirical and experimental data from geology, climatology, sociology, and cardiology with higher sensitivity and specificity than two widely used generic early warning signals -- variance and lag-1 autocorrelation. Since the approach is trained directly on surrogates of historical data, it is not bound by the restricting assumption of a local bifurcation like previous methods. This system-specific approach can contribute to improved early warning signals to help humans better prepare for or avoid undesirable critical transitions.