It probably looked as cool as you think. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Gold ligature surrounding the left central incisor and the right lateral incisor on the mandible of an adult male buried in the East Kirk of the parish church of St Nicholas, Aberdeen, Scotland. Breakthroughs, discoveries, and DIY tips sent six days a week. Today, extensive tooth repair or replacement often requires the installation of a dental bridge made from durable resin and metal.

WiseTech's headquarters in Sydney, where staff fear many jobs will be lost to AI. WiseTech's headquarters in Sydney, where staff fear many jobs will be lost to AI. 'Your craft is obsolete': WiseTech staff in limbo as AI touted as better than humans Staff at WiseTech have been waiting almost three months to be told if they are among the 2,000 people the logistics software company is to cut due to advances in AI, with workers criticising the wait as stressful and "ridiculous". The comments come as its founder on Tuesday told investors an AI agent could learn a human's job in just 15 minutes, according to the Australian Financial Review. The Australian Stock Exchange-listed company announced in late February that it would lay off almost 30% of its workforce across 40 countries, with 2,000 of the 7,000 jobs set to go over the next 18 months. Sign up for the Breaking News Australia email Some areas would be hit harder than others, with product and development and customer service teams expected to be reduced by up to 50%, the chief executive, Zubin Appoo, told an investor briefing in February. "The era of manually writing code as the core act of engineering is over," Appoo said.

Russia ignores Ukraine's unilateral ceasefire and attacks kindergarten Volodymyr Zelensky has accused Russia of breaching a unilateral ceasefire announced by Ukraine by launching a wave of drone attacks on several cities. The Ukrainian president said Russia had carried out active hostilities and terrorist shelling, targeting the front line as well as firing dozens of drones and missiles at civilian areas. In the Sumy border region, one woman was killed when a kindergarten was hit on Wednesday morning, local authorities said. No children were present at the time. Earlier this week the two warring sides had announced rival unilateral ceasefires - with no agreement on their terms, length or monitoring.

To stay or risk the'Road of Death' - Ukrainian civilians trapped in frontline city So, we're stuck here, says Ludmilla, over the phone from the rooftop of a fire-damaged house in southern Ukraine. People are trying their best to survive. Her frontline home city of Oleshky has, according to multiple accounts, been largely cut off from fresh supplies of food or medicine for months. Ludmilla describes being trapped there, and watching it decaying before her eyes. Ukraine's commissioner for human rights has warned of a humanitarian crisis.

Apple has agreed to pay some iPhone buyers a collective $250m (£184m) to end a lawsuit accusing the company of misleading people about new artificial intelligence (AI) features and capabilities. In a settlement filed Tuesday in California federal court, Apple did not admit any wrongdoing, but agreed to a deal that will resolve claims in a large consolidated class action lawsuit filed last year. It accused Apple of false advertising around its AI features on the iPhone, which the company called Apple Intelligence, including an enhancement of its Siri voice assistant. Apple will pay between $25 and $95 to people who bought an iPhone 15 and iPhone 16 between June 2024 and March 2025. An Apple spokeswoman said the lawsuit was focused on the availability of two additional features in a lineup of many released as part of its Apple Intelligence rollout.

This manuscript contains preprint of a chapter under consideration for inclusion in the forthcoming third edition of {\em Cover and Thomas's Elements of Information Theory}, posted with permission from Wiley. The table of contents EIT-3 ToC of the new edition can be found at: https://docs.google.com/document/d/1L-m4oQEJw1PJhoxBeMwrrBD8S_HmvzMEkPbYvS24980/edit?usp=sharing . For feedback, please contact abbas@ee.stanford.edu Learning and information theory intersect in both model training and the characterization of fundamental performance limits. This manuscript provides a concise and accessible treatment of the first intersection, requiring only basic background in information theory and statistics at the senior undergraduate or first-year graduate level. End-of-chapter exercises make the material well suited for classroom use as well as self-study. The chapter focuses on the role of divergence measures in model training, with examples ranging from linear and logistic regression to autoregressive models, variational autoencoders, diffusion models, generative adversarial networks, and score-based models. It introduces the evidence lower bound (ELBO), $f$\!-divergences, and the Fisher divergence. In particular, the treatment of the generative diffusion model provides a more systematic and explicit derivation than is typical in the literature.

We introduce a general framework that extends Bayesian inference by allowing the researcher to explicitly encode confidence in each source of uncertainty within the model. This mechanism provides a new handle for model design and regularisation control. Building on this framework, we develop a general approach for inducing sparsity in statistical models and illustrate its use in linear and logistic regression, as well as in Bayesian neural networks.

Here we introduce Partially Observed Structural Causal Models (POSCMs) that formalize causal systems where latent contexts co-determine both the interaction structure and downstream mechanisms on observed variables. POSCMs provide an extension of structural causal models (SCMs), as a self-contained causal modeling framework for endogenous graphs, allowing for an intervention hierarchy spanning node- and edge-level context and endogenous variable interventions. To enable surgical edge interventions, we adopt a Kolmogorov-Arnold-Sprecher edge-functional decomposition, an existence theorem for representing each node mechanism as a sum of univariate functions of its parents, yielding an explicit parametrization of dyadic functional contributions. We provide an identifiability theory that clarifies which intervention families would suffice to disentangle structure formation from mechanisms. We empirically validate these predictions in a biophysically detailed virtual human retina simulator, constructing intervention protocols that (i) reproduce the non-identifiability predicted when context is latent and no context-level interventions are available, (ii) exhibit structure-mechanism confounding under latent edges when only node interventions are observed, and (iii) recover synaptic input-output relationships via targeted node interventions, consistent with our positive kernel identifiability result. Our work generalizes SCMs in a way that allows it to work in a world closer to the one we live in.

We develop graph-based methods for semi-supervised learning based on label propagation on a data similarity graph. When data is abundant or arrive in a stream, the problems of computation and data storage arise for any graph-based method. We propose a fast approximate online algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local representative points that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We also present graph-based methods for detecting conditional anomalies and apply them to the identification of unusual clinical actions in hospitals. Our hypothesis is that patient-management actions that are unusual with respect to the past patients may be due to errors and that it is worthwhile to raise an alert if such a condition is encountered. Conditional anomaly detection extends standard unconditional anomaly framework but also faces new problems known as fringe and isolated points. We devise novel nonparametric graph-based methods to tackle these problems. Our methods rely on graph connectivity analysis and soft harmonic solution. Finally, we conduct an extensive human evaluation study of our conditional anomaly methods by 15 experts in critical care.

We consider function optimization as a sequential decision making problem under budget constraint. This constraint limits the number of objective function evaluations allowed during the optimization. We consider an algorithm inspired by a continuous version of a multi-armed bandit problem which attacks this optimization problem by solving the tradeoff between exploration (initial quasi-uniform search of the domain) and exploitation (local optimization around the potentially global maxima). We introduce the so-called Simultaneous Optimistic Optimization (SOO), a deterministic algorithm that works by domain partitioning. The benefit of such approach are the guarantees on the returned solution and the numerical efficiency of the algorithm. We present this machine learning approach to optimization, and provide the empirical assessment of SOO on the CEC'2014 competition on single objective real-parameter numerical optimization test-suite.