We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (i) discovering topic prevalence over time, and (ii) learning contemporary multi-scale dependence structures, providing topic and word correlations as a byproduct. The high-dimensional and time-evolving ordinal/rank observations (such as word counts), after an arbitrary monotone transformation, are well accommodated through an underlying dynamic sparse factor model. The framework naturally admits heavy-tailed innovations, capable of inferring abrupt temporal jumps in the importance of topics. Posterior inference is performed through straightforward Gibbs sampling, based on the forward-filtering backward-sampling algorithm. Moreover, an efficient data subsampling scheme is leveraged to speed up inference on massive datasets. The modeling framework is illustrated on two real datasets: the US State of the Union Address and the JSTOR collection from Science .

The race to adopt AI is putting entry-level roles among those at risk. The race to adopt AI is putting entry-level roles among those at risk. Gen Z faces'job-pocalypse' as global firms prioritise AI over new hires, report says Young people entering the workforce are facing a "job-pocalypse", as business leaders invest in artificial intelligence (AI) rather than new hires, according to a study of global business leaders. Bosses are prioritising automation through AI to plug skills gaps and allow them to reduce headcount, instead of training up junior members of staff, a report by the British Standards Institution (BSI) found. Four in 10 (41%) of bosses said AI was allowing them to cut the number of employees in a survey of more than 850 business leaders across seven countries: the UK, US, France, Germany, Australia, China and Japan.

The struggling American chipmaker is betting that a new plant and fresh product line will help turn around its fortunes. After four years of construction, Intel said on Thursday that its Fab 52 semiconductor plant in Chandler, Arizona is now turning out its first chips. The company also shared more details about the long-awaited CPUs that it will be producing in the facility using Intel's brand new 18A process technology. The announcement comes just six weeks after the Trump administration acquired a 9.9 percent stake in Intel in exchange for $8.9 billion in stock. The fab opening, while long in the works, is the first major opportunity for the struggling American chip maker to convince the broader tech industry that it can produce some of the world's most advanced chips at scale--and that the White House's investment might pay off.

Simulation-based inference (SBI) enables amortized Bayesian inference for simulators with implicit likelihoods. But when we are primarily interested in the quality of predictive simulations, or when the model cannot exactly reproduce the observed data (i.e., is misspecified), targeting the Bayesian posterior may be

Competing objectives arise when a model's capabilities

We propose a general symmetrization method that adds rotational equivariance to any given model while preserving all the other requirements.

However, patient outcomes with current technologies are limited.

Personalized federated learning algorithms have shown promising results in adapting models to various distribution shifts.

Although freely accessible, it is provided in individual files for each year, and is difficult to combine in order to analyze spatial or temporal trends.