Survival analysis has become a standard approach for modelling time to default by time-varying covariates in credit risk. Unlike most existing methods that implicitly assume a stationary data-generating process, in practise, mortgage portfolios are exposed to various forms of data drift caused by changing borrower behaviour, macroeconomic conditions, policy regimes and so on. This study investigates the impact of data drift on survival-based credit risk models and proposes a dynamic joint modelling framework to improve robustness under non-stationary environments. The proposed model integrates a longitudinal behavioural marker derived from balance dynamics with a discrete-time hazard formulation, combined with landmark one-hot encoding and isotonic calibration. Three types of data drift (sudden, incremental and recurring) are simulated and analysed on mortgage loan datasets from Freddie Mac. Experiments and corresponding evidence show that the proposed landmark-based joint model consistently outperforms classical survival models, tree-based drift-adaptive learners and gradient boosting methods in terms of discrimination and calibration across all drift scenarios, which confirms the superiority of our model design.

Air pollution, especially the particulate matter 2.5 (PM2.5), has become a growing concern in recent years, primarily in urban areas. Being exposed to air pollution is linked to developing numerous health problems, like the aggravation of respiratory diseases, cardiovascular disorders, lung function impairment, and even cancer or early death. Forecasting future levels of PM2.5 has become increasingly important over the past few years, as it can provide early warnings and help prevent diseases. This paper aims to design, fine-tune, test, and evaluate machine learning models for predicting future levels of PM2.5 over various time horizons. Our primary objective is to assess and compare the performance of multiple models, ranging from linear regression algorithms and ensemble-based methods to deep learning models, such as advanced recurrent neural networks and transformers, as well as large language models, on this forecasting task.

The Director of a Raunchy 3-Hour Dracula Movie Says AI Is Gross and Slimy. That's Why He Used It Radu Jude is the internet's favorite filmmaker. In 2021, the Romanian writer-director bagged the prestigious Golden Bear at the Berlin International Film Festival for, a black comedy about a school teacher whose career is threatened when a hardcore porno she makes with her husband goes viral. Shot largely on the streets of Bucharest during Covid-19 lockdowns, the film documents the eerie, empty aesthetic of urban centers in the era and captures real citizens snarling and cursing at the camera and at the film's lead actress. His follow-up, 2023's, nailed a different strain of post-Covid alienation. Its heroine, Angela (Ilinca Manolache), toils away 9 to 5 making shady workplace safety videos for a faceless multinational while moonlighting on TikTok, pretending to be a misogynist influencer (modeled after Romania's own model of toxic masculinity, Andrew Tate).

Improving the reasoning capabilities of large language models (LLMs) has attracted considerable interest.

Denmark has banned all civilian drone flights this week ahead of a European Union summit in Copenhagen, the country's transport minister said on Sunday. The ministry said the decision was made in order to simplify security work for the police, and they could not accept foreign drones creating uncertainty and disruption. Denmark is one of several European countries that have reported drone incidents in recent weeks, with unidentified drones sighted above Danish military sites as recently as Saturday. Defence ministers from 10 EU countries have agreed to create a drone wall in response to the sightings, and Nato says it has enhanced vigilance across the Baltic. In their statement announcing the ban, the transport ministry said police were on significantly increased alert ahead of this week's summit and that they needed to take care of Danes and our guests.

From Montenegro to northern California, the tech elite dream of building cities where they make the rules. Is this, finally, their moment? The shirtless man in the golden mask and cape has plans to lead his own country one day. There is no location yet, but it will be a crypto-and AI-powered paradise of medical experimentation, filled with people who want to "make death optional," he says. For now, though, he's leading a sparsely attended rave on the second floor of a San Francisco office building. A DJ is spinning at one end of an open room. A handful of people sway and jump on the space cleared out as a dance floor. At a nearby table, coffee is available with many alternative milks.

BUCHAREST - Romania scrambled fighter jets on Saturday when a drone breached the country's airspace during a Russian attack on Ukrainian infrastructure near the border, the defense ministry said. Defense Minister Ionut Mosteanu said the F-16 pilots came close to taking down the drone as it was flying very low before it left national airspace toward Ukraine. A threat of drone strikes also prompted Poland to deploy aircraft and close an airport in the eastern city of Lublin on Saturday, three days after it shot down Russian drones in its airspace with the backing of aircraft from its NATO allies. Romania, a European Union and NATO state which shares a 650-km (400-mile) border with Ukraine, has had Russian drone fragments fall onto its territory repeatedly since Russia began waging war on its neighbor. On Saturday, it scrambled two F-16 fighter jets and later two Eurofighters -- part of German air policing missions in Romania -- and warned citizens in the southeastern county of Tulcea near the Danube and its Ukrainian border to take cover, the defense ministry said in a statement.

Bayesian inference is widely used in many different fields to test hypotheses against observations. In most such applications, an assumption is made of precise input values to produce a precise output value. However, this is unrealistic for real-world applications. Often the best available information from subject matter experts (SMEs) in a given field is interval range estimates of the input probabilities involved in Bayes Theorem. This paper provides two key contributions to extend Bayes Theorem to an interval type-2 (IT2) version. First, we develop an IT2 version of Bayes Theorem that uses a novel and conservative method to avoid potential inconsistencies in the input IT2 MFs that otherwise might produce invalid output results. We then describe a novel and flexible algorithm for encoding SME-provided intervals into IT2 fuzzy membership functions (MFs), which we can use to specify the input probabilities in Bayes Theorem. Our algorithm generalizes and extends previous work on this problem that primarily addressed the encoding of intervals into word MFs for Computing with Words applications.