Topic modeling is a key component in unsupervised learning, employed to identify topics within a corpus of textual data. The rapid growth of social media generates an ever-growing volume of textual data daily, making online topic modeling methods essential for managing these data streams that continuously arrive over time. This paper introduces a novel approach to online topic modeling named StreamETM. This approach builds on the Embedded Topic Model (ETM) to handle data streams by merging models learned on consecutive partial document batches using unbalanced optimal transport. Additionally, an online change point detection algorithm is employed to identify shifts in topics over time, enabling the identification of significant changes in the dynamics of text streams. Numerical experiments on simulated and real-world data show StreamETM outperforming competitors.

Nancy Pelosi explodes at reporter as she's escorted down Capitol Building steps Trump threatens'land strikes' on Venezuela as CIA begins covert operations in Latin American country and Maduro declares'No to coups' She's the dancer caught'going at it' in bed with Britney Spears. Bella Hadid's health battle takes dark turn: Loved ones reveal hellish new details about model... as ominous texts emerge Diane Keaton's cause of death revealed just days after actress' passing at 79 as her family pens emotional message to fans Emily Ratajkowski fans rejoice as she makes Victoria's Secret show debut at age 34 Murderer's final words and hearty last meal as he's executed after 30 years on death row Nepo babies dare to bare! Celebrity offspring leave nothing to imagination as they dominate Victoria's Secret show... what would their parents say? Victoria's Secret show 2025: Bella Hadid rules the runway after her health woes, Jasmine Tookes opens the show at nine months pregnant and Emily Ratajkowski makes her debut aged 34 as legendary Angels and nepo babies unite after failed woke rebrand Popular food can be used to fight resistant viruses ... and it costs just pennies Disney superfan, 31, vanishes from her Midwest home months after announcing pregnancy... then horrific discovery is made at Walt Disney World Chilling new footage shows knifeman on night he'stabbed Ukrainian refugee to death' on a train in murder that shook America Britney Spears unleashes on ex Kevin Federline AND her SONS - accusing him of'gaslighting' and claiming she's seen one boy for just 45 minutes in five years Jailed Diddy's harsh reality check as very unglamorous conditions rapper has to abide by after prison revealed Mel Gibson's highly-anticipated sequel to The Passion of the Christ has sparked outrage from fans, after it was revealed that it is moving forward with a new cast due to the time gap. The original 2004 movie followed the final 12 hours of Jesus Christ's life, and starred Jim Caviezel, 57, as Christ. However, The Resurrection of the Christ -- which takes place three days after Christ's crucifixion on Good Friday -- will now star Finnish actor Jaakko Ohtonen, 36, in the role of Christ.

Reasoning protocols such as Chain of Thought (CoT) and Tree of Thought (ToT) organize internal deliberation but lack an explicit mechanism for external questioning that elicits self-revision. We present FOR-Prompting (From Objection to Revision Prompting), an asymmetric protocol where a Defender proposes an answer, an Objectioner raises question-style objections with no direct fixes, and a Host enforces consistency and closure. On GSM8K we observe about a 22% point gain over single-prompt and accuracy on par with CoT, with more than 10% higher ratings in reasoning and coherence from a uniform GPT 4.1 judge. FOR-Prompting also corrects mistakes without tools or human supervision on tricky queries, and improves performance for small-scale model (approx. 19% accuracy improved on Llama3.2:1b for GSM8K task), highlighting promise for small models and on personal device use. Beyond factual QA, qualitative analyses on open-ended tasks show enhanced exploration and refinement, with dialogue traces that make assumptions and trade-offs explicit. The protocol is model agnostic and operates purely at the prompt level through role-structured turns, so it works with hosted and local models of different sizes without retraining, and it supports large-scale study of objection-guided reasoning.

With his flowing locks, long beard, and worn robes, Jesus is one of the most instantly recognisable figures in the Western world. So it comes as no surprise that his face is also regularly spotted in inanimate objects. This is due to'face pareidolia' - a common brain phenomenon in which a person sees faces in random images or patterns. 'Sometimes we see faces that aren't really there,' explained Robin Kramer, Senior Lecturer in the School of Psychology, at University of Lincoln, in an article for The Conversation. 'You may be looking at the front of a car or a burnt piece of toast when you notice a face-like pattern. 'This is called face pareidolia and is a mistake made by the brain's face detection system.'

The history of art has seen significant shifts in the manner in which artworks are created, making understanding of creative processes a central question in technical art history. In the Renaissance and Early Modern period, paintings were largely produced by master painters directing workshops of apprentices who often contributed to projects. The masters varied significantly in artistic and managerial styles, meaning different combinations of artists and implements might be seen both between masters and within workshops or even individual canvases. Information on how different workshops were managed and the processes by which artworks were created remains elusive. Machine learning methods have potential to unearth new information about artists' creative processes by extending the analysis of brushwork to a microscopic scale. Analysis of workshop paintings, however, presents a challenge in that documentation of the artists and materials involved is sparse, meaning external examples are not available to train networks to recognize their contributions. Here we present a novel machine learning approach we call pairwise assignment training for classifying heterogeneity (PATCH) that is capable of identifying individual artistic practice regimes with no external training data, or "ground truth." The method achieves unsupervised results by supervised means, and outperforms both simple statistical procedures and unsupervised machine learning methods. We apply this method to two historical paintings by the Spanish Renaissance master, El Greco: The Baptism of Christ and Christ on the Cross with Landscape, and our findings regarding the former potentially challenge previous work that has assigned the painting to workshop members. Further, the results of our analyses create a measure of heterogeneity of artistic practice that can be used to characterize artworks across time and space.

It's a mite soon to start grieving, but scientists now project that life on Earth will probably end in about a billion years. A Monday in February, 1,000,002,025, would be my guess. On that inhospitable day, give or take a few million years, the sun will become so hot that the oceans will boil, Earth's oxygen will disappear, and photosynthesis will cease, as will all living things. We should be so lucky. There's a pretty fair chance that life could be wiped out well before then--say, in early June, 2034, or on a cloudy Sunday in November, 3633. Plenty of people do, as it turns out, and, if you want to know who they are, Dorian Lynskey's "Everything Must Go: The Stories We Tell About the End of the World" (Pantheon) is a good place to start. Lynskey, a British journalist and podcaster, has assembled biological, geological, archeological, literary, and cinematic permutations of existential finales, leaving no stone unturned, be it meteor, comet, or asteroid. If a book, a song, a story, a film, a headline, a title, or a study has "world" and "end" in it, Lynskey has unearthed it.

The face on the Shroud of Turin could not have come from Jesus' head – and it's doubtful he ever touched it, an explosive new study suggests. Marked with a faint impression of a body and face, the artifact is believed by many to be the actual fabric used to wrap Christ's corpse after his crucifixion. But its documented history only starts in the mid-14th century, and it's been a source of scepticism for almost as long, with many dismissing it as a medieval forgery. Now a new study has found that the impression on the shroud could not have been made by a three-dimensional human body, but was perhaps from a bas-relief – a shallow carving. To reach this conclusion, Cicero Moraes, author of the new study, created a virtual simulation in which a fabric was placed over a body in a bid to replicate the famous shroud.

Scientists in Italy hit the headlines this week, after claiming the famous Shroud of Turin dates from Jesus' lifetime around 2,000 years ago. Now, AI has reimagined what the son of God might have actually looked like based on the treasured relic, which is said to feature an imprint of Jesus' face. MailOnline asked the AI tool Merlin: 'Can you generate a realistic image of Jesus Christ based on the face in the Shroud of Turin?' The AI-generated result suggests Christ was white with big blue eyes, a trim beard and thorn marks on his face. So, can you see the similarities with the famous holy imprint? The Shroud of Turin is a 14-foot-long linen cloth with a faint image of a crucified man.

When exposed to human-generated data, language models are known to learn and amplify societal biases. While previous works introduced benchmarks that can be used to assess the bias in these models, they rely on assumptions that may not be universally true. For instance, a gender bias dimension commonly used by these metrics is that of family--career, but this may not be the only common bias in certain regions of the world. In this paper, we identify topical differences in gender bias across different regions and propose a region-aware bottom-up approach for bias assessment. Our proposed approach uses gender-aligned topics for a given region and identifies gender bias dimensions in the form of topic pairs that are likely to capture gender societal biases. Several of our proposed bias topic pairs are on par with human perception of gender biases in these regions in comparison to the existing ones, and we also identify new pairs that are more aligned than the existing ones. In addition, we use our region-aware bias topic pairs in a Word Embedding Association Test (WEAT)-based evaluation metric to test for gender biases across different regions in different data domains. We also find that LLMs have a higher alignment to bias pairs for highly-represented regions showing the importance of region-aware bias evaluation metric.

Images showing the internal structure of the Christ the Redeemer statue in Rio de Janeiro, Brazil, have been revealed for the first time thanks to a 3D scan. Geospatial mapping specialist GeoSLAM produced the never-before-seen digital images of the inside of the famous statue ahead of its 90th birthday on October 12. Emblematic of the city of Rio de Janeiro and the nation of Brazil, the concrete clad statue stands 98 feet tall and spans a mammoth 92 feet wide. The digital re-creation of the iconic statue involved more than 180 million points of data - taken from a drone-mounted laser scanner and someone walking up and down the staircases inside the statue using the same scanner. The new digital images will allow people to virtually explore this world-famous monument in ways never before been possible - inside and out. In 2019, the statue was visited over two million times, with people from all over the globe travelling to admire the monument, which soars 2,320 feet above the city.