Accurately dating historical texts is essential for organizing and interpreting cultural heritage collections. This article addresses temporal text classification using interpretable, feature-engineered tree-based machine learning models. We integrate five feature categories - compression-based, lexical structure, readability, neologism detection, and distance features - to predict the temporal origin of English texts spanning five centuries. Comparative analysis shows that these feature domains provide complementary temporal signals, with combined models outperforming any individual feature set. On a large-scale corpus, we achieve 76.7% accuracy for century-scale prediction and 26.1% for decade-scale classification, substantially above random baselines (20% and 2.3%). Under relaxed temporal precision, performance increases to 96.0% top-2 accuracy for centuries and 85.8% top-10 accuracy for decades. The final model exhibits strong ranking capabilities with AUCROC up to 94.8% and AUPRC up to 83.3%, and maintains controlled errors with mean absolute deviations of 27 years and 30 years, respectively. For authentication-style tasks, binary models around key thresholds (e.g., 1850-1900) reach 85-98% accuracy. Feature importance analysis identifies distance features and lexical structure as most informative, with compression-based features providing complementary signals. SHAP explainability reveals systematic linguistic evolution patterns, with the 19th century emerging as a pivot point across feature domains. Cross-dataset evaluation on Project Gutenberg highlights domain adaptation challenges, with accuracy dropping by 26.4 percentage points, yet the computational efficiency and interpretability of tree-based models still offer a scalable, explainable alternative to neural architectures.

Health Nutrition Gastronomists study 100 years of menus to reveal food's political power Menus from 457 diplomatic meals served in Portugal reveal how food can make and break alliances. Breakthroughs, discoveries, and DIY tips sent every weekday. A nice, warm meal is one of the great unifiers. Food communicates everything from love and tradition like a home cooked dinner with all of the trimmings and even political stances. At a state dinner, food has the power to cultivate understanding across cultures-or potentially create tensions.

Understanding the dynamics of consumer credit cycles requires analyzing a complex web of interconnected economic relationships. When unemployment rises, consumers typically reduce spending and increase precautionary savings, while simultaneously facing greater difficulty servicing existing debt obligations. This creates pressure on revolving credit balances as households may increase borrowing to maintain consumption levels, ultimately leading to higher default rates. However, the timing, magnitude, and interaction patterns of these relationships vary dramatically across different economic cycles, creating fundamentally different crisis mechanisms that correlation-based analysis cannot distinguish. Consider the 2008 financial crisis versus the 1990-91 recession.

As Text-to-Image (TTI) diffusion models become increasingly influential in content creation, growing attention is being directed toward their societal and cultural implications. While prior research has primarily examined demographic and cultural biases, the ability of these models to accurately represent historical contexts remains largely underexplored. To address this gap, we introduce a benchmark for evaluating how TTI models depict historical contexts. The benchmark combines HistVis, a dataset of 30,000 synthetic images generated by three state-of-the-art diffusion models from carefully designed prompts covering universal human activities across multiple historical periods, with a reproducible evaluation protocol. We evaluate generated imagery across three key aspects: (1) Implicit Stylistic Associations: examining default visual styles associated with specific eras; (2) Historical Consistency: identifying anachronisms such as modern artifacts in pre-modern contexts; and (3) Demographic Representation: comparing generated racial and gender distributions against historically plausible baselines. Our findings reveal systematic inaccuracies in historically themed generated imagery, as TTI models frequently stereotype past eras by incorporating unstated stylistic cues, introduce anachronisms, and fail to reflect plausible demographic patterns. By providing a reproducible benchmark for historical representation in generated imagery, this work provides an initial step toward building more historically accurate TTI models.

Cuneiform tablets, emerging in ancient Mesopotamia around the late fourth millennium BCE, represent one of humanity's earliest writing systems. Characterized by wedge-shaped marks on clay tablets, these artifacts provided insight into Mesopotamian civilization across various domains. Traditionally, the analysis and dating of these tablets rely on subjective assessment of shape and writing style, leading to uncertainties in pinpointing their exact temporal origins. Recent advances in digitization have revolutionized the study of cuneiform by enhancing accessibility and analytical capabilities. Our research uniquely focuses on the silhouette of tablets as significant indicators of their historical periods, diverging from most studies that concentrate on textual content. Utilizing an unprecedented dataset of over 94,000 images from the Cuneiform Digital Library Initiative collection, we apply deep learning methods to classify cuneiform tablets, covering over 3,000 years of history. By leveraging statistical, computational techniques, and generative modeling through Variational Auto-Encoders (VAEs), we achieve substantial advancements in the automatic classification of these ancient documents, focusing on the tablets' silhouettes as key predictors. Our classification approach begins with a Decision Tree using height-to-width ratios and culminates with a ResNet50 model, achieving a 61% macro F1-score for tablet silhouettes. Moreover, we introduce novel VAE-powered tools to enhance explainability and enable researchers to explore changes in tablet shapes across different eras and genres. This research contributes to document analysis and diplomatics by demonstrating the value of large-scale data analysis combined with statistical methods. These insights offer valuable tools for historians and epigraphists, enriching our understanding of cuneiform tablets and the cultures that produced them.

Words are fundamental linguistic units that connect thoughts and things through meaning. However, words do not appear independently in a text sequence. The existence of syntactic rules induces correlations among neighboring words. Using an ordinal pattern approach, we present an analysis of lexical statistical connections for 11 major languages. We find that the diverse manners that languages utilize to express word relations give rise to unique pattern structural distributions. Furthermore, fluctuations of these pattern distributions for a given language can allow us to determine both the historical period when the text was written and its author. Taken together, our results emphasize the relevance of ordinal time series analysis in linguistic typology, historical linguistics and stylometry.

AI Time Machine is a new tool that allows users to create images of a person in different time periods throughout history using AI-image generator technology. Genealogy website MyHeritage announced the new feature that's based on the image synthesizer model Stable Diffusion and technology licensed from Astria, a company that tailer-makes AI image generation. The app, available on desktop and mobile web browsers, asks the user to upload 10 to 25 photos of the same individual taken from different angles. A model is then made of the individual that can be cast into predefined themes set in different historical eras. The person can then see themselves as an ancient Greek warrior, an Egyptian pharaoh, a medieval knight, a Victorian lady, or a hippie from the 1960s.

The global artificial intelligence (AI) in transportation market is projected to reach $3.5 billion by 2023. Due to the rising concerns for vehicle and driver safety, increasing focus on lowering the transportation costs, and advancements in autonomous vehicles, the artificial intelligence (AI) in transportation market is witnessing significant growth. In 2017, the market valued at $1.4 billion, and it is predicted to generate a revenue of $3.5 billion by 2023, exhibiting a CAGR of 16.5% during the forecast period (2018–2023). AI in transportation involves the use of computer vision, deep learning, and natural language processing technologies. Video camera, radio detection and ranging (RADAR) sensors, and light detection and ranging (LiDAR) equipment are some of the AI-based hardware installed in fully autonomous vehicles which are under trial.

You certainly wouldn't know it from a reading of the CBOE S&P500 Volatility Index (CBOE:VIX), which printed a low of 11.44 on Friday, but there is a great deal of uncertainty about the prospects for the market as we move further into the third quarter, traditionally the most challenging period of the year. Reasons for concern are not hard to fathom, with the Fed on hold and poised to start raising rates, despite anemic growth in the economy; gloom over the "earnings recession"; and an abundance of political risk factors in play, not least of which is the upcoming presidential election. At times like these investors need a little encouragement to stay the course - and where better to look for it than in the history books. More specifically, the question is whether the past has anything to teach us about the prospects for the market, going forward. Academic theory says no; but Wall Street traders controlling trillions of dollars of investments believe that, on the contrary, history contains valuable information that can be helpful in predicting the likely future outcome for the market. There are several difficulties in making historical comparisons.