Composite indicators are widely used to score or classify units evaluated on multiple criteria. Their construction involves aggregating criteria evaluations, a common practice in Multiple Criteria Decision Aiding (MCDA). In MCDA, various methods have been proposed to address key aspects of multiple criteria evaluations, such as the measurement scales of the criteria, the degree of acceptable compensation between them, and their potential interactions. However, beyond producing a final score or classification, it is essential to ensure the explainability and interpretability of results as well as the procedure's transparency. This paper proposes a method for constructing explainable and interpretable composite indicators using " if..., then... " decision rules. We consider the explainability and interpretability of composite indicators in four scenarios: (i) decision rules explain numerical scores obtained from an aggregation of numerical codes corresponding to ordinal qualifiers; (ii) an obscure numerical composite indicator classifies units into quantiles; (iii) given preference information provided by a Decision Maker in the form of classifications of some reference units, a composite indicator is constructed using decision rules; (iv) the classification of a set of units results from the application of an MCDA method and is explained by decision rules. To induce the rules from scored or classified units, we apply the Dominance-based Rough Set Approach. The resulting decision rules relate the class assignment or unit's score to threshold conditions on values of selected indicators in an intelligible way, clarifying the underlying rationale. Moreover, they serve to recommend composite indicator assessment for new units of interest.

Knowledge graphs (KGs) are useful for analyzing social structures, community dynamics, institutional memberships, and other complex relationships across domains from sociology to public health. While recent advances in large language models (LLMs) have improved the scalability and accessibility of automated KG extraction from large text corpora, the impacts of extraction errors on downstream analyses are poorly understood, especially for applied scientists who depend on accurate KGs for real-world insights. To address this gap, we conducted the first evaluation of KG extraction performance at two levels: (1) micro-level edge accuracy, which is consistent with standard NLP evaluations, and manual identification of common error sources; (2) macro-level graph metrics that assess structural properties such as community detection and connectivity, which are relevant to real-world applications. Focusing on affiliation graphs of person membership in organizations extracted from social register books, our study identifies a range of extraction performance where biases across most downstream graph analysis metrics are near zero. However, as extraction performance declines, we find that many metrics exhibit increasingly pronounced biases, with each metric tending toward a consistent direction of either over- or under-estimation. Through simulations, we further show that error models commonly used in the literature do not capture these bias patterns, indicating the need for more realistic error models for KG extraction. Our findings provide actionable insights for practitioners and underscores the importance of advancing extraction methods and error modeling to ensure reliable and meaningful downstream analyses.

--We propose SAL T (Split-Adaptive Lightweight T un-ing), a lightweight model adaptation framework for Split Computing under closed constraints, where the head and tail networks are proprietary and inaccessible to users. In such closed environments, conventional adaptation methods are infeasible since they require access to model parameters or architectures. SAL T addresses this challenge by introducing a compact, trainable adapter on the client side to refine latent features from the head network, enabling user-specific adaptation without modifying the original models or increasing communication overhead. We evaluate SAL T on user-specific classification tasks with CIF AR-10 and CIF AR-100, demonstrating improved accuracy with lower training latency compared to fine-tuning methods. With minimal deployment overhead, SAL T offers a practical solution for personalized inference in edge AI systems under strict system constraints. The increasing scale of deep learning models deployed in cloud-based AI services has raised concerns regarding server-side computational load and inference latency. To address these challenges, Split Computing has emerged as a promising paradigm that offloads part of a large cloud-based model to the client device [1], [2]. In this architecture, the neural network model is partitioned into a head network executed on the client and a tail network retained on the cloud.

This study explores the extent to which national music preferences reflect underlying cultural values. We collected long-term popular music data from YouTube Music Charts across 62 countries, encompassing both Western and non-Western regions, and extracted audio embeddings using the CLAP model. To complement these quantitative representations, we generated semantic captions for each track using LP-MusicCaps and GPT-based summarization. Countries were clustered based on contrastive embeddings that highlight deviations from global musical norms. The resulting clusters were projected into a two-dimensional space via t-SNE for visualization and evaluated against cultural zones defined by the World Values Survey (WVS). Statistical analyses, including MANOVA and chi-squared tests, confirmed that music-based clusters exhibit significant alignment with established cultural groupings. Furthermore, residual analysis revealed consistent patterns of overrepresentation, suggesting non-random associations between specific clusters and cultural zones. These findings indicate that national-level music preferences encode meaningful cultural signals and can serve as a proxy for understanding global cultural boundaries.

In recent years, embedding alignment has become the state-of-the-art machine translation approach, as it can yield high-quality translation without training on parallel corpora. However, existing research and application of embedding alignment mostly focus on high-resource languages with high-quality monolingual embeddings. It is unclear if and how low-resource languages may be similarly benefited. In this study, we implement an established supervised embedding alignment method for word translation from English to Yoruba, the latter a low-resource language. We found that higher embedding quality and normalizing embeddings increase word translation precision, with, additionally, an interaction effect between the two. Our results demonstrate the limitations of the state-of-the-art supervised embedding alignment when it comes to low-resource languages, for which there are additional factors that need to be taken into consideration, such as the importance of curating high-quality monolingual embeddings. We hope our work will be a starting point for further machine translation research that takes into account the challenges that low-resource languages face.

Comparative studies of news coverage are challenging to conduct because methods to identify news articles about the same event in different languages require expertise that is difficult to scale. We introduce an AI-powered method for identifying news articles based on an event FINGERPRINT, which is a minimal set of metadata required to identify critical events. Our event coverage identification method, FINGERPRINT TO ARTICLE MATCHING FOR EVENTS (FAME), efficiently identifies news articles about critical world events, specifically terrorist attacks and several types of natural disasters. FAME does not require training data and is able to automatically and efficiently identify news articles that discuss an event given its fingerprint: time, location, and class (such as storm or flood). The method achieves state-of-the-art performance and scales to massive databases of tens of millions of news articles and hundreds of events happening globally. We use FAME to identify 27,441 articles that cover 470 natural disaster and terrorist attack events that happened in 2020. To this end, we use a massive database of news articles in three languages from MediaCloud, and three widely used, expert-curated databases of critical events: EM-DAT, USGS, and GTD. Our case study reveals patterns consistent with prior literature: coverage of disasters and terrorist attacks correlates to death counts, to the GDP of a country where the event occurs, and to trade volume between the reporting country and the country where the event occurred. We share our NLP annotations and cross-country media attention data to support the efforts of researchers and media monitoring organizations.

Israel activated a new aerial defense system – dubbed "Barak Magen" – for the first time on Sunday night, saying it intercepted and destroyed multiple Iranian drones. Israel activated a new aerial defense system – dubbed "Barak Magen," meaning "lightning shield" – for the first time on Sunday night, saying it intercepted and destroyed multiple Iranian drones. The Israeli Navy intercepted eight Iranian drones using the "Barak Magen" and its long-range air defense (LRAD) interceptor, which were launched from an Israeli navy Sa'ar 6 missile ship, the Israel Defense Forces (IDF) said in a statement. John Hannah, senior fellow at the National Security of America and the co-author of a report published earlier this month on Israel's defense against two massive Iranian missile attacks in 2024, told Fox News Digital on Monday that the air defense system "significantly enhances" the air and missile defense architecture of Israel's navy. "The Barak Magen is simply another arrow in the expanding quiver of Israel's highly sophisticated and increasingly diverse multi-tiered missile defense architecture – which was already, by leaps and bounds, the most advanced and experienced air defense system fielded by any country in the world," Hannah said.

Breakthroughs, discoveries, and DIY tips sent every weekday. As with many mysteries of the mind, science doesn't have one neat answer. "You'll get as many answers to the question'What is the purpose of dreaming?' as there are dream psychologists," says Deirdre Barrett, dream researcher at Harvard University and author of The Committee of Sleep. According to Austrian neurologist and founder of psychoanalysis Sigmund Freud, dreams offered vital clues to unresolved conflicts buried deep within our psyche. But Freud's theory, introduced in his 1899 book The Interpretation of Dreams, sparked plenty of controversy.

Shortly after joining MIT in 2012, Williams created the Civic Data Design Lab to bridge that divide. Over the years, she and her colleagues have pushed the narrative and expository bounds of urban planning data using the latest technologies available--making numbers vivid and accessible through human stories and striking graphics. One project she was involved in, on rates of incarceration in New York City by neighborhood, is now in the permanent collection of the Museum of Modern Art in New York. Williams's other projects have tracked the spread and impact of air pollution in Beijing using air quality monitors and mapped the daily commutes of Nairobi residents using geographic information systems. Cities should be transparent in how they're using AI and what its limitations are.

Russenorsk, a pidgin language historically used in trade interactions between Russian and Norwegian speakers, represents a unique linguistic phenomenon. In this paper, we attempt to analyze its lexicon using modern large language models (LLMs), based on surviving literary sources. We construct a structured dictionary of the language, grouped by synonyms and word origins. Subsequently, we use this dictionary to formulate hypotheses about the core principles of word formation and grammatical structure in Russenorsk and show which hypotheses generated by large language models correspond to the hypotheses previously proposed ones in the academic literature. We also develop a "reconstruction" translation agent that generates hypothetical Russenorsk renderings of contemporary Russian and Norwegian texts.