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.

When knowledge graphs (KGs) are automatically extracted from text, are they accurate enough for downstream analysis? Unfortunately, current annotated datasets can not be used to evaluate this question, since their KGs are highly disconnected, too small, or overly complex. To address this gap, we introduce AffilKG (https://doi.org/10.5281/zenodo.15427977), which is a collection of six datasets that are the first to pair complete book scans with large, labeled knowledge graphs. Each dataset features affiliation graphs, which are simple KGs that capture Member relationships between Person and Organization entities -- useful in studies of migration, community interactions, and other social phenomena. In addition, three datasets include expanded KGs with a wider variety of relation types. Our preliminary experiments demonstrate significant variability in model performance across datasets, underscoring AffilKG's ability to enable two critical advances: (1) benchmarking how extraction errors propagate to graph-level analyses (e.g., community structure), and (2) validating KG extraction methods for real-world social science research.

We present a novel method for learning hierarchical abstractions that prioritize competing objectives, leading to improved global expected rewards. Our approach employs a secondary rewarding agent with multiple scalar outputs, each associated with a distinct level of abstraction. The traditional agent then learns to maximize these outputs in a hierarchical manner, conditioning each level on the maximization of the preceding level. We derive an equation that orders these scalar values and the global reward by priority, inducing a hierarchy of needs that informs goal formation. Experimental results on the Pendulum v1 environment demonstrate superior performance compared to a baseline implementation.We achieved state of the art results.

In this paper, we explore the intriguing similarities between the structure of a discrete neural network, such as a spiking network, and the composition of a piano piece. While both involve nodes or notes that are activated sequentially or in parallel, the latter benefits from the rich body of music theory to guide meaningful combinations. We propose a novel approach that leverages musical grammar to regulate activations in a spiking neural network, allowing for the representation of symbols as attractors. By applying rules for chord progressions from music theory, we demonstrate how certain activations naturally follow others, akin to the concept of attraction. Furthermore, we introduce the concept of modulating keys to navigate different basins of attraction within the network. Ultimately, we show that the map of concepts in our model is structured by the musical circle of fifths, highlighting the potential for leveraging music theory principles in deep learning algorithms.

The House of Commons will vote next week on Theresa May's amended Brexit deal. Ahead of this, MPs are beginning to submit their own amendments. Among the proposals so far are those aimed at: preventing a no-deal Brexit; extending the Article 50 deadline if a deal isn't agreed by 26 February; looking at options including renegotiating with Brussels or holding another referendum. On Monday, the prime minister said she was focusing on altering the Irish backstop, and that she was scrapping proposals for a £65 fee for EU citizens to remain in the UK. But Labour's Jeremy Corbyn argued that Mrs May was in denial about the level of opposition to her plans.

The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying and quantifying such stereotypes and bias in the Man Bookers Prize winning fiction. We consider 275 books shortlisted for Man Bookers Prize between 1969 and 2017. The gender bias is analyzed by semantic modeling of book descriptions on Goodreads. This reveals the pervasiveness of gender bias and stereotype in the books on different features like occupation, introductions and actions associated to the characters in the book.

A group of Amazon shareholders is asking CEO Jeff Bezos to stop selling and marketing facial recognition technology to governments after civil liberties groups warned of the potential for abuse. Earlier this year, a group of advocacy organisations led by the American Civil Liberties Union (ACLU) published a report detailing how Amazon was marketing its Rekognition tool to American law enforcement agencies. In addition to touting the technology as helping to find suspects, Amazon has said it could be used to preemptively identify "persons of interest" and prevent crimes. A letter signed by 19 shareholders - and provided to The Independent by the ACLU - urges Mr Bezos to halt the tool's expansion until those concerns can be addressed. Amazon supplier investigated over'mistreatment' of workers in China How Alexa recorded a family's conversation then sent it to someone Amazon told to stop selling facial recognition tools to police Amazon supplier investigated over'mistreatment' of workers in China How Alexa recorded a family's conversation then sent it to someone Furnishing police and sheriff's departments with the tool would bolster "government surveillance infrastructure technology" and could drive down Amazon's value, the letter warned. It also echoed concerns about the potential for misuse. "While Rekognition may be intended to enhance some law enforcement activities, we are deeply concerned it may ultimately violate civil and human rights", the letter said.

A Tesla's autopilot function was engaged in the minutes before a fiery crash that killed its driver in California earlier this year, according to a federal inquiry. In the roughly 20 minutes before the vehicle slammed into a barrier near Mountain View and burst into flames, the car's autopilot feature was in "continuous operation", the National Transportation Safety Board (NTSB) found in its initial investigation. During the critical 60 seconds leading up to the crash, the NTSB reported, the car's driver repeatedly placed his hands on the steering wheel. Tesla crashes into parked police car in Autopilot mode Wall Street blasts Elon Musk's'truly bizarre' Tesla earnings call Tesla faces labour investigation after allegation of injury undercount But six seconds before the accident, evidence suggests the driver had removed his hands from the steering wheel. The vehicle also accelerated in the final three seconds.