**Abstract:**

Paleobiologists use phylogenetics to study the patterns and processes of organismal evolution. Phylogenetic methods have also been deployed for reconstructing ancestor-descendant relationships using cultural data. Within evolutionary archaeology specifically, phylogenetic analyses based on maximum parsimony and discrete traits dominate, which sets limitations for the downstream role cultural phylogenies, once derived, can play in more elaborate analytical pipelines. Moreover, the use of discrete character traits in these efforts prevails, which in turn sets a number of non-trivial challenges. Recent methodological advances in Bayesian phylogenetics, however, now allow us to infer evolutionary dynamics using continuous characters. Capitalizing on these developments, we here present an exploratory analysis of cultural macroevolution of projectile point shape evolution in the European Final Palaeolithic and earliest Mesolithic (~15,000-11,000 BP) using a Bayesian phylodynamic approach and the fossilised birth-death process model. This model-based approach leaps far beyond the application of parsimony, in that it not only produces a tree, but also divergence times, and diversification rates, while incorporating uncertainties. This allows us to compare rates to the pronounced climatic changes that occurred during our timeframe. While common in cultural evolutionary analyses of language, the extension of Bayesian phylodynamic models to archaeology represents a major methodological breakthrough.

**Speaker's social media:**

Twitter: @david_matzig

RECORDING - A macroevolutionary analysis of European late upper palaeolithic stone tool shape using a Bayesian phylodynamic framework

**Abstract:**

Ontogenetic niche construction has been suggested as a powerful driver influencing learning capabilities and survival across a wide range of species, including humans. A key component of the human ontogenetic niche is play objects. They are ubiquitous ethnographically and display remarkable similarities cross-culturally; they also appear archaeologically in periods often characterized by increased overall rates of innovation. This study explores the role of object play as a cultural variation-generating mechanism that fuels adaption during periods of climatic stress. In a natural experimental paradigm, children’s material culture in the Inuit and Norse societies that inhabited Greenland during the so-called Little Ice Age (~1350-1800 CE) have been compared, demonstrating marked differences in corpus volume, diversity, and change across time. Notably, the Norse settlers disappeared at this time, while Inuit communities thrived. Previous hypotheses for the demise of the Norse in Greenland have been proposed, often deterministically stressing climate or land degradation. As both societies experienced largely identical climatic stress, this case study affords the opportunity to explore what cultural factors – including ontogenetic niche construction and object play – may additionally have promoted survival or collapse. 

**Speaker's social media:**

@MathildeVester5

RECORDING - A natural experiment of history: Ontogenetic niche construction, explorative object play, and adaptation during the Little Ice Age in Greenland

**Abstract:**

Previous cross-cultural research suggests that climate hazards may have profound effects on a society’s culture.  For example, in non-state societies serious food-destroying hazards (typically droughts, some floods, and pest infestations) strongly predict higher warfare frequencies and more customary beyond-household sharing.  The research question addressed here is whether non-food-destroying hazards (such as storms and seismic activity) have similar or different effects on a range of cultural traits hypothesized to be adaptive responses in a worldwide sample of nonindustrial societies. For the past two years our research team has coded the type and frequency of hazards for 132 societies in the ethnographic record using eHRAF World Cultures. Preliminary results suggest that the contrast between frequent food-destroying and frequent non-food-destroying hazard profiles generally make very different predictions. For example, societies with more non-food-destroying hazards tend to have tighter cultures (stronger norms and severe punishment for norm violations), but cultural tightness is not predicted by food-destroying hazards. Food-destroying hazards in non-state societies predict more warfare, but non-food-destroying hazards predict less warfare. We present theory and causal models suggesting explanations for these divergent findings. Understanding how humans have adapted to hazards in the recent past may help us better understand the hazards faced today. 

**Speaker's social media:**



LIVE - Food-destroying versus Non-food-destroying hazards: A worldwide comparison of differential effects on culture

**Abstract:**

Intersections with the global capitalist market system offer challenges and opportunities for Indigenous people living in more or less economically autonomous communities. In contexts of increasing intersection, individuals engage with new globalized culture and economic practices in diverse ways depending on their positions within local social and economic systems. While there is a substantial literature on the emergence of inequality in such settings, there is a gap in research on the pathways through which people initially start shifting towards a cash-dependent subsistence strategy. Guided by cultural evolutionary theory, and drawing on data from 200 semi-structured interviews from one village in the Republic of the Congo, we use a Bayesian probability modeling approach to examine: 1) the role of individual differences as predictors of market intersection, here focused on gender, age, and migration status; and 2) the cultural transmission modes associated with individual’s economic strategies. Our analyses are on-going, but these data have implications for understanding how life histories interact with strategic social learning to motivate behavioral change at the individual level; and how cultural evolutionary forces may scale these up to higher level changes associated with globalization, including increased socio-economic inequality and ecosystem disruption (e.g., via employment for extractive industries).

**Speaker's social media:**

Twitter: @utcp7b2e

RECORDING - Cultural transmission, individual differences, and globalization in a Congo Basin village

**Abstract:**

Context

Polarisation is a growing concern for social cohesion, with individuals aligning with their group's entrenched beliefs. Understanding its causes is key for cultural evolution theory, since cultural group formation is both central and elusive. Previous models typically generate only weak polarisation or rely on strong assumptions.

Purpose

This study examines how trust-based influence in cultural evolution fosters cultural polarisation and group identity formation.

Methods

We developed a mathematical model building on a cultural systems framework where agents trust and learn from those with compatible beliefs (sender filtering). We contrast this with a model where agents focus on the belief itself rather than the sender (belief filtering).

Key results

Sender filtering produces belief clusters, where initially unrelated beliefs (e.g. in climate change and vaccine effectiveness) can become strongly correlated, with the largest factions polarised at the extremes, adopting completely contrary beliefs. These belief clusters signal identity, where adherence to one belief predicts adopting others in the same cluster. In contrast, belief filtering polarises only when beliefs are inherently linked.

Implications

The findings shed new light on polarisation and the emergence of cultural groups. Algorithms like collaborative filtering work similar to our model, suggesting that digital technologies may exacerbate polarisation.

**Speaker's social media:**

@fredrikjansson.bsky.social

LIVE - Trust-based cultural transmission can form polarising group identities

The objective of this project is to use machine learning convolutional neural networks (CNN) to classify marine mammal spectrograms. CNNs excel at image recognition and can identify features and patterns in images. Spectrograms of marine mammal acoustics can be treated as images and put into a standardized format to be used as an input into a CNN model for classification, allowing for a more structured representation of the data than raw time series. The Marine Mammal Monitoring (M3R) on Navy ranges has an extensive data set of both labeled and unlabeled recordings, online sources such as the Watkins marine mammal sound data base have a smaller extent of data but more accessible labeled data. By taking advantage of these online data sets a robust CNN model can be developed and applied to a subset of M3R data to make classifications of marine mammals on navy ranges. 


Marine Mammal Classification using CNNs 

Dive into the realm of advanced AI with our poster presentation on integrating large language models and RAG architectures using Databricks. Discover how this cutting-edge technology transforms operational intelligence, offering unprecedented capabilities in data analysis, decision-making, and strategic operations. Learn how Databricks, with its robust data processing and AI tools, can harness the full potential of large language models, providing the Navy with a formidable tool in information management and operational efficiency. Join us to explore this synergy of technology and defense, paving the way for smarter, faster, and more secure Naval operations.

Empowering Military Intelligence with Next-Gen AI: Unleash the Power of Databricks and Large Language Models

The Naval Postgraduate School Meteorology Department has a developed model, Navy Atmospheric Vertical Surface Layer Model (NAVSLaM), to predict optical turbulence in the surface layer (up to ~100 m above the ocean or land) based upon atmospheric measurements using simple, robust sensors. However, the Physics Department has developed machine learning models of optical turbulence using atmospheric measurements. This research measures optical turbulence over many months using sonic anemometers that served as the baseline to compare prediction from the models. Atmospheric parameters such as air temperature, wind speed, humidity at two different heights; solar flux and ground temperature were simultaneously collected. Those data were used as input for NAVSLaM and machine learning models to predict optical turbulence. We then compared the performance of these prediction models to each other by calculating the root-mean-square error with respect to the baseline data from the sonic anemometers. The results from this research will help determine which model is more reliable to the given environment.

Comparison of Optical Turbulence Prediction Models

A new data-adaptive model via machine learning is investigated as a prospective capability for ocean wave height estimation based on simulated ship motion time-series. Long Short-Term Memory (LSTM) neural networks are trained and tested with physics-based Large Amplitude Motion Program (LAMP) simulation data for the ship geometry and loading conditions of the David Taylor Model Basin (DTMB) Model 5415. From this initial study, data-adaptive machine learning appears to be a suitable candidate for continued investigation of estimation of the ocean wave field based on ship motions.

Ocean Wave Height Estimation via Machine Learning

In the present scenario, Unmanned Surface Vehicles (USVs) or Uncrewed Surface Vessels are playing vital role in naval combat. Many researchers across the globe are working on various applications environmental monitoring and enemy prediction location. While undertaking naval combat missions especially in extreme weather conditions, a real time environment plays a vital role to achieve autonomous navigation and completing tasks for USVs. This is performed for accuracy in detection and recognition of any object that is likely to be an obstacle causing a potential danger to the sailing ships on the water surface. There is a limit in the availability of datasets for many scenarios from publicly available datasets. The data can be collected manually, and this procedure takes several days. This process is expensive and time consuming. Once ample datasets are available, it is easy to train the machine learning model. To address the issue, this research work proposes on implementing synthetic image data from the real images. The image data that is generated, optimizes the data collection process and produces a significant amount of data that could help training ML models with the help of Generative Adversarial Networks (GANs). The implementation of this proposed technique is achieving better accuracy. This research work also finds the generator and discriminator losses and scores of real and synthetic data of the GAN. 


Synthetic data for Naval Combats using Generative Adversarial Networks

The ability to automatically generate accurate protocols for scientific experiments would represent a major step towards the automation of science. Large Language Models (LLMs) have impressive capabilities on a wide range of tasks, such as question answering and the generation of coherent text and code. However, LLMs can struggle with multi-step problems and long-term planning, which are crucial for designing scientific experiments. Moreover, evaluation of the accuracy of scientific protocols is challenging, because experiments can be described correctly in many different ways, require expert knowledge to evaluate, and cannot usually be executed automatically. Here we present an automatic evaluation framework for the task of planning experimental protocols, and we introduce BioProt: a dataset of biology protocols with corresponding pseudocode representations. To measure performance on generating scientific protocols, we use an LLM to convert a natural language protocol into pseudocode, and then evaluate an LLM's ability to reconstruct the pseudocode from a high-level description and a list of admissible pseudocode functions. We evaluate GPT-3 and GPT-4 on this task and explore their robustness. We externally validate the utility of pseudocode representations of text by generating accurate novel protocols using retrieved pseudocode, and we run a generated protocol successfully in our biological laboratory. Our framework is extensible to the evaluation and improvement of language model

BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology | VIDEO

In Psychotherapy, {maladaptive schemas}-- negative perceptions that {an 
individual has of the self, others, or the world that endure despite objective reality}--
often lead to resistance to treatments and relapse of mental health issues such as depression, anxiety, panic attacks etc. Identification of early maladaptive schemas (EMS) is thus a crucial 
step during Schema Therapy-based counseling sessions, 
where patients 
go through a detailed and lengthy EMS questionnaire. However, such an approach is not practical in "offline" counseling scenarios, such as community QA forums which are gaining popularity for people seeking mental health support. In this paper, we 
investigate both LLM (Large Language Models) and non-LLM approaches for identifying EMS labels using resources from Schema Therapy. 
Our evaluation indicates that 
recent LLMs can be effective for identifying EMS but their predictions lack explainability and are too sensitive to precise `prompts'. 
Both LLM and non-LLM methods are unable to reliably address the {null} cases, i.e. cases with no EMS labels. However, we posit that the two approaches show complementary properties and together, 
they can be used to further devise techniques
for EMS identification.

Underline digital video library

RECORDING - A macroevolutionary analysis of European late upper palaeolithic stone tool shape using a Bayesian phylodynamic framework

RECORDING - A natural experiment of history: Ontogenetic niche construction, explorative object play, and adaptation during the Little Ice Age in Greenland

LIVE - Food-destroying versus Non-food-destroying hazards: A worldwide comparison of differential effects on culture

RECORDING - Cultural transmission, individual differences, and globalization in a Congo Basin village

LIVE - Trust-based cultural transmission can form polarising group identities

Marine Mammal Classification using CNNs

Empowering Military Intelligence with Next-Gen AI: Unleash the Power of Databricks and Large Language Models

Comparison of Optical Turbulence Prediction Models

Ocean Wave Height Estimation via Machine Learning

Synthetic data for Naval Combats using Generative Adversarial Networks

BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology | VIDEO

Identifying {Early Maladaptive Schemas} from Mental Health Question Texts

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