Despite significant advances, AI systems struggle with the frame problem: determining what information is contextually relevant from an exponentially large possibility space. We hypothesize that biological rhythms, particularly hormonal cycles, serve as natural relevance filters that could address this fundamental challenge. We develop a framework that embeds simulated menstrual and circadian cycles into Large Language Models through system prompts generated from periodic functions modeling key hormones including estrogen, testosterone, and cortisol. Across multiple state-of-the-art models, linguistic analysis reveals emotional and stylistic variations that track biological phases; sadness peaks during menstruation while happiness dominates ovulation and circadian patterns show morning optimism transitioning to nocturnal introspection. Benchmarking on SQuAD, MMLU, Hellaswag, and AI2-ARC demonstrates subtle but consistent performance variations aligning with biological expectations, including optimal function in moderate rather than extreme hormonal ranges. This methodology provides a novel approach to contextual AI while revealing how societal biases regarding gender and biology ar
This work explores the use of Surface-Enhanced Raman Spectroscopy (SERS) combined with artificial neural network (ANN) models to detect and quantify growth hormone (GH) and testosterone (TE) in the blood of Sprague Dawley (SD) rats. SERS spectra were recorded from blood samples of SD rats injected with GH, TE, both hormones, and non-injected controls using 785 nm laser excitation. The samples were mixed with silver nanoparticles (AgNPs) synthesized in distilled water, applied onto a microscope slide, and air-dried. The resulting SERS spectra displayed similar profiles with intensity variations depending on the hormone, revealing specific bands at 658, 798, 878, 914, 932, 1064, 1190, 1354, 1410, and 1658 cm-1. PCA analysis indicated time-dependent intensity changes in bands centered around 1378 (all groups), 658 and 1614 cm-1 (GH-injected rats), and others for different hormone combinations. These variations reflect subtle biochemical changes induced by hormone injections. The ANN models, trained with six PCA scores of blood spiked with various hormone concentrations, showed high accuracy, with coefficients of determination greater than 87.71% and low root mean square error (RMSE) v
This work reports the potential use of surface enhanced Raman spectroscopy (SERS) in rapid, label-free assaying of testosterone (TE) and growth hormone (GH) in whole blood. Biomarker SERS spectral bands from the two hormones (TE and GH) in intentionally spiked water for injection and in male Sprague-Dawley (SD) rat blood are reported. Abuse of the two hormones (TE and GH) singly or simultaneously is widespread and not only has prolonged side effects such as hypertension and liver failure, but their illegal use by athletes is against clean competition. Currently used highly label-dependent doping detection methods involve complex and time-consuming procedures, rendering them unsuitable for rapid analysis. In blood, the most concentration-sensitive bands (in both TE and GH), as deduced through Principal Component Analysis (PCA) and Analysis of Variance (ANOVA), were around 684 cm-1 (assigned to C-C stretching) and 1614 cm-1 (assigned to C-C stretching) in GH; and 786 cm-1 (assigned to N-H wagging), 856 cm-1 (assigned to C-C stretching), and 1490 cm-1 (assigned to CH2 bending) in TE. In addition, a characteristic variance was noted in the bands around 1510 cm-1 (attributable to CH2 st
Large Language Models have demonstrated remarkable capabilities in generating contextually relevant and grammatically correct text. However, they fundamentally lack the ability to process and respond to emotional context in a manner analogous to human emotional cognition. Current approaches to emotion modeling in NLP systems rely primarily on discrete emotion classification or simplistic sentiment analysis, which fail to capture the continuous, multi-dimensional nature of human emotional states. In this paper, we introduce HormoneT5, a novel architecture that augments transformer language models with a biologically-inspired Hormone Emotion Block that simulates the human endocrine system's role in emotional processing. Our approach computes six continuous hormone-like values through specialized per-hormone attention heads, each with orthogonally initialized learnable queries, temperature-scaled attention mechanisms, and deep output projections. These hormone values are then transformed into an emotional embedding that modulates the encoder hidden states, enabling emotionally-appropriate response generation. We propose a multi-objective training framework combining sequence-to-sequen
We review a framework for the conformal bootstrap that does not rely on positivity and treats the infinite tower of high-dimension OPE contributions to conformal correlators through dispersion relations and neural networks. We apply it to scalar thermal two-point functions on $S^1\times \mathbb R^{d-1}$. We discuss the stability properties of the relevant non-convex optimisation scheme and potential relations to recent discussions of smoothness properties in CFT correlators. We illustrate the numerical application of the method to Generalized Free Fields and 4d holographic CFTs. This is a proceedings contribution to the ``Athens Workshop in Theoretical Physics: 10th Anniversary", held at the National and Kapodistrian University of Athens on December 17-19 2025.
Longitudinal biomarker data and cross-sectional outcomes are routinely collected in modern epidemiology studies, often with the goal of informing tailored early intervention decisions. For example, hormones such as estradiol and follicle-stimulating hormone may predict changes in womens' health during the midlife. Most existing methods focus on constructing predictors from mean marker trajectories. However, subject-level biomarker variability may also provide critical information about disease risks and health outcomes. In this paper, we develop a joint model that estimates subject-level means and variances of longitudinal biomarkers to predict a cross-sectional health outcome. Simulations demonstrate excellent recovery of true model parameters. The proposed method provides less biased and more efficient estimates, relative to alternative approaches that either ignore subject-level differences in variances or perform two-stage estimation where estimated marker variances are treated as observed. Analyses of women's health data reveal larger variability of E2 or larger variability of FSH were associated with higher levels of fat mass change and higher levels of lean mass change acros
Much scholarship considers how U.S. federal agencies govern artificial intelligence (AI) through rulemaking and their own internal use policies. But agencies have an overlooked AI governance role: setting discretionary grant policy when directing billions of dollars in federal financial assistance. These dollars enable state and local entities to study, create, and use AI. This funding not only goes to dedicated AI programs, but also to grantees using AI in the course of meeting their routine grant objectives. As discretionary grantmakers, agencies guide and restrict what grant winners do -- a hidden lever for AI governance. Agencies pull this lever by setting program objectives, judging criteria, and restrictions for AI use. Using a novel dataset of over 40,000 non-defense federal grant notices of funding opportunity (NOFOs) posted to the U.S. federal grants website between 2009 and 2024, we analyze how agencies regulate the use of AI by grantees. We select records mentioning AI and review their stated goals and requirements. We find agencies promoting AI in notice narratives, shaping adoption in ways other records of grant policy might fail to capture. Of the grant opportunities
The transformation to equivalent dimensions that offers a novel approach for investigating earthquake clustering was engaged to analyze the preparatory phase of the 2020 Samos, Greece, Mw7.0 main shock. The analysis considered earthquakes that occurred between 2006 and October 2020, covering an area extended three times the length of the main rupture. Each earthquake was parameterized by its magnitude, the interevent time (interval since the previous earthquake), and the interevent spatial distance (distance between the epicenters of consecutive earthquakes). Transforming these parameters into equivalent dimensions allowed them to be directly compared. The degree of clustering was quantified using the average distance between earthquakes in this transformed parameter space, calculated within consecutive 100 events data windows. Results revealed a distinct pattern, the average distance was increasing steadily during the twelve year period before the main shock. These temporal changes in the average distance were driven by a systematic evolution of earthquake clustering in the used parameter space. Beginning from a two-cluster system, when the distance was minimal, the clustering dev
Objectives: Stress hormones have been associated with temporal discounting. Although time-discount rate is shown to be stable over a long term, no study to date examines whether individual differences in stress hormones could predict individuals' time-discount rates in the relatively distant future (e.g., six month later), which is of interest in neuroeconomics of stress-addiction association. Methods: We assessed 87 participants' salivary stress hormone (cortisol, cortisone, and alpha-amylase) levels and hyperbolic discounting of delayed rewards consisting of three magnitudes, at the time-interval of six months. For salivary steroid assays, we employed a liquid chromatography/ mass spectroscopy (LC/MS) method. The correlations between the stress hormone levels and time-discount rates were examined. Results: We observed that salivary alpha-amylase (sAA) levels were negatively associated with time-discount rates in never-smokers. Notably, salivary levels of stress steroids (i.e., cortisol and cortisone) negatively and positively related to time-discount rates in men and women, respectively, in never-smokers. Ever-smokers' discount rates were not predicted from these stress hormone l
The cellular energy sensor AMP-activated protein kinase (AMPK) is a metabolic regulator that mediates adaptation to nutritional variations in order to maintain a proper energy balance in cells. We show here that suckling-weaning and fasting-refeeding transitions in rodents are associated with changes in AMPK activation and the cellular energy state in the liver. These nutritional transitions were characterized by a metabolic switch from lipid to glucose utilization, orchestrated by modifications in glucose levels and the glucagon:insulin ratio in the bloodstream. We therefore investigated the respective roles of glucose and pancreatic hormones on AMPK activation in mouse primary hepatocytes. We found that glucose starvation transiently activates AMPK, whereas changes in glucagon and insulin levels had no impact on AMPK. Challenge of hepatocytes with metformin-induced metabolic stress strengthened both AMPK activation and cellular energy depletion limited-glucose conditions, whereas neither glucagon nor insulin altered AMPK activation. Although both insulin and glucagon induced AMPK$α$ phosphorylation at its Ser-485/491 residue, they did not affect its activity. Finally, the decreas
This paper investigates citizens' counter-strategies to the use of Artificial Intelligence (AI) by law enforcement agencies (LEAs). Based on information from three countries (Greece, Italy and Spain) we demonstrate disparities in the likelihood of ten specific counter-strategies. We further identified factors that increase the propensity for counter-strategies. Our study provides an important new perspective to societal impacts of security-focused AI applications by illustrating the conscious, strategic choices by citizens when confronted with AI capabilities for LEAs.
This study examines the effect of fiscal austerity measures on infant mortality in Greece. Austerity measures were initiated by the tripartite committee and implemented between 2010 and 2017 to counteract deep fiscal deficit and large public debt. By comparing Greece with a plausible donor pool of OECD and Mediterranean member states in the period 1991-2020, we estimate a series of missing counterfactual scenarios to evaluate the infant mortality effects of large-scale reduction in spending on health care. A series of hybrid synthetic control and difference-in-differences estimates indicate a unique and pervasive increase in infant mortality after the implementation of austerity measures. Compared to a plausible OECD and Mediterranean counterfactual scenario, pro-cyclical austerity measures are associated with derailed and permanently increased infant mortality up to the present day. Our estimates suggest that compared to a plausible counterfactual scenario, the cumulative infant mortality cost of austerity policies exceeds 10,000 infant deaths or slightly less than 850 deaths for each year of the austerity policies. Notably, mortality increases are concentrated among boys. The est
This work explores how to fine-tune large language models using prompt engineering techniques with contextual information for generating an accurate text description of the full story, ready to be forwarded to off-the-shelve speech synthesis tools. We propose to use existing computer vision and optical character recognition techniques to build a grounded context from the comic strip image content, such as panels, characters, text, reading order and the association of bubbles and characters. Then we infer character identification and generate comic book script with context-aware panel description including character's appearance, posture, mood, dialogues etc. We believe that such enriched content description can be easily used to produce audiobook and eBook with various voices for characters, captions and playing sound effects.
Women are at increased risk of bone loss during the menopausal transition; in fact, nearly 50\% of women's lifetime bone loss occurs during this time. The longitudinal relationships between estradiol (E2) and follicle-stimulating hormone (FSH), two hormones that change have characteristic changes during the menopausal transition, and bone health outcomes are complex. However, in addition to level and rate of change in E2 and FSH, variability in these hormones across the menopausal transition may be an important predictor of bone health, but this question has yet to be well explored. We introduce a joint model that characterizes individual mean estradiol (E2) trajectories and the individual residual variances and links these variances to bone health trajectories. In our application, we found that higher FSH variability was associated with declines in bone mineral density (BMD) before menopause, but this association was moderated over time after the menopausal transition. Additionally, higher mean E2, but not E2 variability, was associated with slower decreases in during the menopausal transition. We also include a simulation study that shows that naive two-stage methods often fail t
Monitoring noise pollution in urban areas in a more systematic manner has been gaining traction as a theme among the research community, especially with the rise of smart cities and the IoT. However, although it affects our everyday life in a profound way, monitoring indoor noise levels inside workplaces and public buildings has so far grabbed less of our attention. In this work, we report on noise levels data produced by an IoT infrastructure installed inside 5 school buildings in Greece. Our results indicate that such data can help to produce a more accurate picture of the conditions that students and educators experience every day, and also provide useful insights in terms of health risks and aural comfort.
This paper aims to provide insights related to the impact assessment and evaluation results from the use of CITS services in the Greek pilot of the CRoads Greece project, i.e., Attica Tollway and Egnatia Odos Tollway. The impact assessment and evaluation of the CITS services includes aspects related to user acceptance, real world pilot logs collected from the two pilots, and simulation experiments that were conducted for the impact assessment of the CITS services. The paper concludes with a roadmap and guidelines for the extended deployment of CITS services in the Greek highway and urban road networks.
The results of an alternative methodology for making predictions about the COVID-19 pandemic in Greece are presented. Instead of focusing on the various population profiles (subjected to instabilities introduced by the fitting process), this methodology focuses on the time scale that characterises the intensity and duration of the outbreak phase. Therefore, instead of predicting the peak of active cases, here their inflection point is predicted (the point where the increase of active cases stops accelerating and starts decelerating). Since the inflection point precedes the peak, this methodology can serve as an early warning of the peak. In addition, the paths between the various populations (healthy, exposed, infected, etc) that contribute the most to the outbreak phase are identified.
Greece constitutes a coastal country with a lot of geomorphologic, climatic, cultural and historic peculiarities favoring the development of many aspects of tourism. Within this framework, this article examines what are the effects of tourism in Greece and how determinative these effects are, by applying a macroscopic analysis on empirical data for the estimation of the contribution of tourism in the Greek Economy. The available data regard records of the Balance of Payments in Greece and of the major components of the Balance of the Invisible Revenues, where a measurable aspect of tourism, the Travel or Tourism Exchange, is included. At the time period of the available data (2000-2012) two events of the recent Greek history are distinguished as the most significant (the Olympic Games in the year 2004 and the economic crisis initiated in the year 2009) and their impact on the diachronic evolution in the tourism is discussed. Under an overall assessment, the analysis illustrated that tourism is a sector of the Greek economy, which is described by a significant resilience, but it seems that it has not yet been submitted to an effective developmental plan exploiting the endogenous tou
Objective: To examine the moderation effects of hormonal factors on the associations between vascular risk factors and white matter hyperintensities (WMH) in men and women, separately. Methods: WMH were automatically segmented and quantified in the UK Biobank dataset (N = 18,294). Generalised linear models were applied to examine 1) the main effects of vascular (body mass index, hip to waist ratio, pulse wave velocity, hypercholesterolemia, diabetes, hypertension, smoking status) and hormonal (testosterone levels, contraceptive pill, hormone replacement therapy, menopause) factors on WMH, and 2) the moderation effects of hormonal factors on the relationship between vascular risk factors and WMH volumes. Results: In men with testosterone levels one standard deviation (SD) higher than the mean value, increased body mass index and pulse wave velocity, and smoking were associated with higher WMH volumes. The association between body mass index and WMH was more significant in the periventricular white matter regions, whilst the relationship between pulse wave velocity and WMH was restricted to deep white matter regions. Men with low testosterone levels (one SD below the mean level) show
In recent years, the increased urbanization and industrialization has led to a rising water demand and resources, thus increasing the gap between demand and supply. Proper water distribution and forecasting of water consumption are key factors in mitigating the imbalance of supply and demand by improving operations, planning and management of water resources. To this end, in this paper, several well-known forecasting algorithms are evaluated over time series, water consumption data from Greece, a country with diverse socio-economic and urbanization issues. The forecasting algorithms are evaluated on a real-world dataset provided by the Water Supply and Sewerage Company of Greece revealing key insights about each algorithm and its use.