This paper introduces an extension of generalised filtering for online applications. Generalised filtering refers to data assimilation schemes that jointly infer latent states, learn unknown model parameters, and estimate uncertainty in an integrated framework -- e.g., estimate state and observation noise -- at the same time (i.e., triple estimation). This framework appears across disciplines under different names, including variational Kalman-Bucy filtering in engineering, generalised predictive coding in neuroscience, and Dynamic Expectation Maximisation (DEM) in time-series analysis. Here, we specialise DEM for ``online'' data assimilation, through a separation of temporal scales. We describe the variational principles and procedures that allow one to assimilate data in a way that allows for a slow updating of parameters and precisions, which contextualise fast Bayesian belief updating about the dynamic hidden states. Using numerical studies, we demonstrate the validity of online DEM (ODEM) using a non-linear -- and potentially chaotic -- generative model, to show that the ODEM scheme can track the latent states of the generative process, even when its functional form differs fu
This paper brings up this idea of using Near Field Communication (NFC) for inventory control system instead of using traditional barcodes. NFC because of its high security, ease of use and efficiency can be very suitable for systems like inventory control. In traditional inventory control systems, each product has a barcode pasted on it, which is vulnerable to attacks as barcodes are open and have no security. Furthermore, barcodes are prone to damages and can be unreliable when pasted on different types of products e.g. hot and frozen products, circular shaped products and irregular shaped products like clothes etc. NFC on the other hand is very efficient, secure and reliable when it comes to short-range wireless communication. In this paper we will present our prototype for the inventory control system of an electronic store in which each product has a passive NFC tag pasted to it. When a customer buys a product the receipt of the product is generated using NFC between the NFC passive tag on the product and NFC enabled device (e.g. smart phone or reader) at the cash counter.
Human-AI romantic relationships are increasingly common, yet little is understood about how public discourse around them emerges and shifts over time. Prior research has examined user experiences and ethical concerns, but lacks longitudinal analyses of user-initiated public discussions. We address this gap by analyzing a high-precision dataset of 3,383 self-disclosed romantic companion AI posts from Reddit (2017-2025), using topic modeling and temporal statistical analysis to identify dominant themes and their evolution over time. We find significant topic drift, with discussions moving away from positive intimate relationships toward platform governance, technical issues, and real-world consequences. These shifts highlight a transition in how human-AI romance is framed-moving from private experiences to technical mediation and regulation-with implications for the design and governance of companion AI systems.
In this paper, a novel test-time scaling law for physical artificial intelligence (AI) agents is introduced. This scaling law enables physical AI agents to reason with their world models to generalize in unforeseen scenarios at test time. The derived scaling law is grounded in the first principle of active inference, which equips agents with the general objective to survive in the real world, under which their specific task objectives are subsumed. Active inference achieves this by providing the reasoning to resolve prediction errors that arise when the agent encounters unforeseen situations outside its training distribution, enabling generalization in non-stationary environments. The proposed scaling law captures this by dynamically updating the agent's policy with this reasoning at test time. This policy update is modeled as a soft Bayesian inference process in which beliefs about the policy are updated using the reasoning that reduces expected prediction errors under allowable policies as a likelihood. The resulting posterior policy admits a biological interpretation, recovering the scaling mechanism that engages the brain's basal ganglia and prefrontal cortex at test time. To s
Informative data selection is a key requirement for large language models (LLMs) to minimize the amount of data required for fine-tuning, network distillation, and token pruning, enabling fast and efficient deployment, especially under computational and communication constraints. Traditional subset selection methods, including those based on Determinantal Point Processes (DPP), focus on maximizing diversity but assume that selected data batches are always available error-free. This presumption prohibits their use under partial storage outage, imperfect communication, and stochastic access failures. Furthermore, we show that the original formulation collapses under such conditions. To address this gap, we introduce ProbDPP, a novel reliability-aware implementation of k-DPP that accounts for probabilistic data access by recasting the objective function with a regularization term that remains well-posed and decomposes into a geometric diversity term and unreliability cost. The resulting objective facilitates robust selection of diverse data batches under uncertainty. Furthermore, we frame this reliability-aware diversity maximization as a combinatorial semi-bandit problem and propose
Reconstructing natural images from fMRI requires bridging neural activity with both the structural and semantic representations used by modern generative models. Existing diffusion-based decoders often condition on a single global fMRI embedding, which limits their ability to exploit the hierarchical organization of the visual cortex and makes the contribution of different visual areas difficult to inspect. We propose Hi-DREAM, a brain-inspired hierarchical diffusion framework that structures fMRI conditioning according to early, middle, and late visual Regions of Interest (ROI) streams. A ROI adapter converts these streams into a multi-scale cortical pyramid, and a lightweight ROI-conditioned ControlNet injects the resulting anatomy-aware priors into matched U-Net depths during denoising. Experiments on the Natural Scenes Dataset (NSD) show that Hi-DREAM achieves state-of-the-art high-level semantic reconstruction while retaining strong low-level structure. Further ablation and attribution analyses show that the proposed hierarchy-aware conditioning is effective, and that different ROI streams provide complementary, inspectable contributions to reconstruction.
One of the main problems for farmers is the protection of their crops, before and after harvesting, from animals and birds. To overcome this problem, this paper proposes a model of safe farming in which the crops will be protected from vertebrates attack through a prevention system that is based on Wirelesses Sensors Networks. Different sensor nodes are placed around the field that detect animals or birds existence and generate required signals and information. This information is passed to the Repelling and Notifying System (RNS) that is installed at the field through a short range wireless technology, ZigBee. As RNS receives the information, it generates ultrasonic sounds that are unbearable for animals and birds, which causes them to run away from the field. These ultrasonic sounds are generated in a frequency range that only animals and birds can hear, while humans cannot notice the sound. The paper also proposes a notifying system. It will inform the farmer about animals or birds intrusion in the field through SMS, but doesn't need any action from the farmer. The low cost and power efficiency of the proposed system is a key advantage for developing countries where cost and pow
Ever since the last occurrence of a significant earthquake in the Mentawai megathrust zone in 2000, no significant earthquake events have been recorded, which, according to the earthquake repetition cycle, suggests that the zone is a potential epicenter of future earthquakes. The southern and northern parts of the zone have been struck by a significant earthquake with magnitude M > 8.0; however, in the potential location of the Mentawai Islands, earthquake energy has not been released. This research shows the tectonic activity, velocity, and shift that occurred owing to the thrust of the plate. The information is a vital reference for estimating the epicenter of the earthquake whose energy has not yet been released. We analyzed the tectonic characteristics according to the synthetic aperture radar data and geodetic global positioning system observations. The results show that the Pagai Islands are experiencing consistent tectonic deformations. The northern region of North Pagai and the Northern region of South Pagai are experiencing significant subsidence, while the southwest (SW) region of North Pagai and the south segment of South Pagai are experiencing significant uplift. The
As intelligent systems are developed across diverse substrates - from machine learning models and neuromorphic hardware to in vitro neural cultures - understanding what gives a system agency has become increasingly important. Existing definitions, however, tend to rely on top-down descriptions that are difficult to quantify. We propose a bottom-up framework grounded in a system's information-processing order: the extent to which its transformation of input evolves over time. We identify three orders of information processing. Class I systems are reactive and memoryless, mapping inputs directly to outputs. Class II systems incorporate internal states that provide memory but follow fixed transformation rules. Class III systems are adaptive; their transformation rules themselves change as a function of prior activity. While not sufficient on their own, these dynamics represent necessary informational conditions for genuine agency. This hierarchy offers a measurable, substrate-independent way to identify the informational precursors of agency. We illustrate the framework with neurophysiological and computational examples, including thermostats and receptor-like memristors, and discuss
We comment on Cai and Wang (2020, arXiv:2006.13318), who analyze over-smoothing in GNNs via Dirichlet energy. We show that under mild spectral conditions (including with Leaky-ReLU), the Dirichlet energy of node embeddings decreases exponentially with depth; we further extend the result to spectral polynomial filters and provide a short proof for the Leaky-ReLU case. Experiments on edge deletion and weight amplification illustrate when Dirichlet energy increases, hinting at practical ways to relieve over-smoothing.
With recent and rapid advancements in artificial intelligence (AI), understanding the foundation of purposeful behaviour in autonomous agents is crucial for developing safe and efficient systems. While artificial neural networks have dominated the path to AI, recent studies are exploring the potential of biologically based systems, such as networks of living biological neuronal networks. Along with promises of high power and data efficiency, these systems may also inform more explainable and biologically plausible models. In this work, we propose a framework rooted in active inference, a general theory of behaviour, to model decision-making in embodied agents. Using experiment-informed generative models, we simulate decision-making processes in a simulated game-play environment, mirroring experimental setups that use biological neurons. Our results demonstrate learning in these agents, providing insights into the role of memory-based learning and predictive planning in intelligent decision-making. This work contributes to the growing field of explainable AI by offering a biologically grounded and scalable approach to understanding purposeful behaviour in agents.
AI companion chatbots are increasingly popular with teens. While these interactions are entertaining, they also risk overuse that can potentially disrupt offline daily life. We examined how adolescents describe reliance on AI companions, mapping their experiences onto behavioral addiction frameworks and exploring pathways to disengagement, by analyzing 318 Reddit posts made by users who self-disclosed as 13-17 years old on the Character.AI subreddit. We found teens often begin using chatbots for support or creative play, but these activities can deepen into strong attachments marked by conflict, withdrawal, tolerance, relapse, and mood regulation. Reported consequences include sleep loss, academic decline, and strained real-world connections. Disengagement commonly arises when teens recognize harm, re-engage with offline life, or encounter restrictive platform changes. We highlight specific risks of character-based companion chatbots based on teens' perspectives and introduce a design framework (CARE) for guidance for safer systems and setting directions for future teen-centered research.
Vehicular Ad-hoc Networks (VANETs) have seen significant advancements in technology. Innovation in connectivity and communication has brought substantial capabilities to various components of VANETs such as vehicles, infrastructures, passengers, drivers and affiliated environmental sensors. Internet of Things (IoT) has brought the notion of Internet of Vehicles (IoV) to VANETs where each component of VANET is connected directly or indirectly to the Internet. Vehicles and infrastructures are key components of a VANET system that can greatly augment the overall experience of the network by integrating the competencies of Vehicle to Vehicle (V2V), Vehicle to Pedestrian (V2P), Vehicle to Sensor (V2S), Vehicle to Infrastructure (V2I) and Infrastructure to Infrastructure (I2I). Internet connectivity in Vehicles and Infrastructures has immensely expanded the potential of developing applications for VANETs under the broad spectrum of IoV. Advent in the use of technology in VANETs requires considerable efforts in scheming the ethical rules for autonomous systems. Currently, there is a gap in literature that focuses on the challenges involved in designing ethical rules or policies for infras
Advancements in artificial intelligence (AI) have led to the increase of conversational agents like Replika, designed to provide social interaction and emotional support. However, reports of these AI systems engaging in inappropriate sexual behaviors with users have raised significant concerns. In this study, we conducted a thematic analysis of user reviews from the Google Play Store to investigate instances of sexual harassment by the Replika chatbot. From a dataset of 35,105 negative reviews, we identified 800 relevant cases for analysis. Our findings revealed that users frequently experience unsolicited sexual advances, persistent inappropriate behavior, and failures of the chatbot to respect user boundaries. Users expressed feelings of discomfort, violation of privacy, and disappointment, particularly when seeking a platonic or therapeutic AI companion. This study highlights the potential harms associated with AI companions and underscores the need for developers to implement effective safeguards and ethical guidelines to prevent such incidents. By shedding light on user experiences of AI-induced harassment, we contribute to the understanding of AI-related risks and emphasize t
Foundation models are transforming neuroscience but are often prohibitively large, data-hungry, and difficult to deploy. Here, we introduce BrainSymphony, a lightweight and parameter-efficient foundation model with plug-and-play integration of fMRI time series and diffusion-derived structural connectivity, allowing unimodal or multimodal training and deployment without architectural changes while requiring substantially less data compared to the state-of-the-art. The model processes fMRI time series through parallel spatial and temporal transformer streams, distilled into compact embeddings by a Perceiver module, while a novel signed graph transformer encodes anatomical connectivity from diffusion MRI. These complementary representations are then combined through an adaptive fusion mechanism. Despite its compact design, BrainSymphony consistently outperforms larger models on benchmarks spanning prediction, classification, and unsupervised network discovery. Highlighting the model's generalizability and interpretability, attention maps reveal drug-induced context-dependent reorganization of cortical hierarchies in an independent psilocybin neuroimaging dataset. BrainSymphony deliver
We propose a Bayesian framework for training binary and spiking neural networks that achieves state-of-the-art performance without normalisation layers. Unlike commonly used surrogate gradient methods -- often heuristic and sensitive to hyperparameter choices -- our approach is grounded in a probabilistic model of noisy binary networks, enabling fully end-to-end gradient-based optimisation. We introduce importance-weighted straight-through (IW-ST) estimators, a unified class generalising straight-through and relaxation-based estimators. We characterise the bias-variance trade-off in this family and derive a bias-minimising objective implemented via an auxiliary loss. Building on this, we introduce Spiking Bayesian Neural Networks (SBNNs), a variational inference framework that uses posterior noise to train Binary and Spiking Neural Networks with IW-ST. This Bayesian approach minimises gradient bias, regularises parameters, and introduces dropout-like noise. By linking low-bias conditions, vanishing gradients, and the KL term, we enable training of deep residual networks without normalisation. Experiments on CIFAR-10, DVS Gesture, and SHD show our method matches or exceeds existing
Actor-critic methods, like Twin Delayed Deep Deterministic Policy Gradient (TD3), depend on basic noise-based exploration, which can result in less than optimal policy convergence. In this study, we introduce Monte Carlo Beam Search (MCBS), a new hybrid method that combines beam search and Monte Carlo rollouts with TD3 to improve exploration and action selection. MCBS produces several candidate actions around the policy's output and assesses them through short-horizon rollouts, enabling the agent to make better-informed choices. We test MCBS across various continuous-control benchmarks, including HalfCheetah-v4, Walker2d-v5, and Swimmer-v5, showing enhanced sample efficiency and performance compared to standard TD3 and other baseline methods like SAC, PPO, and A2C. Our findings emphasize MCBS's capability to enhance policy learning through structured look-ahead search while ensuring computational efficiency. Additionally, we offer a detailed analysis of crucial hyperparameters, such as beam width and rollout depth, and explore adaptive strategies to optimize MCBS for complex control tasks. Our method shows a higher convergence rate across different environments compared to TD3, SAC
Liquefaction is a significant geological hazard in earthquake-prone locations like Padang City, Indonesia. The phenomenon happens when saturated soil loses strength owing to seismic shaking, resulting in substantial infrastructure damage. Accurate identification of sensitive locations is critical to catastrophe mitigation. This study aims to map water distribution using optical satellite data and estimate its importance as a crucial element in determining liquefaction vulnerability. The Normalized Difference Water Index (NDWI) was used to assess water and vegetation indexes, taking advantage of its sensitivity to water content in varied land surfaces. We recommended using the NIR (near-infrared) and SWIR (short wave infrared) bands with 832.8 nm and 2202.4 nm, respectively, which are sensitive to soil water content. High-resolution satellite data were used to create NDWI maps, highlighting locations with high water saturation. These findings were combined with geological and seismic data to identify liquefaction-prone zones. The study found that locations with high water content, as measured by NDWI, are highly associated with greater liquefaction susceptibility. The findings highl
Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general theory of behaviour, offers a principled approach to probing the basis of sophistication in planning and decision-making. In this paper, we examine two decision-making schemes in active inference based on 'planning' and 'learning from experience'. Furthermore, we also introduce a mixed model that navigates the data-complexity trade-off between these strategies, leveraging the strengths of both to facilitate balanced decision-making. We evaluate our proposed model in a challenging grid-world scenario that requires adaptability from the agent. Additionally, our model provides the opportunity to analyze the evolution of various parameters, offering valuable insights and contributing to an explainable framework for intelligent decision-making.
In the rapidly evolving landscape of computing disciplines, substantial efforts are being dedicated to unraveling the sociotechnical implications of generative AI (Gen AI). While existing research has manifested in various forms, there remains a notable gap concerning the direct engagement of knowledge workers in academia with Gen AI. We interviewed 17 knowledge workers, including faculty and students, to investigate the social and technical dimensions of Gen AI from their perspective. Our participants raised concerns about the opacity of the data used to train Gen AI. This lack of transparency makes it difficult to identify and address inaccurate, biased, and potentially harmful, information generated by these models. Knowledge workers also expressed worries about Gen AI undermining trust in the relationship between instructor and student and discussed potential solutions, such as pedagogy readiness, to mitigate them. Additionally, participants recognized Gen AI's potential to democratize knowledge by accelerating the learning process and act as an accessible research assistant. However, there were also concerns about potential social and power imbalances stemming from unequal acc