Veintitrés años escribiendo aquí, todos y cada uno de los días, como mínimo un artículo, muchas veces dos, a veces hasta tres, desde aquel 21 de febrero de 2003 en el que decidí empezar algo que, en aquel momento, no tenía ni la más remota idea de que se convertiría en una parte tan esencial de mi vida. Más de un tercio de mi vida escribiendo todos los días. Hoy son 10,671 artículos publicados y 259,027 comentarios, de […]
Massive internet of things microservices require integrating renewable energy harvesting into mobile edge computing (MEC) for sustainable eScience infrastructures. Spatiotemporal mismatches between stochastic task arrivals and intermittent green energy along with complex inter-user interference in multi-antenna (MIMO) uplinks complicate real-time resource management. Traditional centralized optimization and off-policy reinforcement learning struggle with scalability and signaling overhead in dense networks. This paper proposes CADDTO-PPO, a carbon-aware decentralized dynamic task offloading framework based on multi-agent proximal policy optimization. The multi-user MIMO-MEC […]
I’m checking if my understanding is correct. In an MDP, is it accurate to say that: State does NOT directly produce reward or next state. Instead, the structure is always: State → Action → (Reward, Next State) So: Immediate expected reward at state s is the average over actions of p(r | s,a) Future value is the average over actions of p(s’ | s,a) times v(s’) Meaning both reward and transition depend on (s,a), not on s alone. […]
:::tip From political coups (Trump taking in Venezuela’s Maduro) and Epstien files, to eyewatering rallies in metals, agentic AIs breeding anti-human propaganda (clawdbots aggregating on moltbook to create AI language that humans cannot decipher on moltbook) and setting records in the Yen’s Interest Rate; we are halfway into the second month of 2026, and anywhere you turn, it seems as though the sky is falling. ::: “Absolution Before Rebirth” Overdosing on the uncertainty brought on by divisive “us […]
Understanding disease-gene associations is essential for unravelling disease mechanisms and advancing diagnostics and therapeutics. Traditional approaches based on manual curation and literature review are labour-intensive and not scalable, prompting the use of machine learning on large biomedical data. In particular, graph neural networks (GNNs) have shown promise for modelling complex biological relationships. To address limitations in existing models, we propose GLaDiGAtor (Graph Learning-bAsed DIsease-Gene AssociaTiOn pRediction), a novel GNN framework with an encoder-decoder architecture for disease-gene association prediction. […]
Radio maps are essential for efficient radio resource management in future 6G and low-altitude networks. While deep learning (DL) techniques have emerged as an efficient alternative to conventional ray-tracing for radio map estimation (RME), most existing DL approaches are confined to 2D near-ground scenarios. They often fail to capture essential 3D signal propagation characteristics and antenna polarization effects, primarily due to the scarcity of 3D data and training challenges. To address these limitations, we present the RadioGen3D framework. […]
This study presents a hierarchical, network-level traffic flow control framework for mixed traffic consisting of Human-driven Vehicles (HVs), Connected and Automated Vehicles (CAVs). The framework jointly optimizes vehicle-level eco-driving behaviors and intersection-level traffic signal control to enhance overall network efficiency and decrease energy consumption. A decentralized Multi-Agent Reinforcement Learning (MARL) approach by Value Decomposition Network (VDN) manages cycle-based traffic signal control (TSC) at intersections, while an innovative Signal Phase and Timing (SPaT) prediction method integrates a Machine Learning-based […]
Unsupervised multi-view clustering (MVC) aims to partition data into meaningful groups by leveraging complementary information from multiple views without labels, yet a central challenge is to obtain a reliable shared structural signal to guide representation learning and cross-view alignment under view discrepancy and noise. Existing approaches often rely on magnitude-only affinities or early pseudo targets, which can be unstable when different views induce relations with comparable strengths but contradictory directional tendencies, thereby distorting the global spectral geometry and […]
People think that becoming a writer is easy. In some ways, it is. All you need is a pen and paper or a computer, an idea, and the determination to write. Voila, you’re a writer now. Writing is easy; becoming serious about your writing is a whole lot harder and more complex. However, why should it be? Writers should be helping each other navigate this crazy world whenever possible. So, if you’re reading this right now, then that […]
Author(s): Saif Ali Kheraj Originally published on Towards AI. How agentic AI architectures can compress decision cycles in regulated manufacturing with a practical implementation blueprint using CrewAI. Every contract pharmaceutical manufacturer faces the same operational bottleneck. After a medicine batch completes production and passes all laboratory tests, it moves into a quarantine status not because there is any defect in the drug, but because regulatory review has not yet been completed. Figure 1: Process Management by AuthorThis case […]