arXiv:2601.00160v1 Announce Type: new Abstract: End-to-end automatic speech recognition has become the dominant paradigm in both academia and industry. To enhance recognition performance, the Weighted Finite-State Transducer (WFST) is widely adopted to integrate acoustic and language models through static graph composition, providing robust decoding and effective error correction. However, WFST decoding relies on a frame-by-frame autoregressive search over CTC posterior probabilities, which severely limits inference efficiency. Motivated by establishing a more principled compatibility between WFST decoding and CTC […]

Ver mais

Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quantum devices while ensuring data privacy in quantum networks. Despite recent research efforts, existing QFL frameworks struggle to achieve optimal model training performance primarily due to inherent heterogeneity in terms of (i) quantum noise where current quantum devices are subject to varying levels of noise due to varying device quality and susceptibility to quantum decoherence, […]

Ver mais

arXiv:2602.11223v1 Announce Type: new Abstract: Chronic diseases constitute the principal burden of morbidity, mortality, and healthcare costs worldwide, yet current health systems remain fragmented and predominantly reactive. Patient Medical Digital Twins (PMDTs) offer a paradigm shift: holistic, continuously updated digital counterparts of patients that integrate clinical, genomic, lifestyle, and quality-of-life data. We report early implementations of PMDTs via ontology-driven modeling and federated analytics pilots. Insights from the QUALITOP oncology study and a distributed AI platform confirm both feasibility […]

Ver mais

arXiv:2603.03325v1 Announce Type: new Abstract: Large language models (LLMs) have become integral to modern Human-AI collaboration workflows, where accurately understanding user intent serves as a crucial step for generating satisfactory responses. Context-aware intent understanding, which involves inferring user intentions from situational environments, is inherently challenging because it requires reasoning over both the immediate context and the user’s underlying motivations that drive their behavior. Moreover, existing approaches often treat intent understanding as a static recognition task, overlooking users’ accumulated […]

Ver mais

How smart contracts can encode fiscal discipline, aid conditionality, and trust Financial aid and monetary interventions in emerging markets are constrained less by intent than by execution. Capital is typically distributed through multilayered institutional pipelines involving governments, non-governmental organisations, correspondent banks, and local intermediaries. Each layer introduces delay, opacity, and opportunity for misallocation. By the time funds reach their intended recipients, their effectiveness is often diminished. These systems exhibit familiar failure modes. Disbursement delays undermine urgency. Leakage through […]

Ver mais

Low-light image enhancement aims to recover high-quality visuals from poorly illuminated inputs, yet existing methods often suffer from over-enhancement, noise amplification, and semantic inconsistency in complex scenes. In this paper, we propose SeMaNet, a novel semantic-guided framework that integrates textual priors with a hybrid Transformer-Mamba architecture for controllable and efficient low-light enhancement. Our approach begins by leveraging pre-trained CLIP to generate semantically meaningful attention maps from natural language prompts, enabling interpretable region-aware enhancement without requiring pixel-level annotations. These […]

Ver mais

The Problem Isn’t Your AI It’s What’s Around It Enterprise AI investment is accelerating at a pace that would have seemed implausible three years ago. According to McKinsey’s latest data, 72% of organizations are now using generative AI in at least one business function. Agentic AI — systems that don’t just respond but actually execute multi-step tasks — is moving from early pilots into constrained production across industries. The models are getting better. The use cases are getting […]

Ver mais

Hi I’m currently looking to use DQN to implement an ai that plays yugioh (a two player card game), but have had basically no experience with Ml. I don’t know if I am underestimating the complexity of this, given how complex yugioh is, but with how big the size of the state that needs to be fed in is, along with the number of actions that need to be mapped (possibly around 120 total possible moves, though obviously […]

Ver mais