arXiv:2601.00189v1 Announce Type: new Abstract: Mosquitos are the main transmissive agents of arboviral diseases. Manual classification of their neuronal spike patterns is very labor-intensive and expensive. Most available deep learning solutions require fully labeled spike datasets and highly preprocessed neuronal signals. This reduces the feasibility of mass adoption in actual field scenarios. To address the scarcity of labeled data problems, we propose a new Generative Adversarial Network (GAN) architecture that we call the Semi-supervised Swin-Inspired GAN (SSI-GAN). The […]

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arXiv:2601.17005v1 Announce Type: new Abstract: The reliability of survey data is crucial in supply chain decision-making, particularly when evaluating readiness for AI-driven tools such as safety stock optimization systems. However, surveys often attract low-effort or fake responses that degrade the accuracy of derived insights. This study proposes a lightweight AI-based framework for filtering unreliable survey inputs using a supervised machine learning approach. In this expanded study, a larger dataset of 99 industry responses was collected, with manual labeling […]

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Private Companies Are Becoming Strategic Targets in Global Cyber Operations In the shadowed corridors of cyberspace, where digital frontiers stretch beyond the horizon of imagination, a profound transformation has taken root. For much of the past two decades, nation-state cyber operations resembled carefully orchestrated expeditions into the fortress-like domains of government agencies, diplomatic networks, and military systems. Today, that focus has shifted like tectonic plates beneath the digital landscape. Increasingly, private organizations find themselves drawn into the swirling […]

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arXiv:2502.07396v2 Announce Type: replace-cross Abstract: The performance of the Monte Carlo sampling methods relies on the crucial choice of a proposal density. The notion of optimality is fundamental to design suitable adaptive procedures of the proposal density within Monte Carlo schemes. This work is an exhaustive review around the concept of optimality in importance sampling. Several frameworks are described and analyzed, such as the marginal likelihood approximation for model selection, the use of multiple proposal densities, a sequence […]

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For a company built on intellectual property, scale creates a familiar tension. Disney needs to produce and distribute content across many formats and audiences, while keeping tight control over rights, safety, and brand consistency. Generative AI promises speed and flexibility, but unmanaged use risks creating legal, creative, and operational drag. Disney’s agreement with OpenAI shows how a large, IP-heavy organisation is attempting to resolve that tension by putting AI inside its operating system rather than treating it as […]

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arXiv:2603.00540v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) from static instruction-followers to autonomous agents necessitates operating within complex, stateful environments to achieve precise state-transition objectives. However, this paradigm is bottlenecked by data scarcity, as existing tool-centric reverse-synthesis pipelines fail to capture the rigorous logic of real-world applications. We introduce textbf{LOGIGEN}, a logic-driven framework that synthesizes verifiable training data based on three core pillars: textbf{Hard-Compiled Policy Grounding}, textbf{Logic-Driven Forward Synthesis}, and textbf{Deterministic State Verification}. Specifically, […]

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This study addresses the challenge of long-term dependency modeling in agent behavior planning for long-horizon tasks and proposes a memory-driven agent planning framework. The method introduces hierarchical memory encoding and dynamic memory retrieval structures, enabling the agent to selectively retain and effectively utilize historical information across multiple time scales, thereby maintaining policy stability and goal consistency in complex dynamic environments. The core idea is to construct an interaction mechanism between short-term and long-term memory, where attention-guided retrieval integrates […]

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In this comprehensive study, we meticulously investigated multidimensional data analysis techniques, particularly focusing on Tucker decomposition methods, spanning the period from 2000 to 2025. Our primary objective was to discern trends, advancements, and applications of these techniques across various domains of knowledge and how they have evolved over time. An extensive corpus of 288 scientific articles related to tensor decompositions, Tucker models and applications was previously reviewed. Multivariate methods such as text mining using IraMuteq software and MANOVA-Biplot […]

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