This paper introduces a unified theoretical perspective that views deep generative models as probability transformation functions. Despite the apparent differences in architecture and training methodologies among various types of generative models - autoencoders, autoregressive models, generative adversarial networks, normalizing flows, diffusion models, and flow matching - we demonstrate that they all fundamentally operate by transforming simple predefined distributions into complex target data distributions. This unifying perspective facilitates the transfer of methodological improvements between model architectures and provides a foundation for developing universal theoretical approaches, potentially leading to more efficient and effective generative modeling techniques.
In recent decades, numerous algorithms have been proposed for extracting tables from PDF documents, with most designed as general-purpose table extractors. This paper introduces a novel algorithm specifically tailored for extracting order tables, addressing specific issues not covered by existing general-purpose extractors. Our approach, named OTE, identifies leading rows in an order table through a clustering algorithm and employs heuristics to recognize additional row lines and annotate columns. Through an evaluation of 115 order documents from customers of a medium-sized company, we demonstrate that: i) OTE surpasses general-purpose extractors, ii) accurately identifies over 95
This article discusses the importance of effective risk management in Agile projects. The Agile project management philosophy emphasizes open communication, flexibility in product development, and rapid market delivery. Unlike traditional waterfall methods, Agile iterative approach reduces risks by allowing on-going progress assessment. However, Agile alone may not effectively manage risks, especially those stemming from human factors and environmental influences. Existing project management tools lack comprehensive risk identification and management solutions, particularly concerning diverse project components and stakeholder engagement. Decentralization in Agile projects can lead to architectural risks due to documentation inconsistencies and client disengagement. To address these challenges, this article proposes a methodology for identifying risks of Agile project associated with artifacts and attributes of it. By regularly updating attributes' data, potential risks can be unveiled and addressed early, enhancing project team effectiveness and stakeholder clarity through transparent reporting.
