digitado

A “market pulse” screen is basically the tab you keep open when you don’t want to stare at charts all day. It tells you what’s moving right now, what’s unusually volatile, and which names are starting to move together. Not in a research-paper way. In a product way. The kind of feed you could drop into a media platform or investing app and have it feel instantly useful. In this tutorial, we’ll build a minimal version of that […]

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Sometimes there come a time in your career when you want to take the next step in your development, but you may not be sure what that next step is or what opportunities it will open up for you. I like the quote: Life can only be understood backwards, but it must be lived forwards. Kierkegaard On my way I decided to choose Robolectric and answer my questions about the structure of a library with an ambiguous reputation, […]

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This paper deals with some expressions of the fractional generalized Gronwall inequality when associated with both non-negative and non-positive singular kernels and establishes sharp Mittag- Leffler bounds containing different ingredients. The long-term behavior of non-autonomous fractional order systems by means of modified fractional Lyapunov theorems is analyzed. As an application, we give a few examples that use quadratic Lyapunov functions for typical fractional order systems to predict trajectories that ultimately aim to reach vector 0 as t → […]

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Large Language Models (LLMs) have materialised as revolutionary tools across various do- mains, showcasing exceptional capabilities in natural language processing and generation. However, their reliance on static pre-training data limits their ability to access up-to-date and domain-specific information. The existing research often treats augmentation strategies in isolation, and limited efforts have been made to systematically compare them through the lens of information integrity. This review focuses specifically on Retrieval-Augmented Generation (RAG) and Fine-tuning, identifying them as the two […]

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For years, the internet ran on a simple mechanic: You searched. n You scanned links. n You clicked. n You decided. That flow is no longer guaranteed. Increasingly, users are skipping the “list of links” phase entirely and moving straight to synthesized answers generated by AI systems. This is not just a UX improvement. It’s a structural shift in how information is accessed. And it introduces a new problem: If your content isn’t used to generate the answer, […]

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Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous protocols, prioritizing discrimination over temporal interpretability and calibration. This study introduces a survival-oriented benchmark for temporal dropout risk modelling using the Open University Learning Analytics Dataset (OULAD). Two harmonized arms are compared: a dynamic weekly arm, with models in person-period representation, and a comparable continuous-time arm, with an expanded roster of families — tree-based survival, parametric, and neural models. The […]

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This paper studies the probability of a double-spend attack in an Ouroboros-like Proof-of-Stake (PoS) setting when confirmation decisions must be made for a finite number of blocks. Existing security analyses of Ouroboros-family protocols are mainly asymptotic and therefore do not directly provide the attack probability for a fixed confirmation depth. We consider an analytically tractable model that allows empty slots and multiple slot leaders, and assumes fixed stake distribution within an epoch, one-block growth of the public longest […]

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Classifier-free Guidance (CFG) lets practitioners trade-off fidelity against diversity in Diffusion Models (DMs). The practicality of CFG is however hindered by DMs sampling cost. On the other hand, Consistency Models (CMs) generate images in one or a few steps, but existing guidance methods require knowledge distillation from a separate DM teacher, limiting CFG to Consistency Distillation (CD) methods. We propose Joint Flow Distribution Learning (JFDL), a lightweight alignment method enabling guidance in a pre-trained CM. With a pre-trained […]

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