Geometric morphometrics (GMM) is widely used to quantify shape variation, more recently serving as input for machine learning (ML) analyses. Standard practice aligns all specimens via Generalized Procrustes Analysis (GPA) prior to splitting data into training and test sets, potentially introducing statistical dependence and contaminating downstream predictive models. Here, the effects of GPA-induced contamination are formally characterised using controlled 2D and 3D simulations across varying sample sizes, landmark densities, and allometric patterns. A novel realignment procedure is proposed, […]

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arXiv:2601.14333v1 Announce Type: new Abstract: Contextual Bandit (CB) algorithms are widely adopted for personalized recommendations but often struggle in dynamic environments typical of fantasy sports, where rapid changes in user behavior and dramatic shifts in reward distributions due to external influences necessitate frequent retraining. To address these challenges, we propose a Hierarchical Contextual Uplift Bandit framework. Our framework dynamically adjusts contextual granularity from broad, system-wide insights to detailed, user-specific contexts, using contextual similarity to facilitate effective policy transfer […]

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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:2601.20884v1 Announce Type: new Abstract: Multimodal pretraining is effective for building general-purpose representations, but in many practical deployments, only one modality is heavily used during downstream fine-tuning. Standard pretraining strategies treat all modalities uniformly, which can lead to under-optimized representations for the modality that actually matters. We propose Finetune-Informed Pretraining (FIP), a model-agnostic method that biases representation learning toward a designated target modality needed at fine-tuning time. FIP combines higher masking difficulty, stronger loss weighting, and increased decoder […]

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arXiv:2601.12300v1 Announce Type: new Abstract: Biometric payment, i.e., biometric authentication implemented in digital payment systems, can reduce memory demands and streamline payment for older adults. However, older adults’ perceptions and practices regarding biometric payment remain underexplored. We conducted semi-structured interviews with 22 Chinese older adults, including both users and non-users. Participants were motivated to use biometric payment due to convenience and perceived security. However, they also worried about loss of control due to its password-free nature and expressed […]

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arXiv:2602.10173v1 Announce Type: new Abstract: Representation in the family of 3D Gaussian Splats (3DGS) are growing into a viable alternative to traditional graphics for an expanding number of application, including recent techniques that facilitate physics simulation and animation. However, extracting usable objects from in-the-wild captures remains challenging and controllable editing techniques for this representation are limited. Unlike the bulk of emerging techniques, focused on automatic solutions or high-level editing, we introduce an interactive suite of tools centered around […]

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If you invest in crypto, you need to track Bitcoin closely, because Bitcoin drives the whole market. When BTC goes up, most altcoins follow. But the real question is: is Bitcoin still the best choice to make serious money in crypto today, or are there better opportunities with bigger upside? Bitcoin made people rich because they got in early, back when most people laughed at it and called it a bubble. The ones who understood what blockchain really […]

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Standard reward models typically predict scalar scores that fail to capture the multifaceted nature of response quality in non-verifiable domains, such as creative writing or open-ended instruction following. To address this limitation, we propose Rubric-ARM, a framework that jointly optimizes a rubric generator and a judge using reinforcement learning from preference feedback. Unlike existing methods that rely on static rubrics or disjoint training pipelines, our approach treats rubric generation as a latent action learned to maximize judgment accuracy. […]

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From April 2025 to January 2026, I worked through Frankel’s “The Geometry of Physics”. The goal wasn’t to “relearn physics”, but to rebuild a modern geometric toolbox and see which mature ideas from geometry and topology might still be underused in machine learning. The book develops a large amount of machinery—manifolds, differential forms, connections and curvature, Lie groups and algebras, bundles, gauge theory, variational principles, topology—and shows how these arise naturally across classical mechanics, electromagnetism, relativity, and quantum […]

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How baseline strength, churn, and subjectivity determine complexity The post Not All RecSys Problems Are Created Equal appeared first on Towards Data Science.

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