Effective demand forecasting is crucial for reducing food waste. However, data privacy concerns often hinder collaboration among retailers, limiting the potential for improved predictive accuracy. In this study, we explore the application of Federated Learning (FL) in Sustainable Supply Chain Management (SSCM), with a focus on the grocery retail sector dealing with perishable goods. We develop a baseline predictive model for demand forecasting and waste assessment in an isolated retailer scenario. Subsequently, we introduce a Blockchain-based FL model, […]

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arXiv:2602.05036v1 Announce Type: new Abstract: Can multi-task self-supervised learning on graphs be coordinated without the usual tug-of-war between objectives? Graph self-supervised learning (SSL) offers a growing toolbox of pretext objectives: mutual information, reconstruction, contrastive learning; yet combining them reliably remains a challenge due to objective interference and training instability. Most multi-pretext pipelines use per-update mixing, forcing every parameter update to be a compromise, leading to three failure modes: Disagreement (conflict-induced negative transfer), Drift (nonstationary objective utility), and Drought […]

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arXiv:2502.18709v3 Announce Type: replace-cross Abstract: Online structured prediction is a task of sequentially predicting outputs with complex structures based on inputs and past observations, encompassing online classification. Recent studies showed that in the full-information setting, we can achieve finite bounds on the textit{surrogate regret}, textit{i.e.,}~the extra target loss relative to the best possible surrogate loss. In practice, however, full-information feedback is often unrealistic as it requires immediate access to the whole structure of complex outputs. Motivated by this, […]

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Amazon Nova AI Challenge returns with Nova Forge access for competing teams For the first time in an academic competition, students can customize frontier AI models to build trusted software agents Machine learning Staff writer February 02, 02:53 PM February 02, 02:59 PM The Amazon Nova AI Challenge is back for its second year. As ten selected university teams from around the world gather in Seattle this week for bootcamp (February 2-4), they’re preparing to tackle a real-world […]

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arXiv:2601.19934v1 Announce Type: new Abstract: Large language models (LLMs) are widely used for tasks ranging from summarisation to decision support. In practice, identical prompts do not always produce identical outputs, even when temperature and other decoding parameters are fixed. In this work, we conduct repeated-run experiments to empirically quantify baseline behavioural drift, defined as output variability observed when the same prompt is issued multiple times under operator-free conditions. We evaluate two publicly accessible models, gpt-4o-mini and llama3.1-8b, across […]

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Tokens are discrete representations that allow modern deep learning to scale by transforming high-dimensional data into sequences that can be efficiently learned, generated, and generalized to new tasks. These have become foundational for image and video generation and, more recently, physical simulation. As existing tokenizers are designed for the explicit requirements of realistic visual perception of images, it is necessary to ask whether these approaches are optimal for scientific images, which exhibit a large dynamic range and require […]

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arXiv:2602.02602v1 Announce Type: new Abstract: 3D content acquisition and creation are expanding rapidly in the new era of machine learning and AI. 3D Gaussian Splatting (3DGS) has become a promising high-fidelity and real-time representation for 3D content. Similar to the initial wave of digital audio-visual content at the turn of the millennium, the demand for intellectual property protection is also increasing, since explicit and editable 3D parameterization makes unauthorized use and dissemination easier. In this position paper, we […]

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arXiv:2601.16243v1 Announce Type: new Abstract: Topological transitivity is a fundamental notion in topological dynamics and is widely regarded as a basic indicator of global dynamical complexity. For general cellular automata, topological transitivity is known to be undecidable. By contrast, positive decidability results have been established for one-dimensional group cellular automata over abelian groups, while the extension to higher dimensions and to non-abelian groups has remained an open problem. In this work, we settle this problem by proving that […]

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Table of links Abstract 1 Introduction 2 Background and Related Work Software Security Coding Weaknesses Security Shift-Left Modern Code Review Code Review for Software Security Security Concern Handling Process in Code Review 3 Motivating Examples 4 Case Study Design Research Questions Studied Projects Data Collection Coding Weakness Taxonomy Study Overview Security Concern Identification Approach (RQ1) Alignment Analysis of Known Vulnerabilities (RQ2) Handling Process Identification (RQ3) 5 Preliminary Analysis PA1: Prevalence of Coding Weakness Comments PA2: Preliminary Evaluation of […]

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