arXiv:2602.01000v1 Announce Type: new Abstract: Ultrasound imaging is the primary diagnostic modality for detecting Gallbladder diseases due to its non-invasive nature, affordability, and wide accessibility. However, the low resolution and speckle noise inherent to ultrasound images hinder diagnostic reliability, prompting the use of large convolutional neural networks that are difficult to deploy in routine clinical settings. In this work, we propose CortiNet, a lightweight, cortical-inspired dual-stream neural architecture for gallbladder disease diagnosis that integrates physically interpretable multi-scale signal […]

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Introduction I recently saw a question on Reddit that stopped me in my tracks: “Do you feel your work in data analysis is valuable to the organization you work for?” It is the question that haunts every data analyst. We spend hours cleaning data and building complex dashboards. We send them out into the void. And then… silence. We wonder: Is anyone actually reading this? Does this dashboard change anything? If you are just answering ad-hoc requests, the […]

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arXiv:2602.11210v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key paradigm for training software engineering (SWE) agents, but existing pipelines typically rely on per-task containers for isolation. At scale, pre-built container images incur substantial storage overhead, slow environment setup, and require container-management privileges. We propose SWE-MiniSandbox, a lightweight, container-free method that enables scalable RL training of SWE agents without sacrificing isolation. Instead of relying on per-instance containers, SWE-MiniSandbox executes each task in an isolated workspace backed […]

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arXiv:2603.08370v1 Announce Type: new Abstract: Continuous and efficient experimentation is key to the practical success of user-facing applications on the web, both through online A/B-tests and off-policy evaluation. Despite their shared objective — estimating the incremental value of a treatment — these domains often operate in isolation, utilising distinct terminologies and statistical toolkits. This paper bridges that divide by establishing a formal equivalence between their canonical variance reduction methods. We prove that the standard online Difference-in-Means estimator is […]

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:::info Authors: Feiyang Tang Bjarte M. Østvold ::: Table Of Links Abstract 1 Introduction 2 Background 3 Privacy-Relevant Methods 4 Identifying API Privacy-relevant Methods 5 Labels for Personal Data Processing 6 Process of Identifying Personal Data 7 Data-based Ranking of Privacy-relevant Methods 8 Application to Privacy Code Review 9 Related Work Conclusion, Future Work, Acknowledgement And References Abstract Privacy code review is a critical process that enables developers and legal experts to ensure compliance with data protection regulations. […]

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In modern cloud architectures, securing communication between services is paramount. While traditional TLS (Transport Layer Security) protects data in transit, mutual TLS (mTLS) takes security a step further by requiring both parties to authenticate each other. This blog post will help you understand mTLS, how it works in cloud environments, and why it’s becoming a standard practice for service-to-service communication. What is mTLS? Mutual TLS (mTLS) is a security protocol that extends standard TLS by requiring both the […]

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Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning abilities of large language models (LLMs) on mathematics and programming tasks, but standard approaches that optimize single-attempt accuracy can inadvertently suppress response diversity across repeated attempts, narrowing exploration and overlooking underrepresented strategies. We introduce UpSkill, a training time method that adapts Mutual Information Skill Learning (MISL) to LLMs for optimizing pass@k correctness. We propose a novel reward that we implement within Group Relative Policy Optimization (GRPO): a token-level […]

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We’re a small ML team for a project and we keep running into the same wall: large preprocessing jobs (think 50–100GB datasets) running on a single machine take hours, and when something fails halfway through, it’s painful. We’ve looked at Prefect, Temporal, and a few others — but they all feel like they require a full-time DevOps person to set up and maintain properly. And most of our team is focused on the models, not the infrastructure. Curious […]

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Explainable AI (XAI) has become critical as transformer-based models are deployed in high-stakes applications including healthcare, legal systems, and financial services, where opacity hinders trust and accountability. Transformers self-attention mechanisms have proven valuable for model interpretability, with attention weights successfully used to understand model focus and behavior (Xu et al., 2015); (Wiegreffe and Pinter, 2019). However, existing attention-based explanation methods rely on manually defined aggregation strategies and fixed attribution rules (Abnar and Zuidema, 2020a); (Chefer et al., 2021), […]

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There is a lot of tutorials that show how to integrate Google Sign-In in your website, but only a handful show how to integrate it in a REST API. Today I’ll be showing how you can add Google Sign-In and still manage users in your back-end. Overview Here’s a sequence diagram showing how it all works:

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