When using Crossplane to provision Azure resources from Kubernetes, authentication becomes a critical challenge. Traditional approaches using service principal secrets are insecure and operationally complex. This blog post shares how we solved Azure authentication using Workload Identity Federation across three distinct deployment scenarios: Local Development: Kind cluster with Crossplane on developer laptops CI/CD Pipeline: GitHub Actions running Kind cluster with Crossplane for automated testing Production: EKS cluster with Crossplane managing Azure infrastructure Each scenario presented unique challenges, and […]

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arXiv:2601.14492v1 Announce Type: new Abstract: Robotic strawberry harvesting is challenging under partial occlusion, where leaves induce significant geometric uncertainty and make grasp decisions based on a single deterministic shape estimate unreliable. From a single partial observation, multiple incompatible 3D completions may be plausible, causing grasps that appear feasible on one completion to fail on another. We propose an uncertainty-aware grasping pipeline for partially occluded strawberries that explicitly models completion uncertainty arising from both occlusion and learned shape reconstruction. […]

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Split Federated Learning (SFL) enables collaborative training between resource-constrained edge devices and a compute-rich server. Communication overhead is a central issue in SFL and can be mitigated with auxiliary networks. Yet, the fundamental client-side computation challenge remains, as back-propagation requires substantial memory and computation costs, severely limiting the scale of models that edge devices can support. To enable more resource-efficient client computation and reduce the client-server communication, we propose HERON-SFL, a novel hybrid optimization framework that integrates zeroth-order […]

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arXiv:2602.05639v1 Announce Type: cross Abstract: We introduce Variational Joint Embedding (VJE), a framework that synthesizes joint embedding and variational inference to enable self-supervised learning of probabilistic representations in a reconstruction-free, non-contrastive setting. Compared to energy-based predictive objectives that optimize pointwise discrepancies, VJE maximizes a symmetric conditional evidence lower bound (ELBO) for a latent-variable model defined directly on encoder embeddings. We instantiate the conditional likelihood with a heavy-tailed Student-$t$ model using a polar decomposition that explicitly decouples directional and […]

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If there’s one database that promises both performance and cost and also delivers, then it’s Amazon DynamoDB. Single digit millisecond latency, fully managed with automatic scaling, pay per use – DynamoDB is genuinely impressive. But here’s the thing, there are rarely any mentions about what happens when your data model doesn’t fit DynamoDB’s worldview, or when you discover a limitation (I would rather say understand DynamoDB architecture better) three months into production that forces a complete redesign. I’ve […]

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[Updated on 2019-05-27: add the section on Lottery Ticket Hypothesis.] If you are like me, entering into the field of deep learning with experience in traditional machine learning, you may often ponder over this question: Since a typical deep neural network has so many parameters and training error can easily be perfect, it should surely suffer from substantial overfitting. How could it be ever generalized to out-of-sample data points?

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We demonstrate deterministic localization of cryptographic hash preimages within specific layers of deep neural networks trained on information-geometric principles. Using a modified Spin-Glass architecture, MD5 and SHA-256 password preimages are consistently identified in layers ES15-ES20 with >90% accuracy for passwords and >85% for hash values. Analysis reveals linear scaling where longer passwords occupy proportionally expanded layer space, with systematic replication in higher-dimensional layers showing exact topological correspondence.Critically, independent network runs with fresh initialization maintain 41.8% information persistence across […]

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arXiv:2602.07101v1 Announce Type: new Abstract: UAV navigation in unstructured outdoor environments using passive monocular vision is hindered by the substantial visual domain gap between simulation and reality. While 3D Gaussian Splatting enables photorealistic scene reconstruction from real-world data, existing methods inherently couple static lighting with geometry, severely limiting policy generalization to dynamic real-world illumination. In this paper, we propose a novel end-to-end reinforcement learning framework designed for effective zero-shot transfer to unstructured outdoors. Within a high-fidelity simulation grounded […]

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