MICCAI 2026 Results [D]
Results are almost here. Good luck to everyone waiting for the final decision 🙂 submitted by /u/Sea_Muscle_4281 [link] [comments]
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digitado
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Building a Fake Solar Plant for Cybersecurity Research — Part 2
A contained honeypot impersonating a small internet-facing energy site collected 54 days of traffic – roughly 1.7 million events from 16,568 unique sources, discovered within the first hour. Most was commodity automation, but a thin tail spoke real Modbus, including 392 device-identity reads with zero write or control attempts. On ATT&CK for ICS the industrial activity maps to discovery and never reaches impact, while the SSH chain still completed: weak logins led to commodity DDoS, proxy, and backdoor […]
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Graph Clustering for Entity Resolution: Why Union-Find Breaks at Web Scale
Introduction: When engineers first encounter entity resolution, the problem looks deceptively simple: compare records, score likely matches, connect the ones above a threshold, cluster the graph. That mental model works for a whiteboard interview. It does not survive production. Because once you move from a few thousand clean records to hundreds of millions of noisy ones, entity resolution stops being a neat graph exercise and becomes something much messier: a probabilistic system, a data quality system, a correction […]
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ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization
arXiv:2606.13277v1 Announce Type: new Abstract: Recent advances in time series anomaly detection (TSAD) have highlighted the effectiveness of self-supervised classification-based approaches. These methods apply transformations to normal training samples, training a classifier to recognize transformation-specific patterns that help identify anomalies through increased classification errors. Despite their strong performance, a significant challenge is their lack of explainability, as they provide limited insight into the characteristics of flagged anomalies. To address this limitation, we propose ProtoX-AD, a prototype-based self-explainable framework […]
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Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering
arXiv:2606.13146v1 Announce Type: new Abstract: We propose a robust feature-weighted jump model for time-dependent clustering. A penalty is used to encourage smoothness of transitions over time, while robustness is achieved through the use of a Tukey’s biweight loss function. An additional parameter controls the variability of feature weights across states, allowing the model to assign state-specific relevance to each feature. We illustrate in simulation how the method accurately recovers the true cluster sequence and reliably identifies relevant features, […]
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Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression
arXiv:2606.12892v1 Announce Type: new Abstract: This study investigates semiparametric efficient estimation of causal and structural parameters in a semi-supervised setting. In our setting, unlabeled auxiliary regressors are available in addition to labeled observations consisting of outcomes and regressors. Our goal is to construct estimators of causal and structural parameters whose asymptotic variances are smaller than those of estimators constructed using only labeled data. We refer to this framework as prediction-powered causal inference (PPCI). We first derive the efficient […]
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Epistemic Uncertainty Is Not the Reducible Kind
arXiv:2606.12646v1 Announce Type: new Abstract: The standard taxonomy of predictive uncertainty defines epistemic uncertainty as the part removable by collecting more data, while the standard measure identifies it with a mutual-information term. We prove the definition and the measure are extensionally inconsistent. On an explicit construction, the measure assigns all uncertainty to the epistemic class, yet no quantity of training data reduces it. Reducibility is instead a property of the pair (uncertainty, acquisition class), and the dichotomy resolves […]
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Identifiability Without Gaussianity: Symbolic World Models and Near-Infinite Temporal Consistency
arXiv:2606.12471v1 Announce Type: new Abstract: Klindt, LeCun, and Balestriero (arXiv:2605.26379) proved that Joint-Embedding Predictive Architectures (JEPAs) achieve linear identifiability, the linear recovery of the world’s true latent variables, if and only if the world’s latent dynamics follow a Gaussian, stationary process. This Gaussian boundary implies a fundamental limit on temporal consistency: for any non-Gaussian physical system, the representation error of a statistical World Model grows monotonically with time. We prove that this limit is an artifact of the […]
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I built a CLI tool to diff robotics datasets at the episode level (so you can figure out why your imitation learning model regressed)
If you work with LeRobot, ACT, or Diffusion Policy, you know the pain. You retrain your policy and the success rate drops. DVC tells you files changed. MLflow tells you hyperparameters changed. But neither tells you what actually changed in the data at the episode level. Did a teleoperator accidentally add 50 jerky trajectories? Did the task distribution for a specific grasp drop by 75%? Did the average episode length shrink? I built EpisodeVault to solve this. It […]
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How Preply combines AI and human tutors to personalize learning
Preply uses OpenAI to launch AI-generated lesson summaries, providing personalised feedback and language learning exercises.
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