For datasets with low/medium number of categories we used CIFAR-10 and CIFAR-100 (Krizhevsky et al., In the finetuning experiments we used the STL-10 dataset (Coates et al., 2011) For datasets with an high number of categories we used the tiny-ImageNet and SlimageNet (Antoniou et al., We use off-the-shelf Pytorch implementations of ResNets as described in the original paper (He et al., 2016). All the methods could fit on a single one of those GPUs. This baseline consists of standard supervised training. It represents an upper bound. When evaluated for the number of augmentations (Appendix B.6) the same strategy adopted in our method (Appendix A.3) has been used to Clustering has been performed at the beginning of each epoch by using the k-means algorithm available in Scikit-learn.

Following the suggestion in [4], we doubled the weight decay parameter for WRN-28-8 to avoid overfitting. Error rates are reported on a single 250-label split from CIFAR-10. Thefirst row contains the most prototypical images of each class, while the bottom row contains the least prototypicalimages. On each step our batch contains 1024 labeled examples and 5120 unlabeled examples. OnCIFAR-100 with400labeled examples, it reduces the error rate from 49.95% to 40.14%, which is also lower than 44.28% of ReMixMatch.

This work continues the study of the relationship between sample compression schemes and statistical learning, which has been mostly investigated within the framework of binary classification. We first extend the investigation to multiclass categorization: we prove that in this case learnability is equivalent to compression of logarithmic sample size and that the uniform convergence property implies compression of constant size. We use the compressibility-learnability equivalence to show that (i) for multiclass categorization, PAC and agnostic PAC learnability are equivalent, and (ii) to derive a compactness theorem for learnability. We then consider supervised learning under general loss functions: we show that in this case, in order to maintain the compressibility-learnability equivalence, it is necessary to consider an approximate variant of compression. We use it to show that PAC and agnostic PAC are not equivalent, even when the loss function has only three values.

We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-cloud encoder with standardized preprocessing, matched sampling, and a consistent evaluation protocol on a jet classification dataset. We compare contrastive (supervised and self-supervised), masked particle modeling, and generative reconstruction objectives under a common training regimen. In addition, we introduce targeted supervised architectural modifications that achieve state-of-the-art performance on benchmark evaluations. This controlled comparison isolates the contributions of the learning objective, highlights their respective strengths and limitations, and provides reproducible baselines. We position this work as a reference point for the future development of foundation models in particle physics, enabling more transparent and robust progress across the community.

The reliability of secure graphic verification, a key anti-counterfeiting tool, is undermined by poor image acquisition on smartphones. Uncontrolled user captures of these high-entropy patterns cause high false rejection rates, creating a significant'reliability gap'. T o bridge this gap, we depart from traditional perceptual IQA and introduce a framework that predictively estimates a frame's utility for the downstream verification task. W e propose a lightweight model to predict a quality score for a video frame, determining its suitability for a resource-intensive oracle model. Our framework is validated using re-contextualized FNMR and ISRR metrics on a large-scale dataset of 32,000+ images from 105 smartphones. Furthermore, a novel cross-domain analysis on graphics from different industrial printing presses reveals a key finding: a lightweight probe on a frozen, ImageNet-pretrained network generalizes better to an unseen printing technology than a fully fine-tuned model. This provides a key insight for real-world generalization: for domain shifts from physical manufacturing, a frozen general-purpose backbone can be more robust than full fine-tuning, which can overfit to source-domain artifacts.

COCO is available for non-commercial use under the Creative Commons Attribution 4.0 (CC BY 4.0) license.

It has been dubbed'Britain's most confusing junction', thanks to its complex system of mini–roundabouts. But while many drivers struggle to navigate their way around Swindon's Magic Roundabout, the junction proved to be light work for a self–driving car. To put its Full Self Driving (FSD) mode to the test, Tesla sent a Model 3 through the complex intersection. Footage shows the car expertly navigating the roundabout – not just once, but three times – as cars continuously join from seemingly every direction. Fans have flocked to X to discuss the feat, with one calling it'superb'.

Phenotyping is the process of distinguishing groups of patients to identify different types of disease progression. A recent trend employs low-rank matrix and tensor factorization methods for their capability of dealing with multi-modal, heterogeneous, and missing data. Symptom quantification is crucial for understanding patient experiences in inflammatory bowel disease, especially in conditions such as ulcerative colitis (UC). However, patient-reported symptoms are typically noisy, subjective, and significantly more sparse than other data types. For this reason, they are usually not included in phenotyping and other machine learning methods. This paper explores the application of computational phenotyping to leverage Patient-Reported Outcomes (PROs) using a novel supervised coupled matrix-tensor factorization (SCMTF) method, which integrates temporal PROs and temporal labs with static features to predict medication persistence in ulcerative colitis. This is the first tensor-based method that is both supervised and coupled, it is the first application to the UC domain, and the first application to PROs. We use a deep learning framework that makes the model flexible and easy to train. The proposed method allows us to handle the large amount of missing data in the PROs. The best model predicts changes in medication 8 and 20 months in the future with AUCs of 0.853 and 0.803 on the test set respectively. We derive interpretable phenotypes consisting of static features and temporal features (including their temporal patterns). We show that low-rank matrix and tensor based phenotyping can be successfully applied to the UC domain and to highly missing PRO data. We identify phenotypes useful to predict medication persistence - these phenotypes include several symptom variables, showing that PROs contain relevant infromation that is usually discarded.