Abstract
When the available data for a target domain is limited, transfer learning (TL) methods leverage related data-rich source domains to train and evaluate models, before deploying them on the target domain. However, most TL methods assume fixed levels of labeled and unlabeled target data, which contrasts with real-world scenarios where both data and labels arrive progressively over time. As a result, evaluations based on these static assumptions may not reflect how methods perform in practice. To support a more realistic assessment of TL methods in dynamic settings, we propose an evaluation framework that (1) simulates varying data availability over time, (2) creates multiple domains via resampling of a given dataset and (3) introduces inter-domain variability through controlled transformations, e.g., including time-dependent covariate and concept shifts. These capabilities enable the systematic simulation of a large number of variants of the experiments, providing deeper insights into how algorithms may behave when deployed. We demonstrate the usefulness of the proposed framework by performing a case study on a proprietary real-world suite of card payment datasets. To support reproducibility, we also apply the framework on the publicly available Bank Account Fraud (BAF) dataset. By providing a methodology for evaluating TL methods over time and in different data availability conditions, our framework supports a better understanding of model behavior in real-world environments, which enables more informed decisions when deploying models in new domains.

Abstract

Abstract

Abstract
Background and objectives: Timely treatment of pediatric obstructive sleep apnea (OSA) can prevent or reverse neurocognitive and cardiovascular morbidities. However, whether distinct phenotypes exist and account for divergent treatment effectiveness remains unknown. In this study, our goal is threefold: i) to define new data-driven pediatric OSA phenotypes, ii) to evaluate possible treatment effectiveness differences among them, and iii) to assess phenotypic information in predicting OSA resolution. Methods: We involved 22 sociodemographic, anthropometric, and clinical data from 464 children (5-10 years old) from the Childhood Adenotonsillectomy Trial (CHAT) database. Baseline information was used to automatically define pediatric OSA phenotypes using a new unsupervised subject-based association network. Follow-up data (7 months later) were used to evaluate the effects of the therapeutic intervention in terms of changes in the obstructive apnea-hypopnea index (OAHI) and the resolution of OSA (OAHI < 1 event per hour). An explainable artificial intelligence (XAI) approach was also developed to assess phenotypic information as OSA resolution predictor at baseline. Results: Our approach identified three OSA phenotypes (PHOSA1-PHOSA3), with PHOSA2 showing significantly lower odds of OSA recovery than PHOSA1 and PHOSA3 when treatment information was not considered (odds ratios, OR: 1.64 and 1.66, 95 % confidence intervals, CI: 1.03-2.62 and 1.01-2.69, respectively). The odds of OSA recovery were also significantly lower in PHOSA2 than in PHOSA3 when adenotonsillectomy was adopted as treatment (OR: 2.60, 95 % CI: 1.26-5.39). Our XAI approach identified 79.4 % (CI: 69.9-88.0 %) of children reaching OSA resolution after adenotonsillectomy, with a positive predictive value of 77.8 % (CI: 70.3 %-86.0 %). Conclusions: Our new subject-based association network successfully identified three clinically useful pediatric OSA phenotypes with different odds of therapeutic intervention effectiveness. Specifically, we found that children of any sex, >6 years old, overweight or obese, and with enlarged neck and waist circumference (PHOSA2) have less odds of recovering from OSA. Similarly, younger female children with no enlarged neck (PHOSA3) have higher odds of benefiting from adenotonsillectomy.

Abstract

Abstract
Training large Deep Neural Networks (DNNs) is inherently resource-intensive and time-consuming, driving significant research into high-frequency checkpointing for enhanced fault tolerance. However, the overhead associated with checkpointing can prolong overall training time. State-of-the-art (SOTA) solutions break down the checkpointing operation into smaller phases, such as snapshot and persist, and pipeline these phases with foreground training operations. Additionally, some strategies leverage faster dynamic or persistent memory devices to minimize overheads. Despite these advancements, SOTA methods still struggle to eliminate training stalls, primarily due to two critical factors: (i) a bandwidth-limited PCIe bus and (ii) consistency requirements for copying the entire model state from the GPU. We propose Fragment — a novel checkpointing solution that relaxes model consistency requirements for fault tolerance, strategically divides the model state into multiple independent pieces (referred to as fragments ), decreases checkpointing overheads, and increases checkpointing frequency, all without significantly impacting model accuracy. Partitioning complex models into fragments creates challenges that we address by ensuring the integrity of the model state during both creation and restoration, while optimizing the selection of layers. Fragment reduces checkpointing overheads by 15% to 94% while decreasing the total data checkpointed by 43% to 80%, without compromising fault-tolerance needs compared to SOTA solutions. Fragment can also reduce recovery time by up to 97% upon each failure recovery, in the worst case.