CETN: Contrast-enhanced Through Network for CTR Prediction

Click-through rate (CTR) Prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search. Most existing CTR Prediction models utilize explicit feature interactions to overcome the performance bottleneck of implicit feature interactions. Hence, deep CTR models based on parallel structures (e.g., DCN, FinalMLP, xDeepFM) have been proposed to obtain joint information from different semantic spaces. However, these parallel subcomponents lack effective supervisory signals, making it challenging to efficiently capture valuable multi-views feature interaction information in different semantic spaces. To address this issue, we propose a simple yet effective novel CTR model: Contrast-enhanced Through Network for CTR (CETN), so as to ensure the diversity and homogeneity of feature interaction information. Specifically, CETN employs product-based feature interactions and the augmentation (perturbation) concept from contrastive learning to segment different semantic spaces, each with distinct activation functions. This improves diversity in the feature interaction information captured by the model. Additionally, we introduce self-supervised signals and through connection within each semantic space to ensure the homogeneity of the captured feature interaction information. The experiments and research conducted on four real datasets demonstrate that our model consistently outperforms twenty baseline models in terms of AUC and Logloss.

CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence

Maps are fundamental medium to visualize and represent the real word in a simple and 16 philosophical way. The emergence of the 3rd wave information has made a proportion of maps are available to be generated ubiquitously, which would significantly enrich the dimensions and perspectives to understand the characteristics of the real world. However, a majority of map dataset have never been discovered, acquired and effectively used, and the map data used in many applications might not be completely fitted for the authentic demands of these applications. This challenge is emerged due to the lack of numerous well-labelled benchmark datasets for implementing the deep learning approaches into identifying complicated map content. Thus, we develop a large-scale benchmark dataset that includes well-labelled dataset for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. Furthermore, these well-labelled datasets would facilitate the state-of-the-art machine intelligence technologies to conduct map feature detection, map pattern recognition and map content retrieval. We hope our efforts would be useful for AI-enhanced cartographical applications.

FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings

External control arms (ECA) can inform the early clinical development of experimental drugs and provide efficacy evidence for regulatory approval in non-randomized settings. However, the main challenge of implementing ECA lies in accessing real-world data or historical clinical trials. Indeed, data sharing is often not feasible due to privacy considerations related to data leaving the original collection centers, along with pharmaceutical companies' competitive motives. In this paper, we leverage a privacy-enhancing technology called federated learning (FL) to remove some of the barriers to data sharing. We introduce a federated learning inverse probability of treatment weighted (IPTW) method for time-to-event outcomes called FedECA which eases the implementation of ECA by limiting patients' data exposure. We show with extensive experiments that FedECA outperforms its closest competitor, matching-adjusted indirect comparison (MAIC), in terms of statistical power and ability to balance the treatment and control groups. To encourage the use of such methods, we publicly release our code which relies on Substra, an open-source FL software with proven experience in privacy-sensitive contexts.

Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients

Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data is available. In practice, causal networks are difficult to learn and interpret, and limited to relatively small datasets. We report a more reliable and scalable causal discovery method (iMIIC), based on a general mutual information supremum principle, which greatly improves the precision of inferred causal relations while distinguishing genuine causes from putative and latent causal effects. We showcase iMIIC on synthetic and real-life healthcare data from 396,179 breast cancer patients from the US Surveillance, Epidemiology, and End Results program. More than 90\% of predicted causal effects appear correct, while the remaining unexpected direct and indirect causal effects can be interpreted in terms of diagnostic procedures, therapeutic timing, patient preference or socio-economic disparity. iMIIC's unique capabilities open up new avenues to discover reliable and interpretable causal networks across a range of research fields.