Emotional support dialogue systems aim to reduce help-seekers' distress and help them overcome challenges. While human values$\unicode{x2013}$core beliefs that shape an individual's priorities$\unicode{x2013}$are increasingly emphasized in contemporary psychological therapy for their role in fostering internal transformation and long-term emotional well-being, their integration into emotional support systems remains underexplored. To bridge this gap, we present a value-driven method for training emotional support dialogue systems designed to reinforce positive values in seekers. Our model learns to identify which values to reinforce at each turn and how to do so, by leveraging online support conversations from Reddit. The model demonstrated superior performance in emotional support capabilities, outperforming various baselines. Notably, it more effectively explored and elicited values from seekers. Expert assessments by therapists highlighted two key strengths of our model: its ability to validate users' challenges and its effectiveness in emphasizing positive aspects of their situations$\unicode{x2013}$both crucial elements of value reinforcement. Our work validates the effectiveness of value reinforcement for emotional support systems and establishes a foundation for future research.

In an increasingly digital world, connectivity is a necessity. Yet, nearly a third of the global population remains offline, unable to access the services vital to participating in our global digital economy and society. The Edison Alliance at the World Economic Forum has worked to change that by delivering digital connectivity and access to financial, healthcare, and education services to those who need them most. Our partnerships with governments, industries, and non-governmental organizations drive lasting systemic change. The World Economic Forum played a pivotal role in launching and guiding the Alliance's work, providing a platform for stakeholders to come together and commit to a vision with actionable ideas and plans.

Artificial intelligence (AI) methodologies hold great promise for the rapid and accurate diagnosis of coronary artery disease (CAD) from intravascular optical coherent tomography (IVOCT) images. Numerous papers have been published describing AI-based models for different diagnostic tasks, yet it remains unclear which models have potential clinical utility and have been properly validated. This systematic review considered published literature between January 2015 and February 2023 describing AI-based diagnosis of CAD using IVOCT. Our search identified 5,576 studies, with 513 included after initial screening and 35 studies included in the final systematic review after quality screening. Our findings indicate that most of the identified models are not currently suitable for clinical use, primarily due to methodological flaws and underlying biases. To address these issues, we provide recommendations to improve model quality and research practices to enhance the development of clinically useful AI products.

The Brain-Heart Interconnectome (BHI) combines neurology and cardiology but is hindered by inefficiencies in evidence synthesis, poor adherence to quality standards, and research waste. To address these challenges, we developed an AI-driven system to enhance systematic reviews in the BHI domain. The system integrates automated detection of Population, Intervention, Comparator, Outcome, and Study design (PICOS), semantic search using vector embeddings, graph-based querying, and topic modeling to identify redundancies and underexplored areas. Core components include a Bi-LSTM model achieving 87% accuracy for PICOS compliance, a study design classifier with 95.7% accuracy, and Retrieval-Augmented Generation (RAG) with GPT-3.5, which outperformed GPT-4 for graph-based and topic-driven queries. The system provides real-time updates, reducing research waste through a living database and offering an interactive interface with dashboards and conversational AI. While initially developed for BHI, the system's adaptable architecture enables its application across various biomedical fields, supporting rigorous evidence synthesis, efficient resource allocation, and informed clinical decision-making.

Alignments are a well-known process mining technique for reconciling system logs and normative process models. Evidence of certain behaviors in a real system may only be present in one representation - either a log or a model - but not in the other. Since for processes in which multiple entities, like objects and resources, are involved in the activities, their interactions affect the behavior and are therefore essential to take into account in the alignments. Additionally, both logged and modeled representations of reality may be imprecise and only partially represent some of these entities, but not all. In this paper, we introduce the concept of "relaxations" through projections for alignments to deal with partially correct models and logs. Relaxed alignments help to distinguish between trustworthy and untrustworthy content of the two representations (the log and the model) to achieve a better understanding of the underlying process and expose quality issues.

Ensembling is commonly regarded as an effective way to improve the general performance of models in machine learning, while also increasing the robustness of predictions. When it comes to algorithmic fairness, heterogeneous ensembles, composed of multiple model types, have been employed to mitigate biases in terms of demographic attributes such as sex, age or ethnicity. Moreover, recent work has shown how in multi-class problems even simple homogeneous ensembles may favor performance of the worst-performing target classes. While homogeneous ensembles are simpler to implement in practice, it is not yet clear whether their benefits translate to groups defined not in terms of their target class, but in terms of demographic or protected attributes, hence improving fairness. In this work we show how this simple and straightforward method is indeed able to mitigate disparities, particularly benefiting under-performing subgroups. Interestingly, this can be achieved without sacrificing overall performance, which is a common trade-off observed in bias mitigation strategies. Moreover, we analyzed the interplay between two factors which may result in biases: sub-group under-representation and the inherent difficulty of the task for each group. These results revealed that, contrary to popular assumptions, having balanced datasets may be suboptimal if the task difficulty varies between subgroups. Indeed, we found that a perfectly balanced dataset may hurt both the overall performance and the gap between groups. This highlights the importance of considering the interaction between multiple forces at play in fairness.

Cardiac anatomy segmentation is essential for clinical assessment of cardiac function and disease diagnosis to inform treatment and intervention. In performing segmentation, deep learning (DL) algorithms improved accuracy significantly compared to traditional image processing approaches. More recently, studies showed that enhancing DL segmentation with motion information can further improve it. A range of methods for injecting motion information has been proposed, but many of them increase the dimensionality of input images (which is computationally expensive) or have not used an optimal method to insert motion information, such as non-DL registration, non-attention-based networks or single-headed attention. Here, we present a novel, computation-efficient alternative where a novel, scalable temporal attention module (TAM) extracts temporal feature interactions multiple times and where TAM has a multi-headed, KQV projection cross-attention architecture. The module can be seamlessly integrated into a wide range of existing CNN- or Transformer-based networks, providing novel flexibility for inclusion in future implementations. Extensive evaluations on different cardiac datasets, 2D echocardiography (CAMUS), and 3D echocardiography (MITEA) demonstrate the model's effectiveness when integrated into well-established backbone networks like UNet, FCN8s, UNetR, SwinUNetR, and the recent I2UNet. We further find that the optimized TAM-enhanced FCN8s network performs well compared to contemporary alternatives. Our results confirm TAM's robustness, scalability, and generalizability across diverse datasets and backbones.

Complex Query Answering (CQA) over incomplete Knowledge Graphs (KGs) is a challenging task. Recently, a line of message-passing-based research has been proposed to solve CQA. However, they perform unsatisfactorily on negative queries and fail to address the noisy messages between variable nodes in the query graph. Moreover, they offer little interpretability and require complex query data and resource-intensive training. In this paper, we propose a Neural-Symbolic Message Passing (NSMP) framework based on pre-trained neural link predictors. By introducing symbolic reasoning and fuzzy logic, NSMP can generalize to arbitrary existential first order logic queries without requiring training while providing interpretable answers. Furthermore, we introduce a dynamic pruning strategy to filter out noisy messages between variable nodes. Experimental results show that NSMP achieves a strong performance. Additionally, through complexity analysis and empirical verification, we demonstrate the superiority of NSMP in inference time over the current state-of-the-art neural-symbolic method. Compared to this approach, NSMP demonstrates faster inference times across all query types on benchmark datasets, with speedup ranging from 2$\times$ to over 150$\times$.

We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes the hls4ml package and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python APIs. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.

Objective: This study proposes a Natural Language Processing pipeline to evaluate prescription appropriateness in Italian referrals, where reasons for prescriptions are recorded only as free text, complicating automated comparisons with guidelines. The pipeline aims to derive, for the first time, a comprehensive summary of the reasons behind these referrals and a quantification of their appropriateness. While demonstrated in a specific case study, the approach is designed to generalize to other types of examinations. Methods: Leveraging embeddings from a transformer-based model, the proposed approach clusters referral texts, maps clusters to labels, and aligns these labels with existing guidelines. We present a case study on a dataset of 496,971 referrals, consisting of all referrals for venous echocolordopplers of the lower limbs between 2019 and 2021 in the Lombardy Region. A sample of 1,000 referrals was manually annotated to validate the results. Results: The pipeline exhibited high performance for referrals' reasons (Prec=92.43%, Rec=83.28%) and excellent results for referrals' appropriateness (Prec=93.58%, Rec=91.52%) on the annotated subset. Analysis of the entire dataset identified clusters matching guideline-defined reasons - both appropriate and inappropriate - as well as clusters not addressed in the guidelines. Overall, 34.32% of referrals were marked as appropriate, 34.07% inappropriate, 14.37% likely inappropriate, and 17.24% could not be mapped to guidelines. Conclusions: The proposed pipeline effectively assessed prescription appropriateness across a large dataset, serving as a valuable tool for health authorities. Findings have informed the Lombardy Region's efforts to strengthen recommendations and reduce the burden of inappropriate referrals.