We speak of a \textit{computational law} when that law is intended to be enforced by software through an automated decision-making process. As digital technologies evolve to offer more solutions for public administrations, we see an ever-increasing number of computational laws. Traditionally, law is written in natural language. Computational laws, however, suffer various complications when written in natural language, such as underspecification and ambiguity which lead to a diversity of possible interpretations to be made by the coder. These could potentially result into an uneven application of the law. Thus, resorting to formal languages to write computational laws is tempting. However, writing laws in a formal language leads to further complications, for example, incomprehensibility for non-experts, lack of explicit motivation of the decisions made, or difficulties in retrieving the data leading to the outcome. In this paper, we investigate how certain legal principles fare in both scenarios: computational law written in natural language or written in formal language. We use a running example from the European Union's road transport regulation to showcase the tensions arising, and the benefits from each language.

Bag-based Multiple Instance Learning (MIL) approaches have emerged as the mainstream methodology for Whole Slide Image (WSI) classification. However, most existing methods adopt a segmented training strategy, which first extracts features using a pre-trained feature extractor and then aggregates these features through MIL. This segmented training approach leads to insufficient collaborative optimization between the feature extraction network and the MIL network, preventing end-to-end joint optimization and thereby limiting the overall performance of the model. Additionally, conventional methods typically extract features from all patches of fixed size, ignoring the multi-scale observation characteristics of pathologists. This not only results in significant computational resource waste when tumor regions represent a minimal proportion (as in the Camelyon16 dataset) but may also lead the model to suboptimal solutions. To address these limitations, this paper proposes an end-to-end multi-scale WSI classification framework that integrates multi-scale feature extraction with multiple instance learning. Specifically, our approach includes: (1) a semantic feature filtering module to reduce interference from non-lesion areas; (2) a multi-scale feature extraction module to capture pathological information at different levels; and (3) a multi-scale fusion MIL module for global modeling and feature integration. Through an end-to-end training strategy, we simultaneously optimize both the feature extractor and MIL network, ensuring maximum compatibility between them. Experiments were conducted on three cross-center datasets (DigestPath2019, BCNB, and UBC-OCEAN). Results demonstrate that our proposed method outperforms existing state-of-the-art approaches in terms of both accuracy (ACC) and AUC metrics.

Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. As the evolution of biobanks in genetics and systems biology has proved, accessing more extensive and varied data pools leads to a faster and more robust exploration and translation of results. More widespread use of federated learning may have the same impact in bioinformatics, allowing access to many combinations of genotypic, phenotypic and environmental information that are undercovered or not included in existing biobanks. This paper reviews the methodological, infrastructural and legal issues that academic and clinical institutions must address before implementing it. Finally, we provide recommendations for the reliable use of federated learning and its effective translation into clinical practice.

Randomized Controlled Trials (RCTs) are the gold standard for evaluating the effect of new medical treatments. Treatments must pass stringent regulatory conditions in order to be approved for widespread use, yet even after the regulatory barriers are crossed, real-world challenges might arise: Who should get the treatment? What is its true clinical utility? Are there discrepancies in the treatment effectiveness across diverse and under-served populations? We introduce two new objectives for future clinical trials that integrate regulatory constraints and treatment policy value for both the entire population and under-served populations, thus answering some of the questions above in advance. Designed to meet these objectives, we formulate Randomize First Augment Next (RFAN), a new framework for designing Phase III clinical trials. Our framework consists of a standard randomized component followed by an adaptive one, jointly meant to efficiently and safely acquire and assign patients into treatment arms during the trial. Then, we propose strategies for implementing RFAN based on causal, deep Bayesian active learning. Finally, we empirically evaluate the performance of our framework using synthetic and real-world semi-synthetic datasets.

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called "smart pig" in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

Large Language Models (LLMs) offer a promising alternative to traditional survey methods, potentially enhancing efficiency and reducing costs. In this study, we use LLMs to create virtual populations that answer survey questions, enabling us to predict outcomes comparable to human responses. We evaluate several LLMs-including GPT-4o, GPT-3.5, Claude 3.5-Sonnet, and versions of the Llama and Mistral models-comparing their performance to that of a traditional Random Forests algorithm using demographic data from the World Values Survey (WVS). LLMs demonstrate competitive performance overall, with the significant advantage of requiring no additional training data. However, they exhibit biases when predicting responses for certain religious and population groups, underperforming in these areas. On the other hand, Random Forests demonstrate stronger performance than LLMs when trained with sufficient data. We observe that removing censorship mechanisms from LLMs significantly improves predictive accuracy, particularly for underrepresented demographic segments where censored models struggle. These findings highlight the importance of addressing biases and reconsidering censorship approaches in LLMs to enhance their reliability and fairness in public opinion research.

The double hypothesis test (DHT) is a test that allows controlling Type I (producer) and Type II (consumer) errors. It is possible to say whether the batch has a defect rate, p, between 1.5 and 2%, or between 2 and 5%, or between 5 and 10%, and so on, until finding a required value for this probability. Using the two probabilities side by side, the Type I error for the lower probability distribution and the Type II error for the higher probability distribution, both can be controlled and minimized. It can be applied in the development or manufacturing process of a batch of components, or in the case of purchasing from a supplier, when the percentage of defects (p) is unknown, considering the technology and/or process available to obtain them. The power of the test is amplified by the joint application of the Limit of Successive Failures (LSF) related to the Renewal Theory. To enable the choice of the most appropriate algorithm for each application. Four distributions are proposed for the Bernoulli event sequence, including their computational efforts: Binomial, Binomial approximated by Poisson, and Binomial approximated by Gaussian (with two variants). Fuzzy logic rules are also applied to facilitate decision-making.

The integration of Agentic AI into scientific discovery marks a new frontier in research automation. These AI systems, capable of reasoning, planning, and autonomous decision-making, are transforming how scientists perform literature review, generate hypotheses, conduct experiments, and analyze results. This survey provides a comprehensive overview of Agentic AI for scientific discovery, categorizing existing systems and tools, and highlighting recent progress across fields such as chemistry, biology, and materials science. We discuss key evaluation metrics, implementation frameworks, and commonly used datasets to offer a detailed understanding of the current state of the field. Finally, we address critical challenges, such as literature review automation, system reliability, and ethical concerns, while outlining future research directions that emphasize human-AI collaboration and enhanced system calibration. The rapid advancements of Large Language Models (LLMs) (Touvron et al., 2023; Anil et al., 2023; Achiam et al., 2023) have opened a new era in scientific discovery, with Agentic AI systems (Kim et al., 2024; Guo et al., 2023; Wang et al., 2024; Abramovich et al., 2024) emerging as powerful tools for automating complex research workflows. Unlike traditional AI, Agentic AI systems are designed to operate with a high degree of autonomy, allowing them to independently perform tasks such as hypothesis generation, literature review, experimental design, and data analysis. These systems have the potential to significantly accelerate scientific research, reduce costs, and expand access to advanced tools across various fields, including chemistry, biology, and materials science. Recent efforts have demonstrated the potential of LLM-driven agents in supporting researchers with tasks such as literature reviews, experimentation, and report writing. Prominent frameworks, including LitSearch (Ajith et al., 2024), ResearchArena (Kang & Xiong, 2024), SciLitLLM (Li et al., 2024c), CiteME (Press et al., 2024), ResearchAgent (Baek et al., 2024) and Agent Laboratory (Schmidgall et al., 2025), have made strides in automating general research workflows, such as citation management, document discovery, and academic survey generation. However, these systems often lack the domain-specific focus and compliance-driven rigor essential for fields like biomedical domain, where the structured assessment of literature is critical for evidence synthesis.

Computer vision is a field of artificial intelligence that encompasses methods and techniques that provide machines with the ability to learn from image data. This area of computer science includes software, hardware, and imaging techniques required for such methods [1]. In this context, there are several computer vision tasks that can be solved by machines and that find applications in various areas of society, namely: engine fault diagnosis [2], astronomy [3], human-computer interface [4], object detection [5, 6], facial recognition [7], among others. In addition, several deep learning models are proposed to solve such tasks. With their architecture based on convolutional layers, Convolutional Neural Networks (CNNs) [8] dominated computer vision tasks for a few years. Recently, Transformer-based architectures, specifically Vision Transformer (ViT) [9] and Swin Transformer [10], emerged as an alternative based on self-attention layers, a mechanism that learns relationships between different image patches. Thus, Transformers have demonstrated attractive performance, often outperforming CNNs, especially on large datasets [11, 12, 13]. In 2021, Google proposed MLP-Mixer [11], a more concise visual architecture with higher inference speed than ViT. Despite its simple structure, which relies only on Multilayer Perceptron (MLP), MLP-Mixer achieves extremely competitive results, as demonstrated in Tolstikhin (2021).

This paper introduces a novel approach to Dialogue State Tracking (DST) that leverages Large Language Models (LLMs) to generate natural language descriptions of dialogue states, moving beyond traditional slot-value representations. Conventional DST methods struggle with open-domain dialogues and noisy inputs. Motivated by the generative capabilities of LLMs, our Natural Language DST (NL-DST) framework trains an LLM to directly synthesize human-readable state descriptions. We demonstrate through extensive experiments on MultiWOZ 2.1 and Taskmaster-1 datasets that NL-DST significantly outperforms rule-based and discriminative BERT-based DST baselines, as well as generative slot-filling GPT-2 DST models, in both Joint Goal Accuracy and Slot Accuracy. Ablation studies and human evaluations further validate the effectiveness of natural language state generation, highlighting its robustness to noise and enhanced interpretability. Our findings suggest that NL-DST offers a more flexible, accurate, and human-understandable approach to dialogue state tracking, paving the way for more robust and adaptable task-oriented dialogue systems.