In this section, we follow the recommendations in Gebru et al. For what purpose was the dataset created? Who created the dataset ( e.g., which team, research group) and on behalf of which Who funded the creation of the dataset? What do the instances that comprise the dataset represent ( e.g., documents, photos, How many instances are there in total (of each type, if appropriate)? Parts of the dataset have been created programatically.

Unqualified images are removed as described in Appendix A.3. Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data (e.g., to

Unqualified images are removed as described in Appendix A.3. Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data (e.g., to

This chapter explores the convergence of two major developments: the rise of environmental, social, and governance (ESG) investing and the exponential growth of artificial intelligence (AI) technology. The increased demand for diverse ESG instruments, such as green and ESG-linked loans, will be aligned with the rapid growth of the global AI market, which is expected to be worth $1,394.30 billion by 2029. AI can assist in identifying and pricing climate risks, setting more ambitious ESG goals, and advancing sustainable finance decisions. However, delegating sustainable finance decisions to AI poses serious risks, and new principles and rules for AI and ESG investing are necessary to mitigate these risks. This chapter highlights the challenges associated with norm-setting initiatives and stresses the need for the fine-tuning of the principles of legitimacy, oversight and verification, transparency, and explainability. Finally, the chapter contends that integrating AI into ESG non-financial reporting necessitates a heightened sense of responsibility and the establishment of fundamental guiding principles within the spheres of AI and ESG investing.

Recent advances in clinical AI have enabled remarkable progress across many clinical domains. However, existing benchmarks and models are primarily limited to a small set of modalities and tasks, which hinders the development of large-scale multimodal methods that can make holistic assessments of patient health and well-being. To bridge this gap, we introduce Clinical Large-Scale Integrative Multimodal Benchmark (CLIMB), a comprehensive clinical benchmark unifying diverse clinical data across imaging, language, temporal, and graph modalities. CLIMB comprises 4.51 million patient samples totaling 19.01 terabytes distributed across 2D imaging, 3D video, time series, graphs, and multimodal data. Through extensive empirical evaluation, we demonstrate that multitask pretraining significantly improves performance on understudied domains, achieving up to 29% improvement in ultrasound and 23% in ECG analysis over single-task learning. Pretraining on CLIMB also effectively improves models' generalization capability to new tasks, and strong unimodal encoder performance translates well to multimodal performance when paired with task-appropriate fusion strategies. Our findings provide a foundation for new architecture designs and pretraining strategies to advance clinical AI research. Code is released at https://github.com/DDVD233/climb.

A novel framework for Bayesian structural model updating is presented in this study. The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoencoder (VAE). The method facilitates an approximation of the likelihood when dealing with a small number of observations. It is particularly suitable for high-dimensional correlated simultaneous observations applicable to various dynamic analysis models. The proposed approach was benchmarked using a numerical model of a single-story frame building with acceleration and dynamic strain measurements. Additionally, an example involving a Bayesian update of nonlinear model parameters for a three-degree-of-freedom lumped mass model demonstrates computational efficiency when compared to using the original VAE, while maintaining adequate accuracy for practical applications.

Many technical solutions are bio-inspired. Octopus-inspired robotic arms belong to continuum robots which are used in minimally invasive surgery or for technical system restoration in areas difficult-toaccess. Continuum robot missions are bounded with their motions, whereby the motion of the robots is controlled by humans via wireless communication. In case of a lost connection, robot autonomy is required. Distributed control and distributed decision-making mechanisms based on artificial intelligence approaches can be a promising solution to achieve autonomy of technical systems and to increase their resilience. However these methods are not well investigated yet. Octopuses are the living example of natural distributed intelligence but their learning and decision-making mechanisms are also not fully investigated and understood yet. Our major interest is investigating mechanisms of Distributed Artificial Intelligence as a basis for improving resilience of complex systems. We decided to use a physical continuum robot prototype that is able to perform some basic movements for our research. The idea is to research how a technical system can be empowered to combine movements into sequences of motions by itself. For the experimental investigations a suitable physical prototype has to be selected, its motion control has to be implemented and automated. In this paper, we give an overview combining different fields of research, such as Distributed Artificial Intelligence and continuum robots based on 98 publications. We provide a detailed description of the basic motion control models of continuum robots based on the literature reviewed, discuss different aspects of autonomy and give an overview of physical prototypes of continuum robots.

Hence, the use of efficient search methods has experienced a significant growth in the last years, specially for those engineering problems where there are multiple objectives that require to be simultaneously optimized (Marler and Arora, 2004). A recurrent situation in engineering is the need of jointly optimizing energy consumption, cost or time, among others. All these factors constitute a paramount concern to the expert, and represent conflicting objectives, each one having a deep impact on the final solution (Marler and Arora, 2004). Initially applied to single-objective problems, metaheuristics like evolutionary algorithms (EAs) have been successfully applied to the resolution of multi-objective problems (MOPs) in engineering, such as the design of efficient transport systems (Domínguez et al., 2014) or safe civil structures (Zavala et al., 2014). The presence of a large number of objectives has been recently pointed out as an intrinsic characteristic of engineering problems (Singh, 2016), for which the currently applied techniques might not be efficient enough. It is noteworthy that other communities are also demanding novel techniques to face increasingly complex problems, what has led to the appearance of the many-objective optimization approach(von Lücken et al., 2014; Li et al., 2015). This variant of the more general multi-objective optimization (MOO) is specifically devoted to overcome the limits of existing algorithms when problems having 4 or more objectives, known as many-objective problems (MaOPs), have to be faced. Even though each metaheuristic follows different principles to conduct the search, their adaptation to deal with either MOPs or MaOPs share some similarities, such as the presence of new diversity preservation mechanisms or the use of indicators (Li et al., 2015; Mishra et al., 2015). The resulting many-objective algorithms have proven successful in the engineering field too (Li and Hu, 2014; López-Jaimes and Coello Coello, 2014; Cheng et al., 2017), where specialized software tools have begun to appear (Hadka et al., 2015).

Design patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. Automatic methods for DP detection have become relevant but are usually based on the rigid analysis of either software metrics or specific properties of the source code. We propose GEML, a novel detection approach based on evolutionary machine learning using software properties of diverse nature. Firstly, GEML makes use of an evolutionary algorithm to extract those characteristics that better describe the DP, formulated in terms of human-readable rules, whose syntax is conformant with a context-free grammar. Secondly, a rule-based classifier is built to predict whether new code contains a hidden DP implementation. GEML has been validated over five DPs taken from a public repository recurrently adopted by machine learning studies. Then, we increase this number up to 15 diverse DPs, showing its effectiveness and robustness in terms of detection capability. An initial parameter study served to tune a parameter setup whose performance guarantees the general applicability of this approach without the need to adjust complex parameters to a specific pattern. Finally, a demonstration tool is also provided.