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We study a particular class of greedy algorithms for combinatorial optimization problems and present a generalized version of this algorithmic pattern encompassing several previously published algorithms. We analyze the properties of the solutions produced by such algorithms and provide proofs of their optimality through the concept of lexicographic max-ordering. By presenting a unified formulation of this class of greedy algorithms, we hope to facilitate the development of new such algorithms and to provide a deeper understanding on the properties of existing ones. To illustrate the utility of our results, we present two case studies, where we study two previously published optimization algorithms and show that they can be seen as instances of the proposed general algorithmic pattern. In doing so, we provide alternative proofs of correctness for these algorithms and draw new conclusions about their properties.

Standardized microplates are used to conduct large-scale biomedical research. The design of microplate layouts plays an essential role in handling so-called plate effects, i.e., systematic variations across the geometry of a microplate. An effective layout allows us to detect and negate plate effects. The randomized placement of controls and compounds produces layouts of limited effectiveness, so specific approaches are needed. A previously developed system, PLAID, proposed a constraint satisfaction model to construct effective plate layouts. However, PLAID does not scale well with microplate dimensions. To improve on PLAID, we propose Constraint Optimization of MicroPlate Designs (COMPD), which allows for greater flexibility and higher quality of the layouts.

The Water-Energy-Food (WEF) Nexus is high-dimensional and sensitive to control inputs, such as policy changes. Constructing Nexus models to predict policy impacts is time consuming, and the temporal resolution of the available data is often coarse, limiting the use of many data-driven methods. We investigated the applicability Dynamic Mode Decomposition with control (DMDc) as a method of performing policy impact predictions. A high-resolution System Dynamics Model (SDM) of the Nexus in Latvia was used to simulate the impacts of different policies on the Nexus at annual resolution between 2000 and 2050 (m = 50 snapshots). This simulated data was used to assess how well DMDc could reconstruct policy impacts based on data alone. To obtain numerically stable DMDc models with just 50 snapshots, linear interpolation was used to artificially inflate the data to monthly resolution (m = 600). Three policies based on the SDM were tested for two different data sizes,small (n = 15 variables) and large (n = 100). With 5–15 control-policy variables specified, DMDc was able to reconstruct the impacts in both the small and large data sets for two out of the three policies with moderate accuracy (with a Nash-Sutcliffe Efficiency, NSE > 0.4). DMDc was able to capture the general trends in the data but not interannual variability. These findings suggests that DMDc shows promise for impact assessments, but policy variables have to be carefully selected. Improvements to the DMDc pipeline that could improve performance and interpretability are discussed, including data pre-processing steps, architectural changes, and modelconstraints informed by expert- or stakeholder opinion.

Microplates are widely used for high-throughput experiments but are vulnerable to systematic plate effects. Careful placement of materials on a microplate can counteract them. While some tools exist, they often rely on randomization, are proprietary, and web-based, raising privacy concerns. PLAID and COMPD are two constraint programming (CP) models that produce higher quality layouts than randomization. However, they require MiniZinc and CP expertise, with no built-in visualization. MPLACE addresses these barriers as an open-source, Python-based GUI. MPLACE streamlines the workflow with both models by providing convenient data entry, multiple layout configurations, saving, loading, and visualization capabilities.

Liquid-handling instruments are indispensable tools in modern biomedical laboratories, streamlining compound and sample management tasks with precision and efficiency. Compound dispensing from large chemical libraries divided over hundreds of microwell plates can require substantial swapping of plates, particularly if compounds from multiple source plates are to be dispensed in each destination plate. Despite robotisation, plate swapping is a time-consuming necessity for high-throughput experiments, posing a significant bottleneck. In this paper, we explore the application of constraint programming (CP) to the planning of liquid-handling tasks to minimise plate swaps in automated dispensing. We formulate the problem as a combination of a set partitioning problem and the construction of a bipartite network. We present six CP models implemented in MiniZinc and evaluate their performance on synthetic benchmarks using three state-of-the-art constraint solvers. This work highlights the potential of CP to enhance the scalability and efficiency of automated compound transfer systems.

We study a certain class of greedy algorithms for combinatorial optimization, and present a generalized version of this algorithmic pattern encompassing several previously published methods. We analyze the properties of the solutions produced by such algorithms, and provide proofs of their optimality through the concept of lexicographic max-ordering. By presenting a unified formulation of this class of greedy algorithms, we hope to facilitate the development of new such algorithms, and to provide a deeper understanding on the properties of existing algorithms.

Attaining resource security in the water, energy, food, and ecosystem (WEFE) sectors, the WEFE nexus, is paramount. This necessitates the use of quantitative modelling, which presents many challenges, as this is a complex system acting at the intersection of the physical- and social sciences. However, as WEFE data is becoming more widely available, data-driven methods of modelling this system are becoming increasingly viable. Here, we discuss two main problems in WEFE nexus modelling: system identification and control. System identification uses Machine Learning algorithms to obtain dynamical models from data and have shown promise in many disciplines with similar characteristics as the nexus. Meanwhile, control algorithms manipulate a system to achieve objectives and are becoming instrumental in shaping nexus policy. Despite the promise of these algorithms, data-driven modelling is a vast and daunting field, and so here we provide an introductory overview of this field, with emphasis on nexus applications.

In many areas of data-driven science, large datasets are generated where the individual data objects are images, matrices, or otherwise have a clear structure. However, these objects can be information-sparse, and a challenge is to efficiently find and work with the most interesting data as early as possible in an analysis pipeline. We have recently proposed a new model for big data management where the internal structure and information of the data are associated with each data object (as opposed to simple metadata). There is then an opportunity for comprehensive data management solutions to account for data-specific internal structure as well as access patterns. In this article, we explore this idea together with our recently proposed hierarchical storage management framework that uses reinforcement learning (RL) for autonomous and dynamic data placement in different tiers in a storage hierarchy. Our case-study is based on four scientific datasets: Protein translocation microscopy images, Airfoil angle of attack meshes, 1000 Genomes sequences, and Phenotypic screening images. The presented results highlight that our framework is optimal and can quickly adapt to new data access requirements. It overall reduces the data processing time, and the proposed autonomous data placement is superior compared to any static or semi-static data placement policies.

The advent of Large Language Models (LLMs) started a serious discussion among educators on how LLMs would affect, e.g., curricula, assessments, and students' competencies. Generative AI and LLMs also raised ethical questions and concerns for computing educators and professionals.This experience report presents an assignment within a course on professional competencies, including some related to ethics, that computing master's students need in their careers. For the assignment, student groups discussed the ethical process by Lennerfors et al. by analyzing a case: a fictional researcher considers whether to attend the real CHI 2024 conference in Hawaii. The tasks were (1) to participate in in-class discussions on the case, (2) to use an LLM of their choice as a discussion partner for said case, and (3) to document both discussions, reflecting on their use of the LLM.Students reported positive experiences with the LLM as a way to increase their knowledge and understanding, although some identified limitations. The LLM provided a wider set of options for action in the studied case, including unfeasible ones. The LLM would not select a course of action, so students had to choose themselves, which they saw as coherent.From the educators' perspective, there is a need for more instruction for students using LLMs: some students did not perceive the tools as such but rather as an authoritative knowledge base. Therefore, this work has implications for educators considering the use of LLMs as discussion partners or tools to practice critical thinking, especially in computing ethics education.

Microplates are indispensable in large-scale biomedical experiments but the physical location of samples and controls on the microplate can significantly affect the resulting data and quality metric values. We introduce a new method based on constraint programming for designing microplate layouts that reduces unwanted bias and limits the impact of batch effects after error correction and normalisation. We demonstrate that our method applied to dose-response experiments leads to more accurate regression curves and lower errors when estimating IC50/EC50, and for drug screening leads to increased precision, when compared to random layouts. It also reduces the risk of inflated scores from common microplate quality assessment metrics such as Z' factor and SSMD. We make our method available via a suite of tools (PLAID) including a reference constraint model, a web application, and Python notebooks to evaluate and compare designs when planning microplate experiments.