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 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.

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.

Integer time series are often subject to constraints on the aggregation of the features of all occurrences of some pattern within the series. For example, the number of inflexions may be constrained, or the sum of the peak maxima, or the minimum of the valley widths. Many time-series constraints can be described by transducers. The output alphabet of such a transducer consists of symbols that denote the phases of identifying the maximal occurrences of a pattern. It was recently shown how to synthesise automatically a constraint propagator and a constraint checker from such a transducer, which however has to be designed manually from a pattern. Here we define a large class of patterns, present an algorithm for automatically generating a low-level transducer from such a high-level pattern, and prove it correct. This class covers all 20 patterns of the Time-Series Constraint Catalogue, which can now be automatically extended at will.

Automata, possibly with counters, allow many constraints to be expressed in a simple and high-level way. An automaton induces a decomposition into a conjunction of already implemented constraints. Generalised arc consistency is not generally maintained on decompositions induced by counter automata with more than one state or counter. To improve propagation of automaton-induced constraint decompositions, we use automated tools to derive loop invariants from the constraint checker corresponding to the given automaton. These loop invariants correspond to implied constraints, which can be added to the decomposition. We consider two global constraints and derive implied constraints to improve propagation even to the point of maintaining generalised arc consistency.