Invariant universality for quandles and fields

We show that the embeddability relations for countable quandles and for countable fields of any given characteristic other than 2 are maximally complex in a strong sense: they are invariantly universal. This notion from the theory of Borel reducibility states that any analytic quasi-order on a standard Borel space essentially appears as the restriction of the embeddability relation to an isomorphism-invariant Borel set. As an intermediate step we show that the embeddability relation of countable quandles is a complete analytic quasi-order

LakeCC: a tool for efficiently identifying lake basins with application to palaeogeographic reconstructions of North America

Along the margins of continental ice sheets, lakes formed in isostatically depressed basins duringglacial retreat. Their shorelines and extent are sensitive to the ice margin and the glacial history of the region.Proglacial lakes, in turn, also impact the glacial isostatic adjustment due to loading, and ice dynamics by posing amarine‐like boundary condition at the ice margin. In this study we present a tool that efficiently identifies lake basinsand the corresponding maximum water level for a given ice sheet and topography reconstruction. This algorithm,called the LakeCC model, iteratively checks the whole map for a set of increasing water levels and fills isolated basinsuntil they overflow into the ocean. We apply it to the present‐day Great Lakes and the results show good agreement(∼1−4%) with measured lake volume and depth. We then apply it to two topography reconstructions of NorthAmerica between the Last Glacial Maximum and the present. The model successfully reconstructs glacial lakes suchas Lake Agassiz, Lake McConnell and the predecessors of the Great Lakes. LakeCC can be used to judge the quality ofice sheet reconstructions

Formalising extended finite state machine transition merging

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordModel inference from system traces, e.g. for analysing legacy components or generating security tests for distributed components, is a common problem. Extended Finite State Machine (EFSM) models, managing an internal data state as a set of registers, are particularly well suited for capturing the behaviour of stateful components however existing inference techniques for (E)FSMs lack the ability to infer the internal state and its update functions. In this paper, we present the underpinning formalism for an EFSM inference technique that involves the merging of transitions with updates to the internal data state. Our model is formalised in Isabelle/HOL, allowing for the machine-checked validation of transition merges and system properties

A Formal Model of Extended Finite State Machines

This is the final version. Available from AFP via the link in this recordIn this AFP entry, we provide a formalisation of extended finite state machines (EFSMs) where models are represented as finite sets of transitions between states. EFSMs execute traces to produce observable outputs. We also define various simulation and equality metrics for EFSMs in terms of traces and prove their strengths in relation to each other. Another key contribution is a framework of function definitions such that LTL properties can be phrased over EFSMs. Finally, we provide a simple example case study in the form of a drinks machine

Inference of Extended Finite State Machines

This is the final version. Available from AFP via the link in this recordIn this AFP entry, we provide a formal implementation of a state-merging technique to infer extended finite state machines (EFSMs), complete with output and update functions, from black-box traces. In particular, we define the subsumption in context relation as a means of determining whether one transition is able to account for the behaviour of another. Building on this, we define the direct subsumption relation, which lifts the subsumption in context relation to EFSM level such that we can use it to determine whether it is safe to merge a given pair of transitions. Key proofs include the conditions necessary for subsumption to occur and that subsumption and direct subsumption are preorder relations. We also provide a number of different heuristics which can be used to abstract away concrete values into registers so that more states and transitions can be merged and provide proofs of the various conditions which must hold for these abstractions to subsume their ungeneralised counterparts. A Code Generator setup to create executable Scala code is also defined

Algorithms for the self-optimisation of chemical reactions

Self-optimising chemical systems have experienced a growing momentum in recent years, with the evolution of self-optimising platforms leading to their application for reaction screening and chemical synthesis. With the desire for improved process sustainability, self-optimisation provides a cheaper, faster and greener approach to the chemical development process. The use of such platforms aims to enhance the capabilities of the researcher by removing the need for labor-intensive experimentation, allowing them to focus on more challenging tasks. The establishment of these systems have enabled opportunities for self-optimising platforms to become a key element of a laboratory’s repertoire. To enable the wider adoption of self-optimising chemical platforms, this review summarises the history of algorithmic usage in chemical reaction self-optimisation, detailing the functionality of the algorithms and their applications in a way that is accessible for chemists and highlights opportunities for the further exploitation of algorithms in chemical synthesis moving forward

Incorporating Data into EFSM Inference

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record17th International Conference, SEFM 2019 Oslo, Norway, September 18–20, 2019Models are an important way of understanding software systems. If they do not already exist, then we need to infer them from system behaviour. Most current approaches infer classical FSM models that do not consider data, thus limiting applicability. EFSMs provide a way to concisely model systems with an internal state but existing inference techniques either do not infer models which allow outputs to be computed from inputs, or rely heavily on comprehensive white-box traces that reveal the internal program state, which are often unavailable. In this paper, we present an approach for inferring EFSM models, including functions that modify the internal state. Our technique uses black-box traces which only contain information visible to an external observer of the system. We implemented our approach as a prototype