3,969 research outputs found
A Unifying Theory for Graph Transformation
The field of graph transformation studies the rule-based transformation of graphs. An important branch is the algebraic graph transformation tradition, in which approaches are defined and studied using the language of category theory. Most algebraic graph transformation approaches (such as DPO, SPO, SqPO, and AGREE) are opinionated about the local contexts that are allowed around matches for rules, and about how replacement in context should work exactly. The approaches also differ considerably in their underlying formal theories and their general expressiveness (e.g., not all frameworks allow duplication). This dissertation proposes an expressive algebraic graph transformation approach, called PBPO+, which is an adaptation of PBPO by Corradini et al. The central contribution is a proof that PBPO+ subsumes (under mild restrictions) DPO, SqPO, AGREE, and PBPO in the important categorical setting of quasitoposes. This result allows for a more unified study of graph transformation metatheory, methods, and tools. A concrete example of this is found in the second major contribution of this dissertation: a graph transformation termination method for PBPO+, based on decreasing interpretations, and defined for general categories. By applying the proposed encodings into PBPO+, this method can also be applied for DPO, SqPO, AGREE, and PBPO
Cyclic proof systems for modal fixpoint logics
This thesis is about cyclic and ill-founded proof systems for modal fixpoint logics, with and without explicit fixpoint quantifiers.Cyclic and ill-founded proof-theory allow proofs with infinite branches or paths, as long as they satisfy some correctness conditions ensuring the validity of the conclusion. In this dissertation we design a few cyclic and ill-founded systems: a cyclic one for the weak Grzegorczyk modal logic K4Grz, based on our explanation of the phenomenon of cyclic companionship; and ill-founded and cyclic ones for the full computation tree logic CTL* and the intuitionistic linear-time temporal logic iLTL. All systems are cut-free, and the cyclic ones for K4Grz and iLTL have fully finitary correctness conditions.Lastly, we use a cyclic system for the modal mu-calculus to obtain a proof of the uniform interpolation property for the logic which differs from the original, automata-based one
Fragments and frame classes:Towards a uniform proof theory for modal fixed point logics
This thesis studies the proof theory of modal fixed point logics. In particular, we construct proof systems for various fragments of the modal mu-calculus, interpreted over various classes of frames. With an emphasis on uniform constructions and general results, we aim to bring the relatively underdeveloped proof theory of modal fixed point logics closer to the well-established proof theory of basic modal logic. We employ two main approaches. First, we seek to generalise existing methods for basic modal logic to accommodate fragments of the modal mu-calculus. We use this approach for obtaining Hilbert-style proof systems. Secondly, we adapt existing proof systems for the modal mu-calculus to various classes of frames. This approach yields proof systems which are non-well-founded, or cyclic.The thesis starts with an introduction and some mathematical preliminaries. In Chapter 3 we give hypersequent calculi for modal logic with the master modality, building on work by Ori Lahav. This is followed by an Intermezzo, where we present an abstract framework for cyclic proofs, in which we give sufficient conditions for establishing the bounded proof property. In Chapter 4 we generalise existing work on Hilbert-style proof systems for PDL to the level of the continuous modal mu-calculus. Chapter 5 contains a novel cyclic proof system for the alternation-free two-way modal mu-calculus. Finally, in Chapter 6, we present a cyclic proof system for Guarded Kleene Algebra with Tests and take a first step towards using it to establish the completeness of an algebraic counterpart
Language integrated relational lenses
Relational databases are ubiquitous. Such monolithic databases accumulate large
amounts of data, yet applications typically only work on small portions of the data
at a time. A subset of the database defined as a computation on the underlying
tables is called a view. Querying views is helpful, but it is also desirable to update
them and have these changes be applied to the underlying database. This view
update problem has been the subject of much previous work before, but support
by database servers is limited and only rarely available.
Lenses are a popular approach to bidirectional transformations, a generalization
of the view update problem in databases to arbitrary data. However, perhaps surprisingly, lenses have seldom actually been used to implement updatable views in
databases. Bohannon, Pierce and Vaughan propose an approach to updatable views called relational lenses. However, to the best of our knowledge this
proposal has not been implemented or evaluated prior to the work reported in
this thesis.
This thesis proposes programming language support for relational lenses. Language integrated relational lenses support expressive and efficient view updates,
without relying on updatable view support from the database server. By integrating relational lenses into the programming language, application development
becomes easier and less error-prone, avoiding the impedance mismatch of having
two programming languages. Integrating relational lenses into the language poses
additional challenges. As defined by Bohannon et al. relational lenses completely
recompute the database, making them inefficient as the database scales. The
other challenge is that some parts of the well-formedness conditions are too general for implementation. Bohannon et al. specify predicates using possibly infinite
abstract sets and define the type checking rules using relational algebra.
Incremental relational lenses equip relational lenses with change-propagating semantics that map small changes to the view into (potentially) small changes
to the source tables. We prove that our incremental semantics are functionally
equivalent to the non-incremental semantics, and our experimental results show
orders of magnitude improvement over the non-incremental approach. This thesis introduces a concrete predicate syntax and shows how the required checks
are performed on these predicates and show that they satisfy the abstract predicate specifications. We discuss trade-offs between static predicates that are fully
known at compile time vs dynamic predicates that are only known during execution and introduce hybrid predicates taking inspiration from both approaches.
This thesis adapts the typing rules for relational lenses from sequential composition to a functional style of sub-expressions. We prove that any well-typed
functional relational lens expression can derive a well-typed sequential lens.
We use these additions to relational lenses as the foundation for two practical implementations: an extension of the Links functional language and a library written
in Haskell. The second implementation demonstrates how type-level computation can be used to implement relational lenses without changes to the compiler.
These two implementations attest to the possibility of turning relational lenses
into a practical language feature
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Minimum algorithm sizes for self-stabilizing gathering and related problems of autonomous mobile robots
We investigate a swarm of autonomous mobile robots in the Euclidean plane. A
robot has a function called {\em target function} to determine the destination
point from the robots' positions. All robots in the swarm conventionally take
the same target function, but there is apparent limitation in problem-solving
ability. We allow the robots to take different target functions. The number of
different target functions necessary and sufficient to solve a problem is
called the {\em minimum algorithm size} (MAS) for . We establish the MASs
for solving the gathering and related problems from {\bf any} initial
configuration, i.e., in a {\bf self-stabilizing} manner. We show, for example,
for , there is a problem such that the MAS for the
is , where is the size of swarm. The MAS for the gathering
problem is 2, and the MAS for the fault tolerant gathering problem is 3, when
robots may crash, but the MAS for the problem of gathering all
robot (including faulty ones) at a point is not solvable (even if all robots
have distinct target functions), as long as a robot may crash
Preventing Object-centric Discovery of Unsound Process Models for Object Interactions with Loops in Collaborative Systems: Extended Version
Object-centric process discovery (OCPD) constitutes a paradigm shift in
process mining. Instead of assuming a single case notion present in the event
log, OCPD can handle events without a single case notion, but that are instead
related to a collection of objects each having a certain type. The object types
constitute multiple, interacting case notions. The output of OCPD is an
object-centric Petri net, i.e. a Petri net with object-typed places, that
represents the parallel execution of multiple execution flows corresponding to
object types. Similar to classical process discovery, where we aim for
behaviorally sound process models as a result, in OCPD, we aim for soundness of
the resulting object-centric Petri nets. However, the existing OCPD approach
can result in violations of soundness. As we will show, one violation arises
for multiple interacting object types with loops that arise in collaborative
systems. This paper proposes an extended OCPD approach and proves that it does
not suffer from this violation of soundness of the resulting object-centric
Petri nets. We also show how we prevent the OCPD approach from introducing
spurious interactions in the discovered object-centric Petri net. The proposed
framework is prototypically implemented
Automated identification and behaviour classification for modelling social dynamics in group-housed mice
Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results.
This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing.
A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups.
The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup.
Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic
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A model learning based testing approach for kernel P systems
YesKernel P systems have been introduced as a unifying formalism allowing to specify, simulate and analyse various problems. Several applications of this
model have been considered and a powerful tool built in order to support their development and analysis. Testing represents an important aspect of any system analysis and correctness. In this paper we introduce for the first time a bounded test generation approach for kernel P systems by considering bounded input sequences. A learning algorithm for kernel P systems is based on learning X-machine models that are equivalent to these systems for sequences of steps up to a certain limit, â. The Lâ learning algorithm is used.
The testing approach is then devised from the inferred X-machines. The method is applied to a case study illustrating the key parts of the approach.This research was supported by the European Regional Development Fund, Competitiveness Operational Program 2014-2020 through project IDBC (code SMIS 2014+: 121512). Raluca Lefticaru, Savas Konur and Marian Gheorghe have been partially supported by the Royal Society grant IESâČR3âČ213176, 2022-2024. The work of Savas Konur is also supported by EPSRC (EP/R043787/1)
Hierarchical Classification of Design Decisions using pre-trained Language Models
Die Software-Architektur Dokumentation (SAD) ist ein integrales Artefakt eines Software Projektes. Die SAD trĂ€gt zum fortwĂ€hrenden Erfolg eines Software Projektes bei, indem sie ein gemeinsames VerstĂ€ndnis der Software Architektur gewĂ€hrleistet, wichtige Entwurfsentscheidungen dokumentiert und einer Erosion der Software vorbeugt. Um die QualitĂ€t von SADs zu verbessern und nachgelagerte Aufgaben zu unterstĂŒtzen, ist eine automatische Klassifizierung dieser Entwurfsentscheidungen erstrebenswert. In dieser Arbeit implementieren und evaluieren wir einen Ansatz zur automatischen Identifikation und Klassifizierung von Entwurfsentscheidungen auf der Grundlage einer feingranularen Taxonomie, bei der wir eine hierarchische Klassifikationsstrategie mit dem Einsatz von Transfer-Lernen durch vortrainierter Sprachmodelle kombinieren. Der Beitrag dieser Arbeit besteht darin, den Vorteil einer hierarchischen Klassifikationsstrategie fĂŒr die automatische Klassifikation von Entwurfsentscheidungen gegenĂŒber einem nicht-hierarchischen Ansatz zu untersuchen. AuĂerdem untersuchen und vergleichen wir die EffektivitĂ€t der vortrainierten Sprachmodelle RoBERTa, XLNet, BERTOverflow und GPT-3 fĂŒr diese Aufgabe. In unserer Evaluation schnitten die AnsĂ€tze mit vortrainierten Sprachmodellen im Allgemeinen besser ab als die Baseline-AnsĂ€tze. Wir konnten jedoch keinen klaren Vorteil der hierarchischen AnsĂ€tze gegenĂŒber den nicht-hierarchischen AnsĂ€tzen in Kombination mit den Sprachmodellen feststelle. Letztlich sind die Ergebnisse dieser Arbeit durch die GröĂe und das Ungleichgewicht unseres Datensatzes limitiert und erfordern daher weitere Forschung mit einem gröĂeren Datensatz
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