Analysis of Petri Nets and Transition Systems

This paper describes a stand-alone, no-frills tool supporting the analysis of (labelled) place/transition Petri nets and the synthesis of labelled transition systems into Petri nets. It is implemented as a collection of independent, dedicated algorithms which have been designed to operate modularly, portably, extensibly, and efficiently.Comment: In Proceedings ICE 2015, arXiv:1508.0459

Elastic bundles :modelling and architecting asynchronous circuits with granular rigidity

PhD ThesisIntegrated Circuit (IC) designs these days are predominantly System-on-Chips (SoCs). The complexity of designing a SoC has increased rapidly over the years due to growing process and environmental variations coupled with global clock distribution di culty. Moreover, traditional synchronous design is not apt to handle the heterogeneous timing nature of modern SoCs. As a countermeasure, the semiconductor industry witnessed a strong revival of asynchronous design principles. A new paradigm of digital circuits emerged, as a result, namely mixed synchronous-asynchronous circuits. With a wave of recent innovations in synchronous-asynchronous CAD integration, this paradigm is showing signs of commercial adoption in future SoCs mainly due to the scope for reuse of synchronous functional blocks and IP cores, and the co-existence of synchronous and asynchronous design styles in a common EDA framework. However, there is a lack of formal methods and tools to facilitate mixed synchronousasynchronous design. In this thesis, we propose a formal model based on Petri nets with step semantics to describe these circuits behaviourally. Implication of this model in the veri cation and synthesis of mixed synchronous-asynchronous circuits is studied. Till date, this paradigm has been mainly explored on the basis of Globally Asynchronous Locally Synchronous (GALS) systems. Despite decades of research, GALS design has failed to gain traction commercially. To understand its drawbacks, a simulation framework characterising the physical and functional aspects of GALS SoCs is presented. A novel method for synthesising mixed synchronous-asynchronous circuits with varying levels of rigidity is proposed. Starting with a high-level data ow model of a system which is intrinsically asynchronous, the key idea is to introduce rigidity of chosen granularity levels in the model without changing functional behaviour. The system is then partitioned into functional blocks of synchronous and asynchronous elements before being transformed into an equivalent circuit which can be synthesised using standard EDA tools

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding

Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics.

BACKGROUND: The epidermal growth factor receptor (EGFR) signaling pathway plays a key role in regulation of cellular growth and development. While highly studied, it is still not fully understood how the signal is orchestrated. One of the reasons for the complexity of this pathway is the extensive network of inter-connected components involved in the signaling. In the aim of identifying critical mechanisms controlling signal transduction we have performed extensive analysis of an executable model of the EGFR pathway using the stochastic pi-calculus as a modeling language. RESULTS: Our analysis, done through simulation of various perturbations, suggests that the EGFR pathway contains regions of functional redundancy in the upstream parts; in the event of low EGF stimulus or partial system failure, this redundancy helps to maintain functional robustness. Downstream parts, like the parts controlling Ras and ERK, have fewer redundancies, and more than 50% inhibition of specific reactions in those parts greatly attenuates signal response. In addition, we suggest an abstract model that captures the main control mechanisms in the pathway. Simulation of this abstract model suggests that without redundancies in the upstream modules, signal transduction through the entire pathway could be attenuated. In terms of specific control mechanisms, we have identified positive feedback loops whose role is to prolong the active state of key components (e.g., MEK-PP, Ras-GTP), and negative feedback loops that help promote signal adaptation and stabilization. CONCLUSIONS: The insights gained from simulating this executable model facilitate the formulation of specific hypotheses regarding the control mechanisms of the EGFR signaling, and further substantiate the benefit to construct abstract executable models of large complex biological networks.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Deep neural networks architectures from the perspective of manifold learning

Despite significant advances in the field of deep learning in ap-plications to various areas, an explanation of the learning pro-cess of neural network models remains an important open ques-tion. The purpose of this paper is a comprehensive comparison and description of neural network architectures in terms of ge-ometry and topology. We focus on the internal representation of neural networks and on the dynamics of changes in the topology and geometry of a data manifold on different layers. In this paper, we use the concepts of topological data analysis (TDA) and persistent homological fractal dimension. We present a wide range of experiments with various datasets and configurations of convolutional neural network (CNNs) architectures and Transformers in CV and NLP tasks. Our work is a contribution to the development of the important field of explainable and interpretable AI within the framework of geometrical deep learning.Comment: 11 pages, 12 figures, PRAI2023. arXiv admin note: substantial text overlap with arXiv:2204.0862

On the Expressivity of Infinite and Local Behaviour in Fragments of the pi-calculus

The pi-calculus [61] is one the most influential formalisms for modelling and analyzing the behaviour of concurrent systems. This calculus provides a language in which the structure of terms represents the structure of processes together with an operational semantics to represent computational steps. For example, the parallel composition term P | Q, which is built from the terms P and Q, represents the process that results from the parallel execution of the processes P and Q. Similarly, the restriction (v x)P represents a process P with local resource x. The replication !P can be thought of as abbreviating the parallel composition P | P | . . . of an unbounded number of P processes. As for other language-based formalisms (e.g., logic, formal grammars and the pi-calculus) a fundamental part of the research in process calculi involves the study of the expressiveness of fragments or variants of a given process calculus. In this dissertation we shall study the expressiveness of some variants of the pi-calculus focusing on the role of the terms used to represent local and infinite behaviour, namely restriction and replication. The first part of this dissertation is devoted to the expressiveness of the zero-adic variant of the (polyadic) pi-calculus, i.e., CCS with replication (CCS!) [21]. Busi et al [22] show that CCS! is Turing powerful [22]. The result is obtained by encoding Random Access Machines (RAMs) in CCS!. The encoding is said to be non-faithful because it may move from a state which can lead to termination into a divergent one which do not correspond to any configuration of the encoded RAM. I.e., the encoding is not termination preserving. In this dissertation we shall study the existence of faithful encodings into CCS! of models of computability strictly less expressive than Turing Machines. Namely, grammars of Types 1 (Context Sensitive Languages), 2 (Context Free Languages) and 3 (Regular Languages) in the Chomsky Hierarchy. We provide faithful encodings of Type 3 grammars. We show that it is impossible to provide a faithful encoding of Type 2 grammars and that termination-preserving CCS! processes can generate languages which are not Type 2. We finally conjecture that the languages generated by termination-preserving CCS! processes are Type 1 . We also observe that the encoding of RAMs [22] and several encoding of Turing-powerful formalisms in pi-calculus variants may generate an unbounded number of restrictions during the simulation of a given machine. This unboundedness arises from having restrictions under the scope of replication (or recursion) as in e.g., !(v x)P or μX.(v x)(P | X). This suggests that such an interplay between these operators is fundamental for Turing completeness. We shall also study the expressive power of restriction and its interplay with replication. We do this by considering several syntactic variants of CCS! which differ from each other in the use of restriction with respect to replication. We consider three syntactic variations of CCS! which do not allow the generation of unbounded number of restrictions: C2 is the fragment of CCS! not allowing restrictions under the scope of a replication, C3 is the restriction-free fragment of CCS!. The third variant is C4 which extends C2 with Phillips' priority guards [76]. We shall show that the use of an unboundedly many restrictions in CCS! is necessary for obtaining Turing expressiveness in the sense of Busi et al [22]. We do this by showing that there is no encoding of RAMs into C2 which preserves and reflects convergence. We also prove that up to failures equivalence, there is no encoding from CCS! into C2 nor from C2 into C3. Thus up to failures equivalence, we cannot encode a process with an unbounded number of restrictions into one with a bounded number of restrictions, nor one with a bounded number of restrictions into a restriction-free process. As lemmata for the above results we prove that convergence is decidable for C2 and that language equivalence is decidable for C3 but undecidable for C2. As corollary it follows that convergence is decidable for restriction-free CCS. Finally, we show the expressive power of priorities by providing a faithful encoding of RAMs in C4 thus bearing witness to the expressive power of Phillips' priority guards [76]. The second part of this dissertation is devoted to expressiveness of the asynchronous monadic pi-calculus, A [15, 47]. In [70] the authors studied the expressivenessn of persistence in Api [15, 47] wrt weak barbed congruence. The study is incomplete because it ignores divergence. We shall present an expressiveness study of persistence in Api wrt De Nicola and Hennessy's testing scenario which is sensitive to divergence. Following [70],,we consider Api and three sub-languages of it, each capturing one source of persistence: the persistent-input Api-calculus (PIA), the persistent-output Api-calculus (POA) and the persistent Api-calculus (PA). In [70] the authors showed encodings from Api into the semi-persistent calculi (i.e., POA and PIA) correct wrt weak barbed congruence. We show that, under some general conditions related to compositionality of the encoding and preservation of the infinite behaviour, there cannot be an encoding from Api into a (semi)-persistent calculus preserving the must testing semantics. We also prove that convergence and divergence are decidable for POA (and PA). As a consequence there is no encoding preserving and reflecting divergence or convergence from Api into POA (and PA). This study fills a gap on the expressiveness study of persistence in A in [70]

Measurements, Models, Systems and Design

531 s.

Topological Expressiveness of Neural Networks: Topology of Learning

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsGiven a neural network, how many di erent problems can it solve? This important and open question in deep learning is usually referred to as the problem of the expressive power of a neural network. Previous research has tackled this issue through statistical and geometrical methods. This work proposes a new method based on a topological perspective. Topology is the eld of mathematics aimed at describing spaces and functions through robust characterizing features. Topological Data Analysis is the young eld developed to extract topological insight from data. We rst show that topological features of the decision boundary describe the closest notion of the intrinsic complexity of a classi cation problem. These topological features divide classi cation problems into several equivalence classes. Linear-separability is an example of such a class. We establish the topological expressive power of a network architecture as the number of di erent topological classes it is able to express. Being a novel work in a young research eld, most of the thesis is devoted to developing this perspective and creating the tools required. The main objective of this thesis is to tackle neural network’s understanding in general and architecture selection in particular, through a novel approach. Our results show that topological expressiveness has a complex correlation with many features in a neural network’s architecture depending weakly on the total number of parameters. Some of our results recapitulate previous research on geometrical properties, while others are unique to this novel topological point of view, sometimes challenging previous research.Quantos problemas di erentes consegue uma dada rede neuronal resolver? Esta pergunta aberta é central no ramo de aprendizagem profunda e conhecida como o poder expressivo de uma rede neuronal. Tentativas anteriores em resolver este problema zeram-no usando métodos estatísticos ou geométricos. Este trabalho apresenta um novo método baseado numa prespectiva topologica. Topologia é o ramo de matemática responsável por descrever espaços e transformações com base em caracteristicas fundamentais. Topological Data Analysis (Análise Topológica de Dados) é o recente ramo de investigação desenvolvido para extrair conhecimento Topológico de dados. Começamos por mostrar que uma caraterização topológica da barreira de decisão é a noção mais próxima da complexidade de um problema de classi cação. Estas caracteristicas topoólicas dividas os problemas de classi cação em diversas classes de equivalência. O conjunto de problemas separaveis por uma reta são um exemplo de uma destas classes. Establecemos que a expressividade topológica de uma architectura neuronal é equivalente a quantas destas classes consegue resolver. Dado que é um método novo num ramo de investigação recente, grande parte desta tese foca-se em desenvolver esta perspectiva e em criar as ferramentas necessárias para o seu estudo. O objectivo desta dissertação é, apartir de uma abordagem original, enfrentar a falta de compreensão de redes neuronais no geral e, em particular, informar a escolha de arquitecturas. Os resultados obtidos mostram que a expressividade topológica tem correlações complexas com diversos elementos da arquitectura de uma rede, mostrando uma depêndencia ténue no número total de parametros. Alguns resultados seguem a mesma linha que a investigação gemétrica anterior, outros são únicos à perspectiva apresentada e complementando resultados anteriores

An analysis framework for CSCW systems

Software toolkits are under development to help construct applications that support group-working. Toolkit developers adopt different approaches to group-work support in order to tackle different issues and a toolkit is commonly characterised by the approach adopted. It is difficult to compare toolkits because of this lack of apparent commonality and it is difficult to decide which toolkits meet specific application requirements. [Continues.