Reversing Event Structures

Reversible computation has attracted increasing interest in recent years. In this paper, we show how to model reversibility in concurrent computation as realised abstractly in terms of event structures. Two different forms of event structures are considered, namely event structures defined by causation and prevention relations and event structures given by an enabling relation with prevention. We then show how to reverse the two kinds of event structures, and discuss causal as well as out-of-causal order reversibility

Reversing P/T nets

Petri Nets are a well-known model of concurrency and provide an ideal setting for the study of fundamental aspects in concurrent systems. Despite their simplicity, they still lack a satisfactory causally reversible semantics. We develop such semantics for Place/Transitions Petri Nets (P/T nets) based on two observations. Firstly, a net that explicitly expresses causality and conflict among events, e.g., an occurrence net, can be straightforwardly reversed by adding reversal for each of its transitions. Secondly, the standard unfolding construction associates a P/T net with an occurrence net that preserves all of its computation. Consequently, the reversible semantics of a P/T net can be obtained as the reversible semantics of its unfolding. We show that such reversible behaviour can be expressed as a finite net whose tokens are coloured by causal histories. Colours in our encoding resemble the causal memories that are typical in reversible process calculi

MEEDNets: Medical Image Classification via Ensemble Bio-inspired Evolutionary DenseNets

Inspired by the biological evolution, this paper proposes an evolutionary synthesis mechanism to automatically evolve DenseNet towards high sparsity and efficiency for medical image classification. Unlike traditional automatic design methods, this mechanism generates a sparser offspring in each generation based on its previous trained ancestor. Concretely, we use a synaptic model to mimic biological evolution in the asexual reproduction. Each generation's knowledge is passed down to its descendant, and an environmental constraint limits the size of the descendant evolutionary DenseNet, moving the evolution process towards high sparsity. Additionally, to address the limitation of ensemble learning that requires multiple base networks to make decisions, we propose an evolution-based ensemble learning mechanism. It utilises the evolutionary synthesis scheme to generate highly sparse descendant networks, which can be used as base networks to perform ensemble learning in inference. This is specially useful in the extreme case when there is only a single network. Finally, we propose the MEEDNets (Medical Image Classification via Ensemble Bio-inspired Evolutionary DenseNets) model which consists of multiple evolutionary DenseNet-121s synthesised in the evolution process. Experimental results show that our bio-inspired evolutionary DenseNets are able to drop less important structures and compensate for the increasingly sparse architecture. In addition, our proposed MEEDNets model outperforms the state-of-the-art methods on two publicly accessible medical image datasets. All source code of this study is available at https://github.com/hengdezhu/MEEDNets.</p