9 research outputs found
Characterization and computation of ancestors in reaction systems
AbstractIn reaction systems, preimages and nth ancestors are sets of reactants leading to the production of a target set of products in either 1 or n steps, respectively. Many computational problems on preimages and ancestors, such as finding all minimum-cardinality nth ancestors, computing their size or counting them, are intractable. In this paper, we characterize all nth ancestors using a Boolean formula that can be computed in polynomial time. Once simplified, this formula can be exploited to easily solve all preimage and ancestor problems. This allows us to directly relate the difficulty of ancestor problems to the cost of the simplification so that new insights into computational complexity investigations can be achieved. In particular, we focus on two problems: (i) deciding whether a preimage/nth ancestor exists and (ii) finding a preimage/nth ancestor of minimal size. Our approach is constructive, it aims at finding classes of reactions systems for which the ancestor problems can be solved in polynomial time, in exact or approximate way
Reversible Computation: Extending Horizons of Computing
This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first
Reversible Computation: Extending Horizons of Computing
This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first
Bio-inspired multisensory integration of social signals
Emotions understanding represents a core aspect of human communication. Our social behaviours
are closely linked to expressing our emotions and understanding others’ emotional and mental
states through social signals. Emotions are expressed in a multisensory manner, where humans
use social signals from different sensory modalities such as facial expression, vocal changes, or
body language. The human brain integrates all relevant information to create a new multisensory
percept and derives emotional meaning.
There exists a great interest for emotions recognition in various fields such as HCI, gaming,
marketing, and assistive technologies. This demand is driving an increase in research on multisensory
emotion recognition. The majority of existing work proceeds by extracting meaningful
features from each modality and applying fusion techniques either at a feature level or decision
level. However, these techniques are ineffective in translating the constant talk and feedback
between different modalities. Such constant talk is particularly crucial in continuous emotion
recognition, where one modality can predict, enhance and complete the other.
This thesis proposes novel architectures for multisensory emotions recognition inspired by
multisensory integration in the brain. First, we explore the use of bio-inspired unsupervised
learning for unisensory emotion recognition for audio and visual modalities. Then we propose
three multisensory integration models, based on different pathways for multisensory integration
in the brain; that is, integration by convergence, early cross-modal enhancement, and integration
through neural synchrony. The proposed models are designed and implemented using third generation
neural networks, Spiking Neural Networks (SNN) with unsupervised learning. The
models are evaluated using widely adopted, third-party datasets and compared to state-of-the-art
multimodal fusion techniques, such as early, late and deep learning fusion. Evaluation results
show that the three proposed models achieve comparable results to state-of-the-art supervised
learning techniques. More importantly, this thesis shows models that can translate a constant
talk between modalities during the training phase. Each modality can predict, complement and
enhance the other using constant feedback. The cross-talk between modalities adds an insight
into emotions compared to traditional fusion techniques
Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences
Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc