ABSTRACT Reaction systems are a formal framework for modeling processes driven by biochemical rea... more ABSTRACT Reaction systems are a formal framework for modeling processes driven by biochemical reactions. They are based on the mechanisms of facilitation and inhibition. A main assumption is that if a resource is available, then it is present in sufficient amounts and as such, several reactions using the same resource will not compete concurrently against each other; this makes reaction systems very different as a modeling framework than traditional frameworks such as ODEs or continuous time Markov chains. We demonstrate in this paper that reaction systems are rich enough to capture the essential characteristics of ODE-based models. We construct a reaction system model for the heat shock response in such a way that its qualitative behavior correlates well with the quantitative behavior of the corresponding ODE model. We construct our reaction system model based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We conclude with a discussion on the expressivity of reaction systems as compared to that of ODE-based models.
The iterative process of adding details to a model while preserving its numerical behavior is cal... more The iterative process of adding details to a model while preserving its numerical behavior is called quantitative model refinement, and it has been previously discussed for ODE-based models and for kappa-based models. In this paper, we investigate and compare this approach in three different modeling frameworks: rule-based modeling, Petri nets and guarded command languages. As case study we use a model for the eukaryotic heat shock response that we refine to include the acetylation of the heat shock factor. We discuss how to perform the refinement in each of these frameworks in order to avoid the combinatorial state explosion of the refined model. We conclude that Bionetgen (and rule-based modeling in general) is well-suited for a compact representation of the refined model, Petri nets offer a good solution through the use of colors, while the PRISM refined model may be much larger than the basic model.
Object detection from waterborne imagery is an essential aspect in maritime traffic management, n... more Object detection from waterborne imagery is an essential aspect in maritime traffic management, navigation safety and coastal security. Building efficient autonomous systems, which can take decisions in critical situations, requires an integration of complex object detectors in real time. Object detectors trained on generic datasets often give unsatisfactory results in complex scenarios like the maritime environment, since only a fraction of their images contain maritime vessels. Publicly available domain-specific datasets are scarce, and they are limited in the number of images and annotations. Compared to object detection in applications such as autonomous vehicles, maritime vessel detection is considerably reduced in computer vision research. This creates a deficit in exhaustive benchmarking studies for maritime detection datasets. To bridge this gap, we relabel the ABOships dataset and benchmark a state-of-the-art center-based detector, Centernet, on the newly relabeled dataset,...
2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2020
Vessel detection studies conducted on inshore and offshore maritime images are scarce, due to a l... more Vessel detection studies conducted on inshore and offshore maritime images are scarce, due to a limited availability of domain-specific datasets. We addressed this need collecting two datasets in the Finnish Archipelago. They consist of images of maritime vessels engaged in various operating scenarios, climatic conditions and lighting environments. Vessel instances were precisely annotated in both datasets. We evaluated the out-of-the-box performance of three state-of-the-art CNN-based object detection algorithms (Faster R-CNN [1], R-FCN [2] and SSD [3]) on these datasets and compared them in terms of accuracy and run-time. The algorithms were previously trained on the COCO dataset [4]. We explore their performance based on different feature extractors. Furthermore, we investigate the effect of the object size on the algorithm performance. For this purpose, we group all objects in each image into three categories (small, medium and large) according to the number of occupied pixels in the annotated bounding box. Experiments show that Faster R-CNN with ResNet101 as feature extractor outperforms the other algorithms.
There is growing interest in creating large-scale computational models for biological process. On... more There is growing interest in creating large-scale computational models for biological process. One of the challenges in such a project is to fit and validate larger and larger models, a process that requires more high-quality experimental data and more computational effort as the size of the model grows. Quantitative model refinement is a recently proposed model construction technique addressing this challenge. It proposes to create a model in an iterative fashion by adding details to its species, and to fix the numerical setup in a way that guarantees to preserve the fit and validation of the model. In this survey we make an excursion through quantitative model refinement – this includes introducing the concept of quantitative model refinement for reaction-based models, for rule-based models, for Petri nets and for guarded command language models, and to illustrate it on three case studies (the heat shock response, the ErbB signaling pathway, and the self-assembly of intermediate filaments).
Digitalization and Industry 4.0 present a fundamental technological disruption, requiring the ind... more Digitalization and Industry 4.0 present a fundamental technological disruption, requiring the industry, research and government institutions to revisit their roles in the triple helix of innovation ecosystems. In particular, actors in this environment need to understand the logic of value co-creation and alignment of value-systems during interaction and collaboration. The purpose of this study is to increase the understanding of these topics through investigating the triple helix and collaborative capabilities in the Industry 4.0 ecosystem context. It investigates the “Reboot IoT Factory” project, a 13-million-euro research project with seven leading manufacturers, four research partners, and tens of small and medium size enterprises (SMEs). This qualitative single case study presents the project’s novel, experimental operating model that can result in effective collaboration, but also establishes requirements for collaborative capabilities in order to co-create value and innovation...
Availability of domain-specific datasets is an essential problem in object detection. Datasets of... more Availability of domain-specific datasets is an essential problem in object detection. Datasets of inshore and offshore maritime vessels are no exception, with a limited number of studies addressing maritime vessel detection on such datasets. For that reason, we collected a dataset consisting of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including nine types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and we subsequently used the CSRT tracker to trace inconsistencies and relabel inadequate label instances. Moreover, we evaluated the out-of-the-box performance of four prevalent object detection algorithms (Faster R-CNN, R-FCN, SSD and EfficientDet). The algorithms were previously trained on the Microsoft COCO dataset. We compared their accuracy based on feature extra...
ABSTRACT Reaction systems are a formal framework for modeling processes driven by biochemical rea... more ABSTRACT Reaction systems are a formal framework for modeling processes driven by biochemical reactions. They are based on the mechanisms of facilitation and inhibition. A main assumption is that if a resource is available, then it is present in sufficient amounts and as such, several reactions using the same resource will not compete concurrently against each other; this makes reaction systems very different as a modeling framework than traditional frameworks such as ODEs or continuous time Markov chains. We demonstrate in this paper that reaction systems are rich enough to capture the essential characteristics of ODE-based models. We construct a reaction system model for the heat shock response in such a way that its qualitative behavior correlates well with the quantitative behavior of the corresponding ODE model. We construct our reaction system model based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We conclude with a discussion on the expressivity of reaction systems as compared to that of ODE-based models.
ABSTRACT This chapter aims to introduce some of the basics of modeling with ODEs in biology. We f... more ABSTRACT This chapter aims to introduce some of the basics of modeling with ODEs in biology. We focus on computational, numerical techniques, rather than on symbolic ones. We restrict our attention to reaction-based models, where the biological interactions are mechanistically described in terms of reactions, reactants and products. We discuss how to build the ODE model associated to a reaction-based model; how to fit it to experimental data and estimate the quality of its fit; how to calculate its steady state(s), mass conservation relations, and its sensitivity coefficients. We apply some of these techniques to a model for the heat shock response in eukaryotes.
The iterative process of adding details to a model while preserving its numerical behavior is cal... more The iterative process of adding details to a model while preserving its numerical behavior is called quantitative model refinement, and it has been previously discussed for ODE-based models and for kappa-based models. In this paper, we investigate and compare this approach in three different modeling frameworks: rule-based modeling, Petri nets and guarded command languages. As case study we use a model for the eukaryotic heat shock response that we refine to include the acetylation of the heat shock factor. We discuss how to perform the refinement in each of these frameworks in order to avoid the combinatorial state explosion of the refined model. We conclude that Bionetgen (and rule-based modeling in general) is well-suited for a compact representation of the refined model, Petri nets offer a good solution through the use of colors, while the PRISM refined model may be much larger than the basic model.
ABSTRACT One approach to modelling complex biological systems is to start from an abstract repres... more ABSTRACT One approach to modelling complex biological systems is to start from an abstract representation of the biological process and then to incorporate more details regarding its reactions or reactants through an iterative refinement process. The refinement should be done so as to ensure the preservation of the numerical properties of the model, such as its numerical fit and validation. Such approaches are well established in software engineering: starting from a formal specification of the system, one refines it step-by-step towards an implementation that is guaranteed to satisfy a number of logical properties. We introduce here the concepts of (quantitative) data refinement and process refinement of a biomolecular, reaction-based model. We choose as a case study a recently proposed model for the heat shock response and refine it to include some details of its acetylation-induced control. Although the refinement process produces a substantial increase in the number of kinetic parameters and variables, the methodology we propose preserves all the numerical properties of the model with a minimal computational effort.
ABSTRACT Reaction systems are a formal framework for modeling processes driven by biochemical rea... more ABSTRACT Reaction systems are a formal framework for modeling processes driven by biochemical reactions. They are based on the mechanisms of facilitation and inhibition. A main assumption is that if a resource is available, then it is present in sufficient amounts and as such, several reactions using the same resource will not compete concurrently against each other; this makes reaction systems very different as a modeling framework than traditional frameworks such as ODEs or continuous time Markov chains. We demonstrate in this paper that reaction systems are rich enough to capture the essential characteristics of ODE-based models. We construct a reaction system model for the heat shock response in such a way that its qualitative behavior correlates well with the quantitative behavior of the corresponding ODE model. We construct our reaction system model based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We conclude with a discussion on the expressivity of reaction systems as compared to that of ODE-based models.
The iterative process of adding details to a model while preserving its numerical behavior is cal... more The iterative process of adding details to a model while preserving its numerical behavior is called quantitative model refinement, and it has been previously discussed for ODE-based models and for kappa-based models. In this paper, we investigate and compare this approach in three different modeling frameworks: rule-based modeling, Petri nets and guarded command languages. As case study we use a model for the eukaryotic heat shock response that we refine to include the acetylation of the heat shock factor. We discuss how to perform the refinement in each of these frameworks in order to avoid the combinatorial state explosion of the refined model. We conclude that Bionetgen (and rule-based modeling in general) is well-suited for a compact representation of the refined model, Petri nets offer a good solution through the use of colors, while the PRISM refined model may be much larger than the basic model.
Object detection from waterborne imagery is an essential aspect in maritime traffic management, n... more Object detection from waterborne imagery is an essential aspect in maritime traffic management, navigation safety and coastal security. Building efficient autonomous systems, which can take decisions in critical situations, requires an integration of complex object detectors in real time. Object detectors trained on generic datasets often give unsatisfactory results in complex scenarios like the maritime environment, since only a fraction of their images contain maritime vessels. Publicly available domain-specific datasets are scarce, and they are limited in the number of images and annotations. Compared to object detection in applications such as autonomous vehicles, maritime vessel detection is considerably reduced in computer vision research. This creates a deficit in exhaustive benchmarking studies for maritime detection datasets. To bridge this gap, we relabel the ABOships dataset and benchmark a state-of-the-art center-based detector, Centernet, on the newly relabeled dataset,...
2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2020
Vessel detection studies conducted on inshore and offshore maritime images are scarce, due to a l... more Vessel detection studies conducted on inshore and offshore maritime images are scarce, due to a limited availability of domain-specific datasets. We addressed this need collecting two datasets in the Finnish Archipelago. They consist of images of maritime vessels engaged in various operating scenarios, climatic conditions and lighting environments. Vessel instances were precisely annotated in both datasets. We evaluated the out-of-the-box performance of three state-of-the-art CNN-based object detection algorithms (Faster R-CNN [1], R-FCN [2] and SSD [3]) on these datasets and compared them in terms of accuracy and run-time. The algorithms were previously trained on the COCO dataset [4]. We explore their performance based on different feature extractors. Furthermore, we investigate the effect of the object size on the algorithm performance. For this purpose, we group all objects in each image into three categories (small, medium and large) according to the number of occupied pixels in the annotated bounding box. Experiments show that Faster R-CNN with ResNet101 as feature extractor outperforms the other algorithms.
There is growing interest in creating large-scale computational models for biological process. On... more There is growing interest in creating large-scale computational models for biological process. One of the challenges in such a project is to fit and validate larger and larger models, a process that requires more high-quality experimental data and more computational effort as the size of the model grows. Quantitative model refinement is a recently proposed model construction technique addressing this challenge. It proposes to create a model in an iterative fashion by adding details to its species, and to fix the numerical setup in a way that guarantees to preserve the fit and validation of the model. In this survey we make an excursion through quantitative model refinement – this includes introducing the concept of quantitative model refinement for reaction-based models, for rule-based models, for Petri nets and for guarded command language models, and to illustrate it on three case studies (the heat shock response, the ErbB signaling pathway, and the self-assembly of intermediate filaments).
Digitalization and Industry 4.0 present a fundamental technological disruption, requiring the ind... more Digitalization and Industry 4.0 present a fundamental technological disruption, requiring the industry, research and government institutions to revisit their roles in the triple helix of innovation ecosystems. In particular, actors in this environment need to understand the logic of value co-creation and alignment of value-systems during interaction and collaboration. The purpose of this study is to increase the understanding of these topics through investigating the triple helix and collaborative capabilities in the Industry 4.0 ecosystem context. It investigates the “Reboot IoT Factory” project, a 13-million-euro research project with seven leading manufacturers, four research partners, and tens of small and medium size enterprises (SMEs). This qualitative single case study presents the project’s novel, experimental operating model that can result in effective collaboration, but also establishes requirements for collaborative capabilities in order to co-create value and innovation...
Availability of domain-specific datasets is an essential problem in object detection. Datasets of... more Availability of domain-specific datasets is an essential problem in object detection. Datasets of inshore and offshore maritime vessels are no exception, with a limited number of studies addressing maritime vessel detection on such datasets. For that reason, we collected a dataset consisting of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including nine types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and we subsequently used the CSRT tracker to trace inconsistencies and relabel inadequate label instances. Moreover, we evaluated the out-of-the-box performance of four prevalent object detection algorithms (Faster R-CNN, R-FCN, SSD and EfficientDet). The algorithms were previously trained on the Microsoft COCO dataset. We compared their accuracy based on feature extra...
ABSTRACT Reaction systems are a formal framework for modeling processes driven by biochemical rea... more ABSTRACT Reaction systems are a formal framework for modeling processes driven by biochemical reactions. They are based on the mechanisms of facilitation and inhibition. A main assumption is that if a resource is available, then it is present in sufficient amounts and as such, several reactions using the same resource will not compete concurrently against each other; this makes reaction systems very different as a modeling framework than traditional frameworks such as ODEs or continuous time Markov chains. We demonstrate in this paper that reaction systems are rich enough to capture the essential characteristics of ODE-based models. We construct a reaction system model for the heat shock response in such a way that its qualitative behavior correlates well with the quantitative behavior of the corresponding ODE model. We construct our reaction system model based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We conclude with a discussion on the expressivity of reaction systems as compared to that of ODE-based models.
ABSTRACT This chapter aims to introduce some of the basics of modeling with ODEs in biology. We f... more ABSTRACT This chapter aims to introduce some of the basics of modeling with ODEs in biology. We focus on computational, numerical techniques, rather than on symbolic ones. We restrict our attention to reaction-based models, where the biological interactions are mechanistically described in terms of reactions, reactants and products. We discuss how to build the ODE model associated to a reaction-based model; how to fit it to experimental data and estimate the quality of its fit; how to calculate its steady state(s), mass conservation relations, and its sensitivity coefficients. We apply some of these techniques to a model for the heat shock response in eukaryotes.
The iterative process of adding details to a model while preserving its numerical behavior is cal... more The iterative process of adding details to a model while preserving its numerical behavior is called quantitative model refinement, and it has been previously discussed for ODE-based models and for kappa-based models. In this paper, we investigate and compare this approach in three different modeling frameworks: rule-based modeling, Petri nets and guarded command languages. As case study we use a model for the eukaryotic heat shock response that we refine to include the acetylation of the heat shock factor. We discuss how to perform the refinement in each of these frameworks in order to avoid the combinatorial state explosion of the refined model. We conclude that Bionetgen (and rule-based modeling in general) is well-suited for a compact representation of the refined model, Petri nets offer a good solution through the use of colors, while the PRISM refined model may be much larger than the basic model.
ABSTRACT One approach to modelling complex biological systems is to start from an abstract repres... more ABSTRACT One approach to modelling complex biological systems is to start from an abstract representation of the biological process and then to incorporate more details regarding its reactions or reactants through an iterative refinement process. The refinement should be done so as to ensure the preservation of the numerical properties of the model, such as its numerical fit and validation. Such approaches are well established in software engineering: starting from a formal specification of the system, one refines it step-by-step towards an implementation that is guaranteed to satisfy a number of logical properties. We introduce here the concepts of (quantitative) data refinement and process refinement of a biomolecular, reaction-based model. We choose as a case study a recently proposed model for the heat shock response and refine it to include some details of its acetylation-induced control. Although the refinement process produces a substantial increase in the number of kinetic parameters and variables, the methodology we propose preserves all the numerical properties of the model with a minimal computational effort.
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Papers by Bogdan Iancu