The silicon trypanosome

African trypanosomes have emerged as promising unicellular model organisms for the next generation of systems biology. They offer unique advantages, due to their relative simplicity, the availability of all standard genomics techniques and a long history of quantitative research. Reproducible cultivation methods exist for morphologically and physiologically distinct life-cycle stages. The genome has been sequenced, and microarrays, RNA-interference and high-accuracy metabolomics are available. Furthermore, the availability of extensive kinetic data on all glycolytic enzymes has led to the early development of a complete, experiment-based dynamic model of an important biochemical pathway. Here we describe the achievements of trypanosome systems biology so far and outline the necessary steps towards the ambitious aim of creating a , a comprehensive, experiment-based, multi-scale mathematical model of trypanosome physiology. We expect that, in the long run, the quantitative modelling enabled by the Silicon Trypanosome will play a key role in selecting the most suitable targets for developing new anti-parasite drugs

Model Based Systems Engineering for a Typical Smartgrid

The complexity and heterogeneity of today’s large Cyber-Physical Systems (CPS) is addressed by model based design. This class of systems is a direct consequence of our entry into the new era of systems characterized by high complexity, increased software dependency, multifaceted support for networking and inclusion of data and services form global networks. Cyber-Physical Power Systems such as SmartGrids provide perfect example to emphasis heterogeneity and complexity of today’s systems. In this thesis we work towards augmenting the creation and demonstration of a framework for developing an integrated CPS modelling hub with powerful and diverse tradeoff analysis methods and tools for design exploration of CPS

Model Transformation in context of Driver Assistance System: Meta-model based transformation for Simulink an Scicos

In today’s world we see that Embedded Systems forms a major part in the life of a human being. Almost every device today has an electronic chip embedded in it. When it comes to automotive, these electronic devices are multiplying. This has resulted in innovative methods of developing Embedded Systems. Among them, Model Based Development has become very popular and a standard way of developing embedded systems. Now, we can see that most embedded systems, especially the automotive systems, are being developed using Model development tools like Simulink. In the design and development of Driver Assistance System, Model Based Design (MBD) plays an important role from system design and simulation to code generation. Modeling tool Matlab/Simulink is now among the most popular tools. Due to the proprietary nature of Simulink and challenges in requirement elicitation phase the industry is looking towards an open source alternative, such as Scicos. Since, most of the OEMs are still using Simulink, there is a need for interoperability between Simulink and Scicos. The present work proposes metamodels for Simulink and Scicos, and Model transformation using these Metamodels for the inter-operability. In order to develop the model transformation the metamodels for Simulink and Scicos were developed using EMF Ecore. These metamodels conform to OMGs MOF Standards. These metamodels were used in developing the transformation definition using the language QVTo. First a simple model was developed, and transformation rules were applied and verified using it. Then a Simulink subsystem of a cross wind assistance system was subjected to forward transformation. The outputs of the model before transformation and that after transformation were compared. They were found to give the same output as desired. Thus, verifying the transformation definition. An attempt was made to achieve reverse transformation. A subsystem in Scicos was considered for reverse transformation. After subjecting it to transformation, an intermediate model conforming to Simulink metamodel was obtained. This shows that the interoperability between Scicos and Simulink can be achieved

Recursive neuro fuzzy techniques for online identification and control

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresThe main goal of this thesis will be focused on developing an adaptative closed loop control solution, using fuzzy methodologies. A positive theoretical and experimental contribution, regarding modelling and control of fuzzy and neuro fuzzy systems, is expected to be achieved. Proposed non-linear identification solution will use for modelling and control, a recurrent neuro fuzzy architecture. Regarding model solution, a state space approach will be considered during fuzzy consequent local models design. Developed controller will be based on model parameters, being expected not only a stable closed loop solution, but also a static error with convergence towards zero. Model and controller fuzzy subspaces, will be partitioned throughout process dynamical universe, allowing fuzzy local models and controllers commutation and aggregation. With the aim of capturing process under control dynamics using a real time approach, the use of recursive optimization techniques are to be adopted. Such methods will be applied during parameter and state estimation, using a dual decoupled Kalman filter extended with unscented transformation. Two distinct processes one single-input (SISO) other multi-input (MIMO), will be used during experimentation. It is expected from experiments, a practical validation of proposed solution capabilities for control and identification. Presented work will not be completed, without first presenting a global analysis of adopted concepts and methods, describing new perspectives for future investigations

Advancing Cancer Systems Biology: Introducing the Center for the Development of a Virtual Tumor, CViT

Integrative cancer biology research relies on a variety of data-driven computational modeling and simulation methods and techniques geared towards gaining new insights into the complexity of biological processes that are of critical importance for cancer research. These include the dynamics of gene-protein interaction networks, the percolation of sub-cellular perturbations across scales and the impact they may have on tumorigenesis in both experiments and clinics. Such innovative ‘systems’ research will greatly benefit from enabling Information Technology that is currently under development, including an online collaborative environment, a Semantic Web based computing platform that hosts data and model repositories as well as high-performance computing access. Here, we present one of the National Cancer Institute’s recently established Integrative Cancer Biology Programs, i.e. the Center for the Development of a Virtual Tumor, CViT, which is charged with building a cancer modeling community, developing the aforementioned enabling technologies and fostering multi-scale cancer modeling and simulation

Reinforcement Learning for Mitigating Toxicity in Neural Dialogue Systems

Developing a machine that can hold an engaging conversation with a human is one of the main challenges in designing an open-domain dialogue system in the field of natural language processing. With the advancement of deep learning techniques and the availability of large amounts of data on human-to-human conversational interaction, a fully data-driven and holistic approach is considered to design open-domain dialogue systems. Dialogue generation models trained on large corpora of human-to-human interactions learn undesirable features and mimic behaviors from data, including toxic language, gender, and racial biases. Hence, as dialogue systems become more widespread and trusted, developing such systems that account for possible safety concerns is vital. In the first part of the thesis, we address the limitations of training the open-domain dialogue generation model with the log-likelihood method, and we propose the Reinforce Transformer-decoder model, our novel approach for training the Transformer-decoder based conversational model, which incorporates proximal policy optimization techniques from reinforcement learning with the Transformer-decoder architecture. We specifically examine the use of our proposed model for multi-turn open-domain dialogue response generation on the Reddit dialogues data, a real-word human-to-human dataset. Experiments demonstrate that responses generated by our proposed neural dialogue response generation model are diverse and contain information specific to the source prompt based on diversity and relevance evaluation metrics. In the second part of the thesis, we propose a new approach based on the domain adaptation language model and multitask deep neural network to detect and identify the toxic language in the textual content. We argue that the first step in managing toxic language risk is identification, but algorithmic approaches have demonstrated bias. Texts containing some demographic identity terms such as Muslim, Jewish, Asian, or Black are more likely to be labeled as toxic in existing toxic language detection datasets. In many machine learning models introduced for toxic language detection, non-toxic comments containing minority and marginalized community-specific identity terms were given unreasonably high toxicity scores. To address the challenge of bias in toxic language detection, we employ six toxic language detection and identification tasks to train the model to detect toxic contents and mitigate unintended bias in model prediction. We evaluate and compare our model with other state-of-the-art deep learning models using specific performance metrics to measure the model bias. In detailed experiments, we show our approach can identify toxic language in textual content with considerably more robust to model bias towards commonly-attacked identity groups presented in the textual content. Moreover, the experimental results illustrate that jointly training the pretrained language model with a multitask objective can effectively mitigate the impacts of unintended biases and is more robust to model bias towards commonly-attacked identity groups presented in datasets without significantly hurting the model's generalizability. In the third part of the thesis, we propose our approach to mitigate toxic language generation by neural generative language models and conversational AI systems. Transformer-based language models can generate fluent text and efficiently adapt various natural language generation tasks. However, language models that are pretrained on large unlabeled web text corpora have suffered from degenerating toxic content and social bias, hindering their safe deployment for fine-tuning dialogue response generation systems. Various detoxification methods have been proposed to mitigate language model toxicity; however, these methods struggle to detoxify language models when conditioned on prompts that contain specific social identities related to gender, race, or religion. In this study, we propose Reinforce-Detoxify, a reinforcement learning-based method for mitigating toxicity in language models. Reinforce-Detoxify is formulated as an autoregressive LM and uses a multilayer transformer-decoder as the model architecture. We address the effect of detoxification methods on language generation from LMs towards social identities. We propose a reward model based on multitask learning that can mitigate unintended bias related to various social identities in toxicity prediction. We employ our multitask deep neural network model to mitigate unintended bias in toxicity prediction related to various social identities as a reward function for fine-tuning the generative model. Furthermore, to prevent the unfavorable effect of detoxification on language model fluency, we penalize the Kullback Leibler divergence between the learned policy and the original LM that we used to initialize the policy. Empirical results demonstrate that utilizing reinforcement learning for fine-tuning the language models to maximize the reward can mitigate toxic language generation and outperform the current detoxification methods in the literature. Furthermore, we have shown that utilizing a reward model trained to reduce unintended bias towards various social identities successfully enables the language models to mitigate toxicity when conditioned on prompts related to these social identities

Advancing Cancer Systems Biology: Introducing the Center for the Development of a Virtual Tumor, CViT

Integrative cancer biology research relies on a variety of data-driven computational modeling and simulation methods and techniques geared towards gaining new insights into the complexity of biological processes that are of critical importance for cancer research. These include the dynamics of gene-protein interaction networks, the percolation of sub-cellular perturbations across scales and the impact they may have on tumorigenesis in both experiments and clinics. Such innovative ‘systems’ research will greatly benefit from enabling Information Technology that is currently under development, including an online collaborative environment, a Semantic Web based computing platform that hosts data and model repositories as well as high-performance computing access. Here, we present one of the National Cancer Institute’s recently established Integrative Cancer Biology Programs, i.e. the Center for the Development of a Virtual Tumor, CViT, which is charged with building a cancer modeling community, developing the aforementioned enabling technologies and fostering multi-scale cancer modeling and simulation

Automated Teeth Extraction and Dental Caries Detection in Panoramic X-ray

Dental caries is one of the most chronic diseases that involves the majority of people at least once during their lifetime. This expensive disease accounts for 5-10% of the healthcare budget in developing countries. Caries lesions appear as the result of dental biofi lm metabolic activity, caused by bacteria (most prominently Streptococcus mutans) feeding on uncleaned sugars and starches in oral cavity. Also known as tooth decay, they are primarily diagnosed by general dentists solely based on clinical assessments. Since in many cases dental problems cannot be detected with simple observations, dental x-ray imaging is introduced as a standard tool for domain experts, i.e. dentists and radiologists, to distinguish dental diseases, such as proximal caries. Among different dental radiography methods, Panoramic or Orthopantomogram (OPG) images are commonly performed as the initial step toward assessment. OPG images are captured with a small dose of radiation and can depict the entire patient dentition in a single image. Dental caries can sometimes be hard to identify by general dentists relying only on their visual inspection using dental radiography. Tooth decays can easily be misinterpreted as shadows due to various reasons, such as low image quality. Besides, OPG images have poor quality and structures are not presented with strong edges due to low contrast, uneven exposure, etc. Thus, disease detection is a very challenging task using Panoramic radiography. With the recent development of Artificial Intelligence (AI) in dentistry, and with the introduction of Convolutional Neural Network (CNN) for image classification, developing medical decision support systems is becoming a topic of interest in both academia and industry. Providing more accurate decision support systems using CNNs to assist dentists can enhance their diagnosis performance, resulting in providing improved dental care assistance for patients. In the following thesis, the first automated teeth extraction system for Panoramic images, using evolutionary algorithms, is proposed. In contrast to other intraoral radiography methods, Panoramic is captured with x-ray film outside the patient mouth. Therefore, Panoramic x-rays contain regions outside of the jaw, which make teeth segmentation extremely difficult. Considering that we solely need an image of each tooth separately to build a caries detection model, segmentation of teeth from the OPG image is essential. Due to the absence of significant pixel intensity difference between different regions in OPG radiography, teeth segmentation becomes very hard to implement. Consequently, an automated system is introduced to get an OPG as input and gives images of single teeth as the output. Since only a few research studies are utilizing similar task for Panoramic radiography, there is room for improvement. A genetic algorithm is applied along with different image processing methods to perform teeth extraction by jaw extraction, jaw separation, and teeth-gap valley detection, respectively. The proposed system is compared to the state-of-the-art in teeth extraction on other image types. After teeth are segmented from each image, a model based on various untrained and pretrained CNN-based architectures is proposed to detect dental caries for each tooth. Autoencoder-based model along with famous CNN architectures are used for feature extraction, followed by capsule networks to perform classification. The dataset of Panoramic x-rays is prepared by the authors, with help from an expert radiologist to provide labels. The proposed model has demonstrated an acceptable detection rate of 86.05%, and an increase in caries detection speed. Considering the challenges of performing such task on low quality OPG images, this work is a step towards developing a fully automated efficient caries detection model to assist domain experts

Automated Teeth Extraction and Dental Caries Detection in Panoramic X-ray

Dental caries is one of the most chronic diseases that involves the majority of people at least once during their lifetime. This expensive disease accounts for 5-10% of the healthcare budget in developing countries. Caries lesions appear as the result of dental biofi lm metabolic activity, caused by bacteria (most prominently Streptococcus mutans) feeding on uncleaned sugars and starches in oral cavity. Also known as tooth decay, they are primarily diagnosed by general dentists solely based on clinical assessments. Since in many cases dental problems cannot be detected with simple observations, dental x-ray imaging is introduced as a standard tool for domain experts, i.e. dentists and radiologists, to distinguish dental diseases, such as proximal caries. Among different dental radiography methods, Panoramic or Orthopantomogram (OPG) images are commonly performed as the initial step toward assessment. OPG images are captured with a small dose of radiation and can depict the entire patient dentition in a single image. Dental caries can sometimes be hard to identify by general dentists relying only on their visual inspection using dental radiography. Tooth decays can easily be misinterpreted as shadows due to various reasons, such as low image quality. Besides, OPG images have poor quality and structures are not presented with strong edges due to low contrast, uneven exposure, etc. Thus, disease detection is a very challenging task using Panoramic radiography. With the recent development of Artificial Intelligence (AI) in dentistry, and with the introduction of Convolutional Neural Network (CNN) for image classification, developing medical decision support systems is becoming a topic of interest in both academia and industry. Providing more accurate decision support systems using CNNs to assist dentists can enhance their diagnosis performance, resulting in providing improved dental care assistance for patients. In the following thesis, the first automated teeth extraction system for Panoramic images, using evolutionary algorithms, is proposed. In contrast to other intraoral radiography methods, Panoramic is captured with x-ray film outside the patient mouth. Therefore, Panoramic x-rays contain regions outside of the jaw, which make teeth segmentation extremely difficult. Considering that we solely need an image of each tooth separately to build a caries detection model, segmentation of teeth from the OPG image is essential. Due to the absence of significant pixel intensity difference between different regions in OPG radiography, teeth segmentation becomes very hard to implement. Consequently, an automated system is introduced to get an OPG as input and gives images of single teeth as the output. Since only a few research studies are utilizing similar task for Panoramic radiography, there is room for improvement. A genetic algorithm is applied along with different image processing methods to perform teeth extraction by jaw extraction, jaw separation, and teeth-gap valley detection, respectively. The proposed system is compared to the state-of-the-art in teeth extraction on other image types. After teeth are segmented from each image, a model based on various untrained and pretrained CNN-based architectures is proposed to detect dental caries for each tooth. Autoencoder-based model along with famous CNN architectures are used for feature extraction, followed by capsule networks to perform classification. The dataset of Panoramic x-rays is prepared by the authors, with help from an expert radiologist to provide labels. The proposed model has demonstrated an acceptable detection rate of 86.05%, and an increase in caries detection speed. Considering the challenges of performing such task on low quality OPG images, this work is a step towards developing a fully automated efficient caries detection model to assist domain experts

A Model-Based Methodology for the Integration of a System Architecture in a Digital Aircraft Design Process

Due to the current trend towards sustainable, environmentally friendly, and digitally networked aircraft, it is necessary to integrate new revolutionary technology. This increases complexity and necessitates the investigation of novel aircraft designs and configurations early, quickly, and cost-effectively. Innovative concepts and approaches are necessary to handle this complexity. Model-based Systems Engineering (MBSE) is a fundamental approach to support and manage complex system development. Notable benefits are achieved compared to more traditional document-based methods. Therefore, researchers at the DLR Institute of System Architectures in Aeronautics are developing a process to fully digitalize and virtualize an aircraft. This enables it to be completely represented as a virtual product, allowing for the rapid implementation, visualization, and validation of novel design concepts. This work extends the digital design process with a model-based methodology for developing and integrating the functional system architecture. An application use case on passenger service functions serves as a proof of concept (PoC) during the methodology evaluation. At the beginning, the system is analyzed and it's architecture is modeled using the Systems Modeling Language (SysML). The system requirements are defined and all model elements are linked together. This improves the traceability and enables early error detection as well as the validation of requirements. The system model is then linked to existing models. Model integration allows the system architecture to be configured with cabin design parameters from CPACS and the architecture data to be used for geometrical cabin design. To illustrate advantages of architecture integration, multidisciplinary optimization is investigated based on the interaction between the different models. A trade-off analysis is performed using multidisciplinary design parameters regarding electrical power distribution and cable length. The interactions and effects between the design domains are therefore identified and analyzed