| 1. |
- Svahn, Sara L, et al.
(author)
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Dietary polyunsaturated fatty acids increase survival and decrease bacterial load during septic S. aureus infection, and improve neutrophil function in mice
- 2015
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In: Infection and Immunity. - 0019-9567 .- 1098-5522. ; 83:2, s. 514-21
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Journal article (peer-reviewed)abstract
- Severe infection, including sepsis, is an increasing clinical problem that causes prolonged morbidity and substantial mortality. At present, antibiotics are essentially the only pharmacological treatment for sepsis. The incidence of resistance to antibiotics is increasing and it is therefore critical to find new therapies for sepsis. Staphylococcus aureus (S. aureus) is a major cause of septic mortality. Neutrophils play an important role in the defense against bacterial infections. We have shown that a diet with high levels of dietary saturated fatty acids decreases survival in septic mice, but the mechanisms behind remain elusive. The aim of the present study was to investigate how the differences in dietary fat composition affect survival and bacterial load after experimental septic infection and neutrophil function in uninfected mice. We found that, after S. aureus infection, mice fed polyunsaturated high fat diet (HFD/P) for 8 weeks had increased survival and decreased bacterial load during sepsis compared with mice fed saturated high fat diet (HFD/S), and similar to that of mice fed low fat diet (LFD). Uninfected mice fed HFD/P had increased frequency of neutrophils in bone marrow compared with mice fed HFD/S. In addition, mice fed HFD/P had a higher frequency of neutrophils recruited to the site of inflammation in response to peritoneal injection of thioglycollate compared with HFD/S. Differences between the proportion of dietary protein and carbohydrate did not affect septic survival at all. In conclusion, polyunsaturated dietary fat increased both survival and efficiency of bacterial clearance during septic S. aureus infection. Moreover, this diet increased the frequency and chemotaxis of neutrophils, key components of the immune response to S. aureus infections.
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| 2. |
- Andersson, Olov, 1979-, et al.
(author)
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Deep Learning Quadcopter Control via Risk-Aware Active Learning
- 2017
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In: Proceedings of The Thirty-first AAAI Conference on Artificial Intelligence (AAAI). - : AAAI Press. - 9781577357841 ; , s. 3812-3818
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Conference paper (peer-reviewed)abstract
- Modern optimization-based approaches to control increasingly allow automatic generation of complex behavior from only a model and an objective. Recent years has seen growing interest in fast solvers to also allow real-time operation on robots, but the computational cost of such trajectory optimization remains prohibitive for many applications. In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter. As the risk of costly failures is a major concern with real robots, we propose a risk-aware resampling technique. Contrary to prior work this active learning approach is easy to use with existing solvers for trajectory optimization, as well as deep learning. We demonstrate the efficacy of the approach on a difficult collision avoidance problem with non-cooperative moving obstacles. Our findings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller.
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| 3. |
- Andersson, Olov, 1979-, et al.
(author)
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Deep RL for Autonomous Robots: Limitations and Safety Challenges
- 2019
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In: <em>ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning</em>. - : ESANN. - 9782875870650 ; , s. 489-495
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Conference paper (peer-reviewed)abstract
- With the rise of deep reinforcement learning, there has also been a string of successes on continuous control problems using physics simulators. This has lead to some optimism regarding use in autonomous robots and vehicles. However, to successful apply such techniques to the real world requires a firm grasp of their limitations. As recent work has raised questions of how diverse these simulation benchmarks really are, we here instead analyze a popular deep RL approach on toy examples from robot obstacle avoidance. We find that these converge very slowly, if at all, to safe policies. We identify convergence issues on stochastic environments and local minima as problems that warrant more attention for safety-critical control applications.
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| 4. |
- Andersson, Olov, 1979-
(author)
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Learning to Make Safe Real-Time Decisions Under Uncertainty for Autonomous Robots
- 2020
-
Doctoral thesis (other academic/artistic)abstract
- Robots are increasingly expected to go beyond controlled environments in laboratories and factories, to act autonomously in real-world workplaces and public spaces. Autonomous robots navigating the real world have to contend with a great deal of uncertainty, which poses additional challenges. Uncertainty in the real world accrues from several sources. Some of it may originate from imperfect internal models of reality. Other uncertainty is inherent, a direct side effect of partial observability induced by sensor limitations and occlusions. Regardless of the source, the resulting decision problem is unfortunately computationally intractable under uncertainty. This poses a great challenge as the real world is also dynamic. It will not pause while the robot computes a solution. Autonomous robots navigating among people, for example in traffic, need to be able to make split-second decisions. Uncertainty is therefore often neglected in practice, with potentially catastrophic consequences when something unexpected happens. The aim of this thesis is to leverage recent advances in machine learning to compute safe real-time approximations to decision-making under uncertainty for real-world robots. We explore a range of methods, from probabilistic to deep learning, as well as different combinations with optimization-based methods from robotics, planning and control. Driven by applications in robot navigation, and grounded in experiments with real autonomous quadcopters, we address several parts of this problem. From reducing uncertainty by learning better models, to directly approximating the decision problem itself, all the while attempting to satisfy both the safety and real-time requirements of real-world autonomy.
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| 5. |
- Andersson, Olov, 1979-
(author)
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Methods for Scalable and Safe Robot Learning
- 2017
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Licentiate thesis (other academic/artistic)abstract
- Robots are increasingly expected to go beyond controlled environments in laboratories and factories, to enter real-world public spaces and homes. However, robot behavior is still usually engineered for narrowly defined scenarios. To manually encode robot behavior that works within complex real world environments, such as busy work places or cluttered homes, can be a daunting task. In addition, such robots may require a high degree of autonomy to be practical, which imposes stringent requirements on safety and robustness. \setlength{\parindent}{2em}\setlength{\parskip}{0em}The aim of this thesis is to examine methods for automatically learning safe robot behavior, lowering the costs of synthesizing behavior for complex real-world situations. To avoid task-specific assumptions, we approach this from a data-driven machine learning perspective. The strength of machine learning is its generality, given sufficient data it can learn to approximate any task. However, being embodied agents in the real-world, robots pose a number of difficulties for machine learning. These include real-time requirements with limited computational resources, the cost and effort of operating and collecting data with real robots, as well as safety issues for both the robot and human bystanders.While machine learning is general by nature, overcoming the difficulties with real-world robots outlined above remains a challenge. In this thesis we look for a middle ground on robot learning, leveraging the strengths of both data-driven machine learning, as well as engineering techniques from robotics and control. This includes combing data-driven world models with fast techniques for planning motions under safety constraints, using machine learning to generalize such techniques to problems with high uncertainty, as well as using machine learning to find computationally efficient approximations for use on small embedded systems.We demonstrate such behavior synthesis techniques with real robots, solving a class of difficult dynamic collision avoidance problems under uncertainty, such as induced by the presence of humans without prior coordination. Initially using online planning offloaded to a desktop CPU, and ultimately as a deep neural network policy embedded on board a 7 quadcopter.
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| 6. |
- Andersson, Olov, 1979-, et al.
(author)
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Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization
- 2015
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In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI). - : AAAI Press. - 9781577356981 ; , s. 2497-2503
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Conference paper (peer-reviewed)abstract
- Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a model-based reinforcement learning approach for continuous environments with constraints. The approach combines model-based reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.
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| 7. |
- Andersson, Olov, 1979-, et al.
(author)
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Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization
- 2016
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In: IEEE International Conference on Robotics and Automation (ICRA), 2016. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 4597-4604
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Conference paper (peer-reviewed)abstract
- Robots are increasingly expected to move out of the controlled environment of research labs and into populated streets and workplaces. Collision avoidance in such cluttered and dynamic environments is of increasing importance as robots gain more autonomy. However, efficient avoidance is fundamentally difficult since computing safe trajectories may require considering both dynamics and uncertainty. While heuristics are often used in practice, we take a holistic stochastic trajectory optimization perspective that merges both collision avoidance and control. We examine dynamic obstacles moving without prior coordination, like pedestrians or vehicles. We find that common stochastic simplifications lead to poor approximations when obstacle behavior is difficult to predict. We instead compute efficient approximations by drawing upon techniques from machine learning. We propose to combine policy search with model-predictive control. This allows us to use recent fast constrained model-predictive control solvers, while gaining the stochastic properties of policy-based methods. We exploit recent advances in Bayesian optimization to efficiently solve the resulting probabilistically-constrained policy optimization problems. Finally, we present a real-time implementation of an obstacle avoiding controller for a quadcopter. We demonstrate the results in simulation as well as with real flight experiments.
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| 8. |
- Andersson, Olov, 1979-, et al.
(author)
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Real-Time Robotic Search using Structural Spatial Point Processes
- 2020
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In: 35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019). - : Association For Uncertainty in Artificial Intelligence (AUAI). ; , s. 995-1005
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Conference paper (peer-reviewed)abstract
- Aerial robots hold great potential for aiding Search and Rescue (SAR) efforts over large areas, such as during natural disasters. Traditional approaches typically search an area exhaustively, thereby ignoring that the density of victims varies based on predictable factors, such as the terrain, population density and the type of disaster. We present a probabilistic model to automate SAR planning, with explicit minimization of the expected time to discovery. The proposed model is a spatial point process with three interacting spatial fields for i) the point patterns of persons in the area, ii) the probability of detecting persons and iii) the probability of injury. This structure allows inclusion of informative priors from e.g. geographic or cell phone traffic data, while falling back to latent Gaussian processes when priors are missing or inaccurate. To solve this problem in real-time, we propose a combination of fast approximate inference using Integrated Nested Laplace Approximation (INLA), and a novel Monte Carlo tree search tailored to the problem. Experiments using data simulated from real world Geographic Information System (GIS) maps show that the framework outperforms competing approaches, finding many more injured in the crucial first hours.
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| 9. |
- Andersson, Olov, 1979-, et al.
(author)
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Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance
- 2018
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In: 2018 IEEE Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538613955 - 9781538613948 - 9781538613962 ; , s. 4467-4474
-
Conference paper (peer-reviewed)abstract
- A key requirement of autonomous vehicles is the capability to safely navigate in their environment. However, outside of controlled environments, safe navigation is a very difficult problem. In particular, the real-world often contains both complex 3D structure, and dynamic obstacles such as people or other vehicles. Dynamic obstacles are particularly challenging, as a principled solution requires planning trajectories with regard to both vehicle dynamics, and the motion of the obstacles. Additionally, the real-time requirements imposed by obstacle motion, coupled with real-world computational limitations, make classical optimality and completeness guarantees difficult to satisfy. We present a unified optimization-based motion planning and control solution, that can navigate in the presence of both static and dynamic obstacles. By combining optimal and receding-horizon control, with temporal multi-resolution lattices, we can precompute optimal motion primitives, and allow real-time planning of physically-feasible trajectories in complex environments with dynamic obstacles. We demonstrate the framework by solving difficult indoor 3D quadcopter navigation scenarios, where it is necessary to plan in time. Including waiting on, and taking detours around, the motions of other people and quadcopters.
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| 10. |
- Andersson, Olov, 1979-, et al.
(author)
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WARA-PS : a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation
- 2021
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In: Autonomous Intelligent Systems. - : Springer Nature. - 2730-616X. ; 1:1
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Journal article (peer-reviewed)abstract
- A research arena (WARA-PS) for sensing, data fusion, user interaction, planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented. The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges. The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration. This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles. The motivating application for the demonstration is marine search and rescue operations. A state-of-art delegation framework for the mission planning together with three specific applications is also presented. The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles. The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles, and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments. The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility. It would be most difficult to do experiments on this large scale without the WARA-PS research arena. Furthermore, these demonstrator activities have resulted in effective research dissemination with high public visibility, business impact and new research collaborations between academia and industry.
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| 11. |
- Danielsson, Henrik, et al.
(author)
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A Systematic Review of Longitudinal Trajectories of Mental Health Problems in Children with Neurodevelopmental Disabilities
- 2024
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In: Journal of Developmental and Physical Disabilities. - : Springer. - 1056-263X .- 1573-3580. ; 36, s. 203-242
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Research review (peer-reviewed)abstract
- To review the longitudinal trajectories - and the factors influencing their development - of mental health problems in children with neurodevelopmental disabilities. Systematic review methods were employed. Searches of six databases used keywords and MeSH terms related to children with neurodevelopmental disabilities, mental health problems, and longitudinal research. After the removal of duplicates, reviewers independently screened records for inclusion, extracted data (outcomes and influencing factors), and evaluated the risk of bias. Findings were tabulated and synthesized using graphs and a narrative. Searches identified 94,662 unique records, from which 49 publications were included. The median publication year was 2015. Children with attention deficit hyperactivity disorder were the most commonly included population in retrieved studies. In almost 50% of studies, trajectories of mental health problems changed by < 10% between the first and last time point. Despite multiple studies reporting longitudinal trajectories of mental health problems, greater conceptual clarity and consideration of the measures included in research is needed, along with the inclusion of a more diverse range of populations of children with neurodevelopmental disabilities.
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| 12. |
- Granlund, Mats, 1954-, et al.
(author)
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Definitions and operationalization of mental health problems, wellbeing and participation constructs in children with ndd : Distinctions and clarifications
- 2021
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In: International Journal of Environmental Research and Public Health. - : MDPI. - 1661-7827 .- 1660-4601. ; 18:4, s. 1-19
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Journal article (peer-reviewed)abstract
- Children with impairments are known to experience more restricted participation than other children. It also appears that low levels of participation are related to a higher prevalence of mental health problems in children with neurodevelopmental disorders (NDD). The purpose of this conceptual paper is to describe and define the constructs mental health problems, mental health, and participation to ensure that future research investigating participation as a means to mental health in children and adolescents with NDD is founded on conceptual clarity. We first discuss the difference between two aspects of mental health problems, namely mental disorder and mental illness. This discussion serves to highlight three areas of conceptual difficulty and their consequences for understanding the mental health of children with NDD that we then consider in the article: (1) how to define mental health problems, (2) how to define and assess mental health problems and mental health, i.e., wellbeing as separate constructs, and (3) how to describe the relationship between participation and wellbeing. We then discuss the implications of our propositions for measurement and the use of participation interventions as a means to enhance mental health (defined as wellbeing). Conclusions: Mental disorders include both diagnoses related to impairments in the developmental period, i.e., NDD and diagnoses related to mental illness. These two types of mental disorders must be separated. Children with NDD, just like other people, may exhibit aspects of both mental health problems and wellbeing simultaneously. Measures of wellbeing defined as a continuum from flourishing to languishing for children with NDD need to be designed and evaluated. Wellbeing can lead to further participation and act to protect from mental health problems.
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| 13. |
- Simonsson, Olov, 1979-
(author)
-
God Rests in Rwanda : The Role of Religion in the 1994 Genocide in Rwanda
- 2019
-
Doctoral thesis (other academic/artistic)abstract
- This study analyses the role of religion in the Rwandan genocide, providing new explanations to the complex dynamics of devaluation and victimisation processes in genocidal violence. The thesis explains how religion was used in different contexts prior to, during, and after the 1994 genocide. The following questions guide this study: What kinds of religious concepts and arguments were used in the context of the Rwandan genocide, and how? Why were they used and what did these concepts and arguments mean? Finally, did the meanings of the religious arguments change over time and between different contexts, and if so why?Texts from three sources were analysed: the Hutu extremist propaganda in Kangura magazine and in RTLM broadcasts, and testimonies from the ICTR trials. The analysis was guided by Roger Dale Petersen’s theory on Fear, Hatred, and Resentment, as well as theories on devaluation, social identity, self-victimisation, and competitive victimhood. This thesis utilises the computer software MAXQDA to search for concepts and arguments, which are analysed through the contextual approach developed by Quentin Skinner. This thesis demonstrates that the Hutu propagandists used religious mythology to argue that the Tutsis were not of Rwandan origin and therefore had no rights in Rwanda. The devaluation of the Tutsi was not only or even primarily done through downgrading animalistic epithets, but through the elevation of Tutsis with emphasis on the historical, and allegedly divine, superiority of the Tutsi. This devaluation allowed the Hutu extremists to claim victimhood, a necessary conviction to argue that violence committed by the Hutus were acts of self-defence. In the deeply Christian context of Rwanda, the extremist Hutu propagandists constructed a Hutu God, while claiming that the Tutsis were non-Christian, irreligious, or atheists, in order to create different religious identities for the two groups.This study also assesses the judicial aftermath, and argues that religious concepts were used in similar ways in ICTR testimonies to claim innocence, credibility, and victimhood. This thesis thus sheds new light on the importance of religious belief systems in genocidal violence, highlighting the crucial role of religion prior to, during, and after the genocide in Rwanda.
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| 14. |
- Strandberg, Louise, 1981, et al.
(author)
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Mice chronically fed high-fat diet have increased mortality and disturbed immune response in sepsis.
- 2009
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In: PloS one. - : Public Library of Science (PLoS). - 1932-6203. ; 4:10
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Journal article (peer-reviewed)abstract
- BACKGROUND: Sepsis is a potentially deadly disease that often is caused by gram-positive bacteria, in particular Staphylococcus aureus (S. aureus). As there are few effective therapies for sepsis, increased basic knowledge about factors predisposing is needed. METHODOLOGY/PRINCIPAL FINDINGS: The purpose of this study was to study the effect of Western diet on mortality induced by intravenous S. aureus inoculation and the immune functions before and after bacterial inoculation. Here we show that C57Bl/6 mice on high-fat diet (HFD) for 8 weeks, like genetically obese Ob/Ob mice on low-fat diet (LFD), have increased mortality during S. aureus-induced sepsis compared with LFD-fed C57Bl/6 controls. Bacterial load in the kidneys 5-7 days after inoculation was increased 10-fold in HFD-fed compared with LFD-fed mice. At that time, HFD-fed mice had increased serum levels and fat mRNA expression of the immune suppressing cytokines interleukin-1 receptor antagonist (IL-1Ra) and IL-10 compared with LFD-fed mice. In addition, HFD-fed mice had increased serum levels of the pro-inflammatory IL-1beta. Also, HFD-fed mice with and without infection had increased levels of macrophages in fat. The proportion and function of phagocytosing granulocytes, and the production of reactive oxygen species (ROS) by peritoneal lavage cells were decreased in HFD-fed compared with LFD-fed mice. CONCLUSIONS: Our findings imply that chronic HFD disturb several innate immune functions in mice, and impairs the ability to clear S. aureus and survive sepsis.
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