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| 2. |
- Johnson, Calvin W., et al.
(författare)
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White paper: From bound states to the continuum
- 2020
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Ingår i: Journal of Physics G: Nuclear and Particle Physics. - : IOP Publishing. - 0954-3899 .- 1361-6471. ; 47:12
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Forskningsöversikt (refereegranskat)abstract
- This white paper reports on the discussions of the 2018 Facility for Rare Isotope Beams Theory Alliance (FRIB-TA) topical program ‘From bound states to the continuum: Connecting bound state calculations with scattering and reaction theory’. One of the biggest and most important frontiers in nuclear theory today is to construct better and stronger bridges between bound state calculations and calculations in the continuum, especially scattering and reaction theory, as well as teasing out the influence of the continuum on states near threshold. This is particularly challenging as many-body structure calculations typically use a bound state basis, while reaction calculations more commonly utilize few-body continuum approaches. The many-body bound state and few-body continuum methods use different language and emphasize different properties. To build better foundations for these bridges, we present an overview of several bound state and continuum methods and, where possible, point to current and possible future connections.
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- Botteghi, N., et al.
(författare)
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Low dimensional state representation learning with reward-shaped priors
- 2020
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Ingår i: Proceedings - International Conference on Pattern Recognition. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728188089 ; , s. 3736-3743
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Konferensbidrag (refereegranskat)abstract
- Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal policy directly from the sensory inputs, i.e the observations, often requires processing and storage of a huge amount of data. In the context of robotics, the cost of data from real robotics hardware is usually very high, thus solutions that achieve high sample-efficiency are needed. We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space. This mapping is learned with unsupervised learning using loss functions shaped to incorporate prior knowledge of the environment and the task. Using the samples from the state space, the optimal policy is quickly and efficiently learned. We test the method on several mobile robot navigation tasks in a simulation environment and also on a real robot. A video of our experiments can be found at: https://youtu.be/dgWxmfSv95U.
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| 4. |
- Botteghi, N., et al.
(författare)
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Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
- 2021
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Ingår i: IEEE International Conference on Intelligent Robots and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665417150 ; , s. 190-197
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Konferensbidrag (refereegranskat)abstract
- Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fashion without any need for hand-crafted features or policies. Especially in the context of robotics, in which the cost of real-world data is usually extremely high, Reinforcement Learning solutions achieving high sample efficiency are needed. In this paper, we propose a framework combining the learning of a low-dimensional state representation, from high-dimensional observations coming from the robot's raw sensory readings, with the learning of the optimal policy, given the learned state representation. We evaluate our framework in the context of mobile robot navigation in the case of continuous state and action spaces. Moreover, we study the problem of transferring what learned in the simulated virtual environment to the real robot without further retraining using real-world data in the presence of visual and depth distractors, such as lighting changes and moving obstacles. A video of our experiments can be found at: https://youtu.be/rUdGPKr2Wuo.
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| 5. |
- Botteghi, N., et al.
(författare)
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Reinforcement learning helps slam : Learning to build maps
- 2020
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Ingår i: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. - : International Society for Photogrammetry and Remote Sensing. ; , s. 329-336
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Konferensbidrag (refereegranskat)abstract
- In this research, we investigate the use of Reinforcement Learning (RL) for an effective and robust solution for exploring unknown and indoor environments and reconstructing their maps. We benefit from a Simultaneous Localization and Mapping (SLAM) algorithm for real-time robot localization and mapping. Three different reward functions are compared and tested in different environments with growing complexity. The performances of the three different RL-based path planners are assessed not only on the training environments, but also on an a priori unseen environment to test the generalization properties of the policies. The results indicate that RL-based planners trained to maximize the coverage of the map are able to consistently explore and construct the maps of different indoor environments.
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| 6. |
- Einarsdottir, Sigrun, et al.
(författare)
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Vaccination against tick-borne encephalitis (TBE) after autologous and allogeneic stem cell transplantation
- 2021
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Ingår i: Vaccine. - : Elsevier BV. - 0264-410X .- 1873-2518. ; 39:7, s. 1035-1038
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Our aim was to assess response and side effects of 4 doses of TBE vaccine to patients (pts) after allo- and autologous stem cell transplantation (SCT). PATIENTS: Included were 104 pts with leukaemia, myeloma and lymphoma, median age 61 yrs. METHODS: Vaccine (FSME-Immun (R)) was given at 9, 10, 12, and 21 months post-transplant. Serum samples were obtained before and after vaccinations. Healthy controls (n = 27) received 3 vaccinations. Assessments of TBE specific IgG antibodies were performed by Enzygnost anti-TBE ELISA test (Siemens, Sweden). Results: Antibody levels (>12 U/mL; "seropositivity") were seen in 77% and 80% of pts after allo- and autoSCT; IgG levels; 89 vs 94 U/mL. Ongoing chronic GvHD and immunosuppression (n = 29) was associated with sero-negativity in the last sample (p = 0.007). All controls (n = 27) developed protective antibody levels. Conclusions: TBE vaccination was safe, and 4 doses starting 9 months post-SCT, induced seropositivity in a vast majority of pts. (C) 2021 Elsevier Ltd. All rights reserved.
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