| 1. |
- Abelev, Betty, et al.
(författare)
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Long-range angular correlations on the near and away side in p-Pb collisions at root S-NN=5.02 TeV
- 2013
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Ingår i: Physics Letters. Section B: Nuclear, Elementary Particle and High-Energy Physics. - : Elsevier BV. - 0370-2693. ; 719:1-3, s. 29-41
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Tidskriftsartikel (refereegranskat)abstract
- Angular correlations between charged trigger and associated particles are measured by the ALICE detector in p-Pb collisions at a nucleon-nucleon centre-of-mass energy of 5.02 TeV for transverse momentum ranges within 0.5 < P-T,P-assoc < P-T,P-trig < 4 GeV/c. The correlations are measured over two units of pseudorapidity and full azimuthal angle in different intervals of event multiplicity, and expressed as associated yield per trigger particle. Two long-range ridge-like structures, one on the near side and one on the away side, are observed when the per-trigger yield obtained in low-multiplicity events is subtracted from the one in high-multiplicity events. The excess on the near-side is qualitatively similar to that recently reported by the CMS Collaboration, while the excess on the away-side is reported for the first time. The two-ridge structure projected onto azimuthal angle is quantified with the second and third Fourier coefficients as well as by near-side and away-side yields and widths. The yields on the near side and on the away side are equal within the uncertainties for all studied event multiplicity and p(T) bins, and the widths show no significant evolution with event multiplicity or p(T). These findings suggest that the near-side ridge is accompanied by an essentially identical away-side ridge. (c) 2013 CERN. Published by Elsevier B.V. All rights reserved.
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| 2. |
- Abelev, Betty, et al.
(författare)
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Measurement of prompt J/psi and beauty hadron production cross sections at mid-rapidity in pp collisions at root s=7 TeV
- 2012
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Ingår i: Journal of High Energy Physics. - 1029-8479. ; :11
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Tidskriftsartikel (refereegranskat)abstract
- The ALICE experiment at the LHC has studied J/psi production at mid-rapidity in pp collisions at root s = 7 TeV through its electron pair decay on a data sample corresponding to an integrated luminosity L-int = 5.6 nb(-1). The fraction of J/psi from the decay of long-lived beauty hadrons was determined for J/psi candidates with transverse momentum p(t) > 1,3 GeV/c and rapidity vertical bar y vertical bar < 0.9. The cross section for prompt J/psi mesons, i.e. directly produced J/psi and prompt decays of heavier charmonium states such as the psi(2S) and chi(c) resonances, is sigma(prompt J/psi) (p(t) > 1.3 GeV/c, vertical bar y vertical bar < 0.9) = 8.3 +/- 0.8(stat.) +/- 1.1 (syst.)(-1.4)(+1.5) (syst. pol.) mu b. The cross section for the production of b-hadrons decaying to J/psi with p(t) > 1.3 GeV/c and vertical bar y vertical bar < 0.9 is a sigma(J/psi <- hB) (p(t) > 1.3 GeV/c, vertical bar y vertical bar < 0.9) = 1.46 +/- 0.38 (stat.)(-0.32)(+0.26) (syst.) mu b. The results are compared to QCD model predictions. The shape of the p(t) and y distributions of b-quarks predicted by perturbative QCD model calculations are used to extrapolate the measured cross section to derive the b (b) over bar pair total cross section and d sigma/dy at mid-rapidity.
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| 3. |
- Abelev, Betty, et al.
(författare)
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Underlying Event measurements in pp collisions at root s=0.9 and 7 TeV with the ALICE experiment at the LHC
- 2012
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Ingår i: Journal of High Energy Physics. - 1029-8479. ; :7
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Tidskriftsartikel (refereegranskat)abstract
- We present measurements of Underlying Event observables in pp collisions at root s = 0 : 9 and 7 TeV. The analysis is performed as a function of the highest charged-particle transverse momentum p(T),L-T in the event. Different regions are defined with respect to the azimuthal direction of the leading (highest transverse momentum) track: Toward, Transverse and Away. The Toward and Away regions collect the fragmentation products of the hardest partonic interaction. The Transverse region is expected to be most sensitive to the Underlying Event activity. The study is performed with charged particles above three different p(T) thresholds: 0.15, 0.5 and 1.0 GeV/c. In the Transverse region we observe an increase in the multiplicity of a factor 2-3 between the lower and higher collision energies, depending on the track p(T) threshold considered. Data are compared to PYTHIA 6.4, PYTHIA 8.1 and PHOJET. On average, all models considered underestimate the multiplicity and summed p(T) in the Transverse region by about 10-30%.
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| 4. |
- Becker, Joel, et al.
(författare)
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Resource profile and user guide of the Polygenic Index Repository
- 2021
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Ingår i: Nature Human Behaviour. - : Nature Research (part of Springer Nature). - 2397-3374. ; 51:6, s. 694-695
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Tidskriftsartikel (refereegranskat)abstract
- Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the ‘additive SNP factor’. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
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| 5. |
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| 6. |
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| 7. |
- Oskarsson, Emma, et al.
(författare)
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Work-life balance among newly employed officers : a qualitative study
- 2021
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Ingår i: Health Psychology Report. - : Termedia Sp. z.o.o.. - 2353-4184 .- 2353-5571. ; 9:1, s. 39-48
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Tidskriftsartikel (refereegranskat)abstract
- Background: A military career puts great demands on the individual as regards combining working life and private life. The military and the family both demand time, energy, engagement, and commitment from the individual. Finding an appropriate balance between work and non-work might be particularly complex during military training and deployments that require extended periods away from home. The aim of this study was to investigate newly employed officers’ perceptions of work-life balance and its implications for future careers.Participants and procedure: This article is based on 34 semi-structured interviews with newly employed officers and non-commissioned officers in the Swedish Armed Forces (SAF). The interviews were analyzed according to the six-phase approach of coding and theme development by thematic analysis.Results: The analysis resulted in the emergence of three main themes: coping with different loyalties, individual and organizational strategies, and concerns about the future. All officers expressed loyalty to their work and organization, but these perceptions were influenced by significant others in private life. High ambitions in combination with stressful working conditions made organizational supportive strategies important, but these differed between units. Concerns about a constantly high workload and lack of recovery were highlighted, as well as concerns about future career and family building.Conclusions: In order to retain qualified personnel, the SAF should provide support and create conditions that help employees to balance work and non-work. A career in the Armed Forces will inevitably entail a reduced work-life balance, and our results show that the newly employed officers are highly aware of this. To ease the pressure, the SAF could be clearer about the expectations on their new employees.
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| 8. |
- Oskarsson, Joel, 1996-, et al.
(författare)
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Scalable Deep Gaussian Markov Random Fields for General Graphs
- 2022
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Ingår i: Proceedings of the 39th International Conference on Machine Learning. ; , s. 17117-17137
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Konferensbidrag (refereegranskat)abstract
- Machine learning methods on graphs have proven useful in many applications due to their ability to handle generally structured data. The framework of Gaussian Markov Random Fields (GMRFs) provides a principled way to define Gaussian models on graphs by utilizing their sparsity structure. We propose a flexible GMRF model for general graphs built on the multi-layer structure of Deep GMRFs, originally proposed for lattice graphs only. By designing a new type of layer we enable the model to scale to large graphs. The layer is constructed to allow for efficient training using variational inference and existing software frameworks for Graph Neural Networks. For a Gaussian likelihood, close to exact Bayesian inference is available for the latent field. This allows for making predictions with accompanying uncertainty estimates. The usefulness of the proposed model is verified by experiments on a number of synthetic and real world datasets, where it compares favorably to other both Bayesian and deep learning methods.
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| 9. |
- Oskarsson, Joel, 1996-, et al.
(författare)
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Temporal Graph Neural Networks for Irregular Data
- 2023
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Ingår i: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. - : ML Research Press. ; , s. 4515-4531
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a temporal graph neural network model for forecasting of graph-structured irregularly observed time series. Our TGNN4I model is designed to handle both irregular time steps and partial observations of the graph. This is achieved by introducing a time-continuous latent state in each node, following a linear Ordinary Differential Equation (ODE) defined by the output of a Gated Recurrent Unit (GRU). The ODE has an explicit solution as a combination of exponential decay and periodic dynamics. Observations in the graph neighborhood are taken into account by integrating graph neural network layers in both the GRU state update and predictive model. The time-continuous dynamics additionally enable the model to make predictions at arbitrary time steps. We propose a loss function that leverages this and allows for training the model for forecasting over different time horizons. Experiments on simulated data and real-world data from traffic and climate modeling validate the usefulness of both the graph structure and time-continuous dynamics in settings with irregular observations.
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| 10. |
- Westny, Theodor, et al.
(författare)
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Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Given their adaptability and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex ones, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun's can significantly improve predictions.
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| 11. |
- Westny, Theodor, et al.
(författare)
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Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction
- 2023
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Ingår i: 2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV. - : IEEE. - 9798350346916 - 9798350346923
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Konferensbidrag (refereegranskat)abstract
- Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heuns can greatly improve predictions.
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| 12. |
- Westny, Theodor, 1993-, et al.
(författare)
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MTP-GO : Graph-Based Probabilistic Multi-Agent Trajectory Prediction With Neural ODEs
- 2023
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; 8:9, s. 4223-4236
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Tidskriftsartikel (refereegranskat)abstract
- Enabling resilient autonomous motion planning requires robust predictions of surrounding road users’ future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.
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| 13. |
- Westny, Theodor, et al.
(författare)
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MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.
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| 14. |
- Österberg, Johan, 1969-, et al.
(författare)
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Perceptions of Officer Training Among Newly Employed Officers and Specialist Officers in the Swedish Armed Forces : A Qualitative Study
- 2021
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Ingår i: Scandinavian Journal of Military Studies. - : Scandinavian Military Studies. - 2596-3856. ; 4:1, s. 50-61
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Tidskriftsartikel (refereegranskat)abstract
- Professional officer training in Sweden has gone through some major changes in the last 30 years. The current officer system is a two-category system, where officers complete the three-year academic Officers’ Programme, and specialist officers complete 18 months of vocational training at the Swedish Armed Forces’ training schools. The aim of this study was to investigate newly graduated officers’ and specialist officers’ perceptions of their officer training. Results showed that their perceptions could be covered by three overriding themes: identification, vertical versus horizontal career paths and the perceived relevance of the officer training. Furthermore, the respondents’ officer identification seemed to have developed before officer training, and the individual motivators concerned deliberate choices of becoming either an officer or a specialist officer. Coaching was crucial to both officers and specialist officers. However, the officers stated that coaching came at an early stage of their basic military training, whereas specialist officers were coached at a later stage in their career. The implications for the Swedish Armed Forces is that identification and career path are issues that need to be addressed early in a soldier’s military career, and that officer training needs to be more focussed on defining career paths, especially for specialist officers.
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