| 1. |
- Aamodt, K., et al.
(författare)
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Alignment of the ALICE Inner Tracking System with cosmic-ray tracks
- 2010
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Ingår i: Journal of Instrumentation. - 1748-0221. ; 5
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Konferensbidrag (refereegranskat)abstract
- ALICE (A Large Ion Collider Experiment) is the LHC (Large Hadron Collider) experiment devoted to investigating the strongly interacting matter created in nucleus-nucleus collisions at the LHC energies. The ALICE ITS, Inner Tracking System, consists of six cylindrical layers of silicon detectors with three different technologies; in the outward direction: two layers of pixel detectors, two layers each of drift, and strip detectors. The number of parameters to be determined in the spatial alignment of the 2198 sensor modules of the ITS is about 13,000. The target alignment precision is well below 10 mu m in some cases (pixels). The sources of alignment information include survey measurements, and the reconstructed tracks from cosmic rays and from proton-proton collisions. The main track-based alignment method uses the Millepede global approach. An iterative local method was developed and used as well. We present the results obtained for the ITS alignment using about 10(5) charged tracks from cosmic rays that have been collected during summer 2008, with the ALICE solenoidal magnet switched off.
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| 2. |
- Kristan, M., et al.
(författare)
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The Eighth Visual Object Tracking VOT2020 Challenge Results
- 2020
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Ingår i: Computer Vision. - Cham : Springer International Publishing. - 9783030682378 ; , s. 547-601
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net ).
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| 3. |
- Abgrall, N., et al.
(författare)
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The large enriched germanium experiment for neutrinoless double beta decay (LEGEND)
- 2017
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Ingår i: AIP Conference Proceedings. - : Author(s). - 1551-7616 .- 0094-243X. ; 1894
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Konferensbidrag (refereegranskat)abstract
- The observation of neutrinoless double-beta decay (0νββ) would show that lepton number is violated, reveal that neu-trinos are Majorana particles, and provide information on neutrino mass. A discovery-capable experiment covering the inverted ordering region, with effective Majorana neutrino masses of 15 - 50 meV, will require a tonne-scale experiment with excellent energy resolution and extremely low backgrounds, at the level of ∼0.1 count /(FWHM·t·yr) in the region of the signal. The current generation 76Ge experiments GERDA and the Majorana Demonstrator, utilizing high purity Germanium detectors with an intrinsic energy resolution of 0.12%, have achieved the lowest backgrounds by over an order of magnitude in the 0νββ signal region of all 0νββ experiments. Building on this success, the LEGEND collaboration has been formed to pursue a tonne-scale 76Ge experiment. The collaboration aims to develop a phased 0νββ experimental program with discovery potential at a half-life approaching or at 1028 years, using existing resources as appropriate to expedite physics results.
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| 4. |
- Liu, G., et al.
(författare)
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Full-Duplex and C-RAN Based Multi-Cell Non-Orthogonal Multiple Access over 5G Wireless Networks
- 2019
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Ingår i: ICC 2019 - 2019 IEEE International Conference on Communications (ICC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538680889
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose the full-duplex and cloud radio access network (C-RAN) based multi-cell non-orthogonal multiple access schemes over 5G mobile wireless networks. To cope with the severe intra-cell and inter-cell interferences as well as perform the centralized optimization, we adopt the C-RAN architecture, where the baseband processing and resource management are conducted at a central unit (CU). With the goal of maximizing the weighted sum achievable rate, we formulate the sum rate maximization power allocation problem as a non-convex problem. Thanks to the hidden monotonicity structure of the considered problem, the optimal power allocation algorithm is developed by the monotonic optimization method. Besides, we propose another suboptimal algorithm by employing successive convex approximation method to obtain the close-to-optimal solution with a significantly reduced computational complexity. Extensive simulations are conducted to verify the effectiveness of our proposed power allocation schemes, and confirm the superiority of our proposed C-RAN architecture.
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| 5. |
- Teng, F., et al.
(författare)
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A Text Annotation Tool with Pre-annotation Based on Deep Learning
- 2019
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Ingår i: Lect. Notes Comput. Sci.. - Cham : Springer. - 9783030295509 ; , s. 440-451
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Konferensbidrag (refereegranskat)abstract
- In this paper, we introduce an open-source tool, YEDDA, supported by a pre-annotation module based deep learning. EPAD proposes a novel annotation workflow, combining pre-annotation and manual annotation, which improves the efficiency and quality of annotation. The pre-annotation module can effectively reduce the annotation time, and meanwhile improve the precision and recall of annotation. EPAD also contains some of the mechanisms to facilitate the usage of the pre-annotation module. As a collaborative design, EPAD provides administrators with annotation statistics and analysis functions. Experiments showed that EPAD shortened almost 60.0$$\%$$ of the total annotation time, and improved 12.7$$\%$$ of F-measure for annotation quality.
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