| 1. |
- Kan, Siyi, et al.
(författare)
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Risk of intact forest landscape loss goes beyond global agricultural supply chains
- 2023
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Ingår i: One Earth. - : Elsevier BV. - 2590-3322 .- 2590-3330. ; 6:1, s. 55-65
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Tidskriftsartikel (refereegranskat)abstract
- The continued loss of unfragmented intact forest landscapes (IFLs) despite numerous global conservation initiatives indicates the need for improved knowledge of proximate and underlying drivers. Yet the role of non-agricultural activities in forest degradation and fragmentation has not received adequate attention. We focus on IFL loss caused by various economic activities and investigate the influence of global consumption and trade via the multi-regional input-output model. For IFL loss associated with the 2014 world economy, over 60% was related to final consumption of non-agricultural products. More than one-third of IFL loss was linked to export, primarily from Russia, Canada, and tropical regions to mainland China, the EU, and the United States. Of IFL loss associated with export, 51% and 26% was directly caused by logging and mining or energy extraction, respectively. The dispersed nature of IFL loss drivers and their indirect links to individual final consumers call for stronger government engagement and supply chain interventions.
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| 2. |
- Kristan, Matej, et al.
(författare)
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The Ninth Visual Object Tracking VOT2021 Challenge Results
- 2021
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Ingår i: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021). - : IEEE COMPUTER SOC. - 9781665401913 ; , s. 2711-2738
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website(1).
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| 3. |
- Sjöstedt, Evelina, et al.
(författare)
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An atlas of the protein-coding genes in the human, pig, and mouse brain
- 2020
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 367:6482, s. 1090-
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Tidskriftsartikel (refereegranskat)abstract
- The brain, with its diverse physiology and intricate cellular organization, is the most complex organ of the mammalian body. To expand our basic understanding of the neurobiology of the brain and its diseases, we performed a comprehensive molecular dissection of 10 major brain regions and multiple subregions using a variety of transcriptomics methods and antibody-based mapping. This analysis was carried out in the human, pig, and mouse brain to allow the identification of regional expression profiles, as well as to study similarities and differences in expression levels between the three species. The resulting data have been made available in an open-access Brain Atlas resource, part of the Human Protein Atlas, to allow exploration and comparison of the expression of individual protein-coding genes in various parts of the mammalian brain.
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| 4. |
- Zhang, Yichen, et al.
(författare)
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A GPU-based computational framework that bridges neuron simulation and artificial intelligence
- 2023
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such a process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. We investigate how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines. Furthermore, we provide a brief discussion on the potential of DeepDendrite for AI, specifically highlighting its ability to enable the efficient training of biophysically detailed models in typical image classification tasks.
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