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1Open-endedness: The last grand challenge you’ve never heard of
While open-endedness could be a force for discovering intelligence, it could also be a component of AI itself.
2 years ago by @jascal_panetzky
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4Global Vegetation Modeling with Pre-Trained Weather Transformers
P. Janetzky, F. Gallusser, S. Hentschel, A. Hotho, and A. Krause. (2024)
7 months ago by @jascal_panetzky
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3Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
B. Bonev, T. Kurth, C. Hundt, J. Pathak, M. Baust, K. Kashinath, and A. Anandkumar. (2023)
8 months ago by @jascal_panetzky
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6Automatic Speech Detection on a Smart Beehive’s Raspberry Pi
P. Janetzky, P. Lissmann, A. Hotho, and A. Krause. ESC, (2023)
9 months ago by @jascal_panetzky
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1AtmoDist: Self-supervised representation learning for atmospheric dynamics
S. Hoffmann, and C. Lessig. Environmental Data Science, (2023)
9 months ago by @jascal_panetzky
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1Diffusion-Based Audio Inpainting
E. Moliner, and V. Välimäki. arXiv preprint arXiv:2305.15266, (2023)
10 months ago by @jascal_panetzky
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1Solving audio inverse problems with a diffusion model
E. Moliner, J. Lehtinen, and V. Välimäki. ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), page 1--5. IEEE, (2023)
10 months ago by @jascal_panetzky
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1Identifying dynamic memory effects on vegetation state using recurrent neural networks
B. Kraft, M. Jung, M. Körner, C. Requena Mesa, J. Cortés, and M. Reichstein. Frontiers in big Data, (2019)
10 months ago by @jascal_panetzky
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2Adaptive fourier neural operators: Efficient token mixers for transformers
J. Guibas, M. Mardani, Z. Li, A. Tao, A. Anandkumar, and B. Catanzaro. arXiv preprint arXiv:2111.13587, (2021)
12 months ago by @jascal_panetzky
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8Detecting Presence Of Speech In Acoustic Data Obtained From Beehives.
P. Janetzky, P. Davidson, M. Steininger, A. Krause, and A. Hotho. DCASE, page 26--30. (2021)
a year ago by @jascal_panetzky
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1The distribution of tree and grass roots in savannas in relation to soil nitrogen and water
E. February, and S. Higgins. South African Journal of Botany, 76 (3): 517--523 (2010)
a year ago by @jascal_panetzky
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2Influence of competition and rainfall manipulation on the growth responses of savanna trees and grasses
E. February, S. Higgins, W. Bond, and L. Swemmer. Ecology, 94 (5): 1155--1164 (2013)
a year ago by @jascal_panetzky
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1Predicting plant migration rates in a changing world: the role of long-distance dispersal
S. Higgins, and D. Richardson. The American Naturalist, 153 (5): 464--475 (1999)
a year ago by @jascal_panetzky
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1Effects of four decades of fire manipulation on woody vegetation structure in savanna
S. Higgins, W. Bond, E. February, A. Bronn, D. Euston-Brown, B. Enslin, N. Govender, L. Rademan, S. O'Regan, A. Potgieter and 1 other author(s). Ecology, 88 (5): 1119--1125 (2007)
a year ago by @jascal_panetzky
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1Three decades of multi-dimensional change in global leaf phenology
R. Buitenwerf, L. Rose, and S. Higgins. Nature Climate Change, 5 (4): 364--368 (2015)
a year ago by @jascal_panetzky
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4Do red edge and texture attributes from high-resolution satellite data improve wood volume estimation in a semi-arid mountainous region?
P. Schumacher, B. Mislimshoeva, A. Brenning, H. Zandler, M. Brandt, C. Samimi, and T. Koellner. Remote Sensing, 8 (7): 540 (2016)
a year ago by @jascal_panetzky
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1Quantifying interregional flows of multiple ecosystem services--A case study for Germany
J. Kleemann, M. Schröter, K. Bagstad, C. Kuhlicke, T. Kastner, D. Fridman, C. Schulp, S. Wolff, J. Mart\'ınez-López, T. Koellner and 1 other author(s). Global Environmental Change, (2020)
a year ago by @jascal_panetzky
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1Next-generation dynamic global vegetation models: learning from community ecology
S. Scheiter, L. Langan, and S. Higgins. New Phytologist, 198 (3): 957--969 (2013)
a year ago by @jascal_panetzky
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3Impacts of climate change on the vegetation of Africa: an adaptive dynamic vegetation modelling approach
S. Scheiter, and S. Higgins. Global Change Biology, 15 (9): 2224--2246 (2009)
a year ago by @jascal_panetzky
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1Savanna vegetation-fire-climate relationships differ among continents
C. Lehmann, T. Anderson, M. Sankaran, S. Higgins, S. Archibald, W. Hoffmann, N. Hanan, R. Williams, R. Fensham, J. Felfili and 1 other author(s). Science, 343 (6170): 548--552 (2014)
a year ago by @jascal_panetzky
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1Determinants of woody cover in African savannas
M. Sankaran, N. Hanan, R. Scholes, J. Ratnam, D. Augustine, B. Cade, J. Gignoux, S. Higgins, X. Le Roux, F. Ludwig and 1 other author(s). Nature, 438 (7069): 846--849 (2005)
a year ago by @jascal_panetzky
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1Revealing patterns of nocturnal migration using the European weather radar network
C. Nilsson, A. Dokter, L. Verlinden, J. Shamoun-Baranes, B. Schmid, P. Desmet, S. Bauer, J. Chapman, J. Alves, P. Stepanian and 1 other author(s). Ecography, 42 (5): 876--886 (2019)
a year ago by @jascal_panetzky
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1Automatic detection of migrating soaring bird flocks using weather radars by deep learning
I. Schekler, T. Nave, I. Shimshoni, and N. Sapir. Methods in Ecology and Evolution, (2023)
a year ago by @jascal_panetzky
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1The three major axes of terrestrial ecosystem function
M. Migliavacca, T. Musavi, M. Mahecha, J. Nelson, J. Knauer, D. Baldocchi, O. Perez-Priego, R. Christiansen, J. Peters, K. Anderson and 1 other author(s). Nature, 598 (7881): 468--472 (2021)
a year ago by @jascal_panetzky
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1Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally
S. Higgins, and S. Scheiter. Nature, 488 (7410): 209--212 (2012)
a year ago by @jascal_panetzky
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2Shifts in vegetation activity of terrestrial ecosystems attributable to climate trends
S. Higgins, T. Conradi, and E. Muhoko. Nature Geoscience, 16 (2): 147--153 (2023)
a year ago by @jascal_panetzky
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1Ensembles of data-efficient vision transformers as a new paradigm for automated classification in ecology
S. Kyathanahally, T. Hardeman, M. Reyes, E. Merz, T. Bulas, P. Brun, F. Pomati, and M. Baity-Jesi. Scientific Reports, 12 (1): 18590 (2022)
a year ago by @jascal_panetzky
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1Harnessing deep learning in ecology: An example predicting bark beetle outbreaks
W. Rammer, and R. Seidl. Frontiers in plant science, (2019)
a year ago by @jascal_panetzky
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1From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale?
W. Zhi, D. Feng, W. Tsai, G. Sterle, A. Harpold, C. Shen, and L. Li. Environmental science & technology, 55 (4): 2357--2368 (2021)
a year ago by @jascal_panetzky
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1Machine learning and artificial intelligence to aid climate change research and preparedness
C. Huntingford, E. Jeffers, M. Bonsall, H. Christensen, T. Lees, and H. Yang. Environmental Research Letters, 14 (12): 124007 (2019)
a year ago by @jascal_panetzky
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1Machine learning for ecosystem services
S. Willcock, J. Mart\'ınez-López, D. Hooftman, K. Bagstad, S. Balbi, A. Marzo, C. Prato, S. Sciandrello, G. Signorello, B. Voigt and 1 other author(s). Ecosystem services, (2018)
a year ago by @jascal_panetzky
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1Deep learning and process understanding for data-driven Earth system science
M. Reichstein, G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and f. Prabhat. Nature, 566 (7743): 195--204 (2019)
a year ago by @jascal_panetzky
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4FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
J. Pathak, S. Subramanian, P. Harrington, S. Raja, A. Chattopadhyay, M. Mardani, T. Kurth, D. Hall, Z. Li, K. Azizzadenesheli and 3 other author(s). (February 2022)arXiv:2202.11214 physics.
a year ago by @jascal_panetzky
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1A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration
J. Chen, B. Dafflon, A. Tran, N. Falco, and S. Hubbard. Hydrology and Earth System Sciences, 25 (11): 6041--6066 (November 2021)Publisher: Copernicus GmbH.
a year ago by @jascal_panetzky
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1Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks
B. Kraft, M. Jung, M. Körner, C. Requena Mesa, J. Cortés, and M. Reichstein. Frontiers in Big Data, (2019)
a year ago by @jascal_panetzky
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1An Outlook for Deep Learning in Ecosystem Science
G. Perry, R. Seidl, A. Bellvé, and W. Rammer. Ecosystems, 25 (8): 1700--1718 (December 2022)
a year ago by @jascal_panetzky
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1The current and future uses of machine learning in ecosystem service research
M. Scowen, I. Athanasiadis, J. Bullock, F. Eigenbrod, and S. Willcock. Science of The Total Environment, (December 2021)
a year ago by @jascal_panetzky
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Big
Data-driven
Ecosystem
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Methodology
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1BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification
Y. Djoumbou-Feunang, J. Fiamoncini, A. Gil-de-la Fuente, R. Greiner, C. Manach, and D. Wishart. Journal of Cheminformatics, 11 (1): 2 (January 2019)
a year ago by @jascal_panetzky
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Enzyme-substrate
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Machine
Mass
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Metabolite
Microbial
Structure-based
classification
degradation,
ecomodelling
identification,
learning,
pathway,
prediction,
specificity,
spectrometry,
system,
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3Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
X. Shi, Z. Gao, L. Lausen, H. Wang, D. Yeung, W. Wong, and W. WOO. Advances in Neural Information Processing Systems, 30, Curran Associates, Inc., (2017)
a year ago by @jascal_panetzky
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2Deep learning for twelve hour precipitation forecasts
L. Espeholt, S. Agrawal, C. Sønderby, M. Kumar, J. Heek, C. Bromberg, C. Gazen, R. Carver, M. Andrychowicz, J. Hickey and 2 other author(s). Nature Communications, 13 (1): 5145 (September 2022)Number: 1 Publisher: Nature Publishing Group.
a year ago by @jascal_panetzky
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1Recent decline in the global land evapotranspiration trend due to limited moisture supply
M. Jung, M. Reichstein, P. Ciais, S. Seneviratne, J. Sheffield, M. Goulden, G. Bonan, A. Cescatti, J. Chen, R. De Jeu and 23 other author(s). Nature, 467 (7318): 951--954 (October 2010)
a year ago by @jascal_panetzky
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2Skilful precipitation nowcasting using deep generative models of radar
S. Ravuri, K. Lenc, M. Willson, D. Kangin, R. Lam, P. Mirowski, M. Fitzsimons, M. Athanassiadou, S. Kashem, S. Madge and 10 other author(s). Nature, 597 (7878): 672--677 (September 2021)Number: 7878 Publisher: Nature Publishing Group.
a year ago by @jascal_panetzky
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sciences
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1Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model
M. Jung, M. Reichstein, and A. Bondeau. Biogeosciences, 6 (10): 2001--2013 (October 2009)Publisher: Copernicus GmbH.
a year ago by @jascal_panetzky
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1Evaluation of the convection-resolving climate modeling approach on continental scales
D. Leutwyler, D. Lüthi, N. Ban, O. Fuhrer, and C. Schär. Journal of Geophysical Research: Atmospheres, 122 (10): 5237--5258 (2017)\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD026013.
a year ago by @jascal_panetzky
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1Machine-Learned Climate Model Corrections From a Global Storm-Resolving Model: Performance Across the Annual Cycle
A. Kwa, S. Clark, B. Henn, N. Brenowitz, J. McGibbon, O. Watt-Meyer, W. Perkins, L. Harris, and C. Bretherton. Journal of Advances in Modeling Earth Systems, 15 (5): e2022MS003400 (2023)\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022MS003400.
a year ago by @jascal_panetzky
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1Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change
H. Lotze, D. Tittensor, A. Bryndum-Buchholz, T. Eddy, W. Cheung, E. Galbraith, M. Barange, N. Barrier, D. Bianchi, J. Blanchard and 25 other author(s). Proceedings of the National Academy of Sciences, 116 (26): 12907--12912 (June 2019)Company: National Academy of Sciences Distributor: National Academy of Sciences Institution: National Academy of Sciences Label: National Academy of Sciences Publisher: Proceedings of the National Academy of Sciences.
a year ago by @jascal_panetzky
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1A novel framework for spatio-temporal prediction of environmental data using deep learning
F. Amato, F. Guignard, S. Robert, and M. Kanevski. Scientific Reports, 10 (1): 22243 (December 2020)Number: 1 Publisher: Nature Publishing Group.
a year ago by @jascal_panetzky
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3Precipitation Nowcasting with Satellite Imagery
V. Lebedev, V. Ivashkin, I. Rudenko, A. Ganshin, A. Molchanov, S. Ovcharenko, R. Grokhovetskiy, I. Bushmarinov, and D. Solomentsev. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, page 2680--2688. Anchorage AK USA, ACM, (July 2019)
a year ago by @jascal_panetzky
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5Accurate medium-range global weather forecasting with 3D neural networks
K. Bi, L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian. Nature, (July 2023)Publisher: Nature Publishing Group.
a year ago by @jascal_panetzky
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Atmospheric
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dynamics
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AtmosphericComputerdynamicsecomodellingscience,
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2Transformer models: an introduction and catalog
X. Amatriain. (May 2023)arXiv:2302.07730 cs.
a year ago by @jascal_panetzky
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Language
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4Deep learning for multi-year ENSO forecasts
Y. Ham, J. Kim, and J. Luo. Nature, 573 (7775): 568--572 (September 2019)Number: 7775 Publisher: Nature Publishing Group.
a year ago by @jascal_panetzky
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Ocean
dynamics,
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