Zobrazeno 1 - 10
of 14 383
pro vyhledávání: '"A. Blaser"'
Autor:
P.M. Treitz, D.M. Atkinson, A. Blaser, M.T. Bonney, C.A. Braybrook, E.C. Buckley, A. Collingwood, R. Edwards, K. van Ewijk, V. Freemantle, F. Gregory, J. Holloway, J.K.Y. Hung, S.F. Lamoureux, N. Liu, G. Ljubicic, G. Robson, A.C.A. Rudy, N.A. Scott, C. Shang, J. Wall
Publikováno v:
Arctic Science, Vol 10, Iss 2, Pp 281-304 (2024)
The Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Nunavut (74°55′N, 109°34′W) was established in 2003 to examine Arctic ecosystem processes that would be impacted by climate warming and permafrost degradation. This paper pr
Externí odkaz:
https://doaj.org/article/d1b1c9c0d4ad4149a7cc1b09f8aec178
Autor:
Becker, Alexander, Wegner, Jan D., Dawoe, Evans, Schindler, Konrad, Thompson, William J., Bunn, Christian, Garrett, Rachael D., Castro, Fabio, Hart, Simon P., Blaser-Hart, Wilma J.
Reconciling agricultural production with climate-change mitigation and adaptation is one of the most formidable problems in sustainability. One proposed strategy for addressing this problem is the judicious retention of trees in agricultural systems.
Externí odkaz:
http://arxiv.org/abs/2410.20882
Autor:
Blaser, Ethan, Zhang, Shangtong
Tabular average reward Temporal Difference (TD) learning is perhaps the simplest and the most fundamental policy evaluation algorithm in average reward reinforcement learning. After at least 25 years since its discovery, we are finally able to provid
Externí odkaz:
http://arxiv.org/abs/2409.19546
In-context learning refers to the learning ability of a model during inference time without adapting its parameters. The input (i.e., prompt) to the model (e.g., transformers) consists of both a context (i.e., instance-label pairs) and a query instan
Externí odkaz:
http://arxiv.org/abs/2405.13861
The motivation of this paper is to recognize a geometric shape from a noisy sample in the form of a point cloud. Inspired by the HDBSCAN clustering algorithm, we introduce the core dissimilarity, from which we construct the core bifiltration. We also
Externí odkaz:
http://arxiv.org/abs/2405.01214
Autor:
Aragon, Cecilia, Callens, Melissa Vosen, Branham, Stacy M., Anicha, Cali, Blaser, Brianna, Bilen-Green, Canan
In the early stages of the COVID-19 pandemic, many events and conferences hastily converted to a virtual format, and many commercial ventures promptly developed tools promising seamless transitions to virtual spaces. In particular, efforts to expand
Externí odkaz:
http://arxiv.org/abs/2405.05910
The demand for collaborative and private bandit learning across multiple agents is surging due to the growing quantity of data generated from distributed systems. Federated bandit learning has emerged as a promising framework for private, efficient,
Externí odkaz:
http://arxiv.org/abs/2403.00116
Updating machine learning models with new information usually improves their predictive performance, yet, in many applications, it is also desirable to avoid changing the model predictions too much. This property is called stability. In most cases wh
Externí odkaz:
http://arxiv.org/abs/2402.13655
The particle trajectories in irrotational, incompressible and inviscid deep-water surface gravity waves are open, leading to a net drift in the direction of wave propagation commonly referred to as the Stokes Drift, which is responsible for catalysin
Externí odkaz:
http://arxiv.org/abs/2401.14334
Generalizing the problem of counting rational points on curves and surfaces over finite fields, we consider the setting of $n \times n$ matrix points with finite field entries. We obtain exact formulas for matrix point counts on elliptic curves and c
Externí odkaz:
http://arxiv.org/abs/2308.02683