shotput.bib

@article{Ciacci2022,
  title = {Shot Put: Which Role for Kinematic Analysis?},
  volume = {12},
  ISSN = {2076-3417},
  url = {http://dx.doi.org/10.3390/app12031699},
  DOI = {10.3390/app12031699},
  number = {3},
  journal = {Applied Sciences},
  publisher = {MDPI AG},
  author = {Ciacci,  Simone and Merni,  Franco and Semprini,  Gabriele and Drusiani,  Giacomo and Cortesi,  Matteo and Bartolomei,  Sandro},
  year = {2022},
  month = feb,
  pages = {1699}
}

@article{Mastalerz2022,
  title = {Variability of Performance and Kinematics of Different Shot Put Techniques in Elite and Sub-Elite Athletes–A Preliminary Study},
  volume = {19},
  ISSN = {1660-4601},
  url = {http://dx.doi.org/10.3390/ijerph19031751},
  DOI = {10.3390/ijerph19031751},
  number = {3},
  journal = {International Journal of Environmental Research and Public Health},
  publisher = {MDPI AG},
  author = {Mastalerz,  Andrzej and Sadowski,  Jerzy},
  year = {2022},
  month = feb,
  pages = {1751}
}

@article{Caughey2022,
  title={Variables Associated with High School Shot Put Performance},
  author={Caughey, RM and Thomas, C},
  journal={International Journal of Exercise Science},
  volume={15},
  number={6},
  pages={1357--1365},
  year={2022},
  publisher={},
  note={Published 2022 Oct 1}
}

@article{VanBiesen2017,
  title = {Comparison of shot-put release parameters and consistency in performance between elite throwers with and without intellectual impairment},
  volume = {13},
  ISSN = {2048-397X},
  url = {http://dx.doi.org/10.1177/1747954117707483},
  DOI = {10.1177/1747954117707483},
  number = {1},
  journal = {International Journal of Sports Science \& Coaching},
  publisher = {SAGE Publications},
  author = {Van Biesen,  Debbie and McCulloch,  Katina and Vanlandewijck,  Yves C},
  year = {2017},
  month = may,
  pages = {86–94}
}

@article{Linthorne2001,
  title = {Optimum release angle in the shot put},
  volume = {19},
  ISSN = {1466-447X},
  url = {http://dx.doi.org/10.1080/02640410152006135},
  DOI = {10.1080/02640410152006135},
  number = {5},
  journal = {Journal of Sports Sciences},
  publisher = {Informa UK Limited},
  author = {Linthorne,  Nicholas P.},
  year = {2001},
  month = jan,
  pages = {359–372}
}

@article{Landolsi2018,
  title = {Kinematic analysis of the shot‐put: A method of assessing the mechanical work of the hand action force},
  volume = {18},
  ISSN = {1536-7290},
  url = {http://dx.doi.org/10.1080/17461391.2018.1478449},
  DOI = {10.1080/17461391.2018.1478449},
  number = {9},
  journal = {European Journal of Sport Science},
  publisher = {Wiley},
  author = {Landolsi,  Mounir and Labiadh,  Lazhar and Zarrouk,  Fay\c{c}al and Maaref,  Khaled and Ghannouchi,  Slaheddine and Tabka,  Zouhair and Lacouture,  Patrick},
  year = {2018},
  month = jun,
  pages = {1208–1216}
}

@article{Zhu2021,
  title = {Mathematical simulation analysis of optimal testing of shot puter’s throwing path},
  volume = {7},
  ISSN = {2444-8656},
  url = {http://dx.doi.org/10.2478/amns.2021.1.00067},
  DOI = {10.2478/amns.2021.1.00067},
  number = {1},
  journal = {Applied Mathematics and Nonlinear Sciences},
  publisher = {Walter de Gruyter GmbH},
  author = {Zhu,  Wenbing and Hasan,  Hafnida},
  year = {2021},
  month = dec,
  pages = {557–564}
}

@article{NanangHimawanKusuma2021,
  title = {Mathematical simulation approach to diagnose performance limiting factor of shot put technique},
  volume = {1778},
  ISSN = {1742-6596},
  url = {http://dx.doi.org/10.1088/1742-6596/1778/1/012038},
  DOI = {10.1088/1742-6596/1778/1/012038},
  number = {1},
  journal = {Journal of Physics: Conference Series},
  publisher = {IOP Publishing},
  author = {Nanang Himawan Kusuma,  Moh},
  year = {2021},
  month = feb,
  pages = {012038}
}

@misc{WorldAthletics2023,
  author = {{World Athletics}},
  title = {Performance Indicators for the Men's Shot Put},
  year = {2023},
  howpublished = {\url{https://worldathletics.org/download/downloadnsa?filename=9daab72e-6ef2-4748-823d-38e8acec5c80.pdf&urlslug=performance-indicators-for-the-mens-shot-put}},
  note = {Accessed: 2024-03-22}
}

@misc{Li2013,
  author = {PengCheng Li},
  title = {The Impact Statistical Analysis of the Shot Throwing Speed and Angle on Results Based on Numerical Simulation},
  year = {2013},
  howpublished = {\url{https://www.tsijournals.com/articles/the-impact-statistical-analysis-of-the-shot-throwing-speed-and-angle-on-results-based-on-numerical-simulation.pdf}},
  note = {Accessed: 2024-03-22}
}

@misc{IAAF2017WomensShotPut,
  title = {Women's shot put - 2017 IAAF World Championships Biomechanical Report},
  author = {{International Association of Athletics Federations}},
  year = {2017},
  howpublished = {\url{https://worldathletics.org/download/download?filename=7faf6abe-888c-4296-ad4e-4243435d27b4.pdf&urlslug=Women%27s%20shot%20put%20-%202017%20IAAF%20World%20Championships%20Biomechanical%20report}},
  note = {Accessed: 2024-03-22}
}

@misc{IAAF2018WomensShotPutIndoor,
  title = {Women's Shot Put - 2018 IAAF World Indoor Championships Biomechanical Report},
  author = {{International Association of Athletics Federations}},
  year = {2018},
  howpublished = {\url{https://worldathletics.org/download/download?filename=0bd3f5cd-ad88-4c1f-a207-636365911e92.pdf&urlslug=Women%27s%20shot%20put%20-%202018%20IAAF%20World%20Indoor%20Championships%20Biomechanical%20Report}},
  note = {Accessed: 2024-03-22}
}

@misc{1609.04747,
    Author = {Sebastian Ruder},
    Title = {An overview of gradient descent optimization algorithms},
    Year = {2016},
    Eprint = {arXiv:1609.04747},
}

@article{1609.04747v2,
Author        = {Sebastian Ruder},
Title         = {An overview of gradient descent optimization algorithms},
Eprint        = {1609.04747v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Gradient descent optimization algorithms, while increasingly popular, are
often used as black-box optimizers, as practical explanations of their
strengths and weaknesses are hard to come by. This article aims to provide the
reader with intuitions with regard to the behaviour of different algorithms
that will allow her to put them to use. In the course of this overview, we look
at different variants of gradient descent, summarize challenges, introduce the
most common optimization algorithms, review architectures in a parallel and
distributed setting, and investigate additional strategies for optimizing
gradient descent.},
Year          = {2016},
Month         = {Sep},
Url           = {http://arxiv.org/abs/1609.04747v2},
File          = {1609.04747v2.pdf}
}

@article{1912.01703v1,
Author        = {Adam Paszke and Sam Gross and Francisco Massa and Adam Lerer and James Bradbury and Gregory Chanan and Trevor Killeen and Zeming Lin and Natalia Gimelshein and Luca Antiga and Alban Desmaison and Andreas Köpf and Edward Yang and Zach DeVito and Martin Raison and Alykhan Tejani and Sasank Chilamkurthy and Benoit Steiner and Lu Fang and Junjie Bai and Soumith Chintala},
Title         = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
Eprint        = {1912.01703v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Deep learning frameworks have often focused on either usability or speed, but
not both. PyTorch is a machine learning library that shows that these two goals
are in fact compatible: it provides an imperative and Pythonic programming
style that supports code as a model, makes debugging easy and is consistent
with other popular scientific computing libraries, while remaining efficient
and supporting hardware accelerators such as GPUs.
  In this paper, we detail the principles that drove the implementation of
PyTorch and how they are reflected in its architecture. We emphasize that every
aspect of PyTorch is a regular Python program under the full control of its
user. We also explain how the careful and pragmatic implementation of the key
components of its runtime enables them to work together to achieve compelling
performance.
  We demonstrate the efficiency of individual subsystems, as well as the
overall speed of PyTorch on several common benchmarks.},
Year          = {2019},
Month         = {Dec},
Url           = {http://arxiv.org/abs/1912.01703v1},
File          = {1912.01703v1.pdf}
}

@article{2403.14510v1,
Author        = {Ahmed ElGazzar and Marcel van Gerven},
Title         = {Universal Differential Equations as a Common Modeling Language for
  Neuroscience},
Eprint        = {2403.14510v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.CE},
Abstract      = {The unprecedented availability of large-scale datasets in neuroscience has
spurred the exploration of artificial deep neural networks (DNNs) both as
empirical tools and as models of natural neural systems. Their appeal lies in
their ability to approximate arbitrary functions directly from observations,
circumventing the need for cumbersome mechanistic modeling. However, without
appropriate constraints, DNNs risk producing implausible models, diminishing
their scientific value. Moreover, the interpretability of DNNs poses a
significant challenge, particularly with the adoption of more complex
expressive architectures. In this perspective, we argue for universal
differential equations (UDEs) as a unifying approach for model development and
validation in neuroscience. UDEs view differential equations as
parameterizable, differentiable mathematical objects that can be augmented and
trained with scalable deep learning techniques. This synergy facilitates the
integration of decades of extensive literature in calculus, numerical analysis,
and neural modeling with emerging advancements in AI into a potent framework.
We provide a primer on this burgeoning topic in scientific machine learning and
demonstrate how UDEs fill in a critical gap between mechanistic,
phenomenological, and data-driven models in neuroscience. We outline a flexible
recipe for modeling neural systems with UDEs and discuss how they can offer
principled solutions to inherent challenges across diverse neuroscience
applications such as understanding neural computation, controlling neural
systems, neural decoding, and normative modeling.},
Year          = {2024},
Month         = {Mar},
Url           = {http://arxiv.org/abs/2403.14510v1},
File          = {2403.14510v1.pdf}
}