Proceedings 14th International Conference on Automated Deduction in
Geometry
- URL: http://arxiv.org/abs/2401.10725v1
- Date: Fri, 19 Jan 2024 14:42:08 GMT
- Title: Proceedings 14th International Conference on Automated Deduction in
Geometry
- Authors: Pedro Quaresma (University of Coimbra, Portugal), Zolt\'an Kov\'acs
(The Private University College of Education of the Diocese of Linz, Austria)
- Abstract summary: ADG is a forum to exchange ideas and views, to present research results and progress, and to demonstrate software tools.
The 14th edition, ADG 2023, was held in Belgrade, Serbia, in September 20-22, 2023.
- Score: 0.970212766431148
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: ADG is a forum to exchange ideas and views, to present research results and
progress, and to demonstrate software tools at the intersection between
geometry and automated deduction. The conference is held every two years. The
previous editions of ADG were held in Hagenberg in 2021 (online, postponed from
2020 due to COVID-19), Nanning in 2018, Strasbourg in 2016, Coimbra in 2014,
Edinburgh in 2012, Munich in 2010, Shanghai in 2008, Pontevedra in 2006,
Gainesville in 2004, Hagenberg in 2002, Zurich in 2000, Beijing in 1998, and
Toulouse in 1996.
The 14th edition, ADG 2023, was held in Belgrade, Serbia, in September 20-22,
2023. This edition of ADG had an additional special focus topic, Deduction in
Education.
Invited Speakers: Julien Narboux, University of Strasbourg, France
"Formalisation, arithmetization and automatisation of geometry"; Filip Mari\'c,
University of Belgrade, Serbia, "Automatization, formalization and
visualization of hyperbolic geometry"; Zlatan Magajna, University of Ljubljana,
Slovenia, "Workshop OK Geometry"
Related papers
- Proceedings First Workshop on Adaptable Cloud Architectures [0.2864713389096699]
This volume contains the post-proceedings of the Workshop on Adaptable Cloud Architectures (WACA 2025), held on June 20, 2025 in Lille, France, co-located with DisCoTec 2025 - 20th International Federated Conference on Distributed Computing Techniques.
arXiv Detail & Related papers (2025-12-26T15:14:40Z) - PyG 2.0: Scalable Learning on Real World Graphs [70.76634276606693]
We present Pyg 2.0, a comprehensive update that introduces substantial improvements in scalability and real-world application capabilities.<n>We detail the framework's enhanced architecture, including support for heterogeneous and temporal graphs, scalable feature/graph stores, and various optimizations.
arXiv Detail & Related papers (2025-07-22T19:55:09Z) - Proceedings The 13th International Workshop on Theorem proving components for Educational software [0.0]
ThEdu series pursues the smooth transition from an intuitive way of doing mathematics at secondary school to a more formal approach to the subject in STEM education.<n>Papers in this volume are a faithful representation of the wide spectrum of ThEdu.
arXiv Detail & Related papers (2025-05-07T11:30:54Z) - Proceedings of the Fourteenth and Fifteenth International Workshop on Graph Computation Models [0.0]
The workshops took place in Leicester, UK on 18th July 2023 and Enschede, the Netherlands on 9th July 2024.
The aim of the International GCM Workshop series is to bring together researchers interested in all aspects of computation models based on graphs and graph transformation.
arXiv Detail & Related papers (2025-03-25T13:19:26Z) - Proceedings of the 21st International Conference on Quantum Physics and Logic [49.1574468325115]
QPL is an annual conference that brings together academic and industry researchers working on the mathematical foundations of quantum computation, quantum physics, and related areas.
The main focus is on the use of algebraic and categorical structures, formal languages, semantic methods, as well as other mathematical and computer scientific techniques applicable to the study of physical systems, physical processes, and their composition.
arXiv Detail & Related papers (2024-08-09T15:04:17Z) - Proceedings 12th International Workshop on Theorem proving components for Educational software [0.0]
ThEdu series pursues the smooth transition from an intuitive way of doing mathematics at secondary school to a more formal approach to the subject in STEM education.
ThEdu'23 was very successful, with one invited talk, by Yves Bertot (Inria, France), "The challenges of using Type Theory to teach Mathematics", and seven regular contributions.
Seven submissions have been accepted by our reviewers, who jointly produced at least three careful reports on each of the contributions.
arXiv Detail & Related papers (2024-04-04T11:51:26Z) - The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors,
and renal cysts in corticomedullary-phase CT [50.41526598153698]
This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Challenge (KiTS21)
KiTS21 is a sequel to its first edition in 2019, and it features a variety of innovations in how the challenge was designed.
The top-performing teams achieved a significant improvement over the state of the art set in 2019, and this performance is shown to inch ever closer to human-level performance.
arXiv Detail & Related papers (2023-07-05T02:00:14Z) - A General Framework for Sequential Decision-Making under Adaptivity
Constraints [112.79808969568253]
We study general sequential decision-making under two adaptivity constraints: rare policy switch and batch learning.
For the rare policy switch constraint, we provide an algorithm to achieve a $widetildemathcalO(sqrtK+K/B)$ regret with the number of batches.
For the batch learning constraint, we provide an algorithm that provides a $widetildemathcalO(sqrtK+K/B)$ regret with the number of batches.
arXiv Detail & Related papers (2023-06-26T07:20:25Z) - Proceedings 11th International Workshop on Theorem Proving Components
for Educational Software [0.0]
ThEdu series pursues the smooth transition from an intuitive way of doing mathematics at secondary school to a more formal approach to the subject in STEM education.
ThEdu'22 was a vibrant workshop, with two invited talk by Thierry Dana-Picard and Yoni Zohar.
The resulting revised papers are collected in the present volume.
arXiv Detail & Related papers (2023-03-09T16:10:13Z) - VLDB 2021: Designing a Hybrid Conference [32.06949074708552]
This paper describes how we defined the hybrid format for VLDB 2021 going through the key design decisions.
Our goal is to share this knowledge with fellow conference organizers who target a hybrid conference format as well, which is on its way to becoming the norm rather than the exception.
arXiv Detail & Related papers (2022-01-26T15:39:10Z) - Proceedings of the 13th International Conference on Automated Deduction
in Geometry [0.0]
ADG is a forum to exchange ideas and views, to present research results and progress, and to demonstrate software tools at the intersection between geometry and automated deduction.
Previous editions of ADG were held in Nanning in 2018, Strasbourg in 2016, Coimbra in 2014, Edinburgh in 2012, Munich in 2010, Shanghai in 2008, Pontevedra in 2006, in 2004, Zurich in 2000, Beijing in 1998, and Toulouse in 1996.
The 13th edition of ADG was supposed to be held in 2020 in Hagenberg, Austria, but due to the COVID-19 pandemic, it was postponed for 2021, and held online (still hosted by RISC Institute, Hagenberg
arXiv Detail & Related papers (2021-12-28T21:56:13Z) - Changes in European Solidarity Before and During COVID-19: Evidence from
a Large Crowd- and Expert-Annotated Twitter Dataset [77.27709662210363]
We introduce the well-established social scientific concept of social solidarity and its contestation, anti-solidarity, as a new problem setting to supervised machine learning in NLP.
We annotate 2.3k English and German tweets for (anti-)solidarity expressions, utilizing multiple human annotators and two annotation approaches (experts vs. crowds)
Our results show that solidarity became increasingly salient and contested during the COVID-19 crisis.
arXiv Detail & Related papers (2021-08-02T17:03:12Z) - Lorentzian Graph Convolutional Networks [47.41609636856708]
We propose a novel hyperbolic graph convolutional network (LGCN) named Lorentzian graph convolutional network (LGCN)
LGCN rigorously guarantees the learned node features follow the hyperbolic geometry.
Experiments on six datasets show that LGCN performs better than the state-of-the-art methods.
arXiv Detail & Related papers (2021-04-15T14:14:25Z) - Genetic Improvement @ ICSE 2020 [29.082967162994024]
We discuss the eighth international Genetic Improvement workshop, GI-2020 @ ICSE.
Discussion included industry take up, human factors, explainabiloity (explainability, justifyability, exploitability) and GI benchmarks.
We speculate on how the Coronavirus Covid-19 Pandemic will affect research next year and into the future.
arXiv Detail & Related papers (2020-07-31T11:51:38Z) - L$^2$-GCN: Layer-Wise and Learned Efficient Training of Graph
Convolutional Networks [118.37805042816784]
Graph convolution networks (GCN) are increasingly popular in many applications, yet remain notoriously hard to train over large graph datasets.
We propose a novel efficient layer-wise training framework for GCN (L-GCN), that disentangles feature aggregation and feature transformation during training.
Experiments show that L-GCN is faster than state-of-the-arts by at least an order of magnitude, with a consistent of memory usage not dependent on dataset size.
arXiv Detail & Related papers (2020-03-30T16:37:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.