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Darboux Transformations in Integrable Systems
The Darboux transformation approach is one of the most effective methods for constructing explicit solutions of partial differential equations which are called integrable systems and play important roles in mechanics, physics and differential geometry. This book presents the Darboux transformations in matrix form and provides purely algebraic algorithms for constructing the explicit solutions. A basis for using symbolic computations to obtain the explicit exact solutions for many integrable systems is established. Moreover, the behavior of simple and multi-solutions, even in multi-dimensional cases, can be elucidated clearly. The method covers a series of important equations such as various ...
Computational, label, and data efficiency in deep learning for sparse 3D data
Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.
Articulating Citizenship
"At the genesis of the Republic of China in 1912, many political leaders, educators, and social reformers argued that republican education should transform China’s people into dynamic modern citizens—social and political agents whose public actions would rescue the national community. Over subsequent decades, however, they came to argue fiercely over the contents of citizenship and how it should be taught. Moreover, many of their carefully crafted policies and programs came to be transformed by textbook authors, teachers, administrators, and students. Furthermore, the idea of citizenship, once introduced, raised many troubling questions. Who belonged to the national community in China, a...
Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass
This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques.
Computer Vision – ECCV 2022
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Remote Sensing for Field-based Crop Phenotyping
Dynamic monitoring of crop phenotypic traits (e.g., LAI, plant height, biomass, nitrogen, yield et al.) is essential for exploring crop growth patterns, breeding new varieties, and determining optimized strategies for crop management. Traditional methods for determining crop phenotypic traits are mainly based on field sampling, handheld instrument measurement, and mechanized high-throughput platforms, which are time-consuming, and have low efficiency and incomplete spatial coverage. The development of crop science requires more rapid and accurate access to field-based crop phenotypes. Remote sensing provides a novel solution to quantify crop structural and functional traits in a timely, rapi...
ECAI 2023
Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrat...
Nonlinear Physics
These refereed proceedings present recent developments on specific mathematical and physical aspects of nonlinear dynamics. The new findings discussed in here will be equally useful to graduate students and researchers. The topics dealt with cover a wide range of phenomena: solitons, integrable systems, Hamiltonian structures, Bäcklund and Darboux transformation, symmetries, fi- nite-dimensional dynamical systems, quantum and statistical mechanics, knot theory and braid group, R-matrix method, Hirota and Painlevé analysis, and applications to water waves, lattices, porous media, string theory and even cellular automata.
