K. Borras

48.2k total citations
12 papers, 43 citations indexed

About

K. Borras is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Nuclear and High Energy Physics. According to data from OpenAlex, K. Borras has authored 12 papers receiving a total of 43 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 4 papers in Nuclear and High Energy Physics. Recurrent topics in K. Borras's work include Particle physics theoretical and experimental studies (4 papers), Quantum Computing Algorithms and Architecture (4 papers) and Particle Detector Development and Performance (3 papers). K. Borras is often cited by papers focused on Particle physics theoretical and experimental studies (4 papers), Quantum Computing Algorithms and Architecture (4 papers) and Particle Detector Development and Performance (3 papers). K. Borras collaborates with scholars based in Germany, Switzerland and United Kingdom. K. Borras's co-authors include D. Krücker, S. Vallecorsa, Florian Rehm, Michele Grossi, M. Brüggen, Gregor Kasieczka, D. Wegener, Ç. İşsever, Florian Griese and Patrick Connor and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment and Physica Scripta.

In The Last Decade

K. Borras

10 papers receiving 41 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
K. Borras Germany 5 25 17 6 6 6 12 43
Todd B. Krause United States 4 11 0.4× 13 0.8× 12 2.0× 7 1.2× 6 1.0× 12 50
Y. Kishi Japan 4 11 0.4× 12 0.7× 8 1.3× 9 1.5× 6 1.0× 14 53
I. Shapoval Switzerland 4 14 0.6× 20 1.2× 2 0.3× 5 0.8× 5 0.8× 10 37
K. Cho South Korea 5 18 0.7× 47 2.8× 4 0.7× 3 0.5× 7 1.2× 25 63
M. J. Fenton United States 5 25 1.0× 40 2.4× 8 1.3× 2 0.3× 3 0.5× 6 58
D. Schwarz Austria 5 7 0.3× 22 1.3× 3 0.5× 4 0.7× 3 0.5× 9 46
M. Hushchyn Russia 5 12 0.5× 14 0.8× 9 1.5× 2 0.3× 2 0.3× 15 50
E. Gross Israel 6 29 1.2× 73 4.3× 10 1.7× 2 0.3× 4 0.7× 19 93
D. Zuliani Italy 2 33 1.3× 16 0.9× 2 0.3× 13 2.2× 2 0.3× 4 49
K. Potamianos United States 2 42 1.7× 19 1.1× 10 1.7× 10 1.7× 9 58

Countries citing papers authored by K. Borras

Since Specialization
Citations

This map shows the geographic impact of K. Borras's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by K. Borras with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites K. Borras more than expected).

Fields of papers citing papers by K. Borras

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by K. Borras. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by K. Borras. The network helps show where K. Borras may publish in the future.

Co-authorship network of co-authors of K. Borras

This figure shows the co-authorship network connecting the top 25 collaborators of K. Borras. A scholar is included among the top collaborators of K. Borras based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with K. Borras. K. Borras is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
1.
Borras, K., et al.. (2024). Construction and volumetric benchmarking of quantum computing noise models. Physica Scripta. 99(6). 65106–65106. 2 indexed citations
2.
Krücker, D., et al.. (2024). DeepTreeGAN: Fast Generation of High Dimensional Point Clouds. SHILAP Revista de lepidopterología. 295. 9010–9010. 3 indexed citations
3.
Rehm, Florian, et al.. (2023). Precise image generation on current noisy quantum computing devices. Quantum Science and Technology. 9(1). 15009–15009. 3 indexed citations
4.
Borras, K., Lena Funcke, Michele Grossi, et al.. (2023). Impact of quantum noise on the training of quantum Generative Adversarial Networks. Journal of Physics Conference Series. 2438(1). 12093–12093. 11 indexed citations
5.
Rustige, L., Florian Griese, K. Borras, et al.. (2023). Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation. 2(1). 264–277. 8 indexed citations
6.
Rehm, Florian, S. Vallecorsa, Michele Grossi, et al.. (2022). Quantum Angle Generator for Image Generation. CERN Document Server (European Organization for Nuclear Research). 425–429. 1 indexed citations
7.
Rehm, Florian, S. Vallecorsa, K. Borras, & D. Krücker. (2021). QUANTUM MACHINE LEARNING FOR HEP DETECTOR SIMULATIONS. 363–368. 4 indexed citations
8.
Rehm, Florian, S. Vallecorsa, K. Borras, & D. Krücker. (2021). BENCHMARK OF GENERATIVE ADVERSARIAL NETWORKS FOR FAST HEP CALORIMETER SIMULATIONS. 310–315. 1 indexed citations
9.
Rehm, Florian, S. Vallecorsa, K. Borras, & D. Krücker. (2021). Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations. SHILAP Revista de lepidopterología. 251. 3042–3042. 6 indexed citations
10.
Andreev, Vladimir, et al.. (2011). Energy weighting for the upgrade of the hadronic calorimeter of CMS. DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron). 463–465.
11.
12.
İşsever, Ç., K. Borras, & D. Wegener. (2005). An improved weighting algorithm to achieve software compensation in a fine grained LAr calorimeter. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 545(3). 803–812. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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