Zobrazeno 1 - 10
of 12 485
pro vyhledávání: '"Jentsch"'
Publikováno v:
2nd Workshop on Computational Linguistics for Political Text Analysis (CPSS@KONVENS 2022), 47-53
From a monarchy to a democracy, to a dictatorship and back to a democracy -- the German political landscape has been constantly changing ever since the first German national state was formed in 1871. After World War II, the Federal Republic of German
Externí odkaz:
http://arxiv.org/abs/2410.17960
Autor:
Lange, Kai-Robin, Jentsch, Carsten
Publikováno v:
3rd Workshop on Computational Linguistics for Political Text Analysis (CPSS@KONVENS 2024), 19-28
The application of natural language processing on political texts as well as speeches has become increasingly relevant in political sciences due to the ability to analyze large text corpora which cannot be read by a single person. But such text corpo
Externí odkaz:
http://arxiv.org/abs/2410.17886
The Random Forest (RF) algorithm can be applied to a broad spectrum of problems, including time series prediction. However, neither the classical IID (Independent and Identically distributed) bootstrap nor block bootstrapping strategies (as implement
Externí odkaz:
http://arxiv.org/abs/2410.00942
Autor:
Jentsch, Marion
Das Faltblatt informiert rund um die Artenwahl und den erfolgreichen Anbau zweijähriger Schnittblumen. Auch geeignete einjährige Arten, die sich überwintern lassen, sind berücksichtigt. Tipps zur Pflege der Schnittblumen in der Vase runden die Pu
Externí odkaz:
https://slub.qucosa.de/id/qucosa%3A77275
https://slub.qucosa.de/api/qucosa%3A77275/attachment/ATT-0/
https://slub.qucosa.de/api/qucosa%3A77275/attachment/ATT-0/
Handling missing data is crucial in machine learning, but many datasets contain gaps due to errors or non-response. Unlike traditional methods such as listwise deletion, which are simple but inadequate, the literature offers more sophisticated and ef
Externí odkaz:
http://arxiv.org/abs/2405.18878
Autor:
Diefenthaler, M., Fanelli, C., Gerlach, L. O., Guan, W., Horn, T., Jentsch, A., Lin, M., Nagai, K., Nayak, H., Pecar, C., Suresh, K., Vossen, A., Wang, T., Wenaus, T.
Artificial Intelligence is poised to transform the design of complex, large-scale detectors like the ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions,
Externí odkaz:
http://arxiv.org/abs/2405.16279
Autor:
STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kołaś, J., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., Matonoha, O., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pal, S., Pandav, A., Panday, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T. L., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., Zyzak, M.
Publikováno v:
Phys. Rev. C 110, 044908 Published 16 October 2024
With the STAR experiment at the BNL Relativisic Heavy Ion Collider, we characterize $\sqrt{s_\mathrm{NN}}$ = 200 GeV p+Au collisions by event activity (EA) measured within the pseudorapidity range $eta$ $in$ [-5, -3.4] in the Au-going direction and r
Externí odkaz:
http://arxiv.org/abs/2404.08784
Among the various models designed for dependent count data, integer-valued autoregressive (INAR) processes enjoy great popularity. Typically, statistical inference for INAR models uses asymptotic theory that relies on rather stringent (parametric) as
Externí odkaz:
http://arxiv.org/abs/2402.17425
Autor:
Jentsch, Patrick, Lee, Chiu Fan
The Vicsek simulation model of flocking together with its theoretical treatment by Toner and Tu in 1995 were two foundational cornerstones of active matter physics. However, despite the field's tremendous progress, the actual universality class (UC)
Externí odkaz:
http://arxiv.org/abs/2402.01316
Although the statistical literature extensively covers continuous-valued time series processes and their parametric, non-parametric and semiparametric estimation, the literature on count data time series is considerably less advanced. Among the count
Externí odkaz:
http://arxiv.org/abs/2401.14239