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31 results on '"Drozda, Paweł"'

1. Iteratively Comparing Gravitational-Wave Observations to the Evolution of Massive Stellar Binaries

Author

Delfavero, Vera, O'Shaughnessy, Richard, Belczynski, Krzysztof, Drozda, Paweł, and Wysocki, Daniel

Subjects
General Relativity and Quantum Cosmology
Abstract

Gravitational-wave observations have the capability to strongly differentiate between different assumptions for how binary compact objects form. In this work, we show how to carefully interpolate a marginal likelihood between choices of binary evolution model parameters. Using the StarTrack binary evolution code, we compare one- and four-dimensional binary evolution models to the compact binary mergers reported in recent gravitational-wave observing runs. We first consider a one-dimensional model, studying the effect of supernova kick velocity (drawn from a Maxwellian with dispersion {\sigma}_eff) on the simulated population of compact binary mergers, and find support for substantial SN recoil kicks. We follow this up with a four-dimensional study of {\sigma}_eff, mass transfer efficiency (fa) and the efficiency of angular momentum depletion from ejected material (\b{eta}) during Roche-lobe accretion, and an observation-driven reduction in the mass-loss rate estimated from stellar wind models (fwind1). We find that three of them ({\sigma}_eff, fa, and fwind1) can be efficiently limited by these observational comparisons. After initially sampling from a uniform prior in the space of these parameters, we refined our sampling by iteratively estimating a Bayesian likelihood for each simulation and fitting that likelihood to a parametric model (a truncated Gaussian) in order to propose new. Our maximum likelihood simulation (K0559) has parameters: {\sigma}_eff = 108.3 km/s (indicating substantial SN recoil kicks), fa = 0.922 (indicating efficient mass transfer), and fwind1 = 0.328 (indicating support for reduced wind-driven mass loss). Note that our estimates are only valid within one particular model of compact binary formation through isolated binary evolution and do not yet take into account the impact of other uncertain pieces of stellar physics and binary evolution.

Published
2023

2. Professionally Diverse: AI-Generated Faces for Targeted Advertising

Author

Osowski, Maciej, Krasnodebska, Aleksandra, Drozda, Paweł, Scherer, Rafał, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Chbeir, Richard, editor, Manolopoulos, Yannis, editor, Fujita, Hamido, editor, Hong, Tzung-Pei, editor, Nguyen, Le Minh, editor, and Wojtkiewicz, Krystian, editor

Published
2024
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3. Named Entity Recognition Model for Polish Books

Author

Sopyla, Krzysztof, Drozda, Paweł, Ropiak, Krzysztof, Witkowska, Urszula, Sieniewicz, Małgorzata, Jankowski, Sebastian, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Chbeir, Richard, editor, Manolopoulos, Yannis, editor, Fujita, Hamido, editor, Hong, Tzung-Pei, editor, Nguyen, Le Minh, editor, and Wojtkiewicz, Krystian, editor

Published
2024
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4. Galaxy and halo angular clustering in LCDM and Modified Gravity cosmologies

Author

Drozda, Paweł, Hellwing, Wojciech A., and Bilicki, Maciej

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Using a suite of $N$-body simulations we study the angular clustering of galaxies, halos, and dark matter in $\mathrm{\Lambda \text{CDM}}$ and Modified Gravity (MG) scenarios. We consider two general categories of such MG models, one is the $f(R)$ gravity, and the other is the normal branch of the Dvali-Gabadadze-Porrati brane world (nDGP). To measure angular clustering we construct a set of observer-frame lightcones and resulting mock sky catalogs. We focus on the area-averaged angular correlation functions, $W_J$, and the associated reduced cumulants, $S_J\equiv W_J/W_2^{(J-1)}$, and robustly measure them up to the 9th order using counts-in-cells (CIC). We find that $0.15 < z < 0.3$ is the optimal redshift range to maximize the MG signal in our lightcones. Analyzing various scales for the two types of statistics, we identify up to 20\% relative departures in MG measurements from general relativity (GR), with varying signal significance. For the case of halos and galaxies, we find that $3$rd order statistics offer the most sensitive probe of the different structure formation scenarios, with both $W_3$ and the reduced skewness, $S_3$, reaching from $2\sigma$ to $4\sigma$ significance at angular scales $\theta \sim 0.13 ^\circ$. The MG clustering of the smooth dark matter field is characterized by even stronger deviations ($\stackrel{>}{{}_\sim} 5\sigma$) from GR, albeit at a bit smaller scales of $\theta\sim0.08^\circ$, where baryonic physics is already important. Finally, we stress out that our mock halo and galaxy catalogs are characterized by rather low surface number densities when compared to existing and forthcoming state-of-the-art photometric surveys. This opens up exciting potential for testing GR and MG using angular clustering in future applications, with even higher precision and significance than reported here., Comment: 15 pages, 8 figures, 1 table

Published
2022
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5. Differentiating small-scale subhalo distributions in CDM and WDM models using persistent homology

Author

Cisewski-Kehe, Jessi, Fasy, Brittany Terese, Hellwing, Wojciech, Lovell, Mark R., Drozda, Pawel, and Wu, Mike

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Statistics - Applications
Abstract

The spatial distribution of galaxies at sufficiently small scales will encode information about the identity of the dark matter. We develop a novel description of the halo distribution using persistent homology summaries, in which collections of points are decomposed into clusters, loops and voids. We apply these methods, together with a set of hypothesis tests, to dark matter haloes in MW-analog environment regions of the cold dark matter (CDM) and warm dark matter (WDM) Copernicus Complexio $N$-body cosmological simulations. The results of the hypothesis tests find statistically significant differences (p-values $\leq$ 0.001) between the CDM and WDM structures, and the functional summaries of persistence diagrams detect differences at scales that are distinct from the comparison spatial point process functional summaries considered (including the two-point correlation function). The differences between the models are driven most strongly at filtration scales $\sim100$~kpc, where CDM generates larger numbers of unconnected halo clusters while WDM instead generates loops. This study was conducted on dark matter haloes generally; future work will involve applying the same methods to realistic galaxy catalogues., Comment: 17 pages, 11 figures

Published
2022
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6. Evaluating Web Crawlers with Machine Learning Algorithms for Accurate Location Extraction from Job Offers

Author

Drozda, Paweł, Nowak, Bartosz A., Talun, Arkadiusz, Bukowski, Leszek, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Botzheim, János, editor, Gulyás, László, editor, Nunez, Manuel, editor, Treur, Jan, editor, Vossen, Gottfried, editor, and Kozierkiewicz, Adrianna, editor

Published
2023
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7. Previous Opinions is All You Need—Legal Information Retrieval System

Author

Osowski, Maciej, Lorenc, Katarzyna, Drozda, Paweł, Scherer, Rafał, Szałapak, Konrad, Komar-Komarowski, Kajetan, Szymański, Julian, Sobecki, Andrzej, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Botzheim, János, editor, Gulyás, László, editor, Nunez, Manuel, editor, Treur, Jan, editor, Vossen, Gottfried, editor, and Kozierkiewicz, Adrianna, editor

Published
2023
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8. Short Texts Representations for Legal Domain Classification

Author

Zymkowski, Tomasz, Szymański, Julian, Sobecki, Andrzej, Drozda, Paweł, Szałapak, Konrad, Komar-Komarowski, Kajetan, Scherer, Rafał, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor

Published
2023
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9. Assessing the Sentiment of Book Characteristics Using Machine Learning NLP Models

Author

Drozda, Paweł, Sopyła, Krzysztof, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor

Published
2023
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13. FastText and XGBoost Content-Based Classification for Employment Web Scraping

Author

Talun, Arkadiusz, Drozda, Pawel, Bukowski, Leszek, Scherer, Rafał, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor

Published
2020
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14. Accelerating SVM with GPU: The State of the Art

Author

Drozda, Paweł, Sopyła, Krzysztof, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Rutkowski, Leszek, editor, Korytkowski, Marcin, editor, Scherer, Rafał, editor, Tadeusiewicz, Ryszard, editor, Zadeh, Lotfi A., editor, and Zurada, Jacek M., editor

Published
2016
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15. Different Orderings and Visual Sequence Alignment Algorithms for Image Classification

Author

Drozda, Paweł, Sopyła, Krzysztof, Górecki, Przemysław, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Rutkowski, Leszek, editor, Korytkowski, Marcin, editor, Scherer, Rafał, editor, Tadeusiewicz, Ryszard, editor, Zadeh, Lotfi A., editor, and Zurada, Jacek M., editor

Published
2014
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18. Online Crowdsource System Supporting Ground Truth Datasets Creation

Author

Drozda, Paweł, Sopyła, Krzysztof, Górecki, Przemysław, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Rutkowski, Leszek, editor, Korytkowski, Marcin, editor, Scherer, Rafał, editor, Tadeusiewicz, Ryszard, editor, Zadeh, Lotfi A., editor, and Zurada, Jacek M., editor

Published
2013
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19. Ranking by K-Means Voting Algorithm for Similar Image Retrieval

Author

Górecki, Przemysław, Sopyła, Krzysztof, Drozda, Paweł, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Rutkowski, Leszek, editor, Korytkowski, Marcin, editor, Scherer, Rafał, editor, Tadeusiewicz, Ryszard, editor, Zadeh, Lotfi A., editor, and Zurada, Jacek M., editor

Published
2012
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20. SVM with CUDA Accelerated Kernels for Big Sparse Problems

Author

Sopyła, Krzysztof, Drozda, Paweł, Górecki, Przemysław, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Rutkowski, Leszek, editor, Korytkowski, Marcin, editor, Scherer, Rafał, editor, Tadeusiewicz, Ryszard, editor, Zadeh, Lotfi A., editor, and Zurada, Jacek M., editor

Published
2012
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23. IMPROVING THE CREDIBILITY OF THE EXTRACTED POSITION FROM A VAST COLLECTION OF JOB OFFERS WITH MACHINE LEARNING ENSEMBLE METHODS.

Author

Drozda, Paweł, Ropiak, Krzysztof, Nowak, Bartosz A., Talun, Arkadiusz, and Osowski, Maciej

Subjects
JOB offers, MACHINE learning, RANDOM forest algorithms, GRANULATION, WEBSITES
Abstract

The main aim of this paper is to evaluate crawlers collecting the job offers from websites. In particular the research is focused on checking the effectiveness of ensemble machine learning methods for the validity of extracted position from the job ads. Moreover, in order to significantly reduce the training time of the algorithms (Random Forests and XGBoost), granularity methods were also tested to significantly reduce the input training dataset. Both methods achieved satisfactory results in accuracy and F1 measures, which exceeded 96%. In addition, granulation reduced the input dataset by more than 99%, and the results obtained were only slightly worse (accuracy between 1% and 5%, F1 between 3% and 8%). Thus, it can be concluded that the considered methods can be used in the evaluation of job web crawlers. [ABSTRACT FROM AUTHOR]

Published
2023
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24. GOAL -- ORIENTED CONVERSATIONAL BOT FOR EMPLOYMENT DOMAIN.

Author

Drozda, Paweł, Żmijewski, Tomasz, Osowski, Maciej, Krasnodębska, Aleksandra, and Talun, Arkadiusz

Subjects
CHATBOTS, EMPLOYMENT, HUMAN-artificial intelligence interaction, COMPUTER software, OCCUPATIONS
Abstract

This paper focuses of the implementation of the goal -- oriented chatbot in order to prepare virtual resumes of candidates for job position. In particular the study was devoted to testing the feasibility of using Deep Q Networks (DQN) to prepare an effective chatbot conversation flow with the final system user. The results of the research confirmed that the use of the DQN model in the training of the conversational system allowed to increase the level of success, measured as the acceptance of the resume by the recruiter and the finalization of the conversation with the bot. The success rate increased from 10% to 64% in experimental environment and from 15% to 45% in production environment. Moreover, DQN model allowed the conversation to be shortened by an average of 4 questions from 11 to 7. [ABSTRACT FROM AUTHOR]

Published
2023
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31. SVM KERNEL SELECTION FOR CATEGORIZATION OF IMAGES DESCRIBED BY THE BAG OF VISUAL WORDS.

Author

GÓRECKI, PRZEMYSŹAW, SOPYŁA, KRZYSZTOF, and DROZDA, PAWEŁ

Subjects
IMAGE, ACCURACY, KERNEL operating systems, K-means clustering, IMAGING systems
Abstract

Copyright of Studia i Materialy Polskiego Stowarzyszenia Zarzadzania Wiedza / Studies & Proceedings Polish Association for Knowledge Management is the property of Polish Society of Knowledge Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2012

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