Transformation of a nucleon-nucleon potential operator into its SU(3) tensor form using GPUs

Autoři
Langr, D.; Oberhuber, T.; Dytrych, T.; Draayer, J.P.; Launey, K.D.
Rok
2021
Publikováno
Discrete and Continuous Dynamical Systems. Series S. 2021, 14(3), 1111-1122. ISSN 1937-1632.
Typ
Článek
Anotace
Starting from the matrix elements of a nucleon-nucleon potential operator provided in a basis of spherical harmonic oscillator functions, we present an algorithm for expressing a given potential operator in terms of irreducible tensors of the SU(3) and SU(2) groups. Further, we introduce a GPU-based implementation of the latter and investigate its performance compared with a CPU-based version of the same. We find that the CUDA implementation delivers speedups of 2.27x – 5.93x.

Transformation of a nucleon-nucleon potential operator into its SU(3) tensor form using GPUs

Autoři
Langr, D.; Oberhuber, T.; Dytrych, T.; Draayer, J.P.; Launey, K.D.
Rok
2021
Publikováno
Discrete and Continuous Dynamical Systems. Series S. 2021, 14(3), 1111-1122. ISSN 1937-1632.
Typ
Článek
Anotace
Starting from the matrix elements of a nucleon-nucleon potential operator provided in a basis of spherical harmonic oscillator functions, we present an algorithm for expressing a given potential operator in terms of irreducible tensors of the SU(3) and SU(2) groups. Further, we introduce a GPU-based implementation of the latter and investigate its performance compared with a CPU-based version of the same. We find that the CUDA implementation delivers speedups of 2.27x – 5.93x.

A new parallel version of a dichotomy based algorithm for indexing powder diffraction data

Autoři
Rok
2020
Publikováno
Zeitschrift für Kristallographie - Crystalline Materials. 2020, 235(6-7), 203-212. ISSN 2196-7105.
Typ
Článek
Anotace
One of the key parts of the crystal structure solution process from powder diffraction data is the determination of the lattice parameters from experimental data shortly called indexing. The successive dichotomy method is the one of the most common ones for this process because it allows an exhaustive search. In this paper, we discuss several improvements for this indexing method that significantly reduce the search space and decrease the solution time. We also propose a combination of this method with other indexing methods: grid search and TREOR. The effectiveness and time-consumption of such algorithm were tested on several datasets, including orthorhombic, monoclinic, and triclinic examples. Finally, we discuss the impacts of the proposed improvements.

A new parallel version of a dichotomy based algorithm for indexing powder diffraction data

Autoři
Rok
2020
Publikováno
Zeitschrift für Kristallographie - Crystalline Materials. 2020, 235(6-7), 203-212. ISSN 2196-7105.
Typ
Článek
Anotace
One of the key parts of the crystal structure solution process from powder diffraction data is the determination of the lattice parameters from experimental data shortly called indexing. The successive dichotomy method is the one of the most common ones for this process because it allows an exhaustive search. In this paper, we discuss several improvements for this indexing method that significantly reduce the search space and decrease the solution time. We also propose a combination of this method with other indexing methods: grid search and TREOR. The effectiveness and time-consumption of such algorithm were tested on several datasets, including orthorhombic, monoclinic, and triclinic examples. Finally, we discuss the impacts of the proposed improvements.

Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

Rok
2020
Publikováno
Astronomy & Astrophysics. 2020, 643 ISSN 1432-0746.
Typ
Článek
Anotace
Context. Current archives of the LAMOST telescope contain millions of pipeline-processed spectra that have probably never been seen by human eyes. Most of the rare objects with interesting physical properties, however, can only be identified by visual analysis of their characteristic spectral features. A proper combination of interactive visualisation with modern machine learning techniques opens new ways to discover such objects. Aims. We apply active learning classification methods supported by deep convolutional neural networks to automatically identify complex emission-line shapes in multi-million spectra archives. Methods. We used the pool-based uncertainty sampling active learning method driven by a custom-designed deep convolutional neural network with 12 layers. The architecture of the network was inspired by VGGNet, AlexNet, and ZFNet, but it was adapted for operating on one-dimensional feature vectors. The unlabelled pool set is represented by 4.1 million spectra from the LAMOST data release 2 survey. The initial training of the network was performed on a labelled set of about 13 000 spectra obtained in the 400 Å wide region around Hα by the 2 m Perek telescope of the Ondˇrejov observatory, which mostly contains spectra of Be and related early-type stars. The differences between the Ondˇrejov intermediate-resolution and the LAMOST low-resolution spectrographs were compensated for by Gaussian blurring and wavelength conversion. Results. After several iterations, the network was able to successfully identify emission-line stars with an error smaller than 6.5%. Using the technology of the Virtual Observatory to visualise the results, we discovered 1 013 spectra of 948 new candidates of emission-line objects in addition to 664 spectra of 549 objects that are listed in SIMBAD and 2 644 spectra of 2 291 objects identified in an earlier paper of a Chinese group led by Wen Hou. The most interesting objects with unusual spectral properties are discussed in detail.

Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

Rok
2020
Publikováno
Astronomy & Astrophysics. 2020, 643 ISSN 1432-0746.
Typ
Článek
Anotace
Context. Current archives of the LAMOST telescope contain millions of pipeline-processed spectra that have probably never been seen by human eyes. Most of the rare objects with interesting physical properties, however, can only be identified by visual analysis of their characteristic spectral features. A proper combination of interactive visualisation with modern machine learning techniques opens new ways to discover such objects. Aims. We apply active learning classification methods supported by deep convolutional neural networks to automatically identify complex emission-line shapes in multi-million spectra archives. Methods. We used the pool-based uncertainty sampling active learning method driven by a custom-designed deep convolutional neural network with 12 layers. The architecture of the network was inspired by VGGNet, AlexNet, and ZFNet, but it was adapted for operating on one-dimensional feature vectors. The unlabelled pool set is represented by 4.1 million spectra from the LAMOST data release 2 survey. The initial training of the network was performed on a labelled set of about 13 000 spectra obtained in the 400 Å wide region around Hα by the 2 m Perek telescope of the Ondˇrejov observatory, which mostly contains spectra of Be and related early-type stars. The differences between the Ondˇrejov intermediate-resolution and the LAMOST low-resolution spectrographs were compensated for by Gaussian blurring and wavelength conversion. Results. After several iterations, the network was able to successfully identify emission-line stars with an error smaller than 6.5%. Using the technology of the Virtual Observatory to visualise the results, we discovered 1 013 spectra of 948 new candidates of emission-line objects in addition to 664 spectra of 549 objects that are listed in SIMBAD and 2 644 spectra of 2 291 objects identified in an earlier paper of a Chinese group led by Wen Hou. The most interesting objects with unusual spectral properties are discussed in detail.

Active Directory Kerberoasting Attack: Monitoring and Detection Techniques

Autoři
Rok
2020
Publikováno
Proceedings of the 6th International Conference on Information Systems Security and Privacy. Madeira: SciTePress, 2020. p. 432-439. ISSN 2184-4356. ISBN 978-989-758-399-5.
Typ
Stať ve sborníku
Anotace
The paper focus is the detection of Kerberoasting attack in Active Directory environment. The purpose of the attack is to extract service accounts’ passwords without need for any special user access rights or privilege escalation, which makes it suitable for initial phases of network compromise and further pivot for more interesting accounts. The main goal of the paper is to discuss the monitoring possibilities, setting up detection rules built on top of native Active Directory auditing capabilities, including possible ways to minimize false positive alerts.

Active Learning for LSTM-autoencoder-based Anomaly Detection in Electrocardiogram Readings

Autoři
Holeňa, M.; Šabata, T.
Rok
2020
Publikováno
Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020). Aachen: CEUR Workshop Proceedings, 2020. p. 72-77. ISSN 1613-0073.
Typ
Stať ve sborníku
Anotace
Recently, the amount of generated time series data has been increasing rapidly in many areas such as healthcare, security, meteorology and others. However, it is very rare that those time series are annotated. For this reason, unsupervised machine learning techniques such as anomaly detection are often used with such data. There exist many unsupervised algorithms for anomaly detection ranging from simple statistical techniques such as moving average or ARIMA till complex deep learning algorithms such as LSTM-autoencoder. For a nice overview of the recent algorithms we refer to read. Difficulties with the unsupervised approach are: defining an anomaly score to correctly represent how anomalous is the time series, and setting a threshold for that score to distinguish between normal and anomaly data. Supervised anomaly detection, on the other hand, needs an expensive involvement of a human expert. An additional problem with supervised anomaly detection is usually the occurrence of very low ratio of anomalies, yielding highly imbalanced data. In this extended abstract, we propose an active learning extension for an anomaly detector based on a LSTM-autoencoder. It performs active learning using various classification algorithms and addresses data imbalance with oversampling and under-sampling techniques. We are currently testing it on the ECG5000 dataset from the UCR time series classification archive.

Active Learning for LSTM-autoencoder-based Anomaly Detection in Electrocardiogram Readings

Autoři
Holeňa, M.; Šabata, T.
Rok
2020
Publikováno
Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020). Aachen: CEUR Workshop Proceedings, 2020. p. 72-77. ISSN 1613-0073.
Typ
Stať ve sborníku
Anotace
Recently, the amount of generated time series data has been increasing rapidly in many areas such as healthcare, security, meteorology and others. However, it is very rare that those time series are annotated. For this reason, unsupervised machine learning techniques such as anomaly detection are often used with such data. There exist many unsupervised algorithms for anomaly detection ranging from simple statistical techniques such as moving average or ARIMA till complex deep learning algorithms such as LSTM-autoencoder. For a nice overview of the recent algorithms we refer to read. Difficulties with the unsupervised approach are: defining an anomaly score to correctly represent how anomalous is the time series, and setting a threshold for that score to distinguish between normal and anomaly data. Supervised anomaly detection, on the other hand, needs an expensive involvement of a human expert. An additional problem with supervised anomaly detection is usually the occurrence of very low ratio of anomalies, yielding highly imbalanced data. In this extended abstract, we propose an active learning extension for an anomaly detector based on a LSTM-autoencoder. It performs active learning using various classification algorithms and addresses data imbalance with oversampling and under-sampling techniques. We are currently testing it on the ECG5000 dataset from the UCR time series classification archive.

Adapting Stable Matchings to Evolving Preferences

Autoři
Knop, D.; Bredereck, R.; Chen, J.; Luo, J.; Niedermeier, R.
Rok
2020
Publikováno
Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park: AAAI Press, 2020. p. 1830-1837. ISSN 2159-5399.
Typ
Stať ve sborníku
Anotace
Adaptivity to changing environments and constraints is key to success in modern society. We address this by proposing “incrementalized versions” of Stable Marriage and Stable Roommates. That is, we try to answer the following question: for both problems, what is the computational cost of adapting an existing stable matching after some of the preferences of the agents have changed. While doing so, we also model the constraint that the new stable matching shall be not too different from the old one. After formalizing these incremental versions, we provide a fairly comprehensive picture of the computational complexity landscape of Incremental Stable Marriage and Incremental Stable Roommates. To this end, we exploit the parameters “degree of change” both in the input (difference between old and new preference profile) and in the output (difference between old and new stable matching). We obtain both hardness and tractability results, in particular showing a fixed-parameter tractability result with respect to the parameter “distance between old and new stable matching”.

Adapting Stable Matchings to Evolving Preferences

Autoři
Knop, D.; Bredereck, R.; Chen, J.; Luo, J.; Niedermeier, R.
Rok
2020
Publikováno
Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park: AAAI Press, 2020. p. 1830-1837. ISSN 2159-5399.
Typ
Stať ve sborníku
Anotace
Adaptivity to changing environments and constraints is key to success in modern society. We address this by proposing “incrementalized versions” of Stable Marriage and Stable Roommates. That is, we try to answer the following question: for both problems, what is the computational cost of adapting an existing stable matching after some of the preferences of the agents have changed. While doing so, we also model the constraint that the new stable matching shall be not too different from the old one. After formalizing these incremental versions, we provide a fairly comprehensive picture of the computational complexity landscape of Incremental Stable Marriage and Incremental Stable Roommates. To this end, we exploit the parameters “degree of change” both in the input (difference between old and new preference profile) and in the output (difference between old and new stable matching). We obtain both hardness and tractability results, in particular showing a fixed-parameter tractability result with respect to the parameter “distance between old and new stable matching”.

An In-sight into How Compression Dictionary Architecture can Affect the Overall Performance in FPGAs

Autoři
Kubalík, P.; Bartík, M.; Beneš, T.
Rok
2020
Publikováno
IEEE Access. 2020, 2020(8), 183101-183116. ISSN 2169-3536.
Typ
Článek
Anotace
This paper presents a detailed analysis of various approaches to hardware implemented compression algorithm dictionaries, including our optimized method. To obtain comprehensive and detailed results, we introduced a method for the fair comparison of programmable hardware architectures to show the benefits of our approach from the perspective of logic resources, frequency, and latency. We compared two generally used methods with our optimized method, which was found to be more suitable for maintaining the memory content via (in)valid bits in any mid-density memory structures, which are implemented in programmable hardware such as FPGAs (Field Programmable Gate Array). The benefits of our new method based on a “Distributed Memory” technique are shown on a particular example of compression dictionary but the method is also suitable for another use cases requiring a fast (re-)initialization of the used memory structures before each run of an algorithm with minimum time and logic resources consumption. The performance evaluation of the respective approaches has been made in Xilinx ISE and Xilinx Vivado toolkits for the Virtex-7 FPGA family. However the proposed approach is compatible with 99% of modern FPGAs.

An In-sight into How Compression Dictionary Architecture can Affect the Overall Performance in FPGAs

Autoři
Kubalík, P.; Bartík, M.; Beneš, T.
Rok
2020
Publikováno
IEEE Access. 2020, 2020(8), 183101-183116. ISSN 2169-3536.
Typ
Článek
Anotace
This paper presents a detailed analysis of various approaches to hardware implemented compression algorithm dictionaries, including our optimized method. To obtain comprehensive and detailed results, we introduced a method for the fair comparison of programmable hardware architectures to show the benefits of our approach from the perspective of logic resources, frequency, and latency. We compared two generally used methods with our optimized method, which was found to be more suitable for maintaining the memory content via (in)valid bits in any mid-density memory structures, which are implemented in programmable hardware such as FPGAs (Field Programmable Gate Array). The benefits of our new method based on a “Distributed Memory” technique are shown on a particular example of compression dictionary but the method is also suitable for another use cases requiring a fast (re-)initialization of the used memory structures before each run of an algorithm with minimum time and logic resources consumption. The performance evaluation of the respective approaches has been made in Xilinx ISE and Xilinx Vivado toolkits for the Virtex-7 FPGA family. However the proposed approach is compatible with 99% of modern FPGAs.

Approximation Algorithms for Steiner Tree Based on Star Contractions: A Unified View

Autoři
Knop, D.; Hušek, R.; Masařík, T.
Rok
2020
Publikováno
15th International Symposium on Parameterized and Exact Computation (IPEC 2020). Dagstuhl: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2020. p. 16:1-16:18. ISSN 1868-8969. ISBN 978-3-95977-172-6.
Typ
Stať ve sborníku
Anotace
In the Steiner Tree problem, we are given an edge-weighted undirected graph G = (V,E) and a set of terminals R ⊆ V. The task is to find a connected subgraph of G containing R and minimizing the sum of weights of its edges. Steiner Tree is well known to be NP-complete and is undoubtedly one of the most studied problems in (applied) computer science. We observe that many approximation algorithms for Steiner Tree follow a similar scheme (meta-algorithm) and perform (exhaustively) a similar routine which we call star contraction. Here, by a star contraction, we mean finding a star-like subgraph in (the metric closure of) the input graph minimizing the ratio of its weight to the number of contained terminals minus one; and contract. It is not hard to see that the well-known MST-approximation seeks the best star to contract among those containing two terminals only. Zelikovsky’s approximation algorithm follows a similar workflow, finding the best star among those containing three terminals. We perform an empirical study of star contractions with the relaxed condition on the number of terminals in each star contraction motivated by a recent result of Dvořák et al. [Parameterized Approximation Schemes for Steiner Trees with Small Number of Steiner Vertices, STACS 2018]. Furthermore, we propose two improvements of Zelikovsky’s 11/6-approximation algorithm and we empirically confirm that the quality of the solution returned by any of these is better than the one returned by the former algorithm. However, such an improvement is exchanged for a slower running time (up to a multiplicative factor of the number of terminals).

Approximation Algorithms for Steiner Tree Based on Star Contractions: A Unified View

Autoři
Knop, D.; Hušek, R.; Masařík, T.
Rok
2020
Publikováno
15th International Symposium on Parameterized and Exact Computation (IPEC 2020). Dagstuhl: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2020. p. 16:1-16:18. ISSN 1868-8969. ISBN 978-3-95977-172-6.
Typ
Stať ve sborníku
Anotace
In the Steiner Tree problem, we are given an edge-weighted undirected graph G = (V,E) and a set of terminals R ⊆ V. The task is to find a connected subgraph of G containing R and minimizing the sum of weights of its edges. Steiner Tree is well known to be NP-complete and is undoubtedly one of the most studied problems in (applied) computer science. We observe that many approximation algorithms for Steiner Tree follow a similar scheme (meta-algorithm) and perform (exhaustively) a similar routine which we call star contraction. Here, by a star contraction, we mean finding a star-like subgraph in (the metric closure of) the input graph minimizing the ratio of its weight to the number of contained terminals minus one; and contract. It is not hard to see that the well-known MST-approximation seeks the best star to contract among those containing two terminals only. Zelikovsky’s approximation algorithm follows a similar workflow, finding the best star among those containing three terminals. We perform an empirical study of star contractions with the relaxed condition on the number of terminals in each star contraction motivated by a recent result of Dvořák et al. [Parameterized Approximation Schemes for Steiner Trees with Small Number of Steiner Vertices, STACS 2018]. Furthermore, we propose two improvements of Zelikovsky’s 11/6-approximation algorithm and we empirically confirm that the quality of the solution returned by any of these is better than the one returned by the former algorithm. However, such an improvement is exchanged for a slower running time (up to a multiplicative factor of the number of terminals).

At-Most-One Constraints in Efficient Representations of Mutex Networks

Autoři
Rok
2020
Publikováno
Proceedings of the 32nd IEEE International Conference on Tools with Artificial Intelligence. Los Alamitos: IEEE Computer Society, 2020. p. 170-177. ISSN 2375-0197. ISBN 978-1-7281-9228-4.
Typ
Stať ve sborníku
Anotace
he At-Most-One (AMO) constraint is a special case of cardinality constraint that requires at most one variable from a set of Boolean variables to be set to TRUE . AMO is important for modeling problems as Boolean satisfiability (SAT) from domains where decision variables represent spatial or temporal placements of some objects that cannot share the same spatial or temporal slot. The AMO constraint can be used for more efficient representation and problem solving in mutex networks consisting of pair-wise mutual exclusions forbidding pairs of Boolean variable to be simultaneously TRUE.

At-Most-One Constraints in Efficient Representations of Mutex Networks

Autoři
Rok
2020
Publikováno
Proceedings of the 32nd IEEE International Conference on Tools with Artificial Intelligence. Los Alamitos: IEEE Computer Society, 2020. p. 170-177. ISSN 2375-0197. ISBN 978-1-7281-9228-4.
Typ
Stať ve sborníku
Anotace
he At-Most-One (AMO) constraint is a special case of cardinality constraint that requires at most one variable from a set of Boolean variables to be set to TRUE . AMO is important for modeling problems as Boolean satisfiability (SAT) from domains where decision variables represent spatial or temporal placements of some objects that cannot share the same spatial or temporal slot. The AMO constraint can be used for more efficient representation and problem solving in mutex networks consisting of pair-wise mutual exclusions forbidding pairs of Boolean variable to be simultaneously TRUE.

Behavior Anomaly Detection in IoT Networks

Autoři
Čejka, T.; Hynek, K.; Soukup, D.
Rok
2020
Publikováno
Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). Cham: Springer International Publishing, 2020. p. 465-473. Lecture Notes on Data Engineering and Communications Technologies. vol. 49. ISSN 2367-4520. ISBN 978-3-030-43192-1.
Typ
Kapitola v knize
Anotace
Data encryption makes deep packet inspection less suitable nowadays, and the need of analyzing encrypted traffic is growing. Machine learning brings new options to recognize a type of communication despite the heterogeneity of encrypted IoT traffic right at the network edge. We propose the design of scalable architecture and the method for behavior anomaly detection in IoT networks. Combination of two existing semi-supervised techniques that we used ensures higher reliability of anomaly detection and improves results achieved by a single method. We describe conducted classification and anomaly detection experiments allowed thanks to existing and our training datasets. Presented satisfying results provide a subject for further work and allow us to elaborate on this idea.

Bi-directional Transformation between Normalized Systems Elements and Domain Ontologies in OWL

Autoři
Suchánek, M.; Pergl, R.; Mannaert, H.; Uhnák, P.
Rok
2020
Publikováno
Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering. Porto: SciTePress - Science and Technology Publications, 2020. p. 74-85. ISSN 2184-4895. ISBN 978-989-758-421-3.
Typ
Stať ve sborníku
Anotace
Knowledge representation in OWL ontologies gained a lot of popularity with the development of Big Data, Artificial Intelligence, Semantic Web, and Linked Open Data. OWL ontologies are very versatile, and there are many tools for analysis, design, documentation, and mapping. They can capture concepts and categories, their properties and relations. Normalized Systems (NS) provide a way of code generation from a model of so-called NS Elements resulting in an information system with proven evolvability. The model used in NS contains domain-specific knowledge that can be represented in an OWL ontology. This work clarifies the potential advantages of having OWL representation of the NS model, discusses the design of a bi-directional transformation between NS models and domain ontologies in OWL, and describes its implementation. It shows how the resulting ontology enables further work on the analytical level and leverages the system design. Moreover, due to the fact that NS metamodel is metacircular, the transformation can generate ontology of NS metamodel itself. It is expected that the results of this work will help with the design of larger real-world applications as well as the metamodel and that the transformation tool will be further extended with additional features which we proposed.

Biomarkers in Combination with Other Prognostic and Predictive Factors –Individualized Multivariate Statistical Models for Risk and Probability Estimation in Oncology.Implementation into software BIANTA and CRACTES with Some Casuistics

Autoři
Novák, J.; Jiřina, M.; Pecen, L.; Topolčan, O.
Rok
2020
Publikováno
Biomedical Journal of Scientific & Technical Research. 2020, 28(4), 21762-21765. ISSN 2574-1241.
Typ
Článek
Anotace
Each patient has his/her own individual characteristics. It is given by his/her main diagnosis and its detailed description (e.g., in oncology by TNM classification, grading, histological type etc.), comorbidities, results of genetic markers, biomarkers and many other factors. Standard patient care is usually based on the results of large clinical trials. This approach ignores the patient’s individuality. Using patient profiling based on tumor characteristics, genetic markers and biomarkers, it is possible to identify the drug with the highest clinical benefit for small patient groups where, according to the guidelines, treatment choice is no longer defined only by diagnosis and TNM classification. This procedure belongs to the area of personalized medicine. An important methodology is how to find similar patients to a newcomer and how to evaluate clinical benefits and risks of different types of therapies in this group of similar patients. The main focus is on overall survival, but time to progression and adverse reactions to treatment are also important. Based on extensive patient data available in electronic medical records (hospital information systems), similar cases to a newcomer can be identified and some statistics for these similar patients can be provided.

Filtr

Za obsah stránky zodpovídá: doc. Ing. Štěpán Starosta, Ph.D.