Hans-Martin Majewski (1911-1997)

Hans-Martin Majewski wurde am 14.1.1911 in Schlawe (Pommern) als Sohn eines Veterinärrats geboren. Er besuchte zunächst das Gymnasium in Schlawe, bevor er bis 1930 seine Schulausbildung auf einer staatlichen Bildungsanstalt in der Stadt Köslin beendete. Danach nahm er in Königsberg das Studium der Medizin auf. 1932 wechselte Majewski nach Leipzig, studierte am dortigen Konservatorium bei Hermann Grabner, Kurt Thomas (Chorleitung), Robert Teichmüller (Klavier), Max Hochkoffler, Bruno Walter (Dirigieren) und Max Ludwig. Seine Schwerpunkte waren Theorie und Kompositionslehre, Dirigenten- und Opernschule

Intelligent opinion mining and sentiment analysis using artificial neural networks

The article formulates a rigorously developed concept of opinion mining and sentiment analysis using hybrid neural networks. This conceptual method for processing natural-language text enables a variety of analyses of the subjective content of texts. It is a methodology based on hybrid neural networks for detecting subjective content and potential opinions, as well as a method which allows us to classify different opinion type and sentiment score classes. Moreover, a general processing scheme, using neural networks, for sentiment and opinion analysis has been presented. Furthermore, a methodology which allows us to determine sentiment regression has been devised. The paper proposes a method for classification of the text being examined based on the amount of positive, neutral or negative opinion it contains. The research presented here offers the possibility of motivating and inspiring further development of the methods that have been elaborated in this paper.Stuart, KDC.; Majewski, M. (2015). Intelligent opinion mining and sentiment analysis using artificial neural networks. Lecture Notes in Computer Science. 9492:103-110. doi:10.1007/978-3-319-26561-2_13S1031109492Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)Chen, H., Zimbra, D.: AI and opinion mining. IEEE Intell. Syst. 25(3), 74–80 (2010)Majewski, M., Zurada, J.M.: Sentence recognition using artificial neural networks. Knowl. Based Syst. 21(7), 629–635 (2008)Kacalak, W., Stuart, K.D., Majewski, M.: Intelligent natural language processing. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4221, pp. 584–587. Springer, Heidelberg (2006)Kacalak, W., Stuart, K., Majewski, M.: Selected problems of intelligent handwriting recognition. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. Advances in Soft Computing, vol. 41, pp. 298–305. Springer, Cancun (2007)Stuart, K.D., Majewski, M.: Selected problems of knowledge discovery using artificial neural networks. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007, Part III. LNCS, vol. 4493, pp. 1049–1057. Springer, Heidelberg (2007)Stuart, K., Majewski, M.: A new method for intelligent knowledge discovery. In: Castillo, O., Melin, P., Ross, O.M., Cruz, R.S., Pedrycz, W., Kacprzyk, J. (eds.) IFSA 2007. Advances in Soft Computing, vol. 42, pp. 721–729. Springer, Heidelberg (2007)Stuart, K.D., Majewski, M.: Artificial creativity in linguistics using evolvable fuzzy neural networks. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds.) ICES 2008. LNCS, vol. 5216, pp. 437–442. Springer, Heidelberg (2008)Stuart, K.D., Majewski, M.: Evolvable neuro-fuzzy system for artificial creativity in linguistics. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 46–53. Springer, Heidelberg (2008)Stuart, K.D., Majewski, M., Trelis, A.B.: Selected problems of intelligent corpus analysis through probabilistic neural networks. In: Zhang, L., Lu, B.-L., Kwok, J. (eds.) ISNN 2010, Part II. LNCS, vol. 6064, pp. 268–275. Springer, Heidelberg (2010)Stuart, K.D., Majewski, M., Trelis, A.B.: Intelligent semantic-based system for corpus analysis through hybrid probabilistic neural networks. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011, Part I. LNCS, vol. 6675, pp. 83–92. Springer, Heidelberg (2011)Specht, D.F.: Probabilistic neural networks. Neural Netw. 3(1), 109–118 (1990)Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991

Sum rules for total hadronic widths of light mesons and rectilineal stitch of the masses on the complex plane

Mass formulae for light meson multiplets derived by means of exotic commutator technique are written for complex masses and considered as complex mass sum rules (CMSR). The real parts of the (CMSR) give the well known mass formulae for real masses (Gell-Mann--Okubo, Schwinger and Ideal Mixing ones) and the imaginary parts of CMSR give appropriate sum rules for the total hadronic widths - width sum rules (WSR). Most of the observed meson nonets satisfy the Schwinger mass formula (S nonets). The CMSR predict for S nonet that the points $(m,\Gamma{})$ form the rectilinear stitch (RS) on the complex mass plane. For low-mass nonets WSR are strongly violated due to ``kinematical'' suppression of the particle decays, but the violation decreases as the mass icreases and disappears above $\sim 1.5 GeV$. The slope $k_s$ of the RS is not predicted, but the data show that it is negative for all S nonets and its numerical values are concentrated in the vicinity of the value -0.5. If $k_s$ is known for a nonet, we can evaluate ``kinematical'' suppressions of its individual particles. The masses and the widths of the S nonet mesons submit to some rules of ordering which matter in understanding the properties of the nonet. We give the table of the S nonets indicating masses, widths, mass and width orderings. We show also mass-width diagrams for them. We suggest to recognize a few multiplets as degenerate octets. In Appendix we analyze the nonets of $1^+$ mesons.Comment: 20 pages, 3 figures; title and discussion expanded; additional text; final version accepted for publication in EPJ

Complexity of the Havas, Majewski, Matthews LLL Hermite Normal Form algorithm

We show that the integers in the HMM LLL HNF algorithm have bit length O(m.log(m.B)), where m is the number of rows and B is the maximum square length of a row of the input matrix. This is only a little worse than the estimate O(m.log(B)) in the LLL algorithm.Comment: 10 page

Probing the Halo From the Solar Vicinity to the Outer Galaxy: Connecting Stars in Local Velocity Structures to Large-Scale Clouds

(Abridged) This paper presents the first connections made between two local features in velocity-space found in a survey of M giant stars and stellar spatial inhomogeneities on global scales. Comparison to cosmological, chemodynamical stellar halo models confirm that the M giant population is particularly sensitive to rare, recent and massive accretion events. These events can give rise to local observed velocity sequences - a signature of a small fraction of debris from a common progenitor, passing at high velocity through the survey volume, near the pericenters of their eccentric orbits. The majority of the debris is found in much larger structures, whose morphologies are more cloud-like than stream-like and which lie at the orbital apocenters. Adopting this interpretation, the full-space motions represented by the observed velocity features are derived under the assumption that the members within each sequence share a common velocity. Orbit integrations are then used to trace the past and future trajectories of these stars across the sky revealing plausible associations with large, previously-discovered, cloud-like structures. The connections made between nearby velocity structures and these distant clouds represent preliminary steps towards developing coherent maps of such giant debris systems. These maps promise to provide new insights into the origin of debris clouds, new probes of Galactic history and structure, and new constraints on the high-velocity tails of the local dark matter distribution that are essential for interpreting direct detection experiments.Comment: submitted to the Astrophysical Journal, 40 pages, 13 figure

Discovery of a Dynamical Cold Point in the Heart of the Sagittarius dSph Galaxy with Observations from the APOGEE Project

The dynamics of the core of the Sagittarius (Sgr) dwarf spheroidal (dSph) galaxy are explored using high-resolution (R~22,500), H-band, near-infrared spectra of over 1,000 giant stars in the central 3 deg^2 of the system, of which 328 are identified as Sgr members. These data, among some of the earliest observations from the SDSS-III/Apache Point Observatory Galactic Evolution Experiment (APOGEE) and the largest published sample of high resolution Sgr dSph spectra to date, reveal a distinct gradient in the velocity dispersion of Sgr from 11-14 km/s for radii >0.8 degrees from center to a dynamical cold point of 8 km/s in the Sgr center --- a trend differing from that found in previous kinematical analyses of Sgr over larger scales that suggest a more or less flat dispersion profile at these radii. Well-fitting mass models with either cored and cusped dark matter distributions can be found to match the kinematical results, although the cored profile succeeds with significantly more isotropic stellar orbits than required for a cusped profile. It is unlikely that the cold point reflects an unusual mass distribution. The dispersion gradient may arise from variations in the mixture of populations with distinct kinematics within the dSph; this explanation is suggested (e.g., by detection of a metallicity gradient across similar radii), but not confirmed, by the present data. Despite these remaining uncertainties about their interpretation, these early test data (including some from instrument commissioning) demonstrate APOGEE's usefulness for precision dynamical studies, even for fields observed at extreme airmasses.Comment: 15 pages, 3 figure