Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data. In this paper we present an extension of the E2M method in a particular case of incomplete data, where the loss of information is due to both mixture models and censored observations. The prior uncertain infor...

Below a (comparatively large) measure of non-Hermiticity Z = Z (crit) 0 > 0 of a PT symmetrically complexified square well, bound states are constructed non-numerically. All their energies prove real and continuous in the (Hermitian) limit Z → 0. Beyond the threshold Z (crit) 0 (and, in general, beyond Z (crit) m at m = 0, 1, . . .) the lowest two real energies (i.e., E2m and E2m+1) are shown t...

In many applications, data are often imperfect, incomplete or more generally uncertain. This imperfection has to be integrated into the learning process as an information in itself. The E2M decision trees is a methodology that provides predictions from uncertain data modelled by belief functions. In this paper, the problem of rubber quality prediction is presented with a belief function modelli...

This paper considers the problem of identifying multiple sound sources from acoustical measurements obtained by an array of microphones. The problem is solved via maximum likelihood. In particular, an expectation-maximization (EM) approach is used to estimate the sound source locations and strengths, the pressure measured by a microphone being interpreted as a mixture of latent signals emitted ...

background: the use of igys in a variety of methods in different areas of research, diagnostics, medical application and biotechnology should be considered widely. objectives: development of antibodies against extra cellular domain of influenza m2 (m2e) protein in egg yolk of laying hens. methods: a fusion construct harboring c-terminal of bovine heat shock protein 70 (hsp70) and influenza m2e ...

Background: The use of IgYs in a variety of methods in different areas of research, diagnostics, medical application and biotechnology should be considered widely. Objectives: Development of antibodies against extra cellular domain of influenza M2 (M2e) protein in egg yolk of laying hens. Methods: A Fusion construct harboring C-terminal of bovine heat shock protein 70 (Hsp70) and influenza M2e ...

This paper addresses the problem of Hidden Markov Models (HMM) training and inference when the training data are composed of feature vectors plus uncertain and imprecise labels. The “soft” labels represent partial knowledge about the possible states at each time step and the “softness” is encoded by belief functions. For the obtained model, called a Partially-Hidden Markov Model (PHMM), the tra...

In this paper, we propose a new scheme for small group multicast in Mobile Ad hoc Networks (MANETs) named Extended Explicit Multicast (EM), which is built on top of Xcast and introduces mechanisms to make it scalable with number of group members for a given multicast session. EM is based on the novel concept of dynamic selection of Xcast Forwarder (XF) between a source and its potential destina...

Neddylation is a post-translational protein modification process associated with carcinogenesis and cancer development. MLN4924, a pharmaceutical neddylation inhibitor, induces potent anti-cancer effects in multiple types of cancers. In this study, we investigated the effects of MLN4924 on human osteosarcoma (OS). Levels of both NEDD8 activating enzyme E1 (NAE1) and ubiquitin-conjugating enzyme...

In many application data are imperfect, imprecise or more generally uncertain. Many classification methods have been presented that can handle data in some parts of the learning or the inference process, yet seldom in the whole process. Also, most of the proposed approach still evaluate their results on precisely known data. However, there are no reason to assume the existence of such data in a...