Dynamical properties of a gene-protein model

A major limitation of the classical random Boolean network model of gene regulatory networks is its synchronous updating, which implies that all the proteins decay at the same rate. Here a model is discussed, where the network is composed of two different sets of nodes, labelled G and P with reference to “genes” and “proteins”. Each gene corresponds to a protein (the one it codes for), while several proteins can simultaneously affect the expression of a gene. Both kinds of nodes take Boolean values. If we look at the genes only, it is like adding some memory terms, so the new state of the gene subnetwork network does no longer depend upon its previous state only. In general, these terms tend to make the dynamics of the network more ordered than that of the corresponding memoryless network. The analysis is focused here mostly on dynamical critical states. It has been shown elsewhere that the usual way of computing the Derrida parameter, starting from purely random initial conditions, can be misleading in strongly non-ergodic systems. So here the effects of perturbations on both genes’ and proteins’ levels is analysed, using both the canonical Derrida procedure and an “extended” one. The results are discussed. Moreover, the stability of attractors is also analysed, measured by counting the fraction of perturbations where the system eventually falls back onto the initial attractor

High mass photon pairs in lepton+ lepton-gamma gamma events at LEP

High mass photon pairs in lepton+ lepton-gamma gamma events at LEP Adriani, O.; Aguilar-Benitez, M.; Ahlen, S.P.; Alcaraz, J.; Aloisio, A.; Alverson, G.; Alviggi, M.G.; Ambrosi, G.; Linde, F.L. Published in: Physics Letters B DOI: 10.1016/0370-2693(92)91576-U Link to publication Citation for published version (APA): Adriani, O., Aguilar-Benitez, M., Ahlen, S. P., Alcaraz, J., Aloisio, A., Alverson, G., ... Linde, F. L. (1992). High mass photon pairs in lepton+ lepton-gamma gamma events at LEP. Physics Letters B, 295,[337][338][339][340][341][342][343][344][345][346] https://doi.org/10.1016/0370-2693(92)91576-U General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Download date: 28 Jun 2019 Physics Letters B 295 (1992) From the analysis of the reactions e + e-~ g + g-(n?) (g = e, #, ~) we observe four events, one e+e -~'7 and three #+ ~-??, with the invariant mass of the photon pairs close to 60 GeV. These events were selected from a data sample collected in the L3 detector corresponding to 950000 produced Z°'s. More data are necessary to ascertain the origin of these events

Classifier selection approaches for multi-label problems

While it is known that multiple classifier systems can be effective also in multi-label problems, only the classifier fusion approach has been considered so far. In this paper we focus on the classifier selection approach instead. We propose an implementation of this approach specific to multi-label classifiers, based on selecting the outputs of a possibly different subset of multi-label classifiers for each class. We then derive static selection criteria for the macro- and micro-averaged F measure, which is widely used in multi-label problems. Preliminary experimental results show that the considered selection strategy can exploit the complementarity of an ensemble of multi-label classifiers more effectively than selection approaches analogous to the ones used in single-label problems, which select the outputs of the same classifier subset for all classes. Our results also show that the derived selection criteria can provide a better trade-off between the macro- and micro-averaged F measure, despite it is known that an increase in either of them is usually attained at the expense of the other one

A Classification Approach with a Reject Option for Multi-label Problems

We investigate the implementation of multi-label classification algorithms with a reject option, as a mean to reduce the time required to human annotators and to attain a higher classification accuracy on automatically classified samples than the one which can be obtained without a reject option. Based on a recently proposed model of manual annotation time, we identify two approaches to implement a reject option, related to the two main manual annotation methods: browsing and tagging. In this paper we focus on the approach suitable to tagging, which consists in withholding either all or none of the category assignments of a given sample. We develop classification reliability measures to decide whether rejecting or not a sample, aimed at maximising classification accuracy on non-rejected ones. We finally evaluate the trade-off between classification accuracy and rejection rate that can be attained by our method, on three benchmark data sets related to text categorisation and image annotation tasks

Multi-label classification with a reject option

We consider multi-label classification problems in application scenarios where classifier accuracy is not satisfactory, but manual annotation is too costly. In single-label problems, a well known solution consists of using a reject option, i.e., allowing a classifier to withhold unreliable decisions, leaving them (and only them) to human operators. We argue that this solution can be exploited also in multi-label problems. However, the current theoretical framework for classification with a reject option applies only to single-label problems. We thus develop a specific framework for multi-label ones. In particular, we extend multi-label accuracy measures to take into account rejections, and define manual annotation cost as a cost function. We then formalise the goal of attaining a desired trade-off between classifier accuracy on non-rejected decisions, and the cost of manually handling rejected decisions, as a constrained optimisation problem. We finally develop two possible implementations of our framework, tailored to the widely used F accuracy measure, and to the only cost models proposed so far for multi- label annotation tasks, and experimentally evaluate them on five application domains

Spam filtering based on the analysis of text information embedded into images

In recent years anti-spam filters have become necessary tools for Internet service providers to face up to the continuously growing spam phenomenon. Current server-side anti-spam filters are made up of several modules aimed at detecting different features of spam e-mails. In particular, text categorisation techniques have been investigated by researchers for the design of modules for the analysis of the semantic content of e-mails, due to their potentially higher generalisation capability with respect to manually derived classification rules used in current server-side filters. However, very recently spammers introduced a new trick consisting of embedding the spam message into attached images, which can make all current techniques based on the analysis of digital text in the subject and body fields of e-mails ineffective. In this paper we propose an approach to anti-spam filtering which exploits the text information embedded into images sent as attachments. Our approach is based on the application of state-of-the-art text categorisation techniques to the analysis of text extracted by OCR tools from images attached to e-mails. The effectiveness of the proposed approach is experimentally evaluated on two large corpora of spam e-mails

Learning of multilabel classifiers

Developing learning algorithms for multilabel classification problems, when the goal is to maximizing the micro-averaged F measure, is a difficult problem for which no solution was known so far. In this paper we provide an exact solution for the case when the popular binary relevance approach is used for designing a multilabel classifier. We prove that the empirical maximum of the micro-averaged F measure can be attained by iteratively retraining class-related binary classifiers whose learning algorithm is capable of maximizing a modified version of the F measure of a two-class problem. We apply our optimization strategy to an existing formulation of support vector machine classifiers tailored to performance measures like F, and evaluate it on benchmark multilabel data sets

Spam filtering based on the analysis of text information embedded into images

In recent years anti-spam filters have become necessary tools for Internet service providers to face up to the continuously growing spam phenomenon. Current server-side anti-spam filters are made up of several modules aimed at detecting different features of spam e-mails. In particular, text categorisation techniques have been investigated by researchers for the design of modules for the analysis of the semantic content of e-mails, due to their potentially higher generalisation capability with respect to manually derived classification rules used in current server-side filters. However, very recently spammers introduced a new trick consisting of embedding the spam message into attached images, which can make all current techniques based on the analysis of digital text in the subject and body fields of e-mails ineffective. In this paper we propose an approach to anti-spam filtering which exploits the text information embedded into images sent as attachments. Our approach is based on the application of state-of-the-art text categorisation techniques to the analysis of text extracted by OCR tools from images attached to e-mails. The effectiveness of the proposed approach is experimentally evaluated on two large corpora of spam e-mails