neuron-request@HPLMS2.HPL.HP.COM ("Neuron-Digest Moderator Peter Marvit") (02/05/91)
Neuron Digest Monday, 4 Feb 1991 Volume 7 : Issue 7 Today's Topics: tech report available by ftp TR - Integrating Rules and Connectionism for Robust Reasoning report: optimal NN size for classifiers Nips90 Preprint available from neuroprose archive TR-EE 90-63: The Hystery Unit - short term memory tech report: continuous spatial automata Learning algorithms for oscillatory networks TR available from neuroprose; feedforward nets Abstract - Backpropagation Learning in Expert Networks tech rep on overfitting, decision theory, PAC learning, and... preprint - Dynamics of Generalization in Linear Perceptrons Send submissions, questions, address maintenance and requests for old issues to "neuron-request@hplabs.hp.com" or "{any backbone,uunet}!hplabs!neuron-request" Use "ftp" to get old issues from hplpm.hpl.hp.com (15.255.176.205). ------------------------------------------------------------ Subject: tech report available by ftp From: honavar@iastate.edu Date: Mon, 14 Jan 91 13:01:47 -0600 The following technical report is available in postscript form by anonymous ftp (courtesy Jordan Pollack of Ohio State Univ). - ---------------------------------------------------------------------- Generative Learning Structures and Processes for Generalized Connectionist Networks Vasant Honavar Leonard Uhr Department of Computer Science Computer Sciences Department Iowa State University University of Wisconsin-Madison Technical Report #91-02, January 1991 Department of Computer Science Iowa State University, Ames, IA 50011 Abstract Massively parallel networks of relatively simple computing elements offer an attractive and versatile framework for exploring a variety of learning structures and processes for intelligent systems. This paper briefly summarizes the popular learning structures and processes used in such networks. It outlines a range of potentially more powerful alternatives for pattern-directed inductive learning in such systems. It motivates and develops a class of new learning algorithms for massively parallel networks of simple computing elements. We call this class of learning processes \fIgenerative\fR for they offer a set of mechanisms for constructive and adaptive determination of the network architecture - the number of processing elements and the connectivity among them - as a function of experience. Such generative learning algorithms attempt to overcome some of the limitations of some approaches to learning in networks that rely on modification of \fIweights\fR on the links within an otherwise fixed network topology e.g., rather slow learning and the need for an a-priori choice of a network architecture. Several alternative designs, extensions and refinements of generative learning algorithms, as well as a range of control structures and processes which can be used to regulate the form and content of internal representations learned by such networks are examined. ______________________________________________________________________________ You will need a POSTSCRIPT printer to print the file. To obtain a copy of the report, use anonymous ftp from cheops.cis.ohio-state.edu (here is what the transaction looks like): % ftp ftp> open cheops.cis.ohio-state.edu Connected to cheops.cis.ohio-state.edu. 220 cheops.cis.ohio-state.edu FTP server (Version blah blah) ready. Name (cheops.cis.ohio-state.edu:yourname): anonymous 331 Guest login ok, send ident as password. Password: anything 230 Guest login ok, access restrictions apply. ftp> cd pub/neuroprose 250 CWD command successful. ftp> bin 200 Type set to I. ftp> get honavar.generate.ps.Z 200 PORT command successful. 150 Opening BINARY mode data connection for honavar.generate.ps.Z (55121 bytes). 226 Transfer complete. local: honavar.generate.ps.Z remote: honavar.generate.ps.Z 55121 bytes received in 1.8 seconds (30 Kbytes/s) ftp> quit 221 Goodbye. % uncompress honavar.generate.ps.Z % lpr honavar.generate.ps ------------------------------ Subject: TR - Integrating Rules and Connectionism for Robust Reasoning From: Ron Sun <rsun@chaos.cs.brandeis.edu> Date: Tue, 15 Jan 91 17:12:08 -0500 Integrating Rules and Connectionism for Robust Reasoning Technical Report TR-CS-90-154 Ron Sun Brandeis University Computer Science Department rsun@cs.brandeis.edu Abstract A connectionist model for robust reasoning, CONSYDERR, is proposed to account for some common reasoning patterns found in commonsense reasoning and to remedy the brittleness problem. A dual representation scheme is devised, which utilizes both localist representation and distributed representation with features. We explore the synergy resulted from the interaction between these two types of representations, which helps to deal with problems such as partial information, no exact match, property inheritance, rule interaction, etc. Because of this, the CONSYDERR system is capable of accounting for many difficult patterns in commonsense reasoning. This work also shows that connectionist models of reasoning are not just an ``implementation" of their symbolic counterparts, but better computational models of common sense reasoning, taking into consideration of the approximate, evidential and adaptive nature of reasoning, and accounting for the spontaneity and parallelism in reasoning processes. +++ comments and suggestions are especially welcome +++ ------------ FTP procedures --------- ftp cheops.cis.ohio-state.edu >name: anonymous >passwork: neuron >binary >cd pub/neuroprose >get sun.integrate.ps.Z >quit uncompress sun.integrate.ps.Z lpr sun.integrate.ps ------------------------------ Subject: report: optimal NN size for classifiers From: Manoel F Tenorio <tenorio@ecn.purdue.edu> Date: Wed, 16 Jan 91 09:41:37 -0500 This report addresses the analysis of a new criterion for optimal classifier design. In particular we study the effects of the sizing ot the hidden layers and the optimal predicted value by this criterion. Resquest should be sent to: jld@ecn.purdue.edu TR-EE 91-5 There is a fee for requests outside USA,Canada and Mexico. On Optimal Adaptive Classifier Design Criterion - How many hidden units are necessary for an optimal neural network classifier? Wei-Tsih Lee Manoel Fernando Tenorio Parallel Distributed Structures Lab. Parallel Distributed Structures Lab. School of Electrical Engineering School of Electrical Engineering Purdue University Purdue University West Lafayette, IN 47907 West Lafayette, IN 47907 lwt@ecn.purdue.edu tenorio@ecn.purdue.edu Abstract A central problem in classifier design is the estimation of classification error. The difficulty in classifier design arises in situations where the sample distribution is unknown and the number of training samples available is limited. In this paper, we present a new approach for solving this problem. In our model, there are two types of classification error: approximation and generalization error. The former is due to the imperfect knowledge of the underlying sample distribution, while the latter is mainly the result of inaccuracies in parameter estimation, which is a consequence of the small number of training samples. We therefore propose a criterion for optimal classifier selection, called the Generalized Minimum Empirical Criterion (GMEE). The GMEE criterion consists of two terms, corresponding to the estimates of two types of error. The first term is the empirical error, which is the classification error observed for the training samples. The second is an estimate of the generalization error, which is related to the classifier complexity. In this paper we consider the Vapnik-Chervonenkis dimension (VCdim) as a measure of classifier complexity. Hence, the classifier which minimizes the criterion is the one with minimal error probability. Bayes consistency of the GMEE criterion has been proven. As an application, the criterion is used to design the optimal neural network classifier. A corollary to the Bayes optimality of neural network-based classifiers has been proven. Thus, our approach provides a theoretic foundation for the connectionist approach to optimal classifier design. Experimental results are given to validate the approach, followed by discussions and suggestions for future research. ------------------------------ Subject: Nips90 Preprint available from neuroprose archive From: "Terence D. Sanger" <tds@ai.mit.edu> Date: Sat, 19 Jan 91 16:33:00 -0500 The following preprint is available, and will appear in the Nips'90 proceedings: - --------------------------------------------------------------------------- Basis-Function Trees as a Generalization of Local Variable Selection Methods for Function Approximation Terence D. Sanger Local variable selection has proven to be a powerful technique for approximating functions in high-dimensional spaces. It is used in several statistical methods, including CART, ID3, C4, MARS, and others (see the bibliography for references to these algorithms). In this paper I present a tree-structured network which is a generalization of these techniques. The network provides a framework for understanding the behavior of such algorithms and for modifying them to suit particular applications. - --------------------------------------------------------------------------- Bibtex entry: @INCOLLECTION{sanger91, AUTHOR = {Terence D. Sanger}, TITLE = {Basis-Function Trees as a Generalization of Local Variable Selection Methods for Function Approximation}, BOOKTITLE = {Advances in Neural Information Processing Systems 3}, PUBLISHER = {Morgan Kaufmann}, YEAR = {1991}, EDITOR = {Richard P. Lippmann and John Moody and David S. Touretzky}, NOTE = {Proc. NIPS'90, Denver CO} } This paper can be obtained by anonymous ftp from the neuroprose database: unix> ftp cheops.cis.ohio-state.edu # (or ftp 128.146.8.62) Name (cheops.cis.ohio-state.edu:): anonymous Password (cheops.cis.ohio-state.edu:anonymous): <ret> ftp> cd pub/neuroprose ftp> binary ftp> get sanger.trees.ps.Z ftp> quit unix> uncompress sanger.trees.ps unix> lpr -P(your_local_postscript_printer) sanger.trees.ps # in some cases you will need to use the -s switch to lpr. Terry Sanger MIT, E25-534 Cambridge, MA 02139 USA tds@ai.mit.edu ------------------------------ Subject: TR-EE 90-63: The Hystery Unit - short term memory From: tenorio@ecn.purdue.edu (Manoel F Tenorio) Date: Tue, 22 Jan 91 15:15:41 -0500 The task of performing recognition of patterns on spatio-temporal signals is not an easy one, primarily due to the time structure of the signal. Classical methods of handling this problem have proven themselves unsatisfactory, and they range from "projecting out" the time axis, to "memorizing" the entire sequence before a decision can be made. In particular, the latter can be very difficult if no a priori information about signal length is present, if the signal can suffer compression and extension, or if the entire pattern is massively large, as in the case of time varying imagery. Neural Network models to solve this problem have either been based on the classical approach or on recursive loops within the network which can make learning algorithms numerically unstable. It is clear that for all the spatio-temporal processing, done by biological systems, some kind of short term memory is needed, and has been long conjectured. In this report, we have taken the first step at the design of a spatio-temporal system that deals naturally with the problems present in this type of processing. In particular we investigate the exchange of the simple sigmoid function, commonly used, by a hysterisis function. Later, with the addition of an integrator which represents the neuron membrane effect, we construct a simple computational device to perform spatio-pattern recognition tasks. The results are that for bipolar input sequence, this device remaps the entire sequence into a real number. Knowing the output of the device suffices for knowing the sequence. For trajectories embbeded in noise, the device shows superior recognition to other techniques. Furthermore, properties of the device allows the designer to determine the memory length, and explain with simple circuits sensitization and habituation phenomena. The report below deals with the device and its mathematical properties. Other forthcoming papers will concentrate on other aspects of circuits constructed with this device. ---------------------------------------------------------------------- Requests from within US, Canada, and Mexico: The technical report with figures has been/will soon be placed in the account kindly provided by Ohio State. Here is the instruction to get the files: ftp cheops.cis.ohio-state.edu (or, ftp 128.146.8.62) Name: anonymous Password: neuron ftp> cd pub/neuroprose ftp> mget tom.hystery* (type y and hit return) ftp> quit unix> uncompress tom.hystery*.Z unix> lpr -P(your_postscript_printer) tom.hystery.ps unix> lpr -P(your_Mac_laserwriter) tom.hystery_figs.ps Please contact mdtom@ecn.purdue.edu for technical difficulties. ---------------------------------------------------------------------- Requests from outside North America: The technical report is available at a cost of US$22.39 per copy, postage included. Please make checks payable to Purdue University in US dollars. You may send your requests, checks, and full first class mail address to: J. L. Dixon School of Electrical Engineering Purdue University West Lafayette, Indiana 47907 USA Please mention the technical report number: TR-EE 90-63. ---------------------------------------------------------------------- The Hystery Unit - A Short Term Memory Model for Computational Neurons M. Daniel Tom Manoel Fernando Tenorio Parallel Distributed Structures Laboratory School of Electrical Engineering Purdue University West Lafayette, Indiana 47907, USA December, 1990 Abstract: In this paper, a model of short term memory is introduced. This model is inspired by the transient behavior of neurons and magnetic storage as memory. The transient response of a neuron is hypothesized to be a combination of a pair of sigmoids, and a relation is drawn to the hysteresis loop found in magnetic materials. A model is created as a composition of two coupled families of curves. Two theorems are derived regarding the asymptotic convergence behavior of the model. Another conjecture claims that the model retains full memory of all past unit step inputs. ------------------------------ Subject: tech report: continuous spatial automata From: mclennan@cs.utk.edu Date: Wed, 23 Jan 91 16:28:54 -0500 The following technical report is now available: Continuous Spatial Automata B. J. MacLennan Department of Computer Science University of Tennessee Knoxville, TN 37996-1301 maclennan@cs.utk.edu CS-90-121 November 26, 1990 ABSTRACT A _continuous_spatial_automaton_ is analogous to a cellular auto- maton, except that the cells form a continuum, as do the possible states of the cells. After an informal mathematical description of spatial automata, we describe in detail a continuous analog of Conway's ``Life,'' and show how the automaton can be implemented using the basic operations of field computation. Typically a cellular automaton has a finite (sometimes denu- merably infinite) set of cells, often arranged in a one or two dimensional array. Each cell can be in one of a number of states. In contrast, a continuous spatial automaton has a one, two or higher dimensional continuum of _loci_ (corresponding to cells), each of which has a state drawn from a continuum (typically [0,1]). The state is required to vary continuously with the locus. In a cellular automaton there is a transition function that determines the state of a cell at the next time step based on the state of it and a finite number of neighbors at the current time step. A discrete-time spatial automaton is very similar: the future state of a locus is a continuous function of the states of the loci in a (closed or open) bounded neighborhood of the given locus. The report is available as a compressed postscript file in the pub/neuroprose subdirectory; it may be obtained with the Getps script: Getps maclennan.csa.ps.Z For HARDCOPY send your address to: library@cs.utk.edu For other correspondence: Bruce MacLennan Department of Computer Science 107 Ayres Hall The University of Tennessee Knoxville, TN 37996-1301 (615)974-0994/5067 maclennan@cs.utk.edu ------------------------------ Subject: Learning algorithms for oscillatory networks From: prowat@UCSD.EDU (Peter Rowat) Date: Wed, 23 Jan 91 16:19:15 -0800 The following preprint is now available by ftp from neuroprose: Peter Rowat and Allen Selverston (1990). Learning algorithms for oscillatory networks with gap junctions and membrane currents. To appear in: NETWORK: Computation in Neural systems, Volume 2, Issue 1, February 1991. Abstract: One of the most important problems for studying neural network models is the adjustment of parameters. Here we show how to formulate the problem as the minimization of the difference between two limit cycles. The backpropagation method for learning algorithms is described as the application of gradient descent to an error function that computes this difference. A mathematical formulation is given that is applicable to any type of network model, and applied to several models. The standard connectionist model of a neuron is extended to allow gap junctions between cells and to include membrane currents. Learning algorithms are derived for a two cell network with a single gap junction, and for a pair of mutually inhibitory neurons each having a simplified membrane current. For example, when learning in a network in which all cells have a common, adjustable, bias current, the value of the bias is adjusted at a rate proportional to the difference between the sum of the target outputs and the sum of the actual outputs. When learning in a network of n cells where a target output is given for every cell, the learning algorithm splits into n independent learning algorithms, one per cell. For networks containing gap junctions, a gap junction is modelled as a conductance times the potential difference between the two adjacent cells. The requirement that a conductance g must be positive is enforced by replacing g by a function pos(g*) whose value is always positive, for example exp(0.1 g*), and deriving an algorithm that adjusts the parameter g* in place of g. When target output is specified for every cell in a network with gap junctions, the learning algorithm splits into fewer independent components, one for each gap-connected subset of the network. The learning algorithm for a gap-connected set of cells cannot be parallelized further. As a final example, a learning algorithm is derived for a mutually inhibitory two-cell network in which each cell has a membrane current. This generalized approach to backpropagation allows one to derive a learning algorithm for almost any model neural network given in terms of differential equations. It is one solution to the problem of parameter adjustment in small but complex network models. --------------------------------------------------------------------------- Copies of the postscript file rowat.learn-osc.ps.Z may be obtained from the pub/neuroprose directory in cheops.cis.ohio-state.edu. Either use the Getps script or do this: unix-1> ftp cheops.cis.ohio-state.edu # (or ftp 128.146.8.62) Connected to cheops.cis.ohio-state.edu. Name (cheops.cis.ohio-state.edu:): anonymous 331 Guest login ok, sent ident as password. Password: neuron 230 Guest login ok, access restrictions apply. ftp> cd pub/neuroprose ftp> binary ftp> get rowat.learn-osc.ps.Z ftp> quit unix-2> uncompress rowat.learn-osc.ps.Z unix-3> lpr -P(your_local_postscript_printer) rowat.learn-osc.ps (The file starts with 7 bitmapped figures which are slow to print.) ------------------------------ Subject: TR available from neuroprose; feedforward nets From: Eduardo Sontag <sontag@hilbert.RUTGERS.EDU> Date: Thu, 24 Jan 91 16:16:17 -0500 I have deposited in the neuroprose archive the extended version of my NIPS-90 Proceedings paper. The title is: "FEEDFORWARD NETS FOR INTERPOLATION AND CLASSIFICATION" and the abstract is: "This paper deals with single-hidden-layer feedforward nets, studying various measures of classification power and interpolation capability. Results are given showing that direct input to output connections in threshold nets double the recognition but not the interpolation power, while using sigmoids rather than thresholds allows (at least) doubling both." (NOTE: This is closely related to report SYCON-90-03, which was put in the archive last year under the title "sontag.capabilities.ps.Z". No point in retrieving unless you found the other paper of interest. The current paper besically adds a few results on interpolation.) -eduardo ----------------------------------------------------------------------------- To obtain copies of the postscript file, please use Jordan Pollack's service: Example: unix> ftp cheops.cis.ohio-state.edu # (or ftp 128.146.8.62) Name (cheops.cis.ohio-state.edu:): anonymous Password (cheops.cis.ohio-state.edu:anonymous): <ret> ftp> cd pub/neuroprose ftp> binary ftp> get (remote-file) sontag.nips90.ps.Z (local-file) sontag.nips90.ps.Z ftp> quit unix> uncompress sontag.nips90.ps.Z unix> lpr -P(your_local_postscript_printer) sontag.nips90.ps ---------------------------------------------------------------------------- If you have any difficulties with the above, please send e-mail to sontag@hilbert.rutgers.edu. DO NOT "reply" to this message, please. NOTES about FTP'ing, etc: (1) The last time I posted something, I forgot to include the ".Z" in the file name in the above "remote-file" line, and I received many messages telling me that FTP didn't find the file. Sorry for that. Please note that most files in the archive are compressed, and people may forget to mention the ".Z". (2) I also received some email (and saw much discussion in a bboard) concerning the printer errors with the file. Please note that postscript files sometimes require a fair amount of memory from the printer, especially if they contain illustrations, and many smaller printers do not have enough memory. This may result on some pages not being printed, or the print job not being done at all. If you experience this problem with papers you retrieve (mine or from others), I suggest that you ask the author to email you a source file (e.g. LaTex) or a postscript file sans figures. Also, some postscript files are "nonconforming", and this may cause problems with certain printers. ------------------------------ Subject: Abstract - Backpropagation Learning in Expert Networks From: Chris Lacher <lacher@lambda.cs.fsu.edu> Date: Thu, 24 Jan 91 16:16:45 -0500 Backpropagation Learning in Expert Networks by R. C. Lacher, Susan I. Hruska, and David C. Kuncicky Department of Computer Science Florida State University ABSTRACT. Expert networks are event-driven, acyclic networks of neural objects derived from expert systems. The neural objects process information through a non-linear combining function that is different from, and more complex than, typical neural network node processors. We develop backpropagation learning for acyclic, event-driven nets in general and derive a specific algorithm for learning in EMYCIN-derived expert networks. The algorithm combines backpropagation learning with other features of expert nets, including calculation of gradients of the non-linear combining functions and the hypercube nature of the knowledge space. Results of testing the learning algorithm with a medium-scale (97 node) expert network are presented. For a copy of this preprint send an email request with your (snail)MAIL ADDRESS and the TITLE of the preprint to: santan@nu.cs.fsu.edu --- Chris Lacher ------------------------------ Subject: tech rep on overfitting, decision theory, PAC learning, and... From: David Haussler <haussler@saturn.ucsc.edu> Date: Fri, 25 Jan 91 17:31:37 -0800 TECHNICAL REPORT AVAILABLE Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications David Haussler UCSC-CRL-91-02 September, 1989 Revised: December, 1990 haussler@saturn.ucsc.edu Baskin Center for Computer Engineering and Information Sciences University of California, Santa Cruz, CA 95064 Abstract: We describe a generalization of the PAC learning model that is based on statistical decision theory. In this model the learner receives randomly drawn examples, each example consisting of an instance $x \in X$ and an outcome $y \in Y$, and tries to find a hypothesis $h : X \rightarrow A$, where $h \in \cH$, that specifies the appropriate action $a \in A$ to take for each instance $x$, in order to minimize the expectation of a loss $\L(y,a)$. Here $X$, $Y$, and $A$ are arbitrary sets, $\L$ is a real-valued function, and examples are generated according to an arbitrary joint distribution on $X \times Y$. Special cases include the problem of learning a function from $X$ into $Y$, the problem of learning the conditional probability distribution on $Y$ given $X$ (regression), and the problem of learning a distribution on $X$ (density estimation). We give theorems on the uniform convergence of empirical loss estimates to true expected loss rates for certain hypothesis spaces $\cH$, and show how this implies learnability with bounded sample size, disregarding computational complexity. As an application, we give distribution-independent upper bounds on the sample size needed for learning with feedforward neural networks. Our theorems use a generalized notion of VC dimension that applies to classes of real-valued functions, adapted from Pollard's work, and a notion of {\em capacity} and {\em metric dimension} for classes of functions that map into a bounded metric space. The report can be retrieved by anonymous ftp from the UCSC Tech report library. An example follows: unix> ftp midgard.ucsc.edu # (or ftp 128.114.134.15) Connected ... Name (...): anonymous Password: yourname@cs.anyuniversity.edu (i.e. your email address) (Please use your email address so we can correspond with you.) Guest login ok, access restrictions apply. ftp> cd pub/tr ftp> binary ftp> get ucsc-crl-91-02.ps.Z 200 PORT command successful. 150 Opening BINARY mode data connection for ucsc-crl-91-02.ps.Z (576429 bytes). 226 Transfer complete. local: ucsc-crl-91-02.ps.Z remote: ucsc-crl-91-02.ps.Z 576429 bytes received in 10 seconds (70 Kbytes/s) ftp> quit unix> uncompress ucsc-crl-91-02.ps.Z unix> lpr -P(your_local_postscript_printer) ucsc-crl-91-02.ps (Note: you will need a printer with a large memory.) (also: some other UCSC tech reports are available as well and more will be added soon. ftp the file INDEX to see what's there.) If you have any difficulties with the above, please send e-mail to jean@cis.ucsc.edu. DO NOT "reply" to this message, please. -David ------------------------------ Subject: preprint - Dynamics of Generalization in Linear Perceptrons From: hertz@nordita.dk Date: Mon, 28 Jan 91 11:04:05 +0000 The following technical report has been placed in the neuroprose archives at Ohio State University: Dynamics of Generalization in Linear Perceptrons Anders Krogh John Hertz Niels Bohr Institut Nordita Abstract: We study the evolution of the generalization ability of a simple linear perceptron with N inputs which learns to imitate a ``teacher perceptron''. The system is trained on p = \alpha N binary example inputs and the generalization ability measured by testing for agreement with the teacher on all 2^N possible binary input patterns. The dynamics may be solved analytically and exhibits a phase transition from imperfect to perfect generalization at \alpha = 1. Except at this point the generalization ability approaches its asymptotic value exponentially, with critical slowing down near the transition; the relaxation time is \propto (1-\sqrt{\alpha})^{-2}. Right at the critical point, the approach to perfect generalization follows a power law \propto t^{-1/2}. In the presence of noise, the generalization ability is degraded by an amount \propto (\sqrt{\alpha}-1)^{-1} just above \alpha = 1. This paper will appear in the NIPS-90 proceedings. To retrieve it by anonymous ftp, do the following: unix> ftp cheops.cis.ohio-state.edu # (or ftp 128.146.8.62) Name (cheops.cis.ohio-state.edu:): anonymous Password (cheops.cis.ohio-state.edu:anonymous): <ret> ftp> cd pub/neuroprose ftp> binary ftp> get krogh.generalization.ps.Z ftp> quit unix> uncompress krogh.generalization.ps unix> lpr -P(your_local_postscript_printer) krogh.generalization.ps An old-fashioned paper preprint version is also available -- send requests to hertz@nordita.dk or John Hertz Nordita Blegdamsvej 17 DK-2100 Copenhagen Denmark ------------------------------ End of Neuron Digest [Volume 7 Issue 7] ***************************************