Ashak $5.50 Steamed scallion dumpling with yogurt-mint sauce; with or without meat (ground beef).
Badenjan Bouranee $5.50 Sautéed eggplant slices topped with yougurt-mint sauce; with or without meat (ground beef).
Boulanee $4.95 Turnover filled with scallion; with yogurt dip.
Boulanee Kachaloo $4.95 Turnover filled with potatoes, herbs and spices, with yogurt dip.
Boulanee Kadu $4.95 Turnover filled with pumpkin; with yougurt dip.
Combo Turnover $12.50 Sambosa, Boulanee Kadu, and Boulanee Kachaloo.
Humus $5.00 Chick peas spread topped with olive oil and spices.
Mantoo $5.50 Steamed dumpling filled with beef, onion and spices; topped with yogurt, and meat sauce.
Sambosa $4.95 Crispy deep-fried dumpling stuffed with beef, split peas and spices; with yogurt dip.
Ashe Soup $3.50 Traditional vegetable soup with homemade noodles, yogurt; with or without meat (ground beef).
Greek Salad $6.95 Chopped fresh lettuce, tomatoes, and cucumbers, green pepper, olives, and Feta cheese; with oil-vinegar.
Herb Salad $5.95 Chopped fresh lettuce, tomatoes, and cucumbers; topped with fresh herbs.
Masto Khiar Salad $5.00 Homemade yogurt with chopped cucumbers and mint.
Panir Sabzee Salad $4.95 Fresh mint, watercress, basil leaves, scallions, radishes, Feta cheese.
Salad Shirazee Salad $4.95 Sliced cucumbers, tomatoes, scallion and mint leaves, with lemon juice and olives oil dressing.
Charcoal grilled on skewers and served w/choice of rice.
Barg $13.95 Tender pieces of fillet mignon, marinated with saffron and other spices, served with choice of rice and grilled tomatoes.
Chicken Adana $13.95 Grounded chicken marinated with garlic onion herbs and spices served with rice and grill tomato.
Choopan (Shephered`s Kabab) $14.95 Lamb chops marinated with fresh herbs; served with rice and grilled tomatoes.
Combination Kabab $20.00 1/2 skewer of Barg, Koobideh and Morgh; served with rice and house salad (lettuce, tomatoes and cucumber).
Joojeh $12.95 Marinated cornish game hen; served with choice of rice and grilled tomatoes.
Kabab-e-Sultani $16.95 One skewer of Barg and one skewer of Koobideh; served with white basmati rice and grilled tomatoes.
Koobideh $11.95 Mildoy spiced patties of minced lean beef; served with choice of rice and grilled tomatoes.
Morgh $12.95 Tender pieces of boneless chicken, marinated in garlic, onions and spices; served with choice of rice and grilled tomatoes.
Shish Kabab (Bara) $12.95 Chunks of boneless lamb, marinated in onions, garlic and spices; served with rice, grilled tomatoes and onions.
Bamia Chalow $11.50 Okra sautéed with onions, tomatoes and spices; served with white basmai rice.
Bouranee Badenjan $10.50 Eggplant sliced layered over mint yogurt, garnished with fresh coriander. Steamed fresh vegetables, served over white basmati rice.
Kabuli Palow $11.50 Browned basmati rice topped with raisins, slivered carrots and almonds. Served with a choice of spinach, eggplant, pumpkin or red kidney beans.
Kachaloo Chalow $10.50 Potatoes sautéed with onion, tomatoes and spices; served with white rice.
Lubia Palow $9.95 Sautéed red kidney beans with onions, garlic and tomatoes, served with brown rice.
Quorma Badenjan $10.50 Eggplant sautéed with onion, tomatoes and green pepper; served with browned basmati rice.
Quorma Kadu $10.50 Pumpkin sautéed with onions, tomatoes and spices; served with white basmati rice.
Sabze Chalow $10.50 Spicy sautéed chopped spinach, and scallions, served with white rice.
Shireen Palow $11.50 Exotic rice dish cooked with orange peels, saffron, almonds and pistachios; served with a choice of spinach, eggplant, pumpkin or red kidney beans.
Ashak Pasta $10.95 Homemade noodles. Steamed scallion dumpling with yogurt sauce; with or without meat (ground beef).
Asheh Gooshti $9.95 Homemade noodles with yogurt topped with mildly spiced meat sauce.
Asheh Keshida $9.95 Homemade noodles in tangy yogurt sauce, drizzled with sautéed garlic and butter.
Asheh Keshida Lubiah $9.95 Homemade noodles in tangy yogurt sauce, topped with red kidney beans and butter.
Mantoo Pasta $10.95 Homemade noodles. Steamed dumpling filled with minced beef, onions, herbs and spices; topped with yogurt and meat sauce.
Badenjan Bouranee $11.50 Eggplant slices layered over mint yogurt, topped with savory meat sauce and garnished with parsley.
Chicken Curry $12.95 Chicken sautéed with onions, green peppers, tomatoes and curry; served with basmati rice.
Fesenjan Entrée $12.95 Sweet and sour pieces of boneless chicken flavored with walnuts and pomegranate juice; served with white rice.
Kabuli Palow with Lamb $12.50 Brown basmati rice topped with raisins, slivered carrots and almonds; served with tender chunks of lamb.
Kofta Chalow $12.95 Meatballs in tomatoes sauce and spices; served with white rice.
Lamb Curry $12.95 Pieces of lamb sautéed with onions, green peppers, tomatoes and curry; served with basmati rice.
Lowand Chalow $12.50 Tender morsels of boneless chicken sautéed in a savory yogurt sauce seasoned with spices; served with white rice.
Quorma Badenjan with Lamb $11.95 Eggplant sautéed with onions, green peppers, tomatoes and topped with slices of lamb; served with brown basmati rice.
Quorma Chalow $12.50 Lamb, sautéed with onions and tomatoes; served with white basmati rice.
Quorma Sabzee with Lamb $11.95 Spicy sautéed of chopped spinach, coriander and scallions, with morsels of tender lamb; served with white basmati rice.
Shireen Palow Entrèe $12.95 Exotic rice dish cooked with orange peels, saffron, almonds and pistachios, served with grilled cornish game hen.
Dish of Rice $5.00 White rice or brown rice.
Extra bread $1.00 Afghanian bread.
Side Order of Vegetables $4.50 Choice of spinach, eggplant, beans, pumpkin, or okra.
Toorshi $2.50 Mixed pickled vegetables.
Baklava $3.50 Rich layered dessert made with nuts and honey.
Firnee $3.50 Traditional Afghan milk, pudding with pistachios and rose water.
Kabuli Palow with Lamb Brown basmati rice topped with raisins, slivered carrots and almonds; served with tender chunks of lamb.
Koobideh Mildoy spiced patties of minced lean beef; served with choice of rice and grilled tomatoes.
Boulanee Kadu Turnover filled with pumpkin; with yougurt dip.
Firnee Traditional Afghan milk, pudding with pistachios and rose water.
Morgh Tender pieces of boneless chicken, marinated in garlic, onions and spices; served with choice of rice and grilled tomatoes.


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# actually "unissued" on 78 rpm, but previously issued on RCA International INT 1175 !!!
- Won't You Be Kind?
"Remastered from original recordings by Richard Nevins. Records used were from the collections of Richard Nevins, Pete Whelan, Frank Mare, Dave Freeman, Joe Bussard, Chris King, Mark Blaesing, John Coffee, John Tefteller, Richards Spottswood, Don Kent."


Find area information and real estate listings for the Russet Meadows Subdivision, Birmingham, Alabama. Learn more about Russet Meadows, including available homes for sale, school info, and area reviews, from a local RE/MAX real estate agent.
Russet Meadows is in Birmingham off of 459, exit 6, heading towards Helena. Turn left on Woodhaven Road and Russet Meadows is on your right. Russet Meadows is an established subdivision which was developed in the late 90's. There are different styles of homes in the neighborhood. Single level, 1.5 story and 2 story homes. Great access to 459 and just a five to ten minute drive to either Hoover Galleria or Colonial Promenade at Tannehill.


text:
vermont.com | contact | advertising
PHINEAS, THE INJURED KITTY, GETS A NORMAL LIFE THANKS TO FACEBOOK FRIENDS
by Craig Altschul
Phineas, a stray cat, was found by Johnson State's professor Susan Green, says Lamoille's <INews & Citizen.He had been shot several times, including one shot that blew away his lower jaw. Veterinary bills exceeded $3,000 even though veterinary surgeon Dr Kurt Schultz of Williston donated his services. Dr. Schultz stabilized the right jaw with a metal plate; the left side will not be reconstructed, but Phineas still will be able to eat and drink and have a normal life. But more money was needed for Phineas' post-surgical expenses. Green's colleagues stepped into action and sent out Facebook messages to their friends to help raise the money. People from around the country responded, raising close to $2,000. Phineas, now the celebrity cat, was featured on Burlington's WCAX channel 3 garnering a spot on the evening news. All he needs now is time to heal and a good home.
This entry was posted in News of the Day on November 8, 2010 by Craig Altschul.
About Craig Altschul
Craig Altschul is Editorial Director of SnoCountry.com. He is a veteran snow journalist, having covered the sport for more than 40 years. His syndicated snow sports humor column, Ski Tips, ran in newspapers across the country for 20 years. He was Global Editor-in-Chief for OnTheSnow for 12 years and has written for a variety of magazines and newspapers. Altschul is past president of both the Eastern Ski Writers Association and the North American Snow Journalists Association.
View all posts by Craig Altschul →
info@vermont.com
Vermont.com
About Vermont.com
© 1994-2016 Vermont.com / WebMont, Inc.

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Install oracle client 32 bit on Windows Server 2012 64 bit I have installed 64 bit oracle(11.2g) on windows server 2012 64 bit.
When I try to install 32 bit oracle client on windows server 2012 64 bit, I get only the following directories in my install.
cfgtoollogs
install
inventory
jdbc
light
odbc
rdbms

Any suggestions please?

A: Why do you think that's a problem?  You're installing the client not the entire database. The client install is a minimal install consisting of basic libraries required to connect to the database

redpajama_set_name: RedPajamaStackExchange


text:
Q: SignalR + Win RT(Windows 8.1) + using unsigned certificate not working After searching for more than 4 hours, making a Win RT + SignalR work with a selfsigned certificate. Found it! So I share a solution here.
You get the error:
The underlying connection was closed: Could not establish trust relationship for the SSL/TLS secure channel.
The remote certificate is invalid according to the validation procedure.
  public class DefaultHttpClientUnsigned : DefaultHttpClient
{
    protected override HttpMessageHandler CreateHandler()
    {
          var filter = new HttpBaseProtocolFilter();
        filter.IgnorableServerCertificateErrors.Add(ChainValidationResult.Expired);
        filter.IgnorableServerCertificateErrors.Add(ChainValidationResult.Untrusted);
        filter.IgnorableServerCertificateErrors.Add(ChainValidationResult.InvalidName);
        filter.IgnorableServerCertificateErrors.Add(ChainValidationResult.IncompleteChain);
        var handler = new WinRtHttpClientHandler(filter);
        return handler;
    }

}

Then for creating the connection:
var connection = new HubConnection(address);
var client = new DefaultHttpClientUnsigned();
connection.Start(client);

// https://www.nuget.org/packages/WinRtHttpClientHandler
The source code for the WinRtHttpClientHandler  (if it's not available anymore)
public class WinRtHttpClientHandler : HttpMessageHandler
{
    private static readonly Version NoVersion = new Version(0, 0);
    private static readonly Version Version10 = new Version(1, 0);
    private static readonly Version Version11 = new Version(1, 1);
    private readonly rt.HttpClient _client;
    private bool _disposed = false;

    public WinRtHttpClientHandler(IHttpFilter httpFilter)
    {
        _client = new rt.HttpClient(httpFilter);
    }

    protected override async Task<HttpResponseMessage> SendAsync(HttpRequestMessage request, CancellationToken cancellationToken)
    {
        CheckDisposed();

        var rtMessage = await ConvertHttpRequestMessaageToRt(request, cancellationToken).ConfigureAwait(false);

        var resp = await _client.SendRequestAsync(rtMessage).AsTask(cancellationToken).ConfigureAwait(false);

        var netResp = await ConvertRtResponseMessageToNet(resp, cancellationToken).ConfigureAwait(false);

        return netResp;
    }

    internal static async Task<rt.HttpRequestMessage> ConvertHttpRequestMessaageToRt(HttpRequestMessage message, CancellationToken token)
    {
        var rt = new rt.HttpRequestMessage()
        {
            Method = new rt.HttpMethod(message.Method.Method),
            Content = await GetContentFromNet(message.Content).ConfigureAwait(false),
            RequestUri = message.RequestUri,
        };

        CopyHeaders(message.Headers, rt.Headers);

        foreach (var prop in message.Properties)
            rt.Properties.Add(prop);

        return rt;
    }

    internal static async Task<HttpRequestMessage> ConvertRtRequestMessageToNet(rt.HttpRequestMessage message, CancellationToken token)
    {
        var req = new HttpRequestMessage()
        {
            Method = new HttpMethod(message.Method.Method),
            RequestUri = message.RequestUri,
            Content = await GetNetContentFromRt(message.Content, token).ConfigureAwait(false),
        };

        foreach (var header in message.Headers)
            req.Headers.TryAddWithoutValidation(header.Key, header.Value);

        foreach (var prop in message.Properties)
            req.Properties.Add(prop);

        return req;
    }

    internal static void CopyHeaders(IEnumerable<KeyValuePair<string, IEnumerable<string>>> source, IDictionary<string, string> destination)
    {
        var headers = from kvp in source
                      from val in kvp.Value
                      select new KeyValuePair<string, string>(kvp.Key, val);
        foreach (var header in headers)
            destination.Add(header);
    }

    internal static async Task<rt.IHttpContent> GetContentFromNet(HttpContent content)
    {
        if (content == null)
            return null;

        var stream = await content.ReadAsStreamAsync().ConfigureAwait(false);
        var c = new rt.HttpStreamContent(stream.AsInputStream());

        CopyHeaders(content.Headers, c.Headers);

        return c;
    }

    internal static async Task<HttpContent> GetNetContentFromRt(rt.IHttpContent content, CancellationToken token)
    {
        if (content == null)
            return null;

        var str = await content.ReadAsInputStreamAsync().AsTask(token).ConfigureAwait(false);

        var c = new StreamContent(str.AsStreamForRead());

        foreach (var header in content.Headers)
            c.Headers.TryAddWithoutValidation(header.Key, header.Value);

        return c;
    }

    internal static async Task<HttpResponseMessage> ConvertRtResponseMessageToNet(rt.HttpResponseMessage message, CancellationToken token)
    {
        var resp = new HttpResponseMessage((HttpStatusCode)(int)message.StatusCode)
        {
            ReasonPhrase = message.ReasonPhrase,
            RequestMessage = await ConvertRtRequestMessageToNet(message.RequestMessage, token).ConfigureAwait(false),
            Content = await GetNetContentFromRt(message.Content, token).ConfigureAwait(false),
            Version = GetVersionFromEnum(message.Version),
        };

        foreach (var header in message.Headers)
            resp.Headers.TryAddWithoutValidation(header.Key, header.Value);

        return resp;
    }

    internal static Version GetVersionFromEnum(rt.HttpVersion version)
    {
        switch (version)
        {
            case rt.HttpVersion.None:
                return NoVersion;
                break;
            case rt.HttpVersion.Http10:
                return Version10;
                break;
            case rt.HttpVersion.Http11:
                return Version11;
                break;
            default:
                throw new ArgumentOutOfRangeException("version");
        }
    }

    private void CheckDisposed()
    {
        if (_disposed)
            throw new ObjectDisposedException("WinRtHttpClientHandler");
    }

    protected override void Dispose(bool disposing)
    {
        if (disposing && !_disposed)
        {
            _client.Dispose();

            _disposed = true;
        }

        base.Dispose(disposing);
    }
}

Hope this helps some people out there.


text:
A New Projected Quasi-Newton Approach for the Nonnegative Least Squares Problem
Kim, D., Sra, S., Dhillon, I.
(TR-06-54), Univ. of Texas, Austin, December 2006 (techreport)
Kim, D., Sra, S., Dhillon, I. A New Projected Quasi-Newton Approach for the Nonnegative Least Squares Problem (TR-06-54), Univ. of Texas, Austin, December 2006 (techreport)
Probabilistic inference for solving (PO)MDPs
Toussaint, M., Harmeling, S., Storkey, A.
(934), School of Informatics, University of Edinburgh, December 2006 (techreport)
Toussaint, M., Harmeling, S., Storkey, A. Probabilistic inference for solving (PO)MDPs (934), School of Informatics, University of Edinburgh, December 2006 (techreport)
Minimal Logical Constraint Covering Sets
Sinz, F., Schölkopf, B.
(155), Max Planck Institute for Biological Cybernetics, Tübingen, December 2006 (techreport)
We propose a general framework for computing minimal set covers under class of certain logical constraints. The underlying idea is to transform the problem into a mathematical programm under linear constraints. In this sense it can be seen as a natural extension of the vector quantization algorithm proposed by Tipping and Schoelkopf. We show which class of logical constraints can be cast and relaxed into linear constraints and give an algorithm for the transformation.
Sinz, F., Schölkopf, B. Minimal Logical Constraint Covering Sets (155), Max Planck Institute for Biological Cybernetics, Tübingen, December 2006 (techreport)
New Methods for the P300 Visual Speller
Biessmann, F.
(1), (Editors: Hill, J. ), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2006 (techreport)
Biessmann, F. New Methods for the P300 Visual Speller (1), (Editors: Hill, J. ), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2006 (techreport)
Geometric Analysis of Hilbert Schmidt Independence criterion based ICA contrast function
Shen, H., Jegelka, S., Gretton, A.
(PA006080), National ICT Australia, Canberra, Australia, October 2006 (techreport)
Shen, H., Jegelka, S., Gretton, A. Geometric Analysis of Hilbert Schmidt Independence criterion based ICA contrast function (PA006080), National ICT Australia, Canberra, Australia, October 2006 (techreport)
Semi-Supervised Learning
Chapelle, O., Schölkopf, B., Zien, A.
pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.
Chapelle, O., Schölkopf, B., Zien, A. Semi-Supervised Learning pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)
(149), Max Planck Institute for Biological Cybernetics, Tübingen, August 2006 (techreport)
In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Nevertheless, on the first glance spectral clustering looks a bit mysterious, and it is not obvious to see why it works at all and what it really does. This article is a tutorial introduction to spectral clustering. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.
von Luxburg, U. A tutorial on spectral clustering (149), Max Planck Institute for Biological Cybernetics, Tübingen, August 2006 (techreport)
Towards the Inference of Graphs on Ordered Vertexes
Zien, A., Raetsch, G., Ong, C.
We propose novel methods for machine learning of structured output spaces. Specifically, we consider outputs which are graphs with vertices that have a natural order. We consider the usual adjacency matrix representation of graphs, as well as two other representations for such a graph: (a) decomposing the graph into a set of paths, (b) converting the graph into a single sequence of nodes with labeled edges. For each of the three representations, we propose an encoding and decoding scheme. We also propose an evaluation measure for comparing two graphs.
Zien, A., Raetsch, G., Ong, C. Towards the Inference of Graphs on Ordered Vertexes (150), Max Planck Institute for Biological Cybernetics, Tübingen, August 2006 (techreport)
Nonnegative Matrix Approximation: Algorithms and Applications
Sra, S., Dhillon, I.
Univ. of Texas, Austin, May 2006 (techreport)
Sra, S., Dhillon, I. Nonnegative Matrix Approximation: Algorithms and Applications Univ. of Texas, Austin, May 2006 (techreport)
An Automated Combination of Sequence Motif Kernels for Predicting Protein Subcellular Localization
(146), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006 (techreport)
Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions. While many predictive computational tools have been proposed, they tend to have complicated architectures and require many design decisions from the developer. We propose an elegant and fully automated approach to building a prediction system for protein subcellular localization. We propose a new class of protein sequence kernels which considers all motifs including motifs with gaps. This class of kernels allows the inclusion of pairwise amino acid distances into their computation. We further propose a multiclass support vector machine method which directly solves protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. To automatically search over families of possible amino acid motifs, we generalize our method to optimize over multiple kernels at the same time. We compare our automated approach to four other predictors on three different datasets.
Zien, A., Ong, C. An Automated Combination of Sequence Motif Kernels for Predicting Protein Subcellular Localization (146), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006 (techreport)
(147), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006, The version in the "Large Scale Kernel Machines" book is more up to date. (techreport)
Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In this paper, we would like to point out that the primal problem can also be solved efficiently, both for linear and non-linear SVMs, and there is no reason for ignoring it. Moreover, from the primal point of view, new families of algorithms for large scale SVM training can be investigated.
Chapelle, O. Training a Support Vector Machine in the Primal (147), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006, The version in the "Large Scale Kernel Machines" book is more up to date. (techreport)
Cross-Validation Optimization for Structured Hessian Kernel Methods
Seeger, M., Chapelle, O.
Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, February 2006 (techreport)
We address the problem of learning hyperparameters in kernel methods for which the Hessian of the objective is structured. We propose an approximation to the cross-validation log likelihood whose gradient can be computed analytically, solving the hyperparameter learning problem efficiently through nonlinear optimization. Crucially, our learning method is based entirely on matrix-vector multiplication primitives with the kernel matrices and their derivatives, allowing straightforward specialization to new kernels or to large datasets. When applied to the problem of multi-way classification, our method scales linearly in the number of classes and gives rise to state-of-the-art results on a remote imaging task.
Seeger, M., Chapelle, O. Cross-Validation Optimization for Structured Hessian Kernel Methods Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, February 2006 (techreport)
Gaussian Processes for Machine Learning
Rasmussen, CE., Williams, CKI.
pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Rasmussen, CE., Williams, CKI. Gaussian Processes for Machine Learning pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)
Implicit Wiener Series, Part II: Regularised estimation
Gehler, P., Franz, M.
(148), Max Planck Institute, 2006 (techreport)
Gehler, P., Franz, M. Implicit Wiener Series, Part II: Regularised estimation (148), Max Planck Institute, 2006 (techreport)
Popper, Falsification and the VC-dimension
Corfield, D., Schölkopf, B., Vapnik, V.
(145), Max Planck Institute for Biological Cybernetics, November 2005 (techreport)
Corfield, D., Schölkopf, B., Vapnik, V. Popper, Falsification and the VC-dimension (145), Max Planck Institute for Biological Cybernetics, November 2005 (techreport)
A Combinatorial View of Graph Laplacians
Huang, J.
(144), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2005 (techreport)
Discussions about different graph Laplacian, mainly normalized and unnormalized versions of graph Laplacian, have been ardent with respect to various methods in clustering and graph based semi-supervised learning. Previous research on graph Laplacians investigated their convergence properties to Laplacian operators on continuous manifolds. There is still no strong proof on convergence for the normalized Laplacian. In this paper, we analyze different variants of graph Laplacians directly from the ways solving the original graph partitioning problem. The graph partitioning problem is a well-known combinatorial NP hard optimization problem. The spectral solutions provide evidence that normalized Laplacian encodes more reasonable considerations for graph partitioning. We also provide some examples to show their differences.
Huang, J. A Combinatorial View of Graph Laplacians (144), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2005 (techreport)
Beyond Pairwise Classification and Clustering Using Hypergraphs
Zhou, D., Huang, J., Schölkopf, B.
(143), Max Planck Institute for Biological Cybernetics, August 2005 (techreport)
In many applications, relationships among objects of interest are more complex than pairwise. Simply approximating complex relationships as pairwise ones can lead to loss of information. An alternative for these applications is to analyze complex relationships among data directly, without the need to first represent the complex relationships into pairwise ones. A natural way to describe complex relationships is to use hypergraphs. A hypergraph is a graph in which edges can connect more than two vertices. Thus we consider learning from a hypergraph, and develop a general framework which is applicable to classification and clustering for complex relational data. We have applied our framework to real-world web classification problems and obtained encouraging results.
Zhou, D., Huang, J., Schölkopf, B. Beyond Pairwise Classification and Clustering Using Hypergraphs (143), Max Planck Institute for Biological Cybernetics, August 2005 (techreport)
Generalized Nonnegative Matrix Approximations using Bregman Divergences
Univ. of Texas at Austin, June 2005 (techreport)
Sra, S., Dhillon, I. Generalized Nonnegative Matrix Approximations using Bregman Divergences Univ. of Texas at Austin, June 2005 (techreport)
Measuring Statistical Dependence with Hilbert-Schmidt Norms
Gretton, A., Bousquet, O., Smola, A., Schölkopf, B.
(140), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2005 (techreport)
We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.
Gretton, A., Bousquet, O., Smola, A., Schölkopf, B. Measuring Statistical Dependence with Hilbert-Schmidt Norms (140), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2005 (techreport)
Consistency of Kernel Canonical Correlation Analysis
(942), Institute of Statistical Mathematics, 4-6-7 Minami-azabu, Minato-ku, Tokyo 106-8569 Japan, June 2005 (techreport)
Fukumizu, K., Bach, F., Gretton, A. Consistency of Kernel Canonical Correlation Analysis (942), Institute of Statistical Mathematics, 4-6-7 Minami-azabu, Minato-ku, Tokyo 106-8569 Japan, June 2005 (techreport)
Approximate Inference for Robust Gaussian Process Regression
Kuss, M., Pfingsten, T., Csato, L., Rasmussen, C.
(136), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)
Gaussian process (GP) priors have been successfully used in non-parametric Bayesian regression and classification models. Inference can be performed analytically only for the regression model with Gaussian noise. For all other likelihood models inference is intractable and various approximation techniques have been proposed. In recent years expectation-propagation (EP) has been developed as a general method for approximate inference. This article provides a general summary of how expectation-propagation can be used for approximate inference in Gaussian process models. Furthermore we present a case study describing its implementation for a new robust variant of Gaussian process regression. To gain further insights into the quality of the EP approximation we present experiments in which we compare to results obtained by Markov chain Monte Carlo (MCMC) sampling.
Kuss, M., Pfingsten, T., Csato, L., Rasmussen, C. Approximate Inference for Robust Gaussian Process Regression (136), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)
Maximum-Margin Feature Combination for Detection and Categorization
BakIr, G., Wu, M., Eichhorn, J.
Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)
In this paper we are concerned with the optimal combination of features of possibly different types for detection and estimation tasks in machine vision. We propose to combine features such that the resulting classifier maximizes the margin between classes. In contrast to existing approaches which are non-convex and/or generative we propose to use a discriminative model leading to convex problem formulation and complexity control. Furthermore we assert that decision functions should not compare apples and oranges by comparing features of different types directly. Instead we propose to combine different similarity measures for each different feature type. Furthermore we argue that the question: "Which feature type is more discriminative for task X?" is ill-posed and show empirically that the answer to this question might depend on the complexity of the decision function.
BakIr, G., Wu, M., Eichhorn, J. Maximum-Margin Feature Combination for Detection and Categorization Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)
Towards a Statistical Theory of Clustering. Presented at the PASCAL workshop on clustering, London
von Luxburg, U., Ben-David, S.
Presented at the PASCAL workshop on clustering, London, 2005 (techreport)
The goal of this paper is to discuss statistical aspects of clustering in a framework where the data to be clustered has been sampled from some unknown probability distribution. Firstly, the clustering of the data set should reveal some structure of the underlying data rather than model artifacts due to the random sampling process. Secondly, the more sample points we have, the more reliable the clustering should be. We discuss which methods can and cannot be used to tackle those problems. In particular we argue that generalization bounds as they are used in statistical learning theory of classification are unsuitable in a general clustering framework. We suggest that the main replacements of generalization bounds should be convergence proofs and stability considerations. This paper should be considered as a road map paper which identifies important questions and potentially fruitful directions for future research about statistical clustering. We do not attempt to present a complete statistical theory of clustering.
von Luxburg, U., Ben-David, S. Towards a Statistical Theory of Clustering. Presented at the PASCAL workshop on clustering, London Presented at the PASCAL workshop on clustering, London, 2005 (techreport)
Approximate Bayesian Inference for Psychometric Functions using MCMC Sampling
Kuss, M., Jäkel, F., Wichmann, F.
In psychophysical studies the psychometric function is used to model the relation between the physical stimulus intensity and the observer's ability to detect or discriminate between stimuli of different intensities. In this report we propose the use of Bayesian inference to extract the information contained in experimental data estimate the parameters of psychometric functions. Since Bayesian inference cannot be performed analytically we describe how a Markov chain Monte Carlo method can be used to generate samples from the posterior distribution over parameters. These samples are used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. In addition we discuss the parameterisation of psychometric functions and the role of prior distributions in the analysis. The proposed approach is exemplified using artificially generate d data and in a case study for real experimental data. Furthermore, we compare our approach with traditional methods based on maximum-likelihood parameter estimation combined with bootstrap techniques for confidence interval estimation. The appendix provides a description of an implementation for the R environment for statistical computing and provides the code for reproducing the results discussed in the experiment section.
Kuss, M., Jäkel, F., Wichmann, F. Approximate Bayesian Inference for Psychometric Functions using MCMC Sampling (135), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)
Schölkopf, B., Smola, A. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)
Kernel Dependency Estimation
Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V.
(98), Max Planck Institute for Biological Cybernetics, August 2002 (techreport)
We consider the learning problem of finding a dependency between a general class of objects and another, possibly different, general class of objects. The objects can be for example: vectors, images, strings, trees or graphs. Such a task is made possible by employing similarity measures in both input and output spaces using kernel functions, thus embedding the objects into vector spaces. Output kernels also make it possible to encode prior information and/or invariances in the loss function in an elegant way. We experimentally validate our approach on several tasks: mapping strings to strings, pattern recognition, and reconstruction from partial images.
Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V. Kernel Dependency Estimation (98), Max Planck Institute for Biological Cybernetics, August 2002 (techreport)
Global Geometry of SVM Classifiers
Zhou, D., Xiao, B., Zhou, H., Dai, R.
Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2002 (techreport)
We construct an geometry framework for any norm Support Vector Machine (SVM) classifiers. Within this framework, separating hyperplanes, dual descriptions and solutions of SVM classifiers are constructed by a purely geometric fashion. In contrast with the optimization theory used in SVM classifiers, we have no complicated computations any more. Each step in our theory is guided by elegant geometric intuitions.
Zhou, D., Xiao, B., Zhou, H., Dai, R. Global Geometry of SVM Classifiers Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2002 (techreport)
Computationally Efficient Face Detection
Romdhani, S., Torr, P., Schölkopf, B., Blake, A.
(MSR-TR-2002-69), Microsoft Research, June 2002 (techreport)
Romdhani, S., Torr, P., Schölkopf, B., Blake, A. Computationally Efficient Face Detection (MSR-TR-2002-69), Microsoft Research, June 2002 (techreport)
Kernel-based nonlinear blind source separation
Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K.
EU-Project BLISS, January 2002 (techreport)
GZIP [BibTex]
Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K. Kernel-based nonlinear blind source separation EU-Project BLISS, January 2002 (techreport)
A compression approach to support vector model selection
von Luxburg, U., Bousquet, O., Schölkopf, B.
(101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)
In this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization bounds we construct ``compression coefficients'' for SVMs, which measure the amount by which the training labels can be compressed by some classification hypothesis. The main idea is to relate the coding precision of this hypothesis to the width of the margin of the SVM. The compression coefficients connect well known quantities such as the radius-margin ratio R^2/rho^2, the eigenvalues of the kernel matrix and the number of support vectors. To test whether they are useful in practice we ran model selection experiments on several real world datasets. As a result we found that compression coefficients can fairly accurately predict the parameters for which the test error is minimized.
von Luxburg, U., Bousquet, O., Schölkopf, B. A compression approach to support vector model selection (101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)
Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design
Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.
Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2002 (techreport)
Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B. Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2002 (techreport)
Observations on the Nyström Method for Gaussian Process Prediction
Williams, C., Rasmussen, C., Schwaighofer, A., Tresp, V.
A number of methods for speeding up Gaussian Process (GP) prediction have been proposed, including the Nystr{\"o}m method of Williams and Seeger (2001). In this paper we focus on two issues (1) the relationship of the Nystr{\"o}m method to the Subset of Regressors method (Poggio and Girosi 1990; Luo and Wahba, 1997) and (2) understanding in what circumstances the Nystr{\"o}m approximation would be expected to provide a good approximation to exact GP regression.
PostScript [BibTex]
Williams, C., Rasmussen, C., Schwaighofer, A., Tresp, V. Observations on the Nyström Method for Gaussian Process Prediction Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2002 (techreport)
The DELVE user manual
Rasmussen, CE., Neal, RM., Hinton, GE., van Camp, D., Revow, M., Ghahramani, Z., Kustra, R., Tibshirani, R.
Department of Computer Science, University of Toronto, December 1996 (techreport)
This manual describes the preliminary release of the DELVE environment. Some features described here have not yet implemented, as noted. Support for regression tasks is presently somewhat more developed than that for classification tasks. We recommend that you exercise caution when using this version of DELVE for real work, as it is possible that bugs remain in the software. We hope that you will send us reports of any problems you encounter, as well as any other comments you may have on the software or manual, at the e-mail address below. Please mention the version number of the manual and/or the software with any comments you send.
Rasmussen, CE., Neal, RM., Hinton, GE., van Camp, D., Revow, M., Ghahramani, Z., Kustra, R., Tibshirani, R. The DELVE user manual Department of Computer Science, University of Toronto, December 1996 (techreport)
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Schölkopf, B., Smola, A., Müller, K.
(44), Max Planck Institute for Biological Cybernetics Tübingen, December 1996, This technical report has also been published elsewhere (techreport)
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible 5-pixel products in 16 x 16 images. We give the derivation of the method, along with a discussion of other techniques which can be made nonlinear with the kernel approach; and present first experimental results on nonlinear feature extraction for pattern recognition.
Schölkopf, B., Smola, A., Müller, K. Nonlinear Component Analysis as a Kernel Eigenvalue Problem (44), Max Planck Institute for Biological Cybernetics Tübingen, December 1996, This technical report has also been published elsewhere (techreport)
Learning View Graphs for Robot Navigation
Franz, M., Schölkopf, B., Georg, P., Mallot, H., Bülthoff, H.
(33), Max Planck Institute for Biological Cybernetics, Tübingen,, July 1996 (techreport)
We present a purely vision-based scheme for learning a parsimonious representation of an open environment. Using simple exploration behaviours, our system constructs a graph of appropriately chosen views. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. Simulations and robot experiments demonstrate the feasibility of the proposed approach.
Franz, M., Schölkopf, B., Georg, P., Mallot, H., Bülthoff, H. Learning View Graphs for Robot Navigation (33), Max Planck Institute for Biological Cybernetics, Tübingen,, July 1996 (techreport)


Firstly thank you for your excellent service! Your products are excellent quality in manufactuer and great in usability! It's great to see a small English company doing this and being so honest with there products. Personally there's no other product which comes close as I'm allergic to dust mites. Well done wish you all the best!
We are manufacturers of wool duvets, pillows, mattress toppers and natural fibre mattresses. Made in the UK with 100% British wool.


Virgin Active and Laughology share so many values, we could have been siblings in a former life. We are both fun, vibrant, welcoming, innovative, youthful and led by famous beard-wearers (although our leader choses to wax hers).
The synergy was in evidence when a beardless Stephanie Davies worked with the gym and fitness club business this month to explore the concept of happy clubs in more detail, and to investigate how Virgin Active can embed happiness in its culture, both scientifically and structurally.
Stephanie, who like Richard Branson is described as a 'thought leader' and has a mug with this written on it, delivered a half-day session on embedding happiness structure to GMs and leadership team members at the organisation's member service managers conference.


Last night Maher told Democrats to "grow a pair" and go on Fox News, including taking primary debates to the network.
"You want to be in the big leagues but you refuse to ever play an away game? You don't like the questions that Fox News might ask so you're deciding to not take any questions at all? How very Trump of you," he said.
He added that Trump's supporters love that "he fights," while Rachel Campos-Duffy said Republicans go on the "very liberal" news channels all the time.


Like a boring and dull spinoff of Waiting for Godot, Taylor and Corinne continue talking in circles about life, love and Intro to Psych terms. As they talk, their brows both become increasingly creased, until they collapse. Corinne scampers off to tell Nick that everyone hates Taylor and he kisses her for "showing maturity." If Nick sends Taylor home because of this, I will never consider him remotely smart again.
Nick's starting to get serious about some ladies, and has to make some "hard choices." He gives roses to Whitney, Danielle M., Jasmine, Rachel, Jaimi, Josephine, Vanessa, Alexis, Corinne and Taylor.
Sarah and Astrid are sent home. His loss, cuz Sarah is cute as heck.
"Where have you beignet all my life?" Nick asks Rachel in her New Orleans-themed 1-on-1 date card. Finally, it's her turn.
Rachel is probably the most eligible of all the Bachelorettes, what with being a successful attorney who can fit into American Apparel leotards. (Also, let's not forget, she sings to her vacuum to relax — extremely relatable.) Watching Nick with Rachel makes me forget that he might be dumb and also poorly dressed.
They have dinner in a warehouse full of Mardi Gras stuff, which is kinda weird since New Orleans has some of the best restaurants in the country.
"I'm already breaking rules here, but I'm super into you," Nick says. He spends most of the date seemingly prepping to ask Rachel's dad for permission to marry her. Whoa. I think this just officially became a race between Rachel and Vanessa.
Till Death Do Us Part: A Group Date!
Josephine, Kristina, Alexis, Raven, Jaimi, Vanessa, Danielle M., Whitney, Jasmine, Danielle L. and Nick head to a haunted house. The caretaker's name is Boo, although he is not as cute as Boo, world's cutest dog. He orients the ladies to the spookiness at hand. Apparently, this plantation is haunted not by slavery, but by a little girl named Mae.
At this point I'm feeling like a cemetery tour would have been a better idea for a "death do us part" date, but I am not the President of Bachelor Nation.
As they start the game, or whatever it is, Vanessa refuses to play with the ouija board. She just got uninvited from my slumber party!
After lots of bumps in the night, Danielle M. gets the rose. As it's happening, I feel that somewhere, a Shondaland casting agent is watching this and we will see her as a side character on Grey's Anatomy next season.
Side note: Dresses that expose the entirety of one upper thigh keep appearing on this show. Do the producers hand these out?
We all know by now that Corinne is going to win in this battle for Nick's coveted rose. After all, we haven't heard the previewed line yet about her "vagine" being made of platinum yet.
As some kind of metaphor for how scary Nick finds them both, the trio go alligator spectating in the bayou. In the woods, they're approached by a voodoo priestess, and Corinne flinches as if she's a vampire a little too close to garlic.
The tarot reader tells Taylor "not to engage" with a toxic situation, which is actually good advice. Corinne asks her how she can make a voodoo doll specific to a person, and the priestess gives her one.
So far, this is way more boring than Chad's showdown with Alex on JoJo's season. Corinne just doesn't have as much villain charisma, although I feel certain she will mate with him and their child will be psychotic.
As the mosquitos come out in full force, Corinne gets the rose. This cements that Nick lacks a lot of, as Taylor would say, "emotional intelligence." That said, Taylor is 13 years younger than Nick and they don't have any chemistry, so it was only a matter of time for her.
As Nick and Corinne kiss in a tiny boat at sunset, she clutches her voodoo doll against her rose. It's eerily reminiscent of when Prince Eric kisses Ursula disguised as a human in The Little Mermaid.
Are We Ready for Nick's Season of the Bachelor?
The Bachelor Episode 7: Our Moms Weigh In!


text:
Q: Group By alternative in Laravel 5.4 I have a table Schedule with 3 columns (id, ref_number, pay_date). Each ref_number has a pay_date in every month. So the table looks something like this:
id | ref_number | pay_date
-----------------------------
1  | A001       | 2018-06-29
1  | A001       | 2018-07-29
1  | A002       | 2018-06-30
1  | A002       | 2018-07-30
1  | A002       | 2018-08-30
1  | A003       | 2018-06-29

I want to fetch only the earliest record for every ref_number that have pay_date between today and a date (30 or 31 days from today). The below query works fine in Mysql (I would pass the dates dynamically later).
 SELECT id,ref_number,MIN(pay_date) FROM schedule
 WHERE  (pay_date BETWEEN '2018-06-30' AND '2018-07-30')
 GROUP BY ref_number

I know we can turn the mysql "strict" to false in Database config file and the Group By would behave as expected, but without having to change that is there any other way around this problem?
What would be the eloquent equivalent for this query? With or without groupby.

A: After searching for a while, I came across an answer using nested select statement and gave it a try in Laravel. It worked exactly as I wanted. Here's the sweet piece of code:
Schedule::select(DB::raw(id, ref_number, MIN(pay_date) as pay_date))
          ->from(DB::raw("(SELECT *
                       FROM schedule
                       WHERE (pay_date > CURDATE()))
                       temp")
                      )
          ->groupBy('temp.ref_number')
          ->get();


I had read "A Northern Light" by Jennifer Donnelly years ago, and her brilliant writing approach to historical fiction prompted me to take on "Revolution" with little insistence.
Revolution is a novel about the revolution each person has inside of themselves at one point or another in their life. The revolution of thought or resistance to ideas that we've been taught to accept without question. Andi is a relatable character, full of faults and a few virtues scattered here and there. Her passion and obsession for music is believable in the sense that when she lives for her music, she truly means it. She displays suicidal tendencies, and due to the sudden loss of her young brother, Truman, she does all she can to escape the world and the feelings that she was responsible for his death. This includes pills that numb her into oblivion.
The switching of viewpoints between Andi and Alex was easy to follow, although at times portions of Alex's story read dryly—nearly textbook in the way the prose was delivered. I truly enjoyed reading of Andi's struggle with the revolution happening inside of her.
Should she end her life to see Truman again?
Or should she step away from the ledge and live for him?
Those questions are answered in a delicate way, as this book deals with depression, anxiety, suicide, death, living, mental illness, drugs, and alcohol in a way that is fresh and doesn't preach to its audience. "Revolution" is an excellent novel from an author that deserves recognition in the Young Adult fiction community.
Thank you for this great review. I love how you said that "Revolution" is about the revolution that takes place inside us all. You definitely see Andi overcoming her own revolution (through her involvement in the French Revolution), and come out a whole, self-actualized person in the end. I loved this book so much, and Donnelly is definitely one of the most (if not THE most) talented YA writer out there today.
← Find A Girl Who Reads.


I drive a 97 Concorde in perfect mechanical condition, but since the polar vortex hit a few days ago, the brake warning light has been on and so far I can't figure out why.
1) Parking brake is fully released. When setting PB, the warning light gets slightly brighter and when releasing it goes back to slightly dimmer, but doesn't turn off. PB fully sets and fully releases.
2) Brake fluid is nearly full. No leaks.
3) Brake fluid level sensor connector is tight, but I couldn't get my hand in the narrow space underneath to unplug and check terminals for corrosion.
The light has gone off a few times but come back on shortly thereafter.
This vehicle does not have ABS so that is not part of the equation. I don't know of anything else that could trigger this light. Am I missing something?
Can the sensor be resistance checked? Also, how would I remove the sensor...can it be removed w/o losing all the fluid?
Seeing as how it's labeled as a "Switch"...I would think it would either be almost zero resistance or super high resistance.
You could also try removing the connector from the switch and jumper the two pins and verify the warning light goes out. With that test you can verify it's the switch itself or something back in the wiring.
not disable ABS operation. Low brake fluid switch may also be referred to as brake fluid level switch.
And this little bit of info that's useless!
I'd say you'd have to drain the Master Cylinder to replace the switch.
I can't find anything in the parts catalog regarding a part number for the Low Fluid Switch at all. So I don't know if it can be replaced by itself? Might be available only with a replacement master cylinder?
In the troubleshooting flowchart above, it states "Check fluid level switch and master cylinder float for proper operation". Does this reservoir have a float? I can't see thru the fluid to tell what's in there. Not to mention I haven't been successful at locating a replacement part to know what it looks like.
Does your car have ABS? Traction Control?
It's tough to find a good picture of the Reservoir but here's one. It looks like the fluid level switch is molded into the reservoir and not replaceable. You'd have to replace the reservoir.
That master cylinder is available on Ebay for $19.89 with free shipping.
Brakes and e-brake are working fine, fluid is good.
No ABS, no traction control. Thanks for the the info/link on that master cylinder. I'm going to try removing the harness and jumpering the two contacts to see what that does.
When I unplug the switch harness, should that make the light stay ON or OFF?
Last edited by mjgrantham; 02-11-2019 at 10:23 AM.
Ahh, I got it now. I've finally gotten under there to unplug the harness and viola! The light went off. Jumping the connectors turned the light on, so the problem indeed is the switch.
I would like to suction out the reservoir fluid to see the sensor. Do you suppose that could introduce air into the system if I don't step on the brake pedal?
There won't be a problem with air in the system by suctioning the brake fluid out. Just keep the car off and don't touch the brake pedal.
Don't re use the old Brake fluid. Get new fluid. According to the FSM it'll be DOT 3 type.
If the sensor has a float...it could very well be it sprung a leak and is filled with brake fluid and dropped to the bottom of the reservoir.
"If the sensor has a float...it could very well be it sprung a leak and is filled with brake fluid and dropped to the bottom of the reservoir."
That's exactly what I was thinking. And the sensor definitely appears to be molded into the reservoir.
I had in mind to use new brake fluid, but thanks for the tip. It's amazing how grimy it gets. I flushed my lines and reservoir a few years back and it's brown now.
Last edited by mjgrantham; 02-11-2019 at 03:38 PM.


text:
The Evolution of Social Commerce: An Examination from the People, Business, Technology, and Information Perspective
Chingning Wang, Ping Zhang
Social commerce is a form of commerce mediated by social media and is converging both online and offline environments. As a relatively new phenomenon, social commerce has evolved quickly in practice, yet has gained little attention in the IS discipline. With its pervasiveness in businesses and people's lives, social commerce presents ample research opportunities that can have both theoretical and practical significance and implications. This article aims to capture researchers' attention by describing the characteristics of social commerce and its potential future directions. We trace the evolutionary patterns of social commerce chronologically, based on trade articles and academic publications from 2005 to 2011. A framework that combines people, management, technology, and information dimensions is used to provide a systematic analysis of social commerce development. Our examination shows that since 2005, the year the term social commerce was incepted, assumptions and understanding of people in social commerce move from a simple and general description of human social nature to a rich exploration with different angles from social psychology, social heuristics, national culture, and economic situations. On the management dimension, business strategies and models evolve from the short-tail to long-tail thinking, with invented concepts such as branded social networks/communities, niche social networks/communities, niche brands, co-creating, team-buying, and multichannel social networks. Technologically, IT platforms and capabilities for social commerce evolve from blogs, to social networking sites, to media sharing sites, and to smartphones. While Facebook becomes a profit-generating platform, creating the notion of f-commerce, Google and Twitter become strong competitors with great potentials. Information in social commerce evolves from peer-generated, to community-generated (crowdsourcing), to consumer and marketer co-created, and to global crowdsourced. Our examination identifies various conceptualizations, terminologies, views, and perspectives about social commerce and its relation to other well-known concepts such as e-commerce. In light of the evolution of social commerce, we provide possible future directions for research and practice.
Communications of the Association for Information Systems
https://doi.org/10.17705/1cais.03105
G-commerce
Information model
Mediatization
T-commerce
10.17705/1cais.03105
Fingerprint Dive into the research topics of 'The Evolution of Social Commerce: An Examination from the People, Business, Technology, and Information Perspective'. Together they form a unique fingerprint.
Blogs Engineering & Materials Science
Crowdsourcing Engineering & Materials Science
Terminology Engineering & Materials Science
Smartphones Engineering & Materials Science
Profitability Engineering & Materials Science
Wang, C., & Zhang, P. (2012). The Evolution of Social Commerce: An Examination from the People, Business, Technology, and Information Perspective. Communications of the Association for Information Systems, 31(1), 105-127. https://doi.org/10.17705/1cais.03105
The Evolution of Social Commerce: An Examination from the People, Business, Technology, and Information Perspective. / Wang, Chingning; Zhang, Ping.
In: Communications of the Association for Information Systems, Vol. 31, No. 1, 01.01.2012, p. 105-127.
Wang, C & Zhang, P 2012, 'The Evolution of Social Commerce: An Examination from the People, Business, Technology, and Information Perspective', Communications of the Association for Information Systems, vol. 31, no. 1, pp. 105-127. https://doi.org/10.17705/1cais.03105
Wang C, Zhang P. The Evolution of Social Commerce: An Examination from the People, Business, Technology, and Information Perspective. Communications of the Association for Information Systems. 2012 Jan 1;31(1):105-127. https://doi.org/10.17705/1cais.03105
Wang, Chingning ; Zhang, Ping. / The Evolution of Social Commerce: An Examination from the People, Business, Technology, and Information Perspective. In: Communications of the Association for Information Systems. 2012 ; Vol. 31, No. 1. pp. 105-127.
@article{7702f3a1deb943288a8b59448bd26ee1,
title = "The Evolution of Social Commerce: An Examination from the People, Business, Technology, and Information Perspective",
abstract = "Social commerce is a form of commerce mediated by social media and is converging both online and offline environments. As a relatively new phenomenon, social commerce has evolved quickly in practice, yet has gained little attention in the IS discipline. With its pervasiveness in businesses and people's lives, social commerce presents ample research opportunities that can have both theoretical and practical significance and implications. This article aims to capture researchers' attention by describing the characteristics of social commerce and its potential future directions. We trace the evolutionary patterns of social commerce chronologically, based on trade articles and academic publications from 2005 to 2011. A framework that combines people, management, technology, and information dimensions is used to provide a systematic analysis of social commerce development. Our examination shows that since 2005, the year the term social commerce was incepted, assumptions and understanding of people in social commerce move from a simple and general description of human social nature to a rich exploration with different angles from social psychology, social heuristics, national culture, and economic situations. On the management dimension, business strategies and models evolve from the short-tail to long-tail thinking, with invented concepts such as branded social networks/communities, niche social networks/communities, niche brands, co-creating, team-buying, and multichannel social networks. Technologically, IT platforms and capabilities for social commerce evolve from blogs, to social networking sites, to media sharing sites, and to smartphones. While Facebook becomes a profit-generating platform, creating the notion of f-commerce, Google and Twitter become strong competitors with great potentials. Information in social commerce evolves from peer-generated, to community-generated (crowdsourcing), to consumer and marketer co-created, and to global crowdsourced. Our examination identifies various conceptualizations, terminologies, views, and perspectives about social commerce and its relation to other well-known concepts such as e-commerce. In light of the evolution of social commerce, we provide possible future directions for research and practice.",
keywords = "Crowdsourcing, Evolution, F-commerce, G-commerce, Groupon, Information model, Mediation, Mediatization, Social commerce, Social shopping, T-commerce, Tuangou",
author = "Chingning Wang and Ping Zhang",
doi = "10.17705/1cais.03105",
journal = "Communications of the Association for Information Systems",
publisher = "Association for Information Systems",
T1 - The Evolution of Social Commerce: An Examination from the People, Business, Technology, and Information Perspective
AU - Wang, Chingning
AU - Zhang, Ping
N2 - Social commerce is a form of commerce mediated by social media and is converging both online and offline environments. As a relatively new phenomenon, social commerce has evolved quickly in practice, yet has gained little attention in the IS discipline. With its pervasiveness in businesses and people's lives, social commerce presents ample research opportunities that can have both theoretical and practical significance and implications. This article aims to capture researchers' attention by describing the characteristics of social commerce and its potential future directions. We trace the evolutionary patterns of social commerce chronologically, based on trade articles and academic publications from 2005 to 2011. A framework that combines people, management, technology, and information dimensions is used to provide a systematic analysis of social commerce development. Our examination shows that since 2005, the year the term social commerce was incepted, assumptions and understanding of people in social commerce move from a simple and general description of human social nature to a rich exploration with different angles from social psychology, social heuristics, national culture, and economic situations. On the management dimension, business strategies and models evolve from the short-tail to long-tail thinking, with invented concepts such as branded social networks/communities, niche social networks/communities, niche brands, co-creating, team-buying, and multichannel social networks. Technologically, IT platforms and capabilities for social commerce evolve from blogs, to social networking sites, to media sharing sites, and to smartphones. While Facebook becomes a profit-generating platform, creating the notion of f-commerce, Google and Twitter become strong competitors with great potentials. Information in social commerce evolves from peer-generated, to community-generated (crowdsourcing), to consumer and marketer co-created, and to global crowdsourced. Our examination identifies various conceptualizations, terminologies, views, and perspectives about social commerce and its relation to other well-known concepts such as e-commerce. In light of the evolution of social commerce, we provide possible future directions for research and practice.
AB - Social commerce is a form of commerce mediated by social media and is converging both online and offline environments. As a relatively new phenomenon, social commerce has evolved quickly in practice, yet has gained little attention in the IS discipline. With its pervasiveness in businesses and people's lives, social commerce presents ample research opportunities that can have both theoretical and practical significance and implications. This article aims to capture researchers' attention by describing the characteristics of social commerce and its potential future directions. We trace the evolutionary patterns of social commerce chronologically, based on trade articles and academic publications from 2005 to 2011. A framework that combines people, management, technology, and information dimensions is used to provide a systematic analysis of social commerce development. Our examination shows that since 2005, the year the term social commerce was incepted, assumptions and understanding of people in social commerce move from a simple and general description of human social nature to a rich exploration with different angles from social psychology, social heuristics, national culture, and economic situations. On the management dimension, business strategies and models evolve from the short-tail to long-tail thinking, with invented concepts such as branded social networks/communities, niche social networks/communities, niche brands, co-creating, team-buying, and multichannel social networks. Technologically, IT platforms and capabilities for social commerce evolve from blogs, to social networking sites, to media sharing sites, and to smartphones. While Facebook becomes a profit-generating platform, creating the notion of f-commerce, Google and Twitter become strong competitors with great potentials. Information in social commerce evolves from peer-generated, to community-generated (crowdsourcing), to consumer and marketer co-created, and to global crowdsourced. Our examination identifies various conceptualizations, terminologies, views, and perspectives about social commerce and its relation to other well-known concepts such as e-commerce. In light of the evolution of social commerce, we provide possible future directions for research and practice.
KW - Crowdsourcing
KW - Evolution
KW - F-commerce
KW - G-commerce
KW - Groupon
KW - Information model
KW - Mediation
KW - Mediatization
KW - Social commerce
KW - Social shopping
KW - T-commerce
KW - Tuangou
U2 - 10.17705/1cais.03105
DO - 10.17705/1cais.03105
JO - Communications of the Association for Information Systems
JF - Communications of the Association for Information Systems