text:
Steven X. Wang MATH & STAT
List of Algorithms
Professor Steven Xiaogang Wang
Certified Professional Statistician by SSC.
Email: stevenw@yorku.ca
Please note that my previous email address stevenw@mathstat.yorku.ca is no longer used or checked due to email server migration.
Machine Learning, Data Mining, High dimensional optimization , Big Data with Application to Medicine, Disease modelling and Statistical Inference.
Education Background
Postdoc in Data Mining, Pacific Institute of Mathematical Sciences, 2002.
Ph.D. in Statistics, University of British Columbia, 2001.
M.Sc. in Applied Statistics, University of California at Riverside.
B.Sc in Computational Mathematics, Beijing University of Technology.
Selected Publication
Most Recent Publications (last 6 years)
Zana Rashidi, Kasra Ahmadi, Aijun An and Xiaogang Wang (2020). Adaptive Momentum Coefficient for Neural Network Optimization, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD'20).
Lin, Y. Wang, X. and Wu, Y. (2020) A Segmented Generalized Markov Regime-Switching Model with Its Application in Financial Time Series Data. Journal of Statistical Computation and Simulation. Accepted.
Pengjun Zhou, Ziyao, Dandan Xu, Ying Wang, Qi Bai, Yue Feng, Guifeng Su, Pengxiao Chen, Yao Wang, Huizhong Liu, Xiaogang Wang, Rong Zhang and Yifei Wang. (2019). Cepharanthine Hydrochloride Improves Cisplatin Chemotherapy and Enhances Immunity by Regulating Intestinal Microbes in Mice. Frontier in Cellular and Infection Microbiology.
Yuejiao Fu, Yukun Liu, Hsiao-Hsuan Wang and Xiaogang Wang (2019).
Empirical likelihood estimation in multivariate mixture models with repeated measurements. Statistical Theory and Related Fields. In Press.
Chen, L, Zhu, H. and Wang, X. (2018) Modelling Spatial-Temporal Distribution of Mosquito Abundance with Unobservable Environmental Factors. Journal of Medical Entomology. Accepted.
Li, X., Fu, Y., Wang, X., Demeo, D.L., Tantisira, K.,Weiss, S. and Qiu, W. (2018). Detecting Differentially Variable MicroRNAs via Model-based Clustering. International Journal of Genomics. Accepted.
Fok, R., An,A, Rasheid, Z., and Wang, X. (2018) Decoupling the Layers in Residual Networks, International Conference of Learning Representations (ICLR), 2018. Accepted.
Li, X. Fu, Y., Wang, X. and Qiu, W. (2018).Robust Joint Tests in the Application of DNA methylation data analysis. BMC Bioinformatics. To Appear.
Gold, N, Frasch, M., Herry C. , Richardson B and Wang, X. (2018). A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signal. Frontiers in Physiology- Computational Physiology and Medicine. To Appear.
Fok, R., An, A. and Wang, X. (2017). Geodesic and Contour Optimization using Conformal Mapping. Journal of Global Optimization. 69(1): 23-44.
Wang, X., Qiu, X. and Wu, J. (2016). Convergence and Stability Analysis of Mean-shift Type Algorithm on Large Data Sets. Statistics and Its Interface.
Li, X., Jankowski, H., Boonpatcharanon, S., Tran, V., Wang, X. and Heffernan, J.M. (2015). Clustering Neuraminidase Influenza Protein Sequences, Proceedings of the International Symposium on Mathematical and Computational Biology. BIOMAT 2015.
Li, X., Qiu, W., Morrow, J., Weiss, J., Fu, Y. and Wang, X. (2015). A Comparative Study of Tests for Homegeneity of Variances with Application to DNA Methylation Data, PLoS One.
Wang, Q., Gold, N., Frasch, M., Huang, H. and Wang, X. (2015). Mathematical Model of Cardiovascular and Metabolic Responses to Umbilical Cord Occulsions in Fetal Sheep. Bulletin of Mathematical Biology.
Fok, R., An, A. and Wang, X. (2015).Mining Evolving Data Streams with Particle Filers. Computer Intelligence.
Wang, X., Wang, J. Russel, C.m Proctor, P., Bellod, R. Higuchi, K. and Zhu, H. (2014). Clustering of Abundance of West Nile Virus Vector Mosquitoes in Peel Region, Ontario. Environmental and Ecological Statistics.
Wang. X.,Durosier, D, Ross, M. Richardson, S., Frasch, M. (2014). Online Detection of Fetal Academia During Labor by Testing Synchronization of EEG and Heart Rate: A prospective study in fetal sheep. PLoS One.
Chen, S. and Wang. X. (2014). Approximate Risk Analysis using Numerical Integration on Sparse Grid. Journal of Mathematical and Computational Sciences.
Earlier Publications (Most Signifiant Ones)
Andreopoulos, B., An, A., Wang, X., Faloutsos, M and Schroeder, M. (2007). Clustering by Common Friends Finds Locally Significant Proteins Mediating Modules. Bioinformatics, Oxford University Press, 23(9): 1124-1131.
Wang, X., Liang, D, Feng, X. and Ye, L. (2007). A Derivative Free Optimization Algorithm based on Conditional Moments. Journal of Mathematical Analysis and Applications. Vol. 331, No. 2, 1337 - 1360.
Wang, X. (2006). Approximating Bayesian Inference by Weighted Likelihood Method. The Canadian Journal of Statistics. Vol. 34, 279-298.
Wang, X., Qiu, W. and Zamar, R. (2006). An Iterative Non-parametric Clustering Algorithm Based on Local Shrinking. Computational Statistics and Data Analysis.
Zhang, P., Wang, X. and Song, P.X. (2006) Clustering Categorical Data Based on Distance Vectors. The Journal of the American Statistical Association. Vol. 101. No. 473, 355-367.
Wang, X. and Zidek, J.V. (2005). Derivation of Mixture Distribution and Weighted Likelihood as minimizers of KL-divergence subject to constraints. The Annals of the Institute of Statistical Mathematics. Vol 57, 687-701.
Wang, X. and Zidek, J.V. (2005). Selecting likelihood weights by cross-validation. The Annals of Statistics. Vol., 33. No. 2, 463-500.Â
Wang, X., van Eeden, C. and Zidek, J.V. (2004). Asymptotic Properties of Weighted Likelihood Estimators.Journal of Statistical Planning and Inference. Vol 119, pp. 37-54.Â


text:
// (C) Copyright 2015 Martin Dougiamas
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

angular.module('mm.addons.qtype_shortanswer')

/**
 * Directive to render a short answer question.
 *
 * @module mm.addons.qtype_shortanswer
 * @ngdoc directive
 * @name mmaQtypeShortAnswer
 */
.directive('mmaQtypeShortAnswer', function($log, $mmQuestionHelper) {
	$log = $log.getInstance('mmaQtypeShortAnswer');

    return {
        restrict: 'A',
        priority: 100,
        templateUrl: 'addons/qtype/shortanswer/template.html',
        link: function(scope) {
            $mmQuestionHelper.inputTextDirective(scope, $log);
        }
    };
});

redpajama_set_name: RedPajamaGithub


OS400 PF: how to reduce unused space in a BLOB field.
A deletion Job deletes older records periodically. REUSE is set to *YES.
Problem: BLOB field-size will not be reduced, also if new records have smaller documents (because of optimized application) as in the old, deleted records.
to remove deleted records (with larger BLOBs) may reduces filesize.
But there is not enaugh diskspace for a RGZPFM.
Is there any other solution to reduce unused BLOB-space in a BLOB field ?
Please show DDS or SQL DDL for the file.
Also, how big is the file (DSPFD), and how much disk space do you have available?
How many access paths over teh file?
BLOB fields, properly declared, are variable length. Do you have long sections of embedded blanks or leading or trailing blanks that you can recover by reading, compressing, and updating each row?
1) Save and restore a copy of the file to another system with more disk space and CPYF COMPRESS(*YES) or RGZPGM ALWCANCEL(*NO) there, then restore smaller file back to original system.
2) Temporarily free enough disk space to RGZPFM by backing off other libraries or files, then RGZPFM, and restore libs/objects temporarily removed.
3) Depending on your OS version, you may be able use RGZPFM while active, but it will only compress fixed space, not AUX space where blobs are held - and in you case that is probably where all the excess disk space usage is.
is there any common file tool (beneth RPGIV/SQL) to get Information (or stored BLOB documents itself) from a BLOB field, defined in SQL created PF ?
Here is an example of an RPG program to extract BLOBs to a stream file. Is that what you are looking for?
yes, the example is helpful for understandig, how BLOB fields are handled with RPG/SQL.
My concerns - from System perspective - are: how to get those "monsterfiles" with 1,5 TB smaller . . .
All of the data resides in the physical file object - fixed fields and LOBs (BLOBs, CLOBs).
Physical files have a "data space", which contains the fixed length portion of each record, including a pointer for each LOB field in the record.
Physical files also have "auxiliary space", which contains LOBs (and possibly some or all of variable length fields). This pointer in data space points to a location in the "aux space".
Keyed physical files also have an access path - a built-in index.
1) SIMPLY DON'T STORE LOBS IN DATABASE FILES! For a small table, no problem. But for a table that could grow large, this often isn't the most efficient way to manage this type of data.
Instead, store them as stream files (XML, JPG, etc) in the IFS, and store the path in a text field, or store the path in a DATALINK field, or store them in Amazon S3 (or other local store or cloud-based storage). Additional benefits often apply: for example, no need to extract and save to the OS file system to use in web pages and other applications that can directly use the stream files.
2) If you can't remove them, then consider compressing LOBs before storing them, and decompress them when you need to use them. Trim leading and trailing spaces, then compress data using a user-defined function to reduce storage utilization.


text:
Q: Devise - remember me not working? LocalHost Issue? I'm using devise with my rails 3 app. For some reason the sign in with Remember Me is not working.
Could this be due to testing on localhost:3000 ?
in devise.rb, I have the following set:
config.remember_for = 2.weeks

In the logs, when I post a signin I see:
Started POST "/users/sign_in" for 127.0.0.1 at Thu May 12 20:53:04 -0700 2011
  Processing by SessionsController#create as HTML
  Parameters: {"signIn"=>"LOG IN", "authenticity_token"=>"GR09TIq4uSbu6UWxDRhpfQeLWp7qtJTxkCFksLmFzdE=", "utf8"=>"✓", "user"=>{"remember_me"=>"on", "password"=>"[FILTERED]", "email"=>"xxxx@xxxxxxx-inc.com"}}

Is there anything wrong there?
I also have the following in my sessions_controller.rb
class SessionsController < Devise::SessionsController

  prepend_before_filter :require_no_authentication, :only => [ :new, :create ]
  include Devise::Controllers::InternalHelpers

  # GET /resource/sign_in
  def new
    clean_up_passwords(build_resource)
    render_with_scope :new
  end

  # POST /resource/sign_in
  def create
    resource = warden.authenticate!(:scope => resource_name, :recall => "new")
    #set_flash_message :notice, :signed_in
    sign_in_and_redirect(resource_name, resource)
  end

  # GET /resource/sign_out
  def destroy
    #set_flash_message :notice, :signed_out if signed_in?(resource_name)
    sign_out_and_redirect(resource_name)
  end

  protected

  def after_sign_in_path_for(resource)
    if resource.is_a?(User) && resource.banned?
      sign_out resource
      flash[:error] = "This account has been suspended."
      root_path
    else
      super
    end
  end

end

Any ideas why signing in and remembering is not working? Thanks

A: The Devise remember_user_token cookie could be set to 'secure only', in which case it doesn't work with the development rails server on http (browser never sends it back to the server).
Check initializers/devise.rb for rememberable_options = {:secure => true}

A: This happens because remember_me comes in params as "on", but is compared to Devise::TRUE_VALUES, which are [true, 1, '1', 't', 'T', 'true', 'TRUE'].
The easiest way is to make it work is to insure your remember_me comes as one of that values. Example of check-box(notice value="1"):
<input type="checkbox" name="user[remember_me]" value="1" checked="checked" />

Another way if you want to make it work with "on" value you can add "on" to Devise::TRUE_VALUES.
So in your config/initializers/devise.rb just add as the first line:
Devise::TRUE_VALUES << ["on"]

A: Do you have the sessions set aswell with config.timeout_in = 10.minutes?
If so see this contribution on stackoverflow which solves it solution

A: My problem with this was this single line in User.rb (I updated from Michael Hartl login mechanism to devise)
before_save :create_remember_token

I commented it out and it worked.
I also have :
User.rb
class User < ActiveRecord::Base
  # Include default devise modules. Others available are:
  # :token_authenticatable, :confirmable,
  # :lockable, :timeoutable and :omniauthable
  devise :database_authenticatable, :registerable,:token_authenticatable,
         :recoverable, :rememberable, :trackable, :validatable

On devise.rb, I only added Devise::TRUE_VALUES << ["on"] and uncommented config.remember_for = 2.weeks

redpajama_set_name: RedPajamaStackExchange


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These are great replacement pads for magnetic phone mounts! I upgraded to a new phone and needed a new one, and these fit the bill. What I like about these are they are larger than what I had previously, so I don't have to be as precise when placing my phone on the mount. I am able to "hide" it between the layers of my otterbox case and it still has a great grip. The edges are rounded nicely in case you had it on the outside of the case.
Perfect! Also included is a transparent adhesive that goes on your device first then the plate on top. Protecting your device should you decide to remove the plate – you can do so easier – maybe without damaging your device. Either way the plate adhesive is strong and there is excellent contact to my magnetic iPhone holder. They come in a nice plastic resealable plastic envelope/packet. Well done.
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