Reaf Services
Your personal gift card seller.
All you need under one roof.
You're busy, and mastering all the ins and outs of corporate gift card programs can take lots of time. Rely on Reaf to find the very best gift card program for you, and then handle every aspect of it – from sales and marketing, to analysis and reporting, to fulfillment and additional promotional opportunities.
It's a true turnkey solution that lets you reap all the rewards of extra revenue and boosted market share – but spares you all the complexities and time-consuming details.
Reaf has established relationships with top buyers industry wide, and can seamlessly place your gift cards in programs such as:
- Incentives and rewards
- Loyalty – including credit card loyalty rewards
- Fundraising
- Health & wellness
- Safety and many others
What we do for you:
- Act as your company's gift card sales force
- Manage the day-to-day buyer contact including contracts, launching card in program, ordering, customer service to buyers and monitoring inventory arrival
- Help project-manage large program installations, such as 3rd party grocery/drug store programs
- Market your gift card to buyers through emails, direct mail and call campaigns
- Create promotions to increase gift card sales
- Provide monthly sales and forecast reports, so you know your critical stats at all times
- Help analyze backend gift card numbers including lift, breakage and profit
- Fulfill gift cards
What this means for you:
Increased revenue. Corporate gift card sales can bring in extra revenue from alternate channels that traditional business simply can't reach.
Additional sales. Studies show that recipients typically spend 30% more than the issued gift card amount.
Boosted brand awareness and product trial. New customers will be introduced to your brand through gifted cards.
Enhanced branding. People will also see your brand in additional channels – such as third-party gift card programs and credit card loyalty programs.
|


Knowledge space theory by Doignon and Falmagne (1999) <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical framework, which proposes mathematical formalisms to operationalize knowledge structures in a particular domain. The 'kst' package provides basic functionalities to generate, handle, and manipulate knowledge structures and knowledge spaces.
| Version: | 0.5-1 |
| Depends: | R (≥ 3.5.0), proxy, relations (≥ 0.6-7), sets (≥ 1.0-17) |
| Suggests: | Rgraphviz |
| Published: | 2018-11-29 |
| Author: | Christina Stahl [aut], David Meyer [aut], Cord Hockemeyer [aut, cre] |
| Maintainer: | Cord Hockemeyer <cord.hockemeyer at uni-graz.at> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://homepage.uni-graz.at/en/cord.hockemeyer/ |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| In views: | Psychometrics |
| CRAN checks: | kst results |
| Reference manual: | kst.pdf |
| Vignettes: |
KST |
| Package source: | kst_0.5-1.tar.gz |
| Windows binaries: | r-devel: kst_0.5-1.zip, r-release: kst_0.5-1.zip, r-oldrel: kst_0.4-0.zip |
| OS X binaries: | r-release: kst_0.5-1.tgz, r-oldrel: kst_0.4-0.tgz |
| Old sources: | kst archive |
| Reverse suggests: | kstIO |
Please use the canonical form https://CRAN.R-project.org/package=kst to link to this page.


Mvp.Xml Project
This project is developed by Microsoft
MVPs in
XML technologies and
XML Web Services worldwide. It is aimed at supplementing .NET
framework functionality available through the
System.Xml namespace and related namespaces such as
System.Web.Services. It is documented extensively through weblog
posts. All the project's classes contain extensive tests to ensure its
quality, as well as the peer review among this highly focused group of
XML lovers.
Contents:
- Modules
- News
- Get Support
- Downloads
- License
1. Modules
The Mvp.Xml project consists of several modules listed below.
2. News
Mvp.Xml News: (News archive)
Mvp.Xml Releases:
View full
list of the Mvp.Xml project's RSS feeds.
3. Get Support
There are several ways to get support on using Mvp.Xml project's
classes:
- This online documentation.
- Mailing list: mvp-xml-help
(general discussion list for Mvp.Xml users), (online
archive).
- Mvp.Xml Forums at
the XML Lab.
- Tracker: Bugs.
- Tracker: Feature
Requests.
- Browse CVS
repository online.
- Online API
documentation.
4. Downloads
Go to the Downloads
page. You can download Mvp.Xml library release as a whole (all modules
in one
archive) or releases for separate modules.
5. License
The Mvp.Xml library is a subject to the BSD License - OSI approved
free open-source license.
The project is hosted at
SourceForge. Find more at the Mvp.Xml project page at
SourceForge.
$Id: index-old.html,v 1.1 2005/12/22 15:02:37 helgy Exp $


public class PerformanceTracker
extends java.lang.Object
| Constructor and Description |
|---|
PerformanceTracker() |
| Modifier and Type | Method and Description |
|---|---|
static void |
draw(Graphics g,
float x,
float y)
Draws the current values to screen
|
static void |
draw(Graphics g,
float x,
float y,
float targetWidth,
int horizontalAlign)
Draws the current values to screen
|
static void |
drawInBottomLeft(Graphics g)
Draws the current values to the bottom left of the screen
|
static void |
drawInBottomRight(Graphics g)
Draws the current values to the bottom right of the screen
|
static void |
drawInTopLeft(Graphics g)
Draws the current values to the top left of the screen
|
static void |
drawInTopRight(Graphics g)
Draws the current values to the top right of the screen
|
public static void draw(Graphics g, float x, float y)
g
- The Graphics
contextx
- The x coordinate to render aty
- The y coordinate to render atpublic static void draw(Graphics g, float x, float y, float targetWidth, int horizontalAlign)
g
- The Graphics
contextx
- The x coordinate to render aty
- The y coordinate to render attargetWidth
- The target width to render at, or, -1 to use auto-widthhorizontalAlign
- The text alignment. Note: Use Align
to retrieve the
appropriate value. If target width is set to -1 this option is
ignored and the text is left alignedpublic static void drawInTopLeft(Graphics g)
g
- The Graphics
contextpublic static void drawInTopRight(Graphics g)
g
- The Graphics
contextpublic static void drawInBottomLeft(Graphics g)
g
- The Graphics
context


Egyptian Maths
Home
Learn numbers
About
Learn egyptian maths in an easy way
Start
Olympic React Games 2016.


Office hours: Wed. 4:10-5:30pm (N211, Zibin building)
Instructor: Yanwei Fu<EMAIL_ADDRESS>Teaching Assistants: (各位同学，有什么事情，可以给两个助教发邮件，也可以wechat联系助教.
Please email/Wechat the two TAs if you have any problems.）
（1）谢宇 wechat: Y1314941 email<EMAIL_ADDRESS>（2）孙强 wechat: sunqiang6861 email<EMAIL_ADDRESS>Synopsis: As an introduction to statistical learning and machine learning, this course is about learning from data: statistical learning refers to a set of tools for modeling and understanding complex datasets; and machine learning is defined as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty. Thus, the main objectiveness is to present students a unified view of both two fields through the teaching of the methodology, applications and the key ideas behind the methods. The whole course is illustrated with R as well as other statistical programming languages such as Matlab and Python. We aim at gradually cultivating students the abilities of both theoretical analysis and practical problem solving.
Textbook:
1. James, Witten, Hastie and Tibshirani (2013) An
Introduction to Statistical Learning, with applications in R. Springer.
2. Bishop, C.M. (2006), Pattern recognition and Machine Learning, Springer.
3. Hastie, T., Tibshurani, R. and Friedman, J. (2011)
The Elements of Statistical Learning, data mining, inference and
Prediction, 2ndEdition. Springer.
Prerequisites:
Registration:
Grading:
(1) Class attendance (10%), includes class performance, class discussion and critical thinking.
Each
absent: -1%
(2) Weekly homework (20%), is of 5 times. We expect the student can finish each one within 1.5-2.5 hours.
Each:
4% *5;
Late
Submission (after Dec. 14th, 2017) will be penalized by 50% of total
score of each homework, in other word, the highest score for each one
of the late submission is 2%.
(3) Monthly mini-projects (50%), is of 3-4 projects which are selected from the real-world Big-Data problems, including but not limited to, computer vision, pattern recognition, recommendation system, social network, financial data analysis and bioinformatics. In general, the reports should be written in English, and include algorithm skims (3%), critical codes (2%), experimental analysis (3%); and the discussion of proposed method (2%).
About the Submission of mini-projects.
The
report can be written by Word or Latex. Generate a single pdf file
of your mini-projects. The file name should be
SLML_yourname_student-id.pdf. Also put the names and Student ID in
your paper. To submit the report, email the pdf file to<EMAIL_ADDRESS>About the deadline and penalty. In general, you should submit the
paper according to the deadline of each mini-project. The late submission
is also acceptable; however, you will be penalized 10% of scores for each
week's delay.
(4)
Final project (20%) is finished by one team. Each team should have up to
3 students; and will solve a real-world Big-Data problem. In
general, the final report should be written in English. The
main components of the report will cover (1) introduction to background
and potential applications (2%); (2) Review of the state-of-the-art
(3%); (3) Algorithms and critical codes in a nutshell (10%); (4)
Experimental analysis and discussion of proposed methodology (5%).
Reference books:
Math cookbook Linear Algebra Review
Note that:
(1)
mini-projects are not allowed to use any existing toolbox; you have to
write every line codes by yourself.
(2) In final project, you can use the toolbox.
(3) Meanwhile, we will randomly check some students' projects by asking his/her some questions, in order to validate that the projects are done by himself/herself.
| Topic | Slides | Exec &Notes | Other material | |
| 1 | Overview | Introduction | ex1
Notes |
Rcode
|
| 2 | Linear regression | linear regression | ex2 | |
| 3 | Project-1 | Project_1.zip | Deadline: Oct-9 5:00pm send to<EMAIL_ADDRESS>|
|
| 4 | Tutorial: data structure | slides_data_structure | ex3 | |
| 5 | Linear classification | linear classification | ||
| 6 |
SVM(1) |
SVM(1) |
Chap4(Page170) 6, 7; Chap 9 (Page 368) 1, 2, 3 | tutorial:
Latex Latex Example Chinese Intro |
| 7 | SVM(2) | SVM(2) | take a rest | |
| 8 | Project-2 | Project_2.zip | deadline: 5:00pm, Nov 19, 2017 |
Naive Bayes |
| 9 | neural network(1) | neural_network(1) | tensorflow | |
| 10 | neural network(2) | neural_network(2) | BP_on_CNN | |
| 11 | Project-3 | Project_3.zip | Deadline: Dec 3, 2017 5:00pm (extended to Dec. 10, 2017,
5:00pm) send to<EMAIL_ADDRESS><EMAIL_ADDRESS>|
|
| 12 | learning theory | slides | Learning_theory_Andrew_Ng | |
| 13 | Mid-term Review | slides | ex4 | Notes of Andrew_Ng |
| 14 | Unsupervised learning | slides | Notes_on_EM_GMM | |
| 15 | Project-4 | Project_4.zip | ||
| 16 | Final Project | Final_project |
Good Reading Material:


This work is licensed under a
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License,
apart from the remixes, whose copyright is owned by the original artist.
Hand-made by andrew. Contact: cyjet /at/ alphajuliet /dot/ com.


By: Wong Jia Hau (1500181)
FYI: This notebook is uploaded at http://wjh-fashion-mnist.surge.sh/
For this assignment, I will use the default loader provided the authors of Fashion-MNIST.
from utils import mnist_reader
X_train, y_train = mnist_reader.load_mnist('data/fashion', kind='train')
X_test, y_test = mnist_reader.load_mnist('data/fashion', kind='t10k')
The loader does not only helps us to load the data into the kernel, it also help us to do the following:
print(X_train[0].shape)
We can see that the shape of each data is a 1x784 matrix, so it is already flattenized.
x_train_len = len(X_train)
x_test_len = len(X_test)
total = x_train_len + x_test_len
print("Number of training data is", x_train_len)
print("Number of testing data is", x_test_len)
print("Total number of data is ", total)
training_data_percentage = (x_train_len/total*100)
testing_data_percentage = 100 - training_data_percentage
print(f"So there are {training_data_percentage:2.3}% of data is training data")
print(f"And {testing_data_percentage:2.3}% of data is testing data")
After that we can use this benchmarking function to benchmark various classifiers to see their accuracy, training time and predicting performance.
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
import matplotlib.pyplot as plt
import numpy as np
def benchmark(classifier, training_data=X_train, test_data=X_test):
from datetime import datetime
time1 = datetime.now()
cls = classifier()
name = cls.__class__.__name__
cls.fit(training_data, y_train)
time2 = datetime.now()
training_time = time2 - time1
predicted = cls.predict(test_data)
time3 = datetime.now()
predicting_time = time3 - time2
accuracy = accuracy_score(y_test, predicted)
precision = precision_score(y_test, predicted, average='weighted')
recall = recall_score(y_test, predicted, average='weighted')
return {
"classifier" : name,
"training_time" : training_time,
"predicting_time" :predicting_time,
"accuracy" : accuracy,
"precision": precision,
"recall": recall
}
def print_benchmark_result(result):
print("Training data using ", result["classifier"])
print("Time elapsed for training = ", result["training_time"])
print("Time elapsed for prediction = ", result["predicting_time"])
print(f"Accuracy = {result['accuracy']}")
print(f"Precision = {result['precision']}")
print(f"Recall = {result['recall']}")
As mentioned in README, all data are in 28x28 pixels, therefore we need to reshape the data into (28, 28).
# The lables can be obtain from README.md
LABELS = {
0: "T-shirt/top",
1: "Trouser",
2: "Pullover",
3: "Dress",
4: "Coat",
5: "Scandal",
6: "Shirt",
7: "Sneaker",
8: "Bag",
9: "Ankle boot"
}
def plot_data_image(x_train_data):
label_indexes = []
for i in range(0, 10):
label_indexes.append(
[index for index, value in enumerate(y_train) if value == i]
);
fig = plt.figure(figsize=(10, 10))
columns = 10 + 1
rows = 10
for i, labels in enumerate(label_indexes):
for j in range(columns):
ax = fig.add_subplot(rows, columns, i * columns + j + 1)
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
ax.axis('off')
if j == 0:
ax.text(0, 0.5, f'{LABELS[i]} = {i}', horizontalalignment='center')
continue
plt.imshow(x_train_data[labels[j]].reshape(28, 28))
plt.axis('off')
plt.show()
plot_data_image(X_train)
In this section we will train the data using some popular classifier and benchmark their performance and accuracy.
NOTE : We will use the default parameter for each classifier.
from sklearn.neighbors import KNeighborsClassifier
r_KNeighborsClassifier = benchmark(KNeighborsClassifier)
print_benchmark_result(r_KNeighborsClassifier)
from sklearn.tree import DecisionTreeClassifier
r_DecisionTreeClassifier = benchmark(DecisionTreeClassifier)
print_benchmark_result(r_DecisionTreeClassifier)
from sklearn.naive_bayes import GaussianNB
r_GaussianNB = benchmark(GaussianNB)
print_benchmark_result(r_GaussianNB)
from sklearn.neural_network import MLPClassifier
r_MLPClassifier = benchmark(MLPClassifier)
print_benchmark_result(r_MLPClassifier)
from sklearn.ensemble import RandomForestClassifier
r_RandomForestClassifier = benchmark(RandomForestClassifier)
print_benchmark_result(r_RandomForestClassifier)
from sklearn.ensemble import AdaBoostClassifier
r_AdaBoostClassifier = benchmark(AdaBoostClassifier)
print_benchmark_result(r_AdaBoostClassifier)
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
r_QuadraticDiscriminantAnalysis = benchmark(QuadraticDiscriminantAnalysis)
print_benchmark_result(r_QuadraticDiscriminantAnalysis)
There are actually another popular classifier called GaussianProcessClassifier
. However this classifier is a memory beast, thus I can't even run the classifier even I have 8GB of RAM. However, there are actually some hack to get around with this, but I'm just not sure how to apply it.
def plot_graph(x_title, x_labels, title, benchmark_results):
recalls = [x["recall"] for x in benchmark_results]
accuracies = [x["accuracy"] for x in benchmark_results]
precisions = [x["precision"] for x in benchmark_results]
training_times = [x["training_time"].total_seconds() for x in benchmark_results]
predicting_times = [x["predicting_time"].total_seconds() for x in benchmark_results]
bar_width = 0.20
x_indeces = np.arange(len(x_labels))
# Set title and labels
fig , ax1 = plt.subplots()
fig.set_size_inches(8, 8)
ax1.set_title(title)
ax1.set_ylabel('Metric scores')
ax1.set_xlabel(x_title)
plt.xticks(x_indeces + bar_width, x_labels, rotation='90')
# ax1.set_xticklabels(["null"] + x_labels, rotation=90)
x_pos = np.arange(len(x_labels))
# Plot recall
ax1.bar(x_pos, recalls, bar_width, label='Recall', color='lightblue')
# Plot accuracy
ax1.bar(x_pos + bar_width, accuracies, bar_width, label='Accuracy', color='blue')
# Plot precision
ax1.bar(x_pos + (bar_width * 2), precisions, bar_width, label='Precision', color='darkblue')
ax1.legend(bbox_to_anchor=(1.05, 0.85), loc=3)
for i, v in enumerate(precisions):
ax1.text(i + bar_width, v, f'{v:.3g}', color='grey')
# Plot training time
ax2 = ax1.twinx()
ax2.set_ylabel('Time (seconds)')
ax2.plot(x_indeces + bar_width, training_times, color="red", label='Training time', linewidth=5.0)
# Plot predicting time
ax2.plot(x_indeces + bar_width, predicting_times, color="yellow", label="Predicting time", linewidth=5.0)
plt.legend(bbox_to_anchor=(1.05, 1), loc=3)
plt.show()
print(f"Average training time is {sum(training_times)/len(training_times):2.3}s")
print(f"Average precision is {sum(precisions)/len(precisions):2.3}%")
print(f"Max precision is {max(precisions)} when x={x_labels[np.argmax(precisions)]}")
from operator import itemgetter
benchmark_results = sorted([
r_KNeighborsClassifier,
r_DecisionTreeClassifier,
r_GaussianNB,
r_MLPClassifier,
r_RandomForestClassifier,
r_AdaBoostClassifier,
r_QuadraticDiscriminantAnalysis
], key=itemgetter('precision'))
classifiers = [x["classifier"] for x in benchmark_results]
plot_graph("", classifiers, "Metric scores and training-predicting time of various classifier (sorted by precision)",
benchmark_results)
From the graph above we can see that the classifier with the best precision is KNeighbors, but we can see that it is using a horrific 700s for predicting test data (over 11 minutes！). On the contrary, all of the other classifier take less than 10s to do the prediction.
This is because there are actually no training is done for KNeighborsClassifier, all it do is comparing each test data against every train data, thus the terrific predicting time. Meanwhile, the other classifier is really building a model when trained with the data, thus their predicting time is lightning fast.
From my point of view, I thought that the RandomForestClassifier is the best in this situation, because it have top precision, and one of the least training time and predicting time. But does that means RandomForest is good for every case? The answer is NO. It reallys depends on how your data looks like. Just like spoon is good for taking soup, while chopstick is good for grabbing veges. We cannot say that spoon is better than chopstick or vice versa. In the same way, we cannot say some classifier is always better than the other classifier.
In this section, we will play with the parameters of the following classifier:
We will change their default parameter to see if we can get better results.
from sklearn.ensemble import RandomForestClassifier
def randomForest(N_estimators):
return lambda: RandomForestClassifier(n_estimators=N_estimators)
benchmark_results = [benchmark(randomForest(i)) for i in range(1, 50)]
plot_graph("n_estimators", range(1, 50), "Performance of RandomForestClassifier with different n_estimators",
benchmark_results)
From the graph above, we can see that there is a strong positive relation between n_estimators
with training_time
. However, we can see that increasing n_estimators
does did not actually raise the accuracy. No wonder sklearn
defaulted the value of n_estimators
to 10. Thus, brutally increasing n_estimators
will only results in waste of time.
criterion = 'entropy'
¶results = [
benchmark(lambda: RandomForestClassifier(criterion='entropy')),
benchmark(lambda: RandomForestClassifier(criterion='gini'))
]
plot_graph("criterion", ['entropy', 'gini'], "Performance of RandomForestClassifier with different criterion", results)
From the graph we can see that the accuracy difference is not much, but obviously the training time is longer when we use entropy
as the criterion. I think that this may be the reason why gini
is the default criterion.
The explanation (referred from here) is that entropy includes logarithmic calculation while gini does not, and since logarithmic function are computationaly expensive, it is clear that when we use entropy as criterion, the training time will be much longer than that of gini.
from sklearn.neural_network import MLPClassifier
hidden_layer_counts = np.arange(1, 11, 1)
results = []
for n in hidden_layer_counts:
results.append(benchmark(lambda: MLPClassifier(hidden_layer_sizes=((100,)*n))))
CUT_POINT = 82
filtered_results = [x for x in results if x["accuracy"] > CUT_POINT]
filtered_hidden_layer_counts = [x for idx, x in enumerate(hidden_layer_counts) if results[idx]["accuracy"] > CUT_POINT ]
plot_graph("No. of hidden layer", hidden_layer_counts, "Comparing performance of MLP with different number of hidden layer", results)
From the graph above we can see that when the number of hidden layer(NOHL) reach 3, the accuracy increased by 3% at 88%, however further increment for NOHL doesn't seems to improved the accuracy anymore.
Furthermore, we can see that the training time increases when we increase the NOHL.
Thus, for future dicucssion we will use MLP with NOHL=3. since it has top accuracy with reasonable training time.
In this section we will see if the type of activation will affect the performance of MLP.
Note that we will use MLP with NOHL=3 since it is one of the best performing classifier we had ever used.
# Type of activation = {‘identity’, ‘logistic’, ‘tanh’, ‘relu’}. Default is 'relu'
def activation_of(x):
return lambda: MLPClassifier(hidden_layer_sizes=(100,100,100), activation=x)
benchmark_results = [
benchmark(activation_of('identity')),
benchmark(activation_of('logistic')),
benchmark(activation_of('tanh')),
benchmark(activation_of('relu'))
]
plot_graph("Type of activation",
["identity", "logistic", "tanh", "relu"],
"Performance of MLPClassifier using various activation",
benchmark_results
)
In this case, ReLU(rectified linear unit) outperform other type of classifier. If you want to understand the reason behind, you might want to check out this paper written by Krizhevsky et al.
This section is purely for visualizing the loss of MLPClassifier at each iteration.
cls = MLPClassifier()
cls.fit(X_train, y_train)
plt.plot(cls.loss_curve_)
plt.title("Loss of MLPClassifier at each iteration")
plt.ylabel("Loss")
plt.xlabel("No. of iteration")
plt.show()
From the graph above, we can see that the loss decreases as the number of iteration increased.

We can imagine that loss means the mark he (the classifier) loss for every exam he takes.
What exam?
When we train him, he will take an exam after some times of learning, and we will record down the mark he loss for the exam.
Then, we will repeat this process over and over, that is why there is this term called iteration. It means how many times we repeat this process of learning and examining.
Moreover, if we notice carefully, we can see that the loss stop decreasing even when the number of iteration increases, and this is why the training stops. It means that further training meaningless, because the classifier will not get smarter even if we keep on training him.
But how do we decide when to stop?
There is a parameter called tol
which means tolerance, in this case it is defaulted to 0.0001
. It means that if the last training(s) does not decrease the loss by more than 0.0001
, we will terminate the training session.
In this section, we will be using MLPClassifier(with NOHL=3) to plot a confusion matrix to visualize what kind of data is confusing the classifier.
from sklearn.metrics import confusion_matrix
from sklearn.neural_network import MLPClassifier
cls = MLPClassifier(hidden_layer_sizes=((100,) * 3))
cls.fit(X_train, y_train)
predicted = cls.predict(X_test)
cf = confusion_matrix(y_test, predicted)
# The code below is copied from https://stackoverflow.com/questions/35572000/how-can-i-plot-a-confusion-matrix
def plot_confusion_matrix(conf_arr, labels):
norm_conf = []
for i in conf_arr:
a = 0
tmp_arr = []
a = sum(i, 0)
for j in i:
tmp_arr.append(float(j)/float(a))
norm_conf.append(tmp_arr)
fig = plt.figure()
fig.set_size_inches(10, 10)
plt.clf()
ax = fig.add_subplot(111)
ax.set_aspect(1)
res = ax.imshow(np.array(norm_conf), cmap=plt.cm.jet,
interpolation='nearest')
width, height = conf_arr.shape
for x in range(width):
for y in range(height):
ax.annotate(str(conf_arr[x][y]), xy=(y, x),
horizontalalignment='center',
verticalalignment='center')
cb = fig.colorbar(res)
ax.set_xticklabels(labels, rotation=90)
plt.xticks(range(width), labels)
plt.yticks(range(height), labels)
plt.show()
plot_confusion_matrix(cf, list(LABELS.values()))
From the confustion matrix above, by analyzing the rows, we can see that the prediction for Shirt is the worse, because about 50% of the data is mistaken as T-shirt, Pullover, Dress or Coat.
Moreover, we can see that there 146 Shirt mispredicted as T-shirt. To be honest, even my eyes got confused between T-shirt and Shirt.
Now, if we analyze the matrix by columns, we can see that if we are told by the classifier that a data represent a Shirt, we can't really trust it, because it mispredicted:
- 86 T-shirt as Shirt
- 93 Pullover as Shirt
- 54 Coat as Shirt
Therefore, we have to find a way for the classifier to learn specifically on how to differentiate Shirt from others since it is always confused with it.
One approach is to retrain the classifier with those confusing data, but this method does not seems really efficient.
Another method is to preprocess the data such as using normalization. By doing preprocessing, the data might be in a better shape for classification.
from sklearn.preprocessing import Normalizer
import matplotlib.pyplot as plt
def plot_pixels(title, normalizer=None):
sample_x = X_train[1:100]
sample_y = y_train[1:100]
colors = ['red', 'blue']
for i in [1, 2]:
sample = sample_x[np.argwhere(sample_y == i).flatten()]
if normalizer is not None:
sample = n.transform(sample)
for x in sample:
plt.scatter(range(len(x)), x, color=colors[i - 1])
plt.gcf().set_size_inches(30, 10)
plt.title(title)
line1 = plt.scatter([0, 0], [0, 0], color='red')
line2 = plt.scatter([0, 0], [0, 0], color='blue')
plt.legend((line1, line2), ('T-shirt', 'Trousers'))
plt.show()
plot_pixels("Raw data")
plot_pixels("Normalized", Normalizer())
From the graph above, we can see that in raw data, it is quite hard to differentiate the pixels between T-shirt and Trousers. However, after we apply normalization on the data, we can see that it is easier to do the differentiation.
Thus, we may hypothesize that the classifier can work better if they were trained with normalized data.
Now, we will validate this hypothesis by using MLP(where number of hidden layer = 3).
from sklearn.preprocessing import Normalizer
normalized_x_train = Normalizer().fit_transform(X_train, y_train)
normalized_x_test = Normalizer().fit_transform(X_test, y_test)
cls = MLPClassifier(hidden_layer_sizes=(100, 100, 100))
benchmark_result = benchmark(lambda: cls, normalized_x_train, normalized_x_test)
print_benchmark_result(benchmark_result)
Unfortunately, the precision does not seems to improved (which is the same old 88%), not only that, the training time increased by 4 times!
Thus, we can see that the hypothesis have failed when we use MLPClassifier.
In this case we will try to train the RandomForestClassifier with data preprocessed by various preprocessors.
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import RobustScaler
from sklearn.ensemble import RandomForestClassifier
def train(preprocessor=None):
my_X_train = X_train
my_X_test = X_test
if preprocessor is not None:
my_X_train = preprocessor().fit_transform(X_train, y_train)
my_X_test = preprocessor().fit_transform(X_test, y_test)
return benchmark(RandomForestClassifier, my_X_train, my_X_test)
r1 = train()
r2 = train(Normalizer)
r3 = train(MinMaxScaler)
r4 = train(MaxAbsScaler)
r5 = train(StandardScaler)
r6 = train(RobustScaler)
benchmark_results = [r1, r2, r3, r4, r5, r6]
plot_graph("Data preprocessors", [
"Raw",
"Normalizer",
"MinMaxScaler",
"MaxAbsScaler",
"StandardScaler",
"RobustScaler"
], "Training classifier with various preprocessed data", benchmark_results)
Unfortunately, from the graph, our hypothesis is not really true (when using RandomForestClassifier), because the accuracy doesn't seems to improve even if we train that classifier with preprocessed data. Moreover, if the data is preprocess with Normalizer the, training time increases by more than 50%.
In this section we will binarized the training data (as suggested by my friend, Dheeno), that means all pixel that have weight of more than a specified cutting point, we will converted into 1. Then we will if this we improve the performance of the classifier.
def binarize(data, cutting_point):
data[data <= cutting_point] = 0
data[data > cutting_point] = 1
X_train_binarized = np.array(X_train, copy=True)
binarize(X_train_binarized, 9)
plot_data_image(X_train_binarized)
benchmark_results = []
for cutting_point in range(0, 255):
X_train_binarized = np.array(X_train, copy=True)
binarize(X_train_binarized, cutting_point)
X_test_binarized = np.array(X_test, copy=True)
binarize(X_test_binarized, cutting_point)
r = benchmark(RandomForestClassifier, X_train_binarized, X_test_binarized)
benchmark_results.append(r)
plot_graph("Cutting point",
range(0, 255),
"Performance of RandomForestClassifier using different cutting point for binarizing data",
benchmark_results
)
From the graph above, we can see that the precision decreases as the cutting point increases, it means that the data become more and more undistinguishable. Since the precision is highest when the cutting point is 9, now let's try it with MLPClassifier
cutting_point = 9
X_train_binarized = np.array(X_train, copy=True)
binarize(X_train_binarized, cutting_point)
X_test_binarized = np.array(X_test, copy=True)
binarize(X_test_binarized, cutting_point)
r = benchmark(lambda: MLPClassifier(hidden_layer_sizes=((100,) * 3)),
normalized_x_train, normalized_x_test)
print_benchmark_result(r)
From the results above, we can see that the precision did improve, but not really promising, because it only increased about 0.003 from the last best result we got, which is 0.885.
I think I'm stuck at 0.885 precision for now, because no matter what method I tried, it seems to top around 0.88 only.
The methods I tried are:
To be frank, this precision is actually already better than human performance which is 0.835 (documented in README.md of fashion-mnist repo).
In my opinion, I think the reason why I couldn't get more precision is because the library I used, which is Scikit-Learn. This library is actually not meant for production purposes, as mentioned officialy at here.
Therefore, if I would be given more time, I would really like to try out library like Keras or Tensorflow or PyTorch, because all the documented benchmark that have more than 90% accuracy seems to be using these libraries. Moreover, this library will allow me to use Batch Normalization and Deep Learning.
Anyway, I really did learn a lot about Neural networks(NN), and how it works and stuff.
And I did find some interesting readings regarding NN, it is said that until now nobody know exactly how many layers of hidden layer and how many neurons for each layer will be optimum for a model, thus we can only find out the best combination using trial and error.
Thus, I felt like machine learning is kind of like playing lottery, because I need to guess the correct combination of parameter in order to have a better model. Seriously if I have more computational power I would try every possible parameter combination for MLPClassifier.
Another interesting fact is that nobody really knows why more hidden layer (which they call deep learning) will result in a better model. Because in theory, 2 or 3 hidden layers are enough to model any non-linear classification problem.
In my opinion, this deep learning mystery can be related to an article written by Mandelbrot. In this article, he asked "How long is the coast of Britain?", and it turns out that there are not definitive answer, because the output depends on the measuring tool you use, for example, if you use a 1-meter ruler and a 1-cm ruler to measure the length of the coast, you will find that the measurement done with the smaller ruler will result in a longer length.
Therefore I believe that using more hidden layers means to use a smaller measuring tools, which can helps to draw a smoother line between each points to be classified.


Flexitime.
Oddjob Schedules provide a very flexible way of defining the schedule for running a job. Oddjob Schedules work with intervals of time. Here's an interval:
In Oddjob this would be defined with the following XML:
<EMAIL_ADDRESS>examples/userguide/schedules1.xml}Or defined in Oddjob Designer it would look like this:
This interval spans the whole of that Tuesday, that was the 16th of March 2010, from midnight until 1 millisecond before midnight that night.
More commonly an interval is not specified between fixed points in time. This schedule returns the interval that is the next day of the week that is a Tuesday.
<EMAIL_ADDRESS>examples/userguide/schedules2.xml}The Tuesday selected depends on the time now.
On Tuesday the interval is for all of that Tuesday.
And from 00:00 Wednesday the interval is for the following Tuesday.
The weekly schedule allows an interval to be defined from the start of one day of the week to the end of another day of the week. This has exactly the same effect as the above:
<EMAIL_ADDRESS>examples/userguide/schedules2a.xml}Note that this is an interval of 1 day, not 7 days. If the 'on' attribute is given, from and to are ignored.
Many schedules can be constrained with 'from' and a 'to' properties. They are:
With all Constrained Schedules, if the 'from' is after the 'to', then the specified interval is in the next unit of time. For instance:
<EMAIL_ADDRESS>examples/userguide/schedules3.xml}Specifies an interval from this 1st of October to the end of April next year (Or from last October if now is before April this year).
Many schedules allow a refinement schedule to be specified. This is another schedule that 'refines' the interval of its parent.
<EMAIL_ADDRESS>examples/userguide/schedules4.xml}This would schedule a job at 10am every Tuesday. In this instance the
time
schedule has refined the weekly
schedule. The day of week
schedule has also 'limited' the time schedule. It no longer returns an
interval that is every day from 10am to 1pm. The time now only applies
to Tuesdays. This limiting effect applies to the beginning of the interval,
not the end.
Thus the interval is still applicable even if the time is 00:59am Wednesday morning. At 1:00am precisely the interval moves on to the following week.
The interval schedule provides regular fixed length intervals.
<EMAIL_ADDRESS>examples/userguide/schedules6.xml}The schedule provides back to back intervals that are five seconds long. It would be used to schedule something every five seconds.
<EMAIL_ADDRESS>examples/userguide/schedules7.xml}This will provide thirty minute intervals from 10pm Tuesday night until 1:30am Wednesday morning. Intervals are calculated form the parents limits and so would be on the hour and half our. The final interval runs until 1:30am even though it's parent finishes at 01:05. This is because, like the Constrained Schedules the limiting affect of the parent only applies to the beginning of the interval.
The list schedule provides for even greater flexibility.
<EMAIL_ADDRESS>examples/userguide/schedules8.xml}This would schedule a job at 8am every Monday, then at 9am and 3pm every other day.
When a schedule is evaluated each schedule in the list is evaluated and the first due schedule is used as the next due date.
The list of sub schedules can be quite unrelated.
<EMAIL_ADDRESS>examples/userguide/schedules9.xml}We already saw in scheduling how broken schedules can be used to define holidays but here's a little more on how they work.
The intervals defined by the breaks mask those defined by the regular schedule. As with other schedules, the break only masks a interval where its start is in the break interval.
Schedules can moved around breaks. For more information see the examples with the day-after and day-before schedules.


DNSAdmins group DACL privilege escalation
A privilege escalation is possible from the Exchange Windows permissions (EWP) and from the Exchange Trusted Subsystem security groups to control the DNSAdmins group and then compromise the entire prepared Active Directory domain. This second part was described in this work from Shay Ber in 2017
As a matter of fact, any security group inheriting its DACL from the Domain Object can be controlled in this way. The ones giving a privilege escalation to Domain Admins are usually protected by AdminSDHolder, though the DNSAdmins group is not.
DISCLAIMER: This issue has been responsibly disclosed to MSRC in November 2018 and after a few back and forth emails, they closed the case. Basically, they could not reproduce the issue in their testing environments. However, the DACL of the incriminated object on their side appears to be quite different from the DACL that I observed in several live production AD domains: MSRC side DACL has a few Deny ACEs explicitely positioned for the incriminated Exchange security groups, where production side has only Allow ACEs. Not to mention Deny ACEs are bad practice for a case where Allow ACEs should just be removed.
From there, I can only speculate that either they have an unreleased fix on their testing environments, or their path of Exchange deployment on AD is different from what is commonly observed on live domains, which are upgraded and not redeployed on CU releases. If the aformentioned Deny ACEs are set on your environment, it is probably NOT vulnerable.
In up-to-date deployments of Exchange 2016 in Shared permissions (default) or RBAC split permissions, three controlling ACEs are positioned on the "CN=DNSAdmins,CN=Users" group for the Exchange Windows Permissions and for the Exchange Trusted Subsystem security groups. They are inherited from the Domain Object, two of them are documented, the ETS one is not.
This undocumented ACE is probably positioned during an update or a particular CU install. I have been confirmed it exists on other organizations production environments. This is their SDDL representation:
(OA;CIID;WP;bf9679c0-0de6-11d0-a285-00aa003049e2;;<SID of EWP>)
(OA;CIID;WP;0296c120-40da-11d1-a9c0-0000f80367c1;;<SID of EWP>)
(OA;CIID;WD;;bf967a9c-0de6-11d0-a285-00aa003049e2;<SID of ETS>)
Which translates into:
Account ACE type Inheritance Permissions On property/ Applies to Comments
Exchange Windows Permissions Allow ACE All WriteProp ManagedBy /
Exchange Windows Permissions Allow ACE All WriteProp Member /
Exchange Trusted Subsystem Allow ACE All WriteDACL / Group
Technical consequence
Any member of Exchange Windows Permissions can add members to DNSAdmins.
Any member of Exchange Trusted Subsystem can modify DNSAdmins DACL to grant himself permission to add members to it.
The DNSAdmins group has a particular privilege to remotely load DLLs on domain controllers in the SYSTEM context (by design, as MSRC case 38121 reported by Shay Ber).
Security Consequence
Any member of the Organization Management group can execute code remotely on the domain controllers in the SYSTEM context, taking complete control of the Active Directory domain.
Proof of concept
From an Organization Management member account, add yourself to Exchange Windows Permissions This is possible by default and normal
$id = [Security.Principal.WindowsIdentity]::GetCurrent()
$user = Get-ADUser -Identity $id.User
Add-ADGroupMember -Identity "Exchange Windows Permissions" -Members $user
LOG OUT AND RELOG THE USERS SESSION to update groups in token
Add yourself to DNSAdmins This is the problem, it should not be possible
$id = [Security.Principal.WindowsIdentity]::GetCurrent()
$user = Get-ADUser -Identity $id.User
Add-ADGroupMember -Identity "DNSAdmins" -Members $user
LOG OUT AND RELOG THE USERS SESSION to update groups in token
Choose a domain controller and remotely change a registry key to load a dll (needs specific exports) on next DNS service restart:
dnscmd test_domain_controller /config /serverlevelplugindll \\NetworkPath\to\dll
Workaround fix
An elegant way of fixing this problem is including the DNSAdmins group into an AdminSDHolder-protected group.
Alternatively, use the Fix-DNSAdmins-DACL.ps1 Powershell script in this repository. Read it, test it, use it at your own risk.
By default, it checks the three faulty ACEs, this can be done by any user. Use with -Fix switch with Domain Admins privileges will break DACL inheritance on this group and remove the three ACEs.
To revert, simply reenable DACL inheritance.
Alternatively, it can be done manually with LDP: bind as a Domain Admin account with the usual precautions, as you will change the DNSadmins group DACL.
Backup the DACL:
(Get-Acl "AD:\CN=DNSAdmins,CN=Users,DC=...").Sddl | Out-File "dnsadmins_dacl.txt"
Locate the "CN=DNSAdmins,CN=Users,DC=..." security group. Disable ACE inheritance and choose to copy existing ACEs.
Locate and delete the 3 faulty ACE in the DACL: you can sort by Trustee to see Exchange Windows Permissions and Exchange Trusted Subsystem.


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<EMAIL_ADDRESS>Alison House, Nicolson Street, Edinburgh, UK
EH8 9DF


Before Change
image_shape = _preprocess_conv2d_image_shape(int_shape(x), data_format)
else:
image_shape = None
if hasattr(depthwise_kernel, "_keras_shape"):
depthwise_kernel_shape = depthwise_kernel._keras_shape
else:
// Will only work if `depthwise_kernel` is a shared variable.
depthwise_kernel_shape = depthwise_kernel.eval().shape
depthwise_kernel_shape = _preprocess_conv2d_filter_shape(depthwise_kernel_shape, data_format)
if hasattr(pointwise_kernel, "_keras_shape"):
pointwise_kernel_shape = pointwise_kernel._keras_shape
else:
After Change
raise ValueError("Unknown data_format ", data_format)
image_shape = _preprocess_conv2d_image_shape(int_shape(x), data_format)
depthwise_kernel_shape = int_shape(depthwise_kernel)
if depthwise_kernel_shape is None:
depthwise_kernel_shape = depthwise_kernel.eval().shape // in case of a shared variable
depthwise_kernel_shape = _preprocess_conv2d_filter_shape(depthwise_kernel_shape, data_format)
pointwise_kernel_shape = int_shape(pointwise_kernel)
if pointwise_kernel_shape is None:
pointwise_kernel_shape = pointwise_kernel.eval().shape // in case of a shared variable


|
BTK Python wrapper
0.3dev.0
Python bindings for the Biomechanical ToolKit library (BTK)
|
Interface to read/write acquisition's files. More...
Public Member Functions | |
| def | CanReadFile |
| def | CanWriteFile |
| def | GetByteOrder |
| def | GetByteOrderAsString |
| def | GetFileType |
| def | GetInternalsUpdateOptions |
| def | GetStorageFormat |
| def | GetStorageFormatAsString |
| def | HasInternalsUpdateOption |
| def | Read |
| def | SetByteOrder |
| def | SetInternalsUpdateOptions |
| def | SetStorageFormat |
| def | Write |
Static Public Attributes | |
| ASCII = _btk.btkAcquisitionFileIO_ASCII | |
| Binary = _btk.btkAcquisitionFileIO_Binary | |
| DataBasedUpdate = _btk.btkAcquisitionFileIO_DataBasedUpdate | |
| FileFormatOption = _btk.btkAcquisitionFileIO_FileFormatOption | |
| Float = _btk.btkAcquisitionFileIO_Float | |
| IEEE_BigEndian = _btk.btkAcquisitionFileIO_IEEE_BigEndian | |
| IEEE_LittleEndian = _btk.btkAcquisitionFileIO_IEEE_LittleEndian | |
| Integer = _btk.btkAcquisitionFileIO_Integer | |
| MetaDataBasedUpdate = _btk.btkAcquisitionFileIO_MetaDataBasedUpdate | |
| NoUpdate = _btk.btkAcquisitionFileIO_NoUpdate | |
| OrderNotApplicable = _btk.btkAcquisitionFileIO_OrderNotApplicable | |
| StorageNotApplicable = _btk.btkAcquisitionFileIO_StorageNotApplicable | |
| TypeNotApplicable = _btk.btkAcquisitionFileIO_TypeNotApplicable | |
| UpdateNotApplicable = _btk.btkAcquisitionFileIO_UpdateNotApplicable | |
| VAX_LittleEndian = _btk.btkAcquisitionFileIO_VAX_LittleEndian | |
Interface to read/write acquisition's files.
Interface to read/write acquisition's files, cannot be instanced.
| def btk.btkAcquisitionFileIO.CanReadFile | ( | self, | args | ) |
CanReadFile(self, filename) -> bool Checks if the given filename can be read by this AcquisitionFileIO. This methods should try to read the file header instead to check the file's suffix.
| def btk.btkAcquisitionFileIO.CanWriteFile | ( | self, | args | ) |
CanWriteFile(self, filename) -> bool Checks if the given filename can be write by this AcquisitionFileIO. This method should try to determine if the file's suffix corresponds to one of the supported file's suffixes by this btkAcquisitionFileIO.
| def btk.btkAcquisitionFileIO.GetByteOrder | ( | self | ) |
GetByteOrder(self) -> btkAcquisitionFileIO_impl::ByteOrder Gets the byte order of the file (only for binary file).
| def btk.btkAcquisitionFileIO.GetByteOrderAsString | ( | self | ) |
GetByteOrderAsString(self) -> std::string Gets the byte order as a string.
| def btk.btkAcquisitionFileIO.GetFileType | ( | self | ) |
GetFileType(self) -> btkAcquisitionFileIO_impl::FileType Gets the type of the file.
| def btk.btkAcquisitionFileIO.GetInternalsUpdateOptions | ( | self | ) |
GetInternalsUpdateOptions(self) -> int Returns the option(s) used to update internals.
| def btk.btkAcquisitionFileIO.GetStorageFormat | ( | self | ) |
GetStorageFormat(self) -> btkAcquisitionFileIO_impl::StorageFormat Return the format used to store points and analog channels.
| def btk.btkAcquisitionFileIO.GetStorageFormatAsString | ( | self | ) |
GetStorageFormatAsString(self) -> std::string Gets the storage format as a string.
| def btk.btkAcquisitionFileIO.HasInternalsUpdateOption | ( | self, | args | ) |
HasInternalsUpdateOption(self, option) -> bool Returns true if the given @a option is used or false if not.
| def btk.btkAcquisitionFileIO.Read | ( | self, | args | ) |
Read(self, filename, output) Read the file designated by the given filename and fill the given btkAcquisition
| def btk.btkAcquisitionFileIO.SetByteOrder | ( | self, | args | ) |
SetByteOrder(self, b) Sets the byte order of the file.
| def btk.btkAcquisitionFileIO.SetInternalsUpdateOptions | ( | self, | args | ) |
SetInternalsUpdateOptions(self, options) Sets the option(s) used to update internals.
| def btk.btkAcquisitionFileIO.SetStorageFormat | ( | self, | args | ) |
SetStorageFormat(self, s) Set the format used to store points and analog channels.
| def btk.btkAcquisitionFileIO.Write | ( | self, | args | ) |
Write(self, filename, input) Write the file designated by the given filename with the content of the given btkAcquisition.
|
static |
Enum value for ASCII file.
|
static |
Enum value for binary file.
|
static |
Internals are updated using acquisition data (points, analog channels, events).
|
static |
Extension for specific file format extension. It is important to distinguish each new option by adding 2-based integer (i.e. 1, 2, 4, 8, 16, etc.).
|
static |
Acquisition's data are stored as float values.
|
static |
Enum value for a byte order in big endian with a IEEE float representation.
|
static |
Enum value for a byte order in little endian with a IEEE float representation.
|
static |
Acquisition's data are stored as integer values.
|
static |
Internals are updated using acquisition metadata.
|
static |
Same as AcquisitionFileIO::UpdateNotApplicable.
|
static |
Enum value used to indicate the byte order is not yet known.
|
static |
Acquisition's data have no storage format.
|
static |
Enum value used to indicate the type of the file is not yet known.
|
static |
No update is done and existing internals are used.
|
static |
Enum value for a byte order in little endian with a VAX float representation.


--- layout: post title: Java VM as a platform, not a language tags: [] status: publish type: post published: true meta: _edit_last: '1' dsq_thread_id: '17413811' --- There are many sides to Java. Java EE, SE, ME and the hundreds of JSRs that "fit" somewhere else. Vendor collaboration has seen to that. The good news for Javists is that the powers that be seem primed and ready to open their minds to other languages. Microsoft came out of the gate running with the idea of a single virtual machine (VM) supporting multiple languages with their Common Language Runtime (CLR). At first many in the Java camp balked at this idea. "Java as a language is powerful enough to accomplish requisite tasks," they said. But hardware has since caught up to "other" languages making them highly competitive with features much to be desired. Namely, the syntactically terse, extremely expressive, dynamic ones...Python, Ruby, and Perl. So it seems the JRuby folks, who Sun just hired are determined to move Java more dynamic. IT'S ABOUT TIME!..
"Once we've solved JRuby's issues, why not find a way to raise all ships? Support for invokedynamic, open classes, and closures at the VM level? Hooks for code generation, alternative typing systems, and deeper threading, IO, and memory integration? Seamless cross-app connectivity for dynlangs + Java EE with all the tasty agility we've come to love?"Source: "Headius: More FAQs about JRuby and Sun" via JVM futures Ted Leung provides a useful list for tracking dynamic language evolution on the JVM [paraphrasing]:
Source: "Ted Leung on the air"
- Community
- Compatibility
- Performance
- Velocity
- Tooling


Where to redirect in case of errors
Another issue that one must consider when validating data is where to redirect
when an error occurs. How do one redirect the user to another resource using VRaptor3 in case of validation errors?
Easy, just tell your validator to do just that: when you find any validation error, send the user to the specified resource. See the example:
public add(Employee employee) {
//Fluent validation
validator.checking(new Validations(){{
that(!employee.getName().isEmpty(), "error","nameIsRequired");
}});
//Classic validation
if (!employee.getName().equals("John Doe")) {
validator.add(new ValidationMessage("error","invalidName"));
}
validator.onErrorUse(page()).of(EmployeeController.class).form();
dao.add(employee);
}
If your logic may add any validation error you must specify where to go in case of error.
Validator.onErrorUse works just like result.use: you can use any view from Results class.


PML-N biggest enemy of Nawaz Sharif's life: Fawad Chaudhry
by https://www.facebook.com/dunyanewsISLAMABAD (Dunya News) – Federal Minister for Science and Technology Fawad Chaudhry has on Thursday said Pakistan Muslim League-Nawaz (PML-N) and the Sharif family are the biggest enemy of former prime minister Nawaz Shaarif’s life.
While talking to media, the federal minister said Nawaz Sharif was safe at hospital and the government is worried about his health since he has been shifted to his home.
Fawad Chaudhry maintained that the cabinet made a balanced decision, and even Shehbaz Sharif, Maryam Nawaz and Hussain Nawaz are not willing to submit indemnity bond.
PML-N is playing with the life of Nawaz Sharif and it apparently looks like he is not safe at Jati Umra, the federal minister said while condemning the politics that is being done over the former premier’s health.
He affirmed that the politics of Nawaz Sharif and former president Asif Ali Zardari has ended. A race has been ongoing in PML-N for the leadership, he added.
He announced to move privilege motion against PML-N leader Khawaja Asif and said it is regrettable to lie on the floor of the House.


This command toggles the "keep times" option. When the option is on file times (modification time etc) as stored by the filesystem are preserved when you edit a file.
Normally when you view a file it's time of last access is updated to the current time. When you change a file the modification time is also updated. Using this command the times that were in effect when the file was opened are restored when you save the file.
This can be used so that other software or people are unaware that the file has been changed. For example, this could be used for changing comments in source files without causing unnecessary rebuilds due to the makefile dependencies (or other build dependency system).
Shortcuts
Toolbar:


TechLog: The Penta Account Model
By Penta Development Team on Wed, Aug 1, 2018
Starting in August, as part of Penta’s 'universal connector' mission to be a global network that interoperates and interconnects with other projects and off-chain systems we are posting a series of technical documentation articles to Medium. The first of these outlines the Penta Account data model, and how it underpins the rest of the platform architecture.
There are two types of Penta accounts: common user or just user, and contract accounts. External accounts fall into the common user account category, while contract accounts are utilised for smart contract information storage. Together they are core elements of the Penta data model.
In Bitcoin nomenclature addresses, like bank account numbers, are where to deposit bitcoins (BTC). In the Penta data model these are instead referred to as accounts. Ownership of Penta accounts is either on an individual or group basis, and only account holders can transact on the Penta blockchain. Accounts are the fundamental machinery DApp users connect to the Penta blockchain with, and the basic data structure containing user summary data, like a total account balance.
Among the benefits of the Account-Balance Model are:
• Simplified developer (Penta blockchain integration) api usage, reducing the complexity of creating and maintaining a DApp model layer
• Streamlined transaction processing. Basically, only validation of the payer account balance is needed to authorize a transaction, without a computationally intensive review of prior transactions on the chain.


- Reproduce Deep3dPortrait as baseline
- Improve expression manipulation and speed with SOTA 3DMM model
- Funded by Samsung Research
- Reproduce Noiseprint for blind deepfake detection
- Try multiple classification models like ResNet+RNN, C3D, and I3D
- Funded by NIS
- 3D Point Cloud needs sampling of large datapoint
- Try Classification, Registration, Restoration efficiently
- Try unsupervised learning for importance score like pruning
- Failed due to there is no concrete criteria like weight norm
- Network Pruning for Super Resolution
- Finding Lottery Ticket in Decoder (My Korean Blog Article)
- Try with multiple SR models like VDSR, MSRResNet, CARN, SRDenseNet, RDN, RRDB, RCAN
- Try baselines like SNIP, GraSP, and SynFlow
- Image classification with landmark location
- Try multiple models like EfficientNet, ResNet, ResNext, MoCo v2
- Get event prize money for sharing code
- NAVER AI Rush 2020 1 Round
- Advance to the next round with Rank 23
- Try multiple classification models like ResNet, EfficientNet
- Try multiple ways to deal with data imbalance like Focal Loss, Label Smoothing, Temperature Scaling
- Try multiple optimizers and schedulers like RAdam, CosineLRScheduler
Django-based Yonsei University Class Schedule Management Web Service
- Crawl class schedule and mileage history from Yonsei University Database
- Schedule editor like everytime
Gloves Hand recognition with OpenCV
Jingle Bell rings when hands are on each bell on the screen


{# Requires including the js from django_common that enables ajax forms etc. If a template variable cc_form_post_url is passed to the template then this form posts to it, otherwise it posts to the url 'payments_billing'. If a template variable cc_form_success_redirect_url is passed it takes user to that url then after form post has succeeded. #}


TrySemaphore()
语法
Result = TrySemaphore(Semaphore)概要
Decreases the internal count of the semaphore by one only if the count is above 0. This is the same as a WaitSemaphore() operation, but without blocking if the count would fall below 0.
参数
Semaphore The semaphore to use.
返回值
Nonzero if the semaphore count was decreased, or zero if the count could not be decreased because it was already zero.
已支持操作系统
所有


{% extends "base.html" %} {% block head %} {% endblock %} {% block content %}
Redirects you to login with Github
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Redirects you to login with Discord


|
Properties
|
| prev-title |
string |
oneTime |
Previous |
Title of prev button |
| next-title |
string |
oneTime |
Next |
Title of next button |
| prev-icon |
string |
oneTime |
carousel-control-prev-icon |
Icon class for prev button |
| next-icon |
string |
oneTime |
carousel-control-next-icon |
Icon class for next button |
| interval |
number | string |
oneWay |
5000 |
The amount of time to delay between automatically cycling an item. If 0 , carousel will not automatically cycle. |
| keyboard |
boolean | string |
oneWay |
true |
Whether the carousel should react to keyboard events.
|
| pause |
false | 'hover' |
oneWay |
hover |
You should choose between false or 'hover'
|
| ride |
boolean | string |
oneWay |
false |
Autoplays the carousel after the user manually cycles the first item. If true , autoplays the carousel on
load.
|
| wrap |
boolean | string |
oneWay |
true |
Whether the carousel should cycle continuously or have hard stops.
|
| navigator |
boolean | string |
oneWay |
false |
Adding in the previous and next controls |
| indicator |
boolean | string |
oneWay |
false |
Adding in the indicators to the carousel. |
|
Events
|
| bs-slide |
Function |
call |
|
This event fires immediately when the slide instance method is invoked.
|
| bs-slid |
Function |
call |
|
This event is fired when the carousel has completed its slide transition. |


Insult to Ambedkar: Various organisations stage protest
by B J DhanyaprasadMembers of various organisations staged a protest against a recent circular issued by the Department of Education which allegedly insulted the architect of the Constitution, Dr B R Ambedkar, in Chikkamagaluru on Wednesday.
Congress, JD(S), BSP, CPI, Dalit Sangharsh Samiti, Rajya Raitha Sangha, Karnataka Rakshana Vedike, Nava Karnataka Rakshana Vedike and Kannada Sene workers staged a protest.
The protesters took out a procession from taluk office to Azad Park Circle and shouted slogans against the BJP-led Central and state governments.
JD(S) Vice President H H Devaraj said a few political vested interests are working against the Constitution of India and are conspiring against the republic nature of the country.
The circular has insulted Ambedkar, he added.
DCC President Dr K P Amshumanth said, “The Constitution has been emphasising on equality and unity. There is a need to create awareness about the Constitution in every nook and corner of the villages.
Resignation sought
The activists of CPIML district committee staged a protest demanded the resignation of Minister for Primary and Secondary Education Suresh Kumar owning the moral responsibility.
The protesters also urged the government to provide rehabilitation facilities for the flood victims.
The encroached 4.29 acres of government land on survey number 127 at Kammaragodu in Mudigere taluk should be reclaimed, they demanded.


Feeling generous? Donate 0xBitcoin here to top up the faucet's balance.
| Time | Gas | Tokens | ||
|---|---|---|---|---|
| {{ ethBlockNumberToTimestamp(event.block_number) }} | {{ event.gas_price.toFixed(1) }} | {{ event.tokens.toFixed(3) }} | donation! +{{ event.tokens.toFixed(3) }} |
All Ethereum transactions require a certain amount of 'gas' to execute. This contract analyzes the actual gas spent on each request, and returns with a proportional amount of 0xBitcoin. This means that a request at 20 Gwei will pay out twice as many tokens as a request sent at 10 Gwei.
The ratio of 0xBitcoin to Gas is set in the contract, and the owner can adjust the ratio. Periodic adjustments are necessary due to the constantly changing prices of 0xBTC and ETH.
Where does the ETH go? Anyone using this faucet is essentially trading ETH for 0xBTC in the form of gas. These fees are not collected by this contract, they are earned by the miners of the Ethereum blockchain. Future versions of this faucet may 'burn' ETH instead.
Are there any fees? No fees are collected by the faucet; 100% of donated tokens are available for others to withdraw. The contract itself does not handle ETH.
Why not pay out more tokens? If the faucet dispensed more tokens then the price of Gas, there would be an incentive for someone to drain the faucet for profit. Some faucet designs use a different approach to prevent this, usually related to rate-limiting. These approaches have flaws and inconvenience normal users more than abusers. This faucet's goal is to be an easy way to get a small number of tokens to play with; not as a source of income.


Experience
-
Google, Mountain View, CA, United States, 2022.5 -
present
Machine Learning Engineer at Google, working on NLU, LTR, etc.
-
Microsoft, Sunnyvale, CA, United States, 2018.10 -
2022.5
Applied Scientist in Experiences & Devices, working on Bing core search
& AI
-
Microsoft, Redmond, WA, United States, 2017.5 - 2018.10
Researcher in Cloud & AI, working on congnition intelligence
-
University of California at Berkeley, EECS, Berkeley, CA,
United States, 2015.4 - 2017.5
Postdoctoral Researcher in BAIR,
working on hierarchical decision-making and reinforcement learning with
Prof. Stuart Russell
-
Carnegie Mellon University, CSD, Pittsburgh, PA, United
States, 2013.12 - 2015.3
Visiting Research Scholar in
CORAL, working on human-robot
interaction and multi-object tracking with
Prof. Manuela Veloso and
Prof. Reid Simmions
-
University of Science and Technology of China, CSD, Hefei,
Anhui, China, 2009.9 - 2014.11
Research Assistant in
WrightEagle, working on
hierarchical online planning for Markov decision processes with
Prof. Xiaoping Chen
Education
-
University of Science and Technology of China, Hefei,
Anhui, China, 2009.9 - 2014.11
Ph.D. in Computer Science
-
University of Science and Technology of China, Hefei,
Anhui, China, 2005.9 - 2009.7
Bachelor in Computer Science
Publications
-
Posterior Sampling for Monte Carlo Planning under Uncertainty, Aijun Bai, Feng Wu, and Xiaoping Chen,
Applied Intelligence, 2018. [pdf] [bib]
-
Efficient Reinforcement Learning with Hierarchies of Machines by
Leveraging Internal Transitions, Aijun Bai, and Stuart Russell,
Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence (IJCAI), Melbourne, Australia,
August 19 - 25, 2017.
[pdf] [bib] [video]
-
Multi-Object Tracking and Identification via Particle Filtering over
Sets, Aijun Bai, Reid Simmons, and Manuela Veloso,
Proceedings of 20th International Conference on Information Fusion
(FUSION), Xi’an, China, July 10-13, 2017.
[pdf] [bib] [video]
-
Concurrent Hierarchical Reinforcement Learning for RoboCup
Keepaway, Aijun Bai, Stuart Russell, and Xiaoping Chen,
RoboCup-2017: Robot Soccer World Cup XX, Lecture Notes in Artificial
Intelligence (RoboCup), Springer Verlag, Berlin,
2017.
[pdf] [bib] [video]
-
RoboCup 2D Soccer Simulation League: Evaluation Challenges,
Mikhail Prokopenko, Peter Wang, Sebastian Marian, Aijun Bai, Xiao
Li and Xiaoping Chen,
RoboCup-2017: Robot Soccer World Cup XX, Lecture Notes in Artificial
Intelligence (RoboCup), Springer Verlag, Berlin,
2017.
[pdf] [bib]
-
Speeding Up HAM Learning with Internal Transitions,
Aijun Bai, and Stuart Russell,
The Multi-disciplinary Conference on Reinforcement Learning and Decision
Making (RLDM) 2017, Ann Arbor, Michigan, USA, June 11-14,
2017.
[pdf] [bib] [video]
-
Markovian State and Action Abstractions for MDPs via Hierarchical
MCTS, Aijun Bai, Siddharth Srivastava, and Stuart Russell,
Proceedings of the 25th International Joint Conference on Artificial
Intelligence (IJCAI), New York, 2016.
[pdf] [bib]
-
Online Planning for Large Markov Decision Processes with Hierarchical
Decomposition, Aijun Bai, Feng Wu, and Xiaoping Chen,
ACM Transactions on Intelligent Systems and Technology (ACM TIST),6(4):45:1–45:28, July 2015.
[pdf] [appendix] [bib]
-
PLEASE: Palm Leaf Search for POMDPs with Large Observation Spaces, Zongzhang Zhang, David Hsu, Wee Sun Lee, Zhan Wei Lim, and
Aijun Bai,
Proceedings of the 25th International Conference on Automated Planning
and Scheduling (ICAPS), 2015.
[pdf] [bib]
-
PLEASE: Palm Leaf Search for POMDPs with Large Observation Spaces, Zongzhang Zhang, David Hsu, Wee Sun Lee, Zhan Wei Lim, and
Aijun Bai,
Proceedings of the 8th Annual Symposium on Combinatorial Search
(SoCS), 2015.
[pdf] [bib]
-
Intention-Aware Multi-Human Tracking for Human-Robot Interaction via
Particle Filtering over Sets, Aijun Bai, Reid Simmons, Manuela Veloso, and Xiaoping Chen,
AAAI 2014 Fall Symposium: AI for Human-Robot Interaction
(AI-HRI), Arlington, Virginia, United States, November
2014.
[pdf] [bib]
-
Thompson Sampling based Monte-Carlo Planning in POMDPs,
Aijun Bai, Feng Wu, Zongzhang Zhang, and Xiaoping Chen,
Proceedings of the 24th International Conference on Automated Planning
and Scheduling (ICAPS), Portsmouth, New Hampshire, United
States, June 2014.
[pdf] [bib]
-
Bayesian Mixture Modelling and Inference based Thompson Sampling in
Monte-Carlo Tree Search, Aijun Bai, Feng Wu, and Xiaoping Chen,
Advances in Neural Information Processing Systems 26
(NIPS), Lake Tahoe, Nevada, United States, December
2013.
[pdf] [bib]
-
An Intelligent Service System with Multiple Robots, Qiang
Lu, Guanghui Lu, Aijun Bai, Dongxiang Zhang, and Xiaoping Chen,
Robot Competition of International Joint Conference on Artificial
Intelligence (IJCAI), Beijing, China, 2013.
[pdf] [bib]
-
Towards a Principled Solution to Simulated Robot Soccer,
Aijun Bai, Feng Wu, and Xiaoping Chen,
RoboCup-2012: Robot Soccer World Cup XVI, Lecture Notes in Artificial
Intelligence (RoboCup), Vol. 7500, Springer Verlag,
Berlin, 2013.
[pdf] [bib] Best Paper Award Nominee at RoboCup 2012 International
Symposium.
-
Online Planning for Large MDPs with MAXQ Decomposition (Extended
Abstract), Aijun Bai, Feng Wu, and Xiaoping Chen,
Proceedings of the 11th International Conference on Autonomous Agents and
Multiagent Systems (AAMAS), Valencia, Spain, June
2012.
[pdf] [bib]
-
Online Planning for Large MDPs with MAXQ Decomposition,
Aijun Bai, Feng Wu, and Xiaoping Chen,
AAMAS 2012 Autonomous Robots and Multirobot Systems Workshop (ARMS),
Valencia, Spain, June 2012.
[pdf] [bib]
-
WrightEagle and UT Austin Villa: RoboCup 2011 Simulation League
Champions, Aijun Bai, Xiaoping Chen, Patrick MacAlpine, Daniel Urieli,
Samuel Barrett, and Peter Stone,
RoboCup-2011: Robot Soccer World Cup XV, Lecture Notes in Artificial
Intelligence (RoboCup), Vol. 7416, Springer Verlag,
Berlin, 2012.
[pdf] [bib]
Honors and Awards
Scholarships
- Early Researcher Support of ICAPS, 2014.
- Travel Award of NIPS Foundation, 2013.


ANDAMAN
As ANDAMANhas a lot to offer tourists,therefore these Vizag to Andaman tour packages include extensive sightseeing tours which cover all tourist attractions. Places like Havelock Island, Neil Island, North Bay Island, and Baratang Island are covered in these packages. These islands are also home to numerous beaches where one can relax and bask in the tropical weather. Some of the most popular beaches covered in this Andaman tour from Vizag are Radhanagar Beach, Elephant Beach, and Bharatpur Beach.So, wait no more! book Andaman tour packages.
PACKAGE OFFERED:
- DURATION:5 NIGHTS,6 DAY.

- DATE OF JOURNEY:5TH OCTOBER,2021.
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University of British Columbia EECE 310 Software Engineering TEAM 12 Isaac Cheng Natasha Kumar Ishan Sahay Sarah Louise Leong


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Posted for {{lorem | cut:true:30:'...'}} Posted in Thuesday, 12/11/2014
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Multiplayer Flappy Bird
Click on the game area to start, use space bar to jump
Update nickname
Score: 0
Live Leaderboard


{%extends 'base.html'%} {%import 'macros.html' as macro%} {%block content%}
{{blog.blog_text}}
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Specialized on overseeing the creation, developing and presenting the content for fashion oriented magazines and publications. Was responsible for managing teams, putting together photo shoots, deciding what should be published and what shouldn't. Own exceptional writing skills, is an expert in photography and fashion and have great interpersonal communication skills.
Specialized on overseeing the creation, developing and presenting the content for fashion oriented magazines and publications. Was responsible for managing teams, putting together photo shoots, deciding what should be published and what shouldn't. Own exceptional writing skills, is an expert in photography and fashion and have great interpersonal communication skills.


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Sermons
The Persecuted Church.
13 Nov 2011
Series:
Celebration Services
Listen
(60 minutes)
Slides
2011-11-13 Baroness Cox - The Persecuted Church.pdf
Download
2011-11-13 Baroness Cox.mp3
60 minutes
Also in the 'Celebration Services' series
13 Nov 2011 - The Persecuted Church. (Baroness Caroline Cox)
23 Jun 2013 - To know God better.
(John Coles)
22 Sep 2013 - Speaking about his trip to Bethel Church in America.
(Rev. John Valentine)
20 Oct 2013 - Faith.
(Rev. Mark Bailey)
24 Nov 2013 - Dealing with Disappointment.
(Rev. Rich Johnson)
26 Jan 2014 - The Harvest Is Plentiful, the Labourers Few.
(Frog Orr-Ewing)
23 Feb 2014 - Holy Habits.
(Eddie Lyle)
23 Mar 2014 - God has a Good and Perfect Plan for your Life - Hope and a Future.
(Paul Cowley)
18 May 2014 - Have we got our eyes set on what it is that Jesus has called us to?
(Gareth Dickinson)
22 Jun 2014 - What are you passionate about? What's your world view?
(Andrew Wallis)
28 Sep 2014 - Hearing God for Each Other - The Power of Prayer.
(Annie Hughes)
19 Oct 2014 - The Power of Words.
(Mal Calladine)
23 Nov 2014 - Discipleship - Making Disciples.
(Dave Keeper)
25 Jan 2015 - Pursue, Push on Through.
(Hellie Brunt)
Speaker
Baroness Caroline Cox
Also by Baroness Caroline Cox
No other sermons by Baroness Caroline Cox


You are about to donate to the shelter house
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Walking through everything that happens when the first scene is loaded in
RPD
inside main()
- main() is called, and the JSON parser fetches the file passed in
argv[1]
- The singleton containing the core game loop (EngineCore) is
instantiated, and passed the "engine_core" chunk of the JSON blob.
- The EngineCore singleton kicks off the game loop by calling the Run()
function.
Inside EngineCore.Run()
- Once inside the Run() function, more singletons are spawned: Clock,
RenderManager, InputManager, and PhysicsCore
- The Clock singleton is started with its Start() function.
- The first Scene--called
current_scene
is instantiated in
the EngineCore. This Scene instanced is wrapped in a
std::unique_ptr
.
current_scene
is configured by calling the configure()
function, which is passed EngineCore's initial_scene
member.
- A
std::queue
is created for the input data from a
controller/keyboard/etc.
- The main game loop inside the EngineCore is entered. The loop exit
logic is based on a bolean called
running
.
- The input queue is assigned to be the output of the
InputManager singleton's HandleInput() function
- The game loop performs a check to see if there's an exit signal at the
back of the input queue. If so, the clock is stopped, and the loop is
exited.
- Assuming no exit signal, we collect the number of elapsed ticks from
the Clock singleton.
current_scene
is handed the input queue through the
HandleInput function.
- If there this isn't the very first game tick(), we enter anothe control
block where we delegate work to change game state.
- The
current_scene
instance calls its UpdateAll function,
and recieves the number of ticks.
- The PhysicsCore singleton calls its CheckCollisions function, which
receives the Actors, Props, and Stage from
current_scene
.
- We exit the control block checking for the first game tick, and tell
the RenderManager singleton to clear the previous frame SDL Renderer.
current_scene
calls the RenderAll() function, generating
the next frame's worth of content for the RenderManager singleton.
- RengerManager's SceneToScreen() function is called, pushing that frame
into the window.
- The core game loop ends.
Exiting Engine.Run(), back inside main()
- main() calls EngineCore's Cleanup() function.
- The program exits.


| 002:267 Khan | : |
O you who believe! Spend of the good things which you have (legally) earned, and of that which We have produced from the earth for you, and do not aim at that which is bad to spend from it, (though) you would not accept it save if you close your eyes and tolerate therein. And know that Allah is Rich (Free of all wants), and Worthy of all praise. |
| 002:267 Maulana | : |
O you who believe, spend of the good things that you earn and of that which We bring forth for you out of the earth, and aim not at the bad to spend thereof, while you would not take it yourselves unless you connive at it. And know that Allah is Self-sufficient, Praiseworthy. |
| 002:267 Pickthal | : |
O ye who believe! Spend of the good things which ye have earned, and of that which We bring forth from the earth for you, and seek not the bad (with intent) to spend thereof (in charity) when ye would not take it for yourselves save with disdain; and know that Allah is Absolute, Owner of Praise. |
| 002:267 Rashad | : |
O you who believe, you shall give to charity from the good things you earn, and from what we have produced for you from the earth. Do not pick out the bad therein to give away, when you yourselves do not accept it unless your eyes are closed. You should know that GOD is Rich, Praiseworthy. |
| 002:267 Sarwar | : |
Believers, spend for the cause of God from the good things that you earn and from what we have made the earth yield for you. Do not even think of spending for the cause of God worthless things that you yourselves would be reluctant to accept. Know that God is Self-sufficient and Glorious. |
| 002:267 Shakir | : |
O you who believe! spend (benevolently) of the good things that you earn and or what We have brought forth for you out of the earth, and do not aim at what is bad that you may spend (in alms) of it, while you would not take it yourselves unless you have its price lowered, and know that Allah is Self-sufficient, Praiseworthy. |
| 002:267 Sherali | : |
O ye who believe ! spend of the good things you have earned, and from what WE produce for you from the earth; and seek not what is bad that you may spend out of it when you would not take it yourselves except that you connive at it. And know that ALLAH is Self-Sufficient, praiseworthy. |
| 002:267 Yusufali | : |
O ye who believe! Give of the good things which ye have (honourably) earned, and of the fruits of the earth which We have produced for you, and do not even aim at getting anything which is bad, in order that out of it ye may give away something, when ye yourselves would not receive it except with closed eyes. And know that Allah is Free of all wants, and worthy of all praise. |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > صفات الله تعالى > الحميد Pillars of Islam> Islamic> Oneness of Allah> His Glory's Characteristics> The Worthy of All Praise أركان الإسلام > التوحيد > توحيد الله تعالي > صفات الله تعالى > الغنيّ Pillars of Islam> Islamic> Oneness of Allah> His Glory's Characteristics> The Rich(Free of all Needs) أركان الإسلام > التوحيد > توحيد الله تعالي > غناه وافتقار الناس إليه Pillars of Islam> Islamic> Oneness of Allah> His Self-Sufficience and People's need of Him أركان الإسلام > الزكاة والصدقات Pillars of Islam> Due Alms and Charities تنظيم العلاقات المالية > إنفاقها Organizing Financial Relationships>Its Expenditure تنظيم العلاقات المالية > الزكاة Organizing Financial Relationships>Due Alms
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


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Changelog of Toggl Track browser extension
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Syria's Assad says he does not want Turkey as an 'enemy'
DAMASCUS: Syrian President Bashar al-Assad Thursday (Oct 31) said he did not want to make an "enemy" of neighbouring Turkey despite the stand-off between their forces in the north of the country.
Assad said however that Turkish President Recep Tayyip Erdogan himself was an "enemy" due to policies hostile to Syria and opposed by most of his country's political elite.
"We must ensure that we don't turn Turkey into an enemy and here comes the role of friends" such as Russia and Iran, the president said in a pre-recorded interview on state television.
Turkey supports Syrian rebel forces who have battled Assad's government during the eight-year-long war that has killed more than 370,000 people.
This month Ankara launched an operation across Syria's northern border against Kurdish forces.
READ: Syrian and Turkish armies in deadly border clash
The Kurds spearheaded a US-backed military campaign against the Islamic State group that deprived the jihadists' of their final slither of Syrian territory in March this year but Ankara views the Kurdish forces as "terrorists".
Abandoned by their ally Washington -- which early this month pulled its own troops back from the border area, effectively allowing Turkey to attack -- the Kurds turned to Damascus which swiftly deployed and reclaimed swathes of territory it lost years ago.
Assad said the deployment is a prelude to reinstating complete central government control over the region in a process he said would be "gradual" and would "respect new realities on the ground."
Turkey last week struck a deal with Russia to halt its weeks-long operation
The agreement calls for the withdrawal of Syrian Kurdish fighters from areas along Turkey's border with Syria, with a view to setting up a "safe zone" where Ankara plans to repatriate some of the 3.6 million Syrian refugees it currently hosts.
READ: Kurdish forces start Syria-Turkey border pullback
Joint Turkish-Russian patrols are due to start in areas near the Syrian border on Friday after the Kremlin said that Syrian Kurdish fighters had withdrawn in accordance with the terms of the deal between Ankara and Moscow.
On Thursday, Assad said the deal is "temporary."
"We have to distinguish between ultimate or strategic goals... and tactical approaches," he said, stressing that his forces will eventually reclaim territory taken by Ankara in its latest offensive.
The situation around the northwestern region of Idlib showed how the regime could bypass Ankara, he said.
Russia and Turkey last year reached a ceasefire deal that was meant to protect the Idlib region of three million people from an all-out regime assault.
As part of the deal, Turkish forces deployed around Idlib in a bid to ensure the withdrawal of jihadist fighters and allied rebels from a de-escalation zone there.
But Assad's opponents did not pull back and last week Syria's president said that winning the Idlib front would be key to winning the war. He noted that his troops had completed preparations for an offensive.
Idlib province is the last major opposition bastion outside the regime's control.


Optimizers¶
QISKit ACQUA contains a variety of different optimizers for use by quantum variational algorithms, such as VQE. We can logically divide optimizers into two categories:
- Local Optimizers: Given an optimization problem, a local optimizer is a function that attempts to find an optimal value
within the neighboring set of a candidate solution.
- Global Optimizers: Given an optimization problem, a global optimizer is a function that attempts to find an optimal value
among all possible solutions.
Local Optimizers¶
This section presents the local optimizers made available in QISKit ACQUA, and meant to be used in conjunction with a quantum variational
algorithms. Except for Parallel Broyden-Fletcher-Goldfarb-Shann (P-BFGS), all hese optimizers are directly based on the scipy.optimize.minimize
optimization function in
SciPy.org.
They all have a common pattern for parameters. Specifically, the tol
parameter, whose value
must be a float
indicating tolerance for termination,
is from the scipy.optimize.minimize
method itself, while the remaining parameters are
from the options
dictionary,
which may be referred to for further information.
Conjugate Gradient (CG) Method¶
CG is an algorithm for the numerical solution of systems of linear equations whose matrices are symmetric and positive-definite. It is an iterative algorithm in that it uses an initial guess to generate a sequence of improving approximate solutions for a problems, in whicheach approximation is derived from the previous ones. It is often used to solve unconstrained optimization problems, such as energy minimization.
The following parameters are supported:
The maximum number of iterations to perform:
The default is
20
.A Boolean value indicating whether or not to print convergence messages:
disp : bool
The default value is
False
.A tolerance value that must be greater than the gradient norm before successful termination.
gtol : float
The default value is
1e-05
.The tolerance for termination:
tol : float
This parameter is optional. If specified, the value of this parameter must be a number, otherwise, it is
Nonw
. The default isNone
.
Declarative Name
When referring to CG declaratively inside QISKit ACQUA, its code name
, by which QISKit ACQUA dynamically discovers and loads it,
is CG
.
Constrained Optimization BY Linear Approximation (COBYLA)¶
COBYLA is a numerical optimization method for constrained problems where the derivative of the objective function is not known. COBYLA supports the following parameters:
The maximum number of iterations to perform:
maxiter : int
The default is
1000
.A Boolean value indicating whether or not to print convergence messages:
disp : bool
The default value is
False
.Reasonable initial changes to the variable:
rhobeg : float
The default value is
1.0
.The tolerance for termination:
tol : float
This parameter is optional. If specified, the value of this parameter must be of type
float
, otherwise, it isNone
. The default isNone
.
Declarative Name
When referring to COBYLA declaratively inside QISKit ACQUA, its code name
, by which QISKit ACQUA dynamically discovers and loads it,
is COBYLA
.
Limited-memory Broyden-Fletcher-Goldfarb-Shanno Bound (L-BFGS-B)¶
The target goal of L-BFGS-B is to minimize the value of a differentiable scalar function \(f\). This optimizer is a quasi-Newton method, meaning tha, in contrast to Newtons’s method, it does not require :math:f’s Hessian (the matrix of \(f\)‘s second derivatives) when attempting to compute \(f\)‘s minimum value. Like BFGS, L-BFGS is an iterative method for solving unconstrained, non-linear optimization problems, but approximates BFGS using a limited amount of computer memory. L-BFGS starts with an initial estimate of the optimal value, and proceeds iteratively to refine that estimate with a sequence of better estimates. The derivatives of \(f\) are used to identify the direction of steepest descent, and also to form an estimate of the Hessian matrix (second derivative) of \(f\). L-BFGS-B extends L-BFGS to handle simple, per-variable bound constraints.
The following parameters are supported:
The maximum number of function evaluations:
maxfun : int
The default is
1000
.The maximum number of function evaluations:
maxfun : int
The default is
1000
.The maximum number of iterations:
factr : int
The default is
10
.An
int
value controlling the frequency of the printed output showing the optimizer’s operations.iprint : int
The default is
-1
.
Further detailed information on factr and iprint may be found at scipy.optimize.fmin_l_bfgs_b.
Declarative Name
When referring to L-BFGS-B declaratively inside QISKit ACQUA, its code name
, by which QISKit ACQUA dynamically discovers and loads it,
is L_BFGS_B
.
Nelder-Mead¶
The Nelder-Mead algorithm performs unnconstrained optimization; it ignores bounds or constraints. It is used to find the minimum or maximum of an objective function in a multidimensional space. It is based on the Simplex algorithm. Nelder-Mead is robust in many applications, especially when the first and second derivativerds of the objective function are not known. However, if numerical computation of the derivatives can be trusted to be accurate, other algorithms using the first and/or second derivatives information might be preferred for their better performance in the general case, especially in consideration of the fact that the Nelder–Mead technique is a heuristic search method that can converge to non-stationary points.
The following parameters are supported:
The maximum number of iterations:
maxiter : int
This parameter is optional. If specified, the value of this parameter must be of type
int
, otherwise, it isNone
. The default isNone
.The maximum number of functional evaluations to perform:
maxfev : int
The default is
1000
.A
bool
value indicating whether or not to print convergence messages:disp : bool
The default is
False
.A tolerance parameter indicating the absolute error in
xopt
between iterations that will be considered acceptable for convergence.xatol : float
The default value is
0.0001
.The tolerance for termination:
tol : float
This parameter is optional. If specified, the value of this parameter must be of type
float
, otherwise, it isNone
. The default isNone
.
Declarative Name
When referring to Nelder-Mead declaratively inside QISKit ACQUA, its code name
, by which QISKit ACQUA dynamically discovers and loads it,
is NELDER_MEAD
.
Parallel Broyden-Fletcher-Goldfarb-Shann (P-BFGS)¶
P-BFGS is a parallellized version of Limited-memory Broyden-Fletcher-Goldfarb-Shanno Bound (L-BFGS-B), with which it shares the same parameters. P-BFGS can be useful when the target hardware is a quantum simulator running on a classical machine. This allows the multiple processes to use simulation to potentially reach a minimum faster. The parallelization may help the optimizer avoid getting stuck at local mimima. In addition to the parameters of L-BFGS-B, P-BFGS supports the following parameter:
The maximum numer of processes spawned by P-BFGS:
max_processes = 1 | 2 | ...
By default, P-BFGS runs one optimization in the current process and spawns additional processes up to the number of processor cores. An
int
value may be specified to limit the total number of processes (or cores) used. This parameter is optional. If specified, the value of this parameter must be of typeint
, otherwise, it isNone
. The default isNone
.
Note
The parallel processes do not currently work for this optimizer on the Microsoft Windows platform. There, P-BFGS will just run the one optimization in the main process, without spawning new processes. Therefore, the resulting behavior will be the same as the L-BFGS-B optimizer.
Declarative Name
When referring to P-BFGS declaratively inside QISKit ACQUA,
its code name
, by which QISKit ACQUA dynamically discovers and loads it,
is P_BFGS
.
Powell¶
The Powell algorithm performs unconstrained optimization; it ignores bounds or constraints. Powell is a conjugate direction method: it performs sequential one-dimensional minimization along each directional vector, which is updated at each iteration of the main minimization loop. The function being minimized need not be differentiable, and no derivatives are taken.
The following parameters are supported:
The maximum number of iterations:
maxiter : int
This parameter is optional. If specified, the value of this parameter must be of type
int
, otherwise, it isNone
. The default isNone
.The maximum number of functional evaluations to perform:
maxfev : int
The default value is
1000
.A
bool
value indicating whether or not to print convergence messages:disp : bool
The default is
False
.A tolerance parameter indicating the absolute error in
xopt
between iterations that will be considered acceptable for convergence.xtol : float
The default value is
0.0001
.The tolerance for termination:
tol : float
This parameter is optional. If specified, the value of this parameter must be of type
float
, otherwise, it isNone
. The default isNone
.
Declarative Name
When referring to Powell declaratively inside QISKit ACQUA, its code name
, by which QISKit ACQUA dynamically discovers and loads it,
is POWELL
.
Sequential Least SQuares Programming (SLSQP)¶
SLSQP minimizes a function of several variables with any combination of bounds, equality and inequality constraints. The method wraps the SLSQP Optimization subroutine originally implemented by Dieter Kraft. SLSQP is ideal for mathematical problems for which the objective function and the constraints are twice continuously differentiable. Note that the wrapper handles infinite values in bounds by converting them into large floating values.
The following parameters are supported:
The maximum number of iterations:
maxiter : int
The default is
100
.A
bool
value indicating whether or not to print convergence messages:disp : bool
The default is
False
.A tolerance value indicating precision goal for the value of the objective function in the stopping criterion.
gtol : float
The default value is
1e-06
.The tolerance for termination:
tol : number
This parameter is optional. If specified, the value of this parameter must be a number, otherwise, it is
Nonw
. The default isNone
.
Declarative Name
When referring to SLSQP declaratively inside QISKit ACQUA, its code name
, by which QISKit ACQUA dynamically discovers and loads it,
is SLSQP
.
Simultaneous Perturbation Stochastic Approximation (SPSA)¶
SPSA is an algorithmic method for optimizing systems with multiple unknown parameters. As an optimization method, it is appropriately suited to large-scale population models, adaptive modeling,and simulation optimization. Many examples are presented at the SPSA Web site. SPSA is a descent method capable of finding global minima, sharing this property with other methods as simulated annealing. Its main feature is the gradient approximation, which requires only two measurements of the objective function, regardless of the dimension of the optimization problem.
Note
SPSA can be used in the presence of noise, and it is therefore indicated in situations involving measurement uncertainty on a quantum computation when finding a minimum. If you are executing a variational algorithm using a Quantum ASseMbly Language (QASM) simulator or a real device, SPSA would be the most recommended choice among the optimizers provided here.
The optimization process includes a calibration phase, which requires additional functional evaluations. Overall, the following parameters are supported:
Maximum number of trial steps for to be taken for the optimization. There are two function evaluations per trial:
max_trials : int
The default value is
1000
.An
int
value determining how often optimization outcomes should be stored during execution:save_steps : int
SPSA will store optimization outcomes every
save_steps
trial steps. The default value is1
.The number of last updates of the variables to average on for the final objective function:
last_avg : int
The default value is
1
.Control parameters for SPSA:
parameters = list_of_5_numbers
This is an optional parameter, consisting of a list of 5
float
elements. The default value isNone
. SPSA updates the parameters (theta
) for the objective function (J
) through the following equation at iterationk
:J(theta)
is the objective value oftheta
.c0
,c1
,c2
,c3
andc4
are the five control parameters. By default,c0
is calibrated through a few evaluations on the objective function with the initialtheta
.c1
,c2
,c3
andc4
are set as0.1
,0.602
,0.101
,0.0
, respectively.
Declarative Name
When referring to SPSA declaratively inside QISKit ACQUA, its code name
, by which QISKit ACQUA dynamically discovers and loads it,
is SPSA
.
Truncated Newton (TNC)¶
TNC uses a truncated Newton algorithm to minimize a function with variables subject to bounds. This algorithm uses gradient information; it is also called Newton Conjugate-Gradient. It differs from the Conjugate Gradient (CG) Method method as it wraps a C implementation and allows each variable to be given upper and lower bounds.
The following parameters are supported:
The maximum number of iterations:
maxiter : int
The default is
100
.A Boolean value indicating whether or not to print convergence messages:
disp : bool
The default value is
False
.Relative precision for finite difference calculations:
accuracy : float
The default value is
0.0
.A tolerance value indicating the precision goal for the value of the objective function
f
in the stopping criterion.ftol : float
The default value is
-1
.A tolerance value indicating precision goal for the value of
x
in the stopping criterion, after applyingx
scaling factors.xtol : float
The default value is
-1
.A tolerance value indicating precision goal for the value of the projected gradient
g
in the stopping criterion, after applyingx
scaling factors.gtol : float
The default value is
-1
.The tolerance for termination:
tol : number
This parameter is optional. If specified, the value of this parameter must be a number, otherwise, it is
Nonw
. The default isNone
Declarative Name
When referring to TNC declaratively inside QISKit ACQUA, its code name
, by which QISKit ACQUA dynamically discovers and loads it,
is TNC
.
.


#include <TemplateApplication.h>
You can store any data here that is more general than a single document.
Definition at line 16 of file TemplateApplication.h.
Public Member Functions | |
| ConfigSection * | GetConfig () |
| Returns the configuration for the application. | |
| virtual int | InitInstance () |
| Initializes the current instance of the application. | |
| TemplateApplication () | |
| Creates a new application. | |
| ~TemplateApplication () | |
| Destroys the application. | |
Private Attributes | |
| Config * | Config |
| The configuration file. |
|
|
Creates a new application.
|
|
|
Destroys the application.
|
|
|
Returns the configuration for the application.
|
|
|
Initializes the current instance of the application. This method is called each time the application is concurrently started.
|
|
|
The configuration file.
Definition at line 23 of file TemplateApplication.h. |


Micah Zaayer grew up just outside of Portland, OR on a beautiful couple of acres in the Germantown area. Coming fresh out of the Oregon cannabis industry, Micah has ample experinece operating operating in a highly repgulated and competitive market.
At Bridge City Collective, Portland's first mulit-licensed cannabis retail chain, Micah lead BCC's rollout of one of Portland's first cannabis delivery services. Micah was also part of a team that recieved one of Ohio's highly-competitve, for-merit medical cannabis retail licenses in 2018.
While at BCC, Micah worked closely with software designers to create customizable point-of-sales teminals to effectively track inventory, sales trends, deliveries and state taxes. Doing so, Micah saw the infinite power of programming. Seeking new challenges, Micah decided to leave everything he built to start a new path starting at Epicodus.
Micah's hobbies include gem & mineral collecting, mycology, hoticulture, hiking, motorcycles and seeking out delicious food all over the world.
This is the first site Micah made. It uses html and css.
This site uses html, css and bootstrap. This is the first site Micah made using bootstrap.
This is a GH Page link to this site.


Tutorial Flow:
- Determining the problem category (completed)
- Performing detailed diagnosis appropriate locking problems
- Identifying the scope of the SQL statements involved
Detailed Analysis
The detailed diagnosis of the problem suggests that a larger portion of SQL statements are
being compiled rather than retrieving their access plans from the package cache. To analyse the problem further
database administrator monitors elements for SQL compilation.
Metrics related to SQL compilation
- TOTAL_COMPILATIONS
- PKG_CACHE_INSERTS
- PKG_CACHE_LOOKUPS


For this demo the image size should not exceed 200 x 200px. Pause the calculation to save a snapshot or load another image.


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