US Moves Closer to Becoming A Major Shareholder In GM

cnbc, Articles, General Motors Co, Business News, Transportation, Autos, source:tagname:CNBC US Source

The US government is increasingly likely to convert a $13.4 billion loan to General Motors into common stock, sharply reducing the company's debt burden and giving taxpayers a major stake in the struggling auto maker, sources tell CNBC.

,

The move, which is still under discussion, is partly aimed at getting GM's  bondholders and the United Auto Workers union to make more concessions and avert a possible bankruptcy filing, these sources said. The government is pressuring the UAW to make concessions on GM retiree health benefits.

People involved in the talks caution that nothing has been decided yet. Officials from President Obama's auto task force and the UAW had no comment.

The US agreed to loan GM up to $13.4 billion in December provided the auto maker convinced bondholders and the UAW to accept much of what the company owes them in stock rather than cash.

Slideshow: What's new at New York Auto Show

So far, both groups have balked. Both the union and bondholders are worried that if they accept stock, and the company goes bankrupt anyway, they will be left with nothing.

However, if the government converts its loan to equity, it signals to both groups that the White House will not let the company fail, and that the stock will eventually be worth something.

GM owes the UAW $20 billion for its retiree health care benefits, and it owes bondholders $27.5 billion.

Crucial to all of this: a new business plan for GM that reflects the current economic environment. That means fewer plants, fewer models, lower pay for union employees and a balance with far less, if any, debt. If that cannot be achieved, President Obama has said that forcing the company into bankruptcy is a strong possibility.

Previously, GM offered the bondholders 8 cents in cash, 16 and half cents in new debt, and 90% of the company's equity. It offered the union $10 billion amortized over 20 years, and $10 billion in preferred stock with a 9% interest rate. However, the auto task force told GM those offers were too generous because of the unprecedented and precipitous decline in auto sales.

Those previous offers were based on the assumption that auto sales would return to an annual pace of around 11 million cars. Thus far however, they have hovered around 9 million cars per year.

As CNBC reported more than a week ago, GM is planning to offer the bondholders no cash, no new debt, and only as much as 20% of the stock of GM, perhaps even as little as 10%. Again, negotiations are ongoing and nothing has been finalized. Terms of a possible offer could change on a daily or even hourly basis.

Reuters reports they are considering offering the unions only stock as well. If true, that would sit very poorly with the union, as it needs cash to pay for retiree health care benefits. Doctor visits and MRIs can't be paid for with stock.

However, the union retiree benefit plan does have some cash. GM had already paid them $10 billion out of $30 billion owed to them, late last year. Some argue that would justify paying the rest that is owed to them in stock, which down the road they could sell, and use to pay retiree health care benefits.

The US government is increasingly likely to convert a $13.4 billion loan to General Motors into common stock, sharply reducing the company's debt burden and giving taxpayers a major stake in the struggling auto maker, sources tell CNBC.The move, which is still under discussion, is partly aimed at getting GM's  bondholders and the United Auto Workers union to make more concessions and avert a possible bankruptcy filing, these sources said. The government is pressuring the UAW to make concessions on GM retiree health benefits. People involved in the talks caution that nothing has been decided yet. Officials from President Obama's auto task force and the UAW had no comment.The US agreed to loan GM up to $13.4 billion in December provided the auto maker convinced bondholders and the UAW to accept much of what the company owes them in stock rather than cash. Slideshow: What's new at New York Auto ShowSo far, both groups have balked. Both the union and bondholders are worried that if they accept stock, and the company goes bankrupt anyway, they will be left with nothing. However, if the government converts its loan to equity, it signals to both groups that the White House will not let the company fail, and that the stock will eventually be worth something.GM owes the UAW $20 billion for its retiree health care benefits, and it owes bondholders $27.5 billion. Crucial to all of this: a new business plan for GM that reflects the current economic environment. That means fewer plants, fewer models, lower pay for union employees and a balance with far less, if any, debt. If that cannot be achieved, President Obama has said that forcing the company into bankruptcy is a strong possibility. Previously, GM offered the bondholders 8 cents in cash, 16 and half cents in new debt, and 90% of the company's equity. It offered the union $10 billion amortized over 20 years, and $10 billion in preferred stock with a 9% interest rate. However, the auto task force told GM those offers were too generous because of the unprecedented and precipitous decline in auto sales. Those previous offers were based on the assumption that auto sales would return to an annual pace of around 11 million cars. Thus far however, they have hovered around 9 million cars per year. As CNBC reported more than a week ago, GM is planning to offer the bondholders no cash, no new debt, and only as much as 20% of the stock of GM, perhaps even as little as 10%. Again, negotiations are ongoing and nothing has been finalized. Terms of a possible offer could change on a daily or even hourly basis.Reuters reports they are considering offering the unions only stock as well. If true, that would sit very poorly with the union, as it needs cash to pay for retiree health care benefits. Doctor visits and MRIs can't be paid for with stock. However, the union retiree benefit plan does have some cash. GM had already paid them $10 billion out of $30 billion owed to them, late last year. Some argue that would justify paying the rest that is owed to them in stock, which down the road they could sell, and use to pay retiree health care benefits.


Trump: 'Mission accomplished' on 'perfectly executed' Syria strike

cnbc, Articles, George W. Bush, Vladimir Putin, Donald Trump, Chemical weapons, Syria, Politics, Europe News, World News, US: News, source:tagname:CNBC US Source

,

President Donald Trump hailed the U.S.-led intervention in Syria as "perfectly executed," adding that the military campaign to degrade Bashar Assad's chemical weapons capability had accomplished its goals.

Less than a day after U.S., British and French forces targeted suspected chemical weapons sites in retaliation to an attack that left dozens of civilians dead last week, Trump thanked the U.S. coalition partners.

Yet in an echo of former president George W. Bush, Trump used words that ultimately came back to haunt his predecessor, by pronouncing "Mission Accomplished." That characterization raised questions about whether Western forces would intervene again if Assad used chemical weapons again, or if the conflict escalated amid Russia's growing bellicosity.

"A perfectly executed strike last night. Thank you to France and the United Kingdom for their wisdom and the power of their fine Military. Could not have had a better result. Mission Accomplished!" Trump said in a Twitter post.

Defense Secretary James Mattis called the strikes a "one time shot" aimed at the Syrian government's chemical weapons infrastructure.

"Clearly, the Assad regime did not get the message last year," Mattis told reporters on Friday from the Pentagon.

"Together we have sent a clear message to Assad and his murderous lieutenants that they should not perpetrate another chemical weapons attack for which they will be held accountable."

Back in May 2003, Bush prematurely declared the Iraq war as being over, in the wake of U.S. forces successfully toppling the government of Saddam Hussein. The event, staged on a U.S. bomber under a massive banner that screamed "Mission Accomplished," overshadowed the years of conflict and bloodshed that followed.

In a briefing on Saturday, Pentagon officials also described the Syria bombing as having successfully accomplished its goals.

Trump's declaration came as Moscow, which is backing Syria in its long civil conflict, has denounced the bombing campaign with undisguised contempt. In the wake of Friday's strike, Russia's ambassador to the U.S. warned of "consequences," while Russian President Vladimir Putin reportedly called the intervention an "act of aggression."

--CNBC's Amanda Macias contributed to this article.

President Donald Trump hailed the U.S.-led intervention in Syria as "perfectly executed," adding that the military campaign to degrade Bashar Assad's chemical weapons capability had accomplished its goals.Less than a day after U.S., British and French forces targeted suspected chemical weapons sites in retaliation to an attack that left dozens of civilians dead last week, Trump thanked the U.S. coalition partners.Yet in an echo of former president George W. Bush, Trump used words that ultimately came back to haunt his predecessor, by pronouncing "Mission Accomplished." That characterization raised questions about whether Western forces would intervene again if Assad used chemical weapons again, or if the conflict escalated amid Russia's growing bellicosity."A perfectly executed strike last night. Thank you to France and the United Kingdom for their wisdom and the power of their fine Military. Could not have had a better result. Mission Accomplished!" Trump said in a Twitter post.Defense Secretary James Mattis called the strikes a "one time shot" aimed at the Syrian government's chemical weapons infrastructure."Clearly, the Assad regime did not get the message last year," Mattis told reporters on Friday from the Pentagon."Together we have sent a clear message to Assad and his murderous lieutenants that they should not perpetrate another chemical weapons attack for which they will be held accountable."Back in May 2003, Bush prematurely declared the Iraq war as being over, in the wake of U.S. forces successfully toppling the government of Saddam Hussein. The event, staged on a U.S. bomber under a massive banner that screamed "Mission Accomplished," overshadowed the years of conflict and bloodshed that followed.In a briefing on Saturday, Pentagon officials also described the Syria bombing as having successfully accomplished its goals. Trump's declaration came as Moscow, which is backing Syria in its long civil conflict, has denounced the bombing campaign with undisguised contempt. In the wake of Friday's strike, Russia's ambassador to the U.S. warned of "consequences," while Russian President Vladimir Putin reportedly called the intervention an "act of aggression."--CNBC's Amanda Macias contributed to this article.


22. Hexadite

Founders: Eran Barak, Barak Klinghofer (chief product officer), Idan Levin (CTO) Launched: 2014 Headquarters: Boston Funding: $10.5 million   The three founders of Hexadite, a Boston-based cybersecurity firm, have backgrounds in investigating and mitigating attacks for military intelligence units and global defense companies. They knew that these same kinds of automated cyberattacks were plaguing a wide variety of companies, leaving them unprepared when responding to a breach or, worse, preventing a future attack.Hexadite uses artificial intelligence to investigate and remediate every alert a company receives, claiming it can resolve incidents in seconds, freeing up a company's response team to focus on threats that truly need their expertise. Hexadite's Automated Incident Response Solution platform can integrate with customers' existing security technologies and can cut response time by 95 percent, the company says.Co-founder and CEO Eran Barak spent five years in an elite intelligence unit of the Israel Defense Forces before starting Hexadite in 2014 and was also head of Elbit Systems' cybertraining and simulation team. Late last year the company formed an alliance with other cybersecurity companies across the country, including Carbon Black, Check Point Software Technologies and Hewlett Packard Enterprise, to better integrate their products and automate important security processes.The goal of the alliance, explains Barak, is to utilize software to lighten the workload of security teams so that they can focus their time on the most dangerous and complex threats. The company has raised $10.5 million in funding from investors, including Hewlett Packard Ventures and TenEleven Ventures.

Articles, Technology, CNBC Upstart 2018, Boston, Special Reports, Crime, Hardware, Start-ups, Cybersecurity, CNBC Upstart, source:tagname:CNBC US Source


Fewer investors have a 'fear of missing out,' so it may be time to buckle up, market bull suggests

Oppenheimer Asset Management's John Stoltzfus suggests investors may want to fasten their seat belts.The long-time bull expects a wave of near-term volatility to pressure stocks. He's blaming uncertainty surrounding this week's Federal Reserve decision on interest rates, another big batch of second quarter earnings results and a fresh round of U.S.-China trade talks."Any kind of disappointment in a sense, okay, I can kind of take profits today without FOMO [fear of missing out] gripping my soul," the firm's chief investment strategist told CNBC's "Futures Now" last Thursday.With the fear of missing out gripping fewer investors, Stoltzfus contends it's not the time to get aggressive."We're highly selective at this point with the S&P 500 up over 20%," he said.The index, which closed the week at new record highs, has already surpassed Stoltzfus' year-end target of 2,860 by almost 6%. He's planning to tweak the number once there's more visibility."It's under revision at this time. We're going to wait until after the Fed makes its decision on the 31st," he said "At that point, we'll put in our new target for the year-end, depending on how that goes."Stoltzfus notes a more favorable development in the U.S.-China trade war would be an integral part of his bull case. He believes it's the biggest headwind holding back stocks from ripping even higher."It would be confirmation that indeed the markets thought things weren't so bad, were actually pretty good," he said.For now, Stoltzfus is bracing for a 3 to 4% pullback to strike stocks near term. He'd look to add cyclical names such as consumer discretionary, industrials, financials and technology on weakness."Tech looks like a great place to be because technology flows into all of the eleven sectors. Every business needs technology," Stoltzfus said.

cnbc, Articles, S&P 500 Fut (Dec'21), Technology Select Sector SPDR Fund, S&P 500 Industrials Sector, Consumer Discretionary Select Sector SPDR Fund, Wall Street, Markets, Investment strategy, Commodity markets, Futures & Commodities, Futures Now, Futures, Finance, Investing, CNBC TV, source:tagname:CNBC US Source

Oppenheimer Asset Management's John Stoltzfus suggests investors may want to fasten their seat belts.

The long-time bull expects a wave of near-term volatility to pressure stocks. He's blaming uncertainty surrounding this week's Federal Reserve decision on interest rates, another big batch of second quarter earnings results and a fresh round of U.S.-China trade talks.

"Any kind of disappointment in a sense, okay, I can kind of take profits today without FOMO [fear of missing out] gripping my soul," the firm's chief investment strategist told CNBC's "Futures Now" last Thursday.

With the fear of missing out gripping fewer investors, Stoltzfus contends it's not the time to get aggressive.

"We're highly selective at this point with the S&P 500 up over 20%," he said.

The index, which closed the week at new record highs, has already surpassed Stoltzfus' year-end target of 2,860 by almost 6%. He's planning to tweak the number once there's more visibility.

"It's under revision at this time. We're going to wait until after the Fed makes its decision on the 31st," he said "At that point, we'll put in our new target for the year-end, depending on how that goes."

Stoltzfus notes a more favorable development in the U.S.-China trade war would be an integral part of his bull case. He believes it's the biggest headwind holding back stocks from ripping even higher.

"It would be confirmation that indeed the markets thought things weren't so bad, were actually pretty good," he said.

For now, Stoltzfus is bracing for a 3 to 4% pullback to strike stocks near term. He'd look to add cyclical names such as consumer discretionary, industrials, financials and technology on weakness.

"Tech looks like a great place to be because technology flows into all of the eleven sectors. Every business needs technology," Stoltzfus said.

,


['aarhus airport', 'city served', ' aarhus  denmark ']

the aarhus is the airport of aarhus  denmark.


aarhus airport serves the city of aarhus  denmark.


['aarhus airport', 'city served', 'aarhus']

aarhus airport serves the city of aarhus.


['aarhus airport', 'elevation above the sea level', '25.0']

aarhus airport is 25 metres above sea level.


aarhus airport is at an elevation of 25 metres above seal level.


aarhus airport is 25.0 metres above the sea level.


['aarhus airport', 'location', 'tirstrup']

aarhus airport is located in tirstrup.


the location of aarhus airport is tirstrup.


['aarhus airport', 'operating organisation', ' aarhus lufthavn a/s ']

aarhus airport is operated by aarhus lufthavn a/s.


aarhus lufthavn a/s is the operation organisation of aarhus airport.


Fashion MNIST dataset, an alternative to MNIST


load_data function


Loads the Fashion-MNIST dataset.


This is a dataset of 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images. This dataset can be used as a drop-in replacement for MNIST.


The classes are:


i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake


im grabbing a minute to post i feel greedy wrong


i am ever feeling nostalgic about the fireplace i will know that it is still on the property


i am feeling grouchy


ive been feeling a little burdened lately wasnt sure why that was


ive been taking or milligrams or times recommended amount and ive fallen asleep a lot faster but i also feel like so funny


i feel as confused about life as a teenager or as jaded as a year old man


i have been with petronas for years i feel that petronas has performed well and made a huge profit


Upto freebets lol gunners matchedbetting c racing cheltenhamfestival


Leon bailey or Federico cheisa


>

<shape xmlns:android="http://schemas.android.com/apk/res/android"
    android:shape="line">
    <stroke android:color="#CCC" />
    <size android:height="1dp" />
</shape>


The hydrodynamic basis of the vacuum cleaner effect in continuous-flow PCNL instruments: an empiric approach and mathematical model.

Passive removal of stone fragments in the irrigation stream is one of the characteristics in continuous-flow PCNL instruments.

So far the physical principle of this so-called vacuum cleaner effect has not been fully understood yet.

The aim of the study was to empirically prove the existence of the vacuum cleaner effect and to develop a physical hypothesis and generate a mathematical model for this phenomenon.

In an empiric approach, common low-pressure PCNL instruments and conventional PCNL sheaths were tested using an in vitro model.

Flow characteristics were visualized by coloring of irrigation fluid.

Influence of irrigation pressure, sheath diameter, sheath design, nephroscope design and position of the nephroscope was assessed.

Experiments were digitally recorded for further slow-motion analysis to deduce a physical model.

In each tested nephroscope design, we could observe the vacuum cleaner effect.

Increase in irrigation pressure and reduction in cross section of sheath sustained the effect.

Slow-motion analysis of colored flow revealed a synergism of two effects causing suction and transportation of the stone.

For the first time, our model showed a flow reversal in the sheath as an integral part of the origin of the stone transportation during vacuum cleaner effect.

The application of Bernoulli's equation provided the explanation of these effects and confirmed our experimental results.

We widen the understanding of PCNL with a conclusive physical model, which explains fluid mechanics of the vacuum cleaner effect.


DNA sequence and muscle-specific expression of human sarcosin transcripts.

We have characterized two abundant human cDNAs which, through Northern hybridization analysis, are selectively expressed in human sarcomeric muscle.

DNA sequencing was performed and the two cDNAs were found to share sequence identity, with the exception of a 3' UTR extension present on the longer transcript.

Our data suggest that the two transcripts are generated through alternative use of two poly(A) addition signals.

The cDNAs encode a large open reading frame encompassing at least 435 codons.

Through sequence comparisons, both at the DNA and predicted amino acid sequence level, we have been unable to find significant sequence similarity to any other characterized sequence.

Consequently, we have termed this novel human sequence sarcosin.

Although novel, Southern hybridization analysis demonstrated that the sarcosin sequence has been conserved in several mammalian species.


Partnering with patients--a concept ready for action.

This paper is a report of a concept analysis of partnership within the context of a professional-patient relationship.

The concept of partnership has been previously characterized as immature, with a need for further consensus and consistency.

Critical attributes previously reported include relationship, power sharing and negotiation, with empowerment as the primary consequence.

Rodgers' evolutionary method of analysis for concept development was used to re-examine the concept of partnership.

Historical documents and previously published conceptual papers were reviewed for context.

A search of multidisciplinary literature published between 2000 and 2004 was undertaken using the keywords of 'partnership' and 'partnering', combined with nurse/professional/physician-client relationship.

Attributes, uses, antecedents and consequences were inductively derived from the citations analysed (n = 62).

Previous authors affirmed that partnership involves a process and a consistent set of eight attributes.

Relationship, shared power, shared decision-making and patient autonomy are attributes that distinguish partnership from other related concepts.

Most of the literature, however, consisted of expert opinion or descriptive research.

Little progress has been made in applying theory, developing tools to test the process, identifying when partnerships are needed and what specific outcomes occur when they are present.

Partnerships between healthcare providers and patients develop over time.

They are created to support patients in having a greater voice in their care and to empower them in self-management.

A descriptive model of partnership is proposed to support researchers in generating or applying existing theory to the development of research designs and tools that could test how this process actually works.


First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labeled examples. Given these sizes, it should be possible to train models quickly on any machine.

url = 'https://commondatastorage.googleapis.com/books1000/'
last_percent_reported = None
data_root = '.' # Change me to store data elsewhere

def download_progress_hook(count, blockSize, totalSize):
  """A hook to report the progress of a download. This is mostly intended for users with
  slow internet connections. Reports every 5% change in download progress.
  """
  global last_percent_reported
  percent = int(count * blockSize * 100 / totalSize)

  if last_percent_reported != percent:
    if percent % 5 == 0:
      sys.stdout.write("%s%%" % percent)
      sys.stdout.flush()
    else:
      sys.stdout.write(".")
      sys.stdout.flush()

    last_percent_reported = percent

def maybe_download(filename, expected_bytes, force=False):
  """Download a file if not present, and make sure it's the right size."""
  dest_filename = os.path.join(data_root, filename)
  if force or not os.path.exists(dest_filename):
    print('Attempting to download:', filename)
    filename, _ = urlretrieve(url + filename, dest_filename, reporthook=download_progress_hook)
    print('\nDownload Complete!')
  statinfo = os.stat(dest_filename)
  if statinfo.st_size == expected_bytes:
    print('Found and verified', dest_filename)
  else:
    raise Exception(
      'Failed to verify ' + dest_filename + '. Can you get to it with a browser?')
  return dest_filename

train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)
test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)


Extract the dataset from the compressed .tar.gz file.
This should give you a set of directories, labeled A through J.

num_classes = 10
np.random.seed(133)

def maybe_extract(filename, force=False):
  root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz
  if os.path.isdir(root) and not force:
    # You may override by setting force=True.
    print('%s already present - Skipping extraction of %s.' % (root, filename))
  else:
    print('Extracting data for %s. This may take a while. Please wait.' % root)
    tar = tarfile.open(filename)
    sys.stdout.flush()
    tar.extractall(data_root)
    tar.close()
  data_folders = [
    os.path.join(root, d) for d in sorted(os.listdir(root))
    if os.path.isdir(os.path.join(root, d))]
  if len(data_folders) != num_classes:
    raise Exception(
                                                                                'Expected %d folders, one per class. Found %d instead.' % (
        num_classes, len(data_folders)))
  print(data_folders)
  return data_folders

train_folders = maybe_extract(train_filename)
test_folders = maybe_extract(test_filename)


Problem 1
Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.

# Solution for Problem 1
import random
print('Displaying images of train folders')
# Looping through train folders and displaying a random image of each folder
for path in train_folders:
    image_file = os.path.join(path, random.choice(os.listdir(path)))
    display(Image(filename=image_file))

print('Displaying images of test folders')
# Looping through train folders and displaying a random image of each folder
for path in test_folders:
    image_file = os.path.join(path, random.choice(os.listdir(path)))
    display(Image(filename=image_file))


Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.
We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road.
A few images might not be readable, we'll just skip them.

image_size = 28  # Pixel width and height.
pixel_depth = 255.0  # Number of levels per pixel.

def load_letter(folder, min_num_images):
  """Load the data for a single letter label."""
  image_files = os.listdir(folder)
  dataset = np.ndarray(shape=(len(image_files), image_size, image_size),
                         dtype=np.float32)
  print(folder)
  num_images = 0
  for image in image_files:
    image_file = os.path.join(folder, image)
    try:
      image_data = (ndimage.imread(image_file).astype(float) -
                    pixel_depth / 2) / pixel_depth
      if image_data.shape != (image_size, image_size):
        raise Exception('Unexpected image shape: %s' % str(image_data.shape))
      dataset[num_images, :, :] = image_data
      num_images = num_images + 1
    except IOError as e:
      print('Could not read:', image_file, ':', e, '- it\'s ok, skipping.')

  dataset = dataset[0:num_images, :, :]
  if num_images < min_num_images:
    raise Exception('Many fewer images than expected: %d < %d' %
                    (num_images, min_num_images))

  print('Full dataset tensor:', dataset.shape)
  print('Mean:', np.mean(dataset))
  print('Standard deviation:', np.std(dataset))
  return dataset

def maybe_pickle(data_folders, min_num_images_per_class, force=False):
  dataset_names = []
  for folder in data_folders:
    set_filename = folder + '.pickle'
    dataset_names.append(set_filename)
    if os.path.exists(set_filename) and not force:
      # You may override by setting force=True.
      print('%s already present - Skipping pickling.' % set_filename)
    else:
      print('Pickling %s.' % set_filename)
      dataset = load_letter(folder, min_num_images_per_class)
      try:
        with open(set_filename, 'wb') as f:
          pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)
      except Exception as e:
        print('Unable to save data to', set_filename, ':', e)

  return dataset_names

train_datasets = maybe_pickle(train_folders, 45000)
test_datasets = maybe_pickle(test_folders, 1800)


Problem 2
Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.

# Solution for Problem 2
def show_first_image(datasets):
      for pickl in datasets:
        print('Showing a first image from pickle ', pickl)
        try:
            with open(pickl, 'rb') as f:
                letter_set = pickle.load(f)
                plt.imshow(letter_set[0])
        except Exception as e:
            print('Unable to show image from pickle ', pickl, ':', e)
            raise
print('From Training dataset')
show_first_image(train_datasets)
print('From Test Dataset')
show_first_image(test_datasets)


Problem 3
Another check: we expect the data to be balanced across classes. Verify that.

def show_dataset_shape(datasets):
    for pickl in datasets:
        try:
            with open(pickl, 'rb') as f:
                letter_set = pickle.load(f)
                print('Shape of pickle ', pickl, 'is', np.shape(letter_set))
        except Exception as e:
            print('Unable to show image from pickle ', pickl, ':', e)
            raise

print('Shape for Training set')
show_dataset_shape(train_datasets)
print('Shape for Test set')
show_dataset_shape(test_datasets)


Merge and prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune train_size as needed. The labels will be stored into a separate array of integers 0 through 9.
Also create a validation dataset for hyperparameter tuning.

def make_arrays(nb_rows, img_size):
  if nb_rows:
    dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)
    labels = np.ndarray(nb_rows, dtype=np.int32)
  else:
    dataset, labels = None, None
  return dataset, labels

def merge_datasets(pickle_files, train_size, valid_size=0):
  num_classes = len(pickle_files)
  valid_dataset, valid_labels = make_arrays(valid_size, image_size)
  train_dataset, train_labels = make_arrays(train_size, image_size)
  vsize_per_class = valid_size // num_classes
  tsize_per_class = train_size // num_classes

  start_v, start_t = 0, 0
  end_v, end_t = vsize_per_class, tsize_per_class
  end_l = vsize_per_class+tsize_per_class
  for label, pickle_file in enumerate(pickle_files):
    try:
      with open(pickle_file, 'rb') as f:
        letter_set = pickle.load(f)
        # let's shuffle the letters to have random validation and training set
        np.random.shuffle(letter_set)
        if valid_dataset is not None:
          valid_letter = letter_set[:vsize_per_class, :, :]
          valid_dataset[start_v:end_v, :, :] = valid_letter
          valid_labels[start_v:end_v] = label
          start_v += vsize_per_class
          end_v += vsize_per_class

        train_letter = letter_set[vsize_per_class:end_l, :, :]
        train_dataset[start_t:end_t, :, :] = train_letter
        train_labels[start_t:end_t] = label
        start_t += tsize_per_class
        end_t += tsize_per_class
    except Exception as e:
      print('Unable to process data from', pickle_file, ':', e)
      raise

  return valid_dataset, valid_labels, train_dataset, train_labels

"""
train_size = 200000
valid_size = 10000
test_size = 10000
"""
train_size = 20000
valid_size = 1000
test_size = 1000

valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(
  train_datasets, train_size, valid_size)
_, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size)

print('Training:', train_dataset.shape, train_labels.shape)
print('Validation:', valid_dataset.shape, valid_labels.shape)
print('Testing:', test_dataset.shape, test_labels.shape)


Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match.

def randomize(dataset, labels):
  permutation = np.random.permutation(labels.shape[0])
  shuffled_dataset = dataset[permutation,:,:]
  shuffled_labels = labels[permutation]
  return shuffled_dataset, shuffled_labels
train_dataset, train_labels = randomize(train_dataset, train_labels)
test_dataset, test_labels = randomize(test_dataset, test_labels)
valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)


Problem 4
Convince yourself that the data is still good after shuffling!

print('Printing Train, validation and test labels after shuffling')
def print_first_10_labels(labels):
    printing_labels = []
    for i in range(10):
        printing_labels.append(labels[[i]])
    print(printing_labels)
print_first_10_labels(train_labels)
print_first_10_labels(test_labels)
print_first_10_labels(valid_labels)


Finally, let's save the data for later reuse:

pickle_file = os.path.join(data_root, 'notMNIST.pickle')

try:
  f = open(pickle_file, 'wb')
  save = {
    'train_dataset': train_dataset,
    'train_labels': train_labels,
    'valid_dataset': valid_dataset,
    'valid_labels': valid_labels,
    'test_dataset': test_dataset,
    'test_labels': test_labels,
    }
  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)
  f.close()
except Exception as e:
  print('Unable to save data to', pickle_file, ':', e)
  raise

statinfo = os.stat(pickle_file)
print('Compressed pickle size:', statinfo.st_size)


they just said it again i am dying .


i am a yankee by birth and lived much of my life in north i see no diff in level of racism .


jay z verse trash .


friendlyassh0le i miss my bf well i miss sooo shut the fuck up you whiny bitch .


send yall bitch asses fishing with us ikesizzle we aint beat san antonio all season i dont wanna play them nigguhs straightup .


not exactly the part i was but .


can they just send kat and andre home anyway because theyre rude and annoying and score everyone strategically mkr .


i know it was that dana whore if i get rid of them rn will you donate one to me8221 that bitch 8230 .


shy glizzy sound dumb as shit you know nothing bout .


Stop, drop
And drag me into place
And lock the fire escapes
Ill break your pretty face
Oh, you clever little things
The sycophantic teens
What a precious basket case
Now shut your dirty mouth
If I could burn this town
I wouldnt hesitate
To smile while you suffocate and die
And that would be just fine
And what a lovely time
That it would surely be
So bite your tongue
And choke yourself to sleep
You get everything you want
And money always talks
To the idiot savants
Now shut your dirty mouth
If I could burn this town
I wouldnt hesitate
To smile while you suffocate and die
And that would be just fine
And what a lovely time
That it would surely be
So bite your tongue
And choke yourself to sleep
Now shut your dirty mouth
If I could burn this town
I wouldnt hesitate
To smile while you suffocate and die
And that would be just fine, oh
And what a lovely time
That it would surely be
So bite your tongue
And choke yourself


Ha!
Were taking over the world
One kiss at a time
And then Im taking your girl
And Im making her mine
No reason why
Im only doing anything I want to do
Because I do it all the time
Were taking over the world
A little victimless crime
And when Im taking your innocence
Ill be corrupting your mind
No need to cry
Im only doing anything I want to do
Because I do it all the time
Now were so young, but were probably gonna die
Its so fun, were so good at selling lies
We look so good and we never even try
Get your money from a trust fund
Do it all the time
Ha!
Were taking over the world
One kiss at a time
And then Im taking your girl
And Im making her mine, Im making her mine
No reason why
Im only doing anything I want to do
Now were so young, but were probably gonna die
And its so fun, were so good at selling lies
We look so good and we never even try
I dont wanna do it, but Ill do it cause I do it all the time
Now were so young, but were probably gonna die
Its so fun, were so good at selling lies
We look so good and we never even try
Get your money from a trust fund
Do it all the time


Dance
Dance
Thats enough
A modern concussion
The room is on fire
Youre an upstanding model
Of a modern day Cain
With impeccable style
But now youve done a little wrong
And you need to be forgiven
By the vicar and the company you keep
And then you conjure up a fiction
To get the pretty girl to listen
This is the sin
That I will confess to release myself
From consequence
And everyone can tell
The moral objections
To something so profane
But the deepest of convictions
Are the darkest positions
Little remissions for the varicose vain
Now youve done a little wrong
And you swear you didnt do it
But volition left you burdened with a curse
And then you conjure up a fiction
To get the pretty girls to listen
This is, the sin, that I
Will confess to release myself
From consequence
And everyone can tell
This is, the sin, that I
Will confess to release myself
From consequence
And everyone can tell
This is, the sin, that I
Will confess to release myself
From consequence
And everyone can tell
And everyone can tell
And everyone can tell


The full cost of damage in Newton Stewart, one of the areas worst affected, is still being assessed.
Repair work is ongoing in Hawick and many roads in Peeblesshire remain badly affected by standing water.
Trains on the west coast mainline face disruption due to damage at the Lamington Viaduct.
Many businesses and householders were affected by flooding in Newton Stewart after the River Cree overflowed into the town.
First Minister Nicola Sturgeon visited the area to inspect the damage.
The waters breached a retaining wall, flooding many commercial properties on Victoria Street - the main shopping thoroughfare.
Jeanette Tate, who owns the Cinnamon Cafe which was badly affected, said she could not fault the multi-agency response once the flood hit.
However, she said more preventative work could have been carried out to ensure the retaining wall did not fail.
"It is difficult but I do think there is so much publicity for Dumfries and the Nith - and I totally appreciate that - but it is almost like we're neglected or forgotten," she said.
"That may not be true but it is perhaps my perspective over the last few days.
"Why were you not ready to help us a bit more when the warning and the alarm alerts had gone out?"
Meanwhile, a flood alert remains in place across the Borders because of the constant rain.
Peebles was badly hit by problems, sparking calls to introduce more defences in the area.
Scottish Borders Council has put a list on its website of the roads worst affected and drivers have been urged not to ignore closure signs.
The Labour Party's deputy Scottish leader Alex Rowley was in Hawick on Monday to see the situation first hand.
He said it was important to get the flood protection plan right but backed calls to speed up the process.
"I was quite taken aback by the amount of damage that has been done," he said.
"Obviously it is heart-breaking for people who have been forced out of their homes and the impact on businesses."
He said it was important that "immediate steps" were taken to protect the areas most vulnerable and a clear timetable put in place for flood prevention plans.
Have you been affected by flooding in Dumfries and Galloway or the Borders? Tell us about your experience of the situation and how it was handled. Email us on selkirk.news@bbc.co.uk or dumfries@bbc.co.uk.

Summary: Clean-up operations are continuing across the Scottish Borders and Dumfries and Galloway after flooding caused by Storm Frank.


A fire alarm went off at the Holiday Inn in Hope Street at about 04:20 BST on Saturday and guests were asked to leave the hotel.
As they gathered outside they saw the two buses, parked side-by-side in the car park, engulfed by flames.
One of the tour groups is from Germany, the other from China and Taiwan. It was their first night in Northern Ireland.
The driver of one of the buses said many of the passengers had left personal belongings on board and these had been destroyed.
Both groups have organised replacement coaches and will begin their tour of the north coast later than they had planned.
Police have appealed for information about the attack.
Insp David Gibson said: "It appears as though the fire started under one of the buses before spreading to the second.
"While the exact cause is still under investigation, it is thought that the fire was started deliberately."

Summary: Two tourist buses have been destroyed by fire in a suspected arson attack in Belfast city centre.


Ferrari appeared in a position to challenge until the final laps, when the Mercedes stretched their legs to go half a second clear of the red cars.
Sebastian Vettel will start third ahead of team-mate Kimi Raikkonen.
The world champion subsequently escaped punishment for reversing in the pit lane, which could have seen him stripped of pole.
But stewards only handed Hamilton a reprimand, after governing body the FIA said "no clear instruction was given on where he should park".
Belgian Stoffel Vandoorne out-qualified McLaren team-mate Jenson Button on his Formula 1 debut.
Vandoorne was 12th and Button 14th, complaining of a handling imbalance on his final lap but admitting the newcomer "did a good job and I didn't".
Mercedes were wary of Ferrari's pace before qualifying after Vettel and Raikkonen finished one-two in final practice, and their concerns appeared to be well founded as the red cars mixed it with the silver through most of qualifying.
After the first runs, Rosberg was ahead, with Vettel and Raikkonen splitting him from Hamilton, who made a mistake at the final corner on his first lap.
But Hamilton saved his best for last, fastest in every sector of his final attempt, to beat Rosberg by just 0.077secs after the German had out-paced him throughout practice and in the first qualifying session.
Vettel rued a mistake at the final corner on his last lap, but the truth is that with the gap at 0.517secs to Hamilton there was nothing he could have done.
The gap suggests Mercedes are favourites for the race, even if Ferrari can be expected to push them.
Vettel said: "Last year we were very strong in the race and I think we are in good shape for tomorrow. We will try to give them a hard time."
Vandoorne's preparations for his grand prix debut were far from ideal - he only found out he was racing on Thursday when FIA doctors declared Fernando Alonso unfit because of a broken rib sustained in his huge crash at the first race of the season in Australia two weeks ago.
The Belgian rookie had to fly overnight from Japan, where he had been testing in the Super Formula car he races there, and arrived in Bahrain only hours before first practice on Friday.
He also had a difficult final practice, missing all but the final quarter of the session because of a water leak.
Button was quicker in the first qualifying session, but Vandoorne pipped him by 0.064secs when it mattered.
The 24-year-old said: "I knew after yesterday I had quite similar pace to Jenson and I knew if I improved a little bit I could maybe challenge him and even out-qualify him and that is what has happened.
"Jenson is a very good benchmark for me because he is a world champion and he is well known to the team so I am very satisfied with the qualifying."
Button, who was 0.5secs quicker than Vandoorne in the first session, complained of oversteer on his final run in the second: "Q1 was what I was expecting. Q2 he did a good job and I didn't. Very, very good job. We knew how quick he was."
The controversial new elimination qualifying system was retained for this race despite teams voting at the first race in Australia to go back to the 2015 system.
FIA president Jean Todt said earlier on Saturday that he "felt it necessary to give new qualifying one more chance", adding: "We live in a world where there is too much over reaction."
The system worked on the basis of mixing up the grid a little - Force India's Sergio Perez ended up out of position in 18th place after the team miscalculated the timing of his final run, leaving him not enough time to complete it before the elimination clock timed him out.
But it will come in for more criticism as a result of lack of track action at the end of each session. There were three minutes at the end of the first session with no cars on the circuit, and the end of the second session was a similar damp squib.
Only one car - Nico Hulkenberg's Force India - was out on the track with six minutes to go. The two Williams cars did go out in the final three minutes but were already through to Q3 and so nothing was at stake.
The teams are meeting with Todt and F1 commercial boss Bernie Ecclestone on Sunday at noon local time to decide on what to do with qualifying for the rest of the season.
Todt said he was "optimistic" they would be able to reach unanimous agreement on a change.
"We should listen to the people watching on TV," Rosberg said. "If they are still unhappy, which I am sure they will be, we should change it."
Red Bull's Daniel Ricciardo was fifth on the grid, ahead of the Williams cars of Valtteri Bottas and Felipe Massa and Force India's Nico Hulkenberg.
Ricciardo's team-mate Daniil Kvyat was eliminated during the second session - way below the team's expectation - and the Renault of Brit Jolyon Palmer only managed 19th fastest.
German Mercedes protege Pascal Wehrlein managed an excellent 16th in the Manor car.
Bahrain GP qualifying results
Bahrain GP coverage details

Summary: Lewis Hamilton stormed to pole position at the Bahrain Grand Prix ahead of Mercedes team-mate Nico Rosberg.


John Edward Bates, formerly of Spalding, Lincolnshire, but now living in London, faces a total of 22 charges, including two counts of indecency with a child.
The 67-year-old is accused of committing the offences between March 1972 and October 1989.
Mr Bates denies all the charges.
Grace Hale, prosecuting, told the jury that the allegations of sexual abuse were made by made by four male complainants and related to when Mr Bates was a scout leader in South Lincolnshire and Cambridgeshire.
"The defendant says nothing of that sort happened between himself and all these individuals. He says they are all fabricating their accounts and telling lies," said Mrs Hale.
The prosecutor claimed Mr Bates invited one 15 year old to his home offering him the chance to look at cine films made at scout camps but then showed him pornographic films.
She told the jury that the boy was then sexually abused leaving him confused and frightened.
Mrs Hale said: "The complainant's recollection is that on a number of occasions sexual acts would happen with the defendant either in the defendant's car or in his cottage."
She told the jury a second boy was taken by Mr Bates for a weekend in London at the age of 13 or 14 and after visiting pubs he was later sexually abused.
Mrs Hale said two boys from the Spalding group had also made complaints of being sexually abused.
The jury has been told that Mr Bates was in the RAF before serving as a Lincolnshire Police officer between 1976 and 1983.
The trial, which is expected to last two weeks, continues.

Summary: A former Lincolnshire Police officer carried out a series of sex attacks on boys, a jury at Lincoln Crown Court was told.


Patients and staff were evacuated from Cerahpasa hospital on Wednesday after a man receiving treatment at the clinic threatened to shoot himself and others.
Officers were deployed to negotiate with the man, a young police officer.
Earlier reports that the armed man had taken several people hostage proved incorrect.
The chief consultant of Cerahpasa hospital, Zekayi Kutlubay, who was evacuated from the facility, said that there had been "no hostage crises", adding that the man was "alone in the room".
Dr Kutlubay said that the man had been receiving psychiatric treatment for the past two years.
He said that the hospital had previously submitted a report stating that the man should not be permitted to carry a gun.
"His firearm was taken away," Dr Kutlubay said, adding that the gun in the officer's possession on Wednesday was not his issued firearm.
The incident comes amid tension in Istanbul following several attacks in crowded areas, including the deadly assault on the Reina nightclub on New Year's Eve which left 39 people dead.

Summary: An armed man who locked himself into a room at a psychiatric hospital in Istanbul has ended his threat to kill himself, Turkish media report.


Simone Favaro got the crucial try with the last move of the game, following earlier touchdowns by Chris Fusaro, Zander Fagerson and Junior Bulumakau.
Rynard Landman and Ashton Hewitt got a try in either half for the Dragons.
Glasgow showed far superior strength in depth as they took control of a messy match in the second period.
Home coach Gregor Townsend gave a debut to powerhouse Fijian-born Wallaby wing Taqele Naiyaravoro, and centre Alex Dunbar returned from long-term injury, while the Dragons gave first starts of the season to wing Aled Brew and hooker Elliot Dee.
Glasgow lost hooker Pat McArthur to an early shoulder injury but took advantage of their first pressure when Rory Clegg slotted over a penalty on 12 minutes.
It took 24 minutes for a disjointed game to produce a try as Sarel Pretorius sniped from close range and Landman forced his way over for Jason Tovey to convert - although it was the lock's last contribution as he departed with a chest injury shortly afterwards.
Glasgow struck back when Fusaro drove over from a rolling maul on 35 minutes for Clegg to convert.
But the Dragons levelled at 10-10 before half-time when Naiyaravoro was yellow-carded for an aerial tackle on Brew and Tovey slotted the easy goal.
The visitors could not make the most of their one-man advantage after the break as their error count cost them dearly.
It was Glasgow's bench experience that showed when Mike Blair's break led to a short-range score from teenage prop Fagerson, converted by Clegg.
Debutant Favaro was the second home player to be sin-binned, on 63 minutes, but again the Warriors made light of it as replacement wing Bulumakau, a recruit from the Army, pounced to deftly hack through a bouncing ball for an opportunist try.
The Dragons got back within striking range with some excellent combined handling putting Hewitt over unopposed after 72 minutes.
However, Favaro became sinner-turned-saint as he got on the end of another effective rolling maul to earn his side the extra point with the last move of the game, Clegg converting.
Dragons director of rugby Lyn Jones said: "We're disappointed to have lost but our performance was a lot better [than against Leinster] and the game could have gone either way.
"Unfortunately too many errors behind the scrum cost us a great deal, though from where we were a fortnight ago in Dublin our workrate and desire was excellent.
"It was simply error count from individuals behind the scrum that cost us field position, it's not rocket science - they were correct in how they played and we had a few errors, that was the difference."
Glasgow Warriors: Rory Hughes, Taqele Naiyaravoro, Alex Dunbar, Fraser Lyle, Lee Jones, Rory Clegg, Grayson Hart; Alex Allan, Pat MacArthur, Zander Fagerson, Rob Harley (capt), Scott Cummings, Hugh Blake, Chris Fusaro, Adam Ashe.
Replacements: Fergus Scott, Jerry Yanuyanutawa, Mike Cusack, Greg Peterson, Simone Favaro, Mike Blair, Gregor Hunter, Junior Bulumakau.
Dragons: Carl Meyer, Ashton Hewitt, Ross Wardle, Adam Warren, Aled Brew, Jason Tovey, Sarel Pretorius; Boris Stankovich, Elliot Dee, Brok Harris, Nick Crosswell, Rynard Landman (capt), Lewis Evans, Nic Cudd, Ed Jackson.
Replacements: Rhys Buckley, Phil Price, Shaun Knight, Matthew Screech, Ollie Griffiths, Luc Jones, Charlie Davies, Nick Scott.

Summary: Defending Pro12 champions Glasgow Warriors bagged a late bonus-point victory over the Dragons despite a host of absentees and two yellow cards.


Veronica Vanessa Chango-Alverez, 31, was killed and another man injured when an Audi A3 struck them in Streatham High Road at 05:30 GMT on Saturday.
Ten minutes before the crash the car was in London Road, Croydon, when a Volkswagen Passat collided with a tree.
Police want to trace Nathan Davis, 27, who they say has links to the Audi. The car was abandoned at the scene.
Ms Chango-Alverez died from multiple injuries, a post-mortem examination found.
No arrests have been made as yet, police said.
Ms Chango-Alverez was staying at her mother's home in Streatham High Road.
She was born in Ecuador and had lived in London for 13 years, BBC London reporter Gareth Furby said. At the time of the crash, she was on her way to work in a hotel.
The remains of the bus stop, which was extensively damaged in the crash, have been removed.
Flowers have been left at the site in tribute to the victim.
A statement from her brother Kevin Raul Chango-Alverez said: "My family has had its heart torn out, at this Christmas time, we will never be the same again.
"On Friday night we were together as a family with Veronica meeting her newly born nephew and preparing for Christmas.
"I last saw her alive as she left to go to work on Saturday morning, but moments later I was holding her hand as she passed away in the street."
Describing the crash as "horrific" Det Insp Gordon Wallace, said: "The family are devastated. The memory of this senseless death will be with them each time they leave their home.
"The driver fled the scene abandoning the grey Audi, which was extensively damaged.
"We are looking to speak to Mr Nathan Davis in relation to this collision."
The 51-year-old man injured at the bus stop remains in a critical condition in hospital while the condition of the 29-year-old driver of the Volkswagen is now stable.

Summary: A man with links to a car that was involved in a fatal bus stop crash in south London is being sought by police.


Belgian cyclist Demoitie died after a collision with a motorbike during Belgium's Gent-Wevelgem race.
The 25-year-old was hit by the motorbike after several riders came down in a crash as the race passed through northern France.
"The main issues come when cars or motorbikes have to pass the peloton and pass riders," Team Sky's Rowe said.
"That is the fundamental issue we're looking into.
"There's a lot of motorbikes in and around the race whether it be cameras for TV, photographers or police motorbikes.
"In total there's around 50 motorbikes that work on each race.
"We've got a riders union and we're coming together to think of a few ideas, whether we cap a speed limit on how fast they can overtake us.
"Say we put a 10 kilometres per hour limit on it, if we're going 50kph they're only allowed to pass us 60kph or something like that."
Demoitie, who was riding for the Wanty-Gobert team, was taken to hospital in Lille but died later.
The sport's governing body, the UCI, said it would co-operate with all relevant authorities in an investigation into the incident.
The Professional Cyclists' Association (CPA) issued a statement asking what would be done to improve safety.
Despite Demoitie's death, attitudes to road racing will stay the same says Rowe, who has been competing in Three Days of De Panne race in Belgium.
"As soon as that element of fear slips into your mind and you start thinking of things that could happen, that's when you're doomed to fail," he told BBC Wales Sport.
"If you start thinking about crashes and the consequences and what could potentially happen then you're never going to be at the front of the peloton and you're never going to win any races."
In a separate incident, another Belgian cyclist, Daan Myngheer, 22, died in hospital after suffering a heart attack during the first stage of the Criterium International in Corsica.

Summary: Welsh cyclist Luke Rowe says changes to the sport must be made following the death of Antoine Demoitie.


The crash happened about 07:20 GMT at the junction of the A127 and Progress Road in Leigh-on-Sea, Essex.
The man, who police said is aged in his 20s, was treated at the scene for a head injury and suspected multiple fractures, the ambulance service said.
He was airlifted to the Royal London Hospital for further treatment.
The Southend-bound carriageway of the A127 was closed for about six hours while police conducted their initial inquiries.
A spokeswoman for Essex Police said it was not possible comment to further as this time as the "investigation is now being conducted by the IPCC".

Summary: A jogger has been hit by an unmarked police car responding to an emergency call, leaving him with "serious life-changing injuries".


Question: oxybutynin chloride 5 extended release film coated tablet is the ingredients of what routed drug?

Oxybutynin Oral
Oxybutynin Chloride Oral

Oxybutynin chloride 5 extended release film coated tablet

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX : <http://rdf.freebase.com/ns/>
SELECT (?x0 AS ?value) WHERE {
SELECT DISTINCT ?x0  WHERE {
?x0 :type.object.type :medicine.routed_drug .
VALUES ?x1 { :m.0hqs1x_ }
?x0 :medicine.routed_drug.marketed_formulations ?x1 .
FILTER ( ?x0 != ?x1  )
}
}

(AND medicine.routed_drug (JOIN medicine.routed_drug.marketed_formulations m.0hqs1x_))


Question: the leaders of the earliest established religious organization are given what title?

Religious Leadership Title
Religious Organization Leadership
0

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX : <http://rdf.freebase.com/ns/>
SELECT (?x0 AS ?value) WHERE {
SELECT DISTINCT ?x0  WHERE {
?x0 :type.object.type :religion.religious_leadership_title .
?x1 :type.object.type :religion.religious_organization_leadership .
{
SELECT (MIN(?y2) AS ?x2)  WHERE {
?y0 :type.object.type :religion.religious_leadership_title .
?y1 :type.object.type :religion.religious_organization_leadership .
?y0 :religion.religious_leadership_title.leaders ?y1 .
?y1 :religion.religious_organization_leadership.start_date ?y2 .
FILTER ( ?y0 != ?y1 && ?y0 != ?y2 && ?y1 != ?y2  )
}
}
?x0 :religion.religious_leadership_title.leaders ?x1 .
?x1 :religion.religious_organization_leadership.start_date ?x2 .
FILTER ( ?x0 != ?x1 && ?x0 != ?x2 && ?x1 != ?x2  )
}
}

(ARGMIN religion.religious_leadership_title (JOIN religion.religious_leadership_title.leaders religion.religious_organization_leadership.start_date))


Question: on 07/01/1970, which warship v1.1 was hit?

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX : <http://rdf.freebase.com/ns/>
SELECT (?x0 AS ?value) WHERE {
SELECT DISTINCT ?x0  WHERE {
?x0 :type.object.type :user.patrick.default_domain.warship_v1_1 .
VALUES ?x1 { "1970-07-01-08:00"^^<http://www.w3.org/2001/XMLSchema#date> }
?x0 :user.patrick.default_domain.warship_v1_1.struck ?x1 .
FILTER ( ?x0 != ?x1  )
}
}

(AND user.patrick.default_domain.warship_v1_1 (JOIN user.patrick.default_domain.warship_v1_1.struck 1970-07-01^^http://www.w3.org/2001/XMLSchema#date))


Question: what is the language regulator of basque?

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX : <http://rdf.freebase.com/ns/>
SELECT (?x0 AS ?value) WHERE {
SELECT DISTINCT ?x0  WHERE {
?x0 :type.object.type :language.language_regulator .
VALUES ?x1 { :m.017k6 }
?x0 :language.language_regulator.language ?x1 .
FILTER ( ?x0 != ?x1  )
}
}

(AND language.language_regulator (JOIN language.language_regulator.language m.017k6))


Question: when it comes to the food that has the most energy per 100g what is the name of it?

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX : <http://rdf.freebase.com/ns/>
SELECT (?x0 AS ?value) WHERE {
SELECT DISTINCT ?x0  WHERE {
?x0 :type.object.type :food.food .
{
SELECT (MAX(?y1) AS ?x1)  WHERE {
?y0 :type.object.type :food.food .
?y0 :food.food.energy ?y1 .
FILTER ( ?y0 != ?y1  )
}
}
?x0 :food.food.energy ?x1 .
FILTER ( ?x0 != ?x1  )
}
}


Question: a people's history of christianity was edited by what series editor?

Series editor
A People's History of Christianity

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SELECT (?x0 AS ?value) WHERE {
SELECT DISTINCT ?x0  WHERE {
?x0 :type.object.type :book.series_editor .
VALUES ?x1 { :m.012bphrj }
?x0 :book.series_editor.book_edition_series_edited ?x1 .
FILTER ( ?x0 != ?x1  )
}
}

(AND book.series_editor (JOIN book.series_editor.book_edition_series_edited m.012bphrj))


Question: the medical trials conducted on safety and effectiveness of giving indinavir plus stavudine plus lamivudine to hiv-infected children are under the authority of who?

Safety and Effectiveness of Giving Indinavir Plus Stavudine Plus Lamivudine to HIV-Infected Children

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX : <http://rdf.freebase.com/ns/>
SELECT (?x0 AS ?value) WHERE {
SELECT DISTINCT ?x0  WHERE {
?x0 :type.object.type :medicine.medical_trial_health_authority .
VALUES ?x1 { :m.03zbby2 }
?x0 :medicine.medical_trial_health_authority.medical_trials ?x1 .
FILTER ( ?x0 != ?x1  )
}
}

(AND medicine.medical_trial_health_authority (JOIN medicine.medical_trial_health_authority.medical_trials m.03zbby2))


Question: the albums confessions tour is part of what parent genre of a musical genre?

Entity Entity Entity Entity Entity Entity Entity Entity Entity Entity Entity Entity

House music
Dance music
Folk music
Rock music
Rhythm and blues
Classical music
Rock and roll
Rockabilly
World music
Experimental classical music
Techno
Russian pop

Musical genre
Musical genre
confessions tour

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX : <http://rdf.freebase.com/ns/>
SELECT (?x0 AS ?value) WHERE {
SELECT DISTINCT ?x0  WHERE {
?x0 :type.object.type :music.genre .
?x1 :type.object.type :music.genre .
VALUES ?x2 { :m.03fz6lg }
?x1 :music.genre.parent_genre ?x0 .
?x1 :music.genre.albums ?x2 .
FILTER ( ?x0 != ?x1 && ?x0 != ?x2 && ?x1 != ?x2  )
}
}

(AND music.genre (JOIN (R music.genre.parent_genre) (JOIN music.genre.albums m.03fz6lg)))


I got in a fight, I was indisposed
I was in, despite all the wicked prose
But Im only a man, and I do what I can
I got friends in high places
I get out for free
I got in a fight but they dont know me
Cause Im only a man
And I do what I can
B-E-H-A-V-E
Arrest us
Italiana mobster
Looking so precious
B-E-H-A-V-E
Never more
You gave up being good when you declared a state of war
Eh
I dont behave, I dont behave, oh eh
I dont behave, I dont behave, oh eh
I dont behave, I dont behave, oh
Are you going to the party?
Are you going to the show?
Cause Im only a man, do what I can, oh
Cause Im only a man, do what I can
Are you going to the party? Are you going to the show?
I did something bad, maybe I was wrong
Sometimes people say that Im a big time bomb
But Im only a man
And I do what I can
B-E-H-A-V-E
Arrest us
Italiana mobster
Looking so precious
B-E-H-A-V-E
Never more
You gave up being good when you declared a state of war
Eh
I dont behave, I dont behave, oh eh
I dont behave, I dont behave, oh eh
I dont behave, I dont behave, oh
Are you going to the party? Are you going to the show?
Cause Im only a man, do what I can, oh
Cause Im only a man, do what I can
Are you going to the party? Are you going to the show?
Oh, the fire its all right
Cause we can make em all go crazy
We can make em wanna die
Oh, the fire its all right
The people touch it
I cant touch it
Even though its mine
Eh
I dont behave, I dont behave, oh eh
I dont behave, I dont behave, oh eh
I dont behave, I dont behave, oh
Cause Im only a man, do what I can, oh
Cause Im only a man, do what I can
B-E-H-A-V-E
Arrest us
Italiana mobster
Looking so precious
B-E-H-A-V-E
Never more
You gave up being good when you declared a state of war


My heart, I never be, I never see, I never know
Oh, heart, and then it falls, and then I fall, and then I know
My heart, I never be, I never see, I never know
Oh, heart, and then it falls, and then I fall, and then I know
My heart, I never be, I never see, I never know
Oh, heart, and then it falls, and then I fall, and then I know
My heart, I never be, I never see, I never know
Oh, heart, and then it falls, and then I fall, and then I know
My
My
My
Ever see, ever be, ever know my heart
Ever see, ever be, ever know my heart
Home, and I know
Playing the deck above
Its always different
Im the one in love
Home, and I know
Playing the deck above
Its always different
Im the one in love
Home, and I know
Playing the deck above
Its always different
Im the one in love
Home, and I know
Playing the deck above
Its always different
Im the one in love
My heart, I never be, I never see, I never know
Oh, heart, and then it falls, and then I fall, and then I know
My heart, I never be, I never see, I never know
Oh, heart, and then it falls, and then I fall, and then I know
My heart, I never be, I never see, I never know
Oh, heart, and then it falls, and then I fall, and then I know
My heart, I never be, I never see, I never know
Oh, heart, and then it falls, and then I fall, and then I know
Ever see, ever be, ever know my heart
Ever see, ever be, ever know
Home, and I know
Playing the deck above
Its always different
Im the one in love
Home, and I know
Playing the deck above
Its always different
Im the one in love
Home, and I know
Playing the deck above
Its always different
Im the one in love
Home, and I know
Playing the deck above
Its always different
Im the one in love


I never walk about after dark
Its my point of view
Cause someone could break your neck
Coming up behind you
Always coming and youd never have a clue
I never looked behind all the time
I will wait forever
Always looking straight
Thinking counting all the hours you wait
See you on a dark night
See you on a dark night
See you on a dark night
See you on a dark night
And no, Im not a jerk
I would ask if you could help me out
Its hard to understand
Cause when youre runnin by yourself
Its hard to find someone to hold your hand
I know its good to be tough like me
But I would wait forever
I need someone else
To look into my eyes and tell me
Girl, you know youve got to watch your health
To look into my eyes and tell me
La la la la la
To look into my eyes and tell me
La la la la la
La la la la la
La la la la la
La la la la la
La la la la la
See you on a dark night
See you on a dark night
See you on a dark night
See you on a dark night
See you on a dark night
See you on a dark night
See you on a dark night
See you on a dark night
See you on a dark night


The truth runs wild
Like a tear down a cheek
Trying to save face, and daddy heart break
Im lying through my teeth
This voice inside
Has been eating at me
Trying to replace the love that I fake
With what we both need
The truth runs wild
Like kids on concrete
Trying to sedate, my mind in its cage
And numb what I see
Awake, wide eyed
Im screaming at me
Trying to keep faith and picture his face
Staring up at me
Without losing a piece of me
How do I get to heaven?
Without changing a part of me
How do I get to heaven?
All my time is wasted
Feeling like my hearts mistaken, oh
So if Im losing a piece of me
Maybe I dont want heaven?
The truth runs wild
Like the rain to the sea
Trying to set straight the lines that I trace
To find some relief
This voice inside
Has been eating at me
Trying to embrace the picture I paint
And colour me free
Without losing a piece of me
How do I get to heaven?
Without changing a part of me
How do I get to heaven?
All my time is wasted
Feeling like my hearts mistaken, oh
So if Im losing a piece of me
Maybe I dont want heaven?
So Im counting to fifteen
Counting to fifteen, counting to fifteen
So Im counting to fifteen
Counting to fifteen, counting to fifteen
So Im counting to fifteen
Counting to fifteen, counting to fifteen
So Im counting to fifteen
Counting to fifteen, counting to fifteen
Without losing a piece of me
How do I get to heaven?
Without changing a part of me
How do I get to heaven?
All my time is wasted
Feeling like my hearts mistaken, oh
So if Im losing a piece of me
Maybe I dont want heaven?
The truth runs wild
Like a tear down a cheek


Get up, this is what I see
When we were young, we used to get so close to it
And you were scared and you were beautiful
I wanna peer over the edge and see in death
If we are always the same
Oh, I fear that no life will ever be like this again
Cause your love kept me alive and it made me insane, oh, yeah
There were moments when it seemed okay
Whether you go or whether you stay
There was a time when the music would play
Woah-oh-oh-oh-oh
Oh, baby, every morning there are mountains to climb
Taking all my time
Oh, when I get up, this is what I see
Welcome to reality
Oh, baby, every morning there are mountains to climb
Taking all my time
Oh, when I get up, this is what I see
Welcome to reality
When we were young, we used to get so close to it
And you were scared and you were beautiful
I wanna peer over the edge and see in death
If we are always the same, oh, yeah
There were moments when it seemed okay
Whether you go or whether you stay
Oh, oh, oh-oh, oh
Oh, baby, every morning there are mountains to climb
Taking all my time
Oh, when I get up, this is what I see
Welcome to reality
Oh, baby, every morning there are mountains to climb
Taking all my time
Oh, when I get up, this is what I see
Welcome to reality
To reality
Oh, baby, every
To reality
Oh, baby, every
Oh, baby, every
Oh, baby, every
Oh, baby, every
Oh, oh, oh-oh, oh
Oh, baby, every
Oh, baby, every
Oh, baby, every
Oh, baby, every
It sounds like corporate art
Break bread with an infidel
And plunge into the dark


Tang was employed at private-equity firm Friedman Fleischer & Lowe.


San Francisco Mayor Gavin Newsom's withdrawal from the governor's race followed a meeting with top advisers where he was told that, unless he raised $5 million quickly and appeared to be viable, some of the state's biggest unions would throw their support behind Attorney General Jerry Brown.


lol nothing worrying about that.


She told Price she wanted to join the Police Explorers, a Boy Scouts group that lets boys and girls learn law enforcement with local police.


The prime minister is keen to use the autumn pre-budget statement to announce a new "fiscal stimulus", with billions of pounds of extra money for housing, infrastructure projects and training.


Those competencies include mastering fundamental legal skills, support of the firm's culture, demonstration of leadership and business skills, and understanding and effectively managing client needs.


His platform contains plans to fund drainage projects.


"It's a start."


"She is not asking for anything over the top, just to be able to find her daughter and let her know she loves her."


Justice Dinakaran had maintained that he had not acquired an inch of land since his appointment as a judge of HC and even called the collector's report motivated.


---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---

# BERT base model (cased)

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference between
english and English.

Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the Hugging Face team.

## Model description

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:

- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
  the entire masked sentence through the model and has to predict the masked words. This is different from traditional
  recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
  GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
  sentence.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
  predict if the two sentences were following each other or not.

This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.

## Intended uses & limitations

You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
fine-tuned versions on a task that interests you.

Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.

### How to use

You can use this model directly with a pipeline for masked language modeling:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-cased')
>>> unmasker("Hello I'm a [MASK] model.")

[{'sequence': "[CLS] Hello I'm a fashion model. [SEP]",
  'score': 0.09019174426794052,
  'token': 4633,
  'token_str': 'fashion'},
 {'sequence': "[CLS] Hello I'm a new model. [SEP]",
  'score': 0.06349995732307434,
  'token': 1207,
  'token_str': 'new'},
 {'sequence': "[CLS] Hello I'm a male model. [SEP]",
  'score': 0.06228214129805565,
  'token': 2581,
  'token_str': 'male'},
 {'sequence': "[CLS] Hello I'm a professional model. [SEP]",
  'score': 0.0441727414727211,
  'token': 1848,
  'token_str': 'professional'},
 {'sequence': "[CLS] Hello I'm a super model. [SEP]",
  'score': 0.03326151892542839,
  'token': 7688,
  'token_str': 'super'}]
```

Here is how to use this model to get the features of a given text in PyTorch:

```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = TFBertModel.from_pretrained("bert-base-cased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

and in TensorFlow:

```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = BertModel.from_pretrained("bert-base-cased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```

### Limitations and bias

Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-cased')
>>> unmasker("The man worked as a [MASK].")

[{'sequence': '[CLS] The man worked as a lawyer. [SEP]',
  'score': 0.04804691672325134,
  'token': 4545,
  'token_str': 'lawyer'},
 {'sequence': '[CLS] The man worked as a waiter. [SEP]',
  'score': 0.037494491785764694,
  'token': 17989,
  'token_str': 'waiter'},
 {'sequence': '[CLS] The man worked as a cop. [SEP]',
  'score': 0.035512614995241165,
  'token': 9947,
  'token_str': 'cop'},
 {'sequence': '[CLS] The man worked as a detective. [SEP]',
  'score': 0.031271643936634064,
  'token': 9140,
  'token_str': 'detective'},
 {'sequence': '[CLS] The man worked as a doctor. [SEP]',
  'score': 0.027423162013292313,
  'token': 3995,
  'token_str': 'doctor'}]

>>> unmasker("The woman worked as a [MASK].")

[{'sequence': '[CLS] The woman worked as a nurse. [SEP]',
  'score': 0.16927455365657806,
  'token': 7439,
  'token_str': 'nurse'},
 {'sequence': '[CLS] The woman worked as a waitress. [SEP]',
  'score': 0.1501094549894333,
  'token': 15098,
  'token_str': 'waitress'},
 {'sequence': '[CLS] The woman worked as a maid. [SEP]',
  'score': 0.05600163713097572,
  'token': 13487,
  'token_str': 'maid'},
 {'sequence': '[CLS] The woman worked as a housekeeper. [SEP]',
  'score': 0.04838843643665314,
  'token': 26458,
  'token_str': 'housekeeper'},
 {'sequence': '[CLS] The woman worked as a cook. [SEP]',
  'score': 0.029980547726154327,
  'token': 9834,
  'token_str': 'cook'}]
```

This bias will also affect all fine-tuned versions of this model.

## Training data

The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).

## Training procedure

### Preprocessing

The texts are tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form:

```
[CLS] Sentence A [SEP] Sentence B [SEP]
```

With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.

The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.

### Pretraining

The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.

## Evaluation results

When fine-tuned on downstream tasks, this model achieves the following results:

Glue test results:

| Task | MNLI-(m/mm) | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | Average |
|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
|      | 84.6/83.4   | 71.2 | 90.5 | 93.5  | 52.1 | 85.8  | 88.9 | 66.4 | 79.6    |

### BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
  author    = {Jacob Devlin and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
               Understanding},
  journal   = {CoRR},
  volume    = {abs/1810.04805},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.04805},
  archivePrefix = {arXiv},
  eprint    = {1810.04805},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```


Education Details
May 2013 to May 2017 B.E   UIT-RGPV
Data Scientist

Data Scientist - Matelabs
Skill Details
Python- Exprience - Less than 1 year months
Statsmodels- Exprience - 12 months
AWS- Exprience - Less than 1 year months
Machine learning- Exprience - Less than 1 year months
Sklearn- Exprience - Less than 1 year months
Scipy- Exprience - Less than 1 year months
Keras- Exprience - Less than 1 year monthsCompany Details
company - Matelabs
description - ML Platform for business professionals, dummies and enthusiasts.
60/A Koramangala 5th block,
Achievements/Tasks behind sukh sagar, Bengaluru,
India                               Developed and deployed auto preprocessing steps of machine learning mainly missing value
treatment, outlier detection, encoding, scaling, feature selection and dimensionality reduction.
Deployed automated classification and regression model.
linkedin.com/in/aditya-rathore-
b4600b146                           Reasearch and deployed the time series forecasting model ARIMA, SARIMAX, Holt-winter and
Prophet.
Worked on meta-feature extracting problem.
github.com/rathorology
Implemented a state of the art research paper on outlier detection for mixed attributes.
company - Matelabs
description -


Areas of Interest Deep Learning, Control System Design, Programming in-Python, Electric Machinery, Web Development, Analytics Technical Activities q Hindustan Aeronautics Limited, Bangalore - For 4 weeks under the guidance of Mr. Satish, Senior Engineer in the hangar of Mirage 2000 fighter aircraft Technical Skills Programming Matlab, Python and Java, LabView, Python WebFrameWork-Django, Flask, LTSPICE-intermediate Languages and and MIPOWER-intermediate, Github (GitBash), Jupyter Notebook, Xampp, MySQL-Basics, Python Software Packages Interpreters-Anaconda, Python2, Python3, Pycharm, Java IDE-Eclipse Operating Systems Windows, Ubuntu, Debian-Kali Linux Education Details
January 2019 B.Tech. Electrical and Electronics Engineering  Manipal Institute of Technology
January 2015    DEEKSHA CENTER
January 2013    Little Flower Public School
August 2000    Manipal Academy of Higher
DATA SCIENCE

DATA SCIENCE AND ELECTRICAL ENTHUSIAST
Skill Details
Data Analysis- Exprience - Less than 1 year months
excel- Exprience - Less than 1 year months
Machine Learning- Exprience - Less than 1 year months
mathematics- Exprience - Less than 1 year months
Python- Exprience - Less than 1 year months
Matlab- Exprience - Less than 1 year months
Electrical Engineering- Exprience - Less than 1 year months
Sql- Exprience - Less than 1 year monthsCompany Details
company - THEMATHCOMPANY
description - I am currently working with a Casino based operator(name not to be disclosed) in Macau.I need to segment the customers who visit their property based on the value the patrons bring into the company.Basically prove that the segmentation can be done in much better way than the current system which they have with proper numbers to back it up.Henceforth they can implement target marketing strategy to attract their customers who add value to the business.


Education Details
 MCA   YMCAUST,  Faridabad,  Haryana
Data Science internship

Skill Details
Data Structure- Exprience - Less than 1 year months
C- Exprience - Less than 1 year months
Data Analysis- Exprience - Less than 1 year months
Python- Exprience - Less than 1 year months
Core Java- Exprience - Less than 1 year months
Database Management- Exprience - Less than 1 year monthsCompany Details
company - Itechpower
description -


SKILLS C Basics, IOT, Python, MATLAB, Data Science, Machine Learning, HTML, Microsoft Word, Microsoft Excel, Microsoft Powerpoint. RECOGNITION Academic Secured First place in B.Tech.Education Details
August 2014 to May 2018 B.Tech.  Ghatkesar, Andhra Pradesh Aurora's Scientific and Technological Institute
June 2012 to May 2014  Secondary Education Warangal, Telangana SR Junior College
Data Science

Skill Details
MS OFFICE- Exprience - Less than 1 year months
C- Exprience - Less than 1 year months
machine learning- Exprience - Less than 1 year months
data science- Exprience - Less than 1 year months
Matlab- Exprience - Less than 1 year monthsCompany Details
company -
description -


Share my location with Hillary's sister


Send my current location to my father


Share my current location with Jim


Send my location to my husband


Send my location


Always share my location with Lori


Share my location with my boyfriend until I get home


Send my current location to Anna


Share my location with Steve for 3 days


Share my location to mum until I get to school


move the 4 bytes of data at address esi+4*ebx into edx


declare msg string containing 'hello world!'


restore eax from the value we pushed onto the stack at the start


push the double word 0x313a303a onto the stack


A	AH0


A'BODY	EY1 B AA2 D IY0


A'COURT	EY1 K AO2 R T


A'D	EY1 D


A'GHA	EY1 G AH0


A'GOIN	EY1 G OY1 N


A'M	EY1 AH0 M


A'MIGHTY	EY1 M AY1 T IY0


The doctors told his parents it was unhealthy.

His parents understood and decided to make a change.


Carrie had just learned how to ride a bike.

Carrie would sneak rides on her sister's bike.

She got nervous on a hill and crashed into a wall.

The bike frame bent and Carrie got a deep gash on her leg.


She and her boyfriend decided to go for a long walk.

After walking for over a mile, something happened.

Morgan decided to propose to her boyfriend.

Her boyfriend was upset he didn't propose to her first.


Suddenly, a customer barged up to the counter.

He began yelling about how long his food was taking.

Luckily, her coworker intervened and calmed the man down.


She continued to complain about guys flirting with her.

I decided to agree with what she says and listened to her patiently.

After I got home, I got a text from her.


He got a call from his girlfriend, asking where he was.

Frank suddenly realized he had a date that night.

Since Frank was already a bit drunk, he could not drive.

Frank spent the rest of the night drinking more beers.


He decided to go snorkeling on his second day.

While snorkeling, he saw a cave up ahead.

He went into the cave, and he was terrified when he found a shark!

Dave swam away as fast as he could, but the shark caught and ate Dave.


As she stepped out of her car, she realized she forgot something.

It was quite sunny and she forgot her sunglasses.

Sunny got back into her car and heading towards the mall.

Sunny found some sunglasses and headed back to the beach.


Sally was happy when her widowed mom found a new man.

She discovered her siblings didn't feel the same.

Sally flew to visit her mom and her mom's new husband.

Although her mom was obviously in love, he was nothing like her dad.

Sally went home and wondered about her parents' marriage.


Dan hit his golf ball and watched it go.

The ball bounced on the grass and into the sand trap.

Dan pretended that his ball actually landed on the green.

His friends were not paying attention so they believed him.

Dan snuck a ball on the green and made his putt from 10 feet.


e vremea e culcare olly


Finnish airline Finnair is starting the temporary layoffs of cabin crews in February 2010 .


The corresponding increase in the share capital , in total EUR 300,00 was registered in the Finnish Trade Register on May 8 , 2008 .


In the third quarter of fiscal 2008 Efore swung to a net loss of EUR 400,000 versus a net profit of EUR 200,000 for the corresponding period of fiscal 2007 .


ALEXANDRIA , Va. , Oct. 15 -- Aaron Moss of Hampshire , Great Britain , has developed an ornamental design for a handset , the U.S. Patent & Trademark Office announced .


Vaisala Oyj Stock exchange release 26.03.2010 klo 09.00 1 ( 1 ) The Annual General Meeting of Vaisala Oyj decided on March 25 , 2010 to approve the Company 's annual accounts for 2009 .


The product 's front-edge folding maximizes weather resilience and enables a seamless roof finish .


Raute is listed on the Nordic exchange in Helsinki .


KESKO FOOD LTD PRESS RELEASE 04.01.2006 AT 13.00 Kesko Food Ltd is launching a major TV campaign to recruit as many as one hundred new K-food retailers .


A tinyurl link takes users to a scamming site promising that users can earn thousands of dollars by becoming a Google ( NASDAQ : GOOG ) Cash advertiser .


politician signs items after he and politician met with mothers


photographer in the desert taking pictures


mixed race curly girl friends sitting on the couch and watch very scary movie on tv and eat popcorn at home


the outdoor cafe in mountains is completely empty in the early winter morning


illustration of a red balloon with a letter attached


close - up of black and white piano keys on the keyboard


illustration of a traveler with a photo camera .


Selegiline - induced postural hypotension in Parkinson ' s disease : a longitudinal study on the effects of drug withdrawal .


OBJECTIVES : The United Kingdom Parkinson ' s Disease Research Group ( UKPDRG ) trial found an increased mortality in patients with Parkinson ' s disease ( PD ) randomized to receive 10 mg selegiline per day and L - dopa compared with those taking L - dopa alone .


CONCLUSION : This study confirms our previous finding that selegiline in combination with L - dopa is associated with selective orthostatic hypotension .


High doses of vitamin D are known to cause calcification of the artery media in as little as 3 to 4 days .


We then used hyperbaric oxygen at an absolute pressure of 2 atm , 5 days a week for 8 consecutive weeks .


The bleeding ceased completely by the end of treatment and the patient remained free of hematuria thereafter .


No side effect was noted during the course of therapy .


In future , this form of therapy can offer a safe alternative in the treatment of cyclophosphamide - induced hemorrhagic cystitis .


Acute psychosis due to treatment with phenytoin in a nonepileptic patient .


The development of psychosis related to antiepileptic drug treatment is usually attributed to the interaction between the epileptic brain substratum and the antiepileptic drugs .


Context:
A group of teenagers verging on adulthood are to spend a year in a remote village in Indonesia, learning to live on the same terms as the people in that village. Their teacher is going along as well, but he decides to make a little extra money along the way: he cancels out on their scheduled transport to the village's nearest airport and hires a decrepit plane and its pilot, who's seen better days for his income. The deal is that the pilot will make out the receipt for a higher amount than the teacher actually pays.
The pilot, in turn, makes a deal with drug dealers to make a delivery, so when the plane takes off with the students aboard and the teacher in the co-pilot's seat, it isn't flying the direction the teacher and the students require to get to their destination. The plane has mechanical troubles, and lands in the water near an island while the pilot checks it out. He informs the teacher that he'll have to return to an airport for repairs, and teacher says he's going along to make sure the pilot returns. That leaves the students on the island.
While they wait, some of them explore, but one dies in the rising tide after a rock traps his foot against another rock. As night approaches, they decide to leave the beach and find a better campsite. They leave a note for the pilot and teacher that they'll listen for the plane. Meanwhile, the plane is having even more mechanical problems, as the pilot gives a grim comment to the teacher.
Many of them quickly discover they need a leader to make decisions and serve as arbiter in disagreements. Having accepted one boy, they then decide on some rules, such as lavatory rules.
Some days later, the airplane is spotted next to the shore of the island. The leader leads them cross-country to the plane to check it out, and he briefly gets caught in quicksand and panics, but is extricated without threat to his life, although a passing snake rubbing behind him freaks him out.

Question: What is the position of the person that gets freaked out by a snake?


Context: At last, the newly crowned King of Denmark, Edvard, and his wife and Queen, Dr. Paige Morgan, find time to fly to Belavia for their secret honeymoon. They spend their Christmas holidays at a ski resort, but as they take a tour of Belavia's natural beauty, Eddie and Paige discover that the evil Prime Minister Polonius  has given orders to bulldoze the precious forests to drill for oil. Paige and Eddie decide they must do everything they can to save the forest, even if it means putting aside their honeymoon. Then, the couple bump into Paige's ex-boyfriend, Scott, a journalist. Eddie immediately becomes jealous. Even though Edvard suspects Scott cannot be trusted, Eddie and Paige ask him for help with the media to try and stop the minister's evil plans. Scott, however, is being controlled by the evil minister, who tells him to spy on the couple. Scott tries to back out, but fails. Eddie tries to get an audience with the prince of Belavia, but fails, so he and Paige go to the Holiday Ball. Meanwhile, Scott tries to kiss Paige and says he is sorry he let her go. Disgusted, Paige walks away and goes to find Eddie, only to find him drunk.

Question: What is the first name of the person who is asked to spy on the couple?


Context:
The experience was rewarding for Pei, and he agreed immediately to work with the group again. The new project was the Miho Museum, to display Koyama's collection of tea ceremony artifacts. Pei visited the site in Shiga Prefecture, and during their conversations convinced Koyama to expand her collection. She conducted a global search and acquired more than 300 items showcasing the history of the Silk Road.One major challenge was the approach to the museum. The Japanese team proposed a winding road up the mountain, not unlike the approach to the NCAR building in Colorado. Instead, Pei ordered a hole cut through a nearby mountain, connected to a major road via a bridge suspended from ninety-six steel cables and supported by a post set into the mountain. The museum itself was built into the mountain, with 80 percent of the building underground.When designing the exterior, Pei borrowed from the tradition of Japanese temples, particularly those found in nearby Kyoto. He created a concise spaceframe wrapped into French limestone and covered with a glass roof. Pei also oversaw specific decorative details, including a bench in the entrance lobby, carved from a 350-year-old keyaki tree. Because of Koyama's considerable wealth, money was rarely considered an obstacle; estimates at the time of completion put the cost of the project at US$350 million.During the first decade of the 2000s, Pei designed a variety of buildings, including the Suzhou Museum near his childhood home. He also designed the Museum of Islamic Art in Doha, Qatar at the request of the Al-Thani Family. Although it was originally planned for the corniche road along Doha Bay, Pei convinced project coordinators to build a new island to provide the needed space. He then spent six months touring the region and surveying mosques in Spain, Syria, and Tunisia. He was especially impressed with the elegant simplicity of the Mosque of Ibn Tulun in Cairo.
Once again, Pei sought to combine new design elements with the classical aesthetic most appropriate for the location of the building. The rectangular boxes rotate evenly to create a subtle movement, with small arched windows at regular intervals into the limestone exterior. The museum's coordinators were pleased with the project; its official website describes its "true splendour unveiled in the sunlight", and speaks of "the shades of colour and the interplay of shadows paying tribute to the essence of Islamic architecture".
The Macao Science Center in Macau was designed by Pei Partnership Architects in association with I. M. Pei. The project to build the science center was conceived in 2001 and construction started in 2006. The center was completed in 2009 and opened by the Chinese President Hu Jintao.

Question: What is the name of the person that designed the Museum of Islamic Art?