text:
Top Clean Energy Stocks To Own For 2021
Frontier Airlines announced a major expansion in San Antonio, where the ultra low-cost carrier will add nine new routes to its existing schedule.
Once the flights begin, Frontier will fly non-stop from San Antonio to 23 destinations. That, Frontier says, will make it the top carrier in San Antonio as measured by the number of non-stop routes offered. (Scroll down for schedule details on all nine of the new routes)
Frontier's new San Antonio routes are: Albuquerque; Charlotte; Cleveland; Columbus, Ohio; Jacksonville, Fla.; Memphis; Oklahoma City; Omaha and Tulsa.
Frontier will be the only airline to fly on eight of the nine routes. Its new Charlotte-San Antonio service will face direct competition from American, which counts Charlotte as one of its hub cities.
Frontier's existing destinations (including seasonal) from San Antonio are: Atlanta; Cincinnati; Colorado Springs; Denver; Las Vegas; New Orleans; San Diego; San Jose, Calif.; Ontario, Calif.; Orlando; Philadelphia; Phoenix; Raleigh-Durham and Washington Dulles.
Top Clean Energy Stocks To Own For 2021: ChinaCache International Holdings Ltd.(CCIH)
We commenced operations through Beijing Blue I.T. Technologies Co., Ltd., or Beijing Blue I.T., a company incorporated in China in June 1998. In June 2005, we incorporated ChinaCache International Holdings Ltd., or ChinaCache Holdings, under the laws of the Cayman Islands to become our offshore holding company through a series of corporate restructuring transactions.
In August 2005, we established our wholly-owned PRC subsidiary, ChinaCache Network Technology (Beijing) Limited, or ChinaCache Beijing.
In July 2008, we obtained control over Beijing Jingtian Technology Co., Ltd., or Beijing Jingtian, through contractual arrangements.
In October 2010, we completed our initial public offering and our ADSs commenced trading on the NASDAQ Global Market under the symbol "CCIH". Advisors' Opinion:
ChinaCache International Holdings Ltd (NASDAQ:CCIH) shares saw unusually-high trading volume on Tuesday . Approximately 738,018 shares traded hands during mid-day trading, an increase of 559% from the previous session's volume of 112,066 shares.The stock last traded at $1.36 and had previously closed at $1.22.
Intersections (NASDAQ:INTX) and ChinaCache International (NASDAQ:CCIH) are both business services companies, but which is the better business? We will compare the two businesses based on the strength of their institutional ownership, profitability, valuation, analyst recommendations, earnings, risk and dividends.
Top Clean Energy Stocks To Own For 2021: GAIN Capital Holdings, Inc.(GCAP)
GAIN Capital Holdings, Inc. provides trading services and solutions to retail and institutional customers worldwide. The company operates in three segments: Retail, Institutional, and Futures. It specializes in over-the-counter (OTC) and exchange-traded markets. The company offers access to a range of financial products, including spot foreign exchange (forex), precious metals trading, and contracts for differences (CFD) on currencies, commodities, indices, individual equities, bonds, and interest rate products; and spread bets and OTC options on forex, as well as supports the trading of exchange-traded futures and options. It offers services to retail customers through FOREX.com and Cityindex.com, as well as through introducing brokers and white label partners. The company also provides agency execution services; and access to markets and self-directed trading in foreign exchange, commodities, equities, options, and futures to institutional customers through an electronic communications network, as well as offers touch services. In addition, it operates GTX platform, an electronic communications network that provides liquidity in spot and forward foreign exchange and precious metals to buy-side and sell-side firms, including banks, brokers, hedge funds, commodity trading advisors, and asset managers. Further, the company provides execution and risk management services for exchange-traded futures and futures options on U.S. and European futures and options exchanges. As of December 31, 2015, it had 173,738 funded retail accounts. GAIN Capital Holdings, Inc. was founded in 1999 and is headquartered in Bedminster, New Jersey.
Gain Capital Holdings Inc (NYSE:GCAP)Q4 2018 Earnings Conference CallFeb. 28, 2019, 4:30 p.m. ET
Gain Capital Holdings Inc (NYSE:GCAP) Director Alex Goor purchased 17,434 shares of the business's stock in a transaction dated Friday, July 27th. The shares were bought at an average price of $7.50 per share, with a total value of $130,755.00. Following the purchase, the director now directly owns 85,980 shares of the company's stock, valued at $644,850. The purchase was disclosed in a document filed with the Securities & Exchange Commission, which is available through this link.
Acadian Asset Management LLC bought a new position in shares of Gain Capital Holdings Inc (NYSE:GCAP) in the second quarter, according to the company in its most recent filing with the SEC. The fund bought 361,421 shares of the financial services provider's stock, valued at approximately $2,729,000.
Top Clean Energy Stocks To Own For 2021: HubSpot, Inc.(HUBS)
HubSpot, Inc. provides a cloud-based marketing and sales software platform for businesses in the Americas, Europe, and the Asia Pacific. Its software platform includes integrated applications, such as social media, search engine optimization, blogging, Website content management, marketing automation, email, CRM, analytics, and reporting that enables businesses to attract visitors to their Websites, convert visitors into leads, and close leads into customers and delight customers. The company also offers professional, and phone and email-based support services. HubSpot, Inc. was founded in 2005 and is headquartered in Cambridge, Massachusetts.
[By Brian Stoffel]
The two companies we're comparing today — pharmaceutical cloud specialist Veeva Systems (NYSE:VEEV) and inbound-marketing guru Hubspot (NYSE:HUBS) — have both benefited from this trend. And shareholders have reaped the rewards: On average, the stocks have quadrupled over the past three years.
[By Motley Fool Transcription]
HubSpot, Inc. (NYSE:HUBS) Q4 2018 Earnings Conference Call Feb. 12, 2019, 4:30 p.m. ET
HubSpot (NYSE:HUBS) last issued its quarterly earnings data on Tuesday, February 12th. The software maker reported $0.37 EPS for the quarter, topping analysts' consensus estimates of ($0.17) by $0.54. The company had revenue of $144.02 million during the quarter, compared to the consensus estimate of $137.48 million. HubSpot had a negative net margin of 12.44% and a negative return on equity of 17.08%. HubSpot's revenue for the quarter was up 35.2% compared to the same quarter last year. During the same quarter in the previous year, the firm posted $0.12 earnings per share. On average, analysts forecast that HubSpot Inc will post -1.1 earnings per share for the current year.
[By Chris Neiger]
HubSpot (NYSE:HUBS) released its fourth-quarter 2018 and full-year results earlier this month, and the cloud-based marketing software and services company saw its sales climb 35%, year over year, and non-GAAP net income expand from $4.6 million in the year-ago quarter to $15.8 million. Both the company's reported sales and earnings in the fourth quarter beat HubSpot management's own guidance for the quarter.
This entry was posted in Top Stocks and tagged , CCIH, GCAP, HUBS on April 8, 2021 by Admin.
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text:
"""Implements the Hager-Zhang inexact line search algorithm.

Line searches are a central component for many optimization algorithms (e.g.
BFGS, conjugate gradient etc). Most of the sophisticated line search methods
aim to find a step length in a given search direction so that the step length
satisfies the
[Wolfe conditions](https://en.wikipedia.org/wiki/Wolfe_conditions).
[Hager-Zhang 2006](https://epubs.siam.org/doi/abs/10.1137/030601880)
algorithm is a refinement of the commonly used
[More-Thuente](https://dl.acm.org/citation.cfm?id=192132) algorithm.

This module implements the Hager-Zhang algorithm.
"""

import collections

import tensorflow.compat.v2 as tf
from tensorflow_probability.python.internal import broadcast_util
from tensorflow_probability.python.internal import prefer_static

def val_where(cond, tval, fval):
  """Like tf.where but works on namedtuples."""
  if isinstance(tval, tf.Tensor):
    return broadcast_util.where_left_justified_mask(cond, tval, fval)
  elif isinstance(tval, tuple):
    cls = type(tval)
    return cls(*(val_where(cond, t, f) for t, f in zip(tval, fval)))
  else:
    raise Exception(TypeError)

_Secant2Result = collections.namedtuple('_Secant2Result', [
    'active',     # Boolean indicating batch members with work left to do.
    'converged',  # Boolean indicating whether wolfe conditions are satisfied.
    'failed',     # Boolean indicating whether the secant procedure failed.
    'num_evals',  # The total number of objective evaluations performed.
    'left',       # Result of value_and_gradients_function at left end point.
    'right'       # Result of value_and_gradients_function at right end point.
])

def secant2(value_and_gradients_function,
            val_0,
            search_interval,
            f_lim,
            sufficient_decrease_param=0.1,
            curvature_param=0.9,
            name=None):
  """Performs the secant square procedure of Hager Zhang.

  Given an interval that brackets a root, this procedure performs an update of
  both end points using two intermediate points generated using the secant
  interpolation. For details see the steps S1-S4 in [Hager and Zhang (2006)][2].

  The interval [a, b] must satisfy the opposite slope conditions described in
  the documentation for `update`.

  Args:
    value_and_gradients_function: A Python callable that accepts a real scalar
      tensor and returns an object that can be converted to a namedtuple.
      The namedtuple should have fields 'f' and 'df' that correspond to scalar
      tensors of real dtype containing the value of the function and its
      derivative at that point. The other namedtuple fields, if present,
      should be tensors or sequences (possibly nested) of tensors.
      In usual optimization application, this function would be generated by
      projecting the multivariate objective function along some specific
      direction. The direction is determined by some other procedure but should
      be a descent direction (i.e. the derivative of the projected univariate
      function must be negative at 0.).
      Alternatively, the function may represent the batching of `n` such line
      functions (e.g. projecting a single multivariate objective function along
      `n` distinct directions at once) accepting n points as input, i.e. a
      tensor of shape [n], and the fields 'f' and 'df' in the returned
      namedtuple should each be a tensor of shape [n], with the corresponding
      function values and derivatives at the input points.
    val_0: A namedtuple, as returned by value_and_gradients_function evaluated
      at `0.`.
    search_interval: A namedtuple describing the current search interval,
      must include the fields:
      - converged: Boolean `Tensor` of shape [n], indicating batch members
          where search has already converged. Interval for these batch members
          won't be modified.
      - failed: Boolean `Tensor` of shape [n], indicating batch members
          where search has already failed. Interval for these batch members
          wont be modified.
      - iterations: int32 `Tensor` of shape [n]. Number of line search
          iterations so far.
      - func_evals: Scalar int32 `Tensor`. Number of function evaluations
          so far.
      - left: A namedtuple, as returned by value_and_gradients_function,
          of the left end point of the current search interval.
      - right: A namedtuple, as returned by value_and_gradients_function,
          of the right end point of the current search interval.
    f_lim: Scalar `Tensor` of real dtype. The function value threshold for
      the approximate Wolfe conditions to be checked.
    sufficient_decrease_param: Positive scalar `Tensor` of real dtype.
      Bounded above by the curvature param. Corresponds to 'delta' in the
      terminology of [Hager and Zhang (2006)][2].
    curvature_param: Positive scalar `Tensor` of real dtype. Bounded above
      by `1.`. Corresponds to 'sigma' in the terminology of
      [Hager and Zhang (2006)][2].
    name: (Optional) Python str. The name prefixed to the ops created by this
      function. If not supplied, the default name 'secant2' is used.

  Returns:
    A namedtuple containing the following fields.
      active: A boolean `Tensor` of shape [n]. Used internally by the procedure
        to indicate batch members on which there is work left to do.
      converged: A boolean `Tensor` of shape [n]. Indicates whether a point
        satisfying the Wolfe conditions has been found. If this is True, the
        interval will be degenerate (i.e. `left` and `right` below will be
        identical).
      failed: A boolean `Tensor` of shape [n]. Indicates if invalid function or
        gradient values were encountered (i.e. infinity or NaNs).
      num_evals: A scalar int32 `Tensor`. The total number of function
        evaluations made.
      left: Return value of value_and_gradients_function at the updated left
        end point of the interval.
      right: Return value of value_and_gradients_function at the updated right
        end point of the interval.
  """
  with tf.name_scope(name or 'secant2'):
    # This will always be s.t. left <= c <= right
    val_c = value_and_gradients_function(
        _secant(search_interval.left, search_interval.right))
    finished = _is_negative_inf(val_c.f)
    failed = ~search_interval.converged & (
        search_interval.failed | (~finished & ~is_finite(val_c)))
    converged = search_interval.converged | finished | (
        ~failed & _satisfies_wolfe(
            val_0, val_c, f_lim, sufficient_decrease_param, curvature_param))
    new_converged = converged & ~search_interval.converged
    val_left = val_where(new_converged, val_c, search_interval.left)
    val_right = val_where(new_converged, val_c, search_interval.right)

    initial_args = _Secant2Result(
        active=~failed & ~converged,
        converged=converged,
        failed=failed,
        num_evals=search_interval.func_evals + 1,
        left=val_left,
        right=val_right)

    def _apply_secant2_inner():
      return _secant2_inner(
          value_and_gradients_function,
          initial_args,
          val_0,
          val_c,
          f_lim,
          sufficient_decrease_param,
          curvature_param)

    return prefer_static.cond(
        tf.reduce_any(initial_args.active),
        _apply_secant2_inner,
        lambda: initial_args)

def _secant2_inner(value_and_gradients_function,
                   initial_args,
                   val_0,
                   val_c,
                   f_lim,
                   sufficient_decrease_param,
                   curvature_param):
  """Helper function for secant square."""
  # Apply the `update` function on active branch members to squeeze their
  # bracketing interval.
  update_result = update(value_and_gradients_function,
                         initial_args.left,
                         initial_args.right,
                         val_c,
                         f_lim,
                         active=initial_args.active)

  # Update active and failed flags, update left/right on non-failed entries.
  active = initial_args.active & ~update_result.failed
  failed = initial_args.failed | update_result.failed
  val_left = val_where(active, update_result.left, initial_args.left)
  val_right = val_where(active, update_result.right, initial_args.right)

  # Check if new `c` points should be generated.
  updated_left = active & tf.equal(val_left.x, val_c.x)
  updated_right = active & tf.equal(val_right.x, val_c.x)
  is_new = updated_left | updated_right

  next_c = tf.where(
      updated_left, _secant(initial_args.left, val_left), val_c.x)
  next_c = tf.where(
      updated_right, _secant(initial_args.right, val_right), next_c)
  in_range = (val_left.x <= next_c) & (next_c <= val_right.x)

  # Figure out if an extra function evaluation is needed for new `c` points.
  needs_extra_eval = tf.reduce_any(in_range & is_new)
  num_evals = initial_args.num_evals + update_result.num_evals
  num_evals = num_evals + tf.cast(needs_extra_eval, num_evals.dtype)

  next_args = _Secant2Result(
      active=active & in_range,  # No longer active if `c` is out of range.
      converged=initial_args.converged,
      failed=failed,
      num_evals=num_evals,
      left=val_left,
      right=val_right)

  def _apply_inner_update():
    next_val_c = prefer_static.cond(
        needs_extra_eval,
        (lambda: value_and_gradients_function(next_c)),
        (lambda: val_c))
    return _secant2_inner_update(
        value_and_gradients_function, next_args, val_0, next_val_c, f_lim,
        sufficient_decrease_param, curvature_param)

  return prefer_static.cond(
      tf.reduce_any(next_args.active),
      _apply_inner_update,
      lambda: next_args)

def _secant2_inner_update(value_and_gradients_function,
                          initial_args,
                          val_0,
                          val_c,
                          f_lim,
                          sufficient_decrease_param,
                          curvature_param):
  """Helper function for secant-square step."""
  # Fail if `val_c` is no longer finite.
  new_failed = initial_args.active & ~is_finite(val_c)
  active = initial_args.active & ~new_failed
  failed = initial_args.failed | new_failed

  # We converge when we find a point satisfying the Wolfe conditions, in those
  # cases we set `val_left = val_right = val_c`.
  found_wolfe = active & _satisfies_wolfe(
      val_0, val_c, f_lim, sufficient_decrease_param, curvature_param)
  val_left = val_where(found_wolfe, val_c, initial_args.left)
  val_right = val_where(found_wolfe, val_c, initial_args.right)
  converged = initial_args.converged | found_wolfe
  active = active & ~found_wolfe

  # If any active batch members remain, we apply the `update` function to
  # squeeze further their corresponding left/right bracketing interval.
  def _apply_update():
    update_result = update(
        value_and_gradients_function, val_left, val_right, val_c, f_lim,
        active=active)
    return _Secant2Result(
        active=tf.zeros_like(active),  # End of secant2, no actives anymore.
        converged=converged,
        failed=failed | update_result.failed,
        num_evals=initial_args.num_evals + update_result.num_evals,
        left=update_result.left,
        right=update_result.right)

  # Otherwise just return the current results.
  def _default():
    return _Secant2Result(
        active=active,
        converged=converged,
        failed=failed,
        num_evals=initial_args.num_evals,
        left=val_left,
        right=val_right)

  return prefer_static.cond(
      tf.reduce_any(active), _apply_update, _default)

_IntermediateResult = collections.namedtuple('_IntermediateResult', [
    'iteration',  # Number of iterations taken to bracket.
    'stopped',    # Boolean indicating whether bracketing/bisection terminated.
    'failed',     # Boolean indicating whether objective evaluation failed.
    'num_evals',  # The total number of objective evaluations performed.
    'left',       # Result of value_and_gradients_function at left end point.
    'right'       # Result of value_and_gradients_function at right end point.
])

def update(value_and_gradients_function, val_left, val_right, val_trial, f_lim,
           active=None):
  """Squeezes a bracketing interval containing the minimum.

  Given an interval which brackets a minimum and a point in that interval,
  finds a smaller nested interval which also brackets the minimum. If the
  supplied point does not lie in the bracketing interval, the current interval
  is returned.

  The following description is given in terms of individual points evaluated on
  a line function to be minimized. Note, however, the implementation also
  accepts batches of points allowing to minimize multiple line functions at
  once. See details on the docstring of `value_and_gradients_function` below.

  The requirement of the interval bracketing a minimum is expressed through the
  opposite slope conditions. Assume the left end point is 'a', the right
  end point is 'b', the function to be minimized is 'f' and the derivative is
  'df'. The update procedure relies on the following conditions being satisfied:

  '''
    f(a) <= f(0) + epsilon   (1)
    df(a) < 0                (2)
    df(b) > 0                (3)
  '''

  In the first condition, epsilon is a small positive constant. The condition
  demands that the function at the left end point be not much bigger than the
  starting point (i.e. 0). This is an easy to satisfy condition because by
  assumption, we are in a direction where the function value is decreasing.
  The second and third conditions together demand that there is at least one
  zero of the derivative in between a and b.

  In addition to the interval, the update algorithm requires a third point to
  be supplied. Usually, this point would lie within the interval [a, b]. If the
  point is outside this interval, the current interval is returned. If the
  point lies within the interval, the behaviour of the function and derivative
  value at this point is used to squeeze the original interval in a manner that
  preserves the opposite slope conditions.

  For further details of this component, see the procedure U0-U3 on page 123 of
  the [Hager and Zhang (2006)][2] article.

  Note that this function does not explicitly verify whether the opposite slope
  conditions are satisfied for the supplied interval. It is assumed that this
  is so.

  Args:
    value_and_gradients_function: A Python callable that accepts a real scalar
      tensor and returns an object that can be converted to a namedtuple.
      The namedtuple should have fields 'f' and 'df' that correspond to scalar
      tensors of real dtype containing the value of the function and its
      derivative at that point. The other namedtuple fields, if present,
      should be tensors or sequences (possibly nested) of tensors.
      In usual optimization application, this function would be generated by
      projecting the multivariate objective function along some specific
      direction. The direction is determined by some other procedure but should
      be a descent direction (i.e. the derivative of the projected univariate
      function must be negative at 0.).
      Alternatively, the function may represent the batching of `n` such line
      functions (e.g. projecting a single multivariate objective function along
      `n` distinct directions at once) accepting n points as input, i.e. a
      tensor of shape [n], and the fields 'f' and 'df' in the returned
      namedtuple should each be a tensor of shape [n], with the corresponding
      function values and derivatives at the input points.
    val_left: Return value of value_and_gradients_function at the left
      end point of the bracketing interval (labelles 'a' above).
    val_right: Return value of value_and_gradients_function at the right
      end point of the bracketing interval (labelles 'b' above).
    val_trial: Return value of value_and_gradients_function at the trial point
      to be used to shrink the interval (labelled 'c' above).
    f_lim: real `Tensor` of shape [n]. The function value threshold for
      the approximate Wolfe conditions to be checked for each batch member.
    active: optional boolean `Tensor` of shape [n]. Relevant in batching mode
      only, indicates batch members on which the update procedure should be
      applied. On non-active members the current left/right interval is returned
      unmodified.

  Returns:
    A namedtuple containing the following fields:
      iteration: An int32 scalar `Tensor`. The number of iterations performed
        by the bisect algorithm.
      stopped: A boolean `Tensor` of shape [n]. True for those batch members
        where the bisection algorithm terminated.
      failed: A boolean `Tensor` of shape [n]. True for those batch members
        where an error was encountered.
      num_evals: An int32 scalar `Tensor`. The number of times the objective
        function was evaluated.
      left: Return value of value_and_gradients_function at the updated left
        end point of the interval found.
      right: Return value of value_and_gradients_function at the updated right
        end point of the interval found.
  """
  # We should only update if the trial point is within the interval.
  within_range = (val_left.x < val_trial.x) & (val_trial.x < val_right.x)
  if active is not None:
    within_range = within_range & active

  # The new point is a valid left end point if it has negative slope
  # and the value at the point is not too large.
  valid_left = (val_trial.df < 0) & (val_trial.f <= f_lim)

  # If the trial point has a negative slope but the value at that point
  # is too high, bisect can narrow down an interval between the current left
  # and the trial point.
  needs_bisect = within_range & (val_trial.df < 0) & (val_trial.f > f_lim)

  # Note that if `~valid_left` it is because either:
  # - the slope at the trial point is positive, so it is a valid right
  #   point, or
  # - the needs_bisect condition is true.
  # In both cases we want to keep the current left and replace right
  # with the trial point.
  left = val_where(within_range & valid_left, val_trial, val_left)
  right = val_where(within_range & ~valid_left, val_trial, val_right)

  bisect_args = _IntermediateResult(
      iteration=tf.convert_to_tensor(0),
      stopped=~needs_bisect,
      failed=tf.zeros_like(within_range),  # i.e. all false.
      num_evals=tf.convert_to_tensor(0),
      left=left,
      right=right)
  return _bisect(value_and_gradients_function, bisect_args, f_lim)

def bracket(value_and_gradients_function,
            search_interval,
            f_lim,
            max_iterations,
            expansion_param=5.0):
  """Brackets the minimum given an initial starting point.

  Applies the Hager Zhang bracketing algorithm to find an interval containing
  a region with points satisfying Wolfe conditions. Uses the supplied initial
  step size 'c', the right end point of the provided search interval, to find
  such an interval. The only condition on 'c' is that it should be positive.
  For more details see steps B0-B3 in [Hager and Zhang (2006)][2].

  Args:
    value_and_gradients_function: A Python callable that accepts a real scalar
      tensor and returns a namedtuple containing the value filed `f` of the
      function and its derivative value field `df` at that point.
      Alternatively, the function may representthe batching of `n` such line
      functions (e.g. projecting a single multivariate objective function along
      `n` distinct directions at once) accepting n points as input, i.e. a
      tensor of shape [n], and return a tuple of two tensors of shape [n], the
      function values and the corresponding derivatives at the input points.
    search_interval: A namedtuple describing the current search interval,
      must include the fields:
      - converged: Boolean `Tensor` of shape [n], indicating batch members
          where search has already converged. Interval for these batch members
          wont be modified.
      - failed: Boolean `Tensor` of shape [n], indicating batch members
          where search has already failed. Interval for these batch members
          wont be modified.
      - iterations: int32 `Tensor` of shape [n]. Number of line search
          iterations so far.
      - func_evals: Scalar int32 `Tensor`. Number of function evaluations
          so far.
      - left: A namedtuple, as returned by value_and_gradients_function
          evaluated at 0, the left end point of the current interval.
      - right: A namedtuple, as returned by value_and_gradients_function,
          of the right end point of the current interval (labelled 'c' above).
    f_lim: real `Tensor` of shape [n]. The function value threshold for
      the approximate Wolfe conditions to be checked for each batch member.
    max_iterations: Int32 scalar `Tensor`. The maximum number of iterations
      permitted. The limit applies equally to all batch members.
    expansion_param: Scalar positive `Tensor` of real dtype. Must be greater
      than `1.`. Used to expand the initial interval in case it does not bracket
      a minimum.

  Returns:
    A namedtuple with the following fields.
      iteration: An int32 `Tensor` of shape [n]. The number of iterations
        performed. Bounded above by `max_iterations` parameter.
      stopped: A boolean `Tensor` of shape [n]. True for those batch members
        where the algorithm terminated before reaching `max_iterations`.
      failed: A boolean `Tensor` of shape [n]. True for those batch members
        where an error was encountered during bracketing.
      num_evals: An int32 scalar `Tensor`. The number of times the objective
        function was evaluated.
      left: Return value of value_and_gradients_function at the updated left
        end point of the interval found.
      right: Return value of value_and_gradients_function at the updated right
        end point of the interval found.
  """
  already_stopped = search_interval.failed | search_interval.converged

  # We fail if there is a nan at the right end-point that prevents us from
  # making a decision, i.e., either a nan value or a finite value and a nan
  # derivative.
  failed = _bad_nan(search_interval.right)

  # If the right end is -inf, we have already found a minimum.  Passing along to
  # `_bisect` in this state will do the right thing, so just mark those as
  # stopped.
  finished = _is_negative_inf(search_interval.right.f)

  # If the value at the right end is finite and the slope at right end point is
  # positive, step B1 in [2], then the given initial points already bracket a
  # minimum.
  bracketed = _is_rising(search_interval.right)

  # Bisection is needed, step B2, if right end point almost works as a new left
  # end point but the objective value is too high.
  needs_bisect = _needs_bisect(search_interval.right, f_lim)

  # In these three cases bracketing is already `stopped` and there is no need
  # to perform further evaluations. Otherwise, assuming there are no `nan`s,
  # - The right-end point has a finite value,
  # - Whose derivative is non-positive, and
  # - Which is below `f_lim`.
  # This is then a valid left end-point, and the bracketing loop is needed to
  # expand the interval to the right, step B3, until the conditions are met.
  initial_args = _IntermediateResult(
      iteration=search_interval.iterations,
      stopped=already_stopped | failed | finished | bracketed | needs_bisect,
      failed=search_interval.failed | failed,
      num_evals=search_interval.func_evals,
      left=search_interval.left,
      right=search_interval.right)

  def _loop_cond(curr):
    return tf.reduce_any((curr.iteration < max_iterations) & ~curr.stopped)

  def _loop_body(curr):
    """Main body of bracketing loop."""
    # The loop maintains the invariant that curr.stopped is true if we have
    # either: failed, successfully bracketed, or not yet bracketed but need to
    # bisect. On the only remaining case, step B3 in [2], `curr.right` is
    # actually a valid left end-point, and we need to expand to guess another
    # right end-point.
    new_right = value_and_gradients_function(expansion_param * curr.right.x)
    left = val_where(curr.stopped, curr.left, curr.right)
    right = val_where(curr.stopped, curr.right, new_right)

    # Updated the failed, bracketed, and needs_bisect conditions.
    failed = curr.failed | _bad_nan(right)
    finished = _is_negative_inf(right.f)
    bracketed = _is_rising(right)
    needs_bisect = _needs_bisect(right, f_lim)
    return [_IntermediateResult(
        iteration=curr.iteration + tf.cast(~curr.stopped, tf.int32),
        stopped=curr.stopped | failed | finished | bracketed | needs_bisect,
        failed=failed,
        num_evals=curr.num_evals + 1,
        left=left,
        right=right)]

  bracket_result = tf.while_loop(
      cond=_loop_cond, body=_loop_body, loop_vars=[initial_args])[0]

  # Reset the input for the _bisect loop.
  # - `stopped` is now whether `_bisect` should feel already stopped
  # - `left` is adjusted
  #   - to equal `right` if we already found a minimum (i.e., -inf)
  #   - to the starting point if we're actually bisecting, per step B2
  finished = _is_negative_inf(bracket_result.right.f)
  bracketed = _is_rising(bracket_result.right)
  needs_bisect = _needs_bisect(bracket_result.right, f_lim)
  stopped = already_stopped | bracket_result.failed | finished | bracketed
  left = val_where(
      finished, bracket_result.right,
      val_where(needs_bisect, search_interval.left, bracket_result.left))
  bisect_args = bracket_result._replace(stopped=stopped, left=left)
  return _bisect(value_and_gradients_function, bisect_args, f_lim)

def bisect(value_and_gradients_function,
           initial_left,
           initial_right,
           f_lim):
  """Bisects an interval and updates to satisfy opposite slope conditions.

  Corresponds to the step U3 in [Hager and Zhang (2006)][2].

  Tolerates non-finite candidate right end-points.  Specifically:
  - If f(x) = -inf, that's a minimum, so we should just jump both end-points
    there and report success.
  - If f(x) = nan, fail (that is, stop trying to bisect and report failure).
  - If f(x) = +inf, that's fine, but just not an acceptable right
    end-point, so keep bisecting.

  In all of these cases, takes care to ignore the derivative, because (a) it
  doesn't inflence the outcome, and (b) if the value is infinite, the derivative
  should be `nan` (whether the client code arranged for that to be so or not).

  Args:
    value_and_gradients_function: A Python callable that accepts a real scalar
      tensor and returns a namedtuple containing the value filed `f` of the
      function and its derivative value field `df` at that point.
      Alternatively, the function may representthe batching of `n` such line
      functions (e.g. projecting a single multivariate objective function along
      `n` distinct directions at once) accepting n points as input, i.e. a
      tensor of shape [n], and return a tuple of two tensors of shape [n], the
      function values and the corresponding derivatives at the input points.
    initial_left: Return value of value_and_gradients_function at the left end
      point of the current bracketing interval.
    initial_right: Return value of value_and_gradients_function at the right end
      point of the current bracketing interval.
    f_lim: real `Tensor` of shape [n]. The function value threshold for
      the approximate Wolfe conditions to be checked for each batch member.

  Returns:
    A namedtuple containing the following fields:
      iteration: An int32 scalar `Tensor`. The number of iterations performed.
        Bounded above by `max_iterations` parameter.
      stopped: A boolean scalar `Tensor`. True if the bisect algorithm
        terminated.
      failed: A scalar boolean tensor. Indicates whether the objective function
        failed to produce a finite value.
      num_evals: A scalar int32 tensor. The number of value and gradients
        function evaluations.
      left: Return value of value_and_gradients_function at the left end
        point of the bracketing interval found.
      right: Return value of value_and_gradients_function at the right end
        point of the bracketing interval found.
  """
  failed = ~is_finite(initial_left) | tf.math.is_nan(initial_right.f)
  finished = _is_negative_inf(initial_right.f)
  bisect_args = _IntermediateResult(
      iteration=tf.convert_to_tensor(0),
      stopped=failed | finished | ~_needs_bisect(initial_right, f_lim),
      failed=failed,
      num_evals=tf.convert_to_tensor(0),
      left=val_where(finished, initial_right, initial_left),
      right=initial_right)
  return _bisect(value_and_gradients_function, bisect_args, f_lim)

def _bisect(value_and_gradients_function, initial_args, f_lim):
  """Actual implementation of bisect given initial_args in a _BracketResult."""
  def _loop_cond(curr):
    # TODO(b/112524024): Also take into account max_iterations.
    return ~tf.reduce_all(curr.stopped)

  def _loop_body(curr):
    """Narrow down interval to satisfy opposite slope conditions."""
    mid = value_and_gradients_function((curr.left.x + curr.right.x) / 2)

    # If the mid point has a value of -inf, we have found a minimum; jump
    # both ends of the interval there and exit.
    finished = _is_negative_inf(mid.f)

    # Otherwise, fail if the midpoint has a `nan` value or derivative, or if the
    # left/right points are so close to it that we can't distinguish them any
    # more.
    failed = (curr.failed |
              (~finished & ~curr.stopped &
               (_bad_nan(mid) |
                tf.equal(mid.x, curr.left.x) |
                tf.equal(mid.x, curr.right.x))))

    # We will update only non-stopped and non-failed batch members.
    to_update = ~(curr.stopped | failed)

    # If the mid point has a negative slope and the function value at that point
    # is small enough, we can use it as a new left end point to narrow down the
    # interval.  Otherwise it becomes the new right end-point.
    update_left = (mid.df < 0) & (mid.f <= f_lim)
    left = val_where(to_update & (update_left | finished), mid, curr.left)
    right = val_where(to_update & (~update_left | finished), mid, curr.right)
    # If mid point has a finite value and a positive slope, then we have found a
    # suitable right end point to bracket a minimum within opposite slopes.
    # Otherwise, the mid point has a negative slope but the function value at
    # that point is too high to work as left end point (including +inf as a
    # possibility), so we are in the same situation in which we started the
    # loop.  We just update the right end point and continue.
    stopped = curr.stopped | failed | finished | _is_rising(right)

    return [_IntermediateResult(
        iteration=curr.iteration,
        stopped=stopped,
        failed=failed,
        num_evals=curr.num_evals + 1,
        left=left,
        right=right)]

  # The interval needs updating if the right end point has a negative slope and
  # the value of the function at that point is too high. It is not a valid left
  # end point but along with the current left end point, it encloses another
  # minimum. The loop above tries to narrow the interval so that it satisfies
  # the opposite slope conditions.
  return tf.while_loop(
      cond=_loop_cond, body=_loop_body, loop_vars=[initial_args])[0]

def _needs_bisect(val, f_lim):
  """Checks whether a candidate end-point calls for bisection.

  This is the case if the derivative at that point is negative, suggesting that
  this should be a left end-point, but the function value is too high, implying
  that it must instead be a right end-point but that it is unsuitable for secant
  steps and requires bisection steps.

  This check is careful about a +inf value, in which case the derivative is
  ignored and we move to bisection.

  Args:
    val: A namedtuple with fields `f` and `df` holding the function value and
      derivative at the point of interest.  May be batched.
    f_lim: Upper bound on acceptable function values.

  Returns:
    needs_bisect: Boolean Tensor mask giving whether each end-point in the batch
      calls for bisection.
  """
  return (val.f >= float('inf')) | ((val.df < 0) & (val.f > f_lim))

def _is_negative_inf(x):
  return x <= tf.constant(float('-inf'), dtype=x.dtype)

def _is_rising(val):
  """Checks that the value is finite and the derivative is positive.

  This is the stopping condition for the loop U3, and the condition for U1.  The
  check that the value is finite is necessary to ensure that the derivative is
  meaningful.

  Args:
    val: A namedtuple with fields `f` and `df` giving the value and gradient
      of the univariate objective, respectively.  May be batched.

  Returns:
    rising: A Boolean Tensor giving whether this point is a suitable right
      end-point for an interval subject to secant subdivision.
  """
  return tf.math.is_finite(val.f) & (val.df > 0)

def is_finite(val_1, val_2=None):
  """Checks if the supplied values are finite.

  Args:
    val_1: A namedtuple instance with the function value and derivative,
      as returned e.g. by value_and_gradients_function evaluations.
    val_2: (Optional) A namedtuple instance with the function value and
      derivative, as returned e.g. by value_and_gradients_function evaluations.

  Returns:
    is_finite: Scalar boolean `Tensor` indicating whether the function value
      and the derivative in `val_1` (and optionally in `val_2`) are all finite.
  """
  val_1_finite = tf.math.is_finite(val_1.f) & tf.math.is_finite(val_1.df)
  if val_2 is not None:
    return val_1_finite & tf.math.is_finite(val_2.f) & tf.math.is_finite(
        val_2.df)
  return val_1_finite

def _bad_nan(val):
  # If the value is +-inf, nan in the derivative doesn't matter because we
  # always know what to do.
  bad_nan_df = tf.math.is_finite(val.f) & tf.math.is_nan(val.df)
  return tf.math.is_nan(val.f) | bad_nan_df

def _satisfies_wolfe(val_0,
                     val_c,
                     f_lim,
                     sufficient_decrease_param,
                     curvature_param):
  """Checks whether the Wolfe or approx Wolfe conditions are satisfied.

  The Wolfe conditions are a set of stopping criteria for an inexact line search
  algorithm. Let f(a) be the function value along the search direction and
  df(a) the derivative along the search direction evaluated a distance 'a'.
  Here 'a' is the distance along the search direction. The Wolfe conditions are:

    ```None
      f(a) <= f(0) + delta * a * df(0)   (Armijo/Sufficient decrease condition)
      df(a) >= sigma * df(0)             (Weak curvature condition)
    ```
  `delta` and `sigma` are two user supplied parameters satisfying:
   `0 < delta < sigma <= 1.`. In the following, delta is called
   `sufficient_decrease_param` and sigma is called `curvature_param`.

  On a finite precision machine, the Wolfe conditions are difficult to satisfy
  when one is close to the minimum. Hence, Hager-Zhang propose replacing
  the sufficient decrease condition with the following condition on the
  derivative in the vicinity of a minimum.

    ```None
      df(a) <= (2 * delta - 1) * df(0)  (Approx Wolfe sufficient decrease)
    ```
  This condition is only used if one is near the minimum. This is tested using

    ```None
      f(a) <= f(0) + epsilon * |f(0)|
    ```
  The following function checks both the Wolfe and approx Wolfe conditions.
  Here, `epsilon` is a small positive constant. In the following, the argument
  `f_lim` corresponds to the product: epsilon * |f(0)|.

  Args:
    val_0: A namedtuple, as returned by value_and_gradients_function
      evaluated at 0.
    val_c: A namedtuple, as returned by value_and_gradients_function
      evaluated at the point to be tested.
    f_lim: Scalar `Tensor` of real dtype. The function value threshold for
      the approximate Wolfe conditions to be checked.
    sufficient_decrease_param: Positive scalar `Tensor` of real dtype.
      Bounded above by the curvature param. Corresponds to 'delta' in the
      terminology of [Hager and Zhang (2006)][2].
    curvature_param: Positive scalar `Tensor` of real dtype. Bounded above
      by `1.`. Corresponds to 'sigma' in the terminology of
      [Hager Zhang (2005)][1].

  Returns:
    is_satisfied: A scalar boolean `Tensor` which is True if either the
      Wolfe or approximate Wolfe conditions are satisfied.
  """
  exact_wolfe_suff_dec = (sufficient_decrease_param * val_0.df >=
                          tf.math.divide_no_nan(val_c.f - val_0.f, val_c.x))
  wolfe_curvature = val_c.df >= curvature_param * val_0.df
  exact_wolfe = exact_wolfe_suff_dec & wolfe_curvature
  approx_wolfe_applies = val_c.f <= f_lim
  approx_wolfe_suff_dec = ((2 * sufficient_decrease_param - 1) * val_0.df
                           >= val_c.df)
  approx_wolfe = approx_wolfe_applies & approx_wolfe_suff_dec & wolfe_curvature
  is_satisfied = exact_wolfe | approx_wolfe
  return is_satisfied

def _secant(val_a, val_b):
  """Returns the secant interpolation for the minimum.

  The secant method is a technique for finding roots of nonlinear functions.
  When finding the minimum, one applies the secant method to the derivative
  of the function.
  For an arbitrary function and a bounding interval, the secant approximation
  can produce the next point which is outside the bounding interval. However,
  with the assumption of opposite slope condtion on the interval [a,b] the new
  point c is always bracketed by [a,b]. Note that by assumption,
  f'(a) < 0 and f'(b) > 0.
  Hence c is a weighted average of a and b and thus always in [a, b].

  Args:
    val_a: A namedtuple with the left end point, function value and derivative,
      of the current interval (i.e. a).
    val_b: A namedtuple with the right end point, function value and derivative,
      of the current interval (i.e. b).

  Returns:
    approx_minimum: A scalar real `Tensor`. An approximation to the point
      at which the derivative vanishes.
  """
  return tf.math.divide_no_nan(val_a.x * val_b.df - val_b.x * val_a.df,
                               val_b.df - val_a.df)

redpajama_set_name: RedPajamaGithub


text:
Home Gaming Information Yu-Gi-Oh! Master Duel Helps Konami Have One Of Its Most Successful...
Gaming Information
Yu-Gi-Oh! Master Duel Helps Konami Have One Of Its Most Successful Years Ever
Konami has recorded one of its most profitable years in company history.
Konami is not the company it used to be, depending on who you ask. It's always had a hand in pachinko machines and mobile releases. It also had a heavy hand in standard video game releases with franchises such as Metal Gear Solid, Castlevania, etc. Konami isnt too concerned with the latter these days. However, it is releasing the highly-anticipated TMNT: The Cowabunga Collection this year, and its focus has continued to shift into pachinko and mobile releases. That move has paid off because mobile games like Yu-Gi-Oh! Master Duelwhich is also available on consoles and PC, have helped Konami establish one of its most successful years of all time, as reported by Eurogamer,
Here's what Konami has to say about Master Duel and the general Yu-Gi-Oh! franchise, in its financial results,
Global launch of Yu-Gi-Oh! Master duel as a cross-platform title – exceeding 30 million downloads as of April
Yu-Gi-Oh! Trading Card Game continues to gain global support and deliver strong performance amid COVID-19 epidemic.
Between its main pillars of revenue, which are listed as Digital Entertainment, Amusement, Gaming & Systems, Sports, and Eliminations in the financial results, Konami has recorded 299.5 billion yen, which equates to roughly $2.3 billion. Konami reports this is a 9.9% increase in total revenues compared to Fiscal Year 2021's 272.7 billion yen. As far as actual operating profit goes, the company reported 36.6 billion yen last year and 74.4 billion yen this year, which is a massive 103.6% jump. As a result of these large financial increases from Fiscal Year 2021 to Fiscal Year 2022, the company is forecasting that it will earn 320 billion yen in Fiscal Year 2023.
Elsewhere in the company's financial results, Konami president and representative director, Kimihiko Higashio, revealed that the company intends to continue its exploration of NFTs and the metaverse space.
"Since its founding in 1969, the Konami Group has always had a pioneering spirit and the foresight to understand the world's needs," Higashio writes in a press release, "By keeping abreast of the changing trends and technology, it has continuously captured the era's crest of the wave. The field of entertainment and its potential for business growth is expanding with the evolution of technology; such as artificial intelligence (AI), fifth/sixth generation mobile communications (5G/6G), VR/AR, and NFT, the increasing performance of mobile and console game devices, and the emergence of new distribution platforms utilizing cloud technology.
"The market is expected to activate with the spread of new ways to enjoy games, such as the growing recognition of esports, in which games are regarded as a sporting event. The metaverse, a concept in extension of online games, is attracting increasing attention. We believe that there are opportunities to further enhance the value of our content, which has already formed user communities on a global scale."
It seems Konami will continue to explore the realm of NFTs, something it began doing back in January with a Castlevania 35th Anniversary "Memorial NFT" collection,
[Source: Eurogamer]
Have you played Yu-Gi-Oh! Master Duel? Let us know what you think of it in the comments below!
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text:
CNM to resume publication of student newspaper
Without water there is no New Mexico Green Chile
Radon found in state office building in Santa Fe
Wolf-dog hybrid found near Reserve
With national attention, Gov. doesn't budge on mar...
From KOB-TV.com - Central New Mexico Community College has decided to resume publication of the student newspaper the CNM Chronicle. The school had suspended publication of the paper Tuesday and Wednesday in response to an issue that focused on sexual orientation and other sex-related topics.
The CNM publications board met Wednesday and made the decision to resume publishing the paper beginning Thursday. In a statement issued on CNM's website Wednesday, President Katherine Winograd said the school pulled the papers because a high school student was included in the issue. The school, she said, needed to research the legal implications of having a minor in their publication.
She said the school was giving confiscated papers back to the newspaper staff.
Chronicle Editor-inChief Jyllian Roach was pleased with the decision. "We're really really happy with the college's decision, we're excited to see that we're back on and that we get to go back to printing and won't have to stop our publication," Roach said. Read more
© 2013 Michael Swickard, Ph.D. An Old West saying goes: Whiskey is for drinking and water is for fighting. Well, New Mexico is in a fight for its very life and it is about water. There is not enough water for New Mexico's Agriculture Industry. Without Agriculture jobs then the human capital of farm and ranch production leave the state, perhaps, forever. New Mexico is not New Mexico without Agriculture. Without water there is no Green Chile, or at least no New Mexico Green Chile. Water talks and everything else walks.
While there may be enough water for coffee making and toilet flushing; it will not be enough to keep New Mexico Agriculture viable since Agriculture runs on lots of water. We are in the fourth year of a severe drought. Even in good years New Mexico gets ten or less inches of moisture. Native plants are fine but it is not enough for farmers who measure their water needs in acre feet.
During droughts runoff is negligible. So the majority of the water is pumped. As water is increasingly pumped there is a net-loss of water from aquifers. Pumping must slow down or stop because those aquifers are dwindling, not to mention the lawsuits from Texas trying to take the last of our water. Texas could own all of our Agriculture water because of their lawsuits. Then what?
At risk are all non-native trees such as the pecan orchards which must be watered or die. Also, when the farmland goes fallow, there is a risk the farmers will just quit the business. Alfalfa is grown, harvested and transported in New Mexico by an army of highly skilled workers. Stop planting that crop and those people and machines move somewhere else.
Connected to those problems are the livestock producers who depend on alfalfa to supplementally feed their grazing animals. With less acres planted over the last decade the price of alfalfa has risen dramatically. Ranchers have had to go to slaughter with their animals because they could not afford to feed them. With good feed prices the legacy livestock can economically stay on the ranches to create the next generation of New Mexico livestock. These animals form the basis of our livestock future. However, when cattle operations have to sell off their herds, the years of research developing the right animals for our New Mexico climate is lost.
Likewise, high feed costs endanger the milk and cheese producers who feel the increased cost of feeding milk cows. Further, when New Mexico Agriculture stops shipping food there are two problems: first, the money from these Agricultural operations is lost to New Mexico, and, secondly, we no longer control our food supply. Yes, we can buy food from other American producers, but increasingly we see our food coming from areas of the world that do not say, God Bless America. Bells should ring warning us of danger. Read column
Government officials are advising workers to stay out of part of a nearly 80-year-old state office building in Santa Fe to avoid exposure to radon.
Attorney General Gary King said Tuesday that workers in his agency temporarily will not use a section of the Villagra Building, which was built as a New Deal project during the Great Depression. The building is connected to a newer complex for the attorney general's office.
General Services Department spokesman Tim Korte said elevated levels of radon were detected in the building's basement and corrective measures are planned. Radon wasn't a problem in other parts of the building.
Radon is a naturally occurring radioactive gas and enters buildings through basements and foundation cracks. The Environmental Protection Agency says radon exposure can cause lung cancer.
Federal wildlife managers have been working to return the endangered Mexican gray wolf to the American Southwest for the past 15 years.
Every now and then, there's a genetic hiccup. It happens when a wolf breeds with a domestic dog, producing a litter of hybridized pups.
Just last month, an animal that looked like a wolf was spotted in the mountain community of Reserve near the Arizona-New Mexico border. Experts with the wolf management team say the uncollared animal was most likely a wolf-dog hybrid.
While it doesn't happen often, a spokesman for the U.S. Fish and Wildlife Service says hybridization is a concern.
With national attention, Gov. doesn't budge on marriage equality
While the U.S. Supreme Court mulls over California's same-sex marriage law, New Mexico remains a state that does not allow or recognize those marriages.
Same-sex marriage legislation died in the recent session of the state legislature, and supporters vow they'll be back next year, but opposition remains strong.
While polls show many Americans have changed their minds on gay marriage in recent years, there's one New Mexico opponent who remains in a very powerful position – and she hasn't changed her mind one bit.
Gov. Susana Martinez campaigned as a backer of traditional marriage, and she's still that way today. During the 60 day legislative session New Mexicans on both sides of the issue jammed the committee rooms at the State Capitol, hoping to testify.
The legislation died in committee before the end of the session. Even if the legislature does pass same-sex marriage, the governor retains veto power.
Martinez said she is closely following what the Supreme Court does in the California case to see what impact it will have on New Mexico.


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The greatest medicine of all is to teach people how not to need it – and that's exactly what the ENERGY Booster System online course does.
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Recovery from M.E./Chronic Fatigue Syndrome or Burnout doesn't just happen by accident. It isn't something that just happens to some people and not others – it's the result created by deliberate ways of thinking and behaving.
I discovered that there were six areas of my life that I needed to change or improve on in order to not only recover from M.E. but to also avoid any episodes of Burnout ever again, and in this course I show you all of those elements.


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What's my iPhone, iPad, or Cell Phone really worth?
Interested in learning what your iPhone, iPad, or cell phone is worth? Feel free to use our selling resources to get a realistic value of your device. We'll show you where to look for cell phone values and which is best for you, as well as our opinions about the best place to sell your iPhone or cell phone.
uSell.com is simply a cell phone buyback company comparison site. They do provide systems for the companies on their site, such as prepaid labels and order status, but all in all just provide the user with company reviews and a comparison of offers to buy cell phones and iPhones. uSell.com spends money on TV ads and other advertising, so they charge high fees to the companies on their site as commissions. You'll notice many offer prices for cell phones and iPhones will be lower on uSell than on the particular buyback company's website as they will have to pay a heavy commission to uSell. That being said, by selling your iPhone or cell phone through uSell, you do have the protection that uSell will back you up in case something goes wrong.
When selling your iPhone, it is important to fully research the different avenues to sell and the different aspects of each. Make sure you know what your iPhone is worth on each website, look at the time it takes to sell your iPhone, see how long it will take to get the cash for your iPhone, and most importantly, the feedback for that company or iPhone buyer. Just because you may get a little more cash from one place, doesn't mean it's necessarily the best option to sell your iPhone or find out what it is truly worth.
Copyright Gadget Chest, 2016. We are not affiliated with any of the items' original manufacturers.


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Steve Case's Rules for Building Startups in Internet's "Third Wave"
Visible Measures Sold to AcuityAds for $10M in Video Adtech
One of Boston's biggest bets in advertising tech has been acquired for a fraction of what investors pumped into it. Visible Measures, a video analytics company, is getting scooped up by Toronto, Canada-based AcuityAds for $10 million in cash. The deal is expected to close around the end of March.
Visible Measures was founded in 2005 and raised at least $70 million in venture financing. Its investors include General Catalyst Partners, Mohr Davidow Ventures, Northgate Capital, and DAG Ventures. The company developed online-video software that advertisers and publishers could use to analyze user behavior—how viewers interact with content, which parts they watch and share, and so on. More recently, Visible Measures has been using its software platform to do content distribution for brands.
The company is led by founder and CEO Brian Shin, a well-known entrepreneur and angel investor in the Boston tech scene.
"It has been an amazing journey in building Visible Measures into the company that exists today, with a blue-chip customer base and extensive partnerships, and I am incredibly proud of the team and our accomplishments," Shin said in prepared statement. He added that through AcuityAds, "advertisers will gain access to the industry's most comprehensive suite of technology offerings to address their digital marketing needs more successfully."
Visible Measures had about 175 employees in late 2013. The company went through layoffs a year later, and it's unclear how many people remain today, or how many will be joining AcuityAds. We'll update this story if we get more details on the outcome.
Rapid7, Raytheon, Recorded Future Join Cyber Madness in Boston on April 8
Boston Tech Watch: Buoy Health, Cambridge Crops, Polarity & More


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Brad Russell Medal 2019
August 19, 2020 By ERC Community Manager
Congratulations Jesse Colquhoun – Brad Russell Medal 2019
The Brad Russell Medal is awarded to a student from Edmund Rice College in recognition of excellence in Rugby League. It is named after Mr Brad Russell, a past parent of the College and respected member of the local community who volunteered 23 years of service coaching College Rugby League sides from 1996 – 2017. The recipient of the 2019 Brad Russell Medal is Jesse Colquhoun. Jesse represented the College in Rugby League from Yr 7 to Yr 12. He was the captain of the successful Open's Rugby League Team in 2019, and was selected in the NSWCCC Rugby League Team which competed at the Australian All School Titles. Congratulations Jesse on your contribution to Rugby League at Edmund Rice College.


Gary Sieling was born in Bedfordshire and educated at Dunstable Grammar School. He read for his BMus degree at the University of London, Goldsmiths' College, and while a student played for the debut of the London Cantata Choir with whom he is still associated.
He studied organ with Peter Moorse, Nicholas Danby at the Royal College of Music, Dr Peter le Huray at St Catherine's College, Cambridge, and Jane Parker-Smith. He was awarded FRCO in 1981 and MA in Performance from Anglia Ruskin University in 1997, studying organ with Nicholas Kynaston at Caius College, Cambridge.
After 6 years in London as Director of Music at Bromley Parish Church and All Saints Blackheath, Gary has now relocated to Reading to join the music staff at the Abbey School and take on the post of Director of Music at St Mary the Virgin, Henley-on-Thames. The church choir is of high standard, sang High Mass at Brussels Cathedral in 2015 and in Cologne Cathedral in 2016. Meanwhile, he also directs the chamber choir FineChants and works as a freelance organist, harpsichordist, conductor, adjudicator, and examiner for the Royal College of Organists. He spent the summers of 2009 and 2011 as the Music Adjudicator to the Sri Lanka Performing Arts Festival. He is hoping to retire soon to take up a PhD course in Modern Harpsichord Music.
His recent CD recordings include one for Priory Records from Chelmsford Cathedral featuring the major organ works of Stanley Vann, sometime organist of Peterborough Cathedral, and the Kenneth Leighton Memorial Album which Gary compiled and published. Gary has also recorded a CD of harpsichord and organ music at Bromley Parish Church. Organ concert venues in this country include St Paul's, Canterbury and Westminster Cathedrals, King's College Cambridge, and Westminster Abbey. He has made ten international concert tours visiting Denmark, Germany, Italy and USA.
It is disappointing to report that I found no online recording of George Dyson's 1956 Prelude and Postlude, nor of Stanley Vann's Aria and Exultate.
Perhaps this will make up for the missing items - probably the most recent recording we have ever used on these pages !
Daniel Roth is the very accomplished organist of St-Sulpice in Paris, a post he has held since 1985, and in which he follows Widor and Marcel Dupré. The church has two Cavaillé-Coll organs: a 5-manual 102 stop great organ (based on an earlier instrument by Cliquot) and a separate 2-manual choir organ (updated by Mutin in 1903). With his feel for these two instruments and for the space in which they work, Roth has written Quatre pièces pour les deux orgues Cavaillé-Coll de l'église Saint-Sulpice à Paris.
which were a commission from Ken Starr of Boston.


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Dr.Khalil Shikaki is a professor of political science and the director of the Palestinian Center for Policy and Survey Research in Ramallah.
Role of Public Opinion in the Resilience/Resolution of the Palestinian-Israeli Conflict
While the most critical factor that leads to decline in support for a two-state agreement is the perceived lack of feasibility, surveys show that certain incentives can help increase support for a plan among both Palestinians and Israeli Jews.
June Violence in Gaza Weakens Hamas in the West Bank, Increases Support for Early Elections
Dissatisfied with Hamas, but Would Not Vote for Fateh
A public opinion poll assessing the degree of satisfaction with Hamas and Fateh.
Changes in Palestinian Public Attitudes in the Post Arafat Era
Recent polls show increased optimism and decreased support for violence.
'There Is No Returning to the Pre-Intifada Period'
The Al-Aqsa Intifada was a revolt against a failed peace process.
The Principle Facets of the Refugee Problem
An outline of the main obstacles to resolving the refugee problem.
The Geneva Accord and the Palestinian Response
Palestinians have been largely negative in their response to the content of the Geneva Accord.


This anthology of stories has led to an experience similar to what Hirasawa has outlined, and through my own person I am able to transition seamlessly between the pages. This ultimately led to an exceptionally real, profound and personal journey, allowing me to better understand myself as I came to understand what I was reading.
Some critics of this compilation have labelled the stories as disinteresting, claiming nothing of value is ever explored in them. I could not disagree more. In fact, these are some of the best short stories that I've ever read. I'm not fond of short stories; they're my least favourite writing template, and I often cite underdevelopment or lack of immersion as a key reason for this. But Kon's work are the perfect fit for this medium. Particularly when discussing the stories he sets in the modern world, he is able to create compelling, self-contained stories that require no further elaboration or explanation. Instead of creating grandiose settings or using ambitious forms of storytelling, Kon focuses on one issue of existence, and hones in on it using various characters. His characters are just… living. And they FEEL like they're living. In fact, Kon's rejection of any clichè, grandiose motifs in itself provides a sense of depth within the characters.
It's also important to note that Kon's works contain several elements of the surreal, whether that is actual manifestations of spirits, or plots that progress to smoothly to be coincidental. So even when "not much" happens plot wise, the reader must weigh the writing, art and characters in order to truly understand what they may represent.
… But sometimes it really is just uplifting, wholesome entertainment, and I can't help fall in love with the unabashed sentimentality within these stories. By using our current world as a backdrop, Kon can spend his efforts creating compelling plots which flow excellently. The nostalgic sentiments linked with summer, holidays and high school also aid the piece in providing positive undertones within the reader. When reading other short stories, I often find myself detached, and flick on ahead to see how many pages are left. This is mostly done out of worry, as I don't understand how an author can finish off their stories cohesively within such a short timeframe. But Dream Fossil's charming, stylish stories that never bite off more than they can chew kept me fully immersed throughout their entire segment, which always concluded with a satisfying ending.
I never found any weakness with Kon's art or writing style, though some of his settings weren't suitable for short stories. There were a few period pieces in this collection (one set during a war, and another set during feudal Japan), and I felt there wasn't enough time for Kon to fully flesh out the environments. This resulted in the reader learning about characters in an either lacklustre or underdeveloped settings (for example, the war-time piece is just set in a desert), and it often hindered the stories ability to captivate. And as I've mentioned, captivating wasn't an issue for most of these pieces, so I knew Kon was capable of it. But these pieces just had lingering questions in regards to their environment, and when the audience wasn't told, it didn't lead us to speculate (or peak interest), it just looked underdeveloped. Again, when constructing a short story, it's hard to both convey setting, develop characters and progress a story, but Kon had created the perfect marriage between the three in others works, so it was evident that the weakness was coming from the pieces themselves.
While his period pieces were lacklustre, I was intrigued by the way Kon explored science fiction. His sci-fi themed stories were reminiscent of "Akira" a pillar of modern manga in both plot and character design. It was only after reading the epilogue of this piece that I learned Kon was, in fact, the assistant of Katsuhiro Otomo (creator of Akira) at one point in time, which explains their similarities. I had already written off most of Kon's non-normal world stories, until I read the last story in this collection, a piece called "Toriko – Prisoner". It was Kon's debut work, and in my opinion, a highlight, in spite of the piece containing a certain "emptiness" that is only filled when a creator develops further. While some of his other alternate setting works struggle to convey time and atmosphere, Kon seamlessly transitioned the reader from the regular world to this dystopian future. There were enough similarities that the reader could make sense of their environment, and only after this construction did Kon add startling differences to jar the reader. In a genre saturated with poorly constructed and generic worlds, this story hones in on one aspect of Kon's futuristic society, as opposed to trying to create a new world, and explores the themes excellently. Kon was only 21 at the time of this piece's creation, yet his eye for detail and tasteful balance between writing and artistry shows. In saying that, this is the only story were there is a visible drop in artistic quality, but his panel use and action sequences often rectified this. After all, Kon's worst art is still better than most artists at their peak.
While I wouldn't suggest this for a reader looking for a Satoshi Kon introduction, "Dream Fossil" provided me with an experience I was really craving; sophisticated, masterfully mapped content with profound overtones and exceptional storytelling. In a world were overcomplicating is seen as the solution for immersion, these stories provide a refreshing simplicity that leaves the reader content with their journey. I felt much happier than I had before reading the piece, and afterwards was left to bask in a sea of reflection slowly created by Kon's work.


• Content last reviewed June 4, 2018.
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Glossary Home Poetic Forms
Alternate Rhyme
Ahl-tur-nit Rime
An alternate rhyme is a patterned rhyme scheme that's popular in classic and contemporary poetry. It occurs when the poet repeats two end sounds in an alternate pattern. For example, ABAB CDCD.
E.g. E.g. Christina Rossetti's 'Winter Rain' uses a rhyming pattern of ABAB and is a good example of an alternate rhyme scheme.
Related terms: Rhyme scheme, interlocking rhyme scheme, end rhyme, exact rhyme
The rhyme scheme is incredibly common and is often used by first-time poetry writers as a great introduction to composing a simple, easy-to-read rhyme scheme. But, at the same time, it's also used by poets who have been writing for their entire lives are whose work is read around the world.
Explore Alternate Rhyme
1 Alternate Rhyme Definition
2 Examples of Alternate Rhymes in Poetry
4 Related Literary Terms
5 Other Resources
Alternate Rhyme Definition
An alternate rhyme is a pattern found in poetry in which the author intentionally alternates between two end sounds. Usually, the pattern changes from stanza to stanza.
For example, the first stanza rhymes ABAB and the second rhymes CDCD. The same pattern repeats with differently rhymed words. There are innumerable examples of this rhyme scheme seen throughout the history of poetry.
Examples of Alternate Rhymes in Poetry
Jerusalem: And did those feet in ancient time by William Blake
This William Blake poem is a great example of how a poet might use alternate rhymes for one or more stanzas but choose a different pattern at other times. The third stanza of this four-line poem reads:
Bring me my Bow of burning gold:
Bring me my arrows of desire:
Bring me my Spear: O clouds unfold!
Bring me my Chariot of fire!
Here, the poet uses two different end sounds in lines 1-4. The first and third lines rhyme with "gold" and "unfold," with the second and fourth lines rhyming with the words "desire" and "fire."
In contrast, the other stanzas of this famous poem use a different rhyme scheme: ABCB. For instance, stanza one uses the words "time" and "god" in stanzas one and three and "green" and "seen" in lines two and four.
Read more William Blake poems.
Sea Fevers by Agnes Wathall
This, the most famous of Wathall's verses, is a fantastic example of a standard alternate rhyme scheme. The poem uses a consistent pattern of ABAB throughout. This rhyme scheme is particularly effective in this poem due to its subject matter.
The poet discusses the ocean, albatrosses, the sky, the moment of the water, and more. The "tossing" movement of the ship is mimicked by the ABAB rhyme scheme. It moves the reader back and forth from one sound to the next as though they, too, are on the water.
Here are the first four lines:
No ancient mariner I,
Hawker of public crosses,
Snaring the passersby
With my necklace of albatrosses.
The poet rhymes the words "I" and "passerby" in lines one and three and "crosses" and "albatrosses" in this stanza. Amazingly, the poet chose to use the same exact end sounds throughout the rest of the poem (something that's quite uncommon). The next stanza rhymes with "eye" and "ply" in lines one and three and "mosses" and "glosses" in lines two and four. Finally, stanza three uses "sky" and "lie" (maintaining the same vowel sound from stanza one) and "losses" and "tosses" (using the same examples of sibilance).
Explore Agnes Wathall's poetry.
Dust of Snow by Robert Frost
This short Frost poem uses a clear ABAB rhyme scheme in its two quatrains. The poet starts with the lines:
The way a crow
Shook down on me
The end of line one, "crow," rhymes with the end of line three, "snow." The end of line two, "me," rhymes with the end of line four, "tree." These simple end sounds are reflected in the poet's use of clear, colloquial syntax. The poem uses these final two lines:
And saved some part
Of a day I had rued.
The perfect ABAB rhyme scheme gives the poem a simplistic and uplifting tone as it brings readers back to the same sound over and over again.
Discover more Robert Frost poems.
What is an ABAB rhyme scheme example?
The ABAB rhymes theme is an example of a popular rhyme scheme known as an alternate rhyme. It's been used throughout the history of poetry. It can be seen as the solitary pattern of a poem and as a part of a longer rhyme scheme (like a Shakespearean sonnet).
What is an AABB rhyme called?
The AABB rhyme scheme is known as different things by different writers. The most common name for this pattern is a double couplet. It's another very common rhyme scheme found in poetry.
What is a four-line stanza called?
A four-line stanza, or a four-line poem, is known as a quatrain. This stanza length is incredibly popular. It allows writers enough space to create a unique rhyme scheme (or utilize a popular one like the alternate rhyme scheme) or employ a clear metrical pattern.
What is an ABAB rhyme scheme known as?
The ABAB rhyme scheme is known as an alternate rhyme or an interlocking rhyme. This refers to the way that the first end sound is surrounded by a different end sound on both sides.
Related Literary Terms
Masculine Rhyme: a literary device that occurs when the stressed syllables at line endings rhyme together.
Leonine Rhyme: utilizes internal rhyme and a natural pause in the middle of a line. These rhymes were most common in the Middle Ages. Specifically in Latin poetry.
Rhyme Scheme: the pattern of rhyme that's used in a poem. It corresponds with the end sounds that feature in lines of verse.
Exact Rhyme: a literary device that's used in poetry. It occurs when the writer uses the same stressed vowel or consonant sounds.
Tail Rhyme: a specific pattern of end-rhymes and repetition used in poetry. For example, AABCCB or AABCCCB.
Read: Everything You Need to Know about Rhyme Schemes in Poetry
Listen: Poetry | Rhyme Scheme, Rhythm, Repetition
Watch: Types of Poetry
Home » Poetic Forms » Alternate Rhyme
The Definitive Literary Glossary
Crafted by Experts
All terms defined are created by a team of talented literary experts, to provide an in-depth look into literary terms and poetry, like no other.
Discover 876 more terms
"The devil is in the details" is an English proverb that's used to remind someone to pay attention to the details.
Baldwin, Emma. "Alternate Rhyme". Poem Analysis, https://poemanalysis.com/poetic-form/alternate-rhyme/. Accessed 23 January 2023.


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Q: .height goes works only 1 time I have all slides hidden at first and read into an array. Then I use forward and previous buttons to navigate thru the slides. Each one has its own description, inside a div. The slideshow itself works, and the description shows perfectly at every new slide as I navigate. but when I try to show the descriptions text's height, like this:
    $('.slide-desc').click(function () {
        $(this).text($('.slide-desc').height());
    });

it will return 0 in EVERY slide EXCEPT the first one in the array.
but all slide are actually loaded into that array cuz my slide show works.
This is how I turn all slides into an array:
    function getSlides() {
        slidesArray = $('.slide').toArray();
        slidesArrayReversed = $('.slide').toArray().reverse();
    };

Why does it work only for the first?
More details:
You can see the script in action in http://purplerain.com.br/portfolio.html
and here you can see the whole script's code: http://purplerain.com.br/js/fullscreen-slideshow.js
It's just long to post here. As a warning... I tend to code using much more of a Finite State Machine mindset than an Object Oriented mindset.
thanks for the help ^^

A: There may be a problem with your js, use $(this)
$('.slide-desc').click(function () {
    $(this).text($(this).height());
});

You could also try this plugin to get the height of any element in any state (hidden or visible)
https://github.com/dreamerslab/jquery.actual

redpajama_set_name: RedPajamaStackExchange


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Louisiana Medicaid managed care contract awards thrown out
https://www.sfgate.com/news/article/A-FAMILY-S-TRAGEDY-PILOT-HAD-HUNCH-It-led-him-2483566.php
A FAMILY'S TRAGEDY / PILOT HAD HUNCH: It led him to wife, kids
By Jaxon Van Derbeken
Published 4:00 am PST, Thursday, December 7, 2006
John Rachor, a local helicopter pilot in Medford, Or., stands in front of his Robinson J44 helicopter which he was flying when he located Kati Kim and her two children and radioed her position to search and rescue personnel on Monday afternoon, December 4th. Local and state authorities announced that their search for James Kim had concluded on Wednesday, December 6, 2006, after his body was discovered by search and rescue personnel. The San Francisco man whose family was rescued Monday after they were lost in the mountains outside of Merlin, Or., set out on foot to try to find help, but died as he attempted to bring assistance to his wife and two daughers. Photo by Carlos Avila Gonzalez/The San Francisco Chronicle Photo taken on 12/6/06, in Medford, Or, USA. **All names cq (source) less
John Rachor, a local helicopter pilot in Medford, Or., stands in front of his Robinson J44 helicopter which he was flying when he located Kati Kim and her two children and radioed her position to search and ... more
Photo: Carlos Avila Gonzalez
A private helicopter pilot who says he spotted James Kim's footprints in the snow and then located Kim's wife and children alive in the southern Oregon mountains had a hunch the family had made a mistake common to motorists unfamiliar with the area.
That mistake was taking a wrong turn as Bear Camp Road climbs high into the mountains out of Grants Pass. Veer left, and the road continues to Gold Beach, the Kims' intended destination the night of Nov. 25. Veer right, and it's more than 15 miles down a logging road with no outlet.
John Rachor's hunch turned out to be right.
"When I heard the family was lost, immediately I had a pretty good idea where they were," Rachor, a helicopter pilot for 10 years, said Wednesday. "It's real easy to take a wrong turn where they did. A lot of people make that mistake."
Rachor, 58, of Medford, Ore., uses his flame-emblazoned helicopter to fly back and forth among the eight Burger Kings he owns in the southwest part of the state.
He said he went up on his own looking for the Kims on Sunday after reading in the local newspaper that they had gone missing on the way to Gold Beach.
"I saw the picture of the kids in the paper," Rachor said. "I really know that area well. I live out there, so I know it better than most. I fly over it once a week."
Rachor knew that helicopters hired by the Kim family were looking closer to the coast. He said he had contact with government searchers, but they were looking elsewhere as well.
At noon Sunday, he said, he spotted what appeared to be fresh tire tracks down the logging road. He returned to the area Monday and spotted human footprints. Then a few miles down the road, he saw Kim's wife, Kati Kim, "waving an umbrella, running around in circles."
He said she had stomped "SOS" and "out of gas" in the snow. The umbrella was clearly visible from "quite a ways away," he said.
He radioed for a helicopter chartered by the family to pick up Kati Kim and the couple's two young daughters, then waited until it arrived. He said he didn't land himself because he would have had too little clearance for his rotors.
The footprints he had spotted were 5 miles from the Kims' stranded Saab station wagon, about where James Kim turned into a creek ravine where his body eventually was found. Kim would have had to walk 10 more miles up the logging road before he got to Bear Camp Road, Rachor said.
The logging road could easily pass for the main route where it splits off from Bear Camp Road, he said.
"It's a major road because they did a lot of logging -- it is wider than the correct road," he said. "It has a gate, but the gate has been open all winter."
He said he returned Monday to look for James Kim and again Tuesday. But by then, he said, "they had the area covered."
The news that James Kim had been found dead, after the jubilation of finding his wife and girls alive, hit Rachor hard.
"That's a real sad deal," he said.


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760d.61/790: Telegram
The Ambassador in the Soviet Union (Steinhardt) to the Secretary of State
Moscow, December 18, 1939—7 p.m.
[Received December 19—8:35 a.m.]
1107. It is becoming increasing[ly] apparent that the Soviet plans in respect of Finland have seriously miscarried and that the Soviet Government is now faced with the necessity of expending a far greater effort than had been anticipated. From many indications it would appear that Stalin was convinced that a military attack coupled with the formation of the Kuusinen government would produce internal dissension in Finland sufficient to bring about a speedy collapse of Finnish organized resistance and a swift and easy Soviet victory.
Insofar as concerns the military operations in addition to Potemkin's boast to the French Ambassador reported in my 1045, December 8, 2 p.m., I have learned from a number of reliable Soviet sources that the military plans the termination of a 9-day operation against Finland. It is rumored that Voroshilov personally assured Stalin that his motorized columns would have no difficulty in reaching Helsinki within 6 days.
Further evidence of Soviet overconfidence and miscalculation may be found in the prompt repudiation by the Commissariat for Foreign Affairs of the article concerning Rumania in the Communist International which was clearly published to coincide with or follow on the heels of the anticipated collapse of Finland.
Although military information in the Soviet press has been confined to the daily communique which gives few details of the fighting, even accepting these communiques at their face value, it is clear that far from achieving a speedy victory, the Soviet forces, after 19 days on the offensive, have made very little progress against Finland. Reports have reached Moscow from numerous sources that the large number of wounded arriving in Leningrad have necessitated the conversion of schools and other public buildings into temporary hospitals. Up to the present, no indication as to Soviet losses has appeared in the Soviet press. Swedish sources estimate the Soviet dead at 25,000.
The discomfiture of the Soviet Government over the miscarriage of its plans in Finland is reflected in the press which except for the daily communique continues to ignore the war with Finland. Reports from the field correspondents of leading Moscow newspapers which were a feature of the opening days of hostilities have been discontinued. Furthermore, the Soviet no longer makes mention of the whereabouts or the activities of the "Kuusinen government" which has maintained complete silence since the conclusion of the treaty of December 2 with the Soviet Government.
In addition I believe that the Soviet Government did not anticipate the extent of the foreign reaction against Soviet aggression or expulsion from the League. The fact that the Soviet Union apparently preferred to accept expulsion from the League rather than to withdraw of its own accord may have been due to the expectation of the Soviet Government that at least one country represented on the League Council would be sufficiently afraid of Soviet displeasure to block the required unanimous passage of a resolution of expulsion. The tone of the editorials dealing with the League action which have appeared in the leading Moscow newspapers reflect a certain uneasiness as to possible further developments in Soviet relations with foreign countries and the care taken in the editorials to divest the Soviet Union of responsibility for the fact that it is now outside the League may indicate that the Soviet Union while continuing its war with Finland does not desire at this time to precipitate a rupture with England, France or the United States.
The obvious miscarriage of Soviet plans in respect of Finland does not however mean that the Soviet Government has any intention of departing from its announced course in regard to that country. On the contrary, having definitely and publicly committed itself to the Kuusinen government and the Finnish campaign, it is extremely unlikely that the Soviet Government, if only for reasons of military prestige, would be prepared to consider the abandonment of the Finnish venture.
In view of the overwhelming preponderance of the Soviet armed forces the final outcome would appear to be only a matter of time unless Finland is able to obtain sufficient support from other countries. Should this support be forthcoming the effort which would be required of the Soviet Union might complicate Stalin's internal and external situation to such an extent as to endanger the present regime.
Source: Foreign relations of the United States. Diplomatic papers. 1939. Volume I. General. (CONCERN OF THE UNITED STATES OVER SOVIET DEMANDS ON FINLAND AND THE OUTBREAK OF THE WINTER WAR). Department of State 1956, publ. 6242. (University of Wisconsin Digital Collections)
Previous (Dec. 3) | Finland in Great Power politics | Next (Jan. 2, 1940).


This collection of shirts runs the gamut of color.
The definition of gamut is the entire range or extent of something.
An example of gamut is a clothing collection that includes every color of the rainbow.
"gamut." YourDictionary, n.d. Web. 16 April 2019. <https://www.yourdictionary.com/GAMUT>.
A complete range or extent: a face that expressed a gamut of emotions, from rage to peaceful contentment.
Music The entire series of recognized notes.
(music) All the notes in the musical scale.
All the colours available to a device such as a monitor or printer.
1520s, original sense "lowest note of musical scale", from Medieval Latin gamma ut, from gamma ("(Greek letter, corresponding to the musical note G)") + ut ("first solfège syllable, now replaced by do"). In modern terms, "G do" – the first note of the G scale . Meaning later extended to mean all the notes of a scale, and then more generally any complete range.
Christmas cakes run the gamut from plain cakes decorated with red and green icing to fruitcake, Italian Panettone, and King Cake, which is served on the Epiphany to celebrate the arrival of the kings to the manger.
Male celebrity hairstyles run the gamut from sleek and simple to wild and crazy, but not matter what fans may think of any movie, song, or awards show appearance, you can always find something to admire in a great celebrity hair style.
Their site offers links that let you see which styles are most popular, what other customers have to say and a menu of styles to choose from as dress and skirt styles run the fashion gamut from casual and career wear to evening wear.
Grand Canyon University: Featuring several graduate degree programs in healthcare from an MS in Nursing Science to an MBA in healthcare management, Grand Canyon University covers the gamut of online education.
While most individuals automatically think of a full-length ball gown when they hear the term formal wear, the reality is garments for formal occasions run the gamut from evening pantsuits to formal gowns to dressy casual outfits.


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Pontiac Model
Torrent grille guards
Many accidents hit the road everyday, which do not only hurt lives, but also leave the vehicles dilapidated. If air bags are for people, then grille guard is for the cars or trucks. The majority of Pontiac Grill Guards are made with stainless steel tubing, so they are light and they look great. Stainless steel is also great because it will stand up to the test of time and generally will not rust and is very resistant. Chrome grille guards are like a crown in many ways. They are a sign of royalty, they cover the head of your Pontiac, they are powerful, and they let the masses know that you mean business. If you take your truck out on the trail, then grille guards may also be called brush guards or bull bar.
These are several names for the same purpose. Whether you drive in the woods or the highway, the most important thing is that you get a quality Bull Bars made out of chrome. Protecting your truck with quality parts will help you achieve the goal of duration and it will make your truck a unique king of the road for many years to come. When you're in the market for a truck grille guard, there are a few things to bear in mind. The first is that you should only purchase grille guards that are designed to fit your Pontiac model. This eliminates the need for drilling new holes in the underside of the vehicle and provides a perfect fit for your new push bar. The second thing that you need to look for is a complete package, if you want your Pontiac Grill Guards with all the options, including a skid plate as it provides protection below the engine, as well as in front of the truck.
About PONTIAC
For those who desire long-range durability but also crave reciprocal performance, Pontiac has been there, featuring models such as the flagship Bonneville, the ever-popular Trans Am, the sleek but economical Sunfire editions, all the way to the even more contemporary "G" line that has garnered so much attention. A stand out performer on the track as well as the street, Pontiac drivers will always enjoy the power and style that they have come to know and love. It's a relationship as boundless as the name itself.
Thanks to a complete redesign and the success of NBC's Knight Rider, the Firebird created a surge that would help set a tempo for the first part of the decade. The Fiero was introduced two years later in 1984. A two-seater coupe, the Pontiac Fiero provided drivers with a nice chunk of performance at a decent price tag and became an immediate hit. Together with the Grand Prix, Grand Am, and Trans Am still holding strong, Pontiac models were gaining consistent sales. The '90s saw anti-lock brakes, airbags, and GM's vaunted Quad-4 engine as standard Pontiac parts on all models. Revisions to the Firebird in 1993 and 1998, respectively, sparked interest. Pontiac also renewed the Grand Prix, making it comparable to compact stars such as the Accord. As the new millennium came into focus, Pontiac got to work. Thanks to the birth of the Pursuit and the Grand Am's transformation into the stylish G6 in 2004, Pontiac sustained relevancy. Other models such as the sporty G8, the Torrent - a versatile yet stylish SUV, and the legendary GTO all contribute to the ongoing legacy that Pontiac is known for throughout the country.
Since 1926, Pontiac has been recognized for producing high-performing vehicles that deliver on style and dependability. Named after the famous Native American Chief Pontiac, the brand started off in 1900 as the Pontiac Spring & Wagon Works. In 1908, the Oakland Motor Company and Pontiac Spring & Wagon Works merged to form the Oakland Motor Car Company. Soon after, GM purchased the Oakland Motor Car Company. Pontiac quickly took off, not only outselling Oakland, but establishing a standard with its 6-cylinder Cartercar. Throughout the 1920s Pontiac's line of 6-cylinder engines made an immediate impact, most notably with the Chief, which sold almost 77,000 units in its first year. Come the early '30s, Pontiac was assembling affordable sedans which featured inline 8-cylinder engines by borrowing heavily from GM stablemate Chevy. It was during these instances of innovation where Pontiac began to assert itself more and more as a company with a reputation for providing performance-oriented vehicles and strong Pontiac parts the everyday driver could get their hands on.