Special Robot
问题描述 :
Now Bob wants to conduct a performance for his robot. The situation can be described as follows: n balloons stay still on the ground; all of these balloons have an initial position whose Y-coordinates are 0.
Each balloon will move up at certain time. The initial position of the robot is at (0, 0) and if the robot is at position (x, y) at time t, then it can only move to position (x, y-1) or position (x+1, y+1) in a unit time (the robot con not stay still at a position). Its task is to collect as more balloons as possible when it gets to the destination (K,0).
When the robot is moving on the plane, balloons are continuously rising on its path from the ground. Each balloon can reach a unit height in a unit time.
When the robot meets a balloon, it collects the balloon. The situation can be described as the following three examples which are shown in Figure 2:
Example 1: At time t, the robot is at position (x, y), and a balloon is at position (x, y-2). The robot moves down and the balloon moves up; then at time t+1, the robot and the balloon meet at position (x, y-1), so the balloon can be collected by the robot ( which is shown in Figure 2(a)).
Example 2: At time t, there is a balloon at position (x, y-1) and the robot stands at position (x, y). Because the balloon always moves up and the robot can move down, so the robot can collect this balloon because they will meet at some point between position (x, y-1) and (x, y) during their movement (which is shown in Figure 2(b)).
Example 3: At time t, a balloon is at position (x+1, y) and the robot is at position (x, y). Because the robot can arrive at position (x+1, y+1) while the balloon can move up to the same position, the robot could collect the balloon at time t+1 (which is shown in Figure 2(c)).
Maybe it is too easy a problem to control one robot, so Bob intends to control two robots simultaneously.
You need to help him to calculate the maximum balloons these two robots can collect when they get to the destination.
It is possible that the two robots get to the same position at the same time. Note that one balloon could not be collected by two robots and the balloons staying on the ground could not be collected either.
Robot should not move to the underground (y < 0).
输入:
The first integer indicates the position of a balloon and the other indicates the beginning time for it to rise. In the value of n and K, n is the number of balloons and K is the x-coordinate of the destination. The input is ended by two zeros.
输出:
The first integer indicates the position of a balloon and the other indicates the beginning time for it to rise. In the value of n and K, n is the number of balloons and K is the x-coordinate of the destination. The input is ended by two zeros.
样例输入:
3 2 1 1 1 1 1 2 7 4 2 0 2 3 3 4 3 0 3 2 4 0 1 1 0 0
样例输出:
2 6
一个游戏卖15块，圣诞五折促销，花7块钱买到手，原价15块的游戏只花了7块就买到相当于赚了8块，再把买游戏的7块钱从赚的8块钱里扣除，等于白得一个游戏还赚了1块钱。
一个游戏卖15块，圣诞五折促销，花7块钱买到手，原价15块的游戏只花了7块就买到相当于赚了8块，再把买游戏的7块钱从赚的8块钱里扣除，等于白得一个游戏还赚了1块钱。
一个游戏卖15块，圣诞五折促销，花7块钱买到手，原价15块的游戏只花了7块就买到相当于赚了8块，再把买游戏的7块钱从赚的8块钱里扣除，等于白得一个游戏还赚了1块钱。
一个游戏卖15块，圣诞五折促销，花7块钱买到手，原价15块的游戏只花了7块就买到相当于赚了8块，再把买游戏的7块钱从赚的8块钱里扣除，等于白得一个游戏还赚了1块钱。
一个游戏卖15块，圣诞五折促销，花7块钱买到手，原价15块的游戏只花了7块就买到相当于赚了8块，再把买游戏的7块钱从赚的8块钱里扣除，等于白得一个游戏还赚了1块钱。
一个游戏卖15块，圣诞五折促销，花7块钱买到手，原价15块的游戏只花了7块就买到相当于赚了8块，再把买游戏的7块钱从赚的8块钱里扣除，等于白得一个游戏还赚了1块钱。
一个游戏卖15块，圣诞五折促销，花7块钱买到手，原价15块的游戏只花了7块就买到相当于赚了8块，再把买游戏的7块钱从赚的8块钱里扣除，等于白得一个游戏还赚了1块钱。
一个游戏卖15块，圣诞五折促销，花7块钱买到手，原价15块的游戏只花了7块就买到相当于赚了8块，再把买游戏的7块钱从赚的8块钱里扣除，等于白得一个游戏还赚了1块钱。
https://coding.net/moon/miracle
总数为2n，遍历到第n个，就是中间的位置
给力！
有两个重复的话结果是正确的，但解法不够严谨，后面重复的覆盖掉前面的，由于题目数据限制也比较严，所以能提交通过。已更新算法
正有此意，欢迎更多的建议~
学习了
这个问题别太纠结了，不同的编译器结果不一
不是有公式吗？何不直接计算
感觉第二种方法其实也不是很好，不过的确要比第一种方法要好一点。
算法是程序的灵魂，算法分简单和复杂，如果不搞大数据类，程序员了解一下简单点的算法也是可以的，但是会算法的一定要会编程才行，程序员不一定要会算法，利于自己项目需要的可以简单了解。
输入样例作为一个整体输入，带回车
第一句可以忽略不计了吧。从第二句开始分析，说明这个花色下的所有牌都会在其它里面出现，那么还剩下♠和♦。第三句，可以排除2和7，因为在两种花色里有。现在是第四句，因为♠还剩下多个，只有是♦B才能知道答案。
为什么不直接用STL排序呢？
不需要输出，只要函数返回值正确就行


#!/usr/bin/env python
# encoding: utf-8
__copyright__ = "Copyright 2021, AAIR Lab, ASU"
__authors__ = ["Naman Shah", "Chirav Dave", "Ketan Patil", "Pulkit Verma"]
__credits__ = ["Siddharth Srivastava"]
__license__ = "MIT"
__version__ = "1.0"
__maintainers__ = ["Pulkit Verma", "Abhyudaya Srinet"]
__contact__ =<EMAIL_ADDRESS>__docformat__ = 'reStructuredText'
import rospy
from gazebo_msgs.msg import ModelState
from planning.srv import PlaceActionMsg
from planning.srv import PickActionMsg
from std_msgs.msg import String
from planning.srv import RemoveBlockedEdgeMsg
from planning.srv import MoveActionMsg
[docs]class RobotActionsServer:
"""
This class provides APIs for executing the actions needed for Planning Assignment
"""
def __init__(self, object_dict):
"""
:param object_dict: dictionary of objects in the environment.
:type object_dict: dict(str:list(str))
"""
self.failure = -1
self.success = 1
self.object_dict = object_dict
self.empty = True
self.status = String(data='Idle')
self.model_state_publisher = rospy.Publisher("/gazebo/set_model_state",ModelState,queue_size = 10)
self.action_publisher = rospy.Publisher("/actions",String,queue_size= 10)
self.status_publisher = rospy.Publisher("/status",String,queue_size=10)
rospy.Service("execute_place_action",PlaceActionMsg,self.execute_place_action)
rospy.Service("execute_pick_action",PickActionMsg,self.execute_pick_action)
rospy.Service("execute_move_action",MoveActionMsg,self.execute_move_action)
print "Action Server Initiated"
def change_state(self,book_name,target_transform):
model_state_msg = ModelState()
model_state_msg.model_name = book_name
model_state_msg.pose.position.x = target_transform[0]
model_state_msg.pose.position.y = target_transform[1]
model_state_msg.pose.position.z = target_transform[2]
self.model_state_publisher.publish(model_state_msg)
def remove_edge(self,book_name):
rospy.wait_for_service('remove_blocked_edge')
try:
remove_edge = rospy.ServiceProxy('remove_blocked_edge',RemoveBlockedEdgeMsg)
_ = remove_edge(book_name)
except rospy.ServiceException,e:
print "Sevice call failed: %s"%e
[docs] def execute_place_action(self, req):
"""
This action places the book named book_name in the bin named bin_name when the TurtleBot is at State robot_state.
:param book_name: Name of the book that has to placed in the bin.
:type book_name: str
:param bin_name: Name of the bin where the book named book_name will be placed.
:type bin_name: str
:param robot_state: State of the robot and and the bin.
:type robot_state: State
:returns: True if the place action was successful. False, otherwise.
:rtype: bool
.. warning::
This action will fail (Return False) if:
.. hlist::
:columns: 1
* Book name or Bin name is not valid.
* Book size and bin size does not match.
* Subject of the book and bin does not match.
* Robot Location is not within the load location of the bin, i.e. robot is not in the viscinity of the bin.
"""
book_name = req.book_name
bin_name = req.bin_name
robot_state = (req.x , req.y , req.orientation)
if book_name in self.object_dict["books"] and bin_name in self.object_dict["bins"]:
if (robot_state[0],robot_state[1]) in self.object_dict["bins"][bin_name]["load_loc"]:
if self.object_dict["books"][book_name]["size"] == self.object_dict["bins"][bin_name]["size"] and \
self.object_dict["books"][book_name]["subject"] == self.object_dict["bins"][bin_name]["subject"]:
goal_loc = list(self.object_dict["bins"][bin_name]["loc"])
goal_loc[0] = goal_loc[0] + 0.5
goal_loc[1] = goal_loc[1] + 0.5
self.change_state(book_name, goal_loc + [3])
self.empty = True
self.status_publisher.publish(self.status)
return self.success
self.status_publisher.publish(self.status)
return self.failure
[docs] def execute_pick_action(self, req):
"""
This action picks the book named book_name located at State robot_state.
:param book_name: Name of the book
:type book_name: str
:param robot_state: State of the robot.
:type robot_state: State
:returns: True if the place action was successful. False, otherwise.
:rtype: bool
.. warning::
This action will fail (Return False) if:
.. hlist::
:columns: 1
* Book name is not valid.
* Robot Location is not within the load location of the book, i.e. robot is not in the viscinity of the book.
"""
book_name = req.book_name
robot_state = [req.x , req.y ,req.orientation]
if book_name in self.object_dict["books"]:
if (robot_state[0],robot_state[1]) in self.object_dict["books"][book_name]["load_loc"]:
if self.empty:
self.change_state(book_name,robot_state[:2]+[2])
self.empty = False
_ = self.remove_edge(book_name)
self.status_publisher.publish(self.status)
return self.success
self.status_publisher.publish(self.status)
return self.failure
[docs] def execute_move_action(self, req):
"""
This action executes the actions in the action_seq. The actions are executed in the same sequence as they appear in action_seq list.
:param action_seq: Sequence of actions to be executed by the TurtleBot.
:type action_seq: list(str)
:rtype: None
.. warning::
This method will execute the actions even if they are wrong semantically, i.e. do not have the desired effect. It is the responsibility of the caller to ensure that the action sequence is applicable from the current state of the robot.
"""
action_seq = req.actions
self.action_publisher.publish(String(data=action_seq))
return self.success
if __name__ == "__main__":
object_dict = None
RobotActionsServer(object_dict)


A Ma Maniére & Nike Ready Limited Air Force 1 "Hand Wash Cold"
In both high and low iterations.
by Mallory ChinAtlanta-based boutique A Ma Maniére and sportswear giant Nike are gearing up to release a special limited-edition Air Force 1.
Dubbed “Hand Wash Cold,” the silhouette has been fashioned from premium materials including smoothened nubucks, supple leathers and buttery suedes, and utilizing a color palette of greys and dark blacks. The collaborative model is then elevated with hits of contrasting blue outlining the upper’s panels, mis-matching blue and red dubraes, and finally iridescent hardware. Additionally each pair is individually numbered (out of 5,914 for the high and 5,269 for the low).
On the team up, James Whitner of the Whitaker Group shared, “The shoe tells a story of giving and giving back. Everything luxury should be taken care of, and lives in communities are delicate.” He continued, “The AF1 was our choice of shoe before we found luxury. We have to put a luxury expression on it, but do it in a way that feels uniquely ours. I wanted the shoe to look like it fits next to the brands in A Ma Maniére, but still feels natural and true to Air Force 1.”
The A Ma Maniére x Nike Air Force 1 “Hand Wash Cold” will be available in both low and high-top iterations and limited to just 5,269 of the former and 5,914 of the latter. Look for the sneaker to drop via Nike’s SNKRS app and all A Ma Maniére boutiques starting December 7. The low will also be available at select stores across the U.S. and Canada including: Social Status, APB, Concepts, Bodega, Creme, Shoe Gallery, UNKNWN, Trophy Room, Politics, Xhibition, Corporate, Oneness, Rooted, Sole Classics, Undefeated, Bait, Darkside Initiative, Feature, Likelihood, Livestock, Xclucity, and HAVEN.
For more footwear news, Jordan Brand combines four OG Air Jordan 4s in new “What The” colorway.
A Ma Maniére x Nike Air Force 1 “Hand Wash Cold”
Release Date
December 07, 2019 - 12:00 am EST (TBC)
Price
TBC


From:
http://www.lonelyplanet.com/thorntree/thread.jspa?threadID=1384566Try to negotiate a monthly rate with a place that you like. Stay a couple of days beforehand to check it out.
The main street that runs perpendicular to western street (on the non-li river side) has lots of little guest houses and apartments behind the shopfronts. Go up any of the lanes that come off that street and you'll find guest houses tucked away inside. You can negociate cheap rates with them. I stayed for 2 weeks in a room with three beds and my own bathroom/toilet, where they did my washing and even cooked for me sometimes and i payed 40rmb a night. you can always stay somewhere for a few days while you look for a better deal elsewhere. You'll have a great time there.


CARTOON: The Colin Kaepernick saga continues
by Michael Ramirez, Las Vegas Review-JournalBut he was in the middle of taking a selfie …
Review-Journal editorial cartoonist Michael Ramirez is a two-time winner of the Pulitzer Prize and a three-time winner of the Sigma Delta Chi Award.


Log file sizes impose a time limit on the length of time a database may be accessed under transaction protection, before it needs to be dumped and reloaded (see db_dump and db_load). Unfortunately, the limits are potentially difficult to calculate.
The log file name consists of log. followed by 10 digits, with a maximum of 2,000,000,000 log files. Consider an application performing 3000 transactions per second, for 24 hours a day, logged into 10Mb log files, where each transaction is logging approximately 500 bytes of data. The calculation:
(10 * 2^20 *<PHONE_NUMBER>) / (3000 * 500 * 365 * 60 * 60 * 24) = ~443
indicates that the system will run out of log file names in roughly 443 years.
There is no way to reset the log file name space in Berkeley DB. If your application is reaching the end of its log file name space, you should:


[1]
may well have been a necessity in order to survive in today 's economy.
[2]
may well have been a necessity in order to survive in today 's experience nonantum is n't.


Portfolio controller
This controller contains every action relative to the portfolio management.
Located in /application/modules/default/controllers/PortfolioController.php (line 26)
Zend_Controller_Action | --PortfolioController
Buy shares through an Ajax request
Redirect to the list action
Init Ajax context and redirect requests to actions according to the information sent in the HTTP request header.
In case of an Ajax request, the action only renders the portfolio as an HTML content.
Otherwise, it renders a full classic page rendered by the viewscript.
Sell shares through an Ajax request
Documentation generated on Tue, 21 Feb 2012 06:45:24 +0800 by phpDocumentor 1.4.1


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February 03, 2003
He takes fatal OD as Internet pals watch
New York Daily News | Submitted by: Puppy Rapist
"I told u I was hardcore." Those were the last coherent words Brandon Vedas, 21, typed into the computer in his Phoenix bedroom as he showed off for Internet pals watching on a Web cam by swallowing more and more prescription drugs... His mother found him at 1 p.m. the next day sprawled on his bed. The tech whiz's computer had shut down and locked itself automatically, so it wasn't until more than a week later that the family found out his death had had witnesses."
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Read article...
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Couple lost son on Sept. 11 lost niece on Shuttle
Fox News | Submitted by: anonymous
"Astronaut Laurel Clark's death on the space shuttle Columbia was the second sudden and very public tragedy to hit Doug and Betty Haviland in 17 months. On Sept. 11, 2001, television had first brought tragedy. The Havilands were watching after the World Trade Center absorbed the impact of a terrorist-piloted jetliner, burned, then collapsed with their 41-year-old son Timothy inside."
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Read article...
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Man sues police over the Valentine card they sent
London Telegraph | Submitted by: Boat6float
"Williams, 26, will go to the High Court next month to seek a judicial review of the actions of Ken Jones, the chief constable of Sussex. He is seeking damages, arguing that the card was malicious and caused him distress." But wait, he is a car thief and criminal who has racked up 17 convictions since 1990.
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Prison demands their 25 cent overpayment back
Stuff NZ | Submitted by: wingnut
"On payday 8 November 2000 you were overpaid by 25 cents... "This was due to you receiving your annual leave paid in advance and when you returned to normal pay it was not enough to cover your deductions."Would you please give your permission for the 25 cents to be recovered from your pay." That's $.25 NZ"
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Aussie space spiders perish, a nation weeps
Sydney Morning Herald | Submitted by: Nick
"The man behind Melbourne's spiders-in-space project choked back tears today as he recalled hearing the chilling news the space shuttle Columbia had disintegrated. "Obviously the results of the experiment aren't relevant to anybody today," an emotional Mr Manning told AAP." How many more spiders must die?
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Insects as instruments of biological warfare?
Voice of America | Submitted by: comesolito
"The use of insects as agents of warfare is not a new idea. In the Biblical story of the Exodus, one of the plagues visited upon the Egyptians was a horde of locusts. In a short-lived program in the mid-20th century, the U.S. Army reared 100 million yellow fever mosquitoes each week for possible use as a biological weapon against enemy troops."
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For-profit Moon mission slated for October
National Geographic | Submitted by: comesolito
"Earthlings can send personal items to the lunar surface at the rate of US $2,500 per gram. In addition, written messages up the 300 characters long can be etched onto a moon-bound disk for U.S. $16.95. For U.S. $13 more, consumers can send a text message of 9,600 characters... The U.S. government agencies granted TransOrbital permission to launch their moon shoot in September 2002, but only after the company agreed not to harm the lunar environment and to preserve historical sites."
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Remains of cyclops creature found on Crete
CNN | Submitted by: comesolito
"Researchers on the southern Greek island of Crete have unearthed the fossilized tusk, teeth and bones of a Deinotherium Gigantisimum, a fearsome elephant-like creature that might have given rise to ancient legends of one eyed cyclops monsters."
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Win an election: Control the voting machines
Common Dreams | Submitted by: Vomit
"Former conservative radio talk-show host and now Republican U.S. Senator Chuck Hagel was the head of, and continues to own part interest in, the company that owns the company that installed, programmed, and largely ran the voting machines that were used by most of the citizens... votes were counted by computer controlled voting machines put in place by the company affiliated with Hagel. Built by that company. Programmed by that company."
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More Nude Protesters!
Midia Independente | Submitted by: Crazy Quagga
But not PETA this time. "It started when a Mapuche woman who was attending the WSF[World Social Forum] and staying at the international youth camp was arrested with charges of "obscene acts" for bathing nude in a river. Activists saw this and organized over 400 people to protest by nonviolently taking their clothes off in solidarity."
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Spiders like fat chicks
ABC News | Submitted by: Crazy Quagga
Male long-jawed spiders go looking for the heaviest honeys, who are also the most likely to go cannibalistic on the honeymoon. "The joke we tell is for males they mate with anything that moves and a few things that don't move just to make sure you don't miss anything. But this (study) shows... they factor more things into mating than we thought."
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Job from hell
New York Post | Submitted by: wingnut
"In order to be promoted to manager, and earn a salary of $24,000 to $30,000 a year, former employees said they had to have five new trainees working under them - but if their new trainees didn't make at least $40 a day in sales, they had to pay them $40 out of pocket. "I made $14,000 in 2001 for working 80 hours a week, but spent about $20,000 in order to get the job done," said ex-employee Cary Bruce, 25, who worked at the company for 21/2 years and quit in March 2002." Real management material here.
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Putting indigent souls to rest in Cleveland's Potter's Field
Cleveland Plain Dealer | Submitted by: wingnut
"The caskets are made of flakeboard, a siding material made of wood scraps, sawdust and glue. A white cardboard wrap held in place by black plastic straps covers the tops and sides of the caskets. The name of the deceased is scrawled on the cardboard... It's the rare body that cannot be identified. It's not nice to tell them someone died, but we've had families say, "Oh, my God, I had no idea they were still in Cleveland."
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Man drove into woman because she's black
St. Louis Today | Submitted by: Any Mouse
After hitting a black woman waiting for a bus, "Bad" Steven Johnson sat calmly in his car with Jesus until the police arrived. He used several racial slurs, police said. And he claimed to be doing the work of God.
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Pimp of the Year
Atlanta Journal-Constitution | Submitted by: Dirty Sammy
"I think having a pimp convention in and of itself is totally inappropriate, no matter where it is," said former federal prosecutor Janis Gordon, now a DeKalb State Court judge. "I think it is particularly inappropriate to have it in Atlanta when Atlanta has taken such steps to eradicate child prostitution." These people seem unable to distinguish between real pimps and poseur players.
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Human remains from shuttle found
Space.com | Submitted by: yidele
"In Hemphill, near the Louisiana state line, hospital employee Mike Gibbs reported finding what appeared to be a charred torso, thigh bone and skull on a rural road near what appeared to be other debris. Billy Smith, an emergency coordinator for three East Texas counties, confirmed the find... "I wouldn't want anybody seeing what I saw... It was pretty gruesome." On a farm, also in Sabine County, two boys found a charred human leg... "From the hip to the foot, it's all there, scorched from the fire."
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Read article... and
This article...
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Valium turns 40
Observer | Submitted by: Ann Onymous
Happy birthday. Try not to overdose, okay? "They were infinitely safer than barbiturates, which was what people had used to treat anxiety before, and which carried a great risk of fatal overdose. So we were enthusiastic. I myself took enormous quantities to see if there was any toxic effects, but my wife said I was just the same."
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Fear killed these chickens
News Corporation | Submitted by: Taleya
"Nearly 2,000 chickens on a southern China farm died from fright or were suffocated by other terrified birds when teenagers set off firecrackers for Chinese New Year... He rushed out to check his 5,000 chickens and found them terrified and crowded into a corner of the farm furthest from the noise....Some 2,000 of the chickens had already died of fright or from being crushed or suffocated by other chickens."
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Undergraduate: UC San Diego
Dream: Being a fullstack developer
Hobbies: Piano, A lot of Sports, Programming, Video Games


Build Custom Abbreviations
You can use this tool to build a custom list of your atom abbreviations for JSDraw, TouchMol and MolEngine.
1. Use follow JSDraw.Table to build your abbreviations list (Right-click atom to add attach points 1, 2, 3)
2. Once finished, click Save button to save the list into a SDF file
3. To configure JSDraw, TouchMol and MolEngine to use this custom abbreviations list,
have the following in to your configuration file (web.config, scilligence.config, or application.config)
<add key="jsdraw.abbreviations" value="c:\data\custom_abbreviations.sdf" />
Copyright (C) 2016 Scilligence Corporation (http://www.scilligence.com/)
License:


Class: Aws::DynamoDB::Types::ScanInput
| Inherits: |
Struct
|
| Defined in: |
(unknown) |
Overview
Represents the input of a Scan operation.
Note: When passing ScanInput
as input to a Client method, you can use a vanilla Hash
:
{
table_name: "TableName", # required
index_name: "IndexName",
attributes_to_get: ["AttributeName"],
limit: 1,
select: "ALL_ATTRIBUTES", # accepts ALL_ATTRIBUTES, ALL_PROJECTED_ATTRIBUTES, SPECIFIC_ATTRIBUTES, COUNT
scan_filter: {
"AttributeName" => {
attribute_value_list: ["value"], # value <Hash,Array,String,Numeric,Boolean,IO,Set,nil>
comparison_operator: "EQ", # required, accepts EQ, NE, IN, LE, LT, GE, GT, BETWEEN, NOT_NULL, NULL, CONTAINS, NOT_CONTAINS, BEGINS_WITH
},
},
conditional_operator: "AND", # accepts AND, OR
exclusive_start_key: {
"AttributeName" => "value", # value <Hash,Array,String,Numeric,Boolean,IO,Set,nil>
},
return_consumed_capacity: "INDEXES", # accepts INDEXES, TOTAL, NONE
total_segments: 1,
segment: 1,
projection_expression: "ProjectionExpression",
filter_expression: "ConditionExpression",
expression_attribute_names: {
"ExpressionAttributeNameVariable" => "AttributeName",
},
expression_attribute_values: {
"ExpressionAttributeValueVariable" => "value", # value <Hash,Array,String,Numeric,Boolean,IO,Set,nil>
},
consistent_read: false,
}
Instance Attribute Summary collapse
-
#attributes_to_get ⇒ Array<String>
This is a legacy parameter, for backward compatibility. -
#conditional_operator ⇒ String
This is a legacy parameter, for backward compatibility. -
#consistent_read ⇒ Boolean
A Boolean value that determines the read consistency model during the scan:
-
#exclusive_start_key ⇒ Hash<String,Types::AttributeValue>
The primary key of the first item that this operation will evaluate.
-
#expression_attribute_names ⇒ Hash<String,String>
One or more substitution tokens for attribute names in an expression.
-
#expression_attribute_values ⇒ Hash<String,Types::AttributeValue>
One or more values that can be substituted in an expression.
-
#filter_expression ⇒ String
A string that contains conditions that DynamoDB applies after the Scan operation, but before the data is returned to you.
-
#index_name ⇒ String
The name of a secondary index to scan.
-
#limit ⇒ Integer
The maximum number of items to evaluate (not necessarily the number of matching items).
-
#projection_expression ⇒ String
A string that identifies one or more attributes to retrieve from the specified table or index.
-
#return_consumed_capacity ⇒ String
Determines the level of detail about provisioned throughput consumption that is returned in the response:
-
#scan_filter ⇒ Hash<String,Types::Condition>
This is a legacy parameter, for backward compatibility. -
#segment ⇒ Integer
For a parallel Scan request, Segment identifies an individual segment to be scanned by an application worker.
-
#select ⇒ String
The attributes to be returned in the result.
-
#table_name ⇒ String
The name of the table containing the requested items; or, if you provide
IndexName
, the name of the table to which that index belongs. -
#total_segments ⇒ Integer
For a parallel Scan request, TotalSegments represents the total number of segments into which the Scan operation will be divided.
Instance Attribute Details
#attributes_to_get ⇒ Array<String>
This parameter allows you to retrieve attributes of type List or Map; however, it cannot retrieve individual elements within a List or a Map.
The names of one or more attributes to retrieve. If no attribute names are provided, then all attributes will be returned. If any of the requested attributes are not found, they will not appear in the result.
Note that AttributesToGet has no effect on provisioned throughput consumption. DynamoDB determines capacity units consumed based on item size, not on the amount of data that is returned to an application.
#conditional_operator ⇒ String
A logical operator to apply to the conditions in a ScanFilter map:
AND
- If all of the conditions evaluate to true, then the entire map evaluates to true.OR
- If at least one of the conditions evaluate to true, then the entire map evaluates to true.
If you omit ConditionalOperator, then AND
is the default.
The operation will succeed only if the entire map evaluates to true.
Possible values:
- AND
- OR
#consistent_read ⇒ Boolean
A Boolean value that determines the read consistency model during the scan:
If ConsistentRead is
false
, then the data returned from Scan might not contain the results from other recently completed write operations (PutItem, UpdateItem or DeleteItem).If ConsistentRead is
true
, then all of the write operations that completed before the Scan began are guaranteed to be contained in the Scan response.
The default setting for ConsistentRead is false
.
The ConsistentRead parameter is not supported on global secondary indexes. If you scan a global secondary index with ConsistentRead set to true, you will receive a ValidationException.
#exclusive_start_key ⇒ Hash<String,Types::AttributeValue>
The primary key of the first item that this operation will evaluate. Use the value that was returned for LastEvaluatedKey in the previous operation.
The data type for ExclusiveStartKey must be String, Number or Binary. No set data types are allowed.
In a parallel scan, a Scan request that includes ExclusiveStartKey must specify the same segment whose previous Scan returned the corresponding value of LastEvaluatedKey.
#expression_attribute_names ⇒ Hash<String,String>
One or more substitution tokens for attribute names in an expression. The following are some use cases for using ExpressionAttributeNames:
To access an attribute whose name conflicts with a DynamoDB reserved word.
To create a placeholder for repeating occurrences of an attribute name in an expression.
To prevent special characters in an attribute name from being misinterpreted in an expression.
Use the # character in an expression to dereference an attribute name. For example, consider the following attribute name:
Percentile
^
The name of this attribute conflicts with a reserved word, so it cannot be used directly in an expression. (For the complete list of reserved words, see Reserved Words in the Amazon DynamoDB Developer Guide). To work around this, you could specify the following for ExpressionAttributeNames:
`{"#P":"Percentile"}`
^
You could then use this substitution in an expression, as in this example:
#P = :val
^
For more information on expression attribute names, see Accessing Item Attributes in the Amazon DynamoDB Developer Guide.
#expression_attribute_values ⇒ Hash<String,Types::AttributeValue>
One or more values that can be substituted in an expression.
Use the : (colon) character in an expression to dereference an attribute value. For example, suppose that you wanted to check whether the value of the ProductStatus attribute was one of the following:
Available | Backordered | Discontinued
You would first need to specify ExpressionAttributeValues as follows:
{ ":avail":`{"S":"Available"}`, ":back":`{"S":"Backordered"}`,
":disc":`{"S":"Discontinued"}` }
You could then use these values in an expression, such as this:
ProductStatus IN (:avail, :back, :disc)
For more information on expression attribute values, see Specifying Conditions in the Amazon DynamoDB Developer Guide.
#filter_expression ⇒ String
A string that contains conditions that DynamoDB applies after the Scan operation, but before the data is returned to you. Items that do not satisfy the FilterExpression criteria are not returned.
For more information, see Filter Expressions in the Amazon DynamoDB Developer Guide.
#index_name ⇒ String
The name of a secondary index to scan. This index can be any local
secondary index or global secondary index. Note that if you use the
IndexName
parameter, you must also provide TableName
.
#limit ⇒ Integer
The maximum number of items to evaluate (not necessarily the number of matching items). If DynamoDB processes the number of items up to the limit while processing the results, it stops the operation and returns the matching values up to that point, and a key in LastEvaluatedKey to apply in a subsequent operation, so that you can pick up where you left off. Also, if the processed data set size exceeds 1 MB before DynamoDB reaches this limit, it stops the operation and returns the matching values up to the limit, and a key in LastEvaluatedKey to apply in a subsequent operation to continue the operation. For more information, see Query and Scan in the Amazon DynamoDB Developer Guide.
#projection_expression ⇒ String
A string that identifies one or more attributes to retrieve from the specified table or index. These attributes can include scalars, sets, or elements of a JSON document. The attributes in the expression must be separated by commas.
If no attribute names are specified, then all attributes will be returned. If any of the requested attributes are not found, they will not appear in the result.
For more information, see Accessing Item Attributes in the Amazon DynamoDB Developer Guide.
#return_consumed_capacity ⇒ String
Determines the level of detail about provisioned throughput consumption that is returned in the response:
-
INDEXES - The response includes the aggregate ConsumedCapacity for the operation, together with ConsumedCapacity for each table and secondary index that was accessed.
Note that some operations, such as GetItem and BatchGetItem, do not access any indexes at all. In these cases, specifying INDEXES will only return ConsumedCapacity information for table(s).
-
TOTAL - The response includes only the aggregate ConsumedCapacity for the operation.
-
NONE - No ConsumedCapacity details are included in the response.
Possible values:
- INDEXES
- TOTAL
- NONE
#scan_filter ⇒ Hash<String,Types::Condition>
A condition that evaluates the scan results and returns only the desired values.
If you specify more than one condition in the ScanFilter map, then by default all of the conditions must evaluate to true. In other words, the conditions are ANDed together. (You can use the ConditionalOperator parameter to OR the conditions instead. If you do this, then at least one of the conditions must evaluate to true, rather than all of them.)
Each ScanFilter element consists of an attribute name to compare, along with the following:
AttributeValueList - One or more values to evaluate against the supplied attribute. The number of values in the list depends on the operator specified in ComparisonOperator .
For type Number, value comparisons are numeric.
String value comparisons for greater than, equals, or less than are based on ASCII character code values. For example,
a
is greater thanA
, anda
is greater thanB
. For a list of code values, see http://en.wikipedia.org/wiki/ASCII#ASCII_printable_characters.For Binary, DynamoDB treats each byte of the binary data as unsigned when it compares binary values.
For information on specifying data types in JSON, see JSON Data Format in the Amazon DynamoDB Developer Guide.
ComparisonOperator - A comparator for evaluating attributes. For example, equals, greater than, less than, etc.
The following comparison operators are available:
EQ | NE | LE | LT | GE | GT | NOT_NULL | NULL | CONTAINS | NOT_CONTAINS | BEGINS_WITH | IN | BETWEEN
For complete descriptions of all comparison operators, see Condition.
#segment ⇒ Integer
For a parallel Scan request, Segment identifies an individual segment to be scanned by an application worker.
Segment IDs are zero-based, so the first segment is always 0. For example, if you want to use four application threads to scan a table or an index, then the first thread specifies a Segment value of 0, the second thread specifies 1, and so on.
The value of LastEvaluatedKey returned from a parallel Scan request must be used as ExclusiveStartKey with the same segment ID in a subsequent Scan operation.
The value for Segment must be greater than or equal to 0, and less than the value provided for TotalSegments.
If you provide Segment, you must also provide TotalSegments.
#select ⇒ String
The attributes to be returned in the result. You can retrieve all item attributes, specific item attributes, or the count of matching items.
ALL_ATTRIBUTES
- Returns all of the item attributes.ALL_PROJECTED_ATTRIBUTES
- Allowed only when querying an index. Retrieves all attributes that have been projected into the index. If the index is configured to project all attributes, this return value is equivalent to specifyingALL_ATTRIBUTES
.COUNT
- Returns the number of matching items, rather than the matching items themselves.SPECIFIC_ATTRIBUTES
- Returns only the attributes listed in AttributesToGet. This return value is equivalent to specifying AttributesToGet without specifying any value for Select.
If neither Select nor AttributesToGet are specified, DynamoDB
defaults to ALL_ATTRIBUTES
. You cannot use both AttributesToGet and
Select together in a single request, unless the value for Select is
SPECIFIC_ATTRIBUTES
. (This usage is equivalent to specifying
AttributesToGet without any value for Select.)
Possible values:
- ALL_ATTRIBUTES
- ALL_PROJECTED_ATTRIBUTES
- SPECIFIC_ATTRIBUTES
- COUNT
#table_name ⇒ String
The name of the table containing the requested items; or, if you provide
IndexName
, the name of the table to which that index belongs.
#total_segments ⇒ Integer
For a parallel Scan request, TotalSegments represents the total number of segments into which the Scan operation will be divided. The value of TotalSegments corresponds to the number of application workers that will perform the parallel scan. For example, if you want to use four application threads to scan a table or an index, specify a TotalSegments value of 4.
The value for TotalSegments must be greater than or equal to 1, and less than or equal to 1000000. If you specify a TotalSegments value of 1, the Scan operation will be sequential rather than parallel.
If you specify TotalSegments, you must also specify Segment.


Octaskin provides general customer service via email and aims to respond to all queries within 24 hours.
Below is a sample of contact form to demo Materialize CSS form elements.


Image Moderation Instructions
Select images that contain face close-ups with makeup.
NO drawings of people, images must be photos.
NO product shots (shots that show
only
makeup products).
YES full face or closeups (eyes, mouth, lips, etc.).
YES face must have some makeup on.
{% include "simpleamt.html" %}


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How this organisation prescribed for six prescribing measures, compared to the mean for all practices in NHS England.
Prescribing of branded Rosuvastatin Calcium (BNF code 0212000AA) compared with prescribing of generic Atorvastatin (BNF code 0212000B0). Learn more about this metric.
Get more information on {{ orgName }}'s prescribing of Rosuvastatin Calcium vs. Atorvastatin: see how this {{ orgType }} compares to all {{ orgType}}s{% if orgType == 'practice' %} in the same CCG{% else %}, or trends for all {{ practiceCount }} practices in this CCG{% endif %}.
Prescribing of branded Cerazette (BNF code 0703021Q0BB) compared with prescribing of all Desogesterel (BNF code 0703021Q0). Learn more about this metric.
Get more information on {{ orgName }}'s prescribing of branded Cerazette vs all Desogesterel: see how this {{ orgType }} compares to all {{ orgType}}s{% if orgType == 'practice' %} in the same CCG{% else %}, or trends for all {{ practiceCount }} practices in this CCG{% endif %}.
Prescribing of Cephalosporins (BNF section 5.1.2) compared with item-based STAR-PUs for oral antibiotics for this {{ orgType }}. Learn more about this metric.
Get more information on {{ orgName }}'s prescribing of Cephalosporins etc per oral antibiotics STAR-PU:
see how this {{ orgType }} compares to all {{ orgType}}s{% if orgType == 'practice' %} in the same CCG{% else %}, or trends for all {{ practiceCount }} practices in this CCG{% endif %}.
We only have list size data (from which STAR-PUs are derived) back to April 2013: if you know a better source of openly licensed data please get in touch!
Prescribing of Antibacterial Drugs (BNF section 5.1) compared with item-based STAR-PUs for oral antibiotics for this {{ orgType }}. Learn more about this metric.
Get more information on {{ orgName }}'s prescribing of all antibacterial drugs per oral antibiotics STAR-PU:
see how this {{ orgType }} compares to all {{ orgType}}s{% if orgType == 'practice' %} in the same CCG{% else %}, or trends for all {{ practiceCount }} practices in this CCG{% endif %}.
We only have list size data (from which STAR-PUs are derived) back to April 2013: if you know a better source of openly licensed data please get in touch!
Prescribing of Pioglitazone Hydrochloride (BNF code 0601023B0) compared with prescribing of all Antidiabetic Drugs (BNF code 6.1.2). Learn more about this metric.
Get more information on {{ orgName }}'s prescribing of Pioglitazone Hydrochloride vs all Antidiabetic Drugs: see how this {{ orgType }} compares to all {{ orgType}}s{% if orgType == 'practice' %} in the same CCG{% else %}, or trends for all {{ practiceCount }} practices in this CCG{% endif %}.
Prescribing of Celecoxib (BNF code 0801050AY) and Celecoxib (BNF code 1001010AH) compared with prescribing of all Non-Steroidal Anti-Inflammatory Drugs (BNF section 1.1.1). Learn more about this metric.
Get more information on {{ orgName }}'s prescribing of Celecoxib vs all NSAIDs: see how this {{ orgType }} compares to all {{ orgType}}s{% if orgType == 'practice' %} in the same CCG{% else %}, or trends for all {{ practiceCount }} practices in this CCG{% endif %}.
There are currently six standard prescribing measures on the dashboard pages for CCGs and practices, to get you going. These were developed in discussion between Drs Jeff Aronson, Kamal Mahtani, and Ben Goldacre at the University of Oxford, and Richard Croker from South Devon CCG. They are indicative of what the site can do. The interest of each measure is explained below.
We would like to run workshops to develop 100 standard measures (creating a longlist of candidates, choosing the best, and implementing the code). If you are able to fund this, please get in touch.
Statins are the most commonly prescribed class of drug in the UK. Atorvastatin and Rosuvastatin are members of this class, and are both high-potency statins. There will always be reasons why occasional patients do better with a particular drug, but overall there is no good evidence that Rosuvastatin is better than atorvastatin. It is, however, vastly more expensive. When atorvastatin came off patent, and became cheap, practices tended to switch people away from expensive Rosuvastatin.
This is the NHS Business Service Authority's top cost-saver from generic switching. Cerazette and desogestrel are both exactly the same drug, the same molecule, but Cerazette is an expensive branded package, and desogestrel is a cheap generic package.
Cephalosporins are broad spectrum antibiotics which can be used when others have failed. It is important that they are used sparingly, to avoid drug-resistant bacteria developing. This measure looks at the quantity of cephalosporins prescribed per head of population, corrected for the age and sex distribution of that population.
It is important that we don't overuse antibiotics. This shows how many are being prescribed locally.
Rosiglitazone is an antidiabetic drug that turned out to increase the risk of heart problems, and was effectively withdrawn from the market. There is concern that the problems may have been a "class effect", covering other related drugs, and so doctors have tended to also stop using pioglitazone. This shows how local practice reflects that national trend.
Coxib drugs are an interesting illustration of a common phenomenon in medicine: the need to make a trade off between risk and benefit, in different patients. Long term use of NSAID painkillers puts patients at increased risk of gastric bleeds. Coxib painkillers are effective at treating pain, with lower risk of bleeding; but they are much more expensive, and come with a higher risk of cardiovascular problems. Overall, therefore, they are sensible to use in some patients, but if one area is prescribing a lot of coxibs (or very few) then that may mean that they have unusual patients, or it may mean that doctors' thresholds for using them are different to their colleagues nationally.


RNN
The main difference between normal neural
nets and RNNs is that RNNs have two ‘dimensions’ — time t (i.e. along the
sequence length) and the depth l(the usual layers). In fact, in RNNs it is
somewhat incomplete to say ‘the
output at layer l’; we rather say ‘the output at layer l and time t’ .(time here is not
necessarily timestamp but the position ).
The most crucial idea of RNNs which makes
them suitable for sequence problems is that:
The state of the network updates itself as it sees new elements in the
sequence.This is the core idea of an RNN — it updates what it has learnt as it
sees new inputs in the sequence. The ‘next state’ is influenced by the previous
one, and so it can learn the dependence between the subsequent states which is
a characteristic of sequence problems.
Let’s quickly recall the feedforward
equations of a normal neural network:
where,
Wl : weight matrix at layer l
bl : bias at layer l , zl : input into
layer l, fl : activation function at layer l and al : output or activations
from layer l.
We say that there is a recurrent relationship
between al(t+1) and its previous state al(t), and hence the name Recurrent
Neural Networks.
We will provide input to the hidden layer
at each step. A recurrent neuron now stores all the previous step input and
merges that information with the current step input. Thus it also captures some
information regarding the correlation between current data step and the
previous steps. The decision at a time step t-1
affects the decision taken at time t
.
Here, the weights and bias of these hidden
layers are different. And hence each of these layers behave independently and
cannot be combined together. To combine these hidden layers together, we shall
have the same weights and bias for these hidden layers.
We can now combines these layers together,
that the weights and bias of all the hidden layers is the same. All these
hidden layers can be rolled in together in a single recurrent layer.
So they start looking somewhat like this
Consider the following text string: “A
girl walked into a bar, and she said ‘Can I have a drink please?’.
The bartender said ‘Certainly {}”. There
are many options for what could fill in the {} symbol in the above string, for
instance, “miss”, “ma’am” and so on. However, other words could also fit, such
as “sir”, “Mister” etc. In order to get the correct gender of the noun, the
neural network needs to “recall” that two previous words designating the likely
gender (i.e. “girl” and “she”) were used.
For instance, first, we supply the word
vector for “A” to the network F —
the output of the nodes in F are
fed into the “next” network and also act as a stand-alone output (h0). The next
network (though it is really the same network) F at time t=1 takes the next word vector for “girl”
and the previous output h0 into its hidden nodes, producing the next output h1
and so on.
We can say instead of giving all input
togather like in ANN , we give the input one by one after another separated by
‘t’ time frame in sequence.
The RNN feedforward equations are:
This form is not only more concise but also
more computationally efficient. Rather than doing two matrix multiplications
and adding them, the network can do one large matrix multiplication.
There are four types of Recurrent Neural
Networks:
1.
One to One : This type of neural network
is known as the Vanilla Neural Network. It’s used for general machine Example
-learning problems, which has a single input and a single output.prediction
task like image classification.
2.
One to Many : This type of neural
network has a single input and multiple outputs. An example of this is the
image caption.
This type of architecture is generally
used as a generative
model. Among popular use of this architecture are applications
such as generating music (given a genre, for example), generating landscape
images given a keyword, generating text given an instruction/topic, etc.
3. Many to One : This RNN takes a sequence of inputs and generates a single output.
Sentiment analysis is a good example of this kind of network where a given
sentence can be classified as expressing positive or negative sentiments.
4. Many to Many : This RNN takes a sequence of inputs and generates a sequence of
outputs. It further can be of two types :
·
Many to Many : Equal input and output length -In this type of RNN, the input
(X) and output (Y) both are a sequence of multiple entities spread over
timesteps. In this architecture, the network spits out an output at each
timestep. There is a one-to-one correspondence between the input and output at each
timestep. You can use this architecture for various tasks.ex-build a part-of-speech
tagger where each word in the input sequence is tagged
with its part-of-speech at every timestep.
·
Many-to-many RNN: Unequal input and output lengths
— In the previous many-to-many artitecture, we had assumed that the
lengths of the input and output sequences are equal. However, this is not
always the case. There are many problems where the lengths
of the input and output sequences are different. For example,
consider the task of machine translation — the length of a Hindi sentence can
be different from the corresponding English sentence.the encoder-decoder
architecture is used in tasks where the input and output sequences are of
different lengths.
·
The above architecture comprises of two
components — an encoder and a decoder both of which are RNNs themselves. The
output of the encoder, called the encoded
vector (and sometimes also the ‘context vector’),
captures a representation of the input sequence. The encoded vector is then fed
to the decoder RNN which produces the output sequence.
The input and output can now be of
different lengths since there is no one-to-one correspondence between them
anymore. This architecture gives the RNNs much-needed flexibility for
real-world applications such as language translation.
RNNs use a slightly modified version of
backpropagation to update the weights. In a standard neural network, the errors
are propagated from the output layer to the input layer. However, in RNNs,
errors are propagated not only from right to left but also through the time
axis.
Backpropagation breaks down in a recurrent
neural network, because of the recurrent or loop connections.This was addressed
with a modification of the Backpropagation technique called Backpropagation
Through Time or BPTT.
In general we can say -
A recurrent neural network is shown one
input each timestep and predicts one output.Conceptually, BPTT works by unrolling all input
timesteps. Each timestep has one input timestep, one copy of the network, and
one output. Errors are then calculated and accumulated for
each timestep. The network is rolled back up and the weights are updated.
But loss calculation depends on the type
of task and the architecture.
·
In a many-to-one architecture
(such as classifying a sentence as correct/incorrect), the loss is simply the
difference between the predicted and the actual label. The loss
is computed and backpropagated after the entire sequence has been digested by
the network.
·
·
On the other hand, in a many-to-many architecture,
the network emits an output at multiple time steps, and the loss is calculated
at each time step. The total loss (= the sum of
the losses at each time step) is propagated back into the network after the
entire sequence has been ingested.
·
We can now add the losses for all the
sequences (i.e. for a batch of input sequences) and backpropagate the total
loss into the network.
BPTT can be computationally expensive as
the number of timesteps increases.If input sequences are comprised of thousands
of timesteps, then this will be the number of derivatives required for a single
update weight update.
Training
RNNs is considered to be difficult, in order to preserve long-range
dependencies it often meets one of the problems called Exploding
Gradients ( weights become too large that over-fits the
model ) or Vanishing Gradients ( weights become too
small that under-fits the model ). The occurrence of these two problems depends
on the activation functions used in the hidden layer, with the sigmoid
activation function vanishing gradient problem sounds reasonable while with
rectified linear unit exploding gradient make sense.
RNNs are designed to learn patterns in
sequential data, i.e. patterns across ‘time’. RNNs are also capable of learning
what are called long-term
dependencies. For example, in a machine translation task,
we expect the network to learn the interdependencies between the first and the
eighth word, learn the grammar of the languages, etc. This is accomplished
through the recurrent
layers of the net — each state learns the cumulative
knowledge of the sequence seen so far by the network.
Although this feature is what makes RNNs
so powerful, it introduces a severe problem — as the sequences become longer,
it becomes much harder to backpropagate the errors back into the network. The
gradients ‘die out’ by the time they reach the initial time steps during
backpropagation.
You could still use some workarounds to
solve the problem of exploding gradients. You can impose an upper limit to the
gradient while training, commonly known as gradient clipping. By controlling the
maximum value of a gradient, you could do away with the problem of exploding
gradients.
But the problem of vanishing gradients is
a more serious one. The vanishing gradient problem is so rampant and serious in
the case of RNNs that it renders RNNs useless in practical applications.
To solve the vanishing gradients problem,
many attempts have been made to tweak the vanilla RNNs such that the gradients
don’t die when sequences get long. The most popular and successful of these
attempts has been the long,
short-term memory network, or the LSTM . LSTMs have
proven to be so effective that they have almost replaced vanilla RNNs.
Recurrent neural networks (RNNs) are a class of
artificial neural networks which are often used with sequential data. The 3
most common types of recurrent neural networks are
1.
vanilla RNN,
2.
long short-term memory (LSTM),
proposed by Hochreiter
and Schmidhuber in 1997, and
3.
gated recurrent units (GRU),
proposed by Cho et.
al in 2014.
What
is an RNN?
A recurrent neural
network is a neural network that is specialized for processing a sequence of data x(t)= x(1), . . .
, x(τ) with the
time step index t ranging from 1 to τ. For tasks that involve
sequential inputs, such as speech and language, it is often better to use RNNs.
In a NLP problem, if you want to predict the next word in a sentence it is important
to know the words before it. RNNs are called recurrent because they perform the
same task for every element of a sequence, with the output being depended on
the previous computations. Another way to think about RNNs is that they have a
“memory” which captures information about what has been calculated so far.
Architecture : Let us briefly go through a basic RNN network.
The left side of
the above diagram shows a notation of an RNN and on the right side an RNN
being unrolled (or unfolded) into a full network. By unrolling
we mean that we write out the network for the complete sequence. For example,
if the sequence we care about is a sentence of 3 words, the network would be
unrolled into a 3-layer neural network, one layer for each word.
Input: x(t) is taken as the input to the network at time
step t. For
example, x1,could be a one-hot vector corresponding to a word of a
sentence.
Hidden state: h(t) represents a hidden state at time t and acts
as “memory” of the network. h(t) is calculated based on the current input and the
previous time step’s hidden state: h(t)
= f(U x(t)
+ W h(t−1)). The
function f is taken to be a
non-linear transformation such as tanh, ReLU.
Weights: The RNN has input to hidden connections parameterized
by a weight matrix U, hidden-to-hidden recurrent connections parameterized by a
weight matrix W, and hidden-to-output connections parameterized by a weight
matrix V and all these weights (U,V,W) are shared across time.
Output: o(t) illustrates the output of the network. In the figure I just put an arrow
after o(t) which is also often subjected to non-linearity,
especially when the network contains further layers downstream.
Forward
Pass
The ﬁgure
does not specify the choice of activation function for the hidden units. Before
we proceed we make few assumptions: 1) we assume the hyperbolic tangent
activation function for hidden layer. 2) We assume that the output is discrete,
as if the RNN is used to predict words or characters. A natural way to
represent discrete variables is to regard the output o as giving the
un-normalized log probabilities of each possible value of the discrete
variable. We can then apply the softmax operation as a post-processing step to
obtain a vector ŷof normalized probabilities over the output.
The RNN forward
pass can thus be represented by below set of equations.
This is an example
of a recurrent network that maps an input sequence to an output sequence of the
same length. The total loss for a given sequence of x values paired with a
sequence of y values
would then be just the sum of the losses over all the time steps. We assume
that the outputs o(t)are used as the argument to the softmax function to
obtain the vector ŷ of probabilities over the output. We also assume
that the loss L is
the negative log-likelihood of the true target y(t)given the input so far.
Backward
Pass
The gradient
computation involves performing a forward propagation pass moving left to right
through the graph shown above followed by a backward propagation pass moving
right to left through the graph. The runtime is O(τ) and cannot be reduced
by parallelization because the forward propagation graph is inherently
sequential; each time step may be computed only after the previous one. States
computed in the forward pass must be stored until they are reused during the
backward pass, so the memory cost is also O(τ). The back-propagation
algorithm applied to the unrolled graph with O(τ) cost is called
back-propagation through time (BPTT). Because the parameters are shared by all
time steps in the network, the gradient at each output depends not only on the
calculations of the current time step, but also the previous time steps.
Computing
Gradients
Given our loss
function L,
we need to calculate the gradients for our three weight matrices U, V, W, and bias terms b, c and update them with a learning rate α. Similar to normal
back-propagation, the gradient gives us a sense of how the loss is changing
with respect to each weight parameter. We update the weights W to minimize loss
with the following equation:
The same is to be
done for the other weights U, V, b, c as well.
Let us now compute
the gradients by BPTT for the RNN equations above. The nodes of our
computational graph include the parameters U, V, W, b and c as well as the
sequence of nodes indexed by t for x (t), h(t), o(t) and L(t). For each
node n we
need to compute the gradient ∇nL recursively,
based on the gradient computed at nodes that follow it in the graph.
Gradient with respect to output o(t) is calculated assuming the o(t)
are used as the argument to the softmax function to obtain the vector ŷ of probabilities over the
output. We also assume that the loss is the negative log-likelihood of the true
target y(t).
Let us now
understand how the gradient flows through hidden state h(t). This we can
clearly see from the below diagram that at time t, hidden state h(t) has
gradient flowing from both current output and the next hidden state.
Red arrow
shows gradient flow
We work our way
backward, starting from the end of the sequence. At the ﬁnal time step
τ, h(τ) only has o(τ) as a descendant, so its gradient is
simple:
We can then
iterate backward in time to back-propagate gradients through time, from
t=τ −1 down to t = 1, noting that h(t) (for t < τ ) has as
descendants both o(t) and h(t+1). Its gradient is thus given by:
Once the gradients
on the internal nodes of the computational graph are obtained, we can obtain
the gradients on the parameter nodes. The gradient calculations using the chain
rule for all parameters is:
Implementation
We will implement
a full Recurrent Neural Network from scratch using Python. We will try to build
a text generation model using an RNN. We train our model to predict the
probability of a character given the preceding characters. It’s a generative model. Given an existing sequence of
characters we sample a next character from the predicted probabilities, and
repeat the process until we have a full sentence.
General steps to
follow:
1. Initialize weight matrices U, V, W from random distribution and bias b, c with zeros
2. Forward propagation to compute predictions
3. Compute the loss
4. Back-propagation to compute gradients
5. Update weights based on gradients
6. Repeat steps 2–5
1.
RNN can model sequence of data so
that each sample can be assumed to be dependent on previous ones
2.
Recurrent neural network are even used with
convolutional layers to extend the effective pixel neighbourhood.
1.
Gradient vanishing and exploding problems.
2.
Training an RNN is a very difficult task.
3.
It cannot process very long sequences if using tanh or relu as
an activation function.
RNNs can be used in a lot of different
places. Following are a few examples where a lot of RNNs are used.
Given a sequence of word, here we try to
predict the likelihood of the next word. This is useful for translation since
the most likely sentence would be the one that is correct.
Translating text from one language to
other uses one or the other form of RNN. All practical day systems use some
advanced version of a RNN.
Predicting phonetic segments based on
input sound waves, thus formulating a word.
A very big use case is to understand what
is happening inside an image, thus we have a good description. This works in a
combination of CNN and RNN. CNN does the segmentation and RNN then used the
segmented data to recreate the description. It’s rudimentary but the
possibilities are limitless.
This can be used for video search where we
do image description of a video frame by frame.
Long Short-Term Memory (LSTM) networks are
a modified version of recurrent neural networks, which makes it easier to
remember past data in memory. The vanishing gradient problem of RNN is resolved
here. LSTM is well-suited to classify, process and predict time series given
time lags of unknown duration. It trains the model by using back-propagation.
In an LSTM network, three gates are present:
1.
Input gate — discover which value
from input should be used to modify the memory. Sigmoid function
decides which values to let through 0,1. and tanh function
gives weightage to the values which are passed deciding their level of
importance ranging from-1 to 1.
2. Forget gate — discover what details to be discarded from the block. It is decided
by the sigmoid
function. it looks at the previous state(ht-1) and the content input(Xt) and outputs a number
between 0(omit this)and 1(keep this)for each number in the
cell state Ct−1.
3. Output gate — the input and the memory of the block is used to decide the
output. Sigmoid function
decides which values to let through 0,1. and tanh function gives
weightage to the values which are passed deciding their level of importance
ranging from-1 to 1 and multiplied
with output of Sigmoid.
Bidirectional
RNN
Fig 1:
General Structure of Bidirectional Recurrent Neural Networks.
Bidirectional
recurrent neural networks(RNN) are really just putting two independent RNNs
together. The input sequence is fed in normal time order for one network, and
in reverse time order for another. The outputs of the two networks are usually
concatenated at each time step, though there are other options, e.g. summation.
This structure
allows the networks to have both backward and forward information about the
sequence at every time step. The concept seems easy enough. But when it comes
to actually implementing a neural network which utilizes bidirectional
structure, confusion arises…
The
Confusion
The
first confusion is about the way to forward the outputs
of a bidirectional RNN to a dense neural network. For normal RNNs we could just
forward the outputs at the last time step, and the following picture I found
via Google shows similar technique on a bidirectional RNN.
Fig 2: A
confusing formulation.
But wait… if we
pick the output at the last time step, the reverse RNN will have only seen the
last input (x_3 in the picture). It’ll hardly provide any predictive power.
The second
confusion is about the returned hidden states. In seq2seq models, we’ll want
hidden states from the encoder to initialize the hidden states of the decoder.
Intuitively, if we can only choose hidden states at one time step(as in
PyTorch), we’d want the one at which the RNN just consumed the last input in
the sequence. But if the hidden states of time step n (the last one) are
returned, as before, we’ll have the hidden states of the reversed RNN with only
one step of inputs seen.


This tests loading of a HTML page with a JPEG image when the HTTP content-type is set to "NULL" . Expected : This page should have an HTML as text .


'Ever have one of those nights that starts out like any other, but ends up being the *best* night of your life? ' : Gary King
The World's End marks the third part of Pegg and Wright's self-described "Cornetto trilogy."An exhilarating and hilarious comedy, it even surprises you by turning into something completely different when you least expect it. The best part for me about each film in the trilogy is they are all good but in different ways. A thouroughly enjoyable watch from start to finish.
9/10


{% extends "default_form.html" %} {% block head_title %}Marketplace application | {{ market.name }} | Selling online overseas{% endblock head_title %} {% block head_meta_description %}Fill in our short form to apply and get access to our exclusive deals.{% endblock head_meta_description %} {% block back_button %} {% if originating_page %}
The Department for International Trade facilitates the introduction between you and the marketplace directly.
Your application will be 'fast tracked' through this channel, providing a direct route to the marketplace.
Your application for this marketplace will be reviewed for suitability and we will respond to you within 5 working days.
If successfully matched, you may be able to get access to marketplace offers.
We collect certain information or data about you when you use www.export.great.gov.uk. We respect your privacy and will handle your information with care. Read our Privacy and Cookies policy.


WEEK 31 SATURDAY
Year 1 (Year 2)
FIRST READING Romans 16:3-9.16.22-27
“Greet one another with a holy kiss.”
A reading from the Letter of Saint Paul to the Romans
A reading from the holy Gospel according to Mark
Brethren: Greet Prisca
and
The word of the Lord
RESPONSORIAL PSALM Psalm 145:2-3.4-5.10-11 (R. see 1)
R. I will bless your name for ever, my king and my God.
I will bless you day after day, and praise your
name for ever and ever. The LORD is great and highly to be praised; his
greatness cannot be measured.
R. I will bless your name for ever, my king and my
God.
Age to age shall proclaim your works, shall declare
your mighty deeds. They will tell of your great glory and splendour, and
recount your wonderful works.
R. I will bless your name for ever, my king and my
God.
All your works shall thank you, O LORD, and all
your faithful ones bless you. They shall speak of the glory of your reign, and
declare your mighty deeds.
R. I will bless your name for ever, my king and my
God.
FIRST READING Philippians 4:10-19
“I can do all things in him who strengthens me.”
A reading from the Letter of Saint Paul to the Philippians
Brethren: I rejoice in the Lord greatly that now at
length you have revived your concern for me; you were indeed concerned for me,
but you had no opportunity. Not that I complain of want; for I have learned, in
whatever state I am, to be content. I know how to be abased, and I know how to
abound; in any and all circumstances I have learned the secret of facing plenty
and hunger, abundance and want. I can do all things in him who strengthens me.
Yet it was kind of you to share my trouble. And you Philippians yourselves know
that in the beginning of the gospel, when I left Macedonia, no church entered into
partnership with me in giving and receiving except you only; for even in
Thessalonica you sent me help once and again. Not that I seek the gift; but I
seek the fruit which increases to your credit. I have received full payment,
and more; I am filled, having received from Epaphroditus
the gifts you sent, a fragrant offering, a sacrifice acceptable and pleasing to
God. And my God will supply every need of yours according to his riches in
glory in Christ Jesus.
The word of the Lord
RESPONSORIAL PSALM Psalm 112:1b-2.5-6.8a and 9 (R. 1a)
R. Blessed the man who fears the Lord.
Blessed the man who fears the LORD, who
takes great delight in his commandments. His descendants
shall be powerful on earth; the generation of the upright will be blest.
R. Blessed the man who fears the Lord.
It goes well for the man who deals generously and
lends, who conducts his affairs with justice. He will never be moved; for ever
shall the just be remembered.
R. Blessed the man who fears the Lord.
With a steadfast heart he will not fear. Open-handed,
he gives to the poor; his justice stands firm for ever. His might shall be
exalted in glory.
R. Blessed the man who fears the Lord.
R. Alleluia. Though Jesus Christ was rich, yet for your sake he became poor, so that by his poverty you might become rich. R. Alleluia.
GOSPEL Luke 16:9-15
“If then you have not been faithful in the unrighteous mammon, who will entrust to you the true riches?”
A reading from the holy Gospel according to Luke
At that time: Jesus said to his disciples, “Make
friends for yourselves by means of unrighteous mammon, so that when it fails
they may receive you into the eternal habitations. "He
who is faithful in a very little is faithful also in much; and he who is
dishonest in a very little is dishonest also in much. If then you have not been
faithful in the unrighteous mammon, who will entrust to you the true riches?
And if you have not been faithful in that which is another’s, who will give you
that which is your own? No servant can serve two masters; for either he will
hate the one and love the other, or he will be devoted to the one and despise
the other. You cannot serve God and mammon." The Pharisees, who were
lovers of money, heard all this, and they scoffed at him. But he said to them,
"You are those who justify yourselves before men, but God knows your
hearts; for what is exalted among men is an abomination in the sight of God.
The Gospel of the Lord


Magna cuts 2019 outlook on volume lost from GM strike
by The Canadian PressAURORA, Ont. - Magna International Inc. has cut its outlook for the year on lost volume from the nearly six-week strike by GM workers in the United States.
The company, which reports in U.S. dollars, says it now expects total sales this year of between $38.7 billion and $39.8 billion, a reduction of $1.3 billion on the top end of the range.
Magna also estimates 2019 net income of between $1.8 billion and $1.9 billion, a $200 million cut from the top end.
For the third quarter, the company reported a net loss of $233 million, or 75 cents per share, compared with earnings of $554 million or $1.62 per share for the same quarter last year.
The company recorded a $537 million non-cash impairment charge related its German transmission business Getrag in the quarter.
Adjusted earnings came in at $1.41 per share for the quarter ending Sept. 30, compared with $1.56 per share for the same quarter last year.


Vladimir Mikheev
Career path
Working experience
Duration
Google AI
6 month
Google Brain
8 month
Technical Skills
Programming languages:
A
B
C
Python
Python Django
R
Data Science Skills
GLMM
Neural Networks


We introduce a totally different service approach to our new pet customers.
Keep your pets healthy and strong
Have your pets relax a while
Daily walks at the dog park
Meal plans tailored for your pet
A clean grooming station with our world class professional groomer
A high-end pet spa facility for your fur baby to relax and stay pretty
Personal rooms with comfy beds for your pets to enjoy their stay in
A large pen full of chew toys, stuffed toys, and more


Film Search
Home
(current)
Trends
User Rating
Predicted Rating
Overview
Genres
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Revenue
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Trailer


const cluster = require("cluster");
const express = require("express");
const mongoose = require("mongoose");
const { join } = require("path");
const logger = require("./tools/logger");
const cors = require("cors");
let ddos = new Ddos({burst: 10, limit: 15});
/** The Server class is responsible for initializing the Express framework, the Mongoose and MongoDB drivers,
* all the middlewares and all of the controllers as well.
*
* The MVC/MC pattern allows a pretty decent scalability of controllers with zero code on this class and a one liner
* on the applications entry file (index.js). Also unifies MongoDB connections, making the use of models really quick
* by discarding the necessity to establish a new connection at every import of each model.
*
* Also responsible for shutting the service down if anything goes wrong.
*/
module.exports = class Server {
/**
* Instantiates express callbacks, database, middlewares and controllers.
* @param controllers
*/
constructor(controllers) {
this.app = express();
this.DB();
this.initMiddlewares();
this.initControllers(controllers);
}
/**
* Listens in the desired port.
*
* @method
* @listens {Request} Express http request.
*/
listen() {
this.app.listen(process.env.PORT || 5001, () =>
logger.info(`Admin service has been started!`)
);
}
/**
* Initializes middlewares: cors and body parser.
* @method
*/
initMiddlewares() {
this.app.use(ddos.express);
this.app.use(cors());
this.app.use(express.json());
this.app.use(express.urlencoded({ extended: true }));
}
/**
* Loop through and initializes all controllers of the application.
* @method
*/
initControllers(controllers) {
for (let controller of controllers) {
this.app.use("/", controller.router);
}
}
/**
* Standard initialization and connSection of MongoDB with Mongoose and events listener for exceptions and messages.
* @method
* @listens {<http>MongoDB} http MongoDB object events
*/
DB() {
try {
this.connection = mongoose.connect(process.env.MONGO_ACCESS, {
useNewUrlParser: true,
useCreateIndex: true,
useUnifiedTopology: true
});
if (this.connection) logger.info("MongoDB is up and running...");
} catch (e) {
logger.error(`MongoDB ERROR: ${e}`);
process.exit(1);
}
mongoose.connection.on("connected", () => {
logger.info(`Mongoose connection event received!`);
});
mongoose.connection.on("error", err => {
logger.error("Mongoose default connection error: " + err);
});
mongoose.connection.on("disconnected", () => {
logger.info("Mongoose default connection disconnected");
});
process.on("SIGINT", () => {
mongoose.connection.close(function () {
logger.info(
"Mongoose default connection disconnected through app termination"
);
process.exit(0);
});
});
}
};


What are the HTML Viewer Components?
The HTML component set consists of the THtmlViewer, TFrameViewer, and TFrameBrowser components. All three are HTML document display components:
| THtmlViewer | The basic component. THtmlViewer displays single (non-frame) documents. It also forms
the basis for the other two components. |
|
|---|---|---|
| TFrameViewer | Displays both Frame and single HTML documents. TFrameViewer is oriented more for local
file system use. |
|
| TFrameBrowser | Also displays Frame and single HTML documents. However, TFrameBrower is oriented
toward Internet style protocols and URL usage. Additional code and/or components are generally required to get
data from other sources than the local file system. |
These components support most of the HTML 3.2 and HTML 4 specifications with some popular HTML 5 enhancements. Many Cascading Style Sheet properties are also supported.
While HTML documents are normally associated with the Internet, they are also very useful for displaying all kinds of textual material such as documentation, helpfiles, etc. Graphics are easily incorporated in these documents.
In "Demos\Compiled Framedemo" Demo.htm This file *.htm Frame files for Demo.htm FrameDem.exe The TFrameViewer demo program Various image and sound files for demo In "HtmlHelp" THtmlViewer.chm Help file html\*.* Source files of help file images\*.* Source files of help file Various html help project files In "Source" sourcecode for the THtmlViewer, TFrameViewer, and TFrameBrowser components HtmlView.pas, .dcr THtmlViewer FramView.pas, .dcr TFrameViewer FramBrwz.pas, .dcr TFrameBrowser BuffConv.pas BuffConvArrays.pas DitherUnit.pas GDIPL2A.pas HSLUtils.pas HtmlBuffer.pas HtmlCaches.pas HtmlFonts.pas htmlgif1.pas HTMLGif2.pas HtmlGlobals.pas HtmlImages.pas HtmlPrinter.pas Htmlsbs1.pas HTMLSubs.pas HtmlSymb.pas htmlun2.pas MetaFilePrinter.pas ReadHTML.pas StylePars.pas StyleTypes.pas StyleUn.pas URLConn.pas URLSubs.pas vwPrint.pas HtmlMisc.pas Lazarus only HtmlComp.lrs Lazarus only HtmlUn2.lrs Lazarus only WideStringsLcl.pas Lazarus only HTMLCompEdit.pas Designtime only FrameViewerReg.pas Designtime only FrameViewer09.lpk, .pas Lazarus Package source file. Recommended Lazarus Version for best HtmlViewer support >= 1.2 (Win), >= 1.4 (Linux) FrameViewer6.dpk, .res Delphi 6 Package source file. Uses TntControls for better unicode support. FrameViewer7.dpk, .res Delphi 7 Package source file. Uses TntControls for better unicode support. FrameViewer2005.dpk, .res Delphi 2005 Package source file. Uses TntControls for better unicode support. FrameViewer2006.dpk, .res Delphi 2006 Package source file. Uses TntControls for better unicode support. FrameViewer2007.dpk, .res Delphi 2007 Package source file. Uses TntControls for better unicode support. FrameViewer2009.dpk, .res Delphi 2009 Package source file. FrameViewer2010.dpk, .res Delphi 2010 Package source file. FrameViewerXE.dpk, .res Delphi XE Runtime Package source file. FrameViewerXE2.dpk, .res Delphi XE2 Runtime Package source file. FrameViewerXE3.dpk, .res Delphi XE3 Runtime package source file. FrameViewerXE4.dpk, .res Delphi XE4 Runtime package source file. FrameViewerXE5.dpk, .res Delphi XE5 Runtime package source file. FrameViewerXE6.dpk, .res Delphi XE6 Runtime package source file. FrameViewerXE7.dpk, .res Delphi XE7 Runtime package source file. FrameViewerXE8.dpk, .res Delphi XE8 Runtime package source file. FrameViewerXE9.dpk, .res Delphi XE9 Runtime package source file. RAD Studio 10.2\FrameViewer.dpk Delphi 10.2 Tokyo Runtime package source file. RAD Studio 10.3\FrameViewer.dpk Delphi 10.3 Rio Runtime package source file. RAD Studio 10.4\FrameViewer.dpk Delphi 10.4 Sydney Runtime package source file. RAD Studio 11\FrameViewer.dpk Delphi 11 Alexandria Runtime package source file. DclFrameViewerXE.dpk, .res Delphi XE Designtime package source file. DclFrameViewerXE2.dpk, .res Delphi XE2 Designtime package source file. DclFrameViewerXE3.dpk, .res Delphi XE3 Designtime package source file. DclFrameViewerXE4.dpk, .res Delphi XE4 Designtime package source file. DclFrameViewerXE5.dpk, .res Delphi XE5 Designtime package source file. DclFrameViewerXE6.dpk, .res Delphi XE6 Designtime package source file. DclFrameViewerXE7.dpk, .res Delphi XE7 Designtime package source file. DclFrameViewerXE8.dpk, .res Delphi XE8 Designtime package source file. DclFrameViewerXE9.dpk, .res Delphi XE9 Designtime package source file. RAD Studio 10.2\dclFrameViewer.dpk Delphi 10.2 Tokyo Designtime package source file. RAD Studio 10.3\dclFrameViewer.dpk Delphi 10.3 Rio Designtime package source file. RAD Studio 10.4\dclFrameViewer.dpk Delphi 10.4 Sydney Designtime package source file. RAD Studio 11\dclFrameViewer.dpk Delphi 11 Alexandria Designtime package source file. HtmlView.chm The help file (Sorry, but most of it is still version 9.45) Source code for the print preview dialog TBegaHtmlPrintPreviewForm BegaHtmlPrintPreviewForm.pas BegaMetaFilePrinter.pas BegaPreview.pas, .dfm, .lfm BegaPreviewForm.pas, .dfm, .lfm BegaPreviewPanel.pas BegaScrollBox.pas BegaZoom.pas ZoomAndHandCursor.RES In "Demos\Main Demos" Source code for the demo programs HtmlDemo.dpr, .res HtmlViewer demo using TNT Unicode Controls (UTF-16) HtmlDemoW.dpr, .res HtmlViewer demo using VCL Unicode Controls (UTF-16) HtmlDemoLaz.lpr, .res HtmlViewer demo using LCL Unicode Controls (UTF-8) FrameDem.dpr, .res FrameViewer demo using TNT Unicode Controls (UTF-16) FrameDemW.dpr, .res FrameViewer demo using VCL Unicode Controls (UTF-16) FrameDemLaz.lpr, .res FrameViewer demo using LCL Unicode Controls (UTF-8) FrameBrowserDemoIcs.dpr, .res FrameBrowser demo using VCL Unicode Controls and ICS 7/8 Components FrameBrowserDemoId10.dpr, .res FrameBrowser demo using VCL Unicode Controls and Indy 10 Components AuthorizeFormUnit.pas, .dfm, .lfm CacheUnit.pas demounit.pas, .dfm, .lfm DownloadFormUnit.pas, .dfm, .lfm fbHelpIcs7.res fbHelpIndy10.res FBrDemUnit.pas, .dfm, .lfm FDemUnit.pas, .dfm, .lfm FontDlg.pas, .dfm, .lfm GoPage.pas, .dfm, .lfm Grid.res Htmlabt.pas, .dfm, .lfm ImgForm.pas, .dfm, .lfm InfoFormUnit.pas, .dfm, .lfm LogFormUnit.pas, .dfm, .lfm manifest.res OptionsId10.inc PrintStatusForm.pas, .dfm, .lfm ProxyFormUnit.pas, .dfm, .lfm Submit.pas, .dfm, .lfm SubmitTNT.pas, .dfm UrlConIcs.pas UrlConId10.pas UrlConVclZip.pas In "Demos\Other Demos" Some more demo projects and source files. For example the FrameBrowser demos FrameBrowserUsingICSv7 FrameBrowserUsingIndy10
Installing The HTML Components
If you are installing for more than one version of Delphi, use separate directories for the installations.
Installation problems are almost always due to old or incorrect files being accessed. Make sure that the Library Path or Search Path does not point to the directory of any old HTML component files. The Library Path can be edited from the Tools|Environment Options dialog, Library tab. The Search Path from the Project|Options dialog, Directories/Conditionals tab.
Delete all DCU files generated with previous installations.
The Register procedures in HTMLVIEW.PAS
, FRAMVIEW.PAS
, and FRAMBRWZ.PAS
are set to place the components on the palette page tab named Samples. These can be relocated later from
the IDE if desired.
FRAMEVIEWER6|7.DPK
(by default in the \HtmlViewer\package
directory).
\HtmlViewer\package
directory in the searchpath. The
searchpath can be edited by choosing Project|Options|Directories|Conditionals from the main menu.
Delphi 2005 - 2010, XE and later
In the following replace XXX
with the Delpi version name.
FrameViewerXXX
(by default in the \HtmlViewer\package
directory).
FrameViewerXXX.bpl
.
FrameViewerXXX.bpl
.
\HtmlViewer\package
directory in the searchpath. The
searchpath can be edited by choosing Project|Options|Directories/Conditionals from the main menu.
FrameViewer09.lpk
(by default in the \HtmlViewer\package
directory).
\HtmlViewer\package
directory in the
Other Unit Files (-Fu) searchpath. The searchpath can be edited by choosing
Project|Compiler Options... from the main menu.To uninstall the HTML components from Windows:
The following are some of the organizations and individuals who have contributed to the
HTML components:
| SkyLine Tools, Inc. | ImageLib Graphics Library |
|---|---|
| Radek Przybyl | Ideas and code for establishing Print margins |
| Theodor Kleynhans | Animated GIF code |
| Chris Wallace | Original print preview ideas and code |
| Yves Urbain | The Connection concept for protocol support |
| Anders Melander | Color dithering code in DitherUnit.Pas |
| Ron Collins | GIF image code |
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Bayern Munich Reportedly Keeping Eye On Troy Parrott
by Balls TeamIn wake of the news that Jose Mourinho is the new Spurs boss - and the questions it throws up about Troy Parrott's future at the club - comes word that Bayern Munich are supposedly keeping an eye on the Dubliner.
The Daily Mail reports that the Bundesliga champions are "monitoring his situation having noted his continual progress this season".
Parrott made his Spurs senior debut earlier this season in the League Cup against Colchester - that was a game which the Premier League club lost on penalties.
He made his Ireland senior bow in last week's friendly against New Zealand, being credited with the assist for Seani Maguire's goal.
After remaining seated for Ireland's 1-1 draw with Denmark on Monday night, the 17-year-old was dropped into the Ireland U21 team for Tuesday's crucial Euro 2021 qualifier against Sweden.
After a frustrating opening 45 minutes, Parrott was influential in the second period and scored Ireland's third goal as they came from one down at the break to win 4-1.
Parrott, who turns 18 in February, signed his first pro contract with Spurs earlier this year.
Photo by Harry Murphy/Sportsfile


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France grapples with high domestic violence rate
LES MUREAUX, France (AP) - Activists say more than 130 women have died from domestic violence this year alone in France.
European Union studies show France has a higher rate of domestic violence than most of its European peers. Frustrated activists are now drawing national attention to a problem President Emmanuel Macron has called "France's shame."
They have glued posters with the names of the dead to French city walls. By the hundreds, they have walked silently through city streets after each new death.
Two years after Macron made a campaign pledge to tackle the problem, the government has begun to act.
The government will announce measures Monday that are expected to include seizing firearms from suspects, prioritizing police training and formally recognizing "psychological violence" as a form of domestic violence.
Copyright 2019 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.