Night ventilation- or night flushing- works best in climates where day.pdf
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Night ventilation, or night flushing, works best in climates where daytime and nighttime temperatures are similar. True False Question 3 (1 point) An urban heat island is: a city in the developing world that lacks air conditioning an isolated city the phenomenon in which cities are several degrees warmer than the surrounding area an urban area with increased exposure to solar radiation Question 4 (1 point) Sustainable urbanism includes broad streets with expansive block sizes networks of streets with small blocks and frequent intersections.
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#pragma once
#include <functional> // std::less
#include <iostream>
#include <queue> // std::queue
#include <utility> // std::pair
template <typename K, typename V, typename Comparator = std::less<K>>
class BinarySearchTree
{
public:
using key_type = K;
using value_type = V;
using key_compare = Comparator;
using pair = std::pair<key_type, value_type>;
using pointer = pair*;
using const_pointer = const pair*;
using reference = pair&;
using const_reference = const pair&;
using difference_type = ptrdiff_t;
using size_type = size_t;
private:
struct BinaryNode
{
pair element;
BinaryNode *left;
BinaryNode *right;
BinaryNode( const_reference theElement, BinaryNode *lt, BinaryNode *rt )
: element{ theElement }, left{ lt }, right{ rt } { }
BinaryNode( pair && theElement, BinaryNode *lt, BinaryNode *rt )
: element{ std::move( theElement ) }, left{ lt }, right{ rt } { }
};
using node = BinaryNode;
using node_ptr = node*;
using const_node_ptr = const node*;
node_ptr _root;
size_type _size;
key_compare comp;
public:
BinarySearchTree() {
// TODO
}
BinarySearchTree( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree( BinarySearchTree && rhs ) {
// TODO
}
~BinarySearchTree() {
// TODO
}
const_reference min() const { return min( _root )->element; }
const_reference max() const { return max( _root )->element; }
const_reference root() const {
// TODO
}
bool contains( const key_type & x ) const { return contains( x, _root ); }
value_type & find( const key_type & key ) { return find( key, _root )->element.second; }
const value_type & find( const key_type & key ) const { return find( key, _root )-
>element.second; }
bool empty() const {
// TODO
}
size_type size() const {
// TODO
}
void clear() {
clear( _root );
_size = 0;
}
void insert( const_reference x ) { insert( x, _root ); }
void insert( pair && x ) { insert( std::move( x ), _root ); }
void erase( const key_type & x ) { erase(x, _root); }
BinarySearchTree & operator=( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree & operator=( BinarySearchTree && rhs ) {
// TODO
}
private:
void insert( const_reference x, node_ptr & t ) {
// TODO
}
void insert( pair && x, node_ptr & t ) {
// TODO
}
void erase( const key_type & x, node_ptr & t ) {
// TODO
}
const_node_ptr min( const_node_ptr t ) const {
// TODO
}
const_node_ptr max( const_node_ptr t ) const {
// TODO
}
bool contains( const key_type & x, const_node_ptr t ) const {
// TODO
}
node_ptr find( const key_type & key, node_ptr t ) {
// TODO
}
const_node_ptr find( const key_type & key, const_node_ptr t ) const {
// TODO
}
void clear( node_ptr & t ) {
// TODO
}
node_ptr clone ( const_node_ptr t ) const {
// TODO
}
public:
template <typename KK, typename VV, typename CC>
friend void printLevelByLevel( const BinarySearchTree<KK, VV, CC>& bst, std::ostream & out
);
template <typename KK, typename VV, typename CC>
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#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
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#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
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latent_dimensions = 100
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model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
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#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * genera.
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#include<stdio.h>
#include<stdlib.h>
#include<math.h>
#include<string.h>
#define MAX_N 500000
#define MAX_S 10
struct tugboat {
int x, y, dist;
char name[21];
};
int cmp(const void* a, const void* b) {
const struct tugboat* t1 = (const struct tugboat*) a;
const struct tugboat* t2 = (const struct tugboat*) b;
if (t1->dist != t2->dist) {
return t1->dist - t2->dist;
} else {
return strcmp(t1->name, t2->name);
}
}
int main() {
int xs, ys, xn, yn;
scanf("%d%d%d%d", &xs, &ys, &xn, &yn);
int n;
scanf("%d", &n);
struct tugboat tugboats[MAX_N];
int i;
for (i = 0; i < n; i++) {
scanf("%d%d%s", &tugboats[i].x, &tugboats[i].y, tugboats[i].name);
int dx = tugboats[i].x - xs;
int dy = tugboats[i].y - ys;
tugboats[i].dist = dx * dx + dy * dy;
}
qsort(tugboats, n, sizeof(struct tugboat), cmp);
int num_tugs = 0;
char tugs[MAX_N][21];
int prev_x = xs, prev_y = ys;
for (i = 0; i < n; i++) {
if (tugboats[i].x >= prev_x && tugboats[i].y >= prev_y) {
num_tugs++;
strcpy(tugs[num_tugs - 1], tugboats[i].name);
prev_x = tugboats[i].x;
prev_y = tugboats[i].y;
}
}
printf("%d\n", num_tugs);
for (i = 0; i < num_tugs; i++) {
printf("%s\n", tugs[i]);
}
return 0;
}
Here are the instructions:
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Need help with international accounting, please; thanks.
1. How is it possible that companies like Apple, as a U.S. company, do not pay the regular
corporate tax rate?
2. Please explain with examples and particular comments that Apple has been criticized for not
paying appropriate taxes in other foreign countries where it conducts business..
Need help with the GUI buttons- I have the menu and the first section.pdf
Need help with the GUI buttons. I have the menu and the first section to load media. I need help
with the next 2.
Design and implement Java program as follows: 1) Media hierarchy: - Create Media, EBook,
MovieDVD, and MusicCD classes from Week 3 -> Practice Exercise - Inheritance solution. -
Add an attribute to Media class to store indication when media object is rented versus available.
Add code to constructor and create get and set methods as appropriate. - Add any additional
constructors and methods needed to support the below functionality 2) Design and implement
Manager class which (Hint: check out Week 8 Reading and Writing files example): - stores a list
of Media objects - has functionality to load Media objects from files - creates/updates Media
files - has functionality to add new Media object to its Media list - has functionality to find all
media objects for a specific title and returns that list - has functionality to rent Media based on id
(updates rental status on media, updates file, returns rental fee) 3) Design and implement
MediaRentalSystem which has the following functionality: - user interface which is either menu
driven through console commands or GUI buttons or menus. Look at the bottom of this project
file for sample look and feel. (Hint: for command-driven menu check out Week 2: Practice
Exercise - EncapsulationPlus and for GUI check out Week 8: Files in GUI example) - selection
to load Media files from a given directory (user supplies directory) - selection to find a media
object for a specific title value (user supplies title and should display to user the media
information once it finds it - should find all media with that title) - selection to rent a media
object based on its id value (user supplies id and should display rental fee value to the user) -
selection to exit program 4) Program should throw and catch Java built-in and user-defined
exceptions as appropriate 5) Your classes must be coded with correct encapsulation:
private/protected attributes, get methods, and set methods and value validation 6) There should
be appropriate polymorphism: overloading, overriding methods, and dynamic binding 7)
Program should take advantage of the inheritance properties as appropriate.
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Need help with the TODO's (DONE IN C++)
#pragma once
#include <functional> // std::less
#include <iostream>
#include <queue> // std::queue
#include <utility> // std::pair
template <typename K, typename V, typename Comparator = std::less<K>>
class BinarySearchTree
{
public:
using key_type = K;
using value_type = V;
using key_compare = Comparator;
using pair = std::pair<key_type, value_type>;
using pointer = pair*;
using const_pointer = const pair*;
using reference = pair&;
using const_reference = const pair&;
using difference_type = ptrdiff_t;
using size_type = size_t;
private:
struct BinaryNode
{
pair element;
BinaryNode *left;
BinaryNode *right;
BinaryNode( const_reference theElement, BinaryNode *lt, BinaryNode *rt )
: element{ theElement }, left{ lt }, right{ rt } { }
BinaryNode( pair && theElement, BinaryNode *lt, BinaryNode *rt )
: element{ std::move( theElement ) }, left{ lt }, right{ rt } { }
};
using node = BinaryNode;
using node_ptr = node*;
using const_node_ptr = const node*;
node_ptr _root;
size_type _size;
key_compare comp;
public:
BinarySearchTree() {
// TODO
}
BinarySearchTree( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree( BinarySearchTree && rhs ) {
// TODO
}
~BinarySearchTree() {
// TODO
}
const_reference min() const { return min( _root )->element; }
const_reference max() const { return max( _root )->element; }
const_reference root() const {
// TODO
}
bool contains( const key_type & x ) const { return contains( x, _root ); }
value_type & find( const key_type & key ) { return find( key, _root )->element.second; }
const value_type & find( const key_type & key ) const { return find( key, _root )-
>element.second; }
bool empty() const {
// TODO
}
size_type size() const {
// TODO
}
void clear() {
clear( _root );
_size = 0;
}
void insert( const_reference x ) { insert( x, _root ); }
void insert( pair && x ) { insert( std::move( x ), _root ); }
void erase( const key_type & x ) { erase(x, _root); }
BinarySearchTree & operator=( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree & operator=( BinarySearchTree && rhs ) {
// TODO
}
private:
void insert( const_reference x, node_ptr & t ) {
// TODO
}
void insert( pair && x, node_ptr & t ) {
// TODO
}
void erase( const key_type & x, node_ptr & t ) {
// TODO
}
const_node_ptr min( const_node_ptr t ) const {
// TODO
}
const_node_ptr max( const_node_ptr t ) const {
// TODO
}
bool contains( const key_type & x, const_node_ptr t ) const {
// TODO
}
node_ptr find( const key_type & key, node_ptr t ) {
// TODO
}
const_node_ptr find( const key_type & key, const_node_ptr t ) const {
// TODO
}
void clear( node_ptr & t ) {
// TODO
}
node_ptr clone ( const_node_ptr t ) const {
// TODO
}
public:
template <typename KK, typename VV, typename CC>
friend void printLevelByLevel( const BinarySearchTree<KK, VV, CC>& bst, std::ostream & out
);
template <typename KK, typename VV, typename CC>
friend std::ostream& printNode(std::ostream& o, const typename BinarySearchTree<KK, VV,
CC>::node& bn);.
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#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * genera.
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The code I have so far feels about 70% complete. (I plan to get rid of the qsort function and
implement my own merge-sort algo but for now, you can use it.) There are a few errors that
prevent it from running. Here is the code I have so far:
#include<stdio.h>
#include<stdlib.h>
#include<math.h>
#include<string.h>
#define MAX_N 500000
#define MAX_S 10
struct tugboat {
int x, y, dist;
char name[21];
};
int cmp(const void* a, const void* b) {
const struct tugboat* t1 = (const struct tugboat*) a;
const struct tugboat* t2 = (const struct tugboat*) b;
if (t1->dist != t2->dist) {
return t1->dist - t2->dist;
} else {
return strcmp(t1->name, t2->name);
}
}
int main() {
int xs, ys, xn, yn;
scanf("%d%d%d%d", &xs, &ys, &xn, &yn);
int n;
scanf("%d", &n);
struct tugboat tugboats[MAX_N];
int i;
for (i = 0; i < n; i++) {
scanf("%d%d%s", &tugboats[i].x, &tugboats[i].y, tugboats[i].name);
int dx = tugboats[i].x - xs;
int dy = tugboats[i].y - ys;
tugboats[i].dist = dx * dx + dy * dy;
}
qsort(tugboats, n, sizeof(struct tugboat), cmp);
int num_tugs = 0;
char tugs[MAX_N][21];
int prev_x = xs, prev_y = ys;
for (i = 0; i < n; i++) {
if (tugboats[i].x >= prev_x && tugboats[i].y >= prev_y) {
num_tugs++;
strcpy(tugs[num_tugs - 1], tugboats[i].name);
prev_x = tugboats[i].x;
prev_y = tugboats[i].y;
}
}
printf("%d\n", num_tugs);
for (i = 0; i < num_tugs; i++) {
printf("%s\n", tugs[i]);
}
return 0;
}
Here are the instructions:
Tugboat Troubles Objective Give practice with sorting in C. Story The ship's engine has ceased,
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file for sample look and feel. (Hint: for command-driven menu check out Week 2: Practice
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information once it finds it - should find all media with that title) - selection to rent a media
object based on its id value (user supplies id and should display rental fee value to the user) -
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exceptions as appropriate 5) Your classes must be coded with correct encapsulation:
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বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
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Objective:
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www.StokeNewingtonHistory.com
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