text:
from __future__ import print_function
from __future__ import division
import time
import simplejson as json  # supposed to be better than json
import math
import numpy as np
import base64
from functools import wraps
# import time

# are we testing in travis-ci, if so, do fake cv2
# eventually I will fix this
# import os
# env = os.environ
# if 'TRAVIS' in env:
# 	class cv2(object):
# 		def imdecode(self, a, b): pass
# 		def imencode(self, a, b): pass
# else:
# 	import cv2

import cv2

class MessageError(Exception):
	pass

# http://stackoverflow.com/questions/3603502/prevent-creating-new-attributes-outside-init
def froze_it(cls):
	cls.__frozen = False

	def frozensetattr(self, key, value):
		if self.__frozen and not hasattr(self, key):
			# print("Class {} is frozen. Cannot set {} = {}".format(cls.__name__, key, value))
			raise MessageError("Class {} is frozen. Cannot set {} = {}".format(cls.__name__, key, value))
		else:
			object.__setattr__(self, key, value)

	def init_decorator(func):
		@wraps(func)
		def wrapper(self, *args, **kwargs):
			func(self, *args, **kwargs)
			self.__frozen = True
		return wrapper

	cls.__setattr__ = frozensetattr
	cls.__init__ = init_decorator(cls.__init__)

	return cls

@froze_it
class Vector(object):
	def __init__(self, data=None):
		self.Class = 'Vector'
		self.x = 0
		self.y = 0
		self.z = 0
		if data:
			for key in data:
				setattr(self, key, data[key])

	def set(self, x, y, z):
		self.x = x
		self.y = y
		self.z = z

	def __str__(self):
		s = 'x: {:.2f} y: {:.2f} z: {:.2f}'.format(self.x, self.y, self.z)
		return s

	def __eq__(self, v):
		return (self.x == v.x and self.y == v.y and self.z == v.z)

@froze_it
class Quaternion(object):
	def __init__(self, data=None):
		self.Class = 'Quaternion'
		self.x = 0.0
		self.y = 0.0
		self.z = 0.0
		self.w = 1.0
		if data:
			for key in data:
				setattr(self, key, data[key])

	def normalize(self):
		d = math.sqrt(self.w**2 + self.x**2 + self.y**2 + self.z**2)
		if d > 0.0:
			self.x /= d
			self.y /= d
			self.z /= d
			self.w /= d

	def set(self, w, x, y, z):
		self.w = w
		self.x = x
		self.y = y
		self.z = z

	def __str__(self):
		s = 'w: {:.3f} x: {:.3f} y: {:.3f} z: {:.3f}'.format(self.w, self.x, self.y, self.z)
		return s

	def __eq__(self, q):
		return (self.x == q.x and self.y == q.y and self.z == q.z and self.w == q.w)

@froze_it
class Compass(object):
	"""
	Simple tilt compensated compass message, the roll, pitch, and heading angles
	are stored either in radians or degrees.
	"""
	COMPASS_RADIANS = 0
	COMPASS_DEGREES = 1
	kind = {
		0: 'Rads',
		1: 'Deg'
	}

	def __init__(self, data=None, units=COMPASS_RADIANS):
		self.Class = 'Compass'
		self.units = units
		self.roll = 0
		self.pitch = 0
		self.heading = 0
		self.stamp = time.time()
		if data:
			for key in data:
				setattr(self, key, data[key])

	def set(self, x, y, z):
		self.roll = x
		self.pitch = y
		self.heading = z

	def __str__(self):
		s = 'r: {:.2f} p: {:.2f} h: {:.2f} {}'.format(self.roll, self.pitch, self.heading, self.kind[self.units])
		return s

	def __eq__(self, v):
		return self.Class == v.Class

@froze_it
class IMU(object):
	"""
	Simple tilt compensated compass message, the roll, pitch, and heading angles
	are stored either in radians or degrees.
	"""
	IMU_RADIANS = 0
	IMU_DEGREES = 1
	kind = {
		0: 'Rads',
		1: 'Deg'
	}

	def __init__(self, data=None):
		self.Class = 'IMU'
		self.linear_acceleration = Vector()
		self.angular_velocity = Vector()
		self.orientation = Quaternion()
		self.stamp = time.time()

		if data:
			for key in data:
				if key in ['linear_acceleration', 'angular_velocity']:
					setattr(self, key, Vector(data[key]))
				elif key == 'orientation':
					setattr(self, key, Quaternion(data[key]))
				else:
					setattr(self, key, data[key])

	def __str__(self):
		# s = 'a: ' + self.linear_acceleration.__str__()
		# s += 'g: ' + self.angular_velocity.__str__()
		# s += 'q: ' + self.orientation.__str__()
		s = 'IMU: a: {} w: {} q: {}'.format(self.linear_acceleration, self.angular_velocity, self.orientation)
		return s

	def __eq__(self, v):
		return self.Class == v.Class

@froze_it
class Image(object):
	"""
	"""
	def __init__(self, data=None):
		self.depth = -1   # 0-grayscale, 1-bgr ... why?
		self._img = None  # image, either numpy or b64
		self.b64 = False  # is this encoded for transmission?
		self.stamp = time.time()
		self.Class = 'Image'

		if data:
			for key in data:
				setattr(self, key, data[key])

	def __str__(self):
		if self._img is None:
			s = 'image empty'
		elif not self.b64:
			w, h = self.img.shape[:2]
			depth = 'color' if self.depth else 'gray'
			s = 'Image[{}] w:{} h:{}'.format(depth, w, h)
		else:
			s = 'image is b46 encoded'

		return s

	@property
	def img(self):
		return self._img

	@img.setter
	def img(self, image):
		shp = image.shape
		if len(shp) == 2:
			self.depth = 0  # grayscale
		elif len(shp) == 3:
			self.depth = 1  # color ... why not shp[2]
		else:
			print('crap, wrong shape {}'.format(shp))
		self._img = image

	@staticmethod
	def randomImage(size=(5, 5)):
		np.random.randint(0, 255, size=size)

	def decodeB64(self):
		if self.b64:
			ii = base64.b64decode(self._img)
			ii = np.fromstring(ii, dtype=np.uint8)
			ii = cv2.imdecode(ii, self.depth)
			self._img = ii
			self.b64 = False

	def encodeB64(self):
		if not self.b64:
			jpeg = cv2.imencode('.jpg', self._img)[1]
			b64 = base64.b64encode(jpeg)
			self._img = b64
			self.b64 = True

	def __eq__(self, v):
		return self.Class == v.Class

@froze_it
class Twist(object):
	"""
	"""
	def __init__(self, data=None):
		self.Class = 'Twist'
		self.linear = Vector()
		self.angular = Vector()
		self.stamp = time.time()

		if data:
			for key in data:
				if key in ['linear', 'angular']:
					setattr(self, key, Vector(data[key]))
				else:
					setattr(self, key, data[key])

	def __str__(self):
		s = 'linear: {:2f} {:2f} {:2f} '.format(self.linear.x, self.linear.y, self.linear.z)
		s += 'angular: {:2f} {:2f} {:2f}'.format(self.angular.x, self.angular.y, self.angular.z)
		# s += '\n' + str(self.img)
		return s

	def __eq__(self, v):
		return self.Class == v.Class

@froze_it
class Wrench(object):
	"""
	"""
	def __init__(self, data=None):
		self.Class = 'Wrench'
		self.force = Vector()
		self.torque = Vector()
		self.stamp = time.time()

		if data:
			for key in data:
				if key in ['torque', 'force']:
					setattr(self, key, Vector(data[key]))
				else:
					setattr(self, key, data[key])

	def __str__(self):
		return 'Wrench: force: {} torque: {}'.format(self.force, self.torque)

	def __eq__(self, v):
		return self.Class == v.Class

@froze_it
class Pose(object):
	"""
	"""
	def __init__(self, data=None):
		self.Class = 'Pose'
		self.position = Vector()
		self.orientation = Quaternion()
		self.stamp = time.time()

		if data:
			for key in data:
				if key == 'position':
					setattr(self, key, Vector(data[key]))
				elif key == 'orientation':
					setattr(self, key, Quaternion(data[key]))
				else:
					setattr(self, key, data[key])

	def __str__(self):
		s = 'Pose: position: {}, orientation: {}'.format(self.position, self.orientation)
		return s

	def __eq__(self, v):
		return self.Class == v.Class

@froze_it
class Range(object):
	"""
	"""
	UNKNOWN = 0
	ULTRASOUND = 1
	LIDAR = 2
	IR = 3
	kind = {
		0: 'Unknown',
		1: 'Ultrasound',
		2: 'Lidar',
		3: 'IR'
	}

	def __init__(self, data=None):
		self.Class = 'Range'
		self.range = []
		self.fov = 0.0
		self.type = self.UNKNOWN
		self.stamp = time.time()

		if data:
			for key in data:
				setattr(self, key, data[key])

	def __str__(self):
		s = 'Range[{}]: {}'.format(self.kind[self.type], self.range)
		return s

	def __eq__(self, v):
		return self.Class == v.Class

	def __getitem__(self, index):
		return self.range[index]

	def __contains__(self, value):
		if value in self.range:
			return True
		else:
			return False

@froze_it
class Power(object):
	"""
	Power contains: current, voltage, and a timestamp
	"""
	def __init__(self, data=None):
		self.Class = 'Power'
		self.current = 0.0
		self.voltage = 0.0
		self.stamp = time.time()

		if data:
			for key in data:
				setattr(self, key, data[key])

	def __str__(self):
		return 'Power: current: {} voltage: {}'.format(self.current, self.voltage)

	def __eq__(self, v):
		return self.Class == v.Class

@froze_it
class Odom(object):
	"""
	Odom is for robot odometry. It contains: Pose, Twist, and a timestamp
	"""
	def __init__(self, data=None):
		self.Class = 'Odom'
		self.position = Pose()
		self.velocity = Twist()
		self.stamp = time.time()

		if data:
			for key in data:
				if key == 'position':
					setattr(self, key, Pose(data[key]))
				elif key == 'orientation':
					setattr(self, key, Twist(data[key]))
				else:
					setattr(self, key, data[key])

	def __str__(self):
		return 'Odom: position: {} velocity: {}'.format(self.position, self.velocity)

	def __eq__(self, v):
		return self.Class == v.Class

class Axes(object):
	"""
	Axes are a component in Joystick
	"""
	def __init__(self, data=None):
		self.Class = 'Axes'
		self.leftStick = [0.0, 0.0]
		self.rightStick = [0.0, 0.0]
		self.dPad = [0.0, 0.0]
		self.L2 = 0.0
		self.R2 = 0.0

		if data:
			for key in data:
				setattr(self, key, data[key])

	def set(self, ls, rs, dp, l2, r2):
		self.leftStick = ls
		self.rightStick = rs
		self.dPad = dp
		self.L2 = l2
		self.R2 = r2

	def __eq__(self, v):
		return self.Class == v.Class

class Buttons(object):
	"""
	Buttons are a component in Joystick
	"""
	def __init__(self, data=None):
		self.Class = 'Buttons'
		self.x = False
		self.o = False
		self.s = False
		self.t = False
		self.R1 = False
		self.L1 = False
		self.L3 = False
		self.R3 = False
		self.options = False
		self.share = False

		if data:
			for key in data:
				setattr(self, key, data[key])

	def set(self, x, o, s, t, r1, l1, r3, l3, opt, share):
		self.x = x
		self.o = o
		self.s = s
		self.t = t
		self.R1 = r1
		self.L1 = l1
		self.L3 = l3
		self.R3 = r3
		self.options = opt
		self.share = share

@froze_it
class Joystick(object):
	"""
	This is a joystick message and setup to only support a PS4 controller ...
	suck it XBox! :)
	"""
	def __init__(self, data=None):
		self.Class = 'Joystick'
		self.axes = Axes()
		self.buttons = Buttons()
		self.stamp = time.time()

		if data:
			for key in data:
				if key == 'axes':
					setattr(self, key, Axes(data[key]))
				elif key == 'buttons':
					setattr(self, key, Buttons(data[key]))
				else:
					setattr(self, key, data[key])

	def __str__(self):
		return 'Joystick not setup'

	def __eq__(self, v):
		return self.Class == v.Class

@froze_it
class Array(object):
	"""
	A python Array
	"""
	def __init__(self, data=None):
		self.Class = 'Array'
		self.array = []
		# self.stamp = time.time()

		if data:
			for key in data:
				# print('Array >>', key, data[key])
				# setattr(self, key, list(data[key]))
				setattr(self, key, data[key])
				# if key == 'dict':

		# print(self)

	def __str__(self):
		return 'Array[{}]: {}'.format(len(self.array), self.array)

	def __getitem__(self, index):
		return self.array[index]

	def append(self, value):
		self.array.append(value)

	def __contains__(self, value):
		if value in self.array:
			return True
		else:
			return False

	def __eq__(self, v):
		return self.Class == v.Class

@froze_it
class Dictionary(object):
	"""
	A python Dictionary
	"""
	def __init__(self, data=None):
		self.Class = 'Dictionary'
		self.dict = {}
		# self.stamp = time.time()
		# print('data:', data)
		# print('type(data):', type(data))

		if data:
			for key, value in data.items():
				# print('Dictionary >>', key, data[key])

				# setattr(self, key, list(data[key]))
				if key == 'dict':
					for k, v in value.items():
						# print('sub:', k, v)
						getattr(self, key)[k] = v
				else:
					setattr(self, key, data[key])

		# print(self)

	def __str__(self):
		return 'Dictionary[{}]: {}'.format(len(self.dict), self.dict)

	def __getitem__(self, index):
		return self.dict[index]

	def keys(self):
		return self.dict.keys()

	def items(self):
		return self.dict.items()

	def __contains__(self, value):
		if value in self.dict:
			return True
		else:
			return False

	def __eq__(self, v):
		return self.Class == v.Class

def serialize(c):
	"""
	Takes a message and turns it into a json string.
	"""
	if c.Class == 'Image':
		c.encodeB64()
	return json.dumps(c, default=lambda o: vars(o))

idc = {
	'Image': Image,
	'Vector': Vector,
	'Quaternion': Quaternion,
	'Wrench': Wrench,
	'Pose': Pose,
	'Twist': Twist,
	'IMU': IMU,
	'Odom': Odom,
	'Axes': Axes,
	'Buttons': Buttons,
	'Joystick': Joystick,
	'Compass': Compass,
	'Range': Range,
	'Power': Power,
	'Array': Array,
	'Dictionary': Dictionary
}

def deserialize(s):
	"""
	Takes a json string and turns it into a message. If the message is an
	Image, then it automatically decodes it from base64/jpeg
	"""
	j = json.loads(s)
	c = None
	if 'Class' in j and j['Class'] in idc:
		c = idc[j['Class']](j)

		if c.Class == 'Image':
			c.decodeB64()

	return c

def dict_to_class(dct):
	msg = json.dumps(dct)
	clss = deserialize(msg)
	return clss

redpajama_set_name: RedPajamaGithub


text:
Monique Roffey shortlisted for the Rathbones Folio Prize
The Mermaid of Black Conch by UEA alumna Monique Roffey has been shortlisted for the 2021 Rathbones Folio Prize, which is open to all genres and forms of literature and is worth £30,000 to the winner. Monique was born in Trinidad and graduated from the English Literature BA at UEA in 1987. Her first novel Sun Dog was published in 2002. The White Woman On The Green Bicycle, published in 2009, was shortlisted for the 2010 Orange Prize and the 2011 Encore Prize. Archipelago, published in 2012, won the OCM BOCAS prize for Caribbean Literature in 2013, while House of Ashes was shortlisted for both the Costa Fiction Award and the OCM BOCAS prize. The Mermaid of Black Conch is currently also longlisted for the Republic of Consciousness Prize, and was the winner of the 2020 Costa Book Award. It was published by Peepal Tree Press last year.

redpajama_set_name: RedPajamaCommonCrawl


Your day starts when you board Expeditions passenger ferry to Lāna'i from the main loading pier at Lahaina Harbor. During your 45 minute voyage across the Auau channel you will enjoy open ocean viewing of all the islands in Maui County, including Maui, Molokai, Kahoolawe, Molokini and, of course, Lāna'i. You`ll be met upon arrival at Manele Harbor by your guide and be oriented to the lowland area of the island as you are driven up from Manele Harbor, in a Suburban, on the way Lāna'i City.
All tours include; round trip water transportation from Lahaina Harbor, Maui by Expeditions; pick up and return to Manele Harbor, per scheduled tour times; specified 4X4 tour; all safety gear, equipment, bottled water. Meal(s) and gratuities not included. Included are all taxes.
Your experienced guide knows the terrain, the flora and fauna, stories and legends and will share them with you over the course of the tour. At your direction the tour can be as active or relaxing as you like. Weather permitting, you will experience a unique ironwood forest, where the wind is the only voice you`ll hear, on your way to the Keahiakawelo sometimes called "Gardens of the Gods" – a stunning, natural red rock formation in the crater that often inspires visions of a visit to Mars. Other highlights MAY include: Kaiolohia or "Shipwreck Beach", where the breathtaking views of Molokai are interrupted only by the ship, towering just off shore, which brought this beach it`s name; and ancient Petroglyphs.
Activity Providers for this Product are Rabaca Tours and Expeditions Ferry. The Booking Agency or Activity Desk for this product is Lost on Lāna'i.


text:
\section{Introduction}
Query expansion is a method for alleviating the vocabulary mismatch problem present in information retrieval tasks. This is a fundamental problem where users and authors often use different terms describing the same concepts, which in turn harms retrieval performance. In this paper, we aim to distinguish terms helpful to the query expansion process through the use of an artificial neural network classifier.

Various methods for selecting expansion words exist, however, pseudo-relevance feedback (PRF) is the most well-known. PRF assumes that the top retrieved documents for a query will contain terms relevant to the query which can help distinguish other relevant documents from non-relevant documents. However, \cite{cao} showed that not all terms extracted using PRF methods help the retrieval process and many of these terms even have a negative effect. \cite{cao} divided terms into three categories: good, bad and neutral. When used as expansion terms for a query, good terms increase and bad terms decrease retrieval performance. Neutral terms have no effect if added. An ideal query expansion method would add only good terms to the query.
Therefore, \cite{cao} proposed a supervised learning method for the classification of such terms.

\cite{cao} uses a feature vector to train a classifier for separating good expansion terms from bad ones. This feature vector includes the following features: terms distribution in the feedback documents and terms distribution in the whole collection, co-occurrences of the expansion
term with the original query
terms, and proximity of the expansion terms to the query terms.

Another approach for improving the selection of query expansion terms is considering the semantic similarity of the candidate term with the query terms \cite{montazeralghaem2016axiomatic,kuzi,zamani2016embedding,zamani2016estimating,almasri2016comparison}.
As an example, \cite{montazeralghaem2016axiomatic} proposed a semantic similarity constraint for PRF methods and showed that adhering this constraint improves retrieval performance.

Another method that takes semantic similarity into consideration is using word embeddings to expand queries. \cite{kuzi} aimed to expand queries with terms semantically related to query terms. To this end, they trained word embeddings on document corpora using the Word2Vec Continuous bag of words approach \cite{mikolov}. This technique learns low dimensional vectors for words in a vocabulary based on their co-occurrence with other words within a specified window size. These vectors are both semantic and syntactical representations of their corresponding words. The learning is done unsupervised therefore these vectors are query-independent. \cite{kuzi} then uses these word embeddings to expand queries either by adding terms closest to the centroid of the query word embedding vectors (referred to in following sections as Average Word Embedding or AWE) or combining terms closest to individual query terms in the word embedding space. \cite{zamani2016embedding} has a similar approach for query expansion. It first defines a more discriminative similarity metric than cosine-similarity. Next, it introduces two embedding based query expansion models with different simplification assumptions. One model assumes that query terms are independent of each other (referred to in following sections as Embedding Query Expansion 1 or EQE1), and the other one assumes that the semantic similarity between two given terms is independent of the query.

In related work \cite{diaz2016query} showed that training local training of word embeddings using retrieved documents improves query expansion effectiveness.  \cite{zheng2015learning} proposed using supervised training and word embeddings to learn term weights to be used in retrieval models such as BM25.

In this paper, we aim to use pre-trained word embeddings in selecting suitable expansion terms. Furthermore, we propose an artificial neural network (ANN) model for this classification task. We use a siamese neural network architecture inspired by \cite{koch2015siamese} in order to lessen the impact of limited training data. Siamese network architectures have been gaining popularity in recent years in the information retrieval community \cite{yang2017question,dssm,he2016pairwise,severyn2015learning,wang2016compare} and have achieved impressive performance in various tasks. Using the distributed representation of terms, this network learns whether a term is semantically suitable for expanding a query. Our neural network approach intends to go beyond simple vector similarity and learns the latent features present within word embeddings responsible for term effectiveness or ineffectiveness when used for query expansion. In short, the proposed approaches main advantages are that it no longer requires manual feature design and no longer relies on simplistic similarity functions as it uses a trainable classifier.

We evaluate the effectiveness of our approach on four TREC collections.
We compare results with traditional approaches and more recent methods. Results show that integrating term classifications using our novel ANN model significantly improves retrieval performance. We also show that our proposed method is more robust compared to the baselines.

The rest of this paper is organized as follows: In section \ref{sec:termGoodnessSec} we discuss \cite{cao}'s method for labeling expansion terms in greater detail, in \ref{sec:modelsec} we introduce our classifier model and explain its integration with the retrieval process, in section \ref{sec:experiment} we present experiments performed and in section \ref{sec:results} we discuss the results obtained from the experiments.

\begin{table}
\caption{Query expansion term statistics for used collections} \label{tab:stats}
\centering
\begin{tabular}{l N N N N N N N N N N}
        \toprule
         \multirow{2}{*}{} &
         \multicolumn{4}{c}{Embedding-based} &
         \multicolumn{4}{c}{Pseudo Relevance Docs} &
         \multirow{2}{*}{\makecell{QLM \\ (MAP)}}  &
         \multirow{2}{*}{\makecell{QLM \\ +MM \\ (MAP)}} \\
         \cmidrule(lr){2-5}\cmidrule(ll){6-9}
                    &  Good (\%)  & Neutral (\%)  & Bad (\%)   & Oracle (MAP)   & Good (\%)   & Neutral (\%)  & Bad (\%)   & Oracle (MAP)    &  & \\
         \midrule
         AP         & 3.8 & 55.5 & 40.5 & 0.2981 & 16.2 & 53.6 & 30.1 & 0.3983 & 0.2206 & 0.2749   \\
         Robust     & 5.6 & 62.4 & 31.9 & 0.3122 & 21.9 & 55.4 & 22.6 & 0.4021 & 0.2176 & 0.2658   \\
         WT2g       & 6.2 & 37.8 & 55.8 & 0.3442 & 15.7 & 61.3 & 22.9 & 0.4383 & 0.2404 & 0.2593  \\
         WT10g      & 3.4 & 75.6 & 20.9 & 0.2410 & 14.1 & 63.6 & 22.2 & 0.2871 & 0.1837 & 0.1902  \\
         \bottomrule
    \end{tabular}
\end{table}

\section{Good, bad, and neutral expansion terms} \label{sec:termGoodnessSec}
In order to identify terms helpful to query expansion, we follow \cite{cao} and divide candidate terms into three classes: good, bad and neutral. For a particular query, a good term will increase retrieval performance and a bad term will decrease it. Neutral terms have no effect. In order to train our classifier, we require a dataset containing query and candidate expansion terms which have been labeled as one of the three classes mentioned.

For creating this dataset, we take each query and first average the embedding vectors of its query terms; this approach has been proposed in \cite{mikolov} and used in other works such as \cite{kuzi,zamani2016embedding}. Then, we use cosine-similarity to find top 1000 terms that are closest to the averaged vector. Finally, to identify the class of a term, we use the method proposed at \cite{cao}). Briefly, we add the expansion to the query and perform retrieval using Query likelihood method; if the mean average precision increases, the term is a good expansion term, but if it decreases, the term is a bad expansion term. If the change is not tangible, the term is neutral.

To examine the impact of selecting good expansion terms, we explore the performance of an oracle retrieval model that expands queries with only good expansion terms. Table \ref{tab:stats} shows the ratio of the classes of terms along with the performance of the oracle retrieval model. The first set of columns depicts statistics for the top 1000 closest terms to the average of query word embeddings, and the second set is for the terms that exist in first 10 pseudo-relevant documents retrieved using the query likelihood method. The last two columns show the performance of the original query likelihood method and the expanded query by the mixture model. The performance is measured using mean average precision for the top 1000 results. We can see that only a small percentage of expansion terms are good expansion terms in all collections, which may explain the slight improvements achieved by query expansion methods using word embeddings compared to the improvements by pseudo-relevance methods.

\begin{figure}
\centering
\includegraphics[width=\textwidth]{arch.png}
\caption{Architecture of the proposed siamese network. The architecture consists of two identical models projecting two separate inputs (query and expansion term pair) into a common embedding space and then comparing the two projections to get a final similarity score. This score tells us whether the candidate expansion terms of the two classes belong to the same class (good, bad or neutral).} \label{fig:arch}
\end{figure}

\section{Expansion Term Classification} \label{sec:modelsec}
In this section, we first present the model used for the classification task, then we explain how the classifier results are integrated into the retrieval process.
\subsection{Problem Formulation}
Suppose we have a dataset \(\mathcal{D} = \{(q_i, x_i, l_i)\}_{i=1}^N\) where \(q_i = \{q_{i,1}, \cdots, q_{i,k}\}\) represents a query and \(x_i\) is a candidate expansion term. \(l_i \in \{\text{Good}, \text{Neutral}, \text{Bad}\}\) is the label denoting the expansion terms effectiveness for the query. Our goal is to learn a model \(g(\cdot,\cdot)\) with \(\mathcal{D}\). For any query and candidate expression term pair \((q, x)\), \(g(q, x)\) classifies the expansion term as either good, neutral or bad.
\subsection{Model Overview}
We propose the deep expansion classifier (DEC) to model \(g(\cdot, \cdot)\). The architecture is depicted in Figure \ref{fig:arch}. A major roadblock when using ANN approaches in information retrieval tasks is the lack of training data. To overcome this, various methods such as using weak supervision for training ranking networks \cite{dehghani2017neural} have been proposed. In this paper, we use the learning technique proposed in \cite{koch2015siamese}. The \textit{siamese network} proposed was designed to overcome the lack of training data by learning whether two samples are of the same class or not rather than directly predicting which class a sample belongs to. Using this technique we can increase available training samples from \(n\) to \(n(n-1)\). In order to determine which class a query and expansion term pair \((\hat{q}, \hat{x})\) belongs to we can use the trained model to compare it to a set of previously classified pairs \((q,x)\), half of which are classified as good. We calculate the probability of a candidate expansion term being good based on the number of times it is put in the same class as a good pair or a different class than a bad pair.

\subsection{Modeling the Query and Candidate Expansion Term Relation}
Given a query \(q\) and a candidate expansion term \(x\), the model connects them as a sequence, then maps the words to their embeddings through a shared look-up table. A bidirectional long short term memory (LSTM) Network is then used to construct new contextual embeddings for each of the terms in the sequence.
LSTMs \cite{hochreiter1997lstm} are a variant of recurrent neural networks (RNN) designed to overcome the vanishing/exploding gradient problem present in regular RNNs with the usage of memory cells. These cells store information over long histories. The flow of information into and out of these memory cells is controlled by three gates (input, output and forget). Given a sequence of word embeddings including \(k\) query terms and one expansion term \((e_{q,1}, \cdots, e_{q,k},  e_{x})\), an LSTM will output a new sequence where each element captures the contextual information seen before it.

In bidirectional LSTMs the sequence is propagated both forwards and backwards and the two forwards and backwards contextual embeddings are concatenated. The outputs of the bidirectional LSTM layer is fed to a fully connected layer with a softmax activation function. This final layer represents the relation between query terms and the candidate expansion term.

\subsection{Expansion term classification and Term re-weighting}

The representation built must then be compared to the representation of another query-expansion term pair in order to determine whether the two pairs belong to the same class or not. An element-wise subtraction is performed on the two representations and the result is then fed into a fully connected layer with a softmax activation function which outputs the final score depicting whether the expansion terms belong to the same class.

In order to determine whether an expansion term candidate is good for a query, we feed the pair along with a set of query-expansion term pairs whose classes have been previously determined. We calculate the probability of a candidate expansion term being good as \(P(l=\text{Good}|(q,x)) = \frac{N_g + N_{nb}}{N}\) where \(N\) is the total number of pairs we compare to and \(N_g + N_{nb}\) is the number of times the pair being tested was determined to be in the same class a good pair or in a different class than a bad pair.

Finally, we reweight the expansion term weights obtained using the AWE method by multiplying them by \(P(l=\text{Good}|(q,x))\). The AWE method weights the candidate expansion terms based on the cosine similarity of the expansion term embedding and the query term embeddings centroid. So the final weight for an expansion term will be:
\(
     (1 + \alpha \cdot p(l=\text{Good})) \cdot \delta(\Bar{q},x)
\)
where \(\delta\) is the cosine similarity function and \(\alpha\) is a hyper-parameter.

\section{Experiment} \label{sec:experiment}
For evaluating the proposed methods we use four standard TREC collections: AP (Associated Press 1988-1989), Robust (TREC Robust Track 2004), WT2G and WT10G (TREC Web track 1999, 2000-2001). The first two collections contain news articles and the second two are general web crawls. We used the title of topics as queries. The words are stopped using the inquiry stop word list and no stemming was used.
We used pre-trained word embeddings with a dimension of 200 extracted using the GloVe \cite{pennington2014glove} method on a 6 billion token collection (Wikipedia 2014 dump plus Gigawords 5).

The parameters were updated by stochastic gradient descent with the Adam algorithm. Hidden size for the bidirectional LSTM is set to 200. The query and expansion term representation vector is set to 400. Our initial learning rate is 0.001 with a mini-batch size of 32. We train models on a single machine. We use k-fold cross validation (k depends on the number of queries available for each dataset) and average the evaluation metrics.
\subsection{Comparison Approaches}
For evaluation, we only compare to methods that select expansion term candidates based on word embeddings and not other information sources such as the top retrieved documents for each query (PRF). As we use general purpose word embeddings, we also do not compare to methods that train embeddings specifically for query expansion such as \cite{sordoni2014learning,zamani2017relevance}.

For evaluating our proposed method we consider three baselines: (1) the standard query likelihood model using maximum likelihood estimation, (2) AWE where expansion terms closest to the centroid of query term embeddings are selected \cite{kuzi}, and (3) EQE1 where expansion terms are scored by their multiplicative similarity to the query terms  \cite{zamani2016embedding}.

\subsection{Evaluation Metrics}

We use mean average precision (MAP) as our main evaluation metric. We also compare precision for the top 10 retrieved documents (P@10). Statistical significance tests are performed using two-tailed paired t-test at a 95\% confidence level. In order to evaluate the robustness of the proposed methods we use the robustness index (RI) \cite{collins2009reducing} which is defined as \(\frac{N_+-N_-}{|Q|}\) where \(|Q|\) is the number of queries and \(N_+\)/\(N_-\) are the number of queries which have improved/decreased compared to the baseline.

\begin{table} \label{tab:results}
\caption{Evaluation results on the four datasets. The superscripts 1/2/3 denote that the MAP improvements over QLM/AWE/EQE1 are statistically significant. The highest value in each column is marked in bold. }\label{tab:results}
\centering
\begin{tabular}{lllllllllllll}
        \toprule
         \multirow{2}{*}{} &
         \multicolumn{3}{c}{AP} & \multicolumn{3}{c}{Robust} & \multicolumn{3}{c}{WT2g} & \multicolumn{3}{c}{WT10g}\\
         \cmidrule(lr){2-4}\cmidrule(lr){5-7}\cmidrule(lr){8-10}\cmidrule(lr){11-13}
                    &  MAP  & P@10  & RI    & MAP   & P@10  & RI    & MAP   & P@10  & RI    & MAP   & P@10  & RI \\
         \midrule
         QLM        & 0.2206  & 0.3432 & - & 0.2176 & 0.3856 & - & 0.2404 & 0.4167 & -  & 0.1837 & 0.2420 & - \\
         AWE        & $0.2312^{1}$  & 0.3392 & 0.12 & $0.2230^{1}$ & 0.3899 & 0.10 & $0.2456^{1}$ & 0.4169 & 0.08 & 0.1849 & 0.2399 & 0.10 \\
         EQE1       & $0.2344^{12}$  & $\boldsymbol{0.3442}$ & 0.29 & $0.2278^{12}$ & 0.4016 & 0.25 & $0.2463^{1}$ & 0.4188 & $\boldsymbol{0.17}$ & 0.1867 & 0.2432 & 0.18 \\
         DEC        & $\boldsymbol{0.2403}^{123}$  & 0.3434 & $\boldsymbol{0.31}$ & $\boldsymbol{0.2358}^{123}$ & $\boldsymbol{0.4057}$ & $\boldsymbol{0.31}$ & $\boldsymbol{0.2489}^{1}$ & $\boldsymbol{0.4213}$ & 0.16 & $\boldsymbol{0.1891}^{12}$ & $\boldsymbol{0.2434}$ & $\boldsymbol{0.20}$ \\
         \bottomrule
    \end{tabular}
\end{table}

\section{Results and Discussion} \label{sec:results}
Table \ref{tab:results} presents the performance of the baselines and our proposed method. These methods expand queries with semantically related terms. The results show that the query expansion classifier DEC outperforms all baselines in terms of MAP. For all three embeddings-based methods, the performance gains are more pronounced in the two newswire collections. This may be due to the fact that these collections are more homogeneous than the web corpora. Web corpora will generally be noisier which in turn will affect classifier performance. Another reason could be due to the fact that our word-embeddings (GloVe) were pre-trained on Wikipedia and newswire articles. This would make them more suitable for use in newswire collections. Using word embeddings pre-trained using common crawl data may yield better performance in web corpora.

To summarize, the proposed method outperforms other state-of-the-art methods utilizing word embeddings for query expansion. The results are also more robust than previous approaches. This indicates that for selecting candidate expansion terms, simple similarity functions such as cosine similarity can be improved upon using ANN classifiers.

\section{Conclusion and Future Work} \label{sec:conclusion}
In this paper, we proposed a neural network architecture for classifying terms based on their effectiveness in query expansion. The neural network uses only pre-trained word embeddings and no manual feature selection or initial retrieval using the query is necessary. We evaluated the proposed methods using four TREC collections. The results showed that the proposed method significantly outperforms other word embedding based approaches and traditional pseudo-relevance feedback. The method is also shown to be more robust than the baselines.

For future work, one possible method is integrating topic vectors trained using methods such as Latent Dirichlet Allocation into the classification process. Another is using word embeddings trained using methods other than Word2Vec and GloVe (such as Lda2Vec \cite{moody2016mixing} or Paragraph2Vec \cite{le2014distributed}) or training on domain-specific corpora.

\bibliographystyle{splncs04}

redpajama_set_name: RedPajamaArXiv


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text:
The Rise of Self-Concealing Steganography
Digital steganography is the practice of hiding information in plain sight, especially inside other data or images. And a new toolset, which debuted earlier this month at the Black Hat Europe conference, suggests steganography is going to get much more difficult to spot.
Government Seeks to Tweak IT Act Rules
Some privacy and security experts express serious concerns about a new government proposal calling for social media platforms to remove what it deems "unlawful" content within 24 hours of notice.
Why Cybersecurity Remains a Top C-Suite Concern
Healthcare C-suite executives shouldn't have to worry about cybersecurity, contends John Houston, CISO at Pittsburgh-based integrated health delivery network UPMC. But for the second year in a row, cybersecurity was named the top priority for senior executives in a survey of 40 U.S. health systems.
Balancing Privacy, Technology Advancement
Efforts to protect privacy must be carefully balanced against the need to practically implement advanced technologies, argues Jared Ragland, senior director for policy in APAC at BSA/The Software Alliance, an advocacy group for software companies.
Looking Ahead to 2019: Breaches, Regulations and More
Jeremy Kirk • December 24, 2018
What's ahead for the cybersecurity landscape in 2019? We've received many lists of predictions from vendors and analysts for next year, and we've picked out five of the most interesting ones.
Facebook Sued in US Over Cambridge Analytica
Facebook violated consumer protection law by failing to protect personal data that consumers thought they'd locked down, the District of Columbia alleges in a new lawsuit. Plus, Facebook is disputing a New York Times report that it ignored privacy settings and shared data with large companies without consent.
GDPR: EU Sees More Data Breach Reports, Privacy Complaints
The number of data breach reports filed since the EU General Data Protection Regulation went into effect has hit nearly 3,500 in Ireland, over 4,600 in Germany, 6,000 in France and 8,000 in the U.K. Regulators say more Europeans are also filing more complaints about organizations' data protection and privacy practices.
Data Leak Exposes Psychologists' Home Addresses
A large health insurer in Western Australia shared the home addresses of some psychologists to a web-based appointment booking service, according to a news report. The health insurer belated realized after a complaint from one practitioner that some psychologists work from home.
Crafting a Data Protection Law That Works
As India's Parliament prepares to finalize a privacy and data protection law in the weeks ahead, there's still no consensus among security practitioners about what approach the legislation should take. But the government should ensure all companies comply with clearly defined standards - or face punishment.
Equifax Breach 'Entirely Preventable,' House Report Finds
The massive data breach suffered by Equifax in 2017 "was entirely preventable," according to a report released by the House Oversight Committee's Republican majority. Some Democratic lawmakers have slammed the report for failing to advance legislative or oversight changes to help prevent breaches.
GDPR: 8,000 Data Breach Reports Filed So Far in UK
The U.K.'s privacy watchdog says that six months after enforcement of the EU's General Data Protection Regulation began, it's seen a dramatic increase in data breach reports - as well as privacy complaints from the public.
Mobile Apps: Privacy Issues in India
A new study shows mobile apps in India seek access to more data than apps in other nations, says Shivangi Nadkarni, CEO at Arrka Consulting. Nadkarni says the data protection bill being considered in India eventually could help curb access to information via apps.
India's Data Protection Bill: An Analysis
Suparna Goswami • December 7, 2018
Vaishali Bhagwat, cyber lawyer and advocate, contends that the "harms-based" approach in India's data protection bill is more appropriate than the "rights-based" approach taken by the European Union's General Data Protection Regulation
Emails Expose Sensitive Internal Facebook Discussions
Jeremy Kirk • December 6, 2018
A batch of documents meant to be kept under court seal lays bare Facebook's strategic brokering of access to user data to reward partners and punish potential rivals. The material also demonstrates Facebook's views at the time on privacy and the risks of leaking data.
A CISO's Insights on Breach Prevention
A failure to patch systems and slipups that lead to insider threats are two major causes of breaches in the healthcare sector that need to be urgently addressed, says Anahi Santiago of Christiana Care Health System.


text:
Regional Impacts
Breaking Down Barriers to Energy Efficiency
When Elias and Ruth Lumpkins, Jr. bought their two-story house in a verdant neighborhood on the southeast side of Grand Rapids, Michigan, they put on an addition so that Ruth could have the formal dining room she always wanted. But despite that work and the perfectly manicured lawn, the brick and siding house, originally built in 1975, had its flaws. It was cold and drafty in winter, warm and humid in summer, and the utility bills were high.
So when Elias, a former teacher, principal and school administrator who now represents Grand Rapids' Third Ward on the City Commission, heard about a new program offering subsidized home energy assessments, he and Ruth decided to sign up. Home energy use normally isn't something people pay attention to—"you've got bigger fish to fry," he says. "But at that point in time, I was looking to see how we could lower some of those utility bills and use that money for something else."
The energy audit turned up a number of problems, from an inefficient furnace and air conditioner to mold from the dampness. So with help from a $1475 utility rebate and a low cost loan from a part of the program called MichiganSaves, the Lumpkins replaced light bulbs, had the mold removed, and new high efficiency HVAC equipment and water heater, and attic and basement insulation, installed. "It was much more than we wanted to spend, but I think it was worth it," says Elias. The Lumpkins' energy use and utility bills are down 23 percent, saving them more than $640 per year, and the drafts and high humidity are gone. "It's been phenomenal," says Ruth, a community activist and Grand Rapids Public Library Commissioner. Their total out-of-pocket cost was $15,042.53, but more than a third of that was for the mold remediation, so that they will recoup their energy efficiency investment in 12 years or less—and have the 23 percent energy savings far into the future.
Multiply the Lumpkins' story by 90 million—the number of existing single-family homes in the U.S. Then make similar improvements in another 30 million multi-family dwellings and commercial buildings. It all adds up to an enormous opportunity to cut energy use and greenhouse gas emissions in America's homes, offices, stores and other buildings.
In fact, 40 percent of all the U.S.' energy—and 76 percent of all our electricity—is now used to keep our lights and TVs on, our appliances and equipment humming, and our temperatures comfortable. And study after study, such a landmark 2007 roadmap from McKinsey to slashing greenhouse gas emissions, has concluded that major reductions are both possible and profitable. Deutsche Bank figures, for example, that $279 billion spent on retrofitting buildings would bring $1 trillion in savings over 10 years—and create 3.3 million job-years (The Rockefeller Foundation, 2012). The Department of Energy's Quadrennial Technology Review says that "by 2030, building energy use could be cut more than 20 percent using technologies known to be cost effective today and by more than 35 percent if research goals are met.1"
Moreover, energy efficiency is often a cheaper energy 'source' than wind or solar, since every watt saved means one less watt that must be generated.
But the existence of this huge opportunity raises a troubling question. If billions and billions of dollars of saved energy costs are lying on the table for the taking, why have we largely failed to grab that money? America has retrofitted less than 3 percent of its homes and commercial buildings. And more than two-thirds of the new HVAC systems we buy today are far less efficient than equipment already on the market, which would pay back its higher cost in savings in just a few years. In short, why are we still wasting so much energy—and money?
There are many answers. Building owners often don't know or don't much care what their monthly bills are—or have other higher priorities. Landlords have little incentive to cut energy use when tenants pay the bills. Regulators typically allow utilities to get paid only for selling more electricity, not less. And the task itself—upgrading more than 100 million buildings one by one—is enormous and daunting.
So when Michigan launched the effort that would bring energy savings to the Lumpkins and 11,000 other homeowners in the state, one key goal was learning how to surmount the many barriers. "The whole point was to not just deliver a program, but to study what works," says Jacob Corvidae, former executive director of Detroit-based EcoWorks, which participated in the effort.
The story begins in 2008 when Michigan passed its Clean, Renewable and Efficient Energy Act2. The law required utilities to encourage consumers to cut energy use, and led to a small surcharge on energy bills to pay for incentives. Meanwhile, recognizing that coming up with the cash to pay for home upgrades is a challenge for most homeowners, the Michigan Public Service Commission asked a policy research firm in Grand Rapids to devise a good financing model. They proposed getting banks to provide a fund—which ended up totaling $43 million—for low cost loans by holding a modest reserve to cover defaults.
The third piece of the puzzle was a $30 million grant from the U.S. Department of Energy's Better Buildings Neighborhood Program to launch a BetterBuildings for Michigan effort3. The idea: go out into dozens of neighborhoods throughout the state to figure out how to persuade people to sign up first for a $99 energy assessment, and then for the improvements the assessment identified.
It wasn't easy. "We had some theories. Some turned out well, and some failed miserably," recalls Corvidae.
For instance, the team thought homeowners would respond to a slick brochure and a door-to-door campaign that carefully described how energy assessors used a blower door test to find air leaks, and how simple improvements could save them big bucks. Wrong. "People had no idea what we were talking about," says Selma Tucker, director of marketing and communications for MichiganSaves. "At best, they were skeptical, and at worst hostile."
Plus, it was quickly clear that energy efficiency offered little social cachet. "Investing in efficiency has to compete with granite countertops and other traditional improvements," Tucker explains. "I've never gone to a dinner party where the hosts show off their R-49 insulation or new furnace—but they are giddy to show off their remodeled kitchen."
So the program changed its approach. Instead of talking about air leaks and lower utility bills, they focused on comfort, health and safety. Does the house have drafts? Cold floors? Rooms that were freezing without running a space heater—which could be dangerous? "That was a much better message than a blower door test," Tucker says.
But that still wasn't enough. The program burned through a lot of cash trying to go door-to-door and using direct mail to reach people in specific neighborhoods. That didn't work either. People don't want to be bothered after they get home from work, Tucker says, and "if you do mail, you are competing with the Macy's catalog and the other things people are bombarded with."
Then, the team had an "epiphany," says Tucker. Rather than trying to reach people in a geographical neighborhood, the program should target communities such as members of a church or university, or employees of a company. And it should enlist trusted leaders, such as ministers, professors or company officials, to spread the world. "The messenger really matters," says Tucker, who led the effort in the Grand Rapids area.
At Grand Valley State University, for instance, Tucker offered free home energy audits to about a dozen campus leaders in exchange for pictures and testimonials that were put on postcards and sent to all faculty and staff through intercampus mail.
That worked. BetterBuildings for Michigan signed up 215 people (nearly 10 percent of all university employees) for the $99 audit, which included installing more efficient lightbulbs and low-flow showerheads. Perhaps even more impressive, 60 percent made additional upgrades suggested by the audit. That's an astonishingly high rate compared to a national average of only about 5-10 percent of people following up on the audit. In the U.S. in general, "we're still struggling to get people to move beyond the energy audit phase to do additional work—and then not just change one appliance or two, but to do the whole house insulation and upgrade," says Rachel Cluett, Senior Research Analyst at the American Council for an Energy-Efficient Economy4.
Another key lesson from the BetterBuildings for Michigan program is just how easy—and cheap—it is to make major reductions in energy use. In Detroit, few people could afford a $6000 retrofit, even with low cost loans and utility rebates, says Corvidae. So the program offered a pared down energy audit that focused on low cost improvements, such as sealing leaks spotted by the blower door test. "Air sealing is the fastest, cheapest way to cut energy bills in homes," says Corvidae. "We got, on average, a 15 percent energy reduction, and as much as 40 percent."
BetterBuildings for Michigan, of course, has been only one of many successful home efficiency efforts in the U.S. Standouts identified by the ACEEE include5:
National Grid's EnergyWise program in Rhode Island, which does an energy audit for free, then pays for 75 percent of insulation costs up to $2,000 and up to $750 worth of free air sealing for homes.
Columbus Gas of Ohio's Home Performance Solutions program, which offers a $50 energy audit and discounts on insulation and other work.
Xcel Energy's Home Energy Squad in Minnesota, where a $70 trip to a house pays for suite of energy-saving items, such as compact fluorescent light bulbs, programmable thermostats, and weather stripping, that are installed for free.
What all these home retrofit success stories have in common, though, is that they are driven by utilities and government programs, not solely by market forces. As a result, they depend on continued support, and can only reach a small minority of building owners. The BetterBuildings for Michigan outreach effort ended when the DOE grant ended, for instance, though the low cost loans and utility rebates are still available.
Moreover, existing market forces are often counterproductive. Contractors can make a lot more money persuading people to install new windows than to seal air leaks, for instance, but for homeowners—and the nation as a whole—plugging those leaks is a far better deal.
So the conundrum remains. A huge opportunity to save money and energy is lying on the table. But we're only taking advantage of a tiny fraction of that chance. How can we do better?
When Georgetown University's Francis Stakey posed that question to a meeting of mayors he'd organized, "the mayors told us the best way to stimulate more energy efficiency was to hold a competition." So the university created the Georgetown University Energy Prize, which will award $5 million to one community in the U.S., selected from 50 finalists in 26 states, that comes up with the most innovative—and successful—ways to boost energy efficiency6.
Similarly, MichiganSaves' Tucker argues that the biggest challenge on the communications side is making energy efficiency sexy—which might be done by offering awards to homeowners who save the most energy. "No one will probably ever invite me to see their new insulation, but they might feel as proud of an award or plaque in their house as they do about the Cadillac Escalade parked in front," Tucker says. "That's when we can get the most amount of change."
View footnotes
The Rockefeller Foundation. 2012. "Deutsche Bank and The Rockefeller Foundation Release Building Energy Retrofit Study," March 1, available at https://www.rockefellerfoundation.org/about-us/news-media/deutsche-bank-rockefeller-foundation/. ↩
U.S. Department of Energy. 2015. "Increasing Efficiency of Building Systems and Technologies," September, available at http://energy.gov/sites/prod/files/2015/09/f26/QTR2015-05-Buildings.pdf. ↩
Michigan Public Service Commission, "Renewable Energy," available at http://www.michigan.gov/mpsc/0,4639,7-159-16393—,00.html (last accessed August 2016). ↩
U.S. Department of Energy, "Michigan Sweeps Neighborhoods with Energy Upgrades," available at http://energy.gov/eere/better-buildings-neighborhood-program/michigan-sweeps-neighborhoods-energy-upgrades (last accessed August 2016). ↩
American Council for an Energy-Efficient Economy (ACEEE). 2013. "Frontiers of Energy Efficiency: Next Generation Programs Reach for High Energy Savings," January 9, available at http://aceee.org/research-report/u131. ↩
Georgetown University Energy Prize, "The Race for Efficiency," available at https://guep.org (last accessed August 2016). ↩
Next | Energy Storage
Indispensible to a Cleaner, More Resilient Electricity Grid
From Risk to Return
Investing in a Clean Energy Economy
Introduction Report Findings: The Clean Energy Economy Implementation Challenges The Crucial Role of Policy How Business Can Lead: A Call to Action Conclusion: The Time to Act is Now
Bottom Line Benefits for Cities Going Green Policy Can Work: The Story of the Regional Greenhouse Gas Initiative Biofuels: Promise Dimmed by Market and Policy Trends, but Niche Markets Remain Breaking Down Barriers to Energy Efficiency Energy Storage: Indispensible to a Cleaner, More Resilient Electricity Grid The Information Technology Revolution: Coming to the Electricity Grid? Lighter Vehicles Bring Fuel Savings—and Higher Sales Passenger Transportation Innovation Study
A-1 Model, Methodology, Key Results A-2 Technology Cost and Performance Assumptions A-3 Implementation Challenges
Executive Summary Regional Impacts
The Risky Business Project focuses on quantifying and publicizing the economic risks from the impacts of a changing climate.
© 2020 Risky Business. All rights reserved.


text:
Q: Multiple checkbox search wordpress I need a multiple checkbox search to work and I'm stuck. The form is ok but I don't know how to do the query. an someone please help me ?
Form :
<input id="propertytype" class="noborder" type="checkbox" name="propertytype2[]" value="Loft">
Loft

<input id="propertytype" class="noborder" type="checkbox" name="propertytype2[]" value="Studio">
Studio

<input id="propertytype" class="noborder" type="checkbox" name="propertytype2[]" value="2 pieces">
2 pièces

<input id="propertytype" class="noborder" type="checkbox" name="propertytype2[]" value="3 pieces">
3 pièces

<input id="propertytype" class="noborder" type="checkbox" name="propertytype2[]" value="4 pieces">
4 pièces

<input id="propertytype" class="noborder" type="checkbox" name="propertytype2[]" value="5 pieces">
5 pièces

<input id="propertytype" class="noborder" type="checkbox" name="propertytype2[]" value="6 pieces et +">
6 pièces et +

<input id="propertytype" class="noborder" type="checkbox" name="propertytype2[]" value="Proprietes, Hotels particuliers">
Propriétés, Hôtels particuliers

Query :
$search_propertytype = "";
if (isset($_POST['propertytype2'])) {
    $search_propertytype = trim($_POST['propertytype2']);
}

if (get_option('wp_search_propertytype') == "Yes") {
    if($search_propertytype != '')
    {
        $search_propertytype = trim($search_propertytype);
        $query ="SELECT p.* FROM $wpdb->posts p, $wpdb->postmeta p1 WHERE p.ID = p1.post_id AND (p1.meta_key='propertytype_value' AND p1.meta_value='$search_propertytype' OR p1.meta_key='propertytype2_value' AND p1.meta_value='$search_propertytype')";
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}

A: I made a solution by WP_Query and custom meta box. Made my query argument as following:
$args = array(
'post_type' => 'product',
'meta_query' => array(
    'relation' => 'OR',
    array(
        'key' => 'color',
        'value' => 'blue',
        'compare' => 'NOT LIKE'
    ),
    array(
        'key' => 'price',
        'value' => array( 20, 100 ),
        'type' => 'numeric',
        'compare' => 'BETWEEN'
    )
)
);
$query = new WP_Query( $args );

Check wordpress official document: http://codex.wordpress.org/Class_Reference/WP_Query#Custom_Field_Parameters
You can check in this way. May be it can help. Thanks.

redpajama_set_name: RedPajamaStackExchange


text:
  background-image: url('/images/2.png');
}


text:
After Disney Rejects The Divinity Of Jesus Christ During Their Candlelight Processional, Lutheran Leader Calls For Parents To Ditch Disney To Save Their Kids' Hearts And Minds
Lutheran leader Dr. Gregory P. Seltz, the Executive Director of the Lutheran Center for Religious Liberty in Washington, D.C. and the Speaker Emeritus for The Lutheran Hour, called on parents to ditch Disney after the company rejected the divinity of Jesus Christ during their EPCTO International Festival of the Holidays – Candlelight Processional.
Dr. Gregory P. Seltz via The Luterhan Schools Partnership YouTube
Back in December, entertainment analyst and scooper WDW Pro was informed by former Disney graphic artist and historian Joshua L. Harris that Disney had altered their script for the Candlelight Processional, which now denies the divinity of Jesus Christ.
Disney Attacks Christianity for Christmas
The decades-old #CandlelightProcessional at Walt #DisneyWorld and #Disneyland is a super-popular retelling of the nativity story. Now in the ceremony, Disney is declaring in the official script that Jesus was "simply a man".@JLHomni pic.twitter.com/YoItcUsA2r
— wdwpro (@wdwpro1) December 5, 2022
RELATED: The Walt Disney Company Attacks Christianity, Rejects The Divinity Of Christ By Radically Altering EPCOT International Festival Of The Holidays – Candlelight Processional
In this new version of "One Solitary Life," which was read by Shang-Chi and the Legend of the Tens Rings actor Simu Liu it claims that Christ is "simply a man" rather than properly noting that Christ is True God and True Man.
Simu Liu reads, "For all the miracles of Jesus Christ the Prince of Peace himself was simply a man, a human being, and he walked through this world in the most human way possible with love. He didn't have much, but what He did have He shared, and it became plenty: food, water, shelter, care. He met the world around Him with empathy. Saw hunger and shared the food from his plate. Saw thirst and poured from them from his own cup. Saw sickness and offered His time for their healing, soothed them with His human hands."
"Jesus Christ founded in His earthly heart to give of himself to the people who loved Him and even the people who betrayed Him, to all people. That a baby in a manger could grow up to become a man who taught a world of people what it means to love people that is the spirit of this season. And it lives inside each and every one of us. The greatest joy of being human is to love one another and when we share that love that is the greatest gift of all," Liu concluded.
Following this revelation Harris spoke with Christian News Journal where he was asked about how a big a deal Disney's denial of Christ's divinity is.
He responded, "It is nearly impossible to overstate just how much of [a] blasphemous betrayal this is. Beneath a mass of flowery feel-good sentiment, the climax of the Candlelight Processional, a spiritual service held for 64 years, Jesus' divinity is distinctly denied FIVE times – essentially stripping Jesus Christ of his Christ-ness during a deeply religious Christmas service."
"While all faiths have been attacked from the outside throughout all history, I don't believe ANY of them have been so thoroughly defamed from INSIDE as Christianity has been this season," he continued.
Walt Disney via Walt Disney's Wonderful World of Color
RELATED: Report: The Walt Disney Company And Marvel Studios To Educate Actors In Order To Placate Communist China
Harris added, "This is particularly poignant considering the betrayal has occurred from what was once the most prominent Christian company that has ever existed. Walt Disney was a deeply spiritual man who embedded Christian ideals and symbols into almost every project he ever worked on."
"Snow White PRAYS during his first film, Ave Maria is the finale to FANTASIA, Disneyland was prayed over during its opening ceremonies by several Christian leaders," he explained.
Snow White prays in Snow White and the Seven Dwarfs (1937), Walt Disney
Dr. Gregory Seltz would also speak to Christian News Journal and called on parents to ditch Disney.
He informed the outlet, "Sadly, Disney has gone full throttle into the parenting (even the preaching) business, telling children what they should and should not believe, shamelessly willing to obscure the basic truths of Christmas in its new, disinformation campaign all in the name of 'entertainment.'"
"Any Christian parent who allows the glitter and glitz of Disney movies and Disneyland theme parks to take hold of our children's imagination now delivers one's children into the hands of people who actively seek to steal our children's childhoods, and now, even their faith," he declared.
He concluded, "This is about the hearts and minds of our children, not just a ride on Space Mountain. Parents of all faiths need to find other avenues for entertainment rather than anything Disney."
RELATED: Disney CEO Bob Chapek Claims Company Is "A Force For Inclusion" But Doesn't Want It Become A "Political Football"
Dr. Seltz is right that it is indeed about the hearts and minds of our children. Former Disney CEO Bob Chapek also acknowledged this.
During The Walt Disney Company's war with the state of Florida back in February, where they advocated against a bill that would prevent the teaching of gender identity and sexual orientation to children in kindergarten through third grade, Chapek sent an email to Disney employees stating, "Because this struggle is much bigger than any one bill in any one state, I believe the best way for our company to bring about lasting change is through the inspiring content we produce, the welcoming culture we create, and the diverse community organizations we support."
He explained, "There's a reason content is at the top of this list. For nearly a century, our company's stories have opened minds, inspired dreams, shown the world both as it is and how we wish it could be, and now more than ever before, represent the incredible diversity of our society."
"We are telling important stories, raising voices, and I believe, changing hearts and minds," he added.
Bob Chapek at The Walt Disney Company's Investor Day 2020
Later in the email Chapek also stated, "Powerful content that changes hearts and minds only springs from inclusive cultures, which not only attract and retain the best and most diverse talent, but also give those employees the freedom to bring forth ideas that reflect their lives and experiences."
"We must work together to ensure Disney always remains such a place," he declared.
Producer Kevin Feige and Bob Chapek attend the Doctor Strange in the Multiverse of Madness World Premiere at the Dolby Theatre in Hollywood, CA on Monday, May 2, 2022.
(photo: Alex J. Berliner/ABImages)
RELATED: Bob Iger Confirms Disney Will Double Down On Woke Content: "We Can't Lose That, We Just Can't"
This cause was also championed by returning CEO Bob Iger when he was asked about the company's war with the state of Florida and its advocacy seemingly in favoring of grooming minor children.
Iger said, "Well, first of all, our LGBTQ employees are very important to us and we care deeply about them."
"That is a given," he declared. "Secondly, this company has been telling stories for a hundred years, and those stories have had a meaningful, positive impact on the world. And one of the reasons that they've had a meaningful, positive impact is one of our core values is inclusion, acceptance, and tolerance. And we can't lose that. We just can't lose that."
In response to the so-called "Don't Say Gay" legislation, Iger said Disney would still promote "inclusion," but suggested that the company must strike a "delicate balance" and "listen to [its] audience" and "have respect for the people that [it's] serving." This is a retreat. pic.twitter.com/bZBnQdm616
— Christopher F. Rufo ⚔ (@realchrisrufo) November 29, 2022
What do you make of Dr. Seltz's call for parents to ditch Disney after the company rejected the divinity of Jesus Christ?
NEXT: An Interview With Christian Toto: How Hollywood Ruined Entertainment With Woke 'Virtue Bombs'
Bob ChapekBob IgerDisneyDr. Gregory P. SeltzJoshua L. HarrisThe Walt Disney Company
John is the Editor-in-Chief here at Bounding Into Comics. He is a massive Washington Capitals fan, lover of history, and likes to dabble in economics and philosophy.
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Thirroul veteran Jamie Feeney hopes to draw the curtain on a stellar career by finally capturing a premiership which has eluded him since making his debut in the NRL 15 years ago.
It's become part of rugby league folklore that a 22-year-old Johnathan Thurston selflessly gave Canterbury captain Steve Price his premiership ring in the euphoria of the Bulldogs 2004 grand final victory.
What is not as well remembered is that it was Thurston edged out Feeney for a place on the Bulldogs bench in the wake of the knee injury which ruled Price out of the match.
"I warmed up and didn't play," Feeney recalled.
"Pricey was out and Johnno was 18th [man] and I was 19th. We trained all week and then he got the nod on the night and good on him. The rest is history."
Now 36 and at the end of a remarkable second coming as a player, Feeney is looking to make the most of his final 80 minutes and finally snare that elusive ring.
"I haven't played in a lot of grand finals as a senior footballer so I'm looking forward to Sunday," Feeney said.
"The real reason I played was to meet people down here.
"I didn't know anyone besides family so I've ticked that box, had a great year and I feel like I'm part of the furniture at the club now. Hopefully we finish it off really well and it'll be a great way to go out."
A victory in Sunday's grand final would put an exclamation point on a comeback that seemed unlikely when serious back problems brought a premature end to his NRL career with Melbourne in 2007 at just 28 years of age.
"It wasn't the finish I was after in Melbourne and having to retire and give it up," Feeney said.
"I coached for five years and then three years ago I was coaching at Erina and we were a couple of troops short for a trial. I had a run there and just fell in love with playing again."
After recapturing the love for playing the game, Feeney's troublesome back has again caught up with him and will end his playing career after Sunday's match this time - he insists - for good.
"Rugby league's given me a lot but it's also given me a couple of injuries," Feeney said.
"My back's pretty bad and the first two or three days after a game it's really bad and hard to bear so that's why I'm hanging them up. I think it's the body telling me it's time to give it up."
Erina fell one point short of claiming the premiership with Feeney at the helm last year meaning Sunday will be his last throw of the premiership dice but he's confident the Butchers can get it done.
"We've a got a great team and we're playing some great footy so hopefully we can put together and I'll go out on the right note."


Soap is primarily made of fat, animal fat, or vegetable oil (e.g. olive soap) and sodium hydroxide. This explains why soap is alkaline and not great for sensitive skin. (You see, we do beauty tips free of charge here as well!). It can leave a film that can affect absorbency. But the main factor seems to be hard/soft water. Hard water will leave a "fatty scum" on the fibres, no matter how much you rinse, and that will affect absorbency. Soft water is better, as the soap disolves more completely. Soap flakes leave cotton very soft to the touch, but not as absorbent.
As for clogging the machine, it's back to the hard/soft water thing. It shouldn't be a huge problem in the dispenser drawer, but can be in the waste pipes, because you are getting rid of the scummy water at that stage. The inside of the pipes can get clogged until they are totally blocked. However, a good dose of very hot water and washing soda crystals does wonders for that. It declogs the machine as well as the pipes.
Too much froth in the washing machine inhibits the cleaning process, which is why you should be really sparing when using soap flakes instead of detergent. Adding them direct to the drum rather than in the dispenser drawer makes sure that all the flakes get to the nappies rather than stuck on the path from the dispenser drawer.
If you have a toploading machine, there is less of a problem, presumably because there is more room for the froth to get to the top of the water and out of the way. This is probably why both toploader machines and soap flakes are so much more common in France.


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Brent and Janelle Jeal have opened a new plant store in Nambour, Plant Thy seed. Picture: Patrick Woods.
Cafe sale sprouts passionate couples' newest venture
Laura Pettigrew
The former owner of a successful Coast cafe is excited to start a new, more relaxing chapter by opening a specialist store set to "wow" plant lovers.
After eight years of owning and running Woombye's Kimnat Little Market and Cafe Janelle Jeal has along with her husband Brent opened Plant Thy Seed along Howard St in Nambour.
Ms Jeal said she hoped to extend the store's already wide range of plants to include rare plants.
"We are offering a good range of everything there is a high demand on plants at the moment even the wholesalers are struggling to keep up with demand so we are getting everything we possibly can at the moment and hoped to keep growing that," she said.
"We certainly have everything, your monstera, we have your peace lilies, the peperomia, anthuriums and dragon tales, the list is endless.
"We aim for a wow factor and I think we have got that."
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Nambour resident of 29 years Mr Jeal said the business hoped to provide the town with a specialist indoor plant store.
"This year through COVID a lot of people got back into gardening through having to slow down and having to stay home, so I think that has re-sparked a lot of people to get back into gardening and indoor plants," he said.
"And we have always had a passion for it ourselves, so we thought let's supply them at a good price."
Ms Jeal and her husband said they felt it was time to sell Kimnat Little Market and Cafe at the beginning of this year and planned on semi-retiring before they realised they "weren't very good at doing nothing".
Ms Jeal hoped the new owners of the cafe were doing extremely well.
"I loved owning a cafe, it was fun, but I had done it for a long time and its constant and tiring after a while," she said.
"I was looking for a slower pace and plants was more slow, relaxing and calming and at my pace."
Janelle Jeal has opened a new plant store in Nambour, Plant Thy seed. Picture: Patrick Woods.
She said the store continued her life's passion for plants which begun growing up in New Zealand.
"As a child growing up in NZ indoor plants were a big thing and its like they have made a comeback," she said.
"Plants have always been a passion, I love the great outdoors and as an adult whenever I've had my home, I've tried to bring plants inside."
Plant Thy Seed, located next to Arden Dawn Cafe will officially open on November 21, but customers are welcome to begin browsing.
indoor plants kimnat cafe local businesses nambour sunshine coast business woombye


Car shopping near Hudson? Look no further than the new Mazda inventory at Tulley Mazda. We have a wide variety of Mazda cars and SUVs in stock, from the popular Mazda CX-5 to the versatile Mazda3 sedan. Next time you need a reliable vehicle for your Merrimack commute, stop by Tulley Mazda to pursue the Mazda models for sale in Nashua, NH!
Heated Seats, Blind Spot Monitor, Cross-Traffic Alert, iPod/MP3 Input, Aluminum Wheels, Back-Up Camera, Onboard Communications System, Smart Device Integration, Dual Zone A/C, Keyless Start. Mazda3 4-Door Touring trim. Warranty 5 yrs/60k Miles - Drivetrain Warranty; SEE MORE! KEY FEATURES INCLUDE Heated Driver Seat, Back-Up Camera, iPod/MP3 Input, Onboard Communications System, Aluminum Wheels, Keyless Start, Dual Zone A/C, Smart Device Integration, Blind Spot Monitor, Cross-Traffic Alert, Heated Seats. Rear Spoiler, MP3 Player, Remote Trunk Release, Keyless Entry, Child Safety Locks. Mazda Mazda3 4-Door Touring with ETERNAL BLUE MICA exterior and BLACK BLACK interior features a 4 Cylinder Engine with 184 HP at 5700 RPM*. EXPERTS REPORT Edmunds.com's review says The steering delivers excellent on-center feel at highway speed, and your inputs feel direct and precise.. WHY BUY FROM US Tulley Automotive with locations in both Nashua, NH and Manchester, NH treats the needs of each individual customer with paramount concern. We know that you have high expectations, and as a car dealer we enjoy the challenge of meeting and exceeding those standards each and every time. Allow us to demonstrate our commitment to excellence! Horsepower calculations based on trim engine configuration. Please confirm the accuracy of the included equipment by calling us prior to purchase.
Heated Seats, Blind Spot Monitor, Cross-Traffic Alert, iPod/MP3 Input, Aluminum Wheels, Back-Up Camera, Onboard Communications System, Smart Device Integration, Dual Zone A/C, Keyless Start. Mazda3 4-Door Touring trim. Warranty 5 yrs/60k Miles - Drivetrain Warranty; AND MORE! KEY FEATURES INCLUDE Heated Driver Seat, Back-Up Camera, iPod/MP3 Input, Onboard Communications System, Aluminum Wheels, Keyless Start, Dual Zone A/C, Smart Device Integration, Blind Spot Monitor, Cross-Traffic Alert, Heated Seats Rear Spoiler, MP3 Player, Remote Trunk Release, Keyless Entry, Child Safety Locks. Mazda Mazda3 4-Door Touring with MACHINE GRAY METALLIC exterior and BLACK BLACK interior features a 4 Cylinder Engine with 184 HP at 5700 RPM*. VEHICLE REVIEWS Edmunds.com explains The steering delivers excellent on-center feel at highway speed, and your inputs feel direct and precise.. WHO WE ARE Tulley Automotive with locations in both Nashua, NH and Manchester, NH treats the needs of each individual customer with paramount concern. We know that you have high expectations, and as a car dealer we enjoy the challenge of meeting and exceeding those standards each and every time. Allow us to demonstrate our commitment to excellence! Horsepower calculations based on trim engine configuration. Please confirm the accuracy of the included equipment by calling us prior to purchase.
Sunroof, Heated Seats, Cross-Traffic Alert, Blind Spot Monitor, Smart Device Integration, Back-Up Camera, BOSE/MOONROOF/SATELLITE RADIO PACKAGE... Alloy Wheels, iPod/MP3 Input, Dual Zone A/C, Keyless Start, Onboard Communications System. Mazda3 4-Door Touring trim. Warranty 5 yrs/60k Miles - Drivetrain Warranty; SEE MORE! KEY FEATURES INCLUDE Heated Driver Seat, Back-Up Camera, iPod/MP3 Input, Onboard Communications System, Aluminum Wheels, Keyless Start, Dual Zone A/C, Smart Device Integration, Cross-Traffic Alert, Blind Spot Monitor, Heated Seats. Rear Spoiler, MP3 Player, Keyless Entry, Remote Trunk Release, Steering Wheel Controls. OPTION PACKAGES BOSE/MOONROOF/SATELLITE RADIO PACKAGE Bose 9 Speaker Surround Sound Audio System, Centerpoint 2 and AudioPilot 2, SiriusXM Satellite Radio, 4-month subscription, Power Sliding-Glass Moonroof w/Interior Sunshade, 1-touch open feature, Roof-Mounted Shark Fin Antenna. Mazda Mazda3 4-Door Touring with MACHINE GRAY METALLIC exterior and BLACK BLACK interior features a 4 Cylinder Engine with 184 HP at 5700 RPM*. VEHICLE REVIEWS Edmunds.com explains The steering delivers excellent on-center feel at highway speed, and your inputs feel direct and precise.. OUR OFFERINGS Tulley Automotive with locations in both Nashua, NH and Manchester, NH treats the needs of each individual customer with paramount concern. We know that you have high expectations, and as a car dealer we enjoy the challenge of meeting and exceeding those standards each and every time. Allow us to demonstrate our commitment to excellence! Horsepower calculations based on trim engine configuration. Please confirm the accuracy of the included equipment by calling us prior to purchase.
Sunroof, Heated Leather Seats, Dual Zone A/C, Keyless Start, Premium Sound System, Alloy Wheels, Back-Up Camera, Onboard Communications System, iPod/MP3 Input, Satellite Radio. Mazda3 4-Door Grand Touring trim. Warranty 5 yrs/60k Miles - Drivetrain Warranty; SEE MORE! KEY FEATURES INCLUDE Leather Seats, Sunroof, Heated Driver Seat, Back-Up Camera, Premium Sound System, Satellite Radio, iPod/MP3 Input, Onboard Communications System, Aluminum Wheels, Keyless Start, Dual Zone A/C, Heated Seats, Heated Leather Seats Rear Spoiler, MP3 Player, Keyless Entry, Remote Trunk Release, Child Safety Locks. Mazda Mazda3 4-Door Grand Touring with DEEP CRYSTAL BLUE MC exterior and BLACK BLACK interior features a 4 Cylinder Engine with 184 HP at 5700 RPM*. EXPERTS ARE SAYING Edmunds.com's review says The steering delivers excellent on-center feel at highway speed, and your inputs feel direct and precise.. OUR OFFERINGS Tulley Automotive with locations in both Nashua, NH and Manchester, NH treats the needs of each individual customer with paramount concern. We know that you have high expectations, and as a car dealer we enjoy the challenge of meeting and exceeding those standards each and every time. Allow us to demonstrate our commitment to excellence! Horsepower calculations based on trim engine configuration. Please confirm the accuracy of the included equipment by calling us prior to purchase.
Lane Keeping Assist, Smart Device Integration, Cross-Traffic Alert, Blind Spot Monitor, Back-Up Camera, iPod/MP3 Input, SELECT PACKAGE, Aluminum Wheels, Dual Zone A/C, Brake Actuated Limited Slip Differential, Onboard Communications System, Keyless Start. Mazda3 4-Door w/Select Pkg trim. Warranty 5 yrs/60k Miles - Drivetrain Warranty; READ MORE! KEY FEATURES INCLUDE Back-Up Camera, iPod/MP3 Input, Onboard Communications System, Aluminum Wheels, Keyless Start, Dual Zone A/C, Brake Actuated Limited Slip Differential, Smart Device Integration, Lane Keeping Assist, Blind Spot Monitor, Cross-Traffic Alert MP3 Player, Keyless Entry, Remote Trunk Release, Steering Wheel Controls, Child Safety Locks. OPTION PACKAGES SELECT PACKAGE. Mazda Mazda3 4-Door w/Select Pkg with DEEP CRYSTAL BLUE MICA exterior and BLACK interior features a 4 Cylinder Engine with 186 HP at 6000 RPM*. WHY BUY FROM US Tulley Automotive with locations in both Nashua, NH and Manchester, NH treats the needs of each individual customer with paramount concern. We know that you have high expectations, and as a car dealer we enjoy the challenge of meeting and exceeding those standards each and every time. Allow us to demonstrate our commitment to excellence! Horsepower calculations based on trim engine configuration. Please confirm the accuracy of the included equipment by calling us prior to purchase.
Mazda3 4-Door w/Select Pkg trim. Lane Keeping Assist, Brake Actuated Limited Slip Differential, Smart Device Integration, Cross-Traffic Alert, Blind Spot Monitor, Back-Up Camera, Alloy Wheels, SELECT PACKAGE, iPod/MP3 Input, Dual Zone A/C, Keyless Start, Onboard Communications System. Warranty 5 yrs/60k Miles - Drivetrain Warranty; AND MORE! KEY FEATURES INCLUDE Back-Up Camera, iPod/MP3 Input, Onboard Communications System, Aluminum Wheels, Keyless Start, Dual Zone A/C, Brake Actuated Limited Slip Differential, Smart Device Integration, Lane Keeping Assist, Blind Spot Monitor, Cross-Traffic Alert. MP3 Player, Keyless Entry, Remote Trunk Release, Steering Wheel Controls, Child Safety Locks. OPTION PACKAGES SELECT PACKAGE. Mazda Mazda3 4-Door w/Select Pkg with MACHINE GRAY METALLIC exterior and BLACK interior features a 4 Cylinder Engine with 186 HP at 6000 RPM*. OUR OFFERINGS Tulley Automotive with locations in both Nashua, NH and Manchester, NH treats the needs of each individual customer with paramount concern. We know that you have high expectations, and as a car dealer we enjoy the challenge of meeting and exceeding those standards each and every time. Allow us to demonstrate our commitment to excellence! Horsepower calculations based on trim engine configuration. Please confirm the accuracy of the included equipment by calling us prior to purchase.
Why Buy a New Mazda from Tulley Mazda?


text:
New Purecare Eco-Bedding Made With Oprah-Approved Celebrity Doctor
Target Expands Disney Shops-in-Shops Amid Toy Titan's Store-Closing Speculation
By Glenn Taylor
Glenn Taylor
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Disney once again might be shrinking its own store experience in North America, but in doing so, it looks like the entertainment giant is replacing its own brick-and-mortar presence with that of one the nation's biggest and most bankable physical retailers.
WDW News Today, which reports on news related to Disney theme parks and consumer products, said "nearly all" of the remaining Disney Stores across North America will close by the end of September, with the exception of a few Disney Store Outlets.
Though the report said Disney would make an announcement on the impending closures last week on Thursday, the entertainment giant has been mum on any store-closing news. Disney Parks, Experiences and Products didn't immediately reply to a request for comment.
With revenue tanking through the Covid-19 pandemic as Disney saw traffic decrease across its stores and world-famous theme parks, the company originally announced earlier this year that it would shutter 60 of its then-300-plus global stores to focus on digital retail with ShopDisney.com. The e-commerce commitment would prioritize adult apparel collections and artist collaborations, trend-forward streetwear, premium home products and collectibles.
Outside the Disney Stores, the company's brick-and-mortar presence extends to numerous ventures, namely in its theme parks, which are home to more than 600 Disney Parks stores. The company's additional shopping experiences include shop-in-shops, lifestyle and outlet locations and third-party retailers around the world.
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While the closures may be a bummer for many customers who enjoyed the experience of the stores and the "magical moments" they provided, Disney seems to have found a brick-and-mortar mastermind in Target—a partnership that is proving to be very successful so far.
The mass merchant said it will expand the Disney Store at Target concept to more than 160 of its locations across the country by the end of the year. Currently, there are 53 small shop-in-shops inside Target stores, as well as a co-branded online store.
Target started rolling out the Disney shops in October 2019 when the first 25 locations opened. The Disney shops, averaging 750 square feet, are usually adjacent to Target's kids clothing and toy departments.
The mini stores have music, interactive displays, photo opportunities, and a seating area to watch Disney movie clips and to play games.
A list of the 107-plus Target stores that will get a new Disney shop has not yet been made available. Shoppers will find top toys from popular Disney properties, including Star Wars and Raya and the Last Dragon.
Disney gives Target an edge on toy market share gap
The Disney partnership is part of Target's larger toy department expansion and comes at a time when retail's biggest names are looking to capitalize on this lucrative category. On a company earnings call, Target's chief growth officer Christina Hennington told investors that toy sales grew in the "low-20 percent range" year over year during the second quarter.
Even Macy's is trying to get in on the action, recently revealing that it is bringing "full-out [toy] shops" into 400 stores in 2022 as part of yet another resurrection of the Toys "R" Us brand, with the department store expecting to quintuple the size of category. A larger assortment of Toys "R" Us merchandise is now available on Macy's website.
"It was an opportunity for us to look at what the entry category was for Toys 'R' Us kids that are now millennial parents," said Jeff Gennette, CEO of Macy's in a recent earnings call. It was also an opportunity for the company's "under-40 strategy to go after this category…recognizing that those footsteps or those eyeballs will translate into other purchasing."
Toy sales on the whole weren't slowed down at all by the Covid-19 pandemic, with the jumps over the last two years seeing fairly consistent growth, perhaps giving both mass merchants and department stores like more incentive to pursue these kinds of partnerships. The closure and liquidation of Toys "R" Us stores throughout 2018 and 2019 created a major market share gap that many retailers are aiming to fill.
According to data from The NPD Group, toy industry sales increased by 15 percent to $22.45 billion in the first half of 2021 versus the same time period in 2020, and 28 percent when compared to the $17.59 billion generated in the first half of 2019.
Across the top five toy properties sold in the first half of 2021 across 12 top global markets, Pokémon was the largest property, followed by Barbie, Star Wars, L.O.L. Surprise! and Marvel Universe.
"We are delighted to report continuous strong growth for the toy market globally," said Frédérique Tutt, global toys industry analyst at The NPD Group. "Even though Covid-19 restrictions are easing in most countries, consumers continue to turn to toys and games for their entertainment. Whether it is collectible cards, building sets or outdoor toys, demand is still very strong, which is promising as the industry is now preparing for the holiday season."
Another toys industry advisor at NPD Group, Juli Lennett, said signs point to the toy industry continuing to outperform pre-pandemic time periods, in what she referred to as "seismic September" and beyond.
However, ResearchandMarkets' Q2 report on the global toys market, published Monday, warned that supply-chain disruptions, U.S.-China trade tension, and the crackdown in Hong Kong, the "home of the toy industry," are straining the toy sector, which sources 86 percent of its products from China.
In conjunction with the Disney partnership, Target announced the return of its Bullseye's Top Toys list for the holiday season, with the toys ranging from Lego to L.O.L. Surprise! alongside a range of new additions. This includes Target's exclusive, 70-piece toy collection with FAO Schwarz, of which 85 percent of items are new this year, as well as toys from Paw Patrol and Barbie, and the Zoe Doll by Black-owned business Healthy Roots, which inspires girls to love their curls through hair play.
Disney, meanwhile, expanded its range of adaptive character costumes last week, leaning into the consumer call for accessible and inclusive options. New this year, fans of Black Panther and Stars Wars: The Mandalorian can dress up as their icons and outfit their wheelchairs accordingly. Last year, the company launched its first batch of adaptive and accessible costumes with classics and modern favorites like Cinderella, Buzz Lightyear, and The Incredibles. And last month, JCPenney bowed an adaptive children's apparel collection ahead of the back-to-school season.


text:
L11 B17 Elizabeth Lane
McCormick, SC
Huge Price Reduction ... now only $1000. Nice building lot in the Monticello section of Savannah Lakes Village. The lot is approximately 0.30 acres and backs up to POA property for added privacy. Savannah Lakes Village is a lakefront sporting community located at the confluence of Western South Carolina's Little River Blueway Adventure Region and the 71,100-acre Lake Thurmond. Tee it up on three of South Carolina's best lakefront golf courses (two member-owned golf clubs), enjoy special community events at the restaurants, participate in daily programs at the recreation & tennis center, explore hundreds of miles of land and water trails, live out a healthy lifestyle with easy access to outdoor sporting adventures, and stay connected for work and entertainment with the fiber-optic broadband network. While this appeals to retirees, it is not an age-restricted community. Introduce it to your "work from home" families who might appreciate the amenities and less congestion.
Listing Provided by: WEICHERT, REALTORS-CORNERSTONE
Elementary School MCCORMICK ELEMENTARY
High School McCormick
Middle School MCCORMICK MIDDLE
High School McCormick High School
11/5/2019 86% - $1,000
L11 B17 Elizabeth Lane McCormick, SC is a Single Family Home for sale at $1000 and a lot size of Acre(s). L11 B17 Elizabeth Lane McCormick has 0 bedrooms, 0 baths . The property's zip code is 29835 which is in McCormick, SC
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Happy birthday Grace Kelly! Beloved actress and the Princess of Monaco would have been 89 today. This is the fourth year this blogathon is happening and it is the first year I am participating in it. I am very excited to be part of this blogathon! I want to thank The Wonderful World of Cinema and The Flapper Dame for hosting it. This is a nice way to celebrate Grace's birthday and I hope to be part of the blogathon next year as well.
Every April at the mall by my house there is an event called "Book Stock." It's basicaly a week long event where I blow my paycheck on used books to help out local schools in my area. Anyway, at this year's event I found a book I haven't read since childhood. It is a children's book about Grace that came out ten years after she passed away and Frank Sinatra supplied the foreword to this book. He had nothing but kind things to say about his friend and former co-star in this foreword. And if you are wondering, yes, I bought the book. I have decided to focus on their friendship and one co-starring role in this post. I know there are romantic rumors surrounding these two but I will not be jumping down that rabbit hole today.
Grace Kelly and Frank Sinatra met on the set of Mogambo in the early 1950's. Frank, at the time was married to Ava Gardener, who was Grace's co-star in the film. Grace had a tent right next to the Sinatra's and was not happy about since she could hear them fighting all the time. Frank and Ava split for good in 1953, the year Mogambo came out, but didn't divorce until 1957, a year after High Society came out.
High Society came out in 1956 and it stars Grace, Frank, Bing Crosby, and Celeste Holm. It is a musical remake of the 1940 film, The Philadelphia Story, which starred Katherine Hepburn, Cary Grant, and Jimmy Stewart. Grace would take over Hepburn's role and Frank would take over Stewart's. And I am going to say what we were all thinking. We were robbed. The chemistry between these two is amazing and it's just one of those movies you hope changes at the end, but it never does. I wish there had been a film where these two were the leads, but this was Grace's last movie before her marriage to Prince Rainier of Monaco.
Grace and Frank would remain friends for the rest of her life. He would even become good friends with her husband. He happen to see her a couple of weeks before her death when he and his wife went to Monaco to spend some time with Grace and Rainier. Frank would pass away in 1998 and Grace predeceased him by sixteen years. We only have one film starring these two incredible talents, but it is one of my favorite films.
"My memory of Princess Grace is that very real person who shared her fairy tale with the entire world. Her name reflects the lovely woman she was...Grace."
The post was written for "The 4th Wonderful Grace Kelly Blogathon." If you want to see the other posts please go here and here.
I'm so glad you're participating to the blogathon this year! Such a lovely article on Grace and Frank Sinatra's friendship. I remember reading about it in the (not very good) biography of Grace Kelly written by Wendy Leigh but it was a long time ago. Thanks for refreshing my memory on some points!
Thanks so much for your participation to our blogathon!
Thank you so much for hosting and for letting participate.I have a few biographies about Grace, but I don't think I have that one. I will stay clear of it.
I am so excited you've joined us this year!! I wish we could have seen More Frank-Grace movies in the future. You look at all the roles Grace was up for and say- man! Frank and Grace could have been those leads (Designing Woman anyone?) I think it was on youtube - I looked for it but couldnt find it maybe it was taken down- but grace was a speaker and presenter at one of Frank's farewell concerts. she talked of her memories of Mogambo- Frank bringing christmas cheer to the african wilderness! hope to see you around the Film blogging community! and hope to do a blogathon for you in the future!
Grace seemed to have a talent for friendship with most of her co-stars. It's always beautiful reading about how well they remembered her.
I am happy she was close with her co-stars and that they had such sweet things to say abut her throughout the years.
Lovely piece. I didn't realise that Grace and Frank were friends, but I'm so glad that they were. I totally agree that they should have been in more films together. Their chemistry in High Society is off the charts.
Thank you. Yeah, I didn't realize it either until not too long ago actually. And we were robbed! They were perfect in that movie together!
Frank could've been better in High Society, but that wasn't his fault. At least it gave us the chance to see two people of such different backgrounds working together.
I disagree. I think Frank was better than Bing was in the movie. And I did enjoy seeing them together in this film.