text:
2 Samuel 9 :: New Living Translation (NLT)
David's Kindness to Mephibosheth
2Sa 9:1
2Sa 9:1 - One day David asked, "Is anyone in Saul's family still alive—anyone to whom I can show kindness for Jonathan's sake?"
2Sa 9:2 - He summoned a man named Ziba, who had been one of Saul's servants. "Are you Ziba?" the king asked. "Yes sir, I am," Ziba replied.
2Sa 9:3 - The king then asked him, "Is anyone still alive from Saul's family? If so, I want to show God's kindness to them." Ziba replied, "Yes, one of Jonathan's sons is still alive. He is crippled in both feet."
2Sa 9:4 - "Where is he?" the king asked. "In Lo-debar," Ziba told him, "at the home of Makir son of Ammiel."
2Sa 9:5 - So David sent for him and brought him from Makir's home.
2Sa 9:6 - His name was Mephibosheth[fn]; he was Jonathan's son and Saul's grandson. When he came to David, he bowed low to the ground in deep respect. David said, "Greetings, Mephibosheth." Mephibosheth replied, "I am your servant."
2Sa 9:7 - "Don't be afraid!" David said. "I intend to show kindness to you because of my promise to your father, Jonathan. I will give you all the property that once belonged to your grandfather Saul, and you will eat here with me at the king's table!"
2Sa 9:8 - Mephibosheth bowed respectfully and exclaimed, "Who is your servant, that you should show such kindness to a dead dog like me?"
2Sa 9:9 - Then the king summoned Saul's servant Ziba and said, "I have given your master's grandson everything that belonged to Saul and his family.
2Sa 9:10
2Sa 9:10 - You and your sons and servants are to farm the land for him to produce food for your master's household.[fn] But Mephibosheth, your master's grandson, will eat here at my table." (Ziba had fifteen sons and twenty servants.)
2Sa 9:11 - Ziba replied, "Yes, my lord the king; I am your servant, and I will do all that you have commanded." And from that time on, Mephibosheth ate regularly at David's table,[fn] like one of the king's own sons.
2Sa 9:12 - Mephibosheth had a young son named Mica. From then on, all the members of Ziba's household were Mephibosheth's servants.
2Sa 9:13 - And Mephibosheth, who was crippled in both feet, lived in Jerusalem and ate regularly at the king's table.
As in Greek version; Hebrew reads your master's grandson.
As in Greek version; Hebrew reads my table.


text:
The Dragon Wars Saga Arc Four: The Torn Sky Chapter Thirty Six Part Five
July 26th, 2013 | Published in Dragon Wars
First| Previous | Chapter Index |Next
"You'll need to be careful, though," Estara said. "Indaturan will betray most people in a heartbeat if he thinks doing so will aid his cause more than harm it. The exceptions are his court and his kin – both of which he's passionately loyal to."
"But he is upfront about that when he forms alliances," Tarian said. "He's a peculiar person."
"What about the kin of his court?" Lydia asked.
"The ki–" Estara began then paused eyes wide. "Oh, of course. I didn't think of that." She made a thoughtful noise. "Not as much as with his actual court but he'd hesitate to betray someone if he thought it would hurt one of his court."
"Well Adrian did seem genuinely worried about his mother," Lyrrekka said. "So if it is Indaturan that he's working for, that might help. I guess we'll find out."
"I'm sure he cares," Marian said quietly. "Even if he didn't trust me. I can't exactly blame him, though I don't think I could have killed him."
"I'm sure you couldn't," Tara said. "It might have shaken some sense into you."
"No," Marian said. "But the cognitive dissonance would probably have made me collapse. I wonder if he knew what had been done to me?"
"It's possible," Lyrrekka said. "But we'll have to see." She looked around to where one of Estara's goblins was leading Karilya back into the room. "Is that everyone fed?"
"Everyone except you, Lyrrekka-ida," the goblin replied respectfully. "We have some volunteers waiting."
"Oh, I'll be okay for a few days yet," Lyrrekka said. "No need to trouble them."
Estara tutted and shook her head. "Don't neglect yourself. You won't frenzy but you may do yourself harm if you don't feed often enough. You need to feed, Lyrrekka-ida. Check yourself."
"You don't have to be so formal, Estara-mirian. Please call me Lyr." Lyrrekka frowned and pressed one hand to her chest. After a moment her eyes widened and she made a hissing sound. "Oh! I didn't realise I was that low. I guess I will take you up on your offer. Thank you."
"Only if you call me Est," Estara replied. "And you're welcome. When will you be going to this meeting?"
"Later this morning," Lyrrekka said. "We'll have to head back as soon as I've fed because I need to bring the others up to speed before we leave."
"That's interesting," Matthias said after Lyrrekka had described their meeting with Estara. "And very good thinking, Lyd." He gave Lydia an approving nod. He looked around at the rest of the group gathered there. "I've never had much occasion to deal with white or grey dragons – any insights?"
"Mostly what Lyrrekka-ida already said," Naria said. "They both raid sometimes but they aren't wantonly cruel to either their renegades or their prisoners. The white ones are pragmatic and not entirely trustworthy."
"And the grey ones?" Matthias asked. "Since the courts are allied."
"Grey is the colour of… uh what's the word I'm trying to think of?" Naria scowled to herself. "I don't know how to translate sinsaran."
"It's a tough one." Lyrrekka tilted her head as she considered. "Honesty would be closest but it's not quite accurate. But if Kanderian says he'll do something, he'll generally do it as long as there's no trickery on your part."
"So he's reasonable?" Matthias asked.
"Possibly – though he might hesitate to trust you since you're silver," Lyrrekka said. "He'll probably listen to me though."
"I think we should see what Adrian has to say for himself before making any decisions," Sonia said. "We don't know that we're even on the right track here. I can't see any reason those blocks would block him for working with either of them. Anyway, we need to get going if we're to be there before everyone else."

redpajama_set_name: RedPajamaCommonCrawl


Head section ranging from +45?to -90?
Mid-sections range from 10?to 50?in 10?increments.
Foot section range from 0?to 75?in 6 repeatable steps.
Hand-crafted in beautiful solid oak.
The upholstered top includes 2" medium-density foam and durable Naugahyde fabric.
An "H" brace, standard on all models, provides greater stability and strength.
Available in 24", 27", and 30" surface widths; 72" and 78" surface lengths.
Surface height on all models is 30".
1 1/2" medium-density foam, glides and triple-bolted legs.
Traditional tables are sanded smooth with square edges and folded Naugahyde corners.
Offers optimum patient and therapist comfort with electrically-controlled height adjustment. The table's surface is padded with durable, 2.5" high-density foam. Surface height adjusts from 18" to 38" with a convenient foot control. Available in three sizes. An optional hand control is also available.


In a statement, the BHA reported horses from the infected yard raced on Wednesday at Ayr and Ludlow, adding identification of the virus in vaccinated animals presented a "cause for significant concern".
Donald McCain's yard was the only one represented at both tracks.
The BHA statement added: "The action to cancel racing has been viewed as necessary in order to restrict, as far as possible, the risk of further spread of the disease.
"The BHA has worked quickly to identify which yards could have potentially been exposed today and identify the further actions required."
Thursday's cancellations come less than five weeks before the start of this year's Cheltenham Festival - the annual highlight of the National Hunt calendar.
It is not yet known how long the current shut-down of racing may have to last - but inevitably for thousands of racing followers, and of course those directly involved in the industry, there will be uncomfortable echoes of the foot-and-mouth crises of 1967 and 2001.
On each occasion, the racing calendar was affected for two months - and in 2001, the Cheltenham Festival was abandoned.
The governing body's statement added: "The BHA is presently communicating with yards potentially exposed to ensure appropriate quarantine and biosecurity measures are put in place and horse movements restricted to avoid possible further spread of the disease.
"The full extent of potential exposure is unknown, and we are working quickly to understand as much as we can to assist our decision-making."
The action to cancel Thursday's races was taken with unanimous support of the BHA's industry veterinary committee and will affect meetings at Huntingdon, Doncaster, Ffos Las and Chelmsford.
An update on the track's official Twitter account read: "Racing goes ahead as planned here today. First race off at 1:15."


The prospect of securing state funding with a grant-based, cost-forgiveness component motivated the Village of Bellows Falls to dip into some wastewater treatment facility upgrades. The caveat was that design solutions had to be developed up to 90% completion within a six-week timeframe, then approved by the State of Vermont.
All hands were on deck to design and engineer upgrades to the existing Rotating Biological Contactor (RBC) process, to replace old equipment, and to improve treatment. A computer system was included for more efficient facility monitoring and data tracking. This project also included concrete tank repairs, fiberglass cover system upgrades, and the addition of a supervisory control and data acquisition system (SCADA) computer sysetm.
This RBC Upgrade Project was in addition to a recent headworks upgrade and septage handling project which included an automated septage receiving and treatment system, new wastewater screening and replacement of influent pumps, and odor control upgrades.
The fast-tracked deadline for the RBC Upgrade project was met, and upon the State's review, the municipality was granted the necessary funding to allow the project to go forward.


text:
Q: How to set the auto responder in Gmail as a custom HTML? I'm trying to make Gmail send an auto response email in a custom HTML.
What I did is: Settings -> Vacation responder on.
I found the HTML editor, but what I want to do is add my custom HTML code. How can I do this?

A: i solved by copy the design and pasted in the editor

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}\label{sec:intro}
\subsection{Background}

The numerical weather prediction (NWP) approach has been applied to weather
forecasting since the 1950s~\cite{lynch2008origins},
when the first computer ENIAC was invented~\cite{platzman1979eniac}.
John von Neumann, a pioneering computer scientist and meteorologist,
first applied the primitive equation models~\cite{charney1955use} on ENIAC.
Since then meteorologists have
produced reliable weather forecasts, and they have continued to
refine the numerical models, thereby improving the
NWP in recent decades.
Today, the model of the European Centre for Medium-range Weather Forecasts (ECMWF)
and the United States' Global Forecast System (GFS) are two of the most accurate NWP models.
At the 500~hPa geopotential height (HGT) level of the atmosphere, both models
are reported to have annual mean correlation coefficients of nearly
0.9 for 5-day forecasts~\cite{yang2013review}. Other models,
such as UK Met Office Unified model~\cite{lean2008characteristics},
M\'{e}t\'{e}o-France ARPEGE-Climat model~\cite{deque1999documentation},
and Canada Meteorological Centre model~\cite{gauthier1999implementation},
are developed and play important roles in
global and regional weather predictions.

Even though NWP can effectively produce accurate weather
forecasts of the general weather pattern,
it is not always reliable in the prediction
of extreme weather events,
such as severe thunderstorms, hail storms,
hurricanes, and tornadoes, which
cause a significant amount of damage and loss of life
every year worldwide. Unreliable predictions,
either miss or false alarm ones, could
cause massive amounts of loss to the society.
The most well-known failed prediction is the Great Storm of 1987
in UK~\cite{burt1988great}.
The Met Office was criticized for not predicting
the storm timely. More recently, the landfall of 2012 Hurricane
Sandy in the northeastern US was not correctly predicted
by the National Hurricane Center until two days before it
came ashore~\cite{blake2013tropical}.
In January 2015, the impact of winter storm Juno
was overestimated by the US National Weather
Service~\cite{winkler2015importance}. Several cities
in US East Coast suffered from unnecessary travel bans and public
transportation closures. Much of the loss of life and property
due to improper precautions to severe weather events could be
avoided with more accurate (for both severity and location)
weather forecasts.

The numerical methods used in NWP can efficiently process large amounts of
meteorological measurements. However, a major drawback of such
methods is that they are sensitive to noise and initial inputs
due to the complexity of the models~\cite{lorenc20074d,doyle2014initial}.
As a result, although nowadays we have
powerful computers to run complex numerical models,
it is difficult to get accurate predictions computationally,
especially in the forecasts of severe weather.
To some extent, they
do not interpret the data from
a global point of view at a high cognitive level.
For instance, meteorologists can make good judgments of the
future weather conditions by looking at the general cloud patterns
and developing trends from a sequence of satellite
images by using geographical knowledge and their experience of past weather events~\cite{dvorak1975tropical,scofield1987nesdis};
numerical methods do not capture such high-level clues.
Additionally, historical weather records provide valuable
references for making weather forecasts, but numerical methods
do not make best use of them.

To address this weakness of
numerical models, we develop a computational weather forecast
method that takes advantage of
both the global visual clues of satellite data
and the historical records. In particular, we
try to find synoptic scale features of mid-latitude
thunderstorms (to be specified later in Section~\ref{sec:pattern})
by computer vision and machine learning approaches. It is worth mentioning
that the purpose of this work is not to
replace numerical models in forecasting. Instead, by tackling the
problem using different data sources and modeling methodologies,
we aspire to develop a method
that can potentially be used side-by-side with and complement
numerical models.

We analyze the satellite imagery because
it provides important clues as meteorologists view
evolving global weather systems.
Unlike conventional meteorological measures such as
temperature, humidity, air pressure, and wind speed,
which directly reflect the physical conditions
at the sensors' locations,
the visual information in satellite images
is captured remotely from
the orbit, which means a larger geographic
coverage with good resolution.
The brightness in an infrared satellite image indicates
the temperature of the cloud tops~\cite{liu2004satellite}
and therefore reveals
information about the top-level structure of the cloud systems.
Manually interpreting such visual information,
we can trace the evidence that corresponds to certain weather
patterns, particularly those related to severe thunderstorms. Moreover,
certain cloud patterns can be evident in the early stage of
storm development. As a result, the satellite imagery
are very helpful to meteorologists in their work.

Human eyes can effectively capture the visual patterns related to
different types of weather events.
The interpretation requires a high level understanding of
meteorological knowledge, which has been difficult for computers.
However, because the broad spatial and temporal coverage of the
satellite images produces too much information to be fully processed,
there is an emerging need for a computerized way to
analyze the satellite images automatically.
Therefore researchers are seeking computer vision algorithms to
automatically process and interpret the visual features
from the satellite images, aiming to help meteorologists
better analyze and predict weather events.
Traditional satellite image analysis techniques include
cloud detection with complex backgrounds~\cite{srivastava2003onboard},
cloud type classification~\cite{yang2004prep,behrangi2010day,price1990using},
detection of cloud overshooting top~\cite{bedka2010obje,bedka2011over},
and tracking of cyclone movement~\cite{ho2008auto}.
These approaches, however, only capture local visual features
without utilizing many high-level {\it global visual patterns},
and overlook their {\it development
over time}, which provides more clues for weather forecasts.
With the development of advanced computer vision algorithms,
some recent techniques put more focus on
the analysis of cloud motion and
deformation~\cite{zinner2008cb,keil2009displacement,merk2013detection,evans2006cloud}.
In these approaches,
cloud motions are estimated to match and compare cloud patches
between adjacent images in a sequence.
In this paper, the proposed algorithm also uses cloud motion estimation
in image sequences. It is different from existing work
in that it extracts and models certain patterns
of cloud motion, in addition to capturing the cloud displacement.

Historical satellite data archives as well as
meteorological observations are readily available
for recent years. By analyzing large amounts of
past satellite images and inspecting the historical
weather records, our system takes advantage of big
data to produce storm alerts given a certain query
image sequence. The result provides
an alternative prediction that can validate and correct
the conventional forecasts. It can be
embedded into an integrative or information fusion system,
as suggested in~\cite{mcgovern2006open},
to work with data and decisions from other sources
and produce more improved storm forecasts.

\subsection{The Dataset}\label{sec:prelim}

For our research, we acquired all of the GOES-M~\cite{noaa1998earth}
satellite imagery for the year of 2008\footnote{The
satellite imagery is publicly viewable and
the raw data archive can be requested
on the website of US National Oceanic and
Atmospheric Administration (NOAA).},
which was an active year containing
abundant severe thunderstorm cases for us to analyze.
The GOES-M satellite moves on a geostationary orbit and
was continually facing the North America area during that year.
In addition to the imagery data, each record contains
navigation information, which helps us map
each image pixel to its real geo-location.
The data is multi-spectral and contains five channels
covering different ranges of wavelengths, among which
we adopted channel 3 (6.5-7.0$\mu$m) and channel 4 (10.2-11.2$\mu$m)
in our analysis because these two infrared channels
are available both day and night. Both channels are of 4~km resolution
and the observation frequency is four records per hour,
i.e., about 15 minutes between the adjacent images.
To make sure two adjacent images in a sequence have a
noticeable difference, we sampled the original sequence
and used a sub-sequence with a 2-frames-per-hour frame
rate. We select the 30-minute sampling interval
because the algorithm is based on the optical flow analysis
between to adjacent image frames. We need both
evident enough visual difference and relatively
good temporal resolution. The 30-minute interval
is a empirical selection that considers both factors.
In the rest of the paper, every two adjacent images
in a sequence we refer to are 30 minutes away
without specifying.

Using the imagery and navigation data blocks in each raw data entry,
we reconstruct satellite images by an equirectangular
projection~\cite{snyder1987map}.
The pixel mapping from the raw imagery data to the map coordinate
is computed by the navigation data with transformation defined
by~\cite{noaa1998earth}. The map resolution is set to be 4~km (per pixel), which
best utilizes the information of the raw data. We reconstruct
the aligned images within the range
of 60\textdegree W to 124\textdegree W in longitude and
20\textdegree N to 52\textdegree N in latitude,
which covers the Continental US (CONUS) area.
Hereafter
all the image related computations in our approach
are performed on the reconstructed images,
on which the geo-location of each pixel is directly accessible.

We studied the relationship between satellite images and
severe thunderstorms, which may include hailstorms and
tornados\footnote{Severe thunderstorms mostly contain
hailstorms and tornados. Storms with hail and/or tornadoes are
a subset of thunderstorms. Hereafter we generally refer
to severe thunderstorms as storms.}.
To relate the satellite data with severe thunderstorms,
we retrieved the storm report
data from the NOAA National Weather
Service\footnote{http://www.spc.noaa.gov/climo/online/},
where all the time and locations of storms
inside the United States since the year of 2000 are archived.
The records act as ground-truth data in the training
and provide geographic and temporal priors for the
system to make decisions.

\subsection{Synoptic-scale Visual Storm Signatures}\label{sec:pattern}

Being evidently visible on the satellite imagery,
synoptic-scale (spanning several hundred kilometers) storm features
are studied in this paper.
Particularly, we are interested in
the synoptic-scale features of
the mid-latitude ($30^{\circ}$-$60^{\circ}$ in latitude)
thunderstorms located outside of the Hadley cell~\cite{marshall1965atmosphere}
of the atmospheric circulation
because storms cooccurring with these features
are one of the major components
of storms in the CONUS and other
densely populated areas around the world.

Synoptically, one important atmospheric feature
helpful for locating thunderstorms is the subtropical
{\it jet stream}\footnote{Hereafter we refer to the subtropical jet stream as the jet stream for
simplicity, regardless of the existence of the polar jet stream.},
which is the high-altitude (10-16~km) westerly wind
near the boundary between the Hadley cell
and the Ferrel cell; in other words, the cold and warm air masses.
Jet streams move in a meandering path from west to east,
a direction of movement caused
by the {\it Coriolis effect}~\cite{barry2009atmosphere}.
In the northern hemisphere, elongated cold air masses with
low pressure, namely ``troughs'',
will have a dip in the jet stream extending southward at
various longitudes around the earth while in other locations
a ridge of higher pressure with warm air will send the jet
stream bulging northward.
The east side of a
trough, which has a southwest to northeast oriented jet stream, is a region of active
atmospheric storminess~\cite{clark2009climatology},
where cold and warm air masses collide.
Though jet streams themselves are invisible,
they can be revealed by thick cumulus clouds gathering in the
unsettled trough regions and areas of clouds spreading
northeastward along the east side of a trough.
As a result, an elongated cloud-covered area along a southwest
to northeast direction is a useful large-scale clue for us to locate storms.
Fig.~\ref{fig:traugh}
shows a GOES-M channel-4 image taken on February 5, 2008.
Clearly in the mid-latitude region
the southern boundary of the cloud band
goes along a smooth curve, which
indicates the jet stream
(sketched in dashed line).
Usually the cloud-covered areas to
the northwest side of such an easily-perceivable
jet stream are susceptible to storms.
In this case, severe thunderstorms developed two hours
after the time of the image in the boxed area of the
figure. Based on our observation, a large proportion of storms in
the CONUS area have similar cloud
appearances as in this example.
As a result, finding jet streams in the satellite image
is important for storm prediction~\cite{charney1947dynamics}.

A jet stream travels along a large-scale meandering path,
whose wavelength is about
60$^{\circ}$ to 120$^{\circ}$ of longitude long,
as wide as a whole continent.
The full wavelength is therefore usually referred to as a {\em long wave}.
The trough within a long wave
only tells us a general large region that is vulnerable
to storms. To locate individual storm cells more precisely,
meteorologists look for more
detailed smaller scale cloud patterns caused by
{\it short waves} traveling through the long wave.
Short waves are synoptic scale features with the wavelength
of the order of 1,000 km, and indicate the disturbance of
the mid or upper part of the atmosphere. Horizontally
seen from a satellite image, they appear as
smaller waves in the long wave trough.
In many cases, we can observe clouds in
``{\it comma shapes}''~\cite{carlson1980airflow,wallace2006atmospheric}
in short waves, i.e., such a cloud has
a round shape due to the
circular wind in its vicinity and a short tail
towards the opposite direction of its translation.
A comma-shaped cloud patch indicates a turbulent
area of rising air often leading to
convection (thunderstorms), especially
if it lies on the boundary between cold and warm
air masses.
Fig.~\ref{fig:traugh} shows an example of a comma-shaped cloud.
A storm area is marked by an square in the tail of the
comma shape in the figure.
Because of its distinctive pattern,
the comma shape has been one of the most utilized signature
when meteorologists review satellite images for thunderstorms.

It is necessary to mention
that the comma shape is still a signature in the
synoptic scale and therefore used for generally
locating a storm system rather than individual
storms\footnote{Individual thunderstorms typically
develop comma shapes to their clouds,
but not until they have become
e more mature. Such small-scale scale signatures
are also more difficult to detect in the satellite
image and therefore not the focus of this paper.}.
In other words, groupings of thunderstorms
tend to have a comma shape and the shape is used
as a first approximation of where severe
weather is most likely to occur.

The visual cloud patterns introduced above can often be
detected from a single satellite image.
However, a single image is not sufficient to provide
all the crucial information about the
dynamic of a storm system. The cloud shapes
are not always clear due to the irregular nature of clouds.
In addition, the evolution of storm systems, including
cloud areas  emerging, disappearing, and deforming,
cannot be revealed unless different satellite
images in a sequence are compared.

In practice, an important step in producing
weather forecasts is to review satellite images,
during which process meteorologists typically review
a series of images, rather than a single one,
to capture critical patterns.
They use the differences between the key visual
clues (e.g., key points, edges, etc.) to track
the development of storm systems and better locate
the jet streams and comma shapes, among other features.
As mentioned in~\cite{doswell2004weather},
human heuristics are helpful in improving weather forecasts.
Following such guideline, our algorithm attempts to simulate
meteorologists' cognitive processes in analyzing temporally
adjacent satellite image frames.
In particular, two types of motions are regarded
crucial to the storm visual patterns:
the global cloud translation with the jet stream
and the local cloud rotations producing the comma shapes.
We consider both types of motions for extracting synoptic
storm signatures.

\begin{figure}[t]
\centerline{\includegraphics[width=0.8\textwidth]{trough.pdf}}
\caption{A sample GOES-M satellite channel 4 image taken at
16:02~(GMT) on February 5, 2008.
The boxed area underwent a severe
thunderstorm later on that day.
The jet stream (marked in dashed curve) can be
implied from the distribution of clouds.
The storm area is covered by a comma-shaped
cloud patch (surrounded by dashed boundary)
in the trough region.} \label{fig:traugh}
\vspace{-0.1in}
\end{figure}

\subsection{Objectives}
The algorithm proposed in this paper aims at assisting the prediction
of severe thunderstorms from a different perspective from the NWP.
As an initial attempt in this direction,
we focus on short-term severe thunderstorm prediction.
Synoptical scale features abstracted from known visual
storm patterns as aforementioned are extracted from satellite
images and used as clues to locate
regions where thunderstorms tend to happen in the near future.
Machine learning is involved in the classification of
candidate storm regions by inspecting historical thunderstorm data.
The regions, as extracted from synoptical scale features,
are not necessarily associated one-to-one with storms,
i.e., a region may contain several storms and each storm needs
to be more precisely located by other techniques. The algorithm
in fact simulates meteorologists' cognition process
when reviewing satellite images as a part of the weather prediction
process. It could be a helpful tool in automating
and refining the current weather prediction. It can also be used
to provide assistance to meteorologists so that they can be more efficient
and less exhausting.

\subsection{Outline}
The rest of the paper is organized as follows. Section~\ref{sec:feature_extract} introduces
the approach to extract storm signatures and construct storm features. Section~\ref{sec:machine_learning}
reports the approach and results of the machine learning module that accepts or rejects the
extracted storm features. In particular, quantitative benchmarks of the classifier and qualitative
case studies that applies the whole workflow are given to demonstrate the effectiveness of the
proposed algorithm. Finally, we present the future work and conclude the paper in Section~\ref{sec:conclusion}.

\section{Storm Feature Extraction}\label{sec:feature_extract}
Our algorithm extracts synoptic-scale visual thunderstorm features
from satellite images. The cloud motion observed
from image sequences is analyzed in depth.
In order to simulate human cognition and let computers
perceive comma shapes introduced earlier,
the visual signatures are further abstracted into
basic cloud motion components: translation and rotation.
Several properties and measurements related to these two
components are extracted from cloud
motions and analyzed in the following steps.

Fig.~\ref{fig:framework} illustrates the workflow of the whole system.
We employ the {\it optical flow}
between every two adjacent satellite images
to capture the cloud motion and discover vortex
areas that could potentially trigger storms.
Using the historical storm records, vortex features are constructed
and a storm classifier is trained. Given an arbitrary query image
sequence, vortexes are extracted in the same way and then categorized
by the classifier.

In particular, for the feature extraction operations, which will be
applied both to the training data and the query data, two steps are
carried out.
The system first estimates a dense optical flow field
describing the cloud motion between adjacent image frames.
Second, local vortexes are identified with the optical flow
and vortex descriptors are constructed. The vortex descriptors
combine information from both the visual
features and the historical storm records.
The following subsections describe details of these steps.

\begin{figure}
\includegraphics[width=\textwidth]{system_chart2.pdf}
\caption{Workflow of the storm detection system. Optical flows
between adjacent image frames are estimated and vortexes are
extracted base on the flows. Vortex descriptors are then
constructed using both the visual information and historical
storm records. Machine learning is performed on the vortex
descriptors for the storm detection.
}\label{fig:framework}
\end{figure}

\subsection{Robust Optical Flow Estimation}\label{sec:optical_flow}
Optical flow is a basic technique for motion estimation
in image sequences. Given two images $I_t(x,y)$ and
$I_{t+1}(x,y)$, the optical flow $\vec{F}_t(x,y)=\{U_t(x,y),V_t(x,y)\}$
defines a mapping for each pixel
$g:(x,y) \mapsto (x+U_t(x,y), y+V_t(x,y))$, so that
$I_t(g(x,y))\approx I_{t+1}(x,y)$. The vector field
$\vec{F}_t(x,y)$ can be therefore regarded as
the pixel-wise motions from image $I_t(x,y)$ to
$I_{t+1}(x,y)$. Several approaches for optical
flow estimation have been proposed based on different
optimization criteria~\cite{horn1981determining,lucas1981iterative,perez2013tv}. In this work we adopt
the pyramid Lucas-Kanade algorithm~\cite{bouguet2001pyramidal} to estimate a
dense optical flow field between every two neighboring
image frames.

The GOES-M satellite is in a geostationary orbit and thus remains over
the same point on the earth at all times. As a result,
the only objects moving along a satellite image sequence are
the clouds and the optical flow between two images indicates
the cloud motion.
However, the non-rigid and dynamic nature of clouds
makes the optical flow estimation noisy and inaccurate.
Fig.~\ref{fig:process_1} shows the optical flow
estimation result of a satellite
image in respect to its previous frame in the sequence.
Though the result correctly reflects the general cloud
motion, flows in local areas are usually noisy and do not
precisely describe the local motions.
As to be introduced later, the optical flow properties
adopted in this work involve the gradient operation of the
flow field. Thus it is important to get reliable and smooth
optical flow estimation.
To achieve this goal, we
both pre-process the images and post-process the estimation
results. Before calculating the optical flow between
two images, we first enhance both of them using the histogram
equalization technique\footnote{
Each channel is enhanced separately. On a given channel,
the same equalization function (estimated from the first frame
of the sequence) is applied to both images
so that they are enhanced by the same mapping.}~\cite{acharya2005image}.
As a result, more fine details on the images are enhanced
for better optical flow estimation.

After the initial optical flow estimation,
we smooth the optical flow by applying an iterative update operation based
on the Navier-Stokes equation~\cite{temam1984navier} for modeling fluid motions. Given
a flow field $\vec{F}(x,y)$, the equation describes the
evolving of the flow over time $t$:
\begin{equation}\label{equ:ns}
\frac{\partial\vec{F}}{\partial t}=(-\vec{F}\cdot\nabla)\vec{F}+\nu\nabla^2\vec{F}+\vec{f}\;,
\end{equation}
where $\nabla=(\frac{\partial}{\partial x},\frac{\partial}{\partial y})$ is the
gradient operator, $\nu$ is the viscosity of the fluid, and $\vec{f}(x,y)$ is the
external force applied at the location $(x,y)$.

The three terms in Eq.~(\ref{equ:ns}) correspond to the advection, diffusion, and
outer force of the flow field respectively. In the method
introduced in~\cite{stam1999stable}, an initial flow field is updated
to a stable status by iteratively applying these three transformations one
by one. Compared with a regular low-pass filtering to the flow field,
this approach takes into account the physical model of fluid dynamics,
and therefore better approximates the real movement of a flow field.
We adopt a similar strategy to smooth
the optical flow in our case.
The initial optical
flow field $\vec{F_t}(x,y)$ is iteratively updated.
Within each iteration the time is incremented by a
small interval $\Delta t$, and there are three steps to get
$\vec{F}_{t+\Delta t}(x,y)$ from $\vec{F}_t(x,y)$:
\begin{enumerate}
\item add force: $\vec{F}_{t+\Delta t}^{(1)}=\vec{F}_t+\vec{f}\Delta t$\;;
\item advect: $\vec{F}_{t+\Delta t}^{(2)}=adv(\vec{F}_{t+\Delta t}^{(1)}, -\Delta t)$\;;
\item diffuse: $\vec{F}_{t+\Delta t}=FFT^{-1}(FFT(\vec{F}_{t+\Delta t}^{(2)})e^{-\nu k^2\Delta t})$\;.
\end{enumerate}

The first step is simply a linear increment of the flow vectors based on the
external force. In the second step, the advection operator $adv(\cdot)$ uses
the flow $\vec{F}_{t+\Delta t}^1(x,y)$ at each pixel $(x,y)$ to predict its
location $(x_{t-\Delta t}, y_{t-\Delta t})$ $\Delta t$ time ago, and updates
$\vec{F}_{t+\Delta t}^{(2)}(x,y)$ by $\vec{F}_{t+\Delta t}^{(1)}(x_{t-\Delta t}, y_{t-\Delta t})$.
In the last step, the diffusion operation is a low-pass
filter applied in the frequency domain,
where $k$ is the distance from a point to the origin, and the bandwidth of
the filter is determined by the pre-defined fluid viscosity $\nu$ and $\Delta t$ (to be specified later).
We do not enforce the mass conservation like the approach in~\cite{stam1999stable}
because the two-dimensional flow field corresponding to the cloud movement
is not necessarily divergence free.
In fact, we use the divergence as a feature for storm detection
in the following procedures.

After several iterations of the update, the flow field converges to a
stable status and becomes smoother. The iteration number is typically not large,
and we find that the final result is not very sensitive to the parameters
$\nu$ and $\Delta t$. In our system we set the iteration number to 5, the fluid
viscosity $\nu$ to 0.001, and time interval $\Delta t$ to 1. The noisy
optical flow estimation from the previous stage is treated as the external
force field $\vec{f}$, and initially $\vec{F}_t = 0$.
Fig.~\ref{fig:process_2} shows the smoothed flow field
(i.e., $\vec{F}_{t+5\Delta t}$) from the noisy
estimation (Fig.~\ref{fig:process_1}).
Clearly the major motion information is kept
and the flow field is smooth for further analysis.

\begin{figure*}
\centering
\subfigure[Noisy optical flow estimation]{
    \includegraphics[width=0.45\textwidth]{noisy_flow_enhance.png}
    \label{fig:process_1}
}
\subfigure[Smoothed optical flow]{
    \includegraphics[width=0.45\textwidth]{smooth_flow_enhance.png}
    \label{fig:process_2}
}\\
\subfigure[Divergence-free component of the smoothed flow]{
    \includegraphics[width=0.45\textwidth]{vortex_flow_enhance.png}
    \label{fig:process_3}
}
\subfigure[Vorticity-free component of the smoothed flow]{
    \includegraphics[width=0.45\textwidth]{divergence_flow_enhance.png}
    \label{fig:process_4}
}\\
\subfigure[Magnitude of vorticity]{
    \includegraphics[width=0.45\textwidth]{example_vorticity.png}
    \label{fig:process_5}
}
\subfigure[Vortex cores and their expansions]{
    \includegraphics[width=0.45\textwidth]{example_Q.png}
    \label{fig:process_6}
}
\caption{Sample results of the optical flow analysis.
GOES-M satellite images covering the continental US area
are analyzed (to avoid showing too many
missing pixels on the southwest corner, a
small stripe of US west coast is not shown on the figures, though
these pixels are included in our analysis).
Between two adjacent
frames (with 30 minutes' interval) in a satellite image sequence, the optical
flow from the former frame to the latter
one is estimated and processed.
The results are plotted on the second frame.
Only large enough flow vectors are drawn on figure (a-d).
(a) The optical flow estimated
by the Lucas-Kanade algorithm. (b) The smoothed optical flow by applying
an iterative update based on the Navier-Stokes equation. (c) The
divergence-free component of the smoothed flow field. (d) The vorticity-free
component of the smoothed flow field. (e) Visualization of the vorticity.
Vorticity vectors of the 2D flow field are perpendicular to the image plane.
Pixels with vorticity vectors toward the viewer (counter-clockwise rotations)
are tinted in green color; and the pixels with vorticity away from the
viewer (clockwise rotations) are tinted in red color. The saturation
of color means the magnitude of the corresponding vorticity vector.
(f) Vortex regions detected by the $Q$-criterion. For visualization
purpose (to avoid displaying too a large missing corner at
the lower-left, not all areas in CONUS are shown on the above images.
}
\end{figure*}

\subsection{Flow Field Vortex Extraction}
As introduced in Section~\ref{sec:intro}, the rotating and diverging of
local cloud patches are two key signatures for storm detection. In the flow
field, these two kinds of evidence are embodied by the {\it vorticity} and
{\it divergence}. The vorticity of a vector field $\vec{F}(x,y)=\{U(x,y),V(x,y)\}$ is defined as:
\begin{equation}
\begin{aligned}
\vec{\omega}(x,y)&=\nabla \times \vec{F}(x,y)\\
&=\left(\frac{\partial}{\partial x},\frac{\partial}{\partial y},\frac{\partial}{\partial z}\right)\times(U(x,y),V(x,y),0)\\
&=\left(\frac{\partial V(x,y)}{\partial x}-\frac{\partial U(x,y)}{\partial y}\right)\vec{z}\;;
\end{aligned}
\end{equation}
and the divergence is defined as:
\begin{equation}
\begin{aligned}
\text{div}\vec{F}(x,y) &= \nabla \cdot \vec{F}(x,y)\\
&=\left(\frac{\partial}{\partial x},\frac{\partial}{\partial y}\right)\cdot(U(x,y),V(x,y))\\
&=\frac{\partial U(x,y)}{\partial x}+\frac{\partial V(x,y)}{\partial y}\;.
\end{aligned}
\end{equation}

It has been proven that for a rigid body, the magnitude of vorticity at any
point is twice the angular velocity of its self
rotation~\cite{green1995fluid} (the direction of the vorticity vector indicates
the rotation's direction). In our case, even though the cloud is non-rigid,
we can regard the vorticity at a certain point as a description of the local rotation.

To better reveal the rotation and divergence, we apply the
Helmholtz-Hodge Decomposition~\cite{arfken1999mathematical} to decompose the flow field to
a solenoidal (divergence-free) component and a irrotational (vorticity-free)
component. Fig.~\ref{fig:process_3} and Fig.~\ref{fig:process_4} visualize
these two components respectively. In both figures,
areas with densely overlapped vectors plotted are
the places with high vorticity or divergence.

The divergence-free component of the flow field is useful for
detecting vortexes. On this component, we inspect the
local deformation tensor
$$
\nabla \vec{F} = \left[
\begin{matrix}
  {\partial U}/{\partial x} & {\partial V}/{\partial x} \\
  {\partial U}/{\partial y} & {\partial V}/{\partial y}
 \end{matrix}
\right]\;,
$$
which can be decomposed to the symmetric (strain) part
$
S=\frac{1}{2}({\nabla \vec{F} + \nabla \vec{F}^T})
$
and the asymmetric (rotation) part
$
\Omega=\frac{1}{2}({\nabla \vec{F} - \nabla \vec{F}^T})
$.
The $Q$-criterion introduced by~\cite{hunt1988eddies}
takes the difference of their norms:
\begin{equation}\label{equ:Q-cri}
Q=\frac{1}{2}(\|\Omega\|^2-\|S\|^2)=\frac{1}{4}\|\vec{\omega}\|^2-\frac{1}{2}\|S\|^2\;,
\end{equation}
where $\|\cdot\|$ is the Frobenius matrix norm.
The $Q$-criterion measures the dominance of the vorticity.
When $Q>0$, i.e., the vorticity component dominates the local flow, the
corresponding location is regarded to be in a vortex region.
Fig.~\ref{fig:process_5} and Fig.~\ref{fig:process_6} visualize
the vorticity and $Q$-criterion of the flow field in Fig.~\ref{fig:process_3}.
In Fig.~\ref{fig:process_5}, pixels are tinted by the corresponding
magnitude of vorticity, and different
colors mean different rotating directions.
In Fig.~\ref{fig:process_6}, the vortex regions are highlighted in red.
Clearly only pixels with a dominant vorticity component are selected
as vortexes by the $Q$-criterion.
It is apparent that these vortexes are more prone to be located
inside the storm area (highlighted by yellow boundaries)
than the removed high-vorticity pixels.
It is also observed that the vortexes are typically in narrow
bending comma shapes as described in Section~\ref{sec:pattern}.
Therefore, they are properly to be regarded as the potential storm elements.

\subsection{Vortex Descriptor}\label{sec:fea}

Not all the vortex regions in Fig.~\ref{fig:process_6} are related to
storms\footnote{The types of
vortexes outside of the CONUS are unknown because of lack of records.}.
As a result we built a descriptor for each extracted vortex and
apply it to a machine learning module (to be introduced later).
We introduce the vortex descriptor in this subsection.

Denoting a certain vortex region in a satellite image as $\Phi$ and the area
of $\Phi$ as $\Pi(\Phi)$, the following
visual clues, both static ones from a single image and dynamic ones from
the optical flow with respect to the previous image frame, are considered
in our approach.
\begin{enumerate}
\item Mean channel 3 brightness:
$$
w_1(\Phi) = \frac{\sum_{(x,y)\in\Phi}I^{(3)}(x,y)}{\Pi(\Phi)}\;,
$$
where $I^{(3)}(x,y)$ is the brightness of pixel $(x,y)$ in the channel 3 image.
\item Mean channel 4 brightness:
$$
w_2(\Phi) = \frac{\sum_{(x,y)\in\Phi}I^{(4)}(x,y)}{\Pi(\Phi)}\;,
$$
where $I^{(4)}(x,y)$ is the brightness of pixel $(x,y)$ in the channel 3 image.
\item Mean optical flow intensity:
$$
w_3(\Phi) = \frac{\sum_{(x,y)\in\Phi}\|\vec{F}(x,y)\|}{\Pi(\Phi)}\;,
$$
where $\vec{F}(x,y)=(U(x,y), V(x,y))$ is the optical flow at pixel $(x,y)$ computed between the previous and current
image frames.
\item Mean optical flow direction:
$$
w_4(\Phi) = \frac{\sum_{(x,y)\in\Phi}\|\theta(x,y)\|}{\Pi(\Phi)}\;,
$$
where $\theta(x,y)=\tan^{-1}\left(\frac{V(x,y)}{U(x,y)}\right)$ is optical flow $\vec{F}(x,y)$'s direction.
\item Mean vorticity on the solenoidal optical flow component:
$$
w_5(\Phi) = \frac{\sum_{(x,y)\in\Phi}\omega(x,y)}{\Pi(\Phi)}\;,
$$
where $\omega(x,y)$ is the vorticity value of the solenoidal component of
optical flow $\vec{F}(x,y)$ (sign of the value
indicates the vorticity's direction).
\item Mean divergence on the irrotational optical flow component:
$$
w_6(\Phi) = \frac{\sum_{(x,y)\in\Phi}\text{div}\vec{F}(x,y)}{\Pi(\Phi)}\;,
$$
where $\vec{F}(x,y)$ is the divergence of the irrotational component of
optical flow $\vec{F}(x,y)$.
\item Maximal $Q$-value of the vortex:
$$
w_7(\Phi) = \max_{(x,y)\in\Phi}{Q(x,y)}\;,
$$
where $Q(x,y)$ is defined in Eq.~(\ref{equ:Q-cri}).
\end{enumerate}

We also considered the spatial and temporal distributions of the storms that
occurred in the CONUS from 2000 to 2013 using all the historical
storm records through these years\footnote{We noticed that
NWS revised the criteria for hail reporting from 3/4 to 1 inch in
diameter in 2010 so less hails were reported after that.
However, the change has little effect on the storms' relative spatial distribution used in the machine learning system.
}.
This is shown in Fig.~\ref{fig:dense}. We divided the CONUS area into
$4^{\circ} \times 4^{\circ}$ grids. Inside each grid, we count the occurrence
of storms around each given date (e.g., July 4th) in the history.
Storms that occurred from 5 days before to 5 days after the queried
date in each year are counted (e.g., for July 4, storms
from June 29 to July 9 in every year, a total of 154 days,
are included\footnote{We count storms around February 28th in non-leap years
for storm statistics on February 29th.}). Denoting the total
storm count in grid $(i,j)$ (the grid counted $i$-th from the left and
$j$-th from the top) for date $d$ is $N_d(i,j)$
, the average storm density in the grid is
$\rho_d(i,j) = \tfrac{N_d(i,j)}{154}$.

To describe the storm prior for vortex $\Phi$
(extracted from date $d$), we took the average storm densities of
the pixels it covers:
$$
w_8(\Phi) = \frac{\sum_{(x,y)\in\Phi}\phi_d(T(x,y))}{\Pi(\Phi)}\;,
$$
where $T:(x,y) \mapsto (i, j)$ is a mapping from the pixel $(x,y)$ to
the grid index $(i,j)$ it belongs to.

With all the features introduced above, for vortex $\Phi$ we constructed
a vortex descriptor $X(\Phi)=(w_1, w_2, w_3, w_4, w_5, w_6, w_7, w_8)$.
Descriptors of training data are fed into a machine learning algorithm,
and eventually we obtain a classifier $C(\cdot)$.
For an arbitrary vortex $\Phi$, $Y(\Phi) = C(X(\Phi))$
is the predicted status calculated by the classifier. We introduce
the machine learning approach in the next section.

\begin{figure*}
\centering
\includegraphics[width=0.3\linewidth]{01_05_density.png}
\includegraphics[width=0.3\linewidth]{02_05_density.png}
\includegraphics[width=0.3\linewidth]{03_05_density.png}\\
\includegraphics[width=0.3\linewidth]{04_05_density.png}
\includegraphics[width=0.3\linewidth]{05_05_density.png}
\includegraphics[width=0.3\linewidth]{06_05_density.png}\\
\includegraphics[width=0.3\linewidth]{07_05_density.png}
\includegraphics[width=0.3\linewidth]{08_05_density.png}
\includegraphics[width=0.3\linewidth]{09_05_density.png}\\
\includegraphics[width=0.3\linewidth]{10_05_density.png}
\includegraphics[width=0.3\linewidth]{11_05_density.png}
\includegraphics[width=0.3\linewidth]{12_05_density.png}

\caption{
Average storm densities (number of storms
per 30,000~$\text{km}^2$) for the fifth day of each month in US
continent. Darker color means higher storm density.
The statistics are based on the historical records
from 2000 to 2013. Around each given date (+/-5 days), we count the total
number of storms reported in each state across the past 14 years.
The numbers are then divided by the total number of days and the areas of
corresponding states to calculate the average densities.}\label{fig:dense}
\end{figure*}

\section{Storm System Classification and Detection}\label{sec:machine_learning}
The vortex regions with large $Q$ values correspond
to regions with strong cloud rotations and our approach extracts them as vortexes.
However, they are not necessarily related to the storms because
the cloud rotation is only one aspect of a weather system.
In order to create reliable storm detection,
we embedded these vortexes and
their descriptors into a machine learning framework.
Using the descriptors and ground-truth labels
of the extracted candidate vortex regions,
a classifier was trained to distinguish storm-related
vortexes from other vortexes.

\subsection{Training Vortexes and Ground-truth Labels}~\label{sec:gnd_truth}

We extracted vortexes from 2008 GOES-M satellite images and used the storm
reports in the same year to assign a ground-truth label for each vortex
in the CONUS. A classifier is trained to associate the vortex features
with the storm labels. Image sequences in the first ten days of each
month are used for training and those from the 18th to the 22nd days
of each month are used for testing (so that the weather systems
in the training and testing data are relatively far apart and independent). In order to get enough difference yet not to lose
too much detail, we choose the
sampling interval between every two images in a sequence to be
30 minutes.

We used the historical storms to assign ground-truth labels to detected vortexes.
We denote the historical storm database as $\mathscr{D}$ and each storm
entry as $(u_i, v_i, t_i)$ where $(u_i,v_i)$ is the location
(latitude and longitude values in degrees) and $t_i$
is the starting time of the storm.
Assuming a vortex is detected at location $(u, v)$\footnote{$(u, v)$ is
the geometric center of the vortex.} and time $t$,
if at least one record $(u_i, v_i, t_i)\in\mathscr{D}$ within $3^{\circ}$ of
latitude and longitude
exists between the moments $t-0.5hr$ and $t+2hr$
(i.e. $\|u_i-u\| < 3^{\circ} \text{ and } \|v_i-v\| < 3^{\circ} \text{ and }t-0.5hr\le t_i \le t+2hr$),
the vortex is assigned with a positive (storm-related) label;
otherwise it is assigned with a negative (no-storm) label.
We only assigned labels for vortexes inside the CONUS.
For vortexes outside of the CONUS, we had no historical data
indicating whether or not they are storm-related, so we did not
use them for training and testing.

On the 120 selected days (10 for each month), a total of 8,527
storm-related vortexes were discovered by the above method. We
randomly selected the same number of vortexes in the CONUS
that were not storm-related from these days.
A binary classifier is then trained on the 17,054 training vortex samples.

\subsection{Random Forest Classifier}\label{sec:training}
We trained a random forest classifier~\cite{breiman2001random}
using the training data to distinguish storm-related vortexes
from vortexes not affiliated with a storm.
The features for each vortex are
described in Section~\ref{sec:fea} and the ground-truth is defined in
Section~\ref{sec:gnd_truth}.

Using the training data, the random forest learns rules
to determine whether a vortex of certain descriptor causes storms.
The random forest is essentially an ensemble learning
algorithm that makes predictions by combining the results of
multiple individual decision trees trained from random subsets
of the training data.
We chose this approach because it has been found to outperform a single
classifier (e.g., SVM).  In fact, this strategy resembles the {\it
  ensemble forecasting}~\cite{molteni1996ecmwf} approach in
numerical weather prediction, where multiple numerical results with
different initial conditions are combined to make a more reliable
weather forecast.  For both numerical and statistical weather
forecasting approaches, the ensemble approaches help to improve
the prediction qualities.

\subsection{Testing Vortex Samples and Testing Methods}
To develop a benchmark for the performance of the vortex classifier,
we used image sequences from the 18th to the 22nd days
of each month in 2008 as the testing data.
On each selected day,
the image sequence (with a 30-minute interval)
from 10:00~GMT to 18:00~GMT is used to extract dynamic
cloud vortexes.
The test dataset contains a total of 48,698 vortexes,
among which 781 are labeled as positive, and the rest 47,917
samples are negative.
The descriptors of these vortexes are classified
by the classifier. We then evaluate the classification results
in two ways.

Firstly, we assigned the testing vortexes with ground-truth
labels in the same way as how the training ground-truth labels are assigned.
The predicted storm status for each test vortex
is compared with the ground-truth. In this way the classification
accuracy for single vortexes are evaluated. Table~\ref{tab:cls} presents
the classification performance of both the training data (by cross validation)
and the testing data. The classifier shows
consistent performances on both the training set and the test set.
To demonstrate the effect of including geographic storm priors in
the classification, we also trained and tested the random forest classifiers
only on the visual features and the storm density separately.
Clearly none of the visual features and
the historical storm density standalone performs well alone.
Combining visual features with the storm density
significantly enhances the classification performance.

\begin{table}
\renewcommand{\arraystretch}{1.3}
\caption{Classification Performance and Contributions of Visual Features and Historical Priors}\label{tab:cls}
\begin{center}
\begin{tabular}{|c|c|p{0.15\columnwidth}|p{0.15\columnwidth}|p{0.15\columnwidth}|}
\hline
\multicolumn{2}{|c|}{ }& All features & Visual only & Prior only\\\hline
\multirow{3}{*}{\parbox[t]{0.2\columnwidth}{Training set \\(cross validation)}}
& Overall& 83.3\%& 68.8\%& 76.1\%\\\cline{2-5}
& Sensitivity &89.6\%& 67.4\%& 74.8\% \\\cline{2-5}
& Specificity & 76.9\%& 70.3\%&77.3\%\\\hline
\multirow{3}{*}{\parbox[t]{0.2\columnwidth}{Testing set}}
& Overall& 80.3\% & 67.4\%& 77.3\%\\\cline{2-5}
& Sensitivity & 82.1\%& 47.2\%& 77.4\%\\\cline{2-5}
& Specificity & 80.2\%& 67.7\%& 71.2\%\\\hline
\multicolumn{5}{c}{ }
\end{tabular}
\end{center}
{
\footnotesize Note: Training set contains 5,876 storm-related
and 5,876 no-storm vortex regions from
120 days in 2008. 10-fold cross validation is performed in the evaluation. Testing set contains
2,706 storm-related cells and 7,773 no-storm cells from 60 days far away from the training data.
The feature vector for each vortex is composed by both visual features and storm priors (see
Section~\ref{sec:fea}). Beside the merged features (results shown in the first column),
we test the two types of features separately (results shown in the second and third columns).}
\end{table}

Secondly, we evaluated the classifier's ability to predict the future
locations of storms.
Instead of comparing each vortex's classification result with
its ground-truth label, which reflects its status within the period
of $(t-0.5hr, t+2hr)$ ($t$ is the timestamp of the vortex),
we observed how much time in advance a storm can be detected by our algorithm.
Given a testing vortex extracted at the location $(u,v)$ and time $t$,
we find the earliest time $t'=T(u,v,t)$
that a neighboring storm $(u',v',t') \in \mathscr{D}$
developed since two hours before $t$,
i.e.,
$$t'=\min_{t_i}{\left\{(u_i,v_i,t_i) \in \mathscr{D} \text{ and }\|u_i-u\| < 3^{\circ} \text{ and }\|v_i-v\| < 3^{\circ} \text{ and }t_i>t-2hr\right\}}\;.$$
The value $\Delta t = T(u,v,t)-t$ indicates the time difference between an observed vortex
and the first storm to form nearby storm. For a vortex with $\Delta t> 0$,
a future storm is predicted if
the classifier can label a vortex as storm-related. The percentages of storms
predicted for vortexes with different $\Delta t> 0$ are shown in Fig.~\ref{fig:cls_pred}.
The results show that the proposed algorithm can effectively predict future
storms within several hours of the detection of vortexes and
the prediction reliability increases as $\Delta t$ decreases.
Particularly, it shows a reasonable prediction rate for the
development of nearby storms within a period of
4 hours after
the observation of visual vortexes in satellite images.

\begin{figure}
\includegraphics[width=\textwidth]{storm_prediction.pdf}
\caption{Storm detection rate with different amounts of time in advance. The distribution
of the time differences ($\Delta t$) between the testing vortex samples and storms are plotted in red
bars (the last bar is not fully shown because it is much higher than others).
And the percentages of vortexes categorized as storms with different $\Delta t$ are
plotted in a blue curve. The figure shows that the predictivity of the classifier
is generally reliable within few hours of time in advance. The storm detection rate
decreases as the time difference becomes longer. }\label{fig:cls_pred}
\end{figure}

It is necessary to emphasize that the classification is performed
on already extracted candidate vortex regions where local clouds
are rotating. Most of the cloud-covered regions on the satellite
images without obvious rotational motions have been already eliminated
before the algorithm proceeds to the classification stage.
Therefore in practice the system achieves a good overall precision for
detecting the storms. In addition, the classification results for
individual vortex regions can be further corrected and validated based
on their spatial and temporal neighboring relationships.

\subsection{Case Study}
To demonstrate the usefulness of the proposed vortex detection and
classification algorithm in real scenarios, we present several case
studies where our algorithm is applied for automatic storm detection
from satellite images.

Three different cases are presented and the
results are visualized in Fig.~\ref{fig:case_study} (one row for each case).
An active storm reporting day , a clear storm-free day, and a cloudy storm-free day
are selected to demonstrate the adaptiveness of our algorithm.
These three image sequences are not included in the training process
of the storm classifier.
In Fig.~\ref{fig:case_study}, automatically detected vortexes are
filled with colors on each image. Vortexes classified as storms
are colored in red, and the green vortexes are categorized
as storm-free cases by the machine learning module.
The US state boundaries are drawn on each image to show the
alignment between the satellite images and the geo-coordinate system.
In particular, boundaries of states affected by storms
are highlighted by warm colors from red to yellow. The highlighting
color indicates how long after the observation storms were reported
in the corresponding states. As the color turns to yellow, the highlighted
states are affected by storms further in the future.
Specifically, the red color means an ongoing storm and the yellow color
means a storm 6 hours after the observation time.
Fig.~\ref{fig:case_study} only shows the results on the first three
image frames of each case due to space limitation.
Each test image sequence in fact contains more than three frames,
and the storm detection results are consistent
in the subsequent frames for each case.

The first case is from May 25, 2008, which was an active storm day, when
several severe storms hit the Midwest US.
An obvious cloud vortex system is visible on the satellite
images and it is related to storms reported in nearby states.
Our algorithm captures all the major vortexes from the satellite
image sequence, and correctly categories most of them as storms.
The result also shows the capability of the proposed algorithm
to assist in short-term storm forecasting. In most cases there will
be storms detected around regions where storms were reported shortly thereafter.

Different from the first case, the second case is strongly negative
in that little clouds and vortexes can be perceived from the images.
Results on the second row of Fig.~\ref{fig:case_study} shows that
no storm is predicted because few clouds and vortexes can be captured
by the feature extraction stage of our algorithm. In this case, it
is easy for the algorithm to make correct decisions.

In the third case, several cloud systems can be observed above the
CONUS, and several vortexes are detected by the optical flow analysis.
In reality, even though some cloud vortex systems
look dominant on the image sequence,
there is no severe storm reported on that day.
Results shown on the third row of Fig.~\ref{fig:case_study}
demonstrate that the machine learning module successfully
avoids labeling the detected cloud vortexes as storms.
This shows that the system does not over-predict the occurrence of storms.
This benefit is also validated by the standalone test for the classifier
showing that specificity of the classifier is as high as its sensitivity.
\begin{landscape}
\begin{figure*}
\centering
\subfigure[2008/05/25 12:15 GMT]{
    \includegraphics[width=0.25\linewidth]{case_1_1.png}
}
\subfigure[2008/05/25 12:45 GMT]{
    \includegraphics[width=0.25\linewidth]{case_1_2.png}
}
\subfigure[2008/05/25 13:15 GMT]{
    \includegraphics[width=0.25\linewidth]{case_1_3.png}
}\\
\subfigure[2008/09/18 12:15 GMT]{
    \includegraphics[width=0.25\linewidth]{case_3_1.png}
}
\subfigure[2008/09/18 12:45 GMT]{
    \includegraphics[width=0.25\linewidth]{case_3_2.png}
}
\subfigure[2008/09/18 13:15 GMT]{
    \includegraphics[width=0.25\linewidth]{case_3_3.png}
}\\
\subfigure[2008/03/25 12:15 GMT]{
    \includegraphics[width=0.25\linewidth]{case_2_1.png}
}
\subfigure[2008/03/25 12:45 GMT]{
    \includegraphics[width=0.25\linewidth]{case_2_2.png}
}
\subfigure[2008/03/25 13:15 GMT]{
    \includegraphics[width=0.25\linewidth]{case_2_3.png}
}
\caption{Three example cases of applying the proposed algorithm to analyze
satellite image sequences.
Detected storm vortexes are colored in red and rejected vortexes are colored
in green. States hit by storms are highlighted in warm colors. A red state boundary
indicates the state is hit by an ongoing storm based on the historical report.
As the highlighting color turns to yellow, the corresponding state is affected by
more future storms as far as 6 hours away from the observation.
(a-c) Three frames on May 25, 2008. Major storms
occurred in the central, eastern, and southeastern states of US. Multiple
vortexes are detected and classified as storm systems in each frame and
the locations of the vortexes well match the ground-truth.
(d-f) Image frames on September 18, 2008. The US continent area is mostly
clear on the day and no vortex is detected.
(g-i) Image frames on March 25, 2008. No storm is reported on the day.
Though clouds and their rotational motions are perceived on the image
sequence, most of them are categorized as hazardless
cloud systems by the classifier.
}\label{fig:case_study}
\end{figure*}
\end{landscape}

\section{Conclusions and Future Work}\label{sec:conclusion}
In this paper, we present a thunderstorm
detection algorithm that locates storm visual signatures
from satellite images.
The algorithm automatically analyzes the visual features
from satellite images and incorporates the historical meteorological records
to make storm predictions. As opposed to traditional ways of weather forecasting,
our approach primarily relies on the visual information from images and tries
to extract high-level storm signatures as meteorologists usually do manually.
Without using any sensory measurement (e.g., temperature, air
pressure, etc.) as numerical weather forecasting does, the algorithm
captures global cloud patterns solely from the satellite images, which
is helpful in the synoptic-scale storm prediction.
Additionally, the algorithm
takes advantage of big data, using historical storm reports
in the past years together with the satellite image archives
to learn the correspondence between visual
image signatures and the
occurrences of current and future storms. Experiments
and case studies show that
the algorithm is effective and robust.

Properties of optical flows between every two images in a sequence
are the basic visual clues adopted in the work.
The algorithm can estimate robust and smooth optical flows between two
images and determine the rotations in the flow field.
Unlike numerical weather forecasting methods that are sensitive to noise
and initial conditions, our approach can consistently detect
reliable visual storm clues hours before
the occurrence of a storm. Therefore, the results are useful for
weather forecasts, especially storm forecasts.

The application of historical storm records and machine learning
boosts the performance of the storm extraction algorithm.
Standalone vortex detection from the optical flow is not sufficient
to make reliable predictions. The statistical model trained from
historical meteorological data together with the satellite image
visual features further selects the extracted vortexes and removes
vortexes not related to storms. In our current algorithm,
storm detection is based on individual vortex regions. In reality,
multiple vortexes tend to appear near each other in storm systems both temporally
and geographically. Therefore, taking into account nearby vortexes
within a frame and tracking storm systems across a sequence
can improve the overall reliability of the system. In particular,
tracking the development of a storm system will be helpful
for analyzing the future trend of the storm. This problem is nontrivial
because the storm systems (clouds) are non-rigid and highly variant,
and the same vortexes in a storm system are not always
detectable over time. Future work needs to tackle
the problem of non-rigid object tracking to better make use
of the temporal information in the satellite image sequences.

Lastly it should be emphasized that
weather systems are highly complex and chaotic, so it is always
a challenging task to make accurate weather forecasts. The proposed
algorithm based on the satellite image visual features
is not completely accurate and needs to be further validated.
We expect the algorithm or similar approaches
to play a part in producing weather forecasts
rather than making the forecasts alone.
It could be incorporated with other existing weather prediction
techniques (e.g. NWP) and used as an automatic tool to refine the
current forecasts.
It is useful and valuable because it
provides forecasts in a different aspect from the numerical approaches.
The purpose for developing the new algorithm is not to replace the
current weather forecasting models, but to produce additional
independent predictions to be combined with other approaches.
As a result, we will focus the future study on how to integrate
information from multiple data sources and predictions from different
models to produce more reliable and timely storm forecasts.

\section*{Acknowledgments}
\else

  \section*{Acknowledgment}
\fi
This material is based upon work supported by the
National Science Foundation under Grant No. 1027854. Shared
computational infrastructure was provided by the Foundation under
Grant No. 0821527. Part of the work was done when J. Z. Wang and
J. Li were with the Foundation. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of
the authors and do not necessarily reflect the views of the
Foundation. We also thank the US National Oceanic and Atmospheric
Administration (NOAA) for providing the data used in this
research. Siqiong He assisted with data collection. The discussions
with Jose A. Piedra-Fernandez of the University of Almeria, Spain, has
been very helpful.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


90% of the pastors report working between 55 to 75 hours per week.
70% of pastors feel grossly underpaid.
90% feel they are inadequately trained to cope with the ministry demands.
thought it would be like before they entered the ministry.
70% of pastors constantly fight depression.
they could, but have no other way of making a living.
80% believe pastoral ministry has negatively affected their families.
80% of spouses feel the pastor is overworked.
80% spouses feel left out and under-appreciated by church members.
70% do not have someone they consider a close friend.
40% report serious conflict with a parishioner at least once a month.
#1 reason pastors leave the ministry — Church people are not willing to go the same direction and goal of the pastor. Pastors believe God wants them to go in one direction but the people are not willing to follow or change.
50% of the ministers starting out will not last 5 years.
1 out of every 10 ministers will actually retire as a minister in some form.
4,000 new churches begin each year and 7,000 churches close.
However if you want to get further depressed, read the original article on why U.S. pastors leave their churches in Statistics on Pastors by Dr Richard J. Krejcir. Needless to say, we need to pray for all our pastors. And give them regular sabbaticals!
There are actually Christians who believe pastors should never get depressed or burnout if they really know the Lord and have faith in Him. To them the two are contradictory. Why should that happen when the Lord is with them and they have the power of the Spirit, who by the way, brought the world into existence? They forget Elijah, David, Jeremiah and Paul. They forget pastors have feet of clay too. They are made of flesh, blood and have hormones.They go through relational conflicts and experience loss too. They may work under as difficult work environments as executives, as this article USA Today showed……..
What kind of personal pain would cause a 42-year-old pastor to abandon his family, his calling and even life itself? Members of a Baptist church here are asking that question after their pastor committed suicide in his parked car in September. Those who counsel pastors say Christian culture, especially Southern evangelicalism, creates the perfect environment for depression. Pastors suffer in silence, unwilling or unable to seek help or even talk about it. Sometimes they leave the ministry. Occasionally the result is the unthinkable.
Experts say clergy suicide is a rare outcome to a common problem. But Baptists in the Carolinas are soul-searching after a spate of suicides and suicide attempts by pastors. In addition to the September suicide of David Treadway, two others in North Carolina attempted suicide, and three in South Carolina succeeded, all in the last four years.
Being a pastor—a high-profile, high-stress job with nearly impossible expectations for success—can send one down the road to depression, according to pastoral counselors. "We set the bar so high that most pastors can't achieve that," said H.B. London, vice president for pastoral ministries at Focus on the Family, based in Colorado Springs, Colo. "And because most pastors are people-pleasers, they get frustrated and feel they can't live up to that." When pastors fail to live up to demands imposed by themselves or others they often "turn their frustration back on themselves," leading to self-doubt and to feelings of failure and hopelessness, said Fred Smoot, executive director of Emory Clergy Care in Duluth, Georgia.
Most counselors and psychologists interviewed for this article agreed depression among clergy is at least as prevalent as in the general population. As many as 12% of men and 26% of women will experience major depression during their lifetime, according to the American Medical Association. "The likelihood is that one out of every four pastors is depressed," said Matthew Stanford, a professor of psychology and neuroscience at Baylor University in Waco, Texas. But anxiety and depression in the pulpit are "markedly higher" in the last five years, said Smoot. "The current economic crisis has caused many of our pastors to go into depression." Besides the recession's strain on church budgets, depressed pastors increasingly report frustration over their congregations' resistance to cultural change.
Nearly two out of three depressed people don't seek treatment, according to studies by the Depression and Bipolar Support Alliance. Counselors say even fewer depressed ministers get treated because of career fears, social stigma and spiritual taboo. "Clergy do not talk about it because it violates their understanding of their faith," said Scoggin. "They believe they are not supposed to have those kinds of thoughts." Stanford, who studies how the Christian community deals with mental illness, said depression in Christian culture carries "a double stigmatization." Society still places a stigma on mental illness, but Christians make it worse, he said, by "over-spiritualizing" depression and other disorders—dismissing them as a lack of faith or a sign of weakness. Polite Southern culture adds its own taboo against "talking about something as personal as your mental health," noted Scoggin. The result is a culture of avoidance. "You can't talk about it before it happens and you can't talk about it after it happens," said Monty Hale, director of pastoral ministries for the South Carolina Baptist Convention.


text:
Ind3mand
FRED PELLICHERO
JJ MULLOR
LOSH
VICTOR PORFIDIO
Grammy-nominated remixer, producer, writer, & international DJ
When it comes to change, you either get on board or get left behind. No one knows this more than MING. Flipping the script is second nature to this New Yorker. In a city with an ever-changing soundscape, MING remains versatile and adept: essentially a way of life for the producer, songwriter, remixer, DJ, and musician.
In the mid-90s, he was one-half of the pioneering electronic duo MING+FS. Playing guitar, bass, keyboard, and turntables, MING toured the world and gave international audiences an introduction to electronic music, opening up for acts like Sting, RUN-DMC, and Moby.
Over the last decade, MING has pulled the studio more into focus, while still keeping a hand in the touring life. He remixed singles for a star-studded lineup including Beyoncé, Lady Gaga, Katy Perry, Ellie Goulding, and Black Eyed Peas. His original tracks climbed the charts, scoring four Top 40 trap remixes on Beatport. Collaborations on singles like "Disintegration" from Le Castle Vania for deadmau5's Mau5trap label and "King Kong" for Steve Aoki's Dim Mak Records staked claim on dancefloors worldwide. MING received a Grammy nomination in 2014 for the remix of Crossfingers' "Falling Out" (featuring Danny Losito).
No doubt you've heard MING at the club or festival, but his work continues to percolate elsewhere. Habitat Music—MING's music production company—supplies original music to the advertising, film, and television industries. Some of his work has been featured on TV shows such as CBS's "CSI: Miami" and "CSI: NY," HBO's "Sex and the City," and Showtime's "Weeds." Brands like Sears, Nissan, Chevy, Burger King, Nokia, AT&T and Doritos have also leveraged MING's cutting-edge sound for their global advertising campaigns.
MING continues to break through in 2017: MING Presents Warmth, his internationally syndicated weekly radio show, can be heard on Dash Radio, the Main Stage channel of dl.fm, and Future.fm and his mixes now appear on Music Choice, America's premier music and video on demand provider.
MANAGEMENT (EUROPE)
john@ind3mand.nl
MANAGEMENT (WORLDWIDE)
luis@braslive.com
IND3MAND ARTIST & MUSIC MANAGEMENT
Office: Wollegras 42 - 1687 WC Wognum - The Netherlands
E-mail: management@ind3mand.nl
Copyright © 2019 IND3MAND ARTIST & MUSIC MANAGEMENT


The weeks are just whizzing past us, as it's time for another Throwback Thursday! This week's feature is American based artist, Kathryn Selbert. Kathryn has been represented by Advocate Art agency since 2013, and in that time, we have loved watching her evolve as an artist. Kathryn's vibrant colour palette and cute characters have made illustrations perfect for Children's Publishing, especially for readers aged 0-5. Her illustrations are bursting with expressions and emotion, and she has a great ability to bring them to life.
It doesn't stop there... Kathryn's designs have also been popular for Art Licensing, and you can spot her designs on greetings cards for everyday occasions, like birthdays, and other occasions include Easter, Mother's Day, Father's Day and Christmas. Her designs have also branched out onto Art for Posters and Prints and Ceramics and Patterns. You can view more work by Kathryn via her online Advocate portfolio, check it out!
We asked Kathryn to recall some of her first experiences with Advocate, have a read through the Q&A below, and enjoy the images above. If you'd like to work with Kathryn on an upcoming project, be sure to get in touch with one of our agents.
This was a little pirate character I submitted when I was applying to the agency! I still love the way this turned out.
2) First design of yours to sell - what design was it and who was it to.
This was one Christmas card that we developed into a series of four images for Hallmark for Paperchase!
This was for American Greetings - a sweet birthday card of a boy in space.
I've wanted to be an illustrator for as long as I can remember. I was always enamoured with picture books during bedtime and can recall focusing intently on the illustrations. A few of my favourites from when I was little are above.
My second grade teacher wrote and published a children's book while I was in her class. I assume because of this experience, she focused part of our curriculum on learning about the world of illustrators and writing our own books.
The first book I wrote and illustrated, War Dogs, was published before I joined. I still remember opening the box and bursting into tears. I'm still so proud of how it turned out and feel so lucky to have had a chance to complete it. My style has changed in every way since this was published - but it's such a treasure to me.
This was an image for a prospective puzzle - I was so proud at the time of how far I'd come in terms of style. I was happy with my work and starting to get a really strong response - I felt like I was getting to a wonderful place.
Kathryn Selbert was founded by our team of illustration agents based at our UK & USA illustration agencies. If you think any of our illustrators would be suitable for an upcoming project, be sure to reach out to an agent!


text:
Home › Gary Cole Quotes
Gary Cole Quotes
Enjoy the top 41 famous quotes, sayings and quotations by Gary Cole.
"To be in a movie directed by Wolfgang Petersen, and a movie that had a large budget ... I got a taste of what really good filmmaking could be."
— Gary Cole —
Being able to fantasize for a couple of days at being a rock singer surpassed most things I've done on stage.
— Gary Cole
"I think it had something to do with my love of music, especially rock music."
"I miss everything about Chicago, except January and February."
"If someone comes up to me, 90 percent of the time it's about Office Space."
I love going to concerts, so that whole environment is something that intrigues me anyway.
"Karl Malden was quite a mentor. He taught me things he had learned from being in front of a camera so long."
"You always know when something works it's a result of everything firing on all cylinders."
"There is no handbook about how a career is going to go."
"A lot of actors in my age bracket look at being still standing as pretty good."
"I messed around in high school, but I pretty much put it away until I did a television show in San Francisco."
"When you make a movie, it's up to so many things and so many people."
"Yes, I've already done a couple of guest voices."
"I am also a drummer of sorts. I've got an electronic set sitting in my bedroom."
I don't think anyone sets out to do something bad, it's just that it's very difficult.
I grew up with Apocalypse Now and Badlands, so I had a real awe thing going.
I just don't eat too much. That's never been my problem.
I still like to listen to the people that I came of age on.
"I was a little nervous coming in mostly because my first scene was with Martin Sheen, who I'm a huge fan of."
"I will confess I did none of my own singing. I did all my own costume and makeup, though."
"Then, at some point, you get identified with certain things."
"You can go out in a good movie and look bad as well."
"You need someone to tell you how to do things like hitting your marks, or driving a car so it looks right or getting out of a car so it doesn't take a million years of screen time."
"I was initially a leading man, but only on television."
"I like the fact that this kind of family has been seen in a movie a million times: teenage kids, the family is a bit strained and they don't have enough money, but in the background the guy used to be a Gene Simmons type."
Let's focus on where you could end up, not where you were or are.
"It's always best not to be thinking a hell of a lot while you're acting, because you want it to be as spontaneous as possible, not too intellectual. Just behaving and listening to other people who you're doing scenes with. I always like the latter when it looks easy, even though it may not be."
"Clint Eastwood is a very soft-spoken, humble guy, actually, which helped put somebody like me at ease, who had never worked with somebody as huge as that. I'm sure that's not always the case with legendary people."
"Part of you wants to look over at the people watching and say, "Not bad, huh? Me and Clint Eastwood." But you have to get past that and just be an actor."
You make the choice. You look at each scene and you make sure that this is not a person deceiving people.
"You don't really have time to do other than what's written. It's very rigid. Shows have a certain rhythm that nobody wants disturbed. So a lot of that doesn't take place on television, at least the television I was doing at the time when I first started."
"Good decisions don't make life easy, but they do make it easier."
"Improv is not something I had a lot of experience with, because for a long time, my only experience in front of a camera was all television, which is pretty rigid script-wise, except for the occasional scene where you toss in an ad-lib just to elongate something."
If you're onstage and you're improvising and nothing's happening, people are racing for the door. But the director can go shopping later and pick up pieces and moments and insert them.
"But for me, you also have to be conscious of what is going to play. And that includes playing with. Sometimes it's just a vibe. It's what's going to make this scene work. And sometimes there may be something that restricts you that has to do with something that maybe is historically accurate. And then you have to weigh that decision and give up something for a scene to work."
"I look at it scene-by-scene. Whether it's a historical character or not, whatever, on the page is one thing and delving into the history or somebody is one thing, but making something work for an audience in front of a camera is another exercise and you bring whatever authenticity you can to it."
It was really executed well, from the art direction to the wardrobe to everyone else. And I have to say, two really exceptional directors who did three each. Roxann [Dawson] did the first three and Jeremy [Webb] did the second three. And I think they really were very meticulous in getting the right tone because it is both. It isn't dour and it isn't grim, but it's not a romp either. It's truthful and it has room for both of those things.
"Television is a big roulette table on so many levels. That's all it is for actors."
"A show that's been successful that been on a while, chances are it's going to stay that way. At least it's going to maintain some kind of standard. But when a show begins, there's no telling. Even after 13 shows or a whole year, you don't know what will become of it."
"The one nice thing about doing a character for a long time is, you begin to feel more comfortable, and you are thinking less and behaving more."
"What it targets is not something that's really looked at a lot in terms of the war. This is stuff that's off the beaten path in terms of what we think of every time you start a Civil War history or a Civil War presentation. It's usually about the military and the soldiers and all that stuff. And this is not. It's the backdrop to a place and a time and circumstances that didn't have anything to do with that."
Gary Cole Quotes Pictures
Want to see more pictures of Gary Cole quotes? Click on image of Gary Cole quotes to view full size.
Adrian Robinson Quotes
Alison Doody Quotes
Aurelio Voltaire Hernandez Quotes
Burton Hill Quotes
D'arcy Wretzky Quotes
Galen Strawson Quotes
Hanna Martine Quotes
Manny Farber Quotes
Orison S. Marden Quotes
Rishabh Surya Quotes
Ryan Seacrest Quotes
Samuel McMahon Quotes
Shelli NT Quotes
Termina Ashton Quotes
Thuy Trang Quotes
Quotes About Stalin By Historians
Commit To Be Fit Quotes
Execution Is Key To Success Quotes
Quotes About Blue Ivy
Quotes About Calculators
Quotes About Carrying Baby
Quotes About Users And Liars
Quotes About A Baby Sister
Quotes About Being Single But Not Available
Quotes About Being Behind The Wheel
Quotes About Talent And Heart
Barack Obama Audacity Of Hope Quotes
Had To Leave Quotes
Desire And Longing Quotes
The Past Does Not Define You Quotes


Currently, by default, integral curves are repeatedly re-computed in different flow visualization applications, such as FTLE field computation, source-destination queries, etc., leading to unnecessary resource cost. We present a compression-based data reuse framework for integral curves, to greatly reduce their retrieval cost, especially in a resource-limited environment. In our design, a hierarchical and hybrid compression scheme is proposed to balance three objectives, including high compression ratio, controllable error, and low decompression cost. Specifically, we use and combine digitized curve sparse representation, floating-point data compression, and octree space partitioning to adaptively achieve the objectives. Results have shown that our data reuse framework could acquire tens of times acceleration in the resource-limited environment compared to on-the-fly particle tracing, and keep controllable information loss. Moreover, our method could provide fast integral curve retrieval for more complex data, such as unstructured mesh data.
This work is supported by NSFC No. 61672055. This work is also partially supported by NSFC Key Project No. 61232012 and the Strategic Priority Research Program - Climate Change: Carbon Budget and Relevant Issues of the Chinese Academy of Sciences Grant No. XDA05040205.


text:
ilość Australia and New Zealand
Australia and New Zealand by Christine Lindop
Christine Lindop was born in New Zealand where she began her teaching career. She later taught EFL in France and Spain, and has worked as an editor, proofreader, and writer since 1993.
What do you find in these two countries at the end of the world? One is an enormous island, where only twenty million people live – and the other is two long, narrow islands, with ten sheep for every person. One country has the biggest rock in all the world, and a town where everybody lives under the ground. The other has a beach where you can sit beside the sea in a pool of hot water, and lakes that are bright yellow, green, and blue.
Open this book and start your journey – to two countries where something strange, beautiful, or surprisinq waits around every corner.
Seria: FACTFILES
Poziom: 3 Pre-Intermediate
Michael Jackson: The King of Pop


The Black Keys, String Cheese Incident, Avett Brothers, Flaming Lips, Alabama Shakes, Old Crow Medicine Show, and more are inked for the Forecastle Festival, scheduled to take place July 12-14 in Louisville, Kentucky's Waterfront Park.
The Forecastle Festival is excited to announce the initial lineup for its 11th annual three-day music celebration, featuring Grammy Award winners the Black Keys, alongside The String Cheese Incident and the Avett Brothers leading the festival's rich, eclectic offerings. The event will again take place July 12 - 14, 2013 at Louisville's scenic, award-winning Waterfront Park.
The Flaming Lips, Alabama Shakes, Animal Collective, Old Crow Medicine Show, Grace Potter & the Nocturnals, Big Boi, Matt & Kim, Purity Ring, and more than 50 others will round out the lineup. Louisville native Jim James, whose band My Morning Jacket headlined and curated the festival in 2012, will return with his new solo project. A full list of confirmed acts can be found below.
In addition to performances at Waterfront Park, the Forecastle Festival will feature late night shows on the historic Belle of Louisville steamboat and other landmark locations throughout downtown Louisville. Jam masters The String Cheese Incident will perform an exclusive, separately ticketed late night show at Louisville's historic Palace Theater on Saturday, July 13.
Highlighted in Rolling Stone as a top event in 2012, and acclaimed by Outside as one of the "Top 15 Outdoor Festivals in the USA," the Forecastle Festival has evolved into one of the country's premier festival destinations. The event offers an immersive experience that's distinctly Louisville- beyond the first-class musical lineup-highlighting the region's burgeoning culinary and arts scene, Kentucky's unique bourbon heritage and more. The festival continues to emphasize sustainability practices and conservation initiatives, highlighted through the festival's non-profit organization, the Forecastle Foundation.
Weekend passes for Forecastle 2013 will go on sale this Wednesday, March 6 at noon EST at Forecastlefest.com, Ticketmaster.com and all Ticketmaster outlets.
Single day passes and late night show tickets, along with the daily schedule of artists and late-night events will be available in April.
For more information on Forecastle, visit: www.forecastlefest.com.


Erwin Sattler Watches are popular watches for mens and womens.
These watches are really quality and they will last you for a long time.
If you want to buy one of these Erwin Sattler Watches, you can do that on this amazon link.


text:
Q: What is the shortest duration Vibrator.vibrate() can be called with where the device will still vibrate?
*

*100 milliseconds works, but was too long of a vibration for my requirement.

*50 milliseconds works and gives me what I want

*10 milliseconds didn't work on my device

This made me curious, is there a guarantee that 50ms will work on all devices?  Is there a way to determine the vibrator's spec programmatically and set the vibration duration accordingly?  Or possibly a way to tell that the vibrator vibrated at all.

A: Here is the api for the vibrator.
It actually lets you check if your device has a vibrator with
hasVibrator()

But that's about it. It won't tell you if you give it a vibrate duration that isn't long enough.


text:
Becoming Mentally Stronger
By Matt Winkeljohn
Sting Daily
Kristen Adkins and her Georgia Tech softball teammates are in the midst of a rare weekend off, which they can use to recalibrate. After a 6-0 start, the Yellow Jackets have lost four of five, most recently falling 4-1 Wednesday at Kennesaw State.
At 7-4, they are not where you might expect to find a team that ranked in the high teens.
Perhaps nobody on that team might better be able to lead a recalibration process than a certain senior from McKinney, Texas.
Adkins veered off course last season, and has gone through great mental duress to get back on with her faith riding shotgun in a sidecar.
After transferring from Florida following one season with the Gators, Adkins was smack in the middle of the Jackets' drive to the ACC title in 2009. As a pitcher, she was 24-7 with an ERA of 1.69, pitching a whopping 182.1 innings. As a part-time third baseman/defender/pinch hitter, she batted .273 with nine home runs and 40 RBI.
The bottom did not exactly fall all the way out last season, but the floor dropped.
The arrival of freshman pitching/hitting sensation Hope Rush had a lot to do with that.
Adkins knew ahead of time that matters were going to be different, and she thought she was equipped to adapt.
It turned out she wasn't ready to pitch less than half as frequently as she had, and while the damage to her psyche was more notably manifested in her offense, Adkins changed in more ways.
She pitched a modest 88.1 innings, sporting a record of 8-2 with an ERA of 2.30, and her batting average slipped to .183. Her appearances on defense dipped dramatically.
Not exactly an afterthought, Adkins was nonetheless not on par with the player who'd been named second team All-ACC in 2009, when she was also the ACC Tournament MVP.
Talk to her a few times, and the idea of using the word ego doesn't seem at all right, but something of hers took a hit last season. Maybe pride is a better word.
"I knew my role was changing, but at the time I probably wasn't ready to deal with it," she said the other day. "I really just wasn't prepared for it mentally.
"I thought I was ready for it, and obviously I wasn't. I had a little bit of a mental snafu maybe a quarter of the way through the season, and really had to figure out how to pull myself out of a slump."
Adkins said she's a much stronger person – and not just on the field of play – now than she was then. But, "It wasn't easy. It's never easy," to get to this place.
It would not be fair to Adkins to suggest that her entire process of mental re-trenching could be described here because it wasn't so simple. Yet key elements should resonate.
Foremost, she believes that strengthening her relationship with Christ was central.
She also read quite a bit of literature on becoming mentally stronger.
And her relationship with a former teammate who was going through a similar process, former Tech pitcher Jessica Coan, was huge.
Last summer, the two attended a worship-based conference in Jacksonville.
Coan – who along with Adkins and Rush handled virtually all the pitching duties for Tech last season – was looking at other options.
Adkins said that even in the midst of her lowest point, she was never considering transferring. She was looking for a way to pick herself up. Coan was looking elsewhere.
"We were at the conference, and it was like a light came on," Adkins said. "We stayed up all night, discussing matters. She was going through about whether or not to transfer. She was saying that she hadn't been shown a way to go.
"She had been talking to the Auburn coach and the Baylor coach, and literally that night . . . she had two voice mails, one from the Auburn coach and one from the Baylor coach saying `Sorry, things aren't going to work out.' Instantly, she knew."
Coan transferred to North Georgia, where she's studying to become a teacher, and leading the Saints' softball team, which is ranked No. 3 in Division II.
Coan pitched six innings Friday, allowing one hit and striking out 11 in a 7-0 win over California (Pa.) For the season, she's 6-1 with a 0.17 ERA having allowed 13 hits in 41 innings. She has 69 strikeouts.
At that same conference in Jacksonville, Adkins said she too had – her word – an epiphany. "We stayed up all night long, and cried all night long . . . out of happiness. I can remember that clearly," Adkins said.
Her epiphanal moment came in the realization that she had been playing for herself, to satisfy her goals, and that it was a mistake to go about business that way.
Now, "It's not about me. I'm not playing for myself, for my parents, I'm playing for God," Adkins said. "Colossians, 3:23-24: `Whatever you do, work at it with all your heart, as working for the Lord, not for human masters, 24 since you know that you will receive an inheritance from the Lord as a reward.' That's become a mantra for me."
Adkins has been moved, this time more distinctly than in a lesser moment of divine intervention that previously steered her to Tech.
"Moving towards it had a lot to do with my transferring from Florida. Not even 24 hours [after a conversation with the Florida coach], I got a phone call saying that Georgia Tech was looking for a pitcher/third baseman," she said ."I felt like that was him speaking through me, and leading me in a certain direction."
Through her grind last season and summer, Adkins was in contact with Tech coach Sharon Perkins and her staff. They had not misled her. She had to some degree misled herself in the way she prepared for her reduced role.
"It's so hard for [coaches] because they have so many girls," she said. "They have a completely open-door policy. I know I can go there. That time that I'm in there, it's not about softball and that's a great thing. I did take them up on that many times last year."
Ultimately, Adkins had to do a great deal of self-sorting.
She has.
She and Rush are each 3-2 now, although five of the 10 runs Adkins has allowed in seven games have been unearned. Her 1.15 ERA leads the team, as do her 33 strikeouts. She's just behind Rush in innings (30.1 to 31.2) and has walked just 11 to Rush's 23.
There has not been an offensive role for Adkins, but if that's bothering her it's impossible to tell in a 20-minute conversation.
What's impossible to miss is a sense of peace laced with persistence.
"In the words of Pat Summitt . . . I was a huge Lady Vols fan growing up . . . I have become a mental giant rather than a mental midget," Adkins said. "Whatever I take on, I take a relentless attitude regardless of the outside circumstances. Control what I can control, and adjust my plan if I have to adjust."
"I still have a long way to go [pitching], a lot of growing to do. It's still really, really early. I'm just trying to gear for the long haul."
Comments to stingdaily@gmail.com.