Gateway
BOT
27-Aug-19 02:10 PM
[dscrd-hush] <dukeleto> it only happens if that computer has never had KMD or any smart chain ever installed
[dscrd-hush] <DieserMerlin> Works
[dscrd-hush] <dukeleto> so installing KMD first will also fix it (you don't need to even sync, just startup)
[dscrd-hush] <DieserMerlin> Thank you
[dscrd-hush] <dukeleto> @DieserMerlin elite
[dscrd-hush] <dukeleto> !tip @DieserMerlin 5 hush
[dscrd-hush] <Discordtip> @dukeleto sent @DieserMerlin 5.00 HUSH (≈ $0.15).
[dscrd-hush] <dukeleto> enjoy
[dscrd-hush] <DieserMerlin> Thanks
[dscrd-hush] <dukeleto> make sure to move your funds withdrawn from the tipbot (only supports taddrs) to a more private zaddr in your SilentDragon wallet!
[dscrd-hush] <denio> @DieserMerlin pls share this infos with the class
[dscrd-hush] <dukeleto> gosh, we need a new SD release soon
[dscrd-hush] <denio> So not everybody jumps in and ask the same question
[dscrd-hush] <dukeleto> @denio extra credit for students that compile latest master branch from source
[dscrd-hush] <dukeleto> 25 hush instead of 5 hush
[dscrd-hush] <denio> !tip @DieserMerlin 50 Hush
[dscrd-hush] <Discordtip> @denio sent @DieserMerlin 50.00 HUSH (≈ $1.50).
[dscrd-hush] <denio> Compile it, please
(edited)
[dscrd-hush] <DieserMerlin> haha challenge accepted
[dscrd-hush] <dukeleto> @DieserMerlin go go go! there are lots of cool new features
[dscrd-hush] <dukeleto> like Address Validation and the GUI to connect to the android app
[dscrd-hush] <dukeleto> file upload (just small files for now, 512 bytes or less)
[dscrd-hush] <dukeleto> and various bugfixes like the one you hit
[dscrd-hush] <dukeleto> i think your version might still download 1.6gb of files?
[dscrd-hush] <dukeleto> latest doesn't need all that junk
[dscrd-hush] <DieserMerlin> I think it downloaded about 700mb
[dscrd-hush] <dukeleto> @DieserMerlin ok. even that is not needed with latest code!
[dscrd-hush] <dukeleto> only 50MB download, with latest code
[dscrd-hush] <DieserMerlin> Where do I get iostream? I can't compile it because it can't find iostream on my system
[dscrd-hush] <dukeleto> @DieserMerlin you likely don't have various compiler stuff. Look at INSTALL.md
[dscrd-hush] <ITM> its counts as a compile
HUSH fork of KMD. Contribute to MyHush/hush3 development by creating an account on GitHub.
[dscrd-hush] <dukeleto> nice custom release @ITM
[dscrd-hush] <dukeleto> soon, travis will build stuff
[dscrd-hush] <dukeleto> just too many things cookin'
[dscrd-hush] <DieserMerlin> I'm using VS 2017 and QT 5.13
[dscrd-hush] <dukeleto> iostream is a core part of C++, unless you speak of boost::iostream
[dscrd-hush] <dukeleto> which means you don't have boost installed
[dscrd-hush] <dukeleto> boost is installed by the build system, tho
[dscrd-hush] <dukeleto> @DieserMerlin paste the full output of build.sh to a pastebin/github gist, and we can help
[dscrd-hush] <dukeleto> and maybe let's migrate to #developers
Preview of the screen which will allow you to pair your Android phone to your desktop $HUSH SilentDragon wallet, enabling shielded transactions on your phone!!!
It uses WebSockets to pierce firewalls and NAT
Thanks again to our upstream @zecwallet for this awesome c...


Diets and food habits may vary widely from country to country in terms of ingredients and cooking techniques. Nevertheless, dishes from different regions may share similarities in flavours and tastes. Our analysis will be based on Yummly, a platform mainly used in North America containing recipes from different countries.
Exploring cuisines with Yummly
Yummly provides a variety of recipes along with their ingredients and nutritional intake data. We counted 19 unique cuisines in total ranging from the sweetest to the spiciest. Each cuisine counts a number of recipes ranging from 200 to 200K. We are also provided with the flavors defining each recipe.
Let’s dive into data!
Ingredients
Our dataset contains a variety of cuisines representing different regions around the world, implying that we have a large variety of ingredients (~5K). However, a small subset of the ingredients is commonly present across all cuisines (e.g. salt, olive oil..).
Most used ingredients in the world
As expected, spices like salt/pepper is very common across all cuisines. Our curiosity leads us to explore cuisines by characteirizing them with their distinctive ingredients.
While it’s hardly fair to stereotype an entire national cuisine with a few basic ingredients – most are of course just far too varied and diverse – it is hard to deny some flavours evoke dishes from certain parts of the world. Indeed, it is not unknown for countries to almost be defined by their “iconic dishes”. Here is what data says:
Fun fact: Is American cuisine just about burgers?
According to Yummly, less than 7% of american recipes are about burgers. Also, none of the most distinctive ingredients of the US is used to prepare it. Thus, we deduce that Americans eating mostly burgers remains just a stereotype.
Although distinctive ingredients distinguish well enough a cuisine, we often think of a cuisine as a combination of flavours. As flavours originate from a mix of specific ingredients, we will therefore look for distinctive combination of ingredients for each cuisine:
[ Add note explaining how we compute the network ]
As we can see in many cuisines, ingredients orbit around a central ingredient: Fish sauce
for thai food, Enchilada Sauce
for mexican food or Gram masala
for indian food. Moreover, Asian cuisines like Chinese and Japanese share ingredients many ingredients as Italian and French do.
Similarities between cuisines
It is true that cuisines have different distinctive ingredients, nevertheless, similarities between their recipes is not to neglect. Clustering recipes using their ingredients reveals the similarities between recipes within the same cuisine:
Here again, it is clear that recipes from different cuisines do not always share the same ingredients (e.g. French and Indian), However, as the English and Irish clusters overlap, one may conclude that there is an influence of one on the other. One possible reason of this similarity might be the geographic proximity of both countries, or also to the shared history between the two cultures.
One way to further analyse similarities between cuisines is to explore the confusion between recipes of different cuisine. For this purpose, we use a simple machine learning model (logistic regression) and examine its confusion matrix. This will show us the cuisines that our model could not differentiate.
Similarities between cuisines
As expected, we observe similar results, with :
- Spanish and Portuguese
- Irish and English
- Mexican and Cuban
- Chinese and Japanese
- Thai and Chinese
showing strong similarities as they are often confused by our model. Thus, The latter results support our two hypotheses stating that cuisine from closer countries will share more similarities.
Meat’O’Meter
Across several cuisines, meat is always a major component of recipes. Its consumption varies depending on cultural or religious preferences, as well as economic conditions. Therefore, exploring meat preferences across cuisines would give us an idea about a region’s food habit. (Say how cuisines are related to each other) To do so, we analyse the usage of different types of meat across cuisines:
Select a meat and see who are its big consumers
From plotting various meat types, we see different distributions with the following being noticeable:
- Indian and Mexican cuisines use poultry much more than red meats.
- Asian, more specificcaly Japanese recipes contain more seafood compared to other cuisines.
Interestingly, some cuisines do not have recipes containing some types of meat. The results can be explained by the the fact that cuisines mostly use ressources available in their specific region (example of high fish appearance in Japanese recipes which is an island). Another reason is culture and believes, some countries have specific religions or practices forbidding eating specific types of meats (e.g: Beef in India - Pork and bacon Morocco)
Fun fact: Who is Avocado’s biggest fan?
Over the last decade, worldwide comsumption of avocado has tripled showing a high interest in this fruit. Let’s find out from our data what cuisine use it the most:
Cooking Time
Another interesting variable to study when talking about recipes is their corresponding cooking time which may differ depending on the techniques involved in the cooking process (frying, roasting, steaming etc…) or also the types of ingredients (Raw fish in Japan). We aggregate the cooking time of recipes by cuisine and plot their respective cumulative distributions:
Cooking time in cuisines
(Agrandir x/y label)The distribution of the Asian cuisines (Chinese, Japanese and Thai) are quite similar and different from the European cuisines(English, French and German). We also note that Asian recipes are less time demanding compared to the European ones. This can be explained by the eating habits of Asia in which meals are composed of many small dishes (recipes) each taking less time than cooking a single meal (A revoir).
Recipe similarities
As we have now analysed similarities between cuisines and their ingredients, we build a recipe recommender that suggest similar recipes using the cosine similarity metric . For each result, the similarity score will define the similarity degree of similarity of its ingredients with the selected recipe.
Select a recipe and get suggestions
As recipes don’t match exactly in terms of ingredients, they will have a low similarity degree even though the images look alike.
Summary
From this project, we answered various questions by analysing our dataset. We needed to carefully explore and clean the data during which we faced challenges such as correcting the ingredients names and imputing missing values. This helped us get satisfying answers, for instance finding the most iconic ingredients per cuisine, clustering recipes by region and recommending recipes.
Methodology notes: In this section, we go through the methodology used to build different components of our analysis.
-
t-SNE: To create our recipe cluster, we used a dimensionality reduction technique called t-distributed Stochastic Neighbor Embedding. Our input is the one-hot vectors of 5000 dimensions. The output of the t-SNE algorithm mapped recipes to a two-dimensional space based on the similarity (cosine-similarity) of their ingredients.
-
Cosine similarity: Cosine similarity is a common way of calculating the similarity between two vectors by taking the cosine of the angle between them. In our case, that means taking the one hot encoding vector of a recipe and comparing it to that of another. Higher cosine values imply more similarity, with an upper bound of 1 when the vectors are perfectly similar.
For more details concerning the implementation of our analysis, please refer to the github repository associated with the project. More details are provided within the Jupyter Notebook.


Story
Fatta-a-Mano is an Italian pizza franchise that started out as a family business in Sicily, Italy.The name means home made. We bring you the authentic italian experience to your taste buds. Each pizza is carefully curated, every bite is a ‘squisita’ moment.


Cloudify 4
Edit (pull request)cloudify 4 is an opensource orchestrator backed by GigaSpaces that aims to support deployment on various different locations.
This section gives a focus to cloudify 4 orchestrator plugin for ALIEN, a plugin to manage deployment on various cloud using cloudify 4.x.
Alien 4 Cloud cloudify 4 Support
The alien 4 cloud plugin for cloudify 4 exposes several nodes so that TOSCA templates can be deployed on cloudify 4 to various locations, such as Amazon, Openstack, etc… . This allows full portability of topologies (or blueprints) that you have designed.
See Supported locations for more details.
cloudify 4 currently manages the deployment and un-deployment of blueprints and support the ability to trigger custom workflows that have been shipped within the blueprint at runtime.
Out of the box cloudify 4.0 doesn’t support policies like auto-healing.
Auto-healing
As stated previously cloudify 4.3 doesn’t provide support for auto-healing of services. It provide a basic monitoring feature that we implement in the blueprint we generate from the TOSCA model. This basic monitoring is based on Machine status and not software status meaning that if one of the software in a blueprint crash it won’t be detected by the cloudify 4.
We developed as part of Alien 4 Cloud the ability to generate a cron-based mecanism that check the monitoring data in order to trigger an auto-healing workflow. This implementation is quite naïve for now and is disabled by default on deployments but can be enabled per deployment through an orchestrator property.
Scalability
Scalability behavior is currently not deeply specified in TOSCA. When scaling a node, all nodes that are hosted_on the given node are scaled. Also, nodes that are connected or attached to it, this includes block-storage and floating ips.
Since cloudify 3.4, scaling handling has been improved.
However, refers to the table below to see IaaS limitations.
There is currently some missing details in the TOSCA specification on how relationships can be impacted in scaling scenarios and we are working with bot Cloudify and TOSCA to enhance the specification.
IaaS limitations
Here is a table that shows the limitations about scaling per IaaS:
| OpenStack | Amazon | BYON | Azure (Premium ) |
vSphere (Premium ) |
|
|---|---|---|---|---|---|
| Single Compute | |||||
| Compute + Network + Block Storage | N/A |
Block storage recovery limitation
Block storage recovery is the ability to reuse an already created block storage.
Imagine you have a topology containing a compute with a block storage attached on it. You deploy the topology, the VM is started, the block storage is provisionned and attached to the VM, and then some process write data to the disk. If you don’t use a DeletableBlockStorage
, this means that you want the data written to the disk to be persistent : if the topology is undeployed then deployed again, you want the block storage to be reused.
To manage such feature, A4C will keep a trace of the volume ID and store it in the deployment topology. When the topology is deployed again, this volume id is used to find the volume in the the IaaS rather than provisoning another one.


Auckland train network grinds to a halt
Trains across Auckland were temporarily delayed or cancelled on Monday morning.
Just before 11am, Auckland Transport said a "network control matter" knocked the network offline.
At 11:34am, the agency said "most" services had resumed.
The outage followed cancellations and delays on the city's western line after a truck hit a rail overbridge in New Lynn.
The bridge was later cleared by engineers, but traffic in both directions was backed up until the truck could be removed.


Ian Czekala
Models make predictions of stellar properties as a function of mass and age. This relationship is rather uncertain for young stars.
Determined relative to host star properties, and biases or uncertainties strongly affect habitable zone and $\eta_\bigoplus$ (Kane11)
Sub-mm interferometric observations of stars with circumstellar disks. Use Keplerian rotation to derive a very precise and accurate stellar mass ($< 5$%) (Rosenfeld+12)
There are many ways to determine
$\{ T_{\rm eff}, \log g, [{\rm Fe}/{\rm H}] \}$
Depending on your desired precision and the nature of your target, you can use photometric colors, astroseismology, optical/IR interferometry, or spectroscopy.
By far, spectroscopic inference is the most prevalent and general purpose (e.g., spectral typing).
To leverage the high quality synthetic spectra that now exist, we develop a forward model of the observed stellar spectrum.
| Desire |
|
| Problem |
|
| Solution |
|
Spectroscopic fits are different from "normal" $ \chi^2$ fits
Existing stellar synthesis is amazing, but not perfect.
How can you fit ~1,000 or more lines
with only a few parameters?
or at least very difficult to attain in practicefailureperfection is not an option
the residuals are a spectrum $\textbf{R}$
the pixel "variances" are now a covariance matrix $C$
$$p(D | \theta) = \frac{1}{\sqrt{(2 \pi)^N \det(C)}} \exp\left ( -\frac{1}{2} \textbf{R}^T C^{-1} \textbf{R} \right ) $$
$$ \ln[p(D | \theta)] = -\frac{1}{2} \textbf{R}^T C^{-1} \textbf{R} - \frac{1}{2} \ln \det C - \frac{N}{2} \ln 2 \pi $$
the structure of $C$ is set by a kernel function analogous, say, to the two-point correlation function
Matérn $\nu = 3/2$
$k(\lambda_i, \lambda_j | a, l) = a \left (1 + \frac{\sqrt{3} r_{ij}}{l} \right ) \exp \left ( - \frac{\sqrt{3} r_{ij}}{l} \right )$
This covariance matrix accounts for the "global" correlated structure of the residuals
$\sim 5$ pixels correlated
A strong outlier generally looks like a Gaussian $\textbf{R}_\lambda(a, \mu, \sigma) = \frac{a}{\sqrt{2 \pi} \sigma} \exp \left ( - \frac{r^2(\lambda,\, \mu)}{2 \sigma^2} \right ) $
covariance kernel is
$k(\lambda_i, \lambda_j) = \langle \textbf{R}_i \textbf{R}_j \rangle$
$k(\lambda_i, \lambda_j | a, \mu, \sigma) = \frac{1}{2 \pi} \left ( \frac{a}{\sigma} \right)^2 \exp \left ( - \frac{r^2(\lambda_i,\, \mu) + r^2(\lambda_j,\, \mu)}{2 \sigma^2} \right ) $
(courtesy dfm/triangle.py and dfm/emcee)
M dwarf molecular bands are hard to model
...but, some bands tell you a lot about stellar properties
We want to include these areas but weight them appropriately
Workshop topic: custom design of covariance kernels?
This is not unlike how $UBVRI$ photometric passbands
are regressed out (Bessell & Simon 2012)
Pass this along to the stellar modellers.
If you're impatient/a DIY-er, you can create your own self-calibrated semi-empirical libraries of pseudo-synthetic spectra.
Slides at iancze.github.io/exostat
Email: iczekala@cfa.harvard.edu


open fun removeAt(position: SIDE): DockPanel
Removes child from given position in the dock.
position - position in the dock
position
Return current container


How close is matter, really?
Let's find out >
All matter is comprised of a combination of three particles
|
This is a neutron:
It's a small, colorless particle. At just one femtometer, or one millionth of a nanometer, it's pretty small.
Neutrons have a negligable charge.
Protons >


---
layout: post
title: Fielder navigation system
date: '2015-06-14T18:30:46+02:00'
categories:
- Storyboards
picture_url: https://film-Storyboards.tumblr.com/post/121544056516/fielder-navigation-system
---
TOYOTA ECB sponsorship Storyboards.
Title: Fielder Navigation System
Film Director: Bugsy Riverbank Steel
Production: Good Egg


Some bad inference rules
Among many logical mistakes, we often meet bad inference rules, the use of
which can lead to inconsistencies.
For instance, let us consider implication dyslexia (confusion between
P->Q and Q->P) and dyslexic contraposition :
Definition dyslexic_imp := forall P Q:Prop, (P->Q) -> Q -> P.
Definition dyslexic_contrap :=forall P Q:Prop,(P->Q) -> ~P -> ~Q.
Show that if one or the other of the preceding types was inhabited,
one could prove False, hence any proposition.
Solution
Look at file dyslexia.v


Please spend a moment giving people some clues about the area that this atlas covers.
| Thumbnail | Title | Year | Dropbox Image URL |
|---|---|---|---|
| Entrance to San Francisco Bay California. | 1859 | http://dropbox.com/123 | |
| Entrance to San Francisco Bay California. | 1859 | http://dropbox.com/123 | |
| San Francisco Entrance, California. | 1907 | http://dropbox.com/456 |


lib\BrowserID\utils.php
- author
- Benjamin Krämer<EMAIL_ADDRESS>- copyright
- Alien-Scripts.de Benjamin Krämer
- license
- http://www.opensource.org/licenses/mit-license.html MIT License
- package
- BrowserID
- subpackage
- Utils
- Classes
- Utils
Description
Utility library
This class defines some static functions that are used through the project.
LICENSE: Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\Utils
- author
- Benjamin Krämer<EMAIL_ADDRESS>- package
- BrowserID
- subpackage
- Utils
- version
- 1.0.0
- Methods
- base64url_decode
- base64url_encode
- hex_lpad
- is_url_valid
- path_concat
Description
Utility library
Methods
base64url_decode
base64url_decode(
string $data
)
:
string
Decodes data made URL-safe and encoded with MIME base64
Arguments
- $data
- string
The message to decode
Output
- string
- The decoded message
Details
- visibility
- public
- final
- false
- static
- true
base64url_encode
base64url_encode(
string $data
)
:
string
Encodes data with MIME base64 and make it URL-safe
Arguments
- $data
- string
The message to encode
Output
- string
- The encoded message
Details
- visibility
- public
- final
- false
- static
- true
hex_lpad
hex_lpad(
string $str, int $length
)
:
string
Pads a hexadecimal string with 0s to the left
Arguments
- $str
- string
Hexadecimal number to be padded - $length
- int
The length of the final hexadecimal number
Output
- string
Details
- visibility
- public
- final
- false
- static
- true
is_url_valid
is_url_valid(
string $url
)
:
boolean
Checks if a given URL is valid
Arguments
- $url
- string
URL to be verified
Output
- boolean
- true, if the URL is valid
Details
- visibility
- public
- final
- false
- static
- true
path_concat
path_concat(
)
:
string
Concat parts of a file path descriptor into a complete path
Output
- string
Details
- visibility
- public
- final
- false
- static
- true


Whether you are a busy parent with little time left in your day to cook or someone who loves to entertain but just can't figure out how to entertain and cook at the same time, a personal chef may be exactly what you are looking for. A personal chef will customize a menu to your specific tastes, go shopping for ingredients, prepare and cook those ingredients, interact with you and your guests explaining the dishes and cooking techniques used all while leaving your kitchen as clean as it was when they arrived.
Prepare an entire weeks worth of made from scratch meals so your family can reheat at your own convenience during your busy week.
Prepare an elegant 4 course plated dinner or enjoy passed small bites for fun a night with friends or family.
Provide cooking lessons to those who love cooking and want to learn insider chef secrets.
Achef is really only as good as the ingredients he cooks with. That is why Chef Derek Melander focuses on seasonal farm to table cooking with only the freshest of ingredients, sourcing local and in season ingredients whenever possible. With over 15 years culinary experience in New York City as a chef, he has extensive knowledge in many types of cuisines and techniques. With Chef Derek, you will get a highly personalized meal, or set of meals, tailored to your specific needs and dietary requests. He also loves interacting with guests during events, making sure to answer any questions they may have about the menu or cooking in general.
Chef Derek holds a bachelors degree from the University of Delaware and a Swiss higher diploma from SSTH in Switzerland, both for hospitality, as well as a culinary degree from the Institute of Culinary Education in New York City. He holds a food safety certificate from ServeSafe, a nationally recognized leader in food safety education, and has also had extensive on the job training in food safety. You can rest assure that your meal was not only delicious but also prepared and stored in the safest way possible.
To contact Chef Derek regarding any of these services, please email him at<EMAIL_ADDRESS>Follow me on Instagram at ChefDerekMelander


Alberto's Restaurant
Serving delight forever and always
About Us
Alberto's Restaurant is a five-star luxury restaurant located in Calgary, Alberta. We have amazing options for seating as well as the best food this city and province has to offer.
Make a Reservation
To make a reservation, click below.
Location
We are located in the northwest section of Calgary. You can check for directions here on google maps.
Search for directions here
Reserve a table


MTN marathon grows from 1,500 to 25,000 runners
by James BakamaThis year’s marathon due this Sunday has hit a record 25,000 entries.
The MTN Kampala Marathon in 2006. File Photo
Year Participants
2004-1500 runners
2005-2500 runners
2006-3500 runners
2007-6500 runners
2008-12,000 runners
2009-15,000 runners
2010-17,000runners
2011- 20,000runners
2012- 20,000 runner
2013 to 2019 Above 20,000 runners.
From a humble beginning of just 1,500 runners, the MTN Marathon has grown into one of Africa’s biggest events.
This year’s marathon due this Sunday has hit a record 25,000 entries.
MTN Uganda Chief Executive Wim Vanhelleputte’s revealed yesterday that this year’s kit has sold out.
Vanhelleputte had paid a courtesy call to the Speaker of Parliament Rebecca Kadaga to hand over 200 pieces of the kit for the legislators.
The marathon has in recent years according to organisers consistently attracted over 20,000 participants. This is, however, the first time that MTN officially come out to state that 25,000 kits have been sold.
This is a huge improvement from 1500 entrants in the inaugural edition of the annual event.
Apart from the entries, a number of other things have also evolved. Amongst this is the start off and finishing points.
The run initially had its flag-off and finish at Parliamentary avenue. Thereafter these points shifted to Lugogo.
Proceeds from the marathon have over the years also evolved starting assistance to people in Internally Displaced Camps in Northern Uganda.
Clean water has also been accessed after projects in Eastern and Western Uganda started with money from the marathon.
Proceeds from this year’s marathon will go towards the improvement of maternal health. The marathon is today Uganda’s biggest social event.
Vision Group, Stanbic, Huawei, Rwenzori, Uganda Athletics Federation, and Uganda Police are some of the marathon key partners.
World and Olympic champion Stephen Kiprotich and double Commonwealth and African champion Moses Kipsiro are some of the big names that have featured in the annual race.
The race also attracts a big international entry. Prominent amongst these are the Kenyans who have dominated the podium.
There will be five, 10, 21, 42 kilometers and a wheelchair race all flagged off at the National Ceremonial Ground, Kololo.


Landing Page
Special Projects
This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.
TAYLOR JOHNSTON
05.25.17
Entering a new school and a new environment can sometimes have an effect on a student’s immune system.
Dr. Jane Balbo, assistant professor of family medicine and family practice physician at Campus Care, provided some tips for incoming Ohio University freshmen on how to stay healthy in college.
“If someone is not getting enough sleep or good quality sleep, their immune system will suffer,” Balbo said.
Haylee Doran, a rising second-year graduate student in the child and family studies program, said getting adequate sleep helps her stay healthy.
“Also take a lot of Vitamin C, that has helped me so far (to stay healthy),” she said.
Balbo also said students should think about the vaccine-preventable diseases that are prevalent when students live in a more crowded situation, particularly meningitis.
“It’s not a common outbreak, but it can be deadly,” Balbo said. “Getting their meningitis vaccines before they come to college is really important.”
In January, an OU student was hospitalized with bacterial meningitis. The student was discharged later that month.
In addition to vaccines, a healthy diet is important to consider when trying to not get sick, Balbo said.
“The cool thing about Ohio University is the availability of fresh foods in the dining halls has improved drastically,” she said. “It used to just be french fries, pizza, hamburgers and ice cream.”
Trying to incorporate fresh fruit and vegetables into people’s daily diets is going to provide their immune systems with the vitamins and minerals that can help them fight diseases or stay healthy, Balbo said.
Bethany Lilja | FILE
Pictured is the front lobby of the Hudson Campus Care Center.
“Obviously (fried foods) are delicious. You’re living away from home for the first time, and you have access to all this stuff unlimited,” she said. “It is sometimes hard to control yourself.”
Physical activity is another way to help people stay healthy.
Balbo said Ping Recreation Center is great if someone has the time, but she often hears that students say they don’t have the time to go.
“(Ping) is really involved and no wonder someone feels like they don’t have the time because it can take up to an hour and a half for the process of getting there and getting home and getting a great workout,” she said.
She said she tells people about Hockhocking Adena Bikeway, which is located near the residence halls.
“Just thinking about movement, not just going to the gym,” Balbo said. “Just going for a half an hour walk once a day is a wonderful thing for one’s health.”
While students are outside enjoying the Athens scenery, they may experience allergies too.
A lot of students who come from places such as Cleveland, Columbus or other states where they do not have a lot of allergens, come to Campus Care thinking they are sick when they are not, Balbo said.
“Athens is an allergy haven,” she said. “It’s important for them to come get checked out but to also recognize we have a lot more allergens here than many other college campuses.”
She said students should work to keep their allergies at bay before they get to campus, and if they get here and recognize their allergies are out of control, they may need to come in and see a medical provider to tune up their management plan.
“Uncontrolled allergies can lead to sinus infections, ear infections, pneumonia and asthma exacerbations,” Balbo said.
Monica Davis, a rising sophomore studying communication, said she keeps hand sanitizer on her at all times and makes sure she washes her hands regularly.
“Stay away from those who are coughing and make sure you are coughing in your arm, too,” she said.
Back to The Orientation GuideLanding Page
This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.


Color by
Main chain as
Nucleic acid bases as
Side chains as lines
Don't smooth beta-sheets in ribbons
Non-bonded atoms (solvent/ions) as
Small molecules(HETATMs) as
Multiple bond option is for SDF/MOL file ONLY.
Unit cell
Biological assembly (the last one defined)
Crystal packing
Show HETATMs in symmetry mates (slower)


| Property starting with A |
| Name |
Source name |
Type |
Mandatory |
Length |
Scale |
Default value |
Lower range |
Upper range |
Allowed characters |
Auto increment |
Numeric string |
Right aligned |
Unsigned |
Upper case |
Zero equals null |
Zero fill |
Selection values |
|
Assortment
|
Assortment
|
Number
|
|
10
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
AssortmentDescription
|
AssortmentDescription
|
String
|
|
160
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
| Property starting with C |
| Name |
Source name |
Type |
Mandatory |
Length |
Scale |
Default value |
Lower range |
Upper range |
Allowed characters |
Auto increment |
Numeric string |
Right aligned |
Unsigned |
Upper case |
Zero equals null |
Zero fill |
Selection values |
|
ConditionCalculationMethod
|
ConditionCalcMethod
|
String
|
|
1
|
0
|
N
|
|
|
|
|
|
|
|
|
|
|
|
|
CreatedDate
|
SysCreated
|
Date
|
|
10
|
0
|
|
1753-01-01
|
9999-12-31
|
|
|
|
|
|
|
|
|
|
|
Creator
|
SysCreator
|
Number
|
|
10
|
0
|
HUMRESID
|
|
|
|
|
|
|
|
|
|
|
|
| Property starting with D |
| Name |
Source name |
Type |
Mandatory |
Length |
Scale |
Default value |
Lower range |
Upper range |
Allowed characters |
Auto increment |
Numeric string |
Right aligned |
Unsigned |
Upper case |
Zero equals null |
Zero fill |
Selection values |
|
DayOfTheMonth
|
DayOfTheMonth
|
Number
|
|
5
|
0
|
0
|
0
|
99
|
|
|
|
|
|
|
|
|
|
|
Description
|
Description
|
String
|
|
30
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Description1
|
Description1
|
String
|
|
30
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Description2
|
Description2
|
String
|
|
30
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Description3
|
Description3
|
String
|
|
30
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Description4
|
Description4
|
String
|
|
30
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
DiscountSurchargeAmount
|
CS_SD_Amount
|
String
|
|
1
|
0
|
B
|
|
|
|
|
|
|
|
|
|
|
| B,5741,Settlement dsct | | K,10494,Credit surcharge |
|
|
DueDateType
|
DueDateType
|
String
|
|
1
|
0
|
|
|
|
|
|
|
|
|
|
|
|
| 1,14320,Date + payment term | | 2,14519,Date -> end of the month -> day of the month | | 3,14321,Date -> end of the next month -> day of the month | | 4,14323,Date + payment term -> end of the month -> day of the month | | 5,14322,Date + payment term -> end of the next month -> day of the month | | 6,14324,Date + payment term -> x.end of the month -> day of the month | | 7,30935,Date + payment term (in months) | | 8,30936,Date -> end of month + x months | | 9,23921,Terms | | A,13878,Invoice date + payment term | | B,13879,Order date + payment term | | C,13880,Fulfillment date + payment term | | D,13881,Order confirmation date + payment term |
|
|
DueDateType1
|
DueDateType1
|
String
|
|
1
|
0
|
|
|
|
|
|
|
|
|
|
|
|
| 1,14320,Date + payment term | | 2,14519,Date -> end of the month -> day of the month | | 3,14321,Date -> end of the next month -> day of the month | | 7,30935,Date + payment term (in months) | | 8,30936,Date -> end of month + x months |
|
|
DueDateType2
|
DueDateType2
|
String
|
|
1
|
0
|
|
|
|
|
|
|
|
|
|
|
|
| 1,14320,Date + payment term | | 2,14519,Date -> end of the month -> day of the month | | 3,14321,Date -> end of the next month -> day of the month | | 7,30935,Date + payment term (in months) | | 8,30936,Date -> end of month + x months |
|
|
DueDateType3
|
DueDateType3
|
String
|
|
1
|
0
|
|
|
|
|
|
|
|
|
|
|
|
| 1,14320,Date + payment term | | 2,14519,Date -> end of the month -> day of the month | | 3,14321,Date -> end of the next month -> day of the month | | 7,30935,Date + payment term (in months) | | 8,30936,Date -> end of month + x months |
|
|
DueDateType4
|
DueDateType4
|
String
|
|
1
|
0
|
|
|
|
|
|
|
|
|
|
|
|
| 1,14320,Date + payment term | | 2,14519,Date -> end of the month -> day of the month | | 3,14321,Date -> end of the next month -> day of the month | | 7,30935,Date + payment term (in months) | | 8,30936,Date -> end of month + x months |
|
|
DueDateType5
|
DueDateType5
|
String
|
|
1
|
0
|
|
|
|
|
|
|
|
|
|
|
|
| 1,14320,Date + payment term | | 2,14519,Date -> end of the month -> day of the month | | 3,14321,Date -> end of the next month -> day of the month | | 7,30935,Date + payment term (in months) | | 8,30936,Date -> end of month + x months |
|
|
DueDateTypeText
|
DueDateTypeText
|
String
|
|
40
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
| Property starting with G |
| Name |
Source name |
Type |
Mandatory |
Length |
Scale |
Default value |
Lower range |
Upper range |
Allowed characters |
Auto increment |
Numeric string |
Right aligned |
Unsigned |
Upper case |
Zero equals null |
Zero fill |
Selection values |
|
GrossNetInvoicing
|
Gross_Net_Invoicing
|
String
|
|
1
|
0
|
|
|
|
|
|
|
|
|
|
|
|
| B,9817,Calculate VAT on gross amounts | | N,14012,Calculate VAT on net amounts |
|
| Property starting with I |
| Name |
Source name |
Type |
Mandatory |
Length |
Scale |
Default value |
Lower range |
Upper range |
Allowed characters |
Auto increment |
Numeric string |
Right aligned |
Unsigned |
Upper case |
Zero equals null |
Zero fill |
Selection values |
|
ID
|
ID
|
Number
|
|
10
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
InterestRate
|
InterestRate
|
Double
|
|
10
|
2
|
0
|
0
|
1
|
|
|
|
|
|
|
|
|
|
| Property starting with M |
| Name |
Source name |
Type |
Mandatory |
Length |
Scale |
Default value |
Lower range |
Upper range |
Allowed characters |
Auto increment |
Numeric string |
Right aligned |
Unsigned |
Upper case |
Zero equals null |
Zero fill |
Selection values |
|
ModifiedDate
|
SysModified
|
Date
|
|
10
|
0
|
|
1753-01-01
|
9999-12-31
|
|
|
|
|
|
|
|
|
|
|
Modifier
|
SysModifier
|
Number
|
|
10
|
0
|
HUMRESID
|
|
|
|
|
|
|
|
|
|
|
|
| Property starting with N |
| Name |
Source name |
Type |
Mandatory |
Length |
Scale |
Default value |
Lower range |
Upper range |
Allowed characters |
Auto increment |
Numeric string |
Right aligned |
Unsigned |
Upper case |
Zero equals null |
Zero fill |
Selection values |
|
NumberOfDays1
|
NumberOfDays1
|
Number
|
|
3
|
0
|
0
|
0
|
999
|
|
|
|
|
|
|
|
|
|
|
NumberOfDays2
|
NumberOfDays2
|
Number
|
|
3
|
0
|
0
|
0
|
999
|
|
|
|
|
|
|
|
|
|
|
NumberOfDays3
|
NumberOfDays3
|
Number
|
|
3
|
0
|
|
0
|
999
|
|
|
|
|
|
|
|
|
|
|
NumberOfMonths
|
NumberOfMonths
|
Number
|
|
5
|
0
|
0
|
0
|
99
|
|
|
|
|
|
|
|
|
|
| Property starting with P |
| Name |
Source name |
Type |
Mandatory |
Length |
Scale |
Default value |
Lower range |
Upper range |
Allowed characters |
Auto increment |
Numeric string |
Right aligned |
Unsigned |
Upper case |
Zero equals null |
Zero fill |
Selection values |
|
PayInInstallments
|
PayInInstallments
|
Number
|
|
1
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
PaymentConditionCode
|
PaymentConditionCode
|
String
|
|
2
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Percentage
|
Percentage
|
Number
|
|
5
|
2
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Percentage2
|
Percentage2
|
Number
|
|
5
|
2
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Percentage3
|
Percentage3
|
Number
|
|
5
|
2
|
|
|
|
|
|
|
|
|
|
|
|
|
| Property starting with T |
| Name |
Source name |
Type |
Mandatory |
Length |
Scale |
Default value |
Lower range |
Upper range |
Allowed characters |
Auto increment |
Numeric string |
Right aligned |
Unsigned |
Upper case |
Zero equals null |
Zero fill |
Selection values |
|
Term
|
Termijn
|
Number
|
|
3
|
0
|
0
|
0
|
999
|
|
|
|
|
|
|
|
|
|
|
Term1
|
Term1
|
Number
|
|
2
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Term2
|
Term2
|
Number
|
|
2
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Term3
|
Term3
|
Number
|
|
2
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Term4
|
Term4
|
Number
|
|
2
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Term5
|
Term5
|
Number
|
|
2
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|


|
|||||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
See:
Description
| Interface Summary | |
| SingletonStrategy |
SingletonStrategy is an interface used to provide common
factory access for the same object based on an implementation strategy for
singleton. |
| Class Summary | |
| AttributeHelper |
AttributeHelper a number of helper methods for working with
attribute values.
|
| IndexedDocumentFactory |
IndexedDocumentFactory is a factory of XML objects which
create indexed Element implementations to allow quicker lookup via name of
Element and Attributes though at the expense of more memory used to create
the name indexes.
|
| IndexedElement |
IndexedElement is an implementation of Element which
maintains an index of the attributes and elements it contains to optimise
lookups via name.
|
| NodeComparator |
NodeComparator is a Comparator of Node instances which
is capable of comparing Nodes for equality based on their values.
|
| NonLazyDocumentFactory |
NonLazyDocumentFactory is a factory of XML objects which avoid
using the lazy creation pattern. |
| NonLazyElement |
NonLazyElement is the default DOM4J default implementation of
an XML element.
|
| PerThreadSingleton |
PerThreadSingleton is an implementation of the
SingletonStrategy used to provide common factory access to a single object
instance based on an implementation strategy for one object instance per
thread. |
| ProxyDocumentFactory |
ProxyDocumentFactory implements a proxy to a DocumentFactory
which is useful for implementation inheritence, allowing the pipelining of
various factory implementations. |
| SimpleSingleton |
SimpleSingleton is an implementation of the SingletonStrategy
interface used to provide common factory access for the same object instance.
|
| UserDataAttribute |
UserDataAttribute support the adornment of a user data object
on an Element or Attribute instance such that the methods
UserDataAttribute.setData(Object) will get and set the values of a user data object.
|
| UserDataDocumentFactory |
UserDataDocumentFactory is a factory of XML objects which
support the adornment of a user data object on an Element or Attribute
instance such that the methods getData() and
setData() will get and set the values of a user data object.
|
| UserDataElement |
UserDataElement support the adornment of a user data object on
an Element or Attribute instance such that the methods
UserDataElement.setData(Object) will get and set the values of a user data object.
|
| XMLErrorHandler | XMLErrorHandler is a SAX ErrorHandler which turns the
SAX parsing errors into XML so that the output can be formatted using XSLT or
the errors can be included in a SOAP message. |
A collection of utility classes for the dom4j API.
|
|||||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |


Sweet Free Stuff
///////////////////////////////////////////
Free Hallmark Greeting Card at Giant Eagle
Posted: 07 Apr 2016 07:43 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Load the coupon to your Giant Eagle Advantage Card. Coupon valid through
4/13. Free Hallmark Greeting Card at Giant Eagle
The post Free Hallmark Greeting Card at Giant Eagle appeared first on
SweetFreeStuff.com.
///////////////////////////////////////////
Free Subscription to Digital Photo Pro
Posted: 07 Apr 2016 06:29 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Free Subscription to Digital Photo Pro
The post Free Subscription to Digital Photo Pro appeared first on
SweetFreeStuff.com.
///////////////////////////////////////////
Free Premier Protein Bar or Shake
Posted: 06 Apr 2016 04:30 PM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Free Premier Protein Bar or Shake
The post Free Premier Protein Bar or Shake appeared first on
SweetFreeStuff.com.
CommentsIt was gross at least the bar was by RubyLol@tianini. I agree, but
I think all protein shakes taste a … by Monkey SeeI received mine! Tasted
disgusting… but 30 grams of protein! by tianiniYummy by Amber DesiSounds
delicious by Amber DesiPlus 2 more…


UTC +2, summer time UTC +3.
Voltage: 110/220 V
Frequency: 50 Hz
Shops and businesses are typically open Monday through Saturday, 9:00-18:00.
Hours vary, and in summer many establishments close early.
Restaurant hours vary, and many restaurants, especially in Beirut, are open late.
Banking hours are Monday through Saturday, 8:30-12:30.
Working hours for government offices and post offices are typically 8:00-14:00.
Lebanese pound of 100 piastres.
International dialing code for Lebanon is +961.
Most Lebanese use mobile phones, and coverage extends throughout the country.
There are Internet cafés available throughout Lebanon, and many larger hotels now offer high-speed and wireless Internet access for free or for a small fee.
Police - dial 112
IMRAY AND ADLARD COLES PILOT BOOKS are available at a discounted price for Noonsite.com users via World Cruising Club
Mediterranean Almanac 2015/16
By Lucinda Heikell with Rod Heikell
Published by Imray (December 2014)
2016 supplement available from Imray
ISBN –<PHONE_NUMBER>986
Price - £29.50
See this Noonsite review.
Noonsite welcomes information and updates especially regarding clearance, customs and immigration procedures from cruisers visiting this country.
Please E-mail noonsite with any new information, updates or corrections. Even just a short email confirming that the current data is accurate would be most helpful.


Tactical Fuzzing - FI & Uploads Local file inclusion
Core Idea: Does it (or can it) interact with the server file system?
[Liffy] (https://github.com/rotlogix/liffy) is new and cool here but you can also use
[Seclists] (https://github.com/danielmiessler/SecLists/blob/master/Fuzzing/JHADDIX_LFI.txt):
Malicious File Upload
This is an important and common attack vector in this type of testing. A file upload functions need a lot of protections to be adequately secure.
Attacks:
- Upload unexpected file format to achieve code exec (swf, html, php, php3, aspx, ++) Web shells or...
- Execute XSS via same types of files. Images as well!
- Attack the parser to DoS the site or XSS via storing payloads in metadata or file header
- Bypass security zones and store malware on target site via file polyglots
File upload attacks are a whole presentation. Try this one to get a feel for bypass techniques:
As referenced file polyglots can be used to store malware on servers! [See @dan_crowley ‘s talk] (http://goo.gl/pquXC2) [and @angealbertini research:] (corkami.com)
Remote file includes and redirects
Look for any param with another web address in it. Same params from LFI can present here too.
Common blacklist bypasses:
- escape "/" with "/" or “//” with “//”
- try single "/" instead of "//"
- remove http i.e. "continue=//google.com"
- “//\” , “|/” , “/%09/”
- encode, slashes
- ”./” CHANGE TO “..//”
- ”../” CHANGE TO “....//”
- ”/” CHANGE TO “//”
Redirections Common Parameters or Injection points:
- dest=
- continue=
- redirect=
- url= (or anything with “url” in it)
- uri= (same as above)
- window=
- next=
RFI Common Parameters or Injection points:
- File=
- document=
- Folder=
- root=
- Path=
- pg=
- style=
- pdf=
- template=
- php_path=
- doc=


[#include "/admin/common/commons.html"]
[#include "/admin/common/nav.html"]
回到收件箱
Folders
Inbox
12
Sent
Drafts
Junk
65
Trash
Labels
Important
Promotions
Social
Compose New Message
Attachment
Max. 32MB
[#include "/admin/common/footer.html"] [#include "/admin/common/aside.html"]


On the twentieth day of the Hooting Yard Advent Calendar, we show the fair city of Plovdiv, home of Ugo and his blind Ma and his pal Ulf, under an eerie post-apocalyptic sun, like the sun in a John Martin painting. Put that in your pipe and smoke it!, as they used to say, curiously, in British films of the interwar years.
from the Avion Hotel website.


Edit User
Edit Profile of SPV=$rowRegistrationData['registration_id']?>
Email:
Password:
Set Expiry Date
:
From
to
Basic Details
First Name*
Last Name*
Gender*
/>Male
/>Female
Mother Tongue :
Select
Marathi
Arunachali
Assamese
Awadhi
Bengali
Bhojpuri
Brij
Bihari
Chatisgarhi
Dogri
English
French
Garhwali
Garo
Gujarati
Haryanvi
Himachali/Pahari
Hindi
Kanauji
Kannada
Kashmiri
Khandesi
Khasi
Konkani
Koshali
Kumoani
Kutchi
Spoken Languages :
Select
Marathi
Arunachali
Assamese
Awadhi
Bengali
Bhojpuri
Brij
Bihari
Chatisgarhi
Dogri
English
French
Garhwali
Garo
Gujarati
Haryanvi
Himachali/Pahari
Hindi
Kanauji
Kannada
Kashmiri
Khandesi
Khasi
Konkani
Koshali
Kumoani
Kutchi
Religion :
Hindu
Caste :
- Select -
Chambhar
Subcaste :
Profile Created By :
Select
Self
Parents
Sibling
Relative
Friend
Son
Daughter
Sister
Brother
Marital Status :
Unmarried
Divorced
Widowed
Seperated
Children :
- Select -
0
1
2
3
4
5
6
Children Living Status :
- Select -
Living with me
Not Living with me
Never Live with me
Photo
(W:119 X H:119)(Less than 30 KB)
Sub Group
/> Deshastha
/> Kokanastha
Age
Select
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
PAN/ADHAR Card No
About myself :
=$rowRegistrationData['registration_about_myself']?>
Physical Status & Lifestyle
Height* :
Select
4ft
4ft 1in
4ft 2in
4ft 3in
4ft 4in
4ft 5in
4ft 6in
4ft 7in
4ft 8in
4ft 9in
4ft 10in
4ft 11in
5ft
5ft 1in
5ft 2in
5ft 3in
5ft 4in
5ft 5in
5ft 6in
5ft 7in
5ft 8in
5ft 9in
5ft 10in
5ft 11in
6ft
6ft 1in
6ft 2in
6ft 3in
6ft 4in
6ft 5in
6ft 6in
6ft 7in
6ft 8in
6ft 9in
6ft 10in
6ft 11in
7ft 1in
7ft 2in
7ft 3in
7ft 4in
7ft 5in
7ft 6in
7ft 7in
7ft 8in
7ft 9in
7ft 10in
7ft 11in
Weight: Kg
Bloodgroup :
Select
A+
A-
B+
B-
AB+
AB-
O+
O-
Complexion :
Select
Very Fair
Fair
Wheatish
Dark
Bodytype :
Select
Slim
Athletic
Average
Heavy
Spectacles :
No
Yes
Contact Lenses
Physical Status :
/> Normal
/> Disabled
Diet :
- Select -
Vegetarian
Non-Vegetarian
Eggetarian
Astro Details
Birth time* :
Birth Date*
Birthplace :
Country of birth :
Select
India
United States of America
United Arab Emirates
Malaysia
United Kingdom
Australia
Saudi Arabia
Canada
Singapore
Kuwait
Afghanistan
Albania
Algeria
American Samoa
Andorra
Angola
Anguilla
Antarctica
Antigua and Barbuda
Argentina
Armenia
Aruba
Australia
Austria
Azerbaijan
Bahamas
Bahrain
Bangladesh
Barbados
Belarus
Belgium
Belize
Benin
Bermuda
Bhutan
Brazil
Svalbard and Jan Mayen Islands
Swaziland
Sweden
Switzerland
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States of America
Uruguay
Uzbekistan
Vanuatu
Vatican City
Venezuela
Vietnam
Virgin Islands
Wallis and Futuna Islands
Western Sahara
Yemen
Yugoslavia (Former)
Zaire
Zambia
Zimbabwe
Ashmore and Cartier Islands
Solomon Islands
Bassas da India
Congo (Brazzaville)
Coral Sea Islands
Europa Island
Guernsey
Glorioso Islands
Gaza Strip
Isle of Man
Clipperton Island
Cote DIvoire
Jersey
Jan Mayen
Juan de Nova Island
Macedonia
Bodies of water
Paracel Islands
Spratly Islands
Saint Pierre and Miquelon
Saint Helena
Svalbard
Tromelin Island
Undersea features
Saint Vincent & the Grenadines
West Bank
Wallis and Futuna
Serbia and Montenegro
Rashi :
Select
Dhanu (Sagittarius)
Kanya (Virgo)
Kark (Cancer)
Kumbh (Aquarius)
Makar (Capricorn)
Meen (Pisces)
Mesh (Aries)
Mithun (Gemini)
Simha (Leo)
Tula (Libra)
Vrishabh (Taurus)
Vrishchik (Scorpio)
Nakshatra :
Select
Anuradha
Ardra
Ashlesha
Ashwini
Bharani
Chitra
Dhanista
Hastha
Jyesta
Krithika
Magha
Moolam
Mrigasira
Poorvabadrapada
Poorvapalguni
Poorvashada
Punarvasu
Pushya
Revathi
Rohini
Shatataraka
Shravan
Swati
Uttarabadrapada
Uttarapalguni
Uttarashada
Vishaka
Charan :
Gana :
Select
Deva
Manushya
Rakshyasa
Nadi :
Select
Adya
Madhya
Antya
Are you Manglik ?
/>Yes
/>No
Horoscope match required?
/>Yes
/>No
Contact Details
Contact Address* :
City* :
Select
Mumbai
Pune
Thane
Navi Mumbai
Kolhapur
Satara
Nashik
Aurangabad
Bangalore
Hyderabad
Delhi
Chennai
Sindhudurg
Ahmednagar
Baramati
Buldhana
Bhusawal
Latur
Usmanabad
Dahanu
Ratnagiri
Solapur
Nagpur
Jalgaon
Panvel
Goa
Alibaugh
Dhule
Nandurbar
Satana
Shirpur
Pachora
Jalna
Akola
Vadodara
Bangalore
Surat
Kanpur
Jaipur
Lucknow
Patna
Indore
Bhopal
Ludhiana
Agra
Sangali
State :
Select
Andaman & Nicobar
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chandigarh
Chhattisgarh
Dadra & Nagar Haveli
Daman & Diu
Delhi
Goa
Gujarat
Haryana
Himachal Pradesh
Jammu & Kashmir
Jharkand
Karnataka
Kerala
Lakshadeep
Madhya Pradesh
Maharashtra
Manipur
Meghalaya
Mizoram
Nagaland
Orissa
Pondicherry
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttar Pradesh
Uttaranchal
West Bengal
Country :
Select
India
United States of America
United Arab Emirates
Malaysia
United Kingdom
Australia
Saudi Arabia
Canada
Singapore
Kuwait
Afghanistan
Albania
Algeria
American Samoa
Andorra
Angola
Anguilla
Antarctica
Antigua and Barbuda
Argentina
Armenia
Aruba
Australia
Austria
Azerbaijan
Bahamas
Bahrain
Bangladesh
Barbados
Belarus
Belgium
Belize
Benin
Bermuda
Bhutan
Brazil
Svalbard and Jan Mayen Islands
Swaziland
Sweden
Switzerland
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States of America
Uruguay
Uzbekistan
Vanuatu
Vatican City
Venezuela
Vietnam
Virgin Islands
Wallis and Futuna Islands
Western Sahara
Yemen
Yugoslavia (Former)
Zaire
Zambia
Zimbabwe
Ashmore and Cartier Islands
Solomon Islands
Bassas da India
Congo (Brazzaville)
Coral Sea Islands
Europa Island
Guernsey
Glorioso Islands
Gaza Strip
Isle of Man
Clipperton Island
Cote DIvoire
Jersey
Jan Mayen
Juan de Nova Island
Macedonia
Bodies of water
Paracel Islands
Spratly Islands
Saint Pierre and Miquelon
Saint Helena
Svalbard
Tromelin Island
Undersea features
Saint Vincent & the Grenadines
West Bank
Wallis and Futuna
Serbia and Montenegro
Citizenship :
Select
India
United States of America
United Arab Emirates
Malaysia
United Kingdom
Australia
Saudi Arabia
Canada
Singapore
Kuwait
Afghanistan
Albania
Algeria
American Samoa
Andorra
Angola
Anguilla
Antarctica
Antigua and Barbuda
Argentina
Armenia
Aruba
Australia
Austria
Azerbaijan
Bahamas
Bahrain
Bangladesh
Barbados
Belarus
Belgium
Belize
Benin
Bermuda
Bhutan
Brazil
Svalbard and Jan Mayen Islands
Swaziland
Sweden
Switzerland
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States of America
Uruguay
Uzbekistan
Vanuatu
Vatican City
Venezuela
Vietnam
Virgin Islands
Wallis and Futuna Islands
Western Sahara
Yemen
Yugoslavia (Former)
Zaire
Zambia
Zimbabwe
Ashmore and Cartier Islands
Solomon Islands
Bassas da India
Congo (Brazzaville)
Coral Sea Islands
Europa Island
Guernsey
Glorioso Islands
Gaza Strip
Isle of Man
Clipperton Island
Cote DIvoire
Jersey
Jan Mayen
Juan de Nova Island
Macedonia
Bodies of water
Paracel Islands
Spratly Islands
Saint Pierre and Miquelon
Saint Helena
Svalbard
Tromelin Island
Undersea features
Saint Vincent & the Grenadines
West Bank
Wallis and Futuna
Serbia and Montenegro
STD
*
:
Telephone* :
Mobile - 1* :
Mobile - 2 :
Career details
Highest Qualification* :
Select
BE
B Tech
BCA
Aeronautical Engineering
B Arch
B Plan
MCA
PGDCA
ME
M Tech
MS(Engg.)
M Arch
M.C.M
B Phil
BCom
BMS
BSc
BA
BHM
B Ed
Aviation Degree
BFA
BMM
M Phil
M Com
M Sc
MA
MHM
M Ed
MMS
MBA
PGDM
BBA
MD
MS(Medical)
MBBS
MDS
MVSc
MPT
BDS
BVSc
BPT
BHMS
BAMS
M Pharm
B Pharm
ML
LLM
BGL
BL
LLB
CA
ICWA
CS
IAS/IPS/IRS/IES/IFS
Ph D
Trade School
Diploma
D Ed
Higher Secondary School/High School
Less than 10
Less than 12
Undergraduation
MLT
Education Level* :
Select
Bachelors
Masters
Doctorate
Diploma
Undergraduate
Associate Degree
Hounors Degree
Trade School
High School
Less than 10
Less than 12
Education Field :
Select
Advertising
Marketing
Administrative services
Architecture
Armed Forces
Arts
Commerce
Computers/ IT
Education
Engineering / Technology
Fashion
Finance
Fine Arts
Home Science
Journalism/Media
Law
Management
Medicine
Nursing/ Health Sciences
Pharmacy
Office administration
Science
Shipping
Travel and Tourism
Education in Detail :
Occupation :
Select
Service
Manager
Supervisor
Officer
Administrative Professional
Executive
Clerk
Agriculture & Farming Professional
Pilot
Air Hostess
Airline Professional
Architect
Interior Designer
Chartered Accountant
Company Secretary
Accounts/Finance Professional
Banking Service Professional
Auditor
Fashion Designer
Beautician
Civil Services (IAS/IPS/IRS/IES/IFS)
Army
Navy
Airforce
Professor / Lecturer
Teaching / Academician
Education Professional
Hotel / Hospitality Professional
Software Professional
Hardware Professional
Database Professional
Engineer - Non IT
Lawyer & Legal Professional
Law Enforcement Officer
Doctor
Health Care Professional
Paramedical Professional
Nurse
Marketing Professional
Sales Professional
Journalist
Media Professional
Entertainment Professional
Event Management Professional
Advertising / PR Professional
Mariner / Merchant Navy
Scientist / Researcher
CXO / President, Director, Chairman
Consultant
Customer Care Professional
Social Worker
Sportsman
Technician
Arts & Craftsman
Not Working
Employed in :
Select
Service
Government
Private
Business
Defence
Self Employed
Not Required
Occupation in Detail :
Mothnly Income :
Prefer working after marriage ?
Yes
No
Do not know
Family details
Father's Name :
Father's Status :
Select
Retired
Service
Government
Defence
Private
Business
Self Employed
Not Working
Expired
Mother name :
Mother's Status :
Select
Retired
Government
Service
Defence
Private
Business
Self Employed
Not Working
Home Maker
Expired
Brother's Married :
Brother's Unmarried :
Sister Married :
Sister Unmarried :
Living Status :
Select
Living Alone
Living with Friends
Living with Parents
Living with Relatives
Family Type
Joint Family
Nuclear Family
Family Status :
Select
Lower Middle Class
Middle Class
Upper Middle Class
Rich
Affluent
Native Place :
(village)
(taluka)
(district)
(state)
About My Family :
=$rowRegistrationData['registration_myfamily']?>
Partner Preference
Age : Between
Select
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
-
Select
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
Height : Between
Select
4ft
4ft 1in
4ft 2in
4ft 3in
4ft 4in
4ft 5in
4ft 6in
4ft 7in
4ft 8in
4ft 9in
4ft 10in
4ft 11in
5ft
5ft 1in
5ft 2in
5ft 3in
5ft 4in
5ft 5in
5ft 6in
5ft 7in
5ft 8in
5ft 9in
5ft 10in
5ft 11in
6ft
6ft 1in
6ft 2in
6ft 3in
6ft 4in
6ft 5in
6ft 6in
6ft 7in
6ft 8in
6ft 9in
6ft 10in
6ft 11in
7ft 1in
7ft 2in
7ft 3in
7ft 4in
7ft 5in
7ft 6in
7ft 7in
7ft 8in
7ft 9in
7ft 10in
7ft 11in
-
Select
4ft
4ft 1in
4ft 2in
4ft 3in
4ft 4in
4ft 5in
4ft 6in
4ft 7in
4ft 8in
4ft 9in
4ft 10in
4ft 11in
5ft
5ft 1in
5ft 2in
5ft 3in
5ft 4in
5ft 5in
5ft 6in
5ft 7in
5ft 8in
5ft 9in
5ft 10in
5ft 11in
6ft
6ft 1in
6ft 2in
6ft 3in
6ft 4in
6ft 5in
6ft 6in
6ft 7in
6ft 8in
6ft 9in
6ft 10in
6ft 11in
7ft 1in
7ft 2in
7ft 3in
7ft 4in
7ft 5in
7ft 6in
7ft 7in
7ft 8in
7ft 9in
7ft 10in
7ft 11in
Marital Status :
Unmarried
Divorced
Widowed
Seperated
Religion :
Hindu
Caste :
Chambhar
Are you ready for intercaste marriage :
/>Yes
/>No
Manglik Required ?* :
/>Yes
/>No
Diet :
Select
Vegetarian
Non-Vegetarian
Eggetarian
Smoke :
Yes
No
Drink :
Yes
No
Complexion :
Select
Very Fair
Fair
Wheatish
Dark
Bodytype :
Select
Slim
Athletic
Average
Heavy
Education Level :
Select
Any
Bachelors
Masters
Doctorate
Diploma
Undergraduate
Associate Degree
Hounors Degree
Trade School
High School
Less than 10
Less than 12
Employed in :
Select
Any
Service
Government
Private
Business
Defence
Self Employed
Not Required
Occupation :
Monthly Income :
Sub Group :
/>Deshastha
/>Kokanastha
/>Any
City
*
:
-Select-
Any
Select
Mumbai
Pune
Thane
Navi Mumbai
Kolhapur
Satara
Nashik
Aurangabad
Bangalore
Hyderabad
Delhi
Chennai
Sindhudurg
Ahmednagar
Baramati
Buldhana
Bhusawal
Latur
Usmanabad
Dahanu
Ratnagiri
Solapur
Nagpur
Jalgaon
Panvel
Goa
Alibaugh
Dhule
Nandurbar
Satana
Shirpur
Pachora
Jalna
Akola
Vadodara
Bangalore
Surat
Kanpur
Jaipur
Lucknow
Patna
Indore
Bhopal
Ludhiana
Agra
Sangali
State :
-Select-
Any
Select
Andaman & Nicobar
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chandigarh
Chhattisgarh
Dadra & Nagar Haveli
Daman & Diu
Delhi
Goa
Gujarat
Haryana
Himachal Pradesh
Jammu & Kashmir
Jharkand
Karnataka
Kerala
Lakshadeep
Madhya Pradesh
Maharashtra
Manipur
Meghalaya
Mizoram
Nagaland
Orissa
Pondicherry
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttar Pradesh
Uttaranchal
West Bengal
Country :
-Select-
Any
India
United States of America
United Arab Emirates
Malaysia
United Kingdom
Australia
Saudi Arabia
Canada
Singapore
Kuwait
Afghanistan
Albania
Algeria
American Samoa
Andorra
Angola
Anguilla
Antarctica
Antigua and Barbuda
Argentina
Armenia
Aruba
Australia
Austria
Azerbaijan
Bahamas
Bahrain
Bangladesh
Barbados
Belarus
Belgium
Belize
Benin
Bermuda
Bhutan
Brazil
Svalbard and Jan Mayen Islands
Swaziland
Sweden
Switzerland
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States of America
Uruguay
Uzbekistan
Vanuatu
Vatican City
Venezuela
Vietnam
Virgin Islands
Wallis and Futuna Islands
Western Sahara
Yemen
Yugoslavia (Former)
Zaire
Zambia
Zimbabwe
Ashmore and Cartier Islands
Solomon Islands
Bassas da India
Congo (Brazzaville)
Coral Sea Islands
Europa Island
Guernsey
Glorioso Islands
Gaza Strip
Isle of Man
Clipperton Island
Cote DIvoire
Jersey
Jan Mayen
Juan de Nova Island
Macedonia
Bodies of water
Paracel Islands
Spratly Islands
Saint Pierre and Miquelon
Saint Helena
Svalbard
Tromelin Island
Undersea features
Saint Vincent & the Grenadines
West Bank
Wallis and Futuna
Serbia and Montenegro
About My Partner :
=$rowPartnerData['partner_aboutpartner']?>


Groop Chat room
MARUF_BILLAH
hiii everyone
Riya Osman
Hlw maruf
Sabbir Mahmud
today is a nice day
Rizvy
right


See: Description
| Class | Description |
|---|---|
| Main |
The main application.
|
Chainsaw is a GUI log viewer and filter for the log4j package. By default it listens for LoggingEvent objects sent using the SocketAppender and displays them in a table. The events can be filtered based on:
All the details for each event can be displayed by selecting the event in the table.
Chainsaw also supports loading a events logged to a file using the XMLLayout format. This is great for analysing log files, and means you do not need to keep Chainsaw running continously. It is easy to add support for loading events from other sources like JDBC.
A picture is worth a thousand words:
Finally, why is it called chainsaw? Because it cuts your log (file) down to size. :-)
Chainsaw is based on the Swing API which requires JDK 1.2 or later.
You need to include the log4j.jar
in the classpath.
The command line usage is:
java -D<property>=<value> org.apache.log4j.chainsaw.Main
The default behaviour of chainsaw can be changed by setting system properties
using the -D<property>=<value>
arguments to java. The
following table describes what properties can be set:
| Property | Description |
| chainsaw.port | Indicates which port to listen for connections on. Defaults to "4445". |
You will need to configure log4j to send logging events to
Chainsaw. Here is a sample log4j.properties
file
for sending logging events to Chainsaw.
log4j.rootLogger=DEBUG, CHAINSAW_CLIENT log4j.appender.CHAINSAW_CLIENT=org.apache.log4j.net.SocketAppender log4j.appender.CHAINSAW_CLIENT.RemoteHost=localhost log4j.appender.CHAINSAW_CLIENT.Port=4445 log4j.appender.CHAINSAW_CLIENT.LocationInfo=true
Copyright © 1999-2016 Apache Software Foundation. All Rights Reserved.


NBA Data Science
Salary Generator
Points per Game
Age
Height
Plus Minus
Years of Service
Player Search
Salary Generator
Age
Points
Rebounds
Assists
Plus-minus
The player's salary prediction is:
{{salary}}


Khulna University of Engineering & Technology (KUET) is one of the leading public universities of Bangladesh giving special emphasis in the Engineering and Technological Education and research. KUET is well known for offering very high quality educational, research and developmental programs in the major disciplines of engineering as well as basic sciences. It has a sober objective to achieve excellence in quality education, research and progression to address the present needs of the country as well as the South-Western region to make it as the "Center of Excellence".
Khulna University of Engineering & Technology (KUET), Bangladesh established in 1967 as Khulna Engineering College, starts its long cherished Journey in 3 June 1974 after receiving special direction from the Father of Nation Bangabandhu Shiekh Mujibur Rahman. Later to ensure the better academic and research capabilities and environment, it is converted to an autonomous institute called Bangladesh Institute of Technology (BIT), Khulna in July 1986. To meet the demands of the days in the academic and research arena, the institute was upgraded and renamed as Khulna University of Engineering & Technology (KUET) in September 2003.
| Date | Information | Source |
|---|---|---|
| 19.02.2018 | Management policy of M.C | Kuet M.C |
| 18.02.2018 | Notice regarding program of 21th frebruary,2018 | From V.C |
| 20.03.2018 | 3rd convocation of Kuet | Kuet webside |


Rihanna is 'bad at taking pictures' says Seth Meyers
Rihanna might seem absolutely flawless, but it turns out there’s one thing she’s bad at – well, according to Seth Meyer’s anyway.
When appearing on The Tonight Show Starring Jimmy Fallon, the 45-year-old opened up on his iconic day drinking session with the Umbrella hitmaker – which saw her dish out some X-rated advice.
But while gushing over the pop star, he also threw some shade in her direction, as he teased that she’s bad at taking pictures.
Jimmy prompted his confession when he asked the comedian whether or not there’s anything she’s bad at.
Advertisement Advertisement
‘Yes,’ Seth smirked: ‘At one point she took my phone and took a bunch of pictures while we were day drinking.’
He added: ‘And then when I went through them, I was like, “Rihanna is bad at taking pictures.”‘
You know, despite those seemingly perfect selfies!
But despite not getting a good shot to remember the day by, it seems the star’s memories were more than enough to keep him going.
‘It was beyond my wildest dreams,’ he said of their time together: ‘We went day drinking. Why would you say yes to that? But it was amazing.’
The father-of-two added: ‘People have asked, “What was it like drinking with Rihanna?”
‘You know, like there’s some people where the more you drink over the course of the night, the more they start looking like Rihanna, imagine starting there!’
He laughed: ‘You know what I mean? But she was so much fun and is the nicest person in the world and it was a blast.’
When asked if he managed to get her phone number, Seth chuckled: ‘I wouldn’t want it, I think it would be too dangerous to have, I think I’d spend all day like, “Whats a funny thing to send Rihanna?”‘
But while she might be bad at taking photos, it seems the 31-year-old is even worse at dropping new music, as lo and behold we’re still waiting on an album.
Advertisement Advertisement
Teasing a little bit about her ninth studio album, the singer recently revealed that it will be reggae-inspired.
‘I like to look at it as a reggae-inspired or reggae-infused album,’ she explained to Vogue. ‘It’s not gonna be typical of what you know as reggae. But you’re going to feel the elements in all of the tracks.’
Got a showbiz story?
If you've got a story, video or pictures get in touch with the Metro.co.uk Entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.


--- layout: post status: publish published: true title: Edgar Olmos, Cubs Booty Call - Episode 7.82 author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 11650 wordpress_url: http://ivyenvy.com/?p=11650 date: '2016-02-14 18:36:01 +0000' date_gmt: '2016-02-15 00:36:01 +0000' categories: - Podcast - Spring Training tags: [] comments: [] ---
Welcome to the last episode of our 7th season of The Ivy Envy Podcast. Pitchers and catchers report later this week and that officially begins a new Cubs season and a new season of our podcast.
On this episode, we talk about the important Cubs dates in the next month, from pitchers and catchers reporting to Opening Day in Anaheim and everything in-between.
There's lots of Spring Training talk in this episode, too. One of the listener questions is tips on getting the most out of Spring Training and we share a few tips. We have several other questions from listeners on this episode.
To celebrate pitchers and catchers reporting, this week, you can get 20% Ivy Envy shirts at the http://ivyenvy.com/store and use the coupon code "PITCHERS".


selector::pseudo-element { property:value; }
The ::first-line
pseudo-element is used to add a special style to the first line of a text.
The following example formats the first line of the text in all < p > elements:
You can use the ::first-line pseudo-element to add a special effect to the first line of a text.
Some more text. And even more, and more, and more, and more, and more, and more, and more, and more, and more, and more, and more, and more.
You can use the ::first-letter pseudo-element to add a special effect to the first character of a text!
You can combine the ::first-letter and ::first-line pseudo-elements to add a special effect to the first letter and the first line of a text!
The ::before pseudo-element inserts content before the content of an element.


Scott Doyle /<EMAIL_ADDRESS>2020-01-13
Machine Learning (ML) uses collected data to do something useful.
The job of the ML expert is to:
Supervised Learning
Use labeled datasets to classify new, unseen data
Unsupervised Learning
Use unlabeled data to identify natural groups
Semi-Supervised Learning
Use partially labeled data to handle the process
Reinforcement Learning
An agent learns to complete a task policy of rewards
The starting point for all ML algorithms is data.
So… what do we mean by “data”?
Biological structure is primary data.
We can quantify biological structure.
We can model relationships between structure and disease.
Changes in genomic expression manifest as physical changes in tumor morphology
S. S. Badve et al., JCO (2008), Paik et al., N Engl J Med (2004)
Changes in genomic expression manifest as physical changes in tumor morphology
S. S. Badve et al., JCO (2008), Paik et al., N Engl J Med (2004)
How do we begin a machine learning project?
A Brief Overview
What is the problem statement, in one sentence?
NOT the machine learning task, just the gap that needs to be addressed.
Pathologists must review cytology slides to identify malignant versus benign tumors.
What is the current solution?
How is the problem currently addressed, if at all?
What data is currently routinely collected?
Based on cytology images, we can determine by eye whether a sample is benign or malignant.
Domain knowledge identifies useful features.
Pathologists already distinguish benign from malignant tumors.
Our job is to convert qualitative features to quantitative ones.
The pathologist lists cell nuclei features of importance:
Look at the data that is currently collected for your problem!
“A picture might be worth a thousand words; a formula is worth a thousand pictures.” Dr. Edsger Dijkstra
What is the goal of your system?
How will the success of the system be assessed?
This determines your overall performance measure.
At this point…
Is machine learning the right tool for the job?
Machine learning is NOT always the right choice.
The sooner you identify whether ML will help, the better!
Having decided on an ML approach, state the ML task to be completed.
Similar to a problem statement, but a proposed solution to the problem.
Also similar to the hypothesis statement for a scientific proposal.
Given a digital FNA image, we can use image features to predict whether a patient’s tumor is benign or malignant.
How you acquire the dataset is project-specific.
However, do NOT put this off!
Sensitive data (government, military, healthcare) may be difficult to obtain, be embargoed, or censored.
Questions about the dataset:
Ensure that the downloaded data contains what you expect.
For images, look at them!
import matplotlib.pyplot as plt
plt.imshow(data_sample)
plt.show()
For spreadsheets, print their contents!
import pandas as pd
dataset = pd.load_csv(path_to_csv_file)
dataset.head()
dataset.info()
At this point you should split the data into a “training” and “testing” set.
This protects against data snooping bias!
Model design should be completely blind to testing data, to prevent potential overfitting.
If you can hold off on retrieving testing data in the first place, even better!
How much data should go into training and testing?
Approximately $\frac{2}{3}$ in training, $\frac{1}{3}$ in testing.
How should you split your training and testing?
Random versus Stratified splitting?
id
for each sample to ensure reproducibility
# Import functions to calculate a hash for the dataset
from zlib import crc32
def test_set_check(identifier, test_ratio):
'''Return a boolean that states whether the current sample should be included in the testing set.
Calculates a hash value from an identifier, and returns True if the value is in the bottom
(test_ratio)-percent of the maximum possible hash value.
'''
return crc32(np.int64(identifier)) & 0xffffffff < test_ratio * 2**32
def split_train_test_by_id(data, test_ratio, id_column):
'''Return training and testing dataframes given a test ratio and column to use for computing sample hash.
Uses test_set_check to actually compute hash and put the data into training or testing.
'''
ids = data[id_column]
in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio))
return data.loc[~in_test_set], data.loc[in_test_set]
# Apply the above functions to the dataset
train_set, test_set = split_train_test_by_id(df, 0.3, "id")
================
Random Sampling
================
Overall class balance:
B 0.63
M 0.37
Name: diagnosis, dtype: float64
Train set class ratio:
B 0.61
M 0.39
Name: diagnosis, dtype: float64
Test set class ratio:
B 0.67
M 0.33
Name: diagnosis, dtype: float64
# Stratified Split
from sklearn.model_selection import StratifiedShuffleSplit
# Create the splitting object
split = StratifiedShuffleSplit(n_splits=1, test_size=0.3, random_state=42)
# Apply the split to the data frame using the "diagnosis" column as our label
for train_index, test_index in split.split(df, df["diagnosis"]):
train_set = df.loc[train_index]
test_set = df.loc[test_index]
====================
Stratified Sampling
====================
Overall class ratio:
B 0.63
M 0.37
Name: diagnosis, dtype: float64
Train set class ratio:
B 0.63
M 0.37
Name: diagnosis, dtype: float64
Test set class ratio:
B 0.63
M 0.37
Name: diagnosis, dtype: float64
Exploring the Feature Space
Looking at the data can provide insights into your data.
You can evaluate natural groupings of samples and potential correlations between features.
Let’s look at nuclei texture feature as an example for our FNA dataset…
Descriptive: Similar within a class, and different between classes
Relevant: Features should make sense
Invariant: Not dependent on how you measure them
Combining features often yields greater class separation.
Most data you will receive is noisy:
When creating a dataset, use a unique, non-numeric value to fill in missing values.
Do not:
When pre-processing a dataset, decide what to do with missing data:
Text and categorical values should be converted to numeric values.
Ordinal values can be converted directly into numbers:
from sklearn.preprocessing import OrdinalEncoder
ordinal_encoder = OrdinalEncoder()
Non-ordinal values can be encoded with one-hot encoding:
Replace the feature with N new features, where N is the number of categories. Each of the new features is binary, meaning it’s only 0 or 1.
| id | Blood Type |
|---|---|
| 001 | A |
| 002 | B |
| 003 | B |
| 004 | A |
| 005 | B |
| id | Blood Type A | Blood Type B |
|---|---|---|
| 001 | 1 | 0 |
| 002 | 0 | 1 |
| 003 | 0 | 1 |
| 004 | 1 | 0 |
| 005 | 0 | 1 |
from sklearn.preprocessing import OneHotEncoder
cat_encoder = OneHotEncoder()
from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
diagnosis_cat = df['diagnosis']
# Fit the encoder to the categories, and immediately
diagnosis_lab = label_encoder.fit_transform(diagnosis_cat)
# Add the diagnosis label back to the dataframe
df['diagnosis_label'] = diagnosis_lab
Data should always be scaled.
Scaling should be calculated only on the training set, and the proper transform applied to testing.
Min-Max Scaling / Normalization:
$$ x = \frac{x - \min{(x)}}{\max{(x - \min{(x)})}} $$
Standardization:
$$ x = \frac{x - \mu}{\sigma} $$
Not bounded to any specific range, which may be a problem (e.g. for neural networks expecting a 0-1 value), but much less affected by outliers.
Always write code to do your feature pre-processing.
You will be able to apply the same transforms each time, even if datasets grow.
Model selection is an art rather than a science.
Recall Occum’s Razor:
Given a set of possible solutions, the one which makes the fewest assumptions is preferable.
Also often restated as: The simplest solution is best!
So what does a “simple model” mean?
Start looking at linear methods, like a LinearDiscriminant
:
from sklearn.lda import LDA
lin_disc = LDA()
lin_disc.fit(training_data, training_labels)
training_predictions = lin_disc.predict(training_data[:10,:])
Check the performance of this system using root-mean-square error:
from sklearn.metrics import mean_squared_error
lin_mse = mean_squared_error(training_labels[:10], training_predictions)
lin_rmse = np.sqrt(lin_mse)
print("Linear Regressor Root Mean Squared Error: ", lin_rmse)
We can compare this model to more complex ones, like a support vector machine:
from sklearn.svm import SVC
svm_model = SVC()
svm_model.fit(training_data, training_labels)
training_predictions = svm_model.predict(training_data[:10,:])
svm_mse = mean_squared_error(training_labels[:10], training_predictions)
svm_rmse = np.sqrt(svm_mse)
print("Support Vector Machine Root Mean Squared Error: ", svm_rmse)
Evaluation on subsets of training data is the best possible outcome!
Since the system has trained on this data, it is more likely to do well.
Complex models are likely to get 100% performance: This is overfitting!
Complex boundaries fit training perfectly, but do not generalize.
In general, you want the simplest model with the best performance.
There is always a tradeoff; we need to consider the cost.
Cost is defined by our goals and acceptable performance.
Should we prioritize some kinds of errors over others?
Not all mistakes carry the same cost. For example:
To get a real sense of the performance of your system, you should train on your training data and then evaluate on testing data:
testing_predictions = svm_model.predict(testing_data)
svm_mse = mean_squared_error(training_labels, training_predictions)
svm_rmse = np.sqrt(svm_mse)
print("Support Vector Machine Root Mean Squared Error: ", svm_rmse)
However: What if you (randomly) get a really good or bad testing set?
What if you could create several train / test splits, with all of the benefits we discussed earlier?
This is cross-validation: Break the dataset into several (distinct) training and testing sets, and then perform training and testing on each one.
This can be easily done in scikit-learn
:
from sklearn.model_selection import cross_val_score
scores = cross_val_score(svm_model, training_data, training_labels,
scoring="neg_mean_squared_error", cv=10)
svm_rmse_scores = np.sqrt(-scores)
# Display scores
def display_scores(scores):
print("Scores: ", scores)
print("Mean: ", scores.mean())
print("Standard Deviation: ", scores.std())
display_scores(svm_rmse_scores)
Now you have a way to robustly compare models.
After you pick a model, it’s time to make it better!
There are a lot of “knobs” you can turn to opitimize a model.
If you’ve robustly quantified model performance, now it’s okay to tweak away.
This process is parameter optimization: trying all the parameters to see which gives you the best performance.
scikit-learn
enables you to build pipelines to process your data.
These allow you to build a list of different values to try, plus a scoring function, and then returns the best numbers out of what it’s tried.
from sklearn.pipeline import Pipeline
pipe = Pipeline(steps=[
('classification', classifier)
])
from sklearn.model_selection import GridSearchCV
# Set the parameters by cross-validation
param_grid = {
'classification__kernel': ['rbf'],
'classification__gamma': [1e-1, 1e-2, 1e-3, 1e-4],
'classification__C': [1, 1e1, 1e2, 1e3],
}
svm_model = SVC()
grid_search = GridSearchCV(svm_model, param_grid, cv=5,
scoring='neg_mean_squared_error',
return_train_score=True)
grid_search.fit(training_data, training_labels)
Finally, you can predict labels on your testing set:
final_model = grid_search.best_estimator_
final_predictions = final_model.predict(testing_data)
final_mse = mean_squared_error(testing_labels, final_predictions)
final_rmse = np.sqrt(final_mse)
This section is very implementation dependent.
However, there are a few steps…
Takeaway Thoughts
You will often be working as part of a team.
You may only be responsible for some of the workflow steps…
… But understanding them is critical to making your project successful.


|
url2epub
1.1
|
This is a tool for creating a ebook in epub format from an url.
Supported sites:
-h
: display help-d
: enable debug mode. Default: disabled-l
: disable logging mode. Default: enabled-o
: path to output directory. Default: url2epub
inside current directory-c
: path to the cover of story. Default: empty string-p
: the name of the publisher of story. Default: empty string-s
: chapter index to start crawl. Default: crawl from the first chapter-i
: limit total chapters to crawl. Default: 0 - no limiturl
: the url of storyIf the cover image does not appear:
- Check the size of cover image.
- Try to use another format of this image.
Kindles want three objects to be tagged:
1) The cover IMAGE (not the page) 2) The text TOC 3) The Starting position.
Actually you can create an AZW/mobi from an ePub without these and it will work, but better to make it happy.
You can do this in Sigil by right-clicking on the file list and choosing "Add semantics".
1) For the cover PAGE you can tag it "Cover" but you must also be sure that the cover IMAGE is tagged "Cover Image" – may be already done if you used the "Add cover" tool.
2) The TOC choose "Table of Contents" (again, will be set if you used the "Create HTML TOC" tool)
3) The Start page – for this choose "Text" (presumably meaning the first page of text). I usually use the title page as the Start page. Amazon tells you to use Chapter 1, so do that if you're submitting to them. You cannot use the Cover, however, as Kindles do not have a HTML cover page, only the image.
These "semantics" all create entries in the opf file, the TOC and Start in the "Guide" at the end; the cover image in the "meta' and "manifest".


Introduction
Hello there, awesome person!
Tableau Desktop Certified Associate
Tableau Desktop Certified Specialist
ISTQB Advanced Test Manager
ISTQB Advanced Test Analyst
Certified Agile Tester
This is a link to GitHub!


Welcome to
Red Hat
You have successfully logged as
.
You may now close this page.
An error occurred while signing in:


Getting Started
Django-InLaws is a pluggable app that requires very little to get it working in your project.
Add “inlaws” to your INSTALLED_APPS setting in your project’s settings.py file.
Create an “admin” directory in your project’s template directory.
MyProject
+- templates
+- admin
Create a file called change_form.html.
MyProject
+- templates
+- admin
`- change_form.html
The contents of the file should be:
{% extends "inlaws/change_form.html" %}
What items are shown
Several criteria must be met:
- The model has a ForeignKey, ManyToManyField, or OneToOneField field in its model. For example django.contrib.comments.models.Comment has a ForeignKey related to User. Therefore Comment entries will be displayed on the appropriate User‘s admin change form.
- The model has a registered ModelAdmin class. For example, django.contrib.admin.models.LogEntry has a ForeignKey relating to User, but does not have a ModelAdmin registered. Therefore you will not see any LogEntry items on any User change form.
- The model is not in the ADMIN_RELATED_EXCLUDES setting. This setting allows you to specifically exclude models from appearing, even in the first two criteria are met. ADMIN_RELATED_EXCLUDES is simply a list or tuple of strings in 'appname.model' format.


Our Website will bring you to goal
Please Sign Up for new account to get your job dream.
Popular job listing sites like Information Technology are a good job to start.
Please Sign Up for new account to get your job dream.
Popular job listing sites like Information Technology are a good job to start.